Skip to main content

Advertisement

SpringerLink
Log in

Economic growth and environmental degradation: a conditional nonparametric frontier analysis

  • Research Article
  • Published:
Environmental Economics and Policy Studies Aims and scope Submit manuscript

Abstract

This paper examines the effect of economic growth and the association of environmental degradation on economies’ technological change and technological catch-up. Using a conditional nonparametric frontier analysis to a sample of 73 economies over the time period 1980–2014, empirical evidence of the examined relationship is provided both under full and partial frontiers in the constant and variable returns to scale (VRS) models. Specifically, the newly proposed time-dependent conditional nonparametric frontier estimators have been applied. In our case the time-dependent conditional efficiency estimators allow us to model directly the effects of growth and time on economies’ estimated performance without requiring any specification of the production functional form and without assuming the separability condition between time, economic growth and the support of inputs and outputs. The overall results reveal that the efficiency results of full and partial frontiers tend to lead to the same results, except in the cases of full VRS models where energy use and carbon dioxide emissions are incorporated as an additional input and output, respectively. The results demonstrate that countries with a higher environmental efficiency are those that have signed the first agreement between nations (Kyoto Protocol) to mandate country-by-country reductions in greenhouse-gas emissions, while countries that have not signed are relatively inefficient. Ultimately, the empirical findings also suggest that the effect of economic growth is determined by economies’ development stage and geographical region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. As suggested by Mastromarco and Simar (2014) a simple production function with three basic macroeconomic variables can help us to minimize the known DEA problem of ‘dimensionality’.

  2. Previous PWT versions have been criticized for their variability and valuation problems (Johnson et al. 2013), but as Feenstra et al. (2013) emphasise the new version of PWT (v8.0) is more consistent over time and more transparent in its methods. The data can be downloaded from: http://www.ggdc.net/pwt.

  3. Following the metadata glossary of World Bank, CO2 emissions stem from fossil fuels burning as well as the manufacture of cement and include emissions produced during consumption of solid, liquid, and gas fuels and gas flaring (Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States).

  4. Following the metadata glossary of World Bank, energy use refers to primary energy use before transformation to other end-use fuels, equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport (IEA Statistics© OECD/IEA 2014—http://www.iea.org/stats/index.asp subject to https://www.iea.org/t&c/termsandconditions/).

  5. For the calculation of the full and partial frontiers (both for the conditional and the unconditional measures) several LP programs were carried out using STATA (Tauchmann 2012).

  6. Bădin et al. (2012) suggest that median values of \( \alpha \) enable us to investigate the effect of the environmental variables on the distribution of efficiencies (technological catch-up). However, if \( \alpha \to 1 \) (i.e. \( \alpha \ge 0.9 \)) then the shift of the frontiers can also be examined through the Order-α frontiers since \( \lambda_{\alpha } \left( {x,y} \right) \to \lambda \left( {x,y} \right) \).

  7. Due to the enormous quantity of the results obtained, the analytical (per country) efficiency estimates over the examined period cannot be presented. Therefore, in our analysis we present multiple groups of efficiencies in the principles of Henderson and Zelenyuk (2007). We have estimated the frontier using the whole sample size, and then we present the aggregates values of technical efficiencies for different country groups. Analytical results are available upon request.

  8. The Northern America region consists only of Canada and the USA. We decide to include in our analysis the USA since it acts as a natural benchmark for the rest of countries both for conditional and unconditional efficiency estimates.

  9. As mentioned, the results of subfigures 2c are attributed to the higher performance of USA rather the performance of Canada.

  10. Naïve bootstrap is inconsistent for individual DEA applications (Simar and Wilson, 2000) but Kneip et al. (2003) have shown that sub-sampling bootstrap is consistent when the sub-sample size is smaller compared to the initial sample size considered. In general, although time-consuming and computing demanding, we may first correct for the bias in DEA efficiency estimates using appropriate bootstrapping and then to use the bias-corrected estimates in the Li-test (Simar and Zelenyuk, 2004, p. 14). Moreover, Simar and Zelenyuk (2004) propose two alternative bootstraps. The first relies on bootstrapping the Li-statistic using the DEA estimates after trimming the values equal to unity (Algorithm I) while the second relies on relies on bootstrapping the Li-statistic using the DEA estimates where those equal to unity are “smoothed” away from the bound by the addition of a small noise (Algorithm II).

References

  • Ahmed EM (2012) Green TFP intensity impact on sustainable east asian productivity growth. Econ Anal Policy 42(1):67–78

    Article  Google Scholar 

  • Athukorala W, Wilson C (2017) Distributional impacts of irrigation-induced agricultural development in a semi-subsistence economy: new evidence. Environ Econ Policy Stud 19(1):59–75

    Article  Google Scholar 

  • Bădin L, Daraio C, Simar L (2012) How to measure the impact of environmental factors in a nonparametric production model? Eur J Oper Res 223:818–833

    Article  Google Scholar 

  • Bădin L, Daraio C, Simar L (2014) Explaining inefficiency in nonparametric production models: the state of the art. Ann Oper Res 214:5–30

    Article  Google Scholar 

  • Bampatsou C, Halkos G (2017) Energy and CO2 emissions as the determinants of countries’ productivity with different levels of economic development. Int J Glob Energy Issues 40(5):277–293. https://doi.org/10.1504/IJGEI.2017.086840

    Article  Google Scholar 

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092

    Article  Google Scholar 

  • Caves DW, Christensen LR, Diewert WE (1982) The Economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50(6):1393–1414

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Article  Google Scholar 

  • Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51(3):229–240

    Article  Google Scholar 

  • Coelli T, Rao D, O’Donnell C, Battese G (2005) An introduction to efficiency and productivity analysis. Springer, New York

    Google Scholar 

  • Daraio C, Simar L (2007) Advanced robust and nonparametric methods in efficiency analysis: methodology and applications. Springer, New York

    Google Scholar 

  • Dasgupta S, Laplante B, Wang H, Wheeler D (2002) Confronting the environmental Kuznets curve. J Econ Perspect 16(1):147–168

    Article  Google Scholar 

  • Dinda S (2004) A theoretical basis for the environmental Kuznets curve. Ecol Econ 49(4):431–455

    Article  Google Scholar 

  • Everett T, Ishwaran M, Ansaloni GP, Rubin A (2010) Economic growth and the environment, MPRA Paper 23585. University Library of Munich, Germany

    Google Scholar 

  • Färe R, Grosskopf S, Lindgren B, Roos P (1989) Productivity developments in swedish hospitals: a malmquist output index approach. Discussion paper no. 89-3. Southern Illinois University at Carbondale, Carbondale

    Google Scholar 

  • Färe R, Grosskopf S, Lovell CAK (1994) Production frontiers. Cambridge University Press, Cambridge

    Google Scholar 

  • Feenstra RC, Inklaar R, Timmer M (2013) The next generation of the Penn World Table. NBER working papers 19255. National Bureau of Economic Research, Inc

  • Fischer Manfred M, Scherngell Thomas, Reismann Martin (2009) Knowledge spillovers and total factor productivity: evidence using a spatial panel data model. Geograp Anal 41(2):204–220

    Article  Google Scholar 

  • Grosskopf S (1986) The role of the reference technology in measuring productive efficiency. Econ J 96:499–513

    Article  Google Scholar 

  • Grossman G, Krueger A (1995) Economic growth and the environment. Quart J Econ 110:353–377

    Article  Google Scholar 

  • Halkos G (2003) Environmental Kuznets curve for sulphur: evidence using GMM estimation and random coefficient panel data models. Environ Dev Econ 8(4):581–601

    Article  Google Scholar 

  • Halkos G (2013) Exploring the economy—environment relationship in the case of sulphur emissions. J Environ Plan Manag 56(2):159–177

    Article  Google Scholar 

  • Halkos G, Polemis M (2016) The impact of economic growth on environmental efficiency of the electricity sector: a hybrid window DEA methodology for the USA. J Environ Manag 211:334–346

    Article  Google Scholar 

  • Halkos GE, Polemis ML (2017) Does financial development affect environmental degradation? Evidence from the OECD countries. Bus Strategy Environ 26(8):1162–1180

    Article  Google Scholar 

  • Halkos G, Tsilika K (2014) Analyzing and visualizing the synergistic impact mechanisms of climate change related costs. Appl Math Comput 246:586–596

    Google Scholar 

  • Halkos G, Tsilika K (2017) Climate change effects and their interactions: an analysis aiming at policy implications. Econ Anal Policy 53:140–146

    Article  Google Scholar 

  • Halkos G, Tzeremes N (2009) Exploring the existence of Kuznets curve in countries’ environmental efficiency using DEA window analysis. Ecol Econ 68(7):2168–2176

    Article  Google Scholar 

  • Halkos G, Zisiadou A (2018) Examining the natural environmental hazards over the last century. Econ Disasters Clim Change. https://doi.org/10.1007/s41885-018-0037-2

    Google Scholar 

  • Henderson D, Zelenyuk V (2007) Testing for (efficiency) catching-up. South Econ J 73(4):1003–1019

    Google Scholar 

  • Johnson S, Larson W, Papageorgiou C, Subramanian A (2013) Is newer better? Penn World Table revisions and their impact on growth estimates. J Monetary Econ 60(2):255–274

    Article  Google Scholar 

  • Kar AK, Rahman S (2018) Changes in total factor productivity and efficiency of microfinance institutions in the developing world: a non-parametric approach. Econ Anal Policy 60:103–118. https://doi.org/10.1016/j.eap.2018.09.012

    Article  Google Scholar 

  • Kitcher B, McCarthy IP, Turner S, Ridgway K (2013) Understanding the effects of outsourcing: unpacking the total factor productivity variable. Prod Plan Control Manag Oper 24(4–5):308–317

    Article  Google Scholar 

  • Kneip A, Simar L, Wilson P (2003) Asymptotics for DEA estimators in nonparametric frontier models. Discussion Paper #0317, Institut de Statistique, Université Catholique de Louvain, Belgium

  • Kortelainen M (2008) Dynamic environmental performance analysis: a Malmquist index approach. Ecol Econ 64(4):701–715

    Article  Google Scholar 

  • Kumar S, Russell RR (2002) Technological change, technological catch-up, and capital deepening: relative contributions to growth convergence. Am Econ Rev 92:527–548

    Article  Google Scholar 

  • Kumbhakar S, Lovell C (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kuznets S (1965) Economic growth and structural change. Norton, New York

    Google Scholar 

  • Kuznets S (1966) Modern economic growth. Yale University Press, New Haven

    Google Scholar 

  • Li Q, Maasoumi E, Racine JS (2009) A Nonparametric test for equality of distributions with mixed categorical and continuous data. J Economet 148(2):186–200

    Article  Google Scholar 

  • Liu XZ, Heilig GK, Chen JM, Heino M (2007) Interactions between economic growth and environmental quality in Shenzhen, China’s first special economic zone. Ecol Econ 62:559–570

    Article  Google Scholar 

  • Long Xingle, Zhao Xicang, Cheng Faxin (2015) The comparison analysis of total factor productivity and eco-efficiency in China’s cement manufactures. Energy Policy 81:61–66

    Article  Google Scholar 

  • Lopez R (1994) The environment as a factor of production: the effects of economic growth and trade liberalization. J Environ Econ Manag 27:163–184

    Article  Google Scholar 

  • Mahlberg Bernhard, Luptacik Mikulas, Sahoo Biresh K (2011) Examining the drivers of total factor productivity change with an illustrative example of 14 EU countries. Ecol Econ 72:60–69

    Article  Google Scholar 

  • Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de Estadistica 4:209–242

    Article  Google Scholar 

  • Mastromarco C, Simar L (2014) Effect of FDI and time on catching up: new insights from a conditional nonparametric frontier analysis. J Appl Economet 30(5):826–847

    Article  Google Scholar 

  • Mia MA, Ben Soltane BI (2016) Productivity and its determinants in microfinance institutions (MFIs): evidence from South Asian countries. Econ Anal Policy 51:32–45

    Article  Google Scholar 

  • Shephard RW (1970) Theory of cost and production function. Princeton University Press, Princeton

    Google Scholar 

  • Simar L, Wilson P (2000) Statistical inference in nonparametric frontier models: the state of the art. J Prod Anal 13:49–78

    Article  Google Scholar 

  • Simar L, Zelenyuk V (2004) On testing equality of distributions of technical efficiency scores. MPRA Paper 28003. University Library of Munich, Germany

    Google Scholar 

  • Sugiawan Y, Managi S (2016) The environmental Kuznets curve in Indonesia: exploring the potential of renewable energy. Energy Policy 98(C):187–198

    Article  Google Scholar 

  • Tauchmann H (2012) Partial frontier efficiency analysis. Stata J 12(3):461–478

    Article  Google Scholar 

  • Tetsuya T, Managi S (2010) Decomposition of the environmental Kuznets curve: scale, technique, and composition effects. Environ Econ Policy Stud 11(1):19–36

    Google Scholar 

  • The World Bank, World Development Indicators (2017) Washington, DC. © World Bank. License: CC BY 3.0 IGO. https://openknowledge.worldbank.org/handle/10986/26447

  • Wang K, Wei Yi-Ming (2016) Sources of energy productivity change in China during 1997–2012: a decomposition analysis based on the Luenberger productivity indicator. Energy Econ 54:50–59

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for helpful and constructive comments that improved the quality of the paper. Any remaining errors are solely the authors’ responsibility.

Author information

Authors and Affiliations

  1. Laboratory of Operations Research, Department of Economics, University of Thessaly, Vólos, Greece

    George E. Halkos & Christina Bampatsou

  2. Faculty of Economic Sciences, Ionian University, Lefkada, Greece

    Christina Bampatsou

Authors
  1. George E. Halkos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christina Bampatsou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George E. Halkos.

Appendix

Appendix

See Table 3.

Table 3 Country name, code, development stage and major geographical area classification
Full size table

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Halkos, G.E., Bampatsou, C. Economic growth and environmental degradation: a conditional nonparametric frontier analysis. Environ Econ Policy Stud 21, 325–347 (2019). https://doi.org/10.1007/s10018-018-0232-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10018-018-0232-y

Keywords

JEL Classification

Search

Navigation