1. Hsu, A. et al. ClimActor, harmonized transnational data on climate network participation by city and regional governments. Sci. Data (2020) doi:10.1038/s41597-020-00682-0.
2. Reckien, D. et al. How are cities planning to respond to climate change? Assessment of local climate plans from 885 cities in the EU-28. J. Clean. Prod. (2018) doi:10.1016/j.jclepro.2018.03.220.
3. Hsu, A. & Rauber, R. Diverse climate actors show limited coordination in a large-scale text analysis of strategy documents. Commun. Earth Environ. 2021 21 2, 1–12 (2021).
4. Hsu, A., Cheng, Y., Weinfurter, A., Xu, K. & Yick, C. Track climate pledges of cities and companies. Nature 532, 303–306 (2016).
5. Milojevic-Dupont, N. & Creutzig, F. Machine learning for geographically differentiated climate change mitigation in urban areas. Sustain. Cities Soc. 64, 102526 (2021).
6. Intergovernmental Panel on Climate Change & Intergovernmental Panel on Climate Change. Human Settlements, Infrastructure, and Spatial Planning. in Climate Change 2014 Mitigation of Climate Change (2015). doi:10.1017/cbo9781107415416.018.
7. Stehle, F., Hickmann, T., Lederer, M. & Höhne, C. Urban Climate Politics in Emerging Economies: A Multi-Level Governance Perspective: https://doi-org.libproxy.lib.unc.edu/10.1177/2455747120913185 245574712091318 (2020) doi:10.1177/2455747120913185.
8. Boehnke, R., Hoppe, T., Brezet, H., production, K. B.-J. of cleaner & 2019, undefined. Good practices in local climate mitigation action by small and medium-sized cities; exploring meaning, implementation and linkage to actual lowering of. Elsevier.
9. Domorenok, E., Acconcia, G., Bendlin, L. & Campillo, X. R. Experiments in EU Climate Governance: The Unfulfilled Potential of the Covenant of Mayors. Glob. Environ. Polit. 20, 122–142 (2020).
10. Hsu, A. et al. Performance determinants show European cities are delivering on climate mitigation. Nat. Clim. Chang. (2020) doi:10.1038/s41558-020-0879-9.
11. Ibrahim, N., Sugar, L., Hoornweg, D. & Kennedy, C. Greenhouse gas emissions from cities: Comparison of international inventory frameworks. Local Environ. (2012) doi:10.1080/13549839.2012.660909.
12. Rolnick, D. et al. Tackling Climate Change with Machine Learning. (2019).
13. Oda, T. et al. Errors and uncertainties in a gridded carbon dioxide emissions inventory. Mitig. Adapt. Strateg. Glob. Chang. 24, 1007–1050 (2019).
14. Raupach, M. R., Rayner, P. J. & Paget, M. Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions. Energy Policy 38, 4756–4764 (2010).
15. Moran, D. et al. Estimating CO<sub>2</sub> emissions for 108 000 European cities. Earth Syst. Sci. Data 14, 845–864 (2022).
16. Nangini, C. et al. A global dataset of CO2 emissions and ancillary data related to emissions for 343 cities. nature.com.
17. World Resources Institute (WRI). Climate Watch 2020. (2020).
18. Shadish, W. R., Cook, T. D. & Campbell, D. T. Experimental and quasi-experimental designs for generalized causal inference. (2002).
19. van der Heijden, J. Studying urban climate governance: Where to begin, what to look for, and how to make a meaningful contribution to scholarship and practice. Earth Syst. Gov. 1, 100005 (2019).
20. Gordon, D., sustainability, C. J.-C. opinion in environmental & 2018, undefined. City-networks, global climate governance, and the road to 1.5 C. Elsevier.
21. Armstrong, J. H. Modeling effective local government climate policies that exceed state targets. Energy Policy 132, 15–26 (2019).
22. Kousky, C., policy, S. S.-C. & 2003, undefined. Global climate policy: will cities lead the way? Taylor Fr. 3, 359–372 (2003).
23. Broto, V. C. Energy landscapes and urban trajectories towards sustainability. Energy Policy 108, 755–764 (2017).
24. Seto, K. C. et al. From Low- To Net-Zero Carbon Cities- To Next Global Agenda. Annu. Rev. Environ. Resour. 46, 377–415 (2021).
25. Aldy, J. E. The crucial role of policy surveillance in international climate policy. Clim. Change 126, 279–292 (2014).
26. Hale, T. N. et al. Sub- and non-state climate action: a framework to assess progress, implementation and impact. Clim. Policy 21, 406–420 (2021).
27. Bulkeley, H. Can cities realise their climate potential? Reflections on COP21 Paris and beyond. Local Environ. 20, 1405–1409 (2015).
28. Bulkeley, H. et al. Governing climate change transnationally: Assessing the evidence from a database of sixty initiatives. Environ. Plan. C Gov. Policy 30, 591–612 (2012).
29. Chan, S., Falkner, R., Goldberg, M. & van Asselt, H. Effective and geographically balanced? An output-based assessment of non-state climate actions. Clim. Policy 18, 24–35 (2018).
30. Kuramochi, T. et al. Beyond national climate action: the impact of region, city, and business commitments on global greenhouse gas emissions. Clim. Policy (2020) doi:10.1080/14693062.2020.1740150.
31. Pattberg, P., Biermann, F., Chan, C. & Mert, A. Public-private partnerships for sustainable development: Emergence, influence and legitimacy. Public-Private Partnerships Sustain. Dev. Emergence, Influ. Legitimacy (2012) doi:10.4337/9781849809313.
32. Ogle, S. M. et al. Advancing national greenhouse gas inventories for agriculture in developing countries: Improving activity data, emission factors and software technology. Environ. Res. Lett. 8, (2013).
33. Pan, G., Xu, Y. & Ma, J. The potential of CO2 satellite monitoring for climate governance: A review. J. Environ. Manage. 277, 111423 (2021).
34. Boehnke, R. F., Hoppe, T., Brezet, H. & Blok, K. Good practices in local climate mitigation action by small and medium-sized cities; exploring meaning, implementation and linkage to actual lowering of carbon emissions in thirteen municipalities in The Netherlands. J. Clean. Prod. 207, 630–644 (2019).
35. United Nations Framework Convention on Climate Change (UNFCCC). Adoption of the Paris Agreement, 21st Conference of the Parties. https://unfccc.int/sites/default/files/english_paris_agreement.pdf (2015).
36. REVISED NON-PAPER FOR INFORMAL CONSULTATIONS 1 PREPARING FOR THE FIRST GLOBAL STOCKTAKE REVISED NON-PAPER BY THE CHAIRS OF THE SBSTA AND SBI.
37. No, Cities Are Not Actually Leading on Climate. Enough With the Mindless Cheerleading | Greentech Media. https://www.greentechmedia.com/articles/read/hard-truths-about-city-failures-with-clean-energy.
38. politics, T. H.-G. environmental & 2016, undefined. “All hands on deck”: The Paris agreement and nonstate climate action. direct.mit.edu (2016) doi:10.1162/GLEP_a_00362.
39. Hale, T. Catalytic cooperation. Glob. Environ. Polit. 20, 73–98 (2020).
40. Bosilovich, M. G., R. Lucchesi, and M. S. MERRA-2: File Specification. (2015).
41. Spinoni, J. et al. Changes of heating and cooling degree-days in Europe from 1981 to 2100. Int. J. Climatol. 38, e191–e208 (2018).
42. Van Donkelaar, A., Martin, R. V., Li, C. & Burnett, R. T. Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environ. Sci. Technol. 53, 2595–2611 (2019).
43. Engel-Cox, J., Kim Oanh, N. T., van Donkelaar, A., Martin, R. V. & Zell, E. Toward the next generation of air quality monitoring: Particulate Matter. Atmos. Environ. 80, 584–590 (2013).
44. Agency, E. E. Greenhouse gas emissions by aggregated sector. Eurostat (2019).
45. CIESIN. Gridded Population of the World, Version 4. http://dx.doi.org/10.7927/H49C6VBN (2016).
46. Kummu, M., Taka, M. & Guillaume, J. H. A. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Sci. Data 5, (2018).
47. Moritz, S., J., T. B.-B.-R. & 2017, undefined. imputeTS: time series missing value imputation in R. cran.microsoft.com.
48. Kona, A. et al. Global Covenant of Mayors, a dataset of greenhouse gas emissions for 6200 cities in Europe and the Southern Mediterranean countries. Earth Syst. Sci. Data 13, 3551–3564 (2021).
49. Glossary:Local administrative unit (LAU) - Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Local_administrative_unit_(LAU).
50. Jordahl, K. geopandas: Python tools for geographic data. (2014).
51. Haklay, M., computing, P. W.-I. P. & 2008, undefined. Openstreetmap: User-generated street maps. ieeexplore.ieee.org (2008).
52. Perry, M. rasterstats 0.16.0. (2021).
53. Kuhn, M. Building Predictive Models in R Using the caret Package. J. Stat. Softw. 28, 1–26 (2008).
54. Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. doi:10.1145/2939672.
55. Seyedzadeh, S., Pour Rahimian, F., Oliver, S., Rodriguez, S. & Glesk, I. Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making. Appl. Energy 279, 115908 (2020).
56. Si, M. & Du, K. Development of a predictive emissions model using a gradient boosting machine learning method. Environ. Technol. Innov. 20, (2020).
57. Joharestani, M. Z., Cao, C., Ni, X., Bashir, B. & Talebiesfandarani, S. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmos. 2019, Vol. 10, Page 373 10, 373 (2019).
58. Pan, B. Application of XGBoost algorithm in hourly PM2.5 concentration prediction. IOP Conf. Ser. Earth Environ. Sci. 113, 012127 (2018).
59. Si, M. & Du, K. Development of a predictive emissions model using a gradient boosting machine learning method. Environ. Technol. Innov. 20, 101028 (2020).
60. Li, Y. & Sun, Y. Modeling and predicting city-level CO 2 emissions using open access data and machine learning. Environ. Sci. Pollut. Res. 2021 2815 28, 19260–19271 (2021).
61. Yang, L. & Shami, A. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing 415, 295–316 (2020).
62. Chen, T. et al. xgboost: Extreme Gradient Boosting. (2021) doi:10.1145/2939672.2939785.
63. Bernal, J. L., Cummins, S. & Gasparrini, A. The use of controls in interrupted time series studies of public health interventions. Int. J. Epidemiol. 47, 2082–2093 (2018).
64. Kleck, G. & Patterson, E. B. The impact of gun control and gun ownership levels on violence rates. J. Quant. Criminol. 9, 249–287 (1993).
65. (EUCoM), E. C. of M. The Covenant of Mayors for Climate and Energy Reporting Guidelines. https://www.covenantofmayors.eu/IMG/pdf/Covenant_ReportingGuidelines.pdf (2016).
66. Wickham, H. ggplot2 Elegant Graphics for Data Analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) (2016). doi:10.1007/978-3-319-24277-4.