Quantifying the impact of clean air policy interventions for air quality management

Shi, Zongbo and Liu, Bowen and Cheng, Kai and Elliot, Robert and Cole, Matthew and Bryson, John R (2022) Quantifying the impact of clean air policy interventions for air quality management. Project Report. University of Birmingham.

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Identification Number/DOI: 10.25500/epapers.bham.00004040

Abstract

Rising environmental concerns require the implementation of appropriate policies to manage environmental risk. One such risk arises from air pollution. As part of the process of air quality management it is important to understand how effective different policies are to determine whether a policy should be, for example, scrapped, changed, or rolled out across different sectors or regions. However, evaluating clean air policies is a challenge because of the complex physical and chemical processes in the atmosphere and other socioeconomic factors that may also be impacting pollution levels. This briefing document outlines a methodological approach that can be used to provide evidence of the success or otherwise of different clean air policies for different geographical areas and time periods.

Type of Work:	Monograph (Project Report)
School/Faculty:	Colleges (2008 onwards) > College of Life & Environmental Sciences
Department:	School of Geography, Earth & Environmental Sciences, Department of Economics, Department of Strategy and International Business
References:	Abadie, A., Diamond, A. and Hainmueller, J. (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association, 105 (490): 493–505. Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803. Cole, M. A., Elliott, R. J., & Liu, B. (2020). The impact of the Wuhan Covid-19 lockdown on air pollution and health: a machine learning and augmented synthetic control approach. Environmental and Resource Economics, 76(4), 553-580. Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., & Hueglin, C. (2018). Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmospheric Chemistry and Physics, 18(9), 6223-6239. Grange, S. K., & Carslaw, D. C. (2019). Using meteorological normalisation to detect interventions in air quality time series. Science of the Total Environment, 653, 578-588. Shi, Z., Song, C., Liu, B., Lu, G., Xu, J., Van Vu, T., Elliott, R. J., Li, W., Bloss, W.J., Harrison, R. M. (2021). Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science advances, 7(3), eabd6696.
Additional Information:	How to cite: Shi, Z., Liu, B., Cheng, K., Elliott, R.J.R., Cole, M.A., Bryson, J.R., 2022. Quantifying the impact of Clean Air Policy: For air quality management. Working paper, doi: 10.25500/epapers.bham.00004040. Available from: https://doi.org/10.25500/epapers.bham.00004040. Acknowledgements: This work is based on research partially funded by UKRI-NERC (NE/N007190/1; NE/S003487/1). The production of this briefing note is supported by Natural England QR block fund. Thanks to Chantal Jackson for artwork and editorial assistance.
Date:	June 2022
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics
Funders:	Research England, Natural Environmental Research Council
Copyright Status:	© 2021 The Authors. Re-use permitted under CC BY 4.0 creativecommons.org/licenses/by/4.0/
ID Code:	4040

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