Most current cost-benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost-benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost-benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost-benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.
This thesis compiles three papers on different topics in computational economics: First, we present a new method to recursively integrate expectations over serially correlated latent variables with continuous support in maximum likelihood estimations, using highly efficient quadrature rules and interpolation; we apply the method to the dynamic discrete choice model of Rust (1987). Second, we present a method to use the constrained optimization approach to the estimation of dynamic models (MPEC; Su and Judd, 2012) in conjunction with grid adaption for state variables with continuous support; we use grid adaption by node movement, and derive sufficient optimality conditions for the underlying function approximation from the equioscillation theorem, which enables us to solve the estimation and the approximation problem simultaneously. Third, we present a simulation study using agent-based modelling to investigate whether a model of monopolistic competition can reach its equilibrium without common knowledge of aggregate demand and sophisticated utility optimization capabilities of the agents.
This paper examines the role of different material artefacts in the exploration of novel strategic topics. We conceptualize strategic topics as epistemic objects that become instantiated in multiple material artifacts, i.e., partial objects, which not only represent the epistemic object but also energize and direct the exploration process. Based on a longitudinal case study of a company that (in collaboration with other companies) explored the strategic topic of “flexible production”, we develop a new typology of material artefacts in terms of their relation to the strategic topic. We describe the layered nature of materiality, differentiating between different types of objectual and non-objectual material artefacts, and show how their interplay shapes the dynamics of the strategizing process. In particular, we explain how the constellation of material artefacts can lead to a shift of the strategic topic itself. We offer a conceptual model that captures the mechanisms in the dynamic interplay of different types of material artefacts and their effect on the process of exploration.
