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INFRASTRUCTURE NEEDS AND TECHNOLOGY DISRUPTIONDR. JAN MISCHKEGICA, PARIS, JANUARY 2018

McKinsey Global Institute 1

The global gap for 2017–35 as a share of GDP is calculated by adding negative values, converting to dollar terms, then dividing by cumulativeworld GDP. Without adjusting for positive gap, the value is 0.10 percent. This has been calculated from a set of 48 countries for which data areavailable for all sectors. This gap does not include additional investments needed to meet the UN Sustainable Development Goals; NOTE: Not to scale.SOURCE: IHS Global Insight, ITF, GWI, National Statistics; McKinsey Global Institute analysis

If history were a guide, we would know the infrastructure needsEconomic infrastructure; % of GDP

3.4

2.1

2.3

3.4

4.7

5.6

2.1

2.22.32.3

2.53.2

3.74.04.4

5.1

8.3

United States

ItalyFrance

Brazil

Mexico

Saudi Arabia

Canada

Russia

China

Turkey

South Africa

India

Australia

IndonesiaJapan

GermanyUnited Kingdom

Actual infrastructure spending, 2010 - 15

0.50.5

0.21.1

0.51.3

1.2

0.3

0.6

0.7

-0.1

-1.0

-0.2

-0.3-1.0

-0.2

-2.5

Gap between spending and estimated infrastructure needs, 2017- 35

Global gap1 = 0.3%, or $5.5 trillion

McKinsey Global Institute 2

Yet disruptive change lies ahead: Examples

Transport

Autonomous vehicles, shared and electric mobility

Drones

Additive manufacturing, advanced robotics, and changing global supply chains

Renewable energy and shale gas

Telecoms & data

McKinsey Global Institute 3

Autonomous vehicles: At high penetration, they could materially increase road capacity

Number of cars per lane per hour

AV penetration in car parc

3.500

40%0% 20% 60% 80%

3.000

100%

4.000

2.5002.685

+22%

3.960

+80%

3.300

2.200

SOURCE: Expert interviews, Department of Transport; Center of urban transportation research (2013, November). Highway Capacity Impacts of Autonomous Vehicles: An Assessment; Auto2030, Press search

Incidental (accidents)

Structural (imbalance ofsupply and demand)

100%

15%

85%

Percent

1Impact of self-driving vehicles on highway road capacityTypical causes of congestion

Infrastructure implications:▪ Changes in road capacity requirements▪ Charging infrastructure, electrified roads… and for urban transport:▪ Redesign of urban layout and mass transit networks▪ Drop-off and pick up areas▪ Conversion of parking space▪ V2V traffic control

McKinsey Global Institute 4

Drones: What the future of UAS transportation may look likeX Detailed use case

Aerial ridesharing (e.g., Uber Pool, Lyft Line) from Vertiports within less than 10 miles

A Ridesharing

2

Non-urban air ferryF

Sight-seeingG

Urban air ferryE

▪ Ferrying over slow tunnels, bridges, or other specific routes

▪ Pre-programmed trip over iconic areas (e.g., Grand Canyon)

▪ Ferrying between shore and yacht or offshore oil rig

Medical transportH

▪ Medivac and evacuation of people

Thin haul

Long haul

Air taxis

▪ Long-haul transportation (>100 miles)

▪ Transportation that is >10 miles but <100 miles

▪ Aerial taxi (UberX, Lyft) within less than 10 miles

C

D

B

Infrastructure implications:▪ Heliports for landing and takeoff▪ Charging hubs▪ Counter-UAS▪ Air traffic management (ATM)

McKinsey Global Institute 5

3D printing: Between 2 and 4% of total goods trade may be at risk by 2025

635

150

785

1,055

255 1,310

R&D intensive goods

Labor-intensive goods

Total

USD Billions, annual exports

Potential goods trade at risk from 3D printing in 2025 – supply driven estimates

SOURCE: IHS; UNCTAD; McKinsey Global Institute analysis

3High Low

INITIAL ESTIMATE

▪ 4% to 7% of trade in R&D and labor intensive goods

▪ 2% to 4% of total goods trade

▪ 1% to 1.5% of TEUs

Infrastructure implications:Changes to port requirements

McKinsey Global Institute 6

Renewables example: A Europe-wide interconnected market could yield close to €1bn p.a. just solar allocation Spain vs. Germany

PV solar electricity potential in Europe

4

SOURCE: European Commission; Böckers, Haucap and Heimeshoff (2013) "Benefits of an Integrated European Electricity Market"; McKinsey Global Institute analysis

Infrastructure implications:▪ Energy network integration

– Across geographies; e.g. EU– Across types; e.g., electricity, thermal water– With IoT data; e.g. for load optimization

McKinsey Global Institute 7

SOURCE: McKinsey Global Institute, “Digital Globalization: A new era of global flows, February 2016.

+$7.8TIncrease in world GDP from flows –a 10% lift

+$2.8TIncrease in world GDP from data flows—more than goods trade

Telecoms: More GDP uplift from data flows than goods trade5

Infrastructure implications:▪ Broadband connectivity▪ «Soft» cross-border infrastructure like data

regulation more important bottleneck than «hard» infrastructure

McKinsey Global Institute 8

0

90

50

2020 353025

80

40

100

20

70

30

60

2040

10

High scenario L3+L4

L4

Low scenario L3+

Ramp-up as AV availability spreads across popular consumer models

Commercial introduction of full autonomy by new tech players andpremium OEMs

Mass-market leaders introduce full autonomy

SOURCE: McKinsey & Company, Automotive revolution – perspective towards 2030, January 2016.

Technical and regulatory barriers delay commercial-scale introduction of autonomous vehicles

Manufacturing capacity for tech players ramps up gradually

Slow consumer uptake driven by low perceived value proposition or negative publicity follow-ing critical incidents

Disruption will take time (example autonomous vehicles)

New vehicle market share of autonomous vehicles globallyPercent

15%

Similarly:▪ 2030: 24% of fleet electric in US,

Europe, China▪ 2040: 8% of electricity production for

eVehicles in Europe; at-scale use of drones for passenger transport