Thomas S. Fiddaman
Submitted to the Alfred P. Sloan School of Management in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Management
Assessing the economic and ecological impacts of climate change induced by human activity has become a major activity with a substantial modeling community. More than 20 climate-economy models have been developed to address different policy questions. While these integrated models are quite varied, most share some common assumptions and features. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system. Technological potential is represented by elasticities of substitution, exogenous rates of technological improvement, and backstop energy prices. Factor allocation is myopically or intertemporally optimal. The impact of a carbon tax on the energy system at a given time can often be reduced to a simple tradeoff between abatement costs and emissions (though capital stock rigidities complicate the short-run picture in some models). The major endogenous dynamics of these models involve capital accumulation, atmospheric concentrations of greenhouse gases, and the temperature of the atmosphere and ocean system.
These models draw heavily on the energy-economy models of the 70s and 80s, which were motivated by energy security issues and explored the potential impacts of increasing energy prices on economic growth. System dynamics models of that period shared the same motivation, but sought alternatives to the assumptions of optimization and equilibrium. They focused instead on disequilibrium dynamics and feedback complexity, with behavioral decision rules and explicit stocks and flows of capital, labor, and money.
This research builds on earlier system dynamics models of energy economy interactions, creating a model that tests the implications of a number of feedback processes that have not been explored in the climate change context. Among these are endogenous technological change and boundedly rational decision making, with perception delays and biases. Energy requirements are embodied in capital, and energy production capacity depends on explicit capital stocks. The search for optimal policies is decoupled from other decisions, and uses intertemporally fair criteria. To enhance the link between this research and other studies, the model is constructed so that an appropriate parameterization will recover the neoclassical case found in models like Nordhaus' DICE (1994).
The principal purpose of the model is to identify the structural features that have the greatest implications for policy, and thus are worthy of further pursuit. Experiments with the model indicate that depletion of oil and gas resources has critical interactions with climate policy. The inclusion of learning-by-doing and other path-dependent mechanisms suggests that abatement efforts will be more effective and should be more stringent than models with exogenous technology forecasts indicate. Inclusion of delays and biases from structural and behavioral features of the energy system creates higher long-run emissions reduction potential but imposes substantial constraints that prevent rapid reductions. Fair discounting and consideration of intangible damages substantially raise the indicated abatement effort. In both deterministic and uncertain cases, near-term inaction is a poor policy.
The full dissertation is available from the MIT System Dynamics Group, E60, 30 Memorial Drive, Cambridge MA 02142, report # D-4681. You can also obtain it from MIT Archives or by downloading it directly in Adobe Acrobat format: dissertation.pdf (2 MB).