Will environmental regulations affect subjective well-being?-a cross-region analysis in China

Environ Sci Pollut Res Int. 2019 Oct;26(28):29191-29211. doi: 10.1007/s11356-019-06147-1. Epub 2019 Aug 7.

Abstract

China is a vast country with a wide range of difference in local customs and practices, whose governments at all levels have certain flexibility in policy formulation and implementation accordingly. Therefore, it is necessary to compare the impacts of environmental regulations (ERs) on subjective well-being (SWB) in different areas, which was totally overlooked by many scholars. Combining environmental regulations data with subjective well-being data from CGSS (2015), we conduct an empirical study on the linear and non-linear relationships between three different types of ERs and SWB in this study, then, we further verify the lag effects because of the time lag-related policies. Research results provide support that (1) in the eastern region, when command-and-control regulations(CMCER) and market-based regulations (MBER) have a reversed "U"-shaped curve connection with SWB, informal regulations (INFER) would reduce subjective well-being, and (2) for the central region, a "U"-shaped curve relationship exists between CMCER with SWB, while MBER and INFER have no significant impact, and (3) in the western region, MBER can promote SWB more sharply, and CMCER and INFER play negative roles in SWB improvement. Finally, by comparing the hysteresis results of different regions, we find that INFER and MBER are required to be strengthened for all above regions. In addition, implementation of CMCER is the highlight point for western region. Our findings have meaningful policy implications and the government should develop appropriate environmental regulations based on local conditions.

Keywords: Environmental regulations; Regional difference; Subjective well-being.

MeSH terms

  • China
  • Empirical Research
  • Environmental Policy / legislation & jurisprudence*
  • Humans
  • Models, Theoretical
  • Public Opinion
  • Quality of Life*
  • Regression Analysis
  • Time Factors