sathaye galistsky

Slide1: 

Jayant A. Sathaye and Christie Galitsky Lawrence Berkeley National Laboratory, Berkeley, CA Fabian Wagner International Institute for Applied Systems Analysis, Vienna Presented at the Workshop on Energy and Economic Policy Models: A Reexamination of Some Fundamental Issues Washington DC, November 16-17, 2006 With inputs from Ernst Worrell and Amol Phadke Work supported by US Department of Energy, Office of Science, Biological and Environmental Research Program. Representation of Industrial Energy Efficiency Improvement in Integrated Assessment Models

Presentation Contents: 

Presentation Contents Integrated assessment (IA) models Demand-side cost curves Updated cost curves for US steel and cement sectors (preliminary results) Representing US cost curves in a IA model (preliminary results) Conclusions

Climate change mitigation models : 

Climate change mitigation models Long term perspective Cost-effective implementation strategies Least-cost emissions reduction pathway Emissions baseline is critically important to determining costs Defines the size of the reduction required to meet a target

Climate change mitigation bottom-up models: Cost estimates differ widely: 

Climate change mitigation bottom-up models: Cost estimates differ widely Differences can be traced to assumptions about economic growth resource endowments choice of policy instruments extent of no-regrets options cost and availability of new supply- and demand-side technologies technological change This presentation will focus on the last two items and reflects work in progress and preliminary results

B-U Results: Marginal costs of reducing emissions relative to 1990, single region, no trading: 

B-U Results: Marginal costs of reducing emissions relative to 1990, single region, no trading Source: Hourcade and Shukla (2001) Negative values: Future emissions higher than 1990 Positive values: Future emissions lower than 1990 Energy endowment Economic growth Energy intensity Other country-specific conditions

Bottom-up Models: How to explain the cost results? : 

Bottom-up Models: How to explain the cost results? Factors that could increase costs: Transaction costs Hidden costs, such as the risks of using a new technology Rebound effect Real preferences of consumers Factors that could reduce costs: Technological change over time Complete accounting of benefits Policies that remove costlier barriers

Transaction Costs Influence Supply of Traded Carbon: 

Transaction Costs Influence Supply of Traded Carbon Project search costs – Identification and stakeholder consultation May be spread over many projects Feasibility studies costs – engineering, economic, and environmental assessments GHG Baseline estimation and establishing additionality Negotiations costs – obtaining permits, negotiating and enforcing contracts for fuel supply, arranging financing Marketing GHG credits, carbon contracting and enforcement Insurance costs – project risk insurance GHG credit insurance (Difficult to get or too expensive today) Regulatory approval costs (GHG) Project validation and government review (May include both domestic and international validation costs) Monitoring and verification costs (GHG) – During project implementation Monitoring including equipment cost, verification and certification (Spread over many years of project life) Data Set 1: (26 projects) The Nature Conservancy (Forestry) -- Bolivia, and Brazil Indian Institute of Science (Forestry) , LBNL (Household woodstoves) Oregon Climate Trust (Forestry, energy efficiency, renewable energy) Natural Resources Canada (Forestry) Trexler and Associates (Methane, large power plants, energy efficiency, carbon capture) Data Set 2: (13 projects) Ecofys (renewable energy) Ecoenergy (bagasse cogeneration) Data Set 3: (50 projects) – Swedish AIJ Programme (Energy efficiency and renewable energy) Data Set 4: (10 projects) Global Environmental Facility Transportation, energy efficiency, renewable energy

Key Findings of Regression Analysis of Transaction Costs of Multiple Types of Projects: 

Key Findings of Regression Analysis of Transaction Costs of Multiple Types of Projects Dependent variable: Log (Total Transaction Costs (USD)) Independent variables: t C (log) .56** (.08) Forestry - 1.04** (.40) Energy Efficiency - 59 (.36) Multiple objectives - .34 (.30) S. America .75* (.45) Asia -.24 (.41) Mature - .49* .27 Constant 6.08** (1.00) R2 .69 N 48 *Statistical significance at the 10% level **Statistical significance at the 5% level or better Statistical analysis to determine significant influence on costs of Project Size Multiple benefits Technology demonstration, social development, other environmental benefits Forestry, energy efficiency, and renewable energy dummies Regional dummies – Asia and Latin America Mature vs. nascent markets

Slide10: 

. Effect of Barriers on CFL Sales in California in 2005 (18W CFL vs. 75W Incandescent Bulb Used 2.5 Hours per Day) Notes: HH = households; ES = eligible stock; LT = lifetime; S = (cost) savings; K = capital cost

Presentation Contents: 

Presentation Contents Integrated assessment (IA) models Demand-side cost curves Updated cost curves for US steel and cement sectors (preliminary results) Representing US cost curves in a IA model (preliminary results) Conclusions

Cost of Conserved Energy: Accounting for Changes in Capital Costs and Reduction in Energy due to an Energy Efficiency Measure: 

Cost of Conserved Energy: Accounting for Changes in Capital Costs and Reduction in Energy due to an Energy Efficiency Measure where: CCE = Cost of Conserved Energy for the energy efficiency measure, in $/GJ I = Capital cost ($) q = Capital recovery factor S = Annual energy savings (GJ) d = discount rate n = lifetime of the conservation measure (years)

Slide13: 

Source: Brown R. 1993 Estimates of the achievable potential for electricity efficiency improvements in US residences

Cost of Conserved Energy: Accounting for Changes in Capital, Labor and Material Costs : 

Cost of Conserved Energy: Accounting for Changes in Capital, Labor and Material Costs   where: CCE = Cost of Conserved Energy for the energy efficiency measure, in $/GJ I = Capital cost ($) q = Capital recovery factor M = Annual change in labor and material costs ($) S = Annual energy savings (GJ) d = discount rate n = lifetime of the conservation measure (years)

US Steel Industry Cost of Conserved Energy: Other Benefits : 

US Steel Industry Cost of Conserved Energy: Other Benefits

US Steel Industry Supply Curves: Accounting for Changes four categories of benefits (previous slide): 

US Steel Industry Supply Curves: Accounting for Changes four categories of benefits (previous slide) Benefits double cost effective energy efficiency potential to 19% -6 -3 0 3 6 9 12 15 18 21 0 1 2 3 4 5 6 Energy Savings (GJ/tonne) Cost of Conserved Energy ($/GJ) Discount Rate = 30% Cost Curve With Changes in Energy Costs Cost Curve with Changes in Energy and other Benefits Annual Cost-Effective Primary Energy Savings 1994 Weighted Average Primary Fuel Price ($2.14/GJ) Excluding Non-Energy Benefits: 1.9 GJ/tonne Including Non-Energy Benefits: 3.8 GJ/tonne difference: 1.9 J/tonne, approximately 168 PJ/year Source: Worrell et al. (2003)

Effect of Accounting for Changes in Other Benefits on Cost-Effectiveness and Ranking of Measures: 

Effect of Accounting for Changes in Other Benefits on Cost-Effectiveness and Ranking of Measures

Presentation Contents: 

Presentation Contents Integrated assessment (IA) models Demand-side cost curves Updated cost curves for US steel and cement sectors (preliminary results) Representing US cost curves in a IA model (preliminary results) Conclusions

Baseline Changes 1. Structural Changes in the US Cement Industry: 

Baseline Changes 1. Structural Changes in the US Cement Industry Cement: amount of raw materials input; clinker produced (clinker to cement ratio); wet and dry cement produced; types and ages of kilns) Sources: USGS and PCA, various years for throughputs; PCA and Major Industrial Plant Database (MIPD) for kiln technologies

2. Energy intensity changes at each stage of cement production: 

2. Energy intensity changes at each stage of cement production Sources: USGS, MECS, PCA, COWIconsult, CANMET (Canada), Lowes (UK), Folsberg, Ellerbrock, Holnan, ISTUM

3. Changes in fuel mix and energy price – Cement : 

3. Changes in fuel mix and energy price – Cement Sources: MECS, various years

1. Structural Changes in the Steel Industry: 

1. Structural Changes in the Steel Industry Sources: IISI, various years Sources: AISI, various years

2. Energy intensity changes at each stage of steel production: 

2. Energy intensity changes at each stage of steel production Sources: AISI, various years for throughput; Margolis (for DOE) 1994 and 2000 for intensities

3. Changes in fuel mix and energy price – Steel: 

3. Changes in fuel mix and energy price – Steel Sources: MECS, various years

Updating the Cost Curves – critical changes implemented from 1994 to 2004 (cement) or 2002 (steel) in efficiency measures: 

Updating the Cost Curves – critical changes implemented from 1994 to 2004 (cement) or 2002 (steel) in efficiency measures Technology changes – for each individual existing technology Updating costs of technology Updating energy savings relative to current industry practices Applicable share of production for the technology in new year Technology changes –new technology additions which came onto the market (cement only) Requires cost, energy and applicable share of production data for each new technology Comparison of inclusion of energy-only and total benefits

Comparison of Cement Cost Curves for 2004 including total versus only energy benefits: 

Comparison of Cement Cost Curves for 2004 including total versus only energy benefits

Cement Cost Curves – comparison of curves for 2004 and 1994 (30% discount rate): 

Cement Cost Curves – comparison of curves for 2004 and 1994 (30% discount rate)

Iron and Steel Cost Curves –2002 total benefits -integrated, secondary and combined (30% discount rate) : 

Iron and Steel Cost Curves –2002 total benefits -integrated, secondary and combined (30% discount rate)

Iron and Steel Cost Curves –2002 energy only benefits - integrated, secondary and combined (30% discount rate): 

Iron and Steel Cost Curves –2002 energy only benefits - integrated, secondary and combined (30% discount rate)

Presentation Contents: 

Presentation Contents Integrated assessment (IA) models Demand-side cost curves Updated cost curves for US steel and cement sectors (preliminary results) Representing US cost curves in a IA model (preliminary results) Conclusions

Global Atmospheric Stabilization Analysis Using COBRA: A Linear Programming Model: 

Global Atmospheric Stabilization Analysis Using COBRA: A Linear Programming Model COBRA was developed using LBNL data and expertise on bottom-up country-specific models of energy sector mitigation costs and potential, combined with global IEA, WEA, and SRES data assumes perfect foresight Includes 10 global regions, tracks carbon emissions decadally for 16 energy sources and demand sectors, including five industrial sectors, under a stabilization constraint and/or carbon price Source: Wagner and Sathaye, 2006

Global Atmospheric Stabilization Analysis Using COBRA: A Linear Programming Model: 

Global Atmospheric Stabilization Analysis Using COBRA: A Linear Programming Model Small and fast, appropriate for sensitivity analysis treatment of no regrets options energy and total costs of industrial options technological change discount rates alternative stabilization levels and/or carbon prices Model discount rate is 4% Steel and cement cost curves were derived at 15% discount rate

Key Cases Analyzed Using COBRA: 

Key Cases Analyzed Using COBRA Model is calibrated to SRES A1B scenario Baseline with and without no-regrets options (NROs) instantaneous penetration of NROs slowed penetration of NROs Baselines vs. mitigation at alternative carbon prices Energy cost vs. all benefits cost curve Technological change vs. no technological change

US Iron and Steel: 

US Iron and Steel

The effect of carbon prices: Options include energy and non-energy benefits : 

The effect of carbon prices: Options include energy and non-energy benefits

Fuel Mixes: US Iron & Steel: 

Fuel Mixes: US Iron & Steel

US Cement: 

US Cement

US Cement: 

US Cement

The effect of carbon prices: US fuel mix for cement sector: 

The effect of carbon prices: US fuel mix for cement sector

US Steel: Comparison of 1994 and 2002 Supply Curves (Include both energy and other benefits): 

US Steel: Comparison of 1994 and 2002 Supply Curves (Include both energy and other benefits)

Energy Efficiency in the Steel Industry – Electric Arc Furnace: 

Energy Efficiency in the Steel Industry – Electric Arc Furnace

Technological Change: impact on emissions: 

Technological Change: impact on emissions

Conclusions : 

Conclusions Detailed technology representation provides insight and understanding of technology anf fuel mix choices Inclusion of non-energy benefits increases emissions reductions Bottom-up cost curves provide another approach for modeling technological change Technological change increases emissions reduction With a carbon price, potential is lower compared to only price-induced emissions