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.

As data centres consume even more energy to serve the intensive computing needs of artificial intelligence, they could contribute to an estimated 600,000 asthma cases and 1300 premature deaths per year by 2030 – accounting for more than one third of asthma deaths annually in the US. "Public health impacts are direct and tangible impacts on people, and these impacts are substantial and not limited to a small radius of where data centres operate," says Shaolei Ren at the University of California, Riverside. "They affect people across the country." Ren and his colleagues, including Adam Wierman at the California Institute of Technology, developed those estimates based on data centres' projected electricity demand, which produces additional emissions and contributes to air pollution. For instance, the electricity usage required for training large AI models could produce air pollutants equivalent to driving a passenger car for more than 10,000 roundtrips between Los Angeles and New York City, according to the researchers.

Effective disaster response is critical for affected communities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and demands during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics:"health impact," "damage," and "evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response and support their decision-making processes.

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for the environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification are limited in applicability because they require granular and bespoke data on bus operation that can be difficult to procure. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate the energy consumption, the carbon emissions, the health impacts, and the total cost of ownership for each transit route. We demonstrate the scalability of our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. Detailed Affiliation: U.Vijay, S.Woo, and S.J.Moura are at Department of Civil and Environmental Engineering, University of California-Berkeley, Davis Hall, Berkeley, California, 94720, USA. A.Jain is at Department of Electrical Engineering and Computer Sciences, University of California-Berkeley, Soda Hall, Berkeley, California, 94720, USA. D.Rodriguez and E.Mellekas are at Enel X, North America, Inc., One Marina Park Drive, Boston, 02210, MA, USA. S. Gambacorta is at Enel X, Innovation and Sustainability Global, Smart City, Viale Tor di Quinto, Rome, 00191, Italy. G.Ferrara is at Enel X, Innovation and Sustainability Global, Smart City, Passo Martino, Catania, 95121, Italy. L.Lanuzza is at Enel X, Innovation and Sustainability B2C & B2B Innovation Factory, Viale Tor di Quinto, Rome, 00191, Italy. C.Zulberti and C.Papa are at Enel Foundation, Via Bellini, Rome, 00198, Italy. Vehicle electrification is crucial for reducing the climate impact of the transportation sector, which currently accounts for 16.2% of the global greenhouse gas emissions [22]. Zero-emission electric vehicles can significantly improve the air quality, health, and environmental equity [23], [24].