IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v74y2018icp733-745.html
   My bibliography  Save this article

Do we really understand the development of China's new energy industry?

Author

Listed:
  • Xu, Bin
  • Lin, Boqiang

Abstract

Countries all over the world have realized that the key to resolving the dilemma between accelerated energy consumption and reduction in carbon dioxide emission is to actively develop new energy industry. Many scholars have conducted in-depth investigation into the main driving forces of the new energy industry. However, they often adhere to an assumption that the effect of the driving forces on new energy industry is constant across areas, ignoring the spatial heterogeneity in economic phenomena. Geographically weighted regression (GWR) model and the use of local sample data to implement parameter estimation can make up for the inadequacies of existing research. Therefore, this paper uses the GWR model to carefully investigate the new energy industry. The results show that the impact of economic growth on the new energy industry in the eastern region is higher than in the central and western regions owing to the differences in economic structure and fixed asset investment. The impact of foreign energy dependence continuously declines from the eastern region to the central and western regions. This is attributable to the differences in natural gas and oil imports. Technological progress has a similar effect on account of the differences in R&D funding and R&D personnel investments. However, the impact of the agriculture industry in the central region is higher than in the eastern and western regions due to the differences in crop acreage and agricultural output. This study improves our understanding of the new energy industry and would help local authorities to formulate targeted policies for promoting the growth of new energy industry.

Suggested Citation

  • Xu, Bin & Lin, Boqiang, 2018. "Do we really understand the development of China's new energy industry?," Energy Economics, Elsevier, vol. 74(C), pages 733-745.
  • Handle: RePEc:eee:eneeco:v:74:y:2018:i:c:p:733-745
    DOI: 10.1016/j.eneco.2018.07.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988318302743
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2018.07.024?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. Ding, Zhihua & Wang, Guangqiang & Liu, Zhenhua & Long, Ruyin, 2017. "Research on differences in the factors influencing the energy-saving behavior of urban and rural residents in China–A case study of Jiangsu Province," Energy Policy, Elsevier, vol. 100(C), pages 252-259.
    2. Inglesi-Lotz, Roula, 2017. "Social rate of return to R&D on various energy technologies: Where should we invest more? A study of G7 countries," Energy Policy, Elsevier, vol. 101(C), pages 521-525.
    3. He, Yong & Liao, Nuo & Zhou, Ya, 2018. "Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN," Energy, Elsevier, vol. 142(C), pages 79-89.
    4. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Cui, Hantao & Li, Xiaojing, 2017. "Optimal dispatch strategy for integrated energy systems with CCHP and wind power," Applied Energy, Elsevier, vol. 192(C), pages 408-419.
    5. Oseni, Musiliu O. & Pollitt, Michael G., 2017. "The prospects for smart energy prices: Observations from 50 years of residential pricing for fixed line telecoms and electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 150-160.
    6. Kiyotada Hayashi & Hiroki Hondo & Yue Moriizumi, 2016. "Preference Construction Processes for Renewable Energies: Assessing the Influence of Sustainability Information and Decision Support Methods," Sustainability, MDPI, vol. 8(11), pages 1-14, November.
    7. Xu, Bin & Lin, Boqiang, 2016. "Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models," Energy, Elsevier, vol. 101(C), pages 161-173.
    8. 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.
    9. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    10. Ramachandra, T.V. & Shruthi, B.V., 2007. "Spatial mapping of renewable energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(7), pages 1460-1480, September.
    11. Shao, Shuai & Tian, Zhihua & Fan, Meiting, 2018. "Do the rich have stronger willingness to pay for environmental protection? New evidence from a survey in China," World Development, Elsevier, vol. 105(C), pages 83-94.
    12. Daniel A. Griffith & Jean H. P. Paelinck, 2018. "Morphisms for Quantitative Spatial Analysis," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-319-72553-6, July-Dece.
    13. Du, Limin & Wei, Chu & Cai, Shenghua, 2012. "Economic development and carbon dioxide emissions in China: Provincial panel data analysis," China Economic Review, Elsevier, vol. 23(2), pages 371-384.
    14. Mu, Yaqian & Cai, Wenjia & Evans, Samuel & Wang, Can & Roland-Holst, David, 2018. "Employment impacts of renewable energy policies in China: A decomposition analysis based on a CGE modeling framework," Applied Energy, Elsevier, vol. 210(C), pages 256-267.
    15. Yang, Zhenbing & Shao, Shuai & Yang, Lili & Liu, Jianghua, 2017. "Differentiated effects of diversified technological sources on energy-saving technological progress: Empirical evidence from China's industrial sectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1379-1388.
    16. Zhang, Tonglin & Lin, Ge, 2016. "On Moran’s I coefficient under heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 83-94.
    17. Hu, Yuan & Peng, Ling & Li, Xiang & Yao, Xiaojing & Lin, Hui & Chi, Tianhe, 2018. "A novel evolution tree for analyzing the global energy consumption structure," Energy, Elsevier, vol. 147(C), pages 1177-1187.
    18. Yang, Zhenbing & Shao, Shuai & Yang, Lili & Miao, Zhuang, 2018. "Improvement pathway of energy consumption structure in China's industrial sector: From the perspective of directed technical change," Energy Economics, Elsevier, vol. 72(C), pages 166-176.
    19. Kumbaroglu, Gürkan & Madlener, Reinhard & Demirel, Mustafa, 2008. "A real options evaluation model for the diffusion prospects of new renewable power generation technologies," Energy Economics, Elsevier, vol. 30(4), pages 1882-1908, July.
    20. Liu, Hengwei & Liang, Dapeng, 2013. "A review of clean energy innovation and technology transfer in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 486-498.
    21. Shang, Yizi & Hei, Pengfei & Lu, Shibao & Shang, Ling & Li, Xiaofei & Wei, Yongping & Jia, Dongdong & Jiang, Dong & Ye, Yuntao & Gong, Jiaguo & Lei, Xiaohui & Hao, Mengmeng & Qiu, Yaqin & Liu, Jiahong, 2018. "China’s energy-water nexus: Assessing water conservation synergies of the total coal consumption cap strategy until 2050," Applied Energy, Elsevier, vol. 210(C), pages 643-660.
    22. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    23. Yuan, Rong & Behrens, Paul & Rodrigues, João F.D., 2018. "The evolution of inter-sectoral linkages in China's energy-related CO2 emissions from 1997 to 2012," Energy Economics, Elsevier, vol. 69(C), pages 404-417.
    24. Yang, Zhenbing & Fan, Meiting & Shao, Shuai & Yang, Lili, 2017. "Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis," Energy Economics, Elsevier, vol. 68(C), pages 271-282.
    25. Guo, Meiyu & Xu, Yuan, 2018. "Coal-to-liquids projects in China under water and carbon constraints," Energy Policy, Elsevier, vol. 117(C), pages 58-65.
    26. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    27. Paramati, Sudharshan Reddy & Sinha, Avik & Dogan, Eyup, 2017. "The significance of renewable energy use for economic output and environmental protection: Evidence from the next 11 developing economies," MPRA Paper 100087, University Library of Munich, Germany.
    28. Zhang, Xiao-Bing & Qin, Ping & Chen, Xiaolan, 2017. "Strategic oil stockpiling for energy security: The case of China and India," Energy Economics, Elsevier, vol. 61(C), pages 253-260.
    29. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Li, Xiaojing, 2017. "Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process," Applied Energy, Elsevier, vol. 194(C), pages 696-704.
    30. Cevallos-Sierra, Jaime & Ramos-Martin, Jesús, 2018. "Spatial assessment of the potential of renewable energy: The case of Ecuador," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1154-1165.
    31. Destek, Mehmet Akif & Aslan, Alper, 2017. "Renewable and non-renewable energy consumption and economic growth in emerging economies: Evidence from bootstrap panel causality," Renewable Energy, Elsevier, vol. 111(C), pages 757-763.
    32. Su, Meirong & Zhang, Mingqi & Lu, Weiwei & Chang, Xin & Chen, Bin & Liu, Gengyuan & Hao, Yan & Zhang, Yan, 2017. "ENA-based evaluation of energy supply security: Comparison between the Chinese crude oil and natural gas supply systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 888-899.
    33. Zhang, Chuanguo & Lin, Yan, 2012. "Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China," Energy Policy, Elsevier, vol. 49(C), pages 488-498.
    34. Atalla, Tarek & Blazquez, Jorge & Hunt, Lester C. & Manzano, Baltasar, 2017. "Prices versus policy: An analysis of the drivers of the primary fossil fuel mix," Energy Policy, Elsevier, vol. 106(C), pages 536-546.
    35. Zhao, Xu & Luo, Dongkun, 2017. "Driving force of rising renewable energy in China: Environment, regulation and employment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 48-56.
    36. Binz, Christian & Gosens, Jorrit & Hansen, Teis & Hansen, Ulrich Elmer, 2017. "Toward Technology-Sensitive Catching-Up Policies: Insights from Renewable Energy in China," World Development, Elsevier, vol. 96(C), pages 418-437.
    37. Amri, Fethi, 2017. "Intercourse across economic growth, trade and renewable energy consumption in developing and developed countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 527-534.
    38. Wu, Ya & Zhu, Qianwen & Zhu, Bangzhu, 2018. "Comparisons of decoupling trends of global economic growth and energy consumption between developed and developing countries," Energy Policy, Elsevier, vol. 116(C), pages 30-38.
    39. Zhang, Sufang & Andrews-Speed, Philip & Li, Sitao, 2018. "To what extent will China's ongoing electricity market reforms assist the integration of renewable energy?," Energy Policy, Elsevier, vol. 114(C), pages 165-172.
    40. Kahia, Montassar & Aïssa, Mohamed Safouane Ben & Lanouar, Charfeddine, 2017. "Renewable and non-renewable energy use - economic growth nexus: The case of MENA Net Oil Importing Countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 127-140.
    41. Robert H. Rasche & John A. Tatom, 1977. "Energy resources and potential GNP," Review, Federal Reserve Bank of St. Louis, vol. 59(Jun), pages 10-24.
    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. Li, Xiao-Lin & Li, Jingya & Wang, Jia & Si, Deng-Kui, 2021. "Trade policy uncertainty, political connection and government subsidy: Evidence from Chinese energy firms," Energy Economics, Elsevier, vol. 99(C).
    2. Sun, Chuanwang & Zhan, Yanhong & Du, Gang, 2020. "Can value-added tax incentives of new energy industry increase firm's profitability? Evidence from financial data of China's listed companies," Energy Economics, Elsevier, vol. 86(C).
    3. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    4. Jia, Zhijie & Lin, Boqiang, 2020. "Rethinking the choice of carbon tax and carbon trading in China," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    5. Chenghong Shang & Qishen Chen & Kun Wang & Yanfei Zhang & Guodong Zheng & Dehui Zhang & Jiayun Xing & Tao Long & Xin Ren & Kun Kang & Yu Zhao, 2024. "Research on Spatiotemporal Heterogeneity of the Impact of Earthquakes on Global Copper Ore Supply Based on Geographically Weighted Regression," Sustainability, MDPI, vol. 16(4), pages 1-18, February.
    6. Daoyuan Chen & Guoen Wang & Ziwei Yuan & Ershen Zhang, 2023. "Study on the Spatial Pattern and Influencing Factors of China’s New Energy Vehicle Industry—Based on Data of Relevant Listed Companies from 2008–2021," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    7. Cheng, Yuanyuan & Yao, Xin, 2021. "Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Si, Deng-Kui & Li, Xiao-Lin & Huang, Shoujun, 2021. "Financial deregulation and operational risks of energy enterprise: The shock of liberalization of bank lending rate in China," Energy Economics, Elsevier, vol. 93(C).
    9. Wu, Yilin & Huang, Shilei, 2022. "The effects of digital finance and financial constraint on financial performance: Firm-level evidence from China's new energy enterprises," Energy Economics, Elsevier, vol. 112(C).
    10. Apergis, Nicholas & Carmona-González, Nieves & Gil-Alana, Luis Alberiko, 2020. "Persistence in silver prices and the influence of solar energy," Resources Policy, Elsevier, vol. 69(C).
    11. Yangjun Ren & Xin Zhang & Hui Chen, 2022. "The Impact of New Energy Enterprises’ Digital Transformation on Their Total Factor Productivity: Empirical Evidence from China," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    12. Lin, Boqiang & Wu, Nan, 2023. "Climate risk disclosure and stock price crash risk: The case of China," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 21-34.
    13. 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).
    14. 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).
    15. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    16. Wang, Kai-Hua & Su, Chi-Wei & Lobonţ, Oana-Ramona & Moldovan, Nicoleta-Claudia, 2020. "Chinese renewable energy industries’ boom and recession: Evidence from bubble detection procedure," Energy Policy, Elsevier, vol. 138(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. 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.
    2. Xu, Bin & Lin, Boqiang, 2018. "Assessing the development of China's new energy industry," Energy Economics, Elsevier, vol. 70(C), pages 116-131.
    3. 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).
    4. Marques, António Cardoso & Fuinhas, José Alberto & Neves, Sónia Almeida, 2018. "Ordinary and Special Regimes of electricity generation in Spain: How they interact with economic activity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1226-1240.
    5. Akan, Taner & Gündüz, Halil İbrahim & Emirmahmutoğlu, Furkan & Işık, Ali Haydar, 2023. "Disaggregating renewable energy-growth nexus: W-ARDL and W-Toda-Yamamoto approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    6. Adekoya, Oluwasegun B. & Yaya, OlaOluwa S. & Oliyide, Johnson A. & Posu, Sunday M.A., 2022. "Growth and growth disparities in Africa: Are differences in renewable energy use, technological advancement, and institutional reforms responsible?," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 265-277.
    7. Rafiq, Shuddhasattwa & Nielsen, Ingrid & Smyth, Russell, 2017. "Effect of internal migration on the environment in China," Energy Economics, Elsevier, vol. 64(C), pages 31-44.
    8. Tuna, Gülfen & Tuna, Vedat Ender, 2019. "The asymmetric causal relationship between renewable and NON-RENEWABLE energy consumption and economic growth in the ASEAN-5 countries," Resources Policy, Elsevier, vol. 62(C), pages 114-124.
    9. Lijing Zhang & Shuke Fu & Jiali Tian & Jiachao Peng, 2022. "A Review of Energy Industry Chain and Energy Supply Chain," Energies, MDPI, vol. 15(23), pages 1-21, December.
    10. Dincer, Hasan & Yuksel, Serhat, 2019. "Balanced scorecard-based analysis of investment decisions for the renewable energy alternatives: A comparative analysis based on the hybrid fuzzy decision-making approach," Energy, Elsevier, vol. 175(C), pages 1259-1270.
    11. Yang, Zhenbing & Shao, Shuai & Fan, Meiting & Yang, Lili, 2021. "Wage distortion and green technological progress: A directed technological progress perspective," Ecological Economics, Elsevier, vol. 181(C).
    12. Xu, Bin & Lin, Boqiang, 2019. "Can expanding natural gas consumption reduce China's CO2 emissions?," Energy Economics, Elsevier, vol. 81(C), pages 393-407.
    13. David Guan & Ubaldo Comite & Muhammad Safdar Sial & Asma Salman & Boyao Zhang & Stefan B. Gunnlaugsson & Urszula Mentel & Grzegorz Mentel, 2021. "The Impact of Renewable Energy Sources on Financial Development, and Economic Growth: The Empirical Evidence from an Emerging Economy," Energies, MDPI, vol. 14(23), pages 1-13, December.
    14. Wang, Ying & Zhang, Dayong & Ji, Qiang & Shi, Xunpeng, 2020. "Regional renewable energy development in China: A multidimensional assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    15. Gyimah, Justice & Yao, Xilong & Tachega, Mark Awe & Sam Hayford, Isaac & Opoku-Mensah, Evans, 2022. "Renewable energy consumption and economic growth: New evidence from Ghana," Energy, Elsevier, vol. 248(C).
    16. Aunedi, Marko & Pantaleo, Antonio Marco & Kuriyan, Kamal & Strbac, Goran & Shah, Nilay, 2020. "Modelling of national and local interactions between heat and electricity networks in low-carbon energy systems," Applied Energy, Elsevier, vol. 276(C).
    17. Sun, Chuanwang & Zhan, Yanhong & Du, Gang, 2020. "Can value-added tax incentives of new energy industry increase firm's profitability? Evidence from financial data of China's listed companies," Energy Economics, Elsevier, vol. 86(C).
    18. Akram, Rabia & Chen, Fuzhong & Khalid, Fahad & Huang, Guanhua & Irfan, Muhammad, 2021. "Heterogeneous effects of energy efficiency and renewable energy on economic growth of BRICS countries: A fixed effect panel quantile regression analysis," Energy, Elsevier, vol. 215(PB).
    19. Jun Bai & Shixiang Li & Nan Wang & Jianru Shi & Xianmin Li, 2020. "Spatial Spillover Effect of New Energy Development on Economic Growth in Developing Areas of China—An Empirical Test Based on the Spatial Dubin Model," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    20. Pambour, Kwabena Addo & Cakir Erdener, Burcin & Bolado-Lavin, Ricardo & Dijkema, Gerard P.J., 2017. "SAInt – A novel quasi-dynamic model for assessing security of supply in coupled gas and electricity transmission networks," Applied Energy, Elsevier, vol. 203(C), pages 829-857.

    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:74:y:2018:i:c:p:733-745. 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.