Study of the impact of energy consumption structure on carbon emission intensity in China from the perspective of spatial effects

  • Hongwei Xiao
  • Zhongyu Ma
  • Peng Zhang
  • Ming Liu
Original Paper


From now until 2030, China will be in a sprint to achieve reductions of 40–45% in carbon emission intensity by 2020 and 60–65% by 2030 compared to 2005; rigid requirements have thus been imposed for controlling carbon emission intensity. In this study, a spatial Durbin model that integrates a spatial lag model and a spatial error model is used to measure the degree of influence held by the energy consumption structure and other factors over carbon emission intensity and the spatial spillover effect. The results show that there is a spatial demonstration effect on the reduction in interregional carbon emission intensity in China. While the carbon emission intensity in the adjacent region decreases by 1%, the carbon emission intensity in this region will decrease by 0.05%, indicating that China’s regional low-carbon development model is also applicable to neighboring provinces and plays a large role in driving and demonstrating a low-carbon economy. Every additional 1% improvement toward optimizing the energy consumption structure enables the carbon emission intensity of the region to decrease by 0.21%; further, there is a positive spatial spillover effect driving carbon emission intensity decreases in neighboring areas of 0.25%. Industrial structure, energy intensity, energy price, and level of openness are the main factors influencing regional carbon emission intensity. According to the “14th Five-Year Plan,” there is an urgent need to optimize the energy consumption structure in the medium and long term and give full play to its ability to contribute to declines in carbon emission intensity.


Energy consumption structure Spatial Durbin model Carbon emission intensity Demonstration-driving effect Spatial spillover 



This research was supported by the National Key R&D Program of China (2016YFA0602601), Special Items Fund of Beijing Municipal Commission of Education of China, Program of Beijing Energy Development Research Center of China (NYJD20170101), and National Social Science Fund of China (15ZDA011).


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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Economic Forecasting DepartmentState Information CenterBeijingChina
  2. 2.Beijing Energy Development Research CenterBeijingChina
  3. 3.State Information CenterBeijingChina
  4. 4.School of Environment and Natural ResourcesRenmin University of ChinaBeijingChina

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