Agricultural Non-point Source Pollution in China:Evaluation,Convergence Characteristics and Spatial Effects

In this study,an inventory analysis approach was used to investigate the intensity of agricultural non-point source pollution(ANSP)and its spatial convergence at national and provincial levels in China from 1999 to 2017.On this basis,spatial factors affecting ANSP were explored by constructing a spatial econometric model.The results indicate that:1)The intensity of China's ANSP emission showed an overall upward trend and an obvious spatial difference,with the values being high in the eastern and central regions and relatively low in the western region.2)Significant spatial agglomeration was shown in China's ANSP intensity,and the agglomeration effect was increasing gradually.3)In the convergence analysis,a spatial lag model was found applicable for interpretation of the ANSP intensity,with the convergence rate being accelerated after considering the spatial factors but slower than that of regional economic growth.4)The spatial factors affecting the ANSP intensity are shown to be reduced by improving agricultural infrastructure investment,labor-force quality,and crop production ratio,while the expansion of agricultural economy scale and precipitation and runoff have positive impact on ANSP in the study region.However,agricultural research and development(R&D)investment showed no direct significant effect on the ANSP intensity.Meanwhile,improving the quality of the labor force would significantly reduce the ANSP intensity in the surrounding areas,while the precipitation and runoff would significantly increase the pollution of neighboring regions.This research has laid a theoretical basis for formulation and optimization of ANSP prevention strategies in China and related regions.

Have Industrial Robots Improved Pollution Reduction?A Theoretical Approach and Empirical Analysis

To investigate whether industrial robots have improved the ecological environment,this study integrated the adoption of robot technology and pollution abatement into Melitz's heterogeneous firm model.This showed that using robots in production can lower firms'pollution intensity by increasing their abatement investments,and this reduction effect is greater for higher polluting firms and those subject to weaker local environmental regulations.These theoretical expectations were then confirmed through a series of empirical investigations based on Bartik instrument regressions,with multiple robustness checks as well as heterogeneity and mechanism analyses.This paper adds to the literature on the relationships between automation technologies and green transformation.It shows that in the pursuit of economic growth and environmental protection,it is necessary for policymakers to shift from pollution control to technical support for traditional manufacturing firms.

Smarter and Cleaner: The Digital Economy and Environmental Pollution

The ongoing diffusion of digital technology into socio-economic activity has triggered hopes of minimizing environmental pollution.This research aimed to investigate the relationship between the digital economy and the intensity of SO_(2),pollutant emissions and to examine the mechanism that explains how digitalization leads to the reduction of pollutants from the perspective of environmental regulations.We applied the criteria importance through intercriteria correlation approach to construct a comprehensive index system to measurethedigital economy in China.Then weexamined the correlation between the digital economy and SO_(2),pollution intensity.To analyze the mechanism,we investigated the moderating effect of environmental regulations and also took macroscopic mechanisms(such as innovation and industrial structure)into consideration.Our empirical results indicated that the digital economy had a negative effect on SO_(2),pollution intensity.The negative impact was greater for medium-sized provinces with abundant resources in eastern and southern China.We also found that environmental regulations(e.g.,environmental legislation and environmental penalties)moderated the impact of the digital economy on SO_(2),pollution intensity,and the same was true for several macro mechanisms(e.g.,innovation,industrial structure,and labor productivity,etc.).This has implications for how thegovernment implements environmental regulations inthefuture.