The effect of command-and-control regulation on environmental technological innovation in China: a spatial econometric approach

Ren

2021

Front. Environ. Sci.

The spot inspection policy has been widely applied in environmental protection in China. This paper collects environmental enforcement announcements and green patent data published by Chinese government agencies from 2006 to 2015. First, it studies the impact of spot inspection on green innovation with the spatial Durbin model. Then, it analyzes spatial heterogeneity according to the eastern, central, and western regions including 29 provinces. The spot inspection policy significantly increases the green innovation of a current region with a negative spillover effect on neighboring regions. Even though this policy has the best performance in the eastern region, it leads to pollution transfer into the western region, while being ineffective in the central region. Further, analysis on the spatial spillover effects of the 29 provinces proves that 21 provinces have a positive spillover effect, while eight provinces have a negative spillover effect. The research study shows that although spot inspection is generally beneficial to green innovation, pollution transfer and policy failure exist because of spatial heterogeneity.

Section: Hypothesismentioning

confidence: 99%

Impact Analysis of Large-Scale Environmental Spot Inspection Policy on Green Innovation: Evidence From China

Ren

2021

Front. Environ. Sci.

“…The calculation method of Moran's I was described in detail in a previous literature [13]. In quest of contribution degree of population density and public facilities in each street to COVID-19 onset cases number, Spatial error model (SEM) was applied to conduct spatial correlation analysis [14].…”

Section: Discussionmentioning

confidence: 99%

Geographic information system methods tell spatiotemporal transmission and its drivers of COVID-19 in Wuhan, China at street-level

Liu

Wang

Hua

et al. 2020

Preprint

Background: No study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors.Methods: Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods.Results: We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed an obvious global aggregation and a local aggregation in central urban areas, but such aggregations was decreased in the later period of the epidemic. In addition, the analysis at streets-level suggested population density and the number of hospitals were influence factors of spatial difference.Conclusions: The epidemic situation showed obvious global and local spatial aggregations. High population density and directional flow of the Population to hospitals may account for the aggregations. Strong quarantine measures and restrictions on movement of residents in Wuhan make the epidemic under control in a short time.

“…However, due to the profit-seeking nature of both human capital and R&D investment, central regions and economically developed regions will absorb more innovative talents and financial support, which will, instead, inhibit the development of innovation capacities in neighboring regions [27]. By regressing provincial panel data, Li et al (2019) [28] found that the spatial spillover effects of innovation activities are only significant in the eastern provinces, where innovation spatial networks are more closely connected. Taking these findings into consideration, this paper's third research hypothesis is as follows: Hypothesis 3.…”

Section: Research Hypothesesmentioning

confidence: 99%

The Spatial Interaction Effect of Environmental Regulation on Urban Innovation Capacity: Empirical Evidence from China

Zhou

Yun-sheng

Xin-cai

2021

IJERPH

This paper applies a spatial econometric model to measure the impact of environmental regulation on urban innovation capacity from a spatial interaction perspective by using panel data from 41 cities in the Yangtze River Delta urban agglomeration from 2009 to 2018. The study findings are as follows: first, environmental regulation has a significant positive impact on urban innovation capacity and a significant positive spatial spillover effect; second, innovation capacity has significant positive spatial dependence; third, city informatization level, government expenditures on science and technology, city economic scale, and industrial development level all positively affect the innovation capacity of neighboring cities and all have positive spatial spillover effects on the innovation capacity of neighboring cities; and finally, city expansion reduces the innovation capacity of a city and has negative spatial spillover effects on the innovation capacity of neighboring cities.