IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v143y2018icp812-821.html
   My bibliography  Save this article

How to promote energy efficiency through technological progress in China?

Author

Listed:
  • Li, Ke
  • Lin, Boqiang

Abstract

Technological progress plays a key role in promoting energy efficiency. In order to find the suitable path of technological progress to improve energy efficiency, this study adopts the growth-accounting method to investigate the effects of two types of technological progress, namely, the Hicks-neutral and the capital-embodied technological progress on the changes in energy productivity. Furthermore, dynamic panel data models are applied to investigate the various effects of these two types of technological progress on energy productivities 30 Chinese provinces from 1997 to 2012. The main results are: (1) the Hicks-neutral technological progress directly contributes to energy productivity improvement, and its indirect contribution comes from the optimization of manufacturing structure induced by technological catch-up; (2) the capital-embodied technological progress has a direct contribution to energy productivity improvement, while the indirect contribution is seen through its interaction with the upgrading of the manufacturing structure; (3) the energy-saving performance from the capital-embodied technological progress is poor because of energy rebound effect. These findings suggest that the capital-embodied technological progress is effective for energy-saving in China; which can be made possible only by implementing energy price reforms.

Suggested Citation

  • Li, Ke & Lin, Boqiang, 2018. "How to promote energy efficiency through technological progress in China?," Energy, Elsevier, vol. 143(C), pages 812-821.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:812-821
    DOI: 10.1016/j.energy.2017.11.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217318960
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.11.047?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. Chang, Tzu-Pu & Hu, Jin-Li, 2010. "Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China," Applied Energy, Elsevier, vol. 87(10), pages 3262-3270, October.
    2. Chen, Shiyi & Golley, Jane, 2014. "‘Green’ productivity growth in China's industrial economy," Energy Economics, Elsevier, vol. 44(C), pages 89-98.
    3. Marcucci, Adriana & Turton, Hal, 2015. "Induced technological change in moderate and fragmented climate change mitigation regimes," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 230-242.
    4. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    5. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
    6. Wang, Chunhua, 2013. "Changing energy intensity of economies in the world and its decomposition," Energy Economics, Elsevier, vol. 40(C), pages 637-644.
    7. Zeng, Lin & Xu, Ming & Liang, Sai & Zeng, Siyu & Zhang, Tianzhu, 2014. "Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis," Energy Policy, Elsevier, vol. 67(C), pages 640-647.
    8. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    9. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    10. Li, Ke & Lin, Boqiang, 2017. "Economic growth model, structural transformation, and green productivity in China," Applied Energy, Elsevier, vol. 187(C), pages 489-500.
    11. Shao, Shuai & Huang, Tao & Yang, Lili, 2014. "Using latent variable approach to estimate China׳s economy-wide energy rebound effect over 1954–2010," Energy Policy, Elsevier, vol. 72(C), pages 235-248.
    12. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    13. Lin, Boqiang & Liu, Xia, 2012. "Dilemma between economic development and energy conservation: Energy rebound effect in China," Energy, Elsevier, vol. 45(1), pages 867-873.
    14. Jesus Felipe, 1999. "Total factor productivity growth in East Asia: A critical survey," Journal of Development Studies, Taylor & Francis Journals, vol. 35(4), pages 1-41.
    15. Stephen Bond & Anke Hoeffler, 2001. "GMM Estimation of Empirical Growth Models," Economics Series Working Papers 2001-W21, University of Oxford, Department of Economics.
    16. Safarzynska, Karolina, 2012. "Modeling the rebound effect in two manufacturing industries," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1135-1154.
    17. Wang, Zhaohua & Lu, Milin, 2014. "An empirical study of direct rebound effect for road freight transport in China," Applied Energy, Elsevier, vol. 133(C), pages 274-281.
    18. Liu, Jingru & Sun, Xin & Lu, Bin & Zhang, Yunkun & Sun, Rui, 2016. "The life cycle rebound effect of air-conditioner consumption in China," Applied Energy, Elsevier, vol. 184(C), pages 1026-1032.
    19. Hulten, Charles R, 1992. "Growth Accounting When Technical Change Is Embodied in Capital," American Economic Review, American Economic Association, vol. 82(4), pages 964-980, September.
    20. Adetutu, Morakinyo O., 2014. "Energy efficiency and capital-energy substitutability: Evidence from four OPEC countries," Applied Energy, Elsevier, vol. 119(C), pages 363-370.
    21. Yanrui Wu, 2008. "Productivity, Efficiency and Economic Growth in China," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-22825-2.
    22. Wang, Chunhua, 2007. "Decomposing energy productivity change: A distance function approach," Energy, Elsevier, vol. 32(8), pages 1326-1333.
    23. Greenwood, Jeremy & Hercowitz, Zvi & Krusell, Per, 1997. "Long-Run Implications of Investment-Specific Technological Change," American Economic Review, American Economic Association, vol. 87(3), pages 342-362, June.
    24. Fisher-Vanden, Karen & Jefferson, Gary H. & Jingkui, Ma & Jianyi, Xu, 2006. "Technology development and energy productivity in China," Energy Economics, Elsevier, vol. 28(5-6), pages 690-705, November.
    25. Saunders, Harry D., 2013. "Historical evidence for energy efficiency rebound in 30 US sectors and a toolkit for rebound analysts," Technological Forecasting and Social Change, Elsevier, vol. 80(7), pages 1317-1330.
    26. Subodh Kumar & R. Robert Russell, 2002. "Technological Change, Technological Catch-up, and Capital Deepening: Relative Contributions to Growth and Convergence," American Economic Review, American Economic Association, vol. 92(3), pages 527-548, June.
    27. Daniel J. Henderson & R. Robert Russell, 2005. "Human Capital And Convergence: A Production-Frontier Approach ," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(4), pages 1167-1205, November.
    28. Li, Ke & Lin, Boqiang, 2015. "How does administrative pricing affect energy consumption and CO2 emissions in China?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 952-962.
    29. Li, Ke & Lin, Boqiang, 2015. "Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China," Energy Economics, Elsevier, vol. 48(C), pages 230-241.
    30. 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.
    31. Li, Ke & Lin, Boqiang, 2015. "Heterogeneity in rebound effects: Estimated results and impact of China’s fossil-fuel subsidies," Applied Energy, Elsevier, vol. 149(C), pages 148-160.
    32. Zhou, Xiaoyan & Zhang, Jie & Li, Junpeng, 2013. "Industrial structural transformation and carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 57(C), pages 43-51.
    33. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    34. Charles R. Hulten, 1992. "Growth Accounting When Technical Change is Embodied in Capital," NBER Working Papers 3971, National Bureau of Economic Research, Inc.
    35. Li, Ke & Lin, Boqiang, 2014. "The nonlinear impacts of industrial structure on China's energy intensity," Energy, Elsevier, vol. 69(C), pages 258-265.
    36. Zhao, Xiaoli & Ma, Chunbo & Hong, Dongyue, 2010. "Why did China's energy intensity increase during 1998-2006: Decomposition and policy analysis," Energy Policy, Elsevier, vol. 38(3), pages 1379-1388, March.
    37. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    38. Poumanyvong, Phetkeo & Kaneko, Shinji, 2010. "Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis," Ecological Economics, Elsevier, vol. 70(2), pages 434-444, December.
    Full references (including those not matched with items on IDEAS)

    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. Li, Ke & Lin, Boqiang, 2017. "Economic growth model, structural transformation, and green productivity in China," Applied Energy, Elsevier, vol. 187(C), pages 489-500.
    2. Li, Ke & Zhang, Ning & Liu, Yanchu, 2016. "The energy rebound effects across China’s industrial sectors: An output distance function approach," Applied Energy, Elsevier, vol. 184(C), pages 1165-1175.
    3. Chen, Yufen & Liu, Yanni, 2021. "How biased technological progress sustainably improve the energy efficiency: An empirical research of manufacturing industry in China," Energy, Elsevier, vol. 230(C).
    4. You, Jianmin & Zhang, Wei, 2022. "How heterogeneous technological progress promotes industrial structure upgrading and industrial carbon efficiency? Evidence from China's industries," Energy, Elsevier, vol. 247(C).
    5. Meschi, Elena & Taymaz, Erol & Vivarelli, Marco, 2011. "Trade, technology and skills: Evidence from Turkish microdata," Labour Economics, Elsevier, vol. 18(S1), pages 60-70.
    6. William Hauk & Romain Wacziarg, 2009. "A Monte Carlo study of growth regressions," Journal of Economic Growth, Springer, vol. 14(2), pages 103-147, June.
    7. Li, Ke & Jiang, Zhujun, 2016. "The impacts of removing energy subsidies on economy-wide rebound effects in China: An input-output analysis," Energy Policy, Elsevier, vol. 98(C), pages 62-72.
    8. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    9. Rojas-Vallejos, Jorge & Lastuka, Amy, 2020. "The income inequality and carbon emissions trade-off revisited," Energy Policy, Elsevier, vol. 139(C).
    10. Li, Ke & Lin, Boqiang, 2015. "Heterogeneity in rebound effects: Estimated results and impact of China’s fossil-fuel subsidies," Applied Energy, Elsevier, vol. 149(C), pages 148-160.
    11. Khaled Elmawazini, 2014. "FDI Spillovers, Efficiency Change and Host Country Labor Productivity: Evidence from GCC Countries," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 42(4), pages 399-411, December.
    12. Lee, Angela Y. & Aaker, Jennifer L., 2006. "A Monte Carlo Study of Growth Regressions," Research Papers 1836r1, Stanford University, Graduate School of Business.
    13. James Alm & Asmaa El-Ganainy, 2013. "Value-added taxation and consumption," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 20(1), pages 105-128, February.
    14. Khaled Elmawazini & Elias G. Saleeby & Ahmed Ibn el Farouk & Bashayer AL-Naser, 2018. "Tripartite decomposition of labor productivity growth, FDI and human development: evidence from transition economies," Economic Change and Restructuring, Springer, vol. 51(2), pages 153-171, May.
    15. Zheng, Xinye & Li, Fanghua & Song, Shunfeng & Yu, Yihua, 2013. "Central government's infrastructure investment across Chinese regions: A dynamic spatial panel data approach," China Economic Review, Elsevier, vol. 27(C), pages 264-276.
    16. Bobba, Matteo & Coviello, Decio, 2007. "Weak instruments and weak identification, in estimating the effects of education, on democracy," Economics Letters, Elsevier, vol. 96(3), pages 301-306, September.
    17. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    18. Måns Söderbom & Francis Teal, 2003. "Openness and human capital as sources of productivity growth: An empirical investigation," CSAE Working Paper Series 2003-06, Centre for the Study of African Economies, University of Oxford.
    19. Jeffrey Kouton, 2021. "The impact of renewable energy consumption on inclusive growth: panel data analysis in 44 African countries," Economic Change and Restructuring, Springer, vol. 54(1), pages 145-170, February.
    20. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.

    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:143:y:2018:i:c:p:812-821. 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.