IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v312y2022ics0306261922001921.html
   My bibliography  Save this article

Planning energy economy and eco-environment nexus system under uncertainty: A copula-based stochastic multi-level programming method

Author

Listed:
  • Lv, J.
  • Li, Y.P.
  • Huang, G.H.
  • Ding, Y.K.
  • Li, X.
  • Li, Y.

Abstract

Energy consumption, economic growth, and eco-environment protection of urban agglomeration is full of contradictions and continues to be challenges faced by decision makers. It is desired to develop effective methods to realize coordinated management of energy economy and eco-environment nexus (EEEN) system. In this study, a copula-based stochastic multi-level programming (CSMP) approach is first developed, where the conflicting objectives with multiple hierarchical levels and the uncertainty presented as random variables with different probability distributions can be tackled. CSMP can also reflect the complex interactions among random variables and examine the risk of violating joint-probabilistic constraints. The CSMP is then applied to planning EEEN system of Pearl River Delta (PRD) urban agglomeration (China) during the period of 2021–2035. Six scenarios based on joint and individual risk levels for violating energy consumption and GDP constraints are analyzed. Results reveal that the share of tertiary industry and high-tech industry would attain to 62.4% and 14.9% by 2035, the energy and CO2 intensity would decrease by 45.1% and 56.9%, and ecological land would expand to absorb more CO2, indicating that EEEN system would develop towards energy saving, economy and eco-environment friendly pattern. Results also disclose that decisions at high risk level would lead to high GDP, energy consumption and CO2 emissions, occupy ecological land and reduce CO2 absorption, and reduce the reliability of fulfilling system requirements.

Suggested Citation

  • Lv, J. & Li, Y.P. & Huang, G.H. & Ding, Y.K. & Li, X. & Li, Y., 2022. "Planning energy economy and eco-environment nexus system under uncertainty: A copula-based stochastic multi-level programming method," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001921
    DOI: 10.1016/j.apenergy.2022.118736
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922001921
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118736?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. Zhai, Mengyu & Huang, Guohe & Liu, Lirong & Guo, Zhengquan & Su, Shuai, 2021. "Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province," Energy, Elsevier, vol. 231(C).
    2. de Carvalho, Ariovaldo Lopes & Henggeler Antunes, Carlos & Freire, Fausto & Oliveira Henriques, Carla, 2016. "A multi-objective interactive approach to assess economic-energy-environment trade-offs in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1429-1442.
    3. Jia, Zhijie & Wen, Shiyan & Lin, Boqiang, 2021. "The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China," Applied Energy, Elsevier, vol. 302(C).
    4. Guivarch, Céline & Hallegatte, Stéphane & Crassous, Renaud, 2009. "The resilience of the Indian economy to rising oil prices as a validation test for a global energy-environment-economy CGE model," Energy Policy, Elsevier, vol. 37(11), pages 4259-4266, November.
    5. Lv, J. & Li, Y.P. & Shan, B.G. & Jin, S.W. & Suo, C., 2018. "Planning energy-water nexus system under multiple uncertainties – A case study of Hebei province," Applied Energy, Elsevier, vol. 229(C), pages 389-403.
    6. Zhou, Y. & Li, Y.P. & Huang, G.H., 2015. "Planning sustainable electric-power system with carbon emission abatement through CDM under uncertainty," Applied Energy, Elsevier, vol. 140(C), pages 350-364.
    7. Mohamed, Usama & Zhao, Ying-jie & Yi, Qun & Shi, Li-juan & Wei, Guo-qing & Nimmo, William, 2021. "Evaluation of life cycle energy, economy and CO2 emissions for biomass chemical looping gasification to power generation," Renewable Energy, Elsevier, vol. 176(C), pages 366-387.
    8. Fernandes, Mário Correia & Dias, José Carlos & Nunes, João Pedro Vidal, 2021. "Modeling energy prices under energy transition: A novel stochastic-copula approach," Economic Modelling, Elsevier, vol. 105(C).
    9. Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2020. "A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies," Applied Energy, Elsevier, vol. 261(C).
    10. Herrerias, M.J. & Joyeux, R. & Girardin, E., 2013. "Short- and long-run causality between energy consumption and economic growth: Evidence across regions in China," Applied Energy, Elsevier, vol. 112(C), pages 1483-1492.
    11. Wang, Zheng-Xin & Jv, Yue-Qi, 2021. "A non-linear systematic grey model for forecasting the industrial economy-energy-environment system," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    12. Stein, Julian Alexander Cornelius & Braun, Dieter, 2019. "Stability of a time-homogeneous system of money and antimoney in an agent-based random economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 232-249.
    13. Muhammad Azmi & Akihiro Tokai, 2016. "System dynamic modeling of CO2 emissions and pollutants from passenger cars in Malaysia, 2040," Environment Systems and Decisions, Springer, vol. 36(4), pages 335-350, December.
    14. Ruamsuke, Kawin & Dhakal, Shobhakar & Marpaung, Charles O.P., 2015. "Energy and economic impacts of the global climate change policy on Southeast Asian countries: A general equilibrium analysis," Energy, Elsevier, vol. 81(C), pages 446-461.
    15. Choi, Jun-Ki & Bakshi, Bhavik R. & Hubacek, Klaus & Nader, Jordan, 2016. "A sequential input–output framework to analyze the economic and environmental implications of energy policies: Gas taxes and fuel subsidies," Applied Energy, Elsevier, vol. 184(C), pages 830-839.
    16. Ren, Ming & Lu, Pantao & Liu, Xiaorui & Hossain, M.S. & Fang, Yanru & Hanaoka, Tatsuya & O'Gallachoir, Brian & Glynn, James & Dai, Hancheng, 2021. "Decarbonizing China’s iron and steel industry from the supply and demand sides for carbon neutrality," Applied Energy, Elsevier, vol. 298(C).
    17. Domino, Krzysztof & Błachowicz, Tomasz, 2015. "The use of copula functions for modeling the risk of investment in shares traded on world stock exchanges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 142-151.
    18. Xu, Yue & Tian, Shu & Wang, Qingsong & Yuan, Xueliang & Ma, Qiao & Liu, Mengyue & Xu, Zhaopeng & Liu, Jixiang & Xu, Xiang & Liu, Chengqing, 2021. "Optimization path of energy-economy system from the perspective of minimum industrial structure adjustment," Energy, Elsevier, vol. 237(C).
    19. Huang, Shi-Wei & Chung, Yung-Fu & Wu, Tai-Hsi, 2021. "Analyzing the relationship between energy security performance and decoupling of economic growth from CO2 emissions for OECD countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    20. Kailash Lachhwani, 2021. "Solving the general fully neutrosophic multi-level multiobjective linear programming problems," OPSEARCH, Springer;Operational Research Society of India, vol. 58(4), pages 1192-1216, December.
    21. Hawdon, David & Pearson, Peter, 1995. "Input-output simulations of energy, environment, economy interactions in the UK," Energy Economics, Elsevier, vol. 17(1), pages 73-86, January.
    22. Viacheslav M. Shavshukov & Natalia A. Zhuravleva, 2020. "Global Economy: New Risks and Leadership Problems," IJFS, MDPI, vol. 8(1), pages 1-17, February.
    23. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Yin, S., 2018. "Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China," Applied Energy, Elsevier, vol. 212(C), pages 834-849.
    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. Dong, Qianyu & Zhong, Kaiyi & Liao, Yijia & Xiong, Runli & Wang, Fengbo & Pang, Min, 2023. "Coupling coordination degree of environment, energy, and economic growth in resource-based provinces of China," Resources Policy, Elsevier, vol. 81(C).
    2. Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
    3. Ferahtia, Seydali & Rezk, Hegazy & Olabi, A.G. & Alhumade, Hesham & Bamufleh, Hisham S. & Doranehgard, Mohammad Hossein & Abdelkareem, Mohammad Ali, 2022. "Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications," Applied Energy, Elsevier, vol. 327(C).
    4. Yue, Wencong & Li, Yangqing & Su, Meirong & Chen, Qionghong & Rong, Qiangqiang, 2023. "Carbon emissions accounting and prediction in urban agglomerations from multiple perspectives of production, consumption and income," Applied Energy, Elsevier, vol. 348(C).

    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. Cao, R. & Huang, G.H. & Chen, J.P. & Li, Y.P. & He, C.Y., 2021. "A chance-constrained urban agglomeration energy model for cooperative carbon emission management," Energy, Elsevier, vol. 223(C).
    2. Xu, Ye & Tan, Junyuan & Wang, Xu & Li, Wei & He, Xing & Hu, Xiaoguang & Fan, Yurui, 2022. "Synergetic management of water-energy-food nexus system and GHG emissions under multiple uncertainties: An inexact fractional fuzzy chance constraint programming method," Agricultural Water Management, Elsevier, vol. 262(C).
    3. Shu Mo & Ting Wang, 2022. "Synergistic Effects of International Oil Price Fluctuations and Carbon Tax Policies on the Energy–Economy–Environment System in China," IJERPH, MDPI, vol. 19(21), pages 1-17, October.
    4. Soares, N. & Martins, A.G. & Carvalho, A.L. & Caldeira, C. & Du, C. & Castanheira, É. & Rodrigues, E. & Oliveira, G. & Pereira, G.I. & Bastos, J. & Ferreira, J.P. & Ribeiro, L.A. & Figueiredo, N.C. & , 2018. "The challenging paradigm of interrelated energy systems towards a more sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 171-193.
    5. Wang, G.Y. & Li, Y.P. & Liu, J. & Huang, G.H. & Chen, L.R. & Yang, Y.J. & Gao, P.P., 2022. "A two-phase factorial input-output model for analyzing CO2-emission reduction pathway and strategy from multiple perspectives – A case study of Fujian province," Energy, Elsevier, vol. 248(C).
    6. Wang, P.P. & Li, Y.P. & Huang, G.H. & Wang, S.G., 2022. "A multivariate statistical input–output model for analyzing water-carbon nexus system from multiple perspectives - Jing-Jin-Ji region," Applied Energy, Elsevier, vol. 310(C).
    7. Gong, J.W. & Li, Y.P. & Lv, J. & Huang, G.H. & Suo, C. & Gao, P.P., 2022. "Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty," Applied Energy, Elsevier, vol. 308(C).
    8. C. Oliveira & D. Coelho & C. H. Antunes, 2016. "Coupling input–output analysis with multiobjective linear programming models for the study of economy–energy–environment–social (E3S) trade-offs: a review," Annals of Operations Research, Springer, vol. 247(2), pages 471-502, December.
    9. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    10. Tang, Shengwen & Chen, Jingtao & Sun, Peigui & Li, Yang & Yu, Peng & Chen, E., 2019. "Current and future hydropower development in Southeast Asia countries (Malaysia, Indonesia, Thailand and Myanmar)," Energy Policy, Elsevier, vol. 129(C), pages 239-249.
    11. Yang, Honghua & Ma, Linwei & Li, Zheng, 2023. "Tracing China's steel use from steel flows in the production system to steel footprints in the consumption system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    12. Murthy, N. S. & Panda, Manoj & Parikh, Jyoti, 1997. "Economic development, poverty reduction and carbon emissions in India," Energy Economics, Elsevier, vol. 19(3), pages 327-354, July.
    13. Thorne, Rebecca Jayne & Hovi, Inger Beate & Figenbaum, Erik & Pinchasik, Daniel Ruben & Amundsen, Astrid Helene & Hagman, Rolf, 2021. "Facilitating adoption of electric buses through policy: Learnings from a trial in Norway," Energy Policy, Elsevier, vol. 155(C).
    14. Zachary A. Collier & James H. Lambert & Igor Linkov, 2016. "Data analysis and modeling to support policy decisions in environmental, transportation, and energy systems," Environment Systems and Decisions, Springer, vol. 36(4), pages 329-330, December.
    15. Ruben Bibas & C. Cassen & Renaud Crassous & Céline Guivarch & Meriem Hamdi-Cherif & Jean Charles Hourcade & Florian Leblanc & Aurélie Méjean & Eoin Ó Broin & Julie Rozenberg & Olivier Sassi & Adrien V, 2022. "IMpact Assessment of CLIMate policies with IMACLIM-R 1.1. Model documentation version 1.1," Working Papers hal-03702627, HAL.
    16. Boglioni, Michele & Zambelli, Stefano, 2018. "Specialization patterns and reduction of CO2 emissions. An empirical investigation of environmental preservation and economic efficiency," Energy Economics, Elsevier, vol. 75(C), pages 134-149.
    17. Audoly, Richard & Vogt-Schilb, Adrien & Guivarch, Céline & Pfeiffer, Alexander, 2018. "Pathways toward zero-carbon electricity required for climate stabilization," Applied Energy, Elsevier, vol. 225(C), pages 884-901.
    18. Junbo Wang & Liu Chen & Lu Chen & Xiaohui Zhao & Minxi Wang & Yiyi Ju & Li Xin, 2019. "City-Level Features of Energy Footprints and Carbon Dioxide Emissions in Sichuan Province of China," Energies, MDPI, vol. 12(10), pages 1-14, May.
    19. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    20. Wang, Zhibao & Zhao, Nana & Wei, Wendong & Zhang, Qianwen, 2021. "A differentiated energy Kuznets curve: Evidence from mainland China," Energy, Elsevier, vol. 214(C).

    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:appene:v:312:y:2022:i:c:s0306261922001921. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.