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Regional environmental performance and governance quality: a nonparametric analysis

Environmental Economics and Policy Studies volume 17, pages 621–644 (2015)Cite this article

Abstract

This paper applies nonparametric estimators to examine the effect of regional quality of government on the environmental performance in the NUTS 1 regions in France, Germany, and the UK. A novel measure on governance is used, gauging the partiality, corruption and effectiveness of government services in each region. By utilizing regional-level measures of three pollutants (CO2, CH4 and N2O), the effect of governance on environmental efficiency is analyzed. The empirical analysis suggests that there is a nonlinear relationship between regions’ governance quality levels and their environmental performance. It appears that the effect of regional quality of governance is positive up to a certain level, then turning slightly negative. This suggests that higher governance quality will not always result in increased environmental efficiency.

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Fig. 1
Fig. 2

Notes

  1. Since the functional form of regions’ environmental process and the effect of governance quality are not known, the analysis in a fully nonparametric framework will be suitable. According to Li and Racine (2007), nonparametric methods do not require any specification of the functional forms for objects being estimated letting the observed data to determine the resulting model.

  2. There are also some other methodological treatments of bad outputs in a DEA context. For instance, Seiford and Zhu (2002, 2005) uses a linear transformation of the pollutnat (bad output) and it treats it within DEA framework as a regular (good) output. Several other studies (Reinhard et al. 2000; Dyckhoff and Allen 2001; Hailu and Veeman 2001; Korhonen and Luptacik 2004; Mandal and Madheswaran 2010) are treating the pollutant as a regular input. Similarly, Kuosmanen and Kortelainen (2005) and Kortelainen (2008) use the notion of eco-efficiency having in their DEA fornulation only the pollutants as inputs.

  3. According to Bădin et al. (2010, p. 634) the separability condition states that the external/exogenous factors to the environmental production process have no influence on the attainable set, affecting only the probability of being more or less efficient. However, as has been highlighted by Daraio et al. (2010), the external/exogenous variables affect directly both the production possibilities sets and the shape of the inefficiencies’ distribution.

  4. When discussing corruption, we understand this concept according to its most commonly used definition, “the abuse of public power for private gain” (Treisman 2007).

  5. For more information regarding the European NUTS classifications see: http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction.

  6. Regional pollutant data are scarce and therefore our study is limited only for the year 2009 and for 36 NUTS level 1 regions for which the data are available.

  7. Available from: http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/regional_statistics/data/main_tables.

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

  9. Available from: http://prtr.ec.europa.eu/.

  10. For a more detailed description on the survey and the creation of the index, see Charron et al. (2014). Moreover, for extensive sensitivity tests between the regional measure of quality of government and national WGI scores, see also Charron (2010).

  11. For applications, advantages and disadvantages of DEA estimators see also the studies by Managi et al. (2004), Fukuyama et al. (2011), Nakano and Managi (2012) and Halkos and Tzeremes (2013c).

  12. In our case, the regional environmental performance follows the assumption of constant returns to scale (CRS). According to Picazo-Tadeo et al. (2012, p. 802) from an ecological perspective, economic activity is commonly characterized by constant returns to scale.

  13. This is the most common assumption made for directional distance functions when measuring environmental efficiency levels. As has been pointed out by Chen and Delmas (2012) under the chosen direction the proposed approach can lead to misspecified efficient frontier especially when there are some decision-making units producing high amounts of bad and good outputs. However, different directions can also be chosen in order for the researcher to test the environmental efficiency under different environmental policy scenarios (see among others Picazo-Tadeo et al. 2012).

  14. For the theoretical background and the asymptotic properties of nonparametric conditional efficiency measures, see Jeong et al. (2010).

  15. The calculation of bandwidth by Bădin et al. (2010) is based on the least-squares cross-validation (LSCV) criterion introduced by Hall et al. (2004) and Li and Racine (2007).

  16. According to Podinovski and Kuosmanen (2011), the conventional radial Farrell input and output efficiency measures can be obtained as special cases of the directional distance functions.

  17. As indicated by Bădin et al. (2012) and Daraio and Simar (2014) a positive effect indicates a shift of the estimated frontier.

  18. For the construction of the density plots, we have used the ‘normal reference rule-of-thumb’ approach for bandwidth selection (Silverman 1986) and a second-order Gaussian kernel.

  19. In addition, the dotted lines in Fig. 2 indicate the bootstrapped pointwise error bounds (Racine 2008).

  20. According to Daraio and Simar (2014), a positive effect indicates that conditional frontier moves up to the unconditional one when the external variable (i.e., EQI in our case) increases. However, when the effect is negative (indicated by a decreasing nonparametric regression line) the exact opposite phenomenon is observed suggesting that the external variable acts as an extra undesirable output.

  21. “…Corporatism refers to a system of interest representation in which a small number of strategic actors organized associations, represent large parts of the population in an encompassing fashion…” (Crepaz, 1995, pp. 391-392). “…The pluralist form of interest representation is characterised by a large number of atomistic interest groups which are in a competitive struggle over access to the legislative process, using 'pressure politics…” (Crepaz 1995, p. 392).

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Acknowledgments

We would like to thank Professor Shunsuke Managi and two anonymous reviewers for their helpful and constructive comments on earlier version of our manuscript. Any remaining errors are solely the authors’ responsibility.

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Authors and Affiliations

  1. Laboratory of Operations Research, Department of Economics, University of Thessaly, Korai 43, 38333, Volos, Greece

    George E. Halkos & Nickolaos G. Tzeremes

  2. Department of Political Science, The Quality of Government Institute, University of Gothenburg, Box 100, 405 30, Gothenburg, Sweden

    Aksel Sundström

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  1. George E. Halkos
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Correspondence to George E. Halkos.

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Halkos, G.E., Sundström, A. & Tzeremes, N.G. Regional environmental performance and governance quality: a nonparametric analysis. Environ Econ Policy Stud 17, 621–644 (2015). https://doi.org/10.1007/s10018-015-0106-5

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Keywords

  • Quality of governance
  • Environmental performance
  • Regions
  • Nonparametric analysis

JEL Classifications

  • C14
  • Q58
  • R11