instructions for writing a paper for the conference 'energy for ...

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. Cassidy 4 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.

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

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. 2


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 the se 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 construction 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 CO 2 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 3


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 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

 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.

4


Table 2. Data limitations and assumptions Limitations Assumptions Portuguese most recent IO table is for 2008 and RET In line with the data available the baseline year of deployment has substantially evolved since then. this study is 2008, from which projections are made for the year 2020. Unknown when the increase in renewable energy will Employment estimates provided are based on the take place to reach 2020 target 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.

Unit:

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)

MW elec

MW elec

GWh elec

MW elec

MW elec

GWh elec

€ / kW elec

€/ (kW*a) elec

Technologies Geothermal electricity Hydropower large

4.533

Hydropower small

324

29

-

192

29

-

226

2.118

15

6.740

8.540

-

13.613

1.232

16

-

558

394

6,00

916

1.756

16

9,00

Photovoltaic Wind – Onshore

62

47,00

41

647

73,00

1.139

4.430

9

3.058

594,00

5.757

5.242

58,00

11.671

1.152

15

Biogas (incl. CHP)

413

2.272

21

4.025

2.097

15

281

2.000

30

15

6,00

71

59

-

Biomass (incl. CHP)

350

4,00

1.501

755

14,00

Biowaste (incl. CHP)

86

-

281

86

-

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

-

5

-

-


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 6


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).

7


Installation of new facilities

Operation of facilities

Fuels

Biowaste



Fuels

3.000

5.000

Biowaste

Biomass

Biomass

Biogas Biogas

Wind Wind

PV PV

Hydro small Hydro small

Hydro large

Hydro large

Geothermal 0

500

0

1.000 1.500 2.000 2.500 3.000 3.500 4.000

a) Direct employment by RET in 2020

1.000

2.000

4.000

6.000

b) Indirect employment by RET in 2020 Direct Employment Indirect Employment Type 1 Induced Employment

Direct Employment Indirect Employment Type 1 Induced Employment 14.000

16.000

12.000

14.000 5.603

12.000

10.000

6.304 10.000

8.000 8.000 6.000

3.276

6.000

4770 2.242

4.000 1.198

2.000

3.169

2.351

4.000 4.439

653

1779

1489

1263


Fuels

1.884

1224

2.481

2736



2.000

3387

0

0 Installation of new facilities

c) Total employment by life cycle phase – RET 2008

Fuels

d) Total employment by life cycle phase – RET 2020

Figure 1. Illustrative results on jobs obtained for RET

8




Fuels


Oil 0

0

Fuels

Oil 0

Natural Gas 0

Coal 0


2.330

354

0

Natural Gas 0

0

500

1.000

1.500

2.000

Coal 0

223

0

200

2.500

a) Direct employment by CE in 2020 Direct Employment

Indirect Employment

400

600

800

0

1.000

1.200

1.400

1.600

1.800

b) Indirect employment by CE in 2020

Type 1 Induced Employment

Direct Employment

7.000

8.000

6.000

7.000 6.000

2.579

5.000

1.575

Indirect Employment

Induced Employment

2.742

5.000 4.000 4.000 1542

3.000

1798 3.000

2.000 2.000 2388

1.000

0

2684 1.000

269 0 Installation of new facilities

170 Operation of facilities

0

Fuels 36

c) Total employment by life cycle phase – CE 2008

0 Installation of new facilities


0 Fuels

d) Total employment by life cycle phase – CE 2020

Figure 2. Illustrative results on jobs obtained for CE

9


Direct employment 2020

Direct employment 2008 Biogas Biomass 1% 15% Biowaste 0%

Natural Gas 8% Biogas 0%

Wind 36%

CE 25% PV 12%

Coal 12%

Biomass 33%

CE 24%

Wind 9%

Oil 5%

PV 17%

Geothermal 0% Hydro small Hydro large 1% 10%

Natural Gas 21%

Biowaste 0%

Hydro small 2%

Geothermal 0% Hydro large 15%

Oil Coal 0% 3%

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 1,50% 1,50% 2% 2%

Price elasticity -0,5 -1,0 -0,5 -1,0

Job losses 2.430 4.859 3.239 6.477

Total jobs baseline scenario 29.038 29.038 29.038 29.038

Total jobs 2020

Net Jobs

35.421 35.421 35.421 35.421

3.954 1.525 3.144 -93

1 AAGR - Annual average growth rate

Table 5. Expected net job creation for RES-E for different prices and elasticity assumptions. AAGR for Electricity Prices 1,50% 1,50% 2% 2%

Price elasticity -0,5 -1,0 -0,5 -1,0

Job losses 2.431 4.862 3.241 6.483

Total jobs baseline scenario 22.053 22.053 22.053 22.053

Total jobs 2020

Net Jobs

28.197 28.197 28.197 28.197

3.715 1.286 2.905 -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 10


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 11


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. Available from: file:///C:/Users/hp/Downloads/SWD_green-growth_EN.pdf (2012). [Accessed 4 December 2014]. [2] Breitschopf, B., Nathani, C., Resch, G.. ‘Economic and Industrial Development’ EIDEMPLOY. Methodological guidelines for estimating the employment impacts of using renewable energies in electricity generation. Available from: http://iea-retd.org/wpcontent/uploads/2012/12/EMPLOY-Guidelines.pdf, (2012). [Accessed 4 December 12


2014] [3] OECD. Enabling Local Green Growth: Addressing Climate Change Effects of Employment and Local Economic Development. Available from: http://www.oecd.org/regional/leed/49387595.pdf (2012). [Accessed 4 December 2014]. [4] UNEP. Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication. Available from: http://www.unep.org/greeneconomy/Portals/88/documents/ger/ger_final_dec_2011/Gr een%20EconomyReport_Final_Dec2011.pdf (2011). [Accessed 4 December 2014]. [5] ILO. Working towards sustainable development: Opportunities for decent work and social inclusion in a green economy. Available from: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/--publ/documents/publication/wcms_181836.pdf (2012) [Accessed 4 December 2014]. [6] ECORYS. Employment and Labour Force Skills - Overview. Available from: http://ec.europa.eu/environment/enveco/industry_employment/pdf/labor_force.pdf (2008). [Accessed 18 September 2014] [7] Hughes, G.. The Myth of Green Jobs. The Global Warming Policy Foundation GWPF Report 3. Available from: http://www.thegwpf.org/images/stories/gwpfreports/hughes-green_jobs.pdf (2011). [Accessed 18 September 2014] [8] World Bank Issues in estimating the employment generated by energy sector activities. Available from: http://siteresources.worldbank.org/INTOGMC/Resources/Measuring_the_employment _impact_of_energy_sector1.pdf (2011) [Accessed 18 September 2014]. [9] Lambert, J., Silva, P. The challenges of determining the employment effects of renewable energy. Renewable and Sustainable Energy Reviews,16 (7) (2012), pp. 4667-4674. [10] N. Šahović, G. Pereira, P. Silva, C. Henriques, Quantifying renewable energy and energy efficiency employment impacts in Portugal, Proceedings of the 1st South East European Conference on Sustainable Development of Energy, Water and Environment Systems - SEE SDEWES, Ohrid, Macedonia (2014). [11] Wei, M., Patadia, S., Kammen, D. Putting renewables and energy efficiency to work: How many jobs can the clean energy industry generate in the US. Energy Policy, 38 (2) (2010), pp. 919-931. [12] Leontief W. Input–output analysis. In: Leontief W, editor. Input–output economics. New York: Oxford University Press (1985), pp. 19–40. [13] Silva, P., Oliveira, C., Coelho, D. Employment effects and renewable energy policies: applying I - O methodology to Portugal. International Journal of Public Policy, 9 (3) (2012), pp. 147-166. [14] Oliveira, C., Coelho, D., Silva, P., Antunes, C.. How many jobs can the RES-E sectors generate in the Portuguese context? Renewable and Sustainable Energy Reviews, 21, (2013), pp. 444–455. [15] Billman, L., Basoli, D., Energy Input-Output Calculator User Guide, National Renewable Energy Laboratory Technical Report (NREL)/TP-6A20-55584, June 2012. 13


[16] [17] [18] [19] [20] [21] [22]

[23]

[24]

[25]

[26]

Availble from: http://www1.eere.energy.gov/analysis/iocalc/docs/combined_userguide_ML_6-1912.pdf (2012) [Accessed 4 December 2014]. Miller, R., Blair, P. Input-output analysis: Foundations and extensions, 2nd ed. Cambridge University Press, 2009. NREAP. Ministerial Order 20/2013, Diário da República, 1.ª série — N.º 70 — 10 de abril de 2013 (2013), pp. 2022-2091. DGEG. Available from: http://www.dgeg.pt (2014). [Accessed 4 December 2014]. REN. Available from: http://www.centrodeinformacao.ren.pt/ (2008). [Accessed 4 December 2014]. Biamonti, A. G. Renewables Outlook 2007. MSc Dissertation, IST; Technical University of Lisbon (in Portuguese) (2008). Gomes, G. F. Long-term prediction prices for electricity. MSc Dissertation, University of Porto (in Portuguese) (2010). Linden, A. J., Kalantzis, F., Maincent, E., Pienkowski, J. Electricity Tariff Deficit:Temporary or Permanent Problem in the EU? Economic Papers 534, October 2014. Available from: http://ec.europa.eu/economy_finance/publications/economic_paper/2014/pdf/ecp534_e n.pdf (2014) [Accessed 4 December 2014]. OECD, Martinez-Fernandez. C., Hinojosa, C., Miranda, G. (2010). Green jobs and skills: the local labour market implications of addressing climate change. Working document, CFE/LEED, OECD. Available from: http://www.oecd.org/regional/leed/44683169.pdf [Accessed 14 December 2013]. C. Oliveira, Cassidy, N., Coelho, D. Employment effects of electricity generation from renewable energy technologies in the UK, Proceedings of the 22nd International InputOutput Conference, Julho de 2014, Lisboa, Instituto Superior de Economia e Gestão, Portugal. Available from: https://www.iioa.org/conferences/22nd/papers/files/1915_20140509061_Employmente ffectsofelectricitygenerationfromrenewableenergytechnologiesintheUK(DC).pdf (2014) [Accessed 14 December 2014]. GHK. Thematic Expert Work on Green Jobs for DG EMPL/D1, European Employment Observatory Thematic Paper. Available from: http://www.euemployment-observatory.net/resources/reports/GreenJobs-MEDHURST.pdf (2009) [Accessed 14 December 2013]. Mattera, P.. High Road or Low Road? Job Quality in the New Green Economy. Available from: http://www.goodjobsfirst.org/sites/default/files/docs/pdf/gjfgreenjobsrpt.pdf (2009) [Accessed 14 December 2013].

14