A spatially explicit assessment of intra-urban carbon emission inequality and ecological equity: evidence from China’s low-carbon city pilots

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🔬研究者:Novel high-resolution emissions dataset and policy evaluation method for intra-urban inequality.

🏢実務担当者:Insights for urban planners on how low-carbon policies may affect emission distribution within cities.

🏛政策担当者:Consider equity implications when designing low-carbon city policies, especially in developing regions.

Introduction As cities have become the core units of carbon governance, evaluations of the Low-Carbon City Pilot (LCCP) policy have largely focused on changes in total emissions or emission intensity, with limited attention to disparities in emissions across intra-urban spatial units. Using panel data for 284 prefecture-level cities in China from 2005 to 2023, this study examines the impact of LCCP on intra-urban carbon emission inequality (UCEI). Methods We first develop a 1-km high-resolution gridded carbon emission dataset by integrating LightGBM machine learning with spatial Bayesian recalibration. Under constraints of provincial total emissions, we achieve fine-scale spatial disaggregation of emissions. Validation against mainstream datasets, including ODIAC, EDGAR, and CEADS, across multiple spatial scales demonstrates high accuracy and stability at the grid, county, and city levels. Based on this dataset, we combine LandScan population grids to compute per capita carbon emissions at the county level and employ the Theil index to characterize the UCEI. For identification, we exploit the staggered implementation of the policy to construct a multi-period difference-in-differences model, test the parallel trends assumption using an event study framework, and conduct robustness checks through placebo tests, propensity score matching difference-in-differences (PSM-DID), and alternative variable specifications. Results The results indicate that LCCP significantly increases UCEI, and this effect remains robust across various specifications. Heterogeneity analysis shows that the effect is more pronounced in central and western regions and in ordinary prefecture-level cities, whereas it is insignificant or even negative in eastern regions and cities with higher administrative status, suggesting that policy impacts depend on pre-existing development conditions and governance capacity. Mechanism analysis suggests that industrial disparity is strongly associated with the increase in UCEI, while spatial disparities in eco-environmental conditions also show a positive but weaker association. Discussion These results should be interpreted as evidence consistent with the proposed spatial restructuring mechanism rather than as causal mediation effects.

• openalex https://doi.org/10.3389/fevo.2026.1789421first seen 2026-06-29 05:18:06 · last seen 2026-06-29 05:18:13