Benjamin Hampf

Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases

This study explores the reduction potential of greenhouse gases for major pollution emitting countries of the world using nonparametric productivity measurement methods and directional distance functions. In contrast to the existing literature we apply optimization methods to endogenously determine optimal directions for the e ciency analysis. These directions represent the compromise of output enhancement and emissions reduction. The results show that for reasonable directions the adoption of best-practices would lead to sizable emission reductions in a range of about 20 percent compared to current levels.

Optimal Profits under Environmental Regulation: The Benefits from Emission Intensity Averaging

In this paper we analyze the economic effects of implementing EPA’s newly proposed regulations for carbon dioxide (

Carbon dioxide emission standards for U.S. power plants: An efficiency analysis perspective

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Measuring environmentally sensitive productivity growth: An application to the urban water sector.

CO2) on existing U.S. coal-fired power plants using nonparametric methods on a sample of 144 electricity generating units. Moreover, we develop an approach for evaluating the economic gains from averaging emission intensities among the utilities’ generating units, compared to implementing unit-specific performance standards. Our results show that the implementation of flexible standards leads to up to 2.7 billion dollars larger profits compared to the uniform standards. Moreover, we find that by adopting best practices, current profits can be maintained even if an intensity standard of 0.88 tons of

Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?

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Estimating the Bias in Technical Change: A Nonparametric Approach.

CO2 per MWh is implemented. However, our results also indicate a trade-off between environmental and profit gains, since aggregate