Evaluation with stylized facts
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1 Evaluation with stylized facts AMPERE Subgroup on IAM Validation Valeria Jana Schwanitz Potsdam Institute for Climate Impact Research 27. Mai 2013
2 Content Fundamental laws and stylized facts Systematic evaluation of IAMs with stylized facts Evaluating AMPERE models with stylized facts Stylized conclusion and outlook
3 From fundamental laws to observable deep-order patterns Natural Science: Controlled experiments confirm fundamental laws. Example from Newton, F = G m 1m 2 r 2 Social Science (and coupled complex systems): Uncontrolled observations suggest some stylized patterns. ib 1 a 1 c 1 A 1 B 4 C { Sch 1 H 1 neg Z 1 neg sch 7 H 7 Z 9 Example from Propp, Definition of stylized fact: Kaldor 1961 (Economic Growth Theory), Buchanan 2012 (Complex Social Systems) } W 4 L 1 V 1 [ Sch 1 H 1 Z9 = R 4] 3
4 Systematic evaluation of IAMs with stylized facts Proposal for a community-wide list of stylized facts. Criteria for selection: Evaluation value: acceptance, relevance, endogenous and exogenous model results, transperancy Completeness: capturing important system processes and scales Applicability: to a broad class of IAMs of global climate change
5 Identifying relevant system processes
6 Use of PIRT-Tables PIRT - Phenomena Identification and Ranking Tables
7 Proposal: List of stylized facts Focus on endogenous and quasi-endogenous model results Economic development SF 1: Consumption economic growth SF 2: Economic growth investment Energy transition SF 3: Output intensity of final energy 1/(per capita income) SF 4: Consumption of primary energy per capita per cap income SF 5: Share of residential sector in FE 1/per capita income. U-shape relationship for industry share. Service sector share per capita income. Agriculture is small. SF 6: Increasing share in modern energy technologies, solids decrease at the same time. Energy transition/climate change SF 7: Trend towards decarbonization. Technological change SF 8: Extent of technology diffusion duration of tech diffusion SF 9: Spacial acceleration of technology diffusion.
8 Testing stylized facts in AMPERE models: Two examples
9 Testing stylized facts: Consumption and GDP 5 Acemoglu 2: Correlation between consumption and GDP data from PWT 7.0, year: 2000 log ( per capita consumption ) linear regression, slope: 0.89 linear regression, slope: 1.03 MOZ GNB NER AFG BDI ETH SOM SLE LBR MWI GBR ISL BMU CHE AUT ITA FRA GER AUS NLD NOR SWE BEL DNK PLW GRCBHS ESPFIN HKG IRL ARE NZL MLTISR TWN BRB PRT PRI SGP CYPSVN KWT CZE BRN KOR KNAPOLSVK HUN ROWBHR MAC LBN MEX GRD SYC URY LTU ARG HRV EST TTO MHL TUR BIHTON BLZ LVA JAM CRI LCA CHL MKD DOM LAM CUB SAU BGR ATG OMN SLV WSM MUS GTM COL ROM LBY BLR ZAF VEN MNE PAN MYS PER KAZTHA IRN PRY ALBNAM FJI ECU EGY TUN DMA VUT JOR ARM GEO SWZ BWA BOL FSMUKR VCT GAB OAS AZE LKA HND GUY SYR CPV IDN DJI PHL KIR DZA MDA NIC MAR TKM SUR HTI SDN PAK KGZ BTN CH2 LSO CMR IND YEM MNG MDV SEN LAO KEN CIVVNM PNG STP TJK KHM BEN MRT TLS UZB NPL IRQ NGA TZAMDG TGO GHA COM BGD BFA GNQ MLI UGA GIN GMB ZMB ERI SLB COG CAF AGO RWA TCD LUX QAT ZAR ZWE log( per capita GDP )
10 Testing stylized facts: Consumption and GDP 6 IMAGE, AM3S1 log per capita consumption [$2005 PPP] RSAS RSAF EAF WAF UKR RSAM STAN SEAS CEU RCAM NAF SAF UKR ME INDIAINDO RSAM MEX SEAS STAN TUR JAP KOR OCE RCAM RSAS CEU SAF UKR NAF INDIA ME MEX JAP TUR INDO OCE RSAM KOR RSAF STAN RCAM EAF CEU JAP MESAF MEX OCE SEAS RSAS TUR WAF NAF KOR INDIA CEU OCE UKR INDO RSAF JAP RSAM EAF MEX WAF RCAM TUR ME KOR SAF NAF RSAS SEAS STAN RSAF EAF INDIA INDO WAF log per capita GPD [$2005 PPP]
11 Testing stylized facts: Consumption and GDP 6 IMACLIM, AM3S1 log per capita consumption [$2005 GDP] RAS RAS RAL FSU JANZ JANZ FSU RAL FSU JANZ RAS RAL FSU RAS IND RAL JANZ IND IND IND log per capita GDP [$2005 PPP]
12 Testing stylized facts: Consumption and GDP 5 MESSAGE, AM3S1 NAM PAO NAM PAO log per capita consumption [$2005 PPP] SAS PAS CPA LAM FSU SAS EEU PAS EEU CPA FSU LAM SAS PAOCPA EEU LAM FSU SAS PAS NAM NAM PAO CPA EEU LAM FSU PAS log per capita GDP [$2005 PPP]
13 Testing SF (extensive): Transition of energy structure 100 Energy transition - structure - historical data ( ) projections by REMIND Grids (electricity, gases, heat, hydro) 80 FE structure [% of total FE] LIQUIDS EMF27G1 EMF27G17 20 SOLIDS (coal, biomass, trad. biomass) EMF27G EMF27G1 EMF27G17 EMF27G9
![hydro) 80 FE structure [% of total FE] 60 40 LIQUIDS EMF27G1 EMF27G17 20 SOLIDS (coal,](/docs-images/58/3495739/images/page_13.jpg "biomass, trad.")
14 Testing SF (intensive): Transition of energy structure per capita GDP [$2005 PPP] share of solids in final energy consumption XYZ 1990 XYZ 1995 XYZ 2000 XYZ 2005 data from ENERDATA and PWT 6.3 NER NGA NZL BIH ROU FJI MWI GRC MNG DEU ALB TZA CHE AUT THA BGR COL CRI SVK LUX MYS ITA TUR SGP FRA MDG EST BTN KEN MRT CYP PAK BOL BDI SYR CHL GBR VNM AUS DZA PER NPL SVN MAC ARG IRL HKG VEN HTI ISR LBN ZAF UZB BEL HND TUN SLV MKD POL IRN MUS FIN HRV ISL SWE PAN DOM CUB COD HUN MEX DNK ZMB PRT IND URY BWA PHL NOR MAR NLD ESP KOR MOZ TWN BGD SDN LKA JAM ZWE SEN EGY SWZ LAO CZE GTM IDN ARE AFG NER NGA NZL BIH ROU FJI MWI GRC MNG DEU ALB TZA CHE AUT THA BGR COL CRI SVK LUX MYS ITA LTU TUR SGP FRA MDG EST BTN KEN GEO MRT CYP PAK LVA BOL BDI MDA SYR KAZ CHL GBR VNM AUS DZA PER NPL AZE SVN MAC ARG IRL HKG VEN HTI ISR LBN ZAF UZB BEL ARM HND TUN SLV MKD POL IRN MUS BLR FIN HRV ISL SWE PAN DOM CUB COD HUN MEX DNK ZMB PRT IND URY BWA PHL NOR MAR NLD ESP KOR MOZ TWN BGD SDN LKA JAM ZWE UKR SEN EGY SWZ LAO CZE GTM IDN ARE AFG KGZ NER NGA NZL BIH ROU FJI MWI GRC MNG DEU ALB TZA CHE AUT THA BGR COL CRI SVK LUX MYS ITA LTU TUR SGP FRA MDG EST BTN KEN GEO MRT CYP PAK LVA BOL BDI MDA SYR KAZ CHL GBR VNM AUS DZA PER NPL AZE SVN MAC ARG IRL HKG VEN HTI ISR LBN ZAF UZB BEL ARM HND TUN SLV MKD POL IRN MUS BLR FIN HRV ISL SWE PAN DOM CUB COD HUN MEX DNK ZMB PRT IND URY BWA PHL NOR MAR NLD ESP KOR MOZ TWN BGD SDN LKA JAM ZWE UKR SEN EGY SWZ LAO CZE GTM IDN ARE AFG KGZ NERNGA NZL BIH ROU FJI MWI GRC MNG DEU ALB TZA CHE AUT THA BGR COL CRI SVK LUX MYS ITA LTU TUR SGP FRA MDG EST BTN KEN GEO MRT CYP PAK SRB LVA BOL BDI MDA SYR KAZ CHL GBR VNM AUS DZA PER NPL AZE SVN MAC ARG IRL HKG VEN HTI ISR LBN ZAF UZB BEL ARM HND TUN SLV MKD POL IRN MUS BLR FIN HRV ISL SWE PAN DOM CUB COD HUN MEX DNK ZMB PRT IND URY BWA PHL NOR MAR NLD ESP KOR MOZ TWN BGD SDN LKA JAM ZWE UKR SEN EGY SWZ LAO CZE GTM IDN ARE AFG KGZ
15 Testing SF (intensive): Transition of energy structure AM3S1 share of solids in final energy EAF RSAF SSAF IDT WAF EAF SAS INDIA NDE CPA RSAS INDO RSAF WAF RASIA IDT SAF RCAM SEAS OAS KOR SSAF IDT EAF TUR RASIA NDE IDT RJAN CEAM IDT MIEA IDT INDPAS INDIA INDIA JAP COR WRD TUR IDT OAS RJANRSAM RSAS WAF RASIA IDT OCIS IDT OCIS IDT RSAM TUR UKR EEU CPA WRD UKR IDT WRD IDT LAM IND IND JAP RSAS UKR EU12 IDT RSAM WRD IDT COR ROW CEAM IDT EU12 CEU IDT EU15 IDT SAS SSAF IDT OCIS IDT RJAN TUR EU12 IDT CEU RCAM SAF EU15 IDT INDIA KOR RCAM RSAF EU12 WAF EEU IDT RCEU LAM PAS EU15 IDT IDT TUR SSAF IDT RCEU MIEA NDE IDT RASIA IDT RCEU IDT OCIS IDT RSAS JAP SAF KOR EAF TUR TUREEU CEAM EEU RCEU IDT CEAM IND MEX IDT FSUCOR NOAF IDT COR IND RAS KOR RCAM NAF RSAM CPA CEU TUR FSU INDO LAM RJAN SEAS RAS CEU JAP PAO NOAF IDT SAS JANZ PAS PASJANZ RAS RAS INDO FSU JANZ MEX OCE OCE MEXSAF NOAF SEAS IDT PAOLAM ROW ROWE IDT MEX RSAF PAO PAOSTAN STAN ROWE IDT NDE OCE ROWE IDT FSU FSU NAF RSAM UKR RSAM FSU MIEA LAM IDT STAN EU15 FSU IDT OAS ROWE IDT INDO SEAS AM NAF IND RSAM ROW OCE NAM ROW JANZ SAS NAM NOAF IDT NAF LAM RAL RAL STAN RAL ME RAL NAM LAM OAS LAM ME CPA IND MIEA IDT FSU e e+05 2e+05 per capita GDP [$2005 PPP] REMIND 2100 REMIND 2070 REMIND 2040 REMIND 2010 POLES 2100 POLES 2070 POLES 2040 POLES 2010 MESSAGE 2100 MESSAGE 2070 MESSAGE 2040 MESSAGE 2010 IMAGE 2100 IMAGE 2070 IMAGE 2040 IMAGE 2010 IMACLIM 2100 IMACLIM 2070 IMACLIM 2040 IMACLIM 2010
16 Testing SF (intensive): Transition of energy structure AM3S1 share of solids in final energy EAF RSAF SSAF IDT WAF EAF SAS INDIA NDE CPA RSAS INDO RSAF WAF RASIA IDT SAF RCAM SEAS OAS KOR SSAF IDT EAF TUR RASIA NDE IDT RJAN CEAM IDT MIEA IDT INDPAS INDIA INDIA JAP COR WRD TUR IDT OAS RJANRSAM RSAS WAF RASIA IDT OCIS IDT OCIS IDT RSAM TUR UKR EEU CPA WRD UKR IDT WRD IDT LAM IND IND JAP RSAS UKR EU12 IDT RSAM WRD IDT COR ROW CEAM IDT EU12 CEU IDT EU15 IDT SAS SSAF IDT OCIS IDT RJAN TUR EU12 IDT CEU RCAM SAF EU15 IDT INDIA KOR RCAM RSAF EU12 WAF EEU IDT RCEU LAM PAS EU15 IDT IDT TUR SSAF IDT RCEU MIEA NDE IDT RASIA IDT RCEU IDT OCIS IDT RSAS JAP SAF KOR EAF TUR TUREEU CEAM EEU RCEU IDT CEAM IND MEX IDT FSUCOR NOAF IDT COR IND RAS KOR RCAM NAF RSAM CPA CEU TUR FSU INDO LAM RJAN SEAS RAS CEU JAP PAO NOAF IDT SAS JANZ PAS PASJANZ RAS RAS INDO FSU JANZ MEX OCE OCE MEXSAF NOAF SEAS IDT PAOLAM ROW ROWE IDT MEX RSAF PAO PAOSTAN STAN ROWE IDT NDE OCE ROWE IDT FSU FSU NAF RSAM UKR RSAM FSU MIEA LAM IDT STAN EU15 FSU IDT OAS ROWE IDT INDO SEAS AM NAF IND RSAM ROW OCE NAM ROW JANZ SAS NAM NOAF IDT NAF LAM RAL RAL STAN RAL ME RAL NAM LAM OAS LAM ME CPA IND MIEA IDT FSU e e+05 2e+05 per capita GDP [$2005 PPP] REMIND 2100 REMIND 2070 REMIND 2040 REMIND 2010 POLES 2100 POLES 2070 POLES 2040 POLES 2010 MESSAGE 2100 MESSAGE 2070 MESSAGE 2040 MESSAGE 2010 IMAGE 2100 IMAGE 2070 IMAGE 2040 IMAGE 2010 IMACLIM 2100 IMACLIM 2070 IMACLIM 2040 IMACLIM 2010
17 To do: Stylized fact about a brain s capacity to process information within a 20 min talks??? Stop here.
18 Testing stylized facts: Investments and GDP 5 Dosi SF data from PWT 7.0, year: 2005 log ( per capita investment ) ZAR linear regression, slope: 1.09, correlation coefficient: 0.96 linear regression, slope: 0.98, correlation coefficient: 0.97 BDI LBR ISL KWT MAC AUS SGP NOR BHS BHR HKGIRL CHE ARE KOR BEL AUT SUR SYC ESPFIN DNK TTO SVN BMU BRB ITA GRD TWN OMN NZLFRASWE NLD SAU PRT GRC GER GBR ISR BRN KNA EST MHL ATG CZE MLT PRI SVK CYP LVAHRV LBN ROW HUN BWA LCA CHL GNQ KIR GAB MYSMEX PLW MDV THA JAM IRNCRI LTU POL KAZ BTN JOR CH2 MUS BGR DZA TUR ARG LBY ALBECU SRB ROM VEN URY BLZ BLR VCT TUN COL ZAF CPV AZE DMA PAN TON OAS LAM BIH PERMKD DOM IRQ MARFSM NAM VUT MRT GTM MNE MNG GEO VNM SYR FJI ARM HND LKA SLV UKR YEM IND IDN GUYEGY WSM LAONIC PRY SWZ STP SEN MDA UZB SDN BOL GMB ZMB PAK COG DJI PHL BGD PNG AGO GHA NPL CMR TCD KHM LSO GIN CUB TZA BEN TKM MWI AFG MLI TLS UGAKEN SLB KGZ BFA HTI MDG NER ERI TGO COM ETH RWA CIV SLE TJK SOM MOZ NGA CAF GNB LUX QAT ZWE log ( per capita GDP )
19 Testing stylized facts: Investments and GDP 4 EMF27G1, year: linear regression, slope: 0.86, correlation coefficient: 0.95 log ( per capita ESM investments ) IND OAS LAM ROW log ( per capita GDP )
20 Testing stylized facts: Development of FE/cap over population per capita Final energy consumption [GJ] FE/POP vs POP historic aggregate EMF27G1 EMF27G9 EMF27G17 QAT OMN TJK GEO SYR KAZ YEM DZA AZE ISR LBN LBY UZB SAU ARM JOR TUN IRN KWT BHR MAR EGY TKM ARE KGZ Population [billions]
21 Testing stylized facts: Development of FE/cap over GDP/cap per capita Final energy consumption [GJ] FE/POP vs GDP/POP historic aggregate EMF27G1 EMF27G9 EMF27G17 ROU GRC DEU AUT BGR MLT SVK LUX ITA LTU FRA EST CYP LVA GBR SVN IRL BEL POL FIN SWE HUN DNK PRT NLD ESP CZE ANT per capita GDP [ PPP D 2005 ]
22 Testing stylized facts: Distribution dynamics of PE intensity
23 Testing stylized facts: Substitution of energy technologies f/(1-f) Primary energy consumption, Fisher-Pry plot, EMF27G17 world, data from BP 2011, Smil, and WEO oil, own, BP 2011 coal, own, BP 2011 gas,own, BP 2011 nuclear, own, BP 2011 hydro, own, BP 2011 oil, Smil coal, Smil gas, Smil nuclear, Smil hydro, Smil biofuels, Smil oil, WEO coal, WEO gas, WEO nuclear, WEO hydro, WEO biofuels, WEO coal, EMF27G1 oil, EMF27G1 gas, EMF27G1 nuclear, EMF27G1 biofules, EMF27G1 hydro, EMF27G1 wind, EMF27G1 solar, EMF27G1 geothermal, EMF27G years
24 Testing stylized facts: Distribution dynamics of carbon intensity density Dynamic distribution of carbon intensity of FE Efficiency Mix Supply deviation from average / average in
25 Stylized outlook - What can be done should be done Possible ex-ante using stylized facts to evaluate IAMs of global climate change: educated opinion formation about future developments systematic questioning of model behavior (incl. model inter-comparison) community list of stylized facts could serve as a criteria for model quality Only possible ex-post validating forecasting power: Marquardt 1981 citing O Neills Establishing first Marsian mines people living on Mars
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