Exploring the Effects of Traffic Noise on Innovation through Health Mechanism: A Quasi-Experimental Study in China

Abstract

Noise pollution poses a significant hazard to humans by disrupting the maintenance of the quiet environment that is thought to promote innovation. In this study, the causal relationship between traffic noise and innovation was explored using four models. First, the panel data model with fixed effects was applied to determine the impact of traffic noise on innovation. Second, the interaction model was used to estimate the health regulatory effect. Third, the regression discontinuity model was used to identify the natural experience of the impact of traffic noise on innovation and further determine the causal effect of the noise threshold. Finally, the difference-in-differences model was used to identify the micro impact of traffic noise on innovation. The results show that from macro and micro perspectives, traffic noise suppresses innovation, and that health has a differential impact on the traffic noise–innovation relationship. In addition, we identified the critical point at which the impact of traffic noise on innovation is favorable owing to the white noise effect, providing a quantitative basis for policy implementation. Our results show that current environmental noise regulations must be re-examined to determine new measures for improving the innovative acoustic environment, promoting innovation, and achieving sustainable economic development.

Effect of Watering down Environmental Regulation on Residents’ Health in China: A Quasi-Natural Experiment of Local Officials’ Promotion Motivation

“…The discontinuous variation in the residents’ health is caused by the treatment state captured by the distance between the polluter and the water quality monitoring station. Based on previous studies [ 16 , 17 , 18 ], we set the following RDD: where subscript i indicates the individual and t indicates the year, is the state variable (1 for upstream and 0 for downstream), is a running variable that measures the distance from the polluter to the water monitoring station, is a polynomial function that can be set as triangular, Epanechnikov or uniform by Stata 14 statistical software, and are the regional fixed effect and year fixed effect, respectively, and is a random disturbance term. is an intercept coefficient and is a model coefficient that captures the treatment effect.…”

“…The discontinuous variation in the residents' health is caused by the treatment state captured by the distance between the polluter and the water quality monitoring station. Based on previous studies [16][17][18], we set the following RDD:…”