Estimation of PM2.5 Concentration Efficiency and Potential Public Mortality Reduction in Urban China

Yu, Anyu; Jia, Guangshe; You, Jing; Zhang, Puwei

doi:10.3390/ijerph15030529

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…In addition to economic research, single-country environmental energy efficiency analyses have been conducted in Asia, with the research direction being mainly based on regional energy and environmental efficiency comparisons, the impact of new energy on GDP or CO 2 emissions, and new energy policy assessments (Yu, Jia, You, & Zhang, 2018). Honma and Hu (2008) used DEA for a comparative analysis of regional energy efficiency, environmental efficiency, and Target Fabric Energy Efficiency in 47 metropolitan areas in Japan from 1993 to 2003, presenting that the inland sea and coastal areas are more energy efficient than the Pacific coast areas.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

Liu

Chiu

Wang

et al. 2019

Tropical Conservation Science

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Greater and greater attention is being paid to air pollution problems, because of their negative impact on the environment and human health. This article measures energy efficiency, carbon dioxide emissions efficiency, and particulate matter (PM 2.5) concentration efficiency to compare the energy efficiency differences between Organisation for Economic Cooperation and Development (OECD) member countries and non-OECD member countries from 2010 to 2014 using a metafrontier dynamic Data Envelopment Analysis model. We calculate technology gap ratio and input and output efficiency values to measure the energy efficiencies of each economy, finding that (a) OECD countries have a technology gap ratio of 1 or very close to 1; and except for the United Arab Emirates and Singapore, both of which exhibit annual improvements, the non-OECD countries have a significant need for efficiency improvements; (b) the average technology gap ratio of OECD is higher than that of non-OECD countries; that is, while OECD countries' technology gap ratio (TGR) changes are relatively stable, non-OECD countries' TGRs are gradually increasing; (c) non-OECD countries have large PM 2.5 concentration efficiency gaps, with the annual efficiencies in China, India, and Nepal being less than 0.2; (d) Switzerland, Denmark, France, the United Kingdom, Iceland, Luxembourg, Norway, the United States, and the United Arab Emirates all have new and traditional energy efficiency values of 1; and (e) Botswana, Algeria, and Cambodia have poor traditional energy efficiencies, but better new energy efficiencies, whereas Hungary, South Korea, Slovakia, and Slovenia have poor new energy efficiencies and better traditional energy efficiencies.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

Liu

Chiu

Wang

et al. 2019

Tropical Conservation Science

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“…Li et al [38] examined water pollution emissions in China's industrial sector by using a green-biased technological progress model. Yu et al [39] and Ding et al [31] focused on the environmental performance of Chinese urban regions. Clearly, the studies on environmental performance in China differ in terms of both the data used and models applied.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Economic and Environmental Performance of the Chinese Industry Sector

et al. 2019

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This study assesses economic and environmental performance in the Chinese industry sector across 30 provinces during the period of 2006-2017. The study relies on a nonparametric framework and we apply a novel decomposition of the overall inefficiency scores into three components of technical, scale and mix inefficiency at the aggregate level by incorporating undesirable outputs. As we rely on by-production technology, industry performance is split into economic and environmental dimensions. Our results show that Chinese industry inefficiency is equally due to economic and environmental performance during 2006-2017, whereas technical and scale inefficiencies are relatively higher for environmental sub-technology (which relates energy to CO 2 emission) if opposed to the economic sub-technology (which relates all the inputs to the economic value added). This implies that Chinese industry still requires improvements in environmental performance. The eastern region shows a relatively low average economic overall inefficiency if compared to other regions, yet its total OI (overall inefficiency) is the highest among the regions. Thus, environmental performance and misallocation of resources constitute the underlying causes of the total inefficiency. Therefore, structural reforms are necessary besides improvements in the production processes in the eastern region. This is important since China has experienced economic growth, but also policy must pay attention to environmental issues and sustainability.Sustainability 2019, 11, 6804 2 of 17 from energy consumption. In light of these two objectives, it is necessary to measure the efficiency of Chinese industry in this context. Instead of evaluating China's economic growth performance from the perspective of traditional efficiency and productivity, this paper takes into account unintended processes such as the generating of CO 2 . This paper employed the by-production model that can examine the generation of desirable output and undesirable output (CO 2 emission) simultaneously. The by-production model relies on two sub-technologies, one defining the production of desirable outputs and the other defining the generation of the undesirable ones (see Section 3.1 for details).Based on a nonparametric framework, we evaluate economic and environmental performance in the Chinese industrial sector among 30 provinces during 2006-2017. Contributions to the literature include identifying the mix and scale effects in Chinese industry at both an aggregate and individual levels and decomposing the overall performance into economic and environmental components by applying this by-production model. Literature ReviewThe increasing concerns on the sustainability of economic activities have induced the need for appropriate analytical tools [2][3][4][5]. Among different methods, DEA (Data Envelopment Analysis) has appeared as a tool capable of analyzing environmental performance in the context of the neo-classical economic theory. Indeed, there have been a number of studies applying DEA for an...

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“…Thus, traffic emissions recently have contributed more to PM 2.5 concentrations, probably resulting in high exposure since many persons live near traffic routes [17][18][19]. Accurate estimation of the spatiotemporal distribution of PM 2.5 [20][21][22] at a high resolution can help improve health outcome studies of PM 2.5 [8,23], particularly at a local spatial scale. However, PM 2.5 ground monitoring stations are usually limitedly distributed worldwide, e.g., there were 102 PM 2.5 monitoring stations in 2015 for the large Jing-Jin-Ji metropolitan area of China (64,022 km 2 ).…”

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confidence: 99%

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

2020

Remote Sensing

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Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.

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Estimation of PM2.5 Concentration Efficiency and Potential Public Mortality Reduction in Urban China

Cited by 9 publications

References 53 publications

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

Evaluating Economic and Environmental Performance of the Chinese Industry Sector

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

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