Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

In the paper

“The research review on waste management demonstrates the significant contributions of AI in waste management, ranging from enhanced waste sorting to optimized collection routes, predictive maintenance, and Decision Support Systems (DSS).”

In the paper

“Some reviews have been conducted on the link between AI, IoT, and Big Data in smart cities from a more general perspective (e.g., Ref. [1,106,107]), broadly addressing different domains beyond environmental sustainability without providing technical details.”

What the sources say

• Not checked yetcites [1] Bibri S.E. Alahi A. Sharifi A. Krogstie J. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review Energy Inform 6 2023 9 10.1186/s42162-023-00259-2 2023 37032812 PMC10074362 source
• Not checked yetcites [106] Allam Z. Dhunny A.Z. On big data, artificial intelligence and smart cities Cities 89 2019 80 91 source
• Not checked yetcites [107] Navarathna P.J. Malagi V.P. Artificial Intelligence in Smart City Analysis International Conference on Smart Systems Inventive Technology 2018 ICSSIT 44 47 2018 https://doi:10.1109/ICSSIT.2018.8748476

In the paper

“These models and techniques have also been applied to hydro-meteorological forecasting and leak detection [[165], [166], [167]].”

What the sources say

• Not checked yetcites [165] Calp M.H. A hybrid anfis-ga approach for estimation of regional rainfall amount J. Sci. 32 2019 145 162
• Not checked yetcites [166] Ferreira L. Duarte A. Cunha F. Fernandes-Filho E. Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data Acta Sci. Agron. 41 2019 10.4025/actasciagron.v41i1.39880 source
• Not checked yetcites [167] Rojek I. Studzinski J. Detection and localization of water leaks in water nets supported by an ICT system with artificial intelligence methods as a way forward for smart cities Sustainablity vol. 11 2019 518 2 source

In the paper

“Consequently, IoT and Big Data technologies gained further momentum in the pursuit of environmental sustainability across the different domains of smart cities, especially transport, mobility, energy, waste, pollution control, air quality, and planning (e.g., Ref. [[137], [138], [139], [140]]).”

What the sources say

• Not checked yetcites [[137], [138], [139], [140]]

In the paper

“They also demonstrate the clear synergy between the eco-city and smart city solutions as to producing “combined effects greater than the sum of their separate effects — concerning energy efficiency and conservation improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy and material flows.””

What the sources say

• Not checked yetcites [53] Bibri S.E. Krogstie J. Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism Energy Informat. 3 1 2020 29 10.1186/s42162-020-00130-8 source

In the paper

“Energy-related studies have harnessed ML and DSS with techniques such as ANN, FL, SVM, DT, Evolution Strategies (ES), Evolutionary Computing (EC), BN (an algorithmic method that makes the training of DNN faster and more stable), and ANFIS for tasks including energy operation, production, distribution, maintenance, and planning (e.g., Ref. [92,161,[168], [169], [170]]).”

What the sources say

• Not checked yetcites [92,161,[168], [169], [170]]

In the paper

“Overall, policy instruments play a crucial role in the materialization of smart eco-cities by providing a framework for planning, implementing, and regulating various initiatives.”

What the sources say

• Not checked yetcites [35,55,56,[154], [155], [156]]

In the paper

“It is until around 2014 that smart cities started to have a significant impact on eco-cities for environmental sustainability (e.g., Ref. [57,[147], [148], [149], [150], [151]) thanks to the adoption of IoT and Big Data technologies as advanced forms of ICT.”

What the sources say

• Not checked yetcites [57,[147], [148], [149], [150], [151]

In the paper

“Many other eco-cities, mainly from Europe and China, have adopted IoT and Big Data technologies in the domains of energy, transport, waste, water, and planning (e.g., Refs. [35,54,[54], [55], [56]]).”

What the sources say

• Not checked yetcites [35,54,[54], [55], [56]]

In the paper

“Subsequently, they became essential to the functioning of smart cities (e.g., Ref. [40,[141], [142], [143], [144], [145]]).”

What the sources say

• Not checked yetcites [40,[141], [142], [143], [144], [145]]

In the paper

“Smart eco-cities became widespread around 2016/2017 (e.g., Ref. [35,152])”

What the sources say

• Not checked yetcites [35] Caprotti F. Cowley R. Bailey I. Joss S. Sengers F. Raven R. Spaeth P. Jolivet E. Tan-Mullins M. Cheshmehzangi A. Smart Eco-City Development in Europe and China: Policy Directions 2017 University of Exter (SMART-ECO Project
• Not checked yetcites [152] Caprotti F. Cowley R. Flynn A. Joss S. Yu L. Smart-Eco Cities in the UK: Trends and CityProfiles 2016 2016 Exeter: University of Exeter (SMART-ECO Project)

In the paper

“Indeed, governance and smart eco-cities are deeply intertwined in a self-reinforcing relationship.”

What the sources say

• Not checked yetcites [35] Caprotti F. Cowley R. Bailey I. Joss S. Sengers F. Raven R. Spaeth P. Jolivet E. Tan-Mullins M. Cheshmehzangi A. Smart Eco-City Development in Europe and China: Policy Directions 2017 University of Exter (SMART-ECO Project
• Not checked yetcites [53] Bibri S.E. Krogstie J. Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism Energy Informat. 3 1 2020 29 10.1186/s42162-020-00130-8 source
• Not checked yetcites [56] Späth P. Hawxwell T. John R. Li S. Löffler E. Riener V. Utkarsh S. Smart-eco Cities in Germany: Trends and City Profiles 2017 University of Exeter Press
• Not checked yetcites [57] Bibri S.E. Krogstie J. Smart eco-city strategies and solutions for sustainability: the cases of royal seaport, Stockholm, and Western Harbor, Malmö, Sweden Urban Sci. 4 2020 11 10.3390/urbansci4010011 2020 source
• Not checked yetcites [155] Kramers A. Wangel J. Höjer M. Governing the smart sustainable city: the case of the Stockholm Royal Seaport ICT Sustain. 46 2016 99 108 2016

In the paper

“The use and application of AI and AIoT in environmental sustainability, climate change, and smart cities have dramatically increased from 2016 onward.”

What the sources say

• Not checked yetcites [137] Hashem I.A.T. Chang V. Anuar N.B. Adewole K. Yaqoob I. Gani A. Ahmed E. Chiroma H. The role of big data in smart city Int. J. Inf. Manag. 36 5 2016 748 758
• Not checked yetcites [138] Nikitin K. Lantsev N. Nugaev A. Yakavleva A. Data-driven cities: from concept to applied solutions PricewaterhouseCoopers (PWC) 2016
• Not checked yetcites [139] Rathore M.M. Ahmad A. Paul A. Rho S. Urban planning and building smart cities based on the Internet of Things using Big Data analytics Comput. Network. 101 2016 63 80 source

In the paper

“Some studies have adopted ML techniques and DSS, such as ANN, DT, GA, FL, and ANFIS, to analyze water chemistry and assess water quality (e.g., Ref. [163,164]).”

What the sources say

• Not checked yetcites [163] Al-Mukhtar M. Al-Yaseen F. Modeling water quality parameters using data-driven models, a case study Abu-Ziriq Marsh in South of Iraq Hydrology 6 1 2019 24 10.3390/hydrology6010024 source
• Not checked yetcites [164] Najafzadeh M. Ghaemi A. Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods Environ. Monit. Assess. 191 6 2019 380 10.1007/s10661-019-7446-8 31104155 source

In the paper

“The authors show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven IoT solutions adopted to enhance the performance of data-driven smart cities and smart-eco-cities under what they term “environmentally data-driven smart sustainable cities.””

What the sources say

• Not checked yetcites [53] Bibri S.E. Krogstie J. Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism Energy Informat. 3 1 2020 29 10.1186/s42162-020-00130-8 source

In the paper

“ML techniques, such as ANN, SVM, FL, ANFIS (an ANN-based on FL), LR, and Key-Nearest Neighbors (KNN), have been applied to predict stream flow and examine water quality parameters (e.g., Ref. [161,162]).”

What the sources say

• Not checked yetcites [161] Demirci M. Üneş F. Körü S. Modeling of groundwater level using artificial intelligence techniques: a case study of Reyhanli region in Turkey Appl. Ecol. Environ. Res. 17 2019 2651 2663 10.15666/aeer/1702_26512663 source
• Not checked yetcites [162] Poul A.K. Shourian M. Ebrahimi H. A Comparative Study of MLR, KNN, ANN, and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction 2019 Water Resources Management 1 17

In the paper

“ML has been applied to traffic forecasting using ANN, DT, and time series models [171,172].”

What the sources say

• Not checked yetcites [171] Alrukaibi F. Alsaleh R. Sayed T. Applying machine learning and statistical approaches for travel time estimation in partial network coverage 11(14), 3822 10.3390/su11143822 2019
• Not checked yetcites [172] Zhou T. Han G. Xu X. Han C. Huang Y. Qin J. A learning-based multimodel integrated framework for dynamic traffic flow forecasting Neural Process. Lett. 49 1 2019 407 430 10.1007/s11063-018-9804-x source

In the paper

“Efforts to conserve energy and promote renewable sources have been supported by ML techniques such as LR, ANN, SVM, NC, and EC. Researchers and scholars like Akhter et al. [82], Alsadi and Khatib [183], Das et al. [83], Dawoud, Lin, and Okba [184], Khan et al. [84], Youssef, El-Telbany, and Zekry [185], and Wang and Srinivasan [186] have made contributions in this field of study.”

What the sources say

• Not checked yetcites [82] Akhter M.N. Mekhilef S. Mokhlis H. Mohamed Shah N. Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques 13(7), 1009-1023 10.1049/iet-rpg.2018.5649 2019
• Not checked yetcites [183] Alsadi S. Khatib T. Photovoltaic power systems optimization research status: a review of criteria Constrain. Models Techn. Software Tools 8 10 2018 1761 10.3390/app8101761
• Not checked yetcites [83] Das U.K. Tey K.S. Seyedmahmoudian M. Mekhilef S. Idris M.Y.I. Van Deventer W. Horan B. Stojcevski A. Forecasting of photovoltaic power generation and model optimization: a review Renew. Sustain. Energy Rev. 81 2018 912 928 10.1016/j.rser.2017.08.017 source

In the paper

“During the first period, AI research on environmental sustainability was expanding to include smart cities based on various AI models, including ML, DL, CV, NLP, and robotics (e.g., Ref. [107,[206], [207], [208], [209]]).”

What the sources say

• Not checked yetcites [107,[206], [207], [208], [209]]

In the paper

“Several studies have focused on water resources conservation by applying ML and DSS, employing techniques such as ANN, SVM, FL, GA, and ANFIS. Key studies in this area include those by Mehr et al. [180], Oyebode and Stretch [90], Sahoo et al. [181], and Valizadeh et al. [182].”

What the sources say

• Not checked yetcites [180] Mehr A.D. Nourani V. Kahya E. Hrnjica B. Sattar A.M. Yaseen Z.M. Genetic programming in water resources engineering: a state-of-the-art review J. Hydrol. 566 2018 643 667 source
• Not checked yetcites [90] Oyebode O. Stretch D. Neural network modeling of hydrological systems: A review of implementation techniques Nat. Res. Model. 32 1 2019 e12189 source
• Not checked yetcites [181] Sahoo B.B. Jha R. Singh A. Kumar D. Application of support vector regression for modeling low flow time series KSCE J. Civ. Eng. 23 2 2019 923 934 source

In the paper

“Additionally, transport-related studies have employed ML and DSS with ANN, DT, NC, FL, SVM, LR, and time series models (e.g., Ref. [173,174]).”

What the sources say

• Not checked yetcites [173] Abdelkafi N. Täuscher K. Business models for sustainability from a system dynamics perspective Organ. Environ. 29 1 2016 74 96 source
• Not checked yetcites [174] Aymen Flah Chokri Mahmoudi Lassaad Sbita A novel energy optimization approach for electrical vehicles in a smart city Energies 12 5 2019 929 10.20944/preprints201901.0214.v1 source

In the paper

“In empirical AI research on climate change, notable attention has been directed toward specific areas, including scenario analysis, marine resources management, and disaster management and resilience.”

In the paper

“On the other hand, ecosystem services assessment has leveraged ML methodologies such as ARIES, as exemplified in the research by Ochoa and Urbina-Cardona [189].”

What the sources say

• Not checked yetcites [189] Ochoa V. Urbina-Cardona N. Tools for spatially modeling ecosystem services: publication trends, conceptual reflections and future challenges Ecosyst. Serv. 26 2017 155 169 source

In the paper

“Related initiatives have been advanced by applying ML, CV, and DSS utilizing ANN, DT, NC, SVM, FL, and time series models. Key studies in this area include those by Jiang and Zhang [187] and Liyanage et al. [101].”

What the sources say

• Not checked yetcites [187] Jiang W. Zhang L. Geospatial data to images: a deep-learning framework for traffic forecasting Tsinghua Sci. Technol. 24 1 2019 52 64 10.26599/TST.2018.9010033 source
• Not checked yetcites [101] Liyanage S. Dia H. Abduljabbar R. Bagloee S.A. Flexible mobility on-demand: an environmental scan Sustainability 11 5 2019 1262 source

In the paper

“ML techniques, including FL, GA, ARIES, and BN (an algorithmic method used for modeling networks in ecosystems), have been used to address different aspects of biodiversity conservation and assess ecosystem services (e.g., Ref. [98,[175], [176], [177], [178], [179]]).”

What the sources say

• Not checked yetcites [98,[175], [176], [177], [178], [179]]

In the paper

“While the main areas of AI and AIoT research in environmental sustainability continued to attract attention, academic interest slightly decreased compared to the previous period.”

In the paper

“AI and AIoT applications span many domains of smart cities in the field of environmental sustainability.”

In the paper

“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]).”

What the sources say

• Not checked yetcites [19] Raza M. Awais M. Ali K. Aslam N. Paranthaman V.V. Imran M. Ali F. Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform Future Generat. Comput. Syst. 112 2020 1057 1069 source
• Not checked yetcites [240] Khalilpourazari S. Pasandideh S.H.R. Designing emergency flood evacuation plans using robust optimization and artificial intelligence J. Combin. Optim. 41 3 2021 640 677 10.1007/s10878-021-00699-0 source
• Not checked yetcites [241] 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. Tackling climate change with machine learning ACM Comput. Surv. 55 2 2022 1 96 source

In the paper

“The studies on energy conservation and air quality studies significantly enhance their environmental performance and efficiency.”

In the paper

“The experimental results demonstrate the ability of the approach to predict energy consumption with high accuracy.”

What the sources say

• Not checked yetcites [250] Han T. Muhammad K. Hussain T. An efficient deep learning framework for intelligent energy management in IoT networks IEEE IoT J 8 2020 3170 3179

In the paper

“During the second period, 2020–2023, the empirical, theoretical, and literature research on AI increased and expanded across environmental sustainability, climate change, and smart cities.”

In the paper

“However, these two domains have frequently been addressed separately or, more recently, in connection with smart cities, particularly within the context of AI and AIoT, instead of being collectively considered within the framework of smart eco-cities.”

In the paper

“A key distinction lies in AIoT's ability to overcome the limitations of IoT in managing the copious and diverse data generated by the vast network of interconnected devices.”

In the paper

“The starting year was selected because it marked the approval of the 2030 Agenda for Sustainable Development by the United Nations General Assembly as an international policy framework for the 17 SDGs.”

In the paper

“DL has attracted increased attention and has proven useful for improving the intelligence of AIoT applications to handle dynamic and complex environments in the context of unsupervised and reinforcement learning methods [13].”

What the sources say

• Not checked yetcites [13] Zhang J. Tao D. Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things IEEE Internet Things J. 8 10 2021 7789 7817 10.1109/JIOT.2020.3039359 15 May15 source

In the paper

“This was considered reliable when conducting a systematic review [113].”

What the sources say

• Not checked yetcites [113] Talan G. Sharma G.D. Doing well by doing good: a systematic review and research agenda for sustainable investment Sustainability 11 2 2019 source

In the paper

“This suggests that there has been a strong tendency to prioritize one approach over another, specifically the second approach has been given a little attention.”

In the paper

“This is determined by an earlier bibliometric study conducted by Bibri et al. [1], highlighting several urban trends and events highly relevant to the current study.”

What the sources say

• Not checked yetcites [1] Bibri S.E. Alahi A. Sharifi A. Krogstie J. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review Energy Inform 6 2023 9 10.1186/s42162-023-00259-2 2023 37032812 PMC10074362 source

In the paper

“Especially, eco-cities and, by extension, smart eco-cities serve as experimental grounds for innovative solutions and environmental transitions (e.g., Ref. [27,[34], [35], [36]]).”

What the sources say

• Not checked yetcites [27,[34], [35], [36]]

In the paper

“Additionally, AIoT addresses the challenges associated with transmitting the rapid torrent of data from distributed sensor network infrastructure [12,13].”

What the sources say

• Not checked yetcites [12] Mastorakis G. Mavromoustakis C.X. Batalla J.M. Convergence of Artificial Intelligence and the Internet of Things 2020 Springer Berlin
• Not checked yetcites [13] Zhang J. Tao D. Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things IEEE Internet Things J. 8 10 2021 7789 7817 10.1109/JIOT.2020.3039359 15 May15 source

In the paper

“Financial incentives: Governments and local authorities provide financial incentives to encourage the adoption of smart technologies and sustainable practices, including tax incentives, grants, and subsidies for smart eco-city projects that incorporate smart solutions.”

In the paper

“This study explores the key research trends and driving factors behind the emergence of environmentally sustainable smart cities and maps their thematic evolution over time. It demonstrates the rapidly growing trend of this emerging paradigm of urbanism that markedly escalated during 2016–2022 due to the accelerated digitalization and decarbonization agendas — due to COVID-19 and the rapid advance”

What the sources say

• Not checked yetcites Bibri et al. [1] Bibri S.E. Alahi A. Sharifi A. Krogstie J. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review Energy Inform 6 2023 9 10.1186/s42162-023-00259-2 2023 37032812 PMC10074362

In the paper

“As to the starting year of 2016, it has marked the materialization of smart eco-cities (e.g., Ref. [27,35,152]), as discussed in Section 4.”

What the sources say

• Not checked yetcites [27] Bibri S.E. Data-driven smart eco-cities and sustainable integrated districts: a best-evidence synthesis approach to an extensive literature review Eur. J. For. Res. 9 1 2021 16 10.1186/s40309-021-00181-4
• Not checked yetcites [35] Caprotti F. Cowley R. Bailey I. Joss S. Sengers F. Raven R. Spaeth P. Jolivet E. Tan-Mullins M. Cheshmehzangi A. Smart Eco-City Development in Europe and China: Policy Directions 2017 University of Exter (SMART-ECO Project
• Not checked yetcites [152] Caprotti F. Cowley R. Flynn A. Joss S. Yu L. Smart-Eco Cities in the UK: Trends and CityProfiles 2016 2016 Exeter: University of Exeter (SMART-ECO Project)

In the paper

“Ever since, IoT and Big Data technologies and their applied solutions have become instrumental in the functioning of smart eco-cities concerning transport, mobility, energy, waste, pollution control, air quality, and planning.”

In the paper

“IoT and Big Data technologies provide the ability to monitor urban operations, functions, and structures using advanced forms of decision-making in urban intelligence functions for design and planning to improve environmental sustainability.”

In the paper

“The materialization of smart eco-cities is strongly influenced by, in addition to technological advancements and environmental concerns, policy instruments.”

In the paper

“Studies on ocean and cryosphere (e.g., Ref. [199]) and atmospheric forecasting (e.g., Ref. [200,201]) have applied ML using ANN.”

What the sources say

• Not checked yetcites [199] Piasecki A. Jurasz J. Adamowski J.F. Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method Acta Geophys. 66 5 2018 1093 1107 source

In the paper

“These policy instruments create a supportive environment for the materialization of smart eco-cities.”

In the paper

“These endeavors have harnessed various AI models and techniques, as demonstrated by the works of Lu et al. [204] and Wang et al. [205], to address challenges related to the sustainable use and conservation of marine ecosystems.”

What the sources say

• Not checked yetcites Lu et al. [204] Lu H. Li H. Liu T. Fan Y. Yuan Y. Xie M. Qian X. Simulating heavy metal concentrations in an aquatic environment using artificial intelligence models and physicochemical indexes Sci. Total Environ. 694 2019 133591 10.1016/j.scitotenv.2019.133591 31386956
• Not checked yetcites Wang et al. [205] Wang P. Yao J. Wang G. Hao F. Shrestha S. Xue B. Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants Sci. Total Environ. 693 2019 133440 10.1016/j.scitotenv.2019.07.246 31374492

In the paper

“For AI-powered IoT architecture, research tended to focus mainly on the theoretical aspects of ML and data analysis (e.g., Ref. [[213], [214], [215]]), cognitive AI (e.g., Ref. [216,217]), knowledge-based DSS [218], and knowledge-based AI [174].”

What the sources say

• Not checked yetcites [[213], [214], [215]]
• Not checked yetcites [216] Guo K. Lu Y. Gao H. Cao R. Artificial intelligence-based semantic internet of things in a user-centric smart city Sensors 18 5 2018 10.3390/s18051341 PMC5982249 29701679 source
• Not checked yetcites [217] Park J.-h. Salim M. Jo J. Sicato J. Rathore S. Park J. CIoT-Net: a scalable cognitive IoT based smart city network architecture Human-centric Comput. Info. Sci. 9 2019 29 source
• Not checked yetcites [218] Belkhala S. Benhadou S. Boukhdir K. Medromi H. Smart parking architecture based on multi agent system Int. J. Adv. Comput. Sci. Appl. 10 2019 378 382 10.14569/IJACSA.2019.0100349

In the paper

“and CO2 emission [[194], [195], [196]] and natural disaster (e.g. Ref. [197,198], based on ML and FL using ANN, SVM, EC, and neuro-fuzzy.”

What the sources say

• Not checked yetcites [[194], [195], [196]]

In the paper

“analysis across sustainability elements based on ML using ANN (e.g., Ref. [193]);”

What the sources say

• Not checked yetcites [193] Skiba M. Mrówczyńska M. Bazan-Krzywoszańska A. Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: the town of Zielona Góra Appl. Energy 188 2017 356 366 10.1016/j.apenergy.2016.12.006 source

In the paper

“Studies on ocean and cryosphere (e.g., Ref. [199]) and atmospheric forecasting (e.g., Ref. [200,201]) have applied ML using ANN.”

What the sources say

• Not checked yetcites [200] Ise T. Oba Y. Forecasting climatic trends using neural networks: an experimental study using global historical data [original research] 6, 32 10.3389/frobt.2019.00032 2019 PMC7805612 33501048
• Not checked yetcites [201] Yin Z. Feng Q. Yang L. Deo R.C. Wen X. Si J. Xiao S. Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous Inland watershed in North-West China 9(11), 880 10.3390/w9110880 2017

In the paper

“Noteworthy works have performed scenario analysis based on ML, EC, and FL using ANN, BN, SVM, GA, and neuro-fuzzy (e.g., Ref. [[190], [191], [192]]);”

What the sources say

• Not checked yetcites [[190], [191], [192]]

In the paper

“Studies have employed various AI and ML models and techniques.”

In the paper

“IoT, as a fundamental component of AIoT, significantly contributes to the above areas of environmental sustainability by providing a robust technical framework for data collection, analysis, and decision-making.”

In the paper

“It also involved using ML in empirical studies (e.g., Ref. [174,210]).”

What the sources say

• Not checked yetcites [174] Aymen Flah Chokri Mahmoudi Lassaad Sbita A novel energy optimization approach for electrical vehicles in a smart city Energies 12 5 2019 929 10.20944/preprints201901.0214.v1 source
• Not checked yetcites [210] Vázquez-Canteli J. Ulyanin S. Kämpf J. Nagy Z. Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities Sustain. Cities Soc. 45 2018 243 257 10.1016/j.scs.2018.11.021 source

In the paper

“Concerning climate change in the urban context, some studies addressed mitigation concerning urban planning, mobility, and land use (e.g., Ref. [211,212]).”

What the sources say

• Not checked yetcites [211] Barnes E.A. Hurrell J.W. Ebert-Uphoff I. Anderson C. Anderson D. Viewing forced climate patterns through an AI Lens Geophys. Res. Lett. 46 22 2019 13389 13398 10.1029/2019GL084944 source
• Not checked yetcites [212] Huntingford C. Jeffers E.S. Bonsall M.B. Christensen H.M. Lees T. Yang H. Machine learning and artificial intelligence to aid climate change research and preparedness Environ. Res. Lett. 14 12 2019 124007 source

In the paper

“and CO2 emission [[194], [195], [196]] and natural disaster (e.g. Ref. [197,198], based on ML and FL using ANN, SVM, EC, and neuro-fuzzy.”

What the sources say

• Not checked yetcites [197] Mokhtarzad M. Eskandari F. Jamshidi Vanjani N. Arabasadi A. Drought forecasting by ANN, ANFIS, and SVM and comparison of the models Environ. Earth Sci. 76 21 2017 729 10.1007/s12665-017-7064-0 source
• Not checked yetcites [198] Shen R. Huang A. Li B. Guo J. Construction of a drought monitoring model using deep learning based on multi-source remote sensing data Int. J. Appl. Earth Obs. Geoinf. 79 2019 48 57 10.1016/j.jag.2019.03.006

In the paper

“El Himer et al. [251] address the role of AIoT in providing new opportunities in distributed energy resources (DER), focusing on AIoT applications in renewable energy sources, such as solar and wind.”

What the sources say

• Not checked yetcites [251] El Himer S. Ouaissa M. Ouaissa M. Boulouard Z. Artificial intelligence of things (AIoT) for renewable energies systems El Himer S. Ouaissa M. Emhemed A.A.A. Ouaissa M. Boulouard Z. Artificial Intelligence of Things for Smart Green Energy Management. Studies in Systems, Decision and Control vol. 446 2022 Springer Cham 10.1007/978-3-031-04851-7_1

In the paper

“Overall, these studies contribute to understanding how AI can revolutionize waste management by improving waste logistics, classification, and treatment processes.”

In the paper

“optimizing energy production and consumption via domotics (home automation),”

What the sources say

• Not checked yetcites [244] Yigitcanlar T. Desouza K.C. Butler L. Roozkhosh F. Contributions and risks of artificial intelligence (AI) in building smarter cities: insights from a systematic review of the literature Energies 13 6 2020 1473 source

In the paper

“Broadly, AIoT can be used in smart cities to analyze and track how different consumers and residents use energy to make decisions on where and what kind of renewable energy sources could be used, as well as where energy is being wasted and how it can be directed for other uses or conserved.”

In the paper

“Consequently, AI and AIoT technologies gained strong traction because of the accelerated digitalization prompted by COVID-19.”

In the paper

“In more recent years, Blockchain has become an innovative technology and solution for smart cities in environmental sustainability.”

In the paper

“The digitalized transformation triggered by COVID-19 significantly contributed to climate actions [158,159].”

What the sources say

• Not checked yetcites [158] Balogun A.L. Marks D. Sharma R. Shekhar H. Balmes C. Maheng D. Arshad A. Salehi P. Assessing the potentials of digitalisation as a tool for climate change adaptation and sustainable development in urban centres Sustain. Cities Soc. 53 2020 101888 source
• Not checked yetcites [159] Dwivedi Y.K. Hughes L. Kar A.K. Baabdullah A.M. Grover P. Abbas R. Climate change and COP26: are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action Int. J. Inf. Manag. 63 2022 102456

In the paper

“These systems integrate various data sources, including weather conditions, population density, and waste composition, to provide insights and recommendations for effective waste management strategies.”

In the paper

“Furthermore, AI and AIoT have been instrumental in developing advanced waste collection systems that optimize several parameters and maximize efficiency.”

In the paper

“As demonstrated in a recent bibliometric study on environmentally sustainable smart cities, Blockchain is linked to the challenges, services, and resources of smart cities and AI, IoT, and big data analytics [1].”

What the sources say

• Not checked yetcites [1] Bibri S.E. Alahi A. Sharifi A. Krogstie J. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review Energy Inform 6 2023 9 10.1186/s42162-023-00259-2 2023 37032812 PMC10074362 source

In the paper

“In sum, IoT's technical capabilities within AIoT are instrumental in climate change mitigation and adaptation efforts.”

In the paper

“predicting the risks of climate change via ML algorithms and climate models,”

What the sources say

• Not checked yetcites [244] Yigitcanlar T. Desouza K.C. Butler L. Roozkhosh F. Contributions and risks of artificial intelligence (AI) in building smarter cities: insights from a systematic review of the literature Energies 13 6 2020 1473 source

In the paper

“An AIoT system developed by Puri et al. [16] generates energy from different sensors, such as piezoelectric sensors, including from stress caused by human body weight, heat generated by the movement of the human body, and sunlight.”

What the sources say

• Not checked yetcites [16] Puri V. Jha S. Kumar R. A hybrid artificial intelligence and Internet of Things model for generation of renewable resource of energy IEEE Access 7 2019 111181 111191 source

In the paper

“Moreover, AI can be a critical change agent because it enables climate change mitigation through carbon neutrality in energy production, distribution, transportation, buildings, construction, and others [242].”

What the sources say

• Not checked yetcites [242] Stein A.L. Artificial intelligence and climate change Yale J. Regul. 37 2020 890

In the paper

“monitoring changes in the natural environment via remote sensing with autonomous drones,”

What the sources say

• Not checked yetcites [244] Yigitcanlar T. Desouza K.C. Butler L. Roozkhosh F. Contributions and risks of artificial intelligence (AI) in building smarter cities: insights from a systematic review of the literature Energies 13 6 2020 1473 source

In the paper

“The authors built and validated the data collected from the sensors with ANN and ANFIS models to predict generated power output and demonstrated that their system could produce accurate results in predicting the power generated from renewable resources.”

What the sources say

• Not checked yetcites [16] Puri V. Jha S. Kumar R. A hybrid artificial intelligence and Internet of Things model for generation of renewable resource of energy IEEE Access 7 2019 111181 111191 source

In the paper

“In particular, the need to overcome the constraints and complexities associated with conventional approaches (e.g., RFID, GPS, GIS), especially the status and waste level in bins, has driven the development and implementation of various advanced techniques.”

In the paper

“This is likely attributed to COVID-19 taking priority in research during the second period, inducing scholars in smart cities and environmental sustainability to investigate the link between COVID-19 and CO2 emissions reduction and air quality improvement.”

In the paper

“There is a risk of a mismatch between the environmental goals of smarter eco-cities and the opportunities offered by AI and AIoT technologies.”

In the paper

“These suggest the potential for innovative approaches that support localized and community-driven renewable energy production.”

In the paper

“While the relationship between outcomes, investments, and implementations is expected to advance the transition of smart eco-cities toward environmental sustainability, stating a strong causal relationship resulting from such linkages needs to be more accurate.”

In the paper

“In addition, IoT and AI production, distribution, service, and disposal produce vast amounts of e-waste, unsustainable materials, and toxic pollution [[63], [282]].”

What the sources say

• Not checked yetcites [[63], [282]]

In the paper

“For the latter, in a use case of a developed “blockchain-based carbon emission rights verification system,” AI and Big Data are used to learn proven data [262].”

What the sources say

• Not checked yetcites [262] Kim S.-K. Huh J.-H. Blockchain of carbon trading for UN sustainable development goals Sustainability 12 10 2020 4021 10.3390/su12104021 source

In the paper

“In particular, more than 250 studies applied AI to energy conservation and renewable energy during 2015–2019 [17].”

What the sources say

• Not checked yetcites [17] Nishant R. Kennedy M. Corbett J. Artificial intelligence for sustainability: challenges, opportunities, and a research agenda Int. J. Inf. Manag. 53 2020 102104

In the paper

“It is also used in rewards schemes for residents of coastal areas using tokens of cryptocurrencies, which “can later be redeemed for credit to collect and share data on environmental conditions of water bodies” that can aid in enhancing decisions and designing regulations [269].”

What the sources say

• Not checked yetcites [269] Mora H. Mendoza-Tello J.C. Varela-Guzmán E.G. Szymanski J. Blockchain technologies to address smart city and society challenges Comput. Hum. Behav. 122 2021 106854 10.1016/j.chb.2021.106854 source

In the paper

“The focus on the potential of AI and AIoT for energy can be justified by its pivotal role in the transition to smart eco-cities.”

In the paper

“This is due to their indirect, direct, rebound, and systemic effects, which are generated through their development, design, use, application, and disposal (see Ref. [279] for a detailed discussion).”

What the sources say

• Not checked yetcites [279] Bibri S.E. Transitioning from smart cities to smarter cities: the future potential of ICT of pervasive computing for advancing environmental sustainability Smart Sustainable Cities of the Future 2018 Springer 535 599

In the paper

“In particular, the indirect effects are expected to be exacerbated the most due to the increasing demand for AI and AIoT applications.”

In the paper

“AI, IoT, and Big Data technologies pose significant challenges when making smart eco-cities environmentally smarter.”

In the paper

“Overall, AI can provide unsurpassed ways of automating or autonomizing the repetitive, complex, cognitively demanding, and time-consuming tasks associated with the operational functioning and planning of smarter eco-cities.”

In the paper

“The study reveals that AI applications have become integral in urban services, managing various aspects of urban life, such as transport systems, infrastructure, and environmental monitoring.”

What the sources say

• Not checked yetcites [26] Yigitcanlar T. Cugurullo F. The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities Sustainability 12 20 2020 8548 10.3390/su12208548 source

In the paper

“All in all, the applied AI and AIoT solutions for smarter eco-cities are challenged by the effects of high energy-intensive structures, undermining the efforts deployed to avoid the overexploitation of primary resources to achieve carbon neutrality.”

In the paper

“However, the high energy requirements for AI and AIoT applications in the case of the use of non-renewable sources of energy will undermine the efforts to achieve the environmental targets of smarter eco-cities.”

In the paper

“According to current estimates, the global electricity demand for advanced ICT could increase to 20 % compared to around 1 % today [271].”

What the sources say

• Not checked yetcites [271] Vinuesa R. Azizpour H. Leite I. Balaam M. Dignum V. Domisch S. The role of artificial intelligence in achieving the Sustainable Development Goals Nat. Commun. 11 1 2020 233 10.1038/s41467-019-14108-y 31932590 PMC6957485 source

In the paper

“In particular, Blockchain and AI technologies could optimize water management in water-stressed urban areas by facilitating autonomous water distribution and management systems These minimize loss and control quality.”

In the paper

“While these technologies provide new and largely expanded opportunities to understand better and prevent environmental problems, they pose significant challenges that must be addressed and overcome to successfully implement smarter eco-cities.”

In the paper

“While these technologies provide new and largely expanded opportunities to understand better and prevent environmental problems, they pose significant challenges that must be addressed and overcome to successfully implement smarter eco-cities.”

In the paper

“Furthermore, the variance-bias tradeoffs associated with ML [284] have implications for climate change solutions due to the bias and oversimplification inherent in predicting future climate change scenarios.”

What the sources say

• Not checked yetcites [284] Belkin M.D. Hsu S. Ma S. Mandal Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off Proceedings of the National Academy of Sciences of the United States of America vol. 116 2019 15849 15854 32 PMC6689936 31341078

In the paper

“Overall, the study highlighted the significance of AI and AIoT technologies in advancing the transition toward environmental sustainability in smarter eco-cities.”

In the paper

“Non-deterministic ML models are more relevant to transport management, energy management, water management, waste management, and pollution monitoring, where they can detect anomalies and alterations and help provide real-time feedback.”