The Healthcare Cost of Air Pollution: Evidence from the World’s Largest Payment Network

Barwick, Panle Jia; Li, Shanjun; Rao, D. C.; Zahur, Nahim Bin

doi:10.3386/w24688

Cited by 50 publications

(64 citation statements)

References 37 publications

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“…In such cases, the excess deaths caused could be larger. In comparison, COVID-19 may have raised individuals' awareness 9 For example, the morbidity cost of air pollution is found to be around two-thirds of mortality costs in China (Barwick et al, 2018), of their health conditions and made people practice good hygiene. This could significantly reduce deaths from other diseases, particularly influenza.…”

Section: Resultsmentioning

confidence: 99%

COVID-19, City Lockdowns, and Air Pollution: Evidence from China

Pan

Tanaka

2020

Preprint

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The rapid spread of COVID-19 is a global public health challenge. To prevent the escalation of its transmission, China locked down one-third of its cities and strictly restricted human mobility and economic activities. Using timely and comprehensive air quality data in China, we show that these counter-COVID-19 measures led to remarkable improvement in air quality. Within weeks, the Air Quality Index and PM2.5 concentrations were brought down by 25%. The effects are larger in colder, richer, and more industrialized cities. We estimate that such improvement would avert 24,000 to 36,000 premature deaths from air pollution on a monthly basis. : medRxiv preprint Notes: Each column summarizes the mean values and standard deviations of each variable at a weekly level. More information about the dataset is described in Appendix 1.

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Section: Resultsmentioning

confidence: 99%

COVID-19, City Lockdowns, and Air Pollution: Evidence from China

Pan

Tanaka

2020

Preprint

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“…They find that a 10-µg/m3 increase in PM10 increases cardiorespiratory mortality by 8 percent; this impact varies across age cohorts but not across gender. Following the analysis in Barwick et al, (2018) to monetize the mortality impact, the mortality cost amounts to $13.38 billion across the Chinese population from a 10-µg/m3 increase in PM10, or $64.9 per household in Beijing when adjusted for the Beijing per capital income (in 2015 dollars). The morbidity cost of air pollution comes from Barwick et al, (2018), who provide the first comprehensive analysis of the morbidity cost in China based on the universe of credit and debit card spending.…”

Section: Benefit-cost Analysismentioning

confidence: 99%

Community toilet use in Indian slums: willingness-to-pay and the role of informational and supply side constraints

Armand

Augsburg

Bancalari

et al. 2020

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Using big data to evaluate the impacts of transportation infrastructure investment The case of subway systems in Beijing April 2020 About 3ie The International Initiative for Impact Evaluation (3ie) promotes evidence-informed, equitable, inclusive and sustainable development. We support the generation and effective use of high-quality evidence to inform decision-making and improve the lives of people living in poverty in low-and middle-income countries. We provide guidance and support to produce, synthesise and quality assure evidence of what works, for whom, how, why and at what cost. 3ie impact evaluations 3ie-supported impact evaluations assess the difference a development intervention has made to social and economic outcomes. 3ie is committed to funding rigorous evaluations that include a theory-based design and that use the most appropriate mix of methods to capture outcomes and are useful in complex development contexts. About this report 3ie accepted the final version of the report, Using big data to evaluate the impacts of transportation infrastructure investment: the case of subway systems in Beijing, as partial fulfilment of requirements under grant DPW1.1106 awarded through Development Priorities Window 1. The report is technically sound and 3ie is making it available to the public in this final report version as it was received. No further work has been done. The 3ie technical quality assurance team for this report comprises Francis Rathinam, Monica Jain, Deeksha Ahuja, an anonymous external impact evaluation design expert reviewer and an anonymous external sector expert reviewer, with overall technical supervision by Marie Gaarder. The 3ie editorial production team for this report comprises

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“…According to the model selection criteria in Table 1, the simple autoregressive model was most appropriate. We therefore selected model AR (2) to estimate the value of t C . We analyzed periodicity in the AQI residuals after trends and seasonal variations were excluded, and obtained the autocorrelation coefficient and partial autocorrelation coefficient for the residuals (Figure 8).…”

Section: Trends and Seasonal Variation In The Aqi Time Seriesmentioning

confidence: 99%

“…According to the model selection criteria in Table 1, the simple autoregressive model was most appropriate. We therefore selected model AR (2) to estimate the value of C t . based on the 5 years of data used to develop our model.…”

Section: Trends and Seasonal Variation In The Aqi Time Seriesmentioning

confidence: 99%

“…This increases medical expenditures. A Cornell University study showed that for every 10 µg/m 3 decrease in the concentration of particles smaller than 2.5 µm (PM 2.5 ) in China, national medical expenditures would decrease by US$42 billion, which amounted to 7% of China's total medical expenditure in 2015 [2]. Air pollution can also decrease well-being, since it forces us to…”

Section: Introductionmentioning

confidence: 99%

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An Actuarial Pricing Method for Air Quality Index Options

Liu

Zhao

Wang

et al. 2019

IJERPH

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Poor air quality has a negative impact on social life and economic production activities. Using financial derivatives to hedge risks is one of the important methods. Air quality index (AQI) options are designed to help enterprises cope with the operational risk caused by air pollution. First, the expanded Ornstein–Uhlenbeck model is established using an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) method to predict AQI for a city. Next, the average AQI is constructed to be as the underlying index for the AQI options. We then priced AQI options using an actuarial method with an Esscher transform. Meanwhile payoff functions for the options are established to let enterprises hedge against the operational risk caused by air pollution. Finally, we determined the price of AQI options using data from Xi’an, China, and the example of a tourism enterprise as a case study of how AQI options can be applied to hedge against operational risk for enterprises. With AQI options trading, enterprises can hedge against operational risks caused by air pollution. The applicability of AQI options is wide, it can also be applied in other cities or regions.

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The Healthcare Cost of Air Pollution: Evidence from the World’s Largest Payment Network

Cited by 50 publications

References 37 publications

COVID-19, City Lockdowns, and Air Pollution: Evidence from China

COVID-19, City Lockdowns, and Air Pollution: Evidence from China

Community toilet use in Indian slums: willingness-to-pay and the role of informational and supply side constraints

An Actuarial Pricing Method for Air Quality Index Options

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