1. The Second Generation Model The Second Generation Model US Environmental Protection Agency Science Advisory Board SAB-SGM Advisory Panel Jae Edmonds, Ron Sands, Hugh Pitcher, Antoinette Brenkert 04 February 2005 US Environmental Protection Agency Washington, DC

2. 2 OverviewOverview Model Structure and Purpose Hybrid Input-Output Table International Trade Production, Expectations and Market Clearing in the SGM Access to the Model

3. 3 SGM Origins The SGM is a computable general equilibrium model of an economy Original design began in 1988—at a time when there were no CGE models in use to address climate change Designed as a complement to the Edmonds-Reilly model—a long-term partial equilibrium model Designed from the problem back to the model. Primary focus was on emissions of greenhouse gases including CO 2 from energy and land-use emissions and non-CO 2 greenhouse gases Time frame: 5-50 years into the future

4. 4 MiniCAM-SGM Relationship MiniCAM Simpler, long-term, partial equilibrium model Test bed for new ideas SGM Computable general equilibrium Energy-economy and other interactions matter More detailed framework than MiniCAM Medium term (5-year time steps, 50-year time horizon) Tracks capital stocks by vintage

5. 5 The SGM The SGM is a simple model at its base Implements an Economics 101 circular flow diagram Sectors chosen for emphasis reflect a concern for the climate issue in general and the emissions of GHG’s in particular

6. 6 Global Fossil Fuel Carbon Emissions 1999 Source: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory. Fossil fuel use (6.5 PgC/y in 1999) Natural gas 13.7 TgC/EJ Oil 20.2 TgC/EJ Coal 25.5 TgC/EJ Industrial process emissions (e.g. cement) 0.2 Pgc/y Land-use change emissions (1.7; 0.6-2.6 PgC/y) Deforestation Soil cultivation

7. 7 SGM Production Activities

8. 8 Fossil Fuel Carbon Emissions 1999 SGM Regions USA, Western Europe, China, former Soviet Union, Japan, India, Canada, South Korea, Mexico, Eastern Europe, Australia/New Zealand, Brazil, Middle East, Rest of World Source: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory.

9. 9 Key Ideas Emissions—energy, ag-land-use, other Production functions—why CES? Energy production Resources-reserves Energy consumption Model sectors—chosen because they have emissions that matter Households—energy services & transport Sector-subsector Fuels matter Energy-emissions coefficients Region choice—emissions today and tomorrow Time scale—5-50 years Complementarity with MiniCAM Need for vintaged capital stocks

10. 10 Key Features 14 regions of the world USA, Canada, Mexico, Western Europe, Eastern Europe, former Soviet Union, China, India, Brazil, Japan, South Korea, Australia/New Zealand, Middle East, Rest of World Can be operated in single region mode Many regional models are developed in collaboration with in- country researchers 5-year time steps from 1990 to 2050 7 Greenhouse Gases CO 2, CH 4, N 2 O, HFCs, HFC-23, PFCs, SF 6

11. 11 Regional Partnerships SGM regions are developed with regional partners Regional researchers have the best understanding of their regional data, institutions, market structure, and trends Regional partnerships have helped shape the development of most SGM regions

12. 12 Energy Supply in the SGM Finite resources use a resource-reserve- production model Production occurs out of reserves Each period producers bring new reserves on line from the resource base based on expected profitability The resource base is graded Production can occur from more than one grade of the resource simultaneously

13. 13 Anthropogenic GHGs

14. 14 Mapping Emissions from Production Sectors

15. 15 Technology Matters to the Answer MiniCAM Output: Limitation of GMST to 2 o C

16. 16 Technology Vintages in the SGM (1) Technology is embodied in the SGM’s production functions Technology is tracked by vintage Existing technology vintages produce as long as they can cover their operating costs Each existing technology is associated with a level of capital investment that constrains total production

17. 17 Technology Vintages in the SGM (2) New technologies are chosen from the ex ante technology possibility frontier Technologies are chosen based on expected profitability Expectations are treated explicitly in the model (The default is myopic foresight, but others have been tried) The level of investment in the technology depends on expected profitability Aggregate investment in the sector depends on expected demands for the sector

18. 18 Technology Vintages in the SGM (3) The capital market balances the demands and supplies for loanable funds Existing capital stocks are associated with a specific production function (technology) and cannot move from sector to sector Only new investments are perfectly malleable The interest rate clears the market

19. 19 Production Functions All goods are produced with either a non- nested Constant-Elasticity-of-Substitution (CES) production function New investment within the electricity sector (e.g., new coal, gas, oil, etc.) is allocated based on a logit sharing mechanism or Leontief production function For production functions with elasticities of substitution < 0.05 Oil refining Electricity generation from its various energy sources

20. 20 Technological Change Set of parameters that can be used to simulate technical change over time for production sectors Separate parameters are available for each input to each production process (and each vintage) Parameters that determine energy, land, labor, and capital productivity, i.e., non-neutral technical change Labor productivity parameters are the primary determinant of economic growth Both energy efficiency and labor productivity parameters can be altered to construct reference scenarios

21. 21 The Details Ron Sands Hybrid data structure Trade in SGM Hugh Pitcher Production New technologies, vintage structure & expectations Solution mechanism Lots of discussion

22. Hybrid Input-Output Table

23. 23 TerminologyTerminology The term “hybrid” refers to model input data and not model structure No code changes needed Does not limit form of production function The input-output table used to calibrate SGM base year uses hybrid units Joules for energy flows Base-year currency (e.g., 1990 US$) for all other goods Reference: Miller, R. and P. Blair, Input-Output Analysis, Prentice-Hall, 1985, pp. 200-235. (Chapter 6, “Energy Input-Output Analysis”)

24. 24 MotivationMotivation Carbon dioxide emissions are tied closely to energy flows Energy balance tables are the best source of data on energy flows Energy balance tables are actually energy input-output tables Can construct energy “use” and “make” tables One price for each energy commodity ensures quantity balance during model operation Energy commodities are relatively homogeneous Strict energy accounting

25. 25 Typical Energy Balance Table

26. 26 Data Sources Base-year input-output table Locally produced and in local currency, if available Otherwise a composite in US dollars Base-year energy balances Locally produced, if available International Energy Agency Engineering parameters and costs for electric generating technologies

27. 27 Composition of Hybrid Use Table (United States)

28. International Trade

29. 29 OverviewOverview Current Configuration Single Region Global Operation Design Considerations Status of Model Development Comprehensive revision of input data Theoretical considerations

30. 30 Single-Region Configuration Types of commodities Numeraire good (large services sector) is tradable Other tradables (crude oil, natural gas, emissions permits) Trade in remaining goods is fixed at base-year quantity Prices Numeraire price set equal to 1.0 in each period Crude oil and gas price set exogenously (can vary by time period) Other prices solved endogenously each time period Emissions Permits Can be traded in a regional market (endogenous price) Can be a traded in a global market (exogenous price) Balance of payments constraint Specified as an exogenous capital flow (can vary by time period) Any increase in imports must be paid for with exports of another tradable

31. 31 Global Configuration Configuration for recent Energy Modeling Forum studies Emissions permits trade globally or in regional blocks, depending on policy scenario Otherwise, each region operates in same way as its regional configuration Numeraire, crude oil, natural gas are tradable Trade in other goods is fixed at base-year quantity Regional balance of payments constraint Crude oil and natural gas prices set exogenously on a scenario basis Many SGM regions modeled in local currency (USA, India, China, Canada, Mexico, Japan, S. Korea) Trade takes place between each region and a global market Exogenous exchange rate converts world price of traded good to local price (set to base-year market exchange rate) No attempt to track bilateral trade

32. 32 Design Considerations Full global trade in crude oil and natural gas Revisit fossil supply methodology (especially Middle East) Calculation of terms-of-trade component in the cost of a climate policy Full global trade in agricultural products Draw from Battelle-PNNL experience with partial-equilibrium Agriculture and Land Use (AgLU) model Commodity flows calibrated to base-year food balance tables from UN Food and Agriculture Organization Full global trade in energy-intensive goods Shift in production capacity across regions in response to a climate policy Carbon leakage

33. 33 Development Activities Comprehensive revision of input data Selective use of GTAP data Greater coverage of input-output tables Consistent base-year trade data across regions Automate data preparation process Methodology to move input-output table to a different base year Develop data templates applicable to all regions Organize data on national income accounts into a social accounting matrix Evaluate alternatives Fossil energy supply Trade in energy-intensive goods

34. Production, Expectations and Market Clearing in the SGM

35. 35 Production, Expectations and Market Clearing in the SGM Production technology representation Combining technologies (nesting) Expectations and Investment Solving the model

36. 36 ProductionProduction All technologies use either a flat CES or Leontief production function (no nested CES functions)  0 is constrained to be one All technical change occurs through changes in the  i Technologies produce at original capacity until they are retired either by reaching their physical or economic lifetime (they may operate at lower capacity if rate of return is low) Nested energy bundles will not preserve energy balance

37. 37 Technology Characteristics Technologies have larger elasticities of substitution at the point of investment than in subsequent periods Putty-semi putty (or putty-clay) formulation Investment value chosen to be representative of literature—operation value chosen to meet energy conversion constraints (0.1 or lower) Fixed physical lifetime (typically twenty years with no life extension) A vintage has a fixed capacity until it is retired New technologies can be made available in future periods

38. 38 Technology (2) Technologies are represented as a series of vintages—each with its own capital stock which can be operated at full capacity, part capacity or prematurely retired depending on current period profit rate Profits in the SGM are defined as revenue minus variable costs

39. 39 Parameterization Process for Existing Technologies Exogenous value for elasticity of substitution Use IO data and functional specification to derive remaining technology parameters Non-neutral technical change values can be used to tune energy technologies to match EIA forecast Sector specific discount factor derived from historical sector level investments and the investment equation

40. 40 New Technologies Engineering cost studies are the primary source of data on new technologies Issues: Optimism about costs Breaking out inputs into SGM sectors Consistency of discount rates and prices Technologies have individual discount rate which reflects sub-sector specific factor plus cost of capital from capital market Level playing field—Existing technologies reflect additional costs, such as transmission and distribution expenses, regulatory requirements, etc.

41. 41 New Technology (2) Start up is an issue Single digit nameplate problem Penetration rates for new technologies Assigning technology specific discount factor Nesting or other sub-sectors that the technology competes with directly

42. 42 Key Lessons Learned (1) Neutral technical change in energy transformation technologies leads to violations of the conservation of energy principle For example, consider a petroleum refinery that presently converts 80 percent of crude to refined product Under a one percent per year neutral technical change assumption, after 25 years, the refinery will convert 103 percent of crude to refined product Solution Only non-neutral technical change This allows capital and labor efficiency to increase but maintains physical limits

43. 43 Key Lessons Learned (2) Elasticities of substitution in the range of the literature (say.3 to.5) in the presence of carbon prices can result in implied technologies that violate the second law of thermodynamics Existing coal fired electricity plant operating at 33% efficiency Carbon price of $300 a ton Elasticity of Substitution =.5 => Efficiency of 75% Elasticity of Substitution =.05 => Efficiency of 34.5% Solution: Existing energy technologies have sharply limited elasticities of substitution or are Leontief once installed

44. 44 Key SGM Characteristics Technological change in the SGM under a greenhouse gas mitigation strategy is primarily a matter of shifting across technologies The model manages GHG emissions by introducing new technology, not by improving existing technology—essentially bottom up This approach requires explicit new technologies and maintains physical consistency between energy inputs and outputs

45. 45 Advantages of SGM approach The model can maintain a fully consistent energy balance table across time New technologies have to be explicitly identified and parameterized There is no vague implicit or explicit backstop technology The value of new technology can be assessed Explicit representations of technologies enable consideration of additional characteristics of a technology—e.g. safety or local air pollution

46. 46 DisadvantagesDisadvantages Non-standard production characterization makes it difficult to use existing literature on parameter values—i.e. estimates of technical change based are two-digit industries are of limited value It is difficult to foresee technology characteristics beyond the current set of potential new options (What comes after IGCC with capture and disposal?)

47. 47 Nesting of Technologies The specific nature of technologies requires a mechanism to shift across technologies We use a logit approach to share investment across technologies within a sector A flat logit approach violates dominance A logit approach based on expected profits prevents peak load technologies from entering into production—therefore we are using expected costs

48. 48 Why Logit Sharing Rather than Nested CES? Logit mechanism maintains mass balance if components are consistent It is more flexible than a CES nest It does not require all inputs (subsectors) to be positive Logistic sharing does imply equal risk adjusted marginal returns within the industry

49. 49 The Electricity Sector Nest

50. 50 ExpectationsExpectations Expectations enter into production sector decision making through the investment process The model computes either expected cash flow or levelized costs using current and future prices over the lifetime of the investment At present, future prices are projected to be the same as current prices (myopic expectations) Levelized costs (expected profits) are computed using a discount rate which is the sum of price of capital and a sector specific discount factor

51. 51 Expectations and Investment Current investment produces under the same price and output as used to make the investment decision (Expectations are realized in the first period) Current investment is driven by a scale factor representing either (1) previous investment or (2) expected output current period expected cash flow per dollar of investment Investment in subsectors is allocated by a logistic process using subsector levelized costs (or expected profits) as arguments

52. 52 Dynamic Expectations A simple trend extrapolation experiment failed due to instability We plan experiments using simulated values as the basis for price expectations The role of current investment in equating supply and demand may limit potential options

53. 53 Solution Mechanism At solution, supply and demand for each market are equal to within the tolerance given to solution mechanism (typically.01%) Solution requires consistent model in order to solve—budget equations for all decision makers must hold Functions rapidly and reliably as long as the problem posed to the model can be solved using available technology options => low to medium carbon price Single region run time 1-2 minutes Global run <10 minutes

54. 54 Access to the SGM 1.http://www.globalchange.umd.edu/?modelshttp://www.globalchange.umd.edu/?models 2. http://www.globalchange.umd.edu/sgmrelease.phphttp://www.globalchange.umd.edu/sgmrelease.php 3.http://www.globalchange.umd.edu/models/download/http://www.globalchange.umd.edu/models/download/ SGM Users Guide I

55. \ Exec \ SGM_US_A \ Inputs \ Outputs \ USInputs \ Paths sgmctrl.csv RunSGM_US.xls DataViewer_US.xls SGM.exe CE policy files US-specific input files sgmgen2004_us.csv files sgm_us.mbd \screen.csv datamover.exe DONE.csv ddao36.dll: required but Microsoft owned Legal Notice.txt SGM_output.xls files

56. 56 RunSGM_US.xls Scenario Examples USA baseline US CE$100 US CEstab2015..\inputs\USinputs\usa_ba seline_2004_A.csv..\inputs\screen.csv..\inputs\paths\us_CE_$100. csv..\inputs\paths\us_CEstab20 15.csv..\inputs\USInputs\non- CO2_MACs_US.csv..\inputs\screen.csv

57. 57 Dataviewer_US.xls Example of Single Query Output

58. 58 Example of SGMgen2004_US output