Anthropogenic drivers of carbon emissions: scale and counteracting effects

This paper assesses the achievement and the limitation of our path to the stabilization of anthropogenic carbon emissions with economic growth using a stochastic Kaya model. The elasticity of carbon dioxide emissions with respect to anthropogenic drivers such as population, affluence, energy efficiency, fossil-fuel dependence, and emission factor is estimated using panel data of 132 countries from 1960 to 2010. Then the stochastic Kaya model is used for index decomposition analysis. Investigating the scale and the counteracting effects, I find that except a few countries like Germany, most countries have not achieved the goal of carbon reductions with economic growth. In addition, the current path of each nation does not guarantee the achievement of a global long-term goal of emissions reductions, say 50% by 2050 compared to the 1990 level. This is because the scale effect (the sum of the population and affluence effects) is so large that the current level of the technology effects can rarely offset carbon emissions. Should we achieve the global target for carbon reductions a significant amount of technology effects through stringent policy interventions need to be accompanied

Anthropogenic drivers of carbon emissions: scale and counteracting effects

This paper assesses the achievement and the limitation of our path to the stabilization of anthropogenic carbon emissions with economic growth using a stochastic Kaya model. The elasticity of carbon dioxide emissions with respect to anthropogenic drivers such as population, affluence, energy efficiency, fossil-fuel dependence, and emission factor is estimated using panel data of 132 countries from 1960 to 2010. Then the stochastic Kaya model is used for index decomposition analysis. Investigating the scale and the counteracting effects, I find that except a few countries like Germany, most countries have not achieved the goal of carbon reductions with economic growth. In addition, the current path of each nation does not guarantee the achievement of a global long-term goal of emissions reductions, say 50% by 2050 compared to the 1990 level. This is because the scale effect (the sum of the population and affluence effects) is so large that the current level of the technology effects can rarely offset carbon emissions. Should we achieve the global target for carbon reductions a significant amount of technology effects through stringent policy interventions need to be accompanied

Stochastic Kaya model and its applications

This paper develops a stochastic Kaya model. The elasticity of carbon dioxide emissions with respect to population, per capita GDP, energy efficiency, and fossil fuel dependence is estimated using the panel data of 132 countries from 1960 to 2010. As an application of the stochastic Kaya model, we investigate the achievement of each nation to the stabilization of carbon emissions with economic development, using a method of index decomposition analysis. In addition, carbon emissions are projected by 2050 using the model. One of the main findings is that assuming the unit elasticity for each driving force underestimates the scale effect (population change and economic growth) and overestimates the counteracting technology effect. This results in significant differences in quantifying driving forces of the changes in carbon emissions and in future emissions projections

Fat-tailed uncertainty and the learning-effect

One of the recent findings in the economics of climate change is that emissions control plays a significant role in the reduction of the tail-effect of fat-tailed uncertainty on welfare. The current paper gives another perspective: the learning-effect. The effect of emissions control on welfare is decomposed into the direct effect and the learning-effect. Although this has been known for thin-tailed uncertainty in the literature, this paper takes a different approach: the changes in temperature distributions under fat-tailed uncertainty and learning

A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations

A recursive method for solving an integrated assessment model of climate and the economy is developed in this paper. The method approximates value function with a logarithmic basis function and searches for solutions on a set satisfying optimality conditions. These features make the method suitable for a highly nonlinear model with many state variables and various constraints, as usual in a climate-economy model

Fat-tailed uncertainty and the learning-effect

One of the recent findings in the economics of climate change is that emissions control plays a significant role in the reduction of the tail-effect of fat-tailed uncertainty on welfare. The current paper gives another perspective: the learning-effect. The effect of emissions control on welfare is decomposed into the direct effect and the learning-effect. Although this has been known for thin-tailed uncertainty in the literature, this paper takes a different approach: the changes in temperature distributions under fat-tailed uncertainty and learning

A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations

A recursive method for solving an integrated assessment model of climate and the economy is developed in this paper. The method approximates value function with a logarithmic basis function and searches for solutions on a set satisfying optimality conditions. These features make the method suitable for a highly nonlinear model with many state variables and various constraints, as usual in a climate-economy model

The effect of learning on climate policy under fat-tailed risk

The effect of learning on climate policy is not straightforward when climate policy is concerned. It depends not only on the ways that climate feedbacks, preferences, and economic impacts are considered, but also on the ways that uncertainty and learning are introduced. Deep (or fat-tailed) uncertainty does matter for the optimal climate policy in that it requires more stringent efforts to reduce carbon emissions. However, learning may reveal thin-tailed uncertainty, weakening the case for emission abatement: learning reduces the stringency of the optimal abatement efforts relative to the no learning case even when we account for deep uncertainty. In order to investigate this hypothesis, we construct an endogenous (Bayesian) learning model with fat-tailed uncertainty on climate change and solve the model with stochastic dynamic programming. In our model a decision maker updates her belief on the total feedback factors through temperature observations each period and takes a course of action (carbon reductions) based on her belief. With various scenarios, we find that the uncertainty is partially resolved over time, although the rate of learning is relatively slow, and this materially affects the optimal decision: the decision maker with a possibility of learning lowers the effort to reduce carbon emissions relative to the no learning case. This is because the decision maker fully utilizes the information revealed to reduce uncertainty, and thus she can make a decision contingent on the updated information. In addition, with incorrect belief scenarios, we find 2 that learning enables the economic agent to have less regrets (in economic terms, sunk benefits or sunk costs) for her past decisions after the true value of the uncertain variable is revealed to be different from the initial belief