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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