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Measuring Environmental and Economic Efficiency in Italy: an Application of the Malmquist-DEA and Grey Forecasting Model

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  • OA Carboni
  • P. Russu

Abstract

Economic and environmental efficiency has being receiving growing attention among researchers. In general terms, this concept is related to the capability of the economic systems to employ natural resources efficiently, so as to increase economic and human wealth. This clearly implies that both the economic and ecological aspects of decisions ought to be considered. Bearing this in mind, this paper considers economic and ecological performance together, by applying data envelopment analysis (DEA) and the Malmquist productivity index (MPI) to investigating the efficiency of the 20 Italian regions from 2004 to 2011. The results reveal that the northern regions have been more efficient than the southern ones, highlighting the strong geographical differences between the two. Furthemore this paper uses the Grey System Theory to forecast regional economic and environmental efficiency. The results of the forecasting analysis show that the North-south duality remains strong and will possibly increase since the regions in the south get worse in term of environmental and economic efficiency.

Suggested Citation

  • OA Carboni & P. Russu, 2014. "Measuring Environmental and Economic Efficiency in Italy: an Application of the Malmquist-DEA and Grey Forecasting Model," Working Paper CRENoS 201401, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  • Handle: RePEc:cns:cnscwp:201401
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    1. Andrés J. Picazo-Tadeo & José A. Gómez-Limón & Ernest Reig-Martínez, 2010. "Assessing farming eco-efficiency: A Data Envelopment Analysis approach," Working Papers 1004, Department of Applied Economics II, Universidad de Valencia.
    2. Hashimoto, Akihiro & Ishikawa, Hitoshi, 1993. "Using DEA to evaluate the state of society as measured by multiple social indicators," Socio-Economic Planning Sciences, Elsevier, vol. 27(4), pages 257-268, December.
    3. Halkos, George Emm. & Tzeremes, Nickolaos G., 2009. "Exploring the existence of Kuznets curve in countries' environmental efficiency using DEA window analysis," Ecological Economics, Elsevier, vol. 68(7), pages 2168-2176, May.
    4. Dinda, Soumyananda & Coondoo, Dipankor & Pal, Manoranjan, 2000. "Air quality and economic growth: an empirical study," Ecological Economics, Elsevier, vol. 34(3), pages 409-423, September.
    5. Mika Kortelainen & Timo Kuosmanen, 2007. "Eco-efficiency analysis of consumer durables using absolute shadow prices," Journal of Productivity Analysis, Springer, vol. 28(1), pages 57-69, October.
    6. Noelia Somarriba & Bernardo Pena, 2009. "Synthetic Indicators of Quality of Life in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 94(1), pages 115-133, October.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    9. Akihiro Hashimoto & Migaku Kodama, 1997. "Has Livability of Japan Gotten Better for 1956–1990?: a Dea Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 40(3), pages 359-373, May.
    10. Zaim, Osman, 2004. "Measuring environmental performance of state manufacturing through changes in pollution intensities: a DEA framework," Ecological Economics, Elsevier, vol. 48(1), pages 37-47, January.
    11. Pilar Murias & Fidel Martinez & Carlos Miguel, 2006. "An Economic Wellbeing Index for the Spanish Provinces: A Data Envelopment Analysis Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 77(3), pages 395-417, July.
    12. Timo Kuosmanen, 2005. "Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1077-1082.
    13. Timo Kuosmanen & Mika Kortelainen, 2005. "Measuring Eco‐efficiency of Production with Data Envelopment Analysis," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 59-72, October.
    14. Marshall, Elizabeth & Shortle, James, 2005. "Using DEA and VEA to Evaluate Quality of Life in the Mid-Atlantic States," Agricultural and Resource Economics Review, Cambridge University Press, vol. 34(2), pages 185-203, October.
    15. Timo Kuosmanen & Victor Podinovski, 2008. "Weak Disposability in Nonparametric Production Analysis: Reply to Färe and Grosskopf," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(2), pages 539-545.
    16. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    17. D K Despotis, 2005. "A reassessment of the human development index via data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 969-980, August.
    18. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    19. Carlo Milana & Leopoldo Nascia & Alessandro Zeli, 2013. "Decomposing multifactor productivity in Italy from 1998 to 2004: evidence from large firms and SMEs using DEA," Journal of Productivity Analysis, Springer, vol. 40(1), pages 99-109, August.
    20. Cullinane Kevin & Song Dong-Wook & Ji Ping & Wang Teng-Fei, 2004. "An Application of DEA Windows Analysis to Container Port Production Efficiency," Review of Network Economics, De Gruyter, vol. 3(2), pages 1-23, June.
    21. Fare, R. & Grosskopf, S. & Pasurka, C., 1986. "Effects on relative efficiency in electric power generation due to environmental controls," Resources and Energy, Elsevier, vol. 8(2), pages 167-184, June.
    22. Maria Balaguer-Coll & Diego Prior & Emili Tortosa-Ausina, 2013. "Output complexity, environmental conditions, and the efficiency of municipalities," Journal of Productivity Analysis, Springer, vol. 39(3), pages 303-324, June.
    23. Zhang, Bing & Bi, Jun & Fan, Ziying & Yuan, Zengwei & Ge, Junjie, 2008. "Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach," Ecological Economics, Elsevier, vol. 68(1-2), pages 306-316, December.
    24. Wursthorn, Sibylle & Poganietz, Witold-Roger & Schebek, Liselotte, 2011. "Economic-environmental monitoring indicators for European countries: A disaggregated sector-based approach for monitoring eco-efficiency," Ecological Economics, Elsevier, vol. 70(3), pages 487-496, January.
    25. Osman Zaim & Fatma Taskin, 2000. "A Kuznets Curve in Environmental Efficiency: An Application on OECD Countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 17(1), pages 21-36, September.
    26. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    27. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    28. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    29. Daniel Tyteca, 1997. "Linear Programming Models for the Measurement of Environmental Performance of Firms—Concepts and Empirical Results," Journal of Productivity Analysis, Springer, vol. 8(2), pages 183-197, May.
    30. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    31. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
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    Cited by:

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    More about this item

    Keywords

    panel data; Malmquist productivity index (MPI); Grey system theory; forecasting; Data envelopment analysis (DEA);
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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