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Eco-Efficiency and Eco-Productivity change over time in a multisectoral economic system

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  • Mikuláš Luptáèik

    () (University of Economics in Bratislava, Faculty of National Economy, Department of Economic Policy)

  • Bernhard Mahlberg

Abstract

We measure eco-efficiency of an economy by means of an augmented Leontief input-output model extended by constraints for primary inputs. Using a multi-objective optimization model the eco-efficiency frontier of the economy is generated. The results of these multi-objective optimization problems define eco-efficient virtual decision making units (DMUs). The eco-efficiency is obtained as a solution of a data envelopment analysis (DEA) model with virtual DMUs defining the potential and a DMU describing the actual performance of the economy. In this paper the procedure is extended to an intertemporal approach in the spirit of the Luenberger productivity indicator. This indicator permits decomposing eco-productivity change into eco-efficiency change and eco-technical change. The indicator is then further decompounded in a way that enables us to examine the contributions of individual production factors, undesirable as well as desirable outputs to eco-productivity change over time. For illustration purposes the proposed model is applied to investigate eco-productivity growth of the Austrian economy.

Suggested Citation

  • Mikuláš Luptáèik & Bernhard Mahlberg, 2013. "Eco-Efficiency and Eco-Productivity change over time in a multisectoral economic system," Department of Economic Policy Working Paper Series 004, Department of Economic Policy, Faculty of National Economy, University of Economics in Bratislava.
  • Handle: RePEc:brt:depwps:004
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    2. Picazo-Tadeo, Andrés J. & Castillo-Giménez, Juana & Beltrán-Esteve, Mercedes, 2014. "An intertemporal approach to measuring environmental performance with directional distance functions: Greenhouse gas emissions in the European Union," Ecological Economics, Elsevier, vol. 100(C), pages 173-182.
    3. Juan Aparicio & Magdalena Kapelko & Bernhard Mahlberg & Jose L. Sainz-Pardo, 2017. "Measuring input-specific productivity change based on the principle of least action," Journal of Productivity Analysis, Springer, vol. 47(1), pages 17-31, February.
    4. Wang, Ke & Wang, Jiayu & Wei, Yi-Ming & Zhang, Chi, 2018. "A novel dataset of emission abatement sector extended input-output table for environmental policy analysis," Applied Energy, Elsevier, vol. 231(C), pages 1259-1267.
    5. Magdalena Kapelko & Alfons Oude Lansink & Spiro E. Stefanou, 2017. "Input-Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 579-599, June.
    6. Wang, Ke & Wei, Yi-Ming, 2016. "Sources of energy productivity change in China during 1997–2012: A decomposition analysis based on the Luenberger productivity indicator," Energy Economics, Elsevier, vol. 54(C), pages 50-59.
    7. Beltrán-Esteve, Mercedes & Picazo-Tadeo, Andrés J., 2015. "Assessing environmental performance trends in the transport industry: Eco-innovation or catching-up?," Energy Economics, Elsevier, vol. 51(C), pages 570-580.
    8. Beltrán-Esteve, Mercedes & Picazo-Tadeo, Andrés J., 2017. "Assessing environmental performance in the European Union: Eco-innovation versus catching-up," Energy Policy, Elsevier, vol. 104(C), pages 240-252.
    9. Ke Wang & Yujiao Xian & Yi-Ming Wei & Zhimin Huang, 2016. "Sources of carbon productivity change: A decomposition and disaggregation analysis based on global Luenberger productivity indicator and endogenous directional distance function," CEEP-BIT Working Papers 91, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    10. Duman, Yavuz Selman & Kasman, Adnan, 2018. "Environmental technical efficiency in EU member and candidate countries: A parametric hyperbolic distance function approach," Energy, Elsevier, vol. 147(C), pages 297-307.
    11. C. Oliveira & D. Coelho & C. H. Antunes, 2016. "Coupling input–output analysis with multiobjective linear programming models for the study of economy–energy–environment–social (E3S) trade-offs: a review," Annals of Operations Research, Springer, vol. 247(2), pages 471-502, December.
    12. Xiaoping Qiu & Yiping Fang & Xueting Yang & Fubiao Zhu, 2017. "Tourism Eco-Efficiency Measurement, Characteristics, and Its Influence Factors in China," Sustainability, MDPI, Open Access Journal, vol. 9(9), pages 1-19, September.
    13. Zhang, Zibin & Ye, Jianliang, 2015. "Decomposition of environmental total factor productivity growth using hyperbolic distance functions: A panel data analysis for China," Energy Economics, Elsevier, vol. 47(C), pages 87-97.
    14. Sueyoshi, Toshiyuki & Li, Aijun & Liu, Xiaohong, 2019. "Exploring sources of China's CO2 emission: Decomposition analysis under different technology changes," European Journal of Operational Research, Elsevier, vol. 279(3), pages 984-995.
    15. Tenente, Marcos & Henriques, Carla & da Silva, Patrícia Pereira, 2020. "Eco-efficiency assessment of the electricity sector: Evidence from 28 European Union countries," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 293-314.
    16. Tianqun Xu & Ping Gao & Qian Yu & Debin Fang, 2017. "An Improved Eco-Efficiency Analysis Framework Based on Slacks-Based Measure Method," Sustainability, MDPI, Open Access Journal, vol. 9(6), pages 1-21, June.
    17. Mikulas Luptacik & Bernhard Mahlberg, 2018. "Revisiting the Efficiency-Equity Trade-off: A Muli-objective Linear Problem combined with an extended Leontief Input Output Model," Department of Economic Policy Working Paper Series 016, Department of Economic Policy, Faculty of National Economy, University of Economics in Bratislava.
    18. Yang, Fuxia & Yang, Mian, 2015. "Analysis on China's eco-innovations: Regulation context, intertemporal change and regional differences," European Journal of Operational Research, Elsevier, vol. 247(3), pages 1003-1012.
    19. Gurgul, Henryk & Lach, Łukasz, 2019. "Eco-efficiency analysis in generalized IO models: Methods and examples," MPRA Paper 96604, University Library of Munich, Germany.
    20. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    21. Yu, Yantuan & Huang, Jianhuan & Zhang, Ning, 2019. "Modeling the eco-efficiency of Chinese prefecture-level cities with regional heterogeneities: A comparative perspective," Ecological Modelling, Elsevier, vol. 402(C), pages 1-17.

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    More about this item

    Keywords

    Data Envelopment Analysis; Luenberger Indicator; Multi-Objective Optimization; Neoclassical Growth Accounting;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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