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

在CBET/ENG环境可持续性项目和综合活动办公室的NSF 2026基金项目的支持下，研究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的目标，大多数工程师设计和操作制造过程，以尽量减少资源使用和排放，但他们可能没有考虑到，例如，分水岭向所有用户（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，经济学家可能会排除对生态系统影响的考虑。这项研究的愿景是，通过适当的设计，人类活动可以明确地解释生态系统提供的供应，并且可以设计为尊重生态系统的限制。该研究旨在提供一个框架，以设计工业和生态系统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NSF2026创意机器获奖作品“没有浪费的世界”和“设计的大型景观弹性”。TES设计将被表述为一个优化问题。提出了在相关空间和时间尺度上开发设计的新颖和创新的方法来解决优化问题。其中一个需要实现的创新是开发基于物理的代理模型，该模型具有深度神经网络，可以在各种地理、土地覆盖和气象条件下捕获选定区域点源中污染物浓度的时空变化，包括不确定性问题。将采用先进和创新的随机和/或动态规划方法来获得TES设计。作为一个测试案例，开发的TES方法将应用于辛辛那提附近的一家发电厂和周围景观的植被。在这个案例研究中，我们将比较传统设计和工商业污水附加费系统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设计的大型景观复原力，我们将模拟未来气候变化情景，并在区域水资源供应和空气质量方面比较传统和TES设计的景观复原力。这种方法将评估通过工商业污水附加费架构寻求与大自然协同作用的好处。这项工作的结果旨在为进一步的学科融合奠定基础，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。工商业污水附加费方法的目标是随着时间的推移，在工业、城市和农业生态景观网络的规模上实际实施工商业污水附加费。该奖项反映了美国国家科学基金会的法定使命，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。

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.