Promoting renewable energy and energy efficiency in Africa: A framework to evaluate employment generation and cost effectiveness

Abstract

The ongoing debate over the cost-effectiveness of renewable energy (RE) and energy efficiency (EE) deployment often hinges on the current cost of incumbent fossil-fuel technologies versus the long-term benefit of clean energy alternatives. This debate is often focused on mature or 'industrialized' economies and externalities such as job creation. In many ways, however, the situation in developing economies is at least as or even more interesting due to the generally faster current rate of economic growth and of infrastructure deployment. On the one hand, RE and EE could help decarbonize economies in developing countries, but on the other hand, higher upfront costs of RE and EE could hamper short-term growth. The methodology developed in this paper confirms the existence of this trade-off for some scenarios, yet at the same time provides considerable evidence about the positive impact of EE and RE from a job creation and employment perspective. By extending and adopting a methodology for Africa designed to calculate employment from electricity generation in the U.S., this study finds that energy savings and the conversion of the electricity supply mix to renewable energy generates employment compared to a reference scenario. It also concludes that the costs per additional job created tend to decrease with increasing levels of both EE adoption and RE shares.

Full text

LETTER
Promoting renewable energy and energy efficiency in Africa:
a framework to evaluate employment generation and cost
effectiveness
Nicola Cantore
1
, Patrick Nussbaumer
1
, Max Wei
2
and Daniel M Kammen
3,4
1
UNIDO, Austria
2
Lawrence Berkeley National Laboratory, United States of America
3
Energy and Resources Group, University of California, United States of America
4
Goldman School of Public Policy, University of California, United States of America
E-mail: p.nussbaumer@unido.org
Keywords: renewable energy, employment, energy efficiency, Africa
Abstract
The ongoing debate over the cost-effectiveness of renewable energy (RE) and energy efficiency
(EE) deployment often hinges on the current cost of incumbent fossil-fuel technologies versus
the long-term benefit of clean energy alternatives. This debate is often focused on mature or
‘industrialized’economies and externalities such as job creation. In many ways, however, the
situation in developing economies is at least as or even more interesting due to the generally
faster current rate of economic growth and of infrastructure deployment. On the one hand, RE
and EE could help decarbonize economies in developing countries, but on the other hand, higher
upfront costs of RE and EE could hamper short-term growth. The methodology developed in
this paper confirms the existence of this trade-off for some scenarios, yet at the same time
provides considerable evidence about the positive impact of EE and RE from a job creation and
employment perspective. By extending and adopting a methodology for Africa designed to
calculate employment from electricity generation in the U.S., this study finds that energy savings
and the conversion of the electricity supply mix to renewable energy generates employment
compared to a reference scenario. It also concludes that the costs per additional job created tend
to decrease with increasing levels of both EE adoption and RE shares.
1. Introduction
A technology- and policy-driven shift towards renew-
able energy has been advocated on environmental
grounds and to a lesser extent, to improve energy
security (Kammen 2015). Mitigating the adverse
effects of climate change looming or already present
represents an urgent imperative. At the same time, the
need to transform our energy system—essentially
reproducing the Industrial Revolution within just three
decades—opensup vast opportunities for the renewable
energy industry (Kammen 2006, Turkenburg et al
2012). The developing world has a larger share and
much faster growth rate of global energy-related
greenhouse gas emissions (GHG) than OECD countries
(EIA 2013). As a result, a huge potential for low cost de-
carbonization options exists in the developing world as
emphasized in Bowen and Fankhauser (2012). In fact,
the implementation of technologies, policies and
behavioural strategies in the developing world to reduce
the adverse impacts of climate change can—and must—
take place, and can be realized at a relatively low cost
through the promotion of energy efficiency (EE) and
renewable energy (RE).
Increasing the share of RE is also commonly
justified as a means to reduce reliance on energy
imports (Cherp et al 2012), thereby reducing the
vulnerability of developing countries to energy price
shocks (Massa et al 2012). The developing world is also
projected to bear the brunt of shorter term climate
change impacts (IPCC 2014).
The impact of increased deployment of RE and EE
has received less attention, particularly in Africa. One
of the objectives of this paper is to shed light on this
issue and conduct an aggregated analysis to explore the
link between RE, EE and employment.
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PUBLISHED
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Environ. Res. Lett. 12 (2017) 035008 https://doi.org/10.1088/1748-9326/aa51da
©2017 IOP Publishing Ltd

RE continues to grow, both in absolute and relative
terms, globally as well as in Africa. So-called modern
renewables (i.e. excluding traditional biomass)
accounted for approximately 10% of the global energy
mix in 2012 (REN21 2014).
Energy companies are expanding their investment
portfolios and becoming more active in Africa. New
investments in clean energy in Africa and the Middle
East increased from US$ 0.3 billion to US $11.8 billion
between 2004 and 2012 (UNEP/BNEF 2013). Indeed,
business prospects are more appealing in improved
environments in countries with dedicated institutional
and policy frameworks. Also, with the price of
renewables decreasing steadily and the cost of carbon
becoming more internalized through various instru-
ments and strategies (including the phasing out of
fossil fuel subsidies), such options are becoming
increasingly attractive from an investment perspective
compared to conventional energy sources.
Global investment in renewable power capacity, at
$265.8 billion, was more than double allocations to new
coal and gas generation, which was an estimated $130
billion in 2015 (UNEP/BNEF 2016). At the global level,
there are now 144 countries with renewable energy
policies and the share of low income countries with
renewable energy policies grew from 0% to 60% from
2004 to 2014 (REN21 2014).
The grey literature abounds in claims of the
positive impact of promoting RE on employment,
often with little substantiation. The literature on the
impact of EE on employment is even scanter. The
UNIDO Industrial Development Report (2011) states
that energy efficiency may reduce production costs
and increase demand owing to the price elasticity of
demand, but the ‘evidence on the impact of energy
efficiency on employment generation is still limited’
(p 81).
A few attempts have been made to look into the
issue in a more systematic fashion (see Wei et al 2010
for a review of studies). However, pinning down job
numbers is challenging (see, e.g. Bowen 2012), not
least for methodological and definitional reasons.
Kammen et al (2004), for instance, compare the pros
and cons of various models. Employment estimates
rarely capture net effects, self-employment or the
informal economy, especially in developing countries
where reliable and comprehensive data are scarce.
Atherton and Rutovitz (2009) estimate that there
were 9 million jobs in energy globally, with about 20
percent of jobs in 2010 in either the RE industry or in
energy savings realized in the generation of electricity.
Renner et al (2008)‘conservatively’put jobs in RE and
in supplier industries at 2.3 million worldwide.
According to Holdren (2007), India alone may be
able to generate some 900 000 jobs by 2020 from
biomass gasification. Of these, 300 000 jobs are
projected to be from gasifier stove manufacturing
(including masons and metal fabricators), 600 000
from biomass production, supply chain operations
and after-sales services, and 10000 from workers
developing advanced biomass cooking technologies.
Many other contributions in the literature do not
specifically address the quantification of the employ-
ment impact from renewable energy production in
developing countries. del Rio and Burguillo (2008)
define a theoretical framework to develop an
integrated theoretical framework which allows a
comprehensive analysis of the impact of renewable
energy on local sustainability and in particular on
employment but they do not provide quantitative
estimates. Moreno and Lopez (2008) quantify the ratio
of jobs per unit of installed energy power but only for a
Spanish province, Asturias.
As regards to EE, the IEA (2014) estimates values
ranging from 7 to 22 job-years per EUR 1 million
invested. Compared with the same investment in the
fossil fuel industry, EE services reportedly lead to the
generation of three times the number of jobs per
million dollars invested (ACE 2000, Pollin et al 2009).
Wei et al (2010) developed and applied a model to
estimate net job creation in the energy industry,
focusing on the power industry in the United States.
They found that dedicated policy measures can spur
significant positive impacts in terms of employment.
Drawing on this study, we complement the existing
literature by adapting and applying the model to
developing countries. We also expand the methodol-
ogy of Wei et al (2010) to estimate the potential job
‘leakage’to other regions. Additionally, we factor in
reductions in job multipliers due to technology and
their related impact on the jobs dividend. Finally, we
also conduct a cost-benefit analysis for the various
energy scenarios considered.
2. Methodology
We apply scenario analysis to evaluate the employment
potential of an uptake in RE and EE in Africa. We first
develop a reference scenario (or baseline scenario)
with which to compare alternative future scenarios. We
then test the results for robustness using sensitivity
analysis. As mentioned in the previous section, Wei et
al (2010) report that a shift of the US economy from
fossil fuels to RE and EE would lead to net jobs
creation in the energy industry. In this section, we
describe how we adapt and apply their methodology
and assumptions to estimate the potential direct and
indirect job impact of very high increases in RE in
Africa.
We de fine direct job impacts as jobs created (or
lost) in the design, manufacturing, delivery construc-
tion/installation, project management and operation
and maintenance of the different components of the
technology under consideration. Indirect employ-
ment, on the other hand, refers to upstream and
downstream suppliers. Effects on induced jobs (i.e.
employment variation through expenditure-induced
Environ. Res. Lett. 12 (2017) 035008
2

effects in the general economy from changes in
spending patterns by direct and indirect employees) go
beyond the scope of this study
5
.
Our analytical spreadsheet-based model utilizes
the normalization approach of taking average em-
ployment per unit of end use energy produced over
plant lifetime. These coefficients derive from a meta-
study conducted by Wei et al (2010). The model also
computes job losses in the coal and natural gas
industries relative to renewable energy, with the
objective of calculating net employment impacts in the
energy industry.
We take direct and indirect jobs coefficients for
every source of energy from Wei et al (2010)
6
.
Normalized employment multipliers for Africa are
used to calculate job creation and destruction in the
electricity industry based on Rutovitz and Harris
(2012). The underlying idea is that the direct
employment impact of electricity generation is higher
in Africa than in OECD countries, as the production
process would presumably be less efficient.
Conversely, we assume the same coefficients for
indirect employment effects. The literature on the
calculation of indirect job creation is characterized by
high uncertainty. The International Finance Corpora-
tion (IFC 2013) reports that the indirect jobs/direct
jobs ratio lies in the range of 7–25. In our study, we use
a conservative approach, and correct the direct jobs
multipliers of table 1on the basis of coefficients
representing conversion factors of multipliers for
direct employment coefficients of electricity genera-
tion (Rutovitz and Harris 2012), but we do not adjust
indirect jobs multipliers upwards. We implicitly
assume that there are fewer opportunities in Africa
to activate forward and backward linkages for
multiplier effects. We also assume that the direct
jobs/indirect jobs ratio across sources of energy lies in
the range of 0.99–9.0 as in Wei et al (2010).
To estimate net job impact in Africa, we consider
the leakage rate of manufacturing jobs by using
estimates of the share of local manufacturing from
Rutovitz and Harris (2012). They estimate the share of
manufacturing in Africa to represent 30% and 50% in
2010 and 2030, respectively. As in Rutovitz and Harris
(2012), we also assume that jobs multipliers decrease
over time due to technological improvements off-
setting job creation, being the decrease differentiated
across sources of energy and time.
We then take the generation prices
7
for each energy
source from Bosetti et al (2006) to estimate the price of
generation for 2020 and 2030
8
. Intermediate prices are
estimated using interpolation. Generation costs in
Bosetti et al (2006) are applied to the combined
Middle East and North Africa region. To express a cost
for Africa, we take the average of the two values.
Bosetti et al (2006) do not estimate the generation
costs for geothermal and biomass. On the basis of a
study by IRENA (2012), which calculates the weighted
average costs for different sources of energy, we assume
similar costs for geothermal, biomass and hydropower
in Africa. Bosetti et al assume a cost for concentrated
solar power, wind and solar photovoltaics. For the
purpose of crosschecking, we compare interpolated
prices from Bosetti et al (2006) for 2012 with
minimum and maximum weighted prices of geother-
mal/biomass/hydropower (from 3 to 10 cents 2011
constant USD in 2012) and wind/solar (from 10 cents
to 25 cents in constant 2011 USD) by elaborating
IRENA estimates for 2012. Our estimated prices (see
figure 1) fall within that range (7 cents and 11.5 cents
in constant 2011 USD, respectively). Recent estimates
of solar costs (Bosetti et al 2015) indicate a range of
2 cents to 45 cents per KWh in constant USD by 2030,
whereas we use 9.33 cents in constant 2011 USD.
In scenarios in which we introduce reductions in
energy demand, we assume that each unit of saved
energy costs 50% of the average price of electricity
(a share weighted average price of all sources of
energy). This is in line with studies arguing relatively
cheap opportunities or ‘low hanging fruit’in
developing countries (e.g. up to 25% of energy
demand reduction according to McKinsey (2012)) and
in line with Molina (2014, p 39), who claims that
Table 1. Direct and indirect job coefficients (jobs/GWh/year).
Energy efficiency Biomass Conventional hydropower Hydro (small) Municipal solid waste Geothermal
Direct 0.04 0.21 0.15 0.27 0.15 0.25
Indirect 9.0 0.9 0.9 0.9 0.9 0.9
Nuclear Solar PV Concentrated Solar Power (CSP) Wind Coal Natural Gas Oil
Direct 0.14 0.87 0.23 0.17 0.11 0.11 0.11
Indirect 0.9 0.9 0.9 0.9 0.9 0.9 0.9
Source:Weiet al (2010).
5
Like Wei et al (2010), we only consider induced jobs for EE
(presented in table 1as the indirect multiplier), but do not include
induced jobs for RE. We consider both direct and indirect jobs for
RE.
6
In Wei et al (2010), a distinction is made between small and
conventional hydropower direct and indirect jobs. As we only have
data on hydropower (without any distinction between small and
conventional), we take an average of the two.
7
In Bosetti et al (2006), the cost of electricity generation is equal to
the sum of the capital invested in power capacity and the
expenditure for fuels, operation and maintenance.
8
See annex II for WITCH model forecasts of energy prices.
Environ. Res. Lett. 12 (2017) 035008
3

‘electricity efficiency programs are one half to one
third the cost of the alternative of building new power
plants’. In our analysis, we select the more conservative
50% estimate for the reference scenario.
Initial renewable energy shares are taken from IEA
balances for Africa in 2009 and are assumed to increase
by 16% in 2010 to 25% in 2030.
9
Demand for
electricity in Africa is estimated to reach 1311 TWh by
2030. We apply the revised conversion factors to the
electricity generation of our reference scenario. As in
Wei et al (2010), jobs in EE only account for additional
jobs from EE compared with the reference scenario. In
the reference scenario, we assume energy consumption
and shares of RE to be consistent with the IEA’s
CURRENT_POLICIES scenario (figure 2, IEA 2012).
Alternative scenarios are described in table 2and
are consistent with the IEA’s World Energy Outlook
(2012)‘NEW_POLICIES’and ‘450_PPM’storylines.
The former assumes the introduction of new measures
on RE and EE (i.e. above and beyond those considered
in the CURRENT_POLICIES scenario), assuming
that the broad policy commitments that have already
been announced are actually implemented. The latter
depicts a pathway considered to be consistent with the
goal of limiting the global increase in average
temperature to 2 °C. The NEW_POLICIES scenario
assumes a lower energy demand (1224 TWh) than the
CURRENT_POLICIES scenario as well as a lower
share of fossil fuel and nuclear energy (from 75% in
the CURRENT_POLICIES scenario to 70% in the
NEW_POLICIES scenario). 450_PPM is the most
ambitious and environment-friendly scenario, as it
assumes 1106 TWh in electricity demand and a 58%
fossil fuel share in 2030.
3. Results
We provide output results for the following variables
for all scenarios:
Jobs/year
Total generation costs (generation cost per KWh
for different sources of energy) and ratio of the
average cost of RE over the average cost of non-
renewable energy
Generation cost per job per year.
It is interesting to note that the scenario with the
highest level of jobs per year in 2030 is 450_PPM,
which assumes the highest share of both RE and EE
(figure 3). Note that the 450_PPM scenario results in a
2.00
4.00
6.00
8.00
10.00
12.00
14.00
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 20 26 2027 2028 2029 2030
Power generation costs (2011 US$ cents)
Nuclear
Municipal solid waste
CSP wind and photovoltaics
Hydro geothermal and biomass
Coal
Natural gas
Oil
Figure 1. Power generation costs in Africa for each energy source in the reference scenario (2011 cents of US$/KWh).
Source: Adapted from Bosetti et al (2006).
0
200
400
600
800
1000
1200
1400
20302020
Marine
CSP
Solar PV
Geothermal
Wind
Bioenergy
Hydro
Nuclear
Gas
Oil
Figure 2. Electricity generation in Africa under the current
policy scenario. Source: IEA (2012).
Table 2. Key parameters in 2030 for the scenarios considered.
Scenario Share of renewables in
2030 (biomass,
geothermal, municipal
solid waste, solar PV,
solar thermal, small
hydro, wind)
Electricity
demand in
2030 (TWh)
CURRENT_POLICIES 25% 1311
NEW_POLICIES 30% 1224
450_PPM 42% 1106
9
See annex Ifor the IEA energy balance for Africa in 2009.
Environ. Res. Lett. 12 (2017) 035008
4

loss of jobs deriving from the reduction of electricity
generation, but this effect is more than counter-
balanced by the jobs created through the expansion of
EE and RE.
As shown in the table 3in the 450 ppm scenario the
share of jobs from energy efficiency jumps from 0 in the
CURRENT_POLICIES scenario to 17.26% in 2030.
Pollin et al (2009) point out that 30% of total jobs
composed of direct, indirect and induced effects derive
from induced jobs. Surprisingly NEW_POLICIES
(which assumes a higher penetration of renewable
energy than CURRENT_POLICIES in electricity
generation) shows a lower percentage of RE jobs than
CURRENT_POLICIES in 2020. This comes from the
jump of energy efficiency jobs. If we just consider jobs
creation from energy sources (excluding energy
efficiency jobs) the share of renewable energy jobs
raises from 37% in CURRENT_POLICIES to 39% in
NEW_POLICIES to 49% in 450_ppm in 2020 (from
43% in CURRENT_POLICIES to 48% in NEW_
POLICIES to 63% in 450_ppm in 2030).
Over the period 2009–2030, the reference scenario
‘CURRENT_POLICIES’together with the NEW_
POLICES and 450_PPM scenarios, assume an average
cost for RE that is higher than that of non-renewable
energy (nuclear þfossil fuels). In the reference case,
the costs for both RE and fossil fuels decrease, but the
reduction in RE costs slightly exceeds the reduction in
fossil fuel costs (in 2009, the ratio is assumed to be 1.25
and in 2030, it is assumed to be 1.20).
A high number of employees may generate a trade-
off in terms of electricity generation costs. The
450_PPM scenario, which entails the highest renew-
ables cost as well as the largest share of RE, also
displays the highest electricity generation costs
for Africa (figure 4). Interestingly, the NEW_
POLICIES scenario is cheaper than the reference
scenario in 2030. Thus, a higher share of renewables
does not always imply an increase in electricity
generation costs. The savings from EE outweigh the
higher energy costs associated with the increase in the
share of RE. In the 450_PPM scenario, energy savings
cannot compensate for the increase in electricity
generation costs associated with a higher share of RE.
The 450_PPM scenario, which indicates the
highest level of RE share and the lowest level of
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
1,300,000
2030202920282027202620252024202320222021202020192018201720162015201420132012201120102009
Number of jobs per year
CURRENT_POLICIES
NEW_POLICIES
450_ppm
Figure 3. Jobs in different scenarios (jobs/year, vertical axis, year horizontal axis).
Table 3. Share of jobs across sources of energy.
2020 CURRENT_POLICIES NEW_POLICIES 450_ppm
Energy efficiency net of induced jobs 0.00 4.98 9.59
Induced jobs 0.00 2.13 4.11
Renewable energy 36.93 36.52 42.18
Fossil fuels 60.78 53.35 40.50
Nuclear 2.30 3.02 3.63
100 100 100
2030 CURRENT_POLICIES NEW_POLICIES 450_ppm
Energy efficiency net of induced jobs 0.00 6.61 12.08
Induced jobs 0.00 2.83 5.18
Renewable energy 43.16 43.82 51.99
Fossil fuels 54.50 43.09 26.21
Nuclear 2.34 3.49 4.54
Environ. Res. Lett. 12 (2017) 035008
5

energy demand, also entails the lowest generation cost
per worker (figure 5). In other words, the scenario
with the highest level of additional jobs also displays
the lowest electricity generation cost per job created
(figure 6). This result, as already demonstrated in Wei
et al (2010), is, in effect, related to building a new,
clean energy economy. In the 450_PPM scenario, EE
and RE generate additional jobs. The increase in
electricity generation costs in the scenario grows more
slowly than the increase of jobs. Figures 5and 6are
pivotal and illustrate that the economic argument
against the greening of the energy mix is weakened by
the evidence which reveals the savings in terms of costs
per unit of generated employment.
4. Sensitivity analysis
To test the robustness of our results to changes of the
relevant parameters, our key assumptions are modi-
fied in all scenarios. The previous simulations indicate
that EE and RE: 1) create jobs; 2) lead to higher
electricity generation costs; 3) produce a lower
electricity generation cost per job created. We
manipulate: 1) the rate of job losses deriving from a
technology parameter expressing the annual rate of
reduction of the jobs multiplier; 2) the leakage rate of
manufacturing jobs; 3) the price of renewables; 4) the
cost of EE.
We increase the technology parameter expressing
the annual rate of reduction of the jobs multiplier and
the leakage parameter (þ10%, þ30%, þ50%, þ70%)
10
90,000,000
80,000,000
70,000,000
60,000,000
50,000,000
40,000,000
30,000,000
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Total electricity generatio costs (1000 USD)
CURRENT_POLICIESCURRENT_POLICIES
NEW_POLICIESNEW_POLICIES
450_ppm450_ppm
Figure 4. Electricity generation costs (1000 2011 USD).
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
80.00
75.00
70.00
65.00
60.00
55.00
50.00
Generation cost per worker per year (1000
USD per jobs year)
CURRENT_POLICIESCURRENT_POLICIES
NEW_POLICIESNEW_POLICIES
450_ppm450_ppm
Figure 5. Generation cost per worker (1000 2011 USD per jobs/year).
10
The technology effect is incorporated by increasing the annual
decrement of the jobs parameter estimated by Rutovitz and Harris
(for example, for a 10% sensitivity analysis of the technology
parameter, we increase the decrement rates estimated by Rutovitz
and Harris by 10% over the periods 2010–2015, 2016–2020 and
2020–2030. The leakage effect is captured by var ying the leakage rate
estimated by Rutovitz and Harris in 2030 (for example, for a 10%
sensitivity analysis of the leakage parameter, we increase the leakage
rate estimated by Rutovitz and Harris by 10% from 0.5 to 0.55 in
2030). By analysing variations of the leakage effect, the value in 2010
remains unchanged as estimated by Rutovitz and Harris, but the
values of the leakage parameter between 2011–2030 are interpolated
on the basis of the revised value for 2030.
Environ. Res. Lett. 12 (2017) 035008
6

to analyse the extent to which the 450_PPM and the
NEW_POLICIES scenarios continue to generate
additional jobs and a cheaper cost per generated job
when compared with the CURRENT_POLICIES
scenario. Moreover, we increase the price of both RE
and EE (þ10%, þ30%, þ50, þ70%) to analyse the
extent to which the 450_PPM and the NEW_POLICIES
scenarios entail lower electricity generation costs (total
costs and costs per generated job) compared to the
CURRENT_POLICIES scenario. We show results for
the years 2020 and 2030.
We first discuss the results on the technology
parameter and the leakage parameter (tables 4and 5).
The two parameters show similar impacts. In the
CURRENT_POLICIES scenario, not surprisingly,
technology and an increase of leakage of manufactur-
ing jobs reduce the number of jobs. Electricity
generation costs are not affected whereby the
generation cost per worker does increase. In the
NEW_POLICIES scenario, the number of jobs still
remains higher and the generation cost per worker is
lower than in the CURRENT_POLICIES scenario
with an increase of up to 30% of the technology and
leakage parameters (up to 50 percent of the leakage
parameter in 2020). Interestingly, in the 450_PPM
scenario, despite major increases in the technology
and leakage parameters, the number of jobs remains
higher and generation costs per worker remain lower
than in the CURRENT_POLICIES scenario. The
results for 2020 are similar to those for 2030, which
indicate a slightly stronger order of magnitude.
Changes in costs of RE and EE (tables 4and 5)
have no impact on jobs creation
11
. However, we
observe interesting relevant variations in terms of
generation costs and generation cost per worker. An
increase in the cost of renewables results in the worst
case scenario (þ70%) with a 10% increase in
electricity generation costs in 2020 and a 20% increase
in 2030 in the CURRENT_POLICIES scenario. The
CURRENT_POLICIES scenario is not discussed in the
EE sensitivity analysis, because EE is not considered in
that scenario.
In the NEW_POLICIES scenario, the reduction
in electricity generation costs compared to the
CURRENT_POLICIES scenario disappears with a
10% increase in RE costs. The generation cost per
worker is still lower in 2020 despite an increase in RE
costs by up to 30%, and by up to 10% in 2030. In the
450_PPM scenario, the generation cost per worker is
lower than in the CURRENT_POLICIES scenario for
each variation of the cost parameter in 2020, and only
up to a 30% increase of the cost parameter in 2030. EE
costs do not have a significant impact on the
generation cost per worker. As shown tables 4and
5, the NEW_POLICIES and 450_PPM scenarios have
lower generation costs per worker both in 2020 and
2030. This is hardly surprising if we consider that in
the scenario with the highest level of EE (450_PPM),
energy savings only represent 15% of total electricity
generation in the CURRENT_POLICIES scenario.
We also highlight that a simultaneous variation of
all parameters may generate relevant changes in the
overall picture (table 6). By shifting all the parameters
by 10% and 30%, we find that the number of created
jobs remains higher in the 450_PPM scenario and the
NEW_POLICIES scenarios except the scenario as-
suming a 30% increase in the NEW_POLICIES
scenario. The generation cost per worker is higher
than in the CURRENT_POLICIES scenario, except
the scenario assuming a 10% increase in the 450 ppm
scenario.
5. Conclusion
According to our analysis, a transition towards low
carbon power generation in Africa would lead to
additional jobs, but with a potential trade-off in terms
of electricity generation costs. Energy savings do not
always compensate for a higher cost of RE. From a
societal perspective, the results are quite robust and
CURRENT_POLICIESCURRENT_POLICIES
NEW_POLICIESNEW_POLICIES
450_ppm450_ppm
Generation costs per worker yearGeneration costs per worker year
Number of created jobs per yearNumber of created jobs per year
1,300,000
1,250,000
1,200,000
1,150,000
1,100,000
1,050,000
1,000,000
950,000
60.00 65.00 70.00 75.00 80.00
Figure 6. Zoom on 2030. Generation cost per created job per year (vertical axis) vs number of created jobs per year.
11
A general equilibrium approach would be the most appropriate to
capture job variations from RE and/or EE cost parameters.
Environ. Res. Lett. 12 (2017) 035008
7

indicate that policy actions for a higher penetration of
RE and EE generate a social dividend in terms of
additional employment together with lower costs of
generation per additional employee. Higher costs of
renewable energy and employment creation may affect
this positive prospect.
The study adds an additional insights into the
debate on the desirability of RE and EE for
economic, social and environmental sustainability
in low/middle income countries. The results of this
paper reveal that if RE become a competition for
fossil fuels and if at the same time technologies for
Table 4. Change of jobs, generation costs and generation costs per worker based on modifications of the renewable energy costs, energy
efficiency costs, technology and learning parameter. Year: 2020. Changes are expressed as % changes compared to the CURRENT_POLICIES
scenario.
CHANGE OF THE TECHNOLOGY PARAMETER
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 1.51 4.34 6.93 9.30
NEW_POLICIES 4.55 3.46 0.59 2.03 4.43
450_ppm 15.98 14.23 10.95 7.94 5.19
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0.00 0.00 0.00 0.00
NEW_POLICIES 0.39 0.39 0.39 0.39 0.39
450_ppm 2.38 2.38 2.38 2.38 0.39
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 1.53 4.53 7.44 10.26
NEW_POLICIES 4.72 3.72 0.97 1.68 4.23
450_ppm 11.73 10.37 7.72 5.16 2.67
CHANGE OF THE LEAKAGE PARAMETER
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0.86 2.57 4.28 5.99
NEW_POLICIES 4.55 3.65 1.87 0.08 1.71
450_ppm 15.98 14.99 13.00 11.02 9.03
Generation costs 2020 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0.00 0.00 0.00 0.00
NEW_POLICIES 0.39 0.39 0.39 0.39 0.39
450_ppm 2.38 2.38 2.38 2.38 2.38
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0.86 2.63 4.47 6.37
NEW_POLICIES 4.72 3.90 2.21 0.46 1.35
450_ppm 11.73 10.96 9.40 7.78 5.46
CHANGE OF THE RENEWABLE ENERGY COSTS
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0.00 0.00 0.00 0.00
NEW_POLICIES 4.55 4.55 4.55 4.55 4.55
450_ppm 15.98 15.98 15.98 15.98 15.98
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 1.51 4.52 7.54 10.55
NEW_POLICIES 0.39 0.40 3.70 7.00 10.30
450_ppm 2.38 2.57 6.80 11.03 15.26
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 1.51 4.52 7.54 10.55
NEW_POLICIES 4.72 3.97 0.81 2.34 5.50
450_ppm 11.73 11.56 7.92 4.27 0.62
CHANGE OF THE ENERGY EFFICIENCY COSTS
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0 0 0 0
NEW_POLICIES 4.55 4.55 4.55 4.55 4.55
450_ppm 15.98 15.98 15.98 15.98 15.98
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0 0 0 0
NEW_POLICIES 0.39 0.04 0.64 1.33 2.02
450_ppm 2.38 3.15 4.69 6.23 7.77
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 0 0 0 0
NEW_POLICIES 4.72 4.39 3.74 3.08 2.42
450_ppm 11.73 11.06 9.74 8.41 7.08
Environ. Res. Lett. 12 (2017) 035008
8

EE start becoming less expensive, there is a
potential that the greening of the economy
favourably impacts all three pillars of sustainable
development simultaneously. If costs were to
decrease slowly, the higher bill for RE and EE
could be compensated by environmental improve-
ments and may make cost effective contributions to
unemployment reduction in terms of societal costs.
From a policy perspective, these results suggest
justification for a fuller integration of green
technologies beyond the traditional boundaries
of environmental policy.
Table 5. Change of jobs, generation costs and generation costs per worker based on modifications of the renewable energy costs,
energy efficiency costs, technology and learning parameter. Year: 2030. Changes are expressed as % changes compared to the
CURRENT_POLICIES scenario.
CHANGE OF THE TECHNOLOGY PARAMETER
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0 2.12 9.13 9.13 11.91
NEW_POLICIES 6.59 5.94 2.19 0.99 3.69
450_ppm 24.46 21.82 17.12 13.10 9.64
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 0.00 0.00 0.00 0.00
NEW_POLICIES 0.66 0.66 0.66 0.66 0.66
450_ppm 3.51 3.51 3.51 3.51 3.51
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 2.17 6.27 10.05 13.52
NEW_POLICIES 6.80 6.23 2.78 0.34 3.15
450_ppm 16.83 15.03 11.62 8.47 5.59
CHANGE OF THE LEAKAGE PARAMETER
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 1.49 4.48 7.47 10.46
NEW_POLICIES 6.59 5.00 1.81 1.37 4.56
450_ppm 24.46 22.60 18.88 15.16 11.45
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 0.00 0.00 0.00 0.00
NEW_POLICIES 0.66 0.66 0.66 0.66 0.66
450_ppm 3.51 3.51 3.51 3.51 3.51
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 1.52 4.69 8.07 11.68
NEW_POLICIES 6.80 5.39 2.42 0.73 4.09
450_ppm 16.83 15.57 12.93 10.12 7.12
CHANGE OF THE RENEWABLE ENERGY COSTS
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 0.00 0.00 0.00 0.00
NEW_POLICIES 6.59 6.59 6.59 6.59 6.59
450_ppm 24.46 24.46 24.46 24.46 24.46
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 2.92 8.75 14.58 20.41
NEW_POLICIES 0.66 2.73 9.51 16.29 23.07
450_ppm 3.51 8.36 18.05 27.74 37.43
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 0.00 2.92 8.75 14.58 20.41
NEW_POLICIES 6.80 3.62 2.74 9.10 15.46
450_ppm 16.83 12.94 5.15 2.63 10.42
CHANGE OF THE ENERGY EFFICIENCY COSTS
jobs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 00000
NEW_POLICIES 6.59 6.59 6.59 6.59 6.59
450_ppm 24.46 24.46 24.46 24.46 24.46
Generation costs 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 00000
NEW_POLICIES 0.66 0.16 0.84 1.83 2.82
450_ppm 3.51 4.65 6.93 9.21 11.48
Generation costs per worker 2030 0% 10% 30% 50% 70%
CURRENT_POLICIES 00000
NEW_POLICIES 6.80 6.33 5.40 4.47 3.54
450_ppm 16.83 15.92 14.09 12.26 10.43
Environ. Res. Lett. 12 (2017) 035008
9

Acknowledgments
This research expands on background work carried
out for the Industrial Development Reports 2013 and
2016 (UNIDO 2013, UNIDO 2016). The authors fully
acknowledge the input received from various col-
leagues within the scope of that process, in particular
from Camelia Soare of UNIDO. DMK would like to
thank the Karsten Family Foundation and the
Zaffaroni Family for their support of the Renewable
and Appropriate Energy Laboratory.
Annex I: IEA energy balance for Africa
in 2009
Electricity Heat
Unit: GWh Unit:TJ
Coal and peat 250089
Oil 79217
Gas 185582
Biofuels 769
Waste 0
Nuclear 12806
Hydro 101257
Geothermal 1354
Solar PV 26
Solar thermal 0
Wind 1675
Tide 0
Other sources 47
Total production 632822 513
Annex II.: Electricity generation costs –
WITCH model (Bosetti et al 2006) constant
1995 cUSD/KWh
Year 2002 Coal oil Gas Nuclear Hydro Wind and solar
MENA region 4.3 4.5 2.8 6.4 5.6 9.5
SSA region 4.1 8.8 3.4 6.2 5.4 9.2
Year 2030 Coal oil Gas Nuclear Hydro Wind and solar
MENA region 4.8 5.4 2.6 5.8 4.7 7.0
SSA region 4.9 11.0 3.2 5.9 4.8 7.0
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