Spatiotemporal evolution of green development efficiency based on nighttime light data

Abstract

Enhancing Green Development Efficiency (GDE) performance is pivotal for achieving high-quality development in China. Carbon emission reduction is a significant target for sustainable development after the adoption of the ‘dual carbon’ goal. However, the undesirable output of existing GDE research has overlooked carbon emissions. Therefore, this study proposed an improved measurement of GDE by considering carbon emissions as an undesirable output. The data integrating DMSP-OLS and SNPP-VIIRS dataset was used to quantify urban carbon emissions. The super-SBM model was utilized to assess GDE performance. An empirical study of urban agglomerations in the Yellow River Basin was conducted using data from 2013 to 2022. The findings are as follows: (1) The GDE of the seven urban agglomerations exhibit a ‘U-shaped’ trend from temporal evolution perspective. (2) The seven urban agglomerations demonstrate a spatial distribution pattern characterized by higher values in the northern and western regions and lower values in the southern and eastern regions. (3) The GML index of the seven urban agglomerations exhibited an alternating trend of rise and decline. Theoretically, the accuracy of GDE measurement is improved by incorporating an optimized method for calculating urban carbon emissions. Practically, this paper provides a foundation for governments to elevate regional green development performance.