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Differential Quantitative Analysis of Carbon Emission Efficiency of Gansu Manufacturing Industry in 2030

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  • Jingyi Tan

    (Key Laboratory of Western China’s Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730030, China)

  • Shuyang Zhang

    (Key Laboratory of Western China’s Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730030, China)

  • Yun Zhang

    (Key Laboratory of Western China’s Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730030, China)

  • Bo Wang

    (Key Laboratory of Western China’s Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730030, China)

Abstract

Decomposition analysis and forecasting of carbon emissions in manufacturing are crucial for setting sustainable carbon-reduction targets. Given the varied carbon-emission efficiencies across sectors, this study applied the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the drivers of carbon emissions in Gansu’s manufacturing sector, encompassing high, medium, and low-efficiency industries, and it identified vital factors affecting carbon emissions. A localized Long-range Energy Alternatives Planning System (LEAP) model for Gansu was also developed. This model includes six developmental scenarios to project future carbon emissions. The study results are as follows: (1) LMDI decomposition indicates that increased carbon emissions in the manufacturing industry primarily result from economic growth in less efficient sectors and the dominance of moderately efficient ones. (2) Under Optimization Scenario 6, a 50.82 × 10 4 ton reduction in carbon emissions is projected for Gansu’s manufacturing sector by 2030 compared to 2020, marking the carbon peak. These outcomes provide valuable insights for policy reforms in Gansu’s manufacturing industry, aiming for carbon peaking by 2030.

Suggested Citation

  • Jingyi Tan & Shuyang Zhang & Yun Zhang & Bo Wang, 2024. "Differential Quantitative Analysis of Carbon Emission Efficiency of Gansu Manufacturing Industry in 2030," Sustainability, MDPI, vol. 16(5), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2007-:d:1348420
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    References listed on IDEAS

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    1. Ang, B. W. & Pandiyan, G., 1997. "Decomposition of energy-induced CO2 emissions in manufacturing," Energy Economics, Elsevier, vol. 19(3), pages 363-374, July.
    2. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    3. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    4. Chen, Jiandong & Wang, Ping & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2018. "Decomposition and decoupling analysis of CO2 emissions in OECD," Applied Energy, Elsevier, vol. 231(C), pages 937-950.
    5. Hu, Guangxiao & Ma, Xiaoming & Ji, Junping, 2019. "Scenarios and policies for sustainable urban energy development based on LEAP model – A case study of a postindustrial city: Shenzhen China," Applied Energy, Elsevier, vol. 238(C), pages 876-886.
    6. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    Full references (including those not matched with items on IDEAS)

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