Spatio-temporal Dynamic Evolution and Inhibiting Factor Analysis of TFEE in the CCTC Economic Circle

Spatio-temporal Dynamic Evolution and Inhibiting Factor Analysis of TFEE in the CCTC Economic Circle

Автор: Shiyi Zeng, Liyun Su, Tong Zhang, Junjie Peng, Xiangjing Li, Feng Zhao, Dongyang Qiu

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 1 vol.16, 2024 года.

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Accounting for 30.8% of the total economic volume of the western region by 2021, the "fourth pole" of China's economic development is progressively being established as the Chengdu-Chongqing twin-city(CCTC) Zone. Therefore, increasing Total Factor Energy Efficiency (TFEE) and reducing the disparities in energy efficiency amongst cities in the area are important for the economic development of the CCTC Economic Zone. Accordingly, this study employs an output-oriented, super-efficient DEA model with constant returns to scale to measure the total factor energy efficiency of 16 prefecture-level cities in the CCTC economic circle from 2006 to 2020. It also examines the spatial distribution and variation patterns of each city's prefecture-level total factor energy efficiency. Then, spatial autocorrelation and random forest were used to explore the interrelationship among the indicators of the drivers, and the variables were screened according to Gini importance, finally, PCA-GWR models and spatial panel regression models were constructed to dissect the key drivers. The empirical findings indicate that: (1) the average energy efficiency of Chongqing and Chengdu is only 0.708 and 0.788, and the overall efficiency shows a steady increase from 2009 to 2018. (2) The spatial distribution is mainly as follows: H-H agglomerations are distributed in the northeastern cities of the CCTC Economic Zone, L-L agglomerations are distributed in the south, H-L agglomerations and L-H agglomerations are distributed in the north-central part of Chengdu-Chongqing Economic Zone. (3) Industrial structure, population structure, and governmental behavior have significant effects on energy efficiency, with a population mortality rate as the inhibiting factor and the proportion of tertiary industry and policy behavior as the contributing factors. Based on the empirical findings, it is recommended to accelerate the industrial structure adjustment, implement the CCTC economic circle's core cities' target of energy-saving, and build a "community" bridge to improve energy efficiency and promote economic development.

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Super-Efficient DEA, PCA-GWR Model, Spatial panel regression model, Energy Efficiency, CCTC Economic Zone

Короткий адрес: https://sciup.org/15018897

IDR: 15018897   |   DOI: 10.5815/ijieeb.2024.01.01

Список литературы Spatio-temporal Dynamic Evolution and Inhibiting Factor Analysis of TFEE in the CCTC Economic Circle

  • Chen, H., Liu, K., Shi, T., & Wang, L. (2022). Coal consumption and economic growth: A Chinese city-level study. Energy Economics, 109, 105940. https://doi.org/10.1016/j.eneco.2022.105940.
  • Guan, D., Liu, Z., Geng, Y., Lindner, S., & Hubacek, K. (2012). The gigatonne gap in China’s carbon dioxide inventories. Nature Climate Change, 2(9), 672-675. https://doi.org/10.1038/nclimate1560.
  • Liu, J., Zhang, J., & Fu, Z. (2017). Tourism eco-efficiency of Chinese coastal cities–Analysis based on the DEA-Tobit model. Ocean & coastal management, 148, 164-170. https://doi.org/10.1016/j.ocecoaman.2017.08.003
  • Lee, C. C., Wang, C. W., Ho, S. J., & Wu, T. P. (2021). The impact of natural disaster on energy consumption: International evidence. Energy Economics, 97, 105021. https://doi.org/10.1016/j.eneco.2020.105021.
  • Yang, Y., Liu, J., & Zhang, Y. (2017). An analysis of the implications of China’s urbanization policy for economic growth and energy consumption. Journal of Cleaner Production, 161, 1251-1262. https://doi.org/10.1016/j.jclepro.2017.03.207.
  • Wu, Y. (2012). Energy intensity and its determinants in China's regional economies. Energy Policy, 41, 703-711. https://doi.org/10.1016/j.enpol.2011.11.034.
  • Zeng, L., Xu, M., Liang, S., Zeng, S., & Zhang, T. (2014). Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis. Energy Policy, 67, 640-647. https://doi.org/10.1016/j.enpol.2013.11.053.
  • Xu, X., Pan, L. C., Ni, Q. H., & Yuan, Q. Q. (2021). Eco-efficiency evaluation model: a case study of the Yangtze River Economic Belt. Environmental monitoring and assessment, 193(7), 457. https://doi.org/10.1007/s10661-021-09228-2.
  • Zhang, R., & Hanaoka, T. (2021). Deployment of electric vehicles in China to meet the carbon neutral target by 2060: Provincial disparities in energy systems, CO2 emissions, and cost-effectiveness. Resources, Conservation and Recycling, 170, 105622. https://doi.org/10.1016/j.resconrec.2021.105622
  • Chen, J., Lian, X., Su, H., Zhang, Z., Ma, X., & Chang, B. (2021). Analysis of China’s carbon emission driving factors based on the perspective of eight major economic regions. Environmental Science and Pollution Research, 28, 8181-8204. https://doi.org/10.1007/s11356-020-11044-z
  • Dan, S. (2007). Regional differences in China's energy efficiency and conservation potentials. China & World Economy, 15(1), 96-115. https://doi.org/10.1111/j.1749-124X.2007.00052.x
  • Cheng, Z., Li, L., & Liu, J. (2018). Industrial structure, technical progress and carbon intensity in China's provinces. Renewable and Sustainable Energy Reviews, 81, 2935-2946. https://doi.org/10.1016/j.rser.2017.06.103
  • Zhou, P., Ang, B. W., & Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1-14. https://doi.org/10.1016/j.eneco.2006.05.001
  • Li, L. B., & Hu, J. L. (2012). Ecological total-factor energy efficiency of regions in China. Energy Policy, 46, 216-224. https://doi.org/10.1016/j.enpol.2012.03.053
  • Du, H., Matisoff, D. C., Wang, Y., & Liu, X. (2016). Understanding drivers of energy efficiency changes in China. Applied Energy, 184, 1196-1206. https://doi.org/10.1016/j.apenergy.2016.05.002
  • Guo, P., Qi, X., Zhou, X., & Li, W. (2018). Total factor energy efficiency of coal consumption: an empirical analysis of China's energy-intensive industries. Journal of Cleaner Production, 172, 2618-2624. https://doi.org/10.1016/j.jclepro.2017.11.149
  • Lin, B., & Long, H. (2015). A stochastic frontier analysis of energy efficiency of China's chemical industry. Journal of Cleaner Production, 87, 235-244. https://doi.org/10.1016/j.jclepro.2014.08.104.
  • Liu, H., Yang, R., Wu, J., & Chu, J. (2021). Total-factor energy efficiency change of the road transportation industry in China: a stochastic frontier approach. Energy, 219, 119612. https://doi.org/10.1016/j.energy.2020.119612.
  • Shen, X., & Lin, B. (2017). Total factor energy efficiency of China’s industrial sector: A stochastic frontier analysis. Sustainability, 9(4), 646. https://www.mdpi.com/2071-1050/9/4/646#
  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5
  • Chen, Y., Xu, W., Zhou, Q., & Zhou, Z. (2020). Total factor energy efficiency, carbon emission efficiency, and technology gap: evidence from sub-industries of Anhui province in China. Sustainability, 12(4), 1402. https://doi.org/10.3390/su12041402
  • Ratanakuakangwan, S., & Morita, H. (2022). Multi-aspect efficiency measurement of multi-objective energy planning model dealing with uncertainties. Applied Energy, 313, 118883. https://doi.org/10.1016/j.apenergy.2022.118883
  • Zhang, Y., Jin, W., & Xu, M. (2021). Total factor efficiency and convergence analysis of renewable energy in Latin American countries. Renewable Energy, 170, 785-795. https://doi.org/10.1016/j.renene.2021.02.016.
  • Yao, X., Feng, W., Zhang, X., Wang, W., Zhang, C., & You, S. (2018). Measurement and decomposition of industrial green total factor water efficiency in China. Journal of cleaner production, 198, 1144-1156. https://doi.org/10.1016/j.jclepro.2018.07.138.
  • Cheng, X., Li, N., Mu, H., Guo, Y., & Jiang, Y. (2018). Study on total-factor energy efficiency in three provinces of Northeast China based on SBM model. Energy Procedia, 152, 131-136. https://doi.org/10.1016/j.egypro.2018.09.070.
  • Haider, S., Danish, M. S., & Sharma, R. (2019). Assessing energy efficiency of Indian paper industry and influencing factors: A slack-based firm-level analysis. Energy Economics, 81, 454-464. https://doi.org/10.1016/j.eneco.2019.04.027.
  • Zhang, L., Mu, R., Zhan, Y., Yu, J., Liu, L., Yu, Y., & Zhang, J. (2022). Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China. Science of The Total Environment, 852, 158403. https://doi.org/10.1016/j.scitotenv.2022.158403
  • Bai, Y., Deng, X., Jiang, S., Zhang, Q., & Wang, Z. (2018). Exploring the relationship between urbanization and urban eco-efficiency: Evidence from prefecture-level cities in China. Journal of cleaner production, 195, 1487-1496. https://doi.org/10.1016/j.jclepro.2017.11.115
  • Xiong, S., Ma, X., & Ji, J. (2019). The impact of industrial structure efficiency on provincial industrial energy efficiency in China. Journal of cleaner production, 215, 952-962. https://doi.org/10.1016/j.jclepro.2019.01.095
  • Soytas, U., Sari, R., & Ewing, B. T. (2007). Energy consumption, income, and carbon emissions in the United States. Ecological Economics, 62(3-4), 482-489. https://doi.org/10.1016/j.ecolecon.2006.07.009
  • Wen, L., & Li, Z. (2020). Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: combining two-layer LMDI method with spectral clustering. Science of the Total Environment, 700, 134374. https://doi.org/10.1016/j.scitotenv.2019.134374
  • Liu, Y., Xiao, H., Lv, Y., & Zhang, N. (2017). The effect of new-type urbanization on energy consumption in China: a spatial econometric analysis. Journal of Cleaner Production, 163, S299-S305. https://doi.org/10.1016/j.jclepro.2015.10.044.
  • Wu, Y., Ke, Y., Xu, C., Xiao, X., & Hu, Y. (2018). Eco-efficiency measurement of coal-fired power plants in China using super-efficiency data envelopment analysis. Sustainable Cities and Society, 36, 157-168. https://doi.org/10.1016/j.scs.2017.10.011
  • Zhao, Z., Bai, Y., Wang, G., Chen, J., Yu, J., & Liu, W. (2018). Land eco-efficiency for new-type urbanization in the Beijing-Tianjin-Hebei Region. Technological Forecasting and Social Change, 137, 19-26. https://doi.org/10.1016/j.techfore.2018.09.031
  • Lv, Y., Chen, W., & Cheng, J. (2020). Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models. Energy Policy, 147, 111858. https://doi.org/10.1016/j.enpol.2020.111858
  • Yang, G., Gui, Q., Supanyo, P., Zhang, F., Yang, X., & Gong, G. (2023). Temporal and spatial changes and influencing factors of low-carbon economy efficiency in China. Environmental Monitoring and Assessment, 195(1), 55. https://doi.org/10.1007/s10661-022-10599-3
  • Li, G. (2019). Spatiotemporal dynamics of ecological total-factor energy efficiency and their drivers in China at the prefecture level. International Journal of Environmental Research and Public Health, 16(18), 3480. https://doi.org/10.3390/ijerph16183480
  • Zhang, Y., Wang, W., Liang, L., Wang, D., Cui, X., & Wei, W. (2020). Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy. Science of the Total Environment, 739, 140197. https://doi.org/10.1016/j.scitotenv.2020.140197
  • Qi, X., Guo, P., Guo, Y., Liu, X., & Zhou, X. (2020). Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal-intensive industries. Energy, 190, 116354. https://doi.org/10.1016/j.energy.2019.116354
  • Xiang, X., Ma, X., Ma, Z., & Ma, M. (2022). Operational carbon change in commercial buildings under the carbon neutral goal: A LASSO–WOA approach. Buildings, 12(1), 54. https://doi.org/10.3390/buildings12010054
  • Zhou, C., Shi, C., Wang, S., & Zhang, G. (2018). Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models. Ecological Indicators, 86, 67-80. https://doi.org/10.1016/j.ecolind.2017.12.011
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