From the perspective of input and output differentiation, using the SBM directional distance function method and Luenberger productivity index, this paper calculates the index of the green total factor productivity (GTFP) growth of 280 cities in China, from 2004 to 2016. This study also analyzes the growth sources of urban GTFP and the decomposition situation of GTFP in four cases, as well as the factors that affect the growth of GTFP. Finally, the following conclusions are drawn: 1) the overall growth trend of GTFP under four different situations of elements input is basically consistent. As a whole, the growth trend can be divided into three stages. The first rising stage of GTFP growth was from 2004 to 2008; the next declining stage was from 2008 to 2012, and a further rising stage occurred from 2012 to 2016. 2) From the decomposition situation of GTFP growth, we can know that the main source of GTFP growth is in the scale efficiency (LSEC) of GTFP. The decomposition of inefficiency levels in four cases shows that the inefficiency of land resources is the highest among many factors. Land resource inefficiency is also the key factor determining the output efficiency; the input inefficiency and bad-output inefficiency between them account for about 50% of the overall inefficiency level. 3) Among the factors that influence GTFP growth, the negative effect of urban land and energy structure is mainly reflected in the pure technology growth (LPTP) and scale efficiency growth (LSEC) of GTFP. In addition, investment in scientific and technological innovation and foreign direct investment both have a significantly positive effect on GTFP growth, as well as in improving urban population density and road area per capita.

Evaluating the impact of environmental pollution charge system reform is necessary to help formulate a suitable policy to achieve a goal of emission control. The paper examines the relationship between policy adjustment of SO₂ emissions charge standard (PSC) and urban growth of green total factor productivity (GGTFP) using a natural experiment data of 280 cities in China. The results indicated that the improvement of SO₂ emissions charge standard can reduce emission and promote the GGTFP. Furthermore, it reveals an “N” relationship between the policy time period and the GGTFP, and an “Inverted-U” relationship between policy intensity and the GGTFP. The results imply that there is some policy room for policy makers to set a shadow price of pollution charge to maximize policy effect, and it is also important to consider the policy effect in the implementation time and intensity to maximize the policy effect and resource efficiency for the GGTFP and sustainable development.

Analyzing the spatial difference and convergence of ecological common prosperity (ECP) in the Yellow River Basin (YRB) will be beneficial for the environmental governance and multi-regional economic coordination. Based on the panel data of 97 cities in the YRB from 2003 to 2019, this paper measured and analyzed the index of ECP, the Gini coefficient of ECP, and the convergence of ECP. The results indicate that the ECP of YRB shows a steady growth trend (with an average growth rate of 4.71% yearly) and the overall differences are low (average Gini coefficient is 0.1509 from 2003 to 2019). In different areas, the Gini coefficient between the medium-stream and downstream of YRB is the largest (average value of Gini coefficient is 0.1561). From the decomposition of the overall differences of ECP, the contribution degree of the density of transvariation is the highest for annual average, with a contribution rate of 43.37%, the rate of intra-regional and the inter-regional differences are 31.86% and 24.77%, respectively. The results indicate that the overall differences of ECP in YRB are getting smaller because of cooperation and governance, but the differences between and within regions exist because of geographical feature. There is a significant spatial β convergence trend of ECP, the convergence rate in the upstream and downstream area is faster under the economic geographical matrix than others, and the rate in the medium-stream area is faster under the administrative adjacency matrix. Therefore, strengthening economic and environmental cooperation between and within regions is more beneficial to achieve a better quality of life, as well as the long-term goals of 2035.