Adaptive Decision Making for Urban Energy Transformation
Adaptive Decision Making for Urban Energy Transformation
批准号:
EP/R007403/1
负责人:
Katy Roelich
金额:
$105.23万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2017
资助国家:
英国
项目状态:
未结题
起止时间:
2017 至 --
中文摘要
政府在基础设施决策方面的顾问,国家基础设施委员会(NIC),已经确定需要决策支持工具,这些工具可以将多个决策者在不同规模和不同基础设施系统中做出的决策纳入其中。这个团契将特别回应这个需要。存在许多工具和方法,旨在帮助决策者理解和管理与长期决策相关的不确定性。这些不确定性包括基础设施系统之间的相互作用以及社会和环境变化的影响。然而,大多数工具和方法都假设存在一个具有明确目标的决策者,并且他们的偏好随时间保持不变。不同决策者(或通常被称为行动者)之间的相互作用是很少被认识到的额外不确定性。然而,随着我们试图更快地过渡基础设施,以及新技术和工作方式迫使基础设施与决策者之间进行更密切的互动,这一点变得越来越重要。该研究金将根据为支持面对社会和环境变化的决策而制定的长期规划方法,开发一种多参与者、适应性强的决策方法。新的方法将允许决策者也考虑与其他规模和其他系统的决策约束相关的不确定性。它将开发可访问的方法来分析决策者之间的相互作用,并确定改造反映和利用这些相互作用的基础设施所需的活动。它将开发快速和透明的建模方法,以帮助分析决策者的互动如何影响活动的成功实施,以及这如何有助于基础设施转型。它将这些方法和模型应用于案例研究,以制定长期但适应性强的基础设施转型计划。决策者分析方法和建模的产出将用于创建适应性活动途径,这些活动可以改变基础设施,但也可以响应社会和环境变化或来自其他决策者的限制。结合方法和模型来创建适应性路径是多参与者,适应性决策方法,这是本研究的主要成果。我将开发方法、模型和总体方法,以城市能源为例进行测试。气候变化委员会(Committee on Climate Change)和NIC都认为,改造城市能源系统对英国的可持续发展至关重要,迫切需要长期的决策支持,这使其成为一个及时的、全国性的重要试验案例。然而,城市实现这一转型的潜力受到国家、地区和家庭层面以及其他基础设施系统所做出的决定的抑制。这使得城市很难以实现其社会、环境和经济目标的方式参与能源系统。我将使用三个城市能源的案例研究;两个在英国,一个在欠发达国家,与真正的决策者密切合作,并使用参与式技术,以确保方法和模型既健全又相关。我将成立一个地方基础设施委员会,以确定如何更好地协调地方和国家行动者的城市活动。开发的方法和工具将与能源以外的其他基础设施系统以及英国以外的其他环境相关。该奖学金正值我职业生涯的关键时刻,我准备投入大量的时间和资源来建立一个证据库和研究小组,这对于在国际层面上发展我在学术界、政策和工业界的思想领导力和研究影响力至关重要。
英文摘要
The government's advisor on infrastructure decision making, the National Infrastructure Commission (NIC), has identified that there is a need for decision support tools which incorporate decisions taken by multiple decision makers, at multiple scales and in different infrastructure systems. This fellowship will specifically respond to that need. A number of tools and approaches exist, which aim to help decision makers to understand and manage the uncertainties associated with long-term decisions. These uncertainties include the effects of interaction between infrastructure systems and of social and environmental change. However, most tools and approaches assume that there is one decision maker with clearly defined objectives and that their preferences stay the same over time. The interaction between different decision makers (or actors as they are often called) is an additional uncertainty that is rarely recognised. However, it is becoming increasingly important as we try to transition infrastructures more rapidly and as new technologies and ways of working are forcing closer interaction between infrastructures and decision makers.This fellowship will develop a multi-actor, adaptive approach to decision making based on long-term planning approaches developed to support decisions in the face of social and environmental change. The new approach will allow decision makers to also consider uncertainty associated with constraints from decision making at other scales and in other systems. It will develop accessible methods to analyse interactions between decision makers and to identify activities required to transform infrastructure that reflect and capitalise on these interactions. It will develop rapid and transparent modelling methods to help analyse how decision maker interactions affect the successful implementation of activities and how this contributes to infrastructure transformation. It will apply these methods and models to case studies to develop long-term but adaptable plans for infrastructure transformation. The outputs of decision maker analysis methods and modelling will be used to create adaptable pathways of activities that can transform infrastructure but also respond to social and environmental change or constraints from other decision makers. The combination of methods and models to create adaptable pathways is the multi-actor, adaptive decision making approach, which is the main output of this fellowship.I will develop the methods, models and overarching approach using urban energy as a test-case. Both the Committee on Climate Change and the NIC have identified that transforming urban energy systems is essential to the UK's sustainable development and is urgently in need of long-term decision support, which makes it a timely and nationally important test-case. However, the potential for cities to deliver this transformation is stifled by decisions taken at national, regional and household scales and in other infrastructure systems. This makes it very difficult for cities to engage with the energy system in the way that could deliver on their social, environmental and economic objectives. I will use three case studies of urban energy; two in the UK and one in a less developed country, working closely with real decision makers and using participatory techniques to ensure that the approach and models are both robust and relevant. I will set up a Local Infrastructure Commission, to identify how city activity could be better co-ordinated locally and with national actors. The approaches and tools developed will have relevance to other infrastructure systems, beyond energy, and in other contexts, beyond the UK. The fellowship comes at a crucial time in my career, and I am ready to dedicate significant time and resources to building an evidence base and research group, which are essential to developing my thought leadership and research influence in academia, policy and industry at the international level.
期刊论文(10)
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Decision making under deep uncertainties: A review of the applicability of methods in practice
深度不确定性下的决策:方法在实践中的适用性回顾
DOI:
10.1016/j.techfore.2021.120939
发表时间:
2021
期刊:
Technological Forecasting and Social Change
影响因子:
12
作者:
[Stanton M]
通讯作者:
Stanton M
DOI:
--
发表时间:
2020
期刊:
影响因子:
--
作者:
[Roelich K]
通讯作者:
Roelich K
What did infrastructure ever do for us?
基础设施为我们做了什么?
DOI:
10.1111/newe.12200
发表时间:
2020
期刊:
IPPR Progressive Review
影响因子:
--
作者:
[Roelich K]
通讯作者:
Roelich K
Talking about Infrastructure: eliciting public perceptions of infrastructure
谈论基础设施:引发公众对基础设施的看法
DOI:
--
发表时间:
2021
期刊:
影响因子:
--
作者:
[Roelich K]
通讯作者:
Roelich K
Public perceptions of networked infrastructure
公众对网络基础设施的看法
DOI:
10.1080/13549839.2020.1845131
发表时间:
2020
期刊:
Local Environment
影响因子:
2.4
作者:
[Roelich K]
通讯作者:
Roelich K
共 9 条
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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批准号:--
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项目类别:合作创新研究团队
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资助金额:--
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批准年份:2024
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负责人:姚韬
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依托单位: