By blending seminal literature on non-spatial stochastic frontier models with key contributions to spatial econometrics we develop a spatial autoregressive (SAR) stochastic frontier model for panel data. The speci…cation of the SAR frontier allows e¢ ciency to vary over time and across the cross-sections. E¢ ciency is calculated from a composed error structure by assuming a half-normal distribution for ine¢ -ciency. The spatial frontier is estimated using maximum likelihood methods taking into account the endogenous SAR variable. We apply our spatial estimator to an aggregate production frontier for 41 European countries over the period 1990 2011.In the application section, the …tted SAR stochastic frontier speci…cation is used to discuss, among other things, the asymmetry between e¢ ciency spillovers to and from a country.

Energy consumption and greenhouse emissions across many countries have increased overtime despite widespread energy efficiency improvements. One explanation offered in the literature is the rebound effect (RE), however there is a debate about the magnitude and appropriate model for estimating RE. Using a combined stochastic frontier analysis and two-stage dynamic panel data approach for 55 countries covering 1980-2010, we explore these two issues of magnitude and model. Our central estimates indicate that, in the short-run, 100% energy efficiency improvement is followed by 90% rebound in energy consumption, but in the long-run it leads to a 36% decrease in energy consumption. Overall, our estimated cross-country RE magnitudes indicate the need to consider or account for RE when energy forecasts and policy measures are derived from potential energy efficiency savings.

It is common in e¢ ciency studies which analyse the environment for pollution to form part of the production technology. Pollution therefore a¤ects e¢ ciency and the TFP growth decomposition. As an alternative approach we draw on theoretical studies from the environmental economics literature, which demonstrate that TFP a¤ects environmental quality.Along these lines we adopt a two-stage empirical methodology. Firstly, we obtain two estimates of productive performance (e¢ ciency and TFP growth) using a stochastic production frontier framework in Stage 1 for European countries (1995 2008), from which we omit emissions. Secondly, in Stage 2 these measures of productive performance are used as regressors in spatial models of per capita nitrogen and sulphur emissions for European countries.From our preferred Stage 2 spatial models we …nd that a country's TFP growth must fall to reduce its per capita nitrogen and sulphur emissions. This is likely to be because nitrogen and sulphur emissions in the EU have been tightly regulated for a long period of time via air quality standards and consequently, substantial reductions in emissions from cleaner and more productive technology were achieved some time ago.

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