EMRG En Energ rgy-ec econ onomy my mo modeli ling and behavi vioral al re reali lism: m: How mu much h is is us useful ul? Mark rk Jac accard card Simon Frase ser r University rsity Vancou ouve ver Modeling deling Wor orksho kshop Universit iversity y Colle llege ge Londo ndon Energy and M aterials Research Group Apr 2015 1 Simon Fraser University
EMRG Word rds of w f wisd sdom om? All models are wrong. But some are more useful. Energy-economy-climate analysts are like painters. They tend to fall in love with their models. Apr 2015 2
EMRG Apr 2015 3
EMRG Inside the optimization modelers’ clinic Apr 2015 4
EMRG Inside the simulation modelers’ clinic Apr 2015 5
EMRG Key messag ages? s? Make your model useful – and keep reforming it as the critical issues and questions change. Avoid falling in love – be willing to change models if your model is not useful for the next critical questions. Beware a lifetime devoted to pure optimization. Otherwise . . . Apr 2015 6
EMRG Ancien ent t histo tory: ry: to top-down wn vs. . bott ttom-up up Conv. “top - down” econ models: no technology, simple behavior. Conv. “bottom - up” spreadsheet models: tech-rich, naïve behavior, extreme partial equilibrium. Optimization “bottom - up” models: tech-rich, naïve behavior, full energy- economy equilibrium. Hybrid simulation models: tech-rich, behavioral, some energy-economy & macro-economy equilibrium. Jaccard, 2009, “Combining top -down and bottom-up in energy- economy models” in Evans and Hunt (ed.), International Handbook on the Economics of Energy. Apr 2015 7
EMRG CIM IMS: S: a c climate ate-ene nergy rgy policy y simula lation tion model fo for a r a specific ific juri risd sdictio iction Typical tech-rich model: Explicit tech details (cost, lifespan, efficiency, fuel) Semi-endogenous retirement of old stock (time, cost) Semi-endogenous service demand (growth, cost) Exogenous industrial structure (external forecast) Semi-endogenous structural change (macro elasticities) Hybrid in having endogenous micro-econ simulation of new and retrofit tech choices and thus energy supply-demand, especially domestic Apr 2015 8
EMRG CIM IMS: S: why th these design choices? es? Useful to policy-makers in one (or a few) jurisdiction(s) assessing whether their policies would re-direct the energy-economy system to a low emission path. (NEMS-US, CIMS-Canada.) Equally important (!) – useful in exposing “faking it” policies (information, subsidies, soft regs). A counter to climate policy delusions. However, less or not-at-all useful for: Spatial policies (urban form, transit) Redistributive, welfare and competitive impacts. Simulating multi-jurisdictional efforts and global energy markets I’ll next explain behavioral realism in CIMS, and then efforts to go beyond CIMS to achieve other “usefulness” objectives. Apr 2015 9
EMRG CIM IMS: S: sta tandard model str tructu ture Province Z Province Y Energy Supply Province X NG And Demand Supply NG Models Transport Coal Model Coal Supply Models Commerci al Model Electricity NG Model Crude Oil Coal Supply Residentia Models l Model Refining Crude Model Energ rgy y Supply Industrial Chemical Production Models Energ rgy y Demand Industrial Minerals Iron and Steel Metal Smelting Mining Other Manufacturing Apr 2015 10 Pulp and Paper
Pulp and paper paper Fin inal l Products ducts Proc ocesses esses and d Interm ermedia ediate te Prod oducts ucts Auxili xiliarie ies s and Steam am Apr 2015 11
EMRG Tra ranspo port rt New Car Alternative Fuels Apr 2015 12
EMRG Key behaviora oral para rameter ters s fo for r new & re retr trofi fit t te tech choices es LCC v CC j CRF j OC j EC j i j r CRF j MS j n j v 1 (1 r ) CC k CRF k OC k EC k i k Three key behavioural parameters: – Dis iscou count nt rate te (r) r) - time preference as reflected in actual decisions, excluding technology-specific risks – Intangible ngible cos ost (i) – technology-specific decision factors, especially differences in quality of service and cost risks – Mar arket ket heterogen erogeneit eity y (v) v) – reflects the diversity among decision makers in terms of real and perceived costs (logistic curve) Apr 2015 13
EMRG V – para rameter ter re refl flects ts mark rket t hete tero rogen enei eity ty 1 0.9 0.8 Market Share of Tech A 0.7 0.6 0.5 0.4 Power Parameter, v 0.3 100 50 20 10 0.2 6 3 Point where Tech A is 15% 0.1 1 0.5 cheaper than Tech B 0 0 0.25 0.5 0.75 1 1.25 Relative LCC of Tech A to Tech B Apr 2015 14
EMRG Behavi viou oura ral l paramete ter r esti timatio tion 15 years ago, we began discrete choice surveys to estimate the three behavioral parameters. This included stated and revealed preference studies in: - transport mode choice (transit, bus, bike, walking, vehicles), - vehicle choice (efficiency, fuel, motor type) - industrial boilers and cogeneration, - commercial and residential building insulation and HVAC. Increasingly, we focused on cost and non-cost dynamics on technology choices, summarized by “the neighbor effect.” Apr 2015 15
EMRG Discr screte ete choice models to to es esti timate te r, r, i and and v Survey / Empirical CIMS’ r, Observation Model (DCM) i and v Standard discrete choice model for technology choice surveys U CC OC EC e j j CC OC EC j CC j r i AC OC EC j AC AC Use OLS to estimate v for which predictions from CIMS are consistent with those from the DCM model (error term size vs betas). Horne, Jaccard , Tiedemann (2005) “Improving Behavioral Realism in Hybrid Energy -Economy Models Apr 2015 16 Using Discrete Choice Studies of Personal Transportation Decisions,” Energy Economics, V27.
EMRG Earl rlie ier r (i) ) esti timates tes fr from Canada da-US US surv rveys eys Rivers, Jaccard (2006) “Useful models for simulating policies to induce technological change,” Energy Policy, Apr 2015 17
EMRG We us use a st standard rd learn rnin ing g curv rve fo for r capita tal l cost t (c (cc) Declin lining ing capit pital al cos ost func nction: ion: pro rogres gress s rat atio io – Links a technology’s financial cost in future periods to its cumulative production – Reflects economies-of-learning and economies-of-scale – Parameters taken from literature Thus, technology-specific progress ratios (PR) determines the capital cost decline with cumulative production (N). log 2 ( PR ) N ( t ) CC ( t ) CC ( 0 ) N ( 0 ) Apr 2015 18
EMRG We co combine ne th this with th a ma mark rket-sh share re sensiti itive ve fu functi tion on fo for in r inta tangibl ble e cost t (i) Declin lining ing in intangible gible cos ost funct ction ion: neig ighb hbor or effec ect – Links the intangible costs of a technology in a given period (i) with its market share (MS) in the previous period – Reflects improved availability of information and decreased perceptions of risk with rising market share – Estimated from discrete choice surveys that include info on decision maker (income, attitudes to technology risk, environmental attitudes, etc.) i ( 0 ) i ( t ) k * MS 1 Ae t 1 Mau, Eyzaguirre, Jaccard, Collins- Dodd, and Tiedemann (2008) “The neighbor effect: simulating dynamics in consumer preferences for new vehicle technologies.” Ecological Economics, V68. Apr 2015 19
EMRG Combin ined ed eff ffect: t: cost-ado dopti ption on dynamic Increasing returns to adoption: ↑users leads to ↑ consumer acceptance for a given technology LEARNING G BY DOING G + NEIGH GHBOU BOUR ECONOM OMIES ES OF SCALE EFFEC ECT Capita ital Int ntan angibl gible Cost Cost Cumulativ ulative produ roduction ion Mark rket et shar are INCREAS ASIN ING RETURNS S TO ADOPTI TION Apr 2015 20
EMRG CIM IMS-US US policy y simulatio ation with th combine ned d capita tal l and inta tangible le cost t fe feedbacks ks - VES VES – vehicle hicle emission ission standar andard d (ULE ULEV / ZEV EV) - capit pital al and d in intangib ngible le cos osts ts are e annualiz nualized ed for r plu lug-in in hybri brid $25,000 Intangib ible costs ts - - - - = VES $20,000 ____ = BAU $15,000 Capital & Intangible $10,000 Cost $5,000 Capital tal costs ts $0 2005 2010 2015 2020 2025 2030 2035 Year Apr 2015 21
EMRG Oth ther us r usefu ful models needed: : urb rban fo form rm As noted, CIMS is not spatial. In an urban setting, we know that preferences depend in part on urban form (density nodes, mixed land- use, ease of access to alternative mobility options) QUEST project. CIMS used in soft-linking mode with (1) GIS-based model for urban land-use and (2) urban transportation model. Behavioral estimates about location and mobility choices from the urban form and transportation literature, while CIMS simulates technology choices. Combined heat and power often set exogenously. Apr 2015 22
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