Climate Change

Description

This chapter focuses on the mitigation aspect of cities’ climate action, acknowledging that adaptation initiatives are highly context specific and lack globally consistent databases for identification and evaluation.

The landscape of initiatives and mechanisms for subnational actors, such as regions and cities, has evolved significantly over the past decade. Although subnational actors are not official parties to the UNFCCC process, they have gained considerable recognition for their crucial role in achieving the Paris Agreement goals. Their unique ability to directly influence local stakeholders, including businesses and citizens, has made them key players in driving climate action. In addition, multiple initiatives and networks have grown rapidly to encompass a large number of cities, creating opportunities for actors to enter in dialogues with other cities and to pool their global influence, as well as supporting agenda setting, information sharing, and capacity building to its members. An example is GCOM, which now includes over 13,000 cities from across the globe. 

In recent years, there has been a growing emphasis on supporting subnational climate action. Initiatives like the Coalition For High Ambition Multilevel Partnerships (CHAMP), launched at COP28, and the UNFCCC’s Race to Zero initiative, along with other network-led initiatives are actively encouraging and supporting cities to establish climate mitigation plans and targets and report their efforts to voluntary reporting mechanisms such as CDP. As a result of this process, and according to recent reports, the UN’s Global Climate Action Portal features more than 32,000 actors were “engaging in climate action” as of September 2023,7 and more than 3,000 cities and 175 subnational states and regions, accounting for 26.5% of the global total population, have pledged to reduce their greenhouse gas emissions, an increase compared to previous years.8

While these results are important, there remain crucial areas that require further effort from both cities and related initiatives, particularly in target setting and implementation. On the one hand, there has been a proliferation of frameworks, initiatives, and standards that have made the terminology and implications of net zero targets confusing for both actors and the public. If we also consider the phenomenon of greenwashing and the use of net-zero pledges as virtue signaling, it creates conditions that erode the credibility and transparency of climate pledges and targets. As a response to this problem, the United Nations’ High-Level Expert Group on the Net Zero Emissions Commitment of Non-State Entities (HLEG) launched a report at the COP27 UN Climate Conference providing stronger and clearer standards for net-zero emissions pledges by non-state entities – including businesses, investors, cities, and regions.9 A coalition of city and regional networks along with experts then developed specific net-zero guidance for cities and regions in late 2023 called Integrity Matters for Cities, States and Regions Report.10 

Methodology

GHG Emissions Index

The GHG Emissions Index is composed of scores for each GHG (N₂O, CH₄, CO₂) and the value of carbon footprint emissions. The three GHG scores consist of five indicators which include the emissions trend across three distinct time periods: a historical period, a pre-Paris Agreement period, and a Paris Agreement period; the average recent emissions per capita; and the average recent emissions per unit area for each city. 

The three trend indicators are based on specific timeframes using international climate treaties as benchmarks. The historic period indicator is derived from a city’s GHG emissions between 1970-2004 and provides insight into the emission trajectory prior to any global initiative for emissions reductions. The Pre-Paris Agreement period indicator uses the GHG emissions between 2005-2016, using as a start date the year in which the Kyoto Protocol11 entered into force. Although it only involved a limited number of parties, which in turn hindered its success, the agreement is still notable for being the first global effort to address emissions reduction.12 Finally, the Paris Agreement indicator’s period starts in 2017 up to the most recent year available, as the actual Paris Agreement came into force in November 2016. It aligns with the Paris Agreement’s goal to hold “the increase in the global average temperature to well below 2°C above pre-industrial levels” and pursue efforts “to limit the temperature increase to 1.5°C above pre-industrial levels.”13

Based on those periods, the three trend indicators were calculated using a level-1 linear spline model, which fits separate linear functions to different segments of the data corresponding to years 2005 and 2017, allowing us to capture shifts in trends over time. We compared this approach to other commonly-used linear or logarithmic approaches and how closely the model’s predictions match the actual data (RMSE) and how well the model balances accuracy with simplicity (AIC).14 The other two indicators for each GHG, the emission per capita and emission per km² indicators used the emission of the latest available year (2022) and the population and area from official sources when available, or alternatively derived from spatial datasets. 

After calculation, the five indicators were scaled to a 0-1 range using min-max scaling and then combined into a single component score for each GHG, with specific weights assigned to each indicator. The weights for each GHG indicator used in the aggregation were identified using a method we call “Ranked Correlation Optimization.” This approach identifies the optimal combination of weights that maximizes the correlation between each individual indicator and the resulting composite score, while also aligning with the intended importance of the indicators defined by the index developers. The results facilitate the creation of an index that maximizes variability from the original indicators and is guided by strong correlation values. It is intuitive, clearly conveying direction and relative importance, and highly transparent, as it explicitly ranks components according to theoretical considerations and expert opinions from the developers.

This process was performed twice, once for the calculation of each GHG component score, and then for the final GHG Emissions Index that combined the scores of the three GHG indicators - CO₂, CH₄, N₂O - and the value of the carbon footprint emissions. The resulting composite index was also subject to further sensitivity analysis to check the potential effect of other more common alternative weighting schemes, like equal weights, which demonstrated that the selected weight combination did not change the overall distribution of the final score and maintained a stronger association with the underlying data. 

Climate Policy Index

The Climate Policy Index is a composite indicator designed to assess a city's mitigation plans or policies. It draws inspiration from the Integrity Matters for Cities, States, and Regions Report,15 which offers tailored recommendations for subnational governments to enhance the credibility, accountability, and transparency of their net-zero commitments while recognizing their diverse capacities and characteristics. Based on these recommendations, we evaluated the following four components of climate planning:

1. Target Setting: The aspects of the plan or policy that involve the explicit proposal or commitment to mitigation targets and their official recognition.
2. Ambition of climate targets: The quantifiable ambition of their targets as well as the timeframe for completion.
3. Comprehensiveness: Additional details of the plan, including the specific actions to be taken, types of GHG considered, and other explicit considerations related to mitigation strategies.
4. Transparency: The elements of the plan directly related to reporting, disclosure and the periodic review of targets and actions to be implemented.

By leveraging these components and the recommendations from the Integrity Matter for Cities, States, and Regions Report, we reviewed the Net Zero Tracker database and its codebook,16 identifying a total of 17 potential indicators conceptually related to the identified components. Lastly, we assessed data availability and methodological consistency for each variable, ultimately selecting 14 variables to be included in the index. Each variable was transformed using a scoring system that captures the recommendations and preferences proposed in the Integrity Matters for Cities, States and Regions Report (See Table 1 for details on the rationale for each indicator).

After scoring each indicator, the results were scaled using a min-max scaling to achieve a score between 0 and 1. The composite index was created using the ‘Ranked Correlation Optimization’ method previously described.

Results

The overall results of the Climate Policy Index for the 216 cities with available data show a global median value of 43 out of 100 points, and three distinct groups of cities: A first group of approximately 66 cities with very low performance (< 20 points), including 49 cities with a 0 score because they haven’t established a target or plan to evaluate; a second larger group of 93 cities located in the middle of the distribution with scores between 20 and 48, and a final group of 57 cities with scores over 50, including the top five cities with scores over 75 points. The overall distribution of scores shows that approximately 60% of the cities with scores in the analysis fall below the total median score of 53 points, which includes cities without any plans. This suggests that the majority of cities are not proposing climate mitigation measures with enough ambition or comprehensiveness to align with the recommendations of the Integrity Matters Report. Even the highest-scoring cities have room for improvement.

Figure 1 displays the top 10 and bottom 10 scoring cities for the Climate Policy Index, excluding the 49 cities with a score of 0 due to the absence of a target or plan. The highest scoring cities are located primarily in the Global North, including cities like Copenhagen (DNK), Mexico (MEX), both of which also scored highly in our previous climate policy analysis.17 On the other hand, in addition to the cities that lack any climate pledge or plan such as Tianjin (CHN), Moscow (RUS), Montevideo (URY), the bottom 10 cities include cities such as Santo Domingo (DOM), Vienna (AUT) and Seville (ESP). A particularly salient example is the city of Johannesburg (ZAF), which is the highest scoring global south city due to a comprehensive climate action plan that includes explicit references to GHG coverage, Plan Options, and the use of offsets and carbon removal. However, its plan, while less ambitious than some other cities’ climate plans with net-zero pledges, offers more detailed information about its targets including baseline year and interim targets.

Figure 2 provides a deeper look into the geographical distribution of climate policy scores, highlighting two key aspects. On the one hand, we observe a broad range of scores across all regions, with the exception of South Asia and the Middle East and North Africa. These regions include cities with the lowest possible scores (i.e., cities with no climate mitigation plan or pledge) as well as cities scoring above the median value (i.e., at least 43 points out of 100) in the climate policy score, similar to what is seen in Latin America, Asia, and Sub-Saharan Africa. On the other hand, cities with scores over 70 points are mostly located in North America, Europe and Central Asia, and East Asia and the Pacific. This result suggests that cities in more developed regions have the potential to produce more comprehensive and credible climate mitigations strategies but many cities are still falling behind. On the other hand, we can also see that some of the best performing cities in less developed regions are achieving higher scores than the average cities in more developed regions, as in the case of some top performing cities in Sub-Saharan Africa that have climate mitigation policies with a higher score compared to the median North American or European city.

Analyzing the results of the four components of the climate policy score provides valuable insights into the aspects that cities are prioritizing when formulating their climate mitigation strategies (Figure 3). Consistent with the increase of climate pledges and involvement of subnational actors in net-zero or carbon-neutral initiatives, the Target Setting component has some of the highest median scores for most regions, except Sub-Saharan Africa and the Middle East and North Africa, showing that cities participating in such initiatives are taking steps in setting interim targets and formalizing their commitments in policy documents. While this is encouraging, we also observe that cities across all regions consistently scored lower in the Ambition Component, indicating that while many are formalizing their participation in climate mitigation initiatives, their current level of ambition lags behind their peers and falls short when compared to national targets, both in terms of intensity and timeframes. Similarly the Comprehensiveness score for most cities is consistently lower than the Target Setting Score, suggesting that cities might not have fully fleshed specific actions required to meet their GHG reductions. 

Finally, exploring the relation between GDP and Climate Policy Scores in Figure 4, and consistent with previous findings, we find that cities with the lowest level of GDP per capita have some of the lowest Climate Policy Scores. This result suggests that these cities might lack the capacity to engage in climate action. On the other hand, while we do see some of the most developed cities with the highest scores, including Copenhagen (DNK) and Stockholm (SWE), have also some of the highest climate policy scores, this association is not that strong. This trend is even more pronounced among cities in the middle range of GDP per capita, where we see a mix of cities with strong performance like Ottawa (CAN) and Johannesburg (ZAF) alongside many cities that have yet to implement any climate action. These results reveal that many developed cities are performing worse in climate mitigation policy than some of their equally developed counterparts and even some less developed cities. These results underscore the critical need to enhance subnational climate action and translate the pledges and commitments made through various global initiatives into credible, ambitious climate policies for which cities must be held accountable.

GHG Emissions Index

The GHG Emissions Index reveals a range of scores, with all cities falling between 30 and 75 out of 100. Higher scores on the GHG Emissions Index indicate better performance in reducing territorial emissions, while lower scores reflect poorer mitigation efforts. While many cities perform relatively well with a score ~ 55, there is no city that scores over 75. This distribution indicates that, similarly to the Climate Policy Index, all cities can improve in reducing their territorial GHG emissions, which refer to emissions produced within the city’s boundaries.

The top 10 and bottom 10 cities in the GHG Emissions Index can be seen in Figure 5. The high-scoring cities are composed primarily of cities in high or upper income countries like Genova (ITA), Rennes (FRA), Hiroshima City (JPN) and only includes one city located in a low income country: Kinshasa (COD). The highest-scoring city on the GHG Emissions Index is Genova (ITA), with a score nearly ten points higher than the next city, Catania (ITA). However, it's still not perfect, achieving only 75 out of 100. At the bottom of the GHG Emissions Index, we also find cities located in high or upper middle income countries including Singapore (SGP) and Ulsan (KOR) that have the lowest overall emissions indices, particularly lower compared to the rest of the bottom 10 cities.

Exploring the breakdown of the GHG Emissions Index into the individual emission indexes of CH₄, CO₂, N₂O, and Carbon Footprint reveals additional details about the strengths and shortcomings of both the top and bottom 10 cities. For instance, Genova (ITA) that has the highest combined scores, also scores the highest in the individual CH₄ and N₂O indexes and scores 8th in the CO₂ index, indicating low emission levels across these three GHGs, but ranks 128th for Carbon Footprint Score. A more stark case is the city of Singapore that has the lowest overall score, due to having the lowest possible score in its carbon footprint indicator. Overall we find that cities in high-income countries are in the bottom half of carbon footprint scores. 

On the other hand, all cities need to improve their territorial GHG emissions, particularly for N₂O and CO₂, where even the highest scores are around 75 out of 100, indicating significant room for future improvement. For example, the highest scoring city, Genova (ITA) should focus emissions reductions more heavily in high-emitting sectors like Buildings, Road Transportation and Energy. On the other hand, a more industrialized city like Singapore should focus on its Industrial and Energy sectors since those are its highest emitting sectors. A full breakdown of sectoral emissions for each city can be found in Box 2.

The distribution of the GHG Emissions Index by region is displayed in Figure 6. We can observe that most cities in North America, Middle East & North Africa, and East Asia and the Pacific are located below the median value for the GHG Emissions Index (54.7 out of 100). Europe and Central Asia, South Asia, and Latin America and the Caribbean have most of their cities above the median value for the GHG Emissions Index, indicating that most of their cities are performing better than cities in North America and East Asia. Overall, except for East Asia and the Pacific and Europe—which have the cities with the lowest and highest scores, respectively, as well as the widest range of scores—no single region stands out as significantly better or worse than the others. This result suggests that cities in all regions still need to implement effective mitigation strategies to reduce their GHG emissions. 

Finally, looking at the relation between the GHG Emissions Index and GDP in Figure 7, we can more clearly see some of the results we identified before as well as particular associations for some components of the Index. As previously suggested, the association between GDP per capita and the Carbon Footprint Score for the UESI cities reveals that cities with higher GDP per capita have lower Carbon Footprint Score, which indicates higher carbon footprints. This negative relationship between carbon footprint and GDP/income has been well-documented in the literature, especially at national and regional levels. However, our analysis shows that it also exists in urban areas across all geographical regions. This trend is particularly pronounced in North American cities, where both GDP per capita are among the highest and Carbon Footprint Scores are notably low. 

In relation to the territorial GHG Emissions Indices, we can see some distinct patterns depending on the gas in question. For example, in the case of CO₂ and particularly N₂O, for the most part we observe a higher score similarity among cities with low GDP, such as those in Sub-Saharan Africa, and among cities in high GDP areas like those in Europe or North America, as well as particular increase on the variability of scores for cities in the middle range of GDP per capita, such as those in South Asia and Latin America. The wide variability in the scores within these regions suggests that the trajectories and intensity of CO₂ and N₂O emissions are influenced more intensely by other factors beyond just economic development. In contrast to the Carbon Footprint Index, the CH₄ GHG Emissions Index not only reflects the previously observed pattern for CO₂ and N₂O but also shows a clear positive relationship between GDP per capita and CH₄ emissions. This suggests that less developed cities tend to perform worse on this index compared to more developed cities. This relationship could be explained by high methane-emitting sectors that are more heavily related to rural or less urbanized areas like cattling or the operation of facilities that provide wastewater treatment and waste management to nearby cities. 

Both the Climate Policy Index and the GHG Emissions Index highlight the importance of climate mitigation for urban areas, particularly for those in developed countries with high levels of GDP and population. Unfortunately, while we find that most cities in the UESI have developed at least a pledge to mitigate their climate emission, we have identified 30 cities located in upper-middle and high income countries that are still not engaging in these actions, even when they are also found to be among some of the bottom half performing cities in terms of territorial emissions. Similarly, when examining the relationship between GDP per capita and climate action, we see that while some high-GDP cities also achieve high scores in both indices, many cities in upper-middle to high-income countries are currently outperformed by cities in lower and lower-middle-income countries with potentially fewer resources. This finding underscores the leadership of cities in developing regions and the ongoing need to strengthen subnational climate action in developed countries.   

Box 2. Sectoral emissions in EDGAR data and climate action plans

For the GHG Emissions Index, we used aggregated territorial emissions in CO₂e based on total emissions of CO₂, N₂O and CH₄. While this data is valuable on its own, a sectoral breakdown of emissions by GHG is especially important for policymakers, as it helps them identify which sectors to prioritize in the planning and implementation of their mitigation strategies. While the EDGAR v8.0 dataset provides sectoral breakdowns, they are solely consistent with IPCC’s Emission Factor Database30 and less relevant for policy makers. Thus we aggregated the sectors provided into larger policy-relevant sectors and created an application that looks at individual city’s sector emissions and compares cities’ emissions to each other. The aggregated sectors include: Agriculture, Buildings, Energy, Industrial Combustion, Processes, and Product Use (Industrial), Transport, Road Transport, Waste and Wastewater, and Other. The emissions for all UESI cities from 1970 to 2022 are separated into these groups in Figure A.

The aggregation was based primarily on the EDGAR and 2006 IPCC definitions for each sector, as well through a review of other global reports on city emissions and EDGAR reports.31 32 A particular distinction was made for the Road Transport sector, which is not integrated into the larger Transport category due to its specific relevance at a city level compared to other transportation emissions such as aviation and shipping. Energy for buildings was also separated from Energy for the same reason. 

The sector groups we chose are more comprehensive than the emissions that some cities report. Often cities will choose to focus on sectors that they feel they have more direct control over, to the point that some inventories might also have partial coverage of the total territorial emission, however it is important to consider all emissions sectors so cities can directly or indirectly achieve mitigation results even in sectors for which they lack a policy lever. To explore these issues, we compare the sectors used to report emissions by Seattle (USA), Amsterdam (NDL), and Bogota (COL) to the resulting sectors from our aggregation. 

Overall, we do find important omissions from the three explored cities. For instance Seattle’s 2018 climate action plan considers 5 sectors: Passenger Road Transport, Freight Road Transport, Residential Buildings, Commercial Buildings, and Waste, omitting other emissions being produced by the city like from Industrial sources (Industrial Combustion, Processes, and Product Use).33 Similarly, Amsterdam’s 2020 action plan does not account for the emissions produced by waste/waste disposal.34 While Bogota’s 2017 sector emissions are more comprehensive than Seattle’s and Amsterdam’s, it also omits the building sector.35 Figure B provides a visual representation of the sectors used in the different climate action plans, the focus being on the sectors rather than the total emissions due to differences in data collection. The application, which can be accessed here, allows comparison between cities and gives many cities a more comprehensive view of their emissions outputs.

Figure B. Sectoral composition of self-reported emissions as stated on Amsterdam’s and Seattle’s climate action plans and Sectoral composition of territorial emission from EDGAR.

Footnotes

1. IPCC, 2022: Summary for Policymakers [P.R. Shukla, J. Skea, A. Reisinger, R. Slade, R. Fradera, M. Pathak, A. Al Khourdajie, M. Belkacemi, R. van Diemen, A. Hasija, G. Lisboa, S. Luz, J. Malley, D. McCollum, S. Some, P. Vyas, (eds.)]. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.001

2. Dodman, D., B. Hayward, M. Pelling, V. Castan Broto, W. Chow, E. Chu, R. Dawson, L. Khirfan, T. McPhearson, A. Prakash, Y. Zheng, and G. Ziervogel, 2022: Cities, Settlements and Key Infrastructure. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 907–1040, doi:10.1017/9781009325844.008.

3. The Intergovernmental Panel on Climate Change. (2024, April 12). IPCC meets in Latvia to draft outline of Special report on climate change and cities. IPCC. https://www.ipcc.ch/2024/04/12/scoping-meeting-special-report-climate-change-and-cities/ 

4. EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA), and comprising IEA-EDGAR CO₂, EDGAR CH₄, EDGAR N₂O, EDGAR F-GASES version 8.0, (2023) European Commission, JRC (Datasets).

5.  Moran, D., Kanemoto, K., Jiborn, M., Wood, R., Többen, J., & Seto, K. C. (2018). Carbon footprints of 13 000 cities. Environmental Research Letters, 13(6), 064041.

6. Lang, J., Hyslop, C., Chalkley, P., Hale, T., Hans, F., Hay, N., Hsu, A., Kuramochi, T., Manya, D., Smit, S., Smith, S. (2024). Net Zero Tracker. Energy and Climate Intelligence Unit, Data-Driven Envirolab, NewClimate Institute, Oxford Net Zero.

7. Utrecht University and Data-Driven EnviroLab. (2023, September 20). Global Climate Action 2023: Ambition of Cities, Regions, and Companies. Prepared by: Song, K., Hsu, A., Burley, K., Roelfsema, M., Jones, C., Clapper, A., & Du, L. https://datadrivenlab.org/wp-content/uploads/2023/09/20230926_Report_GCC_2023.pdf

8. Song, K., Burley Farr, K., Hsu, A. Doubling Down on Climate Action: Cities and Regions Must Put in 2x the Work to Stay on Track with Climate Goals. (2023). https://datadrivenlab.org/wp-content/uploads/2023/11/DDL_Subnational_Climate_Progress_Nov2023.pdf 

9. High‑Level Expert Group on the Net Zero Emissions Commitments of Non‑State Entities. (2022, November). Integrity matters: Net zero commitments by businesses, financial institutions, cities, and regions. https://www.un.org/sites/un2.un.org/files/high-level_expert_group_n7b.pdf 

10. Global Covenant of Mayors for Climate & Energy and WRI Ross Center for Sustainable Cities. (2023). Integrity Matters for Cities, States, and Regions. https://www.globalcovenantofmayors.org/wp-content/uploads/2023/12/Integrity-Matters-Report-Final3.pdf 

11. United Nations. (n.d.). What is the Kyoto Protocol?. UN Climate Change. https://unfccc.int/kyoto_protocol#:~:text=UNFCCC%20Nav&text=The%20Kyoto%20Protocol%20was%20adopted,Parties%20to%20the%20Kyoto%20Protocol 

12. Bassetti, F. (2022, December 8). Success or failure? The Kyoto Protocol’s Troubled Legacy. Foresight. https://www.climateforesight.eu/articles/success-or-failure-the-kyoto-protocols-troubled-legacy/ 

13. United Nations. (n.d.). The Paris Agreement. UN Climate Change. https://unfccc.int/process-and-meetings/the-paris-agreement#:~:text=The%20Paris%20Agreement%20is%20a,force%20on%204%20November%202016 

14. Linear approaches from Lamb, W. F., Grubb, M., Diluiso, F., & Minx, J. C. (2021). Countries with sustained greenhouse gas emissions reductions: an analysis of trends and progress by sector. Climate Policy, 22(1), 1–17. https://doi.org/10.1080/14693062.2021.1990831 and Mudelsee, M. (2019). Trend analysis of climate time series: A review of methods. Earth-science reviews, 190, 310-322. Logarithmic approach from Ozdemir, M., Pehlivan, S., & Melikoglu, M. (2024). Estimation of greenhouse gas emissions using linear and logarithmic models: a scenario-based approach for Turkiye's 2030 vision. Energy Nexus, 13, 100264. 

15. Global Covenant of Mayors for Climate & Energy and WRI Ross Center for Sustainable Cities. (2023). Integrity Matters for Cities, States, and Regions. https://www.globalcovenantofmayors.org/wp-content/uploads/2023/12/Integrity-Matters-Report-Final3.pdf 

16. Lang, J., Hyslop, C., Manya, D., Smit, S., Chalkley, P., Bervin Galang, J., Green, F., Hale, T., Hans, F., Hay, N., Hsu, A., Kuramochi, T., & Smith, S. (2024). Methodology. Net Zero Tracker. https://zerotracker.net/methodology 

17. Hsu, A., N. Alexandre, J. Brandt, T. Chakraborty, S. Comess, A. Feierman, T. Huang, S. Janaskie, D. Manya, M. Moroney, N. Moyo, R. Rauber, G. Sherriff, R. Thomas, J. Tong, Y. Xie, A. Weinfurter, Z. Yeo (in alpha order).The Urban Environment and Social Inclusion Index. New Haven, CT: Yale University. Available: datadrivenyale.edu/urban.

18.  NewClimate Institute & Data-Driven EnviroLab (2020). Navigating the nuances of net-zero targets. Research report prepared by the team of: Thomas Day, Silke Mooldijk and Takeshi Kuramochi (NewClimate Institute) and Angel Hsu, Zhi Yi Yeo, Amy Weinfurter, Yin Xi Tan, Ian French, Vasu Namdeo, Odele Tan, Sowmya Raghavan, Elwin Lim, and Ajay Nair (Data-Driven EnviroLab).

19. NewClimate Institute & Data-Driven EnviroLab (2020). Navigating the nuances of net-zero targets. Research report prepared by the team of: Thomas Day, Silke Mooldijk and Takeshi Kuramochi (NewClimate Institute) and Angel Hsu, Zhi Yi Yeo, Amy Weinfurter, Yin Xi Tan, Ian French, Vasu Namdeo, Odele Tan, Sowmya Raghavan, Elwin Lim, and Ajay Nair (Data-Driven EnviroLab).

20. NewClimate Institute & Data-Driven EnviroLab (2020).

21. NewClimate Institute & Data-Driven EnviroLab (2020).

22. NewClimate Institute & Data-Driven EnviroLab (2020).

23. NewClimate Institute & Data-Driven EnviroLab (2020).

24. NewClimate Institute & Data-Driven EnviroLab (2020).

25. NewClimate Institute & Data-Driven EnviroLab (2020).

26. NewClimate Institute & Data-Driven EnviroLab (2020).

27. NewClimate Institute & Data-Driven EnviroLab (2020).

28. NewClimate Institute & Data-Driven EnviroLab (2020).

29. Levin, K., Song, J., & Morgan, J. (2015, December 11). COP21 Q&A: What is GHG emissions neutrality in the context of the Paris Agreement?. World Resources Institute. https://www.wri.org/insights/cop21-qa-what-ghg-emissions-neutrality-context-paris-agreement 

30. Intergovernmental Panel on Climate Change. (n.d.). Emission Factor Database. IPCC. https://www.ipcc-nggip.iges.or.jp/EFDB/find_ef.php?ipcc_code=1.A.1&ipcc_level=2 

31. Carbon Neutral Cities Alliance. (n.d.). Our Cities - CNCA. CNCA. https://carbonneutralcities.org/our-cities/ 

32. European Commission. (2023). GHG emissions of all world countries. EDGAR - Emissions Database for Global Atmospheric Research. https://edgar.jrc.ec.europa.eu/report_2023 

33. City of Seattle, Seattle Climate Action (2018). Retrieved from https://durkan.seattle.gov/wp-content/uploads/sites/9/2018/04/SeaClimateAction_April2018.pdf. 

34. City of Amsterdam, New Amsterdam Climate (2020). Retrieved from https://assets.amsterdam.nl/publish/pages/943415/roadmap_amsterdam_climate_neutral_2050_2.pdf. 

35. Inventario de gases de efecto invernadero (2017). https://www.ambientebogota.gov.co/inventario-de-gases-de-efecto-invernadero-ingei