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Probabilistic assessment of realizing the 1.5 °C climate target

Author

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  • Marcucci, Adriana
  • Panos, Evangelos
  • Kypreos, Socrates
  • Fragkos, Panagiotis

Abstract

In this paper we develop a probabilistic assessment of the energy transition and economic consequences of limiting global warming by the end of the century to 1.5 °C. The assessment is made by applying a Monte Carlo analysis in MERGE-ETL, a technology rich integrated assessment model with endogenous learning. We assume a deterministic 1.5 °C target and uncertainty in other factors such as economic growth, resources, and technology costs. The distributions of these variables are obtained by the PROMETHEUS stochastic world energy model. The study assumes early actions and quantifies the market penetration of low carbon technologies, the emission pathways and the economic costs for an efficient reduction of greenhouse gas emissions such that the temperature limit is not exceeded. We find that achieving the 1.5 °C Paris target is technically feasible but it requires immediate and global action. Key pillars in the decarbonization of the energy system are large deployment of renewable energy, early retirement of fossil based power plants, energy efficiency and negative emission technologies. Furthermore, that the availability of biomass resources, the rapid decrease of the costs of renewables and improvements in energy efficiency are the factors with the largest effect on the cost of carbon.

Suggested Citation

  • Marcucci, Adriana & Panos, Evangelos & Kypreos, Socrates & Fragkos, Panagiotis, 2019. "Probabilistic assessment of realizing the 1.5 °C climate target," Applied Energy, Elsevier, vol. 239(C), pages 239-251.
  • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:239-251
    DOI: 10.1016/j.apenergy.2019.01.190
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Panagiotis Fragkos & Francesco Dalla Longa & Eleftheria Zisarou & Bob van der Zwaan & Anastasis Giannousakis & Amir Fattahi, 2023. "Exploring Model-Based Decarbonization and Energy Efficiency Scenarios with PROMETHEUS and TIAM-ECN," Energies, MDPI, vol. 16(18), pages 1-22, September.
    2. Mei, H. & Li, Y.P. & Suo, C. & Ma, Y. & Lv, J., 2020. "Analyzing the impact of climate change on energy-economy-carbon nexus system in China," Applied Energy, Elsevier, vol. 262(C).
    3. Virguez, Edgar & Wang, Xianxun & Patiño-Echeverri, Dalia, 2021. "Utility-scale photovoltaics and storage: Decarbonizing and reducing greenhouse gases abatement costs," Applied Energy, Elsevier, vol. 282(PA).
    4. Panos, Evangelos & Kober, Tom & Wokaun, Alexander, 2019. "Long term evaluation of electric storage technologies vs alternative flexibility options for the Swiss energy system," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Panagiotis Fragkos, 2022. "Decarbonizing the International Shipping and Aviation Sectors," Energies, MDPI, vol. 15(24), pages 1-25, December.
    6. Panos, Evangelos & Glynn, James & Kypreos, Socrates & Lehtilä, Antti & Yue, Xiufeng & Ó Gallachóir, Brian & Daniels, David & Dai, Hancheng, 2023. "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector," Energy Policy, Elsevier, vol. 180(C).
    7. Pizarro-Alonso, Amalia & Ravn, Hans & Münster, Marie, 2019. "Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Silva Herran, Diego & Tachiiri, Kaoru & Matsumoto, Ken'ichi, 2019. "Global energy system transformations in mitigation scenarios considering climate uncertainties," Applied Energy, Elsevier, vol. 243(C), pages 119-131.

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    More about this item

    Keywords

    Probabilistic assessment; Uncertainty; Monte Carlo analysis; Integrated assessment model; 1.5 °C target;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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