Work

Machine learning estimates for G20 subnational urban GHG emissions from 2000–2020

Year: 2026

Type: data-paper

Abstract: Reliable, comparable greenhouse gas (GHG) emissions data at the subnational level remain scarce, despite growing expectations for cities and regions to lead on climate action. Inconsistent reporting, methodological variation, and limited coverage of self-reported inventories hinder efforts to track progress and guide mitigation opportunities. To address these challenges, we develop a machine learning (ML) framework to estimate annual Scope 1 and 2 CO 2 -equivalent emissions for subnational jurisdictions in G20 countries from 2000 to 2020. Our approach integrates publicly available geospatial, socioeconomic, and environmental data with self-reported inventories where available, and aligns predictions with subnational administrative boundaries. Compared to traditional downscaling or proxy-based approaches, our model improves spatial relevance and predictive performance while capturing locally specific emission drivers. This globally consistent, administratively-aligned dataset can serve as a baseline for assessing climate progress, especially in data-poor or inconsistent reporting contexts, and supports more targeted, data-informed policy decisions for urban and regional decarbonization.

Source: Scientific Data

DOI: 10.1038/s41597-026-06691-9

Authors: Ying Yu, Xuewei Wang, Diego Manya, Angel Hsu

Institutions: University of North Carolina at Chapel Hill, Chinese University of Hong Kong, Shenzhen

Language: English

Topic: Energy, Environment, Economic Growth

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Climate action, Sustainable cities and communities, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,490