Haze management: is urban public transportation priority effective?

Nonlinear effects of socio-economic factors on urban haze in China: Evidence from spatial econometric smooth transition autoregressive regression approach

In recent years, China has achieved numerous economic miracles but it has also been plagued by severe air pollution. The frequent hazy weather has severely restricted China's sustainable development. To investigate the nonlinear threshold effect of socio-economic factors on urban haze in China, this study constructs a spatial econometric Smooth Transition Autoregressive Regression (STAR) model based on the STIRPAT theory by using the remote sensing inversion PM2.5 data of 223 prefecture-level and above cities in China mainland during 2004-2016. In this study, the ARAR-STAR model is estimated by quasi-maximum likelihood estimation, and the accuracy of parameter estimation is verified by Monte Carlo simulation, which proves that the ARAR-STAR model constructed in this study is robust. It is concluded that: there is a complex spatial nonlinear relationship between socio-economic factors such as economic development level, population density, advanced industrial structure, energy consumption, opening-up, and haze pollution. The effect of socio-economic factors on haze emission reduction under the spatial influence has complex heterogeneity with the smooth transition between high and low regimes with economic development. The ARAR-STAR model constructed in this paper, which has both individual fixed effects and time fixed effects, expands the form of existing spatial panel nonlinear models and enriches and implements the application of spatial panel smooth transfer threshold models in the environmental field. Not only can it provide policy recommendations for China to achieve "coordinated efficiency in pollution reduction and carbon reduction" as soon as possible, but it also contributes to China's plan to address global climate change and promote global sustainable development.

Innovation city and low-carbon future: a quasinatural experiment from China

Using the difference-in-difference model and panel data from 283 Chinese cities from 2006 to 2019, this study assesses the effect on CO2 emissions of an innovative city pilot policy and analyzes its mechanism using the mediation effect model. The findings demonstrate that the pilot policy significantly reduces urban carbon emissions overall, and this finding holds even after conducting several stability tests. Innovative pilot cities can cut carbon emissions by 11.5% compared to nonpilot cities and thus significantly impact carbon reduction. Reducing carbon emissions is possible through three mechanisms. These are the enrichment of cultural resources, the development of technological innovation levels, and the optimization of industrial structure. There is a significant lag in how the innovative city pilot policy affects this reduction. The emission reduction effects of innovative pilot policies on different pollution levels, regions, and cities of various sizes are heterogeneous. In the long run, the scope of pilot projects needs enlargement in an orderly manner, and specific policies should be implemented according to local conditions. Meanwhile, advanced technologies are required in cities of different scales to build innovative development mechanisms for carbon peaking and carbon neutrality, and environmental regulations should be strengthened to implement in urban areas the concept of green and sustainable ecological development.

Does the low-carbon city pilot contribute to the blue sky defense? Evidence from China

China's goal of ecological civilization construction simultaneously faces the dual strategic tasks of pollution control and carbon reduction (PCCR). In addition to carbon reduction, does the low-carbon city pilot (LCCP) further contribute to the blue sky defense? This study investigates the impact of the LCCP on air pollution by employing a multiperiod difference-in-differences (DID) model based on data from 276 Chinese cities. The results show the following: (1) Compared with nonpilot areas, the LCCP reduces PM2.5 levels in pilot areas by approximately 1.50% on average, which is achieved by "industrial restructuring", "government investment in science and technology (S&T)", and "green lifestyle". (2) The LCCP has heterogeneous effects on air quality across cities with different resource endowments and industrial attributes, showing a greater air quality improvement in nonresource-based cities (NREB cities) and old industrial base cities (OIB cities) than in other city types. (3) The positive impact of the LCCP on air improvement in the pilot areas is derived from "pollution control effects" rather than "pollution transfer effects". This study provides useful policy implications for the comprehensive green transition and exploration of synergistic governance for PCCR in China.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018-2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃ in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018-2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R² > 0.8 except for SO₂) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO₂. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.