IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v107y2022ics0140988322000184.html
   My bibliography  Save this article

Exploring the spatial distribution of distributed energy in China

Author

Listed:
  • Xu, Bin
  • Lin, Boqiang

Abstract

Expanding distributed energy supply can not only make up for the energy shortage, but also help reduce carbon dioxide emissions. Existing studies often ignore the differences in the spatial distribution of distributed energy. To fill this gap, this article uses the geographically weighted regression model to investigate China's distributed energy based on the 2003–2019 panel data. Empirical results display that the impact of technological progress on distributed energy varies across region, because technology investment varies from region to region. Energy infrastructure investment has a greater influence on distributed energy in the eastern region, since it invests more infrastructure construction funds. The energy consumption structure has a greater pulling effect on distributed energy in the central region, because this region has more coal resources. Foreign oil dependence has the greatest effect on distributed energy in the eastern region, since this region imports more oil. Similarly, urbanization has the greatest impact on distributed energy in the eastern region, because this region consumes more natural gas. Therefore, government managers should consider spatial heterogeneity when formulating distributed energy policies.

Suggested Citation

  • Xu, Bin & Lin, Boqiang, 2022. "Exploring the spatial distribution of distributed energy in China," Energy Economics, Elsevier, vol. 107(C).
  • Handle: RePEc:eee:eneeco:v:107:y:2022:i:c:s0140988322000184
    DOI: 10.1016/j.eneco.2022.105828
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988322000184
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2022.105828?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. Sameti, Mohammad & Haghighat, Fariborz, 2018. "Integration of distributed energy storage into net-zero energy district systems: Optimum design and operation," Energy, Elsevier, vol. 153(C), pages 575-591.
    2. Tezer, Tuba & Yaman, Ramazan & Yaman, Gülşen, 2017. "Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 840-853.
    3. Jaganath Behera & Alok Kumar Mishra, 2020. "Renewable and non-renewable energy consumption and economic growth in G7 countries: evidence from panel autoregressive distributed lag (P-ARDL) model," International Economics and Economic Policy, Springer, vol. 17(1), pages 241-258, February.
    4. Ranjan Aneja & Umer J. Banday & Tanzeem Hasnat & Mustafa Koçoglu, 2017. "Renewable and Non-renewable Energy Consumption and Economic Growth: Empirical Evidence from Panel Error Correction Model," Jindal Journal of Business Research, , vol. 6(1), pages 76-85, June.
    5. Mak, Davye & Choeum, Daranith & Choi, Dae-Hyun, 2020. "Sensitivity analysis of volt-VAR optimization to data changes in distribution networks with distributed energy resources," Applied Energy, Elsevier, vol. 261(C).
    6. Yuan, Meng & Zhang, Haoran & Wang, Bohong & Huang, Liqiao & Fang, Kai & Liang, Yongtu, 2020. "Downstream oil supply security in China: Policy implications from quantifying the impact of oil import disruption," Energy Policy, Elsevier, vol. 136(C).
    7. Alam, Md. Mahmudul & Murad, Md. Wahid, 2020. "The impacts of economic growth, trade openness and technological progress on renewable energy use in organization for economic co-operation and development countries," Renewable Energy, Elsevier, vol. 145(C), pages 382-390.
    8. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    9. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    10. Xu, Bin & Chen, Jianbao, 2021. "How to achieve a low-carbon transition in the heavy industry? A nonlinear perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    11. Liu, Ying & Lin, Boqiang & Xu, Bin, 2021. "Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China," Energy, Elsevier, vol. 227(C).
    12. Xu, Bin & Luo, Yuemei & Xu, Renjing & Chen, Jianbao, 2021. "Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model," Energy, Elsevier, vol. 236(C).
    13. Sawle, Yashwant & Gupta, S.C. & Bohre, Aashish Kumar, 2018. "Review of hybrid renewable energy systems with comparative analysis of off-grid hybrid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2217-2235.
    14. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    15. Pagliara, Francesca & Mauriello, Filomena, 2020. "Modelling the impact of High Speed Rail on tourists with Geographically Weighted Poisson Regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 780-790.
    16. Xu, Bin & Lin, Boqiang, 2017. "Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model," Energy Policy, Elsevier, vol. 104(C), pages 404-414.
    17. Luqman, Muhammad & Ahmad, Najid & Bakhsh, Khuda, 2019. "Nuclear energy, renewable energy and economic growth in Pakistan: Evidence from non-linear autoregressive distributed lag model," Renewable Energy, Elsevier, vol. 139(C), pages 1299-1309.
    18. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    19. Xu, Bin & Lin, Boqiang, 2021. "Investigating spatial variability of CO2 emissions in heavy industry: Evidence from a geographically weighted regression model," Energy Policy, Elsevier, vol. 149(C).
    20. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    21. Gilani, Mohammad Amin & Kazemi, Ahad & Ghasemi, Mostafa, 2020. "Distribution system resilience enhancement by microgrid formation considering distributed energy resources," Energy, Elsevier, vol. 191(C).
    22. Chen, Yuan & Lin Lawell, C.-Y. Cynthia & Wang, Yunshi, 2020. "The Chinese automobile industry and government policy," Research in Transportation Economics, Elsevier, vol. 84(C).
    23. Inês, Campos & Guilherme, Pontes Luz & Esther, Marín-González & Swantje, Gährs & Stephen, Hall & Lars, Holstenkamp, 2020. "Regulatory challenges and opportunities for collective renewable energy prosumers in the EU," Energy Policy, Elsevier, vol. 138(C).
    24. Wang, Bing & Wang, Qian & Wei, Yi-Ming & Li, Zhi-Ping, 2018. "Role of renewable energy in China’s energy security and climate change mitigation: An index decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 187-194.
    25. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    26. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
    27. Shahbaz, Muhammad & Raghutla, Chandrashekar & Song, Malin & Zameer, Hashim & Jiao, Zhilun, 2020. "Public-private partnerships investment in energy as new determinant of CO2 emissions: The role of technological innovations in China," Energy Economics, Elsevier, vol. 86(C).
    28. Sueyoshi, Toshiyuki & Qu, Jingjing & Li, Aijun & Liu, Xiaohong, 2021. "A new approach for evaluating technology inequality and diffusion barriers: The concept of efficiency Gini coefficient and its application in Chinese provinces," Energy, Elsevier, vol. 235(C).
    29. Ahsan Anwar & Avik Sinha & Arshian Sharif & Muhammad Siddique & Shoaib Irshad & Waseem Anwar & Summaira Malik, 2022. "The nexus between urbanization, renewable energy consumption, financial development, and CO2 emissions: evidence from selected Asian countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6556-6576, May.
    30. Wang, Xuan & Jin, Ming & Feng, Wei & Shu, Gequn & Tian, Hua & Liang, Youcai, 2018. "Cascade energy optimization for waste heat recovery in distributed energy systems," Applied Energy, Elsevier, vol. 230(C), pages 679-695.
    31. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    32. Dietzenbacher, Erik & Kulionis, Viktoras & Capurro, Filippo, 2020. "Measuring the effects of energy transition: A structural decomposition analysis of the change in renewable energy use between 2000 and 2014," Applied Energy, Elsevier, vol. 258(C).
    33. Yu, Chin-Hsien & Huang, Shih-Kai & Qin, Ping & Chen, Xiaolan, 2018. "Local residents' risk perceptions in response to shale gas exploitation: Evidence from China," Energy Policy, Elsevier, vol. 113(C), pages 123-134.
    34. Sueyoshi, Toshiyuki & Li, Aijun & Liu, Xiaohong, 2019. "Exploring sources of China's CO2 emission: Decomposition analysis under different technology changes," European Journal of Operational Research, Elsevier, vol. 279(3), pages 984-995.
    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. Bu, Yan & Wang, Erda & Möst, Dominik & Lieberwirth, Martin, 2022. "How population migration affects carbon emissions in China: Factual and counterfactual scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Shimei Weng & Jianbao Chen, 2023. "How Does Industrial Upgrading Affect Carbon Productivity in China’s Service Industry?," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    3. Du, Gang & Li, Wendi, 2022. "Does innovative city building promote green logistics efficiency? Evidence from a quasi-natural experiment with 285 cities," Energy Economics, Elsevier, vol. 114(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. Xu, Bin & Luo, Yuemei & Xu, Renjing & Chen, Jianbao, 2021. "Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model," Energy, Elsevier, vol. 236(C).
    2. Lin, Boqiang & Xu, Bin, 2021. "A non-parametric analysis of the driving factors of China's carbon prices," Energy Economics, Elsevier, vol. 104(C).
    3. Xu, Renjing & Xu, Bin, 2022. "Exploring the effective way of reducing carbon intensity in the heavy industry using a semiparametric econometric approach," Energy, Elsevier, vol. 243(C).
    4. Liu, Haiyue & Zhang, Ruchuan & Zhou, Li & Li, Aijun, 2023. "Evaluating the financial performance of companies from the perspective of fund procurement and application: New strategy cross efficiency network data envelopment analysis models," Energy, Elsevier, vol. 269(C).
    5. Rohács, Dániel, 2023. "Analysis and optimization of potential energy sources for residential building application," Energy, Elsevier, vol. 275(C).
    6. Jingjing Qu & Aijun Li & Morié Guy-Roland N’Drin, 2023. "Measuring technology inequality across African countries using the concept of efficiency Gini coefficient," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4107-4138, May.
    7. Wang, Xianzhu & Huang, He & Hong, Jingke & Ni, Danfei & He, Rongxiao, 2020. "A spatiotemporal investigation of energy-driven factors in China: A region-based structural decomposition analysis," Energy, Elsevier, vol. 207(C).
    8. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    9. Abbasi, Kashif Raza & Hussain, Khadim & Haddad, Akram Masoud & Salman, Asma & Ozturk, Ilhan, 2022. "The role of Financial Development and Technological Innovation towards Sustainable Development in Pakistan: Fresh insights from consumption and territory-based emissions," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    10. Bin Xu, 2022. "How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
    11. Li, Raymond & Lee, Hazel, 2022. "The role of energy prices and economic growth in renewable energy capacity expansion – Evidence from OECD Europe," Renewable Energy, Elsevier, vol. 189(C), pages 435-443.
    12. Wang, Kai-Hua & Su, Chi-Wei & Xiao, Yidong & Liu, Lu, 2022. "Is the oil price a barometer of China's automobile market? From a wavelet-based quantile-on-quantile regression perspective," Energy, Elsevier, vol. 240(C).
    13. Chen, Shanshan & Zhang, Ruchuan & Li, Peiwen & Li, Aijun, 2023. "How to improve the performance of China's energy-transport-economy-environment system: An analysis based on new strategy parallel-series input-output data envelopment analysis models," Energy, Elsevier, vol. 281(C).
    14. Opoku, Eric Evans Osei & Aluko, Olufemi Adewale, 2021. "Heterogeneous effects of industrialization on the environment: Evidence from panel quantile regression," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 174-184.
    15. José Luis Torres-Madroñero & Joham Alvarez-Montoya & Daniel Restrepo-Montoya & Jorge Mario Tamayo-Avendaño & César Nieto-Londoño & Julián Sierra-Pérez, 2020. "Technological and Operational Aspects That Limit Small Wind Turbines Performance," Energies, MDPI, vol. 13(22), pages 1-39, November.
    16. Xu, Bin & Lin, Boqiang, 2017. "Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 325-337.
    17. Fahd A. Alturki & Emad Mahrous Awwad, 2021. "Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems," Energies, MDPI, vol. 14(2), pages 1-20, January.
    18. Lin, Boqiang & Xu, Bin, 2018. "Factors affecting CO2 emissions in China's agriculture sector: A quantile regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 15-27.
    19. Kassouri, Yacouba & Altuntaş, Mehmet & Alola, Andrew Adewale, 2022. "The contributory capacity of natural capital to energy transition in the European Union," Renewable Energy, Elsevier, vol. 190(C), pages 617-629.
    20. Muhammad Ramzan & Ummara Razi & Muhammad Umer Quddoos & Tomiwa Sunday Adebayo, 2023. "Do green innovation and financial globalization contribute to the ecological sustainability and energy transition in the United Kingdom? Policy insights from a bootstrap rolling window approach," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(1), pages 393-414, February.

    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:eneeco:v:107:y:2022:i:c:s0140988322000184. 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/eneco .

    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.