Does “National Civilized City” policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis – BMC Public Health

Introduction

With regard to the estimated DID coefficient in the spatial Durbin DID model (Model 11), we find that it is still significant and negative (-0.114) and is smaller than that (-0.153) in the classical DID model because the omission of spatial spillovers can lead to biased estimates, specifically upward bias. The estimation result indicates that the civilized cities could significantly reduce SO₂ emissions because they are highly aware of the honorary title and tend to implement strict environmental regulation to mitigate SO₂ pollution and improve the environment for the purpose of maintaining the award in the process of review in subsequent evaluations. Otherwise, the title will be revoked if they do not meet the environmental standards. Specifically, in the campaign to maintain the golden title, the local government reinforces environmental regulations, notably for highly polluting firms, to curb emissions and strengthen public environmental facilities for pollution control. Local citizens are also encouraged to participate in environmental management to avoid environmental worsening or occurrence of environmental events. In sum, the place-based NCC policy can stimulate local governments to allocate resources effectively and implement regulation through policy intervention [39] to safeguard ‘the public good’, by reducing environmental hazards that have long been ignored [40]. It is therefore conducive to focus on SO₂ reduction and the environment and realize a win–win situation for the promotion probability of local officials and environmental benefits of the city.

More importantly, we notice that the spatial lag of DID, namely, W*DID also has a significant and negative coefficient, indicating that neighboring civilized cities also contribute to reducing SO₂ emissions of their own city. We can verify that the NCC policy can generate positive externalities, as has been confirmed by other researchers (see, amongst others, [18, 41]). One possible explanation is that neighboring cities receiving NCC award will also stimulate their own city to take stringent action to reduce SO₂ emissions and protect the environment due to demonstration and competition effects. If a municipal government is finally granted the title, it can serve as a benchmark for neighboring municipal governments [12]. Spatial spillovers are effective in the yardstick competition and exert powerful influence towards total reductions in SO₂ emissions. All in all, the exemplary role is beneficial to environmental quality improvements across the administrative regions.

Robustness check

Replacing the dependent variable

To confirm our conclusions in a robustness check, we replace the dependent variable, SO₂ emissions, with satellite observed SO₂ concentrations and repeat the estimation of the spatial Durbin DID models with different spatial weights matrices. Results are reported in Table 6.

1. In Table 6 the spatial autoregressive coefficients become larger than those in Table 5 because SO₂ concentrations of Chinese cities present much stronger spatial spillovers compared with SO₂ emissions. However, we
   

   SDGs, Targets, and Indicators

   SDGs Addressed or Connected to the Issues Highlighted in the Article:

   1. SDG 3: Good Health and Well-being
   2. SDG 11: Sustainable Cities and Communities
   3. SDG 13: Climate Action

   Specific Targets Based on the Article’s Content:

   • Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination.
   • Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.

   Indicators Mentioned or Implied in the Article:

   • SO₂ emissions
   • SO₂ concentrations
   • NO₂ concentrations
   • Fiscal autonomy (ratio of fiscal revenue to fiscal expenditure)
   • Share of the secondary industry to GDP

   Table: SDGs, Targets, and Indicators

   SDGs	Targets	Indicators
   SDG 3: Good Health and Well-being	Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination.	SO2 emissions, SO2 concentrations, NO2 concentrations
   SDG 11: Sustainable Cities and Communities	Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.	SO2 emissions, SO2 concentrations, NO2 concentrations
   	Target 13.2: Integrate climate change measures into national policies, strategies, and planning.	SO2 emissions, SO2 concentrations, NO2 concentrations

   Note: The indicators mentioned in the article (SO₂ emissions, SO₂ concentrations, NO₂ concentrations) are relevant to multiple targets under SDGs 3, 11, and 13.

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   Fuente: bmcpublichealth.biomedcentral.com

    

   

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