A study on the global burden of disease conducted by the Institute for Health Metrics and Evaluation (IHME) showed that air pollution is the fifth highest risk factor for mortality worldwide and the leading environmental risk factor; air pollution is responsible for 4.2 million deaths annually1,2. Among various air pollutants, fine particulate matter measuring 2.5 µm or less in aerodynamic diameter (PM2.5) is sufficiently small to penetrate the lungs deeply and pass into the blood stream. This may cause cardiovascular and respiratory diseases, such as lower respiratory infection (LRI), ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), and lung cancer1,2,3.
During the period 2000–2015, when the annual GDP growth rate in India exceeded 8%4, the number of premature deaths attributable to PM2.5 exposure increased from 857,300 to 1,090,400 people1. In 2015, PM2.5-related premature deaths in India accounted for a quarter of global deaths attributed to PM2.5, a level that was comparable to that of China, which has some of the world’s highest air pollution levels1.
India’s rapid economic growth between 1995 and 2009 was mainly due to increasing fixed capital formation (i.e., final demand), and the additional capital formation (i.e., investment) was attributed to a marked increase in coal consumption in India during the same period; coal consumption is one of the major sources of PM2.5 emissions5. Thus, to reduce premature deaths related to PM2.5 emissions in India, it is considered important for Indian policymakers to develop effective demand- and supply-side policy with a focus on higher priority sectors.
In 2019, the Indian government launched the National Clean Air Programme (NCAP) to achieve its sustainable development goals; the proposed national target was a 20–30% reduction in PM2.5 and PM10 levels by 20246. This is the first time-bound commitment concerning air pollution that has been promulgated in India. Although the NCAP mentioned the importance of adopting a multi-sectoral and collaborative approach6, concrete collaborative policies have not yet been developed. To develop effective demand- and supply-side policies, it is important to obtain a deeper understanding of the supply chain structure centered around a critical sector that has contributed to PM2.5 emissions—and therefore, premature deaths—in India.
According to the Regional Emission Inventory in Asia (REAS) database for emissions from 2000 to 20087, the power generation sector is one of the largest contributors of PM2.5 emissions in India, accounting for 822,000 tons of PM2.5 in 2008. In addition, the emissions from the power generation sector increased consistently from 2000 to 2008. Considering energy sources for electrical power generation in India, coal-fired thermal power accounted for 68% of the total 462 TWh generated in 20078. However, coal-fired thermal power plants were responsible for more than 90% of PM2.5 emissions in the power generation sector in 20077, which means that coal-fired thermal power is the most emission-intensive sector and that it plays a critical role in the emissions-related health impact on the people of India. This study examined power generation sector including the coal-fired thermal power and oil-fired thermal power generation, biomass power generation, which account for the remaining 10% of PM2.5 emissions as a critical emission source sector.
PM2.5 emissions from the electric power sector have been increasing due to the increases in electric power consumption that is directly necessary for households, and for industries that produce “final” goods and services. In addition to direct electric power use, it is also important to note that both consumers, i.e., households and industry, also indirectly consume electric power through the production of “intermediate” goods and services (including electric power) that are required to produce the final goods and services. It is also important to note that both direct and indirect electric power consumption generate PM2.5 emissions.
The electric power generation sector plays an important role in the supply chain9. To effectively mitigate the health impacts related to PM2.5 emissions in India, the PM2.5 emissions associated with the indirect use of electricity (i.e., Scope 3 emissions from the electricity sector in line with the greenhouse gas [GHG] protocol10, as well as emissions associated with the direct use of electricity (i.e., Scope 2 emissions from the electricity sector in line with the GHG protocol11) need to be reduced. In other words, it is necessary to identify environmentally important supply chain paths that have the greatest mitigation potential for health impacts in India.
A highly relevant study by Guttikunda and Jawahar (2014)12 focused on coal-fired power plants located in Indian states in 2010 and estimated the total annual PM2.5 emissions in India at around 580,000 tons. These authors also estimated that the annual PM2.5-induced mortalities in India were between 80,000 and 115,000. However, because the study of Guttikunda and Jawahar (2014)12 only examined “production-based” PM2.5 emissions and production-based mortality risks, these results provide a relatively limited understanding of how the final demand of countries such India affects PM2.5-induced mortality risks.
Nansai et al. (2020)13 quantified the mortality-based economic losses (i.e., income loss) attributed to primary and secondary PM2.5 emissions in individual Asian countries that were induced by the final demand of the world’s five largest consuming countries. Their findings showed that in 2010, consumption in the USA, China, Japan, Germany, and the United Kingdom caused approximately 2000, 7700, 2700, 3300, and 3400 deaths in India, respectively. These deaths resulted in economic losses in India of 0.14, 0.26, 0.087, 0.11, and 0.11 billion US dollars in purchasing power parity, respectively. In India, particularly, the export of goods and services from India to these developed countries contributed considerably to PM2.5 emissions, and therefore the high number of premature deaths in India. This situation calls for an analysis of how the global supply chain is impacting health in India in terms of emission responsibility14. In addition, domestic policies need to be introduced to mitigate air pollution inside India, and demand-side policies that consider the role of consumers outside India need to be developed.
Structural path analysis (SPA) is a well-known and effective method that was first introduced by Defourny and Thorbecke (1984)15 to trace important supply chain paths from complex input–output structures by decomposing matrix products into elements (paths). Previous studies addressing PM2.5 emissions have applied this method. For example, Meng et al. (2015)16 identified PM2.5 emission-intensive supply chain paths in China using SPA. However, they only considered PM2.5 emissions and did not consider the reduction potential of health impacts. Nagashima et al. (2017)17 identified critical supply chain paths that contribute toward premature deaths in East Asian countries; however, they did not include secondary PM2.5 generation, which has a marked influence on health, and they did not consider India in their analysis.
This study used EXIOBASE 3 data for 2010 and applied an SPA18,19,20,21 to identify important supply chain paths driven by domestic and international demands that contribute to primary and secondary PM2.5 emissions from the power sector, which is an environmentally critical sector in India. We introduced an atmospheric transport model to fully link final demand via supply chains to the primary emitter that is the power sector in India. Finally, we linked the atmospheric transport of emissions from the emitter to the impact on health in India. To the best of our knowledge, this study is the first attempt to estimate consumption-based PM2.5 emissions as well as the consumption-based mortality risk in India by using a combined approach that is based on an environmentally extended multi-regional input–output (MRIO) analysis and an atmospheric transport model.
The remainder of this manuscript is structured as follows: “Methodology” section explains our methodology, “Data and computation” section describes the data, “Results” section presents and discusses the results, and finally, “Discussion and conclusion” section contains the discussion and conclusions.
Source: Ecology - nature.com
