Reference pathway (REF)—key data and assumptions
STEAM was calibrated to Scottish national statistics for the year 2012 (DfT
2014). We obtained Special Licence Access to the National Travel Survey dataset (Department for Transport
2016) and used SPSS v23 to derive average trip rates, distance travelled and mode splits for Scotland. Due to the smaller sample size of the Scottish sample, the travel demand data were pooled over the years 2010, 2011 and 2012. The ‘Reference’ scenario (REF) broadly describes a projection of transport demand, supply, energy use and emissions as if there were no changes to transport and energy policy beyond current policy. It was modelled using STEAM based on exogenous assumptions and projections of socio-demographic (incl. demand effects of an ageing of the population), economic, technological and (firm and committed) policy developments, including the recently simplified vehicle road tax and relatively complex CO
2-graded company car tax regimes. Economic growth data up to 2015 were based on government figures. Future GDP/capita growth was assumed to average 1.35% p.a. up to 2050. Transport demand projections were modelled based on no changes in trip patterns
4 (i.e. trips and distance travelled per person p.a., and mode split) apart from lower commuting levels due to an ageing population, and average demand elasticities (of GDP/capita, population and generalised cost) for international air and freight transport (Dunkerley et al.
2014; Sims et al.
2014). Fuel price and retail electricity price projections were based on 2014 UK Government forecasts (DECC
2014). Annual road tax and road fuel duties were assumed to remain constant at 2017 levels.
Pre-tax vehicle purchase costs were kept constant over time for established technologies and gradually decreased for advanced and future technologies, thus exogenously simulating improvements in production costs, economies of scale and market push by manufacturers.
5 For example, average purchase prices for BEV cars were assumed to decrease by 2.8% pa from 2015 to 2020, by 1.6% pa until 2030 and 0.6% pa until 2050, based on projected BEV battery cost reductions (Nykvist and Nilsson
2015). The Reference scenario further assumed gradual improvements in specific fuel consumption and tailpipe CO
2 emissions per distance travelled (see Supplementary Materials in Brand et al.
2017). The rates of improvement were based on technological innovation driven entirely by market competition, not on policy or regulatory push.
6 Fuel consumption and CO
2 improvement rates for future car vintages were assumed to be 1.5% p.a.—a somewhat lower and more conservative rate than the average rate of 4% p.a. based on test-cycle data for all new cars between 2008 and 2013 (NB: ‘real-world’ improvements have been significantly lower, as shown by ICCT (
2016) and others). Indirect emissions from fuel supply and vehicle manufacture, maintenance and scrappage have been updated with data from a recent UK-based review (Kay et al.
2013).
Finally, the default electricity generation mix follows central government projections (mainly natural gas, wind and nuclear—with some CCS coal and gas by 2030), implying that the carbon content of retail electricity is gradually decreasing from about 390 gCO2/kWh in 2015 to about 160 gCO2/kWh in 2030. In the absence of any government projections beyond 2030, we have assumed that the carbon content stays constant at this level out to 2050.
The ‘lifestyle’ (LS) scenario: storyline and travel demand modelling
Most transport and energy modelling are based on principles of cost optimisation (Dodds and McDowall
2014), utility maximisation of discrete choices or on the principle that travel-time ‘budgets’ are fixed (Ahmed and Stopher
2014; Bates
2000). Some notable efforts have been made to model behavioural factors endogenously at the level of the wider transport-energy-climate system (Köhler et al.
2009; Pye and Daly
2015; Tattini et al.
2018; Zimmer et al.
2017). Yet, evidence relating to actual travel choices, including vehicle choice, suggests that social change is strongly influenced by concerns relating to health, quality of life, energy use and environmental implications. As such, non-price-driven behaviour (Anable
2005; Cohen et al.
2013; Li et al.
2015; Litman
2017; Schwanen et al.
2011) was deemed to be a dominant driver of energy service demand from transport in this ‘lifestyle’ scenario.
The lifestyle consumer, including ‘fleet’ or company consumers, is more aware of the whole cost of travel (including fixed or sunk costs of owning a vehicle) and the energy and emissions implications of travel choices. People become more sensitive to the rapid normative shifts which alter the bounds of socially acceptable behaviour, e.g. on ‘binge flying’ (Cohen et al.
2011), car choice (Barth et al.
2016) and mobility (in)justice (Mullen and Marsden
2016). Accordingly, the lifestyle scenario assumed that the focus would shift away from
mobility towards
accessibility of services and jobs and from
speed to
quality and
resilience of journeys. Triggered by worsening conditions (e.g. sensitivity to congestion and air quality concerns) and catastrophic events (increased frequency of flooding and/or heatwaves), social norms promote the status of more sustainable and resilient modes of transport and demote single-occupancy car travel, fossil fuelled vehicles, unnecessarily long distances, speeding and air travel.
More efficient, low-energy and zero energy (non-motorised) transport systems (e.g. the use of cycling networks) replace current car-based systems running on petrol and diesel. New models of Mobility as a Service (MaaS) (Mulley
2017) and the Sharing Economy (Ritzer and Jurgenson
2010) are embraced. This includes taxi hailing mobile applications, car clubs
7 and the tendency to hire a shared PHEV for longer distance travel. These are niche markets in which new vehicle technology is fostered. Information and Communication Technology (ICT: telematics, in-car instrumentation, video conferencing, smartcards, e-commerce, connected vehicles) facilitates relatively rapid behavioural change by making cost and energy use transparent to users. This transparency and enablement of responsive choices changes everything from destination choice, substitution of shopping and personal business trips by home delivery, car choice and models of ‘ownership’, driving style and paying for travel, including in the freight sector. As transport and destination choices become more diverse and widely accessible, there is increasing acceptance of restrictive local policies to further accelerate change. It also becomes socially unacceptable to drive children to school. However, capacity constraints limit the pace of change so that mode shift to buses and rail will be moderated.
The new modes and digitalisation, in turn, will result in a new spatial order towards compact cities, mixed land uses and self-contained cities and regions. Average distances travelled are also reduced as distance horizons change partly from the use of cycling and walking and partly from a renewed focus on localism (Ferreira et al.
2017). Some services return to rural areas, though moderated by the ability to carry out much personal business online. The habit of frequent air travel declines as not only does it become socially unacceptable to fly short distances, but also airport capacity constraints as well as a ‘frequent flyer levy’ (Devlin and Bernick
2015) mean that it becomes less attractive. Weekends abroad are replaced by more domestic leisure travel, but this is increasingly carried out by shared low-carbon vehicles, rail and express coach and walking and cycling trips closer to home. As a result, car ownership is lower than in the Reference scenario. An even more radical change takes place through changes in work patterns and business travel not only fuelled by renewed emphasis on quality of life but also facilitated by increasingly sophisticated ways of substituting disproportionally impactful long commuting and business trips by digital technology. The impacts of teleworking and video conferencing are known to be complex, but recent studies have highlighted that they could be potentially important, especially when implemented with the explicit purpose of reducing transport and energy (Gross et al.
2009; Jones et al.
2018; Scottish Government
2013a).
Increased internet shopping (Çelik
2011; Morganti et al.
2014; Suel and Polak
2017) and restrictions on heavy goods vehicles, particularly in town centres,
increase the use of vans, which somewhat offsets the positive effects of decongestion from fewer cars on the road. There is increased relative decoupling of road freight from economic activity due to a return to more localised sourcing (McKinnon
2007), a major shift in the pattern of consumption to services and products of higher value, the digitization of media and entertainment, and an extensive application of new transport-reducing manufacturing technologies such as 3-D printing (Birtchnell et al.
2013). There is some shift towards rail freight and passenger rail from domestic air.
The consequences for travel patterns of these lifestyle changes were first analysed using the STEAM travel demand model, which took as its starting point the figures for current individual travel patterns based on Scottish data in the UK National Travel Survey (Department for Transport
2016). The Scottish data was analysed so as to derive figures for each journey purpose (commuting, travel in the course of work, shopping, education, local leisure, distance leisure and other) in terms of average number of trips, average distance (together producing average journey length). In addition, mode share and average occupancy were altered based on an evidence review (e.g. Cairns et al.
2004,
2008; Petrunoff et al.
2015; Scottish Government
2013a) relating to the impact of transport policies and current variation in travel patterns within and outside Scotland. The ensuing changes in trip rates, average trip lengths and mode shifts by trip length are provided in Tables
3 and
4. Key travel indicators are summarised in Table
2.
Table 2
Summary results of the combined lifestyle and high EV scenario (LS EV)
Average number of trips (per person per year) | 1010 | 1006 | 999 | 955 |
Average distance travelled (km per person per year) | 11,498 | 11,321 | 11,029 | 9845 |
Avg. car occupancy | 1.57 | 1.58 | 1.62 | 1.76 |
Mode split (% distance) |
Cars and motorcycles | 74% | 71% | 61% | 41% |
Slow modes | 3% | 4% | 8% | 17% |
Bus and rail | 14% | 15% | 19% | 28% |
Taxi/‘Uber’, car clubs, other private | 2% | 3% | 4% | 7% |
Domestic air | 7% | 6% | 6% | 6% |
‘On-road fuel efficiency’ |
km affected
|
km affected
|
km affected
|
km affected
|
Cars, 8% better per km | 4% | 17% | 52% | 62% |
Vans, 8% better per km | 2% | 17% | 59% | 70% |
Trucks, 4% better per km | 2% | 17% | 59% | 70% |
International air demand growth (pa) | 1.2% | 0.9% | 0.5% | 0.1% |
Vehicle technology choice, e.g. share of new cars by propulsion/fuel | 98% ICV petrol/diesel | 17% HEV | 2% HEV | 0% HEV |
1% BEV | 13% BEV | 45% BEV |
3% PHEV | 53% PHEV | 40% PHEV |
Direct CO2, reduction over baseline (REF) | n/a | − 4% | − 21% | − 47% |
Lifecycle CO2e, reduction over baseline (REF) | n/a | − 5% | − 20% | − 42% |
Cumulative lifecycle CO2e savings (MtCO2e) | n/a | − 1.9 | − 17.4 | − 97.2 |
Direct NOX, reductions over baseline (REF) | n/a | − 2% | − 12% | − 38% |
Direct PM2.5, reductions over baseline (REF) | n/a | − 2% | − 9% | − 34% |
Table 3
Passenger travel demand indicators, lifestyle scenarios (LS and LS EV)
Number of trips |
Commuting, reduction due to teleworking | 3% | 4% | 5% | 10% | 15% | The uptake in teleworking is reinforced by tax incentives, travel plans, broadband-roll-out and road user charges and parking charges |
Business travel, reduction due to tele/video conferencing | 5% | 6% | 8% | 17% | 25% | Going Smarter report (Scottish Government 2013) concludes that tele/video conferencing could reduce business trips by 18% after 10 years. Extrapolate this on to reach 25% maximum reduction in trips by 2050 on the basis that there are many business trips e.g. nursing which cannot and simply will not be avoided. TC share in 2012 is assumed to be 5%. These proportional reductions will also apply to air trips |
Local leisure, increase due to shift to more local trips | 0% | 1% | 3% | 7% | 10% | There is a general shift in all age groups towards more local leisure trips for at the expense of longer trips, so a small increase is assumed due to this effect |
Long distance leisure in Scotland, increase due to holidaying at home | 0% | 0% | 0% | 0% | 0% | Fewer people travelling abroad means more domestic holidays—however, the increase in weekends away will be neutralised by fewer distance day trips (due to affordability as price of travel increases) with people using their local area more instead |
Shopping, increase due to more walking and cycling | 0% | 2% | 5% | 8% | 10% | Based on figures in Going Smarter report (Scottish Government 2013a) |
Shopping, reduction due to teleshopping | 0% | 1% | 3% | 7% | 10% | Going Smarter report (Scottish Government 2013a) suggests that home shopping could reduce vehicle mileage for shopping by 4% after 10 years. Here, we assume 3% trips by 2030 and 10% by 2050. (However, there will be an effect on van use.) |
Other trips, decrease due to tele-activity | 0% | 1% | 3% | 8% | 12% | It will increasingly be the norm to access many services such as banking and medical care on-line |
Trip length |
Commuting, reduction due to more teleworking | 0% | 1% | 2% | 4% | 6% | Teleworking abstracts the longer commute trips and therefore has a disproportionately large impact on average trips lengths |
Commuting, reduction due to proximity principle | 0% | 1% | 3% | 9% | 15% | The proximity principle assumes that there is a movement towards living closer work places |
Business travel, reduction due to more tele/video conferencing | 0% | 1% | 3% | 9% | 15% | Assumed that the longest trips are increasingly substituted by tele-video conferencing |
Long distance leisure, more weekends away | 0% | 0% | 0% | 0% | 0% | There are fewer day trips and more people cycling and walking from home but some longer holiday trips (weekends away) to replace travel abroad—means that on balance average distance stays the same |
Local leisure, switch to local W&C trips | 0% | 0% | 0% | 0% | 0% | Although there is a shift towards walking and cycling around the local area, this does not reduce the average length of local leisure trips. With leisure, it is mainly modes that change, not the number or length of trips |
School, reduction due to proximity principle | 0% | 1% | 3% | 9% | 15% | School selection policy is revised to insist that ‘local schools’ are chosen |
Shopping, reduction due to more local shopping | 0% | 2% | 5% | 10% | 15% | Restriction of cars in urban areas means that shorter, local journeys become more attractive |
Other trips, reduction due to proximity principle | 0% | 1% | 3% | 9% | 15% | Re-introduction of local clinics, post office/banking services, etc. especially in rural areas. Restriction of cars in urban areas means that shorter, local journeys become more attractive |
Table 4
Mode shift by trip length, lifestyle scenarios (LS and LS EV)
0–1 mi | From car/van driver to walk | 2% | 8% | 20% |
From car/van driver to bicycle | 1% | 5% | 13% |
From car/van driver to local bus | 1% | 3% | 8% |
From car/van passenger to walk | 2% | 8% | 20% |
From car/van passenger to bicycle | 1% | 3% | 8% |
From car/van passenger to local bus | 1% | 3% | 8% |
From local bus to walk | 1% | 5% | 13% |
From local bus to bicycle | 1% | 3% | 8% |
1–2 mi | From car/van driver to walk | 3% | 10% | 25% |
From car/van driver to bicycle | 1% | 5% | 13% |
From car/van driver to motorcycle | 0% | 1% | 2% |
From car/van driver to local bus | 1% | 3% | 8% |
From car/van passenger to walk | 3% | 10% | 25% |
From car/van passenger to bicycle | 1% | 5% | 13% |
From car/van passenger to motorcycle | 0% | 1% | 2% |
From car/van passenger to local bus | 1% | 3% | 8% |
From local bus to walk | 1% | 5% | 13% |
From local bus to bicycle | 1% | 3% | 8% |
2–5 mi | From car/van driver to walk | 1% | 5% | 13% |
From car/van driver to bicycle | 1% | 5% | 13% |
From car/van driver to motorcycle | 0% | 1% | 2% |
From car/van driver to local bus | 1% | 5% | 13% |
From car/van passenger to walk | 1% | 4% | 10% |
From car/van passenger to bicycle | 1% | 4% | 10% |
From car/van passenger to motorcycle | 0% | 1% | 2% |
From car/van passenger to local bus | 1% | 5% | 13% |
From local bus to bicycle | 1% | 5% | 13% |
From rail/underground to bicycle | 1% | 5% | 13% |
5–10 mi | From car/van driver to bicycle | 1% | 3% | 8% |
From car/van driver to motorcycle | 0% | 1% | 2% |
From car/van driver to local bus | 2% | 8% | 20% |
From car/van driver to rail/underground | 1% | 3% | 8% |
From car/van driver to MaaS | 1% | 5% | 13% |
From car/van passenger to bicycle | 1% | 2% | 5% |
From car/van passenger to motorcycle | 0% | 1% | 2% |
From car/van passenger to local bus | 1% | 5% | 13% |
From car/van passenger to rail/underground | 1% | 3% | 8% |
From car/van passenger to MaaS | 1% | 3% | 8% |
10–25 mi | From car/van driver to bicycle | 1% | 2% | 5% |
From car/van driver to motorcycle | 0% | 1% | 2% |
From car/van driver to express coach | 1% | 5% | 13% |
From car/van driver to rail/underground | 3% | 10% | 25% |
From car/van driver to MaaS | 2% | 8% | 20% |
From car/van passenger to bicycle | 0% | 1% | 3% |
From car/van passenger to motorcycle | 0% | 1% | 2% |
From car/van passenger to express coach | 1% | 3% | 8% |
From car/van passenger to rail/underground | 2% | 10% | 25% |
From car/van passenger to MaaS | 1% | 5% | 13% |
25–50 mi | From car/van driver to express coach | 2% | 10% | 25% |
From car/van driver to rail/underground | 2% | 10% | 25% |
From car/van driver to MaaS | 1% | 5% | 13% |
From car/van passenger to express coach | 1% | 5% | 13% |
From car/van passenger to rail/underground | 2% | 10% | 25% |
From car/van passenger to MaaS | 1% | 5% | 13% |
50–100 mi | From car/van driver to express coach | 1% | 5% | 13% |
From car/van driver to rail/underground | 2% | 10% | 25% |
From car/van driver to MaaS | 1% | 5% | 13% |
From car/van passenger to express coach | 1% | 3% | 8% |
From car/van passenger to rail/underground | 1% | 5% | 13% |
From car/van passenger to MaaS | 1% | 3% | 8% |
> 100 mi | From car/van driver to express coach | 1% | 5% | 13% |
From car/van driver to rail/underground | 2% | 10% | 25% |
From car/van driver to MaaS | 1% | 5% | 13% |
From car/van passenger to express coach | 1% | 3% | 8% |
From car/van passenger to rail/underground | 1% | 5% | 13% |
From car/van passenger to MaaS | 1% | 3% | 8% |
From domestic air to express coach | 0% | 1% | 5% |
From domestic air to rail/underground | 1% | 2% | 9% |
In estimating what rate and scale of change seems reasonable, most weight was given to the existing variation in lifestyle observed in societies similar to Scotland, i.e. technologically advanced, liberal democracies. Whilst Scotland has a relatively low average population density due to vast expanses of relatively uninhabited landscape, around 80% of the population live in localities of and around the country’s five largest urban areas of Glasgow, Edinburgh, Aberdeen, Dundee and Inverness. Given this predominance of urban and suburban travel, it seems reasonable to suppose that if a significant fraction of the population (say 5–10%) somewhere in the OECD already behave in a particular way, then it is plausible for this to become a common behaviour in Scotland within the timeframe to 2050. Careful consideration and a more conservative approach was used to take account of specific climate and topographic factors with regard to Scotland, and this led to a lower level of ambition than might have been applied to cycling in particular.
Overall, the Lifestyle scenario implies neither incremental nor step changes in behaviour. It is increasingly clear that incremental changes in efficiency and behaviour will not be effective enough to deliver sustainable energy systems on their own (CCC
2016; Crompton
2008; Maione et al.
2016). Instead, this Lifestyle scenario outlines
far-reaching change leading to
relatively fast transformations and new demand trajectories.
The high electrification pathway (EV)
This scenario combines a transformative pathway developed for the UK’s Committee on Climate Change (CCC
2013,
2015). It focuses on supply measures for plug-in cars and vans as an alternative to fossil fuel vehicles combined with a purchase tax aimed at phasing out petrol/diesel vehicles (ICV, HEV but not PHEV) out of urban areas by 2030. The analysis by the CCC suggested plug-in vehicle deployment targets for 2020 and 2030 at 9 and 60%, respectively. A small number of scenarios were run using STEAM in an iterative process that led to the high electrification pathway. This implied transformational change including the following: significant investment and repositioning towards ultra-low emission vehicles (ULEVs) by the main vehicle manufactures with ultra-low emission vehicles (ULEVs) being available in all car segments (e.g. ‘supermini’, ‘large family’, ‘crossover’) and by all major brands by 2030; petrol/diesel cars, vans and buses are gradually phased out through higher purchase/scrappage taxes, reinforced by low emission zones and increased parking charges in cities/towns; and Scotland-wide consumer awareness and acceptance of ULEV cars by the 2030s driven by comprehensive awareness campaigns and the ‘neighbour effect’ (Mau et al.
2008). In particular, the fiscal and regulatory ‘sticks’ are balanced by the carrots of significant investment in recharging infrastructure (home charging, fast charging stations in and beyond Scotland), reduced (perceived) recharging times, and continued and improved ‘equivalent value support’ (taxation, fuel duty) for ULEVs for both private and company/fleet buyers.
As for the road freight sector, diesel ICV technology prevails for much of the scenario period due to the continued non-availability of gas- and hydrogen-powered vehicles and infrastructure, and the assumed economic and performance advantage of incumbent technology over EVs for long distance haulage and distribution. As for EV trucks, while overhead power supply lines have been explored in some countries (Germany, France), their deployment may be problematic and uneconomic for a small country such as Scotland (few motorways, dense cities, no history of trolley buses).