Measuring Productivity When Technologies are Heterogeneous: A Semi-Parametric Approach for Electricity Generation

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWhile productivity growth in electricity generation is associated with multiple positive effects from an economic and environmental perspective, measuring it is challenging. This paper proposes a framework to estimate and decompose productivity growth for a sector characterized by multiple technologies. Using a metafrontier Malmquist decomposition and frontier estimation based on stochastic non-smooth envelopment of data (StoNED) allows for productivity estimation with few microeconomic assumptions. Additionally, evaluation of productivity at representative hypothetical units permits distribution-free analysis for the whole distribution of power plant sizes. The proposed framework is used to analyze a unique and rich dataset of coal, lignite, gas, and biomass-fired generators operating in Germany from 2003 to 2010. The results indicate stagnating productivity for the sector as a whole, technical progress for biomass plants, and very high productivity for gas-fired plants. JEL-Codes: D24,C14,O13,L94

Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation

“…Thus, the plants could produce 12.6% additional output by using best practice with the same input endowment. This expected inefficiency deviates considerably from the results in Seifert (2015) and Seifert et al (2016), which are both based on similar samples. However, the observation period of both studies end one in 2010, one year before the data in our sample, and one year before the considerable down-turn in gas-fired electricity generation in Germany (see Figure 2).…”

“…The estimates of scale elasticity obtained are in line with more recent studies' findings on scale elasticities in fossil fuel electricity generation, as e.g., Kumbhakar and Tsionas (2016). Compared to Seifert et al (2016) and Seifert (2015) also analyzing German electricity generation, results differ in a few points. Seifert et al (2016) The results of this analysis also need to be interpreted with caution.…”

“…Ghosh and Kathuria (2016) indicate strong positive scale effects for India's coal-fired generation, and Akkemik (2009) finds considerable scale economies in the Turkish electricity generating sector. Based on estimates using DEA and StoNED Seifert et al (2016) and Seifert (2015) indicate that losses due to suboptimal scale size in Germany's electricity generation depend on fuel sources. Overall, the early literature indicates IRS in electricity generation, while more recent studies suggest constant returns-to-scale (CRS) or DRS, supporting the argument that perceived RTS may have been exploited.…”