CAREER: Geospatial life cycle climate change impacts of solar and ocean renewable energy systems

生命周期评估（LCA）的做法是用来确定能源系统的气候变化影响。 然而，这些评估往往错过关键的地理特定因素，如直接土地利用变化（DLUC）的影响，作为非温室气体气候因子的二氧化碳变化，以及与潜在发电相关的地理空间数据。该项目的总体目标是将这些地理空间要素系统地整合到传统和可再生能源系统的生命周期评估中，以提高其在更高空间分辨率下的“碳足迹”准确性，并传播该方法及其结果，为能源转型的可持续特定地点设计、选址和实施决策提供信息。（1）首先更新与化石燃料的基线比较，包括其在美国扩张的DLUC和CO2变化影响;（2）为选定的太阳能和海洋可再生能源技术开发地理空间LCA模型，并确定其生命周期气候变化对美国潜在可用土地和水的影响;以及（3）使地理空间LCA模型适应气候变化下的未来条件。这些能源系统场景的参数化LCA模型将在开源Python代码中开发，由公开的GIS数据集，这些技术的物理特性以及为适应可变环境条件而开发的算法提供信息。为了使能源利益相关者和未来的LCA从业者传播和使用该方法，教育目标是：（1）设计和分享地理空间LCA建模课程;（2）为行业和政策利益相关者开发和推广关于这些方法及其结果的研讨会和网站;和（3）提供一个研究小组的结构，旨在招募，培训和留住学生从代表性不足的背景，在可持续发展工程领域。该项目将制定确定能源技术生命周期气候变化影响的方法，该方法比以前在生命周期评估中使用的方法更精确。这些结果将与相同建模技术的非地理特定LCA结果以及化石燃料的影响进行统计比较，以确定在能源系统LCA中纳入地理空间组件的重要性。开发的地理空间LCA方法也可用于能源技术以外的其他系统的LCA，如采矿，并可用于能源系统规划。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Life cycle assessment (LCA) practices are used to determine the climate change impacts of energy systems. However, these assessments often miss key geographically specific factors, such as direct land use change (DLUC) impacts, albedo change as a non-greenhouse gas climate forcer, and geospatial data relevant to potential electricity generation. The overarching goal of this project is to systematically integrate these geospatial components in LCA of conventional and renewable energy systems to improve the accuracy of their “carbon footprints” at higher spatial resolutions, and to disseminate this methodology and its results to inform sustainable location-specific design, siting, and implementation decisions for the energy transition.The research objectives are to: (1) first update the baseline comparison to fossil fuels by including DLUC and albedo change impacts of their expansion in the US; (2) develop geospatial LCA models for selected solar and ocean renewable energy technologies and determine their life cycle climate change impacts on potentially available land and water in the US; and (3) adapt the geospatial LCA models to future conditions under climate change. Parametric LCA models of these energy system scenarios will be developed in open-source Python code, informed by publicly available GIS datasets, the physics of these technologies, and algorithms developed for flexibility to variable environmental conditions. In order for the methodology to be disseminated and used by energy stakeholders and future LCA practitioners, the education objectives are to: (1) design and share a course in geospatial LCA modeling; (2) develop and promote a workshop and website on these methods and their results for industry and policy stakeholders; and (3) provide a research group structure designed to recruit, train, and retain students from underrepresented backgrounds in sustainability engineering fields. This project will develop methodology for determining life cycle climate change impacts of energy technologies that is more precise than previously used methods in LCA. The results will be statistically compared against non-geographically-specific LCA results of the same modeled technologies and against the impacts of fossil fuels to determine the significance of incorporating geospatial components in energy system LCAs. The developed geospatial LCA methods can also be utilized in LCA of other systems beyond energy technologies, such as mining, and can be used for energy system planning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.