ESTIMATING THE COST OF CLIMATE CHANGE USING THE OPTIMAL COST OF CONTROL METHODOLOGY. Jan Horst Keppler NEA Nuclear Development Division

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1 ESTIMATING THE COST OF CLIMATE CHANGE USING THE OPTIMAL COST OF CONTROL METHODOLOGY Jan Hrst Keppler NEA Nuclear Develpment Divisin

2 Challenges in Estimating Abatement Csts Determining the csts f climate change as well as f abatement csts t reduce GHGs is intrinsically difficult. Kuik, Brander and Tl (Energy Plicy, 2008) indicate n basis f 26 studies fr a stabilisatin target f 450 ppm in 2050 a range f /t Many MACs are ecnmetrically based tp-dwn mdels (e.g., MIT EPPA mdel, frmerly OECD GREEN). Existing bttm-up estimates are frequently eye-balled and apprximate. All have their merits in a necessarily challenging data envirnment. Surce: McKinsey (2010) 2

3 Challenges in Estimating Damage Csts Damage cst estimates display even wider ranges. Only a small fractin f ttal climate change risks are insurable. Climate change impacts are part f uncertainty, uninsurable, residual risk with cllective respnsibility. Gvernments thus have a decisive rle in determining scietal abatement bjectives even in the absence f explicit damage csts measurements. In GHG abatement, ptimality, in the sense f aligning marginal cst and effrt, is implicitly determined thrugh scial, plitical chices. This hlds fr bth damage csts and abatement csts: Damage csts are implicitly determined thrugh the abatement bjective that gvernments have signed up t, i.e., t hld glbal mean temperatures belw 2 C. Abatement csts are implicitly determined thrugh the energy sectr chices cuntries have made r are likely t make as reflected in majr internatinal scenaris (IPCC, WEP, ETP ). 3

4 The Cst f Cntrl Apprach I Attempts t answer questin hw much is climate prtectin really wrth t the glbal cmmunity r individual cuntries? based n empirical data and widely accepted prjectins. Based ultimately n Piguvian apprach, equalising marginal abatement csts with marginal damage csts, the Cst f Cntrl (CC) apprach is based n abatement csts and plitically decided ptimal abatement (2 C bjective) rather than damage csts. In principle, abatement cst estimates can be explicit r implicit: Explicit appraches (e.g., McKinsey) mdel csts f individual technlgies r ecnmetric apprximatins. Implicit appraches (e.g., NEA) derive ttal incremental csts f abatement effrt frm energy scenaris that mdel 2 C bjective (WEO 2015). Key advantages: plicy relevance, based n fficial bjectives and widely accepted scenaris; psitive rather than nrmative apprach. Key drawbacks: Wrking with average rather than (ecnmically relevant) marginal abatement csts, Relying n implicit mdelling assumptins rather than n explicit abatement csts. 4

5 The Cst f Cntrl Apprach II The well-knwn starting pint USD Marginal Abatement Csts (MAC) Marginal Scial Damage Csts (MSC) P* Ttal Abatement Csts t Reach Optimum 0 Q* G tco2 5

6 The Cst f Cntrl Apprach III With unknwn damage cst functin but knwn abatement cst functin: USD Marginal Abatement Csts (MAC) Marginal Scial Damage Csts (MSC) P* crrespnding t Optimising Carbn Tax Officially declared Emissin Objective Ttal Abatement Csts t Reach Optimum 0 2DS BAU Optimal Amunt f Abatement 6

7 The Cst f Cntrl Apprach III With unknwn damage cst functin and unknwn abatement cst functin but knwn ttal incremental cst f reaching the declared target: USD Marginal Abatement Csts (MAC) Marginal Scial Damage Csts (MSC) P* crrespnding t Optimising Carbn Tax (n lnger defined) The Ttal Incremental Cst crrespnds t the cst difference between the 2DS and the BAU Scenari. Average Cst f Reaching Target Officially declared Emissin Objective Ttal Incremental Cst f Reaching Target 0 2DS BAU Optimal Amunt f Abatement 7

8 The Cst f Cntrl Apprach IV NB1: with cntinuusly rising MAC (standard assumptin), marginal csts at the ptimum (and hence an ptimising carbn tax will be higher than average csts. NB2: nly with very unusual MACs wuld marginal csts be radically different frm average cst (e.g., MC >> 2*AC). 8

9 Mdelling the CC f the 2DS in the Electricity Sectr Data Surces: Quantity data fr electricity and CO2 emissins frm IEA Wrld Energy Outlk 2015 fr 2DS and BAU Scenari fr 2040, Cst data (LCOE) frm IEA/NEA Prjected Csts f Generating Electricity: 2015 Editin. The mdest mdelling effrt we perfrmed requested a number f heric assumptins: 50/50 split fr OECD and nn-oecd energy-cnsumptin fr cst calculatins, Cst figures frm EGC 2015 (netted fr CO2) d nt intrduce majr distrtins by nt using cst figures underlying WEO 2015, Generatin cst figures fr 2015 same as in 2040, Data fr 2040 cvers genuine 2 C scenari (usually calibrated n 2050 r 2100 timeframe). 9

10 Results fr Glbal Electricity WORLD 2040 BAU 2DS 450 2DS 450 w/ EEI Ttal Energy (MTOE/TWh) Ttal Cst (USD, net f Ttal CO2 (MtCO2) CO2 per TOE/MWh Unit Cst t g frm BAU t 2DS 450 CO2) (CO2) E E E Tw different methdlgies were pursued in rder t accunt fr the cst f energy efficiency imprvements between 2015 and 2040 that wuld cmpensate fr the reductin in energy use: 1. WEIO 2014 and WEO 2015 prvide rugh summary estimates fr investments in energy end-use efficiency that were added t ttal system csts. 2. N EEI was added but ttal utput was left identical with BAU scenari in rder t simulate identical level. 10

11 Questins 1. Is the methdlgy cnsidered sufficiently rbust and relevant t merit further investment (e.g., use f WEO cst data) and extensin t individual regins and cuntries? 2. Which mdificatins in methdlgy and presentatin are mst relevant? 3. Are results fr average abatement csts t reach the 2DS in the USD/tCO2 range realistic? Cnsider this: fr a marginal abatement cst f USD 20/tCO2, the McKinsey results suggest an abatement ptential f rughly 30 GtCO2 per year. Ging frm BAU t 2DS 450 scenari requires 26 GtCO2. Results indicate qualitatively that carbn prices even at levels that shuld be plitically feasible have a serius chance at making a real difference n the grund. Thank Yu 11