Predicting Public Health Impacts of Electricity Usage

• URL: http://arxiv.org/abs/2511.22031v1
• Date: Thu, 27 Nov 2025 02:33:13 GMT
• Title: Predicting Public Health Impacts of Electricity Usage
• Authors: Yejia Liu, Zhifeng Wu, Pengfei Li, Shaolei Ren,
• Abstract summary: Electric power sector is a leading source of air pollutant emissions, impacting the public health of nearly every community.<n>We introduce HealthPredictor, a domain-specific AI model that provides an end-to-end pipeline linking electricity use to public health outcomes.
• Score: 20.400038737666545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The electric power sector is a leading source of air pollutant emissions, impacting the public health of nearly every community. Although regulatory measures have reduced air pollutants, fossil fuels remain a significant component of the energy supply, highlighting the need for more advanced demand-side approaches to reduce the public health impacts. To enable health-informed demand-side management, we introduce HealthPredictor, a domain-specific AI model that provides an end-to-end pipeline linking electricity use to public health outcomes. The model comprises three components: a fuel mix predictor that estimates the contribution of different generation sources, an air quality converter that models pollutant emissions and atmospheric dispersion, and a health impact assessor that translates resulting pollutant changes into monetized health damages. Across multiple regions in the United States, our health-driven optimization framework yields substantially lower prediction errors in terms of public health impacts than fuel mix-driven baselines. A case study on electric vehicle charging schedules illustrates the public health gains enabled by our method and the actionable guidance it can offer for health-informed energy management. Overall, this work shows how AI models can be explicitly designed to enable health-informed energy management for advancing public health and broader societal well-being. Our datasets and code are released at: https://github.com/Ren-Research/Health-Impact-Predictor.

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