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A Dynamic Benchmark System for Per Capita Carbon Emissions in Low-Carbon Counties of China

Author

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  • Lijie Gao

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

  • Xiaoqi Shang

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    The Institute of Urban Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fengmei Yang

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    The Institute of Urban Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Longyu Shi

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

Abstract

As the most basic unit of the national economy and administrative management, the low-carbon transformation of the vast counties is of great significance to China’s overall greenhouse gas emission reduction. Although the low-carbon evaluation (LCE) indicator system and benchmarks have been extensively studied, most benchmarks ignore the needs of the evaluated object at the development stage. When the local economy develops to a certain level, it may be restricted by static low-carbon target constraints. This study reviews the relevant research on LCE indicator system and benchmarks based on convergence. The Environmental Kuznets Curve (EKC), a dynamic benchmark system for per capita carbon emissions (PCCEs), is proposed for low-carbon counties. Taking Changxing County, Zhejiang Province, China as an example, a dynamic benchmark for PCCEs was established by benchmarking the Carbon Kuznets Curve (CKC) of best practices. Based on the principles of best practice, comparability, data completeness, and the CKC hypothesis acceptance, the best practice database is screened, and Singapore is selected as a potential benchmark. By constructing an econometric model to conduct an empirical study on Singapore’s CKC hypothesis, the regression results of the least squares method support the CKC hypothesis and its rationality as a benchmark. The result of the PCCE benchmarks of Changxing County show that when the per capita income of Changxing County in 2025, 2030, and 2035 reaches USD 19,172.92, USD 24,483.01, and USD 29,366.11, respectively, the corresponding benchmarks should be 14.95 tons CO 2 /person, 14.70 tons CO 2 /person, and 13.55 tons CO 2 /person. For every 1% increase in the county’s per capita income, the PCCE allowable room for growth is 17.6453%. The turning point is when the per capita gross domestic product (PCGDP) is USD 20,843.23 and the PCCE is 15.03 tons of CO 2 /person, which will occur between 2025 and 2030. Prior to this, the PCCE benchmark increases with the increase of PCGDP. After that, the PCCE benchmark decreases with the increase of PCGDP. The system is economically sensitive, adaptable to different development stages, and enriches the methodology of low-carbon indicator evaluation and benchmark setting at the county scale. It can provide scientific basis for Chinese county decision makers to formulate reasonable targets under the management idea driven by evaluation indicators and emission reduction targets and help counties explore the coordinated paths of economic development and emission reduction in different development stages. It has certain reference significance for other developing regions facing similar challenges of economic development and low-carbon transformation to Changxing County to formulate scientific and reasonable low-carbon emission reduction targets.

Suggested Citation

  • Lijie Gao & Xiaoqi Shang & Fengmei Yang & Longyu Shi, 2021. "A Dynamic Benchmark System for Per Capita Carbon Emissions in Low-Carbon Counties of China," Energies, MDPI, vol. 14(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:599-:d:486731
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    References listed on IDEAS

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    1. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alborg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2022. "Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications," Energies, MDPI, vol. 15(8), pages 1-30, April.
    2. Jianshi Wang & Chengxin Wang & Shangkun Yu & Mengcheng Li & Yu Cheng, 2022. "Coupling Coordination and Spatiotemporal Evolution between Carbon Emissions, Industrial Structure, and Regional Innovation of Counties in Shandong Province," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    3. Jingwen Zhang & Yin Dai & Chi-Wei Su & Dervis Kirikkaleli & Muhammad Umar, 2021. "Intertemporal change in the effect of economic growth on carbon emission in China," Energy & Environment, , vol. 32(7), pages 1207-1225, November.

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