Dataset Information

Valuing Air Quality Using Happiness Data: The Case of China.

ABSTRACT: This paper estimates the monetary value of cutting PM2.5, a dominant source of air pollution in China. By matching hedonic happiness in a nationally representative survey with daily air quality data according to the dates and counties of interviews in China, we are able to estimate the relationship between local concentration of particulate matter and individual happiness. By holding happiness constant, we calculate the tradeoff between the reduction in particulate matter and income, essentially a happiness-based measure of willingness-to-pay for mitigating air pollution. We find that people on average are willing to pay ¥258 ($42, or 1.8% of annual household per capita income) per year per person for a 1% reduction in PM2.5.

SUBMITTER: Zhang X

PROVIDER: S-EPMC5626438 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Valuing Air Quality Using Happiness Data: The Case of China.

Zhang Xin X Zhang Xiaobo X Chen Xi X

Ecological economics : the journal of the International Society for Ecological Economics 20170307

This paper estimates the monetary value of cutting PM2.5, a dominant source of air pollution in China. By matching hedonic happiness in a nationally representative survey with daily air quality data according to the dates and counties of interviews in China, we are able to estimate the relationship between local concentration of particulate matter and individual happiness. By holding happiness constant, we calculate the tradeoff between the reduction in particulate matter and income, essentially ...[more]

PMID: 28989234

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Project description:BackgroundWe aimed to examine the effect of ambient air pollution at the district level on adolescents' happiness and their change in happiness over time in a cohort sample from Taiwan.MethodA cohort from the Taiwan Youth Project was evaluated. The adolescents (n = 2571) were in the 7th grade (mean age = 14.3 years) when the study was initiated and resided in 40 districts in three cities and counties in northern Taiwan. We examined the effects of the concentration level of air pollution, including PM2.5, PM10, and NO2, at the district level on adolescents' happiness and their change in happiness over time (7th to 9th grade). Due to the high correlations of the three pollutants, we examined each separately with similar covariates. The analyses were based on both multilevel modeling and latent growth curve modeling.ResultsHigher concentration levels of each of the three air pollutants measured were associated with adolescent happiness such that a higher level of concentration was related to lower levels of adolescents' happiness. These results were observed after controlling for important individual- and district-level covariates. However, further analyses did not reveal that the concentration level of air pollution was associated with the change in happiness in the study period (after 3 years). Some sensitivity checks (e.g., adjusting district size) did not change the substantive results.ConclusionMany previous studies have shown the influence of air pollution on physical health and negative emotions, but only a few using adult samples have shown that air pollution is inversely related to positive wellbeing. This study may be the first to examine the effects of air pollution on adolescents' positive affect. Our results echo recent research on the consequent health burden of air pollution. Given that positive affect has been linked to future adult health, the results of the current study provide empirical grounds for early intervention concerning environmental factors.

Project description:With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

Dataset Information

Valuing Air Quality Using Happiness Data: The Case of China.

Publications

Valuing Air Quality Using Happiness Data: The Case of China.

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