IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v98y2025ics0038012125000114.html
   My bibliography  Save this article

The drivers of forest carbon sink density changes in China under forest area heterogeneity: A production-theoretical decomposition analysis

Author

Listed:
  • Huang, Yan
  • Zhang, Mengjiao
  • Wu, Nan
  • Lin, Jinhuang

Abstract

Global temperatures continue to rise, and mitigating climate change has become a common global challenge. As a nature-based solution, forest carbon sink has received high recognition from the international community for its role in mitigating climate change. However, traditional production theory decomposition analysis (PDA) fails to consider regional heterogeneity when exploring the drivers of forest carbon sink. To better formulate strategies for enhancing forest carbon sink based on the specific conditions of each region, this study proposes a PDA method integrated with the meta-frontier model. The new model applied to the forest carbon sink density (FCSD) of 30 provinces in China in 2018. It analyses the driving mechanisms of forest land area inputs, forest pests and diseases infestations, and heterogeneity on the FCSD in state-owned forest areas, southern collective forest areas, and mixed forest areas. The model decomposes them into three primary factors and three substitute factors. The study results indicate the following: (1) After considering heterogeneity, the FCSD shifted from state-owned forest areas toward southern collective forest areas, further indicating that state-owned forest areas still have significant potential for improving forest carbon sink; (2) Further analysis of the two-stage factor substitution effects unrelated to heterogeneity reveals that the varying degrees of substitution among forest land, labor, and capital have different impacts on FCSD; (3) The primary driving factors differ among different forest areas. Results from multiple factors indicate that mixed forest areas perform poorly, further widening forest carbon sink distribution gaps nationwide.

Suggested Citation

  • Huang, Yan & Zhang, Mengjiao & Wu, Nan & Lin, Jinhuang, 2025. "The drivers of forest carbon sink density changes in China under forest area heterogeneity: A production-theoretical decomposition analysis," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).
  • Handle: RePEc:eee:soceps:v:98:y:2025:i:c:s0038012125000114
    DOI: 10.1016/j.seps.2025.102162
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012125000114
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.seps.2025.102162?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Młynarski, Wojciech & Prędki, Artur & Kaliszewski, Adam, 2021. "Efficiency and factors influencing it in forest districts in southern Poland: Application of Data Envelopment Analysis," Forest Policy and Economics, Elsevier, vol. 130(C).
    2. Hongliang Lu & Min Zhang & Wei Nian, 2023. "The Spatial Spillover Effects of Environmental Regulations on Forestry Ecological Security Efficiency in China," Sustainability, MDPI, vol. 15(3), pages 1-27, January.
    3. Wang, Qunwei & Zhang, Cheng & Cai, Wanhuan, 2017. "Factor substitution and energy productivity fluctuation in China: A parametric decomposition analysis," Energy Policy, Elsevier, vol. 109(C), pages 181-190.
    4. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    5. Yu Lin & Wenhui Chen & Junchang Liu, 2021. "Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    6. Song, Malin & Zhao, Xin & Choi, Yongrok, 2020. "Technical Efficiency of Chinese Forestry and Its Total Factor Productivity for the Adaption of the Climate Change," Journal of Forest Economics, now publishers, vol. 35(2-3), pages 149-175, March.
    7. Fare, Rolf, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    8. Fare, R. & Grosskopf, S. & Hernandez-Sancho, F., 2004. "Environmental performance: an index number approach," Resource and Energy Economics, Elsevier, vol. 26(4), pages 343-352, December.
    9. Zhou, P. & Ang, B.W., 2008. "Decomposition of aggregate CO2 emissions: A production-theoretical approach," Energy Economics, Elsevier, vol. 30(3), pages 1054-1067, May.
    10. Ang, B.W. & Wang, H., 2015. "Index decomposition analysis with multidimensional and multilevel energy data," Energy Economics, Elsevier, vol. 51(C), pages 67-76.
    11. Xu, Chengzhen & Zhu, Qingyuan & Li, Xingchen & Wu, Liangpeng & Deng, Ping, 2024. "Determinants of global carbon emission and aggregate carbon intensity: A multi-region input−output approach," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 418-435.
    12. Wang, Hui & Li, Rupeng & Zhang, Ning & Zhou, Peng & Wang, Qiang, 2020. "Assessing the role of technology in global manufacturing energy intensity change: A production-theoretical decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    13. 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.
    14. Xiaolei Liu & Heng Chen & Cheng Peng & Mingqiu Li, 2022. "Assessing the Drivers of Carbon Intensity Change in China: A Dynamic Spatial–Temporal Production-Theoretical Decomposition Analysis Approach," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, H. & Zhou, P., 2018. "Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach," Energy Economics, Elsevier, vol. 74(C), pages 310-320.
    2. Wang, H. & Zhou, P. & Xie, Bai-Chen & Zhang, N., 2019. "Assessing drivers of CO2 emissions in China's electricity sector: A metafrontier production-theoretical decomposition analysis," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1096-1107.
    3. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    4. Wang, Q.W. & Zhou, P. & Shen, N. & Wang, S.S., 2013. "Measuring carbon dioxide emission performance in Chinese provinces: A parametric approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 324-330.
    5. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    6. Wang, Hui & Yu, Shasha & Yang, Yafei & Wang, Meiyue & Zhou, Peng, 2025. "Assessing carbon emissions along global supply chains from technology perspective: A network production decomposition analysis," Ecological Economics, Elsevier, vol. 233(C).
    7. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    8. Wang, H. & Ang, B.W. & Wang, Q.W. & Zhou, P., 2017. "Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach," Energy Economics, Elsevier, vol. 62(C), pages 70-78.
    9. Wang, Hui & Li, Rupeng & Zhang, Ning & Zhou, Peng & Wang, Qiang, 2020. "Assessing the role of technology in global manufacturing energy intensity change: A production-theoretical decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    10. Zhang, Shanshan & Lundgren, Tommy & Zhou, Wenchao, 2016. "Energy efficiency in Swedish industry," Energy Economics, Elsevier, vol. 55(C), pages 42-51.
    11. Xu, Chong & Wang, Bingjie & Chen, Jiandong & Shen, Zhiyang & Song, Malin & An, Jiafu, 2022. "Carbon inequality in China: Novel drivers and policy driven scenario analysis," Energy Policy, Elsevier, vol. 170(C).
    12. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    13. Hang, Ye & Sun, Jiasen & Wang, Qunwei & Zhao, Zengyao & Wang, Yizhong, 2015. "Measuring energy inefficiency with undesirable outputs and technology heterogeneity in Chinese cities," Economic Modelling, Elsevier, vol. 49(C), pages 46-52.
    14. Li, Jianglong & Lin, Boqiang, 2017. "Does energy and CO2 emissions performance of China benefit from regional integration?," Energy Policy, Elsevier, vol. 101(C), pages 366-378.
    15. Ramli, Noor Asiah & Munisamy, Susila, 2015. "Eco-efficiency in greenhouse emissions among manufacturing industries: A range adjusted measure," Economic Modelling, Elsevier, vol. 47(C), pages 219-227.
    16. Guillen, Maria D. & Charles, Vincent & Aparicio, Juan, 2025. "Enhanced efficiency assessment in manufacturing: Leveraging machine learning for improved performance analysis," Omega, Elsevier, vol. 134(C).
    17. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    18. Mingyue Wang & Yu Liu & Yawen Liu & Shunxiang Yang & Lingyu Yang, 2018. "Assessing Multiple Pathways for Achieving China’s National Emissions Reduction Target," Sustainability, MDPI, vol. 10(7), pages 1-16, June.
    19. Zhang, Wei & Wang, Nan, 2021. "Decomposition of energy intensity in Chinese industries using an extended LMDI method of production element endowment," Energy, Elsevier, vol. 221(C).
    20. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceps:v:98:y:2025:i:c:s0038012125000114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.