Averting catastrophic climate change will require transformational societal change. A key aspect of such change will be in sectors such as agriculture and energy systems. However, there is emerging evidence ((Dunnett et al. 2022) that some options for achieving our climate ambitions risk undermining globally agreed targets relating to biodiversity and ecosystem services. It is therefore essential that future energy and agricultural systems are designed to address both the climate and ecological crisis in parallel. Doing so will require ensuring that land used to meet future energy and food demand does not impact our imperative to maintain and even increase natural ecosystems such as forests. The aim of this project is to develop and empirically test spatial models that assess the degree to which transformational change of landscapes to mitigate climate change can also achieve the protection of natural systems on which human wellbeing depends. Analysis will be conducted at both regional scale to address policy relevant questions for the UK government, and at global scale to examine compatibility between global targets relating to climate and biodiversity. Questions will be examined through the lens of international treaties (e.g UN SDGs) and their regional implementation strategies (e.g. UK 25 Year Environment Plan).
Current methods for predicting land use change are predominantly based on classic economic theory and past change. However, recent land use change is different from past change (Eigenbrod et al. 2020), and will change again due to climate impacts (Agnolucci et al. 2021). During this PhD, the candidate will take advantage of recent advances by the supervisors to link agricultural demand (Agnolucci et al. 2020) and future distributions of energy systems (e.g. Dunnett et al. 2022) to develop methods that capture transformative pathways of change in land use both in the UK and globally. Techniques such as machine learning and cloud-based processing will bring together data across scientific domains to develop a mechanistic understanding of drivers of land use change. From this starting point trade-offs between climate mitigation and biodiversity conservation can be examined, and the influence of key assumptions about the rate of climate change and factors such as dietary or energy system change can be explored. Projections will be linked to policy relevant metrics and you will benefit from the supervisor’s strong links with the energy community (Holland) and wider environmental policy (Eigenbrod) through engagement with organizations such as DEFRA.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the School of Geography and Environmental Science. Specific training will include advanced statistical and spatial modelling skills in R and GIS, programming and data management skills in R, expertise in cloud computing (Google Earth Engine). The student will also be trained in systematic analyses of the broad interdisciplinary literature in land use theory and landscape ecology relevant for the project, as well as training in academic publishing.
Please see https://inspire-dtp.ac.uk/how-apply for details.
Eigenbrod F, Beckmann M, Dunnett S, Graham L, Holland RA, Meyfroidt P, et al. Identifying Agricultural Frontiers for Modeling Global Cropland Expansion. One Earth. 2020 Oct 23;3(4):504–14.
Agnolucci P, Rapti C, Alexander P, De Lipsis V, Holland RA, Eigenbrod F, et al. Impacts of rising temperatures and farm management practices on global yields of 18 crops. Nat Food. 2020 Sep;1(9):562–71.
Dunnett S, Holland RA, Taylor G, Eigenbrod F. Predicted wind and solar energy expansion has minimal overlap with multiple conservation priorities across global regions. Proceedings of the National Academy of Sciences. 2022 Feb 8;119(6):e2104764119.