Transcript Transportation and the NEUJOBS global scenarios

Transportation and the NEUJOBS
global scenarios
Christophe Heyndrickx (TML)
Rodric Frederix (TML)
Joko Purwanto (TML)
Overview
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Transport within Neujobs
Main drivers and expected trends
Scenario matrix definition
Scenario analysis
Conclusion
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Transport within Neujobs
• Neujobs: future possible developments of the labour market
given the upcoming transitions in different fields
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Socio-ecological transition
Societal transition
Skills transition
Territorial transition
• Focus on transport
– Which transitions? …
– Ener
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Economic situation of transport sector
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€ 533 billion in Gross Value Added (GVA) at basic prices
Sector employed around 10.6 million persons (5% total workforce)
+ around 2.3 million people working in manufacturing sector
4.6% of total GDP + 1.7% in manufacturing sector
Land transport (55%)
Sea transport (2%)
Air transport (4%)
Warehousing / storage (22%)
Postage /courrier (17%)
Private household transportation
• € 904 billion (13% of total consumption) spent on transport-related
items in 2010
• 30% on vehicle purchase
• 50% on operation (fuel, maintenance, insurance)
• 20% on transport services
Transport within Neujobs
• Scope: what is the impact of expected trends in the transport
sector on employment, given the upcoming socio-ecological
transitions (SET)?
• Top-down or bottom-up approach?
• Mobility is very much related to economic activities
– Transport sector (+ vehicle manufacturing sector)
– Home-work relationship
• Top-down approach (instead of bottom-up):
1.
2.
3.
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Identification of the main drivers of transport
Translation of SET to trends in drivers of transport
Estimation of effects of these trends on employment in transport
sector, and on society in general with EDIP model
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Overview
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Transport within Neujobs
Main drivers and expected trends
Scenario matrix definition
Scenario analysis
Conclusion
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Main drivers for changes in transport sector
• Based on literature study, we identified 4 main drivers
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Driver 1: Environmental policy
Driver 2: Fossil fuel scarcity
Driver 3: New and more efficient propulsion technologies
Driver 4: Developments in logistics
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Environmental policy
Development of GHG emissions in EU-27
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Conversion
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[Index 1990 = 100] , [%]
Industry
100
Transport
80
Household &
Services
60
Other
40
Share transport
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2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
0
• EU target for 2050: 20% of current GHG emissions
• Transport emits 23% of current GHG emissions, and share is increasing!
→ If EU holds on to this target, this implies environmental policy that will
have a strong effect on transport
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Fossil fuel scarcity
• Demand of crude oil: growth especially in Asia (China, India)
• Supply of crude oil : more controversial
Estimates of Energy Watch Group vs World Energy Outlook
• Much uncertainty, but supply and demand suggest that crude oil prices on
average will increase in the near future
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Propulsion technologies
Fuel efficiency trend
1.2
Index [1990] fuel efficiency
1
0.8
Average car
0.6
Average new car
0.4
0.2
0
1995
2000
2005
2010
2015
Fuel efficiency trend between 1995 and 2012 (source: TREMOVE)
• Fossil fuel combustion engines are in conflict with GHG emission target
and fossil fuel scarcity
• Fuel efficiency for private cars has already increased
• New transport technologies
– Electrification
– Biomassification
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Developments in logistics
• e-Freight Initiative: information sharing along freight transport chains,
especially in the context of multimodal transport
– Gain in cost-efficiency
– Increase in transport volumes
• 3D printing
• e-Commerce
– Effect on transport volumes is small
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Overview
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Transport within Neujobs
Main drivers and expected trends
Scenario matrix definition
Scenario analysis
Conclusion
4/12/2016
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Scenario matrix definition
• Based on scenario matrix by Fischer-Kowalski (2012)
– Background scenario (six megatrends)
– Main policy scenario
Background
Policy
Strategy 1:
Status Quo
Friendly
Tough
S1F ‘Careless and globalized world’
S1T ‘Challenged and ignorant world’
Strategy 2:
S2F ‘Ecologically aware and globalized S2T ‘Challenged, but ecologically aware
Ecological modernization world’
world’
and eco-efficiency
Strategy 3:
Sustainability
transformation
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S3F ‘Sustainable and globalized world’
S3T ‘Challenged and sustainable world’
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Background scenario
Energy transition
No impact on fuel price
Fuel prices +20%
Resource security
No impact on materials
Metal ores +50%
Climate change effects
Low probability for
extreme weather events
Decrease in capital
returns transport
Population dynamics
Population stable
Labour supply decreases
with 10%
Economic development
Exchange rate stable
Depreciation
ICT & Knowledge
Efficient logistics sector
Lower efficiency in
logistics
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FRIENDLY
THOUGH
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Background scenario
• Translation of background scenario in parameters, based on
WP9 & 10 and other recent studies
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Change 2010 - 2030
Yearly GDP growth
Friendly
EU 15: 1.5%
EU 12: 3.0%
Tough
EU 15: 1.0%
EU 12: 2.0%
Comments/ Explanation
GDP growth is one of the main
drivers of transport demand
Price of coal
+10%
+15%
Impact on fuel mix
Price of gas
+20%
+50%
Impact on fuel mix
Price of petrol
+20%
+50%
Impact on fuel mix
Price of metal ores /
metal products
Other raw materials
Price
of
agricultural
products
on
world
market
Exchange rate
+20%
+50%
Construction of transport equipment
+20%
Stable
+50%
+10%
Fuel mix/resource scarcity
Impact on price of bio-fuels
Stable (around
1.3 $/euro)
Raw oil, primary energy inputs and
others are mainly import products
Efficiency
of
logistic
sector
/
transport
margins
Stable
- 10%
(around 1.2
$/ euro)
-10%
Population
dynamics:
Working population
WP 10
WP 10
We assume a reduction in efficiency
of transport and an increase in the
margin of transport in the consumer
products due to congestion and
climate change related extremes.
The population dynamics in friendly
and tough scenarios are based on
WP10 by country results
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Background scenario (2)
• Change in work force by skill level (% change 2010-2030)
Friendly
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AT
BE
BG
CY
CZ
DK
EE
ES
FI
FR
GR
IT
LV
LT
LU
MT
NL
PL
PT
RO
SK
SI
SE
UK
Tough
Low
Medium High
Total
Low
Medium High
Total
-31.9
-4.3
55.8
-0.86
-25.4
-4.8
6.3
-7.74
-28.1
4.6
44.7
6.26
-25.5
8.1
22.2
1.50
-38.3
-16.4
32.7
-12.33
-32.6
-31.0
-16.8
-28.65
-34.9
7.5
62.1
12.44
-27.3
-3.2
30.4
0.41
-33.8
-11.4
65.2
-3.50
-20.8
-14.4
16.3
-10.86
-30.3
-11.1
48.3
-0.12
-26.6
-4.8
24.0
-3.31
-26.3
-11.1
28.4
-2.17
-15.1
-28.2
-16.5
-22.28
-20.5
3.4
53.6
5.75
-24.6
0.4
13.7
-8.00
-37.1
-13.5
30.8
-5.24
-30.6
-5.6
6.9
-7.59
-31.7
-8.7
54.2
0.52
-28.9
-3.7
30.4
-2.74
-30.2
-0.4
46.8
-1.98
-29.9
1.8
15.3
-7.57
-17.1
5.6
80.1
4.77
-28.0
13.5
25.8
-4.09
-46.5
-18.9
32.8
-12.65
-21.8
-34.4
-5.7
-25.47
-42.6
-29.5
34.7
-14.36
-19.9
-36.3
9.3
-21.23
-5.3
7.8
69.5
22.74
-2.8
8.8
44.6
16.32
-27.2
-0.9
87.1
-7.96
-33.0
0.9
24.7
-19.63
-31.2
-5.7
33.0
-2.76
-26.4
-3.6
10.4
-6.78
-44.8
-23.0
61.8
-10.19
-36.7
-24.4
29.6
-15.99
-18.6
-7.0
86.0
-1.91
-28.6
31.4
35.1
-8.28
-38.1
-2.1
83.8
-2.80
-27.8
-22.1
16.1
-19.28
-39.8
-11.7
62.7
-5.05
-28.3
-12.8
20.5
-10.30
-24.9
-12.5
56.9
-1.01
-33.8
-10.2
21.9
-8.60
-28.4
0.4
54.4
8.20
-17.5
-1.1
30.0
3.45
-20.9
-1.6
39.0
5.47
-17.8
2.7
17.8
1.78
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Scenario matrix definition
• Based on scenario matrix by Fischer-Kowalski (2012)
– Background scenario
– Main policy scenario
Strategy 1:
No policy changes
Friendly
Tough
S1F ‘Careless and globalized world’
S1T ‘Challenged and ignorant world’
Strategy 2:
S2F ‘Ecologically aware and globalized S2T ‘Challenged, but ecologically aware
Ecological modernization world’
world’
and eco-efficiency
Strategy 3:
Sustainability
transformation
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S3F ‘Sustainable and globalized world’
S3T ‘Challenged and sustainable world’
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Policy scenario
• Consider 6 relevant transport policy scenario’s, related to the
identified main drivers (environmental policy, fossil fuel
scarcity, propulsion technology, logistics developments)
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increase in energy efficiency (EE)
increase in fuel efficiency (FE)
introduction of electric mobility (ELEC)
internalization of external costs (INT)
increased use of public transport (USE)
e-Freight (EFR)
• 3 main policy scenario’s (Status Quo, Modernization,
Sustainability) indicate the intensity of the transport policy
• Note: other scenario’s possible, selection based on likelihood
and data availability
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Policy scenario
• Translation of policy scenario’s in parameters, based on recent transport
studies
• Distinguish 3 intensities: Status Quo, Modernization, Sustainability
Change in behaviour /
efficiency 2010-2030
MO
SU
Low change
Medium change
High change
EE
Energy
efficiency
increase / year
0.8%
1.2%
1.5%
FE
Fuel
efficiency
cars/year
Electrification of
transport
1.0 %
1.5 %
2.0 %
None
Partial electrification
up to 10% of fleet
Partial electrification
up to 20% of fleet
ELEC
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SQ
of
INT
Internalization of
external costs of
transport
TREMOVE
Basecase 2030
IMPACT project
scenario 2 - 2030
IMPACT project
scenario 5A -2030
USE
Reduced use of own
car transport in favour
of public transit and
car sharing
None
Preference for
private car transport
– 10%
Preference for private
car transport -20%
EFR
Reduction
in
administrative inputs
to transport (e-Freight)
None
Based on e-Freight
project (partial)
Based on e-Freight
project (full)
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Overview
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Transport within Neujobs
Main drivers and expected trends
Scenario matrix definition
Scenario analysis
Conclusion
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EDIP Computable General Equilibrium Model
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EDIP model (developed in REFIT FP6 project)
EU27 + 4 countries (CH, NO, TR, HR)
Strong disaggregation of transport sector
Integrated with SILC micro data for analysis of social effects
Detailed specification of labour market (several skill levels and
occupations)
• Follows 2-digit NACE classification
• Calibrated on recent input-output tables
• CES – functions with econometrically estimated elasticities of substitution
More complex, but more realistic representation of economy
• Caveat: model results indicate the order of magnitude and the direction of
change following from a certain policy measure
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EDIP CGE Model
import/export
Rest of World
foreign investment/savings
Investment
savings
Goods &
services (G&S)
buy G&S
Households
Transport
module
revenues
buy intermediate G&S
wage, capital income
Labour,
capital
Firms
hire capital, labour
hire capital, labour
corporate taxes
income, product taxes
transfers
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Government
buy G&S
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Detail of transport module
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Methodology
• 8 countries from macro-regions in Europe
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Western-European countries: Belgium, Germany, Austria
Nordic countries: Finland
Eastern-European countries: Bulgaria, Poland
Southern-European countries: Spain, Greece
• Base year, reference year and status quo scenario
– Base year: EDIP 2010
– Reference year: EDIP 2010 with constant growth rate till 2030
respective for friendly and tough background scenario
– Status quo: EDIP 2010 with constant growth rate till 2030 respective
for friendly and tough background scenario + Status Quo policy
scenario
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Methodology
• 8 countries from macro-regions in Europe
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Western-European countries: Belgium, Germany, Austria
Nordic countries: Finland
Eastern-European countries: Bulgaria, Poland
Southern-European countries: Spain, Greece
• Base year, reference year and status quo scenario
Additional impact
Sustainability
Additional impact
Modernization
POLICY:
STATUS-QUO
IMPACT
BACKGROUND
SCENARIO
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POLICY:
MODERNIZATION
IMPACT
BACKGROUND
SCENARIO
POLICY:
SUSTAINABILITY
IMPACT
BACKGROUND
SCENARIO
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Methodology
• Indicators: not only employment
Indicator
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Description
Dimension
GDP per capita
Relative change in Gross Domestic Product per
capita, calculated from the demographic change
and the expected average growth rate from 20102030
Measures economic activity and
production. Includes taxes on final
consumption and taxes on income.
GHG per capita
Relative change in Greenhouse Gas Emissions per
capita, calculated from the expected increase in
fuel efficiency and the demographic change from
2010-2030
Measures the emissions of greenhouse
gasses under the proposed changes in
policy
Unemployment
Relative change (in percentage point) in
unemployment rate from baseline unemployment
rate
Measures the amount of
unemployment.
Welfare
Relative change in compensating variation
Measures total consumption of the
population
Transport serv
Relative change in employment in public transport
services
Measures employment in the public
transport sector
Transport eq
Relative change in employment in the transport
equipment and related manufacturing sectors
Measures employment in the
automobile manufacturing sector.
Tax revenues
Relative change in total tax revenues
Measures the government’s tax income
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Results
• Many dimensions:
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Background scenario (friendly, though)
Main policy scenario (status quo, modernization, sustainability)
Countries (AT, BE, BG, ES, FI, GR, PL)
Transport policies (EE, FE, ELEC, INT, USE, EFR, FULL)
• In total 2 × 3 × 8 × 7 × 7 = 336 scenario’s, and 7 indicators for each scenario
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Results
Employment effects in friendly scenario, by transport policy scenario, absolute numbers (FTE’s)
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Total employment and GDP increases in all countries due to transport policies, but
differences in magnitude between countries due to different economic structure
Certain policies have negative effect on employment
– Decrease of fuel tax revenues leads to less employment
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•
Different main policy scenario has impact on magnitude of change
Different background scenario does not influence the impact of the transport
policies very much
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Results
Country
AT
BE
DE
ES
FI
GR
PL
BG
AT
BE
DE
ES
FI
GR
PL
BG
AT
BE
DE
ES
FI
GR
PL
BG
output_sim
Total jobs created
Total jobs created
Total jobs created
Total jobs created
Total jobs created
Total jobs created
Total jobs created
Total jobs created
Transp eq jobs created
Transp eq jobs created
Transp eq jobs created
Transp eq jobs created
Transp eq jobs created
Transp eq jobs created
Transp eq jobs created
Transp eq jobs created
Transp serv jobs created
Transp serv jobs created
Transp serv jobs created
Transp serv jobs created
Transp serv jobs created
Transp serv jobs created
Transp serv jobs created
Transp serv jobs created
Friendly
Tough
ΔMO
ΔSU
ΔMO
ΔSU
9,100
15,100
8,309
15,716
8,100
8,958
7,754
8,600
59,297
117,327
56,994
114,555
68,485
120,039
54,523
127,457
1,465
1,166
161
764
14,952
20,865
12,269
20,177
19,578
29,600
18,068
28,150
5,445
10,730
8,507
11,575
-300
-1,100
-300
-1,100
-700
-5,200
-800
-5,000
-23,900
-98,200
-23,700
-97,500
-5,300
-42,300
-2,400
-42,000
-200
-500
-200
-500
-940
-768
-469
-949
-500
-5,400
-300
-5,100
-100
-200
-100
-200
4,700
14,500
4,700
14,400
7,600
18,100
7,400
17,800
152,800
306,100
152,000
305,800
44,600
99,000
44,100
98,400
4,300
6,500
4,300
5,900
11,579
26,878
12,117
27,044
13,300
34,400
12,900
34,200
6,800
14,200
6,700
13,900
• Increase of employment in transport services, decrease in transport
manufacturing
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Results
• …
• The employment rate increases about 0.25%, with a range between 0.02%
and 0.57%.
• Transport polices increase GDP by around 0.5% , with a range between
0.04% and 1.19%.
• Transport policies reduce emissions of greenhouse gasses and related
pollutants by around 1-9%
– increase in energy efficiency
– reduction in the use of private mobility
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Overview
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Transport within Neujobs
Main drivers and expected trends
Scenario matrix definition
Scenario analysis
Conclusion
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Conclusion
• Transport is being influenced by multiple drivers – we focus on a few that
are important in the near future
• In the SET we see employment shifting from transport manufacturing
towards transport services
• Transport policies increase total employment and GDP in all countries,
while at same time GHG emissions are reduced
– important because one of the main obstacles for introducing policies that reduce
emissions is fear for loss of employment and reduced GDP.
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Thank you for your attention