Spatial optimisation of renewable energy deployment in Great Britain: A natural capital analysis

Abstract

Transitioning global energy systems towards low carbon energy sources will be essential if countries are to reduce their greenhouse gas emissions to limit global warming to less than 2°C degrees. As countries decarbonise their energy systems, the need to determine the best locations for renewable energy infrastructure whilst balancing trade-offs between affordability, food security, and nature protection is of paramount importance. By incorporating the natural capital approach into energy modelling, this thesis presents the ADVENT-NEV model, a spatially-explicit cost minimisation model which determines the optimal locations for solar farms, onshore wind farms, bioenergy power stations and their bioenergy crops in Great Britain (GB) considering both market and non-market costs (i.e. ecosystem services).

This thesis makes several empirical contributions to the energy modelling literature. It highlights that when non-market costs are excluded from decision-making, the welfare loss associated with energy transitions could be up to £5 billion. By applying the natural capital approach, however, the ADVENT-NEV model is able to determine locations for energy infrastructure which minimise the social cost of the energy system. For example, it identifies locations where bioenergy crops could reduce greenhouse gas emissions and increase carbon sequestration. It concludes however that the expansion of bioenergy crops has the potential to result in a net emission of greenhouse gases; this is concerning given the emphasis being placed on bioenergy to provide carbon sequestration services in GB. This thesis also identifies how restricting bioenergy crops from being grown on National Parks, AONB, peatland and high-grade agricultural land results in even low bioenergy targets being infeasible due to the lack of suitable land.

This thesis has demonstrated the critical role that the natural capital approach and high spatial resolution data could play in future energy decision-making. Failure to incorporate spatial environmental data into energy modelling risks overlooking the economic, spatial and social implications of transitioning to a low carbon energy system.