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Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Raphael C Kim¹, Falco J Bargagli-Stoffi², Kevin L Chen¹

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, United States.

Biostatistics (Oxford, England)

|June 15, 2026

Summary

Reducing air pollution from power plants can significantly cut cardiovascular disease. New Q- and A-Learning methods identify optimal emission controls, minimizing ischemic heart disease (IHD) hospitalizations cost-effectively.

Keywords:

environmental policy ischemic heart disease optimal treatment regime power plants

Related Experiment Videos

Area of Science:

Environmental Health
Public Health Policy
Computational Science

Background:

Air pollution from coal-fired power plants significantly impacts cardiovascular disease and mortality.
Emissions-reducing interventions are effective but costly, posing challenges for optimal targeting.
Quantifying health benefits is complex due to bipartite network interference (BNI), where power plant interventions affect distant communities.

Purpose of the Study:

To develop novel policy learning methods for optimal intervention strategies under BNI.
To identify cost-effective power plant emission control policies maximizing health benefits.
To minimize ischemic heart disease (IHD) hospitalizations by optimizing power plant scrubber installation.

Main Methods:

Introduction of novel Q- and A-Learning methods to determine optimal policy under arbitrary BNI.
Derivation of asymptotic properties and demonstration of finite sample efficacy via simulations.
Application of methods to Medicare claims, power plant data, and pollution transport networks.

Main Results:

Optimal policies can reduce annual IHD hospitalization rates.
Reductions range from 23.37 to 55.30 per 10,000 person-years.
Effectiveness is demonstrated under various cost constraints.

Conclusions:

Novel Q- and A-Learning methods provide an effective framework for optimizing pollution control strategies.
These methods can achieve significant reductions in ischemic heart disease hospitalizations.
The approach balances health benefits with realistic cost constraints for public health policy.