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

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.

Who might benefit from this research?
UK government departments, principally BEIS and Defra; the Devolved Administrations (e.g. Scottish Government Rural and Environment Directorate); and the UK Committee on Climate Change.
Major public landowners: the Forestry Commission, the Woodland Expansion Advisory Group, Conservation agencies (SNH, Natural England) and charities (RSPB, International Union of Conservation).

How might they benefit from this research?
Land is a finite resource, and land management can play an important role in mitigating climate change, maintaining water quality, and supporting biodiversity. Our research aims to increase our understanding of the interactions that determine how land use will be affected by future environmental and socio-economic change. With improved understanding, we can better predict the consequences for GHG emissions, and, at governmental level, make more informed decisions about what policies to pursue to mitigate climate change. The findings will inform policy development for climate strategies for implementing the Paris Agreement. To make best use of land-based mitigation measures, we need to be able to put the correct value on the available options (for example, protecting and restoring peatland versus afforestation projects). Only if we understand this can we put the right balance on mitigation policies.
UK government departments (BEIS and Defra), the Devolved Administrations, and the UK Committee on Climate Change have responsibilities for reporting emissions and assessing progress towards the UK's commitments to mitigating climate change. The major public landowners (such as the Forestry Commission, Conservation agencies (SNH, Natural England) and charities (RSPB, International Union of Conservation) need to know the role of land-based activities in climate change mitigation, via effects on emission and sequestration of greenhouse gases. For example, Scotland has an afforestation target to reach 25 %, and the creation of 100,000 ha of woodlands in the period 2012-2022 has been recommended. However, these numbers are largely arbitrary, and not based on any quantitative analysis. This project will provide a robust methodology for MAC curves which underpin and inform future decisions.

Our project team is already involved in the INI programme "Mathematical and statistical challenges in landscape decision making", with Co-I van Oijen participating at INI for the month July 2019. Other project team members will attend INI for the seminar in August 2019, engaging with a wide variety of mathematicians, data scientists, and environmental scientists, and in follow-on activities. CEH and our team are actively engaged with relevant decision-makers. CEH carries out the UK inventory of GHG emissions from land use, land use change and forestry (LULUCF) on behalf of the UK government (Defra and BEIS). PI Levy is on the LULUCF project Scientific Steering Committee, and advises the UK government departments and the Forestry Commission on relevant methodologies and scientific issues. This provides a direct link to policy, and influences a wide range of the stakeholders - conservation agencies, land managers, environmental agencies, and regulatory authorities. To maximise the impact of our work during the short timescale of this project, we will present the outputs directly to particularly relevant stakeholders such as the Woodland Expansion Advisory Group, who meet regularly in Edinburgh. We will exploit our links with the EPSRC-funded Data Science for the Natural Environment (DSNE) project that members of the project team are involved with. Where possible, we will include outputs, challenges and feedbacks from this project within the outreach mechanisms and events planned within the DSNE project (including public lectures and "data science to policy" events).