IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0240460.html
   My bibliography  Save this article

Decision frameworks for restoration & adaptation investment–Applying lessons from asset-intensive industries to the Great Barrier Reef

Author

Listed:
  • Mayuran Sivapalan
  • Jerome Bowen

Abstract

Asset-intensive industries (including water and power utilities, mineral resources and energy) are those which require significant levels of capital investment in their assets in order to operate. These industries face challenges from uncertainty in resource availability and demand for end products, the intricate and complicated nature of their assets, and the complexity of the economic, ecological and social settings in which they operate. In these industries, the application of decision frameworks that account for this uncertainty and complexity in guiding asset investment and development is standard practice. Lessons from asset-intensive industries were applied during the concept feasibility phase of the Reef Restoration and Adaptation Program (RRAP) to establish the investment case for research and development into interventions to help the Great Barrier Reef (GBR) resist, adapt to, and recover from the impacts of climate change. The authors worked with RRAP partners to define a decision framework that included structured decision-making processes (SDM), a cost-benefit analysis (CBA), and a value of information (VoI) analysis, to establish the investment case for intervening on the GBR which led to success in securing Australian Government commitment for the next phase of the Program. With climate change expected to drive increased demand for significant levels of restoration and adaptation investment in large integrated social, ecological and economic assets (such as the GBR), the lessons from RRAP offer insights for the application of decision frameworks to inform public and private investment priorities.

Suggested Citation

  • Mayuran Sivapalan & Jerome Bowen, 2020. "Decision frameworks for restoration & adaptation investment–Applying lessons from asset-intensive industries to the Great Barrier Reef," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-20, November.
  • Handle: RePEc:plo:pone00:0240460
    DOI: 10.1371/journal.pone.0240460
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240460
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240460&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0240460?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
    ---><---

    References listed on IDEAS

    as
    1. Edward Wilson, 2015. "A Practical Guide to Value of Information Analysis," PharmacoEconomics, Springer, vol. 33(2), pages 105-121, February.
    2. Ralph L. Keeney, 1988. "Structuring Objectives for Problems of Public Interest," Operations Research, INFORMS, vol. 36(3), pages 396-405, June.
    3. Ronald A. Howard, 1988. "Decision Analysis: Practice and Promise," Management Science, INFORMS, vol. 34(6), pages 679-695, June.
    4. Gabrielle Wong-Parodi & Tamar Krishnamurti & Alex Davis & Daniel Schwartz & Baruch Fischhoff, 2016. "A decision science approach for integrating social science in climate and energy solutions," Nature Climate Change, Nature, vol. 6(6), pages 563-569, June.
    5. Tilahun, Surafel Luleseged, 2019. "Feasibility reduction approach for hierarchical decision making with multiple objectives," Operations Research Perspectives, Elsevier, vol. 6(C).
    6. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    7. Marttunen, Mika & Belton, Valerie & Lienert, Judit, 2018. "Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis," European Journal of Operational Research, Elsevier, vol. 265(1), pages 178-194.
    8. Jeffrey M. Keisler & Zachary A. Collier & Eric Chu & Nina Sinatra & Igor Linkov, 2014. "Value of information analysis: the state of application," Environment Systems and Decisions, Springer, vol. 34(1), pages 3-23, March.
    Full references (including those not matched with items on IDEAS)

    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. Zou, Guang & Faber, Michael Havbro & González, Arturo & Banisoleiman, Kian, 2021. "Computing the value of information from periodic testing in holistic decision making under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    2. Newbold, Stephen C. & Johnston, Robert J., 2020. "Valuing non-market valuation studies using meta-analysis: A demonstration using estimates of willingness-to-pay for water quality improvements," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    3. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    4. Andrija S Grustam & Nasuh Buyukkaramikli & Ron Koymans & Hubertus J M Vrijhoef & Johan L Severens, 2019. "Value of information analysis in telehealth for chronic heart failure management," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
    5. Tamba, Yvonne & Wafula, Joshua & Whitney, Cory & Luedeling, Eike & Yigzaw, Negusse & Negussie, Aklilu & Muchiri, Caroline & Gebru, Yemane & Shepherd, Keith & Aynekulu, Ermias, 2021. "Stochastic simulation of restoration outcomes for a dry afromontane forest landscape in northern Ethiopia," Forest Policy and Economics, Elsevier, vol. 125(C).
    6. James Love-Koh & Susan Griffin & Edward Kataika & Paul Revill & Sibusiso Sibandze & Simon Walker & Jessica Ochalek & Mark Sculpher & Matthias Arnold, 2019. "Economic analysis for health benefits package design," Working Papers 165cherp, Centre for Health Economics, University of York.
    7. Ian Wadsworth & Lisa V. Hampson & Thomas Jaki & Graeme J. Sills & Anthony G. Marson & Richard Appleton, 2020. "A quantitative framework to inform extrapolation decisions in children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 515-534, February.
    8. Nasuh C. Buyukkaramikli & Peter Wigfield & Men Thi Hoang, 2021. "A MEA is a MEA is a MEA? Sequential decision making and the impact of different managed entry agreements at the manufacturer and payer level, using a case study for an oncology drug in England," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(1), pages 51-73, February.
    9. Hans Olav Vogt Myklebust & Jo Eidsvik & Iver Bakken Sperstad & Debarun Bhattacharjya, 2020. "Value of Information Analysis for Complex Simulator Models: Application to Wind Farm Maintenance," Decision Analysis, INFORMS, vol. 17(2), pages 134-153, June.
    10. Alexis Laurent & Bo P. Weidema & Jane Bare & Xun Liao & Danielle Maia de Souza & Massimo Pizzol & Serenella Sala & Hanna Schreiber & Nils Thonemann & Francesca Verones, 2020. "Methodological review and detailed guidance for the life cycle interpretation phase," Journal of Industrial Ecology, Yale University, vol. 24(5), pages 986-1003, October.
    11. Borgonovo, Emanuele & Hazen, Gordon B. & Jose, Victor Richmond R. & Plischke, Elmar, 2021. "Probabilistic sensitivity measures as information value," European Journal of Operational Research, Elsevier, vol. 289(2), pages 595-610.
    12. Strand,Jon & Siddiqui,Sauleh, 2015. "Value of improved information about forest protection values, with application to rainforest valuation," Policy Research Working Paper Series 7423, The World Bank.
    13. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    14. Valeria Costantini & Francesco Crespi & Giovanni Marin & Elena Paglialunga, 2016. "Eco-innovation, sustainable supply chains and environmental performance in European industries," LEM Papers Series 2016/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Lee, Alice J. & Ames, Daniel R., 2017. "“I can’t pay more” versus “It’s not worth more”: Divergent effects of constraint and disparagement rationales in negotiations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 141(C), pages 16-28.
    16. Hussain, Hadia & Murtaza, Murtaza & Ajmal, Areeb & Ahmed, Afreen & Khan, Muhammad Ovais Khalid, 2020. "A study on the effects of social media advertisement on consumer’s attitude and customer response," MPRA Paper 104675, University Library of Munich, Germany.
    17. A. G. Fatullayev & Nizami A. Gasilov & Şahin Emrah Amrahov, 2019. "Numerical solution of linear inhomogeneous fuzzy delay differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 315-326, September.
    18. Cyril Chalendard, 2015. "Use of internal information, external information acquisition and customs underreporting," Working Papers halshs-01179445, HAL.
    19. Arun Advani & William Elming & Jonathan Shaw, 2023. "The Dynamic Effects of Tax Audits," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 545-561, May.
    20. Philippe Aghion & Ufuk Akcigit & Matthieu Lequien & Stefanie Stantcheva, 2017. "Tax simplicity and heterogeneous learning," CEP Discussion Papers dp1516, Centre for Economic Performance, LSE.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0240460. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.