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

通过改变土地使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方法，而且可以说是扭转这一趋势的唯一经济上可行的方法。为此，大多数国家在其气候变化承诺中列入了通过土地利用、土地利用变化和林业来封存碳的目标(LULUCF)。例如，苏格兰的植树造林目标是达到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，其明确的目标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需求(如粮食生产、体育收入等)的背景下做出的，因此经济学进入了决策过程。我们需要知道哪些政策决定将导致碳封存(从而减缓气候变化)，至少会产生成本，以及随着政策选择的增加，边际成本将如何变化。例如，在低等级的粗糙牧场植树造林可能是划算的，但在高等级耕地上却昂贵得令人望而却步。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。然而，在土地利用、土地利用的变化和林业部门，以前只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、不同土地用途的机会成本和巨大的不确定性。在这里，我们建议在贝叶斯框架下，基于时空动态建模，为土地利用、土地利用的变化和林业部门开发一种更严格的MAC曲线方法。这是建立在以前工作的基础上的，这项工作开发了一种贝叶斯数据同化方法，将不同的数据源结合在一起，对英国过去的土地使用进行时空上明确的(100米和每年)估计。使用马尔可夫链蒙特卡罗方法，我们将有效地探索数以千计的未来景观的实现，这些景观可能会从现在的状态演变而来。该方法在时空上是显性的，必然要考虑土地利用的机会成本，包括土地价值的空间变化和边际成本的变化。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。其结果是对将发生哪些土地利用转变、可能发生土地利用变化、将封存多少碳以及成本等进行了严格的数学和概率分析。这将有助于政策制定者就未来的景观如何有助于缓解气候变化做出明智的、基于证据的决定。

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.