1. SEDS Macroeconomic Module Alan H. Sanstad, LBNL May 7, 2009

2. Macro Module in Context of SEDS Macroeconomics Biomass Coal Natural Gas Oil Biofuels Electricity Hydrogen Liquid Fuels Buildings Heavy Transportation Industry Light Vehicles Macroeconomics Converted Energy Primary Energy End-Use

3. Philosophy and purpose Modeling approach – design principles: —Focus is long-run, economic growth dynamics – not business cycle or labor market (employment) —Theoretical simplicity: Basic Solow model is the departure point Well-suited to Analytica features Facilitates thorough understanding of underlying mechanisms and impacts —Empirical grounding: Closely tied to underlying data Uses standard macro data – National Income and Product Accounts (NIPA), U. S. Bureau of Labor Statistics (BLS) - for calibration and benchmarking Facilitates tuning to Annual Energy Outlook, analysis of AEO assumptions, and testing of other assumptions Role in SEDS: —Alpha version: Provide well-grounded, transparent macroeconomic inputs to energy system, contribute to initial analysis —Going forward: Enable analysis of interactions and feedbacks between macroeconomic and energy markets

4. Model structure: Theoretical background The Solow model with Cobb-Douglas production and exogenous technological change: —Output is a function of capital and labor —Is allocated among consumption, investment, government expenditure, imports, and exports —Behavioral assumption: Fixed savings rate —Growth of capital stock determined by investment net depreciation —Basic story: Long-run per capita growth determined by technological change

5. Equations of model GDP Productivity Capital, Labor Consumption, Investment Government spending Net exports

6. SEDS implementation – data, calibration & testing Economic variables correspond for the most part to NIPA (with some aggregation), in chained 2000 $: Output: Real Gross Domestic Product Consumption, investment, government spending (all levels), imports/exports are NIPA categories Capital stock —Real Fixed Assets (NIPA) Labor —Persons employed in private sector (BLS) Parameters: Capital & labor shares and, productivity and labor force growth, depreciation and savings rates calibrated to historical data for benchmarking

7. Calibrating to AEO Calibration is to real GDP through 2030 from the Annual Energy Outlook 2008, Revised In this simple form of the model, best ad hoc results obtained by varying savings rate and depreciation as well as productivity.

8. Macro data flow AEO Buildings Vehicles Industry Census Bureau Macroeconomic Biofuels NIPA BLS Initial values Data for calibration Population growth rate Labor force growth rate Electric GDP Industrial growth rate Per capita income Interest rate Population Incoming Data Outgoing Data

9. Illustrative results: Productivity and economic growth Private non-farm business sector multi-factor productivity growth – —Average annual rates for recent periods in %, U. S. Bureau of Labor Statistics: 1948-1973:2.0% 1973-1990:0.4% 1990-1995:0.5% 1995-2000:1.1% 2000-2007:1.4% Average annual rate, 2005-2050, in SEDS calibration to AEO 2008: 1.05%

10. Results, cont. SEDS Macro simulations: U. S. real GDP in 2050 as function of productivity growth: —AEO calibration (1.05%): $30.4 Trillion —Sensitivity cases: 1.10%:$31.3 Trillion – 3% higher 1.00%:$29.5 Trillion – 3% lower US Environmental Protection Agency estimates of GDP changes in simulations of Waxman-Markey draft, April 2009: —ADAGE model: 1.6% lower —IGEM model:2.2% lower

11. SEDS Solow-type model with energy In progress, based on general structure used in MARKAL- MACRO, MERGE, possibly others Treating aggregate energy E as an intermediate good (cf., e.g., Hogan and Manne, 1977) with cost c(E), Y as gross output:

12. Ongoing work, issues and next steps Testing and refinement of links Continuing development of model with energy and overall architecture for integration with SEDS energy system —Functional form —Macroeconomic/ energy accounting framework —Details of feedback mechanisms —Stochastic structure and consistency Analysis of influence of economic growth assumptions on impacts and effects of energy and GHG policies, taking account of uncertainties – as in previous example