Landscapes For Sequestering Carbon: a dynamic marginal abatement cost curve approach with Bayesian spatio-temporal modelling

碳封存景观：采用贝叶斯时空建模的动态边际减排成本曲线方法

• 批准号: NE/T003960/1
• 负责人: Peter Levy
• 金额: $ 5.06万
• 依托单位: NERC CEH (Up to 30.11.2019)
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT003960%2F1
• 关键词: Landscapes; Sequestering; Carbon; dynamic; marginal

Sequestering carbon in terrestrial ecosystems by changing land use and management is one means of slowing the rise in atmospheric carbon dioxide, and arguably the only economically feasible means of reversing the trend. To this end, most nations have included targets within their climate change commitments for sequestering carbon through land use, land-use change and forestry (LULUCF). For example, Scotland has an afforestation target to reach 25 % forest cover, and the creation of 100,000 ha of woodlands in the period 2012-2022 has been recommended. More than £30M has already been spent on peatland restoration in the UK since 2012, with a stated aim of sequestering carbon as well as biodiversity conservation. However, decisions on land use and land-use change are made in the context of competing demands for land (e.g. food production, sporting income, etc.), so economics comes into the decision-making. We need to know what policy decisions will result in sequestration of carbon (thereby mitigating climate change) at least cost, and how the marginal costs change as uptake of policy options increases. For example, afforesting low-grade rough grazing land may be cost-effective, but be prohibitively expensive on high-grade arable land. Marginal abatement cost (MAC) curves are an established economic tool for use in making such decisions. However, in the LULUCF sector, these have been applied in only very simplistic ways previously, ignoring these changes in marginal costs, the opportunity costs of the different land uses foregone and the large uncertainties. Here, we propose to develop a much more rigorous MAC curve approach for the LULUCF sector, based on spatio-temporal dynamic modelling in a Bayesian framework. This builds on previous work, which developed a Bayesian data assimilation approach to combine disparate data sources to make spatio-temporally explicit (100-m & annual) estimates of past land use in the UK. Using a Markov chain Monte Carlo approach, we will effectively explore thousands of realisations of future landscapes which could plausibly evolve from the present-day state. Being spatio-temporally explicit, this approach necessarily accounts for the opportunity costs of the land uses foregone, and includes the spatial variation in land value and the changing marginal costs. As a Bayesian approach, we establish the posterior probability density distribution for the MAC curve, and thereby quantify the associated uncertainty. The output is a mathematically and probabilistically rigorous analysis of which land-use transitions will occur, where land-use change is likely to take place, how much carbon will be sequestered, and at what cost. This will help policy-makers to make informed, evidence-based decisions about how future landscapes can help to mitigate climate change.

通过改变土地使用和管理方式，将碳封存在陆地生态系统中，是减缓大气二氧化碳上升的一种手段，也可以说是扭转这一趋势的唯一经济可行的手段。为此，大多数国家在其气候变化承诺中纳入了通过土地利用、土地利用变化和林业（LULUCF）固碳的目标。例如，苏格兰的造林目标是达到25%的森林覆盖率，并建议在2012-2022年期间创造10万公顷的林地。自2012年以来，英国已经花费了超过3000万英镑用于泥炭地恢复，其目的是封存碳和保护生物多样性。然而，关于土地利用和土地利用变化的决定是在对土地的竞争性需求（如粮食生产、体育收入等）的背景下作出的，所以经济学也参与了决策。我们需要知道什么样的政策决定将导致碳固存（从而减缓气候变化）的最低成本，以及边际成本如何随着政策选择的增加而变化。例如，在低等级的粗糙牧场上植树造林可能具有成本效益，但在高等级的可耕地上则过于昂贵。边际减排成本（MAC）曲线是一个既定的经济工具，用于作出这样的决定。然而，在土地利用、土地利用的变化和林业部门，以前只是以非常简单的方式应用这些方法，忽视了边际成本的这些变化、放弃的不同土地用途的机会成本和很大的不确定性。在这里，我们建议开发一个更严格的MAC曲线的方法，土地利用、土地利用的变化和林业部门的时空动态建模的贝叶斯框架的基础上。这建立在以前的工作，开发了贝叶斯数据同化方法，联合收割机不同的数据源，使时空明确（100米和每年）的估计，过去的土地利用在英国。使用马尔可夫链蒙特卡罗方法，我们将有效地探索成千上万的未来景观的实现，这些景观可能从现在的状态演变而来。由于这种方法具有时空上的明确性，因此必须考虑到土地利用的机会成本，并包括土地价值的空间变化和不断变化的边际成本。作为贝叶斯方法，我们建立了后验概率密度分布的MAC曲线，从而量化相关的不确定性。其结果是一个数学和概率上的严格分析，其中的土地利用过渡将发生，土地利用变化可能发生的地方，有多少碳将被封存，以及以什么样的成本。这将有助于政策制定者就未来景观如何有助于减缓气候变化做出明智的、基于证据的决策。