Abstract
The escalating global greenhouse gas emission crisis necessitates a robust scientific carbon accounting framework and innovative development approaches. Achieving emission peaks remains the primary goal for emission reduction. Guangdong Province, a pivotal region in China, faces pressure to reduce carbon emissions. In this study, data was leveraged from the China Carbon Accounting Database (CEADS) and panel data from the “Guangdong Statistical Yearbook” spanning 1997 to 2022. Factors impacting carbon emissions were selected based on Guangdong Province’s carbon reduction goals, macroeconomic development strategies, and economic-population dynamics. To address multicollinearity, lasso regression identified key factors, including population size, economic development level, energy intensity, and technology factors. A novel STIRPAT extended model, combined with the BP neural network optimized using the TPE algorithm, enhanced carbon emission predictions for Guangdong Province. Employing scenario analysis, five scenarios were generated in alignment with the planning policies of Guangdong Province, to forecast carbon emissions from 2020 to 2050. The results suggest that to achieve a win-win situation for both economic development and environmental protection, Guangdong Province should prioritize the energy-saving scenario (S2), which aligns with the “13th Five-Year Plan’s” ecological and green development directives, to reach a projected carbon peak of 637.05Mt by 2030. In conclusion, recommendations for carbon reduction are proposed in the areas of low-carbon transformation for the population, sustainable economic development, and the development of low-carbon technologies.
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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. The data used in this study can be found in the China Emission Accounts and Datasets (CEADs) (https://www.ceads.net.cn/) and the Guangdong Statistical Yearbook (http://stats.gd.gov.cn/).
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Funding
This research was supported by a special grant from the Guangdong Provincial Science and Technology Innovation Strategy under Grant No. pdjh2023b0247, Guangdong Ocean University Undergraduate Innovation Team Project under Grant No. CXTD2023014.
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Ruihan Chen and Minhua Ye contributed equally to all aspects of this work; Zebin Ma and Derong Yang conducted data analysis; Zhi Li and Sheng Li gave some useful comments and suggestions to this work. All authors reviewed the manuscript.
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Chen, ., Ye, M., Li, Z. et al. Empirical assessment of carbon emissions in Guangdong Province within the framework of carbon peaking and carbon neutrality: a lasso-TPE-BP neural network approach. Environ Sci Pollut Res 30, 121647–121665 (2023). https://doi.org/10.1007/s11356-023-30882-1
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DOI: https://doi.org/10.1007/s11356-023-30882-1