Estimating and analyzing energy efficiency in German and Colombian manufacturing industries using DEA and data panel analysis. Part II: Non-energy intensive sectors

ABSTRACT

This study analyzes energy efficiency trends in German and Colombian non-energy intensive sectors using data envelopment analysis (DEA) between 1998 and 2005. The results of the DEA show considerable variation in energy efficiency across non-energy intensive sectors during the sample period. At a second stage, regression analysis using panel data analysis reveals that several factors, including labor productivity, enterprise size, and capital input can be considered determinants of differences in energy efficiency among German and Colombian non-energy intensive sectors. Our results also show that different energy policies should apply in non-energy intensive sectors and that they should encourage the importance of energy efficiency in order to improve it, especially in small and medium enterprises and the industrial sector of developing countries.

KEY WORDS:

• Data envelopment analysis
• data panel analysis
• energy efficiency
• non-energy intensive sectors

Notes

¹ The concept of energy from “efficiency“ has the following perspectives, according to the Wuppertal Institute (2009): in the macroeconomic aggregated perspective, energy efficiency is denoted either as energy intensity (energy input is related to monetary output parameters) or, reciprocally, as energy productivity (the ratio of production is related to energy consumed); the efficiency of energy conversion (the ratio of generated end-use energy to primary energy or to secondary energy used); and the energy end-use efficiency (the proportion of amount of energy used for the satisfaction of personal needs and energy use for non-personal demands).

² NEISs are defined by the concept of German energy tax law as the sectors where the cost of energy is below 3% of total costs.

³ The fundamental feature of DEA is that technical efficiency index of each DMU depends on the performance of the sample of which it forms a part. This means that DEA produces relative, rather than absolute, measures of technical efficiency for each DMU under consideration. DEA evaluates a DMU as technically efficient if it has the best ratio of any output to any input and this shows the significance of the outputs/inputs taken under consideration (Halkos and Tzeremes, 2005).

⁴ DMUs is a more appropriate term than “firm” when, for example, a sector is studying the performance of its branches (Coelli, 1996).

⁵ This variable is calculated taking into account the categories established by the German and Colombian statistical offices based on the number of workers and output levels for every manufacturing industrial sector.