IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v218y2021ics0360544220325500.html
   My bibliography  Save this article

Assessing the impact of energy-saving R&D on China’s energy consumption: Evidence from dynamic spatial panel model

Author

Listed:
  • Huang, Junbing
  • Chen, Xiang
  • Cai, Xiaochen
  • Zou, Hong

Abstract

Increasing energy demand has become a cause of significant concern in China. In previous literature, research and development (R&D) activities have been identified as an important factor in determining energy demand. This study narrowed the R&D down to its energy technological level (i.e., energy-saving R&D). In addition, energy-saving R&D can be differentiated according to the players and their purposes, and the existing literature has basically neglected the temporal and spatial characteristics of energy demand. As a result, this empirical study used the spatial dynamic panel model to analyse the role of energy-saving R&D activities in China’s energy demand by classifying them according to those who undertake them and the purposes of the research. The empirical evidence based on China’s provincial dataset over the period 2000–2016 implies that energy-saving R&D has not played a positive role in influencing energy demand. In addition, both the direct and indirect effects are insignificant. However, the energy-saving R&D conducted by enterprises for the purpose of pursuing utility exerts a positive effect on reducing energy demand through both direct and indirect channels. Based on the empirical evidence, some insightful policy implications for China to effectively control energy demand are presented.

Suggested Citation

  • Huang, Junbing & Chen, Xiang & Cai, Xiaochen & Zou, Hong, 2021. "Assessing the impact of energy-saving R&D on China’s energy consumption: Evidence from dynamic spatial panel model," Energy, Elsevier, vol. 218(C).
  • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220325500
    DOI: 10.1016/j.energy.2020.119443
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220325500
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.119443?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. Paul Lanoie & Jérémy Laurent‐Lucchetti & Nick Johnstone & Stefan Ambec, 2011. "Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 20(3), pages 803-842, September.
    2. Beaudreau, Bernard C., 2005. "Engineering and economic growth," Structural Change and Economic Dynamics, Elsevier, vol. 16(2), pages 211-220, June.
    3. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    4. Barker, Terry & Ekins, Paul & Foxon, Tim, 2007. "The macro-economic rebound effect and the UK economy," Energy Policy, Elsevier, vol. 35(10), pages 4935-4946, October.
    5. Popp, David C., 2001. "The effect of new technology on energy consumption," Resource and Energy Economics, Elsevier, vol. 23(3), pages 215-239, July.
    6. Yang, Yuan & Cai, Wenjia & Wang, Can, 2014. "Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces," Applied Energy, Elsevier, vol. 131(C), pages 117-127.
    7. Yu, Biying & Zhang, Junyi & Fujiwara, Akimasa, 2013. "Evaluating the direct and indirect rebound effects in household energy consumption behavior: A case study of Beijing," Energy Policy, Elsevier, vol. 57(C), pages 441-453.
    8. Sagar, A. D. & Holdren, J. P., 2002. "Assessing the global energy innovation system: some key issues," Energy Policy, Elsevier, vol. 30(6), pages 465-469, May.
    9. Hang, Leiming & Tu, Meizeng, 2007. "The impacts of energy prices on energy intensity: Evidence from China," Energy Policy, Elsevier, vol. 35(5), pages 2978-2988, May.
    10. Jiang, Lei & Folmer, Henk & Ji, Minhe, 2014. "The drivers of energy intensity in China: A spatial panel data approach," China Economic Review, Elsevier, vol. 31(C), pages 351-360.
    11. Soytas, Ugur & Sari, Ramazan, 2006. "Energy consumption and income in G-7 countries," Journal of Policy Modeling, Elsevier, vol. 28(7), pages 739-750, October.
    12. Dong, Kangyin & Sun, Renjin & Hochman, Gal & Li, Hui, 2018. "Energy intensity and energy conservation potential in China: A regional comparison perspective," Energy, Elsevier, vol. 155(C), pages 782-795.
    13. Li, Yi & Sun, Linyan & Feng, Taiwen & Zhu, Chunyan, 2013. "How to reduce energy intensity in China: A regional comparison perspective," Energy Policy, Elsevier, vol. 61(C), pages 513-522.
    14. Liu, Hong & Wang, Chang & Tian, Meiyu & Wen, Fenghua, 2019. "Analysis of regional difference decomposition of changes in energy consumption in China during 1995–2015," Energy, Elsevier, vol. 171(C), pages 1139-1149.
    15. Albino, Vito & Ardito, Lorenzo & Dangelico, Rosa Maria & Messeni Petruzzelli, Antonio, 2014. "Understanding the development trends of low-carbon energy technologies: A patent analysis," Applied Energy, Elsevier, vol. 135(C), pages 836-854.
    16. Wang, Yanqiu & Zhu, Zhiwei & Zhu, Zhaoge & Liu, Zhenbin, 2019. "Analysis of China's energy consumption changing using the Mean Rate of Change Index and the logarithmic mean divisia index," Energy, Elsevier, vol. 167(C), pages 275-282.
    17. Li, Ke & Lin, Boqiang, 2016. "Impact of energy technology patents in China: Evidence from a panel cointegration and error correction model," Energy Policy, Elsevier, vol. 89(C), pages 214-223.
    18. Hualin Xie & Guiying Liu & Qu Liu & Peng Wang, 2014. "Analysis of Spatial Disparities and Driving Factors of Energy Consumption Change in China Based on Spatial Statistics," Sustainability, MDPI, vol. 6(4), pages 1-17, April.
    19. Bentzen, Jan, 2004. "Estimating the rebound effect in US manufacturing energy consumption," Energy Economics, Elsevier, vol. 26(1), pages 123-134, January.
    20. 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.
    21. Gilio, Leandro & Azanha Ferraz Dias de Moraes, Márcia, 2016. "Sugarcane industry's socioeconomic impact in São Paulo, Brazil: A spatial dynamic panel approach," Energy Economics, Elsevier, vol. 58(C), pages 27-37.
    22. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    23. Ajanovic, Amela & Haas, Reinhard, 2012. "The role of efficiency improvements vs. price effects for modeling passenger car transport demand and energy demand—Lessons from European countries," Energy Policy, Elsevier, vol. 41(C), pages 36-46.
    24. Adams, F. Gerard & Shachmurove, Yochanan, 2008. "Modeling and forecasting energy consumption in China: Implications for Chinese energy demand and imports in 2020," Energy Economics, Elsevier, vol. 30(3), pages 1263-1278, May.
    25. Berndt, Ernst R & Wood, David O, 1975. "Technology, Prices, and the Derived Demand for Energy," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 259-268, August.
    26. Borozan, Djula, 2018. "Regional-level household energy consumption determinants: The european perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 347-355.
    27. Huang, Junbing & Du, Dan & Hao, Yu, 2017. "The driving forces of the change in China's energy intensity: An empirical research using DEA-Malmquist and spatial panel estimations," Economic Modelling, Elsevier, vol. 65(C), pages 41-50.
    28. Feng, Taiwen & Sun, Linyan & Zhang, Ying, 2009. "The relationship between energy consumption structure, economic structure and energy intensity in China," Energy Policy, Elsevier, vol. 37(12), pages 5475-5483, December.
    29. Zhang, Yue-Jun & Peng, Hua-Rong & Su, Bin, 2017. "Energy rebound effect in China's Industry: An aggregate and disaggregate analysis," Energy Economics, Elsevier, vol. 61(C), pages 199-208.
    30. Wong, Chan-Yuan & Fatimah Mohamad, Zeeda & Keng, Zi-Xiang & Ariff Azizan, Suzana, 2014. "Examining the patterns of innovation in low carbon energy science and technology: Publications and patents of Asian emerging economies," Energy Policy, Elsevier, vol. 73(C), pages 789-802.
    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. Xiumei Sun & Haotian Zhang & Xueyang Wang & Zhongkui Qiao & Jinsong Li, 2022. "Towards Sustainable Development: A Study of Cross-Regional Collaborative Carbon Emission Reduction in China," Sustainability, MDPI, vol. 14(15), pages 1-21, August.
    2. Chang, Kai & Long, Yu & Yang, Jiahui & Zhang, Huijia & Xue, Chenqi & Liu, Jianing, 2022. "Effects of subsidy and tax rebate policies on green firm research and development efficiency in China," Energy, Elsevier, vol. 258(C).
    3. Liu, Xiaorui & Guo, Wen & Feng, Qiang & Wang, Peng, 2022. "Spatial correlation, driving factors and dynamic spatial spillover of electricity consumption in China: A perspective on industry heterogeneity," Energy, Elsevier, vol. 257(C).
    4. Wang, You & Gong, Xu, 2022. "Analyzing the difference evolution of provincial energy consumption in China using the functional data analysis method," Energy Economics, Elsevier, vol. 105(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. Wu, Shu & Ding, Song, 2021. "Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector," Energy Economics, Elsevier, vol. 99(C).
    2. Huang, Junbing & Lai, Yali & Wang, Yajun & Hao, Yu, 2020. "Energy-saving research and development activities and energy intensity in China: A regional comparison perspective," Energy, Elsevier, vol. 213(C).
    3. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    4. Hongyun Han & Shu Wu, 2018. "Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China," Sustainability, MDPI, vol. 10(12), pages 1-23, December.
    5. Huang, Junbing & Luan, Bingjiang & He, Wanrui & Chen, Xiang & Li, Mengfan, 2022. "Energy technology of conservation versus substitution and energy intensity in China," Energy, Elsevier, vol. 244(PA).
    6. Huang, Junbing & Lai, Yali & Hu, Hanlei, 2020. "The effect of technological factors and structural change on China's energy intensity: Evidence from dynamic panel models," China Economic Review, Elsevier, vol. 64(C).
    7. Wei Li & Tao Zhao & Yanan Wang & Fang Guo, 2017. "Investigating the learning effects of technological advancement on CO2 emissions: a regional analysis in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(2), pages 1211-1227, September.
    8. Huang, Junbing & Du, Dan & Tao, Qizhi, 2017. "An analysis of technological factors and energy intensity in China," Energy Policy, Elsevier, vol. 109(C), pages 1-9.
    9. Hu, Changshuai & Du, Dan & Huang, Junbing, 2023. "The driving effect of energy demand evolution: From the perspective of heterogeneity in technology," Energy, Elsevier, vol. 275(C).
    10. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    11. Huang, Junbing & Xiang, Shiqi & Wu, Panling & Chen, Xiang, 2022. "How to control China's energy consumption through technological progress: A spatial heterogeneous investigation," Energy, Elsevier, vol. 238(PC).
    12. Huang, Junbing & Du, Dan & Hao, Yu, 2017. "The driving forces of the change in China's energy intensity: An empirical research using DEA-Malmquist and spatial panel estimations," Economic Modelling, Elsevier, vol. 65(C), pages 41-50.
    13. Ouyang, Jinlong & Long, Enshen & Hokao, Kazunori, 2010. "Rebound effect in Chinese household energy efficiency and solution for mitigating it," Energy, Elsevier, vol. 35(12), pages 5269-5276.
    14. Hong, Junjie & Shi, Fangyuan & Zheng, Yuhan, 2023. "Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    15. Yan, Junna & Li, Yingzhu & Su, Bin & Ng, Tsan Sheng, 2022. "Contributors and drivers of Chinese energy use and intensity from regional and demand perspectives, 2012-2015-2017," Energy Economics, Elsevier, vol. 115(C).
    16. Shahateet, Mohammed & Bdour, Jaber, 2010. "Consumption of Electricity and Oil in Jordan: A non-parametric analysis using B-splines," MPRA Paper 57352, University Library of Munich, Germany, revised 2010.
    17. Yanli Ji & Jie Xue & Zitian Fu, 2022. "Sustainable Development of Economic Growth, Energy-Intensive Industries and Energy Consumption: Empirical Evidence from China’s Provinces," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    18. Dong, Kangyin & Sun, Renjin & Hochman, Gal & Li, Hui, 2018. "Energy intensity and energy conservation potential in China: A regional comparison perspective," Energy, Elsevier, vol. 155(C), pages 782-795.
    19. Elliott, Robert J.R. & Sun, Puyang & Zhu, Tong, 2017. "The direct and indirect effect of urbanization on energy intensity: A province-level study for China," Energy, Elsevier, vol. 123(C), pages 677-692.
    20. Lee, Hwarang & Kang, Sung Won & Koo, Yoonmo, 2020. "A hybrid energy system model to evaluate the impact of climate policy on the manufacturing sector: Adoption of energy-efficient technologies and rebound effects," Energy, Elsevier, vol. 212(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:energy:v:218:y:2021:i:c:s0360544220325500. 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.