Does Technology Innovation Reduce Haze Pollution? An Empirical Study Based on Urban Innovation Index in China

Abstract

Haze pollution is one of the most concerned environmental issue, it is of great significance to control haze pollution without affecting economic development. Using the panel data composed of PM2.5 concentration and other data from 278 cities in China between 2003 to 2016, this paper empirically investigates the impact of urban innovation on haze pollution and its transmission mechanism. Based on the fixed effect model, the research finds that the increase of urban innovation significantly reduced haze pollution. The result still holds after dealing with possible endogenous problems. Energy consumption and industrial agglomeration are two important transmission channels through which urban innovation affects haze pollution. Furthermore, time heterogeneity analysis shows that the negative effect of urban innovation on haze pollution increases with time. Spatial heterogeneity analysis shows that urban innovation has a greater mitigation effect on haze pollution in eastern cities than in central and western cities in China. This paper indicates that technological innovation as the main driving force for development, can provide strong support for China to achieve the aims of improving the ecological environment.

Research on China's Environmental Governance Mode: Quality Driven or Quantity Driven?

“…Second, for practical reasons, this article cannot list all the factors affecting EG in detail in the regression model, so the influence of missing variables will enter the error term. To control the impact of endogenous problems on the estimation results, this article refers to Xu et al (2021) and He et al (2022) and uses the data of the independent variable lagged for one period as its own instrumental variables for 2SLS regression. The reason for this approach is that the lagged period of the variable has a high correlation with the current term, thus effectively avoiding the endogeneity problems caused by the correlation between the current variable and the current residual term.…”

Table1.docx

“…Second, for practical reasons, this article cannot list all the factors affecting EG in detail in the regression model, so the influence of missing variables will enter the error term. To control the impact of endogenous problems on the estimation results, this article refers to Xu et al (2021) and He et al (2022) and uses the data of the independent variable lagged for one period as its own instrumental variables for 2SLS regression. The reason for this approach is that the lagged period of the variable has a high correlation with the current term, thus effectively avoiding the endogeneity problems caused by the correlation between the current variable and the current residual term.…”