Air pollution lowers Chinese urbanites' expressed happiness on social media

Perception matters: How air pollution influences life satisfaction in China

Academic studies on environmental pollution have convincingly acknowledged the salient relevance of ambient pollutant emissions on individual life satisfaction. However, an understanding of how the different dimensions of air pollution influence public self-assessment of their living condition is required. This research investigates whether objective pollutant emissions and subjective evaluation influence individual life satisfaction. The findings were based on data from the China Environment Yearbook and China Social Survey in 2019. The multi-level linear regression model found that air pollutants emissions, including particulate matter (PM) and sulfur dioxide (SO2), failed to explain the variations in public life satisfaction because of the lag effect of public perception. A significant nexus between perceived air pollution and public life satisfaction was observed at a significance level of 0.01. Specially, as the perceived air pollution by the public increased by one-point, life satisfaction decreased by 0.22 on a scale of 1-10, on average. Heterogeneous analysis based on income further suggested the salient negative effect of PM emissions on life satisfaction only occurred in the high-income group. The findings were robust after various methodological analyses. This study has theoretical implications for understanding the effects of air pollution on public subjective perception and provides guidance for how the government can manage the relationship between environmental governance and life satisfaction.

Effects of extreme temperatures on public sentiment in 49 Chinese cities

The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.

Social media influences National Park visitation

Visitation to National Parks in the United States increased by more than 25% since 2010, rising from roughly 70 to 90 million annual visitors. Anecdotes suggest that this increase was driven by the advent of social media in the early-to-mid 2010s, generating a new form of exposure for parks, and has led to concerns about overcrowding and degradation of environmental quality. However, there is little empirical evidence on the role of social media in influencing recreation decisions. Here, I construct a dataset on social media exposure (SME) for each National Park and relate that exposure to changes in visitation over the last two decades. High SME parks see visitation increase by 16 to 22% relative to parks with less exposure, which comes with a concomitant increase in revenue. Low SME parks have no, or negative, changes in visitation. These estimates account for unobserved park heterogeneity and are based on an instrumental variables strategy that predicts exposure with a park's online popularity prior to the social media era. Additional analysis suggests that recent social media posts that include media attachments increase visitation, while posts with negative sentiment reduce visitation. These results provide insight for the National Park Service-which faces more than $22 billion in deferred maintenance costs and is considering policy options to manage demand-as well as for management of recreation on other public lands.

Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China

China is one of the largest producers and consumers of coal in the world. The National Action Plan on Air Pollution Prevention and Control in China (2013-2017) particularly aimed to reduce emissions from coal combustion. Here, we show whether the acute health effects of PM2.5 changed from 2013 to 2018 and factors that might account for any observed changes in the Beijing-Tianjin-Hebei (BTH) and the surrounding areas where there were major reductions in PM2.5 concentrations. We used a two-stage analysis strategy, with a quasi-Poisson regression model and a random effects meta-analysis, to assess the effects of PM2.5 on mortality in the 47 counties of BTH. We found that the mean daily PM2.5 levels and the SO42- component ratio dramatically decreased in the study period, which was likely related to the control of coal emissions. Subsequently, the acute effects of PM2.5 were significantly decreased for total and circulatory mortality. A 10 μg/m3 increase in PM2.5 concentrations was associated with a 0.16% (95% CI: 0.08, 0.24%) and 0.02% (95% CI: -0.09, 0.13%) increase in mortality from 2013 to 2015 and from 2016 to 2018, respectively. The changes in air pollution sources or PM2.5 components appeared to have played a core role in reducing the health effects. The air pollution control measures implemented recently targeting coal emissions taken in China may have resulted in significant health benefits.

Does national forest city improve residents' health? Evidence from China

Objective

National health is essential for economic and social development. The aim of this article is to examine the relationship, heterogeneity effects and influential mechanisms between National Forest Cities and the residents' health.

Methods

The article matches the China Family Panel Studies data in 2018 (CFPS2018) with the 2016-2018 National Forest Cities Construction List, resulting in a final sample of 20,041. Oprobit, Ologit, Instrumental Variable technique (2SLS) and interaction term analysis were used as the main research methods in this article.

Results

The findings indicate that: (1) The construction of National Forest Cities significantly improves the residents' health in terms of both physical and mental health, and this conclusion is still valid after a series of robustness tests. (2) On the one hand, National Forest Cities promote residents' health by reducing air pollutants such as SO2 and soot to reduce residents' health risk exposure; On the other hand, it promotes residents' health by positively guiding them to engage in healthy behaviors. (3) National Forest Cities have a greater effect on the health of urban residents, older adult and lower-income group, suggesting that National Forest Cities are a public benefit.

Conclusions

The construction of National Forest Cities is a public welfare that promotes residents' health, and it is an important revelation for accelerating the realization of the Healthy China Strategy. The article provides new empirical evidence for understanding the welfare effects of forest cities and offers new practical paths for improving residents' health.

Happiness and air quality: microdata analysis in Indonesia

BACKGROUND

While economics is growing in Indonesia, its Happiness Index remains steady. Regarding the average concentration of dissolved particles, Indonesia is ranked sixth globally. Many factors can affect happiness. Environmental conditions, especially air quality, are considered to influence individual happiness. Therefore, this research investigates the impact of air quality and health on happiness.

METHODS

Data used in this study is the microdata of Indonesia's Happiness Survey (SPTK) in 2021. With more than 70,000 respondents, the study uses Ordered Probit as an analysis method with subjective happiness as the dependent variable. The independent variables used in this study are air quality, age, gender, housing area per capita, marital status, and health status. All independent variables except age are categorical. The variable of interest, air quality, is coded "1" if the IKU achieves the Strategic Plan's target of 84.2 and "0" otherwise. IKU is a regional air quality index that combines two substances.

RESULTS

At a significance level of 5%, there is a positive relationship between subjective happiness and air quality. In other words, if air quality (IKU) meets the Strategic Plan target (≥ 84.2), then a person is more likely to have a higher level of happiness. It can be explained more with the marginal effect. The marginal effect concludes that if the target is achieved, the probability of having a lower level of happiness decreases by up to 2.8%, and a very high level of happiness rises by 5.1%. Regarding health status, the rarer someone gets sick, the happier she/he is.

CONCLUSION

The study finds that air pollution could lower happiness, while better health increases happiness. Therefore, it is important to meet the target of IKU and to improve public health. Some good practices can be adopted to achieve this goal.

Role of metabolic risk factors in the relationship between ambient fine particulate matter and depressive symptoms: Evidence from a longitudinal population study

BACKGROUND

There is growing evidence indicating a connection between fine particulate matter (PM2.5) and depressive symptoms. Metabolic risk factors are critical determinants of depressive symptoms. However, the mediating role of these factors on the association between PM2.5 and depressive symptoms remains elusive. We aimed to investigate whether and to what extent metabolic risk factors mediated the link between long-term PM2.5 exposure and depressive symptoms.

METHODS

This study comprised 7794 individuals aged between 30 and 79 years who participated in two waves of the on-site surveys in the China Multi-Ethnic Cohort. Ambient PM2.5 concentrations were assessed utilizing a random forest method based on satellite data. We employed the Patient Health Questionnaire-9 to assess depressive symptoms at wave 2, and the overall as well as three sub-domain symptom scores (emotional, neurovegetative, and neurocognitive symptoms) were calculated. Three metabolic risk factors, including hypertension, diabetes, and dyslipidemia, were considered. Mediation analyses were conducted to assess the indirect effects of PM2.5 on depressive symptoms through metabolic risk factors.

RESULTS

We found a positive association between chronic exposure to ambient PM2.5 and overall depressive symptoms as well as the three sub-domains. In mediation analyses, metabolic risk factors partially mediated the associations of PM2.5 on depressive symptoms. The natural indirect effects (RR, 95% CI) of PM2.5 on overall, emotional, neurovegetative, and neurocognitive symptoms mediated through metabolic risk factors were 1.004(1.001, 1.007), 1.004 (1.001, 1.008), 1.004 (1.001, 1.007), and 1.003(0.999, 1.007), respectively. Larger indirect effects were found in elderly participants (mediated proportion, 29.3%), females (13.3%), and people who did not consume alcohol (19.6%).

CONCLUSIONS

Metabolic risk factors may act as mediators in the relationship between chronic PM2.5 exposure and depression. Treatment of metabolic risk factors may be an opportunity to reduce the burden of depression caused by long-term exposure to PM2.5.

Characterizing environmental pollution with civil complaints and social media data: A case of the Greater Taipei Area

Environmental pollution is a major cause of nuisance and ill health among urban residents. Complaints are traditionally self-reported through phone-based systems. Social media provide novel channels to detect pollution-related incidents; however, their reliability has not been sufficiently evaluated. This study aimed to compare pollution incidents expressed on Twitter with those extracted from phone-based systems and to identify the built environment and socioeconomic attributes that can predict the likelihood of pollution incidents. A total of 639,746 tweets were retrieved from the Greater Taipei Area in 2017 and 110,716 self-reported pollution incidents were extracted from the Public Nuisance Petition system during the same period. The results suggest that complaints collected from phone-based systems and Twitter were found to have correlated with each other spatially, albeit they differ in temporal profiles and by the proportion of pollution categories. Catering businesses and the entertainment activities they attract appear to be the main sources of pollution complaints and can be precisely captured by geotagged tweets. This can serve as a strong predictor for pollution incidents, more than traditional indicators such as population density or industrial activities, as suggested by earlier studies. Social media analytics, with their ability to monitor and analyze online discussions in a timely manner, can be a valuable supplement to existing phone-based pollution monitoring procedures. The methodologies developed in this study have the potential to support the proactive management of urban environmental pollution, in which resources can be prioritized in key areas to further enhance the quality of urban services.

Living with particles: Disclosure of pollution information, individual responses, and health consequences

Drawing on a panel dataset-the China Health and Retirement Longitudinal Survey (CHARLS)-and other city- and individual-level datasets, this study examines the causal impact of pollution information disclosure on individual outdoor activities and the health status of the middle-aged and elderly. Using city-level variations in disclosure timing, we found that the adoption of pollution information disclosure (PID) significantly reduces the probability of outdoor exercise, especially for those living in more polluted cities. This occurs mainly through enhanced awareness of environmental pollution, particularly for those who are more educated. However, the adoption of PID does not lead to an improvement in health status.

LEIA: Linguistic Embeddings for the Identification of Affect

The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods. We further perform an out-of-domain evaluation on five different datasets of social media and other sources, showing LEIA's robust performance across media, data collection methods, and annotation schemes. Our results show that LEIA generalizes its classification of anger, happiness, and sadness beyond the domain it was trained on. LEIA can be applied in future research to provide better identification of emotions in text from the perspective of the writer.

Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions

The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China. It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment. Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.

Greenspace exposure is conducive to the resilience of public sentiment during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic significantly impacts people's sentiment and mental health, threatening their health and lives. We gathered 4.17 million geotagged social media posts from Weibo and scrutinized the nuances of the collective sentiments of netizens in four megacities in China during the first pandemic wave (from 1 December 2019 to 30 April 2020). Our findings suggest that the COVID-19 outbreak significantly reduced the Sentiment Index (SI) in China's cities, and the collective sentiments expressed in Wuhan were even more negative than those in the other three megacities. We explored the uncharted impacts of exposure to three geographical environment factors (GEFs) on SIs. Public exposure to greenspaces increased, while exposure to indoor built spaces decreased during the lockdown period. The exposure to sidewalks increased in rural areas but decreased in the main urban areas. The contributions of various GEFs to the SIs were the lowest during the lockdown period, and SIs were strongly affected by the pandemic. However, greenspace had the most potent effect on SIs, improving public sentiment resilience and mitigating mental health risks.

How does air pollution affect household energy expenditure: A micro-empirical study based on avoidance behavior

China is one of the world's most highly-pollutant countries, and over the last few years has experienced severe and persistent haze pollution. Exploring the effects of air pollution on household energy expenditure will provide a more comprehensive and accurate understanding of the economic effects of environmental pollution. It is an important question but remains unanswered because of an estimation endogeneity. Household non-clean energy consumption would increase air pollution. A major challenge in estimates is how to accurately and cleanly identify the unwatched effect of air pollution given the endogeneity. Using the global satellite monitoring data and unique micro-household survey data, we try to construct an instrumental variable to identify the net effect of air pollution on Chinese household energy expenditure. We find that the increase in air pollution has a significantly positive effect on household energy expenditure. The results remain robust after a series of pivotal checks. Our findings highlight that stay-home-avoidance behaviors as a potential mechanism behind the energy-associated effects of air pollution on household energy expenditure. The well-educated, high-income, and urban households in southern China are more likely to engage in stay-home-avoidance behaviors. These findings can provide some useful policy enlightenments for the government in terms of environmental regulation and promotion of household clean energy.

Climatic and Economic Background Determine the Disparities in Urbanites' Expressed Happiness during the Summer Heat

Climate-change-induced extreme weather events increase heat-related mortality and health risks for urbanites, which may also affect urbanites' expressed happiness (EH) and well-being. However, the links among EH, climate, and socioeconomic factors remain unclear. Here we collected ∼6 million geotagged tweets from 44 Chinese prefecture-level cities based on Sina Weibo and performed a quadratic regression model to explore the relationships between summer heat and EH. A three-stage analysis was developed to examine spatiotemporal heterogeneity and identify factors contributing to disparities in urbanites' EH. Results show that all cities exhibited a similar hump-shaped relationship, with an overall optimal temperature (OT) of 22.8 °C. The estimated OT varied geographically, with 25.3, 23.8, and 20.0 °C from north to south. Moreover, a 1 standard deviation increase in heatwave intensity was associated with a 0.813 (95% CI: 0.177, 1.449) standard deviation decrease in EH. Notably, within the geographic scope of this study, it was observed that urbanites in northern China and economically underdeveloped cities faced significantly lower heat risks during the summer heat. This research provides insight for future studies and practical applications concerning extreme weather events, urbanites' mental health, and sustainable urban development goal.

Exploring public attention and sentiment toward carbon neutrality: evidence from Chinese social media Sina Weibo

Introduction

Exploring the public's cognition toward carbon neutrality is conducive to improving the quality and effectiveness of policymaking, and promoting the realization of carbon neutrality goals. This study aims to explore the public's attention and sentiment toward carbon neutrality from the perspective of social psychology.

Methods

Using posts on carbon neutrality from the Chinese social media platform Sina Weibo as the data source, this study uses statistical analysis, the Mann-Kendall method, keyword analysis, the BERT model, and the LDA model to explore public attention and sentiment.

Results

The results show that: (1) men, people living east of the Hu line (economically developed regions), and the public in the energy finance market are more concerned about carbon neutrality; (2) high public attention and great dynamic changes in public attention toward carbon neutrality could be trigged by highly credible government or international governmental organizations' information; (3) public sentiment toward carbon neutrality is mostly positive; however, specific topics affect public sentiment differently.

Discussion

The research results contribute to policymakers' better understanding of the trend of public attention and sentiment toward carbon neutrality, and support improvements in the quality and impact of policymaking.

Urban and individual correlates of subjective well-being in China: An application of gradient boosting decision trees

Introduction

Subjective well-being (SWB) is attributable to both individual and environmental attributes. However, extant studies have paid little attention to the contribution of environmental attributes at the urban level to SWB or their nonlinear associations with SWB.

Methods

This study applies a machine learning approach called gradient boosting decision trees (GBDTs) to the 2013 China Household Income Survey data to investigate the relative importance of urban and individual attributes to and their nonlinear associations with SWB.

Results

The urban and individual attributes make similar relative contributions to SWB. Income and age are the most important predictors. Urban facilities make a larger contribution than urban development factors. Moreover, urban attributes exert nonlinear and threshold effects on SWB. Cultural facilities and green space have inverted U-shaped correlations with SWB. Educational facilities, medical facilities, and population size are monotonically associated with SWB and have specific thresholds.

Discussion

Improving urban attributes is important to enhancing residents' SWB.

How does the fiscal pressure of local governments affect China's PM2.5 emissions?

Affected by the epidemic and other factors, the global economy is in a downturn, and countries around the world are under unprecedented debt pressure. How will this affect environmental protection? Taking China as an example, this paper empirically studies the impact of changes in local government behavior on urban air quality under fiscal pressure. This paper uses the generalized method of moments (GMM) to find that fiscal pressure has significantly reduced PM2.5 emissions, with a unit increase in fiscal pressure will increase PM2.5 by about 2%. The mechanism verification shows that three channels affect PM2.5 emissions: (1) fiscal pressure has prompted local governments to relax the supervision of existing pollution-intensive enterprises. (2) Local governments reduce environmental regulations for attracting more pollution-intensive enterprises. (3) Local governments tend to reduce environmental protection investment to save fiscal expenses. The paper's conclusions provide new policy ideas for promoting environmental protection in China, as well as served as a case for analyzing current changes in environmental protection in other countries.

Accounting for the importance of psychological distance in assessing public preferences for air quality improvement policies: an application of the integrated choice and latent variable model

Assessing public preferences for air pollution control is essential to achieving effective air quality improvement, but the internal psychological factors affecting public preferences, especially psychological distance (PD), have only received limited attention. Therefore, this paper explores the role of PD in assessing public preferences for air quality improvement policies. Compared with previous studies that consider psychological factors in the choice model, this study incorporates PD into the choice model as a latent variable by considering both individual responses to measurement questions and socio-economic characteristics in the integrated choice and latent variable model. The results of this study clearly show that PD significantly affects public preferences for air quality improvement policies. Respondents with close PD had obvious preferences for air quality improvement, while those with distant PD were satisfied with the current situation and reluctant to improve it. After considering PD in the analysis, respondents' willingness to pay for one-unit level change of attributes "heavily polluted days," "good air days," "mortality," and "policy postponement" were respectively 10.3791CNY, 10.9005CNY, 11.0427CNY, 28.3412CNY per year. In addition, the paper also found men and respondents with lower levels of education and higher monthly incomes tended to view air pollution as psychologically distant and thus less willing to improve air quality. It is suggested that policy makers should reduce the PD of air pollution among these people by increasing publicity about the hazards of air pollution. This study not only contributes to the literature on the importance of PD in assessing individual preferences, but also provides constructive guidance for policy makers to assess the public's acceptability of air quality improvement.

The long-term impacts of air quality on fine-grained online emotional responses to haze pollution in 160 Chinese cities

Air pollution poses a great threat to public health and social stability by influencing multiple emotions. In particular, the air quality in developing countries is deteriorating along with rapid industrialization and urbanization, and multiple emotions may change along with regulation updates and air quality trending. Monitoring changes in public emotion is crucial for environmental governance. However, limited evidence exists for long-term effects of air quality on fine-grained emotions. Traditional surveys have the drawbacks of spatial limitations and high costs of time and money. Here, we use deep learning models to identify multiple emotions of over 10 million haze-related tweets and evaluate the effect of air quality on emotional predispositions for 160 cities from 2014 to 2019 in China. We find that sadness and joy are persistently associated with air quality, while anger and disgust are not. Surprisingly, the effects on fear vanished in the last three years. Moreover, air pollution initially had a greater impact on expressed fear in cities with higher income, poorer air quality and a greater percentage of women. Through popularity ranking and dynamic topic model, we interpretively revealed that people are no longer overly panicked and their attention is shifting toward policies and sources of haze. Our findings highlight the temporal evolution in the public's emotional response and provide significant implications for equitable public policies.

Air pollution and anti-social behaviour: Evidence from a randomised lab-in-the-field experiment

We conducted a pre-registered randomised lab-in-the-field online experiment in Beijing, China, to explore the relationship between acute air pollution and anti-social behaviour. Our novel experimental design exploits naturally occurring discontinuities in pollution episodes to mimic an experimental setting in which pollution exposure is exogenously manipulated, thus allowing us to identify a causal relationship. Participants were randomly assigned to be surveyed on either high pollution or low pollution days, thereby exogenously varying the degree of pollution exposure. In addition, a subset of individuals surveyed on the high-pollution days received an additional 'pollution alert' to explore whether providing air pollution warnings influences (protective) behaviour. We used a set of well-established incentivised economic games to obtain clean measures of anti-social behaviour, as well as a range of secondary outcomes which may drive the proposed pollution-behaviour relationship. Our results indicate that exposure to acute air pollution had no statistically significant effect on anti-social behaviour, but significantly reduced both psychological and physiological well-being. However, these effects do not remain statistically significant after adjusting for multiple hypothesis testing. We find no evidence that pollution affects cognitive ability, present bias, discounting, or risk aversion, four potential pathways which may explain the relationship between pollution and anti-social behaviour. Our study adds to the growing calls for purposefully designed and pre-registered experiments that strengthen experimental (as opposed to correlational or quasi-experimental) identification and thus allow causal insights into the relationship between pollution and anti-social behaviour.

Joint effects of air PM2.5 and socioeconomic dimensions on posted emotions of urban green space visitors in cities experiencing population urbanization: A pilot study on 50 cities of East China

People may perceive and expose negative sentiments in days with PM_2.5 pollutions, but evidence is still insufficient about the joint effects of PM_2.5 and socioeconomic factors on human sentiments. In this study, a total of 8032 facial photos of urban green space visitors were obtained from Sina Weibo in 50 cities of East China and rated for happy, sad, neutral scores and net positive emotion index (NPE; happy minus sad). Seasonal air PM_2.5 concentrations were collected from days when people exposed faces in cities that were categorized to medium, large, outsize, and mega sizes according to resident populations (RPs). In summer, people posted lower sad score (11.28 %) than in winter (13.51 %; P = 0.0357) and higher NPE (35.86 %) than in autumn (30.92 %; P = 0.0009). Multivariate linear regression on natural logarithms revealed that factors of gross domestic product per capita (parameter estimate: 0.45), RP (0.59), non-production electricity consumption (0.34), and length of road transport (0.34) together generated positive contributions to posted happy score, while the total retail trade of consumer goods (-1.25) and PM_2.5 (-0.50) were perceived as joint depressors on NPE. Overall, cities with more rich households and activated retail sales attracted more people who exposed smiles in weathers with PM_2.5 compared to cities where local economy is reliable on heavy industry. The summertime in mega cities will be recommended to enjoy a higher frequency to perceive satisfaction due to exposure to low PM_2.5.

Anti-social behaviour and economic decision-making: Panel experimental evidence in the wake of COVID-19

We systematically examine the acute impact of exposure to a public health crisis on anti-social behaviour and economic decision-making using unique experimental panel data from China, collected just before the outbreak of COVID-19 and immediately after the first wave was overcome. Exploiting plausibly exogenous geographical variation in virus exposure coupled with a dataset of longitudinal experiments, we show that participants who were more intensely exposed to the virus outbreak became more anti-social than those with lower exposure, while other aspects of economic and social preferences remain largely stable. The finding is robust to multiple hypothesis testing and a similar, yet less pronounced pattern emerges when using alternative measures of virus exposure, reflecting societal concern and sentiment, constructed using social media data. The anti-social response is particularly pronounced for individuals who experienced an increase in depression or negative affect, which highlights the important role of psychological health as a potential mechanism through which the virus outbreak affected behaviour.

Exploration of the Dynamic Evolution of Online Public Opinion towards Waste Classification in Shanghai

Shanghai is one of the fastest-growing metropolises and the first city in China to implement mandatory waste classification. Waste classification policy of Shanghai has attracted widespread attention since its implementation in July 2019. However, previous papers have not focused on online public attitudes surrounding the implementation of a waste classification policy in Shanghai. In order to fill this gap, this paper explored the dynamic evolution of online public attitudes towards waste classification in Shanghai by using sentiment analysis technology and topic modeling technology. It was found that the proportion of negative posts each month was about 20%; therefore, online public sentiment towards waste classification in Shanghai was generally positive. Compared with the first three months of policy implementation, the public sentiment towards Shanghai's waste classification became more positive, with the exception of two special periods. Negative posts in July 2019 mainly discussed waste's environmental hazards and policy provisions. New topics in negative posts in later months focused on some specific problems, including the process of throwing away wet waste, the allocated throwing times, the number of waste cans, takeaway meal disposal, and gathering activities. Improving the factors causing the negative sentiments in the posts will help the government better implement the policy. The paper will help the government to receive higher public support for the waste classification policy in Shanghai. The present findings also have great reference significance for other cities.

Does the built environment of settlements affect our sentiments? A multi-level and non-linear analysis of Xiamen, China, using social media data

Introduction

Humans spend most of their time in settlements, and the built environment of settlements may affect the residents' sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size.

Methods

This study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments.

Results

The results show the following: (1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, and neighborhoods close to water are more likely to improve the residents' sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. (2) The proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value is a basic need and exhibits a nonlinear correlation with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments.

Discussion

The quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents.

Using social media data to assess the impact of COVID-19 on mental health in China

BACKGROUND

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) not only caused an adverse impact on physical health, but also brought about mental health problems among the public.

METHODS

To assess the causal impact of COVID-19 on psychological changes in China, we constructed a city-level panel data set based on the expressed sentiment in the contents of 13 million geotagged tweets on Sina Weibo, the Chinese largest microblog platform.

RESULTS

Applying a difference-in-differences approach, we found a significant deterioration in mental health status after the occurrence of COVID-19. We also observed that this psychological effect faded out over time during our study period and was more pronounced among women, teenagers and older adults. The mental health impact was more likely to be observed in cities with low levels of initial mental health status, economic development, medical resources and social security.

CONCLUSIONS

Our findings may assist in the understanding of mental health impact of COVID-19 and yield useful insights into how to make effective psychological interventions in this kind of sudden public health event.

How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

BACKGROUND

Natural environment might encourage physical exercise, hence enhancing human health and wellbeing. Social media offers an extensive repository of spatiotemporal data, containing details on the feelings and behaviors of individuals. However, investigations on physical activity and public sentiment in the natural environment of the downtown neighborhood are lacking in the existing literature.

METHODS

To extract environmental and behavioral information from social media data and other multi-source data, natural language processing, semantic segmentation, instance segmentation, and fully convolutional neural networks are employed. The research examines how neighborhood blue-green spaces and other health-promoting facilities affect physical activity and public sentiment.

RESULTS

The results reveal that blue space visibility, activity facilities, street furniture, and safety all have a favorable influence on physical activity with a social gradient. Amenities, perceived street safety and beauty positively correlated to public sentiment. The findings from social media about the environment and physical activity are consistent with traditional surveys from the same time period with a 0.588 kappa value.

CONCLUSION

According to our findings, social media data might be utilized to learn more about how urban environments influence people's physical activity patterns. Also, the health-promoting effects of blue space require more investigation.

Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Mental health conditions have the potential to be worsened by air pollution or other climate-sensitive factors. Few studies have empirically examined those associations when we faced to co-exposures, as well as interaction effects. There would be an urgent need to use deep learning to handle complex co-exposures that might interact in multiple ways, and the model performance reinforced by SHapely Additive exPlanations (SHAP) enabled our predictions interpretable and hence actionable. Here, to evaluate the mixed effect of short-term co-exposure, we conducted a time-series analysis using approximately 1.47 million hospital outpatient visits of mental disorders (i.e., depressive disorder-DD, Schizophrenia-SP, Anxiety Disorder-AD, Bipolar Disorder-BD, Attention Deficit and Hyperactivity Disorder-ADHD, Autism Spectrum Disorder-ASD), with matched meteorological observations from 2015 through 2019 in Nanjing, China. The global insights of gated recurrent unit model revealed that most of input features with similar effect size caused the illness risk of SP and ASD increase, and most markedly, 73% of relative humidity, 44.6 µg/m³ of NO₂, and 14.1 µg/m³ of SO₂ at 5-year average level associated with 2.27, 1.14, and 1.29 visits increase for DD, SP, and AD, respectively. Both synergic and antagonistic effect among informative paired-features were distinguished from local feature dependence. Interestingly, variation tendencies of excessive visits of bipolar disorder when atmospheric pressure, PM_2.5, and O₃ interacted with one another were inconsistent. Our results provided added qualitative and quantitative support for the conclusion that short-term co-exposure to ambient air pollutants and meteorological conditions posed threats to human mental health.

Spatial correlation and influencing factors of industrial agglomeration and pollution discharges: a case study of 284 cities in China

Exploring the spatial correlation characteristics and influencing factors of industrial agglomeration and pollution discharge, which is of great significance to reduce industrial pollution discharge and promote China's construction of an ecological civilization. Taking 284 prefecture-level cities in China in 2017 as the research object, this study used spatial autocorrelation analysis method to explore the spatial agglomeration characteristics and spatial correlation of industrial agglomeration and industrial pollution discharge, and spatial econometric analysis method was used to explore the main factors affecting industrial pollution discharge. The research results showed that the level of industrial agglomeration in China exhibited a spatial distribution characteristic of "high in the east and low in the west". The total discharge and discharge intensity of industrial pollutants showed a spatial pattern of "high in the north and low in the south" in general, and industrial agglomeration, total discharge, and discharge intensity of industrial pollution showed significant spatial autocorrelation. Moreover, industrial agglomeration had a strong local spatial correlation with the total and intensity of industrial wastewater, industrial SO₂, and industrial smoke and dust, and the main agglomeration types were high agglomeration-low pollution, low agglomeration-high pollution, and low agglomeration-low pollution. In addition, industrial agglomeration had a positive impact on the total industrial wastewater discharge, and had a negative impact on the total industrial smoke and dust discharge, industrial wastewater discharge intensity, industrial SO₂ discharge intensity, and industrial smoke and dust discharge intensity.

The unbalanced trade-off between pollution exposure and energy consumption induced by averting behaviors

Behavioral responses to environmental risks create gains and losses. We use high-frequency datasets to elucidate such behavior responses against air pollution and find a "double-peaked" time pattern in reducing outdoor exposure and in increasing electricity consumption. Despite that one standard deviation increase in the Air Quality Index induces 2% less outdoor population and 6% more household electricity consumption at peak, most responses fail to match with the intra-day pollution peaks, implying ineffective exposure avoidance. We find an unbalanced trade-off between health benefits and energy co-damages. The behavior-induced change in annual residential power consumption (+1.01% to +1.20%) is estimated to be 20 times more than that in the population-based exposure (-0.02% to -0.05%), and generates 0.13-0.15 million more metric tons of citywide carbon emissions. Our results imply that by targeting peak pollution periods, policies can shrink the trade-off imbalance and achieve mutual improvements in exposure reduction and energy conservation.

Measuring daily-life fear perception change: A computational study in the context of COVID-19

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

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.

Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations

To understand the temporal variation, spatial distribution and factors influencing the public's sensitivity to air pollution in China, this study collected air pollution data from 2210 air pollution monitoring sites from around China and used keyword-based filtering to identify individual messages related to air pollution and health on Sina Weibo during 2017-2021. By analyzing correlations between concentrations of air pollutants (PM_2.5, PM₁₀, CO, NO₂, O₃ and SO₂) and related microblogs (air-pollution-related and health-related), it was found that the public is most sensitive to changes in PM_2.5 concentration from the perspectives of both China as a whole and individual provinces. Correlations between air pollution and related microblogs were also stronger when and where air quality was worse, and they were also affected by socioeconomic factors such as population, economic conditions and education. Based on the results of these correlation analyses, scientists can survey public concern about air pollution and related health outcomes on social media in real time across the country and the government can formulate air quality management measures that are aligned to public sensitivities.

Exploring the association between resource dependence and haze pollution in China: the mediating effect of green technology innovation

Haze pollution has been addressed in extensive studies over the last few years. However, the relationship between resource dependence and haze pollution has not been fully investigated. This study focuses on addressing this problem while considering the mediating role of green technology innovation. A panel dataset of 263 prefecture-level cities in China from 2005 to 2018 is used for the analysis. The results show the following: (1) the two-way fixed-effect model reveals that resource dependence contributes significantly to haze pollution, and this finding remains robust across a series of robustness tests. (2) A mediation analysis indicates that resource dependence is unfavorable for green technology innovation, indirectly affecting the alleviation of haze pollution. (3) The results of panel threshold regression suggest that green technology innovation promotes haze reduction in the weak and medium resource dependence stages, whereas this optimization effect disappears in the strong resource dependence stage due to rebound effects. (4) The results of regional heterogeneity demonstrate that the positive effects of resource dependence on haze pollution exist in eastern and western China but not in central China. Based on these results, policy implications are given to reduce haze pollution.

The impact of air pollution on venture capital: evidence from China

Utilizing a dataset of VC investment from 2005 to 2018, we investigate the relationship between air pollution and venture capital (VC) investment. We find that startups suffering severe air pollution receive less investment from VCs and experience a lower probability of financing from VCs. Our findings are robust to considering endogeneity concerns and various robustness checks. Moreover, air pollution is detrimental to startup innovation ability and entrepreneurial activities through which air pollution negatively affects VC investment. In response, VCs prefer to co-invest with other VCs to mitigate the adverse effect of air pollution. Our paper sheds new light on the impact of air pollution on the private financial markets.

The Impact of Extreme Temperature Shocks on the Health Status of the Elderly in China

With the accelerating process of climate change, long-term exposure to extreme temperatures could threaten individuals' physical health, especially for the vulnerable population. This paper aims to investigate the long-term effects of extreme temperature exposure on the health of the elderly in the context of climate change and aging. Different from most of the existing literature in environmental economics, we define the relative extreme temperature exposure based on the local temperature pattern. By combining a large national household survey and nationwide meteorologic historical data, this study provides empirical evidence that heat exposure days and cold exposure days during the past year both significantly affect the physical health of middle-aged and elderly groups, controlling for city, year, and individual fixed effects. The effect on individual physical health has certain seasonal characteristics and is heterogeneous across populations. Additionally, cooling and heating equipment are effective in alleviating the reverse impact of heat and cold exposure. The estimation is robust and consistent across a variety of temperature measurements and model modifications. Our findings provide evidence of the long-term and accumulative cost of extreme temperature to middle-aged and elderly human capital, contributing to helping the public to better understand the full impact of climate change.

How weather impacts expressed sentiment in Russia: evidence from Odnoklassniki

Prior research suggests that weather conditions may substantively impact people's emotional state and mood. In Russia, the relationship between weather and mood has been studied for certain regions-usually with severe or extreme climatic and weather conditions-but with quite limited samples of up to 1,000 people. Over the past decade, partly due to the proliferation of online social networks and the development of natural language processing techniques, the relationship between weather and mood has become possible to study based on the sentiment expressed by individuals. One of the key advantages of such studies based on digital traces is that it is possible to analyze much larger samples of people in comparison with traditional survey-based studies. In this article, we investigate the relationship between historical weather conditions and sentiment expressed in seven Russian cities based on the data of one of the largest Russian social networks, Odnoklassniki. We constructed a daily city-level expressed positive sentiment metric based on 2.76 million posts published by 1.31 million unique users from Odnoklassniki and studied its dynamics relative to daily weather conditions via regression modelling. It was found that a maximum daily temperature between +20 °C and +25 °C, light breeze (between 5 and 11 km/h) and an increase in the average daily temperature by 20-25 °C compared to the previous day are all associated with higher numbers of expressions of positive sentiment, whereas the difference between the maximum and minimum daily temperatures of 15-20 °C is associated with lower numbers of expressions of positive sentiment.

Behavioral economics: who are the investors with the most sustainable stock happiness, and why? Low aspiration, external control, and country domicile may save your lives—monetary wisdom

Slight absolute changes in the Shanghai Stock Exchange Index (SHSE) corresponded to the city’s immediate increases in coronary heart disease deaths and stroke deaths. Significant fluctuations in the Shenzhen Stock Exchange Index (SZSE) corresponded to the country’s minor, delayed death rates. Investors deal with money, greed, stock volatility, and risky decision-making. Happy people live longer and better. We ask the following question: Who are the investors with the highest and most sustainable stock happiness, and why? Monetary wisdom asserts: Investors apply their deep-rooted values (avaricious love-of-money aspiration and locus of control, Level 2) as a lens to frame critical concerns in the proximal-immediate (Shanghai Stock Exchange Index changes, Level 1) and the omnibus-distal contexts (domicile: city vs. country, Level 2) to maximize expected utility (portfolio changes, Level 1) and ultimate serenity (stock happiness, Level 1). We collected multilevel data—the longitudinal SHSE and 227 private investors’ daily stock happiness and portfolio changes for 36 consecutive trading days in four regions of China. Investors had an average liquid asset of $76,747.41 and $54,660.85 in stocks. This study is not a “one-shot” game with “nothing at stake.” We classified Shanghai and Beijing as the city and Shenzhen and Chongqing as the country. Our cross-level 3-D visualization reveals that regardless of SHSE volatility, investors with low aspiration, external control, and country domicile enjoy the highest and most sustainable stock happiness with minimum fluctuations. Independently, investors with low aspiration, external control, and country domicile tend to make fewer portfolio changes than their counterparts. Behaviorally, less is more, debunking the myth—risky decisions excite stock happiness. Our longitudinal study expands prospect theory, incorporates attitude toward money, and makes robust contributions to behavioral economics and business ethics. We help investors and ordinary citizens make happy, healthy, and wealthy decisions. Most importantly, the life you save may be your own.

The Garden and the Orchestra: Generative Metaphors for Conceptualizing the Complexities of Well-Being

Our understanding of well-being, and related concepts such as health and flourishing, is shaped by the metaphors through which we think about such ideas. Current dominant metaphors-including a pyramid, ladder, and continuum-all have various issues. As such, this paper offers two other metaphors which can better do justice to the nuanced complexities of these notions, namely, a garden and an orchestra. Through these metaphors, this paper articulates a comprehensive framework for conceptualizing and appreciating the nature of well-being (and associated concepts), which it is hoped will generate further insights and research into these valued and sought-after phenomena.

Influencing Factors on Airflow and Pollutant Dispersion around Buildings under the Combined Effect of Wind and Buoyancy-A Review

With the rapid growth of populations worldwide, air quality has become an increasingly important issue related to the health and safety of city inhabitants. There are quite a few factors that contribute to urban air pollution; the majority of studies examining the issue are concerned with environmental conditions, building geometries, source characteristics and other factors and have used a variety of approaches, from theoretical modelling to experimental measurements and numerical simulations. Among the environmental conditions, solar-radiation-induced buoyancy plays an important role in realistic conditions. The thermal conditions of the ground and building façades directly affect the wind field and pollutant dispersion patterns in the microclimate. The coupling effect of wind and buoyancy on the urban environment are currently hot and attractive research topics. Extensive studies have been devoted to this field, some focused on the street canyon scale, and have found that thermal effects do not significantly affect the main airflow structure in the interior of the street canyon but strongly affect the wind velocity and pollutant concentration at the pedestrian level. Others revealed that the pollutant dispersion routes can be obviously different under various Richardson numbers at the scale of the isolated building. The purpose of this review is therefore to systematically articulate the approaches and research outcomes under the combined effect of wind and buoyancy from the street canyon scale to an isolated building, which should provide some insights into future modelling directions in environmental studies.

Adaptability and sustainability of machine learning approaches to traffic signal control

This study investigates how adaptable Machine Learning Traffic Signal control methods are to topological variability. We ask how well can these methods generalize to non-Manhattan-like networks with non-uniform distances between intersections. A Machine Learning method that is highly reliable in various topologies is proposed and compared with state-of-the-art alternatives. Lastly, we analyze the sustainability of different traffic signal control methods based on computational efforts required to achieve convergence and perform training and testing. We show that our method achieves an approximately seven-fold improvement in terms of CO[Formula: see text] emitted in training over the second-best method.

Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China

Air pollution may change people's gym sports behavior. To test this claim, first, we used big data crawler technology and ordinary least square (OLS) models to investigate the effect of air pollution on people' gym visits in Beijing, China, especially under the COVID-19 pandemic of 2019-2020, and the results showed that a one-standard-deviation increase in PM2.5 concentration (fine particulate matter with diameters equal to or smaller than 2.5 μm) derived from the land use regression model (LUR) was positively associated with a 0.119 and a 0.171 standard-deviation increase in gym visits without or with consideration of the COVID-19 variable, respectively. Second, using spatial autocorrelation analysis and a series of spatial econometric models, we provided consistent evidence that the gym industry of Beijing had a strong spatial dependence, and PM2.5 and its spatial spillover effect had a positive impact on the demand for gym sports. Such a phenomenon offers us a new perspective that gym sports can be developed into an essential activity for the public due to this avoidance behavior regarding COVID-19 virus contact and pollution exposure.

Exposures and behavioural responses to wildfire smoke

Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM2.5) concentrations often remain 3-4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.

The COVID-19 pandemic and energy transitions: Evidence from low-carbon power generation in China

The Corona Virus Disease 2019 (COVID-19) has led to a decline in carbon emissions or an improvement in air quality. Yet little is known about how the pandemic has affected the "low-carbon" energy transition. Here, using difference-in-differences (DID) models with historical controls, this study analyzed the overall impact of COVID-19 on China's low-carbon power generation and examined the COVID-19 effect on the direction of the energy transition with a monthly province-specific, source-specific dataset. It was found that the COVID-19 pandemic increased the low-carbon power generation by 4.59% (0.0648 billion kWh), mainly driven by solar and wind power generation, especially solar power generation. Heterogeneous effects indicate that the pandemic has accelerated the transition of the power generation mix and the primary energy mix from carbon-intensive energy to modern renewables (such as solar and wind power). Finally, this study put forward several policy implications, including the need to promote the long-term development of renewables, green recovery, and so on.

The health consequences of greenhouse gas emissions: a potential pathway

Excessive greenhouse gas emissions might be the major culprit for environmental degradation, which have direct and indirect adverse impacts in various ways. As the largest emitter of carbon emissions, China suffered great harm from climate change during the past 40 years. Therefore, it becomes necessary to study the impact of carbon emissions on health issues and their potential mechanism. Using the panel data from 30 provinces in China between 2002 and 2017, this study employes and extends the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and mediating effect model to analyze the direct and indirect effects of carbon emissions. The main results are as follows: (1) Carbon emissions has a certain negative impact on public health, which would increase with the rise of temperature. (2) The increase in carbon emissions has a more significant negative effect on health with the average temperature exceeding 17.75 °C, indicating that the temperature has a threshold effect. (3) The potential health risks become higher with the development of urbanization, but there is no obvious spillover effect in the health consequences. The results remain robust after controlling other factors. This study supplements the literature of climate governance and human health, potentially contributing to the next stage of high-quality and sustainable development.

Air pollution and migration

This paper investigates the impact of air pollution on China’s internal migration using the Migrant Population Dynamic Monitoring Survey and Air Quality Index (AQI) data in 2014. Binary response models suggest that, on average, the probability of the willingness to leave will grow by approximately 0.1 if the average value of AQI is increased by 100 points. The migration effect of sever air pollution still holds using two-stage least squares estimation. In particular, compared to inter-province and intra-city within the same province, interviewees are more willingness to leave in intra-county with the same city. Besides, individuals from the cities in central and western regions, the listed key environmental protection (KEP) cities and cities not listed as resource-based (RB) cities have stronger willingness to leave than those from the cities in eastern region, Non-KEP cities and RB cities. Our results are robust to alternative measures of air pollution and models specification.

Association between built environments and quality of life among community residents: mediation analysis of air pollution

OBJECTIVES

The aim of this study was to analyze the relationship between built environments and quality of life (QoL), and the mediating role of air pollution in that relationship.

STUDY DESIGN

This was a cross-sectional population-based study.

METHODS

Data of 5196 adults residing in 148 communities in three cities in Liaoning Province, China, were analyzed. Objective measures of traffic design included street connectivity, road network density, bus station density, and parking lot density; residential greenness was controlled as a confounder. QoL was evaluated using the 12-Item Short Form Health Survey. The average concentrations of PM_2.5 and SO₂ one month before QoL collection for each community were calculated.

RESULTS

Road network density and parking lot density were negatively associated with the Physical Component Summary (PCS), but street connectivity was positively associated with PCS for the participants. Bus station density, street connectivity, and parking lot density were negatively associated with the Mental Component Summary (MCS), and PM_2.5 and SO₂ mediated this association. In addition, gender and road network density and parking lot density had an interactive effect on the MCS of the participants.

CONCLUSIONS

Dense traffic affects people's health not only directly but also indirectly through air pollution. The effects of built environments and air pollution should be considered when building healthy, supportive communities, and healthy cities.

Winning the Blue Sky Defense War: Assessing Air Pollution Prevention and Control Action Based on Synthetic Control Method

Undoubtedly, the rapid development of urbanization and industrialization in China has led to environmental problems, among which air pollution is particularly prominent. In response, the Chinese government has introduced a series of policies, including the Air Pollution Control and Prevention Action Plan (APPA), which is one of the most stringent environmental regulations in history. The scientific evaluation of the implementation of this regulation is important for China to win the battle of blue sky. Therefore, this study uses a synthetic control method to explore the effects of APPA on air pollution (AP) based on data of 30 provinces from 2000 to 2019. The study concludes that (1) APPA significantly reduces AP in the treatment provinces, and subsequent robustness tests validate our findings. However, the persistence of the policy effect is short in some provinces, and the rate of AP reduction slows down or even rebounds in the later stages of the policy. (2) The reduction effect of APPA varies significantly between regions and provinces. (3) The results of mechanism tests show that APPA reduces AP through high-quality economic development, population agglomeration, control of carbon emissions, and optimization of energy structure. Based on the above findings, targeted recommendations are proposed to promote AP control in China and win the blue sky defense war.

Validating daily social media macroscopes of emotions

Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.