Short-Term and Long-Term Impacts of Air Pollution Control on China's Economy

PM2.5 concentration prediction algorithm integrating traffic congestion index

In this study, a strategy is proposed to use the congestion index as a new input feature. This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter (PM2.5) concentrations. To assess the effectiveness of this strategy, we conducted an ablation experiment on the congestion index and implemented a multi-scale input model. Compared with conventional models, the strategy reduces the root mean square error (RMSE) of all benchmark models by > 6.07 % on average, and the best-performing model reduces it by 12.06 %, demonstrating excellent performance improvement. In addition, even with high traffic emissions, the RMSE during peak hours is still below 9.83 µg/m3, which proves the effectiveness of the strategy by effectively addressing pollution hotspots. This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.

Coordinating public and government responses to air pollution exposure: A multi-source data fusion approach

Aligning public demand with government supply of clean air aids in efficient air pollution control and enhancement of public happiness. However, comparative empirical analyses of public and government attention to air quality changes are still sparse due to data and methodological constraints. Here, we adopt multi-source data fusion approaches to assess the impacts of air pollution exposure on public and government attention. Specifically, remote and social sensing data, alongside keywords extracted from textual data, are utilized to quantify air pollution exposure and corresponding public and government attention levels in 273 Chinese cities from 2011 to 2019, and a two-stage least squares regression model is employed to tackle reverse causality issues underlying the exposure-response relationship. Our findings reveal that, on average, a unit increase in PM2.5 levels would result in a 17.7% growth in public attention and a 12.7% rise in government attention, respectively, suggesting that demand-driven public attention tends to be more sensitive to air quality changes than policy-driven government attention. Results for the spatial-temporal heterogeneity further demonstrate that public attention varies across time and space, whereas government attention remains relatively consistent. Additionally, we have identified 116 cities exhibiting disparities between the public and government responses to air quality changes, calling for environmental policy refinements to better serve the needs of residents. This study emphasizes the necessity of public engagement in environmental governance and offers rich policy implications for air pollution control in China.

Air quality historical correlation model based on time series

Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model-the Gaussian Hidden Markov Model (GHMM)-using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching method and the Fixed Training Set Length method are utilized. Both direct and indirect prediction modes are then used to predict the AQI in the Zhangdian District. Experimental results indicate that the improved GHMM with the indirect mode provides higher accuracy and more stable state estimation results (MAE = 13.59, RMSE = 17.59, mean forecasted value = 117.94). Finally, the air quality historical correlation model is integrated with the air quality meteorological correlation model from a previous study, further improving prediction accuracy (MAE = 11.59, RMSE = 14.87, mean forecasted value = 120.88). This study demonstrates that the GHMM's strong ability to analyze temporal features significantly enhances the accuracy and stability of air quality predictions. The integration of the air quality historical correlation model with the air quality meteorological correlation model from a previous study leverages the strengths of each sub-model in handling different feature groups, leading to even more accurate predictions.

Can factor substitution reduce the shadow price of air pollution embodied in international trade? A worldwide perspective

The calculation of trade-embodied air pollution (TEAP) and its economic losses can be reasonably used to assess the impact of transboundary air pollution. However, these air pollutants, which are associated with international trade, can be easily ignored due to their concealment. Based on this, the global multiregional input‒output model (MRIO) is used to quantify the volume of five air pollutants that are embodied in the trade of 20 countries from 2000 to 2016. Then, the shadow price of trade-embodied air pollution (SPTEAP) and the elasticity of factor substitution (EFS) are both calculated by applying the translog production function. Finally, impulse response analysis is used to study the dynamic impact of EFS on the SPTEAP. The main conclusions are as follows: (1) All countries experienced a mass transfer of TEAP, among which China and the USA are the developing and developed countries with the largest amount of TEAP transfers, respectively. (2) The SPTEAP and EFS vary greatly among countries, and these values are generally higher in developed countries than in developing countries. The relationship between the three EFSs can be expressed as [Formula: see text] in all countries, thus indicating that improving the technological level of a country is the best solution for reducing the TEAP in that country while incurring the lowest cost and the least difficulty. (3) Over the long run, the increase in [Formula: see text] and [Formula: see text] reduces the SPTEAP. Conversely, an increase in [Formula: see text] increases the SPTEAP. Therefore, policymakers should weigh these three factors according to the fluctuation of the SPTEAP and constantly adjust the allocation structure and ratio of these factors to maximize the benefits of transboundary air pollution governance.

Interaction patterns between economic growth and atmospheric environment in China under the "carbon neutrality" target

Clarifying the interaction patterns between economic growth and atmospheric environment (EG-AE) in China is important to achieve the "carbon neutrality" target. A conceptual framework of air pollutant emission in urban economic growth (APEUEG) was proposed to explore the interaction patterns in China from 2007 to 2017. The empirical analysis revealed that a N-shaped EKC exists between aerosol optical depth (AOD) and gross domestic product (GDP), with inflection points of $5000 and $27,000, respectively. Therefore, we speculated that when GDP per capita of a city exceeded $5000, the AOD gradually decreased. However, when GDP per capita of a city gained over $27,000, the economic growth and the atmospheric environment would be coordinated steadily. The interaction of EG-AE experienced three stages-pollution, improvement, and coordination-in China. Spatially, the interaction patterns of EG-AE presented five clusters, which were associated with the spatial distribution of city levels. China's prefecture-level cities have undergone the cluster of low AOD-low GDP (LL), the cluster of high AOD-high GDP (HH), and the cluster of low AOD-high GDP (LH), as urban level improves. By 2017, about 44% of Chinese cities had not completed the coordinated development yet. We found that policymakers should formulate differentiated urban greener economic development policies to reduce APEUEG.

The distributional effects of China'senvironmental taxation: A multi-regional analysis

Environmental taxation is regarded as an effective tool to improve air quality in China, but its distributional effects causing serious disparity among multi-groups and multi-regions are understudied. Here this paper constructs a multi-regional dynamic recursive computable general equilibrium (CGE) model to explore the distributional effects of China's environmental taxation among different income groups and regions, by specifying the elasticity parameters of urban households' consumption in the model, and combining with various micro-data such as household survey data and environmental statistics database. This paper simulates the air pollution reductions of China's environmental taxation, and the impacts on the income and expenditure of households with various environmental tax rates or manners of tax revenue recycling. Results have shown that China's environmental taxation will widen the gap between different income groups and different regions. Also, such adverse distributional effects will be increased by higher environmental tax rates. However, recycling environmental tax revenues to both households and enterprises can reduce the losses of households' income and consumption. Yet recycling revenues to enterprises is more effective in narrowing the gap between income groups and regions while improving regional economic development. Our findings may pave a way to design appropriate environmental tax rates and tax revenue recycling manners for China's future environmental tax policies at the regional level.