Collaborative Research: CAS-Climate: Linking Activities, Expenditures and Energy Use into an Integrated Systems Model to Understand and Predict Energy Futures

合作研究：CAS-气候：将活动、支出和能源使用连接到集成系统模型中，以了解和预测能源未来

• 批准号: 2243099
• 负责人: Eric Williams
• 金额: $ 26.57万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243099&HistoricalAwards=false
• 关键词: Collaborative; Research; CAS; Climate; Linking

Predicting and managing energy demand are crucial tasks for addressing climate change and other environmental impacts of energy use. The mainstream models of energy demand are reductionist, dividing demand into separate categories such as residential, commercial, and transportation, and analyzing each separately. This collaborative research will develop a holistic model of energy demand, one that considers how consumer actions affect multiple sectors at the same time, including residences, vehicles, commercial buildings, server/data networks, and the production of purchased goods. The model will include consumer ownership and use of energy- consuming technologies such as vehicles or home furnaces, and accounts for how people use time and spend money. The new model will be constructed for the U.S. by integrating government micro-data on consumer behavior (American Time Use Survey, Residential Energy Consumption Survey, National Household Travel Survey, Consumer Expenditure Survey), using modern data analysis methods to integrate them. The integrated dataset will provide information about energy device ownership and use, internet use, time spent in commercial buildings, and expenditures on goods. A set of models will map these consumer attributes to energy use and carbon emissions. For commercial buildings, a regression model will be built that links the area of different building types with consumers’ and employees’ use of them. An Economic Input Output Life Cycle Assessment (EIOLCA) will estimate the energy use and emissions from consumer expenditures. The holistic model will help understanding of the broader effects of demand interventions. For example, how are the carbon benefits of electric vehicles affected by induced changes in consumer purchases and activity choices? The model will help assess the effect of behavioral changes not typically considered in policy (e.g. encouraging telework), and thus could broaden the scope of policy options considered. The model advances the state of energy demand modeling on a number of fronts. First, the model combines data on personal expenditures, time, and technology use to provide a household-level estimate of life cycle carbon emissions, accounting for energy use in residences, commercial buildings, servers/networks, transportation, and manufacturing of goods. Part of the contribution is in integrating existing models into a larger holistic framework. Missing modeling elements will be developed, in particular linking consumers’ use of commercial buildings to energy demand, and attributing use of information technology to energy demand in data networks and servers. In methods, the research develops a new approach for assessing how change in time use of a given activity (e.g. shift from driving to biking) leads to changes in other activities as well as the overall energy use from the primary shift in activity. The research involves merging multiple data sources on consumers via representative populations, and this dataset will be made publicly available. Outreach activities include a workshop with energy efficiency modelers and policy advocates, development of an energy demand game, and a summer camp for high school students from underrepresented groups.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.

预测和管理能源需求是解决气候变化和能源使用的其他环境影响的关键任务。能源需求的主流模型是还原主义者，将需求分为独立的类别，例如住宅，商业和运输，并分别分析。这项协作研究将开发一种能源需求的整体模型，该模型考虑了消费者行动如何同时影响多个部门，包括住宅，车辆，商业建筑，服务器/数据网络以及购买商品的生产。该模型将包括消费者所有权和消耗技术（例如车辆或家用炉）的使用，并说明人们如何使用时间和花钱。新模式将通过将政府微型数据整合在消费者行为上（美国时间使用调查，住宅能源消耗调查，国家家庭旅行调查，消费者支出调查）来构建新模型，并使用现代数据分析方法来整合它们。集成的数据集将提供有关能源设备所有权，互联网使用，商业建筑中花费的时间以及商品支出的信息。一组模型将这些消费者属性绘制为能源使用和碳排放。对于商业建筑，将建立一个回归模型，该模型将不同建筑物类型的区域与消费者和员工使用它们联系起来。经济投入产出生命周期评估（EIOLCA）将估计消费者支出的能源使用和排放。整体模型将有助于了解需求干预的更广泛影响。例如，如何受到消费者购买和活动选择的变化影响的电动汽车的碳益处？该模型将有助于评估通常在政策中通常不考虑的行为变化的效果（例如鼓励远程办公），因此可以扩大所考虑的政策选择的范围。该模型在许多方面都在进行了能源需求的状态。首先，该模型结合了有关个人支出，时间和技术使用的数据，以提供家庭级别的生命周期碳排放估计，核算住宅，商业建筑，服务器/网络，运输和商品制造的能源利用。部分贡献是将现有模型集成到更大的整体框架中。将开发缺少的建模元素，特别是将消费者使用商业建筑与能源需求的使用联系，并将信息技术归因于数据网络和服务器中的能源需求。在方法中，研究开发了一种新方法，用于评估给定活动的时间使用变化如何（例如，从驾驶到骑自行车）会导致其他活动的变化以及主要活动的总体能源使用。这项研究包括通过代表人群合并消费者的多个数据源，该数据集将公开使用。推广活动包括一个与能源效率建模者和政策倡导者的研讨会，能源需求游戏的开发以及来自代表性不足的小组的高中生的夏令营。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来获得支持的。

项目成果

How much is US Office Building Space Reduced per Teleworker?

美国每个远程办公人员的办公楼空间减少了多少？

• DOI: 10.32866/001c.115400
• 发表时间: 2024
• 期刊: Findings
• 作者: Liu, Kun;Guhathakurta, Subhrajit;Han, Chaeyeon;Hittinger, Eric;Phoung, Sinoun;Williams, Eric
• 通讯作者: Williams, Eric