See Summary for a quick overview of the EIA analysis.
On July 3, 2003 the Energy Information Administration (EIA) of the U.S. Department of Energy released its economic analysis of Senate Bill 139: the Climate Stewardship Act of 2003. This bill was introduced by Senators John McCain and Joseph Lieberman on January 9, 2003. S.139 represents the first economy-wide “cap-and-trade” bill that reduces greenhouse gas (GHG) emissions primarily through limiting the amount of emissions from key economic sectors and providing flexibility in obtaining GHG reductions through emissions trading and sequestration (or storage) of carbon. The program would apply to greenhouse gas emissions from major sectors – electric utilities, transportation, and industry-- covering roughly 80% of U.S. emissions.
This analysis discusses key features of EIA’s National Energy Modeling System (NEMS) model and relevant assumptions used by EIA in analyzing the costs of S.139. Modeling an economy-wide greenhouse gas trading program presents real challenges. Model results can provide important insights regarding policy design features and their implications for costs but should not be viewed as definitive predictions of future costs. Historically, advance projections of costs of many environmental programs – particularly market-based programs such as the SO2 acid rain trading program – have been much higher than the actual costs of implemented programs. Consumers of any modeling results should understand how model structure, inputs, and assumptions drive the results – which in this case focus only on the costs, but not the benefits, of climate change policy. (For more information on key drivers of cost estimates in modeling, see An Introduction to the Economics of Climate Change Policy .
EIA’s analysis of S.139 using its (NEMS) model is just one of a number of efforts by a range of organizations to model S.139. In addition to the EIA analysis, both MIT and NRDC have released their own review of the potential costs of S.139, and these results are compared with EIA’s below. In addition, the Center is working with Dr. Dale Jorgenson of Harvard University and his colleagues to evaluate possible costs of the bill.
The Center's Director of Policy Analysis, Vicki Arroyo, served as a peer reviewer on the EIA effort. She and other reviewers provided input to EIA, and while some of those comments have been reflected in their analysis (e.g., deleting a side case with zero offsets and including one with a higher limit on offsets), many were not addressed. The discussion below reflects comments submitted during the review process and notes how the cumulative effect of many factors – both structural features of NEMS and assumed inputs – serves to drive the projected costs higher than what they are likely to be.
Characterization of the EIA NEMS Model
As a macro-energy model, NEMS is a useful tool to analyze an economy-wide greenhouse gas trading program. Macro-energy models solve for the most promising solutions to achieving reductions in greenhouse gas emissions, and program costs are calculated from the economy-wide impacts of higher fossil fuel prices, altered productivity, and changing competitive advantages of firms and sectors. However, the trade-off is that macro-energy models lose detail on new technologies, characteristics of individual sectors and opportunities for energy efficiency. As a result, they often miss available opportunities to minimize program costs.
EIA’s NEMS model is considered a conservative macro-energy model and has often produced cost projections in the top quarter of modeling comparisons (for example, in the Energy Modeling Forum’s modeling comparison of the U.S. and the Kyoto Protocol). 1 Some NEMS features yielding higher projected costs are listed below.
Substitution by Firms and Consumers
Inclusion of Benefits of Climate Change Policies
Baseline Estimates of Population, GDP, Energy Use and Hence Emissions
Policy Regime Considered
Key Parameters of McCain-Lieberman S.139
The key characteristics of the S.139 GHG cap-and-trade program are:
The first driver of the costs of reductions is the assumed “business as usual” or “base” case – that is, what emissions would have been in the absence of S.139.
The base case in NEMS assumes strong economic growth (3% per year, despite continuing economic uncertainty), and a continued reliance on fossil fuels with high carbon emissions. In particular, a significant increase of coal for electricity production is forecast, with generation from coal predicted to rise by 32% by 2025 relative to year 2000. In addition, continued expansion of transportation is expected, with petroleum consumption rising by 46% by 2025 relative to year 2000. Additional emissions increases are expected in the industrial, commercial and residential sectors. Despite these high baselines, electricity and fuel prices remain low in the base case, further exaggerating the relative impact of S.139 when costs are imposed.
The future supply -- and hence price -- of natural gas is a crucial component of the costs of controlling GHGs since natural gas is expected to be the primary transition fuel to a lower carbon economy. EIA assumes a tight supply under increased demand for natural gas in their 2003 Annual Energy Outlook, yet in its analysis of S.139, these estimates have been revised to be even higher based on the short-term indications from EIA’s recent Monthly Energy Reviews. This assumption represents a very pessimistic long-term assessment of North American natural gas resources, especially regarding the price level at which new “back-stop” natural gas resources would become available – e.g., from Northern Canada and Alaska, deep water in the Gulf of Mexico, and unconventional gas resources.
In addition, no new policy measures -- including those aimed at reducing local air pollution, improving energy security, developing new technology, promoting hydrogen, or liberalizing the electricity market -- are included in EIA’s analysis. Enactment of these complementary policies is likely to reduce the costs of controlling greenhouse gas emissions over the time period studied (to 2025).
EIA’s Primary Analysis of S.139
In EIA’s primary analysis run of S.139, a number of additional input assumptions drive up the costs of controlling GHGs under this bill. These can be divided into two main categories:
Input data and use of flexibility mechanisms for lower cost reductions:
Technology penetration and energy efficiency opportunities:
As a result of the above assumptions, the majority of emission reductions in this analysis come from anticipated fuel switching in the electricity sector. This leads to higher prices, premature reduction of existing energy equipment, and hence higher costs of the bill.
EIA Sensitivity Analyses of S.139
The report details a number of sensitivity cases in addition to the primary case. Many of these cases were specified by the Senators directing EIA to undertake the analysis. In some cases undertaken by EIA, the specified cases diverged from the recommendations of the reviewers.
Discussion of Results
The cost projections generated by the EIA analysis reflect the input assumptions and model structure of NEMS, and hence are higher than costs are likely to be under the bill as proposed.
Impacts on GDP are reported at a loss of 0.4% by 2025. EIA also reports a higher loss in “real GDP” (down to 0.7% in 2015 before converging with “potential GDP” at 0.4% loss by 2025). This reflects EIA’s assumptions regarding imperfect responses in interest rates and other macro-economic variables. However, this compounded loss in GDP represents only a very small change in annual economic growth rates. The S.139 program would only reduce annual GDP growth in 2001-2025 from 3.04% to 3.02%. That is, rather than growing at 3.04% through 2025, curtailing greenhouse gases under this legislation will result in the economy growing slightly less – at 3.02%. As EIA notes in the Executive Summary, “…other factors that drive the U.S. economy, such as labor force and productivity growth are likely to play a larger role than decisions regarding the enactment of S. 139 in determining the size of the U.S. economy in 2025.”
Specific sectoral impacts are projected to be more pronounced, reflecting the presumed high baseline. The restrictions on natural gas supply, the low level of energy efficiency improvements in the face of sustained price signals (especially in transportation), and low penetration of key technologies that require institutional and regulatory changes for full market penetration, mean that the overwhelming reductions come from fuel and technology switching in the electricity supply industry. As a result, the energy price increases are expected to be significant: e.g., by 2025 prices are projected to increase 27% for petroleum, 46% for natural gas (above an already high base gas price), 475% for coal (because coal is currently very cheap and has more carbon content), and 46% for electricity. In contrast, the MIT analysis of S.139 has far more efficiency improvements, significantly more coal use coupled with carbon sequestration and accelerated penetration of alternative energy supply technologies, including distributed generation and combined heat and power plants. MIT results anticipate a falling price for natural gas under GHG reductions, as higher efficiency and use of alternative fuels weakens demand for natural gas.
Comparison to Other Analyses
Although additional analyses of S.139 are forthcoming, results of EIA’s NEMS runs can be compared with the previously released MIT study (using their EPPA model). As discussed above, a number of different input assumptions in the MIT analysis lead to different principal paths for greenhouse gas reductions, resulting in very different carbon prices and economic impacts. This is illustrated in the table below. Also shown are the results from an analysis for NRDC by the Tellus Institute, which adapts the NEMS model using a more optimistic assessment of opportunities for energy efficiency and the diffusion of lower carbon technologies. In addition, the NRDC analysis includes complementary policies, such as mandatory improvements in vehicle fuel efficiency, controls on local air pollutants and easing of regulatory restrictions that limit combined heat and power technologies.
|Carbon Price in $/tC [$/tCO2]||2010||79 ||62 ||31 ||29 |
|2015||119 ||81 ||40 ||66 |
|2020||178 ||103 ||52 ||81 |
|Welfare % cost||2010||-0.30%||-0.07%||-0.02%||-|
|Total Welfare cost (billion $)||2010||-26.9||-6.1||-1.7||-|
|Cost per Household ($)||2010||228||52||15||53|
The table shows carbon prices, welfare costs and costs per household.
All prices are in $2001.
MIT refers to scenario #9 in that analysis.
Welfare in this case measures lost consumption (or income) by consumers (as leisure effects are ignored). The NRDC analysis does not derive costs per household from overall welfare impacts, instead simply reporting net resource cost changes. Consumption is the major component of GDP (the other components being investment, government expenditures and imports/exports balance). Welfare is a good measure of actual impact on the population.
In year 2000, US GDP was around $10 trillion with consumption at $6.3 trillion.
In year 2000, there were 108 million households in the US with a median income of $41,000, by 2020, there is projected to be 127 million households with a median income of $61,000.
MIT’s analysis of S.139 finds carbon prices to be significantly less for both phases of the bill, including offsets. This reduced impact is even smaller when the model calculates the effects of higher energy prices on overall economic performance and on an individual household basis. Note that if only Phase 1 of S.139 is enacted, the anticipated economic impacts are very small. NRDC’s emphasis on greatly improved energy efficient technologies leads to net benefits from S.139.
The EIA analysis represents an ambitious attempt to provide insights into possible costs related to S.139; however, it should be thought of as an upper bound of likely costs. A more technologically rich and flexible model accompanied by more realistic assumptions regarding modeling inputs would yield lower cost projections.
1 See Weyant J. (ed) 1999, The Costs of the Kyoto Protocol: A Multi-Model Evaluation, Special Issue of the Energy Journal