Three Essays in Energy and Environmental Economics

This dissertation consists of three studies, each examining a different topic in the field of energy and environmental economics. The topics comprise: (i) estimating potentials for greenhouse gas emission reductions of economic sectors, (ii) forecasting the oil production of a region based on historical data from discoveries, and (iii) examining the determinants of electricity price fluctuations. Each of the studies uses a particular statistical method or mathematical model that is specifically adapted to the research question and the data set under investigation. The first study is a stochastic nonparametric efficiency analysis in which greenhouse gas emissions are included as bad outputs. For seven economic sectors and sixteen European countries, this study estimates greenhouse gas emission reduction potentials, i.e., the quantity of emissions that could potentially be reduced by improvements in productive efficiency. The standard DEA method is extended by a specific bootstrapping procedure used to implement a bias correction and to compute confidence intervals. The magnitudes of the emission reduction potentials are compared with the emission reduction targets for 2030 from the European Commission. The results show that improvements in productive efficiency are a quantitatively important element, potentially allowing for a substantial reduction of greenhouse gas emissions in the European Union. The second study presents a stochastic model for forecasting for an oil-producing region the amount of undiscovered oil, the future path of oil discovery and that of oil production. The model combines three submodels: (i) an empirically-founded production model at the level of individual oil fields, (ii) a successive sampling discovery model after Kaufman et al. (1975) for forecasting field sizes, and (iii) a stochastic birth process model for forecasting discovery dates. The model is estimated and evaluated for the oil-producing regions of Norway and the U.S. Gulf of Mexico (the latter further split into shallow- and deep-water parts). The results show that the predictions for oil discovery are somewhat too low compared to the actuals for Norway and for the shallow-water Gulf of Mexico, while for the deep-water Gulf of Mexico the predictions are too high. This is similarly reflected in the predictions for oil production. The third study is a multivariate wavelet analysis of the German wholesale electricity market, which examines the determinants of electricity price fluctuations using daily time series. The possible determinants are coal prices, gas prices, and the residual load (i.e., electricity consumption minus wind and solar generation). The multivariate wavelet method allows for a detailed examination of the relations between the time series in time-frequency space, while also taking into account the interdependencies among the different time series. The results show that the residual load is the key short-run determinant of electricity prices, while coal and gas prices are the key long-run determinants. Also, this study finds that the co-movement relation among the energy prices is time-varying, which is consistent with the findings of other studies (e.g., Sousa et al. (2014); Aguiar-Conraria et al. (2018)).