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Analysis of the Efficiency of Forest Carbon Sinks and Its Influencing Factors—Evidence from China

Author

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  • Junmin Wei

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China)

  • Manhong Shen

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
    Institute of Ecological Civilization, Zhejiang A&F University, Hangzhou 311300, China
    Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou 311300, China)

Abstract

The study of the input–output efficiency and influencing factors of forest carbon sinks is beneficial for the realization of the rational allocation of forest carbon sink resources. Based on the DEA-SBM model, the efficiency of forest carbon sinks is measured and analyzed in 30 provinces (cities) of China from 2005 to 2018; the influencing factors of forest carbon sink efficiency are constructed from the three perspectives of pressure subsystem, state subsystem, and response subsystem with the help of the PSR model and regression analysis is conducted using the FGLS model so that the results of the study can provide a basis for formulating a regionally differentiated forest carbon sink system. The empirical results show that the average annual forest carbon sink efficiency in China is only 0.29, and there is much room for improvement. The level of urbanization, the degree of natural damage to forests, precipitation, and the proportion of financial support for forestry are positively correlated with forest carbon sink efficiency, while temperature is negatively correlated with forest sink efficiency. Additionally, different influencing factors have regional heterogeneity on forest carbon sink efficiency. Based on the above findings, we propose the following policy recommendations: formulate forest carbon sink strategies according to local conditions, adjust and optimize the forestry industry structure at the right time, minimize the intervention in forest ecosystems, improve the supervision mechanism of special forestry funds, improve the level of forestry human capital, and accelerate the transformation of scientific and technological achievements.

Suggested Citation

  • Junmin Wei & Manhong Shen, 2022. "Analysis of the Efficiency of Forest Carbon Sinks and Its Influencing Factors—Evidence from China," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11155-:d:908138
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    References listed on IDEAS

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    1. Johnston, Craig & Buongiorno, Joseph & Nepal, Prakash & Prestemon, Jeff, 2019. "From Source to Sink: Past Changes and Model Projections of Carbon Sequestration in the Global Forest Sector," Journal of Forest Economics, now publishers, vol. 34(1-2), pages 47-72, August.
    2. Daigneault, Adam & Favero, Alice, 2021. "Global forest management, carbon sequestration and bioenergy supply under alternative shared socioeconomic pathways," Land Use Policy, Elsevier, vol. 103(C).
    3. R. A. Houghton, 2002. "Magnitude, distribution and causes of terrestrial carbon sinks and some implications for policy," Climate Policy, Taylor & Francis Journals, vol. 2(1), pages 71-88, March.
    4. Zhang, Kerong & Song, Conghe & Zhang, Yulong & Zhang, Quanfa, 2017. "Natural disasters and economic development drive forest dynamics and transition in China," Forest Policy and Economics, Elsevier, vol. 76(C), pages 56-64.
    5. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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    Cited by:

    1. Ke Zhang & Jing Qian & Zhenhua Zhang & Shijiao Fang, 2023. "The Impact of Carbon Trading Pilot Policy on Carbon Neutrality: Empirical Evidence from Chinese Cities," IJERPH, MDPI, vol. 20(5), pages 1-23, March.
    2. Wu Yang & Zhang Min & Mingxing Yang & Jun Yan, 2022. "Exploration of the Implementation of Carbon Neutralization in the Field of Natural Resources under the Background of Sustainable Development—An Overview," IJERPH, MDPI, vol. 19(21), pages 1-28, October.
    3. Yue Jiang & Yufang Wang & Rui Wang, 2022. "Coupling and Coordination Relationship between Economic and Ecologic-Environmental Developments in China’s Key State-Owned Forest Areas," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    4. Hongyi Liu & Tianyu He, 2023. "Sustainable Management of Land Resources: The Case of China’s Forestry Carbon Sink Mechanism," Land, MDPI, vol. 12(6), pages 1-18, June.

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