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1. Examining impacts of policy, technology, and climate extremes on thermoelectric power production and river thermal pollution in the Midwest and Northeast of the United States

   https://doi.org/10.3389/fenvs.2023.1212211  

   Zhang, Jiaqi; Chen, Mengye; Gao, Shang; Li, Zhi; Vörösmarty, Charles J; Fekete, Balazs; Miara, Ariel; Hong, Yang (August 2023, Frontiers in Environmental Science)

   As one of the components comprising food-energy-water systems (FEWS), the energy sector, especially electricity production, is intimately connected to water. Climate extremes-related impacts on water resources will directly affect the interdependence of water, food, and energy. A better understanding of the extent of climate impacts on energy sector and the options to improve water-energy security are needed for planning an overall resilient FEWS. Therefore, we are motivated to examine the climate stress on the thermoelectric power supply using the Water Balance Model coupled with Thermoelectric Power & Thermal Pollution Model (WBM-TP2M), which can simulate water-energy linkages at the power plant, river reach, and regional scales. Using the Midwest (MW) and Northeast (NE) regions as our study area, we design a group of single- and multi-factor experiments both for historical climate period (1980–2019) and a case where we create a series of intensified extremes (2010–2019). The results show that power generation over the two regions features a gradually increasing trend in the past four decades, while, in contrast, thermal pollution has been decreasing steadily since 2005. Heat waves created temporary dips in the generation of electricity and peaks of heavily thermal-polluted stream length. The experiments reveal the significant role of cooling towers in reducing thermal pollution without losing much capacity to generate power, one important measure of resilience against climate extremes. Constraints placed on effluent temperature from the Clean Water Act lead to interrupted operations, which reduces (up to 20%) power generation, increases sensitivity to climatic extremes, but only show a small reduction in thermal pollution. Coal, as a fuel source, is subject to low thermal efficiency and high-water demand, which results in clearly decreased power generation. Overall, our findings suggest that replacing a less thermal-efficient fuel mix and environmentally destructive once-through cooling can move the energy sector towards several beneficial outcomes. Chief among these is a more efficient power production system that uses less water and does so while fostering clean, less carbon-intense technologies (e.g., combined gas cycle turbines, cooling towers, renewable energy), thus linking positive outcomes that simultaneously and positively impact aquatic ecosystems, regional airsheds and human health. 

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2. Carbon footprint and energy payback time of a micro wind turbine for urban decarbonization planning

   https://doi.org/10.1038/s41598-025-10540-x  

   Pfadt-Trilling, A R; Fortier, M-O P (July 2025, Scientific Reports)

   Micro wind power systems may serve as a source of low-carbon electricity that can be integrated into cities as opposed to utility-scale wind turbines. However, the electricity generation performance of wind turbines of all capacities is highly dependent on conditions at an installation site, which can vary widely even within the same municipal region. We assess the life cycle greenhouse gas emissions (LCGHGE) and energy payback time of a novel microturbine of 2.4-kW capacity with location-specific environmental data. Potential electricity generation was modeled in the areas surrounding two US cities with ambitious decarbonization efforts and abundant wind energy resources in different climates: Austin, Texas and Minneapolis, Minnesota. The effects of system lifetime and hub height on the potential electricity generation were investigated, which identified trade-offs in higher electricity generation for taller turbines yet higher LCGHGE from greater amounts of materials needed. The LCGHGE of micro wind modeled for Austin and Minneapolis range from 53 to 293 g CO2eq/kWh, which is higher than utility-scale wind energy but still lower than fossil fuel sources of electricity. This study highlights the variability in the LCGHGE and energy payback time of micro wind power across locations, demonstrating the value of geospatial analyses for life cycle climate change impact estimates. 

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3. US Clean Energy Futures—Air Quality Benefits of Zero Carbon Energy Policies

   https://doi.org/10.3390/atmos13091401  

   Vasilakos, Petros N.; Shen, Huizhong; Mehdi, Qasim; Wilcoxen, Peter; Driscoll, Charles; Fallon, Kathy; Burtraw, Dallas; Domeshek, Maya; Russell, Armistead G. (September 2022, Atmosphere)

   In this work, we compare the air quality benefits of a variety of future policy scenarios geared towards controlling EGU (electricity generating units) emissions between the present-day conditions and 2050. While these policies are motivated by reducing CO2 emissions, they also yield significant co-benefits for criteria pollutants, such as ozone and PM2.5. An integrated set of clean energy policies were examined to assess the time-varying costs and benefits of a range of decarbonization strategies, including business as usual and the Affordable Clean Energy plan, with a primary focus on others that look to achieve very low, if not zero, CO2 emissions from the EGU sector by 2050. Benefits assessed include mitigation of greenhouse gas emissions as well as air quality co-benefits. In this introductory work, we describe the potential air quality changes from various clean air policies, to set the stage for upcoming work looking at health and monetized benefits. Emission changes for key pollutants are forecast using the Integrated Planning Model (IPM), which are then transformed into emission inputs for the Community Multiscale Air Quality Model (CMAQ). For all primary scenarios considered that achieve large greenhouse gas decreases, significant reductions in ozone and PM are realized, mainly in the eastern US, and all policies produce air quality benefits. 

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4. The unaccounted-for climate costs of materials

   https://doi.org/10.1088/1748-9326/ad796e  

   Colligan, Paikea; Roijen, Elisabeth Van; Kane, Seth; Moore, Frances; Miller, Sabbie A (October 2024, Environmental Research Letters)

   Abstract Materials production is a primary driver of anthropogenic greenhouse gas emissions; yet the externalized costs of these emissions on society are not reflected in market prices. Here, we estimate the externalized climate costs from materials production in the United States at approximately 79 billion USD per annum, and we highlight disparities in materials pricing. Proper accounting for such disparities can be leveraged to drive breakthroughs in technologies used for our material resources and manufacturing. 

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5. Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

   https://doi.org/10.1109/TSTE.2024.3437210  

   Yang, Yijie; Shi, Jian; Wang, Dan; Wu, Chenye; Han, Zhu (October 2024, IEEE Transactions on Sustainable Energy)

   Buildings produce a significant share of greenhouse gas (GHG) emissions, making homes and businesses a major factor in climate change. To address this critical challenge, this paper explores achieving net-zero emission through the carbon-aware optimal scheduling of the multi-energy building integrated energy systems (BIES). We integrate advanced technologies and strategies, such as the carbon capture system (CCS), power-to-gas (P2G), carbon tracking, and emission allowance trading, into the traditional BIES scheduling problem. The proposed model enables accurate accounting of carbon emissions associated with building energy systems and facilitates the implementation of low-carbon operations. Furthermore, to address the challenge of accurately assessing uncertainty sets related to forecasting errors of loads, generation, and carbon intensity, we develop a learning-based robust optimization approach for BIES that is robust in the presence of uncertainty and guarantees statistical feasibility. The proposed approach comprises a shape learning stage and a shape calibration stage to generate an optimal uncertainty set that ensures favorable results from a statistical perspective. Numerical studies conducted based on both synthetic and real-world datasets have demonstrated that the approach yields up to 8.2% cost reduction, compared with conventional methods, in assisting buildings to robustly reach net-zero emissions. 

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