Abstract
Ambitious climate policy is urgently needed to prevent irreversible changes to the climate system. This requires a rapid reduction of carbon emissions caused by the combustion of fossil fuels. Different instruments have been proposed to achieve such a reduction. One approach is to put a price on carbon, which can be done through either a carbon tax or a permit market. Another approach is to regulate emissions directly by setting fixed emission limits for different polluters. All these instruments can either be applied upstream, where fossil fuels are extracted, or downstream, where emissions take place.
The aim of this thesis is to evaluate the relative performance of these different policy approaches, when realistic assumptions are adopted regarding the behavior of polluters. The method of agent-based modeling is used to represent the economy as a complex adaptive system that is based on the interaction of heterogeneous agents. The behavior of these agents follows heuristic rules as they face limited knowledge about future outcomes. On the supply-side, firms have to decide about their prices, production, and the adoption of low-carbon technology. On the demand-side, individuals have to make choices in order to satisfy their needs.
This approach contributes to existing literature in two ways. First, it identifies relevant policy mechanisms that result from the continuous interaction of multiple economic actors. Such dynamics remain hidden within traditional economic equilibrium models that require assumptions of representative and rational behavior. Second, it allows for the assessment of policy impacts upon a wide range of evaluation criteria, including emission reduction, costs, economic output, competitive dynamics, income distributions, and well-being.
The thesis consists of eight chapters. A first one is introductory: it motivates and defines the research focus. The second chapter provides a review of agent-based models applied to climate policy. An overview is given regarding common features, empirical calibration, behavioral assumptions, and policy insights. Gaps in the literature are identified that serve as a basis for the rest of this work. The third chapter introduces a software package for the creation of agent-based models in Python. This package makes it possible to integrate the tasks of model design, numerical experiments, and data analysis within a single environment for interactive computing.
The following two chapters focus on the supply-side of climate policy. The fourth chapter compares a carbon tax and a permit market. A key difference is found in the fact that permit prices can fall when abatement is successful, which may drive emission-efficient firms out of the market. The fifth chapter compares upstream and downstream regulation. Results show how the latter decreases the demand and thus the price of fossil fuels. This reduces the profit margin of fuel suppliers, but it can also lead to emission leakage as it allows firms outside the policymaker’s jurisdiction to buy fuels at a cheaper price.
The next two chapters explore the demand-side of climate policy. The sixth chapter introduces a framework to describe the behavior of individuals who try to improve their quality of life through the satisfaction of multiple human needs. The seventh chapter applies this framework to better understand how a carbon price leads to a shift of demand between sectors that serve different human needs. Results suggest that to achieve emission reductions together with high levels of well-being, a carbon price should be combined with progressive revenue recycling and effective improvements of low-carbon infrastructure. A final and eighth chapter concludes the thesis.
Original language | English |
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Qualification | PhD |
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Award date | 6 Sept 2022 |
Publication status | Published - 6 Sept 2022 |