CAS- Climate. SRS: U.S.-China: Infrastructure-Driven Decision System for Sustainable and Equitable Urban-Rural Development

CAS-气候。

• 批准号: 2214872
• 负责人: Dong Zhao
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2214872&HistoricalAwards=false
• 关键词: CAS; Climate.; SRS; U.S.; China

2214872 (Zhao). Electrification is the future for much of sustainable transportation, and expanded use of electric vehicles (EVs) is a significant part of future urban-rural systems. EVs have the potential to improve environmental sustainability substantially, since 29% of U.S. greenhouse gas emissions are from internal combustion engine vehicles (ICEVs). It is estimated that 18.7 million EVs will be on U.S. roads by 2030, with a 37% annual growth. Charging infrastructure is critical to the continued growth of EV and its upstream industries. A lack of convenient and ubiquitous charging infrastructure is one of the key factors that impedes EV adoption. Increasing investments to the U.S. infrastructure, for example, the recent $1.2 trillion Infrastructure Investment and Jobs Act, envision and plan a network of hundreds of thousands of new charging stations along roads and highways by 2030. Investments in charging infrastructure involve not only financial and economic decisions but also social and environmental decisions, as they influence regional sustainability and equity across urban and rural areas. However, current decision-making models and tools for infrastructure investment focus on project-level economic and engineering efficiency and rarely consider region-level environment and equity. Therefore, the goal of this project is to create a GIS-based decision system to integrate regional economic/engineering efficiency, environmental sustainability, and social equity into EV charging infrastructure planning and design. Built upon preliminary data on engineering-focused optimization models for charging infrastructure design and construction, this project will further (1) assess regional environmental impacts, (2) assess regional social equity, and (3) identify urban-rural spatial mismatches via case studies in real-world testbed regions in the U.S. and China. The project will produce case studies to demonstrate the use of a decision system and the impacts of EV charging infrastructure on regional development. The project will produce new knowledge about the impacts of EV charging infrastructure on region-level economic, environmental, and societal development, and will create a decision system that can assess infrastructure investment plans to ensure economic and engineering efficiency, environmental sustainability, and social equity for regional urban-rural systems. In prior work, the research team developed optimization models to guide the design of EV charging stations (e.g., producing locations and construction plan). The optimization models are to maximize economic cost/benefit based on engineering simulation and computation. Also, the team developed the GIS-based Michigan Spatial Mismatch (MSM) Tool to visualize spatial mismatches and issues of regional equity. To extend the MSM economic/engineering-based decision model (Module #1), this project will add modules that assess regional environmental sustainability (Module #2) and social equity (Module #3). Modules #1-3 together will constitute the decision system. To create the decision system, the research team will perform Tasks 1-3 as follows. Task 1: Assess regional environmental sustainability to be enabled by EV charging infrastructure. Task 2: Assess the regional social equity to be impacted by EV charging infrastructure. Task 3: Demonstrate the decision system to support EV charging infrastructure design. The decision system will (1) enable big data analytics and interactive graphics to visualize regional consistency and mismatches, (2) add layers of environment and equity to complement current models and tools for charging station optimization, and (3) highlight regional interactions and urban-rural typology that is an important factor for EV charging infrastructure investment. Project outcomes will help guide planners, designers, and engineers on charging infrastructure investment towards sustainable and equitable economic development.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.

2214872（赵）。电气化是大部分可持续交通的未来，扩大电动汽车（EV）的使用是未来城乡系统的重要组成部分。电动汽车有潜力大大提高环境的可持续性，因为美国29%的温室气体排放来自内燃机汽车（ICEV）。据估计，到2030年，美国道路上将有1870万辆电动汽车，年增长率为37%。充电基础设施对电动汽车及其上游产业的持续增长至关重要。缺乏方便和无处不在的充电基础设施是阻碍电动汽车采用的关键因素之一。增加对美国基础设施的投资，例如，最近的1.2万亿美元的基础设施投资和就业法案，设想和计划到2030年在公路和高速公路沿线建立数十万个新的充电站网络。对充电基础设施的投资不仅涉及财务和经济决策，还涉及社会和环境决策，因为它们影响到城市和农村地区的区域可持续性和公平性。然而，目前的基础设施投资决策模型和工具主要关注项目层面的经济和工程效率，很少考虑区域层面的环境和公平。因此，本项目的目标是创建一个基于GIS的决策系统，将区域经济/工程效率，环境可持续性和社会公平纳入电动汽车充电基础设施规划和设计中。基于充电基础设施设计和建设的工程优化模型的初步数据，该项目将进一步（1）评估区域环境影响，（2）评估区域社会公平性，以及（3）通过美国和中国真实世界试验区的案例研究来识别城乡空间不匹配。该项目将产生案例研究，以展示决策系统的使用以及电动汽车充电基础设施对区域发展的影响。该项目将产生关于电动汽车充电基础设施对区域经济，环境和社会发展影响的新知识，并将创建一个决策系统，可以评估基础设施投资计划，以确保区域城乡系统的经济和工程效率，环境可持续性和社会公平。在之前的工作中，研究团队开发了优化模型来指导电动汽车充电站的设计（例如，生产地点和施工计划）。优化模型是以工程模拟计算为基础，以经济成本/效益最大化为目标。此外，该团队还开发了基于GIS的密歇根空间不匹配（MSM）工具，以可视化空间不匹配和区域公平问题。为了扩展MSM基于经济/工程的决策模型（模块#1），该项目将增加评估区域环境可持续性（模块#2）和社会公平（模块#3）的模块。模块#1-3将共同构成决策系统。为了创建决策系统，研究小组将执行任务1-3如下。任务1：评估电动汽车充电基础设施所能实现的区域环境可持续性。任务2：评估电动汽车充电基础设施对区域社会公平性的影响。任务3：演示支持电动汽车充电基础设施设计的决策系统。该决策系统将（1）支持大数据分析和交互式图形，以可视化区域一致性和不匹配，（2）添加环境和公平层，以补充当前的充电站优化模型和工具，以及（3）突出区域互动和城乡类型，这是电动汽车充电基础设施投资的重要因素。项目成果将有助于指导规划师、设计师和工程师对基础设施投资进行收费，以实现可持续和公平的经济发展。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。

项目成果

Profit versus Sustainability in Bikeshare

• DOI: 10.1016/j.scs.2023.104512
• 发表时间: 2023-03
• 期刊: Sustainable Cities and Society
• 影响因子: 11.7
• 作者: Huiyi Litan;Ke Rong;Youran Wu;Danxia Xie;Hanzhe Zhang-;Dong Zhao