The impact of digital finance on pollutants emission: evidence from chinese cities.

This paper investigates whether emerging digital finance can reduce environmental pollution in China based on data from 273 of China's prefecture-level cities spanning the period from 2010 to 2017. The dynamic spatial econometric models (DSDM) find a significant negative association between digital finance and pollutants emissions, and the impacts vary among regions and urban development stages. The impact mechanism test proves that digital finance reduces pollutants emissions through technological innovation, structural adjustment, and capital allocation effects. In addition, we explore the different dimensions of digital finance and find that the depth of use has a more practical effect on reducing emissions. Further analyses based on the threshold model show an inverted N-shaped nexus between digital finance and emissions. The threshold effect also exists in terms of the traditional financial level. Our study proves that emerging digital finance crucially affects its potential benefits to environment and provides an empirical basis for policy-makers to accelerate the digitalization of financial markets, particularly paying attention to its emission-reduction effects.

Market Segmentation and Green Development Performance: Evidence from Chinese Cities.

This study is based on 2006-2019 panel data from 282 Chinese cities. Market segmentation and green development performance are empirically investigated to examine their non-linear relationship using static panel, dynamic panel, and dynamic spatial panel models. The results reveal the following: (1) Green development performance is found to have a high degree of temporal and spatial path dependence, exhibiting spatial linkage between cities. (2) Market segmentation stemming from local government protection has a clear inverted U-shaped structure in relationship with the green development performance. (3) Our analysis suggests that the upgrading of industrial structures significantly enhances green development, while factor price distortion inhibits it. The relationship between market segmentation and industrial structure upgrading is also an inverted U-shape. (4) The analysis further reveals that market segmentation has an inverted U-shaped correlation with the green development performance in western, central, and eastern cities. However, the different rates of development of industrial structures within the three regions result in varying degrees of market segmentation according to inflection point values. Moreover, aligned with the theoretical hypothesis of "resource curse," in resource-based cities (exclusively), market segmentation still affects the green development performance with a significant inverted U-shaped structure.

Convergence study of water pollution emission intensity in China: evidence from spatial effects.

One of the challenges that China currently faces is how to reduce the emissions of water pollution. However, the study of water pollution convergence has a certain policy significance for controlling the emissions of water pollution. This article firstly uses chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) as indicators of water pollution. Due to the obvious spillover effect of water in space, this article adds a spatial effect to the convergence model. Based on panel data of 30 provinces and cities from 2006 to 2017, this article uses a dynamic spatial Dubin model to analyze the convergence of water pollution emission intensity to address the endogenous problem in the model. The empirical results of this paper show that there is absolute ß-convergence and conditional ß-convergence in the intensity of water pollution emissions. The spatial autocorrelation test shows that there is a positive spatial autocorrelation of water pollution emissions, which means that the pollution emissions in neighboring areas will affect the emissions in the local area. The industrial structure has a certain promoting effect on the emission of water pollution, which means that adjusting the industrial structure and alleviating the structure of the secondary industry is the trend of future development. Economic growth can curb the emissions of water pollution. The influences of urbanization and foreign investment on the emissions of the two pollutants are inconsistent, and policies can be formulated according to local conditions in the future.

Industrial structure, technological innovation, and total-factor energy efficiency in China.

The promotion of industrial restructuring and technological innovation is the most important and realistic way of improving energy efficiency. This thesis uses the modified Super-SBM method to measure China's total-factor energy efficiency and then uses the dynamic spatial panel model (DSPM) to verify the effect of industrial structure and technological innovation on total-factor energy efficiency. The study found that from 2003 to 2016, China's total-factor energy efficiency showed a fluctuating trend of "falling first and then rising." The inflection point appeared in 2012; total-factor energy efficiency in the Eastern region was significantly higher than the national average, while in the Central and Western regions, it was significantly lower. The results of the analysis show that both the service adjustment of the inter-industry structure and the productivity growth of the intra-industry structure significantly promote improvements in total-factor energy efficiency. However, due to the low conversion rate of scientific and technological achievements in China, the impact of technological innovation input on total-factor energy efficiency is not significant. This is in contradistinction to technological innovation output which does significantly improve total-factor energy efficiency. The above research conclusion is still robust and reliable after changing the measurement method and spatial weight matrix.

The Effect of Manufacturing Agglomeration on Haze Pollution in China.

Based on panel data on 285 Chinese cities from 2003 to 2012, we use a dynamic spatial panel model to empirically analyze the effect of manufacturing agglomeration on haze pollution. The results show that when economic development levels, population, technological levels, industrial structure, transportation, foreign direct investment, and greening levels are stable, manufacturing agglomeration significantly aggravates haze pollution. However, region-specific analysis reveals that the effects of manufacturing agglomeration on inter-regional haze pollution depends on the region: the effect of manufacturing agglomeration on haze pollution is the largest in the Western region, followed by the Central region, and is the least in the Eastern region. Based on the above conclusions, we put forward several specific suggestions, such as giving full play to the technology and knowledge spillover effects of manufacturing agglomeration, guiding manufacturing agglomerations in a scientific and rational way, accelerating the transformation and upgrading of manufacturing industries in agglomeration regions.