The Paris Agreement established that global warming should be limited to “well below” 2oC and encouraged efforts to limit warming to 1.5oC. Achieving this goal presents a significant challenge, especially given the presence of (i) economic inertia and adjustment costs, which penalize a swift transition away from fossil fuels, and (ii) climate uncertainty that, for example, hinders the ability to predict the amount of emissions that can be emitted before a given temperature target is passed, which is often referred to as the remaining carbon budget. This paper presents a modeling framework that explores optimal decarbonization investment strategy when both delayed learning about the remaining carbon budget and adjustment costs are present. The findings show that delaying learning about the remaining carbon budget impacts investment in three ways: (i) the cost of policy increases, especially when adjustment costs are present; (ii) abatement investment is front-loaded relative to the certainty policy; and (iii) the sectoral allocation of investment changes to favor declining investment pathways rather than bell-shaped paths. The latter effect is especially pronounced in hard-to-abate sectors, such as heavy industry. Each of the effects can be traced back to the carbon price distribution inheriting a “heavy tail” when the remaining carbon budget is learned later in the century. The paper highlights how climate uncertainty and adjustment costs combined result in a more aggressive least-cost strategy for decarbonization investment.
Repository name | Type | URI |
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Reproducible Research Repository (World Bank) | https://reproducibility.worldbank.org | |
BMH-delayed-learning-reprod | Github | https://github.com/adam-bauer-34/BMH-delayed-learning-reprod/ |
Paper exhibits were reproduced on a computer with the following specifications:
• OS: Windows 10 Enterprise, version 21H2
• Processor: Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz, 16 Core(s)
• Memory available: 109 GB
• Software version: Python 3.11
~4 hours runtime
To successfully replicate this package, new users must follow these steps: a. setting the environment; b. creating an account and Getting the Gurobi Academic License; c. downloading Gurobi optimization; running in bash script by script in sequential order.
All the data used in this study is taken and/or interpreted from publically available publications and reports. Raw data is used to calibrate the numerical model. Individual numbers used in the simulations can be found in each simulation's codes/data/cal/ files in the reproducibility package.
Author | Affiliation | |
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Adam Michael Bauer | University of Illinois Urbana-Champaign | adammb4@illinois.edu |
Florent McIsaac | World Bank | fmcisaac@worldbank.org |
Stéphane Hallegatte | World Bank | shallegatte@worldbank.org |
2024-04
Location | Code |
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World | WLD |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
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Name | URI |
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MIT license | https://opensource.org/license/mit/ |
Name | Affiliation | |
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Adam Michael Bauer | University of Illinois Urbana-Champaign | adammb4@illinois.edu |
Reproducibility WBG | World Bank | reproducibility@worldbank.org |
Name | Abbreviation | Affiliation | Role |
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Reproducibility WBG | DIME | World Bank - Development Impact Department | Verification and preparation of metadata |
2024-04-22
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