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#170 – Santosh Harish on how air pollution is responsible for ~12% of global deaths — and how to get that number down

80,000 Hours Podcast

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A Reduced Complexity Model For Air Pollution Based On Evidence-Based Acid Rain Policy

Developing a reduced complexity model simplifies the challenging task of predicting the impact of policy interventions on air quality by bridging the gap between atmospheric chemistry expertise and policy analysis. This model provides a computationally light tool that allows experts in various fields to generate scenarios and assess the implications of policy changes on pollution emissions efficiently. By creating a foundation for new quantitative policy analysis, this model enhances the understanding of the effects of alternative interventions on air quality, paving the way for evidence-based governance. While some argue that the value added by such detailed policy analysis might be limited due to the complexity of policy implementation, the significance lies in aligning with successful international models like the US EPA and CLRTAP, which have utilized evidence-based analysis to inform policy decisions. Implementing reduced complexity models can overcome talent constraints and enhance the quality of air quality governance by offering a systematic and data-driven approach to policy-making.

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