Abstract
Environmental information disclosure (EID) is playing an increasingly important role in water pollution management. However, since EID is hard to measure and other endogeneity problems, it is difficult to capture the causal inference between the two. Using the difference-in-difference method, this paper takes advantage of the Pollution Source Regulatory Information Disclosure Index (PITI) program promulgated by China in 2008 as a quasi-natural experiment to empirically capture the net effect of EID on water pollution management and its impact mechanism. The results show that from the average treatment effects perspective, the PITI strongly promote water pollution management. However, the dynamic effects of PITI present a decreasing and weakening trend over time. Further mechanism analysis reveals that PITI improves water pollution management mainly through the technological innovation effect. Several robustness checks justify our findings. Heterogeneity analysis shows that the PITI has a greater positive impact on water pollution management in cities located in the southern areas and with high economic levels and with a larger population.
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Data availability
The datasets used during the current study are available from the corresponding author on reasonable request.
Notes
Actually, the value of τ ranges from − 8 to 9. This is, for the 113 cities implemented PITI in 2008, the value of τ ranges from − 4 to 9, and for the 7 cities implemented PITI in 2013, the value of τ ranges from − 8 to 4. However, we define the value of τ between − 4 and 4. There are two reasons. First, the value between − 4 and 4 covers the whole 120 cities, which can provide more robust results. For example, if we estimate the dynamic effects of the year − 8, − 7, − 6, − 5, then we can only use the 7 expanded cities as our study sample, which may generate biased results. Similarly, if we estimate the dynamic effects of the year 5, 6, 7, 8, and 9, then we can only use the 113 cities as our study sample. Second, we used the Stata command—tvdiff to estimate the dynamic effects. This command also requires that all samples should be included in the time interval of τ. That is to say, the time interval of τ must be between − 4 and 4. If not, the tvdiff will automatically report an error.
It should be noted that the command tvdiff will automatically drop many values when generating the time trend item. For example, in the time item 1 year after PITI, the value of the first year will be deleted. In the time item 2 years after PITI, the values of the first year and the second year will be deleted, and so on. Thus, the observations in the dynamic treatment effects model are significantly less than that in the benchmark model.
It should be noted that the IV estimates in Table 6 are larger than the benchmark estimates. The possible reason may be that the effects of PITI are heterogeneous. Under the condition of heterogeneous effects, the IV estimates the local average treatment effect (LATE), while the benchmark regression estimates the average treatment effect (ATE) over the sample, which will result in a larger IV estimates (Angrist and Pischke 2008).
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Funding
We would like to thank the financial support from the National Youth Talent Support Program, the National Natural Science Foundation of China (No. 71863016; No. 71673123), the Outstanding Youth of Natural Science Foundation in Jiangxi Province (No. 2018ACB21004), and the Science and Technology Research Foundation of Jiangxi Province (No. GJJ170349).
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Conceptualization, Dan Pan; data curation, Wenchao Fan; formal analysis, Wenchao Fan and Dan Pan; funding acquisition, Dan Pan; methodology, Wenchao Fan and Dan Pan; project administration, Dan Pan; software, Wenchao Fan and Dan Pan; writing—original draft, Wenchao Fan and Dan Pan; writing—review and editing, Dan Pan and Wenchao Fan.
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Pan, D., Fan, W. Benefits of environmental information disclosure in managing water pollution: evidence from a quasi-natural experiment in China. Environ Sci Pollut Res 28, 14764–14781 (2021). https://doi.org/10.1007/s11356-020-11659-2
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DOI: https://doi.org/10.1007/s11356-020-11659-2