| Technology and Technical Change in the MIT EPPA Model (2004) | |||||||||||
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| MIT Joint Program the Science and Policy Global Change Technology and Technical Change the MIT EPPA Model Henry Jacoby John Reilly James McFarland and Sergey Paltsev Report July The MIT Joint Program the Science and Policy Global Change organization for research independent policy analysis and public education global environmental change seeks provide leadership understanding scientific economic and ecological aspects this difficult issue and combining them into policy assessments that serve the needs ongoing national and international discussions this end the Program brings together interdisciplinary group from two established research centers MIT the Center for Global Change Science CGCS and the Center for Energy and Environmental Policy Research CEEPR These two centers bridge many key areas the needed intellectual work and additional essential areas are covered other MIT departments collaboration with the Ecosystems Center the Marine Biology Laboratory MBL Woods Hole and short and long term visitors the Program The Program involves sponsorship and active participation industry government and non profit organizations inform processes policy development and implementation climate change research needs focus improving the prediction those variables that are most relevant economic social and environmental effects turn the greenhouse gas and atmospheric aerosol assumptions underlying climate analysis need related the economic technological and political forces that drive emissi. Potential technology change has a strong influence on projections of greenhouse gas emissions and costs of control, and computable general equilibrium (CGE) models are a common device for studying these phenomena. Using the MIT Emissions Prediction and Policy Analysis (EPPA) model as an example, two ways of representing technology in these models are discussed: the sector-level description of production possibilities founded on social accounting matrices and elasticity estimates, and sub-models of specific supply or end-use devices based on engineering-process data. A distinction is made between exogenous and endogenous technical change, and it is shown how, because of model structure and the origin of key parameters, such models naturally include shifts in production process that reflect some degree of endogenous technical change. As a result, the introduction of explicit endogenous relations should be approached with caution, to avoid double counting.. The CGE model underlying this analysis was supported by the US Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-94ER61937), the US Environmental Protection Agency (X-827703-01-0), the Electric Power Research Institute, and by a consortium of industry and foundation sponsors | |||||||||||
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