Lwasa, S. et al. Urban systems and other settlements. In Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) (Cambrige Univ. Press, 2022).
Shukla, P. et al (eds). Climate Change 2022: Mitigation of Climate Change (Cambridge Univ. Press, 2022).
Sethi, M., Lamb, W., Minx, J. & Creutzig, F. Climate change mitigation in cities: a systematic scoping of case studies. Environ. Res. Lett. 15, 093008 (2020).
Google Scholar
Kaack, L. H. et al. Aligning artificial intelligence with climate change mitigation. Nat. Clim. Change 12, 518–527 (2022).
Google Scholar
Rolnick, D. et al. Tackling climate change with machine learning. ACM Comput. Surveys 55, 1–96 (2022).
Google Scholar
Milojevic-Dupont, N. & Creutzig, F. Machine learning for geographically differentiated climate change mitigation in urban areas. Sustain. Cities Soc. 64, 102526 (2021).
Google Scholar
Kim, H. et al. A systematic review of the smart energy conservation system: from smart homes to sustainable smart cities. Renew. Sustain. Energy Rev. 140, 110755 (2021).
Google Scholar
Lai, C. S. et al. A review of technical standards for smart cities. Clean Technol. 2, 290–310 (2020).
Google Scholar
Pereira, G. V., Parycek, P., Falco, E. & Kleinhans, R. Smart governance in the context of smart cities: a literature review. Inform. Polity 23, 143–162 (2018).
Google Scholar
Gracias, J. S., Parnell, G. S., Specking, E., Pohl, E. A. & Buchanan, R. Smart cities–a structured literature review. Smart Cities 6, 1719–1743 (2023).
Google Scholar
Casali, Y., Aydin, N. Y. & Comes, T. Machine learning for spatial analyses in urban areas: a scoping review. Sustain. Cities Soc. 85, 104050 (2022).
Google Scholar
Haddaway, N. R., Bernes, C., Jonsson, B.-G. & Hedlund, K. The benefits of systematic mapping to evidence-based environmental management. Ambio 45, 613–620 (2016).
Google Scholar
Haddaway, N., Macura, B., Whaley, P. & Pullin, A. ROSES flow diagram for systematic reviews, version 1.0. Figshare (2018).
James, K. L., Randall, N. P. & Haddaway, N. R. A methodology for systematic mapping in environmental sciences. Environ. Evidence 5, 1–13 (2016).
Google Scholar
Lamb, W. F., Callaghan, M. W., Creutzig, F., Khosla, R. & Minx, J. C. The literature landscape on 1.5 C climate change and cities. Curr. Opin. Environ. Sustain. 30, 26–34 (2018).
Google Scholar
Sethi, M. & Creutzig, F. Leaders or laggards in climate action? Assessing GHG trends and mitigation targets of global megacities. PLOS Clim. 2, e0000113 (2023).
Google Scholar
Li, X. et al. Short-term forecast of bicycle usage in bike sharing systems: a spatial-temporal memory network. IEEE Trans. Intell. Transp. Syst. 23, 10923–10934 (2021).
Google Scholar
Yusuf, J., Hasan, A. J., Garrido, J., Ula, S. & Barth, M. J. A comparative techno-economic assessment of bidirectional heavy duty and light duty plug-in electric vehicles operation: a case study. Sustain. Cities Soc. 95, 104582 (2023).
Google Scholar
Aziz, H. A., Zhu, F. & Ukkusuri, S. V. Learning-based traffic signal control algorithms with neighborhood information sharing: an application for sustainable mobility. J. Intell. Transp. Syst. 22, 40–52 (2018).
Google Scholar
Seyrfar, A., Ataei, H., Movahedi, A. & Derrible, S. Data-driven approach for evaluating the energy efficiency in multifamily residential buildings. Pract. Periodical Struct. Des. Constr. 26, 04020074 (2021).
Google Scholar
Zhang, J. et al. The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types. Int. J. Geogr. Inform. Sci. 35, 193–211 (2021).
Google Scholar
Nutkiewicz, A., Mastrucci, A., Rao, N. D. & Jain, R. K. Cool roofs can mitigate cooling energy demand for informal settlement dwellers. Renew. Sustain. Energy Rev. 159, 112183 (2022).
Google Scholar
Linton, S., Clarke, A. & Tozer, L. Technical pathways to deep decarbonization in cities: eight best practice case studies of transformational climate mitigation. Energy Res. Soc. Sci. 86, 102422 (2022).
Google Scholar
Verbeek, A. & Lundqvist, M. Artificial Intelligence, Blockchain and the Future of Europe: How Disruptive Technologies Create Opportunities for a Green and Digital Economy, Technical Report (European Investment Bank, 2021).
Maslej, N. et al. The AI Index 2023 Annual Report, Technical Report (Institute for Human-Centered AI, Stanford University, 2023).
Kitsara, I. Artificial Intelligence and the Digital Divide: From an Innovation Perspective. In Platforms and Artificial Intelligence: The Next Generation of Competences (ed. Bounfour, A.) 245–265 (Springer, 2022).
Hao, X., Zhou, Y., Wang, H. & Ouyang, M. Plug-in electric vehicles in China and the USA: a technology and market comparison. Mitig. Adapt. Strateg. Glob. Change 25, 329–353 (2020).
Google Scholar
Ugarteche, O., de León, C. & García, J. China and the energy matrix in Latin America: governance and geopolitical perspective. Energy Policy 177, 113435 (2023).
Google Scholar
A Renovation Wave for Europe—Greening our Buildings, Creating Jobs, Improving Lives, Technical Report (European Commission, 2020).
Bakó, F., Berkes, J. & Szigeti, C. Households’ electricity consumption in Hungarian urban areas. Energies 14, 2899 (2021).
Google Scholar
Stensjö, I. P., Ferreira, C. C. & Loura, R. M. Classification and grouping of Brazilian cities in heating and cooling degrees-day: 1960 to 2013. Urbe Rev. Brasil. de Gestão Urbana 9, 286–300 (2017).
Google Scholar
Praene, J. P., Malet-Damour, B., Radanielina, M. H., Fontaine, L. & Riviere, G. GIS-based approach to identify climatic zoning: a hierarchical clustering on principal component analysis. Build. Environ. 164, 106330 (2019).
Google Scholar
Wu, T.-G. et al. Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities. Environ. Pollut. 294, 118597 (2022).
Google Scholar
Palme, M., Inostroza, L. & Salvati, A. Technomass and cooling demand in South America: a superlinear relationship? Build. Res. Inform. 46, 864–880 (2018).
Google Scholar
Petropoulos, G. P., Kalivas, D. P., Georgopoulou, I. A. & Srivastava, P. K. Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques: case of Athens, Greece. J. Appl. Remote Sens. 9, 096088 (2015).
Google Scholar
Li, X. et al. Developing urban residential reference buildings using clustering analysis of satellite images. Energy Build. 169, 417–429 (2018).
Google Scholar
Yigit, S. A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey. J. Build. Eng. 38, 102225 (2021).
Google Scholar
Adeogba, E., Barty, P., O’Dwyer, E. & Guo, M. Waste-to-resource transformation: gradient boosting modeling for organic fraction municipal solid waste projection. ACS Sustain. Chem. Eng. 7, 10460–10466 (2019).
Google Scholar
Jiang, F., Ma, J. & Li, Z. Pedestrian volume prediction with high spatiotemporal granularity in urban areas by the enhanced learning model. Sustain. Cities Soc. 79, 103653 (2022).
Google Scholar
Manchella, K., Umrawal, A. K. & Aggarwal, V. Flexpool: a distributed model-free deep reinforcement learning algorithm for joint passengers and goods transportation. IEEE Trans. Intell. Transp. Syst. 22, 2035–2047 (2021).
Google Scholar
Lork, C. et al. An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management. Appl. Energy 276, 115426 (2020).
Google Scholar
Tien, P. W., Wei, S., Darkwa, J., Wood, C. & Calautit, J. K. Machine learning and deep learning methods for enhancing building energy efficiency and indoor environmental quality—a review. Energy AI 10, 100198 (2022).
Google Scholar
Campbell-Arvai, V. & Lindquist, M. From the ground up: using structured community engagement to identify objectives for urban green infrastructure planning. Urban For. Urban Green. 59, 127013 (2021).
Google Scholar
Ntakolia, C., Anagnostis, A., Moustakidis, S. & Karcanias, N. Machine learning applied on the district heating and cooling sector: a review. Energy Syst. 13, 1–30 (2022).
Google Scholar
Rong, K. & Luo, Y. Toward born sharing: the sharing economy evolution enabled by the digital ecosystems. Technol. Forecast. Soc. Change 196, 122776 (2023).
Google Scholar
Adler, P. & Florida, R. The rise of urban tech: how innovations for cities come from cities. Reg. Stud. 55, 1787–1800 (2021).
Google Scholar
Albuquerque, V., Sales Dias, M. & Bacao, F. Machine learning approaches to bike-sharing systems: a systematic literature review. ISPRS Int. J. Geo-Inform. 10, 62 (2021).
Google Scholar
Shaygan, M., Meese, C., Li, W., Zhao, X. G. & Nejad, M. Traffic prediction using artificial intelligence: review of recent advances and emerging opportunities. Transp. Res. C 145, 103921 (2022).
Google Scholar
Font Vivanco, D. et al. Rebound effect and sustainability science: a review. J. Ind. Ecol. 26, 1543–1563 (2022).
Google Scholar
Sovacool, B. K., Newell, P., Carley, S. & Fanzo, J. Equity, technological innovation and sustainable behaviour in a low-carbon future. Nat. Hum. Behav. 6, 326–337 (2022).
Google Scholar
McMillan, L. & Varga, L. A review of the use of artificial intelligence methods in infrastructure systems. Eng. Appl. Artif. Intell. 116, 105472 (2022).
Google Scholar
Taiwo, R., Ben Seghier, M. E. A. & Zayed, T. Toward sustainable water infrastructure: the state-of-the-art for modeling the failure probability of water pipes. Water Resour. Res. 59, e2022WR033256 (2023).
Google Scholar
Burley Farr, K., Song, K., Yeo, Z. Y., Johnson, E. & Hsu, A. Cities and regions tackle climate change mitigation but often focus on less effective solutions. Commun. Earth Environ. 4, 439 (2023).
Google Scholar
Chen, S. & Jia, K. How local governments prioritize multiple conflicting goals: beyond the sole-goal perspective. Public Admin. 101, 522–538 (2023).
Google Scholar
Meena, M. et al. Municipal solid waste: Opportunities, challenges and management policies in India: a review. Waste Manag. Bull. 1, 4–18 (2023).
Google Scholar
Moroni, S. & Chiffi, D. Uncertainty and planning: cities, technologies and public decision-making. Perspect. Sci. 30, 237–259 (2022).
Google Scholar
Bright, J. et al. Generative AI is already widespread in the public sector: evidence from a survey of UK public sector professionals. Digit. Gov.: Res. Pract. 6, 1–13 (2025).
Google Scholar
Zhu, D. & Liu, H. City AI: a strategic framework for urban artificial intelligence application and development. Urban Informatics 4, 1–14 (2025).
Google Scholar
Li, Z. et al. Urban computing in the era of large language models. Preprint at (2025).
Luccioni, S., Jernite, Y. & Strubell, E. Power hungry processing: watts driving the cost of AI deployment? In Proc. 2024 ACM Conference on Fairness, Accountability, and Transparency 85–99 (Association for Computing Machinery, 2024).
Dodge, J. et al. Measuring the carbon intensity of AI in cloud instances. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 1877–1894 (Association for Computing Machinery, 2022).
Zhao, F., Fashola, O. I., Olarewaju, T. I. & Onwumere, I. Smart city research: a holistic and state-of-the-art literature review. Cities 119, 103406 (2021).
Google Scholar
João, B., Nascimento, D., Souza, C. L. D. & Serralvo, F. A. A systematic review of smart cities and the internet of things as a research topic. Cadernos EBAPE.BR. 17, 1115–1130 (2020).
Pasgaard, M. & Strange, N. A quantitative analysis of the causes of the global climate change research distribution. Glob. Environ. Change 23, 1684–1693 (2013).
Google Scholar
Tennant, J. P. Web of Science and Scopus are not global databases of knowledge. Eur. Sci. Edit. 46, e51987 (2020).
Google Scholar
Asubiaro, T., Onaolapo, S. & Mills, D. Regional disparities in Web of Science and Scopus journal coverage. Scientometrics 129, 1469–1491 (2024).
Google Scholar
Boeing, G., Pilgram, C. & Lu, Y. Urban street network design and transport-related greenhouse gas emissions around the world. Transp. Res. D 127, 103961 (2024).
Google Scholar
Xiao, C. S. et al. Design effects of cycle infrastructure changes: an exploratory analysis of cycle levels. Transp. Res. Interdisc. Perspect. 22, 100949 (2023).
Oyedemi, T. D. Digital coloniality and ‘next billion users’: the political economy of Google Station in Nigeria. Inform. Commun. Soc. 24, 329–343 (2021).
Google Scholar
Birhane, A. et al. The values encoded in machine learning research. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 173–184 (Association for Computing Machinery, 2022).
Birhane, A. Algorithmic colonization of Africa. SCRIPTed 17, 389 (2020).
Google Scholar
Sietsma, A. J., Ford, J. D. & Minx, J. C. The next generation of machine learning for tracking adaptation texts. Nat. Clim. Change 14, 31–39 (2024).
Google Scholar
Vinuesa, R. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11, 233 (2020).
Google Scholar
Sculley, D. et al. Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems 28 (eds Cortes C., Lawrence N., Lee D., Sugiyama M. & Garnett R.) (Curran Associates, Inc., 2015).
Bulkeley, H. Cities and the governing of climate change. Ann. Rev. Environ. Resour. 35, 229–253 (2010).
Google Scholar
Jiang, W. Bike sharing usage prediction with deep learning: a survey. Neur. Comput. Appl. 34, 15369–15385 (2022).
Google Scholar
Repke, T. & Callaghan, M. NACSOS-nexus: NLP assisted classification, synthesis and online screening with new and extended usage scenarios. Preprint at (2024).
Webersinke, N., Kraus, M., Bingler, J. & Leippold, M. ClimateBERT: a pretrained language model for climate-related text. In Proc. AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges (2022); https://doi.org/10.48550/arXiv.2212.13631
Callaghan, M. & Müller-Hansen, F. Statistical stopping criteria for automated screening in systematic reviews. Syst. Rev. 9, 1–14 (2020).
Google Scholar
Ivanova, D. et al. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 15, 093001 (2020).
Google Scholar
Standard country or area codes for statistical use. United Nations Statistics Division (2025).
Hamel, R. E. The dominance of English in the international scientific periodical literature and the future of language use in science. Aila Rev. 20, 53–71 (2007).
Google Scholar
Petersen, K. & Gerken, J. M. On the road to interactive LLM-based systematic mapping studies. Inform. Softw. Technol. 178, 107611 (2025).
Google Scholar
Losi, E., Manservigi, L., Spina, P. R. & Venturini, M. Data-driven approach for the detection of faults in district heating networks. Sustain. Energy Grids Netw. 38, 101355 (2024).
Google Scholar
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