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The effects of efficiency and TFP growth on pollution in Europe: a multistage spatial analysis


  • Morakinyo Adetutu


  • Anthony Glass


  • Karligash Kenjegalieva


  • Robin Sickles



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. Copyright Springer Science+Business Media New York 2015

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  • Morakinyo Adetutu & Anthony Glass & Karligash Kenjegalieva & Robin Sickles, 2015. "The effects of efficiency and TFP growth on pollution in Europe: a multistage spatial analysis," Journal of Productivity Analysis, Springer, vol. 43(3), pages 307-326, June.
  • Handle: RePEc:kap:jproda:v:43:y:2015:i:3:p:307-326
    DOI: 10.1007/s11123-014-0426-7

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    Cited by:

    1. repec:spr:nathaz:v:91:y:2018:i:2:d:10.1007_s11069-017-3137-3 is not listed on IDEAS
    2. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    3. repec:eee:ecolet:v:163:y:2018:i:c:p:155-157 is not listed on IDEAS
    4. Han, Jaepil & Ryu, Deockhyun & Sickles, Robin, 2015. "How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model," Working Papers 15-015, Rice University, Department of Economics.
    5. Vidoli, Francesco & Cardillo, Concetta & Fusco, Elisa & Canello, Jacopo, 2016. "Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry," Regional Science and Urban Economics, Elsevier, vol. 61(C), pages 153-164.
    6. Dong Tian & Fengtao Zhao & Weisong Mu & Radoslava Kanianska & Jianying Feng, 2016. "Environmental Efficiency of Chinese Open-Field Grape Production: An Evaluation Using Data Envelopment Analysis and Spatial Autocorrelation," Sustainability, MDPI, Open Access Journal, vol. 8(12), pages 1-13, November.

    More about this item


    TFP growth; Atmospheric pollution; Spatial econometrics; Technical efficiency; C23; D24; Q53;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling


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