Estimating CO₂ Emissions for 108,000 European Cities

Abstract

Abstract. City-level CO2 emissions inventories are foundational for supporting the EU’s decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for 116,572 municipal and local government units in Europe. The inventory spatially disaggregates the national reported emissions, using 9 spatialization methods to distribute the 167 line items detailed in the UN's Common Reporting Framework. The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels using a new spatialization approach. All data from this study is available along with an interactive map of results at https://openghgmap.net

Predicting European cities’ climate mitigation performance using machine learning

“…The resulting r 2 = 0.91 indicates our model is strongly predictive overall of cities' self-reported emissions inventories. We further validated our predicted emissions with other studies that report emissions data for European cities, including Moran et al 15 , who estimate 2018 direct (Scope 1) emissions for more than 100,000 European cities and Nangini et al 16 , who combine self-reported inventories with other data for 343 global cities. We found fair correlation (r 2 = 0.57 with Moran et al 15 ; r 2 = 0.62 with Nangini et al 16 ) between our predicted data and these other studies (Supplementary Fig.…”

Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

“…These approaches can provide good estimates for major cities that disclose high-quality energy consumption data 16 . However, they can not be readily applied to a larger scale, where city-specific data are largely absent, especially for smaller cities 19 . Downscaling represents a solution to the scalability issue.…”

“…But the coverage, timeliness, and temporal resolution of these data are not always sufficient to support agile and informed decision making. For example, although several high-quality datasets are available for high-income countries, such as the Covenant of Mayors database ( https://www.globalcovenantofmayors.org/ 25 , 32 ) and OpenGHGmap ( https://openghgmap.net/ 19 ) for EU cities, and the Vulcan and Hestia datasets for U.S. cities ( https://vulcan.rc.nau.edu/ , https://hestia.rc.nau.edu/ 4 , 5 , 33 ), the tension between human development and decarbonization requires an increasing focus on rapidly expanding cities in low-income and emerging regions in South America, South and Southeast Asia, Africa and the Middle East where high-quality emission inventories are lacking 34 , 35 . Moreover, most existing city-level inventories have the issues of long time lag and low temporal resolution.…”