PUTTING RENEWABLES AND ENERGY EFFICIENCY TO WORK:
HOW MANY JOBS CAN THE CLEAN ENERGY INDUSTRY GENERATE?
Max Wei1, Daniel M. Kammen,2,3* Shana Patadia1
1
Haas School of Business
Energy and Resources Group, 3Goldman School of Public Policy
University of California, Berkeley, CA 94720-3050
2
max_wei@mba.berkeley.edu • kammen@berkeley.edu • shanapat@berkeley.edu
EXECUTIVE SUMMARY
The clean energy industry has been targeted as a key area for investment for three primary reasons:
greater energy self-sufficiency, improved environmental outlook due to less CO2 emissions, and
significant, positive economic impacts. Job creation is an especially urgent issue as we face the most
severe recession in decades with a collapse in the financial system and mounting job losses across
multiple industries. By developing domestic sources of clean energy, less wealth is exported abroad and
more jobs can be created at home. By investing in energy efficiency measures, money otherwise spent
on energy costs can be redirected to create a large number of jobs. And by replacing or retrofitting
outdated infrastructure, especially in the electric power sector, a foundation is built for future domestic
stability and growth, much as the federal highway system spurred domestic development and economic
expansion through more efficient travel and transportation in the 1950s and 60s.
This study provides a projection of employment impacts in the electric power sector based on 15
independent reports and studies that analyze the economic and employment impacts of the clean
energy industry. We have developed a job creation model from 2009 to 2030 which effectively
synthesizes the results of the various studies. To put the data from each study on comparable footing,
we utilize normalized job creation metrics, and both direct and indirect jobs are included. Our study
allows user-specified scenarios for a variety of assumptions, such as total electricity growth and national
renewable portfolio standards (RPS), and as output produces net direct and indirect job creation by
sector over and above status quo (“business-as-usual”) assumptions. This permits sensitivity analysis of
job creation as a function of various technologies, as well as a better understanding of how a prespecified job target in the future can be met. We include renewable energy technologies such as
biomass, geothermal, solar and wind as well as the option to include “low carbon” emitting technologies
such as carbon capture and storage, nuclear power and conventional hydroelectric power.
* Address correspondence to: Professor Daniel M. Kammen, Energy and Resources Group, 310 Barrows Hall #3050,
University of California, Berkeley, CA 94720-3050. URL: http://socrates.berkeely.edu/~kammen.
1
Several key conclusions emerge from an analysis of the electricity sector:
(1) The renewable energy sector generates more jobs than the fossil fuel-based sector per unit of
energy delivered (i.e. per average megawatt). For example, a 20% national RPS in 2020 produces
more than a million additional FTE jobs than the case where this 20% of generation is produced by
coal and natural gas (see Table ES-2 below).
(2) Many sectors can contribute to both very low CO2 emissions and significant job creation. Half a
million jobs can be created in 2020 by each of the following scenarios: reducing energy growth by
0.5X over BAU levels through greater energy efficiency measures (0.5% per year annual growth vs.
1% BAU); increasing RPS to 25% from BAU 7%; or increasing nuclear power generation capacity to
30% of overall generation from BAU 20% (see Table ES-2 below).
(3) Among the common RPS technologies, solar PV creates the most jobs per average megawatt. A 20%
RPS with BAU portfolio of technologies produces 399,000 FTE jobs in 2020, while an RPS with a twice
as much solar (2% solar PV vs. BAU 1%) produces 732,000 jobs.
(4) A national RPS of 25% in 2025 coupled with 0.5X electricity growth can generated over two million
jobs, and further increasing low carbon sources by about 1.5X can generate an additional million
jobs (see Table ES-3 below).
(5) Carbon capture and storage appears to shift jobs from traditional coal and/or natural gas plants to
jobs associate with CCS technology, and as such, does not appear to be a significant driver for
expanding employment opportunities.
2
Energy
Technology
Source of Numbers
Total
#jobs/MWa
Capacity
Factor
Equipment
lifetime
(years)
Biomass
REPP 2006
2.0
85%
25
Geothermal
CDEAC 2005
2.2
90%
35
Solar PV 1
REPP 2006
4.5
20%
25
Solar PV 2
IDAE 2005
7.4
20%
25
Solar PV 3
CALPIRG 2002
2.5
20%
25
Solar PV 4
EPIA/Greenpeace 2006
12.2
20%
25
Solar Thermal 1
Skyfuels/NREL 2009
1.8
40%
25
Solar Thermal 2
NREL 2006
1.4
40%
25
Solar Thermal 3
CALPIRG 2002
2.3
30%
25
Wind1
EWEA 2008
2.1
35%
25
Wind 2
REPP 2006
0.6
35%
25
Wind 3
Carbon Capture &
Storage
VESTAS 2006
1.8
35%
25
J. Friedmann 2009
1.0
80%
40
Nuclear
INEEL 2004
1.3
90%
40
Coal
REPP 2001
1.0
80%
40
Natural Gas
CALPIRG 2002
1.0
85%
40
Energy Efficiency 1
ACEEE 2008
1.5
100%
20
Energy Efficiency 2
J. Goldemberg 2009
5.2
100%
20
Table ES-1: Average employment for different energy technologies. “MWa” refers to average installed
megawatts de-rated by the capacity factor of the technology; for a 1MW solar facility operating on
average 20% of the time, the power output would be 0.20 MWa.
3
Approach
Energy Efficiency (BAU 24% electricity growth to 2030 BAU)
12% growth (0.5X BAU)
0% growth
RPS Percentage
BAU 7.4% of overall generation
10% of overall generation
20% of overall generation [U.S. target]
30% of overall generation
0% of overall generation
Distribution Of RPS Technologies
20% of overall generation with BAU distribution
20% of overall generation with 10% solar PV (2X over BAU)
20% of overall generation with 55% wind (2X over BAU)
Carbon Capture and Storage
25% of coal generation
50% of coal generation
Nuclear Power Options (BAU 18.7%)
25% of overall generation
30% of overall generation
35% of overall generation
Net Jobs-Yrs in 2020
481,403
1,092,427
64,412
399,562
707,094
(657,300)
399,562
732,308
396,352
332,358
595,378
858,398
Table ES-2. Job creation sensitivity in 2020 for increased energy efficiency, RPS standards, carbon
capture and storage, and nuclear power. Net job creation is the number of jobs produced by each line
item relative to the number of jobs created in the BAU or reference case, with all other factors held
constant at BAU levels. [Note 1: “BAU distribution of technologies” is the projected percentages of RPS
technologies in 2020 according to the EIA roadmap: approximately 50% biomass and 30 % wind].
4
Scenario
RPS 20% 2020 base case
Add 0.5X BAU growth
Increase to 30% nuclear
Add 25% Carbon Capture & Storage
Increase to 10% conventional hydro
RPS 25% 2025 base case
Add 0.5X BAU growth
Increase to 30% nuclear
Add 25% Carbon Capture & Storage
Increase to 10% conventional hydro
RPS 33% 2020 base case
Add 0.5X BAU growth
Increase to 30% nuclear
Add 25% Carbon Capture & Storage
Increase to 10% conventional hydro
%Renewable
Energy
% Low
Carbon
20%
20%
20%
20%
20%
25%
25%
25%
25%
25%
33%
33%
33%
33%
33%
24.6%
25.7%
36.2%
61.2%
65.0%
24.6%
25.7%
36.2%
61.2%
65.0%
24.6%
25.7%
36.2%
61.2%
65.0%
%Renewable + Net Total JobLow Carbon
Years
44.6%
45.7%
56.2%
81.2%
85.0%
49.6%
50.7%
61.2%
86.2%
90.0%
57.6%
58.7%
69.2%
94.2%
98.0%
399,562
849,688
1,383,310
1,383,310
1,458,458
1,162,127
2,095,403
3,096,949
3,096,949
3,236,893
794,912
1,230,323
1,763,944
1,763,944
1,839,093
Table ES-3. Scenarios in the power sector. Each scenario starts with an RPS baseline case and
successively adds the following: improved energy efficiency with 50% less growth than BAU, increased
nuclear power from 20% BAU to 30%, addition of carbon capture and storage for 25% of coal generation,
and increased conventional hydro power from 6% BAU to 10%. A 25% RPS in 2025 can create over two
million jobs with 0.5X electricity growth and over three million jobs by increasing low carbon sources.
Scenario
RPS 20% 2020 base case
Add Zero growth
Increase to 30% nuclear
25% Carbon Capture & Storage
Increase to 10% Hydro
RPS 25% 2025 base case
Add Zero growth
Increase to 30% nuclear
25% Carbon Capture & Storage
Increase to 10% Hydro
RPS 33% 2020 case
Add Zero growth
Increase to 30% nuclear
25% Carbon Capture & Storage
Increase to 10% Hydro
%Renewable
Energy
% Low
Carbon
20%
20%
20%
20%
20%
25%
25%
25%
25%
25%
33%
33%
33%
33%
33%
24.6%
27.3%
36.6%
61.6%
65.0%
24.6%
27.3%
36.6%
61.6%
65.0%
24.6%
27.3%
36.6%
61.6%
65.0%
%Renewable + Net Total JobLow Carbon
Years
44.6%
47.3%
56.6%
81.6%
85.0%
49.6%
52.3%
61.6%
86.6%
90.0%
57.6%
60.3%
69.6%
94.6%
98.0%
399,562
1,420,278
1,871,545
1,871,545
1,936,162
1,162,127
3,230,066
4,050,168
4,050,168
4,168,633
794,912
1,781,479
2,232,746
2,232,746
2,297,363
Table ES-4. Scenarios in the power sector. Each scenario starts with an RPS baseline case and
successively adds the following: improved energy efficiency with 0% growth, increased nuclear power
from 20% BAU to 30%, addition of carbon capture and storage for 25% of coal generation, and increased
conventional hydro power from 6% BAU to 10%. A 25% RPS in 2025 can create over three million jobs
with 0.5X electricity growth and over four million jobs by increasing low carbon sources.
5
INTRODUCTION AND METHODOLOGY
An increasing number of studies are finding that greater use of renewable energy systems and energy
efficiency provides economic benefits through job creation while at the same time protecting the
economy from political and economic risks associated with over-reliance on a limited suite of energy
technologies and fuels. This report reviews the range of recent studies on job creation potential of the
renewable energy industry. We critically analyze the studies and synthesize a job creation model from
the studies with a view to answering three main questions.
-
What are the job creation sensitivities to the various clean energy approaches?1
How would large scale growth in the renewable energy sector impact affect overall employment
taking into account job losses in the fossil fuel sector?
What are the clean energy portfolio options to achieve target levels of job creation?
A summary of all studies and their respective methodologies is provided in Appendix 1. We next discuss
the methodology and framework we use to provide comparisons for employment across different
technologies.
Table 1 contains a list of the studies reviewed. A complication is that the studies report their data in
different forms using different methods and in different units. We follow the approach described in
detail in Kammen (2004) to normalize the data from each study. This follows two general steps. First,
construction, installation, and manufacturing (CIM) jobs are converted to average jobs per megawatt
over the lifetime of the plant. This assumes that a large number of facilities of a given type are being
built (and eventually replaced) throughout the economy. With this assumption, the CIM number in jobs
per peak MW can be added to the operations, maintenance, and fuel processing job component which
is typically reported in jobs per peak MW. Second, the total jobs per peak (or nameplate) megawatt
(MWp) is normalized to total jobs per average megawatt (MWa) by dividing jobs per peak megawatt by
the capacity factor, where the capacity factor is the fraction of time that the facility is operation. This
follows since lower capacity technologies will have to build more plants than higher capacity
technologies. Table 2 allows for simple comparison between the jobs created per unit of average power
delivered from each energy technology.
1
In this paper, energy efficiency is viewed as both a source of clean energy and a source of renewable energy,
since by definition it reduces energy use for the same level of service and is renewable in the sense that historically
energy services have become more energy efficient over time as technology improves.
6
No. Year
Author
1 2009 Isabel Blanco and Christian
Kjaer
2 2009 Julio Friedmann - personal
communcation, 13 February
2009
3 2009 José Goldemberg - personal
communcation, 13 February
2009
4 2009 SkyFuels - personal
communication 21 March
2009
5 2008 John A. "Skip" Laitner and
Vanessa McKinney
Affiliation
European Wind Energy Association
(EWEA)
Lawrence Livermore National
Laboratory
Study (Model type)
Wind at Work: Wind energy and job creation in the EU
(Analytical model)
Carbon capture and storage job impacts
State of São Paulo, Brazil
Energy efficiency and jobs data
National Renewable Energy
Laboratory
Solar Thermal jobs data. Jobs and Economic Development
Impact (JEDI) model (Analytical model)
American Council for an Energy
Efficient Economy
Positive Returns: State Energy Efficiency Analyses Can Inform
U.S. Energy Policy Assessments (I/O Model)
University of California, Berkeley
Energy Efficiency, Innovation, and Job Creation in California
(I/O model)
Solar Generation: Solar Electricity for Over One Billion People
and Two Million Jobs by 2020 (Analytical model)
6
2008 David Roland-Holst
7
2006 Winfried Hoffman, Sven Teske European Photovoltaic Industry
Association (EPIA) and Greenpeace
8
2006 Frithjof Staiss, et al.
Forschungsvorhaben im Auftrag des Erneuerbare Energien: Arbeitsplatzeffekte (Analytical model)
Bundesministeriums für Umwelt,
Naturschutz und Reaktorsicherheit,
Federal Republic of Germany.
9
2006 George Sterzinger
Jobs and Renewable Energy Project (Analytical model)
10
2006 L. Stoddard, J. Abiecunas, R.
O'Connell
2006 Vestas
Renewable Energy Policy Project
(REPP)
National Renewable Energy
Laboratory
Windpower and Development: Jobs,
Industry and Export
Western Governors' Association:
Geothermal Task Force
11
Economic, Energy, and Environmental Benefits of
Concentrating Solar Power in California (I/O model)
Renewable Energy Vermont; Strom Thurmond Institute:
McKinsey (Analytical model)
Clean and Diversified Energy Initiative (CDEAC)
12
2005 Doug Arent, John Tschirhart,
Dick Watsson
13
2005 Jose Gil and Hugo Lucas
Institute for Diversification and
Saving of Energy (Instituto para la
Diversificacion y Ahorro de la
Energia, IDAE)
Plan for Renewable Energy in Spain 2005-2010 (Plan de
Energias Renovables en Espana 2005-2010)
14
2004 Daniel M. Kammen, Kamal
Kapadia, and Matthias Fripp
Energy and Resources Group,
Universtiy of California, Berkeley.
Putting Renewables to Work: How Many Jobs Can the Clean
Energy Industry Generate? (Analytical model)
15
2004 C.R. Kenley, et al.
Idaho National Engineering and
Environmental Laboratory (INEEL)
and Bechtel BWXT Idaho, LLC
U.S. Job Creation Due to Nuclear Power Resurgence in the
United States (Analytical model)
16
2002 Brad Heavner and Susannah
Churchill
CALPIRG (California Public Interest
Research Group) Charitable Trust
Job Growth from Renewable Energy Development in
California (Analytical model)
Table 1: List of Studies reviewed.
7
Employment Components
Work-hrs per year
2000
Energy
Technology
Source of Numbers
Total
#jobs/MWa
Capacity
Factor
Equipment
lifetime
(years)
CIM (personyears/MWp)
O&M
(jobs/MWp)
Total jobs/MWp
Fuel extraction &
processing
CIM
(personyrs/GWh)
0.20
0.14
Total jobs/MWa
Total person-yrs/GWh
O&M and
fuel
processing
CIM
O&M and
fuel
processing
CIM
O&M and fuel
processing
sum
Biomass
REPP 2006
2.0
85%
25
3.38
0.04
1.53
0.16
1.80
0.02
0.21
0.22
Geothermal
CDEAC 2005
1.0
190%
35
6.43
1.79
0.00
0.18
1.79
0.10
0.94
0.01
0.11
0.12
Solar PV 1
REPP 2006
4.5
20%
25
21.97
0.19
0.00
0.88
0.19
4.39
0.95
0.50
0.11
0.61
Solar PV 2
IDAE 2005
7.4
20%
25
82.80
0.40
0.00
3.31
0.40
16.56
2.00
1.89
0.23
2.12
Solar PV 3
CALPIRG 2002
2.5
20%
25
9.67
0.12
0.00
0.39
0.12
1.93
0.60
0.22
0.07
0.29
Solar PV 4
EPIA/Greenpeace 2006
12.2
20%
25
36.00
1.00
0.00
1.44
1.00
7.20
5.00
0.82
0.57
1.39
Solar Thermal 1
Skyfuels/NREL 2009
1.8
40%
25
10.31
1.00
0.00
0.41
1.00
1.03
2.50
0.12
0.29
0.40
Solar Thermal 2
NREL 2006
1.4
40%
25
4.50
0.38
0.00
0.18
0.38
0.45
0.95
0.05
0.11
0.16
Solar Thermal 3
CALPIRG 2002
2.3
30%
25
11.69
0.22
0.00
0.47
0.22
1.56
0.73
0.18
0.08
0.26
Wind1
EWEA 2008
2.1
35%
25
10.10
0.33
0.00
0.40
0.33
1.15
0.94
0.13
0.11
0.24
Wind 2
REPP 2006
0.6
35%
25
2.87
0.09
0.00
0.11
0.09
0.33
0.25
0.04
0.03
0.07
Wind 3
Carbon Capture &
Storage
VESTAS 2006
1.8
35%
25
10.96
0.18
0.00
0.44
0.18
1.25
0.50
0.14
0.06
0.20
J. Friedmann 2009
1.0
80%
40
15.00
0.40
0.38
0.40
0.47
0.50
0.05
0.06
0.11
Nuclear
INEEL 2004
1.3
90%
40
9.35
0.93
0.23
0.93
0.26
1.03
0.03
0.12
0.15
Coal
REPP 2001
1.0
80%
40
8.50
0.18
0.06
0.21
0.59
0.27
0.74
0.03
0.08
0.11
Natural Gas
CALPIRG 2002
1.0
85%
40
8.5
0.18
0.06
0.21
0.63
0.25
0.74
0.03
0.08
0.11
Table 2. Comparison of jobs/MWp, jobs/MWa and person-years/GWh across technologies. (“CIM” = Construction, Installation, Manufacturing).
8
Studies that focus on calculating the employment impacts of the renewable industry can be divided into
two main types: (a) those that use input-output (I-O) models of the economy; and (b) those that use
simpler, largely spreadsheet-based analytical models. Among the studies reviewed and listed in Table 1,
reports number 5, 6 and 10 are based on I-O models, and the rest are based on analytical models. Both
types of models have advantages and disadvantages as discussed in Kammen (2004). We also note that
some studies were consulted but not included in this report due to lack of information regarding their
quoted job estimates. The more comprehensive papers, which presented jobs/MW data along
with person-years data, were used most intensively.
The definitions of direct, indirect, and induced jobs vary widely by study. Here we describe our
definitions and usage of these categories. Direct employment includes those jobs created in the design,
manufacturing, delivery, construction/installation, project management and operation and maintenance
(O&M) of the different components of the technology, or power plant, under consideration. This data
can be collected directly from existing facilities and manufacturers in the respective phases of operation.
Indirect employment refers to the “supplier effect” of upstream and downstream suppliers. For
example, the task of installing wind turbines is a direct job, whereas manufacturing the steel that is used
to build the wind turbine is an indirect job. Induced employment comprehends expenditure-induced
effects in the general economy due to the economic activity and spending of direct and indirect
employees, e.g. non-industry jobs created such as teachers, grocery store clerks, and postal workers.
When discussing energy efficiency, a large portion of the induced jobs are the jobs created by the
household savings due to the energy efficiency measures.
Most analytical models calculate direct employment impacts only but an increasing number include
indirect jobs as well (reports 1, 8, 10, and 15 in Table 1). I-O models have the capability to calculate
direct and indirect employment as well as the induced employment due to economic impacts of
spending by workers in the new jobs. I-O models provide the most complete picture of the economy as
a whole. They capture employment multiplier effects, as well as the macroeconomic impacts of shifts
between sectors; that is to say, they account for losses in one sector (e.g. coal mining) created by the
growth of another sector (e.g. the wind energy industry).
For the purposes of this study and simplicity of output, we report “direct” and “indirect” jobs, with
direct as defined above, but with the meaning of indirect employment varying by sector. For the
renewable sector and low carbon technologies with analytical model-based studies, “indirect”
employment refers to the supplier effect multiplier, while for approaches with I/O based models (energy
efficiency in this case) “indirect” employment actually refers to the induced employment which in the
case of energy efficiency is due to the economic impacts of additional spending from energy savings.
These job multiplier numbers (jobs/MWa) are expected to evolve downward as industries advance on
their respective learning curves, but for simplicity are held constant in this study.
For our analytical model we use a “net job” creation model. Previous studies have focused on
renewable energy job creation under various RPS or technology scenarios, e.g. “a 20% national RPS in
2020 produces 160,000 direct jobs.” While this may be a correct statement, we ask what amount of net
jobs can be created over and above what is projected from existing policies and accounting for any job
9
losses that may occur in the fossil fuel industry. For example, in the power sector, we take as our
baseline reference the EIA roadmap of electricity generation and contributing electricity sources out to
2030, and calculate the baseline numbers of direct and indirect jobs created with this amount of
generation and partitioning of energy sources. Our calculator then computes how the job picture shifts
with greater or lower energy efficiency, varying amounts of renewable energy, and differing portfolio
mixes of RPS and low carbon technologies. A net job creation number is calculated by factoring in job
loss impacts to the coal and natural gas industry due to increases in renewable energy or low carbon
technologies. Currently our model does not include a separate category for the utility industry, but
while utilities may be losing jobs from a reduction in coal and natural gas supply, they may be gaining
jobs from increasing their supply of renewable energy or low carbon sources. For reference, screen
shots of our calculator’s input and output sheets in Excel are attached in Appendix 2 and 3.
We note that the recently enacted U.S. Federal Stimulus Package of 2009 will make a large impact upon
the renewable energy sector, although there is debate as to how many green collar jobs it will create.
An estimated 5.3%, or $41.4 billion, of the $787 billion bill is targeted to renewable energy. Based upon
the numerical breakdowns published by the Wall Street Journal2 and ProPublica.org3, the largest
portions of this energy portion of the stimulus, were put towards energy efficiency (EE) and smart grid
technology.
According to the Apollo Alliance’s research, it is projected that about 13 FTE jobs4 are created per million
dollars invested into EE, and these jobs are due to both direct installation and production of relevant
materials and products. Thus, about 150,000 new FTE jobs are expected to result from the $11 billion
targeted for increasing energy efficiency ($6.3 billion in grants for increasing energy efficiency at the
state and city level, $4.5 billion for increasing energy efficiency in federal buildings, and $0.25 billion for
improving energy efficiency in government subsidized housing).
Much of this investment into energy efficiency is projected to begin paying for itself overtime. The
California Sustainable Building Task Force estimated that for an initial investment of $100,000 in energy
efficiency in a $5 million construction project, there would be savings of $1 million over the life of the
building.
Finally, Roland-Holst (2008) in his study of job creation in California suggests that the induced job
growth due to energy efficiency savings is tremendous. His research estimates that about 1.5 million
induced FTE jobs with a total payroll of $45 billion were created due to energy efficiency savings of $56
billion in the 34 year period from 1972-2006. Similar trends in induced job creation are expected from
the most recent stimulus spending.
2
“Getting to $787 Billion,” Wall Street Journal, 17 February 2009. Accessed at
http://online.wsj.com/public/resources/documents/STIMULUS_FINAL_0217.html on 23 March 2009.
3
Michael Grabell and Christopher Weaver, “The Stimulus Plan: A Detailed List of Spending ,” Pro Publica, 13
February 2009. Accessed at http://www.propublica.org/special/the-stimulus-plan-a-detailed-list-of-spending on
23 March 2009.
4
One “FTE job” is equivalent to one person employed full-time for one year. Note then that “50 FTE jobs” could
mean either five full time jobs over 10 years, 25 jobs over two years, or other such combinations.
10
DISCUSSION OF RESULTS
Table 2 presents a detailed summary of the studies that were analyzed. Some technologies were
represented by many studies (solar and wind); some technologies were not studied as frequently
(geothermal, biomass); and for some, job estimates were not readily available (small hydro, biogas). For
the latter we adopted placeholder values of 0.15 jobs/GWh. A large amount of variation is found in the
various studies on solar and wind. This may be due to implicit differences in data collection and analysis
methodology between different studies. For technologies with more than one study, our approach of
averaging the studies thus reduces the weight of any one study. Having said this, solar PV has the
highest job multiplier with a large gap between it and the next highest technologies (solar thermal and
biomass). Carbon capture and storage is estimated to have the same job multiplier (Jobs/GWh) as coal
and natural gas, so to first order increasing CCS shifts jobs from conventional fossil fuel technology to
CCS technology. (It is also possible that this is an underestimate of CCS job impact since we were unable
to find recent coal and natural gas jobs data and the numbers in Table 2 may be too high).
For indirect jobs, we took the average multiplier from three reports, a solar study5 from the United
States, a European wind report by Blanco (2009), a German renewable energy study by Staiss (2006).
This gave an indirect multiplier of 0.9 that for simplicity we applied to all renewable energy
technologies, coal and natural gas. (For example if the direct multiplier is 0.2 jobs/GWh, the indirect
multiplier is 0.2 x 0.9 = 0.18 jobs/GWh and the total jobs produced is 0.38 jobs/GWh). For nuclear
power we applied an indirect multiplier of 1.7, taken from Kenley (2004). Again, these numbers are
quite rough estimates and the distinction between direct and indirect is probably blurred between the
studies.
For energy efficiency, we used a multipler of 0.38 jobs per GWh6 of energy savings that is the average of
Goldemberg (2009) and Laitner (2008). We assume that 90% of these jobs are induced jobs and only
10% are direct jobs associated with energy efficiency products or installation, as the ACEEE has used in
the past.7 The “BAU” case of energy demand already assumes a certain amount of energy savings and
energy efficiency induced jobs due to existing building codes and appliance standards, industry
improvement, and implicit programs,8 so our energy efficiency net job gains are additional jobs above
and beyond this implicit baseline level.
5
Roger Bezdek (Management Information Services, Inc.), Renewable Energy and Energy Efficiency: Economic
Drivers for the 21st Century (Boulder, CO: American Solar Energy Society, 2007), p. 24.
6
A third reference, Roland-Holst (2008), was also consulted for energy efficiency job dividend sensitivity, but this
study of job creation in California from 1972-2007 does not quote a parametrized number for jobs vs energy
savings. Still, a rough model of energy savings versus cumulative FTE job creation over this time frame was found
to yield a multiplier of about 0.4 jobs/GWh saved.
7
Howard Geller, John DeCicco, and Skip Laitner, Energy Efficiency and Job Creation, ACEEE 1992. Accessed at
http://www.aceee.org/pubs/ed922.htm on 10 February, 2009.
8
O. Siddiqui, Assessment of Achievable Potential from Energy Efficiency and Demand Response Programs in the
U.S. (2010–2030), EPRI 2009. Accessed at http://mydocs.epri.com/docs/public/000000000001018363.pdf on 17
March 2009.
11
Job creation sensitivities and different scenarios to create two to four million FTE jobs (or job-years) in
2025 are shown in the Executive Summary. We now provide a more detailed discussion of those results.
Many paths can produce both lower CO2 emissions and create a significant number of jobs (Table ES-2).
About 500,000 job-years are produced in 2020 by each of the following paths: (1) 0.5X growth in
electricity; (2) just under 25% RPS standard; and (3) increasing nuclear to just below 30% of overall
generation.
As the national RPS percentage is increased, jobs are created to varying degrees according to the
technology mix. Coal and natural gas jobs are lost but at a lower rate than RPS job creation. For
example, 20% RPS creates about 400,000 jobs compared to the BAU baseline of 7.4% RPS, while zeroing
out the RPS and moving this 20% generation to coal and natural gas would reduce net jobs by over
600,000 compared to the BAU case. Thus a 20% RPS has a net gain of over a million jobs versus the case
of having no RPS and moving this 20% to fossil fuels. Solar PV continues to be the most job intensive
technology with over 700,000 jobs created in 2020 with a 10% solar fraction of RPS vs. 400,000 jobs with
a “BAU” distribution of 50% biomass, 30% wind and 5% solar. An even greater number of jobs could be
created with higher solar fractions, but higher solar fractions are likely neither realistic nor costeffective.
Our calculator readily models various scenarios that can meet job creation targets. If for example, the
target for net job creation in 2020 is 3 million net new jobs in the clean tech industry with 1 million from
the electricity sector, Tables ES-3 illustrates two scenarios for reaching this target. Starting with a 20%
RPS target in 2020 and increasing energy efficiency measures so as to slow electricity growth by 50%
produces 850,000 FTE jobs. Adding to this an increase in nuclear power from 19% to 25% would result
in over a million net jobs. Alternately a more aggressive 33% RPS target in 2020 would generate almost
800,000 net FTE jobs and adding a slower rate of electricity growth by 50% produces 1.2 million jobs.
CONCLUSION AND POLICY OUTLOOK
Large scale federally supported investment in targeted industries is a controversial political and
economic issue. Central governments have a mixed record of picking winners and losers among myriad
technology options and the allocation of resources outside of market forces is apt to be economically
inefficient. However, only the federal government has sufficient reach to impact national energy
systems in a coordinated and effective manner. The energy industry is already one of the most
regulated industries in the world, dependence on fossil fuels continues to cause global warming, and the
existing energy infrastructure is quickly becoming outdated, thus changes are necessary.
The current economic crisis with its collapse in both aggregate demand and the credit market provides
added urgency for federal infrastructure investment, similar to the New Deal in the 1930s, and levels of
deficit spending not seen since the 1940s may be required to revive the U.S. and global economies. An
increasing number of studies are finding that greater use of renewable energy systems and energy
efficiency provides economic benefits through job creation while at the same time protecting the
12
economy from political and economic risks associated with over-reliance on a limited suite of energy
technologies and fuels.
This work provides a summary of existing job reports in the electricity industry. It provides a framework
for understanding the sensitivities of job creation by technology and readily lends itself to scenario
analysis and job projections in time. Key results of this study are the following:
(1) The renewable energy sector generates more jobs than the fossil fuel-based sector per unit of
energy delivered (i.e. per average megawatt). For example, a 20% national RPS in 2020 produces
more than a million additional FTE jobs than the case where this 20% of generation is produced by
coal and natural gas (see Table ES-2 below).
(2) Many sectors can contribute to both very low CO2 emissions and significant job creation. Half a
million jobs can be created in 2020 by each of the following scenarios: reducing energy growth by
0.5X over BAU levels through greater energy efficiency measures (0.5% per year annual growth vs.
1% BAU); increasing RPS to 25% from BAU 7%; or increasing nuclear power generation capacity to
30% of overall generation from BAU 20%.
(3) Among the common RPS technologies, solar PV creates the most jobs per average megawatt. A 20%
RPS with BAU portfolio of technologies produces 399,000 FTE jobs in 2020, while an RPS with a twice
as much solar (2% solar PV vs. BAU 1%) produces 732,000 jobs.
(4) A national RPS of 25% in 2025 coupled with 0.5X electricity growth can generated over two million
jobs, and further increasing low carbon sources by about 1.5X can generate an additional million
jobs.
(5) Carbon capture and storage appears to shift jobs from traditional coal and/or natural gas plants to
jobs associate with CCS technology, and as such, does not appear to be a significant driver for job
creation.
Our modeling provides quantitative analysis for the 2009 federal stimulus bill, and this topic will be
more fully explored in a future policy memo. Other key questions for the policy maker include: what is
the cost/benefit ratio for each technology; what policies can be put in place to maximize the number of
domestic manufacturing jobs (assumed to be 100% domestic here); how do job creation effects vary by
region and state; and how much change in job creation is expected over time as renewable energy
becomes more widely adopted and has a reduced job dividend?
Finally, we note that current studies are from developed nations and largely focused on wind, solar, and
energy efficiency. More study is needed on emerging technologies such as ocean energy and CCS as
well as the electricity grid and storage. More study is also needed in developing nations where
renewable energy may play a large role in economic development and traditional power systems may be
less deployed.
13
APPENDIX 1. SUMMARY OF STUDIES REVIEWED
No.
1
2
Year Author - Affiliation
2009 Isabel Blanco and Christian Kjaer European Wind Energy Association
Study
Wind at Work: Wind energy and job
creation in the EU
Method
Assumes that wind energy creates 15 jobs (man years)
per MW of annual installation, turbine manufacturing,
component manufacturing,
wind farm development, installation and
indirect employment. O&M work contributes an
additional 0.33 jobs per MW of total installed capacity.
Scenarios used
WInd sector employment in EU increasing from 154k in 2007 to 377k in 2030. 180
GW of wind energy will be operating in the EU in 2020 and 300 GW by the end of
2030. Over that period, an increasing share of the installations will be offshore.
2009 Julio Friedmann - Lawrence Livermore Personal communcation, 13 February
National Laboratory
2009, on Carbon capture and storage job
impacts
2009 José Goldemberg - State of São Paulo, Personal communcation, 13 February
Brazil
2009, on Energy efficiency and jobs data
Model three paths for CCS: (1) pulverized coal; (2) IGCC; Consider situation where all three paths occur and take average of employment
(3) Natural gas carbon capture.
effects.
4
2009 SkyFuels - National Renewable Energy Personal communication 21 March 2009
Laboratory
on Solar Thermal jobs data.
Jobs and Economic Development Impact (“JEDI”) model 1000 MW online by 2014, total projected CSP project job creation through 2014 ~
33,300 FTE jobs
5
2008 John A. "Skip" Laitner and Vanessa
McKinney - American Council for an
Energy Efficient Economy
Positive Returns: State Energy Efficiency
Analyses Can Inform U.S. Energy Policy
Assessments
Summary of state level studies.
6
2008 David Roland-Holst - University of
California, Berkeley
Energy Efficiency, Innovation, and Job
Creation in California (I/O model)
7
2006 Winfried Hoffman, Sven Teske European Photovoltaic Industry
Association (EPIA) and Greenpeace
2006 Frithjof Staiss, et al. Forschungsvorhaben im Auftrag des
Bundesministeriums für Umwelt,
Naturschutz und Reaktorsicherheit,
Federal Republic of Germany.
Solar Generation: Solar Electricity for Over Information provided by industry
One Billion People and Two Million Jobs by
2020.
Erneuerbare Energien: Arbeitsplatzeffekte
3
8
Based on a review of 48 different assessments, this report highlights the findings of
a wide variety of studies that explore the many possibilities of further gains in
energy efficiency, especially at the regional and state level. The studies reviewed
here show an average 23 percent efficiency gain with a nearly 2 to 1 benefit-cost
ratio. From analyzing this set of studies, we estimate that a 20 percent to 30
percent energy efficiency gain within the U.S. economy might lead to a net gain of
500,000 to 1,500,000 jobs by 2030.
Detailed I/O tables aggregated to 50-sector framework Modeled 1972-2006 period in California households with redirected expenditures
over period 1972-2006 for detailed historical
from energy efficiency savings creating about 1.5 million FTE jobs with a total
employment impact.
payroll of $45 billion, driven by household energy savings of $56 billion.
Global PV systems output 589 TWh in 2025, 276 TWh in 2020.
14
No.
9
Year Author - Affiliation
Study
2006 George Sterzinger - Renewable Energy Jobs and Renewable Energy Project
Policy Project (REPP)
10
2006 L. Stoddard, J. Abiecunas, R. O'Connell - Economic, Energy, and Environmental
National Renewable Energy Laboratory Benefits of Concentrating Solar Power in
California
11
2008 Vestas
12
2005 Doug Arent, John Tschirhart, Dick
Watsson - Western Governors'
Association: Geothermal Task Force
13
2005 Jose Gil and Hugo Lucas - Institute for
Diversification and Saving of Energy
(Instituto para la Diversificacion y
Ahorro de la Energia, IDAE)
Plan for Renewable Energy in Spain 20052010 (Plan de Energias Renovables en
Espana 2005-2010)
14
2004 Daniel M. Kammen, Kamal Kapadia,
and Matthias Fripp - Energy and
Resources Group, Universtiy of
California, Berkeley.
2004 C.R. Kenley, et al. - Idaho National
Engineering and Environmental
Laboratory (INEEL) and Bechtel BWXT
Idaho, LLC
Putting Renewables to Work: How Many
Jobs Can the Clean Energy Industry
Generate?
Meta-analysis of 13 studies on renewable energy job
creation. Normalization of job creation by average
power over lifetime of plant.
Comparison of average employement from five electricity generation scenarios.
Considers photovoltaics, wind, biomass and coal.
U.S. Job Creation Due to Nuclear Power
Resurgence in the United States
Industry/expert estimates for manufacturing and
construction/operations jobs: Indirect/induced jobs via
NEI (Nuclear Energy Institute) economic impact studies
and U.S. Census Data IMPLAN modeling tool
33-41 Gen III units, 1200-1500 Mwe for 50,000 Mwe by 2020. Construction from
2009-24. 1-2 plants/yr online starting 2014 to 4-5 plants online 2020-2024. 40,000
manufacturing jobs, 80,000 construction/operations jobs and 500,000 total with
direct: indirect: induced ratios of 1:1.7:1.7.
2002 Heavner and Churchill - CALPIRG
(California Public Interest Research
Group) Charitable Trust
Job Growth from Renewable Energy
Development in California
Report detailing job creation potential of renewable
energy industry in California. Data is yielded from CEC
(Califonia Energy Commission) research, and a CEC
funded EPRI (Electric Power Research Institute) study
from 2001.
Comparison of employment projections from CEC and data from existing plants was
used to derive employment rates for wind, geothermal, solar PV, solar thermal, and
landfill/digester gas.
15
16
Windpower and Development: Jobs,
Industry and Export
Clean and Diversified Energy Initiative
(CDEAC)
Method
Used enhanced version of 2002 REPP Jobs Calculator
and Nevada RPS standards to yield labor information
about wind, PV, biomass co-firing, and geothermal
technologies.
Study focusing on economic return, energy supplyl
impact, and environmental benefits of CSP
(Concentrating Solar Power) in California.
Scenarios used
100 MW parabolic trough plant with 6 hours of storage was used as a
representative CSP plant. Cumulative deployment scenarious of 2100 MW and
4000 MW were assumed for 2008 to 2020. Assumed that technological
improvements would result in 150 and 200 MW plants in 2011 and 2015,
respectively. Included learning curve estimations based on NREL data.
Sources: Renewable Energy Vermont; Strom Thurmond Jobs generated by an onshore and on offshore park, considering development and
Institute: McKinsey (2006)
installation jobs and operations and maintenance jobs.
Study synthesizing views and research of 24 members
of geothermal community.
15
APPENDIX 2. SAMPLE INPUT DECK FROM JOBS CALCULATOR
16
APPENDIX 2. SAMPLE OUTPUT DECK FROM JOBS CALCULATOR
CUMULATIVE TOTAL
Jobs created (BAU ref)
Direct (BAU ref)
Indirect (BAU ref)
Jobs created (modeled)
Direct (modeled)
Indirect (modeled)
Net Jobs (modeled - BAU)
Net Direct Jobs (modeled - BAU)
Net Indirect Jobs (modeled - BAU)
Cumulative Jobs-Yrs
Net Direct Job-Yrs
Net Indirect Jobs-Yrs
2020
1,210,849
582,680
628,169
1,529,601
713,797
815,805
318,752
131,117
187,635
1,603,248
616,058
987,190
2021
1,223,923
589,303
634,620
1,564,355
728,499
835,855
340,432
139,196
201,235
1,943,680
755,254
1,188,426
2022
1,237,815
596,553
641,263
1,604,938
744,683
860,255
367,122
148,130
218,993
2,310,802
903,384
1,407,418
2023
1,253,513
604,814
648,700
1,651,381
762,344
889,036
397,868
157,531
240,337
2,708,670
1,060,915
1,647,755
2024
1,267,687
612,272
655,415
1,694,949
779,335
915,614
427,262
167,063
260,200
3,135,932
1,227,977
1,907,955
2025
1,279,562
618,521
661,041
1,731,484
794,625
936,859
451,922
176,104
275,818
3,587,855
1,404,081
2,183,773
17