Minh Ha-Duong

Influence of socioeconomic inertia and uncertainty on optimal CO2-emission abatement

Following the United Nations Framework Convention on Climate Change, governments will negotiate, in Kyoto this December, an agreement to mitigate anthropogenic greenhouse-gas emissions. Here we use a model approach to examine optimal CO2-emission abatement paths for specified long-term constraints on atmospheric CO2 concentrations. Our analysis highlights the interplay of uncertainty (in target greenhouse-gas concentrations) and the inertia in the energy systems that produce CO2 emissions. We find that the ‘integrated assessment’ models previously applied to these issues under-represent inertia. A more appropriate representation of inertia increases the costs of deferring abatement and makes it optimal to spread the effort of abatement across generations. Balancing the costs of early action against the potentially higher costs of more rapid and forced later action, we show that early attention to the carbon-emitting potential of new and replacement energy investments will minimize the risk to environmental and economic systems. We conclude that if there is a significant probability of having to maintain atmospheric greenhouse gas concentrations below about double those of the pre-industrial era, then the economic risks associated with deferring abatement justify starting to limit CO2 emissions from energy systems immediately.

An abrupt stochastic damage function to analyse climate policy benefits

This paper studies uncertainty about the non-linearity of climate change impact. The DIAM 2.3 model is used to compute the sensitivity of optimal CO2 emissions paths with respect to damage function parameters. This builds upon results of the EMF-14 uncertainty subgroup study by explicitly allowing for the possibility of threshold effects and hockey stick damage functions. It also extends to the cost-benefits framework previous studies about inertia of energy systems. Results show that the existence of a threshold in the damage function is critical to precautionary action. Optimal path are much less sensitive to uncertainty on the scale of the damages than on the threshold values.