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

生命周期评估（LCA）实践用于确定能源系统对气候变化的影响。然而，这些评估往往忽略了关键的地理特定因素，如直接土地利用变化（DLUC）影响、反照率变化作为非温室气体气候驱动因素，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系统地将这些地理空间成分整合到传统和可再生能源系统的LCA中，以提高其“碳足迹”在更高空间分辨率下的准确性，并传播这种方法及其结果，为可持续的特定地点设计、选址和能源转型的实施决策提供信息。研究目标是：(1)首先更新化石燃料的基线比较，包括它们在美国的扩张对DLUC和反照率变化的影响；(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；(3)使地理空间LCA模型适应未来气候变化条件。这些能源系统场景的参数化LCA模型将在开放源代码的Python代码中开发，由公开可用的GIS数据集、这些技术的物理特性和为适应可变环境条件而开发的算法提供信息。为了使该方法能够被能源利益相关者和未来的LCA从业者传播和使用，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；(3)提供一个研究小组结构，旨在招募、培训和留住来自可持续发展工程领域代表性不足背景的学生。该项目将开发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LCA中使用的方法更精确。将结果与相同建模技术的非地理特定LCA结果进行统计比较，并与化石燃料的影响进行比较，以确定将地理空间成分纳入能源系统LCA的重要性。所开发的地理空间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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.