IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i11p2115-d119392.html
   My bibliography  Save this article

Allocation of Energy Consumption among Provinces in China: A Weighted ZSG-DEA Model

Author

Listed:
  • Siqin Xiong

    (School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
    College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China)

  • Yushen Tian

    (School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
    College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China)

  • Junping Ji

    (School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
    College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China)

  • Xiaoming Ma

    (School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
    College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China)

Abstract

To realize the sustainable development of energy, the Chinese government has formulated a series of national goals of total energy control and energy structure optimization. Under the national constraints, how to efficiently allocate the constrained total amount of energy consumption to each province is a fundamental problem to be solved. Based on a data envelopment analysis (DEA) model and a zero-sum game theory (ZSG), this paper constructs a weighted zero-sum game data envelopment analysis (ZSG-DEA) model to allocate the energy consumption quota. Additionally, this paper compares the results with the current administrative targets, to examine the efficiency and feasibility of each allocation mechanism. Finally, this paper employs the proposed model to determine the optimal energy structure for each province in China. The results indicate that by 2020, the national goal of energy structure adjustment will be realized, and energy structure will be diversified in most regions, whereas the coal-dominated status in primary energy consumption will not change. Additionally, the weighted ZSG-DEA model focuses on allocation efficiency while the government considers more regional economic disparity. Therefore, this study suggests a mixture of the two allocation mechanisms in accordance with specific conditions.

Suggested Citation

  • Siqin Xiong & Yushen Tian & Junping Ji & Xiaoming Ma, 2017. "Allocation of Energy Consumption among Provinces in China: A Weighted ZSG-DEA Model," Sustainability, MDPI, vol. 9(11), pages 1-12, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2115-:d:119392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/11/2115/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/11/2115/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, P. & Wang, M., 2016. "Carbon dioxide emissions allocation: A review," Ecological Economics, Elsevier, vol. 125(C), pages 47-59.
    2. Wen Guo & Tao Sun & Hongjun Dai, 2017. "Efficiency Allocation of Provincial Carbon Reduction Target in China’s “13·5” Period: Based on Zero-Sum-Gains SBM Model," Sustainability, MDPI, vol. 9(2), pages 1-18, January.
    3. Zhang, Yue-Jun & Wang, Ao-Dong & Da, Ya-Bin, 2014. "Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method," Energy Policy, Elsevier, vol. 74(C), pages 454-464.
    4. Shihong Zeng & Jiuying Chen, 2016. "Forecasting the Allocation Ratio of Carbon Emission Allowance Currency for 2020 and 2030 in China," Sustainability, MDPI, vol. 8(7), pages 1-28, July.
    5. Foo, Dominic C.Y. & Tan, Raymond R. & Ng, Denny K.S., 2008. "Carbon and footprint-constrained energy planning using cascade analysis technique," Energy, Elsevier, vol. 33(10), pages 1480-1488.
    6. Yue-Jun Zhang & Jun-Fang Hao, 2017. "Carbon emission quota allocation among China’s industrial sectors based on the equity and efficiency principles," Annals of Operations Research, Springer, vol. 255(1), pages 117-140, August.
    7. Park, Ji-Won & Kim, Chae Un & Isard, Walter, 2012. "Permit allocation in emissions trading using the Boltzmann distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4883-4890.
    8. Wang, Ke & Zhang, Xian & Wei, Yi-Ming & Yu, Shiwei, 2013. "Regional allocation of CO2 emissions allowance over provinces in China by 2020," Energy Policy, Elsevier, vol. 54(C), pages 214-229.
    9. 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.
    10. Wu, Jie & Zhu, Qingyuan & Liang, Liang, 2016. "CO2 emissions and energy intensity reduction allocation over provincial industrial sectors in China," Applied Energy, Elsevier, vol. 166(C), pages 282-291.
    11. Lins, Marcos P. Estellita & Gomes, Eliane G. & Soares de Mello, Joao Carlos C. B. & Soares de Mello, Adelino Jose R., 2003. "Olympic ranking based on a zero sum gains DEA model," European Journal of Operational Research, Elsevier, vol. 148(2), pages 312-322, July.
    12. Ji-Won Park & Chae Un Kim & Walter Isard, 2011. "Permit Allocation in Emissions Trading using the Boltzmann Distribution," Papers 1108.2305, arXiv.org, revised Mar 2012.
    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. Jiekun Song & Rui Chen & Xiaoping Ma, 2021. "Collaborative Allocation of Energy Consumption, Air Pollutants and CO 2 Emissions in China," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    2. 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.
    3. Feng Dong & Bolin Yu & Jixiong Zhang, 2018. "What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach," Sustainability, MDPI, vol. 10(6), pages 1-26, May.
    4. Han, Rong & Li, Jianglong & Guo, Zhi, 2022. "Optimal quota in China's energy capping policy in 2030 with renewable targets and sectoral heterogeneity," Energy, Elsevier, vol. 239(PA).
    5. Fei Ye & Lixu Li & Zhiqiang Wang & Yina Li, 2018. "An Asymmetric Nash Bargaining Model for Carbon Emission Quota Allocation among Industries: Evidence from Guangdong Province, China," Sustainability, MDPI, vol. 10(11), pages 1-18, November.
    6. Wei Yang & Zudi Lu & Di Wang & Yanmin Shao & Jinfeng Shi, 2020. "Sustainable Evolution of China’s Regional Energy Efficiency Based on a Weighted SBM Model with Energy Substitutability," Sustainability, MDPI, vol. 12(23), pages 1-22, December.
    7. Jianguo Zhou & Yushuo Li & Xuejing Huo & Xiaolei Xu, 2019. "How to Allocate Carbon Emission Permits Among China’s Industrial Sectors Under the Constraint of Carbon Intensity?," Sustainability, MDPI, vol. 11(3), pages 1-21, February.

    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. Fang, Kai & Zhang, Qifeng & Long, Yin & Yoshida, Yoshikuni & Sun, Lu & Zhang, Haoran & Dou, Yi & Li, Shuai, 2019. "How can China achieve its Intended Nationally Determined Contributions by 2030? A multi-criteria allocation of China’s carbon emission allowance," Applied Energy, Elsevier, vol. 241(C), pages 380-389.
    2. Yue-Jun Zhang & Jun-Fang Hao, 2017. "Carbon emission quota allocation among China’s industrial sectors based on the equity and efficiency principles," Annals of Operations Research, Springer, vol. 255(1), pages 117-140, August.
    3. Feng, Zhiying & Tang, Wenhu & Niu, Zhewen & Wu, Qinghua, 2018. "Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1122-1135.
    4. Yali Zhang & Yihan Wang & Xiaoshu Hou, 2019. "Carbon Mitigation for Industrial Sectors in the Jing-Jin-Ji Urban Agglomeration, China," Sustainability, MDPI, vol. 11(22), pages 1-13, November.
    5. Yongjun Li & Wenhui Hou & Weiwei Zhu & Feng Li & Liang Liang, 2021. "Provincial carbon emission performance analysis in China based on a Malmquist data envelopment analysis approach with fixed-sum undesirable outputs," Annals of Operations Research, Springer, vol. 304(1), pages 233-261, September.
    6. Yu, Anyu & Lee, Andy & Chen, Yao, 2021. "Carbon allocation targeting with abatement capability: A firm-level study," International Journal of Production Economics, Elsevier, vol. 235(C).
    7. Sun, J. & Wen, W. & Wang, M. & Zhou, P., 2022. "Optimizing the provincial target allocation scheme of renewable portfolio standards in China," Energy, Elsevier, vol. 250(C).
    8. Wen-Chi Yang & Wen-Min Lu, 2023. "Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    9. Chu, Junfei & Hou, Tianteng & Li, Feng & Yuan, Zhe, 2024. "Dynamic bargaining game DEA carbon emissions abatement allocation and the Nash equilibrium," Energy Economics, Elsevier, vol. 134(C).
    10. Zhou, P. & Wang, M., 2016. "Carbon dioxide emissions allocation: A review," Ecological Economics, Elsevier, vol. 125(C), pages 47-59.
    11. Baochen Yang & Chuanze Liu & Yunpeng Su & Xin Jing, 2017. "The Allocation of Carbon Intensity Reduction Target by 2020 among Industrial Sectors in China," Sustainability, MDPI, vol. 9(1), pages 1-19, January.
    12. Zhu, Qingyuan & Li, Xingchen & Li, Feng & Wu, Jie & Zhou, Dequn, 2020. "Energy and environmental efficiency of China's transportation sectors under the constraints of energy consumption and environmental pollutions," Energy Economics, Elsevier, vol. 89(C).
    13. Qianting Zhu & Wenwu Tang, 2017. "Regional-Level Carbon Allocation in China Based on Sectoral Emission Patterns under the Peak Commitment," Sustainability, MDPI, vol. 9(4), pages 1-18, April.
    14. Yang, Mian & Hou, Yaru & Fang, Chao & Duan, Hongbo, 2020. "Constructing energy-consuming right trading system for China's manufacturing industry in 2025," Energy Policy, Elsevier, vol. 144(C).
    15. Anyu Yu & Hao Zhang & Hu-chen Liu & Yu Shi & Weilong Bi, 2024. "Dynamic centralized resource allocation approach with contextual impacts: analyzing Chinese carbon allocation plans," Annals of Operations Research, Springer, vol. 341(1), pages 451-483, October.
    16. Shaofu Du & Jun Qian & Tianzhuo Liu & Li Hu, 2020. "Emission allowance allocation mechanism design: a low-carbon operations perspective," Annals of Operations Research, Springer, vol. 291(1), pages 247-280, August.
    17. Yang, Mian & Hou, Yaru & Ji, Qiang & Zhang, Dayong, 2020. "Assessment and optimization of provincial CO2 emission reduction scheme in China: An improved ZSG-DEA approach," Energy Economics, Elsevier, vol. 91(C).
    18. Xin Zheng & Shenya Mao & Siqi Lv & Sheng Wang, 2022. "An Optimization Study of Provincial Carbon Emission Allowance Allocation in China Based on an Improved Dynamic Zero-Sum-Gains Slacks-Based-Measure Model," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    19. Zhu, Bangzhu & Jiang, Mingxing & He, Kaijian & Chevallier, Julien & Xie, Rui, 2018. "Allocating CO2 allowances to emitters in China: A multi-objective decision approach," Energy Policy, Elsevier, vol. 121(C), pages 441-451.
    20. Yanbin Li & Zhen Li & Min Wu & Feng Zhang & Gejirifu De, 2018. "Regional-Level Allocation of CO 2 Emission Permits in China: Evidence from the Boltzmann Distribution Method," Sustainability, MDPI, vol. 10(8), pages 1-16, July.

    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:gam:jsusta:v:9:y:2017:i:11:p:2115-:d:119392. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.