The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis
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DOI: 10.1016/j.energy.2020.119517
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- Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
- Hansen, Bruce E., 1999.
"Threshold effects in non-dynamic panels: Estimation, testing, and inference,"
Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
- Bruce E. Hansen, 1997. "Threshold effects in non-dynamic panels: Estimation, testing and inference," Boston College Working Papers in Economics 365, Boston College Department of Economics.
- Rafiq, Shuddhasattwa & Salim, Ruhul & Nielsen, Ingrid, 2016. "Urbanization, openness, emissions, and energy intensity: A study of increasingly urbanized emerging economies," Energy Economics, Elsevier, vol. 56(C), pages 20-28.
- Li, Ke & Lin, Boqiang, 2018. "How to promote energy efficiency through technological progress in China?," Energy, Elsevier, vol. 143(C), pages 812-821.
- Dargahi, Hassan & Khameneh, Kazem Biabany, 2019. "Energy intensity determinants in an energy-exporting developing economy: Case of Iran," Energy, Elsevier, vol. 168(C), pages 1031-1044.
- Li, Li & Hong, Xuefei & Wang, Jun, 2020. "Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach," Energy, Elsevier, vol. 195(C).
- Huang, Junbing & Hao, Yu & Lei, Hongyan, 2018. "Indigenous versus foreign innovation and energy intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1721-1729.
- Song, Feng & Zheng, Xinye, 2012. "What drives the change in China's energy intensity: Combining decomposition analysis and econometric analysis at the provincial level," Energy Policy, Elsevier, vol. 51(C), pages 445-453.
- Zhang, Fan & Deng, Xiangzheng & Phillips, Fred & Fang, Chuanglin & Wang, Chao, 2020. "Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
- Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
- Majumdar, Devleena & Kar, Saibal, 2017. "Does technology diffusion help to reduce emission intensity? Evidence from organized manufacturing and agriculture in India," Resource and Energy Economics, Elsevier, vol. 48(C), pages 30-41.
- Stephanie Kremer & Alexander Bick & Dieter Nautz, 2013.
"Inflation and growth: new evidence from a dynamic panel threshold analysis,"
Empirical Economics, Springer, vol. 44(2), pages 861-878, April.
- Kremer, Stephanie & Bick, Alexander & Nautz, Dieter, 2009. "Inflation and growth: new evidence from a dynamic panel threshold analysis," Discussion Papers 2009/9, Free University Berlin, School of Business & Economics.
- Kremer, Stephanie & Bick, Alexander & Nautz, Dieter, 2009. "Inflation and growth: New evidence from a dynamic panel threshold analysis," SFB 649 Discussion Papers 2009-036, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Joakim Westerlund, 2005.
"New Simple Tests for Panel Cointegration,"
Econometric Reviews, Taylor & Francis Journals, vol. 24(3), pages 297-316.
- Westerlund, Joakim, 2005. "New Simple Tests for Panel Cointegration," Working Papers 2005:8, Lund University, Department of Economics.
- Buylova, Alexandra, 2020. "Spotlight on energy efficiency in Oregon: Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption," Energy Policy, Elsevier, vol. 140(C).
- 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.
- Zhao, Hongli & Lin, Boqiang, 2019. "Will agglomeration improve the energy efficiency in China’s textile industry: Evidence and policy implications," Applied Energy, Elsevier, vol. 237(C), pages 326-337.
- Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
- Shuxing Chen & Xiangyang Du & Junbing Huang & Cheng Huang, 2019. "The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
- Zhu, Junpeng & Lin, Boqiang, 2020. "Convergence analysis of city-level energy intensity in China," Energy Policy, Elsevier, vol. 139(C).
- Wenming Cao & Shuanglian Chen & Zimei Huang, 2020. "Does Foreign Direct Investment Impact Energy Intensity? Evidence from Developing Countries," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, March.
- Ji, Qiang & Zhang, Dayong, 2019. "How much does financial development contribute to renewable energy growth and upgrading of energy structure in China?," Energy Policy, Elsevier, vol. 128(C), pages 114-124.
- Antonietti, Roberto & Fontini, Fulvio, 2019.
"Does energy price affect energy efficiency? Cross-country panel evidence,"
Energy Policy, Elsevier, vol. 129(C), pages 896-906.
- Roberto Antonietti & Fulvio Fontini, 2018. "Does energy price affect energy efficiency? Cross-country panel evidence," SEEDS Working Papers 1218, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Oct 2018.
- Wu, Haitao & Hao, Yu & Weng, Jia-Hsi, 2019. "How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models," Energy Policy, Elsevier, vol. 127(C), pages 24-38.
- 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.
- Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
- Wu, Tao & Kung, Chih-Chun, 2020. "Carbon emissions, technology upgradation and financing risk of the green supply chain competition," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
- Xinxuan Cheng & Longfei Fan & Jiachen Wang, 2018. "Can Energy Structure Optimization, Industrial Structure Changes, Technological Improvements, and Central and Local Governance Effectively Reduce Atmospheric Pollution in the Beijing–Tianjin–Hebei Area," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
- 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.
- Arčabić Vladimir & Tica Josip & Lee Junsoo & Sonora Robert J., 2018. "Public debt and economic growth conundrum: nonlinearity and inter-temporal relationship," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(1), pages 1-20, February.
- 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.
- 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.
- 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.
- Tom Doan, "undated". "RATS program to replicate Arellano-Bond 1991 dynamic panel," Statistical Software Components RTZ00169, Boston College Department of Economics.
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Keywords
Industrial structure adjustment; Industrial structure optimisation; Energy intensity; Dynamic threshold panel model;All these keywords.
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