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This inequality exacerbates the disparity in carbon emissions across regions, hindering efforts to achieve sustainable development and environmental justice. Previous research has primarily focused on the structure of carbon footprints and their influencing factors, but there has been limited quantitative research on carbon emission inequality, particularly from a multi-scale perspective. This study constructs a 250 m-high-resolution consumption-based carbon footprint grid for China and uses the Theil index to reveal significant spatial inequalities in carbon footprints. The results indicate that smaller-scale analyses better reveal the spatiotemporal heterogeneity of carbon footprints within regions. At the county level, carbon footprints exhibit significant inequalities, with hotspots concentrated in regions such as Beijing\u2013Tianjin\u2013Hebei, the Yangtze River Delta, and the Pearl River Delta. The top 5% of areas with the highest carbon footprints (139 cities) contributed 19.6% of the national total, indicating a concentration in a few large cities. The decomposition of the Theil index shows that county-level cities contributed 55% of the national carbon inequality. The study also reveals the complex relationship between carbon footprints and income, as well as urban-rural disparities. The underdeveloped central and western regions exhibit a pronounced spatial lag effect, with the growth rate of carbon footprints in rural areas surpassing that of urban areas. Carbon footprints in impoverished areas and inter-provincial marginal areas overlap significantly with low-emission zones, demonstrating characteristics of \u201clow-carbon growth\u201d. To achieve carbon peak and carbon neutrality targets, China must adopt comprehensive measures to reduce carbon footprints and their inequalities, including strengthening multi-scale carbon inequality monitoring, implementing differentiated carbon reduction policies, and promoting coordinated emission reduction development at the county level.<\/jats:p>","DOI":"10.3390\/ijgi14020049","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T09:42:23Z","timestamp":1737970943000},"page":"49","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploring Inequality: A Multi-Scale Analysis of China\u2019s Consumption Carbon Footprint"],"prefix":"10.3390","volume":"14","author":[{"given":"Feng","family":"Xu","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8850-0912","authenticated-orcid":false,"given":"Xinqi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"},{"name":"Frontiers Science Center for Deep-Time Digital Earth, China University of Geosciences, Beijing 100083, China"},{"name":"Observation and Research Station of Beijing Fangshan Comprehensive Exploration, Ministry of Natural Resources of the People\u2019s Republic of China, Beijing 100083, China"},{"name":"Technology Innovation Center for Territory Spatial Big-Data, Ministry of Natural Resources of the People\u2019s Republic of China, Beijing 100036, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0407-1319","authenticated-orcid":false,"given":"Minrui","family":"Zheng","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Territory Spatial Big-Data, Renmin University of China, Beijing 100036, China"},{"name":"Digital Government and National Governance Lab, Renmin University of China, Beijing 100872, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6853-3370","authenticated-orcid":false,"given":"Dongya","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"},{"name":"Frontiers Science Center for Deep-Time Digital Earth, China University of Geosciences, Beijing 100083, China"}]},{"given":"Yin","family":"Ma","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, China Geological Survey, Beijing 100083, China"}]},{"given":"Jizong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, China"}]},{"given":"Ye","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"given":"Xu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Economics and Finance, Huaqiao University, Quanzhou 362000, China"}]},{"given":"Mengdi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing 100037, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,26]]},"reference":[{"key":"ref_1","first-page":"52","article-title":"Does Digitalization Mitigate Regional Inequalities? 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