Transcript energy sector

Employment effects of
selected scenarios from
the Energy Roadmap 2050
WG Meeting of the European Sectoral Social Dialogue
Committee for the electricity sector
15.11.2013 Brussels
[email protected]
DG ENER, A1
Outline
1. Context and timeline
2. Project details
3. Main results
Energy
1. Context and timeline (1)
The Energy Roadmap to 2050
• Nov. 2008 2nd SER: EC to prepare an energy policy
roadmap towards a low carbon energy system; in line with
the EU growth agenda set out in the Europe 2020 strategy
• Feb. 2009, Oct. 2009
The European Parliament and the
EU Council support an EU objective to reduce GHG by 8095% wrt 1990 levels, as estimated by IPCC
• Feb. 2011 The EU Council reconfirms the reduction
commitment, recognizes it will require a revolution in the
EU energy systems; fixing intermediary targets discussed
• Dec. 2011 The EC adopts the Communication, IA and
scenario analysis of the Energy Roadmap to 2050
Energy
1. Context and timeline (2)
The study of empl. effects of RM2050 scenarios
• 2012
Following a recommendation from the IAB, DG
ENER commissioned a study analysing potential impacts of
decarbonisation scenarios on jobs and skills
• Dec. 2012 - Oct. 2013
Work on the study
• Nov. 2013 – Dec. 2013
stakeholders
Discussion
of
results
with
• Dec. 2013 – Jan. 2014
DG ENER to decide on the
dissemination of the findings and conclusions of the report
Energy
2. Project details
The tender under an existing framework contract was
awarded to a Consortium led by
• COWI
which included
• Cambridge Econometrics,
• Exergia E3M Lab, NTUA,
• Enrst&Young
• Warwick Institute for Employment Research
Final draft (159 p.), appendices (57 p.) and additional data
structured along the tasks identified in the Terms of
References
Energy
3. Main results (1)
3.1 Collection of disaggregated statistical and
market employment data in the energy sector
• Approach
Combined input from ESTAT LFS, ESTAT SBS, commercial providers (such as
Bureau Van Dijk and the EurObserv’ER consortium)
• Methodology
Prioritization of data sources by relevance and importance (ESTAT LFS, ESTAT
SBS, Amadeus micro data, EurObserv’ER, Consortium calculations)
Apportioning used. The tables are filled gradually giving priority of the first choice
data source. If elements of the tables are not available, then a lower order data
choice is taken and the data from it is apportioned to match comparable
aggregated values from the higher order data choice.
Energy
3. Main results (2)
3.1 Collection of disaggregated statistical and
market employment data in the energy sector
• 2,5 million people directly employed
in the energy sectors across EU28
•
It represents about 1% of the total
employment in all sectors
Energy
3. Main results (3)
3.1 Collection of disaggregated statistical and
market employment data in the energy sector
• 0.6 million directly employed in
power generation (employment
numbers are given in brackets):
fossil fuels (32 800),
hydro (160 400),
nuclear (141 700),
solar (88 200),
wind (55 200),
geothermal (8 000),
biomass (106 500) and
tidal (100)
•
0.5 million directly employed in
transmission (67 500) and
distribution (425 900) of electricity
and about 140 000 were employed
in transmission and distribution of
natural gas
Energy
3. Main results (4)
3.1 Collection of disaggregated statistical and
market employment data in the energy sector
The statistical chapter contains detailed tables and charts on:
•
Number of companies per relevant sector;
•
Direct and indirect employment in the renewable sectors
(including a split on manufacturing, installation, operation
and maintenance);
•
Direct employment by MS and by NACE sector with the power
generation sector broken down by generation technology
Energy
3. Main results (5)
3.2 Literature review
• What are the methods used in literature to estimate the
employment impacts of energy policies?
• What type of workers are most/least sensitive to different
energy policies?
• Which sectors benefit most/least from different types of
energy policies (e.g. energy-efficiency policies, introduction of
low-carbon technologies)?
• What is the potential for workers from declining sectors to
move into new growing sectors? To what extent will these
new sectors be competing for skilled labour?
• What are the potential labour market impacts of the
structural change anticipated in the Energy Roadmap?
Energy
3. Main results (5)
3.3 The models
Cambridge Econometrics uses E3ME, a structural
(Keynesian) macroeconometric model of Europe’s economic and
energy systems and the environment.
Exergia E3M Lab from the National Technical University of
Athens uses GEM-E3, a multi-regional, multi-sectoral,
recursive dynamic computable general equilibrium (CGE) model
which provides details on the macro-economy and its interaction
with the environment and the energy system.
Employment in the models is determined by a combination of
structural change, the revenue recycling, aggregate GDP effects
and the reaction in the labour market
Energy
3. Main results (6)
3.3 The decarbonisation scenarios
Energy
3. Main results (7)
3.3 Selected empl. results – broader economy
Energy
3. Main results (8)
3.3 Selected empl. results – broader economy
Energy
3. Main results (9)
3.3 Selected empl. results – energy sector
• Decomposed results for the whole energy sector by NACE
(such as in Section 3.1) are not available (energy sector
spread around several lines in the previous slide)
• Employment results in the power generation sector in the
electricity sector are determined by:
input assumptions on the electricity fuel mix (consistent
between the models);
coefficients used to determine number of jobs per unit
of generation capacity.
• (Not by differences in modelling specification)
Energy
3. Main results (10)
3.3 Selected empl. results – power gen sector
Baseline
Energy
3. Main results (11)
3.3 Selected empl. results – power gen sector
Baseline vs other scenarios
Employment in fossil fuel sectors decreases across all scenarios
examined.
Employment in other power technologies depends on their
deployment suggested by the different scenarios
Energy
3. Main results (11)
3.3 Selected results – sensitivity analysis
Results across models are fairly robust.
• Relatively low sensitivity
Labour intensity of new technologies (measured as jobs
per GW capacity);
baseline rates of GDP growth
• Relatively high sensitivity
Recycling options of carbon tax revenues (income tax,
VAT reduction, direct lump sum transfer to households);
Fossil fuel prices (oil price depends partly on the level of
decarbonisation ambitions of the EU trading partners);
Investment crowding out effects
Energy
Thank you
Energy