Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (6): 1316-1330.doi: 10.11821/dlxb202006016

• Urban-Rural Integration and Regional Development • Previous Articles    

Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model

WANG Shaojian1, GAO Shuang1, HUANG Yongyuan2, SHI Chenyi1   

  1. 1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
    2. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2019-04-25 Revised:2020-03-12 Online:2020-06-25 Published:2020-08-25
  • Supported by:
    Fundamental Research Funds for the Central Universities(19lgzd09);Guangdong Special Support Program;Pearl River S&T Nova Program of Guangzhou(201806010187)


Climate change caused by CO2 emissions has become an environmental issue globally in recent years, and improving carbon emission performance is an important way to reduce carbon emissions. Although some scholars have discussed the carbon emission performance at the national scale and industry level, literature lacks studies at the city- level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we used the super-efficiency SBM model to measure the urban carbon emission performance, and the traditional Markov probability transfer matrix and spatial Markov probability transfer matrix are constructed to explore the spatio-temporal dynamic evolution characteristics of urban carbon emission performance in China for the first time and to predict its long-term evolution trend. The study shows that urban carbon emission performance in China presents a trend of steady increase in the fluctuation, but the overall level is still at a low level, so there is still a great improvement space in urban carbon emission performance, with huge potential for energy conservation and emission reduction. The spatial pattern of national urban carbon emission performance shows the characteristics of "high in the south and low in the north", and there is a significant difference in the level of carbon emission performance between cities. The spatial Markov probabilistic transfer matrix results show that the transfer of carbon emission performance type in Chinese cities is stable, thus it forms the "club convergence" phenomenon, and the geographical background plays an important role in the process of the transfer. From the perspective of long-term trend prediction, the future evolution of urban carbon emission performance in China is relatively optimistic. The carbon emission performance will gradually improve over time, and the distribution of carbon emission performance presents a trend of high concentration. Therefore, in the future, China should continue to strengthen research and development to improve the performance level of urban carbon emissions and achieve the national target of energy conservation and emission reduction. At the same time, neighboring cities with different geographical backgrounds should establish a sound linkage mechanism of economic cooperation to pursue coordinated development between economic growth, energy conservation and emission reduction, so as to realize low-carbon city construction and sustainable development.

Key words: urban carbon emission performance, super-efficiency SBM model, spatial Markov chain, spatio-temporal evolution, trend prediction, China