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Bayesian Functional Emulation of CO2 Emissions on Future Climate Change Scenarios

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  • Luca Aiello
  • Matteo Fontana
  • Alessandra Guglielmi

Abstract

We propose a statistical emulator for a climate-economy deterministic integrated assessment model ensemble, based on a functional regression framework. Inference on the unknown parameters is carried out through a mixed effects hierarchical model using a fully Bayesian framework with a prior distribution on the vector of all parameters. We also suggest an autoregressive parameterization of the covariance matrix of the error, with matching marginal prior. In this way, we allow for a functional framework for the discretized output of the simulators that allows their time continuous evaluation.

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  • Luca Aiello & Matteo Fontana & Alessandra Guglielmi, 2022. "Bayesian Functional Emulation of CO2 Emissions on Future Climate Change Scenarios," Papers 2209.05767, arXiv.org.
    • Handle: RePEc:arx:papers:2209.05767
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    References listed on IDEAS

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    1. Jeff Goldsmith & Tomoko Kitago, 2016. "Assessing systematic effects of stroke on motor control by using hierarchical function-on-scalar regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(2), pages 215-236, February.
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