IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_10892.html
   My bibliography  Save this paper

Balancing the Risk of Tipping: Early Warning Systems from Detection to Management

Author

Listed:
  • Florian Diekert
  • Daniel Heyen
  • Frikk Nesje
  • Soheil Shayegh

Abstract

Early warning signals (EWS) of imminent regime shifts can be identified through the observation of a system’s behavior under increasing stress and before crossing a tipping point. Despite many advances in the detection of EWS in recent years, EWS are yet to find direct application in management. Here, we focus on operationalizing the EWS information in an early warning system consisting of a tipping indicator (e.g., autocorrelation), whose value increases as the system approaches the tipping point, and a trigger value, beyond which an EWS is sent. We demonstrate how such an early warning system allows managers to balance the risk of tipping by providing information for updating their belief about the location of the tipping point. In particular, deployment of an early warning system results in taking more cautious early steps while it encourages more risk taking behavior in later stages if no EWS is sent. We uncover a tension between better information about the location of the tipping point and increased risk of crossing it as a result of EWS. Our framework complements the emerging EWS knowledge in the natural sciences with a better understanding of how, when, and why EWS improve management.

Suggested Citation

  • Florian Diekert & Daniel Heyen & Frikk Nesje & Soheil Shayegh, 2024. "Balancing the Risk of Tipping: Early Warning Systems from Detection to Management," CESifo Working Paper Series 10892, CESifo.
    • Handle: RePEc:ces:ceswps:_10892
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp10892.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yongyang Cai & Thomas S. Lontzek, 2019. "The Social Cost of Carbon with Economic and Climate Risks," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2684-2734.
    2. Sumit Sarkar & Ram S. Sriram, 2001. "Bayesian Models for Early Warning of Bank Failures," Management Science, INFORMS, vol. 47(11), pages 1457-1475, November.
    3. Polasky, Stephen & de Zeeuw, Aart & Wagener, Florian, 2011. "Optimal management with potential regime shifts," Journal of Environmental Economics and Management, Elsevier, vol. 62(2), pages 229-240, September.
    4. Friederike E. L. Otto, 2016. "The art of attribution," Nature Climate Change, Nature, vol. 6(4), pages 342-343, April.
    5. Diekert, Florian K., 2017. "Threatening thresholds? The effect of disastrous regime shifts on the non-cooperative use of environmental goods and services," Journal of Public Economics, Elsevier, vol. 147(C), pages 30-49.
    6. Saed Alizamir & Francis de Véricourt & Shouqiang Wang, 2020. "Warning Against Recurring Risks: An Information Design Approach," Management Science, INFORMS, vol. 66(10), pages 4612-4629, October.
    7. Dirk Bergemann & Stephen Morris, 2016. "Information Design, Bayesian Persuasion, and Bayes Correlated Equilibrium," American Economic Review, American Economic Association, vol. 106(5), pages 586-591, May.
    8. Chris A. Boulton & Timothy M. Lenton & Niklas Boers, 2022. "Pronounced loss of Amazon rainforest resilience since the early 2000s," Nature Climate Change, Nature, vol. 12(3), pages 271-278, March.
    9. Timothy M. Lenton & Johan Rockström & Owen Gaffney & Stefan Rahmstorf & Katherine Richardson & Will Steffen & Hans Joachim Schellnhuber, 2019. "Climate tipping points — too risky to bet against," Nature, Nature, vol. 575(7784), pages 592-595, November.
    10. Peter Ditlevsen & Susanne Ditlevsen, 2023. "Warning of a forthcoming collapse of the Atlantic meridional overturning circulation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    11. Derek Lemoine & Christian Traeger, 2014. "Watch Your Step: Optimal Policy in a Tipping Climate," American Economic Journal: Economic Policy, American Economic Association, vol. 6(1), pages 137-166, February.
    12. Yuval Hart & Maryam Vaziri-Pashkam & L Mahadevan, 2020. "Early warning signals in motion inference," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-16, May.
    13. Yanlan Liu & Mukesh Kumar & Gabriel G. Katul & Amilcare Porporato, 2019. "Reduced resilience as an early warning signal of forest mortality," Nature Climate Change, Nature, vol. 9(11), pages 880-885, November.
    14. Niklas Boers, 2021. "Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation," Nature Climate Change, Nature, vol. 11(8), pages 680-688, August.
    15. Claude Henry, 1974. "Investment decisions under uncertainty: The "irreversibility effect"," ULB Institutional Repository 2013/327343, ULB -- Universite Libre de Bruxelles.
    16. Haoyu Wen & Massimo Pica Ciamarra & Siew Ann Cheong, 2018. "How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    17. Henry, Claude, 1974. "Investment Decisions Under Uncertainty: The "Irreversibility Effect."," American Economic Review, American Economic Association, vol. 64(6), pages 1006-1012, December.
    18. Gary E. Bolton & Elena Katok, 2018. "Cry Wolf or Equivocate? Credible Forecast Guidance in a Cost-Loss Game," Management Science, INFORMS, vol. 64(3), pages 1440-1457, 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. Wagener, Florian & de Zeeuw, Aart, 2021. "Stable partial cooperation in managing systems with tipping points," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    2. Timothy M. Lenton & Jesse F. Abrams & Annett Bartsch & Sebastian Bathiany & Chris A. Boulton & Joshua E. Buxton & Alessandra Conversi & Andrew M. Cunliffe & Sophie Hebden & Thomas Lavergne & Benjamin , 2024. "Remotely sensing potential climate change tipping points across scales," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Dominika Czyz & Karolina Safarzynska, 2023. "Catastrophic Damages and the Optimal Carbon Tax Under Loss Aversion," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(2), pages 303-340, June.
    4. Ahlvik, Lassi & Iho, Antti, 2018. "Optimal geoengineering experiments," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 148-168.
    5. Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
    6. Frederick Ploeg & Aart Zeeuw, 2019. "Pricing Carbon and Adjusting Capital to Fend Off Climate Catastrophes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 29-50, January.
    7. Zeeuw, Aart de, 2024. "Climate change, tipping points, and economics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 109(C).
    8. Charles Sims & David Finnoff, 2016. "Opposing Irreversibilities and Tipping Point Uncertainty," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(4), pages 985-1022.
    9. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    10. Nkuiya, Bruno & Diekert, Florian, 2023. "Stochastic growth and regime shift risk in renewable resource management," Ecological Economics, Elsevier, vol. 208(C).
    11. Jin, Wei, 2021. "Path dependence, self-fulfilling expectations, and carbon lock-in," Resource and Energy Economics, Elsevier, vol. 66(C).
    12. Attanasi, Giuseppe Marco & Montesano, Aldo, 2010. "Testing Value vs Waiting Value in Environmental Decisions under Uncertainty," TSE Working Papers 10-154, Toulouse School of Economics (TSE).
    13. Maria Arvaniti & Chandra K. Krishnamurthy & Anne-Sophie Crépin, 2019. "Time-consistent resource management with regime shifts," CER-ETH Economics working paper series 19/329, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    14. Can Askan Mavi & Nicolas Quérou, 2020. "Common pool resource management and risk perceptions," DEM Discussion Paper Series 20-25, Department of Economics at the University of Luxembourg.
    15. Edouard Civel & Marc Baudry, 2018. "The Fate of Inventions. What can we learn from Bayesian learning in strategic options model of adoption ?," EconomiX Working Papers 2018-47, University of Paris Nanterre, EconomiX.
    16. Guillouet, Louise & Martimort, David, 2023. "Acting in the Darkness: Towards some Foundations for the Precautionary Principle," TSE Working Papers 23-1411, Toulouse School of Economics (TSE), revised Jul 2024.
    17. Gustav Engström & Johan Gars, 2016. "Climatic Tipping Points and Optimal Fossil-Fuel Use," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(3), pages 541-571, November.
    18. Marcello Basili, 2006. "A Rational Decision Rule with Extreme Events," Risk Analysis, John Wiley & Sons, vol. 26(6), pages 1721-1728, December.
    19. Baixun Li & Meng Li & Chao Liang, 2023. "Cry‐wolf syndrome in recommendation," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 347-358, February.
    20. Long, Shaobo & Zhang, Rui, 2022. "The asymmetric effects of international oil prices, oil price uncertainty and income on urban residents’ consumption in China," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 789-805.

    More about this item

    Keywords

    catastrophic regime shifts; tipping points; early warning signals; learning; optimal ecosystem management;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ces:ceswps:_10892. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    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.