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Mobilising finance for green investments

Home > Energy Demand Research Network (EDRN) > Mobilising finance for green investments

2018 to 2021

PI: Daire McCoy

Funded by: ESRC Centre for Climate Change Economics and Policy (CCCEP)

Collaborators:

UNEP FI; Climate Strategies and Partners

Partners and stakeholders:

HSBC; Lloyds; BEIS

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Introduction

Are “green” assets lower risk than “brown” assets? Answering this question is a key factor in unlocking finance for energy efficiency. If improved energy efficiency of portfolios can reduce risk for lenders, accurately quantifying this difference could potentially create incentives for lenders to direct finance towards lower carbon investment, provide borrowers with preferential treatment for green investments, and reduce the reliance on public funds to support energy efficiency programmes. Despite the importance of this question, robust empirical evidence on the relationship between energy performance and credit performance is limited.

Summary

This research aims to provide evidence of the link between energy performance and credit performance in the residential sector. The research team have secured access to the UK loan books of two global banks, comprising approximately 25% of the UK residential mortgage market. This data will be linked with the publicly available Energy Performance Certificate (EPC) database and the confidential UK Government National Energy Efficiency frame-work Directive (NEED) database. This will allow the research team to create a panel dataset of credit performance and energy performance from 2009-2018.

Key questions assessed will include: (i) Is energy performance correlated with credit risk? (ii) How does this vary by observed household characteristics and loan characteristics (iii) Is there evidence of a causal link between improved energy performance and credit risk?

The empirical analysis will model the probability of default and/or prepayment as a function of the energy efficiency of the dwelling and other loan and borrower characteristics typically used in credit-risk modelling. A range of modelling frameworks such as survival analysis and other advanced statistical techniques will be used.

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