2015 - Employment effects of electricity production from renewable energy in Portugal – an IO LCA...

Energy for Sustainability 2015

Sustainable Cities: Designing for People and the Planet

Coimbra, 14-15 May, 2015

EMPLOYMENT EFFECTS OF ELECTRICITY PRODUCTION FROM

RENEWABLE ENERGY IN PORTUGAL – AN IO LCA APPROACH

Carla O. Henriques1,3

*, Dulce H. Coelho2,3

, Natalie L. Cassidy4

1: Polytechnic Institute of Coimbra, ISCAC

Quinta Agrícola, Bencanta, 3040-316 Coimbra, Portugal

e-mail: [email protected], web: http://www.iscac.pt

2: Polytechnic Institute of Coimbra, ISEC

Rua Pedro Nunes, Quinta da Nora, 3030-199, Coimbra, Portugal

e-mail: [email protected], web: http://www.isec.pt

3: INESC Coimbra

Rua Antero de Quental, 199, 3030-030 Coimbra, Portugal

4: Maastricht University

[email protected]

Keywords: RES-E, net jobs, IO analysis, power plant life cycle, NREAP

Abstract In response to the need of reducing Green House Gas (GHG) emissions, energy

strategies in Europe advocate increasing the share of electricity generated from

renewable energy sources (RES-E). RES-E production is considered to be a priority in the

crusade against climate change, with a crucial role in Europe’s energy security. Several

studies even suggest that renewable energy technology (RET) deployment will also be

responsible for the creation of a large number of jobs. Nevertheless, the exact number of

jobs created varies enormously across recent studies. Employment estimates may vary not

only with the use of different methodologies but also with the consideration of different

assumptions even when the same methodology is applied. The aim of this paper is to

provide a clearer understanding of what are the implications of government support for

RES-E on jobs, and to see if current claims over employment benefits are too optimistic or

even pessimistic in light of these findings. Taking Portugal as a case study, this paper

conveys an assessment of the impact of renewable energy targets for electricity generation

on employment for the year 2020. The analysis is conducted by means of the Input-Output

(IO) approach (quantity and price models) and considering the different life cycle stages

of RES-E and conventional energy (CE) power plants.

http://www.isec.pt/

Carla O. Henriques, Dulce H. Coelho and Natalie L. Cassidy

2

1. INTRODUCTION

The recent literature on employment vis-à-vis the environment suggests that switching to a

‘low carbon’ economy will have significant repercussions for Europe’s labour market and

jobs (see e.g. [1], [2], [3]). Climate change policies ultimately force adjustments to occur in

both production and consumer habits, which industries and thus the labour market must

respond to, in order to meet the rising employment pressures coming from the expansion of

some sectors (e.g. RES-E) whilst at the expense of a decrease in others (CE – electricity

produced by fossil-fuel based industries). A distinction is usually made between four

employment outcomes that are foreseen as a consequence of switching to a low carbon

economy: additional jobs will be created; jobs will be substituted; jobs will be eliminated; and

existing jobs will be transformed [4]. A further outcome of ‘job displacement’ could also

transpire as a consequence of ‘carbon leakage’ [5]. Gross employment forecasts for Europe, in

the year of 2020, range between 2.3 million to 21 million [6]. Besides differing definitions,

discrepancy between employment estimates is also caused by a diverse range of

methodologies being used, making it difficult to accurately draw comparisons between results

[2]. This problem is further exacerbated by the fact that the approach taken and assumptions

underlying estimates are not always explicitly stated [7].

Several methods have been used to assess the impact of RES-E targets on employment that

can be categorised into bottom-up and top-down approaches, i.e. the analytical or the IO

method, respectively [8], [9]. The analytical method is aimed at quantifying job effects of a

precise energy project or industry as it uses survey or model plant data to establish the

employment required to manufacture and operate a plant or a certain piece of equipment [8].

This approach is used in a specific context and it is said to be more transparent than the IO

framework (in Portugal a tentative study of this kind has been conducted in [10]). However, it

is less suited for forecasting economy-wide impacts as it cannot take into account the indirect

and induced employment effects [11]. On the other hand, IO method allows for the estimation

of direct, indirect and induced employment effects and it is typically used to quantify the

number of employed persons at the national/regional level [8]. The basic structure of IO based

models represents each sector’s production process through a vector of structural coefficients

that describes the relationship between the intermediate inputs consumed in the production

process and the total output. The supply side is split into several processing industries that

deliver their total output (production) for intermediate consumption or final demand [12].

Nevertheless, because the RES-E sector brings relatively new concerns, current IO tables are

not sufficiently disaggregated to straightforwardly arrive at employment estimates. Therefore

a different approach has been used by considering the different life cycle phases of each RET

and CE (i.e. installation, operation and maintenance (O&M) and fuel) which are further

decomposed into their corresponding activities/components.

The purpose of this paper is to assess the implications of RES-E promotion on job generation,

and to discuss if the current policy assumptions regarding employment benefits are overly

optimistic. The remainder of this paper is structured as follows: section 2 provides a brief

overview of the methodology and assumptions followed; section 3 presents the discussion of

some illustrative results and section 4 draws the main conclusions and future work

developments.


3

2. METHODOLOGY AND ASSUMPTIONS

IO tables cannot identify in their present form the number of jobs that are likely to be created

from an increase in the demand for RES-E/CE, but only the impact of an increase in demand

for electricity in general. One way to overcome this problem is to build the RES-E distinct

vectors within the IO matrix (see [13], [14]). But another way to surmount this problem is to

decompose RETs/CE into their various activities/components and associated costs, and then

match these to the sectors identified in the IO table of the economy under analysis and obtain

the relevant employment coefficients and multipliers to arrive at employment estimates [2].

In order to assess the impacts on employment, the economic impulses that originate these

impacts must be identified [2]. Therefore, the life cycle of a RES-E/CE power plant is

divided into different life cycle phases. The life cycle phases can be seen as economic

activities that provide impulses in the form of expenditures that can generate different

economic effects. Impulses (e.g. expenditures for O&M, manufacturing and construct ion

of RET) are regarded as exogenously determined parameters that trigger an economic

mechanism that leads to several effects. Effects (e.g. a direct positive effect could be an

increase in RES-E production; a negative induced effect could be a decrease in the

consumption of goods) relate to how impulses influence the economy – positively,

negatively, directly, indirectly or induced. They provide the economic impacts, which are

the final outcomes measured here as the number of jobs or changes in employment . The

most important impulses herein analysed are: investment and O&M expenditures, fuel

supply and exports of RET and CE electricity equipment, including impacts in upstream

industries (direct and indirect effects); the impulse from household income due to

employment changes in the RETs and CE (induced effect of type 1); and the impulse due

to changes in electricity prices (e.g. the CO2 emission costs avoided with the additional

production of RES-E, if the RES-E targets are met, and the expected impact of the tariff

deficit generated from the support of the RES-E sectors) and affecting consumption

expenditures in households and cost structures in industries (induced effect of type 2). An

exemplification of the methodology herein followed is briefly summarised in Table 1.

Direct and indirect employments are computed in two steps, offering the possibility to fine

tune this approach by considering results on direct employment from other sources (e.g.

industry surveys). In order to obtain the direct employment in the RES-E/CE industry (for

further details on this methodology, please see [2] and [15]) we compute the direct

employment for operating RES-E/CE facilities directly from labour costs by assuming an

average compensation per full time employment (FTE). Then, direct employment for any

other activity is obtained by multiplying domestic output with an industry-specific direct

employment factor that relates employment to industry output (in monetary units).

Indirect employment effects include employment in upstream industries that supply and

support the RES-E/CE activities (e.g. intermediate inputs like steel, synthetics, software,

etc. for RES-E/CE plants, or for equipment, facilities, O&M). Finally, the impacts from

investments and net exports are included in final demand. Changes in expenditures for

O&M and fuel supply (e.g. domestic production) are treated as additional final demand.

The impacts on output induced by changes in final demand can then be assessed using the


4

closed IO model. Induced impacts represent the employment triggered by consumption

expenditures of persons employed in the RE/CE industry and in supplying industries. The

type 2 induced effects were obtained through the IO price model. The IO price model

calculates the impact of the electricity price change on the prices of different sectoral

outputs. The household consumption and labour (primary input) are “endogenised”, while

the electricity sector and all other primary inputs are considered to be exogenous inputs

(see [16]). In order to obtain type 2 induced employments, the electricity price change is the

key input to the closed price IO model; whereas the outputs are the resulting price changes of

all other sectors. While in the quantity IO closed model the exogenous variable is final

demand (without household consumption) and the endogenous variable is total output

(quantity), in the price model, the exogenous variables are the prices of primary inputs (except

labour) and electricity, and the endogenous variables are the prices of all industry goods

except electricity. The aim is to assess the impact of changes in electricity prices on the prices

of all other industries and primary input labour [2]. Finally, it is assumed that price increases

lower real purchasing power of final consumers and, therefore, reduce the final demand for all

goods and services. In our study the change in final demand for goods and services is

estimated based on assumptions about the price reactions of household consumption (by

assuming certain price elasticities). Although this should be calculated for the total final

demand (investments, net exports, private consumption and government consumption), in our

work it is only referred to changes in household consumption.

Table 1. Methodology application

Manufacturing and Installation;

O&M;

Fuel (for Biomass and CE).

The RET the activities/components used are provided in [2] - e.g. large hydropower:

Manufacturing and installation - planning: regulatory activities;

construction work; steel hydro construction; hydro turbine; electromechanics; electronic control; installation; electric connection to

net; other; large hydropower.

O&M - labour costs; waste management; maintenance; spare parts;

insurance; other).

TE CE activities/components used are provided in [15].

Total expenditure connected to each life cycle phase cost share of each

relevant activity/component as % of life cycle phase.

The RET shares were obtained from [2].

The CE shares were obtained from [15].

The match of the domestic output of each relevant activity/component of

RET to the industries within the I-O table was based on [2].

The match of the domestic output of each relevant activity/component of

electricity from CE to the industries within the I-O table was based on [15].

Use employment multipliers, to arrive at indirect and induced employment effects.

Divide into life cycle phase

Decompose phases into their

activities/components

Calculate total output of each relevant activity/

component

Match the domestic output of each relevant activity/ component of RET/CE to industry in

I-O table

Calculate the employment effect of each

activity/component


5

Table 2. Data limitations and assumptions

Limitations Assumptions

Portuguese most recent IO table is for 2008 and RET

deployment has substantially evolved since then.

In line with the data available the baseline year of

this study is 2008, from which projections are made

for the year 2020.

Unknown when the increase in renewable energy will

take place to reach 2020 target

Employment estimates provided are based on the

assumption that each individual RET target will be

met according to NREAP (2013).

Unknown increase of electricity prices in 2020. The price change can be obtained via the feed-in levy

on current electricity prices or, if available, via

electricity market models. However, since no reliable

data was available, employment estimates provided

are based on the assumption that electricity prices

will have an annual average increase of 1,5% and

2%.

Unknown final demand vector in 2020. The consumption structure is considered as constant,

and a change in price will affect the consumption of

all goods proportionately. Two scenarios regarding

price elasticity were assumed, reflecting a more and

less elastic demand regarding price changes.

Table 3. Installed capacity, annual capacity increase, energy output considered and specific investment

and O&M costs considered per technology.

Installed

Capacity

2008

Annual

Capacity

increase

2008

Annual

energy

output

2008

Installed

Capacity

2020

Annual

Capacity

increase

2020

Annual

energy

output

2020

Specific

investment

costs

Specific

O&M costs

(without

fuel costs)

Unit: MW elec MW elec GWh

elec MW elec MW elec GWh

elec € / kW elec €/ (kW*a)

elec

Technologies

Geothermal

electricity 29 - 192 29 - 226 2.118 15

Hydropower

large 4.533 9,00 6.740 8.540 - 13.613 1.232 16

Hydropower

small 324 - 558 394 6,00 916 1.756 16

Photovoltaic 62 47,00 41 647 73,00 1.139 4.430 9

Wind –

Onshore 3.058 594,00 5.757 5.242 58,00 11.671 1.152 15

Biogas (incl.

CHP) 15 6,00 71 59 - 413 2.272 21

Biomass (incl.

CHP) 350 4,00 1.501 755 14,00 4.025 2.097 15

Biowaste (incl.

CHP) 86 - 281 86 - 281 2.000 30

Coal 1.871 - 11.196 576 - 2.618 1.152 58

Natural Gas 2.376 - 15.198 6.757 - 45.601 576 17

Oil 2.634 - 4.154 - - - 516 15


6

There are a number of assumptions upon which the results of IO analysis rest. It is assumed

that there are constant returns to scale. The technological coefficients are fixed, and do not

allow for the possibility that there could be technological advancements or economies of scale

that decrease the cost per unit of output. It is also assumed that there is no substitution among

inputs in the production of any good or service and that there is only one process used for the

production of each output. Besides these limitations, Table 2 refers other issues identified and

assumptions made in this particular study.

The Portuguese Ministerial Order 20/2013 [17] has set a global target for RET of about

15,8 GW installed capacity in Portugal by 2020 (an increase of 7 GW regarding 2008).

Table 3 illustrates the contribution/expected contribution of each RET and CE in 2008

[18], [19] and by 2020 [17] and the specific installation and O&M costs (based on [20]

and [21]).

In 2020, large hydropower is expected to have the largest installed capacity at 8 540 MW,

followed by natural gas (6 757 MW) and onshore wind (5 242 MW). However, photovoltaic

(PV) is expected to have the highest installed capacity increase (943,5%), followed by biogas

(293,3%) and natural gas (184,4%). The contrast is the result of the fact that there is a lot of

potential for deploying PV due to technological advances, whereas options to decommission

oil and coal power plants started in 2011. Nonetheless, when looking to the actual electricity

generated for the year 2020, which is expected to be approximately 80 503 GWh, natural gas

produces the most electricity (56,6% of total generated), followed by large hydropower

(16,9%) and wind energy (14,5%). PV accounts for only 1,4% of total expected electricity

generation. Generators do not operate at their full capacity and in some instances do not

generate electricity at all at given times of the year or day. The reasons are manifold, ranging

from cost considerations to the conditions of the power plant. On the other hand, wind and PV

technologies heavily depend on weather conditions to generate electricity.

3. ILLUSTRATIVE RESULTS

According to our analysis, 22 053 jobs were estimated for 2008, with 7 191 direct, 5 710

indirect and 9 152 induced jobs of type 1 associated with the increase in installed capacity

of RETs (see Fig. 1 c)). Regarding CE 6 985 jobs were estimated for the same year, with

2 425 direct, 1 712 indirect and 2 848 induced of type 1 (see Fig. 2 c)). From our analysis

it is estimated that there will be 28 197 jobs, with 8 605 direct and 7 878 indirect and 11 715

induced jobs of type 1, associated with the increase in installed capacity of RET in 2020 (see

Fig. 1 d)). The increase in installed capacity (mainly natural gas) of CE in 2020 will be

responsible for a total of 7 224 jobs, with 2 684 direct, 1 798 indirect and 2 742 induced of

type 1 (see Fig. 2 d)).

In 2008, the installation of new RET facilities accounts for the majority of jobs with both

direct and indirect totalling to 7 716. When contrasting the number of direct jobs created due

to installation of new facilities (4 439 jobs) with those in O&M (1 489 jobs), the results

suggest that the installation and construction of new facilities is generally more labour

intensive. The indirect employment effect is understandably larger for the installation (3 276

jobs) compared to O&M (653 jobs) as the demands on the supply chain are likely to be much


7

greater due to the need for different materials and services to construct the new facilities. In

2020 the difference between jobs in the installation of new RETs facilities and O&M will be

reduced (see Fig. 1 a), b) and d)) since it is assumed that the installed capacity of biomass and

biogas and large hydropower will be fully exhausted in 2016 and 2017, respectively.

Regarding RET, PV (3 342 direct and indirect jobs) and biomass (8 773 direct and indirect

jobs) will be the main responsible for job creation in 2020.

When comparing the number of potential employed persons in the year 2020 to the baseline

year in 2008, although there is a decrease of direct and indirect jobs in the installation phase

from 7 716 to 4 365 jobs resulting from the exhaustion of the installed capacity of RETs

before 2020, there is an increase of the total number of direct and indirect jobs from 12 900 to

16 483. The increase of jobs generated in the O&M phase from 2 142 to 3 960, where jobs are

usually more permanent, and in fuel for bio energy from 3 042 to 8 158 due to the increase of

biomass installed capacity are the main responsible for this result (see Fig. 3). The

decommissioning of coal and oil power plants is surpassed by the additional installed capacity

of natural gas that becomes responsible for the direct and indirect employment of 4 482

persons in 2020 against the 3 931 persons in the base year (see Fig 2). Since the installed

capacity of natural gas will be fully exhausted in 2017 only O&M jobs will be generated by

2020.

For all technologies (with the exception of biomass whereby more jobs are created indirectly

as a result of fuel input), the indirect effect is smaller than the direct employment effect. The

indirect effect is large for PV due to the heavy demands that installation will place on the

supply chain. In general, the installation of new facilities is the cause of most indirect

employment effects in the RET sector.

Finally, the computation of negative impulses regarding type 2 employment job losses due to

the expected electricity price increase by 2020 (in particular due to RES-E support

mechanisms implemented in Portugal) are presented in Table 4 and Table 5. The RES-E

support mechanisms implemented, in particular since 2007-2008, have certainly contributed

to the deployment of more expensive technologies, in particular in Portugal, where the high

share of RES-E in the gross electricity generation mix corresponds to high average level of

support per unit of electricity produced [22]. In 2013, Portugal faced one of the highest

cumulated tariff deficits reaching estimated values ranging from 2,2% (estimated by the

regulator at EUR 3.7 billion) to 2,6% of its GDP (according to other government estimates at

EUR 4.4 billion) [22]. In the case of Portugal, the Portuguese authorities have formally

recognized the right of the affected utilities to recover the corresponding amount with

interests. Therefore, in our analysis we have considered a real average annual increase of

electricity prices ranging from 1,5% to 2%. From our computations it is possible to conclude

that the overall net impact on jobs resulting from the targets imposed on RES-E consistent

with [17] are modest when positive, ranging from 1 282 jobs to 3 713 jobs and can even be

negative under stringent conditions (see Table 4 and Table 5).


8

0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000

Geothermal

Hydro large

Hydro small

PV

Wind

Biogas

Biomass

Biowaste

Installation of new facilities Operation of facilities Fuels

0 1.000 2.000 3.000 4.000 5.000 6.000

Hydro large

Hydro small

PV

Wind

Biogas

Biomass

Biowaste


a) Direct employment by RET in 2020 b) Indirect employment by RET in 2020

4.439

1489 1263

3.276

6531779

5.603

1.198

2.351

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

Installation of new

facilities

Operation of facilities Fuels

Direct Employment Indirect Employment Type 1 Induced Employment

2.481 27363387

1.884 1224

47703.169

2.242

6.304

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

Installation of new

facilities



c) Total employment by life cycle phase – RET

2008 d) Total employment by life cycle phase – RET 2020

Figure 1. Illustrative results on jobs obtained for RET


9

0

0

0

354

2.330

0

0

0

0

0 500 1.000 1.500 2.000 2.500

Coal

Natural Gas

Oil


0

0

0

223

1.575

0

0

0

0 200 400 600 800 1.000 1.200 1.400 1.600 1.800

Coal

Natural Gas

Oil


a) Direct employment by CE in 2020 b) Indirect employment by CE in 2020

0

2388

360

1542

1700

2.579

269

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

Installation of new

facilities



0

2684

00

1798

00

2.742

0 0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

Installation of new

facilities


Direct Employment Indirect Employment Induced Employment

c) Total employment by life cycle phase – CE 2008 d) Total employment by life cycle phase – CE 2020

Figure 2. Illustrative results on jobs obtained for CE


10

Geothermal

0%Hydro large

10%

Hydro small

1%

PV

12%

Wind

36%

Biogas

1%Biomass

15% Biowaste

0%

Coal

12%

Natural Gas

8%

Oil

5%

CE

25%

Direct employment 2008

Geothermal

0%Hydro large

15%

Hydro small

2%

PV

17%

Wind

9%

Biogas

0%

Biomass

33%

Biowaste

0%

Coal

3%

Natural Gas

21%

Oil

0%

CE

24%

Direct employment 2020

Figure 3. Contribution of each electricity technology to direct employment.

Table 4. Expected total net job creation for different prices and elasticity assumptions.

AAGR1 for

Electricity

Prices

Price

elasticity Job losses

Total jobs

baseline

scenario

Total jobs

2020 Net Jobs

1,50% -0,5 2.430 29.038 35.421 3.954

1,50% -1,0 4.859 29.038 35.421 1.525

2% -0,5 3.239 29.038 35.421 3.144

2% -1,0 6.477 29.038 35.421 -93

1AAGR - Annual

average growth rate

Table 5. Expected net job creation for RES-E for different prices and elasticity assumptions.

AAGR for

Electricity

Prices

Price

elasticity Job losses

Total jobs

baseline

scenario

Total jobs

2020 Net Jobs

1,50% -0,5 2.431 22.053 28.197 3.715

1,50% -1,0 4.862 22.053 28.197 1.286

2% -0,5 3.241 22.053 28.197 2.905

2% -1,0 6.483 22.053 28.197 -333

4. CONCLUSIONS

Policy decision making is rather influenced by the official employment estimates provided by

different sources, particularly in the current sluggish economic context where the

unemployment faces high rates. Therefore, we aimed at assessing whether such estimates

produce reasonable results and whether, as a consequence, policy makers are sufficiently

aware of what the likely impact will be on the workers. By taking Portugal as a case study,

this paper provided an assessment of the impact RES-E targets will have on employment. The


11

modelling approach herein used combines bottom-up technology-specific data regarding the

capacities, costs and cost structures with top-down economic modelling, by means of IO

analysis. One of the advantages of its use is that it allows estimating employment in the RET

industry within a comprehensive and consistent framework [2]. Moreover, with this

methodology indirect impacts are fully taken into account. One limitation of this approach

regards the assumption that the industries embedded in the IO model are suitable proxies for

the companies of the RET/CE industry and its supply chain with regard to cost structures,

import relations and employment per unit of output. This problem can be mitigated by partly

including additional, technology-specific information in the estimation of direct employment,

for example according to data from enterprise surveys or industry experts.

It was found that if each RET met its individual target for installed capacity by 2020, then the

industry would support, directly, indirectly and induced (type 1), just under 30 000 jobs.

Whilst the employment implications of exploiting RES-E are positive to the Portuguese

workforce, when comparing the number of jobs estimated in this paper to other estimates such

as those made by [17] (70 000 jobs will be created with RES by 2020), the impact appears

modest. A small fraction of NREAP’s [17] estimate relates to jobs borne out of an increase in

the use of renewables for heat and transport, which were not accounted for in the analysis of

this paper. The employment estimates presented in this paper are based on the assumption that

the 2020 renewable energy targets for each technology will be met and therefore should be

considered as a best-case scenario. In reality, with the Portuguese government failing to keep

on top their renewable commitments, the number of jobs associated with renewable energy

could be considerably less than forecasted. Furthermore, the analysis showed that the majority

of jobs would be in the installation of the new facilities, and therefore many of these jobs are

likely to be only temporary, as opposed to in O&M, where the jobs are usually more

permanent. Finally, it can also be inferred from the analysis that the labour intensity of RES-E

tends to decline as experience in installing and O&M of the technology increases. As

hydropower and wind have been deployed in Portugal over a longer period, these

technologies have become more efficient with time, both because of improved capabilities

and technological advances, which means they require less labour input per MW of installed

capacity. Therefore, given that IO analysis assumes that the technological coefficients are

fixed, including the labour coefficient, it is possible that the number of jobs in 2020 will be

less than predicted in this paper, as newer technologies such as wind and PV learn how to use

less labour to produce the same amount of output. This paper has brought to light the urgent

need for more robust data on RES-E in Portugal. The lack of clarity and consensus over

employment estimates stems largely from unreliable and missing statistics on the sector.

Whilst this study provides employment estimates in order to develop a better understanding of

the situation in Portugal, these estimates are only as good as the data that underpins it. Further

effort to revise and collect data that is necessary for producing employment estimates is

therefore encouraged so as to put an end to the disparities currently haunting the literature.

For example, an up-to-date IO table and cost structures of RET would help to improve the

accuracy of estimates which are derived from an IO analysis. Ideally IO tables would be

expanded to allow for the inclusion of RES-E as its own separate industry. By identifying the

number of jobs that will be created and what kind of jobs they are, i.e. whether in


12

construction, O&M etc., it is important to then know what type of skills are needed to perform

these roles. However, thus far, this kind of information has been limited – largely because of

the unpredictability associated with the transition and also because it is likely that the skill

needs will be different according to local contexts [3]. At a general level, it has been

acknowledged by organisations such as the OECD [23] that there will be a need for highly

skilled and qualified labour [23]. As with any structural change the speed and the extent of the

transition will depend considerably on how well technical skills are aligned to new job

requirements; researchers and innovators will be needed so that low carbon ideas can be

easily brought to market; and workers with technical capabilities will be needed to put these

ideas into practice. However, along with the need for high skilled workers, there will also be a

demand for low skilled workers. First, in the short term, in jobs associated with construction

and manufacturing; and second, in the long term, as the employment effects are expected to

‘trickle down’ to society at wide, with every job set to become a ‘green’ job. To therefore

ensure new demands are met and that the labour force are ready to take advantage of new

opportunities, it is important that further research is carried out that can map out the specific

skill sets which will be required [24]. Finally it is increasingly recognised that along with

determining the quantitative impact on employment, the qualitative impact also needs to be

addressed to fully appreciate the consequences of moving to a low carbon economy. There

tends to be an assumption in much of the past literature that green jobs are also of good

quality that are well paid and with good working conditions [25]. Nevertheless ‘one of the

greatest risks is that, in our haste to create a large quantity of new green jobs, we pay too little

attention to their quality’; ‘green’ after all does not necessarily mean social [26]. Whilst a

quantitative analysis, as presented in this paper, provides a significant and vital step towards

understanding the employment effects of switching to a low carbon economy, it is important

to go beyond the numbers to truly understand how the transition will impact the workers.

ACKNOWLEDGEMENTS

The authors acknowledge the Portuguese Science and Technology Foundation (FCT) project

PEst-OE/EEI/UI0308/2014. This work was framed under the Energy for Sustainability

Initiative of the University of Coimbra and supported by the R&D Project EMSURE (Energy

and Mobility for Sustainable Regions, CENTRO 07 0224 FEDER 002004).

REFERENCES

[1] European Commission. Exploiting the employment potential of green growth.

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