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Description
Global warming has become one of the major challenges facing the world today, and the effective control of carbon emissions as a significant source of greenhouse gas emissions is particularly critical.As a major country in charge, the Chinese government proposed at the 75th United Nations General Assembly that carbon dioxide emissions should aim to peak by 2030 and strive to achieve carbon neutrality by 2060, demonstrating China's determination to actively address climate change. Nevertheless, in contrast to the absolute carbon emission reduction achieved by developed countries, China's current coal-based energy consumption structure is challenging to swiftly modify. Consequently, enhancing GDP-related energy efficiency has emerged as a viable option for achieving energy savings and emissions reduction. Concurrently, as the strategic support and growth pole of China's future economic development, population and industry will continue to concentrate in urban agglomerations, and it is of great importance to effectively improve urban carbon emission efficiency and build low-carbon cities to achieve the dual-carbon goal. The six major urban agglomerations in China selected for the present study are as follows: Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, the middle reaches of the Yangtze River, Chengdu-Chongqing, and the Central Plains urban agglomerations. Firstly, a carbon emission estimation model was constructed based on XGBoost machine learning with Bayesian optimization, and the carbon emissions of 119 cities from 2014 to 2023 were measured. Secondly, the super-efficient SBM model was employed to measure the carbon emission efficiency, taking into account the undesirable outputs. The different characteristics of urban agglomerations in terms of their carbon emission efficiency and spatial agglomeration characteristics were then analysed. The cities were also classified into megacities, metropolis and large cities to compare the differences in carbon emission efficiency between different types of cities. The STIRPAT model was then constructed, with five indicators: population density, industrial structure, economic development, energy intensity and scientific and technological innovation, were selected as influencing factors. These factors were analysed to determine their impact on different types of cities. The results are as follows: (1) The accuracy of the carbon emission estimation model reaches 96.2%; the total carbon emissions of each urban agglomeration show an increasing trend, and the growth rate decreases after 2022, among which the total carbon emissions of the urban agglomerations in the middle reaches of the Yangtze River and the Beijing-Tianjin-Hebei urban agglomeration decrease in 2023. (2) The average carbon emission efficiency of the six major urban agglomerations shows a downward and then upward trend, with a U-shaped dynamic development during the study period. (3) The regional differences in carbon emission efficiency of the six major urban agglomerations show an increasing trend, and there are obvious spatial clustering characteristics, and the carbon emission efficiency of each type of city shows the difference characteristics of "megacities > large cities > metropolis". (4) Economic development and scientific and technological innovation have obvious promoting effects on carbon emission efficiency, and population density has a two-way promoting and inhibiting effect. Based on the conclusions of the study, appropriate emission reduction policies are proposed for different urban agglomerations and cities.
| Keywords | Urban carbon emission efficiency；Urban agglomerations；Influencing factors；Spatio-temporal evolution |
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| Best Congress Paper Award | No |
