Co-desIgning Robust natural Capital LandscapEs (CIRCLE)

研究翻译研究员英国政府正在为英格兰设计一项新的环境土地管理(ELM)政策，这将在未来几个世纪改变我们农村景观的面貌和作用。这将是50多年来对农业政策的最大一次干预，目前每年高达30亿GB的农业补贴将被重新安排为“公共产品的公共资金”--向土地管理者支付报酬，让他们提供清洁水和碳捕获等环境效益。环境管理将成为政府实现其在25年环境计划和其他方面设定的环境目标的核心政策工具；包括到2050年实现净零碳排放，创造和恢复50万公顷野生动物丰富的栖息地，以及改善水质和数量。为了做到这一点，榆树的一部分将鼓励永久的土地使用变化，从农业转向林地、湿地和盐沼；并改善泥炭地。然而，尽管这种变化的好处可能需要几十年的时间才能实现，但必须现在就如何实现这些变化做出决定。为了做出这些决定，Defra必须解决三个关键问题：土地使用变化应该发生在哪里？应该如何鼓励它呢？它的更广泛的影响是什么？回答这些问题的一个重要信息来源是模型，这些模型可以模拟不同政策设计引起的土地利用变化，并预测其对环境的影响及其对社会的好处。Defra对这些建模能力的获取和理解是有限的。这项研究翻译奖学金的核心目的是在Defra的榆树团队中嵌入一名学术土地利用模型专家。他们将把景观决策方案中的模型证据直接提供给政策设计过程。土地利用模型的现实情况是，预测带有不确定性，这些不确定性源于模型的不准确和未来的内在不确定性(例如气候、粮食价格)。在像ELM这样的项目中，由于景观的广泛变化，以及未来几十年的长期后果，了解这些不确定性对于确保实现预期的结果至关重要。为此目的，该研究金将利用正在通过ADVANCES(景观决策中的自然资本推进分析)项目开发的新的不确定性分析方法。该奖学金将把这些方法扩展到埃克塞特大学土地环境经济与政策研究所(LEEP)运行的一套土地利用决策模型，并将该模型套件应用于支持ELM设计。我们将与Defra合作，确定产生良好结果的土地利用变化模式(例如碳捕获)，以及风险水平(即这些数量的不确定性)。例如，我们可以确定土地利用变化的结果对不确定性敏感的地方的组合；并将那些带来不太有利的结果的地方与其他地方进行比较，但这些结果具有更大的确定性，因此风险较小。然后，政策制定者可以在决定ELM下土地使用变化的方向时平衡结果和风险。该项目还将为Defra提供模拟政策的能力，并比较它们鼓励土地管理者做出所需土地使用变化的能力。与Defra合作，我们将研究不同支付类型的政策(例如，预付款；年度支付，奖金)，支付的东西(例如，碳捕获，娱乐准入)；以及定价的方式(例如，固定价格，拍卖)。最后，该研究员将评估榆树的更广泛影响，包括对粮食安全、农场企业的生存能力和国家碳足迹。综上所述，该奖学金旨在赋予Defra以尖端科学投入，使其能够设计出强有力的榆树政策，并在国家环境质量方面带来阶段性变化。

Research Translation FellowshipThe UK Government is designing a new Environmental Land Management (ELM) policy for England which will change how our rural landscape looks, and what it does, for centuries to come. It will be the biggest intervention in agricultural policy for more than 50 years, and will see up to £3bn a year of current agricultural subsidies redirected into 'public money for public goods' - paying land managers to provide environmental benefits like clean water and carbon capture.ELM will be the central policy tool through which the Government intends to deliver its environmental targets, set out in the 25 Year Environment Plan and elsewhere; including net zero Carbon emissions by 2050, creation and restoration of half-a-million hectares of wildlife-rich habitat, and improving water quality and quantity. To do this, part of ELM will incentivise permanent land use change out of agriculture, to woodland, wetland, and saltmarshes; and improving peatland. However whilst the benefits of such changes may take many decades to come about, the decisions on how to make these happen must be taken now.To make those decisions, Defra must address three key questions: Where should land use change happen? How should it be encouraged? And what are its wider impacts? One important source of information to answer those questions comes from models that can simulate land use changes arising from different policy designs; and predict their impacts on the environment and their benefits to society. Defra's access to, and understanding of, those modelling capabilities is limited. The core purpose of this research translation fellowship is to embed an academic land use modelling expert in Defra's ELM team. They will deliver modelling evidence from the Landscapes Decisions Programme directly into the policy design process.The reality of land use modelling is that predictions come with uncertainties, arising from model inaccuracy and inherent uncertainty of the future (e.g. climate, food prices). In a programme like ELM, with widespread changes across landscapes, and long-term consequences many decades in the future, understanding these uncertainties is critical to ensure the desired outcomes are delivered. To that end, this fellowship will draw on new methods of uncertainty analysis that are being developed through the ADVANCES (Advancing analysis of natural capital in landscape decisions) project. The fellowship will extend those methods to a suite of land use decision models run by the Land Environment Economics and Policy (LEEP) Institute at the University of Exeter, and apply that model suite to support ELM design.In collaboration with Defra we will identify patterns of land use change that deliver good outcomes (e.g. carbon capture), and at what level of risk (i.e. uncertainties over those amounts). For example, we can identify combinations of places where the outcomes of land use change are sensitive to uncertainties; and compare those to others that deliver less favourable outcomes, but with more certainty, and hence less risk. Policymakers can then balance outcomes and their risk when deciding where land use change under ELM should go.The project will also provide Defra with the capacity to simulate policies, and compare their ability to encourage land managers to make the desired land use changes. Working with Defra we will examine policies with different payment types (e.g. up-front payments; annual payments, bonuses), the things that are paid for (e.g. carbon capture, recreation access); and the way prices are set (e.g. fixed prices, auctions).Finally the fellowship will assess the wider impacts of ELM, including on food security, viability of farm businesses, and national carbon footprints. Taken together this fellowship aims to empower Defra with cutting edge science inputs that will enable the design of a robust ELM policy, and deliver a step-change in the quality of the nation's environment.