On the dynamics of eco-efficiency performance in the European Union

Highlights

• We evaluate the evolution of environmental performance in the context of the EU.

• We estimate specific efficiencies for CO₂e, SO₂, NO_x and an eco-efficiency indicator.

• We propose using a model of explicit distribution dynamics.

• Results indicate that the dynamics of the indicators are complex.

• Despite the recent progresses, the abatement opportunities are still remarkable.

Abstract

This paper evaluates the evolution of environmental performance in the context of the European Union (EU), over the period 1993–2010. The context is particularly relevant, due to the traditionally high concerns of the EU about these issues, which has triggered off several initiatives and regulations on environmental protection. In this setting, we conduct a two-stage analysis which develops environmental performance indicators in the first stage for each pair country-year, and evaluates its evolution in the second. More specifically, in the first stage we estimate specific efficiencies for three air-pollutants (CO₂e, SO₂, NO_x), along with an eco-efficiency indicator, for which we use the slack-free directional distance functions in the Data Envelopment Analysis framework (as opposed to the more extended intensity ratios), whereas in the second stage we propose to using a model of explicit distribution dynamics which takes into account how the entire distributions of these indicators evolve. Our results indicate that the dynamics underlying the evolution of the indicators analyzed are indeed remarkable. Although the eco-efficiency indicator has improved over the last two decades, it has been during the last decade when performance has shown a more convergent path. However, in the case of the more traditional indicators (CO₂e, SO₂, NO_x) the abatement opportunities are still remarkable, especially in the case of SO₂.

Introduction

There is compiled evidence about the relationship between global warming/climate change and the amount of greenhouse gases (GHG) released into the atmosphere [34]. Similar anthropogenic interactions have also been established between acid rain, acidification, eutrophication¹ or ground-level ozone and emissions of pollutants like sulphur dioxide (SO₂), nitrogen oxides (NO_x), non-methane volatile organic compounds (NMVOC) and ammonia (NH₃).² These problems are closely linked to the fact that pollution is mainly a by-product of the manufacturing activity, and as a result of this, there is a branch of studies suggesting that a correct long-term assessment of economic performance should also incorporate costs resulting from environmental degradation or benefits of environmental improvements [75].

The complexity of the interactions among global environmental changes and its influence on economic and social life has motivated a significant response to this challenge among the scientific community, with an increasing concern according to which, unless a principle of sustainability is included in productive processes, the long term growth of human welfare will be jeopardised by environmental destruction [78].³ More recently, we find a series of studies dealing with economic and ecological efficiency, more popularly known as eco-efficiency, with the aim of measuring the ability of firms, industries, regions or economies to produce more goods and services with less impact on the environment and less consumption of natural resources [37], [15], [27], [43].⁴

In the case of the European Union (EU), [2] have highlighted the importance of eco-efficiency in this specific context. In particular, more efficient city planning and transportation systems, greener products and services, new business models and, in general, more efficient use of resources (including energy and, raw materials, and water) would be benefitial for the society as a whole. Therefore, and ultimately, by promoting an internal and external market for eco-efficiency it might be easier to achieve the objectives of the ‘Europe 2020’ strategy, helping to drive a more sustainable and inclusive growth and, ultimately, increasing Europe׳s competitiveness.

In this context, the aim of this study is to jointly evaluate the economic and ecological performance of EU countries (EU27) during the 1993–2010 period. This study is carried out using a direct approach based on Data Envelopment Analysis (DEA) techniques, via a two-stage analysis. In the first stage, we compute an eco-efficiency score and three pollutant pressures-specific indicators, through the proposal by [51], but making use of the Directional Distance Function (DDF) instead of a radial approach. Throughout this analysis we obtain specific pollutant pressure indicators for the following pollutants: Carbon Dioxide equivalent (CO₂e), Nitrogen Oxides (NO_x) and Sulphur Oxides (SO_x)—the last one being responsible for the acidification of soil and the decrease in the richness of plant species. In the second stage, we study the dynamics of these specific pollutant pressure indicators and the eco-efficiency indicator using the model of explicit distribution dynamics initially devised by [55] for analysing their convergence (or divergence) patterns.

We attempt to make a twofold contribution to the eco-efficiency literature. Firstly, although DEA-DDF models have been applied in the pollutants literature [41], [74], [51], fewer have considered the presence of non-directional slacks on this particular model [30], and some theoretical contributions [29], [5] have pointed out that neglecting the existence of slacks leads to over-estimated efficiency indicators (see also [61]).

Secondly, several research initiatives have analysed convergence in emissions through econometric techniques using either indirect [44], [3], [70], [60] or direct approaches [17], [49]. In our case, we analyse the convergence and dynamics of these indicators using an explicit model of distribution dynamics which operates in three stages, namely, analysing the evolution of the external shapes of the distributions, examining if intra-distibution mobility (or churning) exists, and computing the stationary distribution of the efficiencies. This detailed analysis of how distributions evolve over time encodes meaningful information which is usually difficult to summarise considering other methodologies.

In the particular context of the European Union, a careful evaluation of eco-efficiency dynamics is particularly relevant. As indicated in [2], although the ‘Europe 2020’ strategy seeks to achieve smarter and more sustainable and inclusive growth, and that both European industries and citizens are more aware of their carbon and environmental footprints, this is happening “too slowly” [2, p.3]. However, the existing initiatives to measure how slow, or how fast this is actually taking place have been, up to now, limited. In the second stage of our analysis, by applying a model of explicit distribution dynamics to the indicators constructed in the first stage, we will try to shed some light on the speed to which the EU is achieving its climate and energy targets.

Our results show that there the underlying dynamics of the indicators of interest are too rich to be captured by few summary statistics, for all indicators considered. Although the general tendency is of a positive convergence, regardless of the aspect of the evaluation of dynamics considered (evolution of the external shape, intra-distribution mobility or ergodic distribution), there are remarkable differences among indicators. Specifically, the stationary state for each of the indicators considered varies greatly, since some of them show long-run tendencies which are not far from the current trends, whereas in other cases the improvements and general convergence will take place much faster. From an environmental policy point of view we consider this is a relevant result, since it would indicate that more emphasis should be placed on some specific indicators, for which disparities are persistent over time.

The paper is organised as follows. After this introduction, Section 2 is devoted to present a brief literature review on eco-efficiency indicators. Section 3 is devoted to the development of the model, methodology and construction of the indicators. Section 4 briefly explains the model of distribution dynamics. Section 5 describes the data and sources, followed by Section 6 which focuses on the results. Finally, Section 7 outlines some conclusions.