Effectiveness of air pollution standards in reducing mortality in India

Sankar, Ashwini; Coggins, Jay S.; Goodkind, Andrew L.

doi:10.1016/j.reseneeco.2020.101188

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…We run the following model, with the natural log of AMR in district i and year t as the dependent variable against the natural log of PM 2.5 (ln (PM 2.5 )) for each district-year, and control for district level fixed effects (δ i ), year fixed effects (τ t ), district-specific linear and quadratic trends (denoted by δ i × t and δ i × t 2 respectively), and urban literacy rates (X it ) (see S.2 in supplemental information for alternative estimated models) [9].…”

Section: Econometric Analysismentioning

confidence: 99%

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The air pollution tradeoff in India: saving more lives versus reducing the inequality of exposure

Sankar,

Goodkind,

Coggins

2023

Environ. Res. Lett.

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Chronic exposure to ambient fine particulate matter (PM2.5) represents one of the largest global public health risks, leading to millions of premature deaths annually. For a country facing high and spatially variable exposures, prioritizing where to reduce PM2.5 concentrations leads to an inherent tradeoff between saving the most lives and reducing inequality of exposure. This tradeoff results from the shape of the concentration-response function between exposure to PM2.5 and mortality, which indicates that the additional lives saved per unit reduction in PM2.5 declines as concentrations increase, suggesting that more lives can be saved by reducing exposures in clean locations than in dirty locations. We estimated this concentration-response function for urban areas of India, finding that a 10 µgm-3 reduction in PM2.5 in already-clean locations will reduce the mortality rate substantially (4.2% for a reduction from 30 to 20 µgm-3), while a 10 µgm-3 reduction in the dirtiest locations will reduce mortality only modestly (1.2% for a reduction from 90 to 80 µgm-3). Policymakers face a troubling tradeoff between maximizing lives saved and reducing the inequality of exposure. Many air pollution policies impose an upper limit on exposure, thereby cleaning the dirtiest locations and reducing exposure inequality. We explore the implications of this PM2.5/mortality relationship by considering a thought experiment. If India had a fixed amount of resources to devote to PM2.5 concentration reductions across urban areas, what is the lives saved/inequality of exposure tradeoff from three different methods of deploying those resources? Across our three scenarios: 1) which reduces exposures for the dirtiest districts, 2) which reduces exposures everywhere equally, and 3) which reduces exposures to save the most lives—scenario 1 saves 18,000 lives per year while reducing the inequality of exposure by 65%, while scenario 3 saves 126,000 lives per year, but increases inequality by 19%.

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Section: Econometric Analysismentioning

confidence: 99%

“…Fifteen of the 20 most polluted cities in the world are in India, leading to significant numbers of premature deaths each year [4][5][6][7][8]. Reducing air pollution, specifically, fine particulate matter (PM 2.5 ), would reap benefits for India in terms of improved health [9,10].…”

Section: Introductionmentioning

confidence: 99%

The air pollution tradeoff in India: saving more lives versus reducing the inequality of exposure

Sankar,

Goodkind,

Coggins

2023

Environ. Res. Lett.

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“…Our dataset is at the urban district level, which is an administrative division within a state that is a conglomeration of towns and cities. We run the following model, with the natural log of the mortality rate as the dependent variable and control for linear and quadratic district-speci c time trends, year xed effects, and urban literacy rates 9 . 1 where is the log of adjusted mortality rate in district and year , is the log of PM 2.5 for each district-year, and is the control variable we use, i.e., urban literacy rate.…”

Section: Econometric Estimatesmentioning

confidence: 99%

The Air Pollution Tradeoff in India: Saving More Lives Versus Reducing the Inequality of Exposure

Sankar

Goodkind²,

Coggins³

2022

SSRN Journal

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“…Our dataset is at the urban district level, which is an administrative division within a state that is a conglomeration of towns and cities. We run the following model, with the natural log of the mortality rate as the dependent variable and control for linear and quadratic district-speci c time trends, year xed effects, and urban literacy rates 9 . ln(AMR it ) = α + βX it + γln(PM 2.5 , it ) + δ i + τ t + δ i × t + δ i × t 2 + ϵ it ,…”

Section: Econometric Estimatesmentioning

confidence: 99%

“…Fifteen of the 20 most polluted cities in the world are in India, leading to signi cant numbers of premature deaths each year [4][5][6][7][8] . Reducing air pollution, speci cally, ne particulate matter (PM 2.5 ), would reap bene ts for India in terms of improved health 9,10 .…”

Section: Introductionmentioning

confidence: 99%

The Air Pollution Tradeoff in India: Saving More Lives Versus Reducing the Inequality of Exposure

Sankar

Goodkind²,

Coggins

2021

SSRN Journal

Self Cite

View full text Add to dashboard Cite

Chronic exposure to ambient ne particulate matter (PM2.5) represents one of the largest global public health risks, leading to millions of premature deaths annually. For a country facing high and spatially variable exposures, prioritizing where to reduce PM2.5 concentrations leads to an inherent tradeoff between saving the most lives and reducing inequality of exposure. This tradeoff results from the shape of the concentration-response function between exposure to PM2.5 and mortality, which indicates that the additional lives saved per unit reduction in PM2.5 declines as concentrations increase. We estimate this concentration-response function for urban areas of India, nding that a 10 unit reduction in PM2.5 in already-clean locations will reduce the mortality rate substantially (4.2% for a reduction from 30 to 20 µgm-3), while a 10 unit reduction in the dirtiest locations will reduce mortality only modestly (1.2% for a reduction from 90 to 80 µgm-3). We explore the implications of this PM2.5/mortality relationship by considering a thought experiment. If India had a xed amount of resources to devote to PM2.5 concentration reductions across urban areas, what is the lives saved/inequality of exposure tradeoff from three different methods of employing those resources? Across our three scenarios-1) which reduces exposures for the dirtiest districts, 2) which reduces exposures everywhere equally, and 3) which reduces exposures to save the most lives-scenario 1 saves 18,000 lives per year while reducing the inequality of exposure by 65%, while scenario 3 saves 126,000 lives per year, but increases inequality by 19%. Signi cance StatementDesigning policies to reduce exposure to PM 2.5 is complicated by the apparent supralinear relationship between PM 2.5 and premature mortality. This relationship-which we estimate for Indian urban areasindicates that more lives can be saved by reducing exposures in the already-clean locations than in the dirtiest locations. Thus, policymakers face a troubling tradeoff, between maximizing lives saved and reducing the inequality of exposure to pollution. Many air policies impose an upper limit on exposure, thereby cleaning the dirtiest locations while reducing exposure inequality. We illustrate the tradeoff between lives saved and inequality, highlighting the challenge facing policy makers charged with protecting human health equitably.

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Effectiveness of air pollution standards in reducing mortality in India

Cited by 9 publications

References 27 publications

The air pollution tradeoff in India: saving more lives versus reducing the inequality of exposure

The air pollution tradeoff in India: saving more lives versus reducing the inequality of exposure

The Air Pollution Tradeoff in India: Saving More Lives Versus Reducing the Inequality of Exposure

The Air Pollution Tradeoff in India: Saving More Lives Versus Reducing the Inequality of Exposure

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