22 research outputs found

    Potential Gains from Mergers in Local Public Transport: An Efficiency Analysis Applied to Germany

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    We analyze potential gains from hypothetical mergers in local public transport using the non-parametric Data Envelopment Analysis with bias corrections by means of bootstrapping. Our sample consists of 41 public transport companies from Germany's most densely populated region, North Rhine-Westphalia. We merge them into geographically meaningful, larger units that operate partially on a joint tram network. Merger gains are then decomposed into individual technical efficiency, synergy and size effects following the methodology of Bogetoft and Wang [Bogetoft, P., Wang, D., 2005. Estimating the Potential Gains from Mergers. Journal of Productivity Analysis, 23(2), 145-171]. Our empirical findings suggest that substantial gains up to 16 percent of factor inputs are present, mainly resulting from synergy effects

    Measurement of the 'Underlying Energy Efficiency' in Chinese Provinces

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    China is one of the largest consumers of energy globally. The country also emits some of the highest levels of CO2 globally. In 2009, 18% of the world’s total energy was consumed in China and the growth rate of energy consumption in China is 6.4% per year. In recent years, the Chinese government decided to introduce several energy policy instruments to promote energy efficiency. For instance, reduction targets for the level of energy intensity have been defined for provinces in China. However, energy intensity is not an accurate proxy for energy efficiency because changes in energy intensity are a function of changes in several socioeconomic factors. For this reason, in this paper we present an empirical analysis on the measurement of the persistent and transient “underlying energy efficiency” of Chinese provinces. For this purpose, a log-log aggregate energy demand frontier model is estimated by employing data on 29 provinces observed over the period 1996 to 2008. Several econometric model specifications for panel data are used: the random effects model and the true random effects model along with other versions of these models. Our analysis shows that energy intensity cannot measure accurately the level of efficiency in the use of energy in Chinese provinces. Further, our empirical analysis shows that the average value of the persistent “underlying energy efficiency” is around 0.78 whereas the average value of the transient “underlying energy efficiency” is approximately 0.93
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