This thesis addresses problems in the economic modelling of renewable electricity generation industries, with a particular focus on the UK onshore wind industry. Many governments worldwide have instituted steep reduction targets for emissions of gases linked with climate change, in particular carbon dioxide. In order to achieve such reductions, the UK has chosen to support the growth of low-carbon renewable electricity generation industries such as wind, solar, wave and tidal power. Chapter 3 sets out a mathematical model of four financial support mechanisms for renewable energy, and considers the problem of how to control these mechanisms so that they produce the greatest degree of growth at the lowest cost possible. The mathematical model includes the concept of learning, through which production costs decline as experience of production increases. A 1992- 2010 UK onshore wind dataset was used to fit the models developed during the course of this thesis. This was stored in a novel networked database, described in chapter 2. Within economics, learning effects are often assumed to occur at the level of a technology, rather than within an individual company or organisation. Chapter 4 considers the opposite case, in which learning takes place at the level of an individual firm. Firms may choose to share the production cost reductions they gain from learning with other firms either unilaterally or through multilateral reciprocal arrangements known as cliques. The implications of state intervention to prevent monopolies are considered. It is shown that tax-enforced market share limits are superior to limit-by-dictat. However, any form of state intervention in a learning industry is shown to cause average productiOli costs to rise, potentially harming the interests of consumers. Finally, chapter 5 considers the question: is there a relationship between the propensity of a firm to experiment with a range of technologies and its likelihood of finding long-term success?
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:649369 |
| Date | January 2014 |
| Creators | Oak, Neeraj |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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