Abstract
As industry is the world’s leading carbon emitter, promoting industrial carbon reduction is of key significance to carbon peak and carbon neutrality. Using a data-driven method, based on the collection and processing of relevant data from statistical yearbooks and others, we analyze the efficiency and amount of carbon emission of each industrial sector after processing multi-dimensional data by the improved IPCC EF method of calculating carbon emissions. In addition, we adopt the LMDI decomposition method for data modeling to measure the contribution of energy efficiency, industrial structure, GDP per capita, and population size to carbon emission changes, to identify targets for industrial carbon reduction, and to propose a targeted optimization path for carbon emission. We show how the method is implemented by taking the statistics of Anhui Province from 2010 to 2019 as an example and advises on an optimization path for carbon emission in Anhui Province. This study is of both theoretical and practical significance as it provides theoretical and methodological support for the low-carbon development of the regional industry, and provides a reference for other countries and regions to explore the path of low-carbon and environment-friendly green transformation and upgrading.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This research is supported by a Key Project of Anhui Philosophy and Social Science Planning (AHSKXZX2020D13).
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Conceptualization, X.W.; methodology, X.W. and G.X.; investigation, S.Z.; resources, C.L.; data curation, X.W. and G.X.; writing—original draft preparation, S.Z. and X.W.; writing—review and editing, C.L. and Y.Z.; all authors read and approved the final manuscript.
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Wu, X., Zhou, S., Xu, G. et al. Research on carbon emission measurement and low-carbon path of regional industry. Environ Sci Pollut Res 29, 90301–90317 (2022). https://doi.org/10.1007/s11356-022-22006-y
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DOI: https://doi.org/10.1007/s11356-022-22006-y