Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in electricity mix of local electric grid to better understand the end user environmental impacts. We offer hourly estimates of the US electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority. While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions. The attached dataset provides static data from 2018 to 2022, as detailed in the publication: Siddik, M. A. B., Shehabi, A., Rao, P., & Marston, L. T. (2024). Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use. Water Resources Research, 60(12), e2024WR038350. For continuous updates and the latest version of the data product, please visit the following link: https://industrialapplications.lbl.gov/water-impact-tool美国电力生产伴随着大量水资源消耗与温室气体排放。然而，由于电网架构复杂、电源结构动态多变，精准估算此类环境影响颇具挑战。现有电力使用环境影响的估算方法多在大区域尺度上通用，忽略了水资源消耗与排放的时空差异。因此，在减缓环境影响的相关工作中，电网动态特征（如可再生能源的时序波动）常被忽视。 美国能源部（U.S. Department of Energy, DOE）已启动韧性能源域管理系统的开发工作，此类系统亟需掌握本地电源结构及其环境影响的详细信息。本研究契合DOE的目标，纳入了本地电网电源结构的地理与时序变化特征，以更深入地解析终端用户面临的环境影响。 我们提供了美国各电网平衡机构（grid balancing authority）的逐小时电源结构估算数据，详细涵盖燃料类型、取水强度与耗水强度。尽管本研究的核心聚焦于水资源强度因子，但本数据集与用于历史及实时分析的编程脚本，还在同一建模框架内纳入了二氧化碳（当量）强度的评估。这种集成化方法可全面解析电力生产与使用相关的环境足迹，助力制定科学决策，以有效减少范围2（Scope 2）的水资源消耗与排放。 本次附带的数据集提供了2018年至2022年的静态数据，相关细节已发表于以下论文：Siddik, M. A. B., Shehabi, A., Rao, P., & Marston, L. T. (2024). 美国电力生产与使用的时空精细化水足迹与碳足迹。《水资源研究》(Water Resources Research), 60(12), e2024WR038350. 如需获取持续更新的最新版本数据产品，请访问以下链接：https://industrialapplications.lbl.gov/water-impact-tool