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Coordinated and published by The Grassroots Institute, the Grassroots Journal of Natural Resources (GJNR) is an international journal dedicated to the latest advancements in natural resources throughout the world. The goal of this journal is to provide a platform for scientists, social scientists, policy analysts, managers and practitioners (on all academic and professional levels) all over the world to promote, discuss and share various new issues and developments in different arenas of natural resources.
Kassem Alshar Wajiha*1, Szabolcs Rámháp2
1Doctoral School of Regional and Business Administration Sciences, Széchenyi István University, Győr, Hungary.
Email: wajiha.k.alshar@gmail.com | ORCID: https://orcid.org/0009-0004-7967-2711
2Department of Corporate Leadership and Marketing, Széchenyi István University, Győr, Hungary.
Email: ramhap@ga.sze.hu | ORCID: https://orcid.org/0000-0001-5178-5942
*Corresponding author
Grassroots Journal of Natural Resources, 8(3): 1-24. Doi: https://doi.org/10.33002/nr2581.6853.080301
Received: 26 June 2025
Reviewed: 18 October 2025
Provisionally Accepted: 19 October 2025
Revised: 12 November 2025
Finally Accepted: 16 November 2025
Published: 25 December 2025
Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.
Artificial intelligence; Business resilience; Climate adaptation; Sustainable business models; Systematic literature review
Akter, S., Wamba, S.F., Mariani, M. and Hani, U. (2021). How to build an AI climate-driven service
analytics capability for innovation and performance in industrial markets? Industrial Marketing Management, 97: 258–273. DOI: https://doi.org/10.1016/j.indmarman.2021.03.001.
Alonso Leal, S., Maldonado, S., Martínez, J.I., Bertazzo, S., Quijada, S. and Vairetti, C. (2025).
Enhancing environmental governance: A text-based artificial intelligence approach for project evaluation involvement. Environmental Impact Assessment Review, 107: 107707. DOI:
https://doi.org/10.1016/j.eiar.2024.107707.
Biagini, B. and Miller, A. (2013). Engaging the private sector in adaptation to climate change in
developing countries: Importance, status, and challenges. Climate and Development, 5(3): 242–252. DOI: https://doi.org/10.1080/17565529.2013.821053.
Busch, T. (2011). Organizational adaptation to disruptions in the natural environment: The case of
climate change. Scandinavian Journal of Management, 27(4): 389–404. DOI:
https://doi.org/10.1016/j.scaman.2011.07.001.
Chen, J., Huang, S., BalaMurugan, S. and Tamizharasi, G.S. (2020). Artificial intelligence-based e-waste
management for environmental planning. Environmental Impact Assessment Review, 85: 106498. DOI: https://doi.org/10.1016/j.eiar.2020.106498.
Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A.I., Farghali, M. and Yap, P.S. (2023). Artificial
intelligence‐based solutions for climate change: A review. Environmental Chemistry Letters, 21: 2525–2557. DOI: https://doi.org/10.1007/s10311-023-01617-y.
Cheong, S.M., Sankaran, K. and Bastani, H. (2022). Artificial intelligence for climate change adaptation.
WIREs Data Mining and Knowledge Discovery, 12(5): e1459. DOI: https://doi.org/10.1002/widm.1459.
Di Vaio, A., Palladino, R., Hassan, R. and Escobar, O. (2020). Artificial intelligence and business models
in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121: 283–314. DOI: https://doi.org/10.1016/j.jbusres.2020.08.019.
DiBella, J. (2020). The spatial representation of business models for climate adaptation: An approach for
business model innovation and adaptation strategies in the private sector. Business Strategy and Development, 3(2): 245–260. DOI: https://doi.org/10.1002/bsd2.92.
Dubey, R., Gunasekaran, A., Childe, S.J., Bryde, D.J., Giannakis, M., Foropon, C., Roubaud, D. and
Hazen, B.T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226: 107599. DOI: https://doi.org/10.1016/j.ijpe.2019.107599.
Dwivedi, Y.K., Hughes, L., Kar, A.K., Baabdullah, A.M., Grover, P., Abbas, R., Andreini, D.,
Abumoghli, I., Barlette, Y., Bunker, D. and Chandra Kruse, L. (2022). Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action. International Journal of Information Management, 63: 102456. DOI: https://doi.org/10.1016/j.ijinfomgt.2021.102456.
Füssel, H.M. (2007). Adaptation planning for climate change: Concepts, assessment approaches, and key
lessons. Sustainability Science, 2: 265–275. DOI: https://doi.org/10.1007/s11625-007-0032-y.
Hallegatte, S. (2009). Strategies to adapt to an uncertain climate change. Global Environmental Change,
19(2): 240–247. DOI: https://doi.org/10.1016/j.gloenvcha.2009.01.003.
Hewawasam, V. and Matsui, K. (2024). An analysis of policy frameworks on business formulization and
disaster management to mitigate flood loss and damage among informal businesses in Sri Lanka. Social Sciences, 13: 573. DOI: https://doi.org/10.3390/socsci13110573.
Huiskamp, U., ten Brinke, B. and Kramer, G.J. (2022). The climate resilience cycle: Using scenario
analysis to inform climate-resilient business strategies. Business Strategy and the Environment, 31(4): 1763–1775. DOI: https://doi.org/10.1002/bse.2982.
Jarrahi, M.H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in
organizational decision making. Business Horizons, 61(4): 577–586. DOI:
https://doi.org/10.1016/j.bushor.2018.03.007.
Lemma, T.T., Lulseged, A. and Tavakolifar, M. (2021). Corporate commitment to climate change action,
carbon risk exposure, and a firm's debt financing policy. Business Strategy and the Environment, 30(8): 3919–3936. DOI: https://doi.org/10.1002/bse.2849.
Leal Filho, W., Salvia, A.L., Balogun, A., Pereira, M.J.V., Mucova, S.A.R., Ajulo, O.M., Ng, A., Gwenzi,
J., Mashonjowa, E., Aina, Y.A., Li, C., Totin, E., Pinho, P., Campbell, D., Chanza, N. and Setti, A.F. (2023). Towards more sustainable responses to natural hazards and climate change challenges via transformative adaptation. Cities, 141: 104525. DOI: https://doi.org/10.1016/j.cities.2023.104525.
Leal Filho, W., Wall, T., Mucova, S.A.R., Nagy, G.J., Balogun, A.L., Luetz, J.M., Ng, A.W., Kovaleva,
M., Azam, F.M.S., Alves, F., Guevara, Z., Matandirotya, N.R., Skouloudis, A., Tzachor, A., Malakar, K. and Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180: 121662. DOI:
https://doi.org/10.1016/j.techfore.2022.121662.
Linnenluecke, M.K. and Griffiths, A. (2010). Beyond adaptation: Resilience for business in light of
climate change and weather extremes. Business & Society, 49(3): 477–511. DOI:
https://doi.org/10.1177/0007650310368814.
Linnenluecke, M.K. and Griffiths, A. (2012). Assessing organizational resilience to climate and weather
extremes: Complexities and methodological pathways. Climatic Change, 113(3): 933–947. DOI: https://doi.org/10.1007/s10584-011-0365-5.
Linnenluecke, M.K., Griffiths, A. and Winn, M. (2012). Extreme weather events and the critical
importance of anticipatory adaptation and organizational resilience in responding to impacts. Business Strategy and the Environment, 21(1): 17–32. DOI: https://doi.org/10.1002/bse.708.
Linnenluecke, M.K., Griffiths, A. and Winn, M. (2013). Firm and industry adaptation to climate change:
A review of climate adaptation studies in the business and management field. Wiley Interdisciplinary Reviews: Climate Change, 4(5): 397–416. DOI: https://doi.org/10.1002/wcc.214.
Liu, W., Zhao, J., Du, L., Padwal, H.H. and Vadivel, T. (2020). Intelligent comprehensive evaluation
system using artificial intelligence for environmental evaluation. Environmental Impact Assessment Review, 85: 106495. DOI: https://doi.org/10.1016/j.eiar.2020.106495.
Mbanyele, W. and Muchenje, L.T. (2022). Climate change exposure, risk management and corporate
social responsibility: Cross-country evidence. Journal of Multinational Financial Management, 66: 100771. DOI: https://doi.org/10.1016/j.mulfin.2022.100771.
Melkonyan, A., Hollmann, R., Gruchmann, T. and Daus, D. (2024). Climate mitigation and adaptation
strategies in the transport sector: An empirical investigation in Germany. Transportation Research Interdisciplinary Perspectives, 21: 101102. DOI: https://doi.org/10.1016/j.trip.2024.101102.
Miglionico, A. (2022). The use of technology in corporate management and reporting of climate-related
risks. European Business Organization Law Review, 23: 119–141. DOI:
https://doi.org/10.1017/ebor.2022.6.
Moher, D., Shamseer, L., Clarke, M. (2015). Preferred reporting items for systematic review and meta-
analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4: 1. DOI:
https://doi.org/10.1186/2046-4053-4-1.
Moser, S.C. and Ekstrom, J.A. (2010). A framework to diagnose barriers to climate change adaptation.
Proceedings of the National Academy of Sciences of the United States of America, 107(51): 22026–22031. DOI: https://doi.org/10.1073/pnas.1007887107.
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges,
opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104.
Rahman, M.S., Bag, S., Gupta, S. and Sivarajah, U. (2023). Technology readiness of B2B firms and AI-
based customer relationship management capability for enhancing social sustainability performance. Journal of Business Research, 156: 113525. DOI: https://doi.org/10.1016/j.jbusres.2022.113525.
Sakhel, A. (2017). Corporate climate risk management: Are European companies prepared? Journal of
Cleaner Production, 165: 103–118. DOI: https://doi.org/10.1016/j.jclepro.2017.07.093.
Shakou, L.M., Wybo, J.L., Reniers, G. and Boustras, G. (2019). Developing an innovative framework for
enhancing the resilience of critical infrastructure to climate change. Safety Science, 118: 364–378. DOI: https://doi.org/10.1016/j.ssci.2019.05.019.
Shankar, R. and Gupta, L. (2024). An integrated AI framework for managing organizational risk and
climate change concerns in the B2B market. Industrial Marketing Management, 117: 173–187. DOI: https://doi.org/10.1016/j.indmarman.2023.12.019.
Singh, S., Goyal, M. and Kumar, A. (2023). Enhancing climate resilience in businesses: The role of
artificial intelligence. Journal of Cleaner Production, 394: 136343. DOI:
https://doi.org/10.1016/j.jclepro.2022.136343.
Todaro, N.M., Testa, F., Daddi, T. and Iraldo, F. (2021). The influence of managers’ awareness of climate
change, perceived climate risk exposure and risk tolerance on the adoption of corporate responses to climate change. Business Strategy and the Environment, 30(2): 1232–1248. DOI:
https://doi.org/10.1002/bse.2683.
Truong, Y. and Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review
and future research agenda. Technological Forecasting and Social Change, 183: 121852. DOI:
https://doi.org/10.1016/j.techfore.2022.121852.
Waltersmann, L., Kiemel, S., Stuhlsatz, J., Sauer, A. and Miehe, R. (2021). Artificial intelligence
applications for increasing resource efficiency in manufacturing companies — A comprehensive review. Sustainability, 13(12): 6689. DOI: https://doi.org/10.3390/su13126689.
Wedawatta, G., Ingirige, B. and Amaratunga, D. (2010). Building up resilience of construction sector
SMEs and their supply chains to extreme weather events. International Journal of Strategic Property Management, 14(4): 362–375. DOI: https://doi.org/10.3846/ijspm.2010.27.
Wittneben, B.B.F. and Kiyar, D. (2009). Climate change basics for managers. Management Decision,
47(7): 1122–1132. DOI: https://doi.org/10.1108/00251740910978331.
Yuan, X.C., Wei, Y.M., Wang, B. and Mi, Z. (2017). Risk management of extreme events under climate
change. Journal of Cleaner Production, 166: 1169–1174. DOI: https://doi.org/10.1016/j.jclepro.2017.08.089.
Zeng, F., Guo, Y., Fan, Q. and Wang, C. L. (2023). AI-orientation and company climate action: The
moderating role of dependency structure and innovation capability. Industrial Marketing Management, 117: 148–160. DOI: https://doi.org/10.1016/j.indmarman.2023.12.018.
Zhong, Q., Zhang, Q. and Yang, J. (2025). Can artificial intelligence empower energy enterprises to cope
with climate policy uncertainty? Energy Economics, 141: 108088. DOI:
https://doi.org/10.1016/j.eneco.2024.108088.
Wajiha, K.A. and Rámháp, S. (2025). Artificial Intelligence in Climate Adaptation: Opportunities and Challenges for Sustainable Business Models. Grassroots Journal of Natural Resources, 8(3): 1-24. Doi: https://doi.org/10.33002/nr2581.6853.080301
Wajiha, K.A., & Rámháp, S. (2025). Artificial Intelligence in Climate Adaptation: Opportunities and Challenges for Sustainable Business Models. Grassroots Journal of Natural Resources, 8(3), 1-24. https://doi.org/10.33002/nr2581.6853.080301
Wajiha K.A., Rámháp S. Artificial Intelligence in Climate Adaptation: Opportunities and Challenges for Sustainable Business Models. Grassroots Journal of Natural Resources, 2025, 8 (3), 1-24. https://doi.org/10.33002/nr2581.6853.080301
Wajiha, Kassem Alshar, Rámháp, Szabolcs. 2025. “Artificial Intelligence in Climate Adaptation: Opportunities and Challenges for Sustainable Business Models”. Grassroots Journal of Natural Resources, 8 no. 3: 1-24. https://doi.org/10.33002/nr2581.6853.080301
Wajiha, Kassem Alshar and Szabolcs Rámháp. 2025. “Artificial Intelligence in Climate Adaptation: Opportunities and Challenges for Sustainable Business Models”. Grassroots Journal of Natural Resources, 8 (3): 1-24. https://doi.org/10.33002/nr2581.6853.080301
| Internet Archive: | https://archive.org/details/nr.08-03-01-wajiha-ramhap |
| WorldCat: | https://search.worldcat.org/title/11057325865 |
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