IDEAS home Printed from https://ideas.repec.org/r/eee/rensus/v28y2013icp525-530.html
   My bibliography  Save this item

Regional carbon emission performance in China according to a stochastic frontier model

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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).
  2. Jiekun Song & Rui Chen & Xiaoping Ma, 2021. "Collaborative Allocation of Energy Consumption, Air Pollutants and CO 2 Emissions in China," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
  3. Wen Guo & Tao Sun & Hongjun Dai, 2017. "Efficiency Allocation of Provincial Carbon Reduction Target in China’s “13·5” Period: Based on Zero-Sum-Gains SBM Model," Sustainability, MDPI, vol. 9(2), pages 1-18, January.
  4. Weiming Li & Zhaoyang Cai & Shixiong Cao, 2021. "What has caused regional income inequality in China? Effects of 10 socioeconomic factors on per capita income," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13403-13417, September.
  5. Tateishi, Henrique Ryosuke & Bragagnolo, Cassiano & de Faria, Rosane Nunes, 2020. "Economic and environmental efficiencies of greenhouse gases’ emissions under institutional influence," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
  6. Bagchi, Prantik & Sahu, Santosh Kumar & Kumar, Ajay & Tan, Kim Hua, 2022. "Analysis of carbon productivity for firms in the manufacturing sector of India," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
  7. Lingchun Hou & Yuanping Wang & Yingheng Zheng & Aomei Zhang, 2022. "The Impact of Vehicle Ownership on Carbon Emissions in the Transportation Sector," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
  8. Zhou, Yang & Liu, Yansui & Wu, Wenxiang & Li, Yurui, 2015. "Effects of rural–urban development transformation on energy consumption and CO2 emissions: A regional analysis in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 863-875.
  9. Jiang, Wei & Sun, Yifei, 2023. "Which is the more important factor of carbon emission, coal consumption or industrial structure?," Energy Policy, Elsevier, vol. 176(C).
  10. Xian’En Wang & Shimeng Wang & Xipan Wang & Wenbo Li & Junnian Song & Haiyan Duan & Shuo Wang, 2019. "The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
  11. Cai, Bofeng & Guo, Huanxiu & Ma, Zipeng & Wang, Zhixuan & Dhakal, Shobhakar & Cao, Libin, 2019. "Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data," Applied Energy, Elsevier, vol. 242(C), pages 994-1009.
  12. Shi Wang & Hua Wang & Li Zhang & Jun Dang, 2019. "Provincial Carbon Emissions Efficiency and Its Influencing Factors in China," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
  13. Lin, Boqiang & Wang, Xiaolei, 2015. "Carbon emissions from energy intensive industry in China: Evidence from the iron & steel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 746-754.
  14. Yang, Jun & Zhang, Tengfei & Sheng, Pengfei & Shackman, Joshua D., 2016. "Carbon dioxide emissions and interregional economic convergence in China," Economic Modelling, Elsevier, vol. 52(PB), pages 672-680.
  15. Li, Guohao & Chen, Xue & You, Xue-yi, 2023. "System dynamics prediction and development path optimization of regional carbon emissions: A case study of Tianjin," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  16. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
  17. Wang, Zhaohua & Zhang, Bin & Liu, Tongfan, 2016. "Empirical analysis on the factors influencing national and regional carbon intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 34-42.
  18. Jiekun Song & Rui Chen & Xiaoping Ma, 2022. "Provincial Allocation of Energy Consumption, Air Pollutant and CO 2 Emission Quotas in China: Based on a Weighted Environment ZSG-DEA Model," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
  19. Jinkai Li & Jingjing Ma & Wei Wei, 2020. "Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
  20. Kekui Chen & Jianming Fu & Yun Gong & Jian Wang & Shilin Lv & Yajie Liu & Jingyun Li, 2022. "Study on the Influencing Factors of CO 2 from the Perspective of CO 2 Mitigation Potentials," Sustainability, MDPI, vol. 14(15), pages 1-22, July.
  21. Fan Zhang & Gui Jin & Junlong Li & Chao Wang & Ning Xu, 2020. "Study on Dynamic Total Factor Carbon Emission Efficiency in China’s Urban Agglomerations," Sustainability, MDPI, vol. 12(7), pages 1-17, March.
  22. Lili Guo & Sihang Guo & Mengqian Tang & Mengying Su & Houjian Li, 2022. "Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China," IJERPH, MDPI, vol. 19(12), pages 1-19, June.
  23. Lv, Miaochen & Bai, Manying, 2021. "Evaluation of China's carbon emission trading policy from corporate innovation," Finance Research Letters, Elsevier, vol. 39(C).
  24. Jingwen Yi & Yuchen Zhang & Kaicheng Liao, 2021. "Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry," IJERPH, MDPI, vol. 18(24), pages 1-25, December.
  25. Li, Wei & Sun, Wen & Li, Guomin & Jin, Baihui & Wu, Wen & Cui, Pengfei & Zhao, Guohao, 2018. "Transmission mechanism between energy prices and carbon emissions using geographically weighted regression," Energy Policy, Elsevier, vol. 115(C), pages 434-442.
  26. Wang, Jianda & Dong, Kangyin & Sha, Yezhou & Yan, Cheng, 2022. "Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  27. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
  28. Feng Dong & Yifei Hua & Bolin Yu, 2018. "Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review," Sustainability, MDPI, vol. 10(8), pages 1-34, August.
  29. Feng Dong & Bolin Yu & Jixiong Zhang, 2018. "What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach," Sustainability, MDPI, vol. 10(6), pages 1-26, May.
  30. Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, vol. 11(10), pages 1-32, October.
  31. Yung-Hsiang Lu & Ku-Hsieh Chen & Jen-Chi Cheng & Chih-Chun Chen & Sian-Yuan Li, 2019. "Analysis of Environmental Productivity on Fossil Fuel Power Plants in the U.S," Sustainability, MDPI, vol. 11(24), pages 1-27, December.
  32. Meng Sun & Yue Zhang & Yaqi Hu & Jiayi Zhang, 2022. "Spatial Convergence of Carbon Productivity: Theoretical Analysis and Chinese Experience," IJERPH, MDPI, vol. 19(8), pages 1-19, April.
  33. Hongxu Guo & Zihan Xie & Rong Wu, 2021. "Evaluating Green Innovation Efficiency and Its Socioeconomic Factors Using a Slack-Based Measure with Environmental Undesirable Outputs," IJERPH, MDPI, vol. 18(24), pages 1-20, December.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.