Analysis on Effectiveness of SO2 Emission Reduction in Shanxi, China by Satellite Remote Sensing

Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining

This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO₂), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO₂ concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO₂ concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO₂ concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO₂ concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO₂ concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

Air pollution characteristics, health risks, and source analysis in Shanxi Province, China

China is confronting an unprecedented air pollution problem. This study discussed the characteristics of air pollution and its risks on human health and conducted source analysis combined with local development in Shanxi Province in 2016 and 2017. Results demonstrated that the air pollution situation in Shanxi was deteriorating, with Taiyuan, Yangquan, Changzhi, Jincheng, Jinzhong, and Linfen being heavily polluted districts. Particulate matter (PM) was considered the major pollutant, but nitrogen dioxide and ozone showed a dominant trend recently. Furthermore, the health risks evaluated on the basis of a comprehensive air quality index (AAQI) and an aggregated risk index revealed a relatively high-risk level in Shanxi. Among the pollutants, the largest contributor was PM, followed by sulfur dioxide and ozone. Southern Shanxi had the largest pollution level and health risks, whereas Datong was the least polluted region. Source analysis suggested that the main driving forces of air pollution, besides natural factors, were urbanization, population size, civil vehicles, coal-based heavy industries, and high-energy consumption. Therefore, strengthening urban greening, vigorously adjusting and optimizing the industrial structure, and formulating a multi-domain cooperative control regime on air pollution, especially PM and ozone, should be promoted.

Multi-perspective comparisons and mitigation implications of SO2 and NO x discharges from the industrial sector of China: a decomposition analysis

This study is the first attempt to investigate the drivers of Chinese industrial SO₂ and NO _x emissions from both periodic and structural perspectives through a decomposition analysis using the logarithmic mean Divisia index (LMDI). The two pollutants' emissions were decomposed into output effects, structural effects, clean production effects, and pollution abatement effects. The results showed that China's industrial SO₂ discharge increased by 1.14 Mt during 2003-2014, and the contributions from the four effects were 23.17, - 1.88, - 3.80, and - 16.36 Mt, respectively. Likewise, NO _x discharge changed by - 3.44 Mt over 2011-2014, and the corresponding contributions from the four effects were 2.97, - 0.62, - 1.84, and - 3.95 Mt. Thus, the output effect was mainly responsible for the growth of the two discharges. The average annual contribution rates of SO₂ and NO _x from output were 14.33 and 5.97%, respectively, but pollution abatement technology presented the most obvious mitigating effects (- 10.11 and - 7.92%), followed by the mitigating effects of clean production technology (- 2.35 and - 3.7%), and the mitigation from the structural effect was the weakest (- 1.16 and - 1.25%, respectively), which meant pollutant reduction policies related to industrial structure adjustment should be a long-term measure for the two discharges. In addition, the sub-sectors of I20 (manufacture of raw chemical materials and chemical products), I24 (manufacture of non-metallic mineral products), and I26 (smelting and pressing of non-ferrous metals) were the major contributors to both discharges. Thus, these sub-sectors should be given priority consideration when designing mitigation-related measures. Last, some particular policy implications were recommended for reducing the two discharges, including that the government should seek a technological discharge reduction route.

SO2 Emissions in China - Their Network and Hierarchical Structures

SO₂ emissions lead to various harmful effects on environment and human health. The SO₂ emission in China has significant contribution to the global SO₂ emission, so it is necessary to employ various methods to study SO₂ emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO₂ emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO₂ emissions from power generation sector were highly individualized as small-sized clusters, the SO₂ emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO₂ emissions from residential sector was not impacted by time, and the SO₂ emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO₂ emissions from all four sectors and is potentially useful to find out similar patterns of SO₂ emissions, which can provide information on understanding the mechanisms of SO₂ pollution and on designing different environmental measure to combat SO₂ emissions.