IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v88y2017i2d10.1007_s11069-017-2915-2.html
   My bibliography  Save this article

Investigating the learning effects of technological advancement on CO2 emissions: a regional analysis in China

Author

Listed:
  • Wei Li

    (Tianjin University)

  • Tao Zhao

    (Tianjin University)

  • Yanan Wang

    (Northwest A&F University)

  • Fang Guo

    (Tianjin University)

Abstract

Technological advancement plays a crucial role in CO2 emissions mitigation and has attracted great attention around the world. A multitude of literatures mainly focused on single technological impact on environmental issues at national level, while comprehensive studies concerning technological factors at regional level are rare. This paper employs environmental learning curve model to investigate the learning effects of different technological channels on CO2 emissions at the national and regional levels using panel data of China’s 29 provinces from 1997 to 2014. The technological advancement is disaggregated into indigenous research and development (R&D), foreign technology import and technological revolution. Furthermore, to comprehend the characteristics of various provinces with regard to CO2 emissions and emission efficiency, China’s 29 provinces are divided into four regions according to the features of “CO2 emissions-efficiency”. Empirically results manifest that technical renovation is the paramount driver to mitigate the national CO2 emissions. The CO2 learning abilities of indigenous R&D in high emissions regions are greater than those in the low ones, while boosting the investment of foreign technology import in low emission regions has significantly positive impacts on CO2 emissions, and the technical renovation is effective in abating CO2 emissions in all regions. The findings not only enrich technology innovation theories, but also deserve special attention from policymakers.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:88:y:2017:i:2:d:10.1007_s11069-017-2915-2
    DOI: 10.1007/s11069-017-2915-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-017-2915-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-017-2915-2?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. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    2. Wang, Zhaohua & Yang, Zhongmin & Zhang, Yixiang & Yin, Jianhua, 2012. "Energy technology patents–CO2 emissions nexus: An empirical analysis from China," Energy Policy, Elsevier, vol. 42(C), pages 248-260.
    3. Beck, Nathaniel & Katz, Jonathan N., 1995. "What To Do (and Not to Do) with Time-Series Cross-Section Data," American Political Science Review, Cambridge University Press, vol. 89(3), pages 634-647, September.
    4. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    5. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    6. Zhang, Chuanguo & Zhou, Xiangxue, 2016. "Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 943-951.
    7. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    8. Ang, James B., 2009. "CO2 emissions, research and technology transfer in China," Ecological Economics, Elsevier, vol. 68(10), pages 2658-2665, August.
    9. 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.
    10. Peter Pedroni, 1999. "Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 653-670, November.
    11. Maddala, G S & Wu, Shaowen, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    12. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    13. Daniel Hoechle, 2007. "Robust standard errors for panel regressions with cross-sectional dependence," Stata Journal, StataCorp LP, vol. 7(3), pages 281-312, September.
    14. Fang Guo & Tao Zhao & Yanan Wang & Yue Wang, 2016. "Estimating the abatement potential of provincial carbon intensity based on the environmental learning curve model 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. 84(1), pages 685-705, October.
    15. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    16. Wang, Jianjun & Li, Li, 2016. "Sustainable energy development scenario forecasting and energy saving policy analysis of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 718-724.
    17. Zhu, Bangzhu & Wang, Kefan & Chevallier, Julien & Wang, Ping & Wei, Yi-Ming, 2015. "Can China achieve its carbon intensity target by 2020 while sustaining economic growth?," Ecological Economics, Elsevier, vol. 119(C), pages 209-216.
    18. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    19. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    20. 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.
    21. 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.
    22. Pedroni, Peter, 1999. "Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 653-670, Special I.
    23. Pakes, Ariel, 1985. "On Patents, R&D, and the Stock Market Rate of Return," Journal of Political Economy, University of Chicago Press, vol. 93(2), pages 390-409, April.
    24. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    25. Fisher-Vanden, Karen & Jefferson, Gary H. & Liu, Hongmei & Tao, Quan, 2004. "What is driving China's decline in energy intensity?," Resource and Energy Economics, Elsevier, vol. 26(1), pages 77-97, March.
    26. 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.
    27. Noailly, Joëlle & Smeets, Roger, 2015. "Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 15-37.
    28. Boeing, Philipp & Mueller, Elisabeth & Sandner, Philipp, 2016. "China's R&D explosion—Analyzing productivity effects across ownership types and over time," Research Policy, Elsevier, vol. 45(1), pages 159-176.
    29. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    30. Yu, Shiwei & Zhang, Junjie & Zheng, Shuhong & Sun, Han, 2015. "Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method," Energy Policy, Elsevier, vol. 77(C), pages 46-55.
    31. Xu, Peng & Xu, Tengfang & Shen, Pengyuan, 2013. "Energy and behavioral impacts of integrative retrofits for residential buildings: What is at stake for building energy policy reforms in northern China?," Energy Policy, Elsevier, vol. 52(C), pages 667-676.
    32. Rubin, Edward S & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A, 2004. "Learning curves for environmental technology and their importance for climate policy analysis," Energy, Elsevier, vol. 29(9), pages 1551-1559.
    33. 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.
    34. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    35. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    36. 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. Haoran Yang & Qun Wu, 2019. "Land Use Eco-Efficiency and Its Convergence Characteristics Under the Constraint of Carbon Emissions in China," IJERPH, MDPI, vol. 16(17), pages 1-17, August.
    2. Qian, Yuan & Scherer, Laura & Tukker, Arnold & Behrens, Paul, 2020. "China's potential SO2 emissions from coal by 2050," Energy Policy, Elsevier, vol. 147(C).
    3. Yanan Wang & Wei Chen & Minjuan Zhao & Bowen Wang, 2019. "Analysis of the influencing factors on CO2 emissions at different urbanization levels: regional difference in China based on panel estimation," 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. 96(2), pages 627-645, March.
    4. Wang, Yanan & Li, Xinbei & Kang, Yanqing & Chen, Wei & Zhao, Minjuan & Li, Wei, 2019. "Analyzing the impact of urbanization quality on CO2 emissions: What can geographically weighted regression tell us?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 127-136.
    5. Jing Sun & Ningning Zhai & Jichao Miao & Huaping Sun, 2022. "Can Green Finance Effectively Promote the Carbon Emission Reduction in “Local-Neighborhood” Areas?—Empirical Evidence from China," Agriculture, MDPI, vol. 12(10), pages 1-13, September.
    6. Haoran Yang & Hao Zheng & Hongguang Liu & Qun Wu, 2019. "NonLinear Effects of Environmental Regulation on Eco-Efficiency under the Constraint of Land Use Carbon Emissions: Evidence Based on a Bootstrapping Approach and Panel Threshold Model," IJERPH, MDPI, vol. 16(10), pages 1-20, May.

    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. Yanan Wang & Wei Chen & Minjuan Zhao & Bowen Wang, 2019. "Analysis of the influencing factors on CO2 emissions at different urbanization levels: regional difference in China based on panel estimation," 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. 96(2), pages 627-645, March.
    2. Zhang, Chuanguo & Zhou, Xiangxue, 2016. "Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 943-951.
    3. Qamruzzaman, Md & Jianguo, Wei, 2020. "The asymmetric relationship between financial development, trade openness, foreign capital flows, and renewable energy consumption: Fresh evidence from panel NARDL investigation," Renewable Energy, Elsevier, vol. 159(C), pages 827-842.
    4. 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.
    5. Lee Chien-Chiang, 2011. "Does Insurance Matter for Growth: Empirical Evidence from OECD Countries," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-28, June.
    6. Aladejare, Samson Adeniyi, 2022. "Natural resource rents, globalisation and environmental degradation: New insight from 5 richest African economies," Resources Policy, Elsevier, vol. 78(C).
    7. Jos Alberto Fuinhas & Ant nio Cardoso Marques & Alcino Pinto Couto, 2015. "Oil-Growth Nexus in Oil Producing Countries: Macro Panel Evidence," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 148-163.
    8. Mehmet Balcilar & Ojonugwa Usman & George N. Ike, 2023. "Investing green for sustainable development without ditching economic growth," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(2), pages 728-743, April.
    9. Acikgoz, Senay & Ben Ali, Mohamed Sami, 2019. "Where does economic growth in the Middle Eastern and North African countries come from?," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 172-183.
    10. Sung, Bongsuk & Song, Woo-Yong & Park, Sang-Do, 2018. "How foreign direct investment affects CO2 emission levels in the Chinese manufacturing industry: Evidence from panel data," Economic Systems, Elsevier, vol. 42(2), pages 320-331.
    11. Breitung, Jörg & Pesaran, Mohammad Hashem, 2005. "Unit roots and cointegration in panels," Discussion Paper Series 1: Economic Studies 2005,42, Deutsche Bundesbank.
    12. Omri, Anis, 2018. "Entrepreneurship, sectoral outputs and environmental improvement: International evidence," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 46-55.
    13. SENTURK, Mehmet & AKBAS, Yusuf Ekrem & OZKAN, Gokcen, 2014. "Cross Sectional Dependence and Cointegration Analysis among the GDP-Foreign Direct Investment and Aggregate Credits: Evidence from Selected Developing Countries," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 4(11), pages 1485-1501, November.
    14. McCarl, Bruce A. & Villavicencio, Xavier & Wu, Ximing, 2009. "The Effect of Climate Change over Agricultural Factor Productivity: Some Econometric Considerations," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49452, Agricultural and Applied Economics Association.
    15. Tiwari, Sunil & Si Mohammed, Kamel & Guesmi, Khaled, 2023. "A way forward to end energy poverty in China: Role of carbon-cutting targets and net-zero commitments," Energy Policy, Elsevier, vol. 180(C).
    16. Trofimov, Ivan D., 2022. "Determinants of the profit rates in the OECD economies: A panel data analysis of the Kalecki's profit equation," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 380-397.
    17. Dehghan Shabani, Zahra & Shahnazi, Rouhollah, 2019. "Energy consumption, carbon dioxide emissions, information and communications technology, and gross domestic product in Iranian economic sectors: A panel causality analysis," Energy, Elsevier, vol. 169(C), pages 1064-1078.
    18. Karikallio, Hanna, 2015. "Cross-commodity Price Transmission and Integration of the EU Livestock Market of Pork and Beef: Panel Time-series Approach," 2015 Conference, August 9-14, 2015, Milan, Italy 211832, International Association of Agricultural Economists.
    19. Mirza, Faisal Mehmood & Sinha, Avik & Khan, Javeria Rehman & Kalugina, Olga A. & Zafar, Muhammad Wasif, 2022. "Impact of Energy Efficiency on CO2 Emissions: Empirical Evidence from Developing Countries," MPRA Paper 111923, University Library of Munich, Germany, revised 2022.
    20. Bhattacharya, Mita & Narayan, Paresh, 2015. "Output and labor productivity in organized manufacturing: A panel cointegration analysis for India," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 171-177.

    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:spr:nathaz:v:88:y:2017:i:2:d:10.1007_s11069-017-2915-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.