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

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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.