权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

NSF2026: EAGER: Spatio-Temporal Design of Techno-Ecological Synergies for a World without Waste and Resilient Landscapes

NSF2026：EAGER：技术生态协同效应的时空设计，打造一个没有废物和有弹性景观的世界

基本信息

批准号：
2036982
负责人：
Bhavik Bakshi
金额：
$ 29.99万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036982&HistoricalAwards=false
关键词：
NSF2026 EAGER Spatio Temporal Design

项目摘要

With support from the CBET/ENG Environmental Sustainability program and the NSF 2026 Fund Program in the Office of Integrated Activities, the investigators are researching the ecological capacity to provide goods and services in the face of demands imposed by a technological society. To meet sustainability goals, most engineers design and operate manufacturing processes to minimize resource use and emissions, but they may not account, for example, for the capacity of a watershed to provide fresh water to all users (including non-human users) or of the atmosphere to absorb emitted CO2. Similarly, economists may exclude consideration of the impact on ecosystems. The vision of this research is that through appropriate design, human activities can explicitly account for the provisions supplied by ecosystems, and can be designed to respect ecosystem limits. The research seeks to provide a framework for designing industries and ecosystems simultaneously to operate in a mutually beneficial or synergistic manner. The resulting Techno-Ecological Synergies (TES) will rely on designing ecosystems of the future, that in fundamental concept include the built environment, to enrich the NSF2026 Idea Machine winning entries of a "World without Waste," and "Large Landscape Resilience by Design."TES design will be formulated as an optimization problem. Novel and innovative approaches for developing designs at relevant spatial and temporal scales are proposed for solving the optimization problem. One such innovation to be realized is the the development of physics-based surrogate models with deep neural networks to capture the spatio-temporal variation of pollutant concentration in a selected region for a point source under various geographical, land cover, and meteorological conditions, embracing uncertainty issues. Advanced and innovative stochastic and/or dynamic programming methods will be employed to obtain TES designs. As a test case, the developed TES approach will be applied to a power plant near Cincinnati and vegetation on the surrounding landscape. For this case study, conventional and TES designs will be compared in terms of their contribution to reducing waste at the landscape and life cycle scales. To assess large landscape resilience for conventional and TES designs, future climate change scenarios will be simulated and landscape resilience compared for conventional and TES designs in terms of regional water availability and air quality. This approach will assess the benefits of seeking synergies with nature through the TES framework. The results of this work are targeted to lay the foundation for further work toward the convergence of disciplines including ecology, sociology, economics, public policy, statistics, environmental science, and engineering. The aspiration of the TES approach is to see, with time, practical implementation of TES on the scale of industrial, urban, and agro-ecological landscape networks.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.

在CBET/ENG环境可持续性计划和综合活动办公室的NSF 2026基金计划的支持下，调查人员正在研究面对技术社会的需求提供商品和服务的生态能力。为了实现可持续发展目标，大多数工程师设计和操作制造工艺，以尽量减少资源使用和排放，但他们可能没有考虑到，例如，流域为所有用户（包括非人类用户）提供淡水的能力或大气吸收排放的二氧化碳的能力。同样，经济学家也可能不考虑对生态系统的影响。这项研究的愿景是，通过适当的设计，人类活动可以明确说明生态系统提供的规定，并可以设计尊重生态系统的限制。该研究旨在为设计产业和生态系统提供一个框架，同时以互利或协同的方式运作。由此产生的技术生态协同效应（TES）将依赖于设计未来的生态系统，其基本概念包括建筑环境，以丰富NSF 2026创意机器的获奖作品“没有废物的世界”和“设计的大型景观弹性”。他说：“污水附加费的设计会以最佳化的方法来处理。在相关的空间和时间尺度上开发设计的新颖和创新的方法，提出了解决优化问题。要实现的一个创新是开发基于物理学的代理模型和深度神经网络，以捕获在各种地理，土地覆盖和气象条件下选定区域的点源污染物浓度的时空变化，包括不确定性问题。先进的和创新的随机和/或动态规划方法将被用来获得TES设计。作为一个测试案例，开发的工商业污水附加费的方法将适用于辛辛那提附近的发电厂和周围景观的植被。在本案例研究中，将比较传统设计和工商业污水附加费设计在景观和生命周期尺度上对减少废物的贡献。为了评估传统和TES设计的大景观弹性，将模拟未来的气候变化情景，并在区域水资源供应和空气质量方面比较传统和TES设计的景观弹性。这一办法将评估通过环境贸易服务框架寻求与自然协同增效的好处。这项工作的结果是有针对性的，为进一步的工作，包括生态学，社会学，经济学，公共政策，统计学，环境科学和工程学科的融合奠定了基础。TES方法的愿望是看到，随着时间的推移，TES的工业，城市和农业生态景观网络的规模上的实际实施。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。