Our Energy Tomorrow

An interactive guide to the future of energy

Welcome to the Future of Energy! There are lots of opinions about how we should power our world in the coming decades, but how do you separate fact from fiction? This unique web experience is designed to let you explore many possible futures, using information from the federal Energy Information Administration.

You can compare up to three scenarios at once, enabling you to see side-by-side the realities of our energy future. Take the trip, explore, and share what you find.

Energy is inseparable from America’s economic growth and job creation, upon which rests a thriving quality of life and secure future for generations to come. There are many opinions surrounding the best energy policy for our nation. We also know there are a wide range of factors that can impact our future, so how do you separate realities from ideals, or best guesses?

In its 2014 Annual Energy Outlook (AEO) the U.S. Energy Information Administration (EIA) identified 31 possible scenarios, projecting what American energy may look like in the year 2040. In addition, 7 of these scenarios have been updated to reflect new projections for AEO 2015, and are also included in the dashboard. These scenarios are based on a series of variables, including natural resource availability, technology development, and federal legislation. This unique interactive dashboard was created to make it easy to explore these many possible futures, giving you the ability to see the potential of our energy tomorrow.

How to use

1EIA AEO

This yearly report focuses on the factors that shape the U.S. energy system over the long term. The EIA considers each scenario equally likely.

2American Petroleum Institute (API)

Here, you can easily compare and contrast all 38 scenarios. This tool is built from data researched by the U.S. government.

3Your Role

We encourage you to use the dashboard to explore projected scenarios and their potential impacts on our America’s coming energy future, and to draw your own conclusions.

Explore Visualization

A product of Energy Tomorrow

Our Energy Tomorrow - Methodology

Our Energy Tomorrow is presented by the American Petroleum Institute as a tool to explore the potential projections of the U.S. energy system through 2040. The interface was custom designed and built to enable users to easily and productively sort through data compiled by the U.S. Energy Information Administration (EIA) in its Annual Energy Outlook (AEO) for 2014. It should be noted that all data in the AEO are projections (including 2012 and 2013 data) and should not be used for historical modeling.

All data in the Future of Energy (excepting Contextual Data) was obtained from the EIA AEO for 2014, except for the following: Scenarios 1-5, 18, and 31. Data for these scenarios was obtained from the EIA AEO for 2015. In Scenario 31 (AEO2014 Reference Case), data for GDP and Real Disposable Personal Income was NOT updated by the EIA, therefore those data reflect the AEO for 2014.

The AEO2014, prepared by the EIA, presents long term annual projections of energy supply, demand, and prices focused on the U.S. through 2040, based on results from EIA's National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases.

The EIA has identified 31 possible scenarios through its AEO report. Projections in AEO2014 are generated using NEMS, developed and maintained by the Office of Energy Analysis of the EIA. In addition to its use in developing the AEO projections, NEMS is used to complete analytical studies for the U.S. Congress, the Executive Office of the President, other offices within the U.S. Department of Energy (DOE), and other federal agencies. In addition, AEO projections are used by analysts and planners in other government agencies and nongovernmental organizations.

The projections in NEMS are developed with the use of a market-based approach, subject to regulations and standards. For each fuel and consuming sector, NEMS balances energy supply and demand, accounting for economic competition across the various energy fuels and sources. The time horizon of NEMS extends to 2040. Complete regional and detailed results are available on the EIA Analyses and Projections Home Page.

To focus more resources on rapidly changing energy markets and the ways in which they might evolve over the next few years, the U.S. Energy Information Administration (EIA) is revising the schedule and approach for production of the Annual Energy Outlook (AEO). Starting with this Annual Energy Outlook 2015 (AEO2015), EIA is adopting a two-year release cycle for the AEO, with full and shorter editions of the AEO produced in alternating years. AEO2015 is a shorter edition of the AEO.

The shorter AEO includes a limited number of model updates, which are selected predominantly to reflect historical data updates and changes in legislation and regulations. The shorter edition includes a Reference case and five alternative cases: Low Oil Price, High Oil Price, Low Economic Growth, High Economic Growth, and High Oil and Gas Resource.

Legislation in AEO2014

The version of NEMS used for AEO2014 generally represents current legislation and environmental regulations, including recent government actions for which implementing regulations were available as of October 31, 2013, as discussed in the Legislation and Regulations section of the AEO. The potential effects of proposed federal and state legislation, regulations, or standards — or of sections of legislation that have been enacted but require funds or implementing regulations that have not been provided or specified — are not reflected in NEMS. Many of the pending provisions are examined, however, in alternative cases included in AEO2014 or in other analysis completed by EIA. For complete AEO methodology, please see Assumptions to the Annual Energy Outlook 2014. A list of the specific federal and selected state legislation and regulations included in the AEO, including how they are incorporated, is provided in Appendix A.

Data Selection

Data points were selected in order to represent a broad spectrum of relevant metrics. This includes production, consumption, imports and exports for a range of fuel types. It also includes economic metrics such as GDP, manufacturing jobs, etc, as well as consumer metrics, such as electricity and motor gas prices. These were chosen to provide an overview of the specific conditions in 2040 as described in each of the 31 scenarios. All data categories are consistent across all scenarios.

Some data does not appear across all metrics. Nuclear energy, for example, provides data for Consumption and Production, but no data for Imports or Exports. In such cases, this is because the AEO2014 does not provide said information. This is largely because many energy sources are used to generate electricity. This electricity is sometimes imported and exported, but for the purposes of this application, that data was not relevant.

In the case of Oil, Production reflects "Crude oil and lease condensate;" consumption reflects "Petroleum and other liquids", which includes ethanol, biodiesel, natural gas plant liquids, crude oil, coal-based synthetic liquids, and petroleum coke. This is how the data is presented in AEO2014.

As presented in AEO2014, the source data points for Oil Production and Oil Consumption do not align 1:1. That is, not every component of Oil Production has a direct representation in Oil Consumption.

Oil Imports reflect crude oil imports. Not reflected is "Petroleum and other liquids," which includes finished petroleum products, unfinished oils, alcohols, ethers, blending components, and renewable fuels such as ethanol. Oil Exports reflect "Petroleum and other liquids," a combination of crude oil, petroleum products, ethanol, and biodiesel. This is how the data is presented in AEO2014.As presented in AEO2014, the source data points for Oil Imports and Oil Exports do not align 1:1. That is, not every component of Oil Imports has a direct representation in Oil Exports.

In the case of Coal, "Imports" are not reflected, as AEO2014 reports Coal imports along with coke and net electricity as "Other." Because coal import numbers are thusly bundled, only Coal Export figures are included in this dashboard.

In the case of Natural Gas, Production reflects both "Dry natural gas" and "Natural gas plant liquids." See the "Calculations" section of this document for combination methodology. Consumption reflects "Natural gas" ("natural gas plant liquids" consumption is reflected in "Oil Consumption").

Following is a breakdown of the specific filters used to obtain each data category from the AEO table browser. All data was obtained using Publication: AEO2014 and Region: No Regional Tables. All data is listed in the format "Subject Filter/Table."

Total Jobs: Macroeconomic/Macroeconomic Indicators
Manufacturing Jobs: Macroeconomic/Macroeconomic Indicators
Total GDP: Macroeconomic/Macroeconomic Indicators
Population: Macroeconomic/Macroeconomic Indicators
Real Disposable Personal Income Per Capita: Macroeconomic/Macroeconomic Indicators
Total Energy Production: All Tables/Total Energy Supply, Disposition and Price Summary
Total Energy Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Total Energy Imports: All Tables/Total Energy Supply, Disposition and Price Summary
Total Energy Exports: All Tables/Total Energy Supply, Disposition and Price Summary
Total Energy-related CO2 Emissions: All Tables/Total Energy Supply, Disposition and Price Summary
Oil Price: All Tables/Total Energy Supply, Disposition and Price Summary
Oil Production: All Tables/Total Energy Supply, Disposition and Price Summary
Oil Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Oil Imports: All Tables/Total Energy Supply, Disposition and Price Summary
Oil Exports: All Tables/Total Energy Supply, Disposition and Price Summary
Natural Gas Price: All Tables/Total Energy Supply, Disposition and Price Summary
Total Natural Gas Production: All Tables/Total Energy Supply, Disposition and Price Summary
Natural Gas Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Natural Gas Imports: All Tables/Total Energy Supply, Disposition and Price Summary
Natural Gas Exports: All Tables/Total Energy Supply, Disposition and Price Summary
Coal Price: All Tables/Total Energy Supply, Disposition and Price Summary
Coal Production: All Tables/Total Energy Supply, Disposition and Price Summary
Coal Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Coal Exports: All Tables/Total Energy Supply, Disposition and Price Summary
Renewable Energy Production: All Tables/Total Energy Supply, Disposition and Price Summary
Renewable Energy Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Hydropower Energy Production: All Tables/Total Energy Supply, Disposition and Price Summary
Hydropower Energy Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Nuclear Energy Production: All Tables/Total Energy Supply, Disposition and Price Summary
Nuclear Energy Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Geothermal Energy Consumption: Renewable Energy/Renewable Energy Consumption by Sector and Source
Biogenic Energy Consumption: Renewable Energy/Renewable Energy Consumption by Sector and Source
Solar Thermal Energy Consumption: Renewable Energy/Renewable Energy Consumption by Sector and Source
Solar Photovoltaic Energy Consumption: Renewable Energy/Renewable Energy Consumption by Sector and Source
Wind (Onshore) Energy Consumption: Renewable Energy/Renewable Energy Consumption by Sector and Source
Biomass Energy Production: All Tables/Total Energy Supply, Disposition and Price Summary
Biomass Energy Consumption: All Tables/Total Energy Supply, Disposition and Price Summary
Electricity Price: All Tables/Total Energy Supply, Disposition and Price Summary
Motor Gas Price: Energy Prices/Petroleum and other Liquid Prices

WHY 2012/2013 DATA?

2012 or 2013 was used as the baseline data point in The Future of Energy because it is the most recent year for which there is complete historical data, as opposed to forecast or estimated data. It has not been singled out in any way by EIA as "significant". This enables users to compare each scenario's projected future with data that is as close as possible to reality.

The Reference Case, so-called because it is "conditionally neutral," was not selected as the baseline for changes because it provides a comparison point only with one of many projected futures. The reference case forecast assumes that "normal" inventories and weather — as well as current laws and regulations — will hold throughout the forecast period. This does not mean that EIA believes the reference case forecast is the most likely outcome. This means that the "high economic growth case" forecast may be considered just as likely an outcome as the reference case — or even as the "low economic growth case" forecast.

As with AEO2014, in the case of the scenarios that have been updated for AEO2015, 2013 was selected as the baseline year as it is the most recent year for which there is complete historical data.

It should be noted that the calculations for Real Disposable personal income per capita are based on 2005 chain-weighted dollars, EXCEPT in the case of those scenarios updated in AEO2015. In those cases this figure represents 2009 chain-weighted dollars.

It should be noted that the calculations for GDP are based on 2005 chain-weighted dollars, EXCEPT in the case of those scenarios updated in AEO2015. In those cases this figure represents 2009 chain-weighted dollars.

It should further be noted that all other price-based figures, including Home Heating Index, are given in 2012 dollars, EXCEPT in the case of those scenarios updated in AEO2015. In those cases prices are given in 2013 dollars.

Assumptions by Fuel Type

Every fuel type is associated with a particular set of conditions related to optimizing that fuel as a source of energy. These conditions range from the unique challenges of extracting a given fuel, to technological limitations on conversion into energy, to a necessary large upfront investment in infrastructure, etc. Taken together, these conditions help to explain, in part, the relative extent of various energy types across the US. In this section, those key conditions, per AEO2014 Assumptions, have been summarized. These assumptions are also used in the updated cases in AEO2015.

  • Wind
    • Much of the available windy land is unsuitable for wind energy production due to terrain slope, forestation, urbanization, etc.
    • Offshore production is limited by the above but explicitly by water depth.
    • Because of this, wind is considered a finite resource. The NEMS submodule calculates max available capacity by EMM region.
    • 90% of available windy land produces commercially viable energy only after a 50% to 100% increase in cost.
    • Due to spacing requirements for turbines, typical generation by wind is 6 MW/km2
    • Supply costs are affected by the following modeling measures: average wind speed; distance from existing transmission lines; and resource degradation.
    • A learning curve factor is included with offshore production to account for the substantial cost of establishing the unique construction infrastructure required for this technology.
    • Lead time for new generation (onshore) is 3 years for 100 MW of capacity, at $2205/kWh.
    • Lead time for new generation (offshore) is 4 years for 400 MW of capacity, at $6192/kWh.
    • 5%/20% learning curve factor for onshore/offshore (this represents the demonstrated tendency to underestimate actual costs for new technology).
  • SOLAR
    • NEMS considers PV and solar thermal energy together.
    • NEMS represents the Energy Policy Act of 1992 permanent 10% investment tax credit for solar electric power generation by tax-paying entities. In addition, the current 30% income tax credit, scheduled to expire at the end of 2016, is also represented to qualifying new capacity installations.
    • Capacity expansion is based on installations under construction before expiration of 30% tax credit.
    • Capacity factors are based on time of day and season as well as region.
    • Lead time for new generation is 2 years for 150 MW of capacity, at $3564/kWh.
    • Total learning curve factor of 10%.
  • BIOMASS
    • Biomass co-firing can occur in up to only 15% of fuel used in coal-fired generation plants.
    • NEMS assumes additional cropland needed for energy crops will displace agricultural cropland.
    • Maximum resources from forestry are fixed
    • Lead time for new generation is 4 years for 50 MW of capacity, at $3919/kWh.
    • Total learning curve factor of 10%.
  • HYDROELECTRICITY
    • Represents potential for new hydroelectric generation of greater than 1 MW from new and existing dams.
    • Only sites with estimated costs of $.1/kWh or lower are included.
    • NEMS does not include efficiency or operational improvements without capital additions. That is to say all systemic improvements require money.
    • Only those hydroelectric sites whose cost per kWh are less than the least cost of other technologies delivered in the previous decision cycle are included. This means that hydroelectricity is only modeled when it is demonstrably cheaper than other regionally available energy sources.
    • Lead time for new generation is 4 years for 500 MW of capacity, at $2435/kWh.
    • Total learning curve factor of 5%.
  • COAL
    • Capacity can be added only when capacity utilization (the rate at which coal energy is actually used) reaches 80%. When capacity utilization declines, capacity may be reduced. The volume of capacity expansion is based on: capacity utilization; the supply region (of 14) in question; mining type; and historical patterns of expansion. NEMS accounts for 41 individual supply curves based on region, mining type, and coal type.
    • Between 1980 and 2000, coal productivity increased 6.6%/year. This was due to interfuel price competition and technological improvements. Since 2000, productivity has been declining 2.4%/year. In the Central Appalachian Coal Basin, for instance, productivity has declined 52% during that period.
    • Labor productivity is assumed to decline, as technological gains are offset by high strip ratios and more expansive mines.
    • This is partially due to regulatory restrictions, such as MATS (Mercury Air Toxics Standard, and partially to fragmentation of underground reserves.
    • Different rates of productivity improvement are assumed for all 41 supply curves.
    • Major coal rail carriers have begun implementing fuel surcharges.
    • Lead time for new generation is 4 years for 1300 MW of capacity, at $2925/kWh.
    • Total learning curve factor of 5%.
  • NATURAL GAS
    • Long-term viability of natural gas as an energy source is partially determined by estimating remaining technically recoverable resource. This is the amount of natural gas still in the ground that can be recovered, accounting for technological and management improvements. These estimates tend to be highly uncertain.
    • Technology advances, including improved drilling practices, as well as advanced processing operations, are included in NEMS to determine direct impacts on supply, reserves, and various economic parameters.
    • Most of the Lower 48 production comes from deepwater off the Gulf of Mexico. Production from currently producing fields largely determines short term oil production projections.
    • Currently producing fields are assumed to have a 20% exponential decline in productivity, except for natural gas fields in shallow water off the Gulf of Mexico, which are assumed to have a 30% exponential decline rate.
    • Lead time for new generation is 2 years for 85 MW of capacity, at $971/kWh.
    • Total learning curve factor is 5%.
  • OIL
    • Domestic crude oil production is highly dependent on the profile of independent wells over time, including operating costs and revenue generation.
    • Long-term viability of oil as an energy source is partially determined by estimating remaining technically recoverable resource. This is the amount of oil still in the ground that can be recovered, accounting for technological and management improvements. These estimates tend to be highly uncertain.
    • This year's TRR estimate jumped from 5.4 billion barrels to 238 billion barrels, due in part to revised geological data. This estimate is assumed to adjust as new data becomes available.
    • Technology advances, including improved drilling practices, as well as advanced processing operations, are included in NEMS to determine direct impacts on supply, reserves, and various economic parameters.
    • Most of the Lower 48 production comes from deepwater off the Gulf of Mexico. Production from currently producing fields largely determines short term oil production projections.
    • Currently producing fields are assumed to have a 20% exponential decline in productivity.
    • Lead time for new generation is 3 years for 620 MW of capacity, at $915/kWh.
    • Total learning curve factor is 5%.
  • NUCLEAR
    • Generating units are assumed to retire when it is no longer economical to continue running them. NEMS determines, each year, whether the market price of electricity is sufficient to support the continued operation of existing generators.
    • Going-forward costs include fuel, operations and maintenance costs, and annual capital additions. Average capital additions $22/kW.
    • Beyond 30 years of age, $33/kW is additionally factored into capital expenses when determining plant viability.
    • NEMS assumes increased capacity rating at plants through power uprates. These are changes to the licensure at a given plant. NEMS only takes into account those uprate applications reported to the EIA, which account for an additional .7 GW of capacity.
    • Lead time for new generation is 6 years for 2234 MW at $5501/kWh.
    • Total learning curve factor is 10%.

Calculations

  • RDPI (Real Disposable Personal Income) per Capita: (RDPI(billions of dollars)) / (population(millions) x 1000)
  • Natural Gas Production: (dry natural gas) + (liquid natural gas)
  • Percent of Total Production & Consumption: By energy source, (production) / (total energy production)
  • Percent change from 2012: ((Scenario data) - (2012 data)) / (2012 data)
  • Quantity change from 2012: (Scenario data) - (2012 data)
  • Total Energy-Related CO2 Emissions: (Energy-Related CO2 Emissions per capita) x (population)
Home Heating Index

What it means: The Home Heating Index provides an average yearly cost (in 2012 dollars) per U.S. household of the energy used for heating (this includes electricity, natural gas, distillate fuel oil, and propane). The Index provides this information for each of the 31 scenarios as projected for the year 2040.

How it was derived:

  • "Delivered Energy Consumption by Fuel (quad Btu)" is recorded. This gives the energy consumption by fuel for each scenario. This is measured in the industry standard units, quads (quadrillion Btu's). These figures are for space heating for each of the above-mentioned fuel types.
  • "Delivered Energy Consumption by Fuel (million Btu)" is derived from the Delivered Energy Consumption by Fuel (quad Btu). This is done because energy prices are reported on a per million Btu basis. These numbers are reached by multiplying the corresponding numbers from the first section by 1,000,000.
  • "Residential prices by Fuel Source, per million Btu" is recorded. This gives average prices for the above fuel sources in the Residential sector, per million Btu, in 2040 for each scenario.
  • "Total Expenditure for Space Heating by Fuel (2012 Dollars)" is derived by multiplying "Residential prices by Fuel Source, per million Btu" and "Delivered Energy Consumption by Fuel (million Btu)" and provides the total amount spent nationally on each fuel type for residential space heating.
  • The figures from all 4 fuel types are totalled to provide an overall expenditure on residential space heating across all fuel types.
  • Home heating expenditure per household values were rounded up to their nearest whole number.

Sources for Contextual Data

(i)
Bazilian, M., & Roques, F. (Eds.). (2009). Analytical methods for energy diversity and security. Elsevier Science.
(ii)
http://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population
(iii)
http://www.bbc.com/news/magazine-17429786
(iv)
http://www.bls.gov/news.release/laus.nr0.htm
(v)
http://www.eia.gov/tools/faqs/faq.cfm?id=24&t=10
(vi)
http://geography.about.com/library/faq/blqzcircumference.htm
(vii)
http://www.toyota.com/prius/#!/Welcome
(viii)
http://phx.corporate-ir.net/phoenix.zhtml?c=176060&p=irol-newsArticle&ID=1868098&highlight=
(ix)
http://en.wikipedia.org/wiki/Oil_tanker#cite_ref-hay3_44-0
(x)
http://www.eia.gov/countries/index.cfm
(xi)
http://www.ge.com/about-us/fact-sheet
(xii)
http://www.eia.gov/countries/index.cfm?topL=con
(xiii)
http://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC
(xiv)
http://www.eia.gov/tools/faqs/faq.cfm?id=97&t=3
(xv)
http://en.wikipedia.org/wiki/List_of_countries_by_total_primary_energy_consumption_and_production
(xvi)
http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=1&pid=1&aid=2&cid=regions&syid=2008&eyid=2012&unit=QBTU
(xvii)
http://en.wikipedia.org/wiki/Montana#CITEREFCensus_Estimate2013
(xviii)
http://www.eia.gov/tools/faqs/faq.cfm?id=97&t=3
(xix)
http://theenergycollective.com/jemillerep/114236/why-have-us-oil-imports-declined-recent-years
(xx)
http://www.eia.gov/tools/faqs/faq.cfm?id=23&t=10
(xxi)
http://www.eia.gov/tools/faqs/faq.cfm?id=23&t=10
(xxii)
http://www.eia.gov/state/?sid=OR
(xxiii)
http://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population
(xxiv)
http://www.eia.gov/tools/faqs/faq.cfm?id=97&t=3
(xxv)
http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8
(xxvi)
http://www.eia.gov/tools/faqs/faq.cfm?id=23&t=10
(xxvii)
http://en.wikipedia.org/wiki/Greenhouse_gas_emissions_by_the_United_States#mediaviewer/File:CO2_emissions_China_USA_1990-2006.svg
(xxviii)
http://www.eia.gov/tools/faqs/faq.cfm?id=23&t=10
(xxix)
http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=1&pid=1&aid=2&cid=regions&syid=2008&eyid=2012&unit=QBTU
(xxx)
Bazilian, M., & Roques, F. (Eds.). (2009). Analytical methods for energy diversity and security. Elsevier Science.
(xxxi)
http://en.wikipedia.org/wiki/List_of_largest_employers_in_the_United_States