Spatial environmental efficiency indicators in regional waste generation: a nonparametric approach

Abstract

This paper computes and analyses, for the first time, environmental efficiencies in waste generation of 116 European regions in NUTS 2 level in five European countries over the period of 2008–2010. For this reason, different data envelopment analysis (DEA) model formulations are used for modeling the pollutant in the form of waste generation as a regular output and as a regular input. In the latter case, we also use the notion of eco-efficiency. The empirical findings reveal environmental inefficiencies among the regions, indicating the lack of a uniform regional environmental policy among the European countries. This finding is observed not only for regions between different countries but also among the regions within countries, implying the need for implementation of unified appropriate municipal environmental policies in waste management.

Keywords:

• environmental efficiency
• waste generation
• European regions
• data envelopment analysis

JEL codes:

• C6
• O13
• O52
• Q50
• Q53
• Q56
• R11

Acknowledgements

We would like to thank the editor Dr Neil Powe and two anonymous reviewers for their useful and constructive comments. Any remaining errors are the authors’ responsibility.

Notes

1. According to the 1961 Brussels Conference on Regional Economies, NUTS 2 regional classification is the most common framework used by Member States to apply their regional policies and therefore is the most appropriate level for analysing regional environmental problems (EUROSTAT 2007).

2. Available from: http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/introduction.

3. Available from: http://stats.oecd.org/Index.aspx?DataSetCode=REG_LAB_TL3.

4. Since regional capital stock is not available from any regional database, it is calculated following the perpetual inventory method (Feldstein and Foot 1971; Verstraete 1976; Epstein and Denny 1980) as where represents the regional gross capital stock in current year, is the regional gross capital stock in the previous year that is the regional gross fixed capital formation and δ represents the depreciation rate of capital stock (it is set to 6%).

5. The analytical results under all DEA formulations are available upon request.

6. However, it must be mentioned that, as in any modeling technique, some disadvantages of the applied models can be mentioned, in relation to the treatment of bad output as input (Seiford and Zhu 2002; Färe and Grosskopf 2004) or the choice of translation vector W (Sahoo, Luptacik, and Mahlberg 2011).

7. The different results reported may also be attributed to a potential measurement bias which is related to the bad output used (municipality waste generation). As has been reported by EEA (2013, 11), countries define municipal waste differently. For instance, some countries include in their measurement bulky waste or garden waste; as a result, they will appear to have higher values of bad output compared to those countries which have excluded these types of waste in their measurement. In addition, some other countries report in their measurement only waste from households, whereas other countries include both wastes from households and the wastes derived from commercial activities and offices.

Additional information

Funding

This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: Thalis – Athens University of Economics and Business – Optimal Management of Dynamic Systems of the Economy and the Environment