Directed International Technological Change and Climate Policy: New Methods for Identifying Robust Policies Under Conditions of Deep Uncertainty.

摘要

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.;For this study I developed an exploratory dynamic integrated assessment model (EDIAM) which is used as the scenario generator in a large computational experiment. The scope of the experimental design considers an ample set of climate and technological scenarios. These scenarios combine five sources of uncertainty: climate change, elasticity of substitution between renewable and fossil energy and three different sources of technological uncertainty (i.e. R&D returns, innovation propensity and technological transferability). The performance of eight different GCF and non-GCF based policy regimes is evaluated in light of various end-of-century climate policy targets. Then I combine traditional scenario discovery data mining methods (Bryant and Lempert, 2010) with high dimensional stacking methods (Suzuki, Stem and Manzocchi, 2015; Taylor et al., 2006; LeBlanc, Ward and Wittels, 1990) to quantitatively characterize the conditions under which it is possible to stabilize greenhouse gas emissions and keep temperature rise below 2°C before the end of the century.;Finally, I describe a method by which it is possible to combine the results of scenario discovery with high-dimensional stacking to construct a dynamic architecture of low cost technological cooperation. This dynamic architecture consists of adaptive pathways (Kwakkel, Haasnoot and Walker, 2014; Haasnoot et al., 2013) which begin with carbon taxation across both regions as a critical near term action. Then in subsequent phases different forms of cooperation are triggered depending on the unfolding climate and technological conditions.;I show that there is no single policy regime that dominates over the entire uncertainty space. Instead I find that it is possible to combine these different architectures into a dynamic framework for technological cooperation across regions that can be adapted to unfolding climate and technological conditions which can lead to a greater rate of success and to lower costs in meeting the end-of-century climate change objectives agreed at the 2015 Paris Conference of the Parties.;Keywords: international technological change, emerging nations, climate change, technological uncertainties, Green Climate Fund.