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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. 

Novel energy technologies, especially decentralized electricity generation systems, are increasingly being designed and implemented. However, potential environmental impacts are frequently recognized after installing new energy systems at full scale, at which point modification comes at a high cost. Life cycle assessment (LCA) can be used throughout the design-to-commercialization process to prevent this outcome, despite the challenges of emerging energy technology LCAs, like comparability, lack of data, scale-up difficulties, and uncertainties that are not typically faced while evaluating existing and established systems. The complexity and urgency of evaluating climate change impacts of novel energy technologies during the research and development stage reveal the need for guidance, presented in this study, with an emphasis on data collection, data processing, and uncertainty analysis. We outline best practices in choosing among several methods that have been employed in LCA studies to fill gaps in input data, including machine learning. Additionally, we discuss how design can be guided by LCA through assessment setting and delineation of scenarios or case studies, in order to prevent unnecessary effort and maximize the amount of useful, interpretable results. We also discuss the utility of complementary analyses, including global sensitivity analysis, neural network, Monte Carlo analysis that differentiates between uncertainty and variability parameters, and optimization. This guidance has the potential to make emerging electricity generation system implementation ultimately effective in reducing greenhouse gas emissions, through the methodological use of LCA in the design process. 

Thorough accounting of the climate change impacts of natural gas is crucial to guide the energy transition towards climate change mitigation, as even decarbonization roadmaps project continued natural gas use into the future. The climate change impacts of natural gas extraction have not previously been assessed at the well pad level, accounting for a multitude of geospatial differences between individual pads. Well pads constructed across a varied landscape lead to a range of well pad areas, earth flattening needs, well pad lifetimes, total gas production, and direct land use change (DLUC) effects such as loss of original biomass, soil organic carbon loss, change in net primary productivity, and altering the surface albedo of the site. Using existing well pad data, machine learning techniques, and satellite imagery, the spatial extents of thousands of well pads in New Mexico were delineated for site-specific data collection. A parametric life cycle assessment (LCA) model of natural gas-producing well pads was developed to integrate geospatial differences and DLUC effects, yielding scenario analysis results for each identified well pad. The DLUC effects contributed a median of 14.4% and a maximum of 59.0% to natural gas extraction carbon footprints. The use of well pad-level data revealed that the carbon footprint of natural gas extraction ranges across orders of magnitude, from 0.016 to 46.4 g CO2eq per MJ. The results highlight the need to quantify the climate change impacts of establishing a well pad and extracting natural gas case-by-case, with geographically specific data, to guide new installations towards lower emissions.