Drivers and Decoupling Effects of PM2.5 Emissions in China: An Application of the Generalized Divisia Index

Although economic growth brings abundant material wealth, it is also associated with serious PM_2.5 pollution. Decoupling PM_2.5 emissions from economic development is important for China's long-term sustainable development. In this paper, the generalized Divisia index method (GDIM) is extended by introducing innovation indicators to investigate the main drivers of PM_2.5 pollution in China and its four subregions from 2008 to 2017. Afterwards, a GDIM-based decoupling index is developed to examine the decoupling states between PM_2.5 emissions and economic growth and to identify the main factors leading to decoupling. The obtained results show that: (1) Innovation input scale and GDP are the main drivers for increases in PM_2.5 emissions, while innovation input PM_2.5 intensity, emission intensity, and emission coefficient are the main reasons for reductions in PM_2.5 pollution. (2) China and its four subregions show general upward trends in the decoupling index, and their decoupling states turn from weak decoupling to strong decoupling. (3) Innovation input PM_2.5 intensity, emission intensity, and emission coefficient contribute largely to the decoupling of PM_2.5 emissions. Overall, this paper provides valuable information for mitigating haze pollution.

Socio-economic driving forces of PM2.5 emission in China: a global meta-frontier-production-theoretical decomposition analysis

PM2.5 is a bad output of China's improved industrialization and rapid economic development, which seriously threatens people's health and greatly hinders the sustainable economic development. Studying the socio-economic driving factors of PM2.5 emissions is of great significance for reducing air pollution and realizing green development. Therefore, based on the simultaneous consideration of space technology differences and time technology progress, this paper constructs an index decomposition analysis-production-theoretical decomposition analysis decomposition model under the global meta-frontier-production theory. Then, we decompose the PM2.5 emission concentration of 30 provinces in China from 2005 to 2018 into nine driving factors and discuss the impact of different factors from the national, regional, and provincial levels. The results reveal that economic activity is still the main factor to promote the increase of PM2.5 emission, but its effect decreases, while the inhibitory effect of catch-up effect on PM2.5 concentration increases gradually. In addition, economic activities have the greatest impact on the East China, while the time catch-up effect has a more significant impact on the Central and Western China. Moreover, the influence of energy intensity effect, space technology catch-up effect, and time technology catch-up effect is gradually increasing, which have become important factors to inhibit the PM2.5 emission. Based on the above results, we put forward relevant policy suggestions.

What factors influence PM2.5 emissions in China? An analysis of regional differences using a combined method of data envelopment analysis and logarithmic mean Divisia index

This study uses a combined data envelopment analysis and logarithmic mean Divisia index (DEA-LMDI) method to decompose affecting factors for PM2.5 emissions into effects related to the potential emission intensity (PEI), environmental efficiency and technology, production efficiency and technology, regional economic structure, and national economic growth, and investigates differences in the effects on PM2.5 emissions, considering the diversity among different areas and periods in China. This study provides a new insight in the decomposition method, which can decompose the emissions into new effects compared with the exiting studies. This study reveals that the regional environmental-based technology (EBT) effect is the key curbing factor for PM2.5 emissions, followed by the regional PEI effect. The curbing effect of regional EBT on PM2.5 emissions is strong in East China and weak in Northeast China. The environment-oriented scale efficiency (ESE), environment-oriented management efficiency (EME), production-oriented scale efficiency (PSE), production-oriented management efficiency (PME), and production-based technology (PBT) had relatively small effects on PM2.5 emissions on the whole. The effects differ among different areas and periods in China. The emission reduction potential of these efficiency effects has not been realized. The national economic growth greatly promotes PM2.5 emissions. The regional economic structure effect slightly increases PM2.5 emissions because of the unbalanced development of regional economy. The relative policy suggestions are put forward based on the findings of this study.

How is COVID-19 affecting environmental pollution in US cities? Evidence from asymmetric Fourier causality test

This paper aims to examine the effects of the COVID-19 pandemic on PM_2.5 emissions in eight selected US cities with populations of more than 1 million. To this end, the study employs an asymmetric Fourier causality test for the period of January 15, 2020 to May 4, 2020. The outcomes indicate that positive shocks in COVID-19 deaths cause negative shocks in PM_2.5 emissions for New York, San Diego, and San Jose. Moreover, in terms of cases, positive shocks in COVID-19 cause negative shocks in PM_2.5 emissions for Los Angeles, Chicago, Phoenix, Philadelphia, San Antonio, and San Jose. Overall, the findings of the study highlight that the pandemic reduces environmental pressure in the largest cities of the USA. This implies that one of the rare positive effects of the virus is to reduce air pollution. Therefore, for a better environment, US citizens should review the impact of current production and consumption activities on anthropogenic environmental problems.

Comparison of PM2.5 emission rates and source profiles for traditional Chinese cooking styles

The number of restaurants is increasing rapidly in recent years, especially in urban cities with dense populations. Particulate matter emitted from commercial and residential cooking is a significant contributor to both indoor and outdoor aerosols. The PM_2.5 emission rates and source profiles are impacted by many factors (cooking method, food type, oil type, fuel type, additives, cooking styles, cooking temperature, source surface area, pan, and ventilation) discussed in previous studies. To determine which cooking activities are most influential on PM_2.5 emissions and work towards cleaner cooking, an experiment design based on multi-factor and level orthogonal tests was conducted in a laboratory that is specifically designed to resemble a professional restaurant kitchen. In this cooking test, four main parameters (the proportion of meat in ingredients, flavor, cooking technique, oil type) were chosen and five levels for each parameter were selected to build up 25 experimental dishes. Concentrations of PM_2.5 emission rates, organic carbon/elemental carbon (OC/EC), water-soluble ions, elements, and main organic species (PAHs, n-alkanes, alkanoic acids, fatty acids, dicarboxylic acids, polysaccharides, and sterols) were investigated across 25 cooking tests. The statistical significance of the data was analyzed by analysis of variance (ANOVA) with ranges calculated to determine the influence orders of the 4 parameters. The PM_2.5 emission rates of 25 experimental dishes ranged from 0.1 to 9.2 g/kg of ingredients. OC, EC, water-soluble ions (WSI), and elements accounted for 10.49-94.85%, 0-1.74%, 10.09-40.03%, and 0.04-3.93% of the total PM_2.5, respectively. Fatty acids, dicarboxylic acids, n-alkanes, alkanoic acids, and sterols were the most abundant organic species and accounted for 2.32-93.04%, 0.84-60.36%, 0-45.05%, and 0-25.42% of total PM_2.5, respectively. There was no significant difference between the 4 parameters on PM_2.5 emission rates, while a significant difference was found in WSI, elements, n-alkanes, and dicarboxylic acids according to ANOVA. Cooking technique was found to be the most influential factor for PM_2.5 source profiles, followed by the proportion of meat in ingredients and oil type which resulted in significant difference of 183.19, 185.14, and 115.08 g/kg of total PM_2.5 for dicarboxylic acids, n-alkanes, and WSI, respectively. Strong correlations were found among PM_2.5 and OC (r = 0.854), OC and sterols (r = 0.919), PAHs and n-alkanes (r = 0.850), alkanoic acids and fatty acids (r = 0.877), and many other species of PM_2.5.

Globalization and pollution: tele-connecting local primary PM2.5 emissions to global consumption

Globalization pushes production and consumption to geographically diverse locations and generates a variety of sizeable opportunities and challenges. The distribution and associated effects of short-lived primary fine particulate matter (PM_2.5), a representative of local pollution, are significantly affected by the consumption through global supply chain. Tele-connection is used here to represent the link between production and consumption activity at large distances. In this study, we develop a global consumption-based primary PM_2.5 emission inventory to track primary PM_2.5 emissions embodied in the supply chain and evaluate the extent to which local PM_2.5 emissions are triggered by international trade. We further adopt consumption-based accounting and identify the global original source that produced the emissions. We find that anthropogenic PM_2.5 emissions from industrial sectors accounted for 24 Tg globally in 2007; approximately 30% (7.2 Tg) of these emissions were embodied in export of products principally from Brazil, South Africa, India and China (3.8 Tg) to developed countries. Large differences (up to 10 times) in the embodied emissions intensity between net importers and exporters greatly increased total global PM_2.5 emissions. Tele-connecting production and consumption activity provides valuable insights with respect to mitigating long-range transboundary air pollution and prompts concerted efforts aiming at more environmentally conscious globalization.