Abstract
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 employ spatial Durbin model (SDM) to investigate the influencing factors of CRP. The results are obtained as follows: (1) 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. (2) 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 moderation carbon reduction potential (MCRP), fairness carbon reduction potential (FCRP), and efficiency carbon reduction potential (ECRP) shows a slight spatial polarization feature. (3) 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.
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Data availability
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Notes
The “Min–Max” normalization method converts zi to si by Si = (zi − min z)/(maxz − min z). The variable of the carbon shadow price is reverse transformed.
Different from considering 7 energy types, it is found that the measurement accuracy is significantly improved after supplementing ten types of energy consumption data by constructing the accuracy improvement rate index; the results are presented as supplementary material.
Liu et al. (2015) pointed out that the carbon emission factor in the IPCC report is approximately higher than the value in China’s “United Nations Framework Convention on Climate Change (UNFCCC)” report. Therefore, we use the net calorific value provided in the China Energy Statistical Yearbook, which is more suitable for China’s national conditions.
The change trend plots of carbon reduction potential under moderation, fairness, and efficiency scenarios between 2000 and 2017 are presented in supplementary material.
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All authors contributed to the study conception and design. Zhangwen Li acquired, analyzed, and interpreted the data; drafted the article or revised it critically for important intellectual content; and was a major contributor in writing the manuscript. Caijiang Zhang revised it critically for intellectual content and approved the version to be published. Yu Zhou acquired the data and made substantial contributions to the conception or design of the work. All authors read and approved the final manuscript.
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Li, Z., Zhang, C. & Zhou, Y. Spatio-temporal evolution characteristics and influencing factors of carbon emission reduction potential in China. Environ Sci Pollut Res 28, 59925–59944 (2021). https://doi.org/10.1007/s11356-021-14913-3
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DOI: https://doi.org/10.1007/s11356-021-14913-3