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Worldwide Evidences in the Relationships between Agriculture, Energy and Water Sectors

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  • Peri, Massimo
  • Vandone, Daniela
  • Baldi, Lucia

Abstract

Water, food and energy (WFE) are strongly interconnected: each depends on the other for a lot of concerns, spanning from guaranteeing access to services, to environmental, social and ethical impact issues, to price relations. The development, use, and waste generated by demand for these resources drive global changes and fears of resource scarcity. To date, a new approach to the concept of sustainable development is emerging and a joint analysis of these three areas is needed. “Demand for water, food and energy is expected to rise by 30-50% in the next two decades, while economic disparities incentivize short-term responses in production and consumption that undermine long-term sustainability. Shortages could cause social and political instability, geopolitical conflict and irreparable environmental damages. Any strategy that focuses on one part of the WFE relationships without considering its interconnections risks serious unintended consequences” (World Economic Forum, 2011).

Suggested Citation

  • Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Worldwide Evidences in the Relationships between Agriculture, Energy and Water Sectors," 2014 International European Forum, February 17-21, 2014, Innsbruck-Igls, Austria 199346, International European Forum on System Dynamics and Innovation in Food Networks.
    • Handle: RePEc:ags:iefi14:199346
    DOI: 10.22004/ag.econ.199346
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    References listed on IDEAS

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    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    3. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    4. Annastiina Silvennoinen & Timo Teräsvirta, 2009. "Multivariate GARCH Models," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 9, pages 201-229, Springer.
    5. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    6. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    7. Chen, Shiu-Sheng, 2009. "Predicting the bear stock market: Macroeconomic variables as leading indicators," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 211-223, February.
    8. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    9. Gulnur Muradog Lu & Kivilcim Metin & Reha Argac, 2001. "Is there a long run relationship between stock returns and monetary variables: evidence from an emerging market," Applied Financial Economics, Taylor & Francis Journals, vol. 11(6), pages 641-649.
    10. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    11. Bazilian, Morgan & Rogner, Holger & Howells, Mark & Hermann, Sebastian & Arent, Douglas & Gielen, Dolf & Steduto, Pasquale & Mueller, Alexander & Komor, Paul & Tol, Richard S.J. & Yumkella, Kandeh K., 2011. "Considering the energy, water and food nexus: Towards an integrated modelling approach," Energy Policy, Elsevier, vol. 39(12), pages 7896-7906.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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