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: 2243100
• 负责人: SUBHRAJIT GUHATHAKURTA
• 金额: $ 23.42万
• 依托单位: Georgia Tech Research Corporation
• 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=2243100&HistoricalAwards=false
• 关键词: Collaborative; Research; CAS; Climate; Linking

2243099 (Williams) and 2243100 (Guhathakurta). 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 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 includes 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.

2243099（威廉姆斯）和2243100（古哈塔库塔）。预测和管理能源需求是应对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简化的，将需求分为不同的类别，如住宅，商业和交通，并分别进行分析。这项研究将开发一个能源需求的整体模型，该模型考虑消费者行为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。该模型包括消费者所有权和使用能源消耗技术，如车辆或家庭炉，并占人们如何使用时间和花钱。新模型将通过整合政府关于消费者行为的微观数据（美国时间使用调查，住宅能源消费调查，全国家庭旅行调查，消费者支出调查），使用现代数据分析方法来整合它们。综合数据集将提供有关能源设备所有权和使用、互联网使用、在商业建筑中花费的时间以及商品支出的信息。一套模型将把这些消费者属性映射到能源使用和碳排放。就商业楼宇而言，将建立一个回归模型，将不同楼宇类型的面积与消费者和雇员的使用情况联系起来。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。整体模型将有助于理解需求干预的更广泛影响。例如，电动汽车的碳效益如何受到消费者购买和活动选择的诱导变化的影响？该模型将有助于评估政策中通常没有考虑到的行为变化的影响（例如鼓励远程工作），从而可以扩大所考虑的政策选择的范围。该模型在许多方面推进了能源需求建模的状态。首先，该模型结合了个人支出、时间和技术使用的数据，以提供家庭层面的生命周期碳排放估计，并考虑了住宅、商业建筑、服务器/网络、交通和商品制造中的能源使用。部分贡献是将现有模式纳入一个更大的整体框架。将开发缺少的建模元素，特别是将消费者对商业建筑的使用与能源需求联系起来，并将信息技术的使用归因于数据网络和服务器的能源需求。在方法上，研究开发了一种新的方法来评估特定活动的时间使用变化（例如从驾驶转向骑自行车）如何导致其他活动的变化以及活动主要转变的整体能源使用。该研究涉及通过代表性人群合并消费者的多个数据源，该数据集将公开提供。推广活动包括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群体的高中生夏令营。该奖项反映了NSF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。