IDEAS home Printed from https://ideas.repec.org/a/kap/enreec/v84y2023i4d10.1007_s10640-022-00744-4.html
   My bibliography  Save this article

A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty

Author

Listed:
  • N. Englezos

    (University of Piraeus)

  • X. Kartala

    (Athens University of Economics and Business)

  • P. Koundouri

    (Athens University of Economics and Business
    Technical University of Denmark)

  • M. Tsionas

    (Montpellier Business School
    Lancaster University)

  • A. Alamanos

    (Athens University of Economics and Business)

Abstract

The optimal management of scarce transboundary water resources among competitive users is expected to be challenged by the effects of climate change on water availability. The multiple economic and social implications, including conflicts between neighbouring countries, as well as competitive sectors within each country are difficult to estimate and predict, to inform policy-making. In this paper, this problem is approached as a stochastic multistage dynamic game: we develop and apply a novel framework for assessing and evaluating different international strategies regarding transboundary water resources use, under conditions of hydrological uncertainty. The Omo-Turkana transboundary basin in Africa is used as a case study application, since it increasingly faces the above challenges, including the international tension between Kenya and Ethiopia and each individual country’s multi-sectoral competition for water use. The mathematical framework combines a hydro-economic model (water balance, water costs and benefits), and an econometric model (production functions and water demand curves) which are tested under cooperative and non-cooperative conditions (Stackelberg “leader–follower” game). The results show the cross-country and cross-sectoral water use—economic trade-offs, the future water availability for every game case, the sector-specific production function estimations (including residential, agriculture, energy, mining, tourism sectors), with nonparametric treatment, allowing for technical inefficiency in production and autocorrelated Total Factor Productivity, providing thus a more realistic simulation. Cooperation between the two countries is the most beneficial case for future water availability and economic growth. The study presents a replicable, sophisticated modelling framework, for holistic transboundary water management.

Suggested Citation

  • N. Englezos & X. Kartala & P. Koundouri & M. Tsionas & A. Alamanos, 2023. "A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 975-1030, April.
    • Handle: RePEc:kap:enreec:v:84:y:2023:i:4:d:10.1007_s10640-022-00744-4
    DOI: 10.1007/s10640-022-00744-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10640-022-00744-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10640-022-00744-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. A. Alamanos & D. Latinopoulos & A. Loukas & N. Mylopoulos, 2020. "Comparing Two Hydro-Economic Approaches for Multi-Objective Agricultural Water Resources Planning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4511-4526, November.
    2. Peter Xue‐Kun Song, 2000. "Multivariate Dispersion Models Generated From Gaussian Copula," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(2), pages 305-320, June.
    3. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.
    4. Edward B. Barbier, 2004. "Water and Economic Growth," The Economic Record, The Economic Society of Australia, vol. 80(248), pages 1-16, March.
    5. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    6. Dinar, Ariel & Hogarth, Margaret, 2015. "Game Theory and Water Resources Critical Review of its Contributions, Progress and Remaining Challenges," Foundations and Trends(R) in Microeconomics, now publishers, vol. 11(1-2), pages 1-139, June.
    7. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    8. Adriano Vinca & Simon Parkinson & Keywan Riahi & Edward Byers & Afreen Siddiqi & Abubakr Muhammad & Ansir Ilyas & Nithiyanandam Yogeswaran & Barbara Willaarts & Piotr Magnuszewski & Muhammad Awais & A, 2021. "Transboundary cooperation a potential route to sustainable development in the Indus basin," Nature Sustainability, Nature, vol. 4(4), pages 331-339, April.
    9. Sungho Park & Sachin Gupta, 2012. "Handling Endogenous Regressors by Joint Estimation Using Copulas," Marketing Science, INFORMS, vol. 31(4), pages 567-586, July.
    10. Putnam, Robert D., 1988. "Diplomacy and domestic politics: the logic of two-level games," International Organization, Cambridge University Press, vol. 42(3), pages 427-460, July.
    11. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    12. Pitt, Michael K. & Silva, Ralph dos Santos & Giordani, Paolo & Kohn, Robert, 2012. "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter," Journal of Econometrics, Elsevier, vol. 171(2), pages 134-151.
    13. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(5), pages 933-956, October.
    14. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    15. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    16. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    17. Roberto Casarin & Jean-Michel Marin, 2007. "Online data processing: comparison of Bayesian regularized particle filters," Working Papers 0703, University of Brescia, Department of Economics.
    18. Erik Ansink & Arjan Ruijs, 2008. "Climate Change and the Stability of Water Allocation Agreements," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 41(2), pages 249-266, October.
    19. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    20. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    21. Lin, Ming & Chen, Rong & Mykland, Per, 2010. "On Generating Monte Carlo Samples of Continuous Diffusion Bridges," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 820-838.
    22. Tang, Chang & Irfan, Muhammad & Razzaq, Asif & Dagar, Vishal, 2022. "Natural resources and financial development: Role of business regulations in testing the resource-curse hypothesis in ASEAN countries," Resources Policy, Elsevier, vol. 76(C).
    23. Gareth O. Roberts & Jeffrey S. Rosenthal, 1998. "Optimal scaling of discrete approximations to Langevin diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 255-268.
    24. Thomas Bernauer & Tobias Böhmelt, 2020. "International conflict and cooperation over freshwater resources," Nature Sustainability, Nature, vol. 3(5), pages 350-356, May.
    25. Mike G. Tsionas, 2017. "“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 948-965, July.
    26. Dinar, Ariel & Blankespoor, Brian & Dinar, Shlomi & Kurukulasuriya, Pradeep, 2010. "Does precipitation and runoff variability affect treaty cooperation between states sharing international bilateral rivers?," Ecological Economics, Elsevier, vol. 69(12), pages 2568-2581, October.
    27. Tsionas, Mike G. & Mamatzakis, Emmanuel, 2019. "Further results on estimating inefficiency effects in stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1157-1164.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Phoebe Koundouri & Angelos Alamanos & Jeffrey D Sachs, 2024. "Innovating for Sustainability: The Global Climate Hub," DEOS Working Papers 2403, Athens University of Economics and Business.
    2. Phoebe Koundouri & Georgios I. Papayiannis & Athanasios Yannacopoulos, 2022. "Optimal Control Approaches to Sustainability under Uncertainty," DEOS Working Papers 2215, Athens University of Economics and Business.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Delis, Manthos D. & Tsionas, Mike G., 2018. "Measuring management practices," International Journal of Production Economics, Elsevier, vol. 199(C), pages 65-77.
    2. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Bayesian analysis of chaos: The joint return-volatility dynamical system," MPRA Paper 80632, University Library of Munich, Germany.
    3. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 95-107.
    4. Lien, Gudbrand & Kumbhakar, Subal C. & Alem, Habtamu, 2018. "Endogeneity, heterogeneity, and determinants of inefficiency in Norwegian crop-producing farms," International Journal of Production Economics, Elsevier, vol. 201(C), pages 53-61.
    5. Emmanuel Mamatzakis & Mike Tsionas, 2018. "A Bayesian dynamic model to test persistence in funds' performance," Working Paper series 18-23, Rimini Centre for Economic Analysis.
    6. Jingfang Zhang & Emir Malikov, 2023. "Detecting Learning by Exporting and from Exporters," Journal of Productivity Analysis, Springer, vol. 60(1), pages 1-19, August.
    7. Li, Mingyang & Jin, Man & Kumbhakar, Subal C., 2022. "Do subsidies increase firm productivity? Evidence from Chinese manufacturing enterprises," European Journal of Operational Research, Elsevier, vol. 303(1), pages 388-400.
    8. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    9. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2019. "A time-varying true individual effects model with endogenous regressors," Journal of Econometrics, Elsevier, vol. 211(2), pages 539-559.
    10. Michael L. Polemis & Mike G. Tsionas, 2022. "Endogenous productivity: a new Bayesian perspective," Annals of Operations Research, Springer, vol. 318(1), pages 425-451, November.
    11. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    12. Koppenberg, Maximilian & Hirsch, Stefan, 2020. "Comparing methods for markup estimation with an application to EU food retailing," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304272, Agricultural and Applied Economics Association.
    13. Peng, Jiaying & Xie, Rui & Ma, Chunbo & Fu, Yang, 2021. "Market-based environmental regulation and total factor productivity: Evidence from Chinese enterprises," Economic Modelling, Elsevier, vol. 95(C), pages 394-407.
    14. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
    15. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    16. Maximilian Koppenberg & Stefan Hirsch, 2022. "Markup estimation: A comparison of contemporary methods at the example of European food retailers," Agribusiness, John Wiley & Sons, Ltd., vol. 38(1), pages 108-133, January.
    17. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
    18. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    19. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    20. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:enreec:v:84:y:2023:i:4:d:10.1007_s10640-022-00744-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.