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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 UK's strategy be for integrated provision of NI in the long term?
Programme Overview
7
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
CuQng through the complexity
9
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
ITRC Stakeholder communica)ons
• Newslefer (over 450 contacts)
• Website (www.itrc.org.uk) • Twifer
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
Overview of the FTA Methodology
19
Highgrowth
Medium growth
Lowgrowth
Capacity-intensive (CI)
strategy
Capacity-constrained
(CC) strategyDecentralisation
(DC) strategy
Decision-maker goals & key questions
Sector analysismodels
Policy & technologyevaluation of performance
FTA growth scenarios Cross-sectoral transition strategies Performance evaluation
Figure 1
HIGH
HIGH
HIGH
LOW
LOW
LOW
Populationgrowth
Economicg rowth
Energycosts
Populationgrowth
Economicg rowth
Energycosts
Developing 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
Scenarios
Figure 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
Scenarios
Population of Great Britain projections 2008–2100
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
120
90
60
30
GB High projectionGB Principal projectionGB Low projection
Popu
latio
n (m
illio
ns)
90+85–8980–8475–7970–7465–6960–6455–5950–5445–4940–4435–3930–3425–2920–2415–1910–14
5–90–4
Figure 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
Sector Models
22
Water demand projections and capacity implications from climate change
20,000
15,000
10,000
5000
02010 2020 2030 2040 2050
Population scenarios: Low growth Medium growth High growth
Climate change Low impact Central impact High impact
Wat
er d
eman
d &
cap
acity
(MI/d
)
Figure 4
Transi)on Strategies: Key Ques.ons
• growing demand for infrastructure services?
• investment constraints and infrastructure capacity?
23
Strategies
What are the implica)ons of…
• a carbon-‐constrained future?
• a decentralised na)onal infrastructure system?
• interdependence between infrastructure sectors?
Transi)on Strategies: Dimensions
24
Strategies
High investment
Low investment
Centralised provision Decentralised provision
Capacity-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
Cost
Emssions
Security of supply
Low growth scenario
Medium growth scenario
High growth scenario
High performance(e.g. low cost, low emissions, high supply security)
Medium performance
Low performance(e.g. high cost, high emissions,low supply security)
L
M
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Sector
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
Figure 7
2.50
2.25
2.00
1.75
1.50
1.25 2010 2015 2020 2025 2030 2035 2040 2045 2050
Capacity-intensive (CI)
Capacity-constrained (CC)
Decentralisation (DC)
Shan
non–
Wie
ner I
ndex
(uni
tless
)
Example energy strategy performance for single metric
Figure 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
Highgrowth
Medium growth
Lowgrowth
Capacity-intensive (CI)
strategy
Capacity-constrained
(CC) strategyDecentralisation
(DC) strategy
Decision-maker goals & key questions
Sector analysismodels
Policy & technologyevaluation of performance
FTA growth scenarios Cross-sectoral transition strategies Performance evaluation
Cost
Emssions
Security of supply
Low growth scenario
Medium growth scenario
High growth scenario
High performance(e.g. low cost, low emissions, high supply security)
Medium performance
Low performance(e.g. high cost, high emissions,low supply security)
L
M
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Sector
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
Summary of results
Page 27 Cost
Emssions
Security of supply
Low growth scenario
Medium growth scenario
High growth scenario
High performance(e.g. low cost, low emissions, high supply security)
Medium performance
Low performance(e.g. high cost, high emissions,low supply security)
L
M
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Energy
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Transport
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Water
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Wastewater
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Solid waste
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
ICT
2010–2030
2030–2050
Cost
Emssions
Security of supply
Low growth scenario
Medium growth scenario
High growth scenario
High performance(e.g. low cost, low emissions, high supply security)
Medium performance
Low performance(e.g. high cost, high emissions,low supply security)
L
M
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Energy
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Transport
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Water
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Wastewater
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Solid waste
2010–2030
2030–2050
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
ICT
2010–2030
2030–2050
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 28
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
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L
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M
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M
M
M
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H
HH
H
Aggregatecomparativeperformance
2010–2030
2030–2050
Capacity-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
System
Surroundings
System
System System
System of Systems
System of Systems Analysis Strategies future conditions
(“Scenarios”) Surroundings
smaller set of drivers
Decision analysis
increasing returns to scale for the nonemitting technologies, therate at which agents learn from one another about the perfor-mance of new technologies, the agents’ risk aversion, and theheterogeneity of the agents’ price-performance preferences fornew technologies. Then, we viewed a series of interactive computer visualizations using different combinations of these fourkey uncertain inputs as independent variables (as well as a fifthuncertainty, the damages caused by climate change), each onecomparing the performance of the ‘‘Limits-Only’’ and ‘‘Com-bined Strategy.’’ Each visualization showed the performance ofthe two strategies as surface plots, measured as the present valueof the GDP over the 21st century (reflecting both the costs andbenefits of each strategy) as a function of two of the uncertain-ties, with the other inputs held constant at fixed values. A clearpattern emerged: the Limits-Only strategy is preferable in a
world 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 there
Fig. 3. Adaptive decision strategy for adjusting carbon taxes (Left) and technology incentives (Right) over time.
Fig. 4. A Landscape of Plausible Futures showing a wide range of future GHG emissions paths, all of which are consistent with available information.[Reproduced with kind permission from figure 3 of ref. 18 (Copyright 2000, Kluwer Academic Publishers).]
7312 ! www.pnas.org"cgi"doi"10.1073"pnas.082081699 Lempert
Lempert et al., 2002
Large ensemble simulation Robust control approach
14
S T R AT E G I E S F O R N AT I O N A L I N F R A S T R U C T U R E P R O V I S I O N I N G R E AT B R I TA I N : E X E C U T I V E S U M M A R Y
Figure 12: Summary
of transition strategy
performance assessment
using cross-sectoral metrics
of cost, emissions and
security of supply. In the
transport sector, the ‘security
of supply’ metric relates to
congestion.
Cost
Emssions
Security of supply
Low growth scenario
Medium growth scenario
High growth scenario
High performance(e.g. low cost, low emissions, high supply security)
Medium performance
Low performance(e.g. high cost, high emissions,low supply security)
L
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M
M
M
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H
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H
Energy
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
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H
L
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L
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Transport
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
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Water
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
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LL
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M
M
M
M
M
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Wastewater
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
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H
L
LL
L
LL
M
M
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H
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L
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L
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M
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M
M
M
M
HH
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HH
H
Solid waste
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
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L
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L
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ICT
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
An integrated Capacity & Demand framework
Dem
and
man
agem
ent
Capital investment &
other scenario variables
Demographics Economics
Scenarios
Performance measures
Energy
Transport
Water
Waste water
Waste
Demand
Household Industry
Supply Capacity
Demand modelling Household appliances (amenity)
Electricity
Gas
Biomass
Biogas
(Delivered) heat
Oil (petrol, diesel)
Solar
LPG
Biofuel
Hydrogen
Private vehicle transportation (cars, vans, motorcycles)
Freight transportation - LGV, HGV, rail, ship
Aviation - private, business travel, commercial (cargo)
Water purification
Water extraction & delivery
Waste water collection & processing
Waste collection and disposal - transportation
Waste processing
H2
LPG
Waste
(Waste)Water
Digital communication (network, servers, routers)
Data center operation
Computing services - excl. cloud/data centers
Transport
Mass Transportation - Rail, Bus
Agriculture
Plowing, harvesting & fertilizer application
Irrigation - water extraction, pumping
Food processing
Residential
Industry / Commercial
Mechanical - motor, drives, cranes Process heating - low & high temperature
Drying/separation
Compressed air processes
Site transportation (raw/processed material moving)
Other (inc. power production)
ICT
Cooking
Lighting (illumination)
Space heating (thermal comfort)
Space cooling/Air-conditioning (thermal comfort)
Water heating
H2
H2
H2
H2
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
18
S T R AT E G I E S F O R N AT I O N A L I N F R A S T R U C T U R E P R O V I S I O N I N G R E AT B R I TA I N : E X E C U T I V E S U M M A R Y
Work Stream 1 (WS1) is developing a system of quanti!ed capacity/demand assessment modules (CDAM) for analysis of long term strategies for infrastructure provision. In that sense it will resemble the FTA but will be based upon more quanti!ed and more fully integrated models including:
• A micro-simulation model for generation of high resolution demographic and demand scenarios.
• A regional economic model that will generate regional multi-sectoral projections of industrial demand for infrastructure services.
• A model of the UK electricity and gas networks and a new disaggregated energy demand module.
• A national strategic model of trunk road, rail, port and airport infrastructure.
• A national water resources system model, coupled with a model of wastewater treatment systems.
• A national solid waste assessment model.
ICT will be excluded from the WS1 analysis, as the FTA has illustrated that new capacity has being provided historically and this can be expected to continue for the foreseeable future. Further the demand is very sensitive to unforeseen technological developments which makes future analysis di"cult.
These models will be coupled in an overall simulation framework in which the main scenario uncertainties are extensively sampled, expanding upon the small number of scenarios analysed in the FTA. A set of infrastructure investment options will be developed for each sector and assembled #exibly into cross-sectoral packages, representing a major extension of the three transition strategies analysed in the FTA. New tools will be developed to explore and visualise the results of the analysis.
High resolution demographic projections
Regional multi-sectoral economic model
National infrastructuredatabase andanalysis archive
Module for:1 Sampling scenarios & uncertainties 2 Specifying options & strategies for infrastructure provisions3 Specifying CDAM model runs4 Post-processing & visualising results
Capacity/Demand Assessment Module (CDAM) for each NI sector
Energy
Transport
Water & wastewater
Solid waste
Figure 14: Structure of the
system of assessment models
and databases now under
development in Work Streams 1
and 4 of ITRC.
WS1 Scenario Genera)on Process
1. Compiling a list of interesting, high level policy relevant questions about the future performance of the interdependent infrastructure system
2. Deriving a set of dimensions relevant for answering the policy questions and partitioning of these dimensions into small sets of significantly different “levels”
3. Combining the “level” values of different dimensions to an overall strategy (accompanied by a narrative that accounts for synchronisation and consistency across the strategy dimensions)
4. Link the “level” values within the strategy dimensions to the “strategy variables” on the modelling level
Future conditions
Strategy variables
External Variables
smaller set of drivers
Policy Questions
Strategy Dimensions
WS1 Scenario Generation
1. Partitioning the space of external assumptions into “strategy variables” and “future conditions”
2. Linking the “future conditions” to a shorter list of underlying drivers
3. Sampling the underlying drivers (acknowledging interdependencies between different drivers)
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 … …
9
S T R AT E G I E S F O R N AT I O N A L I N F R A S T R U C T U R E P R O V I S I O N I N G R E AT B R I TA I N : E X E C U T I V E S U M M A R Y
High investment
Low investment
Centralised provision Decentralised provision
Capacity-intensive (CI)
Capacity-constrained (CC)
Decentralisation(DC)
Figure 6: Relative location of
the FTA infrastructure transition
strategies in relation to
investment requirements and
centralisation/decentralisation.
Highgrowth
Medium growth
Lowgrowth
Capacity-intensive (CI)
strategy
Capacity-constrained
(CC) strategyDecentralisation
(DC) strategy
Decision-maker goals & key questions
FTA drivers: Population growth • Economic growth • Energy cost
Sector analysismodels
Policy & technologyevaluation of performance
FTA growth scenarios Cross-sectoral transition strategies Performance evaluation
Figure 7: Summary of the Fast
Track Analysis methodology.
Single Scenario
FTA-‐like small set with narra.ves
Larger Monte Carlo Ensembles
Complete Factor analysis
Sampling Procedures – Future condi)ons (Scenarios)
unit
Time (units) temporal resolution
}
1 2 3 4
11+ aT be.g.
Parameterised time series
• Linking CDAM input parameters to a set of underlying drivers • Quantifying uncertainty of the underlying drivers • Implementing a formal sampling procedure
7
S T R AT E G I E S F O R N AT I O N A L I N F R A S T R U C T U R E P R O V I S I O N I N G R E AT B R I TA I N : E X E C U T I V E S U M M A R Y
THE ITRC FAST TRACK ANALYSIS ME THODOLOGY
National Infrastructure systems have to cope with the implications of long term changes in population, the economy, society and the environment. The nature of these changes is hard to predict in the long term, so the ITRC is adopting an approach in which plausible ranges of these future changes are analysed. A simpli!ed version of this methodology has been developed for the FTA, in which three primary scenario dimensions that are common to all infrastructure sectors have been analysed: demographic change, energy prices and economic growth (Figure 4).
Whilst the ITRC modelling tools that are now under development will enable the analysis of many combinations of these and other scenario dimensions, in the FTA the analysis has been restricted to only three combinations, representing low, medium, and high growth scenarios (Table 2, and Figure 5).
Table 2: Summary of the FTA scenarios
Low growth scenario
Medium growth scenario
High growth scenario
GB population (see Figure 5)
Low ONS projection
Principal ONS projection
High ONS projection
GDP growth per year
1.6% 2.3% 3.0%
Energy cost1 DECC High fossil fuel prices
DECC Central fossil fuel price
DECC Low fossil fuel price
1 DECC (2010). Updated energy and emissions projections. Department for Energy and Climate Change, London: TSO.
Figure 4: The dimensions of the
FTA scenario space.
HIG
H
HIGH
HIGH
LOW
LOW
LOW
Population growth
Economicgrowth
Energy costs
FTA driver space
Revisi)ng the level of resolu)on issue
LAD Regions
Finding acceptable levels of resolution for each sector
Twin-Track approach of low- and high resolution CDAM versions
Investigating the impact of NI management on different levels (e.g. Waste CDAM)
Incorpora)on of Interdependencies D
eman
d m
anag
emen
t
Capital investment &
other scenario variables
Demographics Economics
Scenarios
Performance measures
Energy
Transport
Water
Waste water
Waste
Demand
Household Industry
Supply Capacity
Transport (Southampton)
Water (Newcastle)
Solid Waste (Southampton)
Waste Water (Newcastle)
Energy (Oxford/Cardiff)
2… 3 1… Evaluation order
Sector CDAMs
Potential for interdependencies Current approach: • Common set of assumptions
• Solving order allows for one way dependencies
Iterative solution of the modelling framework…
Visuliza)on of Results 352 | THE ATLAS OF ECONOMIC COMPLEXITY
2008
ZIMBABWE
EVOLUTION OF EXPORT COMPOSITION
PRODUCT SPACE
2008
PRODUCT EXPORTED WITH RCA>1
PRODUCT NOT EXPORTED WITH RCA>1
NODE SIZE IS PROPORTIONAL TO WORLD TRADE
ECONOMIC COMPLEXITY INDEX [2008] ! -0.327 / (80/6) EXPECTED GDPPC GROWTH * ! 3.79% / (6/1)
2008 EXPORT OPPORTUNITY SPECTRUM
FRACTION OF PRODUCTS WITH RCA > 1
22% OF WT
6% OF WT
2% OF WT
0.8% OF WT
0.06% OF WT
*Expected annual average for the 2009-2020 period.
0.9 0.91 0.92 0.93 0.94
-2
-1
0
1
2
3
Distance
Avera
ge Co
mplex
ity of
Miss
ing Pr
oduc
ts
0.01 0.02 0.03 0.04 0.05 0.06
-2
-1
0
1
2
3
Opportunity Gain
Avera
ge Co
mplex
ity of
Miss
ing Pr
oduc
ts
MAPPING PATHS TO PROSPERITY | 353
2927 (12%) Flora 1212 (5.5%) Wholly or partly strippedtobacco
1222 (1.6%) Cigarretes
1211 1213
2631 (5.5%) Raw cotton 0612 (1.7%)Refined sugar
0572(0.99%)Fresh or driedcitrus N.E.S
0813
2872 (18%) Nickel 2731 2789
3232 (0.99%)
6612 (0.97%)
6672 (0.9%)Not mounteddiamonds
7821 (3.5%) Trucks& vans
8743 (3.3%) Gas,liquid & electriccontrol instruments
8946
6991
6954 7188
6716 (3%) Ferro-alloys 7711 (2.6%) Electricaltransformers
6513 (3.4%) Cotton yarn
6535 (0.74%)
8422 (2.5%) Men'ssuits
8219 (1%) 2482(0.98%)
2471
8960 (2.7%) Worksof art
0545 (1.6%)Other fresh orchilledvegetables
0586 0546(0.78%)
6421
5137 (1.7%) Monocarboxylicacids & derivatives
8748 (1.5%) Electricalmeasuring & controllinginstruments N.E.S.
5232(0.71%)
5231(0.67%)
8928 (4.1%) Printed matterN.E.S.
* Data are from 2008. Numbers indicate: Value (World Ranking / Regional Ranking). Sub-Saharan Africa.
2008
1968
EXPORTTREEMAP
EXPORT TREEMAP
* 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: 24.65 M / 228.33 B (0.01%) 1988 EXPORT TREEMAP TOTAL EXPORTS: 1.13 B / 2.79 T (0.04%)
TOTAL EXPORTS: 1.72 B / 15.56 T (0.01%)
ELEC
TRON
ICS
MACH
INER
Y
AIRCR
AFT
BOILE
RS
SHIPS
META
L PRO
DUCT
S
CONS
TR. M
ATL.
& EQ
PT.
HOME
& OF
FICE
PULP
& PA
PER
CHEM
ICALS
& HE
ALTH
AGRO
CHEM
ICALS
OTHE
R CHE
MICA
LS
INOR
. SAL
TS &
ACID
S
PETR
OCHE
MICA
LS
LEAT
HER
MILK
& CH
EESE
ANIM
AL FI
BERS
MEAT
& EG
GS
FISH &
SEAF
OOD
TROP
ICAL A
GRIC.
CERE
ALS &
VEG.
OILS
COTT
ON/R
ICE/S
OY &
OTHE
RS
TOBA
CCO
FRUI
T
MISC
. AGR
ICULT
URE
NOT C
LASS
IFIED
TEXT
ILE &
FABR
ICS
GARM
ENTS
FOOD
PROC
ESSIN
G
BEER
/SPIR
ITS &
CIGS
.
PREC
IOUS
STON
ES
COAL OI
L
MINI
NG
GDP ! USD 4.2 B / (124/22)
GDPPC ! USD 341 / (125/23)
EXPORTS PER CAPITA ! USD 138 / (119/19)
EXPORTS AS SHARE OF GDP ! 41 % (44/9)
POPULATION ! 12 M / (62/17)
TOTAL EXPORTS ! USD 1.7 B / (118/22)
MIT Atlas of economic complexity
Investigating different methods and methodologies for complex data visualisation…
Visulisa)on of Results
14
S T R AT E G I E S F O R N AT I O N A L I N F R A S T R U C T U R E P R O V I S I O N I N G R E AT B R I TA I N : E X E C U T I V E S U M M A R Y
Figure 12: Summary
of transition strategy
performance assessment
using cross-sectoral metrics
of cost, emissions and
security of supply. In the
transport sector, the ‘security
of supply’ metric relates to
congestion.
Cost
Emssions
Security of supply
Low growth scenario
Medium growth scenario
High growth scenario
High performance(e.g. low cost, low emissions, high supply security)
Medium performance
Low performance(e.g. high cost, high emissions,low supply security)
L
M
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Energy
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Transport
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Water
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Wastewater
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
Solid waste
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
L
LL
L
LL
M
M
M
M
M
M
HH
H
HH
H
ICT
2010–2030
2030–2050
Capacity-intensive (CI) Capacity-constrained (CC) Decentralisation (DC)
352 | THE ATLAS OF ECONOMIC COMPLEXITY
2008
ZIMBABWE
EVOLUTION OF EXPORT COMPOSITION
PRODUCT SPACE
2008
PRODUCT EXPORTED WITH RCA>1
PRODUCT NOT EXPORTED WITH RCA>1
NODE SIZE IS PROPORTIONAL TO WORLD TRADE
ECONOMIC COMPLEXITY INDEX [2008] ! -0.327 / (80/6) EXPECTED GDPPC GROWTH * ! 3.79% / (6/1)
2008 EXPORT OPPORTUNITY SPECTRUM
FRACTION OF PRODUCTS WITH RCA > 1
22% OF WT
6% OF WT
2% OF WT
0.8% OF WT
0.06% OF WT
*Expected annual average for the 2009-2020 period.
0.9 0.91 0.92 0.93 0.94
-2
-1
0
1
2
3
Distance
Avera
ge Co
mplex
ity of
Miss
ing Pr
oduc
ts
0.01 0.02 0.03 0.04 0.05 0.06
-2
-1
0
1
2
3
Opportunity Gain
Avera
ge Co
mplex
ity of
Miss
ing Pr
oduc
ts
MIT Atlas of economic complexity
…and applying them to the different WS1 outputs.
www.itrc.org.uk
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