How Consistent are Alternative Short-Term Climate Policies with Long-Term Goals?
Valentina Bosetti, Marzio Galeotti, Alessandro Lanza
Climate policy,Long-term climate targets,Climate sensitivity uncertainty,capping radiative forcing
Climate Change and Sustainable Development
Choosing long-term goals is a key issue in the climate policy agenda. Targets should be easily measurable and feasible, but also effective in damage control. Once goals are set globally, given the uncertainty affecting long-term strategies and region-specific preferences for different policy instruments, policies will be better represented by a diversified portfolio to be revised over time, rather than "once and forever" decisions. It therefore becomes crucial to understand to what extent different strategies (or policy portfolios) are consistent with long-term targets, that is, when they imply emission paths which do not irreversibly diverge from globally set goals. The present paper aims to investigate emission paths implied by plausible policy scenarios against those derived by imposing alternative long-term targets, comparing, for example, differences in peak periods. Plausible policy scenarios are for instance Kyoto-type targets with or without participation by the U.S. and/or by developing countries. Different long-term targets considered focus on stabilisation of CO2 concentrations, radiative forcing and the increase in atmospheric temperature relative to pre-industrial levels. In order to account for the uncertainty surrounding the climate cycle, for each long-term goal multiple paths of emission – the most probable, the optimistic and the pessimistic ones – are considered in the comparison exercise. Comparative analysis is performed using a newly developed version of the FEEM-RICE model, a regional economy-climate model of optimal economic growth which is based on Nordhaus and Boyer’s RICE model crucially extended in order to account for induced technical change. In particular, both carbon and energy intensity are affected by a new endogenous variable – Technical Progress – which captures both the role of Learning by Researching and of Learning by Doing. These are in turn determined by the optimal levels of Research and Development and of Emission Abatement.