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Energy Efficiency
Erschienen in: Energy Efficiency 1/2011

01.02.2011

Energy efficiency development in German and Colombian non-energy-intensive sectors: a non-parametric analysis

verfasst von: Clara Inés Pardo Martínez

Erschienen in: Energy Efficiency | Ausgabe 1/2011

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Abstract

This paper measures energy efficiency development in non-energy-intensive sectors (NEISs) in Germany and Colombia from a production-based theoretical framework using Data Envelopment Analysis (DEA). Using data from the German and Colombian Annual Surveys of Industries from 1998 to 2005, the analysis compares energy efficiency performances in German and Colombian NEISs at two levels of aggregation and then applies several alternative models. The results show considerable variation in energy efficiency performance in the NEISs of both countries. Comparing the results across models, it was found that in the German and Colombian NEISs, the measures of energy efficiency are similar, indicating that an appropriate combination of technical efficiency and cost minimisation are necessary to improve energy efficiency. However, energy efficiency based on cost minimisation is greater in both countries, demonstrating that energy prices in this sector are not the key variable for improving energy efficiency. This is due to the low share of energy costs, making it preferable to change other inputs rather than energy. A second-stage regression analysis reveals that in the German and Colombian NEISs, labour productivity and investments are fundamental to changes in energy efficiency. Finally, the energy efficiency measures of the DEA models show significant correlations with the traditional energy efficiency measure, indicating that energy efficiency as measured through DEA could be complementary to measures of energy intensity when analysing other key elements of energy efficiency performance in the industrial sector.
Vorheriger Artikel Technical and cost assessment of energy efficiency improvement and greenhouse gas emission reduction potentials in Thai cement industry

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Fußnoten
1
DEA assumes that there are n DMUs to be evaluated. Each DMU consumes different amounts of i inputs and produces y different outputs—that is, DMU j consumes x ji amounts of input to produce y ir amounts of output (Charnes et al. 1994; Cooper et al. 2000).
 
2
The first development of non-parametric approach DEA was by Charnes, Cooper and Rhodes (CCR 1978) to measure the efficiency of individual DMUs.
 
3
Energy use efficiency (μ*)= ratio of minimum energy cost / actual energy cost
 
4
TFP growth measures how much productivity grows or declines over time. When there are more outputs relative to the quantity of given inputs, then TFP has grown or increased. TFP can grow when adopting innovations such as application of energy-efficient technologies (e.g. heat recovery, cogeneration, high efficiency boilers, etc.) which it calls TC. TFP can also grow when industry uses their existing technology and economic inputs more efficiently; they can produce more while using the same inputs (e.g. capital, energy, labour and technology) or more generally by increases in TE. TFP change from 1 year to the next is, therefore, comprised of technological change and changes in technical efficiency.
 
5
For more information on Malmquist index, see Fare et al. (1994), Coelli et al. (1997, 2005).
 
6
The undesirable output may be analysed as an input because it has the characteristic of an input (less of it is preferable), but it is difficult the interpretation of results (Schuschny (2007) used this method to analysis energy sector and CO2 emissions in Latin American and the Caribbean); another possibility is to assign a negative sign to undesirable outputs. However, this method also shows difficulties because many DEA models are not invariant with respect to adding different signs between inputs and outputs (Lowell and Pastor 1995), and another method may be using the reciprocals of undesirable output to incorporate the feature that less undesirable outputs are preferred and this method solves the difficulties of previous methods (Ramanathan 2006 and Zhou et al. 2008 used this approaches in their models to evaluate energy efficiency and emissions).
 
7
In the Colombian case, the economic variables were measured using exchange rates. In both countries, the economic variables in Euro were deflated by the wholesale price index reported by German statistical office.
 
8
In this study, NEISs at two aggregate levels are considered as DMUs. The manufacturing industrial sectors at different aggregation levels have been used as DMUs by other researchers in the field DEA. (See, for example, Ali and Lerme 1990; Cooper et al. 1995; Dinc and Haynes 1999; Hirschberg and Lloyd 2000; Mahadevan 2002; Azadeh et al. 2007; Mukherjee 2008a, b).
 
9
Including final energy consumption as soon as transformation input consumption of the energy sector and final non-energy consumption.
 
10
Energy consumption by type of energy source comes from each country’s respective statistic offices according to energy balances.
 
11
DEA analysis defines three types of frontiers: (1) the contemporaneous builds from only the cross-section data from a given period, (2) the sequential considers all current and past observations as feasible and (3) the inter-temporal uses observations from all the periods in the sample (Tulkens and Eeckaut 1995).
 
12
The assumption of technical progress or regress means that during sample period NEIS have could achieve significant improvements in technology or maintain or decrease their technology level.
 
13
German energy tax law defines the EISs as the sectors where the cost of energy is above 3% of total costs. Moreover, to confirm this criterion applied cluster analysis using as criteria the energy intensity, the share of energy cost and energy consume by every industrial sector at the two- and three-digit level with the aim to exclude energy intensive sectors of some non-energy intensive sectors that include at three-digit level both energy intensive sectors and non-energy intensive sectors.
 
14
Higher NEISs are defined as sectors where energy intensity is above 2 GJ/€ in Germany and 3 GJ/€ in Colombia and lower NEISs are sectors where energy intensity is below 2 GJ/€ in Germany and 3 GJ/€ in Colombia.
 
15
These results suggests that the energy efficiency performance in the industrial sector is dependent on economic factors and that energy intensity performance is more sensitive to economic and political changes in Colombian NEISs due to the fact that industrial output is so closely linked to economic growth and prosperity which concurs with results of Cotte (2007) in the Colombian case.
 
16
Note that in German case, there is no slack associated with this input in the optimal solution to model (1) and ε = θ and in both measurements the reduction potential of energy consumption is 17%.
 
17
Nwaokoro (2003) found evidence of substitutability among inputs where workers, materials and energy are substitutes according to cost minimisation and Metcalf (2008) found that higher energy prices contributes to declines in energy intensity, primarily through improvements in energy efficiency and where the substitution among inputs become a key factor.
 
18
For instance, Germany, to be a leader in energy efficiency technology, considered that it has responsibility to support the efforts to increase energy efficiency undertaken by emerging and developing countries in particular (Federal Ministry of Economics and Technology (BMWI) 2008).
 
19
This variable was calculated taking into account the categories established by German and Colombian statistic office based in number of workers and output levels for every industrial sector.
 
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Metadaten
Titel
Energy efficiency development in German and Colombian non-energy-intensive sectors: a non-parametric analysis
verfasst von
Clara Inés Pardo Martínez
Publikationsdatum
01.02.2011
Verlag
Springer Netherlands
Erschienen in
Energy Efficiency / Ausgabe 1/2011
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
DOI
https://doi.org/10.1007/s12053-010-9078-2

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