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Linking TIAM and KLEM: Economic Impacts of WB2D mitigation pathways

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Linking TIAM and KLEM: Economic Impacts of WB2D mitigation pathways

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Linking TIAM and KLEM: Economic Impacts of WB2D mitigation pathways

  1. 1. Linking TIAM-KLEM: Economic impacts of WB2D mitigation pathways (Work in Progress, please don’t cite) James Glynn, Frédéric Ghersi, Brian Ó Gallachóir 70th ETSAP Workshop CIEMAT, MADRID | 17th – 18th November 2016
  2. 2. Outline • What is the key motivation? • Understand feedbacks, structural changes and welfare effects due to energy system decarbonisation • Update on soft-link method between TIAM and KLEM • KLEM Computable General Model • Harmonising updated World Bank/OECD Driver to TIAM • Some precursory results, aggregated at world level highlighting the difference between BASE economic outlook and current macroeconomic outlook from SSP/OECD/World Banks
  3. 3. Why Hybrid Linking? • Update TIAM Macroeconomic outlook(s) • Harmonisation of energy service demands with changing economic outlook. • Aim for best of both worlds. • Technological Explicitness • Macroeconomic realism • Sectoral Dynamics (Energy, Non Energy, Households) • Demand response is considerable in decarbonisation scenarios. • Moving forward from TIAM-MACRO/MSA • Investigate multi-sector dynamics • Aim for better representation socio-economic dynamics
  4. 4. Overview of linkage • TIMES-MACRO (Remme & Blesl, 2006) • TIAM-KLEM TIAM Sectoral Energy p&qs KLEM Labour ES investment Households’ consumption Public consumption International trade Investment Non-E/E Capital Non-E output ?Non-E prices?
  5. 5. KLEM Prerequisites • National accounting framework to access • Complete cost structures K, L, E, M(1,…,n) • Inter-industry flows i.e. structural change, dematerialisation • Market instruments recycling options • Distributive issues, at least firms/government/households • Dual accounting in monetary and physical units • To keep track of energy volumes in stand-alone versions • To model agent-specific pricing • Explicit investment profiles • To account for transitional strain on shorter time intervals
  6. 6. KLEM at a glimpse • CGEM with 2 primary factors L and K, 1 E good, 1 non-E good • Recursive dynamics driven by • Exogenous L supply and productivity (SSP) • K accumulation via exogenous investment & depreciation rates • Public expenses constant share of GDP, constant (rough) tax system • Operates on hybrid energy/economy matrix obtained from crossing GTAP and TIAM data 6/16
  7. 7. B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E 430 627 249 - - 269 1 574 L net 5 859 41 L taxes 2 060 15 Y taxes 649 87 K 5 681 137 M 1 980 461 SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 Base year (2007) IOT, WEU
  8. 8. Base year (2007) IOT, WEU B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E 430 627 249 - - 269 1 574 L net 5 859 41 L taxes 2 060 15 Y taxes 649 87 K 5 681 137 M 1 980 461 SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 E uses and imports are TIAM data with explicit p x q decomposition
  9. 9. Base year (2007) IOT, WEU B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E 430 627 249 - - 269 1 574 L net 5 859 41 L taxes 2 060 15 Y taxes 649 87 K 5 681 137 M 1 980 461 SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 Remainder of E resource structure scaled up/down from GTAP to balance uses E uses and imports are TIAM data with explicit p x q decomposition
  10. 10. Base year (2007) IOT, WEU B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E 430 627 249 - - 269 1 574 L net 5 859 41 L taxes 2 060 15 Y taxes 649 87 K 5 681 137 M 1 980 461 SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 Calibrated zero-sum specific margins warrant agent-specific E prices E uses and imports are TIAM data with explicit p x q decomposition Remainder of E resource structure scaled up/down from GTAP to balance uses
  11. 11. Base year (2007) IOT, WEU B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E 430 627 249 - - 269 1 574 L net 5 859 41 L taxes 2 060 15 Y taxes 649 87 K 5 681 137 M 1 980 461 SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 Non-E data deduced from GTAP totals
  12. 12. KLEM behavioural assumptions • Output sequential trade-off of K vs. L then KL vs. E then KLE vs. ‘M’ (aggregate of non-E goods) • K vs. L, KLE vs. M settled by CES functions • KL (VA) vs. E from TIAM under a maintained CES assumption for KLE • Aggregate savings rate exogenous (recursive dynamics) • Households’ E consumption from TIAM • International trade • E trade from TIAM • Non-E trade: ratio of M to Y isoelastic to terms of trade; X settled by international good CES of exported goods (Armington) 12/16
  13. 13. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net 5 859 41 L taxes 2 060 15 Y taxes 649 87 K 5 681 ### M 1 980 ### SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 TIAM trajectory prescribes E uses and imports as well as E investment requirements, which drive KE accumulation K rental price adjusts to balance remainder of K supply and K demand by non-E production
  14. 14. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E 14 085 90 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net 5 859 ### L taxes 2 060 ### Y taxes 649 87 K 5 681 ### M 1 980 ### SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 116 Resources 32 000 1 574 Labour intensity of E production assumed constant, wage adjusts to balance remainder of L supply and L demand by non-E production Optional imperfect L market magnifies cost of E investment crowding out non-E investment
  15. 15. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E 14 085 ### 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net 5 859 ### L taxes 2 060 ### Y taxes 649 ### K 5 681 ### M 1 980 ### SM non-E - 103 SM E - -14 SM C - -58 SM X - -30 Sales taxes 1 257 ### Resources 32 000 1 574 ‘M’ (non-E) intensity of E production trades off with KLE aggregate under a constant elasticity of substitution assumption Output and sales taxes constant ad valorem rates
  16. 16. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E 14 085 ### 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net 5 859 ### L taxes 2 060 ### Y taxes 649 ### K 5 681 ### M 1 980 ### SM non-E - ### SM E - ### SM C - ### SM X - ### Sales taxes 1 257 ### Resources 32 000 ### Specific margins adjust to have E end-use prices match TIAM agent- specific prices
  17. 17. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E 14 085 ### 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net ### ### L taxes ### ### Y taxes 649 ### K ### ### M 1 980 ### SM non-E - ### SM E - ### SM C - ### SM X - ### Sales taxes 1 257 ### Resources 32 000 ### In non-E production K and L trade off with constant elasticity to produce aggregate KL (VA) considering wage and rent adjusted to clear markets Optional imperfect L market magnifies cost of E investment crowding-out non-E investment Resulting K, L and E combine into aggregate KLE following CES specification
  18. 18. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E ### ### 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net ### ### L taxes ### ### Y taxes 649 ### K ### ### M 1 980 ### SM non-E - ### SM E - ### SM C - ### SM X - ### Sales taxes 1 257 ### Resources 32 000 ### In non-E production Non-E intensity of non-E production and KLE aggregate trade off to produce domestic output Y The price of the non-E good is the weighted average of domestic and import prices
  19. 19. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E ### ### 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net ### ### L taxes ### ### Y taxes 649 ### K ### ### M ### ### SM non-E - ### SM E - ### SM C - ### SM X - ### Sales taxes 1 257 ### Resources 32 000 ### In non-E production The ratio of imports to domestic output in (volumes) is isoelastic to the ratio of their prices
  20. 20. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E ### ### 9 022 3 235 3 410 2 158 32 000 E ### ### ### - - ### ### L net ### ### L taxes ### ### Y taxes ### ### K ### ### M ### ### SM non-E - ### SM E - ### SM C - ### SM X - ### Sales taxes 1 ### ### Resources 32 000 ### In non-E production Exogenous tax rates
  21. 21. At each period from 2010 to 2100 B$ Non-E E C G I X Uses Non-E ### ### ### ### ### ### ### E ### ### ### - - ### ### L net ### ### L taxes ### ### Y taxes ### ### K ### ### M ### ### SM non-E - ### SM E - ### SM C - ### SM X - ### Sales taxes 1 ### ### Resources ### ### Final non-E consumptions G and I are exogenous shares of GDP X trades off with Xs of other regions at constant elasticity of substitution (Armington) to provide sum of Ms Closure of accounting balance defines C
  22. 22. Overview of linkage • TIMES-MACRO (Remme & Blesl, 2006) • TIAM-KLEM TIAM Energy p&qs KLEM Labour ES investment Households’ consumption Public consumption International trade Investment Non-E/E Capital Non-E output Simultaneously Iteratively ?Non-E prices?
  23. 23. Harmonising Drivers • Why? – Initial calibration run not converging with significant differences in BASE calibration • OECD-SSP2 economic outlook is quite different to existing ETSAP-TIAM macroeconomic drivers. • SSP2 • OECD_SSP2 IIASA-DB • GDP, POP, Urbanisation • OECD – ENV-LINKS model Sectoral projections • PAGR, PSERV, PCHEM, PISNF, POEI, POI • OECD in Paris were happy to provide baseline SSP2 consistent Gross production and value added for results for their GTAP CGE model (25 regions/ 35 sectors) • World Bank • Historical value added by sector • 2005 – 2010 • Full data not available to 2015 yet.
  24. 24. New generation of scenarios In the lead up to the IPCC’s Sixth Assessment Report new scenarios have been developed to more systematically explore key uncertainties in future socioeconomic developments Five Shared Socioeconomic Pathways (SSPs) have been developed to explore challenges to adaptation and mitigation. Shared Policy Assumptions (SPAs) are used to achieve target forcing levels (W/m2). Source: Riahi et al. 2016; IIASA SSP Database; Global Carbon Budget 2016
  25. 25. CO2 Budgets +SSP2 drivers BASE BASE SSP2 2DS 66% 2DS 66% SSP2
  26. 26. Driver Differences to SSP2 0 10 20 30 40 50 60 70 2000 2025 2050 2075 2100 GDP: AFR BASE 15R BASE 15R SSP2 0 5 10 15 20 25 2000 2025 2050 2075 2100 GDP: CHI 0 10 20 30 40 2000 2025 2050 2075 2100 GDP: IND 0 1 2 3 4 2000 2025 2050 2075 2100 GDP: WEU 0 0.5 1 1.5 2 2.5 3 3.5 2000 2025 2050 2075 2100 POP: AFR BASE 15R BASE 15R SSP2 1 1.1 1.2 1.3 1.4 1.5 1.6 2000 2025 2050 2075 2100 POP: IND 0.5 0.7 0.9 1.1 1.3 2000 2025 2050 2075 2100 POP: CHI 0.8 0.9 1 1.1 1.2 2000 2025 2050 2075 2100 POP: WEU
  27. 27. Driver Differences to SSP2 0 20 40 60 80 100 2000 2025 2050 2075 2100 SERV: AFR BASE 15R BASE 15R SSP2 0 20 40 60 80 2000 2025 2050 2075 2100 SERV: IND 0 1 2 3 4 5 2000 2025 2050 2075 2100 SERV: WEU 0 50 100 150 200 2000 2025 2050 2075 2100 ISNF: AFR BASE 15R BASE 15R SSP2 0 200 400 600 800 2000 2025 2050 2075 2100 ISNF: IND 0 0.5 1 1.5 2 2.5 3 2000 2025 2050 2075 2100 ISNF: WEU 0 10 20 30 40 50 2000 2025 2050 2075 2100 ISNF: CHI 0 5 10 15 20 25 2000 2025 2050 2075 2100 SERV: CHI ??
  28. 28. Primary Energy 0 200 400 600 800 1000 1200 BASE BASE BASE SSP2 2DS 66% 2DS 66% SSP2 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2005 2030 2050 ExaJoules Coal Oil Gas Nuclear Hydro Biomass Renewable except hydro and biomass 0 500 1000 1500 2000 2500 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2100 ExaJoules
  29. 29. Final Energy - 100 200 300 400 500 600 700 BASE BASE BASE SSP2 2DS 66% 2DS 66% SSP2 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2005 2030 2050 ExaJoules Coal Oil Products Gas Electricity Biomass (excludes liquid biofuels) Biodiesel Alcohol Other Renewable Heat Hydrogen - 200 400 600 800 1,000 1,200 1,400 1,600 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2100ExaJoules
  30. 30. Electricity Capacity - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2005 2030 2050 GW Electricity Generation Installed Capacities (GW) - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 GW Solar Thermal Solar PV Geo, Tidal and Wave Wind Biomass CCS Biomass Hydro Nuclear Gas CCS Gas and Oil Coal
  31. 31. Electricity Generation 0 20 40 60 80 100 120 140 160 BASE BASE BASE SSP2 2DS 66% 2DS 66% SSP2 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2005 2030 2050 Exajoules Total Electricity Generation 0 100 200 300 400 500 600 BASE BASE SSP2 2DS 66% 2DS 66% SSP2 2100 Exajoules Solar Thermal Solar PV Geo, Tidal and Wave Wind Biomass CCS Biomass Hydro Nuclear CH4 Options Gas CCS Gas and Oil
  32. 32. KLEM eventual Outputs • GDP change • Consumption change across sectors • Employment change • Re-estimated output/drivers • Residential, • Energy firms • Non-Energy – Commercial/Services
  33. 33. Conclusions • This hybrid type of approach steps towards sectoral specific dynamics of decarbonising from a bottom up technology explicit perspective • Unemployment, structural changes, sectoral outputs, drivers • A broader approach to assess demand uncertainty using the SSP narratives could be integrated into TIAM/regional/or National Models • A CGE such as KLEM or GTAP is required to generate sectoral SSP drivers. (disaggregate GDP) • Is it appropriate that we generally don’t have multiple drivers scenarios? • Long term OECD-SSP2 drivers cause extreme growth indices post 2060 in emerging economies. – review elasticities… • A Hybrid (GE) TIAM is ideal for NDC analysis given TIAM’s bottom up nature and a hybrid general equilibrium with the economy. • Technology specific NDCs - USA • Economic Intensity NDCs - INDIA/CHINA • Carbon limits - Europe
  34. 34. Environmental Research Institute Instiúd Taighde Comshaoil Energy Policy and Modelling Group www.ucc.ie/energypolicy @james_glynn james.glynn@umail.ucc.ie
  35. 35. Lahinch Beach, Co Clare, IR Thanks you QUESTIONS?
  36. 36. @james_glynn is a Postdoctoral researcher in @MaREIcentre @ERIUCC @UCC working on global integrated assessment models linking detailed energy-economy-climate models, to assess equitable, ambitious and secure decarbonisation of the energy system eMail: james.glynn@ucc.ie Twitter: @james_glynn Web: www.ucc.ie/energypolicy Profile: http://www.ucc.ie/en/energypolicy/people/jamesglynn/
  37. 37. Energy Policy & Modelling Collaborators and Funders
  38. 38. GLOBAL ETSAP-TIAM model • Linear programming bottom-up energy system model of IEA-ETSAP • Integrated model of the entire energy system • Prospective analysis on medium to long term horizon (2100) • Demand driven by exogenous energy service demands • Partial and dynamic equilibrium (perfect market) • Optimal technology selection • Minimizes the total system cost • Environmental constraints • Integrated Climate Model • 15 Region Global Model • Price-elastic demands • Macro Stand Alone • Single consumer-producer, multi-regional, inter-temporal general equilibrium model which maximises regional utility. • The utility is a logarithmic function of the consumption of a single generic consumer. • Production inputs are labour, capital and energy. • Energy demand and energy costs from ETSAP-TIAM model. • MSA Re-estimates Energy Service Demands based on energy cost
  39. 39. IPCC INDC Synthesis Report
  40. 40. ETSAP-TIAM Reference Energy System Source: Loulou, R., Labriet, M., 2008. ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure. Comput. Manag. Sci. 5, 7–40. doi:10.1007/s10287-007-0046-z
  41. 41. ETSAP-TIAM 15 Regions AFR CAN USA MEX CSA WEU EEU MEA IND CHI SKO JPN AUS ODA
  42. 42. GDP losses & Costs of Delayed Action 0 2 4 6 8 10 12 14 16 18 20 2DS 50% 2DS 50% DA20 2DS 50% 2DS 50% DA20 2DS 50% 2DS 50% DA20 2DS 50% 2DS 50% DA20 2030.2050.2070.2100 GDP Loss % Former Soviet Union Australia & NZ South Korea Other Developing Asia Canada Middle East China East Europe Africa India West Europe Japan USA Central South America Mexico
  43. 43. ETSAP-TIAM MSA (TMSA) Macro Stand Alone 𝑀𝑎𝑥 𝑈 = 𝑡=1 𝑇 𝑟 𝑛𝑤𝑡 𝑟 . 𝑝𝑤𝑡𝑡. 𝑑𝑓𝑎𝑐𝑡 𝑟,𝑡. 𝑙𝑛 𝐶𝑟,𝑡 (1) (MSA OBJz) 𝑌𝑟,𝑡 = 𝐶𝑟,𝑡 + 𝐼𝑁𝑉𝑟,𝑡 + 𝐸𝐶𝑟,𝑡 + 𝑁𝑇𝑋(𝑛𝑚𝑟) 𝑟,𝑡 (2) 𝑌𝑟,𝑡 = 𝑎𝑘𝑙 𝑟 ∙ 𝐾𝑟,𝑡 𝑘𝑝𝑣𝑠 𝑟∙𝜌 𝑟 ∙ 𝑙 𝑟,𝑡 (1−𝑘𝑝𝑣𝑠 𝑟)𝜌 𝑟 + 𝑘 𝑏 𝑟,𝑘 ∙ 𝐷𝐸𝑀𝑟,𝑡,𝑘 𝜌 𝑟 1 𝜌 𝑟 (3) • nwt – Negishi Weights • pwt – weight Multiplier • dfact – utility discount factor • C - Consumption • Y – Production • INV – Investment • EC – Energy Cost • NTX – Net exports • akl – production fn constant • K – Capital • kpvs – capital value share • l - Labour annual growth • b – Demand coefficient • p – elasticity of substitution • DEM - Energy Demands      R r YEARSy yREFYR yr yrANNCOSTdNPVMin 1 , ),()1( (TIAM OBJz)
  44. 44. TIMES Energy System Model Cost and emissions balance GDP Process Heating area Population Light Comms Power Person kilometers Freight kilometers Service Demands Coal processing Refineries Power plants and Transportation CHP plants and district heat networks Gas network Industry Commercial and Tertiary Households Transport Final energyPrimary energy Domestic sources Imports Demands Energyprices,Resourceavailability

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