Spatial Climate-Economic Models in the Design of Optimal Climate Policies across Locations 1 William Brock a , Gustav Engstrm b and Anastasios Xepapadeas c a University of Wisconsin, Department of Economics, wbrock@ssc.wisc.edu b The Beijer Institute of Ecological Economics and University of Stockholm, gustav.engstrom@beijer.kva.se c Athens University of Economics and Business, xepapad@aueb.gr August 26, 2012 Abstract We couple a one-dimensional energy balance climate model with heat transportation across latitudes, with an economic growth model. We derive temperature and damage distributions across locations and optimal taxes on fossil fuels which, in contrast to zero-dimensional Integrated Assessment Models, account for cross latitude externalities. We analyze the impact of welfare weights on the spatial structure of optimal carbon taxes and identify conditions under which these taxes are spatially nonhomogeneous and are lower in latitudes with relatively lower per capita income populations. We show the way that heat transportation a/ects local economic variables and taxes, and locate su¢cient conditions for optimal mitigation policies to have rapid ramp-up initially and then decrease over time. Keywords: One-dimensional energy balance model, heat transport, lat- itudes, temperature distribution, damage distribution, social planner, com- petitive equilibrium, local welfare weights, optimal taxes. JEL Classication: Q54, Q58, R11 1 This research has been co-nanced by the European Union (European Social Fund  ESF) and Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thalis  Athens University of Economics and Business - Optimal Management of Dynamical Systems of the Economy and the Environment. W. Brock is grateful for nancial and scientic support received from The Center for Robust Decision Making on Climate and Energy Policy (RDCEP) which is funded by a grant from the National Science Foundation (NSF) through the Decision Making Under Uncertainty (DMUU) program. He is also grateful to the Vilas Trust for nancial support. None of the above are responsible for any errors, opinions, or shortcomings in this article. The authors thank K. Judd and E. Moyer for valuable discussions and J. Stefan for editing assistance. 1