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This study first attempts to use the parameterized quadratic Directional Distance Function (DDF) approach to calculate China's provincial carbon abatement cost and carbon reduction potential (CRP) under different scenarios from 2000 to 2017. Afterward, considering three different scenarios, we analyze the Spatio-temporal characteristics and the dynamic evolution pattern of CRP. We also conduct the spatial autocorrelation test and spatial Durbin model to analyze the spatial spillover effects and influencing factors of CRP. The results are obtained as follows: CRP across the three scenarios varies considerably across provinces and different-located groups. CRP higher areas are mainly located in the economically developed eastern coastal regions, while most provinces with low CRP are concentrated in the western region. The spatial autocorrelation test indicated that provinces with a similar CRP showed a significant geographic agglomeration, and the agglomeration effect was strengthened first and then weakened. Simultaneously, the local spatial distribution of MCRP, FCRP, and ECRP shows a slight spatial polarization feature. Finally, through the SDM analysis and spillover effect decomposition, we find that improvement of regional CRP not only depends on economic development, industrial structure adjustment, and energy efficiency elevation, but also involves energy structure optimization, low-carbon innovation, and population. The low-carbon innovation provides critical support for local CRP under the efficiency scenario but restrains the local CRP under the fairness scenario. Therefore, the central government should emphasize local conditions and the ex-ante scenario assessment, strengthen regional interactive governance, optimize energy efficiency, and promote the application of clean energy to enhance CRP.

This study first attempts to use the parameterized quadratic Directional Distance Function (DDF) approach to calculate China's provincial carbon abatement cost and carbon reduction potential (CRP) under different scenarios from 2000 to 2017. Afterward, considering three different scenarios, we analyze the Spatio-temporal characteristics and the dynamic evolution pattern of CRP. We also conduct the spatial autocorrelation test and spatial Durbin model to analyze the spatial spillover effects and influencing factors of CRP. The results are obtained as follows: CRP across the three scenarios varies considerably across provinces and different-located groups. CRP higher areas are mainly located in the economically developed eastern coastal regions, while most provinces with low CRP are concentrated in the western region. The spatial autocorrelation test indicated that provinces with a similar CRP showed a significant geographic agglomeration, and the agglomeration effect was strengthened first and then weakened. Simultaneously, the local spatial distribution of MCRP, FCRP, and ECRP shows a slight spatial polarization feature. Finally, through the SDM analysis and spillover effect decomposition, we find that improvement of regional CRP not only depends on economic development, industrial structure adjustment, and energy efficiency elevation, but also involves energy structure optimization, low-carbon innovation, and population. The low-carbon innovation provides critical support for local CRP under the efficiency scenario but restrains the local CRP under the fairness scenario. Therefore, the central government should emphasize local conditions and the ex-ante scenario assessment, strengthen regional interactive governance, optimize energy efficiency, and promote the application of clean energy to enhance CRP.

The role of digital financial inclusion in economic development has been widely appreciated, and its carbon emission mitigating effect on the household sector needs to be noticed. This study investigates the impact of digital financial inclusion on household carbon emissions based on panel data for 30 Chinese provinces from 2011 to 2020. The results show that digital financial inclusion has a significant and robust mitigation effect on household carbon emissions and that digital financial inclusion impacts mainly from the breadth of coverage and the degree of digitization. The heterogeneity test results show that this mitigation effect is mainly found in the central and western inland regions as well as in the northern regions with high winter heating demand. In addition, this mitigation effect is mainly found in urban rather than rural areas. The results of the mechanism analysis show that digital financial inclusion reduces household carbon emissions through two pathways, electricity consumption and natural gas consumption share, and no significant mediating effect is observed for residential consumption share. The results of this study shed light on the relationship between digital financial inclusion and carbon emissions in the household sector and provide a reference for decision-making to address household carbon emission mitigation in China.

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