Dr Philip Holden

Staff time for Phil Holden for a sub-contract with the University of Exeter.

The original pioneering NERC-funded project identified significant financial risks associated with the rapid shift to sustainable energy systems. These included the potential loss of value in fossil fuel assets and infrastructure which could lead to financial shocks and instability in the financial sector. TREX will extend this methodology to provide a robust characterisation and quantification of transition risks and opportunities for individual firms adjacent to the fossil fuel industry. Tailored methodologies will be used to derive a Transition Value at Risk, a dollar value corresponding to the likely transition-related revenue upside, or downside, for listed firms in the following industrial groupings: • Dependent suppliers to oil and gas e.g. oil and gas services, mining equipment. • Clients of oil and gas e.g. electricity utilities, producers of steel, cement, chemicals, fertilisers. • Producers of fossil-fuel consuming products e.g. transport and heating manufacturers. • Component suppliers to fossil-fuel technologies e.g. manufacturers of components of combustion engines, gas turbines, blast furnaces.

The Bank of England has recently drawn attention to the risks to the UK economy posed by the impacts of climate change and climate policy. The risks that extreme climatic events pose to assets and infrastructure have already been the subject of substantial research. However, the risks to financial assets of changes in the policy regime designed to achieve climate targets, notably risks surrounding the impacts of stranded fossil fuel assets (SFFA), have received much less attention. In this project, we propose to study the risk of SFFA shocks to the UK economy, its resilience to such shocks, and the policy options that could improve this resilience. Using in-depth knowledge and expertise on the financial sector, we will develop new tools to determine the risks that SFFA pose for financial stability. We will also further develop our stakeholder engagement methodology, through which we co-develop strategies to assess and understand risk propagation, and policy to minimise the risk of instability. Award value is £250,000 at 80%FEC

The Paris agreement commits nations to pursuing efforts to limit the global temperature rise to 1.5 degrees. This represents a level of transformation of the socio-economic and energy systems that substantially exceeds the scenarios that have been found using most conventional integrated assessment models (IAMs) based on equilibrium assumptions. Such strong mitigation also violates the pattern scaling assumptions used to derive environmental impacts in IAMs because of the rapid reversal in emissions growth. We will use a new, fully dynamic IAM that does not rely on equilibrium or pattern scaling assumptions to provide a set of more realistic dynamic pathways to reach the 1.5 degree target. The assessment will identify policy options and the degree of negative emissions required.

The 1.5C programme will achieve a high level of policy impact through publication of results in the IPCC special report SR1.5. But this alone does not necessarily provide answers to the specific questions of UK policymakers. To achieve the programme's full impact potential for UK policy demands an in-depth dialogue between programme participants and stakeholders, together with detailed policy impact and effectiveness modelling to answer the specific questions arising from the dialogue in the light of an integrated view of the new results generated by the 1.5C programme.

The project aim is to build a robust decision-making tool for future investment in new wind turbines, which takes account of climate change scenarios, and government policy scenarios in energy and climate change, as well as the business operations and profitability of the stakeholders. Research questions include: • How is wind power potential in the UK impacted by both uncertain future climate states and policy trajectories? • What strategies for investing in new wind farms would provide the foundation for a more robust strategy under climate change and/or diverse policy outcomes? • What vulnerabilities still remain, and how can they be reduced (e.g. through modelling studies, or monitoring external factors deemed relevant to the decision-making process)?

BUMPER is an “embedded research” project, aimed towards strengthening interdisciplinary connections and funded by ReCoVER (Research on Changes of Variability and Environmental Risk). The project addresses transfer functions, widely used tools to infer past climate from microfossil assemblages preserved in lake and ocean sediments. In collaboration with ecologists at the Florida Institute of Technology and NIMBioS (National Institute for Mathematical and Biological Synthesis, University of Tennessee), BUMPER is developing a computationally-fast Bayesian transfer function to produce a user-friendly tool for paleoecologists. The principal motivation for developing a Bayesian approach is the calculation of reconstruction-specific uncertainty that, for instance, greatly increases the power of multi-proxy reconstructions.

Large parts of the Antarctic ice sheet lie on bedrock below sea level and may be vulnerable to a positive feedback known as Marine Ice Sheet Instability (MISI), a self-sustaining retreat of the grounding line triggered by oceanic or atmospheric changes. There is growing evidence MISI may be underway throughout the Amundsen Sea Embayment (ASE) of West Antarctica. If this is sustained the region could contribute up to 1-2 m to global mean sea level, and if triggered in other areas the potential contribution to sea level on centennial to millennial timescales could be two to three times greater. However, physically plausible projections of Antarctic MISI are challenging: numerical ice sheet models are either too low in spatial resolution to explicitly resolve grounding line processes or else too computationally expensive to assess modeling uncertainties. The proposed work brings together and analyses two new datasets that complement each other in model complexity – a large ensemble generated with a low resolution model, and a small ensemble from a high resolution model – by constructing a new emulator of the relationship between them.