mitigation cost. They have also started to analyze implications for worldwide emissions trading. In particular, they are modeling scenarios for varying carbon allocation rules.

IMAGE

The Netherlands Environmental Assessment Agency (MNP) operates a global coverage model similar to the MIT-EPPA model. IMAGE represents 26 regions at a resolution of 0.5 degree by 0.5 degree. It integrates energy, land use, food security, climate, and air quality in one framework and is used to analyze long-term (2050-2100) issues. From a technology perspective, IMAGE is relatively aggregated, though it does include advanced options such as biomass power plants with CCS. Climate change feedback is represented and affects land use and energy demand. The MNP is also now working to disaggregate regional data, with the hope of shedding some light on the positions of different countries—some countries will likely experience damages that exceed their expected GHG mitigation costs—and this information may be useful in formulating a more equitable global response to climate change.

Haiku

Resources for the Future (RFF) models the U.S. electric utility sector using the Haiku model. It solves for the electricity sector in 21 U.S. regions, and unlike some other electricity market models, it explicitly accounts for the structure of cost recovery in different regions of the country (whether it is market-based or based on regulation of the cost of service). It also includes a fuel price response to demand. Dallas Burtraw noted that, although the Haiku model may not handle uncertainty any better than other models, RFF is using the Delta method when looking at safety valves, and at the influence of a price cap and price floor on variability in allowance price transactions. RFF has also linked to an investor behavior model and is using stochastic dynamic optimization to analyze how those who invest in CCS would respond to equilibrium conditions, given the uncertainty in that market.

Scenario Planning

Within GE, analysts are using scenario planning to help the business units understand the social, economic, and political ramifications of climate change. Their primary interest is in technology change, both its rate of deployment and its effects on factors like energy efficiency. Evans provided three reasons that GE takes this approach: to deal with rapid change and uncertainty in national and international energy markets; to improve long-term technology investment decisionmaking within the company; and to enhance organizational learning. They use three scenario frameworks: Asia Rising, Changing Climates, and Global Rifts. Each scenario provides a unique framework for thinking about how the future could unfold and the technology pathways that might result.

Data Sources and Limitations

Baseline assumptions are the biggest source of uncertainty for several of the models. Assumptions about world oil prices, technological progress, or population growth can have important implications, particularly beyond 2030. These sorts of assumptions determine the “business as usual” scenario. In the shorter-term, even the changes in EIA’s Annual Energy Outlook 2008 (notably the Energy Independence and Security Act) affect the baseline for many models. John Conti noted that Congress would like NEMS to model at the state or district level, but sufficient data does not exist. End-use data and consumption surveys would aid the EIA in providing a richer level of detail, and from his perspective, industrial sector data is most in need of improvement. DOE has programs that extend NEMS results out to 2050, but it becomes difficult to fill in the detailed data assumptions that would drive the model beyond its 25-year timeframe. John Reilly stated that MIT is developing a U.S. regional model that



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