Closing the green finance gap in the UK: policy recommendations and economic implications, using the system dynamics Green Investment Barrier Model (GIBM)

In 2019, the UK pledged to achieve a net-zero carbon emission economy by 2050. While the so-called ‘green finance gap’ is generally acknowledged in this context, tailored policy recommendations on how to address it are missing. The focus of this thesis lies on the upscaling of private finance (e.g. from institutional investors).This thesis builds a new system dynamics energy-economy model – called the Green Investment Barrier Model (GIBM) – that includes as a main novelty the representation of a green finance gap. System dynamics is most appropriate to model complex systems and to understand the likely long-term trends. In terms of key contributions, first the qualitative investigation demonstrates that key investment barriers form a complex system characterised by path dependency, lock-in and non-linearity. Therefore, the adoption of a systems policy, drawing on a long-term and holistic systems perspective and tackling identified key green investment barriers is recommended to close the green finance gap. Second, in terms of modelling contributions and as shown by GIBM, when a green finance gap exists, the introduction of a finance systems policy leads to multiple co-benefits, including an emission reduction, a decline in unemployment and a drop in the unit costs of energy, while increasing GDP by 2050. Further, GIBM results reveal that reaching the UK zero carbon targets for the electricity sector by 2050 requires the implementation of additional low-carbon energy policies besides a finance system policy. Finally in terms of modelling results, the recommended energy policy scenario includes a step-wise linear halt in brown energy infrastructure until 2050. Co-benefits of this latter policy scenario include higher GDP, lower energy system costs and lower unemployment. Third, in terms of theoretical contributions, it is demonstrated that the theoretical underpinning of models influences not only the magnitude of the impact but also the sign of their results and consequently policy formulation, it is therefore argued that more transparency on this among policy-makers is required, along with increased knowledge on how models with different theoretical frameworks should or should not be applied in combination.