Public Abstract

DE-SC0012042: Methods for Decision under Technological Change Uncertainty and Risk Assessment for Integrated Assessment of Climate Change

Award Status: Inactive

Institution: The Pennsylvania State University, University Park, PA
UEI: NPM2J7MSCF61
DUNS: 003403953

Most Recent Award Date: 06/17/2014
Number of Support Periods: 1
PM: Vallario, Robert

Current Budget Period: 07/01/2014 - 06/30/2015
Current Project Period: 07/01/2014 - 06/30/2015
PI: Webster, Mort David

Supplement Budget Period: N/A

Public Abstract

The United States and other nations face the challenge of developing appropriate responses to global climate change. In general, such responses include both mitigation, to reduce future risks from climate change, and adaptation, to reduce damages from the climate change that does occur. Choosing appropriate actions requires an understanding of the several complex interacting systems, including economic growth and energy systems, the physical earth system, and other economic activities that are affected by changes in climate. Uncertainties in our understanding of these systems, their interactions, and their future evolution further complicate this task. A primary set of tools for supporting climate change decision-making are integrated assessment models (IAMs), which represent the human-climate coupled system. IAMs have contributed to our understanding of many issues in climate change, such as the development of internally consistent global emissions scenarios and the exploration of economic, technological, emissions, and climate impacts of alternative mitigation strategies. However, the evolving demands on decision makers and uncertainties in our ability to project future impacts on the human-climate system raise a set of scientific challenges for integrated assessment research. A recent report for the U.S. Department of Energy Office of Science outlined several key scientific challenges for IAMs. The proposed research aims to contribute to closing identified gaps in two of these areas, the representation of technological change, and the treatment of risk and uncertainty. In addition, some methods developed in this project could also be applied in the future to a third challenge, that of improving the spatial and temporal resolution of climate impacts. Specifically, the objective of this research is to develop improved methods in three areas: Modeling of climate mitigation as sequential decisions under uncertainty, Modeling of uncertainty in technological change that represents structural uncertainty, Reduced-form models of complex earth system processes to improve the ability to analyze the risks of climate impacts. The models and methods to be developed would improve the representation of technology and technological change and the treatment of uncertainty in IAMs. Decision makers at all levels of government and other stakeholders will be making critical choices regarding the investment in energy technologies over the next few decades, and these choices will likely determine the technological options available later in the century if more stringent climate targets are pursued. Yet analyses of the future energy technology mix using IAMs are predominantly deterministic scenarios. The methods that will be developed here will enable the explicit investigation of near-term technological strategies accounting for technological uncertainty and the ability to learn and adjust policies over time. The end result will be to improve the ability of IAMs to provide useful decision support as climate change is addressed over the coming decades. In this final year of the project, we will integrate the methodologies developed into a representative integrated assessment model.

The United States and other nations face the challenge of developing appropriate responses to global climate change. In general, such responses include both mitigation, to reduce future risks from climate change, and adaptation, to reduce damages from the climate change that does occur. Choosing appropriate actions requires an understanding of the several complex interacting systems, including economic growth and energy systems, the physical earth system, and other economic activities that are affected by changes in climate. Uncertainties in our understanding of these systems, their interactions, and their future evolution further complicate this task.

A primary set of tools for supporting climate change decision-making are integrated assessment models (IAMs), which represent the human-climate coupled system. IAMs have contributed to our understanding of many issues in climate change, such as the development of internally consistent global emissions scenarios and the exploration of economic, technological, emissions, and climate impacts of alternative mitigation strategies. However, the evolving demands on decision makers and uncertainties in our ability to project future impacts on the human-climate system raise a set of scientific challenges for integrated assessment research. A recent report for the U.S. Department of Energy Office of Science outlined several key scientific challenges for IAMs. The proposed research aims to contribute to closing identified gaps in two of these areas, the representation of technological change, and the treatment of risk and uncertainty. In addition, some methods developed in this project could also be applied in the future to a third challenge, that of improving the spatial and temporal resolution of climate impacts. Specifically, the objective of this research is to develop improved methods in three areas:

Modeling of climate mitigation as sequential decisions under uncertainty,
Modeling of uncertainty in technological change that represents structural uncertainty,
Reduced-form models of complex earth system processes to improve the ability to analyze the risks of climate impacts.

The models and methods to be developed would improve the representation of technology and technological change and the treatment of uncertainty in IAMs. Decision makers at all levels of government and other stakeholders will be making critical choices regarding the investment in energy technologies over the next few decades, and these choices will likely determine the technological options available later in the century if more stringent climate targets are pursued. Yet analyses of the future energy technology mix using IAMs are predominantly deterministic scenarios. The methods that will be developed here will enable the explicit investigation of near-term technological strategies accounting for technological uncertainty and the ability to learn and adjust policies over time. The end result will be to improve the ability of IAMs to provide useful decision support as climate change is addressed over the coming decades. In this final year of the project, we will integrate the methodologies developed into a representative integrated assessment model.