Applying the improved EBM and spatial statistical models to examining carbon emission performance: Evidence from Yellow River Basin urban agglomerations
Affiliations: [a] College of Economics and Management, Shandong University of Science and Technology, Qingdao, Shandong Province, China
| [b] Coal Industry Planning and Design Research Institute Co., Ltd., Beijing, China
Correspondence:
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Corresponding author. Chengyu Li, College of Economics and Management, Shandong University of Science and Technology, Qingdao, Shandong Province, 266590, China. E-mail: woailaoer5555@outlook.com.
Abstract: Global warming caused by excessive emissions of carbon dioxide (CO2) has become a hot topic globally in today’s society, and optimizing carbon emission performance (CEP) is an effective way to alleviate CO2 emissions. Many studies have explored CEP at the global, national, provincial and sector levels. However, due to the difficulty in obtaining energy consumption data, there is a lack of studies at the urban agglomeration and city levels. Taking the urban agglomeration dimension as the starting point, this paper constructs an improved epsilon-based measure (EBM) model to measure the CEP of the Yellow River Basin. A spatial data analysis model was introduced to explore the regional spatial characteristics of CEP. The newly developed spatial statistical model was used to study the driving factors of CEP. The results showed that: (1) The overall CEP of the Yellow River Basin was relatively high, showing an upward trend of volatility. There were significant differences between the seven urban agglomerations and 69 cities. (2) The CEP of the Yellow River Basin showed a trend of spatial agglomeration. The urban agglomerations of the eastern region showed a low-value agglomeration phenomenon, and the urban agglomerations of the central and western regions showed a trend of high-value agglomeration. (3) Economic development level (PGGDP), technological progress (TP), industrialization level (IND) and human capital (HC) can play a positive role in promoting the improvement in CEP, and population structure (PD) and energy structure (ES) can play a negative role in promoting the improvement in CEP. Industrial agglomeration (IA) and CEP show a “U"-shaped relationship that first inhibits and then promotes. In addition, foreign direct investment (FDI), IND, and HC have significant spatial spillover effects on neighboring cities.
