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
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 uncertain.es • Financially feasible • Within an appropriate governance framework
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
Challenges in delivering the vision • Analysing the long term state of NI systems • Uncertain.es 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
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.
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?
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 authori.es • Insurers • NGOs • U.lity companies On-‐going collabora.on and dissemina.on arrangements
The ITRC Fast Track Analysis Objec.ves: 1. Ensure that the ITRC research programme is building upon exis.ng 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 consor.um’s partners in government and industry.
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
Harnessing stakeholder and partner par.cipa.on Ben Kidd
ITRC Partners Over 40 partners in industry and government: • Government departments, agencies and local authori.es • U.lity companies • Engineering and mul.-‐disciplinary consultants • Contractors • Insurers • Research organisa.ons and data providers • Engineering ins.tu.ons • NGOs
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
Co-‐produc)on in prac)ce Good engagement via FTA report development – Produc.ve stakeholder review workshop (30th Oct 2011) – Electronic and paper-‐based reviews of drais of the FTA report – “Comments Log”, providing an audit trail
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
Fast Track Analysis Methodologies and Results Dr Jus)n Henriques
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
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
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
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?
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
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
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)
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
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…
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
ITRC complex systems concepts, methodologies and the modelling framework Alex Lorenz
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).]pnas.org͞cgi͞doi͞10.1073͞pnas.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)
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
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.
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)
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
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)
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…
Visuliza)on of Results 352 | THE ATLAS OF ECONOMIC COMPLEXITY2008ZIMBABWEEVOLUTION OFEXPORT COMPOSITIONPRODUCTSPACE2008PRODUCTEXPORTEDWITHRCA>1PRODUCTNOTEXPORTEDWITHRCA>1NODESIZEISPROPORTIONALTOWORLDTRADEECONOMIC COMPLEXITY INDEX  ≥ -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…
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  ≥ -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.