Prior to the launch of the EU Emissions Trading System (EU ETS) in 2005, the electricity sector was widely proclaimed to have more low-cost emission abatement opportunities than other sectors. If this were true, effects of the EU ETS on carbon dioxide (CO2) emissions would likely be visible in the electricity sector. Our study looks at the effect of the price of emission allowances (EUA) on CO2 emissions from Swedish electricity generation, using an econometric time series analysis for the period 2004-2008. We control for effects of other input prices and hydropower reservoir levels. Our results do not indicate any link between the price of EUA and the CO2 emissions of Swedish electricity production. A number of reasons may explain this result and we conclude that other determinants of fossil fuel use in Swedish electricity generation probably diminished the effects of the EU ETS.

By using a novel approach in this paper, ;2 -analysis, we have found that electricity prices most of the time have increased in stability and decreased in volatility when the Nordic power market has expanded and the degree of competition has increased. That electricity prices at Nord Pool have been generated by a stochastic dynamic system that most often has become more stable during the step-wise integration of the Nordic power market means that this market is less sensitive to shocks after the integration process than it was before this process. This is good news.JEL codes: C14; C22; D43.

The aim of this paper is to illustrate how the stability of a stochastic dynamic system is measured using the Lyapunov exponents. Specifically, we use a feedforward neural network to estimate these exponents as well as asymptotic results for this estimator to test for unstable (chaotic) dynamics. The data set used is spot electricity prices from the Nordic power exchange market. Nord Pool, and the dynamic system that generates these prices appears to be chaotic in one case.

The aim of this paper is to illustrate how the stability of a stochastic dynamic system is measured using the Lyapunov exponents. Specifically, we use a feedforward neural network to estimate these exponents as well as asymptotic results for this estimator to test for unstable (chaotic) dynamics. The data set used is spot electricity prices from the Nordic power exchange market. Nord Pool, and the dynamic system that generates these prices appears to be chaotic in one case.

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