Abstract
It is common in efficiency studies which analyse the environment for pollution to form part of the production technology. Pollution therefore affects efficiency and the TFP growth decomposition. As an alternative approach we draw on theoretical studies from the environmental economics literature, which demonstrate that TFP affects environmental quality. Along these lines we adopt a two-stage empirical methodology. Firstly, we obtain two estimates of productive performance (efficiency and TFP growth) using a stochastic production frontier framework in Stage 1 for European countries (1995–2008), from which we omit emissions. Secondly, in Stage 2 these measures of productive performance are used as regressors in spatial models of per capita nitrogen and sulphur emissions for European countries. From our preferred Stage 2 spatial models we find that a country’s TFP growth must fall to reduce its per capita nitrogen and sulphur emissions. This is likely to be because nitrogen and sulphur emissions in the EU have been tightly regulated for a long period of time via air quality standards and consequently, substantial reductions in emissions from cleaner and more productive technology were achieved some time ago.
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Notes
The approach in Reinhard et al. (1999, 2000) and Glass et al. (2013a) involves estimating a standard input distance frontier where the negative externality is modelled as an input. Atkinson and Dorfman (2005), on the other hand, use an input distance frontier but instead of the negative externality being modelled as an input, the externality is allowed to shift the best practice frontier.
We can include both efficiency and TFP growth as regressors in models of per capita \(NO_{x}\) and \(SO_{x}\) emissions to capture different aspects of productive performance for two reasons. Firstly, in contrast to TFP growth, efficiency is a level variable. Secondly, it will become clear further in the paper that the efficiency change component of TFP growth is relatively small. In addition, although we are not aware of an empirical study which uses measures of productive performance from a fitted stochastic frontier model as independent variables in a second-stage model of emissions, this approach is common in the extensive literature on banking efficiency. For example, Wheelock and Wilson (2000) use cost inefficiency as an explanatory variable in a model of competing risks in US banking; Cipollini and Fiordelisi (2012) explain the financial distress of European banks using, among other things, profit efficiency; and cost efficiency is a regressor in a model of bank competitiveness in Casu and Giradone (2009).
Most emissions of a transboundary pollutant are internalised (i.e. emissions come to rest within the borders of the country which is responsible) as they fall to ground in their dry form within 300 km of the source. Sulphuric acid rain, on the other hand, is often externalised (i.e. it comes to rest outside of the borders of the country which is responsible) as it can have a long-range impact and may fall to ground up to 2,000 km from its source (Maddison 2007).
As pointed out by an anonymous referee, Anselin (2001) outlines some of the issues which arise when using spatial econometric techniques to model environmental quality. To illustrate, one issue which is often encountered is the spatial scale mismatch between economic data for administrative units and the measurement of environmental quality which may take the form of values for a regular grid of squares or pixels. This is not an issue in our empirical analysis because the economic data, emissions data and EMEP source-receptor tables all relate to individual European countries.
As a result, relatively little of the impact associated with \(NO_{x}\) and \(SO_{x}\) emissions is felt by countries such as Moldova and Latvia. To illustrate, gaseous sulphur dioxide emissions have been found to preceed small particulate matter which have been linked to premature mortality (Pope et al. 1995). Also, these particles impair visibility in urban areas and are thought to alter planetary reflectivity masking temporarily the effects of climate change (Stern and Kaufmann 2000).
Countries do not cooperate when each country only considers the national marginal damage of its emissions. Alternatively, countries cooperate when each country considers the marginal damage of its emissions across Europe.
See Figure 10.15 in Perman et al. (2003).
An \(SO_{2}\) scrubber system is the informal name for flue gas desulphurisation technology, which removes or ‘scrubs’ \(SO_{2}\) emissions from the exhaust of coal-fired power plants.
Although global spatial estimators such as that which we use in Stage \(2\) can be extended to unbalanced panel data, their asymptotic properties may become problematic if the reason why data are missing is not known (Elhorst 2009). Extending global spatial estimators from balanced to unbalanced panel data therefore involves making a strong assumption about why observations are missing. For example, Pfaffermayr (2013) assumes that data are missing at random for an unbalanced spatial panel.
We thank Joseph Pearlman for suggesting this approach to estimate real capital stock for the first year of the sample. Although this is not the usual approach to estimate real capital stock for the first year of a sample, it will become apparent that the capital elasticities in Stage 1 using this approach are sensible. The conventional approach to estimate real capital stock for the first year of a sample is to use fully depreciated real GDP but this would require several years of additional data, which was not available for all the countries in the sample.
As noted in Sect. 3.1.1 above, all the specifications of \(\mathbf {W}\) in Stage 1 are row-normalized so the spatial lags of the inputs and exogenous variable which shift the production frontier technology in Eq. 1 are weighted averages of observations for neighbouring units. The specification of \(\mathbf {W}\) in Stage 2 is also row-normalised. The spatial lag of the dependent variable in Stage 2 is therefore a weighted average of observations for the dependent variable for neighbouring units. As result, in Stages 1 and 2 spillovers are positively related to the relative (and not the absolute) measure of proximity used to configure \(\mathbf {W}\).
We control for the possibility of an EKC relationship but this is not a relationship which we focus on in this paper. This is because, firstly, the empirical focus of Stage 2 is the direct and indirect effects of \(TFPG\) and \(TE\) on \(NO_{x}/Pop\) and \(SO_{x}/Pop\). Secondly, the EKC literature is very well developed. For an up-to-date appraisal of the EKC literature see Carson (2010). Furthermore, we explored including \(\left( RGDP/Pop\right) ^{3}\) to capture the possibility of a further turning point but for reasons which are explained in the analysis of the results this variable was dropped.
The impact of trade on the environment is an issue which has received a lot of attention in recent years. We control for the effect of trade on the environment but we do not focus on this relationship in the analysis of the results because our interests lie elsewhere. For a recent survey of the literature on the trade-environment nexus see Frankel (2009).
We follow the spatial analysis of sulphur emissions in Europe by Ivanova (2011) and do not include dummy variables for international environmental agreements (IEAs). This is because a lot of the empirical evidence on the effects of IEAs suggests that they are symbolic, as they mandate reductions in pollution which would have been achieved in their absence (e.g. Murdoch and Sandler 1997; Murdoch et al. 1997). See Ivanova (2011) for a discussion of the empirical and game theoretic rationales for not including dummy variables relating to IEAs.
To further illustrate, the LR test statistics range from \(68.98\) \((\mathbf {W }_{4Near})-180.35\) \((\mathbf {W}_{7Near})\) for the eleven tests.
We experimented with a range of other efficiency estimators by allowing the non-spatial and spatial variables which shift the frontier technology to also affect the mean of the pre-truncated inefficiency distribution or affect the variance of the inefficiency distribution and/or the variance of the idiosyncratic disturbance. Despite a number of countries in the sample being at different stages of development and transition we obtained the most sensible set of efficiencies using the time varying decay estimator.
The fitted local spatial stochastic frontier models which are not reported are available from the corresponding author upon request.
It was noted in footnote 10 above that the assumption about the value of real capital stock in the first year of the sample yields reasonable estimates of the capital elasticities at the sample mean for the non-spatial and local spatial frontier models. This assumption about the initial value of real capital stock, however, is not the conventional approach to obtain a starting value for the stock and was made because of data availability issues.
When the FEs and REs are correlated with variables like this, the estimated parameters can be biased and inconsistent.
\(\frac{-10.00 }{1.21}=-8.26\) and \(\frac{-10.00}{0.74}=-13.51\), where 1.21 and 0.74 are the significant own \(TFPG\) and \(TE\) parameters, respectively, from model \(3\).
\(\frac{-10.00}{1.381}=-7.24\), where 1.381 is the significant total \(TFPG\) parameter from model 9. Then \(-7.24\times \frac{ 1.112}{1.381}=-5.83\) and \(-7.24+5.83=-1.41\), where 1.112 is the significant direct \(TFPG\) parameter from model 9.
References
Anselin L (2001) Spatial effects in econometric practice in environmental and resource economics. Am J Agric Econ 83:705–710
Atkinson SE, Dorfman JH (2005) Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution. J Econom 126:445–468
Badunenko O, Henderson DJ, Zelenyuk V (2008) Technological change and transition: relative contributions to worldwide growth during the 1990s. Oxf Bull Econ Stat 70:461–492
Baltagi BH, Griffin JM, Xiong W (2000) To pool or not to pool: Homogenous versus heterogenous estimators applied to cigarette demand. Rev Econ Stat 82:117–126
Baltagi BH, Levin D (1986) Estimating dynamic demand for cigarettes using panel data: the effects of bootlegging, taxation and advertising reconsidered. Rev Econ Stat 68:148–155
Battese GE, Coelli TJ (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. J Product Anal 3:153–169
Burnside C, Eichenbaum M, Rebelo S (1995) Capital utilization and returns to scale. In: Bernanke BS, Rotemberg JJ (eds) The NBER macroeconomics annual 1995, vol 10. MIT Press, Cambridge
Carson RT (2010) The environmental Kuznets curve: seeking empirical regularity and theoretical structure. Rev Environ Econ Policy 4:3–23
Casu B, Giradone C (2009) Testing the relationship between competition and efficiency in banking: a panel data analysis. Econ Lett 105:134–137
Caves DW, Christensen LR, Diewert WE (1982) The economic theory of index numbers and the measurement of input, output and productivity. Econometrica 50:1393–1414
Chimeli AB (2007) Growth and the environment: are we looking at the right data? Econ Lett 96:89–96
Chimeli AB, Braden JB (2005) Total factor productivity and the environmental Kuznets curve. J Environ Econ Manag 49:366–380
Cipollini A, Fiordelisi F (2012) Economic value, competition and financial distress in the European banking system. J Bank Financ 36:3101–3109
Coelli T, Estache A, Perelman S, Trujillo L (2003) A primer on efficiency measurement for utilities and transport regulators. World Bank Institute, Washington
Cole MA (2007) Corruption, income and the environment: an empirical analysis. Ecol Econ 62:637–647
Cornwell C, Schmidt P, Sickles RC (1990) Production frontiers with cross-sectional and time-series variation in efficiency levels. J Econom 46:185–200
Cuesta RA, Lovell CAK, Zofio JL (2009) Environmental efficiency measurement with translog distance functions: a parametric approach. Ecol Econ 68:2232–2242
Druska V, Horrace WC (2004) Generalized moments estimation for spatial panel data: Indonesian rice farming. Am J Agric Econ 86:185–198
Elhorst JP (2012) MATLAB software for spatial panels. Int Reg Sci Rev (Forthcoming)
Elhorst JP (2009) Spatial panel data models. In: Fischer MM, Getis A (eds) The handbook of applied spatial analysis. Springer, Berlin
Färe R, Grosskopf S, Lovell CAK, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71:90–98
Färe R, Grosskopf S, Tyteca D (1996) An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities. Ecol Econ 18:161–175
Fernández C, Koop G, Steel MFJ (2005) Alternative efficiency measures for multiple-output production. J Econom 126:411–444
Førsund F, Hjalmarsson L (2011) Renewable energy expansion and the value of balance regulation power. In: Johansen P-O, Kriström B (eds) Modern cost-benefit analysis of hydropower conflicts. Edward Elgar, Cheltenham
Førsund F, Hjalmarsson L (1988) Choice of technology and long-run technical change in energy-intensive industries. Energy J 9:79–97
Frankel J (2009) Environmental effects of international trade. John F. Kennedy School of Government, Harvard University, Research Working Paper 09–006
Glass AJ, Kenjegalieva K, Sickles RC (2014) Estimating efficiency spillovers with state level evidence for manufacturing in the US. Econ Lett 123:154–159
Glass AJ, Kenjegalieva K, Sickles RC (2013a) How efficiently do U.S. cities manage roadway congestion? J Product Anal 40:407–428
Glass AJ, Kenjegalieva K, Paez-Farrell J (2013b) Productivity growth decomposition using a spatial autoregressive frontier model. Econ Lett 119:291–295
Halkos G, Hutton J (1993) Acid Rain Games in Europe. Discussion Papers 93/12, Department of Economics, University of York.
Hernandez-Sancho F, Picazo-Tadeo A, Reig-Martinez E (2000) Efficiency and environmental regulation—an application to Spanish wooden goods and furnishings industry. Environ Resour Econ 15:365–378
Heston A, Summers R, Aten B (2011) Penn World Table Version 7.0. Center for International Comparisons of Production, Income and Prices, University of Pennsylvania
Ireland PN (2004) A method for taking models to data. J Econ Dyn Control 28:1205–1226
Ivanova K (2011) Corruption and air pollution in Europe. Oxf Econ Pap 63:49–70
Johnson S, Larson W, Papageorgiou C, Subramanian A (2013) Is newer better? Penn World Table revisions and their impact on growth estimates. J Monet Econ 60:255–274
Kelejian HH, Prucha IR (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. J Econom 157:53–67
Kelejian HH, Prucha IR (1999) A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev 40:509–533
Kelejian HH, Prucha IR (1998) A generalized spatial two stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. J Real Estate Financ Econ 17:99–121
Koop G, Tole L (2008) What is the environmental performance of firms overseas?: an empirical investigation of the global gold mining industry. J Product Anal 30:129–143
Lee L-F, Yu J (2010) Estimation of spatial autoregressive panel data models with fixed effects. J Econometrics 154(2):165–185
LeSage J, Pace RK (2009) Introduction to spatial econometrics. CRC Press, Boca Raton
Maddison D (2007) Modelling sulphur emissions in Europe: a spatial econometric approach. Oxf Econ Pap 59:726–743
Maddison D (2006) Environmental Kuznets curves: a spatial econometric approach. J Environ Econ Manage 51:218–230
Murdoch JC, Sandler T (1997) The voluntary provision of a pure public good: the case of reduced CFC emissions and the Montreal Protocol. J Pub Econ 63:331–349
Murdoch JC, Sandler T, Sargent K (1997) A tale of two collectives: sulphur versus nitrogen oxides emission reduction in Europe. Economica 64:281–301
Neyman J, Scott EL (1948) Consistent estimates based on partially consistent observations. Econometrica 16:1–32
Orea L (2002) Parametric decomposition of a generalized Malmquist productivity index. J Product Anal 18:5–22
Perman R, Ma Y, McGilvray J, Common M (2003) Natural resource and environmental economics. Pearson Addison Wesley, Edinburgh
Pfaffermayr M (2009) Conditional \(\beta\)- and \(\sigma\)-convergence in space: a maximum likelihood approach. Reg Sci Urban Econ 39:63–78
Pfaffermayr M (2013) The Cliff and Ord test for spatial correlation of the disturbances in unbalanced panel models. Int Reg Sci Rev 36:492–506
Pope CA III, Thun MJ, Namboodiri MM, Dockery DW, Evans JS, Speizer FE, Heath CW Jr (1995) Particulate air pollution as a predictor of mortality in a prospective study of US adults. Am J Respir Crit Care Med 151:669–674
Reinhard S, Lovell CAK, Thijssen GJ (2000) Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur J Operat Res 121:287–303
Reinhard S, Lovell CAK, Thijssen GJ (1999) Econometric estimation of technical and environmental efficiency: an application to Dutch dairy farms. Am J Agric Econ 81:44–60
Saal DS, Parker D, Weyman-Jones T (2007) Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh water and sewerage industry: 1985–2000. J Product Anal 28:127–139
Schmidt P, Sickles RC (1984) Production frontiers and panel data. J Bus Econ Stat 2:367–374
Smets F, Wouters R (2003) An estimated dynamic stochastic general equilibrium model of the euro area. J Eur Econ Assoc 1:1123–1175
Stern DI, Kaufmann RK (2000) Detecting a global warming signal in hemispheric temperature series: a structural time series analysis. Clim Change 47:411–438
Stevenson RE (1980) Likelihood functions for generalized stochastic frontier estimation. J Econom 13:57–66
Tyteca D (1997) Linear programming models for the measurement of environmental performance of firms—concepts and empirical results. J Product Anal 8:183–197
Weber WL, Domazlicky BR (2001) Productivity growth and pollution in state manufacturing. Rev Econ Stat 83:195–199
Wheelock DC, Wilson PW (2000) Why do banks disappear? The determinants of U.S. bank failures and acquisitions. Rev Econ Stat 82:127–138
Zaim O, Taskin F (2000) A Kuznets curve in environmental efficiency: an application on OECD countries. Environ Resour Econ 17:21–36
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Appendices
Appendix 1
Countries in the sample
Albania | Denmark | Latvia | Romania |
Armenia | Estonia | Lithuania | Russia |
Austria | Finland | Luxembourg | Slovakia |
Azerbaijan | France | Macedonia | Slovenia |
Belarus | Germany | Malta | Spain |
Belgium | Greece | Moldova | Sweden |
Bulgaria | Hungary | Netherlands | Switzerland |
Croatia | Iceland | Norway | Turkey |
Cyprus | Ireland | Poland | Ukraine |
Czech Republic | Italy | Portugal | UK |
Appendix 2
Values of the information criteria for the stochastic frontier models
Model | AIC | BIC |
|---|---|---|
Base non-spatial | −1,323.05 | −1,245.15 |
\(\mathbf {W}_{All}\) | −1,467.57 | −1,368.03 |
\(\mathbf {W}_{3Near}\) | −1,398.26 | −1,298.72 |
\(\mathbf {W}_{4Near}\) | −1,382.03 | −1,282.48 |
\(\mathbf {W}_{5Near}\) | −1,421.76 | −1,322.21 |
\(\mathbf {W}_{6Near}\) | −1,479.07 | −1,379.52 |
\(\mathbf {W}_{7Near}\) | −1,493.40 | −1,393.86 |
\(\mathbf {W}_{3Big}\) | −1,425.49 | −1,325.95 |
\(\mathbf {W}_{4Big}\) | −1,455.53 | −1,355.98 |
\(\mathbf {W}_{5Big}\) | −1,483.98 | −1,384.44 |
\(\mathbf {W}_{6Big}\) | −1,481.38 | −1,381.84 |
\(\mathbf {W}_{7Big}\) | −1,459.40 | −1,359.86 |
Appendix 3
Average TE scores and average TE ranks
Country | Model 1-\(\text {No Spatial} \text {Dependence}\) | Model 2-\(\mathbf {W}_{All}\) | Model 3-\(\mathbf {W}_{3Near}\) | Model 4-\(\mathbf {W}_{7Near}\) | Model 5-\(\mathbf {W}_{3Big}\) | Model 6-\(\mathbf {W}_{7Big}\) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Av \(TE\) | Av \(TE\) Rank | Av \(TE\) | Av \(TE\) Rank | Av \(TE\) | Av \(TE\) Rank | Av \(TE\) | Av \(TE\) Rank | Av \(TE\) | Av \(TE\) Rank | Av \(TE\) | Av \(TE\) Rank | |
Albania | 0.252 | 36 | 0.155 | 39 | 0.219 | 36 | 0.259 | 35 | 0.227 | 36 | 0.158 | 37 |
Armenia | 0.210 | 39 | 0.161 | 38 | 0.148 | 39 | 0.137 | 39 | 0.214 | 37 | 0.135 | 38 |
Austria | 0.820 | 12 | 0.805 | 7 | 0.723 | 9 | 0.789 | 12 | 0.857 | 9 | 0.691 | 9 |
Azerbaijan | 0.215 | 38 | 0.205 | 37 | 0.157 | 38 | 0.138 | 38 | 0.188 | 39 | 0.135 | 39 |
Belarus | 0.335 | 34 | 0.688 | 12 | 0.235 | 34 | 0.210 | 36 | 0.292 | 35 | 0.219 | 35 |
Belgium | 0.832 | 11 | 0.332 | 31 | 0.762 | 8 | 0.833 | 9 | 0.925 | 5 | 0.666 | 12 |
Bulgaria | 0.430 | 30 | 0.429 | 27 | 0.318 | 31 | 0.433 | 29 | 0.438 | 28 | 0.279 | 31 |
Croatia | 0.458 | 29 | 0.752 | 9 | 0.422 | 26 | 0.485 | 25 | 0.460 | 27 | 0.330 | 28 |
Cyprus | 0.627 | 19 | 0.454 | 26 | 0.535 | 21 | 0.570 | 21 | 0.504 | 23 | 0.473 | 20 |
Czech Republic | 0.604 | 21 | 0.495 | 21 | 0.574 | 19 | 0.607 | 18 | 0.605 | 18 | 0.452 | 23 |
Denmark | 0.847 | 9 | 0.478 | 23 | 0.716 | 11 | 0.857 | 8 | 0.836 | 10 | 0.545 | 18 |
Estonia | 0.485 | 27 | 0.240 | 34 | 0.402 | 27 | 0.439 | 28 | 0.416 | 29 | 0.284 | 30 |
Finland | 0.778 | 14 | 0.819 | 6 | 0.620 | 16 | 0.830 | 10 | 0.745 | 14 | 0.585 | 16 |
France | 0.898 | 4 | 0.603 | 16 | 0.952 | 4 | 0.937 | 7 | 0.885 | 6 | 0.890 | 3 |
Germany | 0.865 | 7 | 0.975 | 2 | 0.856 | 5 | 0.954 | 6 | 0.831 | 11 | 0.860 | 4 |
Greece | 0.749 | 15 | 0.948 | 3 | 0.465 | 24 | 0.505 | 24 | 0.724 | 15 | 0.588 | 15 |
Hungary | 0.611 | 20 | 0.634 | 13 | 0.580 | 18 | 0.650 | 17 | 0.579 | 19 | 0.437 | 24 |
Iceland | \(0.838\) | 10 | \(0.343\) | 30 | \(0.720\) | 10 | \(0.773\) | 13 | \(0.752\) | 13 | \(0.767\) | 7 |
Ireland | \(0.804\) | 13 | \(0.477\) | 24 | \(0.644\) | 15 | \(0.693\) | 15 | \(0.720\) | 16 | \(0.584\) | 17 |
Italy | \(0.857\) | 8 | \(0.752\) | 10 | \(0.688\) | 12 | \(0.805\) | 11 | \(0.882\) | 7 | \(0.825\) | 5 |
Latvia | \(0.461\) | 28 | \(0.579\) | 17 | \(0.366\) | 29 | \(0.419\) | 30 | \(0.410\) | 30 | \(0.252\) | 33 |
Lithuania | \(0.511\) | 24 | \(0.840\) | 5 | \(0.383\) | 28 | \(0.456\) | 27 | \(0.463\) | 26 | \(0.323\) | 29 |
Luxembourg | \(0.980\) | 2 | \(0.362\) | 29 | \(0.981\) | 1 | \(0.982\) | 1 | \(0.983\) | 1 | \(0.983\) | 1 |
Macedonia | \(0.386\) | 32 | \(0.984\) | 1 | \(0.251\) | 33 | \(0.345\) | 32 | \(0.355\) | 32 | \(0.221\) | 34 |
Malta | \(0.701\) | 17 | \(0.206\) | 36 | \(0.654\) | 14 | \(0.579\) | 20 | \(0.554\) | 22 | \(0.638\) | 13 |
Moldova | \(0.148\) | 40 | \(0.070\) | 40 | \(0.100\) | 40 | \(0.101\) | 40 | \(0.115\) | 40 | \(0.061\) | 40 |
Netherlands | \(0.984\) | 1 | \(0.210\) | 35 | \(0.966\) | 3 | \(0.977\) | 4 | \(0.981\) | 2 | \(0.799\) | 6 |
Norway | \(0.966\) | 3 | \(0.769\) | 8 | \(0.813\) | 6 | \(0.979\) | 3 | \(0.972\) | 4 | \(0.708\) | 8 |
Poland | \(0.486\) | 26 | \(0.876\) | 4 | \(0.488\) | 23 | \(0.563\) | 23 | \(0.483\) | 24 | \(0.478\) | 19 |
Portugal | \(0.501\) | 25 | \(0.606\) | 15 | \(0.436\) | 25 | \(0.478\) | 26 | \(0.480\) | 25 | \(0.398\) | 26 |
Romania | \(0.312\) | 35 | \(0.560\) | 18 | \(0.221\) | 35 | \(0.325\) | 33 | \(0.318\) | 34 | \(0.256\) | 32 |
Russian Fed | \(0.370\) | 33 | \(0.494\) | 22 | \(0.316\) | 32 | \(0.271\) | 34 | \(0.344\) | 33 | \(0.462\) | 21 |
Slovakia | \(0.554\) | 23 | \(0.300\) | 32 | \(0.541\) | 20 | \(0.587\) | 19 | \(0.565\) | 21 | \(0.382\) | 27 |
Slovenia | \(0.572\) | 22 | \(0.616\) | 14 | \(0.494\) | 22 | \(0.566\) | 22 | \(0.577\) | 20 | \(0.456\) | 22 |
Spain | \(0.740\) | 16 | \(0.454\) | 25 | \(0.681\) | 13 | \(0.685\) | 16 | \(0.706\) | 17 | \(0.678\) | 11 |
Sweden | \(0.865\) | 6 | \(0.416\) | 28 | \(0.767\) | 7 | \(0.962\) | 5 | \(0.865\) | 8 | \(0.685\) | 10 |
Switzerland | \(0.692\) | 18 | \(0.560\) | 19 | \(0.616\) | 17 | \(0.743\) | 14 | \(0.753\) | 12 | \(0.593\) | 14 |
Turkey | \(0.428\) | 31 | \(0.284\) | 33 | \(0.363\) | 30 | \(0.364\) | 31 | \(0.408\) | 31 | \(0.419\) | 25 |
Ukraine | 0.226 | 37 | 0.558 | 20 | \(0.182\) | 37 | \(0.163\) | 37 | \(0.195\) | 38 | \(0.181\) | 36 |
UK | 0.892 | 5 | 0.728 | 11 | 0.979 | 2 | 0.982 | 2 | 0.973 | 3 | 0.971 | 2 |
Sample Av \(TE\) | 0.607 | 0.530 | 0.533 | 0.576 | 0.580 | 0.484 | ||||||
Sample \(TE\) Std Dev | 0.244 | 0.246 | 0.247 | 0.266 | 0.252 | 0.238 | ||||||
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Adetutu, M., Glass, A.J., Kenjegalieva, K. et al. The effects of efficiency and TFP growth on pollution in Europe: a multistage spatial analysis. J Prod Anal 43, 307–326 (2015). https://doi.org/10.1007/s11123-014-0426-7
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DOI: https://doi.org/10.1007/s11123-014-0426-7