Essays on Renewable Energy Technology Development and Voluntary Carbon Offsets

Abstract: This thesis consists of an introduction and five self-contained papers addressing the issues of renewable energy technology development and voluntary carbon offsets, respectively. Paper I presents the results from a semi-experimental study of Swedish students’ stated willingness to purchase emission allowances for carbon dioxide within the European emissions trading scheme. Methodologically the analysis draws on recent developments in the literature on integrating norm-motivated behaviour into neoclassical consumer theory. The results indicate that students’ willingness to purchase emission allowances is determined by both price and the presence of norms; people who feel personally responsible for contributing to reduced climate damages are also the ones who appear more inclined to purchase emission allowances. The empirical findings are also consistent with the notion that perceptions about others’ stated willingness to purchase emission allowances imply improvements in people’s self-image and ultimately behavioural change. In paper II we conduct a conceptual review and a meta-analysis of wind power learning rates, including an assessment of a number of important model specification and data issues that influence these learning rates. The econometric analysis in this paper relies on over 100 learning rate estimates presented in 35 studies. The results indicate that the choice of the geographical domain of learning, and thus implicitly of the assumed presence of learning spillovers, is an important determinant of wind power learning rates. We also find that the use of extended learning curve concepts, thus integrating, most notably, public R&D effects into the analysis, tends to result in lower learning rates than those generated by the so-called single-factor learning curve model specification. In paper III a critical analysis of the choice of model specification in learning curve analyses of wind power costs is presented. Special attention is devoted to the choice of either national or global cumulative capacities as learning indicators, as well as the inclusion of other independent variables such as public R&D, scale effects and a time trend. A data set of pooled annual time series over eight European countries is used. The empirical results indicate that the estimates of the learning rates may differ considerably across different model specifications. The presence of global learning for wind power appears more important than that of national learning. Moreover, the use of extended learning curve concepts, thus integrating either scale effects or public R&D (or both) into the analysis, adds to our understanding of cost decreases in wind power technology. In paper IV we examine how effective different public policies have been in encouraging innovation in the wind energy sector. The analysis is conducted using patent counts data on a panel of European countries over the time period 1977-2009. The contribution of the paper lies primarily in its in-depth empirical efforts to address the innovation impacts of different public policies, including tests of different model specifications and important policy interaction effects. An important result is that the marginal impact of public R&D support to wind power has a more profound effect on patenting activity when implemented jointly with a feed-in tariff scheme. Finally, paper V provides an econometric analysis of the technology development patterns in the European wind power sector. The invention, innovation and diffusion phases of wind power development are brought together to assess important interaction effects. The dataset covers the time period 1991-2008 for eight western European wind power countries. We find evidence of national and international knowledge spillovers in the invention model. The results from the innovation models show that there exists global learning, but also that the world market price of steel has been an important determinant of wind power investment costs. The diffusion model results indicate that investment cost is an important determinant of the development of installed wind power capacity. The results also identify natural gas prices and feed-in tariffs as vital factors behind the observed wind power diffusion patterns.