关键词: 生态效率, Super-SBM模型, 时空演变, 时空地理加权回归模型, 驱动因素

Abstract: Under the context of China’s carbon peak and carbon neutrality goals, improving urban eco-efficiency has become an important topic for green development. Based on panel data from nine cities in the Pearl River Delta urban agglomeration from 2011 to 2019, we used the Super-SBM model to quantitatively measure urban eco-efficiency. The spatio-temporal evolution pattern was revealed by combining kernel density estimation and trend surface analysis. Furthermore, the geographically and temporally weighted regression model (GTWR) was employed to explore the effects of the driving factors and their spatio-temporal differences from the temporal and spatial perspectives. The results showed that: (1) The overall eco-efficiency of the Pearl River Delta fluctuated and rose during the study period. From 2011 to 2014, the average eco-efficiency of the Pearl River Delta decreased from 0.61 to 0.44, with a rate of -27.93%. From 2015 to 2019, it increased from 0.57 to 0.71, with a growth rate of 23.18%. The kernel density curve showed a “catch-up effect” from low-efficiency cities to high-efficiency cities. (2) The eco-efficiency of the cities showed a spatial pattern of high in the middle and low in the two wings, with the Pearl River Estuary as the core. This pattern showed an “inverted U” shape in both east-west and north-south directions, which weakened over time and presented a trend differentiation of central part > eastern part > western and northern parts > southern part. (3) The driving factors of eco-efficiency in the cities exhibited spatio-temporal heterogeneity. Temporally, the positive driving effect of urbanization on eco-efficiency decreased with fluctuation over time, the inhibitory effect of environmental regulation weakened gradually, and the impact of industrial structure changed from positive to negative. Spatially, the impact coefficient of urbanization increased from southwest to northeast, the inhibitory effect of environmental regulation mainly occurred in the central cities such as Zhongshan and Guangzhou, and the impact coefficient of industrial structure presented a pattern of positive in the west and negative in the east. Our results provided spatial decision-making support for the green and low-carbon development of the Pearl River Delta urban agglomeration and offered important insights for ecosystem management and research of other large urban agglomerations.

Key words: eco-efficiency, Super-SBM model, spatio-temporal evolution, geographically and temporally weighted regression model (GTWR), driving factor.