Tackling climate change with machine learning
Open accessAbstract
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
AI snapshot
Written from a shortened version of the paper’s full text (it is longer than we read).
TL;DR
This review argues that machine learning can be a powerful tool for climate change mitigation and adaptation across electricity, transportation, buildings, industry, land use, CO2 removal, climate prediction, societal impacts, geoengineering, individual action, collective decisions, education, and finance. It maps high-impact problems, research questions, and deployment opportunities, while stressing that ML is not a silver bullet and requires cross-disciplinary collaboration and policy.
Key findings
- The paper identifies ML opportunities across mitigation and adaptation domains, with “High Leverage,” “Long-term,” and “Uncertain Impact” flags; examples include smart grids, disaster management, precision agriculture, materials discovery, climate model emulation, and carbon markets.
- It reports sectoral magnitudes: electricity systems are responsible for about a quarter of human-caused GHG emissions each year; transportation accounts for about a quarter of energy-related CO2 emissions; buildings account for a quarter of global energy-related emissions; land use by humans is estimated at about a quarter of global GHG emissions; cement and steel production together account for over 10% of all global GHG emissions; deforestation contributes approximately 10% of global GHG emissions.
- It cites quantified potentials and examples: existing building emissions could be reduced by up to 90%; about a third of GHG emissions reductions could come from better land management and agriculture; food loss/waste is 1.3 billion metric tons per year, one-third of all food produced; peatlands cover only 3% of Earth’s land area but hold twice the total carbon in all the world’s forests; standby power is roughly 8% of residential electricity demand; behavior-change programs can cost as little as 3 cents to save a kilowatt-hour versus 5–6 cents with coal or wind and 10 cents with solar; one energy savings program improved enrollment by 2–3x.
- Cross-cutting themes include ML for monitoring, accelerated science, optimization, and hybrid physical models; the paper warns ML can also worsen climate change (e.g., fossil fuel exploration, energy-intensive models) and that technology alone is insufficient.
Methods
- Design: a review/perspective/roadmap article, not an empirical study; it synthesizes opportunities and recommendations across application domains and includes expert consultation in preparation.
- Data/sample: no primary dataset or sample; it draws on prior literature and examples, with tables mapping climate solution domains to ML areas and cross-cutting objectives.
- Analysis: qualitative synthesis and expert judgment; it identifies high-impact problems, research questions, business opportunities, and a call for collaboration, with no formal meta-analysis or quantitative modeling.
Limitations
- Stated: the authors cannot cover everything and may have omitted or erroneously dismissed applications; the High Leverage/Long-term/Uncertain Impact flags are not definitive and reflect subjective evaluation; ML is not a silver bullet, can worsen climate change, and technology/policy alone are insufficient.
- Stated: data can be proprietary, sensitive, heterogeneous, or unrepresentative; low-data settings and transfer learning are challenges; rebound effects and deployment barriers can undermine emissions benefits; many CO2 removal and geoengineering applications are speculative or early-stage.
- Evident from text: as a broad review, it relies on expert judgment rather than a systematic search or quantitative synthesis, and many listed applications are potential opportunities rather than demonstrated outcomes.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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- Interpretationreasoning
Demand for low-carbon electricity will grow as buildings, transportation, and other sectors seek to replace GHG-emitting fuels.
“as buildings, transportation, and other sectors seek to replace GHG-emitting fuels (Section 3-4), demand for low-carbon electricity will grow.”
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Some ML models are themselves energy-intensive to train and run.
“some ML models are themselves energy-intensive to train and run [69,459,720,764]”
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ML can also be applied in ways that make climate change worse.
“ML can also be applied in ways that make climate change worse.”
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Many ML methods have been used to forecast electricity supply and demand.
“To date, many ML methods have been used to forecast electricity supply and demand.”
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- Interpretationcitation
In the future, the buffer role is expected to be played by energy storage technologies such as batteries, pumped hydro, or power-to-gas.
“In the future, this role is expected to be played by Fig. 1.Selected opportunities to reduce GHG emissions from electricity systems using ML, as described in Section 2. energy storage technologies such as batteries (Section 3.3), pumped hydro, or power-to-gas [226]; see [27] for an overview.”
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- Interpretationreasoning
Scheduling will become even more complex as electricity systems include more storage, variable generators, and flexible demand.
“Further, scheduling will become even more complex as electricity systems include more storage, variable generators, and flexible demand, since operators will need to manage even more system components while simultaneously solving scheduling problems more quickly to account for real-time variations in electricity production.”
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Recent work has shown that deep learning may enable insights that generalize to multiple reactors.
“recent work has shown that deep learning may enable insights that generalize to multiple reactors [413].”
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- Interpretationcitation
ML can help prevent the leakage of methane from natural gas pipelines and compressor stations.
“ML can help prevent the leakage of methane (an extremely potent GHG) from natural gas pipelines and compressor stations.”
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Platforms such as PowerTAC and Grid2Op can provide large-scale simulated environments on which to perform these tests.
“platforms such as PowerTAC [638] and Grid2Op [519] can provide large-scale simulated environments on which to perform these tests.”
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ML can automate the process of materials discovery by combining existing heuristics with experimental data, physics, and reasoning to apply and even extend existing physical knowledge.
“ML can automate this process by combining existing heuristics with experimental data, physics, and reasoning to apply and even extend existing physical knowledge.”
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Techniques such as deep RL, agent-based models, online optimization, and dynamic programming can help maximize profits for decentralized storage, demand, and generation, given real-time prices.
“Techniques such as (deep) RL [291,292,810,881], agent-based models [173,662,663,863], online optimization [99], and dynamic programming [704] can then help maximize profits for decentralized storage, demand, and generation, given real-time prices.”
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- Interpretationcitation
ML can help control batteries located at solar and wind farms to increase these farms' profits, for instance by storing their electricity when prices are low and then selling it when prices are high.
“ML can also help control batteries located at solar and wind farms to increase these farms' profits, for instance by storing their electricity when prices are low and then selling it when prices are high; prior work has used ML to forecast electricity prices [460,838] or RL to control batteries based on current and historical prices [825].”
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Recent work has used tools from ML, AI, optimization, and physics to figure out a proposed material's crystal structure, with the goal of accelerating materials discovery for solar fuels.
“recent work has used tools from ML, AI, optimization, and physics to figure out a proposed material's crystal structure, with the goal of accelerating materials discovery for solar fuels [49,298,773].”
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ML can help integrate rooftop solar panels into the electric grid, particularly in the United States and Europe.
“ML can also help integrate rooftop solar panels into the electric grid, particularly in the United States and Europe.”
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ML can help implement interventions that reduce GHG emissions in transportation, for example by providing better demand forecasts.
“ML can help in implementing such interventions, for example, by providing better demand forecasts.”
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ML-based modeling of demand can help mitigate climate change by improving operational efficiency of high-CO2 modes such as aviation.
“Similarly, ML-based modeling of demand can help mitigate climate change by improving operational efficiency of modes that emit significant CO 2 , such as aviation.”
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Shared mobility can lead to higher utilization of each vehicle, meaning more efficient use of materials.
“On the other hand, shared mobility can lead to higher utilization of each vehicle, which means a more efficient use of materials [341].”
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- Interpretationcitation
ML can help predict runway demand and aircraft taxi time to reduce excess fuel burned due to airport congestion.
“ML can help predict runway demand and aircraft taxi time in order to reduce the excess fuel burned in the air and on the ground due to congestion in airports [379,474].”
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ML is particularly relevant for deducing information from novel data, such as learning about public transit users from smart card data.
“ML is particularly relevant for deducing information from novel data-for example, learning about the behavior of public transit users from smart card data [280,515].”
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ML can turn raw sensor data into useful information.
“In recent years, new types of sensors have become available, and ML can turn this raw data into useful information.”
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Consolidation enables shipments to use rail and water modes for part of the journey.
“As rail and water modes require much larger loads than trucks, consolidation also enables shipments to use these modes for part of the journey [407].”
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ML methods such as SVMs and neural networks have made it easier to classify roads with similar traffic patterns.
“ML methods, such as SVMs and neural networks, have made it easier to classify roads with similar traffic patterns [270,454,796].”
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Mobile phone sensors provide new means to understand personal travel demand and urban topology, such as walking route choices.
“Also, mobile phone sensors provide new means to understand personal travel demand and the urban topology, such as walking route choices [795].”
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ML can predict if a customer decides to share a ride with other passengers from an on-demand ride service.
“For example, they can be used to predict if a customer decides to share a ride with other passengers from an ondemand ride service [133].”
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Short-term forecasting of public transit ridership can improve with ML.
“Also, short-term forecasting of public transit ridership can improve with ML; see for example [158,586].”
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It is largely unclear what the impact of shared mobility development will be on GHG emissions.
“However, it is largely unclear what the impact of this development will be on GHG emissions.”
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Misaligned incentives in early stages of technological development could result in lock-in to a service with high GHG emissions.
“Misaligned incentives in the early stages of technological development could result in the lock-in to a service with high GHG emissions [36,44].”
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ML offers opportunities to optimize the complex interaction of shipment sizes, modes, origin-destination pairs, and service requirements.
“ML offers opportunities to optimize this complex interaction of shipment sizes, modes, origin-destination pairs, and service requirements.”
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ML makes it possible to estimate origin-destination demand from traffic counts.
“For example, ML makes it possible to estimate origin-destination demand from traffic counts [504]”
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ML can provide information about mobility patterns necessary for agent-based travel demand models.
“ML can provide information about mobility patterns, which is directly necessary for agent-based travel demand models, one of the main transport planning tools [864].”
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ML methods can help understand the energy impact of shared mobility concepts.
“ML methods can help to understand the energy impact of shared mobility concepts.”
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Additive manufacturing has limited potential to reduce freight transport by producing lighter goods and enabling production closer to the consumer.
“For example, additive manufacturing (AM) or 3-D printing has (limited) potential to reduce freight transport by producing lighter goods and enabling production closer to the consumer [407].”
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ML methods can help understand driving practices that are more energy efficient.
“ML methods can also help to understand driving practices that are more energy efficient.”
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In-vehicle sensors and communication data offer an opportunity to understand travel and charging behavior of EV owners, informing placement of charging stations or battery swapping stations.
“Also, in-vehicle sensors and communication data are increasingly becoming available and offer an opportunity to understand travel and charging behavior of EV owners, which can for example inform the placement of charging stations [779] or alternatives such as battery swapping stations.”
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ML has been shown to help pick good algorithms and parameters to solve auction markets.
“Proposed planning approaches include designing allocation algorithms and freight auctions, and ML has for example been shown to help pick good algorithms and parameters to solve auction markets [708].”
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Aerodynamic improvements can be made by rearranging load.
“Aerodynamic improvements can not only be made by vehicle design but also by rearranging load.”
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ML can be a valuable tool for improving AM processes.
“ML can be a valuable tool for improving AM processes [859].”
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ML is vital for a range of problems related to EVs.
“ML is vital for a range of different problems related to EVs.”
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There is evidence that ML can improve upon operations research methods, in particular mixed-integer linear programming.
“There is evidence that ML can improve upon these methods, in particular mixed-integer linear programming [70].”
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- Interpretationcitation
ML can help develop AV technologies specifically aimed at reducing energy consumption.
“ML can help to develop AV technologies specifically aimed at reducing energy consumption.”
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Jiménez et al. use smartphone sensor data to identify driving behavior that leads to higher energy consumption in EVs.
“For example, Jiménez et al. [392] use data from smart phone sensors to identify driving behavior that leads to higher energy consumption in EVs.”
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- Interpretationreasoning
Modeling charging behavior will be useful for grid operators predicting electric load.
“Modeling charging behavior will be useful for grid operators looking to predict electric load.”
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ML is applied in advanced combustion engine design.
“For example, ML is applied in advanced combustion engine design [384].”
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ML can help improve virtual communication.
“ML can also help to improve virtual communication [733].”
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Hybrid EVs rely on power management methods that can be improved with ML.
“Hybrid EVs, which are more efficient than combustion engines alone, rely on power management methods that can be improved with ML [15].”
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If passenger trips are replaced by telepresence, travel demand can be reduced, as shown in public agencies and scientific teams.
“If passenger trips are replaced by telepresence, travel demand can be reduced, as has been shown for example in public agencies [34] and for scientific teams [518].”
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ML can play a role in research and development of batteries, a decisive technology for EV costs and usability.
“Finally, ML can also play a role in the research and development of batteries, a decisive technology for EV costs and usability.”
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Consumers are expected to be reluctant to agree to vehicle-to-grid services because they might not want to compromise driving range.
“However, consumers are expected to be reluctant to agree to such services, as they might not want to compromise their driving range [343].”
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- Interpretationreasoning
Many engineering techniques for reducing vehicle energy use can benefit from ML.
“These different strategies require a broad range of engineering techniques, many of which can benefit from ML.”
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ML is applied to improve AM processes through failure detection or material design.
“ML is applied to improve those processes, for example through failure detection [722,730] or material design [309].”
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Work in battery ML has focused on predicting battery state, degradation, and remaining lifetime using supervised learning, fuzzy logic, and clustering.
“Work in this area has focused on predicting battery state, degradation, and remaining lifetime using supervised learning techniques, fuzzy logic, and clustering [42,104,218,358,414,725,735,819,854].”
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AM can produce lighter parts in vehicles that reduce energy consumption.
“AM (3-D printing) can produce lighter parts in vehicles, such as road vehicles and aircraft, that reduce energy consumption [341,407].”
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Wu et al. develop AV controllers based on RL to smooth out traffic involving non-AVs.
“For example, Wu et al. [851,852] develop AV controllers based on RL to smooth out traffic involving non-AVs.”
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Aerodynamic efficiency improvements need turbulence modeling that relies heavily on ML-based surrogate models.
“Aerodynamic efficiency improvements need turbulence modeling that is often computationally intensive and relies heavily on ML-based surrogate models [865].”
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It is possible to analyze residential EV charging behavior from aggregate electricity load.
“For this application, it is possible to analyze residential EV charging behavior from aggregate electricity load (energy disaggregation, see also Section 4.1) [827].”
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Energy consumed in buildings is responsible for a quarter of global energy-related emissions.
“While the energy consumed in buildings is responsible for a quarter of global energy-related emissions [372]”
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A combination of easy-to-implement fixes and state-of-the-art strategies could reduce emissions for existing buildings by up to 90%.
“a combination of easy-to-implement fixes and state-of-the-art strategies 16 could reduce emissions for existing buildings by up to 90% [802].”
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- Interpretationreasoning
As energy storage, electrofuels and hydrogen could provide grid services by enabling flexible power use and balancing variable generators.
“Thus, as a form of energy storage, these fuels could provide services to the electricity grid by enabling flexible power use and balancing variable electricity generators (Section 2.1.1).”
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Electrofuels and biofuels have potential as low-carbon drop-in fuels retaining fossil fuel properties and compatibility with existing vehicles and infrastructure.
“Electrofuels and biofuels have the potential to serve as low-carbon drop-in fuels that retain the properties of fossil fuels, such as high energy density, while retaining compatibility with the existing fleet of vehicles and the current fuel infrastructure [407].”
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Much of the transportation sector is highly dependent on liquid fossil fuels.
“Much of the transportation sector is highly dependent on liquid fossil fuels.”
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Intermodal transportation relies on low-carbon modes such as rail and water for part of the journey.
“Such intermodal transportation relies on low-carbon modes such as rail and water for part of the journey [407].”
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- Interpretationcitation
ML can help solve the bike sharing rebalancing problem by improving forecasts of bike demand and inventory.
“ML can also help solve the bike sharing rebalancing problem, where shared bikes accumulate in one location and are lacking in other locations, by improving forecasts of bike demand and inventory [675].”
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Buildings offer some of the lowest-hanging fruit for reducing GHG emissions.
“Buildings offer some of the lowest-hanging fruit when it comes to reducing GHG emissions.”
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Supervised ML based on survey data can improve passenger mode choice models.
“Some recent studies have shown that supervised ML based on survey data can improve passenger mode choice models [322,571,597].”
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Some high-GHG modes such as trucks can be cost-competitive in regions with lax enforcement because they can overload and not obey labor or safety rules.
“Some modes with high GHG emissions, such as trucks, can be particularly cost-competitive in regions with lax enforcement of regulation, as they can benefit from overloading and not obeying labor or safety rules [407].”
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Intelligent transport systems of different modes could be combined and enable more efficient multimodal freight transportation.
“Intelligent transport systems of different modes could be combined and enable more efficient multimodal freight transportation [407].”
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- Premisecitation
In freight, ML has been applied to analyze modal trade-offs, for example by imputing data on counterfactual mode choices.
“Also in the freight sector, ML has been applied to analyze modal trade-offs, for example, by imputing data on counterfactual mode choices [706].”
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ML can improve understanding of passengers' travel mode choices, informing transportation planning such as where public transit should be built.
“ML can improve our understanding about passengers' travel mode choices, which in turn informs transportation planning, such as where public transit should be built.”
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ML can help improve operation of low-carbon modes, for example by reducing rail operations and maintenance costs and predicting track degradation.
“ML can also help to improve the operation of low-carbon modes, for example, by reducing the operations and maintenance costs of rail [383] and predicting track degradation [746].”
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Many academic battery models are based on laboratory data that do not account for real-world factors such as environmental conditions.
“However, many models developed in academia are based on laboratory data that do not account for real-world factors such as environmental conditions [735,819,854].”
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ML studies help understand how usage patterns for bike stations depend on their immediate urban surroundings.
“ML studies help to understand how usage patterns for bike stations depend on their immediate urban surroundings [364].”
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ML can assist public institutions with enforcing their regulations.
“ML can assist public institutions with enforcing their regulations.”
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Image recognition can help law enforcement detect overloading of trucks.
“For example, image recognition can help law enforcement detect overloading of trucks [888].”
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It is possible today for buildings to consume almost no energy.
“It is possible today for buildings to consume almost no energy [595].”
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By producing accurate travel time estimates, ML can provide tools that help integrate bike shares with other modes.
“By producing accurate travel time estimates, ML can provide tools that help to integrate bike shares with other modes of transportation [281].”
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ML can reduce energy usage by allowing devices and systems to adapt to usage patterns.
“Specifically, ML can reduce energy usage by allowing devices and systems to adapt to usage patterns.”
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Urban planning and public policy can play a major role in reducing emissions via infrastructure, financial incentives, or energy standards.
“Urban planning and public policy can play a major role in reducing emissions by providing infrastructure, financial incentives, or energy standards for buildings [536].”
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Wang et al. treated HVAC fault-detection as a one-class classification problem using only temperature readings.
“Wang et al. [829] treated HVAC fault-detection as a one-class classification problem, using only temperature readings for their predictions.”
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Many critical systems inside buildings can be made radically more efficient.
“Many critical systems inside buildings can be made radically more efficient.”
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With changing climate, more people will need access to air conditioning in regions where deadly heat waves become common.
“For example, with the changing climate, more people will need access to air conditioning in regions where deadly heat waves will become common [553,554].”
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ML can be used to gather and make sense of data to inform policymakers in urban planning.
“At the level of urban planning, ML can be used to gather and make sense of data to inform policymakers (Section 4.2).”
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ML can accelerate building emission reduction strategies by modeling energy consumption data and optimizing energy use.
“ML can accelerate these strategies by (i) modeling data on energy consumption and (ii) optimizing energy use (in smart buildings).”
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Energy flexibility in buildings is increasingly useful to schedule consumption when supply is high.
“Thus, energy flexibility in buildings is increasingly useful to schedule consumption when supply is high [683].”
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Occupancy detection represents an opportunity for ML algorithms, ranging from decision trees to deep neural networks using various inputs.
“Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [193,882] to deep neural networks [896] that take input from occupancy sensors [193], WiFi signals [896,897], or appliance power consumption data [882].”
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Deep autoencoders can simplify machine operation information so deep neural networks can more easily predict multiple kinds of faults.
“Deep autoencoders can be used to simplify information about machine operation so that deep neural networks can then more easily predict multiple kinds of faults [387].”
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ML can automate building diagnostics and maintenance through fault-detection.
“Finally, ML can automate building diagnostics and maintenance through fault-detection.”
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Burlig et al. used Lasso regression on hourly electricity consumption data from California schools to find that energy efficiency interventions fall short of expected savings.
“For example, Burlig et al. [102] used Lasso regression on hourly electricity consumption data from schools in California to find that energy efficiency interventions fall short of the expected savings.”
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- Interpretationcitation
Transfer learning from modeling one building to another can make it easier to render precise estimations of more buildings.
“Learning how to transfer the knowledge gained from modeling one building to another can make it easier to render precise estimations of more buildings.”
Quote in the paperNot a substantial claim - Premisereasoning
Two major challenges for buildings are heterogeneity and inertia.
“Two major challenges are heterogeneity and inertia.”
Quote in the paperSubstantial claim - Premisecitation
Promising ML approaches to energy disaggregation include hidden Markov models, sparse coding, harmonic analysis, and deep neural networks.
“Promising ML approaches to this problem include hidden Markov models [445], sparse coding algorithms for structured prediction [443], harmonic analysis that picks out the "signatures" of individual appliances [750], and deep neural networks [423].”
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- cites [443]
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- cites [750] source
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- Interpretationcitation
Statistical ML offers methods for causal inference to verify success or failure of energy efficiency interventions.
“To verify the success or failure of energy efficiency interventions, statistical ML offers methods for causal inference.”
Quote in the paperNot a substantial claim - Premisecitation
Making sense of increasing data from meters and home energy monitors is an essential step towards energy efficiency.
“An essential step towards energy efficiency is making sense of the increasing amounts of data produced by meters and home energy monitors (see for example [723]).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [723]
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- Premisecitation
ML approaches such as deep belief networks can potentially increase temperature forecasting accuracy with less computational expense.
“however, ML approaches such as deep belief networks can potentially increase accuracy with less computational expense [4,255] (see also Section 5.3).”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Numerous technologies can reduce GHG emissions in designing new and improving existing buildings, often saving money.
“In designing new buildings and improving existing ones, there are numerous technologies that can reduce GHG emissions, often saving money in the process [276,499,595,754,802].”
Quote in the paperNot a substantial claim0 of 5 sources checked - Premisenone given
Buildings vary by age, construction, usage, and ownership, so optimal strategies vary widely by context.
“Buildings vary according to age, construction, usage, and ownership, so optimal strategies vary widely depending on the context.”
Quote in the paperSubstantial claim - Interpretationcitation
Edge computing and deep RL can efficiently schedule energy use at building, microgrid, and grid levels.
“Edge computing can be used to process data from distributed sensors and other Internet of Things devices, and deep RL can then use this data to efficiently schedule energy use [489], at the level of single or multiple buildings, or at the microgrid and grid level [631].”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationcitation
Smart building technologies have potential drawbacks.
“While smart building technologies have the capability to significantly increase efficiency, we should note that there are potential drawbacks [346].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [346] source
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- Interpretationcitation
ML can help building systems dynamically adapt to changes in occupancy patterns.
“ML can help these systems dynamically adapt to changes in occupancy patterns [669].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [669] source
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- Premisecitation
Simple building characteristics and surrounding environmental factors can be used for retrofit potential.
“Simple building characteristics and surrounding environmental factors-both potentially available at scale-can be used [83,432].”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationcitation
ML is key to preprocessing large amounts of data in large sensor networks, allowing only relevant data to be transmitted instead of all raw data.
“ML is key to preprocessing large amounts of data in large sensor networks, allowing only what is relevant to be transmitted, instead of all the raw data that is being collected [480,672,804].”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationcitation
Similar ML techniques help reduce the amount of energy consumed during data transmission.
“Similar techniques also help to reduce the amount of energy consumed during transmission itself [563].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [563] source
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- Premisecitation
Segmentation of high-resolution satellite images can now generate exact building footprints at a national scale.
“Segmentation of high-resolution satellite images can now generate exact building footprints at a national scale [535].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [535]
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- Interpretationcitation
ML can predict energy consumption from UBEM features.
“ML can be used to held predict energy consumption from such features.”
Quote in the paperSubstantial claim - Interpretationnone given
Where energy-use data on individual buildings exist, ML can derive higher-level patterns.
“Where energy-use data on individual buildings exist, ML can be used to derive higher-level patterns.”
Quote in the paperNot a substantial claim - Premisecitation
Nutkiewicz et al. used deep neural networks for hybrid ML-physical modeling to provide precise energy demand forecasts for all buildings on a campus.
“Using deep neural networks for hybrid ML-physical modelling, Nutkiewicz et al. provided precise energy demand forecasts that account for inter-building energy dynamics and urban microclimate factors for all buildings on a campus [589].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [589] source
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- Premisecitation
Geiß et al. used remote sensing data to cluster buildings into types to assess district heat potential in a German town.
“For example, using remote sensing data, Geiß et al. [273] clustered buildings into types to assess the potential of district heat in a German town.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [273] source
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- Premisecitation
Smart building devices and connection networks consume energy themselves.
“First, smart building devices and connection networks, like wireless sensor networks, consume energy themselves.”
Quote in the paperSubstantial claim - Premisecitation
Building heights can be predicted without elevation data from real estate records, census data and features characterizing the neighborhood of each building.
“Recent research showed that heights can be predicted even without such elevation data, as demonstrated by [77,537], who predicted these from real estate records, census data and features characterizing the neighborhood of each building.”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Widespread adoption of smart meters raises concerns about mineral use and embodied energy from their production.
“Another concern in the case of widespread adoption of smart meters is the impact on mineral use and embodied energy use arising from their production [736].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [736]
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- Premisecitation
Energy-relevant building attributes, such as the presence of photovoltaic panels, can be retrieved from high-resolution satellite images.
“Energy-relevant building attributes, such as the presence of photovoltaic panels, can also be retrieved from these images [868] (see Section 2.1.1).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [868] source
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- Premisecitation
Robinson et al. predict commercial building energy use across large American cities using five commonly accessible features.
“Using five commonly accessible features of buildings and climate, Robinson et al. predict commercial building energy use across large American cities [689].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [689] source
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- Interpretationcitation
Specifics about building infrastructure can often be predicted using ML techniques.
“Specifics about building infrastructure can often be predicted using ML techniques.”
Quote in the paperNot a substantial claim - Interpretationcitation
ML can distill information on urban issues related to climate change through web-scraping and text-mining.
“ML can also distill information on urban issues related to climate change through web-scraping and text-mining, e.g., [775].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [775] source
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- Premisecitation
Relevant information on the urban space can be learned from social media activity, e.g., on Twitter.
“Relevant information on the urban space can also be learned from social media activity, e.g., on Twitter, as reviewed in [367,703].”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationcitation
Buildings' features can be used by ML algorithms to pinpoint which buildings have highest retrofit potential.
“Beyond energy prediction, buildings' features can be used by ML algorithms to pinpoint which buildings have the highest retrofit potential.”
Quote in the paperNot a substantial claim - Premisecitation
Clustering anonymized credit card payments makes it possible to model different communities and lifestyles, whose sustainability can be assessed.
“For example, clustering anonymized credit card payments makes it possible to model different communities and lifestyles-of which the sustainability can be assessed [181].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [181] source
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- Interpretationnone given
If control systems optimize for costs, interventions do not necessarily translate into energy efficiency or GHG reductions.
“Third, if control systems optimize for costs, interventions do not necessarily translate into energy efficiency measures or GHG reductions.”
Quote in the paperNot a substantial claim - Premisecitation
It is possible to produce a global map of human settlement footprints at meter-level resolution from satellite radar images.
“It is possible to produce a global map of human settlement footprints at meter-level resolution from satellite radar images [224].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [224] source
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- Interpretationcitation
As online shopping and just-in-time manufacturing become more prevalent, better demand forecasts will be needed on a regional level to efficiently distribute inventory.
“As online shopping and just-in-time manufacturing become more prevalent and websites offer more product types than physical storefronts, better demand forecasts will be needed on a regional level to efficiently distribute inventory without letting unwanted goods travel long distances only to languish in warehouses [685].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [685] source
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- Interpretationcitation
ML may be able to mitigate overproducing and/or overstocking goods by improving demand forecasting.
“ML may be able to mitigate these issues of overproducing and/or overstocking goods by improving demand forecasting [12,797].”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
The challenge in using recommender systems for climate-friendly options lies in hunting down usable data on every relevant material and production process from metal ore extraction through production, shipping, and eventual use and disposal of a product.
“The challenge here lies in hunting down usable data on every relevant material and production process from metal ore extraction through production, shipping, and eventual use and disposal of a product [336,674].”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
ML and finite element modeling have been used to simulate the physical processes of 3D printing in order to improve the quality of finished products.
“Novel manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material but may be impossible to produce through traditional metal-casting or poured concrete; ML and finite element modeling have been used to simulate the physical processes of 3D printing in order to improve the quality of finished products [62].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [62] source
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- Premisecitation
Corporate sales estimates diverged from actual sales by an average of 40%.
“This excess may be in part due to mis-estimation of demand, as the same organization noted that corporate sales estimates diverged from actual sales by an average of 40% [847].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [847]
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- Interpretationcitation
To transfer strategies across cities, it is possible to cluster similar cities based on climate-relevant dimensions.
“To transfer strategies across cities, it is possible to cluster similar cities based on climate-relevant dimensions [326,494].”
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- cites [494] source
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In 2006, at least two Scottish seafood firms flew hundreds of metric tons of shrimp from Scotland to China and Thailand for peeling, then back to Scotland for sale because they could save on labor costs.
“In 2006, at least two Scottish seafood firms flew hundreds of metric tons of shrimp from Scotland to China and Thailand for peeling, then back to Scotland for sale-because they could save on labor costs [147].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [147]
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- Premisecitation
Global excess inventory in 2011 amounted to about $8 trillion worth of goods.
“Global excess inventory in 2011 amounted to about $8 trillion worth of goods, according to the Council of Supply Chain Management Professionals [847].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [847]
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- Interpretationcitation
ML can potentially assist with other issues related to food waste, such as helping develop sensors to identify when produce is about to spoil, so it can be sold faster or removed from a storage crate before it ruins the rest of the shipment.
“ML can also potentially assist with other issues related to food waste, such as helping develop sensors to identify when produce is about to spoil, so it can be sold faster or removed from a storage crate before it ruins the rest of the shipment [256].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [256]
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- Premisecitation
ML-based analysis has shown that the development of efficient public transportation is dependent on both the extent of urban sprawl and the local development around transportation hubs.
“ML-based analysis has shown that the development of efficient public transportation is dependent on both the extent of urban sprawl and the local development around transportation hubs [547,737].”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Researchers have combined ML with generative design to develop structural products that require less raw material, thus reducing the resulting GHG emissions.
“To reduce the use of cement and steel, researchers have combined ML with generative design to develop structural products that require less raw material, thus reducing the resulting GHG emissions [416].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [416] source
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- Premisecitation
The production, shipment, and climate-controlled warehousing of excess products is a major source of industrial GHG emissions, particularly for time-dependent goods such as perishable food or retail goods that quickly fall out of fashion.
“The production, shipment, and climate-controlled warehousing of excess products is a major source of industrial GHG emissions, particularly for time-dependent goods such as perishable food or retail goods that quickly fall out of fashion [828].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [828] source
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- Premisecitation
Urban sprawl influences the energy use from transport, as wider cities tend to be more car-oriented.
“For instance, urban sprawl influences the energy use from transport, as wider cities tend to be more car-oriented [151,189,228].”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
Creutzig et al. related the energy use of 300 cities worldwide to historical structural factors such as fuel taxes, which have a strong impact on urban sprawl.
“Creutzig et al. [151] related the energy use of 300 cities worldwide to historical structural factors such as fuel taxes (which have a strong impact on urban sprawl).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [151] source
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- Interpretationreasoning
The availability of large quantities of data, combined with affordable cloud-based storage and computing, indicates that industry may be an excellent place for ML to make a positive climate impact.
“The availability of large quantities of data, combined with affordable cloudbased storage and computing, indicates that industry may be an excellent place for ML to make a positive climate impact.”
Quote in the paperSubstantial claim - Interpretationreasoning
There are a number of highly impactful applications where ML can help reduce GHG emissions in industry, with several caveats.
“As we have seen, there are a number of highly impactful applications where ML can help reduce GHG emissions in industry, with several caveats.”
Quote in the paperSubstantial claim - Premisecitation
Researchers are experimenting with supervised learning and thermal imaging systems to rapidly identify promising catalysts and chemical reactions.
“Researchers are also experimenting with supervised learning and thermal imaging systems to rapidly identify promising catalysts and chemical reactions [140,686], as described in Section 2.1.1.”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationcitation
On the production side, ML can potentially improve the efficiency of HVAC systems and other industrial control mechanisms if given necessary data about all relevant processes.
“On the production side, ML can potentially improve the efficiency of HVAC systems and other industrial control mechanisms-if given necessary data about all relevant processes.”
Quote in the paperSubstantial claim - Premisecitation
According to one estimate, about a third of GHG emissions reductions could come from better land management and agriculture.
“According to one estimate [335], about a third of GHG emissions reductions could come from better land management and agriculture.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [335]
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- Interpretationcitation
Deep neural networks could be used for adaptive control in a variety of industrial networking applications, enabling energy savings through self-learning about devices' surroundings.
“Deep neural networks could also be used for adaptive control in a variety of industrial networking applications [8], enabling energy savings through self-learning about devices' surroundings.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] source
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- Interpretationreasoning
ML may be particularly useful for enabling more flexible operation of industrial electrical loads, through optimizing a firm's demand response to electricity prices.
“ML may be particularly useful for enabling more flexible operation of industrial electrical loads, through optimizing a firm's demand response to electricity prices (see also Section 2).”
Quote in the paperSubstantial claim - Premisecitation
Synthesizing nitrogen-based fertilizers consumes about 2% of global energy consumption.
“Synthesizing these fertilizers consumes massive amounts of energy, about 2% of global energy consumption [551] (see Section 5.2).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [551] source
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- Premisereasoning
We can observe GHGs with hyperspectral cameras because they interact with sunlight.
“While GHGs are invisible to our eyes, they must by definition interact with sunlight.This means that we can observe these compounds with hyperspectral cameras [428,711].”
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- cites [711] source
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- Interpretationcitation
ML will become a much more viable option for industry when factory workers can identify, develop, implement, and monitor their own solutions internally instead of relying upon outside experts.
“ML will become a much more viable option for industry when factory workers can identify, develop, implement, and monitor their own solutions internally instead of relying upon outside experts [337].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [337]
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- Premisecitation
Some nitrogen fertilizer is converted to nitrous oxide, a GHG that is about 300 times more potent than CO2.
“Moreover, while some of this nitrogen is absorbed by plants or retained in the soil, some is converted to nitrous oxide, 24a GHG that is about 300 times more potent than CO 2 .”
Quote in the paperNot a substantial claim - Premisereasoning
Crop production is a significant source of GHG emissions.
“Crop production is a significant source of GHG emissions.”
Quote in the paperSubstantial claim - Interpretationnone given
Digital twins can reduce production waste by identifying broken or about-to-break machines before the actual factory equipment starts producing damaged products.
“Digital twins can also reduce production waste by identifying broken or about-to-break machines before the actual factory equipment starts producing damaged products.”
Quote in the paperNot a substantial claim - Premisecitation
Using semi-supervised generative models and concrete compression data, Ge et al. proposed novel, low-emission concrete formulas that could satisfy desired structural characteristics.
“Using semi-supervised generative models and concrete compression data, for example, Ge et al. proposed novel, low-emission concrete formulas that could satisfy desired structural characteristics [271].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [271]
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- Premisecitation
Many satellites are equipped with hyperspectral cameras and can perform, to some extent, estimations of CO2, CH4, H2O, and N2O emissions.
“Many satellites are equipped with such cameras and can perform, to some extent, estimations of CO 2 , CH 4 (methane), H 2 O, and N 2 O (nitrous oxide) emissions [73,378].”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationcitation
ML can help in proving that tracts of forest are indeed being preserved, thereby providing verification of carbon credits or other incentive structures for land custodians or owners.
“ML can also help in proving that tracts of forest are indeed being preserved (integrating data sources such as satellite imagery, UAV-based monitoring, and indigenous participatory mapping), thereby providing verification of carbon credits or other incentive structures for land custodians or owners [264, 501].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [264]
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- cites [501]
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- Interpretationcitation
Significantly reducing emissions may require a shift in agricultural paradigms, for example, widespread adoption of regenerative agriculture, silvopasture, and tree intercropping.
“Moreover, significantly reducing emissions may require a shift in agricultural paradigms, for example, widespread adoption of regenerative agriculture, silvopasture, and tree intercropping [335].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [335]
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- Premisenone given
Small forest fires are part of natural forest cycles.
“On the other hand, small forest fires are part of natural forest cycles.”
Quote in the paperSubstantial claim - Interpretationcitation
ML can be used to help build incentive structures for sustainable forest management.
“ML can also be used to help build incentive structures for sustainable forest management.”
Quote in the paperSubstantial claim - Premisecitation
A single peat fire in Indonesia in 1997 is reported to have released emissions comparable to 20%-50% of global fossil fuel emissions during the same year.
“A single peat fire in Indonesia in 1997 is reported to have released emissions comparable to 20%-50% of global fossil fuel emissions during the same year [606].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [606] source
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- Premisecitation
Only 17% of the world's forests are legally protected.
“Only 17% of the world's forests are legally protected [506].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [506]
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- Premisecitation
ML has been used successfully to classify some extreme weather events.
“ML has been used successfully to classify some extreme weather events.”
Quote in the paperNot a substantial claim - Premisecitation
Citizen science can enable dataset collection at a scale impossible in individual studies.
“Citizen science can also enable dataset collection at a scale impossible in individual studies [93,533,634,767].”
Quote in the paperNot a substantial claim0 of 4 sources checked - Premisereasoning
Intermediate time scales, of weeks to months, are exceptionally difficult to predict.
“Intermediate time scales, of weeks to months, are exceptionally difficult to predict, although Cohen et al. [139] argue that ML could bridge that gap by making good predictions on four to six week timescales [363].”
Quote in the paperSubstantial claim - Premisecitation
Computer vision can be used to classify species from camera traps, supporting a real-time, less labor-intensive species count.
“For instance, camera traps take photos automatically whenever a motion sensor is activated—computer vision can be used to classify the species that pass by, supporting a real-time, less labor-intensive species count [64,585,643].”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
ML can predict localized flooding patterns from past data.
“For example, ML can predict localized flooding patterns from past data [625], which could inform individuals buying insurance or homes.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [625]
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- Interpretationreasoning
Many of the ML applications outlined in the carbon dioxide removal section are either speculative or in the early stages of development or commercialization.
“As such, many of the ML applications we outline in this section are either speculative or in the early stages of development or commercialization.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Recent trends have created opportunities for ML to advance the state-of-the-art in climate prediction.
“Recent trends have created opportunities for ML to advance the state-of-the-art in climate prediction (Figure 6).”
Quote in the paperSubstantial claim- Rests on an assumption: New and cheaper satellites are creating petabytes of climate observation data.
- Rests on an assumption: Massive climate modeling projects are generating petabytes of simulated climate data.
- Rests on an assumption: ML methods are becoming increasingly fast to train and run.
- Premisereasoning
Many climate prediction problems are irremediably datalimited.
“Many climate prediction problems are irremediably datalimited.”
Quote in the paperSubstantial claim - Interpretationreasoning
The next most important targets for climate model improvements are ice sheet dynamics and sea level rise.
“The next most important targets for climate model improvements are ice sheet dynamics and sea level rise.”
Quote in the paperNot a substantial claim - Premisecitation
Manual, on-the-ground observation can be accelerated by annotation of remote sensing data.
“This has traditionally been done via manual, on-the-ground observation, but the process can be accelerated by annotation of remote sensing data [91,92,513,637] (see also Section 6.1).”
Quote in the paperNot a substantial claim0 of 4 sources checked - Premisecitation
ML can reduce waste along food supply chains.
“As detailed in Section 5, ML can reduce waste along these chains; we emphasize that for adaptation, it is important that supply chains also be made robust to unexpected disruptions [203,568,639,665].”
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- cites [568]
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- cites [639]
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- cites [665] source
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- Interpretationcitation
Replacing select climate model components with neural network approximators may improve both the cost and the accuracy of global climate models.
“Replacing select climate model components with neural network approximators may thus improve both the cost and the accuracy of global climate models.”
Quote in the paperSubstantial claim - Premisecitation
Deep learning can help speed up simulation of carbon dioxide plume migration in sequestration reservoirs.
“Deep learning can also help speed up simulation of carbon dioxide plume migration in sequestration reservoirs [836].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [836] source
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- Premisecitation
Individuals whose livelihoods depend on one activity, and who have less access to community resources, are those who are most at risk.
“Individuals whose livelihoods depend on one activity, and who have less access to community resources, are those who are most at risk [7,692].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [7]
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- cites [692] source
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- Premisecitation
It is possible to incorporate flood hazard and traffic information in order to uncover vulnerable stretches of road.
“In road networks, it is possible to incorporate flood hazard and traffic information in order to uncover vulnerable stretches of road, especially those with few alternative routes [314].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [314] source
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- Premisecitation
Oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces.
“Oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces [31].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] source
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- Premisecitation
The best climate predictions are synthesized from ensembles of 20+ climate models.
“The best climate predictions are synthesized from ensembles of 20+ climate models [782].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [782] source
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- Premisecitation
It is possible to distill relevant signals from mobile phones, credit card transactions, and social media data.
“For real-time and near-term systems, it is possible to distill relevant signals from mobile phones, credit card transactions, and social media data [171,434,645].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [171]
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- cites [434] source
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- cites [645]
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- Premisecitation
Existing climate models deal with this limitation by relying heavily on physical laws.
“Existing climate models deal with this limitation by relying heavily on physical laws, such as thermodynamics [214,300].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [214]
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- cites [300]
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- Premisecitation
Decreases in crop yield, due to drought and other factors, will pose a threat to food security.
“Decreases in crop yield, due to drought and other factors, will pose a threat to food security, as already evidenced by long periods of drought in North America, West Africa, and East Asia [157,636].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [157] source
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- cites [636]
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- Premisecitation
Climate forecasts are computationally expensive.
“Third, climate forecasts are computationally expensive [115] (the simulations in [415] took three weeks to run on NCAR supercomputers), while ML methods are becoming increasingly fast to train and run, especially on next-generation computing hardware.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [115]
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- Interpretationcitation
Many components of large climate models can be replaced with ML models at lower computational costs.
“The examples above have shown that many components of large climate models can be replaced with ML models at lower computational costs.”
Quote in the paperSubstantial claim - Interpretationcitation
ML can help properly surface insights from social media data, compressing large volumes of social media data into key takeaways that disaster managers can act upon.
“ML can help properly surface these insights, compressing large volumes of social media data into the key takeaways, which can be acted upon by disaster managers [118,368,596].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [118]
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- Rests on an assumption: Social media data can contain kernels of insight—places without water, clinics without supplies—which can inform relief efforts.
- Premisecitation
ML has had recent success in predicting specific chemical, material, and optical properties without the need for expensive experimentation or brute-force simulation, including in aerosols.
“ML has had recent success in predicting specific chemical, material, and optical properties without the need for expensive experimentation or brute-force simulation [105,299,490,653], including in aerosols [465,500].”
Quote in the paperSubstantial claim0 of 6 sources checked - Premisecitation
Temperature observations after volcanic eruptions, which release sulfates into the stratosphere when sufficiently large, provide data for sulfate aerosol research.
“(These data are largely in the form of temperature observations after volcanic eruptions, which release sulfates into the stratosphere when sufficiently large [691].)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [691] source
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- Premisecitation
Accurate and well-annotated maps can inform evacuation planning, retrofitting campaigns, and delivery of relief.
“Accurate and wellannotated maps can inform evacuation planning, retrofitting campaigns, and delivery of relief [59,200].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [59] source
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- cites [200]
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- Premisecitation
Aerial imagery can assist damage assessment by comparing scenes immediately pre- and post-disaster.
“Further, this imagery can assist damage assessment, by comparing scenes immediately pre-and post-disaster [315,816].”
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[247] use Gaussian processes to emulate climate model outputs based on nine possible aerosol parameter settings, allowing them to establish plausible parameter ranges with only 350 climate model runs instead of >100,000.
“As a recent example, [247] use Gaussian processes to emulate climate model outputs based on nine possible aerosol parameter settings, allowing them to establish plausible parameter ranges (and thus much better calibrated error-bars) with only 350 climate model runs instead of >100,000.”
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- Premisecitation
The potential for solar geoengineering to moderate some of the most catastrophic hazards of climate change is well-established.
“However, the potential for solar geoengineering to moderate some of the most catastrophic hazards of climate change is well-established [374], and it has received increasing attention in the wake of societal inaction on mitigation.”
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- Premisecitation
Disease surveillance and outbreak forecasting systems can be built from web data and specially-designed apps, in addition to traditional surveys.
“Disease surveillance and outbreak forecasting systems can be built from web data and specially-designed apps, in addition to traditional surveys [394,467,627].”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
Sulfates come with a well-studied risk of ozone loss.
“Sulfates come with a well-studied risk of ozone loss [210], and they make sunlight slightly more diffuse, which can impact agriculture [642].”
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- Premisecitation
Solar geoengineering comes with the risk of termination shock (fast, catastrophic warming if humanity undertakes solar geoengineering but stops suddenly).
“Moreover, unlike CO 2 removal, it cannot simply reverse the effects of climate change (average temperatures may return to pre-industrial levels, but location-specific climates still change), and also comes with the risk of termination shock (fast, catastrophic warming if humanity undertakes solar geoengineering but stops suddenly) [615].”
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- Premisecitation
A variety of ML techniques have been used to effectively disaggregate household energy, such as spectral clustering, Hidden Markov Models, and neural networks.
“A variety of ML techniques have been used to effectively disaggregate household energy, such as spectral clustering, Hidden Markov Models, and neural networks [33].”
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- Premisecitation
Many environmental groups and governmental bodies have come down on the side of supporting further research on solar geoengineering.
“and ultimately many environmental groups and governmental bodies have come down on the side of supporting further research [145,258,592].”
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These predictions can be used to tailor customized interventions for high-emissions households.
“These predictions can be used to tailor customized interventions for highemissions households [398].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [398] source
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- Premisecitation
Combining these predictions with disaggregated energy data allows for the efficient automation of household energy consumption.
“Combining these predictions with disaggregated energy data allows for the efficient automation of household energy consumption, ideally through products that present interpretable insights to the consumer (e.g., [721,760]).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
ML has revealed that cross-cultural support for international climate programs is not reduced, even when individuals are exposed to information about other countries' climate behavior.
“Similarly, ML has revealed that cross-cultural support for international climate programs is not reduced, even when individuals are exposed to information about other countries' climate behavior [65].”
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- Premisecitation
Data show that the size and composition of household carbon footprints varies dramatically across geographic regions and demographics.
“all the more so because data show that the size and composition of household carbon footprints varies dramatically across geographic regions and demographics [399].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [399] source
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- Interpretationcitation
Interventions minimizing psychological distance to the effects of climate change may be most effective.
“This suggests that interventions minimizing psychological distance to the effects of climate change may be most effective.”
Quote in the paperSubstantial claim - Premisecitation
Methods like RL can be used to learn how to optimally schedule household appliances to consume energy more efficiently and sustainably.
“Methods like RL can be used to learn how to optimally schedule household appliances to consume energy more efficiently and sustainably [543,679].”
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Standby power consumption accounts for roughly 8% of residential electricity demand.
“standby power consumption accounts for roughly 8% of residential electricity demand [507].”
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- Premisecitation
Path analysis has shown that an individual's psychological distance to climate change fully mediates their level of climate change concern.
“For example, path analysis has shown that an individual's psychological distance to climate change (on geographic, temporal, social, and uncertainty dimensions) fully mediates their level of climate change concern [397].”
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- Premisecitation
Such techniques have improved the enrollment rate of customers in an energy savings program by 2-3x.
“Such techniques have improved the enrollment rate of customers in an energy savings program by 2-3x [14].”
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- Premisecitation
A large meta-analysis found that significant residential energy savings can be achieved by targeting the right interventions to the right households.
“A large meta-analysis found that significant residential energy savings can be achieved [215], by targeting the right interventions to the right households [14,16,17].”
Quote in the paperSubstantial claim0 of 4 sources checked - Premisecitation
Multi-agent RL can be applied to understand the behavior of groups of agents who need to cooperate.
“Multi-agent RL can also be applied to understand the behavior of groups of agents who need to cooperate; see [607] for an overview and [385,475] for recent examples.”
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ML can help identify the roles of conditions directly from data in ABMs.
“Here, ML can help identify the roles of these conditions directly from data [58].”
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- Premisecitation
ML can help determine an individual's carbon footprint from their personal and household data.
“Fortunately, ML can help determine an individual's carbon footprint from their personal and household data [790].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [790]
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- Premisecitation
Many carbon mitigation strategies also provide cost savings to consumers.
“studies have shown that many carbon mitigation strategies also provide cost savings to consumers [399].”
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- Premisecitation
Image generation techniques such as CycleGANs have been used to visualize the potential consequences of extreme weather events on houses and cities.
“To make the effects of climate change more real for consumers, and thus help motivate those who wish to act, image generation techniques such as CycleGANs have been used to visualize the potential consequences of extreme weather events on houses and cities [716].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [716]
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- Premisecitation
Solar geoengineering could be a moderating force against the uncertainties climate change already introduces.
“However, although it creates new risks, solar geoengineering could actually be a moderating force against the terrifying uncertainties climate change already introduces [374,509]”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Residences are responsible for a large share of GHG emissions.
“Residences are responsible for a large share of GHG emissions [372] (see also Section 4).”
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- Premisecitation
Multi-agent learning has been applied to coordinate groups of homes to balance energy consumption and keep peak demand low.
“Multi-agent learning has also been applied to this problem, to ensure that groups of homes can coordinate to balance energy consumption to keep peak demand low [662,863].”
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ML can predict a household's emissions in transportation, energy, water, waste, foods, goods, and services, as a function of its characteristics.
“ML can predict a household's emissions in transportation, energy, water, waste, foods, goods, and services, as a function of its characteristics [399].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [399] source
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- Premisecitation
ABMs have been applied to a multitude of problems relevant to climate change, in particular to study low-carbon technology adoption.
“ABMs have been applied to a multitude of problems relevant to climate change, in particular to study low-carbon technology adoption [320,584,656,878].”
Quote in the paperNot a substantial claim0 of 4 sources checked - Premisecitation
Climate change may diminish educational outcomes for some populations, due to its negative effects on agricultural productivity and household income.
“As climate change itself may diminish educational outcomes for some populations, due to its negative effects on agricultural productivity and household income [666,667], this makes providing high-quality educational interventions globally all the more important.”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Natural language processing methods are already used in the field of political science to analyze political texts and legislation.
“In particular, natural language processing methods are already used in the field of political science to analyze political texts and legislation [307]; these approaches could be promising for systematically studying climate change policies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [307] source
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- Premisecitation
ML has been used to examine the factors influencing the financial viability of carbon offset projects.
“ML has been used to examine the factors influencing the financial viability of such projects [425].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [425] source
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- Premisecitation
Natural language processing, network analysis, and clustering techniques can be used to analyze social media data to understand public opinions and discourse around climate change.
“Natural language processing, network analysis, and clustering techniques can also be used to analyze social media data to understand public opinions and discourse around climate change [436,812,844].”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
Climate change poses substantial financial risks to global assets measured in the trillions of dollars.
“Climate change poses a substantial financial risks to global assets measured in the trillions of dollars [185]”
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- Premisereasoning
The available data may not be representative of global use cases.
“Moreover, the available data may not be representative of global use cases.”
Quote in the paperSubstantial claim - Interpretationreasoning
Tools from transfer learning and domain adaptation will likely prove essential in low-data settings.
“Tools from transfer learning and domain adaptation will likely prove essential in low-data settings.”
Quote in the paperNot a substantial claim - Interpretationcitation
ML can help create personalized educational tools for climate learning, for instance by generating images of potential future impacts of extreme weather events based on a learner's address or by anchoring learning in a digital replica of their real-life location.
“In these contexts, ML can help create personalized educational tools, for instance by generating images of potential future impacts of extreme weather events based on a learner's address [716] or by anchoring an individual's learning experience in a digital replica of their real-life location and allowing them to explore the way that climate change will impact a specific location [28].”
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- cites [28] source
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- Interpretationreasoning
The current financial system's focus on quarterly or yearly performance fails to incentivize the prediction of medium or long-term risks, which include most climate change-related exposures.
“This fails to incentivize the prediction of medium or long-term risks, which include most climate change-related exposures such as physical impacts on assets or distribution chains, legislative impacts on profit generation, and indirect market consequences such as supply and demand.”
Quote in the paperNot a substantial claim- Rests on an assumption: The majority of the current financial system is based on quarterly or yearly performance.
- Classified as probably not a substantial claim. 194
- May combine two claims. 212
Classified as probably not a substantial claim.
PremisereasoningVariable electricity generators are supported by some mix of natural gas plants, storage, or other controllable sources ready to buffer changes in their output.
“This means that solar panels, wind turbines, and other variable electricity generators are supported by some mix of natural gas plants, storage, or other controllable sources ready to buffer changes in their output”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningML algorithms of the future will need to incorporate domain-specific insights.
“While much of this previous work has used domain-agnostic techniques, ML algorithms of the future will need to incorporate domain-specific insights.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningForecasts will need to become increasingly accurate, span multiple horizons in time and space, and better quantify uncertainty to support these use cases.
“forecasts will need to become increasingly accurate, span multiple horizons in time and space, and better quantify uncertainty to support these use cases.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisereasoningThe scheduling and dispatch process is slow and complex, as it is governed by NP-hard optimization problems such as unit commitment and optimal power flow that must be coordinated across multiple time scales.
“This process is slow and complex, as it is governed by NP-hard optimization problems such as unit commitment and optimal power flow that must be coordinated across multiple time scales (from sub-second to days ahead).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningSince weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting and in hybrid physics-plus-ML modeling techniques.
“since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting (Section 8) and in hybrid physics-plus-ML modeling techniques [161,818,822].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S4 (based).
Premisenone givenToday, the buffer for variable generators is often provided by coal and natural gas plants run in a CO2-emitting standby mode called spinning reserve.
“Today, this buffer is often provided by coal and natural gas plants run in a CO 2emitting standby mode called spinning reserve.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationDynamic scheduling and (safe) reinforcement learning could also be used to balance the electric grid in real time; some electricity system operators have started to pilot similar methods at small, test case-based scales.
“Dynamic scheduling [225,546] and (safe) reinforcement learning (RL) could also be used to balance the electric grid in real time; in fact, some electricity system operators have started to pilot similar methods at small, test case-based scales [520].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningBetter short-term forecasts can allow system operators to reduce their reliance on polluting standby plants and to proactively manage increasing amounts of variable sources.
“Better shortterm forecasts can allow system operators to reduce their reliance on polluting standby plants and to proactively manage increasing amounts of variable sources.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationClimate change effects are increasingly visible; storms, droughts, fires, and flooding have become stronger and more frequent.
“The effects of climate change are increasingly visible. 1 Storms, droughts, fires, and flooding have become stronger and more frequent [239].”
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InterpretationcitationML can help improve the existing centralized process of scheduling and dispatch by speeding up power system optimization problems and improving the quality of optimization solutions.
“ML can help improve the existing (centralized) process of scheduling and dispatch by speeding up power system optimization problems and improving the quality of optimization solutions [333,608].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [333] source
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- cites [608]
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Premisenone givenRecent work has examined how to (at least partially) decentralize scheduling and dispatch using energy storage, flexible demand, low-carbon generators, and other resources connected to the electric grid.
“recent work has examined how to (at least partially) decentralize scheduling and dispatch using energy storage, flexible demand, low-carbon generators, and other resources connected to the electric grid.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningML can both reduce emissions from today's standby generators and enable the transition to carbon-free systems by helping improve forecasting, scheduling, and control and by helping create advanced electricity markets.
“ML can both reduce emissions from today's standby generators and enable the transition to carbon-free systems by helping improve necessary technologies (namely forecasting, scheduling, and control) and by helping create advanced electricity markets that accommodate both variable electricity and flexible demand.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationnone givenAnother strategy is to let storage, demand, and generation respond to real-time prices that reflect how emissions-intensive electricity currently is.
“Another strategy is to let storage, demand, and generation respond to real-time pricesfoot_11 that reflect (for example) how emissions-intensive electricity currently is.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationML is used widely to accelerate activities such as fossil fuel exploration and extraction.
“ML is used widely to accelerate activities such as fossil fuel exploration and extraction [303,406,814]”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [303]
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- cites [406]
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- cites [814]
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InterpretationreasoningSince variable generation and electricity demand both fluctuate, they must be forecast ahead of time to inform real-time electricity scheduling and longer-term system planning.
“Since variable generation and electricity demand both fluctuate, they must be forecast ahead of time to inform real-time electricity scheduling and longer-term system planning.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningBetter long-term forecasts can help system operators and investors determine where and when variable plants should be built.
“Better long-term forecasts can help system operators (and investors) determine where and when variable plants should be built.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningScheduling processes must improve significantly for operators to manage systems with a high reliance on variable sources.
“Scheduling processes must therefore improve significantly for operators to manage systems with a high reliance on variable sources.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationML can be used to approximate or simplify existing optimization problems, find good starting points for optimization, identify redundant constraints, learn from the actions of power system control engineers, or do some combination of these.
“ML can be used to approximate or simplify existing optimization problems [75,242,311,871], find good starting points for optimization [52,196,382], identify redundant constraints [541], learn from the actions of power system control engineers [197], or do some combination of these [858].”
Quote in the paperSubstantial claim0 of 8 sources checked- cites [75]
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- cites [242] source
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- cites [311]
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- cites [871] source
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- cites [52]
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- cites [196]
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- cites [382]
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- cites [541]
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Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenML contributions to electricity systems include accelerating development of clean energy technologies, improving forecasts of demand and clean energy, improving electricity system optimization, and enhancing system monitoring.
“Such contributions include accelerating the development of clean energy technologies, improving forecasts of demand and clean energy, improving electricity system optimization, and enhancing system monitoring.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationHybrid physics-plus-ML techniques can help improve short-to medium-term forecasts, and are also necessary for ML to contribute to longer-term forecasts since weather distributions shift over time.
“Such techniques can help improve short-to medium-term forecasts, and are also necessary for ML to contribute to longer-term (e.g., year-scale) forecasts since weather distributions shift over time [404].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [404] source
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InterpretationreasoningML can contribute to reducing GHG emissions from electricity systems by informing the research, deployment, and operation of electricity system technologies.
“ML can contribute on all fronts by informing the research, deployment, and operation of electricity system technologies (Figure 1).”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationOne strategy is to explicitly design local control algorithms; recent work has controlled energy storage and solar inverters using supervised learning techniques trained on historical optimization data.
“One strategy is to explicitly design local control algorithms; for instance, recent work has controlled energy storage and solar inverters using supervised learning techniques trained on historical optimization data [191,192,410,411].”
Quote in the paperNot a substantial claim0 of 4 sources checkedMay combine two claims.
InterpretationcitationML can help both to design real-time prices and to respond to these prices.
“In this case, ML can help both to design real-time prices and to respond to these prices.”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe 2018 intergovernmental report on climate change estimated that the world will face catastrophic consequences unless global GHG emissions are eliminated within 30 years.
“The 2018 intergovernmental report on climate change estimated that the world will face catastrophic consequences unless global greenhouse gas (GHG) emissions are eliminated within 30 years [372].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [372]
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Interpretationnone givenLow-carbon electricity sources are essential to tackling climate change.
“Low-carbon electricity sources are essential to tackling climate change.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationNuclear fusion reactors have the potential to produce safe and carbon-free electricity using a virtually limitless hydrogen fuel supply, but currently consume more energy than they produce.
“Nuclear fusion reactors [577] have the potential to produce safe and carbon-free electricity using a virtually limitless hydrogen fuel supply, but currently consume more energy than they produce [146].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [577]
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InterpretationreasoningFusion reactors require intelligent experimental design because they have a large number of tunable parameters; ML can help prioritize which parameter configurations should be explored during physical experiments.
“Fusion reactors require intelligent experimental design because they have a large number of tunable parameters; ML can help prioritize which parameter configurations should be explored during physical experiments.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrior work has tried to preemptively detect disruptions for tokamak reactors, using supervised learning methods such as support vector machines, adaptive fuzzy logic, decision trees, and deep learning on previous disruption data.
“Prior work has tried to preemptively detect disruptions for tokamak reactors, using supervised learning methods such as supportvector machines, adaptive fuzzy logic, decision trees, and deep learning [110,413,567,811,846,850] on previous disruption data.”
Quote in the paperNot a substantial claim0 of 6 sources checkedMay combine two claims.
PremisecitationThe process of discovering new materials can be slow and imprecise; the physics behind materials are not completely understood, so human experts often manually apply heuristics to understand a proposed material's physical properties.
“However, the process of discovering new materials can be slow and imprecise; the physics behind materials are not completely understood, so human experts often manually apply heuristics to understand a proposed material's physical properties [49,105].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPrevious work has used RL and dynamic programming to set real-time electricity prices and more broadly for power market design.
“Previous work has used RL and dynamic programming to set real-time electricity prices [30,90] and more broadly for power market design [30,881].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSince the locations and sizes of rooftop solar panels are often unknown to electricity system operators, previous work has used computer vision techniques on satellite imagery to generate size and location data for rooftop solar panels.
“Since the locations and sizes of rooftop solar panels are often unknown to electricity system operators, previous work has used computer vision techniques on satellite imagery to generate size and location data for rooftop solar panels [165,514,868].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningML for accelerated science may also have significant impacts outside electricity systems, e.g., by helping design alternatives to cement or create better CO2 sorbents.
“ML for accelerated science may also have significant impacts outside electricity systems, e.g., by helping design alternatives to cement (Section 5.2) or create better CO 2 sorbents (Section 7.1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationML techniques including supervised learning, active learning, and generative models have been used to help synthesize, characterize, model, and design materials.
“ML techniques including supervised learning, active learning, and generative models have been used to help synthesize, characterize, model, and design materials, as described in reviews [105,490] and more recent work [299].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenMuch more work is needed to test and scale existing decentralized solutions.
“In general, much more work is needed to test and scale existing decentralized solutions”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationML can inform policy for accelerated materials science; previous work has applied natural language processing to patent data to understand the solar panel innovation process.
“ML can inform policy for accelerated materials science; for instance, previous work has applied natural language processing to patent data to understand the solar panel innovation process [813].”
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PremisecitationPrevious and ongoing work has used sensor and/or satellite data to proactively suggest pipeline maintenance or detect existing leaks.
“Previous and ongoing work has used sensor and/or satellite data to proactively suggest pipeline maintenance [213,898] or detect existing leaks [81,749,823,826]”
Quote in the paperNot a substantial claim0 of 6 sources checkedClassified as probably not a substantial claim.
PremisereasoningRooftop solar and other distributed energy resources have created a two-way flow of electricity on distribution grids.
“However, rooftop solar and other distributed energy resources have created a”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPrior work has attempted to maximize electricity production by controlling solar panels or wind turbine blades using RL or Bayesian optimization.
“Prior work has attempted to maximize electricity production by controlling solar panels [1,3,678] or wind turbine blades [2,833] using RL or Bayesian optimization.”
Quote in the paperNot a substantial claim0 of 5 sources checkedMay combine two claims.
InterpretationcitationML can help accelerate fusion research by guiding experimental design and monitoring physical processes.
“ML can help accelerate this work by, e.g., guiding experimental design and monitoring physical processes; see also [360].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [360] source
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Interpretationnone givenReducing emissions from fossil fuels is a necessary stopgap while society transitions towards low-carbon electricity.
“Reducing emissions from fossil fuels is a necessary stopgap while society transitions towards low-carbon electricity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOther work seeking to improve battery storage technologies has combined first-principles physics calculations with support-vector regression to design conducting solids for lithium-ion batteries.
“Other work seeking to improve battery storage technologies has combined first-principles physics calculations with support-vector regression to design conducting solids for lithium-ion batteries [257].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [257] source
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InterpretationreasoningControllable low-carbon electricity sources can help achieve climate change goals while requiring very few changes to how the electric grid is run.
“Controllable low-carbon electricity sources can help achieve climate change goals while requiring very few changes to how the electric grid is run (since today's fossil fuel power plants are also controllable).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenML can support existing controllable technologies while accelerating the development of new technologies such as nuclear fusion power plants.
“ML can support existing controllable technologies while accelerating the development of new technologies such as nuclear fusion power plants.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationNovel challenges for ML in materials science include coping with moderately sized datasets and inferring physical principles from trained models.
“novel challenges for ML in materials science include coping with moderately sized datasets and inferring physical principles from trained models [800].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [800] source
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PremisecitationVehicles can be detected in high-resolution satellite images with high accuracy, and image counts can estimate average vehicle traffic.
“Vehicles can also be detected in high-resolution satellite images with high accuracy [177,390,566,747], and image counts can serve to estimate average vehicle traffic [405].”
Quote in the paperNot a substantial claim0 of 5 sources checkedMay combine two claims.
PremisereasoningChanging routing so trucks do not return empty reduces trips and GHG emissions.
“The same is true for changing routing so that trucks do not have to return empty.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationML methods can help impute missing data for precise bottom-up estimation of GHG emissions and are applied in simulation models of vehicle emissions.
“Similarly, ML methods can help with imputing missing data for precise bottom-up estimation of GHG emissions [583] and they are also applied in simulation models of vehicle emissions [46].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenML strategies are most effective in tandem with strong public policies.
“Typically, ML strategies are most effective in tandem with strong public policies.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisereasoningModeling demand and planning new infrastructure can significantly shape trip length and transport mode choice, and can reduce GHG emissions.
“Modeling demand and planning new infrastructure can significantly shape how long trips are and which transport modes are chosen by passengers and shippers-for example, discouraging sprawl and creating new transportation links can both reduce GHG emissions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningThe use of newer and more efficient vehicles, ideally electric, could increase with vehicle-sharing concepts, reducing GHG emissions.
“The use of newer and more efficient vehicles, ideally electric ones, could increase with vehicle-sharing concepts, reducing GHG emissions.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationOn-demand taxi services add mileage when traveling without a customer, possibly negating GHG emission savings.
“Similarly, on-demand taxi services add mileage when traveling without a customer, possibly negating any GHG emission savings [765].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [765] source
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PremisecitationShared mobility is disrupting travel and vehicle ownership, and ML plays an integral part in running and optimizing these services.
“In the passenger sector, shared mobility (such as on-demand ride services or vehicle-sharing 14 ), is undoubtedly disrupting the way people travel and think about vehicle ownership, and ML plays an integral part in running and optimizing these services (e.g., [768,837]).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [768]
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- cites [837] source
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Interpretationnone givenWith ML, vehicle-miles traveled can be reduced by making long trips less necessary, increasing loading, and optimizing vehicle routing.
“With the help of ML, the number of vehiclemiles traveled can be reduced by making long trips less necessary, increasing loading, and optimizing vehicle routing.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenShared cars can cause more people to travel by car instead of public transportation.
“For example, shared cars can actually cause more people to travel by car, as opposed to using public transportation.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationML offers new methods for spatio-temporal road traffic forecasting that do not always outperform other statistical methods but may transfer well between areas.
“and it offers new methods for spatio-temporal road traffic forecasting-which do not always outperform other statistical methods [223] but may transfer well between areas [776].”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
InterpretationcitationUsing autonomous shared vehicles alone could increase total vehicle-miles traveled and do not necessarily lead to lower emissions if vehicles have internal combustion engines or dirty electricity.
“However, using autonomous shared vehicles alone could increase the total vehicle-miles traveled and therefore do not necessarily lead to lower emissions as long as the vehicles have internal combustion engines (or electrical engines on a "dirty" electrical grid) [131,495].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenML can help reduce emissions from freight transportation of solid fuels, identify and manage storage sites for CO2 sequestered from power plant flue gas, and optimize power plant parameters to reduce CO2 emissions.
“ML can help reduce emissions from freight transportation of solid fuels (Section 3), identify and manage storage sites for CO 2 sequestered from power plant flue gas (Section 7.2), and optimize power plant parameters to reduce CO 2 emissions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTraffic monitoring with video systems, especially counting pedestrians and cyclists, can be automated with computer vision.
“Traffic is sometimes monitored with video systems, in particular when counting pedestrians and cyclists, which can be automated with computer vision [870].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [870] source
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InterpretationcitationCar-sharing services using AVs could yield GHG emission savings when they encourage public transit use or with autonomous EVs.
“Car-sharing services using AVs could yield GHG emission savings when they encourage people to use public transit for part of the journey [552] or with autonomous EVs [408].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [552] source
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- cites [408]
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Premisenone givenMany areas of transportation lack data, and decision-makers often design infrastructure and policy with uncertain information.
“Many areas of transportation lack data, and decision-makers often design infrastructure and policy with uncertain information.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningWhile some electricity losses are unavoidable, others can be significantly mitigated to reduce waste and emissions.
“While some of these losses are unavoidable, others can be significantly mitigated to reduce waste and emissions.”
Quote in the paperSubstantial claimMay combine two claims.
PremisereasoningFreight consolidation dramatically reduces the number of trips and therefore GHG emissions.
“Bundling shipments together, which is referred to as freight consolidation, dramatically reduces the number of trips (and therefore the GHG emissions).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenMuch of global transport mileage occurs inefficiently, resulting in needless GHG emissions.
“A colossal amount of transport occurs each day across the world, but much of this mileage occurs inefficiently, resulting in needless GHG emissions.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationSmall AVs such as delivery robots and drones could reduce energy consumption of last-mile delivery, though with regulatory challenges.
“First, small AVs, such as delivery robots and drones, could reduce the energy consumption of last-mile delivery [758], though they come with regulatory challenges [517].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRigas et al. detail methods by which ML can improve charge scheduling, congestion management, and vehicle-to-grid algorithms.
“Rigas et al. [684] detail methods by which ML can improve charge scheduling, congestion management, and vehicle-to-grid algorithms.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [684] source
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PremisecitationOther proposed and deployed ML applications include predicting arrival times or demand, identifying and planning around transportation disruptions, and clustering suppliers by location and shipping destinations.
“Other proposed and deployed applications of ML include predicting arrival times or demand, identifying and planning around transportation disruptions [279], and clustering suppliers by their geographical location and common shipping destinations.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [279]
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PremisecitationML is essential in the development of AVs, including following the road and detecting obstacles.
“ML is essential in the development of AVs, including in such basic tasks as following the road and detecting obstacles [87].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [87]
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InterpretationcitationTrucks can reduce energy consumption by platooning, alleviating challenges of electrifying long-distance road freight.
“Second, trucks can reduce energy consumption by platooning (driving very close together to reduce air resistance), thereby alleviating some of the challenges that come with electrifying long-distance road freight [318].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [318] source
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Premisenone givenVehicle-to-grid technology comes with private and social financial benefits.
“Vehicle-to-grid technology comes with private and social financial benefits.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationMost vehicles are not very efficient compared to what is technically possible; aircraft carbon intensity is expected to decline by more than a third from 2012 with newer models.
“Most vehicles are not very efficient compared to what is technically possible: for example, aircraft carbon intensity is expected to decline by more than a third with respect to 2012, simply by virtue of newer models replacing aging jets [713].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [713]
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InterpretationreasoningDisruptive technologies based on ML could replace or reduce transportation demand.
“Disruptive technologies that are based on ML could replace or reduce transportation demand.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenBoth vehicle design and operation can increase fuel economy.
“Both the design of the vehicle and the way it is operated can increase the fuel economy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationIt is uncertain to what extent virtual meetings replace physical travel or may give rise to more face-to-face meetings.
“However, it is uncertain to what extent virtual meetings replace physical travel, or if they may actually give rise to more face-to-face meetings [762].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [762] source
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PremisecitationML plays a role in studying consumer preferences in vehicle markets.
“Encouraging drivers to adopt more efficient vehicles is also a priority; while we do not focus on this here, ML plays a role in studying consumer preferences in vehicle markets [103].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [103] source
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PremisecitationML methods have been applied to battery energy management and to detect faults and lateral misalignment in wireless charging of EVs.
“ML methods have also been applied to battery energy management (for example charge estimation [330] or optimization in hybrid EVs [15]), and to detect faults and lateral misalignment in wireless charging of EVs [780].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationAVs could reduce energy consumption by reducing traffic congestion and inducing eco-driving, but could also increase overall road traffic and nullify efficiency gains.
“While AVs could reduce energy consumption-for example, by reducing traffic congestion and inducing efficiency through eco-driving-it is also possible that AVs will lead to an increase in overall road traffic that nullifies efficiency gains.”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLai et al. use computer vision to detect aerodynamically inefficient loading on freight trains.
“Lai et al. [461] use computer vision to detect aerodynamically inefficient loading on freight trains.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [461] source
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InterpretationreasoningAs more people drive EVs, understanding their use patterns will become more important.
“As more people drive EVs, understanding their use patterns will become more important.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationBattery EVs are typically not used for more than a fraction of the day, allowing them to act as energy storage for the grid at other times.
“Battery EVs are typically not used for more than a fraction of the day, allowing them to act as energy storage for the grid at other times, where charging and discharging is controlled for example by price signals [265] (see Sections 2.1.1 and 2.2).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [265] source
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Premisenone givenEVs can have very low GHG emissions depending on the carbon intensity of electricity.
“EVs can have very low GHG emissionsdepending, of course, on the carbon intensity of the electricity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningML can help decarbonize transportation by providing data, gaining knowledge from data, planning, and automation.
“ML can help decarbonize transportation by providing data, gaining knowledge from data, planning, and automation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenML can make low-carbon freight modes more competitive by helping coordinate intermodal transport.
“ML can also make low-carbon freight modes more competitive by helping to coordinate intermodal transport.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenShifting passengers and freight to low carbon-intensity modes is one of the most important means to decarbonize transport.
“Shifting passengers and freight to low carbon-intensity modes is one of the most important means to decarbonize transport.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningML is fundamental to shared mobility, AVs, EVs, and smart public transit, which with right incentives can enable significant GHG reductions.
“Moreover, ML is fundamental to shared mobility, AVs, EVs, and smart public transit, which, with the right incentives, can be used to enable significant reductions in GHG emissions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenFuels such as electrofuels and hydrogen can be produced using electricity-intensive processes and stored at lower cost than electricity.
“Fuels such as electrofuels and hydrogen can be produced using electricity-intensive processes and can be stored at lower cost than electricity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenCoordination between modes resulting in faster and more reliable transit times could increase the amount of people or goods traveling on low-carbon modes such as rail.
“Coordination between modes resulting in faster and more reliable transit times could increase the amount of people or goods traveling on low-carbon modes such as rail.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationreasoningDecarbonizing transport is essential to a low-carbon society, and there are numerous applications where ML can make an impact.
“Decarbonizing transport is essential to a low-carbon society, and there are numerous applications where ML can make an impact.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSun et al. use SVMs and neural networks for analyzing preferences of high-speed rail customers in China.
“Sun et al. [770] use SVMs and neural networks for analyzing preferences of customers traveling by high speed rail in China.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [770] source
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Premisenone givenTransportation causes a large share of GHG emissions, but reducing them has been slow and complex.
“This is because transportation causes a large share of GHG emissions, but reducing them has been slow and complex.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
PremisereasoningMany low-carbon modes must be integrated with other modes to deliver the same level of service.
“Many low-carbon modes must be integrated with other modes of transportation to deliver the same level of service.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThere is a rich literature exploring ML methods to predict bus arrival times and their uncertainty.
“For example, there is a rich literature exploring ML methods to predict bus arrival times and their uncertainty [18,526].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [18]
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- cites [526] source
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Interpretationnone givenML techniques may present opportunities for improvement at various stages of R&D of alternative fuels.
“ML techniques may present opportunities for improvement at various stages of research and development of alternative fuels (similar to applications in Section 2.1.1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningMerging academic and industry perspectives could yield significant benefits for battery ML.
“Merging these two perspectives could yield significant benefits for the field.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationcitationML algorithms could be applied to make public transportation faster and easier to use.
“ML algorithms could be applied to make public transportation faster and easier to use.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMany emerging bike and scooter sharing services are dockless, meaning they are parked anywhere in public space and can block sidewalks.
“Many emerging bike and scooter sharing services are dockless, which means that they are parked anywhere in public space and can block sidewalks [25].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [25]
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenModal shift in passenger transportation can involve providing public transit, which requires analyzing mode choice and travel demand data.
“This modal shift in passenger transportation can for example involve providing people with public transit, which requires analyzing mode choice and travel demand data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenBike sharing and electric scooter services can offer low-carbon alternatives for urban mobility that do not require ownership and integrate well with public transportation.
“Bike sharing and electric scooter services can offer low-carbon alternatives for urban mobility that do not require ownership and integrate well with public transportation.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationML can contribute by improving predictions of estimated time of arrival or weight/volume of expected freight.
“ML can contribute by improving predictions of the estimated time of arrival (for example, of freight trains [56]) or the weight or volume of expected freight (for example, for roll-on/roll-off transport-often abbreviated as Ro-Ro [560]).”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationML has been applied to monitor public sentiment about bike shares via tweets.
“ML has been applied to monitor public sentiment about such bike shares via tweets [775].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [775] source
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Interpretationnone givenSolutions for transport decarbonization are likely very technical, highly dependent on existing infrastructure, and require detailed understanding of behavior.
“Solutions are likely very technical, are highly dependent on existing infrastructure, and require detailed understanding of passengers' and freight companies' behavior.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningGiven their long-term importance and early stage, alternative fuels present a critical opportunity to mitigate climate change.
“Given their relative long-term importance and early stage of development, they present a critical opportunity to mitigate climate change.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSingla et al. propose a pricing mechanism based on online learning to provide monetary incentives for bike users to help rebalancing.
“Singla et al. [739] propose a pricing mechanism based on online learning to provide monetary incentives for bike users to help rebalancing.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [739]
Not checked in the free run.
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Premisenone givenIndustry lags behind in ML modeling, but real-world operational data are readily available.
“By contrast, industry lags behind in ML modeling, but real-world operational data are readily available.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningThere are many opportunities for ML to facilitate better integration of modes in passenger and freight sectors.
“There are many opportunities for ML to facilitate a better integration of modes, both in the passenger and freight sectors.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAviation, long-distance road transportation, and ocean shipping require high energy density fuels and are not conducive to electrification.
“Aviation, long-distance road transportation, and ocean shipping require fuels with high energy density and thus are not conducive to electrification [162].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [162] source
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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationcitationML could provide tools and information for regulators to ensure public space can be used by everyone.
“ML could also provide tools and information for regulators to ensure that public space can be used by everyone [741].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [741]
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PremisecitationThere is work on inferring people's travel modes and destinations from social media or mobile phone sensors such as GPS.
“There is also work on inferring people's travel modes and destinations from social media or various mobile phone sensors such as GPS (transportation mode detection), e.g., [156,798].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenUnderstanding which appliances drive energy use (energy disaggregation) is crucial for targeting efficiency measures and can motivate behavioral changes.
“Within a single building, understanding which appliances drive energy use (energy disaggregation) is crucial for targeting efficiency measures, and can motivate behavioral changes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationcitationDeep learning methods for counterfactual prediction could be useful for such problems.
“Such problems could represent a useful application of deep learning methods for counterfactual prediction [332].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [332]
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InterpretationreasoningThere are several promising ways to enhance HVAC operating performance, each providing substantial opportunities for ML: forecasting temperatures, better control, and fault detection.
“There are several promising ways to enhance HVAC operating performance, each providing substantial opportunities for using ML: forecasting what temperatures are needed throughout the system, better control to achieve those temperatures, and fault detection.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationBuildings have very long lifespans, so it is necessary to create new energy-efficient buildings and retrofit old ones.
“Buildings also have very long lifespans; thus, it is necessary both to create new, energy-efficient buildings, and to retrofit old buildings to be as efficient as possible [150].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [150] source
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May combine two claims.
Premisenone givenBuildings can respond to electricity grid signals, providing flexibility to grid operators and lowering consumer costs.
“Further, buildings can respond to signals from the electricity grid, providing flexibility to the grid operator and lowering costs to the consumer (Section 2.1.1).”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationEnergy game-theoretic frameworks can incentivize occupants to minimize energy demand, and ML can help develop tailored incentives.
“Energy game-theoretic frameworks can also incentivize occupants to actively minimize their energy demand, and ML can help here to develop tailored incentives based on different energy usage behaviors [159].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [159] source
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PremisecitationMany building systems can adjust operation based on occupancy, improving occupant comfort and energy use.
“Many systems within buildings-such as lights and heating-can also adjust how they operate based on whether a building or room is occupied, thereby improving both occupant comfort and energy use [614].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [614] source
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PremisecitationAutomated demand-side response can respond to electricity prices, smart meter signals, or learned user preferences.
“For this, automated demand-side response [357] can respond to electricity prices, smart meter signals, or learned user preferences [393].”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationMocanu et al. modeled building load profiles with RL and deep belief networks and used transfer learning to predict new buildings.
“For instance, Mocanu et al. [544] modeled building load profiles with RL and deep belief networks using data on commercial and residential buildings; they then used approximate RL and transfer learning to make predictions about new buildings, enabling the transfer of knowledge from commercial to residential buildings, and from gas-to power-heated buildings.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [544] source
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InterpretationreasoningML can help select strategies tailored to individual buildings and implement them via smart control systems.
“At the level of building management, ML can help select strategies that are tailored to individual buildings, and can also contribute to implementing those strategies via smart control systems (Section 4.1).”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationHVAC systems are notoriously inefficient and account for more than half of energy consumed in buildings.
“we focus on the example of heating, ventilation, and air conditioning (HVAC) systems, both because they are notoriously inefficient and because they account for more than half of the energy consumed in buildings [499].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [499]
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PremisecitationEnergy efficiency of cooling systems can degrade if refrigerant levels are low, and ML approaches are well-suited to detect faults.
“For example, the energy efficiency of cooling systems can degrade if refrigerant levels are low [435]; ML approaches are well-suited to detect faults in these systems.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [435] source
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PremisecitationIntelligent control systems in buildings can decrease carbon footprint by reducing energy consumed and integrating lower-carbon sources.
“Intelligent control systems in buildings can decrease their carbon footprint both by reducing the energy consumed and by providing means to integrate lower-carbon sources into the electricity mix [277].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [277] source
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PremisecitationKazmi et al. used deep RL to achieve a scalable 20% reduction of energy while requiring only three sensors.
“For control, Kazmi et al. [417] used deep RL to achieve a scalable 20% reduction of energy while requiring only three sensors: air temperature, water temperature, and energy use (see also Section 5.3 for similarly substantial gains in datacenter cooling).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [417] source
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InterpretationreasoningML provides critical tools for supporting building managers and policymakers in reducing GHG emissions.
“ML provides critical tools for supporting both building managers and policymakers in their efforts to reduce GHG emissions (Figure 3).”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationMany energy efficiency measures result in overall cost savings and yield other benefits such as cleaner air.
“Many of these energy efficiency measures actually result in overall cost savings [754] and simultaneously yield other benefits, such as cleaner air for occupants.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [754]
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InterpretationcitationML has the potential to greatly speed up building energy demand computations by ignoring physical knowledge, incorporating it, or learning surrogate models.
“ML has the potential to speed up these computations greatly, either by ignoring physical knowledge of the building entirely [452,617], by incorporating it into the computation [195], or by learning to approximate the physical model to reduce the need for expensive simulation (surrogate models) [274].”
Quote in the paperNot a substantial claim0 of 4 sources checkedMay combine two claims.
InterpretationcitationSmart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, including data matching, data fusion, and ensemble learning.
“Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, which includes data matching [89,190], data fusion [885], and ensemble learning [451].”
Quote in the paperNot a substantial claim0 of 4 sources checkedMay combine two claims.
PremisecitationLiDAR data are often used to retrieve heights or classify buildings at city scale, but its collection is expensive.
“To generate 3D models, LiDAR data are often used to retrieve heights or classify buildings at city scale [339,496], but its collection is expensive.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [339]
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- cites [496] source
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Premisenone givenFor many impactful mitigation strategies, coordination at district and city level is essential.
“For many impactful mitigation strategies-such as district heating and cooling, neighborhood planning, and large-scale retrofitting of existing buildings-coordination at the district and city level is essential.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThere have been attempts to upscale individual-building energy models to district scale.
“There have also been attempts to upscale individual-building energy models to the district scale.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenUrban Building Energy Models (UBEMs) provide simplified information on energy use of all buildings across a city.
“Urban Building Energy Models (UBEMs) provide simplified information on the energy use of all buildings across a city.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationML is uniquely capable of predicting energy consumption and GHG mitigation potential at scale from other available data, guiding policy design.
“ML is uniquely capable of predicting energy consumption and GHG mitigation potential at scale from other types of available data, thereby guiding policy design [536].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [536]
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PremisecitationKolter and Ferreira used Gaussian process regression to predict energy use from features such as property class or central air conditioning.
“For example, Kolter and Ferreira used Gaussian process regression to predict energy use from features such as property class or the presence of central air conditioning [444].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [444]
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Premisenone givenPolicymakers use instruments such as building codes, subsidies, public utility investments, and public-private partnerships to reduce GHG emissions without compromising equity.
“Policymakers use instruments such as building codes, retrofitting subsidies, investments in public utilities, and public-private partnerships in order to reduce GHG emissions without compromising equity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenUBEMs include features such as location, geometries, building footprint, usage, material, roof type, and surroundings.
“UBEMs include features such as the location, geometries, and various other attributes of interest like building footprint, usage, material, roof type, immediate surroundings, and the like.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationZhang et al. matched survey data with public use microdata to estimate residential energy consumption at neighborhood level.
“Zhang et al. [879] matched residential energy consumption survey data with public use microdata samples to estimate residential energy consumption at the neighborhood level.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [879] source
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PremisecitationRemote sensing is key to inferring infrastructure data, as satellite data are globally available and largely consistent.
“Remote sensing is key to inferring infrastructure data [79,224,339,496,535,868] as satellite data 19 present a source of information that is globally available and largely consistent worldwide.”
Quote in the paperSubstantial claim0 of 6 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationSmart home applications present security and privacy risks that require adequate regulation.
“Finally, smart home applications present security and privacy risks [144] that require adequate regulation.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [144] source
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Premisenone givenMany regions have almost no energy consumption data, making it difficult to design targeted mitigation strategies.
“However, many regions of the world have almost no energy consumption data, which can make it difficult to design targeted mitigation strategies.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationRebound effects are likely in certain cases, leading to additional building energy consumption typically between 10 and 20%.
“Second, rebound effects are likely to happen in certain cases [45], leading to additional building energy consumption typically ranging between 10 and 20% [671].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenUBEMs can be used for planning and operating district heating and cooling.
“UBEMs can for example be used for planning and operating district heating and cooling, where a central plant supplies many households in a district.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationUBEMs have yet to be adopted at scale but are expected to become fundamental for localized action by city planners.
“While UBEMs have yet to be adopted at scale, they are expected to become fundamental for enabling localized action by city planners [677].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [677] source
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PremisecitationDeep neural networks can monitor and optimize smart building operations.
“Deep neural networks can be used to monitor and optimize such operations [39].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [39] source
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PremisecitationDistrict heating and cooling reduces HVAC energy consumption and can provide flexible load, but needs large amounts of district-level data.
“In turn, district heating and cooling reduces HVAC energy consumption and can provide flexible load [808], but it needs large amounts of data at the district level for implementation and operation.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [808] source
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PremisereasoningUBEMs differ from individual building models by modeling all buildings with less detail and temporal granularity.
“These are different from individual building models, which model energy use of only specific buildings, but with finer details and temporal granularity (Section 4.1).”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationKontokosta and colleagues used ML to predict energy use of New York City's 1.1 million buildings, analyzed energy disclosure effect, and developed a ranking system.
“Based on energy data disclosed by residents of New York City, Kontokosta and colleagues used various ML methods to predict the energy use of the city's 1.1 million buildings [448], analyzed the effect of energy disclosure on the demand [610], and developed a system for ranking buildings based on energy efficiency [611].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationHard sensing is the primary data collection paradigm in many smart city applications, as it is adapted to precisely meet the application requirements.
“Hard sensing is the primary data collection paradigm in many smart city applications, as it is adapted to precisely meet the application requirements.”
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Interpretationnone givenMunicipal governments have a unique opportunity to mitigate climate change because most of the resources of the world are ultimately channeled into cities.
“Since most of the resources of the world are ultimately channeled into cities, municipal governments have a unique opportunity to mitigate climate change.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn developing countries, 40% of food waste occurs between harvest and processing or retail, while over 40% of food waste in industrialized nations occurs at the end of supply chains, in retail outlets, restaurants, and consumers' homes.
“In developing countries, 40% of food waste occurs between harvest and processing or retail, while over 40% of food waste in industrialized nations occurs at the end of supply chains, in retail outlets, restaurants, and consumers' homes [316].”
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InterpretationcitationAssuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project and the UCI Machine Learning Repository to invent new, climate-friendly materials.
“Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project [380] and the UCI Machine Learning Repository [271] to invent new, climate-friendly materials [830].”
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- The authors state this as an assumption: Future advances in materials science will occur, enabling ML research to draw upon open databases to invent new, climate-friendly materials.
May combine two claims.
InterpretationcitationML can help minimize emissions from cement and steel by reducing the need for carbon-intensive materials, by transforming industrial processes to run on low-carbon energy, and even by redesigning the chemistry of structural materials.
“ML can help minimize these emissions by reducing the need for carbon-intensive materials, by transforming industrial processes to run on low-carbon energy, and even by redesigning the chemistry of structural materials.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningWe have shown many different ways that ML can help to reduce GHG emissions from buildings and cities.
“We have shown many different ways that ML can help to reduce GHG emissions from buildings and cities.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationML and AI can help coordinate district heating and cooling networks, solar power generation, and charging stations for EVs and bikes, and can improve public lighting systems by regulating light intensity based on historical patterns of foot traffic.
“ML and AI can help, for example, to coordinate district heating and cooling networks, solar power generation, and charging stations for EVs and bikes [602], and can improve public lighting systems by regulating light intensity based on historical patterns of foot traffic [169].”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationThe global industrial sector spends billions of dollars annually gathering data on factories and supply chains, aided by improvements in the cost and accessibility of sensors and other data-gathering mechanisms.
“Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains [310]-aided by improvements in the cost and accessibility of sensors and other data-gathering mechanisms (such as QR codes and image recognition).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [310]
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InterpretationreasoningRecommender systems can potentially direct consumers and purchasing firms toward climate-friendly options, as long as one can obtain information about GHG emissions throughout the entire life-cycle of some product.
“Recommender systems can potentially direct consumers and purchasing firms toward climatefriendly options, as long as one can obtain information about GHG emissions throughout the entire life-cycle of some product.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationGreater efficiency may increase the production of goods and thus GHG emissions via rebound effects unless industrial actors have sufficient incentives to reduce overall emissions.
“However, it is worth noting that greater efficiency may increase the production of goods and thus GHG emissions (via rebound effects) unless industrial actors have sufficient incentives to reduce overall emissions [748].”
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PremisecitationIndustrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions.
“Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions [162].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [162] source
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InterpretationcitationDue to inherent variability in energy demand and supply, there is a need for uncertainty estimation, e.g., using Markov chain Monte Carlo methods or Gaussian processes.
“Due to inherent variability in energy demand and supply, there is a need for uncertainty estimation, e.g., using Markov chain Monte Carlo methods or Gaussian processes [602].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [602] source
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Premisenone givenA central challenge in the buildings and cities sector is the availability of high-quality data for training algorithms, which rarely go beyond main cities or represent the full spectrum of building types.
“A central challenge in this sector is the availability of high-quality data for training the algorithms, which rarely go beyond main cities or represent the full spectrum of building types.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningTechniques for obtaining high-quality data can themselves be an important application for ML, e.g., via computer vision algorithms to parse satellite imagery.
“Techniques for obtaining these data, however, can themselves be an important application for ML (e.g., via computer vision algorithms to parse satellite imagery).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationGlobally, society loses or wastes 1.3 billion metric tons of food each year, which translates to one-third of all food produced for human consumption.
“Globally, society loses or wastes 1.3 billion metric tons of food each year, which translates to onethird of all food produced for human consumption [316].”
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PremisecitationCement and steel production together account for over 10% of all global GHG emissions; the cement industry alone emits more GHGs than every country except the USA and China.
“Cement and steel production together account for over 10% of all global GHG emissions [243]; the cement industry alone emits more GHGs than every country except the USA and China [477].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [243]
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Interpretationnone givenFor many supply-chain applications to reduce emissions, firms' financial incentives must also align with climate change mitigation through carbon pricing or other policy mechanisms.
“However, for many of these applications to reduce emissions, firms' financial incentives must also align with climate change mitigation through carbon pricing or other policy mechanisms.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenML can help reduce emissions in supply chains by intelligently predicting supply and demand, identifying lower-carbon products, and optimizing shipping routes.
“ML can help reduce emissions in supply chains by intelligently predicting supply and demand, identifying lower-carbon products, and optimizing shipping routes.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationWhen smart city projects are properly integrated into urban planning, they can make cities more sustainable and foster low-carbon lifestyles.
“When smart city projects are properly integrated into urban planning, they can make cities more sustainable and foster low-carbon lifestyles (see [563,602,853] for extensive reviews on this topic).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
InterpretationcitationML can potentially reduce global emissions by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels.
“In particular, ML can potentially reduce global emissions (Figure 4) by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels [74,227,416,880].”
Quote in the paperSubstantial claim0 of 4 sources checkedMay combine two claims.
InterpretationreasoningML demonstrates considerable potential for reducing industrial GHG emissions when there is enough accessible high-quality data, firms have incentives to share data, aspects of production can be fine-tuned, and firms' incentives align with reducing emissions.
“ML demonstrates considerable potential for reducing industrial GHG emissions under the following circumstances:”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe clothing industry sells an average of only 60% of its wares at full price, but some brands can sell up to 85% due to just-in-time manufacturing and clever intelligence networks.
“For example, the clothing industry sells an average of only 60% of its wares at full price, but some brands can sell up to 85% due to just-in-time manufacturing and clever intelligence networks [294].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [294]
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PremisecitationThe literature on city-scale mitigation strategies is strongly biased towards the Global North, while key mitigation challenges are expected to arise from the Global South.
“The literature on city-scale mitigation strategies is also strongly biased towards the Global North [466], while key mitigation challenges are expected to arise from the Global South [570].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationcitationRealizing the potential of data-driven urban infrastructure can advance mitigation goals while improving the well-being of citizens.
“Realizing the potential of data-driven urban infrastructure can advance mitigation goals while improving the well-being of citizens [50,150,802].”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
InterpretationcitationML can help reduce food waste by optimizing delivery routes and improving demand forecasting at the point of sale, as well as improving refrigeration systems.
“ML can help reduce food waste by optimizing delivery routes and improving demand forecasting at the point of sale (see Section 5.1), as well as improving refrigeration systems [532] (see Section 5.3).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [532] source
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PremisecitationBerral et al. proposed a framework that demonstrates how such optimization algorithms might be combined with RL, digitized controls, and feedback systems to enable the autonomous control of industrial processes.
“Berral et al. proposed a framework that demonstrates how such optimization algorithms might be combined with RL, digitized controls, and feedback systems to enable the autonomous control of industrial processes [74].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [74] source
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PremisecitationMisjudgments can be very costly for manufacturers and retailers, leading most managers to adopt risk-averse strategies towards relatively untested technologies such as ML.
“Finally, misjudgments can be very costly for manufacturers and retailers, leading most managers to adopt risk-averse strategies towards relatively untested technologies such as ML [337].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [337]
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Interpretationnone givenIncentives for cleaner production and distribution are not always aligned with reduced costs, though policies can play a role in aligning these incentives.
“First, incentives for cleaner production and distribution are not always aligned with reduced costs, though policies can play a role in aligning these incentives.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationOnline algorithms for optimizing demand response can reduce overall power usage for computer servers by dynamically shifting the internet traffic load of data providers to underutilized servers, although most of this research has focused on minimizing costs rather than GHG emissions.
“Online algorithms for optimizing demand response can reduce overall power usage for computer servers by dynamically shifting the internet traffic load of data providers to underutilized servers, although most of this research, again, has focused on minimizing costs rather than GHG emissions [99,352].”
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- cites [352] source
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PremisecitationMany companies are now writing decarbonization strategies in response to increasing pressure from governments, financial institutions, and stockholders.
“Many companies are now writing decarbonization strategies in response to increasing pressure from governments, financial institutions, and stockholders (see, e.g., [620,793]).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [620]
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- cites [793]
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Premisenone givenSatellite images make it possible to estimate the amount of carbon sequestered in a given area of land, as well as track GHG emissions from it.
“Satellite images make it possible to estimate the amount of carbon sequestered in a given area of land, as well as track GHG emissions from it.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningML could contribute to predictive maintenance by more accurately modelling the wear and tear of machinery that is currently in use, and interpretable ML could assist factory owners in developing a better understanding of how best to minimize GHG emissions for specific equipment and processes.
“ML could also contribute to predictive maintenance (see also Sections 2.2 and 9.2) by more accurately modelling the wear and tear of machinery that is currently in use, and interpretable ML could assist factory owners in developing a better understanding of how best to minimize GHG emissions for specific equipment and processes.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningSuch optimization can contribute to cutting GHG emissions as long as firms have a financial incentive to optimize for minimal emissions, maximal low-carbon energy, or minimum overall power usage.
“Such optimization can contribute to cutting GHG emissions as long as firms have a financial incentive to optimize for minimal emissions, maximal low-carbon energy, or minimum overall power usage.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningGiven the globalized nature of international trade and the urgency of climate change, decarbonizing the industrial sector is becoming a key priority for both policymakers and factory owners worldwide.
“Given the globalized nature of international trade and the urgency of climate change, decarbonizing the industrial sector is becoming a key priority for both policymakers and factory owners worldwide.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAmmonia production for fertilizer use relies upon natural gas to heat up and catalyze the reaction, and accounts for around 2% of global energy consumption.
“Ammonia production for fertilizer use relies upon natural gas to heat up and catalyze the reaction, and accounts for around 2% of global energy consumption [551].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [551] source
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May combine two claims.
InterpretationcitationML can potentially assist in reducing overall electricity consumption, streamlining factories' HVAC systems, and developing models for electrifying industrial processes so they can be run on low-carbon energy instead of coal, oil, or natural gas.
“ML can potentially assist in reducing overall electricity consumption, streamlining factories' HVAC systems, and developing models for electrifying industrial processes so they can be run on low-carbon energy instead of coal, oil, or natural gas [305].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [305]
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May combine two claims.
PremisecitationDeepMind has used RL to optimize cooling centers for Google's internal servers by predicting and optimizing the power usage effectiveness, thus lowering HFC emissions and reducing cooling costs.
“DeepMind has used RL to optimize cooling centers for Google's internal servers by predicting and optimizing the power usage effectiveness, thus lowering HFC emissions and reducing cooling costs [227,268].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [227]
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- cites [268]
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PremisecitationResearchers have suggested combining optimization-based control algorithms with ML techniques such as image recognition, regression trees, and time delay neural networks to reduce GHG emissions from HVAC systems.
“To reduce GHG emissions from HVAC systems, researchers have suggested combining optimization-based control algorithms with ML techniques such as image recognition, regression trees, and time delay neural networks [5,202] (see also 4.1).”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningData on emissions make it possible to set effective targets, and pinpointing the sources of emissions makes it possible to enforce regulations.
“For example, data on emissions make it possible to set effective targets, and pinpointing the sources of emissions makes it possible to enforce regulations.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationCreating a digital twin model of some industrial equipment or process could enable a manufacturer to virtually experiment with a new piece of code before uploading it to the factory floor, and to test out scenarios for lower GHG emissions without slowing down production.
“For example, creating a digital twin model of some industrial equipment or process could enable a manufacturer to virtually experiment with a new piece of code before uploading it to the factory floor, and to test out scenarios for lower GHG emissions without slowing down production [290,777].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [290]
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- cites [777] source
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May combine two claims.
PremisecitationThe permafrost is now melting, peat bogs are drying, and forest fires are becoming more frequent as a consequence of climate change itself, all of which release yet more carbon.
“In addition to this direct release of carbon through human actions, the permafrost is now melting, peat bogs are drying, and forest fires are becoming more frequent as a consequence of climate change itself -all of which release yet more carbon [573].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [573]
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InterpretationcitationGiven the potential of ML for predicting chemical reactions, ML may also be able to help with the discovery of new materials for electrocatalysts and/or proton conductors to facilitate ammonia production.
“Given the potential of ML for predicting chemical reactions [140], ML may also be able to help with the discovery of new materials for electrocatalysts and/or proton conductors to facilitate ammonia production.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [140] source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
InterpretationreasoningStandard satellite imagery provides RGB images with much higher resolution, which could be used in an ML algorithm to fill the gaps in hyperspectral data and obtain more precise information about emissions.
“Standard satellite imagery provides RGB images with much higher resolution, which could be used in an ML algorithm to fill the gaps in hyperspectral data and obtain more precise information about emissions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe higher the incentives for reducing carbon emissions, the more likely that firms will optimize for low-carbon energy use.
“Again, the higher the incentives for reducing carbon emissions, the more likely that firms will optimize for low-carbon energy use.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationOverall, land use by humans is estimated to be responsible for about a quarter of global GHG emissions, and this may be an underestimate.
“Overall, land use by humans is estimated to be responsible for about a quarter of global GHG emissions [370] (and this may be an underestimate [511]).”
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- cites [511] source
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Premisenone givenMost of these satellites have very coarse spatial resolution and large temporal and spatial gaps, making them unsuitable for precise tracking of emissions.
“While extremely useful for studying climate change, most of these satellites have very coarse spatial resolution and large temporal and spatial gaps, making them unsuitable for precise tracking of emissions.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationML can help in disease detection, weed detection, and soil sensing.
“ML can also help in disease detection, weed detection, and soil sensing [482,699,821].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningIndustrial systems can employ similar models to predict which pipes are liable to spring leaks, thus minimizing the direct release of GHGs such as HFCs and natural gas.
“Industrial systems can employ similar models to predict which pipes are liable to spring leaks, thus minimizing the direct release of GHGs such as HFCs and natural gas.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningGiven the heterogeneity across industrial sectors and the secrecy of industrial data, firms will need to tailor the requisite sensors and data analysis systems to their individual processes.
“Given the heterogeneity across industrial sectors and the secrecy of industrial data, firms will also need to tailor the requisite sensors and data analysis systems to their individual processes.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIntelligent irrigation systems can save large amounts of water while reducing pests that thrive under excessive moisture.
“Intelligent irrigation systems can save large amounts of water while reducing pests that thrive under excessive moisture [335].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [335]
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May combine two claims.
InterpretationreasoningML can help monitor the health of forests and peatlands, predict the risk of fire, and contribute to sustainable forestry.
“ML can help monitor the health of forests and peatlands, predict the risk of fire, and contribute to sustainable forestry (Figure 5).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenFirms are unlikely to adopt new materials or change existing practices without financial incentives, so widespread adoption might require subsidies for low-carbon alternatives or penalties for high GHG emissions.
“Firms are unlikely to adopt new materials or change existing practices without financial incentives, so widespread adoption might require subsidies for low-carbon alternatives or penalties for high GHG emissions.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationHaving real-time maps of GHGs could help quantify emissions from agriculture and forestry practices, which remain relatively uncertain, as well as monitor emissions from other sectors.
“Having real-time maps of GHGs could help us quantify emissions from agriculture and forestry practices, which remain relatively uncertain [371], as well as monitor emissions from other sectors (Section 2.2).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [371]
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PremisecitationDespite the proliferation of industrial data, much of the information is proprietary, low-quality, or very specific to individual machines or processes; practitioners estimate that 60%-70% of collected industrial data goes unused.
“Second, despite the proliferation of industrial data, much of the information is proprietary, low-quality, or very specific to individual machines or processes; practitioners estimate that 60%-70% of collected industrial data goes unused [138,310].”
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InterpretationcitationDemand response optimization algorithms can help firms adjust the timing of energy-intensive processes such as cement crushing and powder-coating to take advantage of electricity price fluctuations, although published work on the topic has to date used relatively little ML.
“Demand response optimization algorithms can help firms adjust the timing of energy-intensive processes such as cement crushing [880] and powder-coating [43] to take advantage of electricity price fluctuations, although published work on the topic has to date used relatively little ML.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [880]
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- cites [43]
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May combine two claims.
InterpretationcitationNew factory equipment can be very expensive to purchase and set up, so firms' cost-benefit calculations may dissuade them from retrofitting existing factories to run using low-carbon electricity or to save a few kilowatts.
“New factory equipment can be very expensive to purchase and set up, so firms' cost-benefit calculations may dissuade them from retrofitting existing factories to run using low-carbon electricity or to save a few kilowatts [288,633,778].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationTo develop cleaner ammonia, chemists may be able to invent electrochemical strategies for lower-temperature ammonia production.
“To develop cleaner ammonia, chemists may be able to invent electrochemical strategies for lower-temperature ammonia production [551,849].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [551] source
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- cites [849]
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenPrecision agriculture could reduce carbon release from the soil and improve crop yield, which in turn could reduce the need for deforestation.
“Precision agriculture could reduce carbon release from the soil and improve crop yield, which in turn could reduce the need for deforestation.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisereasoningModern industrial crop production involves land stripping, tilling, and nitrogen-based fertilizers, which release carbon and produce nitrous oxide.
“First, the land is generally stripped of existing vegetation, releasing carbon sequestered there.Second, the process of tilling exposes topsoil to the air, thereby releasing carbon that had been bound in soil aggregates and disrupting organisms in the soil that contribute to sequestration.Finally, because such farming practices strip soil of nutrients, nitrogen-based fertilizers must be added back”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationDeforestation contributes to approximately 10% of global GHG emissions as vegetation is burned or decays.
“The rest are subject to deforestation, which contributes to approximately 10% of global GHG emissions [370] as vegetation is burned or decays.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [370]
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PremisecitationSince most of a forest's carbon is stored in above-ground biomass, tree species and heights are a good indicator of the carbon stock.
“Since most of a forest's carbon is stored in above-ground biomass [693], tree species and heights are a good indicator of the carbon stock.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [693] source
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InterpretationreasoningInterfacing between these different stakeholders is a practical challenge for meaningful work in this area.
“Interfacing between these different stakeholders is a practical challenge for meaningful work in this area.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationPreventing small forest fires causes biomass to accumulate on the ground and increases the chances of large fires, which can then burn all trees to the ground and erode top soil, resulting in high CO2 emissions, biodiversity loss, and a long recovery time.
“Preventing them causes biomass to accumulate on the ground and increases the chances of large fires, which can then burn all trees to the ground and erode top soil, resulting in high CO 2 emissions, biodiversity loss, and a long recovery time [550].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [550]
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Premisenone givenThe height of trees can be estimated fairly accurately with LiDAR devices mounted on UAVs, but this technology is not scalable and many areas are closed to UAVs.
“The height of trees can be estimated fairly accurately with LiDAR devices mounted on UAVs, but this technology is not scalable and many areas are closed to UAVs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAbout 80% of global deforestation is the result of agriculture, while other significant causes include mining, logging, and urban development.
“About 80% percent of global deforestation is the result of agriculture (clearing land for pasture or crop production), while other significant causes include mining, logging, and urban development [353].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [353] source
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InterpretationreasoningEliminating emissions entirely may be tricky, given the sheer diversity of sources such as airplanes and cows.
“Eliminating emissions entirely may also be tricky, given the sheer diversity of sources (such as airplanes and cows).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenAgriculture involves a complex mix of large-scale farming interests, small-scale farmers, agricultural equipment manufacturers, and chemical companies, each with different interests and access to different portions of data.
“Agriculture, for example, involves a complex mix of large-scale farming interests, small-scale farmers, agricultural equipment manufacturers, and chemical companies.Each stakeholder has different interests, and each often has access to a different portion of the data that would be useful for impactful ML applications.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationWhen peat dries, it releases carbon through decomposition and also becomes susceptible to fire.
“When peat dries, however, it releases carbon through decomposition and also becomes susceptible to fire [245,612].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [245]
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- cites [612]
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PremisecitationAccording to one estimate, up to 0.9 billion hectares of extra canopy cover could theoretically be added globally.
“According to one estimate, up to 0.9 billion hectares of extra canopy cover could theoretically be added [60] globally.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [60] source
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PremisecitationSome companies are using ML-enabled tools to quantify the carbon impact of forestry decisions, enabling landowners to choose more beneficial actions as well as profit by selling carbon offsets.
“Some companies are using ML-enabled tools to quantify the carbon impact of forestry decisions, enabling landowners to choose more beneficial actions as well as profit by selling carbon offsets [578,603].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [578]
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- cites [603]
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Classified as probably not a substantial claim.
InterpretationcitationMonitoring peatlands and protecting them from artificial drainage or droughts is essential to preserve the carbon sequestered in them.
“Monitoring peatlands and protecting them from artificial drainage or droughts is essential to preserve the carbon sequestered in them [349,400].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [349] source
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- cites [400]
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningLiDAR data are often not equally distributed across regions or seasons, so domain adaptation and transfer learning techniques may help algorithms to generalize better.
“For example, LiDAR data are often not equally distributed across regions or seasons.Hence domain adaptation and transfer learning techniques may help algorithms to generalize better.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationGlobally, agriculture constitutes a $2.4 trillion industry, and there is already a significant economic incentive to increase efficiency.
“Globally, agriculture constitutes a $2.4 trillion industry [785], and there is already a significant economic incentive to increase efficiency.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [785]
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Classified as probably not a substantial claim.
PremisecitationMany experts argue that to meet critical climate goals, global emissions must become net-negative-that is, we must remove more CO2 from the atmosphere than we release.
“Instead, many experts argue that to meet critical climate goals, global emissions must become net-negative-that is, we must remove more CO 2 from the atmosphere than we release [259,269].”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationML can be used with audio instead of visual data; one such project installs old smartphones powered by solar panels in the forest, which enables the detection of chainsaw sounds within a one-kilometer radius.
“ML can also be used with audio instead of visual data; one such project installs (old) smartphones powered by solar panels in the forest, which enables the detection of chainsaw sounds within a one-kilometer radius [657].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [657]
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PremisecitationDrought forecasting is helpful in predicting regions that are more at risk, as is estimating the water content in the tree canopy.
“Drought forecasting [682] is helpful in predicting regions that are more at risk, as is estimating the water content in the tree canopy [94].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [682]
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- cites [94] source
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PremisecitationStartups like Dendra Systems and Droneseed are developing UAVs that are capable of planting seed packets more quickly and cheaply than traditional methods, while Restor uses ML to learn from past afforestation projects about effective strategies for ecosystem restoration.
“For example, startups like Dendra Systems and Droneseed are developing UAVs that are capable of planting seed packets more quickly and cheaply than traditional methods [176,204], while Restor uses ML to learn from past afforestation projects about effective strategies for ecosystem restoration [681].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [176]
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- cites [204]
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- cites [681]
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InterpretationcitationML can be used with remote sensing imagery to pinpoint changes in forest cover, or proxies for deforestation such as smoke from fires set to clear vegetation, as well as to differentiate selective cutting from clearcutting.
“ML can be used with remote sensing imagery to pinpoint changes in forest cover [241,329,369], or proxies for deforestation such as smoke from fires set to clear vegetation [505], as well as to differentiate selective cutting from clearcutting [342,487].”
Quote in the paperSubstantial claim0 of 6 sources checkedMay combine two claims.
PremisecitationML was applied to features extracted from remote sensing data to estimate the thickness of peat and assess the carbon stock of tropical peatlands.
“In [538], ML was applied to features extracted from remote sensing data to estimate the thickness of peat and assess the carbon stock of tropical peatlands.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [538] source
Not checked in the free run.
May combine two claims.
InterpretationcitationML can be used to predict the LiDAR's outcome from satellite imagery, and the learned estimator can perform predictions at scale.
“To address this challenge, ML can be used to predict the LiDAR's outcome from satellite imagery [48,599,693].From there, the learned estimator can perform predictions at scale.”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningAdvanced ML could potentially help develop precise monitoring tools at low cost, as well as predicting the risk of fire.
“Advanced ML could potentially help develop precise monitoring tools at low cost, as well as predicting the risk of fire.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenAfforestation that comes at the expense of farmland (or ecosystems such as peat bogs) could result in a net increase of GHG emissions.
“Afforestation that comes at the expense of farmland (or ecosystems such as peat bogs) could result in a net increase of GHG emissions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenEven if we could cut emissions to zero today, we would still face significant climate consequences from GHGs already in the atmosphere.
“Even if we could cut emissions to zero today, we would still face significant climate consequences from GHGs already in the atmosphere.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenPlanting trees, also called afforestation, can be a means of sequestering CO2 over the long term.
“Planting trees, also called afforestation, can be a means of sequestering CO 2 over the long term.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationA central database for peatland-monitoring has been established, but considerable data gaps remain.
“A central database for peatland-monitoring has been established, but considerable data gaps remain [121].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [121]
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May combine two claims.
PremisecitationForest fires release CO2 into the atmosphere, which in turn increases the rate of forest fires.
“Besides their potential for harming people and property, forest fires release CO 2 into the atmosphere (which in turn increases the rate of forest fires [839]).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [839] source
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenFarms and forests make up a large portion of global GHG emissions, but reducing these emissions is challenging.
“Farms and forests make up a large portion of global GHG emissions, but reducing these emissions is challenging.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationRL is used to predict the spatial progression of fire, which helps firefighters decide when to let a fire burn and when to stop it.
“In [266,267], RL is used to predict the spatial progression of fire.This helps firefighters decide when to let a fire burn and when to stop it [355].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenPlanting trees without regard for local conditions and native species can reduce the climate impact of afforestation as well as negatively affecting biodiversity.
“Moreover, planting trees without regard for local conditions and native species can reduce the climate impact of afforestation as well as negatively affecting biodiversity.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationML can be helpful in automating large-scale afforestation by locating appropriate planting sites, monitoring plant health, assessing weeds, and analyzing trends.
“ML can be helpful in automating large-scale afforestation by locating appropriate planting sites, monitoring plant health, assessing weeds, and analyzing trends.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationPeatlands cover only 3% of the Earth's land area, yet hold twice the total carbon in all the world's forests, making peat the largest source of sequestered carbon on Earth.
“Peatlands (a type of wetland ecosystem) cover only 3% of the Earth's land area, yet hold twice the total carbon in all the world's forests, making peat the largest source of sequestered carbon on Earth [612].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [612]
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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenThe scope of the problem is highly globalized, but the necessary actions are highly localized.
“The scope of the problem is highly globalized, but the necessary actions are highly localized.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationML can guide crop yield prediction and macroeconomic models that help farmers predict crop demand and decide what to plant at the beginning of the season.
“ML can guide crop yield prediction [866] and even macroeconomic models that help farmers predict crop demand and decide what to plant at the beginning of the season [503].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [866]
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- cites [503] source
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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationcitationML tools for policymakers and agronomists could potentially encourage climate-positive action, e.g., remote sensing with UAVs and satellites could perform methane detection and carbon stock estimation, which could be used to incentivize farmers to sequester more carbon and reduce emissions.
“ML tools for policymakers and agronomists [126] could potentially encourage climate-positive action: for example, remote sensing with UAVs and satellites could perform methane detection and carbon stock estimation, which could be used to incentivize farmers to sequester more carbon and reduce emissions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [126] source
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Interpretationnone givenEfficiency gains do not necessarily translate into reduced GHG emissions, e.g., via rebound effects increasing consumption of particularly emissions-intensive products.
“However, efficiency gains do not necessarily translate into reduced GHG emissions (e.g., via rebound effects increasing consumption of particularly emissions-intensive products).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenML models are likely to be more accurate or less expensive than other models where there are plentiful data but it is hard to model systems with traditional statistics, or where there are good models but they are too computationally expensive to use in production.
“ML models are likely to be more accurate or less expensive than other models where: (1) there are plentiful data, but it is hard to model systems with traditional statistics, or (2) there are good models, but they are too computationally expensive to use in production.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisenone givenTwo strategies for efficiently managing limited maintenance resources are predictive maintenance and anomaly detection.
“Two strategies for efficiently managing limited maintenance resources are predictive maintenance and anomaly detection; both can be applied to electrical, water, and transportation infrastructure (see also Sections 2.2 and 5.3).”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationGentine et al. trained a deep neural network to emulate the behavior of a high-resolution cloud simulation, and found that the network gave similar results for a fraction of the cost and was stable in a simplified global model.
“Gentine et al. trained a deep neural network to emulate the behavior of a high-resolution cloud simulation, and found that the network gave similar results for a fraction of the cost [275] and was stable in a simplified global model [670].”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationAlthough there has been significant progress in negative emissions research, the actual CO2 removal industry is still in its infancy.
“Although there has been significant progress in negative emissions research [260,540,575,580,707], the actual CO 2 removal industry is still in its infancy.”
Quote in the paperSubstantial claim0 of 5 sources checkedProbably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationNew ML models for climate will be most successful if they are closely integrated into existing scientific models.
“New ML models for climate will be most successful if they are closely integrated into existing scientific models.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Premisenone givenTo preserve ecosystems, it is important to know which are most at risk.
“To preserve ecosystems, it is important to know which are most at risk.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationDirect air capture requires little land and has minimal negative environmental impacts.
“While this direct air capture (DAC) approach faces technical hurdles, it requires little land and has, according to current understanding, minimal negative environmental impacts [152].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [152] source
Not checked in the free run.
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Premisenone givenThe Earth will generate at most one year of new climate data per year.
“No matter how many weather stations we construct, how many field campaigns we run, or how many satellites we deploy, the Earth will generate at most one year of new climate data per year.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationModels of mass loss from the Antarctic ice-sheet are highly uncertain and models of the extent of Antarctic sea ice do not match reality well.
“In particular, models of mass loss from the Antarctic ice-sheet are highly uncertain [449] and models of the extent of Antarctic sea ice do not match reality well [263].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Premisenone givenForecasts are most actionable if they are specific and local.
“Forecasts are most actionable if they are specific and local.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAnderson and Lucas used random forests to make high-resolution predictions from a mix of high-and low-resolution models, which could reduce the costs of building multi-model ensembles.
“More recently, Anderson and Lucas used random forests to make high-resolution predictions from a mix of high-and low-resolution models, which could reduce the costs of building multi-model ensembles [23].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] source
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PremisecitationML is providing new solutions to assess the impact of ecological interventions and prevent poaching.
“ML is providing new solutions to assess the impact of ecological interventions [13,502,664] and prevent poaching [860].”
Quote in the paperNot a substantial claim0 of 4 sources checkedMay combine two claims.
PremisereasoningData can be used to monitor the risk of food insecurity in real time, to forecast near-term shortages, and to identify areas at risk in the long-term.
“Data can be used to monitor the risk of food insecurity in real time, to forecast near-term shortages, and to identify areas at risk in the long-term, all of which can guide interventions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGagne et al. recently used an ensemble of random forests to improve hail predictions within a major weather model.
“For example, Gagne et al. recently used an ensemble of random forests to improve hail predictions within a major weather model [262].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [262] source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Premisenone givenSocial systems are just as critical to the smooth functioning of society as any physical infrastructure.
“While less tangible, the social systems we construct are just as critical to the smooth functioning of society as any physical infrastructure, and it is important that they adapt to changing climate conditions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenML can support two types of activities: design and maintenance.
“From this more general perspective of improving social processes, ML can support two types of activities: design and maintenance.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenBiomass, mineral, and ocean-based CO2 removal methods would not likely benefit significantly from ML.
“However, although these biomass, mineral, and ocean-based methods are all promising enough as techniques to merit mention, they may have drawbacks in terms of land use and potentially serious environmental impacts, and (more relevantly for this article) they would not likely benefit significantly from ML.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Premisenone givenAccurate estimates of species populations are the foundation on which conservation efforts are built.
“Accurate estimates of species populations are the foundation on which conservation efforts are built.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisereasoningClimate change will affect exposure to health hazards, and ML can play a role in measuring and mitigating their impacts across subpopulations.
“Climate change will affect exposure to health hazards, and ML can play a role in measuring and mitigating their impacts across subpopulations.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPoint-of-care diagnostics have enjoyed a renaissance, thanks in part to ML.
“Beyond surveillance, point-of-care diagnostics have enjoyed a renaissance, thanks in part to ML [598,648].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [598]
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- cites [648] source
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May combine two claims.
Premisenone givenHuman populations move in response to threats and opportunities, and ML can be used to predict large-scale migration patterns.
“Human populations move in response to threats and opportunities, and ML can be used to predict large-scale migration patterns.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationML has emerged as an important tool in epidemiology and public health.
“It should be no surprise that ML has emerged as an important tool in these disciplines, supporting a variety of research efforts, from increasing the efficiency of disease simulators to supporting the fine-grained measurement of exposures and their health impacts [433,705].”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationThere has been recent progress in RL for control, which could be useful for fine-tuning geoengineering interventions such as deciding where and when to release aerosols.
“Additionally, there has been recent progress in RL for control [21,84,545], which could be useful for fine-tuning geoengineering interventions such as deciding where and when to release aerosols.”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationClimate change will increase the range of vector and water-borne diseases, elevating the likelihood that these new environments experience epidemics.
“Climate change will increase the range of vector and water-borne diseases, elevating the likelihood that these new environments experience epidemics [324].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [324] source
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May combine two claims.
PremisecitationSolar geoengineering cannot simply reverse the effects of climate change; average temperatures may return to pre-industrial levels, but location-specific climates still change.
“Moreover, unlike CO 2 removal, it cannot simply reverse the effects of climate change (average temperatures may return to pre-industrial levels, but location-specific climates still change), and also comes with the risk of termination shock (fast, catastrophic warming if humanity undertakes solar geoengineering but stops suddenly) [615].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [615] source
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PremisecitationSulfates make sunlight slightly more diffuse, which can impact agriculture.
“Sulfates come with a well-studied risk of ozone loss [210], and they make sunlight slightly more diffuse, which can impact agriculture [642].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [642] source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSulfates are the best understood due to volcanic eruption data, but many other aerosols have been studied, including zirconium dioxide, titanium dioxide, calcite, and synthetic diamond.
“Although sulfates are the best understood due to volcanic eruption data, many others have been studied, including zirconium dioxide, titanium dioxide, calcite (which preserves ozone), and even synthetic diamond [209].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [209] source
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May combine two claims.
PremisecitationDisaster response and public health are mature disciplines and have already benefited extensively from ML methodology.
“Furthermore, while crises may be some of the most distressing consequences of climate change, disaster response and public health are mature disciplines in their own right, and have already benefited extensively from ML methodology [118,531,862].”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWidespread awareness of solar geoengineering's very possibility may undermine mainstream efforts to cut emissions (moral hazard).
“widespread awareness of its very possibility may undermine mainstream efforts to cut emissions [485].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [485]
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Premisenone givenImmigrants and refugees are vulnerable groups, and systems that surveil them can easily be exploited by bad actors.
“It is worth cautioning that immigrants and refugees are vulnerable groups, and systems that surveil them can easily be exploited by bad actors.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationHeat waves will become more frequent and outdoor and indoor air quality will deteriorate.
“Two of the most relevant expected shifts are (1) heat waves will become more frequent and (2) outdoor and indoor air quality will deteriorate [324,710].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSolar geoengineering proposals are real proposals for solar radiation management.
“Airships floating through the sky, spraying aerosols; robotic boats crisscrossing the ocean, firing vertical jets of spray; arrays of mirrors carefully positioned in space, micro-adjusted by remote control; these images seem like science fiction, but they are actually real proposals for solar radiation management, commonly called solar geoengineering [375,418,419,728].”
Quote in the paperNot a substantial claim0 of 4 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation[179] use deep neural networks to estimate the effects of aerosols on human health, while [148] use them to estimate the effects of solar geoengineering on agriculture.
“For example, [179] use deep neural networks to estimate the effects of aerosols on human health, while [148] use them to estimate the effects of solar geoengineering on agriculture.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [179] source
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- cites [148]
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Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Premisenone givenGuiding interventions based on purely observational, potentially unrepresentative data poses risks.
“Across social applications, there are worthwhile research challenges—guiding interventions based on purely observational, potentially unrepresentative data poses risks.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe sulfate injection process is reversible but must be continually maintained.
“As a result, the process is reversible, but must also be continually maintained.”
Quote in the paperNot a substantial claim- Rests on an assumption: Once injected, sulfates circulate globally and remain aloft for 1 to 2 years.
May combine two claims.
PremisecitationInjecting sulfate aerosols into the stratosphere is considered a leading candidate for solar geoengineering because of its economic and technological feasibility and because we have data.
“Injecting sulfate aerosols into the stratosphere is considered a leading candidate for solar geoengineering both because of its economic and technological feasibility [527,743] and because of a reason that should resonate with the ML community: we have data.”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Premisenone givenNatural disasters and health crises are not entirely unpredictable-they can be prepared for, risks can be reduced, and coordination can be streamlined.
“Perhaps counterintuitively, natural disasters and health crises are not entirely unpredictable—they can be prepared for, risks can be reduced, and coordination can be streamlined.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThere has been significant debate about whether it is ethically responsible to research solar geoengineering.
“Because of these issues, there has been significant debate about whether it is ethically responsible to research this topic [420,640].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [420]
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- cites [640] source
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationReferences [101,187] use relatively simple local and polynomial regression techniques applied to extensive empirical data to estimate the past and future effects of temperature change on economic production.
“References [101,187] use relatively simple local and polynomial regression techniques but applied to extensive empirical data to estimate the past and future effects of temperature change on economic production.”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe primary candidate methods for geoengineering are marine cloud brightening, cirrus thinning, and stratospheric aerosol injection.
“The primary candidate methods for geoengineering are marine cloud brightening [396] (making low-lying clouds more reflective), cirrus thinning [759] (making high-flying clouds trap less heat), and stratospheric aerosol injection [668] (which we discuss below).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationUsing ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics, and thus predict who will be most amenable to new technologies and sustainable behavior change.
“Using ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics (e.g., [66,116,166,261,861]), and thus predict who will be most amenable to new technologies and sustainable behavior change.”
Quote in the paperSubstantial claim0 of 5 sources checkedMay combine two claims.
PremisecitationBehavior change programs can cost as little as 3 cents to save a kilowatt hour of electricity, whereas generating one kWh would cost 5-6 cents with a coal or wind power plant, and 10 cents with solar.
“behavior change programs can cost as little as 3 cents to save a kilowatt hour of electricity, whereas generating one kWh would cost 5-6 cents with a coal or wind power plant, and 10 cents with solar [207,313].”
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PremisecitationTechniques from game theory can be valuable in modeling behavior, e.g., to explore cooperation in the face of a depleting resource.
“In addition to ABMs, techniques from game theory can be valuable in modeling behavior, e.g., to explore cooperation in the face of a depleting resource [344].”
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PremisecitationOther works have used ML to predict how much consumers are willing to pay to avoid potential environmental harms of energy consumption, finding that some groups were totally insensitive to cost and would pay the maximum amount to mitigate harm, while other groups were willing to pay nothing.
“Other works have used ML to predict how much consumers are willing to pay to avoid potential environmental harms of energy consumption [167], finding that some groups were totally insensitive to cost and would pay the maximum amount to mitigate harm, while other groups were willing to pay nothing.”
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PremisecitationML can be used to predict the marginal emissions of energy consumption in real time, on a scale of hours, potentially allowing consumers to schedule activities such as charging an EV when emissions and prices will be lowest.
“ML can also be used to predict the marginal emissions of energy consumption in real time, on a scale of hours [832], potentially allowing consumers to effectively schedule activities such as charging an EV when the emissions (and prices [439]) will be lowest [143].”
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- cites [143]
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PremisecitationOther applications of ABMs include modeling how behavior under social norms changes with external pressures, how the economy and climate may evolve given a diversity of political and economic beliefs, and how individuals may migrate in response to environmental changes.
“Other applications of ABMs include modeling how behavior under social norms changes with external pressures [715], how the economy and climate may evolve given a diversity of political and economic beliefs [272], and how individuals may migrate in response to environmental changes [787].”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationHousehold energy disaggregation breaks down overall electricity consumption into energy use by individual appliances, which can help facilitate behavior change.
“Household energy disaggregation breaks down overall electricity consumption into energy use by individual appliances (see also Section 4.1) [33], which can help facilitate behavior change [772].”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationThere are actions which can meaningfully reduce each person's carbon footprint, and, if widely adopted, could have a significant impact on mitigating global emissions.
“In fact, there are actions which can meaningfully reduce each person's carbon footprint, and, if widely adopted, could have a significant impact on mitigating global emissions [335,845].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [335]
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PremisecitationML has the potential to help with sensitivity and uncertainty analysis, with finding numerical solutions for computationally expensive submodels, and assessing the validity of models.
“ML also has the potential to help with sensitivity and uncertainty analysis [386], with finding numerical solutions for computational expensive submodels [206,714], and assessing the validity of the models [556].”
Quote in the paperNot a substantial claim0 of 4 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThis approach has been applied in a number of climate change-related fields, including energy and infrastructure planning, industry, land use, and more.
“This approach has been applied in a number of climate change-related fields, including energy and infrastructure planning [38,325,525,635,732,856], industry [122,334], land use [462,809], and more [127,317,539,761].”
Quote in the paperNot a substantial claim0 of 10 sources checked- cites [38] source
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- cites [732] source
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- cites [122] source
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- cites [334] source
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PremisecitationCombined with mechanism design, such approaches can be used to design methods for cooperation that lead to mutually beneficial outcomes, for example when formalizing procedures around international climate agreements.
“Combined with mechanism design, 30 such approaches can be used to design methods for cooperation that lead to mutually beneficial outcomes, for example when formalizing procedures around international climate agreements [522,641].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationHedonic pricing can help identify how much different consumers may be willing to pay for an environmental good or service, which is a noisy measure for the monetary value of that good or service; these values are typically inferred using regression or ML techniques on historical market data.
“Hedonic pricing can also help identify how much different consumers may be willing to pay for a environmental good or a service, which is a noisy measure for the monetary value of that good or service; these values are typically inferred using regression or ML techniques on historical market data [175,594,613,629].”
Quote in the paperNot a substantial claim0 of 4 sources checkedMay combine two claims.
PremisecitationML can be used to analyze prices within cap-and-trade markets, for example by predicting prices via supervised learning or analyzing the main drivers of prices via hierarchical clustering.
“ML can be used to analyze prices within these markets, for example by predicting prices via supervised learning [769,834,890,892] or analyzing the main drivers of prices via hierarchical clustering [891].”
Quote in the paperNot a substantial claim0 of 5 sources checkedMay combine two claims.
PremisecitationSelf-organizing maps have been used to analyze how R&D investment in green technologies changes in response to fuel prices, while a game theoretical framework using neural networks has been used to study optimal production strategies for companies under carbon quotas.
“For example, self-organizing maps have been used to analyze how R&D investment in green technologies changes in response to fuel prices [55], while a game theoretical framework using neural networks has been used to study the optimal production strategies for companies under carbon quotas [884].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFollowing seminal research on modeling pricing in markets as a bandit problem, many works have applied bandit and other RL algorithms to determine prices or other market values.
“Following seminal research on modeling pricing in markets as a bandit problem [700], many works have applied bandit and other RL algorithms to determine prices or other market values.”
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PremisecitationML can also help solve auctions in supply chains.
“ML can also help solve auctions in supply chains [708].”
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PremisecitationStudies have considered effects of carbon pricing on economic growth and energy intensity, or on electricity prices.
“Studies have considered effects of carbon pricing on economic growth and energy intensity [233,234], or on electricity prices [569].”
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PremisecitationPrevious work has employed parallel surrogate search, assisted by ML, to efficiently solve multi-objective optimization problems.
“Previous work has also employed parallel surrogate search, assisted by ML, to efficiently solve multi-objective optimization problems [11].”
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PremisecitationIAMs are used to explore future societal pathways that are consistent with climate goals and play a prominent role in the IPCC assessments.
“IAMs are used to explore future societal pathways that are consistent with climate goals (e.g., 1.5 • C mean global temperature increase), and play a prominent role in the IPCC assessments [561].”
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PremisecitationThe actions required to address climate change, both in mitigation and adaptation, require making policies at the local, national, and international levels.
“The actions required to address climate change, both in mitigation and adaptation, require making policies 31 at the local, national, and international levels [756].”
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PremisecitationEducation can both improve the resilience of communities, particularly in developing countries that will be disproportionately affected by climate change, and empower individuals, especially from developed countries, to adopt more sustainable lifestyles.
“For instance, education can both improve the resilience of communities, particularly in developing countries that will be disproportionately affected by climate change [801], and empower individuals, especially from developed countries, to adopt more sustainable lifestyles [244].”
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PremisecitationClustering can help identify permit allocation schemes that maximize social welfare, and supervised learning has been used to predict winners and losers from changing electricity tariff schemes.
“Here, clustering can help identify permit allocation schemes that maximize social welfare [855], and supervised learning has been used to predict winners and losers from changing electricity tariff schemes [302].”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationRL has been applied to predict bids and market power in electricity markets, and to set dynamic prices in more general settings.
“For example, RL has been applied to predict bids [654] and market power [572] in electricity markets, and to set dynamic prices in more general settings [510].”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationOutputs with high resolution may be aggregated via clustering methods to provide insights, while at coarser resolution, statistical downscaling can help to disaggregate data to an appropriate spatial resolution, as seen in applications such as crop yield, wind speed or surface temperature.
“Outputs with high resolution may be aggregated via clustering methods to provide insights [183], while at coarser resolution, statistical downscaling can help to disaggregate data to an appropriate spatial resolution, as seen in applications such as crop yield [249], wind speed [479] or surface temperature [481].”
Quote in the paperNot a substantial claim0 of 4 sources checkedMay combine two claims.
PremisecitationML can play a role in causal inference, including in policy problems and in climate-relevant scenarios such as estimating the effects of temperature on human mortality and the effects of World Bank projects on vegetative cover.
“ML can play a role in causal inference [41,340,619], including in the context of policy problems [40,453] and in climate-relevant scenarios such as estimating the effects of temperature on human mortality [356] and the effects of World Bank projects on vegetative cover [883].”
Quote in the paperSubstantial claim0 of 7 sources checked- cites [41]
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- cites [40] source
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- cites [453] source
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- cites [356]
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PremisecitationIntelligent Tutoring Systems can adapt their behavior in real time according to the needs of individuals or to support collaborative learning.
“One important area of AIED research has been Intelligent Tutoring Systems (ITSs), which can adapt their behavior in real time according to the needs of individuals or to support collaborative learning [124].”
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PremisecitationEducational activities centered on climate change and carbon footprints can engage learners in understanding the connection between personal and collective actions and their impact on global climate, and can enable individuals to make climate-friendly lifestyle choices such as reducing energy use.
“Research has shown that educational activities centered on climate change and carbon footprints can engage learners in understanding the connection between personal and collective actions and their impact on global climate, and can enable individuals to make climate-friendly lifestyle choices such as reducing energy use [142].”
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InterpretationreasoningThe nature of climate-relevant data poses challenges and opportunities.
“The nature of climate-relevant data poses challenges and opportunities.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningML plays a central role in these strategies, and can be a powerful tool in leveraging the financial sector to mitigate climate change and in reducing the financial impacts of climate change on society.
“ML plays a central role in these strategies, and can be a powerful tool in leveraging the financial sector to mitigate climate change and in reducing the financial impacts of climate change on society.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationRecent approaches have applied ML techniques such as multi-armed bandit techniques to adaptively personalize sequences of learning activities, LSTMs to generate questions to evaluate language comprehension, and RL to improve the strategies used within the ITS.
“While ITSs have traditionally been defined and constructed by hand, recent approaches have applied ML techniques such as multi-armed bandit techniques to adaptively personalize sequences of learning activities [135], LSTMs to generate questions to evaluate language comprehension [205], and RL to improve the strategies used within the ITS [365,441].”
Quote in the paperNot a substantial claim0 of 4 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Premisenone givenDatasets may include information from heterogeneous sources, which must be integrated using domain knowledge.
“Datasets may include information from heterogeneous sources, which must be integrated using domain knowledge.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenMany of the problems discussed in the paper highlight cutting-edge areas of ML, such as interpretability, causality, and uncertainty quantification.
“Many of the problems we have discussed here highlight cutting-edge areas of ML, such as interpretability, causality, and uncertainty quantification.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThis investment strategy is creating major shifts in certain sectors of the market (e.g., utilities and energy) towards renewable energy alternatives, which are seen as having greater growth potential than traditional energy sources such as oil and gas.
“This investment strategy is creating major shifts in certain sectors of the market (e.g., utilities and energy) towards renewable energy alternatives, which are seen as having a greater growth potential than traditional energy sources such as oil and gas [72].”
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Interpretationnone givenML can accelerate the process of scientific discovery, e.g., by suggesting new materials for batteries, construction, and carbon capture.
“It can accelerate the process of scientific discovery (e.g., by suggesting new materials for batteries, construction, and carbon capture).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationcitationGiving teachers guidance derived from computational teaching algorithms or heuristics could help them design better educational curricula and improve student learning outcomes.
“Furthermore, giving teachers guidance derived from computational teaching algorithms or heuristics could help them design better educational curricula and improve student learning outcomes [172].”
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PremisecitationClimate investment is the current dominant approach in climate finance and involves investing money in low-carbon assets.
“Climate investment, the current dominant approach in climate finance, involves investing money in low-carbon assets [229].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [229] source
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Interpretationnone givenAIED applications can be used either as a standalone tool for independent learners or as an educational resource that frees up teachers to have more one-on-one time with students.
“In this context, AIED applications can be used either as a standalone tool for independent learners or as an educational resource that frees up teachers to have more one-on-one time with students.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenML can enable automatic monitoring through remote sensing, e.g., by pinpointing deforestation, gathering data on buildings, and assessing damage after disasters.
“ML can enable automatic monitoring through remote sensing (e.g., by pinpointing deforestation, gathering data on buildings, and assessing damage after disasters).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenML can accelerate computationally expensive physical simulations through hybrid modeling, e.g., climate models and energy scheduling models.
“And it can accelerate computationally expensive physical simulations through hybrid modeling (e.g., climate models and energy scheduling models).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenClimate analytics involves analyzing investment portfolios, funds, and companies to pinpoint areas with heightened risk due to climate change.
“Climate analytics involves analyzing investment portfolios, funds, and companies in order to pinpoint areas with heightened risk due to climate change, such as timber companies that could be bankrupted by wildfires or water extraction initiatives that could see their sources polluted by shifting landscapes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenMeaningful action on climate problems requires dialogue with fields within and outside computer science and can lead to interdisciplinary methodological innovations, such as improved physics-constrained ML techniques.
“Moreover, meaningful action on climate problems requires dialogue with fields within and outside computer science and can lead to interdisciplinary methodological innovations, such as improved physics-constrained ML techniques.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationMajor financial institutions take the climate investment approach mainly by creating green financial indexes focused on low-carbon energy, clean technology, and/or environmental services, or by designing carbon-neutral investment portfolios that remove or under-weight companies with relatively high carbon footprints.
“The dominant ways in which major financial institutions take this approach are by creating”
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PremisecitationApproaches used in climate analytics include NLP for identifying climate risks and investment opportunities in company disclosures, NLP for analyzing media climate coverage to dynamically hedge climate change risk, econometric approaches for arbitrage strategies using carbon risk factor, and ML for forecasting carbon price in emission exchanges.
“Approaches used in this field include: natural language processing techniques for identifying climate risks and investment opportunities in disclosures made by companies [254,442,498,752] as well as for analyzing the evolution of climate coverage in the media to dynamically hedge climate change risk [221]; econometric approaches for developing arbitrage strategies that take advantage of the carbon”
Quote in the paperNot a substantial claim0 of 6 sources checkedClassified as probably not a substantial claim.
PremisereasoningForecasts of electricity demand based on a dataset from the US will not necessarily generalize to India, where patterns of demand may be different.
“For example, forecasts of electricity demand based on a dataset from the US will not necessarily generalize to India, where patterns of demand may be different.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenFor many of the applications identified, data can be proprietary or include sensitive personal information.
“For many of the applications we identify, data can be proprietary or include sensitive personal information.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenIn each application, ML is only one part of the solution; it is a tool that enables other tools across fields.
“We emphasize that in each application, ML is only one part of the solution; it is a tool that enables other tools across fields.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenML can optimize systems to improve efficiency, e.g., by consolidating freight, designing carbon markets, and reducing food waste.
“ML can optimize systems to improve efficiency (e.g., by consolidating freight, designing carbon markets, and reducing food waste).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningML can potentially have greater impact in climate analytics because it is a predictive approach to addressing climate change from a financial perspective.
“Since this is a predictive approach to addressing climate change from a financial perspective, it is one where ML can potentially have greater impact.”
Quote in the paperSubstantial claim- Rests on an assumption: It is hard to forecast where, how, or when climate change will impact the stock price of a given company, or even the debt of an entire nation.
May combine two claims.
PremisecitationClimate analytics is the other main approach to climate finance, aims to predict the financial effects of climate change, and is still gaining momentum in the mainstream financial community.
“The other main approach to climate finance is climate analytics, which aims to predict the financial effects of climate change, and is still gaining momentum in the mainstream financial community [229].”
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InterpretationreasoningApplying ML to tackle climate change has the potential both to benefit society and to advance the field of ML.
“Applying ML to tackle climate change has the potential both to benefit society and to advance the field of ML.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningThere is potential in applying deep learning for portfolio selection and investment timing to maximize the impact and scope of climate investment strategies.
“While this approach currently does not utilize ML directly, we see the potential in applying deep learning both for portfolio selection (based on features of the stocks involved) and investment timing (using historical patterns to predict future demand), to maximize both the impact and scope of climate investment strategies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenWhere datasets exist, they may not be organized with a specific task in mind, unlike typical ML benchmarks that have a clear objective.
“Where datasets exist, they may not be organized with a specific task in mind, unlike typical ML benchmarks that have a clear objective.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationScalable, adaptive online courses could give hundreds of thousands of learners access to learning resources that they would not usually have in their local educational facilities.
“For instance, scalable, adaptive online courses could give hundreds of thousands of learners access to learning resources that they would not usually have in their local educational facilities [694].”
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InterpretationreasoningIn the fight against climate change, ML has significant contributions to offer across domain areas.
“In the fight against climate change, ML has significant contributions to offer across domain areas.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenFor some tasks, it may be feasible to augment learning with carefully simulated data.
“For some tasks, it may also be feasible to augment learning with carefully simulated data.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Global GHG emissions rise year after year.
“Yet year after year, these emissions rise.”
The paper relies on this without support.
All electric grids share the same underlying system physics.
“(especially since all electric grids share the same underlying system physics)”
The paper relies on this without support.
Small microgrids are even harder to balance than country-scale electric grids.
“since small microgrids are even harder to balance than country-scale electric grids.”
The authors state this as an assumption.
Future advances in materials science will occur, enabling ML research to draw upon open databases to invent new, climate-friendly materials.
“Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project [380] and the UCI Machine Learning Repository [271] to invent new, climate-friendly materials [830].”
The paper relies on this without support.
New and cheaper satellites are creating petabytes of climate observation data.
“First, new and cheaper satellites are creating petabytes of climate observation Fig. 6.Schematic of a climate model, with selected opportunities to improve climate change predictions using ML, as described in Section 8. data.”
The paper relies on this without support.
Massive climate modeling projects are generating petabytes of simulated climate data.
“Second, massive climate modeling projects are generating petabytes of simulated climate data.”
The paper relies on this without support.
ML methods are becoming increasingly fast to train and run.
“while ML methods are becoming increasingly fast to train and run, especially on next-generation computing hardware.”
The paper relies on this without support.
Climate scientists have recently begun to explore ML techniques.
“As a result, climate scientists have recently begun to explore ML techniques, and are starting to team up with computer scientists to build new and exciting applications.”
The paper relies on this without support.
Numerous ML weather models are already running in production.
“Numerous ML weather models are already running in production.”
The paper relies on this without support.
In the past few years, new satellite campaigns have illuminated the Arctic and Antarctic with hundreds of terabytes of data.
“In the past few years, however, new satellite campaigns have illuminated them with hundreds of terabytes of data.”
The paper relies on this without support.
The CO2 removal industry is growing.
“Although many of these applications are speculative, the industry is growing, which will create more data and more opportunities for ML approaches to help.”
The paper relies on this without support.
Once injected, sulfates circulate globally and remain aloft for 1 to 2 years.
“Once injected, sulfates circulate globally and remain aloft for 1 to 2 years.”
The paper relies on this without support.
Atmospheric aerosol physics is not perfectly captured by current climate models.
“One reason that sulfates have been the focus for aerosol research is that atmospheric aerosol physics is not perfectly captured by current climate models, so having natural data is important for validation.”
The paper relies on this without support.
Even if current aerosol models are correct, their best-fit parameters must still be determined using historical data, which comes with uncertainty and computational difficulty.
“Furthermore, even if current aerosol models are correct, their best-fit parameters must still be determined (using historical data), which comes with uncertainty and computational difficulty.”
The paper relies on this without support.
It is most likely impossible to preserve all relevant climate characteristics in all locations through geoengineering.
“Although it is possible to stabilize global mean temperature and even regional temperatures through geoengineering, it is most likely impossible to preserve all relevant climate characteristics in all locations.”
The paper relies on this without support.
Addressing climate change requires swift and effective decision-making by groups at multiple levels.
“Addressing climate change requires swift and effective decision-making by groups at multiple levels-communities, unions, NGOs, businesses, governments, intergovernmental organizations, and many more.”
The paper relies on this without support.
Many data-driven approaches lose the interpretability for which ABMs are valued.
“While the hope of learning or tuning behavior from data is promising for generalization, many data-driven approaches lose the interpretability for which ABMs are valued; work in interpretable ML methods could potentially help with this.”
The paper relies on this without support.
Prices within cap-and-trade markets are highly sensitive to control elements such as the number of permits released at a given time.
“Prices within such cap-and-trade markets are highly sensitive to control elements such as the number of permits released at a given time.”
The paper relies on this without support.
Social media data can contain kernels of insight—places without water, clinics without supplies—which can inform relief efforts.
“Social media data can contain kernels of insight-places without water, clinics without supplies-which can inform relief efforts.”
The paper relies on this without support.
The design space for aerosols is far from fully explored.
“However, the design space is far from fully explored.”
The paper relies on this without support.
Individuals may lack the data and knowledge to know which decisions are most impactful.
“We as individuals are constantly confronted with decisions that affect our carbon footprint, but we may lack the data and knowledge to know which decisions are most impactful.”
The paper relies on this without support.
Citizens can have difficulty understanding existing laws and policies due to their complexity.
“Citizens who would like to engage with policy decisions, or explore different options to reduce their personal carbon footprint, can have difficulty understanding existing laws and policies due to their complexity.”
The paper relies on this without support.
GHG emissions can be seen as a negative externality: the cost is often not reflected in the market price of goods or services that cause GHG emissions.
“In economics, GHG emissions can be seen as a negative externality: while a changing climate results in a cost for society, this cost is often not reflected in the market price of goods or services that cause GHG emissions.”
The paper relies on this without support.
A flat carbon tax on gasoline will have a larger effect on lower-income populations, as fuel expenses are a bigger share of their total budget.
“For example, a flat carbon tax on gasoline will have a larger effect on lower-income populations, as fuel expenses are a bigger share of their total budget.”
The paper relies on this without support.
Education contributes to improving quality of life, helps individuals make informed decisions, and trains the next generation of innovators.
“Education contributes to improving quality of life, helps individuals make informed decisions, and trains the next generation of innovators.”
The paper relies on this without support.
There remains much work to be done to bridge the performance gap between digital and human tutors.
“However, there remains much work to be done to bridge the performance gap between digital and human tutors, and ML-based approaches have an important role to play in this endeavor”
The paper relies on this without support.
It is hard to forecast where, how, or when climate change will impact the stock price of a given company, or even the debt of an entire nation.
“it is hard to forecast where, how, or when climate change will impact the stock price of a given company, or even the debt of an entire nation.”
The paper relies on this without support.
The majority of the current financial system is based on quarterly or yearly performance.
“the majority of the current financial system is based on quarterly or yearly performance.”
The paper relies on this without support.
To date, the field of climate finance has been largely neglected within the larger scope of financial research and analysis.
“To date, the field of climate finance has been largely neglected within the larger scope of financial research and analysis.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI can be an effective tool to mitigate climate change, but greenhouse gases emitted by the computationally intensive training processes of complex AI-based systems can exacerbate existing social and ethical challenges linked to AI.
“Although they can be an effective tool to mitigate climate change [93,94], greenhouse gases emitted by the computationally intensive training processes of complex AI-based systems can exacerbate existing social and ethical challenges linked to AI [95].”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [93]
Disaster resilience and management, including early warning systems, resilience and planning, and simulation and prediction, is one of the areas that received more focus in theoretical and empirical research on AI applications for climate change.
“one of the areas that received more focus in the theoretical and empirical research on AI applications for climate change is disaster resilience and management, including early warning systems, resilience and planning, and simulation and prediction (e.g., Ref. [19,240,241]).”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [241]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Bird-counting citation points to a study of invasive plants
MinorThe citation offered for counting birds points to a reference about mapping invasive species in a coastal marsh, not bird counts.
“It is also possible to use aerial imagery to estimate the size of large herds [805] or count birds [282].”
“282. Dana M. Ghioca-Robrecht, Carol A. Johnston, and Mirela G. Tulbure. 2008. Assessing the use of multiseason Quick- Bird imagery for mapping invasive species in a Lake Erie coastal marsh.”
MacMartin and Kravitz citation points to a different author
MinorThe text attributes the quoted phrase to MacMartin and Kravitz at reference [508], but reference 508 is a work by David MacKay. The MacMartin and Kravitz work appears at reference 509.
“Efficient emulations and error-bars will be essential for what MacMartin and Kravitz [508] call "The Engineering of Climate Engineering."”
“508. David MacKay. 2008. Sustainable Energy-Without the Hot Air. UIT Cambridge. 509. Douglas G. MacMartin and Ben Kravitz. 2019. The engineering of climate engineering.”
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- Authors
- Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., & Bengio, Y.
- Affiliations
- Mila - Quebec Artificial Intelligence Institute (CA)
- Carnegie Mellon University (US)
- Hertie School (DE)
- University of Colorado Boulder (US)
- ServiceNow (United States) (US)
- University of Wisconsin–Madison (US)
- Harvard University (US)
- Mercator Research Institute on Global Commons and Climate Change (DE)
- University of California, Berkeley (US)
- Massachusetts Institute of Technology
- Stanford University (US)
- Lawrence Berkeley National Laboratory (US)
- University of Pennsylvania (US)
- Cornell University (US)
- Google DeepMind (United Kingdom) (GB)
- Google (United States) (US)
- ORCID
- Rolnick, David
- Donti, Priya L.
- Kaack, Lynn H.
- Kochanski, Kelly
- Lacoste, Alexandre
- Sankaran, Kris
- Ross, Andrew Slavin
- Milojevic-Dupont, Nikola
- Jaques, Natasha
- Luccioni, Alexandra Sasha
- Maharaj, Tegan
- Sherwin, Evan D.
- Mukkavilli, S. Karthik
- Kording, Konrad P.
- Gomes, Carla P.
- Ng, Andrew Y.
- Hassabis, Demis
- Platt, John C.
- Creutzig, Felix
- Chayes, Jennifer
- Bengio, Yoshua
- Venue
- ACM Computing Surveys
- Published
- 2022-02-07
- Volume
- 55
- Issue
- 2
- Pages
- 1-96
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2950398529
- Semantic Scholar
- 998039a4876edc440e0cabb0bc42239b0eb29644
- Cited by
- 336
- References
- 806
- Citations by year
- 2026: 5 · 2025: 25 · 2024: 29 · 2023: 48 · 2022: 57 · 2021: 92 · 2020: 64 · 2019: 16
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Computing Surveys (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion, cc-by)
- DepositOnce (submittedVersion, cc-by)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Technische Universität Berlin – Universitätsbibliothek (cc-by)
- ETH Zürich Research Collection
- Funders
- National Science Foundation (1803547)
- Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation and Carnegie Mellon University (SES-00949710)
- US Department of Energy (DE-FG02-97ER25308)
- Natural Sciences and Engineering Research Council of Canada
- MIT Media Lab Consortium
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Keywords
- climate change, machine learning applications, greenhouse gas emissions reduction, climate change adaptation, smart grid, disaster management, interdisciplinary collaboration, research directions, business opportunities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-08.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-08.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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