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En-ROADS Review Andrew Jones, Lori Siegel, Jack Homer, and John Sterman November 26, 2014.

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Presentation on theme: "En-ROADS Review Andrew Jones, Lori Siegel, Jack Homer, and John Sterman November 26, 2014."— Presentation transcript:

1 En-ROADS Review Andrew Jones, Lori Siegel, Jack Homer, and John Sterman November 26, 2014

2 Agenda Welcome, Introductions and Context Model background Operating the model (brief demonstration of reference scenario) Model structure, reference scenario vs. data, and policy/sensitivity testing Questions, comments, and “what-ifs” How to proceed

3 Model Development Team for En-ROADS Dr. Tom Fiddaman, Ventana Systems Dr. Jack Homer, Homer Consulting Andrew Jones, Climate Interactive Dr. Phil Rice, Climate Interactive Dr. Beth Sawin, Climate Interactive Dr. Lori Siegel, Climate Interactive Stephanie McCauley, Climate Interactive Prof. John Sterman, MIT System Dynamics Group

4 Purpose of the External Review: Assessment from experts of En-ROADS ’ appropriateness relative to its purpose Determine areas for improvement Purpose of En-ROADS: Improve understanding of important energy, land use, and climate dynamics among non- scientists as a means to effective action by: Policymakers Educators The Public Purposes

5 Building Confidence in This Simulation Policy relevance Evidence-based structure and parameters Fit to history Reference run compared with others’ projections and structurally explained Policy/sensitivity results, structurally explained

6 The Simulation in Action

7 What global changes over the next decade do you think are required and possible to drive the energy transition? * As a driver of energy demand, not technological innovation or investment ** New Zero C: technological innovation leading to a new zero carbon energy supply, such as Thorium fission Population and Economic Growth* Energy Efficiency and Demand Energy SupplyOther Population growth No change Decreas e Increase GDP per capita Mobile Stationary No change Modest increase Big increase Coal Oil Gas Solar & Wind Biofuels New Zero C ** Nuclear CCS No change LessMore No change $/ton Achieved by (year) Land use emissions Other green- house gases No change Small decrease Big decrease Carbon price like methane & N 2 O Other Policy and Regulatory (Example Handout for Workshop Participants)

8 EMF Model Suite BP Energy Outlook HYDE (PBL) US EIA WEO LBL HADCRUT IPCC Incorporate structure, equations, and data from diverse research teams  DOE  UN  IEA  GISS  CDIAC NCDC NOAA  MIT EPPA  V. Smil  Maddison  Houghton

9 Historical Data GDPHyde energy consumption by fuel type, Hyde historical data for Kaya identity, Total Final Energy Demand Hyde energy consumption by fuel type, Hyde historical data for Kaya identity, WEO 2012 data for final and primary energy by source, BP Statistical Review of World Energy June 2014 Electricity production WEO 2012 data for final and primary energy by source, CO2 emissions from energy Hyde energy consumption by fuel type, Hyde historical data for Kaya identity, WEO 2012 data for final and primary energy by source, Primary Energy Demand by Source WEO 2012 data for final and primary energy by source, Energy IntensityWorld Resources Institute (2011), US Energy Information Administration (2014), International Energy Agency (2011), Lawrence Berkeley National Laboratory (1998)

10 Published Projections Projected Energy Mix, GHG emissions, atmospheric concentrations, and temperature Energy Information Agency (EIA) International Energy Outlook 2014 World Energy Outlook (WEO) 2012 Energy Modeling Forum (EMF) Special Report on Emissions Scenarios (SRES) Representative Concentration Pathways (RCP) Hyde 2010 British Petroleum (BP) Statistical Review of World Energy Projected Population United Nations, Department of Economic and Social Affairs, Population Division (2014). World Population Prospects: The 2010 Revision, CD-ROM Edition. Medium, Low, and High Scenarios. Energy PricesBritish Petroleum (BP) Statistical Review of World Energy Annual Energy Outlook 2014 Early Release 2013

11 Studies on Carbon-Temperature Dynamics Carbon Cycle and Temperature Bolin, B Fiddaman. T.S Nordhaus, W. D. 1992, 1994, 2000 Goudriaan, J. and P. Ketner Oeschger, H., U. Siegenthaler, et al Rotmans, J Schwartz, S.E Schneider, S.H., and S.L. Thompson Wullschleger, S. D., W. M. Post, et al

12 Studies Providing Key Parameter Estimates Commercialization Time Akiner, S. & Aldis, A. (2004) Smil,V. (2006) Progress Ratios Junginger, M., et al. (2010) McDonald, A., Schrattenholzer, L (2001) Non-Renewable Energy Resources IPCC. (2007) World Energy Council. (2010) Renewable Energy Resources IPCC. (2011) Jacobson, M. Z. (2009) Construction Materials J. Sullivan, et al. (2010) Kris R. Voorspools, et al. (2000) Development Time Jacobson, M. Z. (2009) US Department of Energy (2008) Construction Time Jacobson, M. Z. (2009) US Department of Energy (2008) Lifecycle Emissions Hiroki, H. (2005) White, S. & Kulcinski, G. (1998) Building EfficiencyUS Department of Energy (2011) TransportationUS Bureau of Transportation Statistics (2011)

13 En-ROADS and C-ROADS Scope Other GHG emissions Population and GDP/capita (1 or 6 regions) En-ROADS System of Energy/Economics/Climate Land use Energy Demand, Supply, and Prices Technology and Policies CO 2 emissions GHG cycles Climate Tempera- ture C-ROADS Other forcings Impacts (pH, SLR) GHG emissions by regions GHG emissions

14 Simulation Demonstration

15 Overview of Structure and Reference Scenario Assumptions

16 En-ROADS Simulation Structure Energy Supply Carbon intensity by source Costs, learning, R&D success, complementary assets, resource availability Energy Demand Energy intensity Stationary & mobile Elec & Non-elec Aging, efficiency, & retrofits Economy GDP/capita Population Climate Emissions Concen- trations Temp. Sea level rise GHGs emissions and removals Other gases Land use CO 2 Energy CO 2 Emissions Policies and Scenarios Carbon price Subsidies/Tax Tech. breakthrough Prices Market-clearing Utilization

17 Hydro Nuclear Stationary Electric Non-Elec Mobile Electric Non-Elec Electricity Production Elec thermal CCS Renewables Hydro New Tech Nuclear Nonelectric Consumption s s Oil Natural Gas Coal Biofuels Extracted Fuels s s Oil Natural Gas Coal Biofuels Delivered FuelsCarriersDemand Renewables (Solar, Wind, Geothermal) New Tech En-ROADS Energy Flows

18 En-ROADS, though aggregate, captures realistic energy/economy dynamics 1.Separate pricing of 4 fuel types (extracted/spot, delivered) and electricity 2.Short-term and long-term consumer responses to price: curtailment, rebound, energy efficiency, choice of fuels and electricity 3.Extracted fuel prices fluctuate via endogenous commodity cycle 4.Delivered fuel prices affected by extracted prices, but more stable 5.Electricity source decisions based largely on cost comparison, but also network complementarities and performance standards 6.Learning curves reduce energy production costs 7.Time delays (and possible “overheating”) in building new supply capacity 8.Fossil fuel production costs increased by resource depletion 9.Other production costs (biofuel, hydro, renewables) potentially increased by flow limits

19 In the reference scenario, extracted fuel prices are set equal to their historical values during , and change endogenously thereafter. It is possible to reproduce the cyclical nature of extracted prices broadly but not all of the historical ups and downs energy volumes, which we want to reproduce, are affected by extracted prices We want to give the model the proper head start to produce plausible future cycles; without that head start, the 2013 disequilibrium is muffled and the future cycles are less prominent as a result The reference scenario assumes no carbon pricing or other policy interventions UN medium population scenario Growth in GDP per capita at a decreasing rate Reference Scenario Assumptions

20 Kaya Variables Compared with EMF suite to 2100 Energy Supply by Source Compared with WEO to 2020 Energy Prices by Source Fossil fuel prices compared with EIA to 2040 Reference Scenario Results

21 Kaya Variables to 2100 vs. EMF27

22 Global GDP to 2100

23 Energy Intensity to 2100

24 Energy Use to 2100

25 Carbon Intensity of Energy to 2100

26 Carbon Intensity of GDP to 2100

27 CO2 Emissions from Energy to 2100

28 Energy Supply by Source to 2020 vs. WEO

29 Fuel Production to 2020 INCLUDES Renewables e.g., solar, wind, geothermal Hydro EXCLUDES Bio Note: Blue line = En-ROADS output Red dots represent single data points from WEO 2012.

30 Note: Blue line = En-ROADS output Red dots represent single data points from WEO Fuel End-Use to 2020

31 INCLUDES Renewables e.g., solar, wind, geothermal EXCLUDES Hydro Bio * Electricity Production to 2020 Note: Blue line = En-ROADS output Red dots represent single data points from WEO 2012.

32 Fossil Fuel Prices to 2040 vs. EIA

33 Note: En-ROADS extracted prices for oil, coal, and gas are set equal to their historical values during , and are simulated starting thereafter; delivered fuel prices are simulated throughout. Oil Prices 1990 to 2040 vs. EIA

34 Coal and Gas Prices to 2040 vs. EIA

35 Policy/Sensitivity Testing

36 Two Settings for Each Policy: High & Moderate Policy LeverSetting Carbon price Base: 0 $/TonCO2 High: 100 $/TonCO2 Moderate: 50 $/TonCO2 Electric subsidy Base: 0 $/GJ High: 20 $/GJ (~0.072 $/kWh) Moderate: 10 $/GJ (~0.036 $/kWh) Source subsidy renewables Base: 0 $/GJ High: 10 $/GJ (~0.036 $/kWh) Moderate: 5 $/GJ (~0.018 $/kWh) Coal tax Base: 0 $/GJ High: 5 $/GJ (~147 $/tce) Moderate: 1 $/GJ (~29 $/tce) Energy efficiency improvement (e.g., in response to performance standards) Base: Stationary: 1.2 %/year Mobile: 0.5 %/year High: 5 %/year Moderate: 2 %/year

37 Key Uncertain Parameters for Sensitivity Testing ParameterRef. Run ValueMinMaxNotes Carrier network sensitivity [“CNS”] Exponent (positive) for the effect of current carrier share on carrier attractiveness. Early adoption of electricity is slow but facilitates more rapid adoption later on. Demand elasticity of fuels [“DE”] Fuels: 0.1 Electricity: Short-term elasticity (negative) of end-use demand to effective energy price (i.e., price adjusted for end-use energy efficiency). Affects expressed energy demand and market- clearing prices. End use carrier share cost sensitivity [“EUCS”] 213 Exponent (negative) for the effect of aggregate fuel vs. electricity cost on the shares of new end-use capital investment. Long term GDP growth rate Global long-term GDP growth rate approached gradually 2014 to Initial available resource remaining in EJ [“IARR”] Coal: , Oil: 12500, Gas: 9000 Coal: 70000, Oil: 6000, Gas: 6000 Coal: , Oil: 25000, Gas: Recoverable resource remaining as of Profit effect on desired extraction capacity [“EC”] Determines the rate of expansion for extraction capacity in response to profitability. Progress ratio renewables (“PR”) Ratio of unit cost per doubling of cumulative production. Equals 1 minus the learning rate.

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39 Results most sensitive to uncertainty in initial available resources remaining (IARR).

40 Summary of Policy/Sensitivity Results – 2100 (% of reference scenario result; sensitivity conditions) Policy LeverSettingCO2 EmissionsEnergy IntensityCarbon Intensity Carbon price High: 100 $/TonCO % % % Moderate: 50 $/TonCO % % % Electricity subsidy to consumers High: 20 $/GJ % % % Moderate: 10 $/GJ % % % Source subsidy renewables High: 10 $/GJ % % % Moderate: 5 $/GJ % % % Coal tax High: 5 $/GJ % % % Moderate: 1 $/GJ % % % Energy efficiency improvement High: 5 %/year % % % Moderate: 2 %/year % % %

41 How to Proceed Feedback on this presentation Set date for next review meeting Invitations

42 Goals or principles We want to have Disciplines: three groups of people Modeling professionals of two types: Energy and Climate. Some people are both. Learning, communications, interface design people. User representatives. Policy people with a science/economics scholarly background. Diversity At least 1 or 2 women At least 1 from developing world, likely China At least 1 from EU Recognition Naki, Wigley, or Edmonds. IE, perhaps we set a goal of at least one of the three (unless someone else nominates someone for that list)

43 Draft invite ( from John Weyant) Dear [insert name] – I’m writing to invite you to review the En-ROADS simulation of Climate Interactive and MIT Sloan.En-ROADS simulationClimate InteractiveMIT Sloan No travel would be necessary – you would attend a small webinar, review the PPT deck and experiment with the simulation online if you want, and share your comments with me. I’m chairing the review because I think this simulation is so important – it extends their earlier C- ROADS simulation and complements the Energy Model Forum suite of models (indeed, they calibrated to the suite and included all the results in their software), aiming at policymaker use, online/app accessibility and broad education. We’ve used the simulation with great success for the last three years in a workshop with our incoming grad students here at Stanford – a two minute video of the event is here. A MIT video is here. And they’ve engaged policymakers in London and elsewhere. A short abstract is here.here Londonhere If you are willing to help, please reply to this and share your availability here on a doodle poll. We are hoping to hold two webinars (you attend the more convenient one) on TKTK. Please use the doodle link to let us know which days are best for you. If necessary, we could schedule a private meeting with you.here on a doodle poll I appreciate your urgency on this. We want to be ready for the Paris COP and other engagements. Thank you for your help. I think it could make a big difference in the world. Sincerely, John Weyant

44 Reviewers to Invite Modeling Geeks Chair John Weyant (yes) Nebojsa (Naki) Nakicenovic, IIASA Or Kewyan Riahi Rich Richels Ottmar Idenhoffer Brian O’Neil Bill Moomaw, Susan Solomon, MIT Jae Edmonds Policy and Users Jonathan Pershing, DOE Learning, Communications, Interface George Richardson, Elke Weber from Columbia (female) China Zhao Xiusheng, Tsinghua, John W says either Jae OR Rich Richels

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46 Appendix A: Supply & Demand Curves

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52 Price sensitivity of demand = 1.0

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54 Demand Elasticity of Fuels = 0.1 (for each fuel type)

55 Demand Elasticity of Electricity = 0.2

56 Appendix B: More Policy/Sensitivity Results 56

57 57

58 58

59 Summary of Policy/Sensitivity Results – 2050 (% of base run result; range across sensitivity conditions) 59 Policy LeverSettingCarbon EmissionsEnergy IntensityCarbon Intensity Carbon tax High: 100 $/TonCO % % % Moderate: 50 $/TonCO % % % Electricity subsidy to consumers High: 20 $/GJ % % % Moderate: 10 $/GJ % % % Source subsidy renewables High: 10 $/GJ % % % Moderate: 5 $/GJ % % % Coal Tax High: 5 $/GJ % % % Moderate: 1 $/GJ % % % Energy efficiency improvement High: 5 %/year % % % Moderate: 2 %/year % % %

60 60 Scenario tonsCO2/EJ% of BasetonsCO2/EJ% of Base Base C tax High %20.146% Moderate %27.763% Electricity subsidy High %39.590% Moderate %41.494% Renewables subsidy High %30.369% Moderate %38.487% Coal tax High %29.066% Moderate %39.490% Energy efficiency improvement High %40.191% Moderate %43.499% Policy Impacts on Carbon Intensity of Energy with Default Parameter Estimates

61 61 Scenario EJ/Trillion $% of BaseEJ/Trillion $% of Base Base C tax High 5.282%3.584% Moderate 5.890%3.891% Electricity subsidy High %8.0192% Moderate %5.7136% Renewables subsidy High %5.0120% Moderate %4.5107% Coal tax High 5.992%3.891% Moderate 6.398%4.198% Energy efficiency improvement High 4.164%2.049% Moderate 5.891%2.662% Policy Impacts on Energy Intensity of GDP with Default Parameter Estimates

62 62 Scenario EJ/year% of BaseEJ/year% of Base Base C tax High % % Moderate % % Electricity subsidy High % % Moderate % % Renewables subsidy High % % Moderate % % Coal tax High % % Moderate % % Energy efficiency improvement High % % Moderate % % Policy Impacts on Energy Use with Default Parameter Estimates

63 63 Scenario GtonsCO2/year% of BaseGtonsCO2/year% of Base Base C tax High %30.239% Moderate %44.857% Electricity subsidy High % % Moderate % % Renewables subsidy High %64.783% Moderate %73.093% Coal tax High %47.060% Moderate %69.088% Energy efficiency improvement High %34.945% Moderate %47.861% Policy Impacts on CO2 from Energy with Default Parameter Estimates

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74 Appendix C: Causal Loop Diagram

75 Key Model Dynamics to 2100

76 Where the Levers Fit In: 5 Examples

77 * Coal equivalent weight = 1 Primary Energy Equivalence by Source

78 * Oil equivalent weight = 1 Primary Energy Equivalence by Source

79 * Natural gas equivalent weight = 1 Primary Energy Equivalence by Source

80 * Bio equivalent weight = 1 Primary Energy Equivalence by Source

81 * Nuclear equivalent weight = 1 Primary Energy Equivalence by Source

82 * Renewables equivalent weight = 3 Primary Energy Equivalence by Source

83 Appendix D: Electricity Price to 2010 vs. EIA (US)… and simulated to 2100

84 En-ROADS Electricity Price to 2010 vs. EIA (US)

85 Where US electricity price stands globally

86 Electricity Price to 2100

87 Appendix E: Reference Scenario Stacked Graphs

88 Energy End Use by Segment to 2100

89 CO2 Emissions by End Use Carrier to 2100

90 Fuel Production by Source to 2100

91 Electricity Production by Source to 2100

92 Other slides

93 Fossil Fuel & Biofuel Prices Simulated to 2100

94 Carbon tax ($/tonCO2): –Tax on delivered fuel according to its carbon intensity ($/tonCO2) Subsidies or taxes ($/GJ): –May apply to specified source(s) of extracted fuel, delivered fuel, or electricity, or to electricity consumers in general Fractional cost reductions from technical innovations: –May apply to fuel extractors, producers of delivered fuel, or electricity producers Other electricity levers: –Performance standard (TonCO2/TJ) –Thermal efficiency improvement –Reduction in the loss of efficiency due to CCS Other end-use levers: –Efficiency improvements for mobile and/or stationary end uses Users decide magnitude and timing Energy Sector Policy and Scenario Levers

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98 Oil Prices to 2040 vs. EIA En-ROADS extracted prices for oil, coal, and gas are set equal to their historical values during , and are simulated starting thereafter; delivered fuel prices are simulated throughout. Extracted (crude) oil price is projected to decline because of capacity overexpansion, which itself is a response to prior high price (commodity cycle). The crude price decline is passed to refiners as lower costs, thus higher profitability, encouraging higher capacity utilization. This higher supply tends to suppress market- clearing price, but the price decline in delivered oil is mitigated by greater end use demand. Greater oil end-use and refinery utilization, in turn, prop up demand for extracted oil, leading to a new upswing in oil price starting after 2030.


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