Inês M L Azevedo’s research while affiliated with NOVA School of Business and Economics and other places

California wildfires have been increasing in frequency and severity. However, there is a limited understanding to date about people’s experiences with wildfire occurrences, how that experience shapes their perceptions of risk and how it affects their response in terms of actions that can reduce their household risk. In 2021, we fielded an online survey to 1204 people living in California aimed at understanding experience, risk perceptions, and decision-making strategies related to wildfires. We find that 70% of all survey respondents participants experienced poor air quality and 39% experienced a power outage in their home. Other experiences elicited (such as experiencing a school closure, a home evacuation, or home damages) were reported by less than 1/5 of our respondents. A significant portion of our survey respondents has undertaken actions to reduce the risk from wildfire to their household by having air filters, smoke-protecting masks and using informational air quality tools, and investing in back up power. Despite low number of reported prior experiences and the low perception of likelihood for the need to evacuate, a relatively large percent of respondents has taken actions to prepare for evacuation with 41% of the respondents stating packed a ‘go bag’, 32% securing a place to stay in case of evacuation, 35% reported having created a defensible space, and 18% have pursued retrofits to their house. Our regression models suggest that while the perception of risk is associated with action, other factors, such as being a homeowner or deriving livelihood off their land are much more likely to drive people to act. The results from our study suggest that while experience may lead to action, other policy interventions, such as clear communication on risks and consequences for households in high-risk areas, may be warranted to drive further action.

Air pollution from coal electricity generation is a major driver of poor air quality in India and its effects on human health have been extensively studied. Despite considerable evidence that the same pollution also reduces crop productivity, we lack similar quantitative assessments of coal electricity’s crop damages. Here, we estimate rice and wheat crop losses from coal generation’s nitrogen dioxide (NO 2 ) emissions using a regression model that combines station-level electricity generation and wind direction, satellite-measured NO 2 , and its association with crop productivity. Coal emissions impact yields up to 100 km away from power stations. In parts of West Bengal, Madhya Pradesh, and Uttar Pradesh heavily exposed to coal-linked NO 2 , annual yield losses exceed 10%, equivalent to approximately 6 y worth of average annual yield growth in both rice and wheat in India between 2011 and 2020. While station-specific crop damages (value of lost output) are almost always lower than mortality damages (monetized value of annual premature PM 2.5 -related deaths), crop damage intensity (crop damage per GWh of electricity generated) is frequently higher than mortality damage intensity (mortality damage/GWh). Rice damage intensity exceeds mortality damage intensity at 58, and wheat damage intensity at 35 of the 144 power stations studied. The stations associated with the largest crop losses differ from those associated with the highest mortality. Co-optimizing for crop gains and mortality reduction slightly increases and meaningfully changes the distribution of social benefits from reducing emissions, highlighting the importance of considering crop losses alongside health impacts when regulating coal electricity emissions in India.

Recycling lithium-ion batteries (LIBs) can supplement critical materials and improve the environmental sustainability of LIB supply chains. In this work, environmental impacts (greenhouse gas emissions, water consumption, energy consumption) of industrial-scale production of battery-grade cathode materials from end-of-life LIBs are compared to those of conventional mining supply chains. Converting mixed-stream LIBs into battery-grade materials reduces environmental impacts by at least 58%. Recycling batteries to mixed metal products instead of discrete salts further reduces environmental impacts. Electricity consumption is identified as the principal contributor to all LIB recycling environmental impacts, and different electricity sources can change greenhouse gas emissions up to five times. Supply chain steps that precede refinement (material extraction and transport) contribute marginally to the environmental impacts of circular LIB supply chains (<4%), but are more significant in conventional supply chains (30%). This analysis provides insights for advancing sustainable LIB supply chains, and informs optimization of industrial-scale environmental impacts for emerging battery recycling efforts.

Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations. Here, we develop open-source daily fine particulate matter (PM 2.5 ) datasets at a 10-kilometer resolution for India from 2005 to 2023 using a two-stage machine learning model validated on held-out monitor data. Analyzing long-term air quality trends, we find that PM 2.5 concentrations increased across most of the country until around 2016 and then declined partly due to favorable meteorology in southern India. Recent reductions in PM 2.5 were substantially larger in wealthier areas, highlighting the urgency of air quality control policies addressing all socioeconomic communities. To advance equitable air quality monitoring, we propose additional monitor locations in India and examine the adaptability of our method to other countries with scarce monitoring data.

Wind and solar generation constitute an increasing share of electricity supply globally. We find that this leas to shifts in the operational dynamics of thermal power plants. Using fixed effects panel regression across seven major U.S. balancing authorities, we analyze the impact of renewable generation on coal, natural gas combined cycle plants, and natural gas combustion turbines. Wind generation consistently displaces thermal output, while effects from solar vary significantly by region, achieving substantial displacement in areas with high solar penetration such as the California Independent System Operator but limited impacts in coal reliant grids such as the Midcontinent Independent System Operator. Renewable energy sources effectively reduce carbon dioxide emissions in regions with flexible thermal plants, achieving displacement effectiveness as high as one hundred and two percent in the California Independent System Operator and the Electric Reliability Council of Texas. However, in coal heavy areas such as the Midcontinent Independent System Operator and the Pennsylvania New Jersey Maryland Interconnection, inefficiencies from ramping and cycling reduce carbon dioxide displacement to as low as seventeen percent and often lead to elevated nitrogen oxides and sulfur dioxide emissions. These findings underscore the critical role of grid design, fuel mix, and operational flexibility in shaping the emissions benefits of renewables. Targeted interventions, including retrofitting high emitting plants and deploying energy storage, are essential to maximize emissions reductions and support the decarbonization of electricity systems.

Long-haul freight shipment in the United States relies on diesel trucks and constitutes ∼3% of U.S. greenhouse gas emissions and a significant share of local air pollution. Here, we compare the climate and air pollution-related health damages from electric versus diesel long-haul truck fleets. We use truck commodity flows to estimate tailpipe emissions from diesel trucks and regional grid emissions intensities to estimate charging emissions from electric trucks under various grid scenarios. We use a reduced complexity air quality model combined with valuation of air pollution-related premature deaths (using two hazard ratios (HRs)) and quantify the distributional health impacts in different scenarios. We find that annual health and climate costs of the current diesel fleet are 195–249/capita compared to 174–205/capita for a new diesel fleet, and 156–177/capita for an electric fleet, depending on the HR. We find that freight electrification could avoid $6.2–8.5 billion in health and climate damages annually when compared to a fleet of new diesel vehicles (with even higher benefits when compared to the current diesel fleet). However, the Midwest and parts of the Gulf Coast would experience an increase in health damages due to vehicles charging using electricity from coal power plants. If old coal power plants (operating in 1980 or earlier) are replaced with zero-emission generation, electrification of all U.S. freight would result in$32.3–39.2 billion in avoided damages annually and health benefits throughout the U.S. Electrifying transport of consumer manufacturing goods (including electronics, transport equipment, and precision instruments) and food, beverage, and tobacco products would provide the largest absolute health and climate benefits, whereas mixed freight and manufacturing goods would result in the largest benefits per tonne-km. We find small variations in health damages across race and income. These results will help policymakers prioritize electrification and charging investment strategies for the freight transportation sub-sector.

As Chile's electric power sector advances toward a future powered by renewable energy, accurate forecasting of renewable generation is essential for managing grid operations. The integration of renewable energy sources is particularly challenging due to the operational difficulties of managing their power generation, which is highly variable compared to fossil fuel sources, delaying the availability of clean energy. To mitigate this, we quantify the impact of increasing intermittent generation from wind and solar on thermal power plants in Chile and introduce a hybrid wind speed forecasting methodology which combines two custom ML models for Chile. The first model is based on TiDE, an MLP-based ML model for short-term forecasts, and the second is based on a graph neural network, GraphCast, for medium-term forecasts up to 10 days. Our hybrid approach outperforms the most accurate operational deterministic systems by 4-21% for short-term forecasts and 5-23% for medium-term forecasts and can directly lower the impact of wind generation on thermal ramping, curtailment, and system-level emissions in Chile.

Wind and solar electricity generation account for 14% of total electricity generation in the United States and are expected to continue to grow in the next decades. In low carbon systems, generation from renewable energy sources displaces conventional fossil fuel power plants resulting in lower system-level emissions and emissions intensity. However, we find that intermittent generation from renewables changes the way conventional thermal power plants operate, and that the displacement of generation is not 1 to 1 as expected. Our work provides a method that allows policy and decision makers to continue to track the effect of additional renewable capacity and the resulting thermal power plant operational responses.

California contributes 0.75% of global greenhouse gas (GHG) emissions and has a target of reaching economy-wide net zero emissions by 2045, requiring all sectors to rapidly reduce emissions. Nearly 8% of California’s GHG emissions are from the heavy-duty transportation sector. In this work, we simulate decarbonization strategies for the heavy-duty vehicle (HDV) fleet using detailed fleet turnover and air quality models to track evolution of the fleet, GHG and criteria air pollutant emissions, and resulting air quality and health impacts across sociodemographic groups. We assess the effectiveness of two types of policies: zero emission vehicle sales mandates, and accelerated retirement policies. For policies including early retirements, we estimate the cost of early retirements and the cost-effectiveness of each policy. We find even a policy mandating all HDV sales to be zero emission vehicles by 2025 would not achieve fleetwide zero emissions by 2045. For California to achieve its goal of carbon neutrality, early retirement policies are needed. We find that a combination of early retirement policies and zero emission vehicle sales mandates could reduce cumulative CO2 emissions by up to 64%. Furthermore, we find that decarbonization policies will significantly reduce air pollution-related mortality, and that Black, Latino, and low-income communities will benefit most. We find that policies targeting long-haul heavy-heavy duty trucks would have the greatest benefits and be most cost-effective.