CoChat
Sign Up
Cited by 266Climate Change and Environmental ImpactClimate Change and Sustainable DevelopmentArtificial Intelligence Applications

Deploying artificial intelligence for climate change adaptation

Leal Filho, Walter, Wall, Tony, Rui Mucova, Serafino Afonso, Nagy, Gustavo J., Balogun, Abdul-Lateef, Luetz, Johannes M., Ng, Artie W., Kovaleva, Marina +8 more·2022·Technological Forecasting and Social Change
DOI 10.1016/j.techfore.2022.121662
Open this paper in CoChat — free account

AI snapshot

Written from the paper’s full text.

TL;DR

This paper combines a systematic literature review and a 104-respondent, 51-country survey to examine how AI is deployed for climate change adaptation. It finds most reviewed AI studies relate to adaptation (95%), especially water-related issues (38%), while survey respondents broadly endorse AI’s governance and sustainability potential but also express concerns about risk, privacy, and unequal digital connectivity.

Key findings

  • In the systematic review, about 95% of assessed studies related to climate change adaptation and 5% to mitigation; water-related issues attracted the most research attention (38%), followed by agriculture, land and wildfire combined (31%), disaster management/regional and global deployment (27%), and energy management (5%).
  • The survey had 104 respondents from 51 countries: Africa contributed 32%, Europe 31%, and Oceania-Australia 3%; respondents were 68% male, 30% female, and about 2% did not state their gender.
  • The strongest agreements were that digitisation and AI can strengthen governance practices and policy coherence in climate change (90%), strengthen environmental sustainability and reduce climate vulnerability (89%), and deliver economic and environmental benefits in climate change management (88%).
  • Respondents also recognised risks: 30% indicated AI and related technologies pose some danger to mankind, while 90% agreed on the need to keep the public well informed with full transparency and accountability, 89% agreed digitalisation and AI need privacy and data protection procedures, and 85% agreed limitations in digital connectivity disproportionately affect developing countries.

Methods

  • A systematic literature review adapted from Leal Filho et al. (2019), using Web of Science, Scopus, Google Scholar, specialised organisational reports, and Google; literature was analysed by theme/category, AI application, implications, and region.
  • A global online questionnaire was administered for approximately two weeks in February 2021 via Google Forms; it comprised sixteen closed-ended questions, three of which allowed brief qualitative answers, and used purposive plus snowball sampling of climate change researchers.
  • Analysis included descriptive statistics, content analysis of qualitative responses, and inferential analysis in SPSS using Pearson’s chi-square test (significance set at <0.05).

Limitations

  • The paper states that the literature review contains relatively recent data because AI has evolved over the past few years, and that the sampling was limited to only 104 people from 51 countries.
  • Evident from the text: the purposive and snowball sampling is non-probability, and some continental subsamples are very small (e.g., Oceania-Australia at 3%), with the authors themselves cautioning that Oceania/Australia results should be treated cautiously due to small sample size.
  • Evident from the text: the narrative states that 54% of respondents disagreed that digital technologies will strengthen governance and policy coherence, while Table 1 reports 54% strongly agree and 90% agree/strongly agree, an inconsistency that complicates interpretation.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.techfore.2022.121662

Cites

116 of 116 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 42 cited sources checked · 0 supported · 0 flagged

  • Findingown data
    § Systematic literature review

    Fewer than ten studies addressed the application of AI in disaster risk reduction and response.

    “In terms of humanitarian responses in the wake of extreme weather events, fewer than ten studies addressed the application of AI in disaster risk reduction and response.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Systematic literature review

    Nearly half of the literature assessed (27 out of 56) focused heavily on the significant role of AI in understanding future outlooks.

    “Nearly half of the literature assessed (27 out 56) focused heavily on the significant role of AI in understanding future outlooks, where issues related to forecasting, projection, and modelling of extreme weather events, resource use, and the even impact of conservation and adaptation efforts were predominant.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Systematic literature review

    Only six studies underscored AI's application in inland and natural coastal capitals (blue economy).

    “Similarly, little attention was given to AI's role in unlocking the blue economy's potential, as only six studies underscored its application in inland and natural coastal capitals.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Systematic literature review

    AI was noted to have gained traction in biotechnology, agronomy, and plant science.

    “Among the literature assessed, AI was noted to have gained traction in terms of its application in biotechnology, agronomy, and plant science, particularly at a time when drought and pest-resistant crop varieties are most sought in many places around the world.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Systematic literature review

    The place of AI in the clinical characterisation of mental health cases emerged only once.

    “The place of AI in the clinical characterisation of mental health cases emerged only once where virtual reality (VR) is relevant in assessing, treating, and managing posttraumatic stress disorder (PTSD).”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 48
  • May combine two claims. 41

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Deploying Artificial Intelligence for Climate Change Adaptation Abstract

    The surveyed articles are classified under nine areas.

    “The surveyed articles are classified under nine areas, e.g., Global/Earth Related; Disaster Response; Water-related Issues and Agriculture, 95% of which are related to adaptation.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingnone given
    § Systematic literature review

    The assessed studies were either reviews (4), case studies (17), or experimental studies (35).

    “The assessed studies were either reviews (4), case studies (17), or experimental studies (35).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data
    § Systematic literature review

    The case studies involved Asia (8), Europe (6), Australia (2), and North America (1).

    “The case studies involved Asia (8), Europe (6), Australia (2), and North America (1), respectively.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingnone given
    § Systematic literature review

    The use of AI for adaptation is frequently performed for monitoring climate dynamics, and in predicting the adverse impacts of climate change in cities and the urban environment.

    “In particular, the use of AI for adaptation is frequently performed for monitoring climate dynamics, and in predicting the adverse impacts of climate change in cities and the urban environment.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Deploying Artificial Intelligence for Climate Change Adaptation Abstract

    95% of the surveyed/assessed articles are related to climate change adaptation, whereas 5% refer to mitigation.

    “The surveyed articles are classified under nine areas, e.g., Global/Earth Related; Disaster Response; Water-related Issues and Agriculture, 95% of which are related to adaptation.”
    • Also stated in: § Systematic literature review · plain
      “Around 95% of these studies are related to climate change adaptation, whereas 5% of them refer to mitigation.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    The lowest percentage contribution came from the Oceanic-Australia block, which contributed 3%.

    “The lowest percentage contribution came from the Oceanic-Australia block, which contributed 3%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Systematic literature review

    Fears related to impacts on employment are nuanced and noticeable across all continents, although at close to 60% Europeans are more cautious than North Americans.

    “Fears related to impacts on employment (Figure 5b) are nuanced and noticeable across all continents, although at close to 60% Europeans are more cautious than North Americans.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • May combine two claims.

    Findingown data
    § Systematic literature review

    Asia and North America are both positive but had relatively lower values (both over 35%) than Australia and Oceania regarding need for privacy and data protection procedures.

    “Although Asia and North America are both positive, they had relatively lower values (both over 35%) than Australia and Oceania.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • May combine two claims.

    Findingown data
    § 5. Conclusions

    There is still considerable fear about the potential risks posed by AI concerning humanity, as some respondents felt AI is an existential threat to humanity.

    “The study has also identified the fact that there is still considerable fear about the potential risks posed by AI concerning humanity, as some respondents felt that AI is an existential threat to humanity.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    36% of respondents agreed that DTs could be of assistance in the development of climate change governance policies.

    “In comparison, 36% agreed that DTs could be of assistance in the development of climate change governance policies”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given
    § Systematic literature review

    Most respondents in all regions (except Australia) expect DT and AI to strengthen governance practices and policy coherence in climate change.

    “Thus, most respondents in all regions (except Australia) expect DT and AI to strengthen governance practices and policy coherence in climate change.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    38% of respondents agreed that digital tools could reduce inequalities due to climate change.

    “Finally, concerning DTs being a tool to reduce inequalities (Figure 4d), 38% of respondents agreed that digital tools could reduce inequalities due to climate change.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Systematic literature review

    40% and 44% of respondents from across all continents agreed that leveraging DTs and AI can be valuable tools in increasing research in climate change-related issues and the implementation of SDG13, respectively.

    “From the survey, 40% and 44% of respondents from across all the continents agreed that leveraging DTs and AI can be valuable tools in increasing research in climate change-related issues and the implementation of SDG13 (Figure 6a and 6b).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § 5. Conclusions

    Some respondents felt that they fear that robots might take their job.

    “Others felt that they fear that robots might take their job.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    54% of respondents disagreed that DTs will strengthen the governance practice and policy coherence in climate change.

    “The survey indicated that 54% of respondents disagreed that DTs will strengthen the governance practice and policy coherence in climate change (Figure 4a).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § 5. Conclusions

    Some respondents expressed concerns regarding potential intrusion on privacy from deployment of AI, mainly if left unregulated and unchecked.

    “Also, some respondents expressed some concerns regarding the potential intrusion on privacy that comes from the deployment of AI, mainly if left unregulated and unchecked.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    Respondents strongly felt that connectivity and infrastructure issues could negatively affect the capacity of developing countries to take full advantage of advancements in AI and other digital tools.

    “Respondents also strongly felt that connectivity and infrastructure issues could negatively affect the capacity of developing countries to take full advantage of advancements in AI and other digital tools (Figure 5c).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given
    § Systematic literature review

    The systematic literature review was complemented by a survey of 104 respondents from 51 countries.

    “The systematic literature review was complemented by a survey of 104 respondents from 51 countries.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingcitation
    § Systematic literature review

    40% and 41% of respondents respectively believed that AI and DTs would not be good tools to aid research in climate change and the implementation of SDG13.

    “On the other hand, 40% and 41% of respondents respectively, believed that AI and DTs would not be good tools to aid research in climate change and the implementation of SDG13 as some of the AI techniques are high energy demanders, and particularly where non-carbon neutral energy sources are used (Vinuesa et al., 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Vinuesa et al., 2020 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Findingown data
    § 5. Conclusions

    DTs were valuable tools that can be deployed towards supporting research on issues related to climate change adaptation and the implementation of SDG13.

    “Our study also ascertained that from the respondent's views, DTs were also valuable tools that can be deployed towards supporting research on issues related to climate change adaptation and the implementation of SDG13.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Systematic literature review

    Predicted future institutional investments in digitisation indicated affirmative results from Oceania (100%), North America (85%), Africa (64%), and Europe (63%).

    “In terms of predicted future institutional investments in digitisation (Figure 3d), findings indicated affirmative results from Oceania (100%), North America (85%), Africa (64%), and Europe (63%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    Most respondents agree or strongly agree that the timely implementation of DTs and AI delivers economic and environmental benefits in climate change management.

    “Concerning that the timely implementation of DTs and AI deliver economic and environmental benefits in climate change management (Figure 4b), most respondents agree (particularly from South America and Europe) or strongly agree (particularly from Oceania and Europe).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Systematic literature review

    A more positive affirmation was verified in Oceania (100%) and North America (more than 65%) regarding expectation that use of DTs and AI will be critical for climate-resilient and sustainable COVID-19 recovery.

    “A more positive affirmation was verified in the Oceania region, with 100%, and North America, with more than 65%.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • May combine two claims.

    Findingown data
    § 5. Conclusions

    80% of respondents from North America and 75% from South America agreed that these tools are essential hence being frequently used.

    “This was evidenced by the high levels of optimism as indicated by 80 % of respondents from North America and 75 % from South America agreeing that these tools are essential hence being frequently used.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Self-reported importance and frequency of use of DTs and AI correspond to actual extensive application and utilisation.

  • May combine two claims.

    Findingown data
    § Systematic literature review

    40% of respondents strongly agreed and 45% agreed that DTs might reduce climate change vulnerability.

    “Regarding the capacity of DTs to reduce climate change vulnerability, 40% and 45% of respondents strongly agreed and agreed, respectively, that DTs might reduce climate change vulnerability”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data
    § Systematic literature review

    The respondents came from all continents, with a majority coming from Africa at 32%, followed by Europe (31%).

    “The respondents came from all continents (Figure 2), with a majority coming from Africa at 32%, followed by Europe (31%).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data
    § Systematic literature review

    The respondents were 68% male and 30% female, whereas about 2% did not state their gender.

    “Furthermore, the respondents varied among countries and the rate was 68% male and 30% female, whereas about 2% did not state their gender.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingnone given
    § Systematic literature review

    North and South America, with more than 80% and 75%, respectively, were more optimistic about the rapid advances in digitisation and accelerated implementation of AI.

    “Nevertheless, North and South America, with more than 80% and 75%, respectively, were more optimistic about the rapid advances in digitisation and accelerated implementation of AI.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • May combine two claims.

    Findingown data
    § Systematic literature review

    With more than 50%, Africa was more favourable than the Asian region (more than 40%) and Europe (more than 35%) regarding expectation that use of DTs and AI will be critical for climate-resilient and sustainable COVID-19 recovery.

    “With more than 50%, Africa was more favourable than the Asian region (more than 40%) and Europe (more than 35%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • Classified as probably not a substantial claim. May combine two claims.

    Findingown data
    § Systematic literature review

    Figure 5d shows agreement and convergence regarding digitalisation, and AI needs to be paralleled by privacy and data protection procedures for all regions.

    “Figure 5d shows agreement and convergence regarding digitalisation, and AI needs to be paralleled by privacy and data protection procedures for all regions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Systematic literature review

    The highest percentage of concern about AI posing danger to humankind came from Australia (100%), Oceania (50%), and South America (38%).

    “The highest percentage of concern came from Australia (100%), Oceania (50%), and South America (38%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Systematic literature review

    Respondents from Oceania and Australia totally agreed that DTs will strengthen governance practice and policy coherence in climate change.

    “From a continental angle, respondents from Oceania and Australia totally agreed with the view; however, this analysis needs to be treated with caution due to the small size of the sample from the two continents.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of their respective continents' views on AI and climate change adaptation.
    • Rests on an assumption: Continental-level percentages are meaningful despite small and uneven sample sizes.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation
    § 1. Introduction: Using Artificial Intelligence in Climate Change Research

    The computational architecture and power that AI encapsulates are particularly fitting for addressing pressing climate change threats.

    “In this context, the computational architecture and power that AI encapsulates are particularly fitting in grappling with such pressing climate change threats, a domain of global environmental changes described by massive data availability, processing and forecasting challenges (Stein, 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Stein, 2020

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 2. Artificial Intelligence and Climate Change Adaptation

    Google used machine learning to increase wind generation efficiency and predict power output, increasing the value of their wind energy by a 20% factor.

    “A key example is Google using machine learning to increase wind generation efficiency and predict power output, increasing the value of their wind energy by a 20% factor (Elkin & Witherspoon, 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Elkin & Witherspoon, 2019

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 1. Introduction: Using Artificial Intelligence in Climate Change Research

    AI applications may upscale climate engagement of the public and stimulate collective action by predicting or visualising climate change risks and aid decision-support efforts by monitoring extreme weather disasters in real-time.

    “AI applications may also upscale climate engagement of the public and stimulate collective action by predicting or visualising climate change risks and aid decision-support efforts by monitoring extreme weather disasters (remotely and) in real-time (Alemany et al., 2019; Huntingford et al., 2019; Walsh et al., 2020).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Alemany et al., 2019

      Not checked in the free run.

    • cites Huntingford et al., 2019 source

      Not checked in the free run.

    • cites Walsh et al., 2020

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductcitation
    § 3. Methods

    Literature searches were based on reputable online databases including Web of Science, Scopus, and Google Scholar, and reports from specialised organisations.

    “Literature searches were based on some of the most reputable online databases of scientific research data, including Web of Science, Scopus, and Google Scholar. We also consulted reports from specialised organisations (e.g. research centres and societies, UN bodies, and the OECD).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § 3. Methods

    The purposive sampling ensured that the survey instrument was appropriate for the target group of academics in climate change disciplines.

    “The purposive sampling ensured that the survey instrument was appropriate for the envisaged target group, which comprised academics working in disciplines related to climate change, including mitigation and adaptation.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisereasoning
    § 2. Artificial Intelligence and Climate Change Adaptation

    AI has great potential in the energy industry.

    “AI has great potential in the energy industry.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation
    § 3. Methods

    The data collection instrument was pre-tested, which led to minor adjustments but overall confirmed its adequacy.

    “Following the instrument's conceptual development, the data collection instrument was pre-tested, which led to minor adjustments, but overall confirmed its adequacy (Bryman 2016, pp. 260-261).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bryman 2016

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation
    § 3. Methods

    Care was taken to select reliable sources for the research data.

    “Care was taken to select reliable sources for the research data since top quality resources are essential requirements for a good literature search (Balogun et al., 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Balogun et al., 2020 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 2. Artificial Intelligence and Climate Change Adaptation

    Machine learning can efficiently process data faster than previous models, which is beneficial in predicting extreme weather events.

    “However, machine learning can efficiently process data faster, which is beneficial in predicting extreme weather events, and models are now being redesigned or modified to include machine learning (Rolnick et al., 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Rolnick et al., 2019

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationcitation
    § Systematic literature review

    These findings echo broad application areas previously studied, but extend previous studies by representing the spread and reach of AI application in climate change adaptation.

    “These findings echo the broad application areas previously studied, but extend previous studies by representing the spread and reach of AI application in climate change adaptation (Walsh et al, 2020)”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Walsh et al, 2020

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § Systematic literature review

    AI can easily be leveraged in drought forecasting, which can subsequently reduce the cost of food insecurity interventions as well as assist in saving lives.

    “Belayneh et al. (2016), who assert that AI can easily be leveraged in drought forecasting, which can subsequently reduce the cost of food insecurity interventions as well as assist in saving lives.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Belayneh et al. (2016)

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation
    § Systematic literature review

    AI can be integrated with improving renewable energy capabilities and energy efficiency, thus reducing total greenhouse gas emissions and carbon footprint.

    “Vinuesa et al. (2020), who assert that AI can be integrated with improving renewable energy capabilities and energy efficiency, thus reducing the total greenhouse gas emissions and carbon footprint.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Vinuesa et al. (2020) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation
    § Systematic literature review

    These reflect the positive intentions of AI reported in the literature.

    “These reflect the positive intentions of AI reported in the literature (Stein, 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Stein, 2020

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning
    § Systematic literature review

    Even though South America, Australia and Oceania did not have a high number of participants, these continents are also expected to suffer several adverse climate change effects; hence adaptation capacity may be increased by integrating AI and other related DTs.

    “Even though South America, Australia and Oceania did not have a high number of participants, these continents are also expected to suffer several adverse climate change effects; hence adaptation capacity may be increased by integrating AI and other related DTs.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: South America, Australia and Oceania are expected to suffer several adverse climate change effects.

  • May combine two claims.

    Premisecitation
    § Systematic literature review

    AI could assist in improving forecast models as several AI techniques can extract information through blending observational data and forecast model information; hence quality outcomes can be derived.

    “Furthermore, McGovern et al. (2017) indicated that AI could assist in improving forecast models as several AI techniques can extract information through blending observational data and forecast model information; hence quality outcomes can be derived.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites McGovern et al. (2017)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Systematic literature review

    The prevalence of DTs (e.g., big data analytics) in all continents enhances urban resilience.

    “Balogun et al. (2020) reported the prevalence of DTs (e.g., big data analytics) in all continents to enhance urban resilience.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Balogun et al. (2020) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Systematic literature review

    ANNs could be applied in future predictions of future natural or human-induced disturbances of climatic systems.

    “They established that ANNs could be applied in future predictions of future natural or human-induced disturbances of climatic systems.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Buckland et al. (2019)

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § Systematic literature review

    AI will assist in better understanding the climate change phenomenon as well as modelling the impacts thereof.

    “Furthermore, evidence exists that shows that AI will assist in better understanding the climate change phenomenon as well as modelling the impacts thereof (Vinuesa et al., 2020); however, this will not be possible in situations where DT distribution is already uneven between rich and developing countries (WHO, 2019).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Vinuesa et al. (2020) source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § Systematic literature review

    The extended employment of DTs during the COVID-19 pandemic facilitates stakeholder engagement, project cooperation, communication, research and teaching.

    “The extended employment of DTs is evident during the COVID-19 pandemic to facilitate stakeholder engagement, project cooperation, communication, research and teaching (Leal Filho et al., 2021a).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Leal Filho et al. (2021a)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductreasoning
    § 5. Conclusions

    The literature review contains relatively recent data since AI is a topic that has evolved over the past few years.

    “The first one is that the literature review contains relatively recent data since AI is a topic that has evolved over the past few years.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § Systematic literature review

    Many farmers in Africa still rely on rainfed agriculture, increasing vulnerability to impacts of climate change.

    “For instance, many farmers in Africa still rely on rainfed agriculture, increasing vulnerability to impacts of climate change (Serdeczny et al., 2017).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Serdeczny et al. (2017)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Systematic literature review

    Innovative digital tools, methods, and approaches support a wide range of project cooperation, including aspects of climate change action.

    “Also, Mckinley et al. (2021) highlighted the emergence of innovative digital tools, methods, and approaches to support a wide range of project cooperation, including aspects of climate change action.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Mckinley et al. (2021)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Systematic literature review

    Models and data-driven machine learning can provide warnings and decision-making support, notably drought predictions.

    “Huntingford et al. (2019), who encouraged the use of models and data-driven machine learning to provide warnings and decision-making support, notably drought predictions.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Huntingford et al. (2019) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § Systematic literature review

    The number of African respondents to the survey can offer valuable insights to make up for the absence of case studies from the region.

    “Thus, number of African respondents to the survey can offer valuable insights to make up for the absence of case studies from the region.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Africa is disproportionately affected by climate change.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Appendix: Systemic Literature Review: Data Matrix

    The European Union is finalising plans for an ambitious 'digital twin' of planet Earth that would simulate the atmosphere, ocean, ice, and land with unrivalled precision.

    “European Union is finalising plans for an ambitious “digital twin” of planet Earth that would simulate the atmosphere, ocean, ice, and land with unrivalled precision,”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Voosen (2020)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning
    § Systematic literature review

    This assertion further highlights the fear which has gripped both the scientific community and the general population regarding AI.

    “This assertion further highlights the fear which has gripped both the scientific community and the general population regarding AI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation
    § Systematic literature review

    This aligns with the outcome of the literature review and confirms opportunities to use AI and digital tools to find practical adaptive approaches to climate change.

    “This aligns with the outcome of the literature review and confirms opportunities to use AI and digital tools to find practical adaptive approaches to climate change.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning
    § Systematic literature review

    The less favourable position of the Australian region can be justified likely due to the existence of similar tools that can play and support climate resilience and sustainable COVID-19 pandemic recovery, and climate change and COVID-19 pandemic are consequences of human behaviour, which can be positively changed if there is a will, decision, commitment, and all this anchored to well-founded and有效

    “We understand that the less favourable position of the Australian region (100%) can be justified likely due to (1) the existence of similar tools that can play and support climate resilience and sustainable COVID -19 pandemic recovery, and (2) climate change and COVID-19 pandemic are consequences of human behaviour, which can be positively changed, if there is a will, decision, commitment, and all”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Appendix: Systemic Literature Review: Data Matrix

    The digital twin of Zurich city planning is a digital twin for urban planning with a focus on climate change adaptation measures.

    “Digital twin for urban planning, with a focus on CC adaptation measures”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Schrotter and Hürzeler (2020)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning
    § Systematic literature review

    This may require advanced technological options such as AI to detect, predict, record, and report extreme weather events, sudden events, predictability of impacts, and mitigation and adaptation options.

    “This may require advanced technological options such as AI to detect, predict, record, and report extreme weather events, sudden events, predictability of impacts, and mitigation and adaptation options.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation
    § Systematic literature review

    These disagreements, mainly in Africa, may be associated in part due to limited use of technologies and digitisation in various productive sectors, limited information on the current dynamics of technologies and AI, and reduced opportunities for technological production and transformation.

    “These disagreements, mainly in Africa, maybe associated in part due to limited use of technologies and digitisation in various productive sectors, limited information on the current dynamics of technologies and AI, and reduced opportunities for technological production and transformation (Van Rensburg et al., 2019; Cariolle, 2020).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Van Rensburg et al., 2019

      Not checked in the free run.

    • cites Cariolle, 2020

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationown data
    § Systematic literature review

    The research results allow the authors to highlight the existence of imbalances on the understanding of technological advances, or whether digitalisation and AI will be deployed much faster and on a broader scale than in the past.

    “The research results allow the authors to highlight the existence of imbalances on the understanding of technological advances, or whether digitalisation and AI will be deployed much faster and on a broader scale than in the past (Figure 6c).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation
    § Systematic literature review

    Another set of risks can be classified as cyber physical and structural imbalances, thus limiting equitable AI adoption by all countries.

    “Furthermore, another set of risks can be classified as cyber physical and structural imbalances (Jelinek et al., 2021) thus limiting equitable AI adoption by all countries.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Jelinek et al. (2021)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation
    § Systematic literature review

    These findings add new insight into the perception of ongoing investment in AI, an area currently unexplored in the literature.

    “These findings add new insight into the perception of ongoing investment in AI, an area currently unexplored in the literature (Walsh et al, 2020), but indicative of future intent and activity.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Walsh et al, 2020

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation
    § Systematic literature review

    These findings sit in contrast to previous findings as they provide a more skeptical picture of how AI can be applied in policy and governance fields.

    “These findings sit in contrast to previous findings as they provide a more skeptical picture of how AI can be applied in policy and governance fields (Rolnick et al, 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Rolnick et al, 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given
    § Systematic literature review

    Such optimism could mean acquiring most of the knowledge, production capacity, technological transformation, and digitisation of these regions.

    “Such optimism could mean acquiring most of the knowledge, production capacity, technological transformation, and digitisation of these regions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationreasoning
    § Systematic literature review

    These results might reflect current commitments to maintain technological advancements in ongoing climate change activity or potential maintenance and utilisation of previous institutional investments in technology.

    “These results might reflect current commitments to maintain technological advancements in ongoing climate change activity (e.g. in Oceania) or potential maintenance and utilisation of previous institutional investments in technology (e.g. as demonstrated in China and other parts of Asia in recent years).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationreasoning
    § Systematic literature review

    These findings might reflect limited national resources and the stage of technology diffusion in climate change action across the continents.

    “These findings might reflect limited national resources and the stage of technology diffusion in climate change action across the continents.”
    Quote in the paperNot a substantial claim
70 of 70

  • The paper relies on this without support.

    The survey respondents, recruited by purposive and snowball sampling, are sufficiently representative of the multinational and interdisciplinary climate change research community for their agreement percentages to indicate expert consensus.

    “The purposive sampling ensured that the survey instrument was appropriate for the envisaged target group, which comprised academics working in disciplines related to climate change, including mitigation and adaptation. Additionally, a snowball sampling was used to leverage the research networks of the authors and 'capitalise on the connectedness of individuals in research networks' (Bryman 2016, p”

  • The paper relies on this without support.

    The classification of articles into adaptation/mitigation and thematic areas accurately reflects their content.

    “Appendix 1 provides a summary of the identified research publications, which are categorised under the Adaptation or Mitigation category. Each article is further classified under the following main areas: 1 = Global or Earth Related; 2 = City or Urban Related; 3 = Disaster Response; 4 = Water-related Issues; 5 = Agricultural, Land or Tree; 6 = Energy; 7 = Wildfire; 8 = Specific AI Technique (s).”

  • The paper relies on this without support.

    Africa is disproportionately affected by climate change.

    “It is one of the continents is disproportionately affected by climate change; therefore, as part of building the adaptation capacity on the continent, participation in such studies is vital.”

  • The paper relies on this without support.

    South America, Australia and Oceania are expected to suffer several adverse climate change effects.

    “Even though South America, Australia and Oceania did not have a high number of participants, these continents are also expected to suffer several adverse climate change effects; hence adaptation capacity may be increased by integrating AI and other related DTs.”

  • The paper relies on this without support.

    The survey respondents are representative of their respective continents' views on AI and climate change adaptation.

    “A second limitation is the sampling since only 104 people from 51 countries took part in it.”
    “From a continental angle, respondents from Oceania and Australia totally agreed with the view; however, this analysis needs to be treated with caution due to the small size of the sample from the two continents.”

  • The paper relies on this without support.

    Self-reported importance and frequency of use of DTs and AI correspond to actual extensive application and utilisation.

    “This was evidenced by the high levels of optimism as indicated by 80 % of respondents from North America and 75 % from South America agreeing that these tools are essential hence being frequently used.”
    “One of the main findings from the study is that respondents from North America and South America are already extensively applying and utilising DTs and AI as tools to increase climate change adaptation.”

  • The paper relies on this without support.

    Continental-level percentages are meaningful despite small and uneven sample sizes.

    “A second limitation is the sampling since only 104 people from 51 countries took part in it.”
    “From a continental angle, respondents from Oceania and Australia totally agreed with the view; however, this analysis needs to be treated with caution due to the small size of the sample from the two continents.”
7 of 7

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?

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.

  • Agreement percentages are presented as opposition to AI tools

    Major

    The passage attributes opposition to 40% and 41% of respondents. In Table 1, those figures are in the "Strongly agree" column for the teaching-and-research and SDG13 statements, respectively.

  • Governance responses are described with the opposite polarity

    Major

    The text says 54% disagreed that digital technologies would strengthen governance. Table 1 places 54% under "Strongly agree"; its two disagreement columns total only 8%.

  • Agreement on reducing climate vulnerability is misstated

    Minor

    The text reports 45% agreeing, but Table 1 reports 49% in the "Agree" column for the corresponding statement. Both give 40% as strongly agreeing.

  • The number of literature-review areas changes from nine to eight

    Minor

    The abstract says the articles are classified into nine areas, while the results give an exhaustive numbered list ending at eight.

4 of 4

Cites

1 retracted

Flagged first, then by citations

Research works printed but not linked: 5

  1. Abarca-Alvarez, 2019, European strategies for adaptation to climate change with the mayors adapt initiative by self-organising maps, Appl. Sci.-Basel, 9
  2. Mohamadi, 2016, Assessing the desertification trend using neural classification and object-oriented techniques, J. Faculty Forestry Istanbul Univ.
  3. Nabipour, 2020, Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system, Eng. Appl. Comput. Fluid Mech., 14, 491
  4. Omdena, 2020, AI for disaster response: predicting relief during cyclones, Omdena
  5. Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., . . . Bengio, Y. (2019). Tackling climate change with machine learning. Online ahead of print, 1–111. https://arxiv.org/abs/1906.05433. · arXiv

Cited by

Sorted by citations

Related

Authors
Leal Filho, W., Wall, T., Rui Mucova, S. A., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Safiul Azam, F. M., Alves, F., Guevara, Z., Matandirotya, N. R., Skouloudis, A., Tzachor, A., Malakar, K., & Gandhi, O.
Affiliations
  • Liverpool John Moores University (GB)
  • Universidad de la República de Uruguay (UY)
  • Universiti Teknologi Petronas (MY)
  • Alphacrucis University College (AU)
  • University of Waterloo (CA)
  • HAW Hamburg (DE)
  • Tecnológico de Monterrey (MX)
  • North-West University (ZA)
  • University of the Aegean (GR)
  • Reichman University (IL)
  • Indian Institute of Technology Madras (IN)
  • University of Nairobi (KE)
Corresponding author
Kovaleva, Marina
ORCID
Venue
Technological Forecasting and Social Change
Publisher
Elsevier BV
Published
2022-07
Volume
180
Pages
121662
ISSN
0040-1625 (ISSN-L)
DOI
10.1016/j.techfore.2022.121662
OpenAlex
W4223435904
Semantic Scholar
58f2e4056e09a9a9a064db0ef0d6ab5e5ccb0a62
Cited by
266
References
116
Citations by year
2026: 53 · 2025: 89 · 2024: 72 · 2023: 39 · 2022: 13
FWCI
21.22
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Technological Forecasting and Social Change (publishedVersion)
  • e-space (Manchester Metropolitan University) (acceptedVersion)
  • Liverpool John Moores University (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Sustainable Development
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence, climate change adaptation, climate change research, water management, digitization, climate governance, policy coherence, climate policy

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 ›

Are you an author, or do you see a problem with this page? Request a review