Energy Efficiency Management across EU Countries: A DEA Approach

Abstract

We examine energy efficiency in the European Union (EU) using an integrated model that connects labor and capital as production factors with energy consumption to produce GDP with a limited amount of environmental emissions. The model is a linear output-oriented BCC data envelopment analysis (DEA) that employs variables with non-negative values to calculate efficiency scores for a sample of 28 EU member states in the period 2010–2018. We assume variable returns to scale (VRS) considering the natural inclination of countries to adopt technologies that allow them to produce higher outputs over extended periods of time, which we observed through the trends of increasing labor productivity and decreasing energy intensity over the analyzed period. The average EU inefficiency margin in the sample period is 16.0%, with old member states being significantly more efficient (4.2%) than new member states (29.5%). Energy efficiency management does not improve over time, especially in new member states that had substantially worse efficiency by 2018 than in 2010. New member states could increase energy efficiency through the liberalization of the energy market, the support of energy-saving and technologically advanced industries, and the introduction of measures aimed at increasing the productivity levels in the economy.

The Effect of Marshallian and Jacobian Knowledge Spillovers on Jobs in the Solar, Wind and Energy Efficiency Sector

“…In order to evaluate the effect of technological change in the green energy sector on efficiency indicator, a twofold procedure is employed: (i) the technical efficiency is estimated through data envelopment analysis (DEA) [85]; (ii) the tobit model [86] allows to identify the knowledge spillovers' impact on efficiency score. In particular, the DEA is a method commonly used in the energy field [87][88][89][90][91].…”

“…As DEA efficiency scores are continuous on the interval [0, 1], in the second step, the tobit model [86] is used to estimate the linear relationship between the efficiency score as the dependent variable, and the Marshallian and Jacobian spillovers stemmed from R&D investments in the energy and wind field. Stata 16 software has been used for the empirical results.…”

Energy efficiency decision-making in non-energy intensive industries: content and social network analysis

“…Among the theoretical studies (Finnerty et al, 2017;Palm & Thollander, 2010;Thollander & Palm, 2015;Bhanot et al, 2017;Cooremans, 2011Cooremans, , 2012Henriques & Catarino, 2016;Reddy, 2013;Trianni et al, 2017;Cagno et al, 2018;Neri et al, 2018;Rotzek et al, 2018;Mawson & Hughes, 2019;Çoban et al, 2020;Simeonovski et al, 2021), there are publications focusing on strategic character. The topic indicators are treated theoretically in twelve publications, practices are the object of theoretical study in eight publications.…”

“…The other studies address the theme of indicators and measurements of performance in energy efficiency as complementary elements for the development of models to assist in the evaluation and the decision-making using different approaches, such as linear programming (Karlsson, 2011), dynamics modeling systems (Adane et al, 2014), multicriteria decision (Boehner, 2015;Li et al, 2020), DEA (Kim et al, 2015;Simeonovski et al, 2021), computational simulation (Cassettari et al, 2017;Ionescu & Darie, 2020), mathematical model (Sarkar et al, 2019) and fuzzy logic (Dong & Huo, 2017;Çoban et al, 2020).…”

“…Despite being comprehensive, this approach presents a linear process, without considering the possible interrelationships between internal and external variables. Other studies have used techniques such as Monte Carlo (Cassettari et al, 2017;Knobloch & Mercure, 2016), simulation of discrete events (Cassettari et al, 2017;Sáenz et al, 2012;Horschig & Thrän, 2017), optimization (Karlsson, 2011;Sáenz et al, 2012;Hasan & Trianni, 2020;Sarkar et al, 2019;Ionescu & Darie, 2020;Simeonovski et al, 2021;Roemer & Strassburger, 2019) and system dynamic modeling (Adane et al, 2014;Zeng et al, 2015;Horschig & Thrän, 2017;Martins et al, 2020).…”