Aggregate energy intensity in the United States has been declining steadily since the mid-1970s and the first oil shock. Energy intensity can be reduced by improving efficiency in the use of energy or by moving away from energy-intensive activities. At the national level, I show that roughly three-quarters of the improvements in U.S. energy intensity since 1970 results from efficiency improvements. This should reduce concerns that the United States is off-shoring its carbon emissions. A state-level analysis shows that rising per capita income and higher energy prices have played an important part in lowering energy intensity. Price and income predominantly influence intensity through changes in energy efficiency rather than through changes in economic activity. In addition, the empirical analysis suggests that little policy intervention will be needed to achieve the Bush Administration goal of an 18 percent reduction in carbon intensity by the end of this decade.

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This paper measures the direct and indirect incidence of a carbon tax using current income and two measures of lifetime income to rank households. Our results suggest that carbon taxes are more regressive when annual income is used as a measure of economic welfare than when proxies for lifetime income are used. Further, the direct component of the tax, in any given year, is significantly more regressive than the indirect component. In fact, for 1987, the indirect component of the tax is mildly progressive. We observe a modest shift over time with the direct component of carbon taxes becoming less regressive and the indirect component becoming more regressive. These effects mostly offset each other and the distribution of the total tax burden has not changed much over time. In addition we find that regional variation has fluctuated over the years of our anlaysis. By 2003 there is little systematic variation in carbon tax burdens across regions of the country.

Change combines cutting-edge scientific research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being data-driven, the Joint Program uses extensive Earth system and economic data and models to produce quantitative analysis and predictions of the risks of climate change and the challenges of limiting human influence on the environmentessential knowledge for the international dialogue toward a global response to climate change.To this end, the Joint Program brings together an interdisciplinary group from two established MIT research centers: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers-along with collaborators from the Marine Biology Laboratory (MBL) at Woods Hole and short-and long-term visitors-provide the united vision needed to solve global challenges.At the heart of much of the program's work lies MIT's Integrated Global System Model. Through this integrated model, the program seeks to discover new interactions among natural and human climate system components; objectively assess uncertainty in economic and climate projections; critically and quantitatively analyze environmental management and policy proposals; understand complex connections among the many forces that will shape our future; and improve methods to model, monitor and verify greenhouse gas emissions and climatic impacts.This reprint is intended to communicate research results and improve public understanding of global environment and energy challenges, thereby contributing to informed debate about climate change and the economic and social implications of policy alternatives. Given uncertainty in long-term carbon reduction goals, how much non-carbon generation should be developed in the near-term? This research investigates the optimal balance between the risk of overinvesting in non-carbon sources that are ultimately not needed and the risk of underinvesting in non-carbon sources and subsequently needing to reduce carbon emissions dramatically. We employ a novel framework that incorporates a computable general equilibrium (CGE) model of the U.S. into a two-stage stochastic approximate dynamic program (ADP) focused on decisions in the electric power sector. We solve the model using an ADP algorithm that is computationally tractable while exploring the decisions and sampling the uncertain carbon limits from continuous distributions.The results of the model demonstrate that an optimal hedge is in the direction of more non-carbon investment in the near-term, in the range of 20-30% of new generation. We also demonstrate that the optimal share of non-carbon generation is increasing in the variance of the uncertainty about the long-term carbon targets, and that with greater uncertainty in the future policy regime, a balanced portfolio of non-carbon, natural gas, and coal generation is desirable.