The current misplaced focus on short-term climate policies is a product both of domestic political exigencies and badly flawed technical analyses. A prime example of the latter is a recent U.S. Department of Energy study, prepared by five national laboratories. The 5-Labs study assumes — incorrectly — that technical solutions are readily at hand. Worse, advocates of short-term emissions targets under the Framework Convention on Climate Change are using this study to justify the subsidy of existing energy technologies — diverting resources from the effective long-term technology response that will be needed if the climate picture darkens.

About the book: In the nineteenth century, horse transportation consumed vast amounts of land for hay production, and the intense traffic and ankle-deep manure created miserable living conditions in urban centers. The introduction of the horseless carriage solved many of these problems but has created others. Today another revolution in transportation seems overdue. Transportation consumes two-thirds of the world's petroleum and has become the largest contributor to global environmental change. Most of this increase in scale can be attributed to the strong desire for personal mobility that comes with economic growth.

In Transportation in a Climate-Constrained World, the authors present the first integrated assessment of the factors affecting greenhouse gas (GHG) emissions from passenger transportation. They examine such topics as past and future travel demand; the influence of personal and business choices on passenger travel's climate impact; technologies and alternative fuels that may become available to mitigate GHG emissions from passenger transport; and policies that would promote their adoption. And most important, taking into account all of these options are taken together, they consider how to achieve a more sustainable transportation system in the next thirty to fifty years.

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The United States has adopted fuel economy standards that require increases the on-road efficiency of new passenger vehicles, with the goal of reducing petroleum use, as well as (more recently) greenhouse gas (GHG) emissions. Understanding the cost and effectiveness of this policy, alone and in combination with economy-wide policies that constrain GHG emissions, is essential to inform coordinated design of future climate and energy policy. In this work we use a computable general equilibrium model, the MIT Emissions Prediction and Policy Analysis (EPPA) model, to investigate the effect of combining a fuel economy standard with an economy-wide GHG emissions constraint in the United States. First, a fuel economy standard is shown to be at least five to fourteen times less cost effective than a price instrument (fuel tax) when targeting an identical reduction in cumulative gasoline use. The GHG emissions reduction under a fuel economy standard alone is also shown to be proportionally less than the reduction in gasoline use, in part because GHG emissions from electricity production used in grid-connected electric vehicles are excluded from the regulation. Second, when combined with a cap-and-trade (CAT) policy, the fuel economy standard increases the cost of meeting the GHG emissions constraint by forcing expensive reductions in passenger vehicle gasoline use, replacing other more cost-effective abatement opportunities. Third, the impact of adding a fuel economy standard depends on the availability and cost of abatement opportunities in transport—if advanced biofuels provide a cost-competitive alternative to gasoline, the fuel economy standard does not bind and passenger vehicles provide a significantly larger contribution to GHG emissions abatement.

The purpose of this study is to develop a strategy for investment in power generation technologies in the future given the uncertainties in climate policy and fuel prices. First, such studies are commonly conducted using deterministic methods which assume a given likelihood of the carbon and gas price levels. In this study a probabilistic approach is used to address these uncertainties. Secondly, capacity expansion models conventionally apply average estimates to predict the amount of power that each generator will produce based on the technology chosen. I propose an alternate method which determines the actual generation hour-by-hour of a generator. Using this method, I also capture the variability of wind generation across the year.

To accomplish this goal, I used the Electric Reliability Council of Texas (ERCOT) as a case study. I investigated the effect of different scenarios of generation technology investments projected over a period of twenty years. I conducted two sets of analyses; first assuming that Carbon Capture and Storage (CCS) technologies will be available after 2020, then assuming that they will not. Using a dispatch model, I simulated the hours of a load duration curve for 2020 and 2030. In the first period 2010-2020, I assumed the price of carbon to either be $0 or $50/ton CO2. In the second period, I take the carbon price to be at either a low of $25/ton of CO2 or a high of $100/ton of CO2. The price of natural gas used was either a high of $15/MMBtu or a low of $3MMBtu in both periods. Using a Monte Carlo, I sample the wind generation based on the season and the time of dat. The system costs with the new investment scenarios were then evaluated in a decision tree to establish the socially optimal solution.

I find that the optimal strategy to be taken today depends on the availability of CCS technologies in 2030. Assuming that there is CCS in 2030, the more dominant strategy would be to build natural gas generators today. If we assume that there is no CCS in 2030, the strategy would depend on the probabilities of the levels of gas and carbon prices in 2020.