Incorporating carbon capture and storage technologies in integrated assessment models
Introduction
Over the past century, technological change has accounted for a significant portion of economic growth and is, in part, responsible for the dramatic increases in greenhouse gas emissions attendant with this growth. Despite this historic linkage between economic growth and greenhouse gas emissions, technological change also holds much promise for decoupling economic growth from emissions growth. Both the magnitude and rate of technological change toward low- or no-carbon emitting processes govern future anthropogenic emissions (IPCC, 2001). A multitude of integrated assessment models have been developed to characterize future greenhouse gas emissions resulting from human economic activity and to analyze the cost of policies for reducing such emissions (see, e.g., Weyant and Hill, 1999). These models, containing various representations of technological change, form a key component of integrated climate policy analysis.
This paper examines the representation of carbon capture and storage (CCS) technologies, one form of technological change, in integrated assessment models. As a test case, we use the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium (CGE) model of the world economy. EPPA simulates both economic growth and anthropogenic greenhouse gas emissions and performs a key role in the MIT Integrated Global System Model (Prinn et al., 1999). CGE models are the most complete in characterizing economy-wide interactions, including international trade, energy supply and demand, inter-industry supply and final demand for goods and services, and consumer demands. However, these models typically offer minimal technological detail compared to bottom–up engineering activity models (see, e.g., van der Zwaan et al., 2002). As Jacoby et al. (2004) notes, specific technologies are but one form of technical change within the EPPA model. This paper highlights our efforts and future plans to represent low carbon-emitting, evolutionary technologies in the electricity sector of a CGE model.1
The broad class of CCS technologies includes all systems that harness CO2 from industrial processes or the air and sequester the CO2 in geologic reservoirs, the deep ocean, or biomass. The CCS technologies represented in this paper apply only to the capture and storage of CO2 from electric power plants. The electric power sector accounts for a substantial portion of greenhouse gas emissions in the developed world and a growing fraction in developing countries. In the U.S., electricity generation accounts for about one-third of all greenhouse gas emissions. Herein, we describe the representation of three electricity generation options that compete with existing electricity generation technologies in the EPPA model. The three new power generation technologies are (1) a natural gas combined cycle technology (advanced gas) without carbon capture and storage, (2) a natural gas combined cycle technology with carbon capture and storage (gas capture), and (3) an integrated coal gasification technology with carbon capture and storage (coal capture). These compete in the EPPA model's electricity sector with conventional fossil generation, nuclear, hydro, wind and solar, and biomass power generation. This paper addresses three main issues with representing CCS technologies in a top–down model: proper characterization of the technical system, translation of bottom–up engineering information into a CGE model, and the portrayal of factors that affect technology adoption rates. Many of the lessons learned in our research and the cost and performance data that have been developed can be applied to other integrated assessment models.
Section 2 describes both the framework of the EPPA model and the bottom–up engineering cost models of technical systems that are to be represented within it. The translation of bottom–up engineering cost models into EPPA's general equilibrium framework is handled in Section 3. These sections summarize our efforts to characterize the relative economics of technologies competing in the electricity sector. Having dealt with the attributes that determine the economic attractiveness of a technology in 2 Characterizing technology costs using bottom–up information, 3 Translating bottom–up information into top–down models, Section 4 examines the factors that affect the rate of technology adoption, reviews their treatment in integrated assessment models, and outlines areas of further research.
Section snippets
Characterizing technology costs using bottom–up information
Incorporation of novel technologies into top–down models requires a complete accounting and characterization of costs that are consistent with the both the engineering system in which the technology functions and the model's structure and underlying data. This section begins with a brief description of the EPPA model, previewing the final economic analysis framework. It is followed by a characterization of the individual technologies and the economic and physical systems in which they operate.
Translating bottom–up information into top–down models
Having accounted for the relevant bottom–up technology costs, we now demonstrate how to translate these technology costs for application in a top–down macro-economic model. This section reviews three aspects of this translation: converting bottom–up engineering costs into factors of production, specifying elasticities of substitution between factors of production, and specifying costs relative to existing technologies.8
Modeling technology adoption rates
The above sections describe a procedure for, and the challenges with, obtaining the correct relative economics for introducing non-extant technologies into CGE models. Yet, they do not address the rate of technology penetration—a critical factor in responding to constraints on greenhouse gas emissions. Evidence that a new technology gradually takes over a market such that the share of the market controlled by the technology plotted against time follows an S-shaped function is widely observed (
Conclusions
We described a method for incorporating CCS technologies into the EPPA model, a top–down CGE model using bottom–up engineering information. By defining consistent cost model boundaries that compare homogenous goods, the methodology is applicable to other technologies not widely available today. The translation of bottom–up engineering information into CGE models involves the specification of factor shares, elasticities of substitution, and relative economics. The treatment of plant level
Acknowledgements
This work was conducted with support by the US Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-02ER63473).
The EPPA model used in much of 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. Thanks are due to Henry Jacoby and John
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