The nonlinear relationship between air quality and housing prices by machine learning.

Using a dataset encompassing 228 cities in China spanning from 2005 to 2019, this study explores the nonlinear relationship between air quality and housing prices and devises a strategy that incorporates the instrumental variable and machine learning to address the endogeneity issue. Both traditional models and machine learning models find air pollution affects housing prices in a diminishing manner. The negative impact of air pollution on housing prices decreases when the degree of air pollution intensifies. Such a characteristic is more pronounced in Eastern China and cities with fewer land resource constraints and larger populations. Mechanism analysis also reveals that air pollution could affect residents' perceived air quality and the industrial structure, further contributing to the nonlinear relationship between air quality and housing prices. The further SHapley Additive exPlanations (SHAP) evaluates the importance of air quality in determining housing prices and finds that air quality's contribution outweighs educational and medical resources. The contribution of air quality also shows a distinct regional disparity and has become increasingly important in recent years. The findings refine the benefit assessment accuracy related to air quality improvement.

The spatiotemporal effects of environmental regulation on green innovation: Evidence from Chinese cities.

Environmental regulation is expected to stimulate green innovation for the promotion of urban sustainability, while the effectiveness of this stimulus has long been debated under the Porter hypothesis and the crowding out theory. Empirical studies under different contexts have not reached a consistent conclusion yet. Based on the data of 276 cities in China from 2003 to 2013, this study captures the spatiotemporal non-stationarity in the effects of environmental regulation on green innovation with the combination of Geographically and Temporally Weighted Regression (GTWR) and Dynamic Time Warping (DTW) algorithm. The results show that environmental regulation has an overall U-shape impact on green innovation, indicating that the Porter hypothesis and the crowding out theory are not in conflict, but are theoretical interpretations of different stages of local responses to environmental regulation. Specifically, the effects of environmental regulation on green innovation present to be diverse in patterns that include enhancing, stagnant, undermining, U-shape, and inverted U-shape. These contextualized relationships are shaped by local industrial incentives and innovation capacities of pursing green transformations. The spatiotemporal findings allow policymakers to better understand the multi-staged and geographically diverse impacts of environmental regulation on green innovations, and formulate targeted policies for different localities.

Can Environmental Regulation Promote Green Innovation and Productivity? The Moderating Role of Government Interventions in Urban China.

Can environmental regulation promote green innovation and the productivity of cities? The "Compliance Cost" (CC) perspective and the "Porter Hypothesis" (PH) offer contrasting views, whereas the existing empirical results are inconclusive. This paper aims to highlight the roles of multifaceted government interventions, including government-to-firm subsidies, tax levies on firms, and environmental infrastructure provisions, in moderating environmental regulation for realizing PH. Based on the fixed-effects models for Chinese prefecture cities from 2005-2013, we found that environmental regulation positively impacted green innovation but negatively affected productivity. The results of moderating effects suggest that environmental regulation can better promote green innovation if it is compounded with more government-to-firm subsidies, lower firm tax burdens, and increased environmental infrastructure provisions. We further decomposed the impacts of these interventions across seven fields of green innovation and found that subsidy and tax burden relief were especially effective in facilitating more GI in the sector of transportation and alternative energy production. This paper amplifies the theoretical framework of PH by accentuating the analytical lens of multifaceted government interventions but also provides insights into how local governments can effectively design "carrot-and-stick" policies to realize PH at the city level.

Emission reduction target, complexity and industrial performance.

To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and profitability of a given industry. However, this negative effect is weaker for more complex industries. Only for industries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time.