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.

通过改变土地利用和管理来封存陆地生态系统中的碳是减缓大气二氧化碳上升的一种手段，而且可以说是扭转这一趋势的唯一经济上可行的手段。为此，大多数国家已将目标纳入其气候变化承诺中。通过土地利用、土地利用变化和林业 (LULUCF) 封存碳 例如，苏格兰的造林目标是达到 25% 的森林覆盖率，并创造 100,000 公顷的森林。自 2012 年以来，英国已在 2012 年至 2022 年期间投入超过 3000 万英镑用于泥炭地恢复，其既定目标是固碳和生物多样性保护。用途的改变是在对土地的竞争性需求（例如粮食生产、体育收入等）的背景下进行的，因此我们需要知道哪些政策决定会导致封存。例如，在低等级粗糙牧场上造林可能具有成本效益，但在高等级耕地上造林成本却高得令人望而却步。边际减排成本（MAC）曲线是用于做出此类决策的既定经济工具，但是，在土地利用、土地利用和林业部门中，这些曲线以前仅以非常简单的方式应用，忽略了边际的这些变化。在此，我们建议基于贝叶斯框架中的时空动态建模，为 LULUCF 部门开发一种更加严格的 MAC 曲线方法。工作，开发了一种贝叶斯数据同化方法，结合不同的数据源，使用马尔可夫链蒙特对英国过去的土地利用进行时空明确（100 米和年度）估计。卡罗方法，我们将有效地探索未来景观的数千种实现，这些景观可能从当前状态演变而来。由于时空明确，这种方法必然考虑到放弃土地利用的机会成本，并包括空间变化。作为贝叶斯方法，我们建立了 MAC 曲线的后验概率密度分布，从而量化了相关的不确定性，其输出是对土地利用转变的数学和概率严格分析。土地利用可能发生变化、封存多少碳以及成本如何，这将有助于政策制定者就未来景观如何帮助缓解气候变化做出明智的、基于证据的决策。