A Two-stage Approach for Energy Efficiency Analysis in European Union Countries.

AuthorMakridou, Georgia
  1. INTRODUCTION

    In the 1970s and early 1980s, energy efficiency emerged as a major issue for sustainable economic growth. Even after the 1986 counter oil shock and the decline in oil prices, environmental concerns continued to rise, especially in the context of the growing debates on global warming and climate change, which gave energy efficiency improvement a new perspective. The latter, along with the 1993 world energy crisis, and in combination with the sharp increase in oil prices during the 2000s, today have put energy efficiency on the policy agenda of many countries as a top priority issue.

    Governments are increasingly aware of the urgent need to make better use of energy resources. The benefits of more efficient energy use are well known, including reduced investments in energy infrastructure, lower fossil fuel dependency, increased competitiveness, and improved consumer welfare. Efficiency gains also deliver environmental benefits by reducing greenhouse gas emissions and air pollution. Therefore, it is not surprising that tracking economy-wide energy efficiency trends is being undertaken in many countries on a regular basis (Ang, Mu and Zhou, 2010).

    Energy efficiency has now been recognized as an essential component of sustainable development policies, which seek to achieve a well-balanced trade-off between economic growth and competitiveness, energy security, and environmental sustainability. As noted by Filippini and Hunt (2011), policy making in this area has adopted energy intensity (i.e., energy consumption to gross domestic product [GDP]) as the main indicator for evaluating energy efficiency. However, as Patterson (1996) noted, changes in energy intensity cannot be solely attributed to energy efficiency policies, as there are other important factors that affect energy intensity (e.g., the sector mix of the economy, the mix of the energy inputs, etc.). This is further confirmed by the empirical results presented by Filippini and Hunt (2011) for OECD countries, who also noted the importance of introducing alternative measures controlling for structural economic and energy-related factors. In a wider context, Ryan and Campbell (2012) emphasized the importance of going beyond the analysis of energy-related outcomes when evaluating energy efficiency policies. The framework proposed by the authors suggests the adoption of a broader socioeconomic perspective, which would enable policy makers to generate accurate impact assessments considering a comprehensive range of benefits and costs that result from energy efficiency programs.

    Adopting the context introduced in such studies, in this paper the evaluation of energy efficiency is based on a multidimensional context, considering a disaggregated view of energy consumption and economic outputs. Furthermore, following the framework proposed by Ryan and Campbell (2012), we also consider the introduction of an evaluation model that enables policy makers and analysts to consider the trade-offs between the different benefits of energy efficiency. The analysis is based on data collected for European Union countries over the period 2000-2010.

    On the methodological side, at the first stage, we use data envelopment analysis (DEA) to measure the relative efficiency of the countries. DEA is a popular, non-parametric efficiency analysis technique with many applications in energy management and environmental planning (see, among others, Boyd and Pang, 2000; Hu and Kao, 2007; Ramanathan, 2005; Zhou, Ang and Poh, 2008a). At the second stage, the DEA efficiency classifications are used as inputs to a MCDA approach, which is used to build an operational model that combines energy efficiency with economic and environmental indicators. Two-stage approaches are often employed in an explanatory setting to identify relationships between efficiency estimates and external factors using parametric regression methods (e.g., OLS, truncated or tobit regression), based mainly on linear models. Instead, in this study we follow a decision-making approach based on a non-parametric multicriteria additive model. The additive model retains the simplicity and transparency of linear models, but it provides the flexibility needed to consider possible nonlinear relationships between energy efficiency and a set of multiple factors that describe its drivers and benefits. The construction of the additive MCDA model is based on a non-parametric approach using linear programming, thus being in accordance with the non-parametric framework of DEA. The resulting multicriteria model complements and enhances the technical efficiency estimates of DEA through the introduction of a transparent composite indicator that enables the evaluation of all countries in a common setting. Thus, the proposed two-stage DEA/MCDA approach provides a framework that policy makers can use to construct a standardized and comprehensible composite energy efficiency and performance evaluation indicator, which can be easily used for benchmarking purposes, allowing the formulation of a complete ranking of all countries under consideration, as well as the monitoring of the performance of any country over time, without having to resort to relative efficiency analyses every time an evaluation is sought. The introduction of the multicriteria approach also enables policy makers to evaluate different types of benefits that result from energy efficiency programs, without restricting the analysis solely to an input/output energy-economic context.

    The remainder of this paper has the following structure: In Section 2, a literature review is presented, followed in Section 3 by the presentation of the main methodological tools used in the analysis. In Section 4, the data and variables used in the analysis are described, and in Section 5, the results are presented and described. Finally, in Section 6, the paper concludes, and future research directions are outlined.

  2. LITERATURE REVIEW

    Energy efficiency is a difficult concept to define. It is often confused with energy conservation, but conservation simply means using less energy, whereas efficiency implies meeting a given demand of energy required to provide products and services with a lower use of resources (Gunn, 1997). The directive on energy end-use efficiency and energy services of the European Council and the Parliament defines energy efficiency as "a ratio between an output of performance, service, goods or energy, and an input of energy" (European Union, 2006). An even trickier task than defining energy efficiency is measuring it. To measure energy efficiency changes over time at the economy-wide level, and to be able to make cross-country comparisons, a rich body of research has emerged. On one hand, various efficiency-related indicators have been developed, with the ratio of total national primary energy consumption to GDP (energy intensity) among the most popular ones. On the other hand, most researchers focus on developing methods to decompose accurately the aggregate energy intensity into the true change in intensities at the disaggregated sectorial levels, and to understand the effects of structural changes in the economy.

    Another line of research examines energy efficiency within a framework where energy is one of the many inputs of production, with the most widely used technique being DEA. A recent literature survey by Zhou, Ang and Poh (2008b) listed 100 studies published from 1983 to 2006 using DEA in energy and environmental analysis. According to the survey, 72 of these studies were published between 1999 and 2006, which shows a rapid increase in the number of studies using DEA. Zhou and Ang (2008) presented several DEA-type linear programming methods for measuring economy-wide energy efficiency performance using labor, capital stock, and energy consumption as inputs, and GDP as the desirable output. DEA has also been widely used in energy efficiency studies at the sector, sub-sector, and firm levels.

    Bampatsou and Hadjiconstantinou (2004) used DEA to develop an efficiency index, which combines economic activity, C[O.sub.2] emissions, and energy consumption of the production process in 31 European countries for 2004. The study also provides estimates for the capability of the countries to achieve sustainable economic development through the reduction of their reliance on fossil fuels. In a similar context, Ramanathan (2005) used DEA to analyze the performance of 17 countries in the Middle East and North Africa in terms of four indicators of energy consumption and C[O.sub.2] emissions for the period 1992-1996. The authors concluded that oil-rich countries show no indication of following carbon-friendly policies for their economic development.

    Lozano and Gutierrez (2008) applied a number of non-parametric, linear programming models for measuring energy efficiency in 21 OECD countries from 1990 to 2004, using the environmental DEA technology concept. Lanfang and Jingwan (2009) proposed a non-parametric method based on DEA to measure energy efficiency, taking into account undesirable factors such as water, gas, and solid wastes. In another study, Yu (2010) used a panel data set of 16 OECD countries to estimate the relationship between overall energy efficiency and the behavior of households regarding energy consumption. Ceylan and Gunay (2010) analyzed Turkey's economy-wide energy efficiency and its energy-saving potential with cross-country comparisons and benchmarking with EU countries, for the period 1995-2007, using a non-parametric frontier approach.

    Table 1 presents a brief overview of other studies that used DEA in measuring energy efficiency at the country level.

    Furthermore, DEA has gained popularity in environmental performance measurement. Fare, Grosskopf and Hernandez-Sancho (2004) provided a formal index number of environmental performance using DEA with three pollutants (C[O.sub.2], [SO.sub.X], and [NO.sub.X]) as undesirable outputs. The...

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