Dr. Matthew Ives, University of Oxford. ISNGI 2017 Wednesday, September 13th, 2017 Institution of Civil Engineers, London

Transcription

1 Mapping out the landscape of long-term national infrastructure demands for the U.K. S National Infrastructure Assessment Ives, M.C. 1, Silberman, A. 2, Letti, B. 2, Large, J. 2, Blainey, S. 3, Choundry, M. 4, Baruah, P. 5, Robson, C. 6, Usher, W. 1, Hall, J.W. 1 Dr. Matthew Ives, University of Oxford ISNGI 2017 Wednesday, September 13th, 2017 Institution of Civil Engineers, London 1. University of Oxford, 2. National Infrastructure Commission, 3. University of Southampton, 4. Cardiff University, 5. Global Green Growth Institute (GGGI), 6. Newcastle University

2 National Infrastructure Assessment The National Infrastructure Commission has been tasked by the government to provide expert, independent advice on pressing infrastructure issues and to produce an in-depth assessment of the UK s major infrastructure needs out to In order to identify the UK s future infrastructure needs, the National Infrastructure Assessment will consider a range of scenarios, which will explore different future pressures on infrastructure. The scenarios are tested using the national infrastructure systems model (NISMOD), as well as models used by government. The analysis assumes a do minimum policy, which provides a baseline for the Commission against which it can compare policy options and recommendations as part of the Assessment. Assessments are being undertaken in energy, transport, water, waste, digital communications and flood risk.

3 What is ITRC? ITRC is the Infrastructure Transitions Research Consortium The ITRC is a consortium of seven leading UK universities led by University of Oxford ITRC has been funded by EPSRC from

4 A great idea Problem: How to provision for national infrastructure in an uncertain future given immense system complexity and sector interdependencies? Common challenges faced by infrastructure decision makers: Broad spatial and temporal scales Poor quality of evidence and information System Complexity Fragmented information Lack of transparency Limited cross-sectoral assessment available Solution: Develop and demonstrate a new generation of simulation models and tools to inform the analysis, planning and design of national infrastructure

5 ITRC and NISMOD Applications Assembled a database of UK infrastructure assets and networks Implemented a national infrastructure planning model for the development of long-term strategic approaches to infrastructure provision (NISMOD). Numerous publications covering the entire span of U.K. infrastructure ( Provided HM Treasury with a evaluation of the current U.K. national infrastructure plan Currently working closely with the National Infrastructure Commission to develop the U.K. government s vision for national infrastructure

6 Infrastructure System-of-systems Energy from waste The transport sector represents 34% of energy demand in the UK* Waste Electricity generation is responsible for 32% of non-tidal water abstractions* Climate Socio-economics *(Defra, 2009) Water Digital communications

7 Great Britain s Integrated electricity network Medium complexity models

8 NISMOD: National Infrastructure Systems MODel The NISMOD system-of-systems model

9 NIC Scenarios Driver Central variant Low variant High variant Additional variant Economic growth Long-term average and productivity growth of GDP per capita of 1.9% per year based on the latest OBR long-term Population demography and Climate change and environment economic growth projections in the 2017 Fiscal Sustainability Report. Population will grow in line with the ONS central population projection to reach 77.5m in Climate projections based on the UKCP09 medium emissions scenario (B1). Technology New technologies are developed and made available in infrastructure systems at a steady pace, similar to that observed in recent years. Long-term average growth of GDP per capita of 0.7% per year, consistent with very weak productivity growth since 2008 and techno-pessimism. This is consistent with projecting forward the OBR weak productivity scenario in the November 2016 EFO. Population will grow in line with the ONS low migration population projection to reach 73.7m in Climate projections based on the UKCP09 low emissions scenario. Population will grow in line with the ONS high fertility population projection to reach 80.1m in Climate projections based on the UKCP09 high emissions scenario (A1F1). New technologies are developed and made available in infrastructure systems, at a faster pace than observed in recent years. Long-term average growth of GDP per capita of 1.7% per year based on projecting forward the trend rate of productivity growth at the end of the five year forecast horizon of the OBR s November 2016 Economic and Fiscal Outlook (EFO). Population will grow in line with the ONS central projection, but with the shift in population distribution motivated by trends in house building (less skewed towards London).

10 Scenarios This offers too many possible combinations to analyse and explain so a smaller set of scenarios was chosen based on the following criteria: Diversity scenarios that are diverse enough to provide robustness against future uncertainty Plausibility scenarios that are realistic, internally consistent and probable Relevance to the Commission s objectives and to interdependencies across sectors

11 Transport - Roads Historic road traffic levels have risen steadily in the last few decades to over 500 billion vehicle kilometres per year (black line). Historic and modelled U.K. traffic, all vehicles, billion vehicle kms In the NISMOD model road travel over the next 25 years is estimated to increase from 30% to 45% Road travel forecasts from DfT range between 20% and 60% (transparent) Road travel to remain the main form of transport Source: National Travel Survey 2016

12 Connected and Automated Vehicles (CAVs)

13 Transport - Rail Estimated trips range from 40% to 61% in NISMOD Historic and modelled U.K. rail passenger journeys (millions) The unconstrained increase in rail travel in U.K. between 2015 and 2037, as measured by rail passenger journeys, ranges from 11% to 33% in the DfT projections. The increase in rail journeys over the preceding 23 year period ( ) was 113%

14 Transport - Rail NISMOD elasticities are based on PDFH 5.1 whereas the DfT elasticities are mostly PDFH 5.1, with a few from PDFH 5.0 and 4.0. Some key differences include: Income elasticity (per capita) is 0.55 for NISMOD and 0.89 for DfT Population elasticity is 1.0 in NISMOD and 0.69 in DfT (averaged across all journey types). DfT model uses origin/destination matrices and timetables. NISMOD does not. The DfT model depresses rail demand due to declining car ownership costs while the NISMOD model predicts increasing rail demand from increasing car fuel costs.

15 Energy - Heat In NISMOD gas demand increases between 2015 and 2050 from 20 to 80 TWh The FES outputs decrease by 20 to 250 TWh by 2050 Reduction of heating demands, boiler efficiency, heat pumps, government manipulation of fuel prices for cleaner fuels, reduction in use of gas for electricity generation.

16 Energy - Electricity Electricity increases between 2015 and 2050 ranging from 90 and 230 TWh by 2050 in NISMOD The FES outputs ranging from 0 to 50 TWh by 2050 Efficiency gains in appliances, demandside response (e.g. smart metering), take-up of batteries, fuel cells, micro CHP

17 Energy demand and economic growth

18 Energy - Electricity Electricity increases between 2015 and 2050 ranging from 90 and 230 TWh by 2050 in NISMOD The FES outputs ranging from 0 to 50 TWh by 2050 Efficiency gains in appliances, demandside response (e.g. smart metering), take-up of batteries, fuel cells, micro CHP Need beyond 2030 technologies

19 Cross-sector interdependencies In ec_pc_th electricity demand from Services decrease by 20 TWh and Residential demands by 15 TWh due to efficiency improvements However, Transport increases around 25 TWh due to CAVs & EVs British Gas Smart Meter ( The Tesla Model S Electric Vehicle (

20 Modelling Conclusions A sobering outlook for policy planners, not only in the relentless rise in demands, but in the significant level of uncertainty associated with the estimates. Despite the apparent simplicity of this analysis comparing the results of alternative models is not a simple exercise The clearest differences in the models were in their alternative representations of market-driven technological and behavioural change in the demand for energy, and in their representation of drivers of demand and the potential impact of autonomous vehicles in transport. Commendable levels of documentation and transparency around departmental modelling NISMOD s flexibility enabled model comparisons and the modelling of interdependencies between sectors

21 NISMOD s NIC-friendly design Problem: How to provision for national infrastructure in an uncertain future? Solution: Develop a new generation of simulation models and tools to inform the analysis, planning and design of national infrastructure Efficient models that are built by sector experts at the appropriate level of complexity A system design that enables: A systematic evaluation of infrastructure strategies under a consistent set of scenarios of uncertainty Exploration of the consequence of key decisions & assumptions The flexibility to introduce your own data and strategies Complete tracking of model-run inputs and outputs and code versioning Allows for an explorative, iterative approach to decision-maker engagement

22 National Infrastructure Database, Modelling, Simulation and Visualisation Facilities (DAFNI)

23 MISTRAL MISTRAL: Multi-scale Infrastructure Systems TRansition AnaLytics - Downscale from ITRC s representation of national networks to the UK s 25.7 million households and 5.2 million businesses - Upscale from the national perspective to incorporate global interconnections - Across-scale to other national settings outside the UK.

24 Thank you Dr Matthew Ives Infrastructure Systems Modeller Environmental Change Institute University of Oxford