Multi-Sectoral Environmental Concerns and Air Pollution : Evidence from Large-Scale Text Data

Efforts to promote pollution control strategies by the government and the media can lower exposure to ambient pollution. However, few empirical studies have quantified multi-sectoral control measures using big data text mining algorithms to explore the relationship and mechanism between environmental concern and air pollution at the city level. Here, we quantify media and government environmental concerns with the text-mining algorithm. The Dynamic Spatial Durbin model is used to explore the relationship between environmental concerns and air pollution. We find that environmental concerns from government, media, and their synergy are associated with a 3.12%, 3.05% and 3.61% decreased air pollution with spatial spillover effect, respectively. And the government has the problem of incomplete implementation in pollution control, but media environmental concerns can force the government to increase the intensity in pollution control. In addition, industrial restructuring, technological innovation and foreign direct investment mediated the association between environmental concerns and haze pollution are confirmed. This study provides a feasible solution for the mitigation of air pollution in the cooperation of multi-sectoral synergistic governance