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  • 1. Introduc)on  to  the  UK  Infrastructure  Transi)ons  Research  Consor)um  Prof  Jim  Hall  FREng    Principal  Inves.gator  ITRC  Director,  Environmental  Change  Ins.tute,  University  of  Oxford    29  February  2012  
  • 2. What  are  the  desirable  a@ributes  of  a  Na)onal  Infrastructure?  •  Priori.sing  the  capacity  constraints  to  economic  prosperity    •  Achieving  carbon  reduc.on  commitments  •  Ensuring  energy  security  •  Well  adapted  to  a  changing  climate  •  Resilient  to  natural  and  man-­‐made  hazards  •  Robust  to  a  full  range  of  future  •  Financially  feasible  •  Within  an  appropriate  governance  framework  
  • 3. 2011  Na)onal  Infrastructure  Plan  •  Underlines  the  economic  importance  of  na.onal  infrastructure  •  Sets  out  a  detailed  plan  for  infrastructure  delivery  in  the  coming  years  •  Explores  new  sources  of  funding  and  finance  •  Proposes  metrics  for  monitoring  infrastructure  performance   3  
  • 4. Challenges  in  delivering  the  vision  •  Analysing  the  long  term  state  of  NI  systems  •  e.g.  in  demand,  economic  condi.ons,  costs,  performance  •  The  complexity  of  mul.ple  governance  arrangements  and  projects  •  The  capacity  of  UK  industry  to  compete  in  globalised  markets  for  infrastructure  services  
  • 5. ITRC  Aim  and  Ambi)on  Aim:  To  develop  and  demonstrate  a  new  genera.on  of  simula.on  models  and  tools  to  inform  the  analysis,  planning  and  design  of  na.onal  infrastructure    Ambi%on:  Enabling  a  revolu.on  in  the  strategic  analysis  of  NI  provision  in  the  UK…  whilst  at  the  same  .me  becoming  an  interna.onal  landmark  programme  recognised  for  novelty,  research  excellence  and  impact.    
  • 6. ITRC  Key  Ques)ons  and  Objec)ves  6  1.  How  can  infrastructure  capacity  and  demand  be  balanced  in  an  uncertain  future?    2.  What  are  the  risks  of  infrastructure  failure  and  how  can  we  adapt  Na.onal  Infrastructure  to  make  it  more  resilient?    3.  How  do  infrastructure  systems  evolve  and  interact  with  society  and  the  economy?    4.  What  should  the  UKs  strategy  be  for  integrated  provision  of  NI  in  the  long  term?      
  • 7. Programme  Overview  7  
  • 8. Consor)um  Lead  Universi)es    •   Cardiff  University    •   University  of  Leeds    •   University  of  Southampton    •   Newcastle  University    •   University  of  Oxford    •   University  of  Sussex    •   University  of  Cambridge    Support  •   Engineering  and  Physical  Science  Research  Council  Programme  Grant  £4.7  million  •   University  contribu.ons  £1  million  •   Industry  contribu.ons  £1.6  million  Partnership  Over  40  partners  in  industry  and  government:    •   Contractors  •   Engineering  &  mul.-­‐disciplinary  consultants  •   Engineering  ins.tu.ons  •   Government  departments,  agencies  &  local  •   Insurers  •   NGOs  •   U.lity  companies  On-­‐going  collabora.on  and  dissemina.on  arrangements    
  • 9. CuQng  through  the  complexity  9  
  • 10. The  ITRC  Fast  Track  Analysis  Objec.ves:  1. Ensure  that  the  ITRC  research  programme  is  building  upon  knowledge.  2. Refine  the  scope  of  the  ITRC  research.  3. Pilot  and  communicate  new  analysis  concepts.    4. Strengthen  the  rela.onship  between  the  research  team  and  the’s  partners  in  government  and  industry.    
  • 11. Agenda  16:40  Harnessing  stakeholder  and  partner  par%cipa%on      in  the  co-­‐produc%on  of  transi%on  strategies  16:50  Fast  Track  Analysis  Methodologies  and  Results  17.10  ITRC  complex  systems  concepts,  methodologies  and      the  modelling  framework  17.30  Discussion  session  18:00  Drink  recep%on    
  • 12. Harnessing  stakeholder  and  partner  par.cipa.on  Ben  Kidd  
  • 13. ITRC  Partners  Over  40  partners  in  industry  and  government:  •  Government  departments,  agencies  and  local  •  U.lity  companies  •  Engineering  and  mul.-­‐disciplinary  consultants  •  Contractors    •  Insurers    •  Research  organisa.ons  and  data  providers      •  Engineering  ins.tu.ons    •  NGOs    
  • 14. Coordinated  research  &  knowledge  exchange  Linking  in  with  affiliated  and  other  similar  projects:  •  ARCC  Coordina.on  Network  •  LWEC  Infrastructure  Challenge  •  EPSRC-­‐funded  projects  •  EU-­‐funded  projects    
  • 15. ITRC  Stakeholder  communica)ons  •  Newslefer  (over  450  contacts)  •  Website  (    •  Twifer  
  • 16. Co-­‐produc)on  in  prac)ce  Good  engagement  via  FTA  report  development  –  stakeholder  review  workshop  (30th  Oct  2011)  –  Electronic  and  paper-­‐based  reviews  of  drais  of  the  FTA  report  –  “Comments  Log”,  providing  an  audit  trail  
  • 17. ITRC  impact  -­‐  Informing  policy  and  prac)ce  Early  impacts:    •  Infrastructure  UK  engagement  •  ICE  State  of  the  Na.on:  Water  resources  engagement  •  England  Waste  Strategy  (Defra,  ICE)  •  RSSB  futures  work  
  • 18. Fast  Track  Analysis  Methodologies  and  Results  Dr  Jus)n  Henriques  
  • 19. Overview  of  the  FTA  Methodology  19  HighgrowthMediumgrowthLowgrowthCapacity-intensive (CI)strategyCapacity-constrained(CC) strategyDecentralisation(DC) strategyDecision-maker goals & key questionsSectoranalysismodelsPolicy &technologyevaluation ofperformanceFTA growth scenarios Cross-sectoral transition strategies Performance evaluationFigure 1
  • 20. HIGHHIGHHIGHLOWLOWLOWPopulationgrowthEconomicgrowthEnergycostsPopulationgrowthEconomicgrowthEnergycostsDeveloping  Scenarios:  Drivers  of  Change  Primary  drivers  of  change  •  Demographic  change  •  Energy  prices    •  Economic  growth  Secondary  drivers    •  Climate  change  •  Carbon  emission  targets  •  EU  direc.ves  and  Na.onal  standards  •  Others  20  ScenariosFigure 2
  • 21. Developing  Scenarios  Low  Growth  •  Popula.on  (Fig  4)  •  GDP  growth:  1.6%  •  Energy  costs:  DECC  high*  Medium  growth  •  Popula.on  (Fig  4)  •  GDP  growth:  2.3%  •  Energy  costs:  DECC  central*  High  Growth  •  Popula.on  (Fig  4)  •  GDP  growth:  3.0%  •  Energy  costs:  DECC  low*    *assump.ons  of  fossil  fuel  price  21  ScenariosPopulation of Great Britain projections 2008–21002000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100120906030GB High projectionGB Principal projectionGB Low projectionPopulation(millions)90+85–8980–8475–7970–7465–6960–6455–5950–5445–4940–4435–3930–3425–2920–2415–1910–145–90–4Figure 3
  • 22. Demand  and  Supply  Models  For  each  sector…  •  Created  demand  projec.ons  based  on  the  three  scenarios  •  Constructed  supply-­‐side  and  demand  management  op.ons  for  each  strategy  •  Evaluated  performance  –  Common  set  of  performance  measures,  including  cost,  emissions,  and  security  of  supply    SectorModels22  Water demand projections and capacity implications from climate change20,00015,00010,000500002010 2020 2030 2040 2050Population scenarios:Low growthMedium growthHigh growthClimate changeLow impactCentral impactHigh impactWaterdemand&capacity(MI/d)Figure 4
  • 23. Transi)on  Strategies:  Key  Ques.ons  •  growing  demand  for  infrastructure  services?    •  investment  constraints  and  infrastructure  capacity?    23  StrategiesWhat  are  the  implica)ons  of…  •  a  carbon-­‐constrained  future?    •  a  decentralised  na)onal  infrastructure  system?    •  interdependence  between  infrastructure  sectors?  
  • 24. Transi)on  Strategies:  Dimensions  24  StrategiesHigh investmentLow investmentCentralised provision Decentralised provisionCapacity-intensive(CI)Capacity-constrained(CC)Decentralisation(DC)Capacity-­‐intensive  High  investment  in  new  capacity  to  keep  up  with  demand  and  maintain  good  security  of  supply  (except  transport)  Decentralisa%on   Reorienta.on  to  more  distributed  systems  involving  a  combina.on  of  supply  and  demand-­‐side  measures  Capacity-­‐constrained  Emphasis  on  demand  management  measures,  low  infrastructure  investment   Figure 5
  • 25. Performance  Evalua)on  25  CostEmssionsSecurity of supplyLow growth scenarioMedium growth scenarioHigh growth scenarioHigh performance(e.g. low cost, low emissions,high supply security)Medium performanceLow performance(e.g. high cost, high emissions,low supply security)LMHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHSector2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)Figure 72.502.252.001.751.501.252010 2015 2020 2025 2030 2035 2040 2045 2050Capacity-intensive (CI)Capacity-constrained (CC)Decentralisation (DC)Shannon–WienerIndex(unitless)Example energy strategy performance for single metricFigure 6
  • 26. Summary  of  the  FTA  methodology  1.  Developing  scenarios  •  IdenAfy  the  primary  drivers  that  impact  the  future  demand  and  capacity  of  infrastructure  services  •  Construct  three  possible  futures  through  varia.on  of  these  drivers  to  2050    2.  Sector  modelling  •  Build  models  to  project  future  demand  across  the  three  scenarios  for  each  NI  sector  •  Construct  three  transi)on  strategies  and  IdenAfy  key  performance  metrics  3.  Evalua)on  •  Evaluate  the  performance  of  the  transi.on  strategies  across  the  scenarios  •  Construct  visualisa.on  summary  of  performance  26  HighgrowthMediumgrowthLowgrowthCapacity-intensive (CI)strategyCapacity-constrained(CC) strategyDecentralisation(DC) strategyDecision-maker goals & key questionsSectoranalysismodelsPolicy &technologyevaluation ofperformanceFTA growth scenarios Cross-sectoral transition strategies Performance evaluationLMHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHSector2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
  • 27. Summary of resultsPage 27CostLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHEnergy2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHTransport2010–20302030–2050LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHWater2010–20302030–2050LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHWastewater2010–20302030–2050LLLMMMHHH LLLMMMHHH LLLMMMHHHSolid waste2010–2030CostEmssionsSecurity of supplyLow growth scenarioMedium growth scenarioHigh growth scenarioHigh performance(e.g. low cost, low emissions,high supply security)Medium performanceLow performance(e.g. high cost, high emissions,low supply security)LMHLMH LMH LMHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHWater2010–20302030–2050LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHWastewater2010–20302030–2050LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHSolid waste2010–20302030–2050LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHICT2010–20302030–2050
  • 28. Sample  of  cross-­‐sectoral  findings  Decentraliza)on    •  Greater  diversity  of  supply  could  lead  to  greater  supply  security  •  Capitalize  on  interdependencies  (local  waste  to  energy  conversion);  Constrained  investment  •    cost,  however,  erosion  of  supply  security,  especially  in  high  growth  scenario  •  Demand  reduc.on  may  improve  efficiently  (energy),  but  can  also  adversely  impact  economy  and  society  (transport)  Page 28LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHAggregatecomparativeperformance2010–20302030–2050Capacity-Intensive (CI) Capacity-Constrained (CC) Decentralisation (DC)Implications of…
  • 29. Contribu)ons  29  1.  Iden.fica.on  of  key  drivers  of  change  of  demand  for  NI  services  2.  Enabling  the  cross-­‐sectoral  analysis  of  NI  strategies  under  long  term  uncertainty  3.  Incorporates  a  process-­‐based  understanding  of  NI  demand  and  capacity  4.  Visualisa.on  of  NI  strategy  performance  over  mul.ple  metrics  and  .me  periods  
  • 30. ITRC  complex  systems  concepts,  methodologies  and  the  modelling  framework    Alex  Lorenz  
  • 31. General  methodology  of  Capacity/Demand  modelling  within  ITRC  SystemSurroundingsSystemSystem SystemSystem of SystemsSystem of Systems AnalysisStrategiesfuture conditions(“Scenarios”) Surroundingssmaller setof driversDecision analysiseturns to scale for the nonemitting technologies, theh agents learn from one another about the perfor-ew technologies, the agents’ risk aversion, and thety of the agents’ price-performance preferences forogies. Then, we viewed a series of interactive comizations using different combinations of these fourn inputs as independent variables (as well as a fifththe damages caused by climate change), each onehe performance of the ‘‘Limits-Only’’ and ‘‘Com-gy.’’ Each visualization showed the performance ofegies as surface plots, measured as the present valueover the 21st century (reflecting both the costs andach strategy) as a function of two of the uncertain-e other inputs held constant at fixed values. A clearerged: the Limits-Only strategy is preferable in aworld where the agents’ technology preferences are homoge-neous, imperfect information effects are small, and the damagescaused by climate change emerge slowly. When these conditionsdo not hold, the Combined (tax and subsidy) Strategy quicklybecomes more attractive.The robust region map in Fig. 5 summarizes these results. Thefigure shows the expectations about the future that should causea decision-maker to prefer the Limits-Only strategy to theCombined Strategy. The horizontal axis represents the range ofexpectations a decision-maker might have for how likely itis—from very unlikely (Left) to very likely (Right)—that factorssuch as the potential number of early adopters and the amountof increasing returns to scale will significantly influence thediffusion of new technologies. The vertical axis represents therange of expectations a decision-maker might have that thereFig. 3. Adaptive decision strategy for adjusting carbon taxes (Left) and technology incentives (Right) over time.ndscape of Plausible Futures showing a wide range of future GHG emissions paths, all of which are consistent with available information.with kind permission from figure 3 of ref. 18 (Copyright 2000, Kluwer Academic Publishers).]͞cgi͞doi͞10.1073͞pnas.082081699 LempertLempert et al., 2002Large ensemble simulation Robust control approachSTRATEGIES FOR NATIONAL INFRASTRUCTURE PROVISION IN GREAT BRITAIN: EXECUTIVE SUMMARYFigure 12: Summaryof transition strategyperformance assessmentusing cross-sectoral metricsof cost, emissions andsecurity of supply. In thetransport sector, the‘securityof supply’metric relates tocongestion.LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHEnergy2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHTransport2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
  • 32. An  integrated  Capacity  &  Demand  framework  DemandmanagementCapitalinvestment&otherscenariovariablesDemographics EconomicsScenariosPerformance measuresEnergyTransportWaterWaste waterWasteDemandHousehold IndustrySupply Capacity
  • 33. Demand  modelling  Householdappliances(amenity)ElectricityGasBiomassBiogas(Delivered)heatOil(petrol, diesel)SolarLPGBiofuelHydrogenPrivate vehicletransportation(cars, vans, motorcycles)Freight transportation- LGV, HGV, rail, shipAviation - private, businesstravel, commercial (cargo)Water purificationWater extraction & deliveryWaste water collection & processingWaste collection and disposal- transportationWaste processingH2LPGWaste(Waste)WaterDigital communication(network, servers, routers)Data center operationComputing services- excl. cloud/datacentersTransportMass Transportation - Rail, BusAgriculturePlowing, harvesting &fertilizer applicationIrrigation- water extraction, pumpingFood processingResidentialIndustry / CommercialMechanical- motor, drives,cranesProcess heating- low & high temperatureDrying/separationCompressed air processesSite transportation(raw/processed materialmoving)Other (inc. powerproduction)ICTCookingLighting(illumination)Space heating(thermalcomfort)Space cooling/Air-conditioning(thermal comfort)Water heatingH2H2H2H2
  • 34. Issues  for  an  integrated  Modelling  framework  Issues     Want  to  integrate  the  sector  CDAMs  +  interdependency  Combined  single  model  is  computa.onal  infeasible    General  equilibrium  approach  (soi  link)  also  infeasible  Trade  off  between  single  model  detail  and  level  of  integra.on  
  • 35. Linking  architecture  wastewater treatment systems.• A national solid waste assessmentmodel.High resolutiondemographic projectionsRegional multi-sectoraleconomic modelNationalinfrastructuredatabase andanalysisarchiveModule for:1 Sampling scenarios &uncertainties2 Specifying options & strategiesfor infrastructure provisions3 Specifying CDAM model runs4 Post-processing & visualisingresultsCapacity/Demand AssessmentModule (CDAM) for each NI sectorEnergyTransportWater & wastewaterSolid waste: Structure of theassessment modelsbases now underment in Work Streams 1TRC.
  • 36. WS1  Scenario  Genera)on  Process  1.  Compiling a list of interesting, high level policy relevantquestions about the future performance of theinterdependent infrastructure system2.  Deriving a set of dimensions relevant for answering thepolicy questions and partitioning of these dimensions intosmall sets of significantly different “levels”3.  Combining the “level” values of different dimensions to anoverall strategy (accompanied by a narrative that accountsfor synchronisation and consistency across the strategydimensions)4.  Link the “level” values within the strategy dimensions to the“strategy variables” on the modelling levelFutureconditionsStrategyvariablesExternal Variablessmaller setof driversPolicyQuestionsStrategyDimensionsWS1 Scenario Generation1.  Partitioning the space of externalassumptions into “strategy variables”and “future conditions”2.  Linking the “future conditions” to ashorter list of underlying drivers3.  Sampling the underlying drivers(acknowledging interdependenciesbetween different drivers)
  • 37. Itera)ve  ensemble  approach  Scenario  #  Decision  variables    Underlying  Drivers  d1   d2   …   dM   r1   r2   …   rK  2   1   1   …   1  of  BM   1   1   …   1  3   2   1   …   1  of  BM   1   1   …   2  3   3   1   …   …   3  4   …   …  5   …   …  …   …   …  7   …   …  9Low investmentCentralised provision Decentralised provisionCapacity-constrained(CC)HighgrowthMediumgrowthLowgrowthCapacity-intensive (CI)strategyCapacity-constrained(CC) strategyDecentralisation(DC) strategyDecision-maker goals & key questionsFTA drivers: Population growth • Economic growth • Energy costSectoranalysismodelsPolicy &technologyevaluation ofperformanceFTA growth scenarios Cross-sectoral transition strategies Performance evaluationSingle  Scenario  FTA-­‐like  small  set  with  narra.ves  Larger  Monte  Carlo  Ensembles  Complete  Factor  analysis  
  • 38. Sampling  Procedures  –  Future  condi)ons  (Scenarios)  unitTime (units)temporal resolution}12 3 411+ aTbe.g.Parameterised time series•  Linking CDAM input parameters to a set of underlying drivers•  Quantifying uncertainty of the underlying drivers•  Implementing a formal sampling procedureTRACK ANALYSISMETHODOLOGYNational Infrastructure systems have tocope with the implications of long termchanges in population, the economy,society and the environment. The nature ofthese changes is hard to predict in the longterm, so the ITRC is adopting an approachin which plausible ranges of these futurechanges are analysed. A simplified versionof this methodology has been developedfor the FTA, in which three primary scenariodimensions that are common to allinfrastructure sectors have been analysed:demographic change, energy prices andeconomic growth (Figure 4).Whilst the ITRC modelling tools that arenow under development will enable theanalysis of many combinations of these andother scenario dimensions, in the FTA theanalysis has been restricted to only threecombinations, representing low, medium,and high growth scenarios (Table 2, andFigure 5).Table 2: Summary of the FTA scenariosLow growthscenarioMedium growthscenarioHigh growthscenarioGB population(see Figure 5)Low ONSprojectionPrincipal ONSprojectionHigh ONSprojectionGDP growth peryear1.6% 2.3% 3.0%Energy cost1 DECC High fossil DECC Central DECC Low fossilFigure 4: The dimensions of theFTA scenario space.HIGHHIGHHIGHLOWLOWLOWPopulationgrowthEconomicgrowthEnergycostsFTA driver space
  • 39. Revisi)ng  the  level  of  resolu)on  issue  LAD RegionsFinding acceptable levels ofresolution for each sectorTwin-Track approach of low-and high resolution CDAMversionsInvestigating the impact of NImanagement on differentlevels (e.g. Waste CDAM)
  • 40. Incorpora)on  of  Interdependencies  DemandmanagementCapitalinvestment&otherscenariovariablesDemographics EconomicsScenariosPerformance measuresEnergyTransportWaterWaste waterWasteDemandHousehold IndustrySupply CapacityTransport(Southampton)Water(Newcastle)Solid Waste(Southampton)Waste Water(Newcastle)Energy(Oxford/Cardiff)2… 31…Evaluation orderSector CDAMsPotential for interdependenciesCurrent approach:•  Common set of assumptions•  Solving order allows for one waydependenciesIterative solution of the modelling framework…
  • 41. Visuliza)on  of  Results  352 | THE ATLAS OF ECONOMIC COMPLEXITY2008ZIMBABWEEVOLUTION OFEXPORT COMPOSITIONPRODUCTSPACE2008PRODUCTEXPORTEDWITHRCA>1PRODUCTNOTEXPORTEDWITHRCA>1NODESIZEISPROPORTIONALTOWORLDTRADEECONOMIC COMPLEXITY INDEX [2008] ≥ -0.327 / (80/6) EXPECTED GDPPC GROWTH * ≥ 3.79% / (6/1)2008 EXPORT OPPORTUNITYSPECTRUMFRACTIONOFPRODUCTSWITHRCA>122%OFWT6%OFWT2%OFWT0.8%OFWT0.06%OFWT*Expected annual average for the 2009-2020 period.0.9 0.91 0.92 0.93 0.94-2-10123DistanceAverageComplexityofMissingProducts0.01 0.02 0.03 0.04 0.05 0.06-2-10123OpportunityGainAverageComplexityofMissingProducts2927(12%)Flora 1212(5.5%)Whollyorpartlystrippedtobacco1222(1.6%)Cigarretes1211 12132631(5.5%)Rawcotton 0612(1.7%)Refinedsugar0572(0.99%)FreshordriedcitrusN.E.S08132872(18%)Nickel 2731 27893232(0.99%)6612(0.97%)6672(0.9%)Notmounteddiamonds7821(3.5%)Trucks&vans8743(3.3%)Gas,liquid&electriccontrolinstruments894669916954 71886716(3%)Ferro-alloys 7711(2.6%)Electricaltransformers6513(3.4%)Cottonyarn6535(0.74%)8422(2.5%)Menssuits8219(1%) 2482(0.98%)24718960(2.7%)Worksofart0545(1.6%)Otherfreshorchilledvegetables0586 0546(0.78%)64215137(1.7%)Monocarboxylicacids&derivatives8748(1.5%)Electricalmeasuring&controllinginstrumentsN.E.S.5232(0.71%)5231(0.67%)8928(4.1%)PrintedmatterN.E.S.2008 EXPORTTREEMAP* Numbers indicate SITC-4 rev 2 codes. Parenthesis indicate percentage of total exports. Treemap Headers show: Total Trade/Total World Trade (share of world trade represented by the country).TOTAL EXPORTS: 1.72 B / 15.56 T (0.01%)MIT Atlas of economic complexityInvestigating different methods andmethodologies for complex datavisualisation…
  • 42. Visulisa)on  of  Results  STRATEGIES FOR NATIONAL INFRASTRUCTURE PROVISION IN GREAT BRITAIN: EXECUTIVE SUMMARYFigure 12: Summaryof transition strategyperformance assessmentusing cross-sectoral metricsof cost, emissions andsecurity of supply. In thetransport sector, the‘securityof supply’metric relates tocongestion.CostEmssionsSecurity of supplyLow growth scenarioMedium growth scenarioLMLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHEnergy2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHTransport2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHWater2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHWastewater2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)LLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHLLLLLLMMMMMMHHHHHHSolid waste2010–20302030–2050Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)352 | THE ATLAS OF ECONOMIC COMPLEXITYZIMBABWEPRODUCTSPACE2008PRODUCTEXPORTEDWITHRCA>1PRODUCTNOTEXPORTEDWITHRCA>1NODESIZEISPROPORTIONALTOWORLDTRADEECONOMIC COMPLEXITY INDEX [2008] ≥ -0.327 / (80/6) EXPECTED GDPPC GROWTH * ≥ 3.79% / (6/1) *Expected annual average for the 2009-2020 period.MIT Atlas of economic complexity…and applying them tothe different WS1outputs.
  • 43.