IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v216y2021ics0360544220324087.html
   My bibliography  Save this article

The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model

Author

Listed:
  • Teng, Xiangyu
  • Liu, Fan-peng
  • Chiu, Yung-ho

Abstract

Although China is the largest energy consumer and carbon emitter in the world, it also has experienced the largest increase in green leaf area, as evidenced from 2007 to 2017 when it hit 66.156 million hectares, accounting for 6.891% of the country’s total land area. This study considered carbon sequestration in afforestation and introduced it as an exogenous variable into a modified dynamic Slacks-Based Measure (SBM) model to find the change in China’s energy and carbon emissions efficiency. Different from previous studies, China’s western region had the best efficiency value of 0.718, while the eastern and central regions were 0.699 and 0.590. In some provinces with more restoration of trees, their efficiency value changed greatly, such as Yunnan, Qinghai, Inner Mongolia and Guizhou. The results highlighted that afforestation has become the most effective strategy for China to improve energy and carbon emissions efficiency and for dealing with climate change, which can be ensured through carbon tax and market mechanism policies.

Suggested Citation

  • Teng, Xiangyu & Liu, Fan-peng & Chiu, Yung-ho, 2021. "The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model," Energy, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220324087
    DOI: 10.1016/j.energy.2020.119301
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220324087
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.119301?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. Li, Lan-Bing & Hu, Jin-Li, 2012. "Ecological total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 46(C), pages 216-224.
    2. Myroslava Lesiv & Anatoly Shvidenko & Dmitry Schepaschenko & Linda See & Steffen Fritz, 2019. "A spatial assessment of the forest carbon budget for Ukraine," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 985-1006, August.
    3. Kao, Chiang, 2013. "Dynamic data envelopment analysis: A relational analysis," European Journal of Operational Research, Elsevier, vol. 227(2), pages 325-330.
    4. Li, Aijun & Zhang, Aizhen & Huang, Huijie & Yao, Xin, 2018. "Measuring unified efficiency of fossil fuel power plants across provinces in China: An analysis based on non-radial directional distance functions," Energy, Elsevier, vol. 152(C), pages 549-561.
    5. Xie, Qiwei & Hu, Ping & Jiang, An & Li, Yongjun, 2019. "Carbon emissions allocation based on satisfaction perspective and data envelopment analysis," Energy Policy, Elsevier, vol. 132(C), pages 254-264.
    6. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    7. Roger Sedjo & Brent Sohngen, 2012. "Carbon Sequestration in Forests and Soils," Annual Review of Resource Economics, Annual Reviews, vol. 4(1), pages 127-144, August.
    8. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    9. Xiangyu Teng & Liang Chun Lu & Yung-ho Chiu, 2018. "Considering Emission Treatment for Energy-Efficiency Improvement and Air Pollution Reduction in China’s Industrial Sector," Sustainability, MDPI, vol. 10(11), pages 1-18, November.
    10. Jebali, Eya & Essid, Hédi & Khraief, Naceur, 2017. "The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach," Energy, Elsevier, vol. 134(C), pages 991-1000.
    11. Guo, Xiaoying & Lu, Ching-Cheng & Lee, Jen-Hui & Chiu, Yung-Ho, 2017. "Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China," Energy, Elsevier, vol. 134(C), pages 392-399.
    12. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    13. Ramanathan, Ramakrishnan, 2005. "An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa," Energy, Elsevier, vol. 30(15), pages 2831-2842.
    14. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    15. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2012. "A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs?," Energy Policy, Elsevier, vol. 46(C), pages 574-584.
    16. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    17. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    18. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.
    19. 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.
    20. Yeh, Tsai-lien & Chen, Tser-yieth & Lai, Pei-ying, 2010. "A comparative study of energy utilization efficiency between Taiwan and China," Energy Policy, Elsevier, vol. 38(5), pages 2386-2394, May.
    21. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peng, Honggang & Xiao, Zhi & Wang, Jianqiang & Li, Jian, 2021. "A decision support framework for new energy selection in rural areas from the perspectives of information reliability and criterion non-compensation," Energy, Elsevier, vol. 235(C).
    2. Fang, Tao & Fang, Debin & Yu, Bolin, 2022. "Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants," Energy Policy, Elsevier, vol. 165(C).
    3. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    4. Pan Jiang & Mengyue Li & Yuting Zhao & Xiujuan Gong & Ruifeng Jin & Yuhan Zhang & Xue Li & Liang Liu, 2022. "Does Environmental Regulation Improve Carbon Emission Efficiency? Inspection of Panel Data from Inter-Provincial Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    5. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    6. Wang, Jianda & Dong, Kangyin & Sha, Yezhou & Yan, Cheng, 2022. "Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. Luwei Wang & Yizhen Zhang & Qing Zhao & Chuantang Ren & Yu Fu & Tao Wang, 2023. "Horizontal CO 2 Compensation in the Yangtze River Delta Based on CO 2 Footprints and CO 2 Emissions Efficiency," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    8. Teng, Xiangyu & Liu, Fan-peng & Chang, Tzu-han & Chiu, Yung-ho, 2023. "Measuring China’s energy efficiency by considering forest carbon sequestration and applying a meta dynamic non-radial directional distance function," Energy, Elsevier, vol. 263(PC).
    9. Liu, Wan-Yu & Chiang, Yi-Hua & Lin, Chun-Cheng, 2022. "Adopting renewable energies to meet the carbon reduction target: Is forest carbon sequestration cheaper?," Energy, Elsevier, vol. 246(C).
    10. Li, Mengjie & Du, Weijian, 2021. "Can Internet development improve the energy efficiency of firms: Empirical evidence from China," Energy, Elsevier, vol. 237(C).
    11. Mengyao Ci & Lu Ye & Changhao Liao & Li Yao & Zhiqin Tu & Qiao Xing & Xuguang Tang & Zhi Ding, 2023. "Long-Term Dynamics of Ecosystem Services and Their Influencing Factors in Ecologically Fragile Southwest China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    12. Liang Chun Lu & Yung-ho Chiu & Shih-Yung Chiu & Tzu-Han Chang, 2022. "Do Forests help environmental development of Cities in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6602-6629, May.

    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. Xiangyu Teng & Danting Lu & Yung-ho Chiu, 2019. "Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China," Energies, MDPI, vol. 12(2), pages 1-18, January.
    2. Xiangyu Teng & Fan‐peng Liu & Yung‐ho Chiu, 2020. "The impact of coal and non‐coal consumption on China's energy performance improvement," Natural Resources Forum, Blackwell Publishing, vol. 44(4), pages 334-352, November.
    3. Ying Li & Yung-Ho Chiu & Liang Chun Lu, 2018. "Regional Energy, CO 2 , and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach," Energies, MDPI, vol. 11(8), pages 1-20, August.
    4. Teng, Xiangyu & Zhuang, Weiwei & Liu, Fan-peng & Chang, Tzu-han & Chiu, Yung-ho, 2023. "China's path of carbon neutralization to develop green energy and improve energy efficiency," Renewable Energy, Elsevier, vol. 206(C), pages 397-408.
    5. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    6. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.
    7. Ze Tian & Fang-Rong Ren & Qin-Wen Xiao & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Cross-Regional Comparative Study on Carbon Emission Efficiency of China’s Yangtze River Economic Belt Based on the Meta-Frontier," IJERPH, MDPI, vol. 16(4), pages 1-19, February.
    8. Liang Chun Lu & Yung-ho Chiu & Shih-Yung Chiu & Tzu-Han Chang, 2022. "Do Forests help environmental development of Cities in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6602-6629, May.
    9. Teng, Xiangyu & Liu, Fan-peng & Chang, Tzu-han & Chiu, Yung-ho, 2023. "Measuring China’s energy efficiency by considering forest carbon sequestration and applying a meta dynamic non-radial directional distance function," Energy, Elsevier, vol. 263(PC).
    10. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "Energy and Environmental Efficiency in Different Chinese Regions," Sustainability, MDPI, vol. 11(4), pages 1-26, February.
    11. Chen, Weidong & Geng, Wenxin, 2017. "Fossil energy saving and CO2 emissions reduction performance, and dynamic change in performance considering renewable energy input," Energy, Elsevier, vol. 120(C), pages 283-292.
    12. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, vol. 11(8), pages 1-21, August.
    13. Xiangyu Teng & Liang Chun Lu & Yung-ho Chiu, 2018. "Considering Emission Treatment for Energy-Efficiency Improvement and Air Pollution Reduction in China’s Industrial Sector," Sustainability, MDPI, vol. 10(11), pages 1-18, November.
    14. Zhu, Lin & Wang, Yong & Shang, Peipei & Qi, Lin & Yang, Guangchun & Wang, Ying, 2019. "Improvement path, the improvement potential and the dynamic evolution of regional energy efficiency in China: Based on an improved nonradial multidirectional efficiency analysis," Energy Policy, Elsevier, vol. 133(C).
    15. Fang, Tao & Fang, Debin & Yu, Bolin, 2022. "Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants," Energy Policy, Elsevier, vol. 165(C).
    16. 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.
    17. Tianbo Tang & Jianxin You & Hui Sun & Hao Zhang, 2019. "Transportation Efficiency Evaluation Considering the Environmental Impact for China’s Freight Sector: A Parallel Data Envelopment Analysis," Sustainability, MDPI, vol. 11(18), pages 1-24, September.
    18. Yu, Junqing & Zhou, Kaile & Yang, Shanlin, 2019. "Regional heterogeneity of China's energy efficiency in “new normal”: A meta-frontier Super-SBM analysis," Energy Policy, Elsevier, vol. 134(C).
    19. Svetlana Ratner & Andrey Lychev & Aleksei Rozhnov & Igor Lobanov, 2021. "Efficiency Evaluation of Regional Environmental Management Systems in Russia Using Data Envelopment Analysis," Mathematics, MDPI, vol. 9(18), pages 1-21, September.
    20. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.

    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:energy:v:216:y:2021:i:c:s0360544220324087. 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.journals.elsevier.com/energy .

    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.