Haze Influencing Factors: A Data Envelopment Analysis Approach

Abstract

This paper investigates the meteorological factors and human activities that influence PM2.5 pollution by employing the data envelopment analysis (DEA) approach to a chance constrained stochastic optimization problem. This approach has the two advantages of admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint. Furthermore, by utilizing the meteorological observation data incorporated with the economic and social data for Jiangsu Province, the chance constrained stochastic DEA model was solved to explore the relationship between the meteorological elements and human activities and PM2.5 pollution. The results are summarized by the following: (1) Among all five primary indexes, social progress, energy use and transportation are the most significant for PM2.5 pollution. (2) Among our selected 14 secondary indexes, coal consumption, population density and civil car ownership account for a major portion of PM2.5 pollution. (3) Human activities are the main factor producing PM2.5 pollution. While some meteorological elements generate PM2.5 pollution, some act as influencing factors on the migration of PM2.5 pollution. These findings can provide a reference for the government to formulate appropriate policies to reduce PM2.5 emissions and for the communities to develop effective strategies to eliminate PM2.5 pollution.

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

“…Tables 3 and 4 respectively show the efficiency scores under the assumption of CRS (δ � 0) and VRS (δ � 1). We use four groups of values (α i � 0.5, α i � 0.1, α i � 0.05, and α i � 0.01) to study the effects of α i on the results of efficiency evaluation, and we select α i � 0.05 as the risk criterion in this study as it is set 0.05 in the related literature [39][40][41][42][43].…”

“…However, it is impossible to get precise data related to carbon emissions, which could be caused by measurement errors, natural uncertainty of carbon emissions, and so on [39]. e results would be erroneous if decision-makers do not consider the uncertainty of carbon emissions when measuring the ECEE [39][40][41][42][43]. As a matter of fact, the deterministic DEA model is only a particular case of the uncertain DEA model [39].…”

“…Wu et al [40], Jin et al [41], Zha et al [39], Chen et al [42], and Zhou et al [43] used the chance-constrained DEA method to evaluate the environmental efficiency considering uncertain undesirable outputs. However, as far as we know, there is no literature on measuring the efficiency of regional transportation systems (RTS) by taking the uncertainty of carbon emissions into account, especially China's RTS.…”

Bayesian network reasoning and machine learning with multiple data features: air pollution risk monitoring and early warning

“…To a large extent, air pollution creates the biggest threat to people's health (Fan et al 2019). From 2013 to 2016, China's average PM2.5 concentration (57.75 lg/m 3 ) was five times higher than the WHO's standard (10 lg/m 3 ) and six times higher than the USA (8.47 lg/m 3 ) (Zhou et al 2019). Air pollution causes more than 3 million deaths worldwide each year (Lelieveld et al 2015), and China alone accounts for 41.2% of these deaths.…”

“…In addition to monitoring air pollution, in recent years, scholars have conducted studies on factors affecting air quality. Zhou et al (2019) used data envelopment analysis (DEA) to analyze the factors that contribute to PM2.5 and determined the impact of meteorological factors and human activities on pollutants. Yang et al (2015) used the North China Plain as an example to study the characteristics and formation mechanisms of continuous haze in China and to study the influence of meteorological factors on various pollutants.…”