Can PM2.5 concentration reduced by China's environmental protection tax?

The responsibility of enhancing environmental quality is shouldered by China's Environmental Protection Tax (EPT), which constitutes a vital element of China's tax system greening initiative. Using the difference-in-differences (DID) method, the effects of the EPT on PM2.5 concentration were empirically examined in this study, through panel data of 218 cities in China from 2015 to 2021. The results indicate that the EPT can effectively reduce PM2.5 concentration by approximately 2.4 %, and this conclusion remained unchanged after a series of robustness tests. In the channel analysis, it can be found that the reduction of PM2.5 concentration by the EPT was achieved through the alleviation of financing constraints, technological advancements, and optimization of industrial structure. Heterogeneity analysis indicates that the negative impact of the EPT on PM2.5 concentration was more significant in northern cities, inland cities and non-national environmental protection model cities. Further analysis found that EPT has a stronger inhibitory effect on PM2.5 concentration within 100 % of tax increase. The conclusions remain consistent when spatial spillover effects of PM2.5 are taken into account. This paper provides important empirical evidence to support the effectiveness of emission reductions of EPT and provides valuable insights for the future improvement of EPT.

Factors that influence the intent to pursue a master's degree: evidence from Shandong Province, China.

Introduction: In recent years, the pursuit of a master's degree has become a social phenomenon of wide concern. It is essential to understand why large number of students choose to pursue master's degree. This study aims to empirically analyze the factors that influence the intent to pursue a master's degree. Method: Based on the extended theory of planned behavior, this study conducts a questionnaire survey of university students in Shandong Province, which has had the highest number of people taking the postgraduate entrance examination in China for several years. A total of 440 questionnaires were finally collected, including 417 valid questionnaires. And then ordinary least squares (OLS) regression was used to analyze the factors that influence the intent to pursue a master's degree. Results: In general, the intent to pursue a master's degree is positively influenced by attitude (ß = 0.161, p < 0.01) and subjective norms (ß = 0.208, p < 0.01), and negatively influenced by risk perception (ß = -0.084, p < 0.05). Compared with male students, female students' intent is more likely to be influenced by risk perception (ß = -0.144, p < 0.05) and social factors (ß = 0.140, p < 0.05). The intent of upperclass students tends to be positively influenced by perceived behavioral control (ß = 0.125, p < 0.05), whereas the negative impact of risk perception (ß = -0.219, p < 0.05) on the intent is significant for underclass students. The intent of students in rural areas are more sensitive to risk perception (ß = -0.194, p < 0.01) than those of students in cities. In private universities, social factors (ß = 0.445, p < 0.05) significantly affect the intent to pursue a master's degree. In ordinary public universities, the intent of students is more likely to be influenced by risk perception (ß = -0.082, p < 0.05). Conclusion: The study is helpful to strengthen the understanding of the influencing factors of the intent to pursue a master's degree. In general, the intent to pursue a master's degree is mainly influenced by attitude, subjective norms and risk perception. Moreover, the influencing factors vary among different groups (e.g., female vs. male, rural areas vs. cities). Furthermore, attitude, subjective norms, perceived behavioral control, risk perception, and social factors have greater impacts on the intent of students from low-income households than those from high-income households. This study can provide policy implications for universities to take targeted educational measures to encourage students to make a choice that suits their own development after graduation.

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019.

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78-0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014-2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

Multifunctional Short-Chain 2-Thiophenealkylammonium Bromide Ligand-Assisted Perovskite Quantum Dots for Efficient Light-Emitting Diodes.

Lead halide perovskite quantum dots (QDs) have attracted great interest for application in light-emitting diodes (LEDs) due to their high photoluminescence quantum yield (PLQY), solution processability, and high color purity, showing great potential for next-generation full-color display and lighting technologies. Conventional long-chain insulating oleic acid (OA)/oleamine (OAm) ligands exhibit dynamic binding to the surface of QDs, resulting in a plethora of extra surface defects and inferior optoelectronic properties. Herein, a sole multifunctional ligand with optimized carbon chain length, that is, 2-thiophenepropylamine bromide (ThPABr), was creatively designed and introduced into CsPbBr3 QDs, which not only replaces OAm and provides a bromine source but also coordinates with the uncoordinated surface Pb2+ of QDs through the thiophene, passivating surface defects and increasing the PLQY of the film to 83%. More importantly, the interaction between the electron donor-thiophene ring and QDs can enhance electron injection and improve carrier balance. The resulting green LED exhibited significant performance improvement, showing ultrahigh spectral stability under high operating voltage, achieving a maximum external quantum efficiency of 10.5%, and extending the operating lifetime to 5-fold that of the reference. Designing a single multifunctional ligand presents a promising and convenient strategy for selecting surface ligands that can enhance the performance of LEDs or other optoelectronic devices.

Does environmental regulation lessen health risks? Evidence from Chinese cities.

Introduction: Atmospheric pollution is a severe problem confronting the world today, endangering not only natural ecosystem equilibrium but also human life and health. As a result, governments have enacted environmental regulations to minimize pollutant emissions, enhance air quality and protect public health. In this setting, it is critical to explore the health implications of environmental regulation. Methods: Based on city panel data from 2009 to 2020, the influence of environmental regulatory intensity on health risks in China is examined in this study. Results: It is discovered that enhanced environmental regulation significantly reduces health risks in cities, with each 1-unit increase in the degree of environmental regulation lowering the total number of local premature deaths from stroke, ischemic heart disease, and lung cancer by approximately 15.4%, a finding that remains true after multiple robustness tests. Furthermore, advances in science and technology are shown to boost the health benefits from environmental regulation. We also discover that inland cities, southern cities, and non-low-carbon pilot cities benefit more from environmental regulation. Discussion: The results of this research can serve as a theoretical and empirical foundation for comprehending the social welfare consequences of environmental regulation and for guiding environmental regulation decision-making.

The Impact of Pollution Fee Reform on the Emission of Water Pollutants: Evidence from Manufacturing Enterprises in China.

With the development of China's industrial economy and urbanization, water pollution has become serious and gradually exposed to the public. The pollution fee policy is an important tool to force enterprises to reduce pollution. This study used the panel data of manufacturing enterprises during 2006-2013 and the multiperiod difference in differences (DID) method to systematically analyze the impact of water pollution fee reform on emissions of manufacturing enterprises in China. In general, enterprises facing improved pollution fee collection standards reduce COD emissions by approximately 4.1%. However, significant location heterogeneities are captured in China. The rising water pollution fees have promoted the emission reduction of enterprises in northern China and resource-based cities, but the effect is not significant in southern China and nonresource-based cities. Furthermore, the mechanism analysis shows that enterprises mainly reduced emissions through terminal treatment and reducing production. This study provided micro evidence for research on the effect of pollution fee reform and supplied a reference for the improvement of the environmental protection tax system in China.

Are House Prices Affected by PM2.5 Pollution? Evidence from Beijing, China.

With the progress of high-quality development in China, residents have begun to focus on the air quality of their residential areas in an effort to reduce the health threats of air pollution. Gradually, the risk associated with air pollution has become an important factor affecting housing prices. To quantitatively analyze the impact of air pollution on house prices, panel data, including data for fine particulate matter (PM2.5) concentrations, house prices and other auxiliary variables from 2009 to 2018, were collected from 16 districts in Beijing, China. Based on this dataset, ordinary least squares (OLS), moderating effect and threshold effect models were constructed for empirical investigation. Within the studied decade, PM2.5 pollution shows a significant decreasing trend of −3.79 µg m−3 yr−1 (p < 0.01). For house prices, the opposite trend was found. The empirical results indicate that PM2.5 pollution has a negative effect on house prices and that every 1% increase in PM2.5 causes an approximately 0.541% decrease in house prices. However, the inhibition of PM2.5 on housing prices is moderated by regional educational resources, especially in areas with high education levels. In addition, per capita disposable income can also cause heterogeneities in the impact of PM2.5 on house prices, whereby the threshold is approximately CNY 101,185. Notably, the endogeneity problems of this study are solved by the instrumental variable method, and the results are robust. This outcome suggests that the coordinated control of air pollution and balanced educational resources among regions are required for the future sustainable development of the real estate market.

Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region.

Surface ozone (O3) is an important atmospheric trace gas, posing an enormous threat to ecological security and human health. Currently, the core objective of air pollution control in China is to realize the joint treatment of fine particulate matter (PM2.5) and O3. However, high-accuracy near-surface O3 maps remain lacking. Therefore, we established a new model to determine the full-coverage hourly O3 concentration with the WRF-Chem and random forest (RF) models combined with anthropogenic emission data and meteorological datasets. Based on this method, choosing the Beijing-Tianjin-Hebei (BTH) region in 2018 as an example, full-coverage hourly O3 maps were generated at a horizontal resolution of 9 km. The performance evaluation results indicated that the new model is reliable with a sample (station)-based 10-fold cross-validation (10-CV) R2 value of 0.94 (0.90) and root mean square error (RMSE) of 14.58 (19.18) µg m-3. In addition, the estimated O3 concentration is accurately determined at varying temporal scales with sample-based 10-CV R2 values of 0.96, 0.98 and 0.98 at the daily, monthly, and seasonal scales, respectively, which is highly superior to traditional derivation algorithms and other techniques in previous studies. An initial increase and subsequent decrease, which constitute the diurnal variation in the O3 concentration associated with temperature and solar radiation variations, were captured. The highest concentration reached approximately 112.73 ± 9.65 µg m-3 at 15:00 local time (1500 LT) in the BTH region. Summertime O3 posed a high pollution risk across the whole BTH region, especially in southern cities, and the pollution duration accounted for more than 50% of the summer season. Additionally, 43 and two days exhibited light and moderate O3 pollution, respectively, across the BTH region in 2018. Overall, the new method can be beneficial for near-surface O3 estimation with a high spatiotemporal resolution, which can be valuable for research in related fields.

Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) µg/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 µg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions.

Aerosol-induced direct radiative forcing effects on terrestrial ecosystem carbon fluxes over China.

Atmospheric aerosols can change vegetation photosynthesis through the effects of aerosols on radiation, which will affect the peak carbon dioxide emissions and carbon neutrality at global scales. In this study, we quantify the aerosol-induced direct radiation forcing (ADRF) in China from 2001 to 2014 based on the radiation flux simulation used by the Fu-Liou radiation transfer model under with-aerosols and no-aerosols scenarios. Using the radiation simulation results, we modify the atmospheric forcing datasets to drive Community Land Model 4.5 (CLM4.5) to gain the changes in carbon fluxes in China caused by ADRF. The results show that these two models are accurate in estimating radiation (R2 = 0.78-0.88) and carbon fluxes (R2 = 0.73-0.75) in China. High levels of ADRFs were captured in China, especially with increasing diffuse fraction, resulting in the diffusing fertilization effect occurring in most areas of China. The ADRF can increase cumulative gross primary productivity (GPP) and total ecosystem respiration (ER) by 3.20 gC m-2 and 5.13 gC m-2 per year, respectively. From 2001 to 2014, the diffusing fertilization effects experienced trends of increasing first and then decreasing. However, ADRFs in some regions of China show negative effects on carbon fluxes due to vulnerable vegetation functional types and high aerosol loading. The ADRF will also enable soil temperature decreases and volumetric soil water increases, which is closely related to changes in carbon fluxes. Meanwhile, due to changes in soil water and heat conditions, N2O and CH4 production will also be disturbed, and ADRF increases the global warming potential (GWP) for both greenhouse gases. This phenomenon indicated that atmospheric aerosol pollution control is far-reaching significance for peaking carbon dioxide emissions before 2030.

The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China.

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) µg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 µg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.

Spatiotemporal variations and relationships of aerosol-radiation-ecosystem productivity over China during 2001-2014.

Several air pollution episodes occurred in China in the past decade, and high levels of aerosols load also caused the changes of radiation, which could further influence the gross primary productivity (GPP) in the terrestrial ecosystem. This paper focuses on the spatiotemporal variations and relationship of aerosol-radiation-GPP in China during a heavy pollution period (2001-2014). For this purpose, the Fu-Liou radiation transfer mechanism model was used to estimate total radiation (TR) and diffuse radiation (DIFR) at the spatial resolution of 1° × 1° based on the satellite aerosol optical depth (AOD) and other auxiliary data. This model shows excellent performance with an R2 of 0.88 and 0.79 for TR and DIFR, respectively. A significant increasing trend (0.23 W m-2 year-1) in TR was found in China in this phase, and it was mainly attributed to DIFR. Furthermore, a scenario without aerosols (AOD = 0) was simulated as a comparison to quantify the aerosol radiative forcing, which indicated that aerosols play a catalytic role in DIFR, increasing it by approximately 19.55%. Despite all this, aerosols have weakened the brightening of China due to the negative forcing on direct radiation. Meanwhile, 0.65-4.20 kgC m-2 year-1 increase of GPP was also captured in seven regions of China during this phase.However, the significant negative response of GPP to aerosol was found in most ecosystems in the growing season of vegetation, and the highest correlation of -0.76 (p < .01) existed in the central China forest regions. It suggests although aerosol causes a diffuse fertilization effect, GPP is still lost due to high levels of aerosol load in most areas of China during growing season of vegetation. This paper aims to determine the relationship among the aerosol-radiation-ecosystem productivity in different regions of China, which could provide a reference for the divisional strategy formulation and classification management in different ecosystems.

π-Expanded Coumarins: One-Pot Photo Synthesis of 5H-Benzo[12,1]tetrapheno[7,6,5-cde]chromen-5-ones and Photophysical Properties.

The synthesis of a unique type of π-expanded coumarin derivatives, bearing six fused phenyl rings, was achieved via one-pot Suzuki reaction and visible light-driven electrocyclization. The large π-expanded 5H-benzo[12,1]tetrapheno[7,6,5-cde]chromen-5-ones were obtained in good to high yields from 1-bromo-2H-phenaleno[1,2,3-de]chromen-2-ones, and the intriguing optical properties were explored by altering the attached functional groups. 2-Arylaminosubstituted-5H-benzo[12,1]tetrapheno[7,6,5-cde]chromen-5-ones showed a large Stokes shift (4005 cm-1) or excellent fluorescence quantum yield (Φf = 0.75) along with significant bathochromic shift in tetrahydrofuran.

Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China.

The PM2.5 concentration is an important evaluation index for the global Sustainable Development Goals (SDGs) for its negative impacts on human health. Last decade, several fine particulate pollution episodes occurred in the vast area of China. In response to this, the Chinese government has stepped up efforts to tackle air pollution. In this paper, the temporal trends of PM2.5 and the quantitative potential impact of environmental governance on PM2.5 are analyzed for China. Due to the lack of historical records, a two-stage model was used to estimate the historical PM2.5 concentrations, combined with the newly released satellite-based aerosol optical depth (AOD) product (MODIS Collection 6.1) and other data. The estimated PM2.5 concentrations showed strong consistency with the surface observations. Furthermore, significant seasonal variations existed in the PM2.5 concentrations and the temporal trends were captured, especially in city clusters. Then eight major city clusters were selected as typical samples. All the city clusters showed decrease trends in recent years, with PM2.5 concentrations in these regions decreased by 0.269-1.604 µg m-3 year-1. From 2006 to 2017, the annual PM2.5 concentrations decreased by 7.83%-26.35% in the major city clusters among China. Technological innovation and environmental governance play an important role in the decrease of PM2.5. In order to quantify the influence of governance, environmental regulation intensity and synergy were applied as the indicators of the internal governance and co-governance in each city cluster. In most city clusters, PM2.5 concentrations were significantly negatively correlated with regional internal governance and co-governance (R = -0.596 to -0.930, p < 0.05), and the effect on PM2.5 lasted for several years. However, 1- to 2-year lagged effect was found for governance, which means that the regulatory measures should be enhanced to decrease PM2.5 in the future to achieve the SDGs in China.

Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China.

Particulate matter with aerodynamic diameters ≤1 µm (PM1) has a greater impact on the human health but has been less studied due to fewer ground observations. This study attempts to improve the retrieval accuracy and spatial resolution of satellite-based PM1 estimates using the new ground-based monitoring network in China. Therefore, a space-time extremely randomized trees (STET) model is first developed to estimate PM1 concentrations at a 1 km spatial resolution from 2014 to 2018 across mainland China. The STET model can derive daily PM1 concentrations with an average across-validation coefficient of determination of 0.77, a low root-mean-square error of 14.6 µg/m3, and a mean absolute error of 8.9 µg/m3. PM1 concentrations are generally low in most areas of China, except for the North China Plain and Sichuan Basin where intense human activities and poor natural conditions are prevalent, especially in winter. Moreover, PM1 pollution has greatly decreased over the past 5 years, benefiting from emission control in China. The STET model, incorporating the spatiotemporal information, shows superior performance in PM1 estimates relative to previous studies. This high-resolution and high-quality PM1 data set in China (i.e., ChinaHighPM1) can be greatly useful for air pollution studies in medium- or small-scale areas.