Hi, I'm Yixin Sun, but most people call me Sunny.

One of the promising opportunities offered by AI to support the decarbonization of electricity grids is to align demand with low-carbon supply, yet there is little causal evidence on household acceptance of AI managed control, and its impact on their consumption. We evaluated the effects of the world's largest AI managed EV charging tariff (a retail electricity pricing plan) using a large-scale natural field experiment. This tariff is designed to optimize EV charging by dynamically controlling charging in response to real-time wholesale electricity prices. We randomized financial incentives to encourage enrollment onto the tariff and observed consumption for more than a year. We found that the tariff led to a 42% reduction in household electricity demand during peak hours, with 100% of this demand shifted to lower-cost and lower-carbon-intensity periods. The tariff generated substantial consumer savings, while demonstrating potential to lower producer costs, energy system costs, and carbon emissions through significant load shifting. Overrides of the AI algorithm were low, suggesting that this tariff was likely more efficient than a real-time-pricing tariff without AI, given our theoretical framework. We also showed that experimental estimates differed meaningfully from those obtained via non-randomized difference-in-differences analysis, due to differences in the potential outcomes of the samples in the two evaluation strategies, although we can reconcile the estimates somewhat with observables. Our findings highlight the potential for scalable managed charging and its substantial welfare gains for the electricity system and society.

Exposure to fine particulate matter (PM2.5) poses major health risks, especially in rapidly urbanizing cities. As urbanization accelerates, people in low- and middle-income countries spend more time indoors, where pollution risks remain poorly understood. We present evidence from over 152,000 monitor-hours of indoor PM2.5 measurements across homes in Jakarta, Indonesia, one of the world's largest and most polluted cities. We find that mean daily indoor and outdoor PM2.5 levels are both dangerously high, eight times above World Health Organization's (WHO) health-based guidelines. In addition, indoor PM2.5 frequently reach hazardous levels — 40 to 100 times the WHO guideline, levels that outdoor monitors do not capture. Unlike in developed settings, most indoor pollution originates from outdoor infiltration. Survey data also reveal large inequalities: lower-income households experience double the mean indoor PM2.5 of higher-income households. Our findings show that indoor air pollution remains both severe and unequally distributed, even in this population where most people have adopted cleaner cooking fuels. Researchers and policymakers should integrate outdoor air quality mapping with demographically representative indoor monitoring to close key data gaps, enabling more accurate exposure estimates and better-targeted environmental health policies.