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Scientific evidence indicates that greenhouse gases emissions related to human activity are a significant contributor to global climate change. This paper investigates the impact of policy prescriptions and technologies for reducing U.S. greenhouse gas emissions. The analysis uses NERA's NewERA integrated model, which combines a top-down general equilibrium macro model of the U.S. economy with a detailed bottom-up model of the North American electricity sector. It examines the cost of cutting emissions by 0% to 80% of 2005 levels by 2050 under several scenarios, which consider different assumptions about policy choices ranging from purely market-based policy such as a cap-and-trade program to purely command-and-control policies and technology involving availability and efficacy of nuclear, Carbon Capture and Storage, renewables, and end-use efficiency technology. Our analysis shows a distinct efficiency advantage for market-based mechanisms and interaction of command-and-control mandates with market-based policies increase market distortions leading to higher welfare loss. We show that under such a mixed policy regime, carbon price is an unsuitable indicator of economic costs. [ABSTRACT FROM AUTHOR]

This paper examines a range of technological and regulatory approaches to reducing greenhouse gas (GHG) emissions. Availability of new technologies will control how the economy and energy infrastructure respond to any future climate policies. How such policies interact with other types of environmental regulations will also influence the best options for meeting emissions goals. To investigate these effects, the ADAGE model is used to examine policy impacts for several climate and technology scenarios, focusing on key factors such as emissions, technology deployment, energy prices and macroeconomic indicators. In general, the simulations indicate that reductions in GHG emissions can be accomplished with limited economic adjustments, although the impacts depend on both the regulatory approaches used and the future availability of new low-carbon technologies. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the study design and the results of the EMF 24 U.S. Technology Scenarios. The EMF 24 U.S. Technology Scenarios engaged nine top energy-environment-economy models to examine the implications of technological improvements and technological availability for reducing U.S. greenhouse gas emissions by 50% and 80% by 2050. The study confirms that mitigation at the 50% or 80% level will require a dramatic transformation of the energy system over the next 40 years. The study also corroborates the result of previous studies that there is a large variation among models in terms of which energy strategy is considered most cost-effective. Technology assumptions are found to have a large influence on carbon prices and economic costs of mitigation. [ABSTRACT FROM AUTHOR]

This paper assesses endogenous technical change (ETC) in climate-economy models, using the models in the Innovation Modeling Comparison Project (IMCP) as a representative cross-section. ETC is now a feature of most leading models. Following the new endogenous growth literature and the application of learning curves to the energy sector, there are two main concepts employed: knowledge capital and learning curves. The common insight is that technical change is driven by the development of knowledge capital and its characteristics of being partly non-rival and partly non-excludable. There are various different implementations of ETC. Recursive CGE models face particular difficulties in incorporating ETC and increasing returns. The main limitations of current models are: the lack of uncertainty analysis; the limited representation of the diffusion of technology; and the homogeneous nature of agents in the models including the lack of representation of institutional structures in the innovation process. [ABSTRACT FROM AUTHOR]

This paper uses the ENTICE-BR model to study the effects of various climate stabilization policies. Because the ENTICE-BR model includes benefits from reduced climate damages, it is possible to calculate the net economic impact of each policy. In general, only the least restrictive concentration limit is welfare enhancing. While the policies are welfare enhancing in simulations using optimistic assumptions about the potential of the backstop energy technology, such assumptions mean that the backstop is also used in the no-policy base case, so that climate change itself is less of a problem. Finally, assumptions about the nature of R&D markets are important. Removing the assumption of partial crowding out from energy R&D nearly doubles the gains from policy-induced energy R&D. [ABSTRACT FROM AUTHOR]

We assess the effect of ITC in a global growth model, -- DEMETERICCS -- with learning-by-doing, where energy savings, energy transition and carbon capturing and sequestration (CCS) are the three main options for emissions reductions. The model accounts for technological change based on learning by doing, embodied in capital installed in previous periods. We run five scenarios: one baseline scenario with no climate change policy and four stabilization scenarios in which atmospheric CO2 concentrations are stabilized at 550, 500, 450, and 400 ppmv. We find that the timing of emissions reductions and the investment strategy is relatively independent of the endogeneity of technological change. More important is the vintages' structure of production. ITC does reduce costs by approximately a factor of 2, however, these benefits only materialize after some decades. [ABSTRACT FROM AUTHOR]

Modeling of technological change has been a major empirical and analytical ob-stacle for many years. One approach to modeling technology is learning or ex-perience curves, which originated in techniques used to estimate cost functions in manufacturing. These have recently been introduced in policy models of energy and climate-change economics to make the process of technological change en-dogenous--that is, allow technologies to vary with economic conditions. It is not widely appreciated that using learning in modeling raises major potential prob-lems. The present note has three points. First, it shows that there is a fundamental statistical identification problem in trying to separate learning from exogenous technological change and that the estimated learning coefficient will generally be biased upwards. Second, we present two empirical tests that illustrate the potential bias in practice and show that learning parameters are not robust to alternative specifications. Finally, we show that an overestimate of the learning coefficient will provide incorrect estimates of the total marginal cost of output and will therefore bias optimization models to tilt toward technologies that are incorrectly specified as having high learning coefficients. [ABSTRACT FROM AUTHOR]