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Measurement of Environmental Efficiency in the Countries of the European Union with the Enhanced Data Envelopment Analysis Method (DEA) During the Period 2005–2012 †

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Measurement of Environmental Efficiency in the Countries of the European Union with the Enhanced Data Envelopment Analysis Method (DEA) During the Period 2005–2012 † Proceedings Measurement of Environmental Efficiency in the Countries of the European Union with the Enhanced Data Envelopment Analysis Method (DEA) during the Period 2005–2012 † Manuel Jesús Hermoso-Orzáez 1,*, Miriam García-Alguacil 2, Julio Terrados-Cepeda 1 and Paulo Brito 3 1 Department of Engineering Graphics, Design and Projects, University of Jaén, 23071 Jaén, Spain; [email protected] 2 Faculty of Experimental Sciences, (Environmental Sciences), University of Jaén, 23071 Jaén, Spain; [email protected] 3 lPPortalegre. Campus Politécnico, 10|7300-555 Portalegre, Portugal; [email protected] * Correspondence: [email protected] † Presented at Presented at the 5th Ibero-American Congress on Entrepreneurship, Energy, Environment and Technology—CIEEMAT, Portalegre, Portugal, 11–13 September 2019. Published: 18 June 2020 Abstract: In recent years there has been growing interest in measuring the environmental efficiency of the different territories, countries and/or nations. This has led to the development of different methods applied to the evaluation of environmental efficiency such as the Data Envelopment Analysis (DEA) method. This method, supported by different studies, allows measuring relative environmental efficiency and is consolidated as a very reliable method to measure the effectiveness of environmental policies in a specific geographical area. The objective of our study is the calculation of the environmental efficiency of the 28 member countries of the European Union (EU) through the DEA method. We will collect the data regarding the last years in which there are reliable comparative data in all. We will study in reference to them, the results of the environmental policies applied in the different countries, in order to make comparisons between countries and classify them according to their environmental efficiency. Using this, two variants of calculation within the DEA method to compare in a contrasted way the results of environmental efficiency for the 28 countries of the European Union (EU) analyzed and propose possible solutions for improvement. Contributing in this work as main novelty the application of a new variant of the DEA Method, which we will call Improved Analysis Method (MAN) and that aims to agglutinate and assess more objectively, the results of the two DEA methods applied. The results show that there are 14 of the 28 countries that have a high relative environmental efficiency. However, we also find countries with very low environmental efficiency that should improve in the coming years. Coinciding precisely in this last group with countries recently admitted to the EU and where environmental policies have not yet been applied effectively and with positive results. Keywords: DEA method; environmental efficiency; sustainable development; undesirable outputs; CO2 emissions; economy 1. Introduction The progressive increase of the population, especially in large cities, the depletion of natural resources, the generation of waste and its impact, is having direct consequences on the pollution of Proceedings 2019, 38, 20; doi:10.3390/proceedings2019038020 www.mdpi.com/journal/proceedings Proceedings 2019, 38, 20 2 of 30 the natural environment, causing natural disasters, associated with climate change [1]. All this requires a change on a global scale defining sustainable environmental policies. The rational and sustainable use of natural and energy resources is key to solving this problem. The concept of sustainable development that implies “meeting the needs of the present generation but without compromising the needs of future generations”, now makes more sense than ever. [2]. The II Earth Summit of Rio de Janeiro in 1992, [3], the Kyoto Protocol for the reduction of greenhouse gas emissions [4] until reaching the Paris Agreement in 2015 where the main objective was defined as “strengthening the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty“ (UNFCCC, 2015) [5]. They have provoked an evolution of the concept of sustainable development oriented towards energy efficiency or eco- efficiency. Eco-efficiency is defined as an attempt to provide goods and services at a competitive price, satisfying human needs and quality of life, by progressively reducing the environmental impact and the intensity of the use of resources throughout the life cycle, up to level compatible with the estimated capacity that the Planet can support [6]. Likewise, the countries of the European Union have established as strategic objectives the “green growth” that implies the development of integrated policies that promote a sustainable environmental framework. (European Union, 2014), protect nature, favor sustainable development based on sustainable economic growth and the protection of the environment. In addition to the H2020 objective (20% reduction of greenhouse gas emissions, 20% of renewable energy, 20% of the improvement of environmental efficiency to achieve these objectives before 2020) [7]. Measuring eco-efficiency is not easy and depends on many factors. However, environmental efficiency data can be obtained using the DEA (Data Envelopment Analysis) method [8]. The DEA method [9], is a method used to calculate the relative efficiency of different units that are called DMUs (decision making units, “decision making units”). For this, they are based on the existence of certain input variables (inputs) that give rise to certain output variables (outputs). Both inputs and outputs are common in the different decision-making units and are assigned certain weights. Charnes et al. in 1978 they proposed a method to calculate technical efficiency by solving a problem of nonlinear programming. Considering that we have n DMUs denoted as j = (1, 2, …, n) and for each DMU we have m inputs xij (i = 1, 2, …, m) and s outputs yrj (r = 1, 2, …, s), this problem of linear programming can be expressed in the following way [10]: e0 = máx ∑ /∑ s.t. ∑ − ∑ ≤ 0 (1) , ≥ Being the inputs, the outputs, j the set of decision-making units, r the number of outputs, i the number of inputs and , and the weights assigned to the outputs and to the inputs, respectively, and is a parameter to force the variables to be positive. This method is called CCR by the first letter of the names of its designers [9] and is also known as the CRS (constant retuns to scale) method [10]. However, this problem of non-linear programming can be translated into linear programming in the following way [10]: e0 = max ∑ s.t. ∑ = 1 (2) ∑ − ∑ ≤ 0 , ≥ where = t y = t, being t = (∑ ) . After the development of the CRS model, others emerged as: Proceedings 2019, 38, 20 3 of 30 • RSV (variable returns to scale) [13]. • the additive method [12]. • measures based on slacks-based measures [14]. • the Russell measure (The Russel measure) [15–17]. • and other non-radial models, such as RAM (range adjusted measure) [18]. The DEA method has numerous applications for calculating efficiency and since its development has been widely used, becoming more important in recent years. This method can be used in different fields (such as engineering, industry, economics) [19]. It can also be used to calculate environmental efficiency [20,21]. In the DEA-based method for the calculation of environmental efficiency, the output variables or outputs are to be divided into desirable outputs (which we want to be high, for example, income per capita) and undesirable outputs (which suits us that are as low as possible to achieve greater efficiency, for example, contaminants). The objective of our study is to calculate the environmental efficiency of the 28 member countries of the European Union through the DEA method during the years in which reliable comparative results have been obtained in all the countries analyzed. To be able to study progress in environmental efficiency in different countries and to make comparisons between countries and classify them according to their environmental efficiency. Likewise, different solutions or alternatives will be proposed to improve the situation in the different countries based on the results obtained. We will also use two calculation options within the DEA method to compare the results obtained. Contributing in this work as a novelty an Improved Analysis Method (MAN) that aims to use the results of the two previous methods to improve the final classification in a more objective way. 2. Methodology In our case study, we used the 28 countries of the European Union as decision-making units. The data of the inputs and outputs to calculate environmental efficiency were taken from 2005 to 2012 because there were no more recent published data from some of the countries, which did not allow us to do a closer study. The selected inputs have been the production of electricity from coal, petroleum and nuclear origin (all these data expressed in percentages of the total electricity produced in each country), the annual industrial production index (expressed as a percentage) calculated as the average of the twelve indices of monthly industrial production and the volume of vehicles (understood as the number of vehicles that work with fossil fuels in use in each one of the countries). These inputs are shown in Table 1. In terms of outputs are divided into desirable outputs that are those that will contribute to that country has greater efficiency and the undesirable outputs that are what detract from efficiency in that country. As desirable outputs, GDP and per
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