Abstract
Existing researches about environment regulation mainly focus on its effect on enterprises’ production decision-making behavior but neglects the effect on the individual and household behavior. Based on the micro survey data from the Chinese General Social Survey 2010 and corresponding city-level macroeconomic data, this paper investigates the effect of environmental regulations on residents’ willingness to pay (WTP) for protecting the environment. We find that environmental regulation has a significantly positive effect on residents’ WTP, especially where residents are at higher income levels, pollution levels, and government trust levels. The heterogeneous test shows that boosting the government’s credibility in environmental governance has become the key to improve the environmental preferences of the entire society. Finally, we show that reducing the expected cost and expected benefit of environmental protection is the main channel through which environmental regulation affected the residents’ WTP.
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Notes
Although in existing studies, the contingent valuation (CV) method is usually used to value people’s WTP for environmental protectuon (Burkhardt and Chan 2017), the CV survey lack of the reference/starting point of the evaluation really and difficult to allow for assigning a monetary value to the individuals valuation of environmental protection. The defects need to be overcome in future work.
The closer the value of VIF is to 1, the more the multiple co-linearity is not serious. 10 is usually used as the judgment boundary. When \(\hbox {VIF} < 10\), there is no multiple co-linearity; when \(10 \le \hbox {VIF} < 100\), there is strong multiple co-linearity; when \(\hbox {VIF} \ge 100\), there is severe multiple co-linearity.
From the European Centre for Medium-Range Weather Forecasts (ECMWF)
In ERA-Interim database, mixing height is the boundary layer height.
If WTP is a continuous variable, we can adopt the traditional IV-2SLS for estimation (not repeated here). However, WTP is an ordered discrete variable, and there is no direct method for solving the endogeneity. We tried to combine the Instrumental Variables method (IV) with the two-step method to solve the endogeneity between WTP and Ers. In the first stage, we estimate the effect of the instrumental variable and exogenous explanatory variable to obtain the fitted value \(\hbox {Ers}_{i}^{\star \star }\) of latent variable \(\hbox {Ers}_{i}^{\star }\) by
$$\begin{aligned} \hbox {Ers}_{i}= & {} \alpha _{1}+\beta _{1} \hbox {lnVC}_{i}+\gamma X+\varepsilon _{i}\\ \hbox {Ers}_{i}^{**}= & {} \hat{\alpha }_{1}+\hat{\beta }_{1} \hbox {lnVC}_{i}+\hat{\gamma } X \end{aligned}$$where \(\hbox {Ers}_{i}\) represents the environmental regulation of city i, \(\hbox {Ers}_{i}^{**}\) represents the fitted value of environmental regulation latent variable \(\hbox {Ers}_{i}^{*}\). \(\hbox {lnVC}\) is the instrumental variable, that is, the air circulation coefficient. The X vector represents the control variable (see Table 1 for the variable description). In the second stage, we used the ordered Logit to estimate the WTP, the fitted values of latent variables, exogenous explanatory variables, and residuals. We can get the consistent estimate of \(\beta ^{\star }\) through two-stage regression.
\(\hbox {WTP}_{i}=\left( \beta ^{\star } \text{ Ers } _{i}^{\star \star }+\gamma \varvec{X}_{i}+\varepsilon _{i}\right) \)
Grading standards: (a) Low-income class: Income-self \(\le 2\); Middle and high-income class: Income-self \(\ge 3\); (b) Low pollution: Pollution-self \(\le 3\); High pollution: Pollution-self \(\ge 4\); (c) Distrust: Trustgov \(\le 3\); Trust: Trustgov \(\ge 4\).
For simplification of analysis, we assume that the control variables remain unchanged, consistent with the results of Eq. 6.
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Acknowledgements
The authors are listed in alphabetical order. Dr. HE is a full professor of economics. ZHANG is a Ph.D. candidate supervised by Dr. He. The authors contribute equally in the project. Dr. HE conceived the whole project and acquired funding supports for it. LIN calculated the results under Dr. HE’s supervision. HE and LIN analyzed the data, discussed the results, and co-wrote the manuscript. The authors declare that there are no conflict of interest involved. This project is supported by the National Natural Science Foundation of China (Grant Nos. 71874070, 71573258), and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019).
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He, LY., Zhang, HZ. Spillover or crowding out? The effects of environmental regulation on residents’ willingness to pay for environmental protection. Nat Hazards 105, 611–630 (2021). https://doi.org/10.1007/s11069-020-04326-9
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DOI: https://doi.org/10.1007/s11069-020-04326-9