The Future of Enhanced Mobility | The Big Picture
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Electric Vehicles
- Connected Intelligence
Publication | Update: Mar 2020
According to a UK Government Office for Science study on the Future of Mobility, there are many potential gains from the rise of self-driving vehicles. Transport Systems Catapult suggests that they could add up to £2.1 billion gross value added to the UK economy by 2035, and support up to 47,000 jobs. Advanced safety features, such as autonomous braking systems, could reduce road casualties by 30% by 2033. SMMT suggests a wider economic benefit of £51 billion per year by 2030 due to fewer accidents, improved productivity and increased trade.
According to Anirban Ghosh is Chief Sustainability Officer of the Mahindra Group, new business models can be developed once access to EVs becomes a norm. Drivers and fleet operators of EVs could play the role of prosumers i.e. of customers who design products for themselves and start producing it. Another opportunity is for producers and consumers of energy services such as vehicle-to-everything (V2X), which is the passing of information from a vehicle to any entity that may affect the vehicle, and vice-versa through smart charging. These new energy services will create additional opportunities for revenue sharing between the vehicle owners and the energy suppliers that would reduce the total cost of ownership of EVs and accelerate their market penetration.
An example of such an exercise can be seen at the EUREF campus of Schneider on the outskirts of Berlin, where the EV charging stations are integrated in the local micro smart-grid with solar and wind generation. Schneider Electric has collaborated with the Innovation Center for Mobility and Societal Change to complete a micro smart-grid that features artificial intelligence and machine-to-machine learning capacity that actively optimizes EV charging. The Enel Group, which is one of Italy’s leading energy companies, has also successfully carried out pilots on Vehicle-to-Grid (V2G) applications in Europe -which is a system in which plug-in EVs communicate with the power grid to sell demand response services by either returning electricity to the grid or providing frequency regulation services.. Second life value chain of EV batteries could also potentially disrupt the market – bringing down the capex of an EV and opening new markets for business. According to a UK Government Office for Science study on the Future of Mobility, with a growing demand for mobility, the potential for scaled adoption of EVs across different customer segments, including passenger movement (shared and personal) as well as freight movement is significant.
Transport users, whether individuals, households or businesses want a safe, reliable and affordable way of getting to their destination. Yet sometimes people have further expectations of the transport system. According to Whittle, it is unclear which set of factors are the most influential in mobility decisions and it is likely that their influence is cumulative.
Factors that Influence Mobility Decisions
Other emerging trends, such as the shift from ownership to user ship, may bring incremental changes on their own, but their cumulative effect could be transformational. For example, a rise in the use of electric vehicles, increased lift-sharing and a decline in private car ownership would lead to revenues from fuel duty decreasing. Left unchecked, further unintended impacts on the transport system are likely. The potential impacts of automation on the livelihoods of the driving workforce needs to be anticipated, for example.
Automation could also increase travel demand, with knock-on effects on car use, urban sprawl and congestion. By considering all uses (private, public and freight) and users, and how they interact with new technology and connect using data, government can shape the future in a positive direction. Technological progress will be another major shaper of future transport scenarios. As automation develops, it is likely to have positive impacts on road safety and emissions per mile, and improve accessibility for less mobile people. Yet without intervention, there could be negative impacts such as increased congestion, that could also be worsened as the price of vehicle batteries falls, the uptake of EVs will reduce travel costs and potentially induce further demand.
By contrast, if self-driving vehicles or Mobility-as-a-Service trips remain expensive, they may only be affordable for the wealthiest travelers. There will be further impacts of technology on society: automation will create new jobs, but is likely to decrease the role of drivers in the transport sector. Demands on the freight system are changing rapidly. There is potential for electrification of the freight sector. Vehicle manufacturers including BMW, Mercedes-Benz and Tesla have recently announced prototype electric HGVs. Yet according to Timmers and Achten, decarbonizing large freight vehicles through electrification is a significant challenge. Current battery constraints relating to vehicle size, load weights and distances travelled make them unsuitable for many journeys.
Without intervention, LCVs will contribute further to congestion in and around urban centers, as rapid delivery services continue to grow. Meanwhile, Greening has explored UK scenarios and ways to decarbonize road freight, suggesting that if government takes no action in this regard, CO2 emission reductions from road freight by 2040 may only be 61% (compared to 1990 levels), making it harder for the UK to meet its 2050 target. Travel behaviours are expected to change. Car-sharing and ride-sharing will grow, but are unlikely to be transformative without clear incentives from government or industry to boost their uptake. These are currently constrained by their low ease of use, cost, social norms and potential risks (perceived or real) of travelling with strangers. One potential future outcome – driven by the possible decline of public transport, combined with the wider limitations of the transport system, and users’ habits and unwillingness to share – is a UK dominated by privately-owned self-driving vehicles that increase congestion and urban sprawl.
Increasing data use and connectivity will also have a greater role to play in the future. The rapid uptake of new modes of transport, Mobility-as-a-Service and automation are all heavily dependent on public acceptance and, particularly, people’s willingness to share data. Who will benefit from the new data that will be generated? Public attitudes will be important, such as resistance to change or skepticism about new developments.According to Anirban Ghosh is Chief Sustainability Officer of the Mahindra Group, access to finance will play an important role. With the initial capex of an EV on the higher side as compared to an ICE vehicle as well as investment required for charging and parking infrastructure, availability of low-cost capital will be critical.
Rapid changes caused by new business models or changing transport provision may mean government has to respond quickly to ensure beneficial outcomes are realized. Finally, the overall policy framework in terms of an integrated transport and energy policy will go a long way in providing the necessary support to drive the e-mobility transition.
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.