City sizes are rapidly expanding, and urban air pollution is a serious challenge in China. PM_2.5 (fine particulate matter) is the primary pollutant of urban pollution. This study aimed to examine the correlations between PM_2.5 and city size. In this paper, using the panel data of 278 cities in China from 2007 to 2016, we constructed a static and dynamic panel model based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) analytical framework. We found that there was a significantly inverted N-shaped correlation between PM_2.5 and city size. Two inflection points were found at 949,200 and 3,736,100. We found no evidence to support the EKC (Environmental Kuznets Curve) hypothesis, while the “Pollution Haven Hypothesis” gained support. The contradiction between PM_2.5 and city size will exist for the long term. Policy recommendations were proposed based on our findings. Controlling the city size does not seem to be necessary for very large cities as they have passed the second inflection point. Cities with a growing population are under great pressure to prevent PM_2.5 pollution and need to implement greater measures to reduce pollution.

Based on the panel data of 283 cities in China from 2009 to 2018, this paper analyzes the effect of urban green scientific and technological innovation enhancement on hazardous air pollutants using the GS2SLS method, which simultaneously controls for model endogeneity and spatial spillover effects and reveals the transmission mechanism of urban green scientific and technological innovation level. It was found that (1) There is a significant spatial spillover effect of hazardous air pollutants between regions, both in China as a whole and in the eastern, central, and western parts of the country, and the spatial spillover effect of hazardous air pollutants is significantly greater in the eastern and central parts of China than in the western parts. (2) Green technological innovation has a significant inhibitory effect on hazardous air pollutants in cities in eastern and central China. An extended study found that the improvement in green technology levels in innovative cities has a better effect on controlling hazardous air pollutants than in non-innovative cities. (3) The energy- saving and green economy effects have a mediating influence on the effect of green technological innovation on hazardous air pollutants in cities, and the simultaneous occurrence of these two effects in green technological innovation serves to enhance the transmission of hazardous air pollutants in order to facilitate the long-term management of haze.

Based on the Driver-Pressure-State-Impact-Response (DPSIR) framework, this paper constructs the input, expected output, and unexpected output of China’s sustainable development. This paper calculates the sustainable development efficiency of 31 provinces and cities in China using a super-slack-based measure (SBM) model that eliminates the influence of scale factors through a three-stage data envelope analysis (DEA) approach. Taking the level of science and technology as the control variable, and the relative scale efficiency as the threshold variable, this paper calculates the effects of the absolute scale of labor force, education, economy, enterprise, and transportation on sustainable development efficiency. The results show that: (1) there is an upward trajectory for sustainable development efficiency of China’s provinces and municipalities overall from 0.65 in 2004 to 0.68 in 2017, with significant regional differences in which the ecological efficiency in the Eastern region is 0.26 higher than that of the Central region; (2) it enhances the absolute scale of the labor force, education, and transportation, in order to reduce the inhibition on sustainable development efficiency; and (3) shifts our attention to the promotion of absolute economic scale to the promotion of green economic development, and increases control of polluting enterprises.

Innovation agglomeration plays a decisive role in improving the input–output scale and marginal output efficiency of factors. This paper takes carbon emissions as the unexpected output and energy consumption as the input factor into the traditional output density model. The dynamic spatial panel Durbin model is used to analyze the mechanism for innovation agglomeration and energy intensity to affect carbon emissions from 2004 to 2017 in thirty Chinese provinces. Then, we test the possible mediating effect of energy intensity between innovation agglomeration and carbon emissions. The major findings are as follows. (1) The carbon emission intensity has time-dependence and positive spatial spillover effect. That is, there is a close correlation between current and early carbon emissions, and there is also a high-degree correlation between regional and surrounding areas’ carbon emissions. (2) Carbon emissions keep a classical inverted U-shaped relation with innovation agglomeration, as well as with energy intensity. However, the impact of innovation agglomeration on carbon emissions in inland regions of China does not appear on the right side of the inverted U-shaped curve, while carbon emissions are subject to a positive nonlinear promoting effect from energy intensity. (3) When the logarithm of innovation agglomeration is more than 3.0309, it first shows the inhibition effect on energy intensity. With the logarithm of innovation agglomeration exceeding 5.0100, it will show the dual effect of emission reduction and energy conservation. (4) Energy intensity could work as the intermediary variable of innovation agglomeration’s influence on carbon emissions. Through its various positive externalities, innovation agglomeration can produce a direct impact on carbon emissions, and through energy intensity, it can also affect carbon emissions indirectly.

Foreign trade in subdeveloped regions may lead to serious environmental problems. The direct and possible indirect ways in which foreign trade correlates with green economic efficiency in subdeveloped regions are qualitatively analyzed. Empirical tests are conducted based on the data of 80 prefecture-level cities in Central China. Results show that: (1) Foreign trade in Central China has a significant parabolic type of relationship with green economic efficiency. (2) Foreign trade in Central China will indirectly correlate with green economic efficiency through stimulating industries agglomerated in big cities, changing the level of industrial specialization and adjusting policy and technical environments. Whether the indirect relation between foreign trade and green growth is positive is determined by the scale of foreign trade itself. (3) The relation between foreign trade and green economic efficiency in different cities of Central China is heterogeneous. From the perspective of spatial heterogeneity, the negative relation of cities along major rivers and cities not belonging to capital city groups is more significant. From the perspective of city characteristic heterogeneity, a parabolic type of relationship of foreign trade with green economic efficiency is significant in large cities or cities with a pleasant policy environment, whereas the relationship is negative in small and medium sized cities or cities with a high proportion of manufacturing. Policy implications are proposed.

This paper intends to clarify whether launching a cross-border freight railway project will cause adverse impacts on cities’ green innovation intensity. Based on the construction and operation of the China Railway Express, the impact mechanism of this cross-border freight railway project on cities’ green innovation is theoretically analyzed. Through using the data of 284 prefecture-level cities in China from 2017 to 2019, the effects of the China Railway Express on green innovation intensity are empirically tested by adopting the differences-in-differences method. The results show the following: 1. launching the China Railway Express project has a direct negative impact on cities’ green innovation intensity; 2. the China Railway Express has indirect positive impacts on green innovation intensity through increasing the foreign trade volume of cities as well as reducing the level of industrial specialization, and has an indirect negative impact on green innovation intensity through reducing the strictness of environmental regulations; 3. the radius of the impact coverage of the China Railway Express is 150 km, and its impact on surrounding cities is funnel-shaped; 4. the China Railway Express will cause a negative impact on green innovation intensity in large cities, cities with lower pollution emissions, and cities of Eastern China and Western China. Taking these findings into account, some policy implications are proposed.