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How can energy system models align with the real-world tragedy of the horizon? The impact of decision-makers’ short-term biases and temporally-inconsistent preferences

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Koutsandreas,  Diamantis       
Potsdam Institute for Climate Impact Research;

Keppo,  Ilkka
External Organizations;

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Koutsandreas, D., Keppo, I. (2026 online): How can energy system models align with the real-world tragedy of the horizon? The impact of decision-makers’ short-term biases and temporally-inconsistent preferences. - Applied Energy, 412, 127718.
https://doi.org/10.1016/j.apenergy.2026.127718


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_34170
Zusammenfassung
Decision makers often operate on short-term horizons, placing more weight on the short term and revising their decisions later as new information arises. What’s more, literature has shown that the implicit discount rate at which decisions are made for the future isn’t constant, but changes as a function of the point in the future for which the decision is made. While the myopic foresight of decision makers has been extensively studied in the energy system modeling literature, the focus has been on a binary representation in which time periods beyond the defined decision horizon are completely ignored, and changes in strategies are a result of new information, rather than different valuation of a specific future cost. This study introduces a temporal inconsistency framework for energy system modeling. In this approach, decision-makers do plan for the full time horizon, but assess future value based on a changing discount rate and, as time is moved forward, reassess this value at each one year step. This approach advances the state of the art by simulating temporally–inconsistent decision making, in which optimal decision made for a given year changes as a function of when those plans were made. We demonstrate this framework by simulating decarbonization scenarios for the Greek power sector with varying levels of path dependency and commitment over 2019–2070. The results highlight that logarithmic discounting can lead to irreversible sub-optimal pathways, whereas exponential discounting produces sub-optimal early decisions, which however can later be revised, moving closer to the time-consistent pathway (up to 10 times less natural gas-fired power generation in 2050 compared to initial plans).