Open Access
SHS Web Conf.
Volume 170, 2023
2023 International Conference on Digital Economy and Management Science (CDEMS 2023)
Article Number 02015
Number of page(s) 5
Section Economic Innovation and Talent Development Technology
Published online 14 June 2023
  1. World Meteorological Organization. New Climate Predictions Increase Likelihood of Temporarily Reaching 1.5 ° in Next 5 Years [R]. 2021-05-27. [Google Scholar]
  2. Wang H., Chen Z. P., Wu X. Y., et al. Can a carbon trading system promote the transformation of a low- carbon economy under the framework of the porter hypothesis? -Empirical analysis based on the PSMDID method[J]. Energy policy, 2019, 129(6):930–938. [CrossRef] [Google Scholar]
  3. Ana F., Manuel D. P., Jorge D. B., et al. Combined carbon and energy intensity benchmarks for sustainable retail stores[J]. Energy, 2018, 165:877–889. [CrossRef] [Google Scholar]
  4. Meng F. Y., Su B., Elspeth T., et al. Measuring China’s regional energy and carbon emission efficiency with DEA models: a survey[J]. Applied energy, 2016, 183:1–21. [CrossRef] [Google Scholar]
  5. Zhang Y. J., Sun Y. F., Huang J. L. Energy efficiency, carbon emission performance, and technology gaps: evidence from CDM project investment[J]. Energy policy, 2018, 115(4):119–130. [CrossRef] [Google Scholar]
  6. Zhang Y. J., Hao J. F., Song J. The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: evidence from the regional level[J]. Applied energy, 2016, 174:213–223. [CrossRef] [Google Scholar]
  7. Yu Xiangyu, Chen Huiying, Li Yue. Impact of carbon emission trading mechanism on carbon performance based on synthetic control method[J]. China Population, Resources and Environment, 2021, 31(04):51–61. [Google Scholar]
  8. Wang Shaojian, Gao Shuang, Huang Yongyuan, et al. Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model[J]. Acta Geographica Sinica, 2020, 75(06):1316–1330. [Google Scholar]
  9. Li K., Lin B. Q. Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China[J]. Energy economics, 2015, 48:230–241. [CrossRef] [Google Scholar]
  10. Chang L. Y., Hao X. G., Song M., et al. Carbon emission performance and quota allocation in the bohai rim economic circle[J]. Journal of cleaner production, 2020, 258(C):120722. [CrossRef] [Google Scholar]
  11. Zeng L. G., Lu H. Y., Liu Y. P., et al. Analysis of regional differences and influencing factors on China’s carbon emission efficiency in 2005-2015[J]. Energies, 2019, 12(16):1–21. [Google Scholar]
  12. Zhao P. J., Zeng L. G., Li P. L., et al. China’s transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model[J]. Energy, 2022, 238:121934. [CrossRef] [Google Scholar]
  13. Wu F., Fan L. W., Zhou P., et al. Industrial energy efficiency with CO2 emissions in China: a nonparametric analysis[J]. Energy policy, 2012, 49:164–172. [CrossRef] [Google Scholar]
  14. Lan Hong, Wang Liu-Yuan. Research on Regional Carbon Emission Performance and Environmental Regulation Threshold Effect under Green Development[J]. Soft Science, 2019, 33(08):73–77+97. [Google Scholar]
  15. Zhang C. Q., Chen P. Y. Industrialization, urbanization, and carbon emission efficiency of Yangtze river economic belt-empirical analysis based on stochastic frontier model[J]. Environmental science and pollution research international, 2021, 28(47):1–16. [Google Scholar]
  16. Cheng Yuan, Qiao Guanmin, Mei Siyu, et al. Spatial- temporal differences of carbon emission efficiency in Zhejiang province based on SBM-DEA model[J]. Resource Development & Market, 2022, 38(03):272–279. [Google Scholar]
  17. Li J. B., Huang X. J., Kwan M. P., et al. The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze river delta, China[J]. Journal of cleaner production, 2018, 188(1):38–48. [CrossRef] [Google Scholar]
  18. Song W. F., Mao H., Han X. F. The two-sided effects of foreign direct investment on carbon emissions performance in China[J]. Science of the total environment, 2021, 791:148331 [CrossRef] [Google Scholar]
  19. Wang Yaqin, Yao Shunbo, Hou Mengyang, et al. Spatial-temporal differentiation and its influencing factors of agricultural eco-efficiency in China based on geographic detector[J]. Chinese Journal of Applied Ecology, 2021, 32(11):4039–4049. [Google Scholar]
  20. Wang Lili, Liu Xiaojie, Li Ding, et al. Geographical Detection of Spatial Heterogeneity and Drivers of PM2. 5 in the Yangtze River Economic Belt[J]. Environmental Science, 2022, 43(3):11. [Google Scholar]
  21. Li Qingsong, Zhang Fengtai, Su Weici, et al. Measurement of green efficiency of agricultural water in the Yangtze river economic belt and analysis of influencing factors-based on the super efficiency EBM-Geodetector model[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(5):40–52. [Google Scholar]
  22. Tone K., Tsutsui M. An epsilon-based measure of efficiency in DEA-a third pole of technical efficiency[J]. European journal of operational research, 2010, 207(3):1554–1563. [CrossRef] [Google Scholar]
  23. Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(01):116–134. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.