It is widely recognized that international environmental technological change is key to reduce the rapidly rising greenhouse gas emissions of emerging nations. In 2010, the United Nations Framework Convention on Climate Change (UNFCCC) Conference of the Parties (COP) agreed to the creation of the Green Climate Fund (GCF). This new multilateral organization has been created with the collective contributions of COP members, and has been tasked with directing over USD 100 billion per year towards investments that can enhance the development and diffusion of clean energy technologies in both advanced and emerging nations (Helm and Pichler, 2015). The landmark agreement arrived at the COP 21 has reaffirmed the key role that the GCF plays in enabling climate mitigation as it is now necessary to align large scale climate financing efforts with the long-term goals agreed at Paris 2015.
This study argues that because of the incomplete understanding of the mechanics of international technological change, the multiplicity of policy options and ultimately the presence of climate and technological change deep uncertainty, climate financing institutions such as the GCF, require new analytical methods for designing long-term robust investment plans. Motivated by these challenges, this dissertation shows that the application of new analytical methods, such as Robust Decision Making (RDM) and Exploratory Modeling (Lempert, Popper and Bankes, 2003) to the study of international technological change and climate policy provides useful insights that can be used for designing a robust architecture of international technological cooperation for climate change mitigation.
Table of Contents
International Technological Change and Climate Policy in the Presence of Deep Uncertainty
Framing International Technological Change: Theories, Implications, Evidence and Relevant Knowledge Gaps
An Exploratory Dynamic Integrated Assessment Model (EDIAM) for Analyzing International Environmental Technological Change and Climate Policy
Optimal Policy Response and Climate Deep Uncertainty: A Robust Decision Making Analysis
Mapping Optimal Climate Policy Across Multiple Climate and Technological Scenarios
Derivation of Intertemporal Equilibrium Conditions for the EDIAM Model
Game Theory Analysis of the No-Regret Policy
Calibrating EDIAM's Climate Change Parameters Using the CMIP5 Data Ensemble
Additional Comparisons between EDIAM Simulated Output and Original CMIP5 Models' Temperature Rise Trajectories and RCP Emissions Scenarios
Additional Scenario Discovery Results