Environmental Modeling & Assessment

, Volume 24, Issue 5, pp 533–546 | Cite as

Investigating the Interdependence Between Non-Hydroelectric Renewable Energy, Agricultural Value Added, and Arable Land Use in Argentina

  • Mehdi Ben JebliEmail author
  • Slim Ben Youssef


We examine the dynamic relationships between per capita carbon dioxide emissions, real gross domestic product (GDP), non-hydroelectric renewable energy (NHRE) consumption, agricultural value added (AVA), and agricultural land (AGRL) use for the case of Argentina over the period 1980–2013 by employing the autoregressive distributed lag bound approach to cointegration and Granger causality tests. The Fisher statistics of the Wald test are examined, and the existence of a long-run cointegration between variables is proved. There are long-run bidirectional causalities between all considered variables. The short-run Granger causality suggests bidirectional causality between AVA and agricultural land use, unidirectional causalities running from AGRL to NHRE and from NHRE to AVA. Long-run elasticity estimates suggest that increasing AGRL reduces carbon emissions; increasing AVA increases GDP and reduces pollution, AGRL, and NHRE; and increasing NHRE reduces AVA and AGRL. Thus, it seems that agriculture and renewable energy are substitute activities and compete for land use. We recommend that Argentina should continue to encourage agricultural production. The substitutability between agricultural and non-hydroelectric renewable energy productions, and their competition for agricultural land use, should be at least reduced or even stopped by encouraging research and development in second-generation (or even in third-generation) biofuel production and in new technologies for renewable energy and for agriculture more efficient in land use.


Autoregressive distributed lag Granger causality Non-hydroelectric renewable energy Agricultural value added Agricultural land Argentina 

JEL Classifications

C32 O54 Q15  Q42  Q54 


  1. 1.
    Al-Mulali, U., Solarin, S. A., & Ozturk, I. (2016a). Biofuel energy consumption-economic growth relationship: an empirical investigation of Brazil. Biofuels, Bioproducts and Biorefining, 10, 753–775.CrossRefGoogle Scholar
  2. 2.
    Al-Mulali, U., Solarin, S. A., Sheau-Ting, L., & Ozturk, I. (2016b). Does moving towards renewable energy causes water and land inefficiency? An empirical investigation. Energy Policy, 93, 303–314.CrossRefGoogle Scholar
  3. 3.
    Apergis, N., & Payne, J. E. (2010a). Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy Policy, 38, 656–660.CrossRefGoogle Scholar
  4. 4.
    Apergis, N., & Payne, J. E. (2010b). Renewable energy consumption and growth in Eurasia. Energy Economics, 32, 1392–1397.CrossRefGoogle Scholar
  5. 5.
    Apergis, N., & Payne, J. E. (2011). The renewable energy consumption–growth nexus in Central America. Applied Energy, 88, 343–347.CrossRefGoogle Scholar
  6. 6.
    Apergis, N., Payne, J. E., Menyah, K., & Wolde-Rufael, Y. (2010). On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecological Economics, 69, 2255–2260.CrossRefGoogle Scholar
  7. 7.
    Ben Jebli, M. (2016). On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: The case of Tunisia. Environmental Science and Pollution Research, 22, 16699–16715.CrossRefGoogle Scholar
  8. 8.
    Ben Jebli, M., & Ben Youssef, S. (2015). The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renewable and Sustainable Energy Reviews, 47, 173–185.CrossRefGoogle Scholar
  9. 9.
    Ben Jebli, M., & Ben Youssef, S. (2016). Combustible renewables and waste consumption, agriculture, CO2 emissions and economic growth in Brazil. MPRA Paper No. 69694. Accessed at:
  10. 10.
    Ben Jebli, M., & Ben Youssef, S. (2017a). Renewable energy consumption and agriculture: evidence for cointegration and Granger causality for Tunisian economy. International Journal of Sustainable Development & World Ecology, 24, 149–158.CrossRefGoogle Scholar
  11. 11.
    Ben Jebli, M., & Ben Youssef, S. (2017b). The role of renewable energy and agriculture in reducing CO2 emissions: evidence for North Africa countries. Ecological Indicators, 74, 295–301.CrossRefGoogle Scholar
  12. 12.
    Ben Jebli, M., & Ben Youssef, S. (2017c). Renewable energy, arable land, agriculture, CO2 emissions, and economic growth in Morocco. MPRA Paper No. 76798. Accessed at:
  13. 13.
    Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relations over time. Journal of the Royal Statistical Society, Series B, 37, 149–163.Google Scholar
  14. 14.
    Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431.Google Scholar
  15. 15.
    Diogo, V., Hilst, F. V. D., Eijck, J. V., Verstegen, J. A., Hilbert, J., Carballo, S., Volante, J., & Faaij, A. (2014). Combining empirical and theory-based land-use modelling approaches to assess economic potential of biofuel production avoiding iLUC: Argentina as a case study. Renewable and Sustainable Energy Reviews, 34, 208–224.CrossRefGoogle Scholar
  16. 16.
    Di Sbroiavacca, N., Nadal, G., Lallana, F., Falzon, J., & Calvin, K. (2016). Emissions reduction scenarios in the Argentinean energy sector. Energy Economics, 56, 552–563.CrossRefGoogle Scholar
  17. 17.
    Dogan, E. (2016). Analyzing the linkage between renewable and non-renewable energy consumption and economic growth by considering structural break in time-series data. Renewable Energy, 99, 1126–1136.CrossRefGoogle Scholar
  18. 18.
    Dogan, E., Sebri, M., & Turkekul, B. (2016). Exploring the relationship between agricultural electricity consumption and output: new evidence from Turkish regional data. Energy Policy, 95, 370–377.CrossRefGoogle Scholar
  19. 19.
    Energy Information Administration, 2017. International Energy Outlook. Accessed at:
  20. 20.
    Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica, 55, 251–276.CrossRefGoogle Scholar
  21. 21.
    International Renewable Energy Agency, 2015. Renewable Energy Policy Brief: Argentina. Accessed at:
  22. 22.
    Karkacier, O., Goktolga, Z. G., & Cicek, A. (2006). A regression analysis of the effect of energy use in agriculture. Energy Policy, 34, 3796–3800.CrossRefGoogle Scholar
  23. 23.
    Menyah, K., & Wolde-Rufael, Y. (2010). CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy, 38, 2911–2915.CrossRefGoogle Scholar
  24. 24.
    Mushtaq, K., Abbas, F., & Ghafour, A. (2007). Energy use for economic growth: cointegration and causality analysis from the agriculture sector of Pakistan. The Pakistan Development Review, 46, 1065–1073.CrossRefGoogle Scholar
  25. 25.
    Navia, T., Sewell, A., & Avila, J. (2016). Argentina launches innovative renewables program. Accessed at:
  26. 26.
    Omri, A., Daly, S., & Nguyen, D. K. (2015). A robust analysis of the relationship between renewable energy consumption and its main drivers. Applied Economics, 47, 2913–2923.CrossRefGoogle Scholar
  27. 27.
    Pao, H. T., & Fu, H. C. (2013a). Renewable energy, non-renewable energy and economic growth in Brazil. Renewable and Sustainable Energy Reviews, 25, 381–392.CrossRefGoogle Scholar
  28. 28.
    Pao, H. T., & Fu, H. C. (2013b). The causal relationship between energy resources and economic growth in Brazil. Energy Policy, 61, 793–801.CrossRefGoogle Scholar
  29. 29.
    Pesaran, M. H., & Pesaran, B. (1997). Working with Microfit 4.0: interactive econometric analysis. Oxford: Oxford University Press.Google Scholar
  30. 30.
    Pesaran, M. H., & Smith, R. P. (1998). Structural analysis of cointegratingVARs. Journal of Economic Survey, 12, 471–505.CrossRefGoogle Scholar
  31. 31.
    Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326.CrossRefGoogle Scholar
  32. 32.
    Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regressions. Biometrika, 75, 335–346.CrossRefGoogle Scholar
  33. 33.
    Qureshi, M. I., Awan, U., Arshad, Z., Rasli, A. M., Zaman, K., & Khan, F. (2016). Dynamic linkages among energy consumption, air pollution, green house gas emissions and agricultural production in Pakistan: sustainable agriculture key to policy success. Natural Hazards, 84, 367–381.CrossRefGoogle Scholar
  34. 34.
    Rafiq, S., Salim, R., & Apergis, N. (2016). Agriculture, trade openness and emissions: an empirical analysis and policy options. Australian Journal of Agricultural and Resource Economics, 60, 348–365.CrossRefGoogle Scholar
  35. 35.
    REN21, 2012. Renewables 2012 Global Status Report. Accessed at:
  36. 36.
    Sebri, M., & Abid, M. (2012). Energy use for economic growth: a trivariate analysis from Tunisian agriculture sector. Energy Policy, 48, 711–716.CrossRefGoogle Scholar
  37. 37.
    Shahbaz, M., Islam, F., & Butt, M. S. (2016). Finance–growth–energy nexus and the role of agriculture and modern sectors: evidence from ARDL bounds test approach to cointegration in Pakistan. Global Business Review, 17, 1037–1059.CrossRefGoogle Scholar
  38. 38.
    Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies. Energy Policy, 37, 4021–4028.CrossRefGoogle Scholar
  39. 39.
    Tan, K. T., Lee, K. T., & Mohamed, A. R. (2008). Role of energy policy in renewable energy accomplishment: the case of second-generation bioethanol. Energy Policy, 36, 3360–3365.CrossRefGoogle Scholar
  40. 40.
    Tang, C. F., & Shahbaz, M. (2013). Sectoral analysis of the causal relationship between electricity consumption and real output in Pakistan. Energy Policy, 60, 885–891.CrossRefGoogle Scholar
  41. 41.
    Tugcu, C. T., Ozturk, I., & Aslan, A. (2012). Renewable and non-renewable energy consumption and economic growth relationship revisited: evidence from G7 countries. Energy Economics, 34, 1942–1950.CrossRefGoogle Scholar
  42. 42.
    Turkekul, B., & Unakitan, G. (2011). A co-integration analysis of the price and income elasticities of energy demand in Turkish agriculture. Energy Policy, 39, 2416–2423.CrossRefGoogle Scholar
  43. 43.
    Viglizzo, E. F., & Frank, F. C. (2014). Energy use in agriculture: Argentina compared with other countries (Chapter 4). In S. Reiter (Ed.), Energy consumption: impacts of human activity, current and future challenges, environmental and socio-economic effects (pp. 77–98). N York: NOVA Science Publishers, Inc.Google Scholar
  44. 44.
    World Bank, 2017. World Development Indicators. Accessed at:
  45. 45.
    World Bank; CIAT; CATIE, 2014. Supplementary material to climate-smart agriculture in Argentina. CSA country profiles for Latin America series. Washington D.C.: The World Bank Group.Google Scholar
  46. 46.
    Zivot, E., & Andrews, D. (1992). Further evidence of great crash, the oil price shock and the unit root hypothesis. Journal of Business and Economic Statistics, 10, 251–270.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Jendouba, FSJEG de JendoubaJendoubaTunisia
  2. 2.Univ. Manouba, ESCT, QUARG UR17ES26, Campus Universitaire ManoubaManoubaTunisia

Personalised recommendations