Domestic renewable energy systems have enjoyed considerable government support in Europe and the United States. Yet despite their ubiquity there is a gap in evaluating their effectiveness. We examine the impact of the 2017 Home Energy Loan & Grant Scheme (HELS) in Scotland using administrative data on the universe of domestic solar photovoltaic installations in the United Kingdom since 2008. Exploiting the devolved nature of renewable energy support policy in the UK we implement a matching strategy between Scottish and English locales to provide a causal assessment of the scheme’s effectiveness. Our findings are three-fold. First the HELS increased domestic solar photovoltaic installations. Second the HELS’ funding ceiling biased installation sizes downward. Third a complementary distributional analysis reveals that the vast majority of the HELS benefits accrued to the top three deciles by house value.

Current growth theories do not allow for the study of the bias of technical change and the evolution of factor shares, neither at aggregate nor sectoral level, without strong assumptions on the elasticity of substitution between capital and labour. We present a growth accounting framework that disentangles the different factor-saving directions of technical change and factor substitution. We build the framework for two primary factors, capital and labour. We represent technical change as the shift of a Leontief production function to a new function which is the convex hull of two shifts of this Leontief production function - one purely labour-saving, the other purely capital-saving. We apply this framework to industry-level data to answer the following questions; What has been the bias of technical change? Does an increase in the price of one factor spurs specific factor-saving innovation? Can we forecast the evolution of factor shares? We find that most industries are capital-biased but with a growing trend of labour-saving technical change. In some industries, we find significant evidence of labour-saving technical change induced by the cost of labour. The framework is validated by better forecasting the evolution of the factors shares than CES, Cobb-Douglas and Leontief functions.

Transitioning away from dirty and towards clean technologies is critical to reduce carbon emissions, but the race between clean and dirty technologies is taking place against the backdrop of improvements in general-purpose technologies (GPT) such as information and communication technologies (ICT) and artificial intelligence (AI). We show how, in theory, a GPT can affect the direction of technological change and, in particular, the competition between clean and dirty technologies. Second, we use patent data to show that clean technologies absorb more spillovers from AI and ICT than dirty technologies and that energy patenting firms with higher AI knowledge stocks are more likely to absorb AI spillovers for their energy inventions. We conclude that ICT and AI have the potential to accelerate clean energy innovation