Modeling Uncertain Climate Impacts and Adaptation for the Integrated Assessment of Carbon Policy. Diaz, D. B. 2015. ![Modeling Uncertain Climate Impacts and Adaptation for the Integrated Assessment of Carbon Policy. [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Carbon mitigation policies should be designed to balance the costs of reducing emissions and the benefits of avoided climate change. However, both costs and benefits are challenging to characterize because of pervasive uncertainty and complex inter actions among important physical, natural, and human systems. This dissertation reviews, assesses, and seeks to improve the representation of the benefits in the cost benefit oriented integrated assessment models (IAMs) used for global assessment and climate policy analysis. One prominent measure of these benefits is the social cost of carbon (SCC), a monetary estimate of the climate change damages to society from an additional emission of carbon dioxide (CO2). The opening dissertation research project presents the first in-depth model diagnostic and inter-comparison examination of the three IAMs used to quantify the US Government SCC – DICE, FUND, and PAGE – to reveal how each model uniquely determines damages from climate change. This study's diagnostic analysis improves public understanding of the SCC, informs future SCC estimation, and identifies research priorities for climate impacts modeling that will be addressed in the remainder of the dissertation. The subsequent studies focus on climate impacts of sea level rise (SLR). The second dissertation project presents a new model to inform global estimates of coastal impacts, the Coastal Impact and Adaptation Model (CIAM). CIAM improves the credibility of coastal impact estimates along many analytical and empirical dimensions: geographical scope, spatial resolution, temporal dynamics, and the inclusion of optimal local adaptation, uncertain flooding from storm surge extremes, wetland loss, and the effects of relative SLR. While the CIAM approach informs incremental climate change damage estimates, these are not the only factor in carbon mitigation policy design. Another important consideration is the risk of uncertain and potentially irreversible catastrophes, which have not yet been well incorporated into IAMs. The final project of this dissertation presents a multistage stochastic programming framework for modeling climate catastrophes with endogenous uncertainty, applied to a benchmark IAM. The specific catastrophe considered is the uncertain collapse of the West Antarctic Ice Sheet (WAIS), characterized in accordance with recent expert elicitations, empirical results, physical relationships, and economic consequences, as represented with the both the DICE and CIAM coastal damage functions. The stochastic DICE-WAIS model introduced here investigates the optimal policy response to the ice sheet collapse threat.
@misc{diaz_modeling_2015,
title = {Modeling {Uncertain} {Climate} {Impacts} and {Adaptation} for the {Integrated} {Assessment} of {Carbon} {Policy}.},
url = {https://searchworks.stanford.edu/view/11061330},
abstract = {Carbon mitigation policies should be designed to balance the costs of reducing emissions and the benefits of avoided climate change. However, both costs and benefits are challenging to characterize because of pervasive uncertainty and complex inter actions among important physical, natural, and human systems. This dissertation reviews, assesses, and seeks to improve the representation of the benefits in the cost benefit oriented integrated assessment models (IAMs) used for global assessment and climate policy analysis. One prominent measure of these benefits is the social cost of carbon (SCC), a monetary estimate of the climate change damages to society from an additional emission of carbon dioxide (CO2). The opening dissertation research project presents the first in-depth model diagnostic and inter-comparison examination of the three IAMs used to quantify the US Government SCC -- DICE, FUND, and PAGE -- to reveal how each model uniquely determines damages from climate change. This study's diagnostic analysis improves public understanding of the SCC, informs future SCC estimation, and identifies research priorities for climate impacts modeling that will be addressed in the remainder of the dissertation. The subsequent studies focus on climate impacts of sea level rise (SLR). The second dissertation project presents a new model to inform global estimates of coastal impacts, the Coastal Impact and Adaptation Model (CIAM). CIAM improves the credibility of coastal impact estimates along many analytical and empirical dimensions: geographical scope, spatial resolution, temporal dynamics, and the inclusion of optimal local adaptation, uncertain flooding from storm surge extremes, wetland loss, and the effects of relative SLR. While the CIAM approach informs incremental climate change damage estimates, these are not the only factor in carbon mitigation policy design. Another important consideration is the risk of uncertain and potentially irreversible catastrophes, which have not yet been well incorporated into IAMs. The final project of this dissertation presents a multistage stochastic programming framework for modeling climate catastrophes with endogenous uncertainty, applied to a benchmark IAM. The specific catastrophe considered is the uncertain collapse of the West Antarctic Ice Sheet (WAIS), characterized in accordance with recent expert elicitations, empirical results, physical relationships, and economic consequences, as represented with the both the DICE and CIAM coastal damage functions. The stochastic DICE-WAIS model introduced here investigates the optimal policy response to the ice sheet collapse threat.},
urldate = {2017-06-29},
publisher = {Stanford University},
author = {Diaz, Delavane B.},
year = {2015},
keywords = {DR, Untagged},
}
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One prominent measure of these benefits is the social cost of carbon (SCC), a monetary estimate of the climate change damages to society from an additional emission of carbon dioxide (CO2). The opening dissertation research project presents the first in-depth model diagnostic and inter-comparison examination of the three IAMs used to quantify the US Government SCC – DICE, FUND, and PAGE – to reveal how each model uniquely determines damages from climate change. This study's diagnostic analysis improves public understanding of the SCC, informs future SCC estimation, and identifies research priorities for climate impacts modeling that will be addressed in the remainder of the dissertation. The subsequent studies focus on climate impacts of sea level rise (SLR). The second dissertation project presents a new model to inform global estimates of coastal impacts, the Coastal Impact and Adaptation Model (CIAM). CIAM improves the credibility of coastal impact estimates along many analytical and empirical dimensions: geographical scope, spatial resolution, temporal dynamics, and the inclusion of optimal local adaptation, uncertain flooding from storm surge extremes, wetland loss, and the effects of relative SLR. While the CIAM approach informs incremental climate change damage estimates, these are not the only factor in carbon mitigation policy design. Another important consideration is the risk of uncertain and potentially irreversible catastrophes, which have not yet been well incorporated into IAMs. The final project of this dissertation presents a multistage stochastic programming framework for modeling climate catastrophes with endogenous uncertainty, applied to a benchmark IAM. 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