100% Clean and Renewable Wind, Water, and Sunlight (WWS) AllSector Energy Roadmaps for 139 Countries of the World
April 24, 2016
Note: this is a draft (version 3) – not the final version – modifications are expected
By
Mark Z. Jacobson1,, Mark A. Delucchi2, Zack A.F. Bauer1, Savannah C.
Goodman1, William E. Chapman1, Mary A. Cameron1, Alphabetical:
Cedric Bozonnat1, Liat Chobadi3, Jenny R. Erwin1, Simone N. Fobi1, Owen
K. Goldstrom1, Sophie H. Harrison1, Ted M. Kwasnik1, Jonathan Lo1,
Jingyi Liu1, Chun J. Yi1, Sean B. Morris1, Kevin R. Moy1, Patrick L.
O’Neill1, Stephanie Redfern1, Robin Schucker1, Mike A. Sontag1, Jingfan
Wang1, Eric Weiner1, Alex S. Yachanin1
1
*
Atmosphere/Energy Program, Dept. of Civil and Env. Engineering, Stanford University
Corresponding author. T: 650-723-6836; F: 650-723-7058; E: jacobson@stanford.edu
2
Institute of Transportation Studies, U.C. Berkeley
3
Technical University of Berlin
Abstract
We develop roadmaps for converting the all-purpose energy (electricity, transportation,
heating/cooling, industry, and agriculture/forestry/fishing) infrastructures of each of 139
countries of the world to ones powered by wind, water, and sunlight (WWS). As of the end
of 2014, 3.6% of the WWS energy generation capacity needed for a 100% world has already
been installed in these countries, with Norway (55%), Paraguay (53%), and Iceland (38%)
the furthest along The roadmaps envision 80% conversion by 2030 and 100% conversion of
all countries by 2050. The transformation reduces 2050 power demand relative to a
business-as-usual (BAU) scenario by ~31.4% due to the higher work to energy ratio of
WWS electricity over combustion and the elimination of energy for mining, transporting,
and processing fuels, and another ~6.9% due to end-use efficiency beyond that already
occurring in the BAU case. Remaining annually averaged 2050 demand may be met with a
mean of ~19.8% onshore wind, ~12.7% offshore wind, ~40.8% utility-scale photovoltaic
(PV), ~6.5% residential rooftop PV, ~7.0% commercial/government/parking rooftop PV,
~7.7% concentrated solar power (CSP), ~0.73% geothermal power, ~0.38% wave power,
~0.06% tidal power, and ~4.3% hydropower. The new plus existing nameplate capacity of
generators across all 139 countries is ~45.8 TW, which represents only ~0.5% of the
technically possible installed capacity. An additional ~0.94 TW nameplate capacity of new
CSP, ~5.1 TW of new solar thermal for heat, and ~0.07 TW of existing geothermal heat in
combination with low-cost storage is needed to balance supply and demand economically.
The capital cost of all new generators (50.3 TW nameplate) is ~$102 trillion in 2013 USD,
1
or ~$2.04 million/MW. Over the 139 countries, converting will create an estimated 25.0
million 35-year construction jobs and 26.4 million 35-year operation jobs for energy
facilities alone, the total outweighing the 27.7 million jobs lost by ~23.7 million. Converting
will eliminate ~4.6 (1.3-8.0) million premature air pollution mortalities per year today and
3.3 (0.8-7.0) million/year in 2050 in the 139 countries, avoiding ~$23.2 ($4.0-$70.8)
trillion/year in 2050 air-pollution damage costs (2013 USD), equivalent to ~7.2 (1.2-22)
percent of the 2050 139-country gross domestic product. It will further eliminate ~$17 (9.636) trillion/year in 2050 global warming costs (2013 USD) due to 139-country emissions. A
2050 WWS versus BAU infrastructure will save the average person worldwide $143/year in
fuel costs, ~2,632/year (14 ¢/kWh) in air-pollution damage costs, and $1,930/year (10
¢/kWh) in climate costs (2013 USD). The new footprint over land required for adding the
WWS infrastructure is equivalent to ~0.25% of the 139-country land area, mostly in deserts
and barren land, without accounting for land gained from eliminating the current energy
infrastructure. The new spacing area between wind turbines, which can be used for
farmland, ranchland, grazing land, or open space, is equivalent to 0.68% of the 139-country
land area. Aside from virtually eliminating air pollution morbidity and mortality and global
warming, the implementation of these roadmaps will create net jobs worldwide, stabilize
energy prices because fuel costs are zero; reduce international conflict by creating energyindependent countries; reduce energy poverty; reduce power disruption by decentralizing
power; and avoid exploding CO2 levels and catastrophic climate change.
Keywords: Renewable energy; air pollution; global warming; sustainability
1. Introduction
We develop roadmaps for converting the all-purpose energy (electricity, transportation,
heating/cooling, industry, and agriculture/forestry/fishing) infrastructures of 139 countries to
ones powered by wind, water, and sunlight (WWS). These roadmaps represent highresolution country-specific WWS plans that improve upon and update the general world
roadmap developed by Jacobson and Delucchi (2009, 2011) and Delucchi and Jacobson
(2011) and expand upon the individual U.S. state energy roadmaps for New York,
California, Washington State, and the 50 United States developed in Jacobson et al. (2013,
2014, 2016a, 2015a), respectively.
The roadmaps here are developed with a consistent methodology across all countries and
with the goal of minimizing emissions of both air pollutants and greenhouse gases and
particles. Previous clean-energy plans have been limited to individual countries or regions,
selected sectors, partial carbon emission reductions, and/or emission reductions of carbon
only rather than air pollution and carbon (e.g., Parsons-Brinckerhoff, 2009 for the UK;
Price-Waterhouse-Coopers, 2010 for Europe and North Africa; Beyond Zero Emissions,
2010 for Australia; ECF, 2010 for Europe; EREC, 2010 for Europe; Zero Carbon Britain,
2013 for Great Britain; ELTE/EENA, 2014 for Hungary; Connolly and Mathiesen, 2014 for
Ireland; Hooker-Stroud et al., 2015 for the UK; Mathiesen et al., 2015 for Denmark;
Negawatt Association, 2015 for France; Teske et al., 2015 for several world regions; and
DDDP, 2015 for 16 countries).
This paper provides the original country-specific estimates of
2
(1) Future demand (load) in each energy sector in BAU and WWS cases;
(2) The number of WWS generators and corresponding footprint and spacing areas needed
to meet load in the WWS case;
(3) Rooftop areas and solar photovoltaic (PV) potentials on buildings, carports, garages,
parking lots, and parking structures;
(5) Levelized costs of energy today and in 2050 in the BAU and WWS cases;
(6) Reductions in air-pollution mortality, morbidity and associated costs due to WWS;
(7) Reductions in ambient CO2 and global-warming costs due to WWS; and
(8) Changes in job numbers and revenue due to WWS.
This paper further provides a transition timeline, energy efficiency measures, and possible
policy measures to implement the roadmaps. Delucchi et al. (2016) contain the spreadsheet
calculations and methodology used here.
2. WWS Technologies
Quantifying the number of WWS generators in each country begins with 2012 energy use
data (IEA, 2015) in each energy sector of 139 countries for which data are available. Energy
use in each sector of each country is then projected to 2050 in a BAU scenario. The
projections account for increasing demand, modest shifts from coal to gas, biofuels,
bioenergy, and some WWS, and some end use energy efficiency improvements.
All energy-consuming processes in each sector are then electrified, and the resulting end-use
energy required for a fully electrified all-purpose energy infrastructure is estimated. Some
end-use electricity in each country is used to produce hydrogen for some transportation and
industrial applications. Modest additional end-use energy efficiency improvements are then
applied. Finally, the remaining power demand is supplied by a set of WWS technologies,
the mix of which varies by country with available resources and rooftop, land, and water
areas.
The WWS electricity generation technologies include wind, concentrated solar power
(CSP), geothermal, solar PV, tidal, wave, and hydropower. These are existing technologies
found to reduce health and climate impacts the most among several technologies while
minimizing land and water use and other impacts (Jacobson, 2009).
Technologies for ground transportation include battery electric vehicles (BEVs) and
hydrogen fuel cell (HFC) vehicles, where the hydrogen is electrolytic (produced by
electrolysis, or passing electricity through water). BEVs with fast charging or battery
swapping will dominate long-distance, light-duty ground transportation; battery electricHFC hybrids will dominate heavy-duty ground transportation and long-distance water-borne
shipping; batteries will power short-distance shipping (e.g., ferries); and electrolytic
cryogenic hydrogen plus batteries will power aircraft. HFCs will not be used to generate
electricity because of the relative inefficiency and cost of this process.
3
Air heating and cooling will be performed by ground-, air-, or water-source electric heat
pumps. Water heat will be generated by heat pumps with an electric resistance element for
low temperatures and/or solar hot water preheating. Cook stoves will be electric induction.
Electric arc furnaces, induction furnaces, dielectric heaters, resistance heaters, and some
combusted electrolytic hydrogen will power high-temperature industrial processes. In this
study, ~9.0% of all 2050 WWS electricity (44.5% of the transportation load and 4.8% of the
industrial load) is for producing, storing, and using hydrogen.
The roadmaps assume the adoption of new energy-efficiency measures but exclude the use
of nuclear power, coal with carbon capture, liquid or solid biofuels, or natural gas because
all result in more air pollution and climate-relevant emissions than do WWS technologies, in
addition to other issues (Jacobson and Delucchi, 2011; Jacobson et al., 2013).
This study calculates the number of generators of each type needed to power each country
based on the 2050 power demand in the country after all sectors have been electrified but
before considering grid reliability and neglecting energy imports and exports. However,
results from a grid reliability study for the continental U.S. (Jacobson et al., 2015b) are used
to estimate the additional generators needed worldwide and by country to ensure a reliable
electric power grid while considering that all energy sectors have been electrified with some
use of electrolytic hydrogen.
In reality, energy exchanges among countries will occur in 2050 as they currently do.
However, we assume each country can generate all of its annually averaged power
independently of other countries, since ultimately this goal may reduce international
conflict. However, because it can be more profitable for countries with higher grade WWS
resources to produce more power than they need for their own use and export the rest, the
real system cost will likely be less than that proposed here since the costs of, for example
solar, are higher in low-sunlight countries than in countries that might export solar
electricity. An optimization study that determines the ideal tradeoff between generation and
additional transmission costs is left for future work.
3. Changes in Each Country’s Power Load upon Conversion to WWS
Table 1 projects 139-country BAU and WWS end-use power demands to 2050. End-use
power is the power in delivered electricity or fuel (e.g., gasoline) that is actually used to
provide services such as heating, cooling, lighting, and transportation. It excludes losses
during the production and transmission of the electricity or fuel. All end uses that feasibly
can be electrified use WWS power directly, and remaining end uses (some transportation
and industrial processes) use WWS to produce electrolytic hydrogen.
Table 1. 1st row of each country: estimated 2050 total end-use load (GW) and percent of total load by sector if
conventional fossil-fuel, nuclear, and biofuel use continue from today to 2050 under a BAU trajectory. 2nd row
of each country: estimated 2050 total end-use load (GW) and percent of total load by sector if 100% of BAU
end-use all-purpose delivered load in 2050 is instead provided by WWS. The last column shows the percent
reduction in total 2050 BAU load due to switching from BAU to WWS, including the effects of assumed
policy-based improvements in end-use efficiency beyond those in the BAU case (6.9%), inherent reductions in
energy use due to the higher work to energy ratio of electricity over combustion, and the elimination of energy
use for the upstream mining, transport, and/or refining of coal, oil, gas, biofuels, bioenergy, and uranium.
4
Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and
Herzegovina
Botswana
Brazil
Brunei
Darussalam
Bulgaria
Cambodia
Cameroon
Canada
Chile
China
Chinese Taipei
Colombia
Residential
per-cent
of total
Commercial
percent of
total
Industrial
percent of
total
Transport
percent of
total
Ag/For
/Fishing
percent of
total
Other
percent
of total
Scenario
2050
Total
end-use
load
(GW)
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
4.7
2.7
105.0
55.4
21.4
13.8
145.4
86.2
4.9
3.5
170.3
90.9
44.7
29.7
20.8
11.3
14.2
6.7
58.8
42.5
56.0
39.7
64.6
39.5
5.7
3.2
12.9
7.7
7.7
4.6
4.8
3.2
627.5
396.9
6.6
2.3
25.1
15.4
9.3
6.4
11.1
7.0
412.1
240.2
76.1
50.4
5044.7
3302.8
170.0
113.8
60.8
32.6
24.54
31.70
33.10
42.70
46.85
52.10
30.39
34.94
32.42
31.62
10.92
14.70
22.60
25.57
30.53
39.29
9.98
16.19
45.54
44.49
19.66
22.89
17.73
20.20
48.10
61.07
13.72
16.50
24.32
30.78
21.70
23.65
9.63
11.32
9.11
19.22
20.39
25.40
46.75
48.74
54.59
62.19
13.93
17.20
16.05
17.50
24.62
27.50
11.87
13.20
18.33
24.21
12.44
16.65
0.04
0.07
8.98
10.77
9.97
13.31
9.82
10.81
7.48
10.98
11.28
14.35
10.84
15.82
9.26
15.28
1.22
1.38
12.45
15.56
12.83
16.79
10.16
14.58
3.95
5.24
0.00
0.00
8.27
9.56
9.13
11.22
11.63
25.48
15.53
20.08
4.17
4.69
10.32
13.04
15.58
21.23
9.11
10.74
7.22
8.85
9.45
11.00
7.73
11.38
22.01
26.26
37.79
31.03
21.12
25.87
28.51
28.62
19.30
21.38
41.30
42.65
33.47
34.80
24.91
20.89
56.21
52.15
34.41
39.03
29.15
27.14
35.17
40.75
1.12
1.78
37.62
41.77
30.03
31.22
34.76
43.93
49.78
53.37
60.63
38.90
33.46
34.80
31.64
37.83
17.26
15.22
53.32
48.64
50.52
58.80
41.92
46.70
55.37
60.58
28.99
32.27
37.36
20.00
21.87
14.04
22.85
10.99
27.81
18.50
26.68
22.53
33.66
21.41
23.62
14.08
29.67
17.59
24.48
16.22
11.00
5.97
16.75
8.67
30.08
16.60
40.62
22.57
34.28
21.45
31.64
16.84
26.98
12.52
26.73
17.68
18.62
16.40
29.08
17.57
16.36
7.38
16.96
8.38
14.83
9.50
22.94
11.25
22.21
11.37
18.95
8.97
39.80
24.08
3.39
4.97
0.54
0.96
0.13
0.17
3.32
4.62
0.57
0.80
2.03
3.19
2.08
2.63
4.05
6.40
0.07
0.15
7.51
8.68
3.97
4.90
1.75
2.34
0.00
0.00
7.36
10.49
0.24
0.41
1.44
1.93
4.52
6.14
0.00
0.00
1.54
2.15
0.00
0.00
0.19
0.30
2.35
3.44
1.37
1.71
1.40
1.87
1.16
1.54
5.04
7.88
0.25
0.43
6.65
11.20
0.07
0.09
0.00
0.00
11.21
12.85
4.60
7.07
6.95
8.57
0.00
0.00
0.00
0.00
0.32
0.45
18.02
20.83
2.45
3.32
0.00
0.00
3.07
4.57
13.77
20.75
6.86
8.42
0.21
0.28
0.00
0.00
0.00
0.00
1.08
1.36
0.68
0.88
0.00
0.00
0.00
0.00
2.63
3.71
3.20
4.70
0.11
0.18
Overall
percent
change
in enduse
power
with
WWS
-41.80
-47.17
-35.25
-40.70
-28.45
-46.63
-33.44
-45.69
-53.00
-27.67
-29.09
-38.75
-43.92
-40.56
-40.46
-32.87
-36.74
-64.61
-38.58
-30.96
-36.94
-41.71
-33.82
-34.53
-33.02
-46.33
5
Congo
Congo, Dem.
Republic of
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican
Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Gibraltar
Greece
Guatemala
Haiti
Honduras
Hong Kong,
China
Hungary
Iceland
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
2.3
1.2
42.4
31.5
7.4
4.1
12.2
8.3
15.6
9.2
14.5
10.4
4.7
2.4
38.6
25.8
24.3
15.8
11.4
6.2
24.0
10.9
173.3
104.5
5.2
2.9
0.8
0.6
5.2
3.3
53.3
37.8
39.8
29.1
242.5
156.6
4.5
3.1
7.2
4.5
375.8
260.9
15.1
9.7
3.3
1.1
30.9
17.1
14.5
8.6
4.4
2.9
7.7
4.8
59.5
30.8
24.5
16.0
4.4
3.5
39.51
53.28
56.80
54.22
13.62
18.64
58.09
61.38
25.14
31.54
15.45
16.06
14.76
21.36
24.75
27.13
29.20
37.22
15.90
21.30
11.31
18.04
22.22
27.02
22.18
29.04
73.64
77.59
29.38
37.90
86.38
87.33
22.19
25.04
27.72
31.04
38.73
41.04
31.97
36.31
23.56
24.30
30.11
34.10
0.00
0.00
23.56
31.10
52.29
62.89
71.55
78.50
34.91
40.59
7.87
10.34
34.45
37.77
16.11
17.08
0.81
1.24
0.25
0.26
12.58
17.82
12.09
14.12
17.52
23.41
5.01
5.44
18.16
27.38
15.38
19.15
15.59
20.74
5.21
7.41
4.52
7.71
9.86
12.68
3.64
5.12
9.41
10.91
15.55
20.37
1.96
2.19
9.44
10.03
17.72
21.93
3.97
4.51
8.93
11.27
15.35
17.86
6.00
7.30
0.00
0.00
12.63
17.81
5.11
6.74
2.15
2.64
6.70
8.36
23.64
35.52
21.67
27.03
11.80
12.41
9.03
10.91
39.57
43.87
20.90
31.40
15.36
16.20
25.09
24.86
59.07
64.55
7.58
11.34
36.87
37.85
22.34
19.79
24.14
36.52
26.03
31.25
41.51
40.27
24.13
35.07
5.49
6.27
23.45
22.89
6.12
7.28
44.71
46.78
22.97
24.73
39.16
45.00
25.14
29.42
28.68
29.23
32.00
41.28
0.13
0.31
26.07
24.83
9.65
12.87
8.03
10.13
23.28
30.64
12.50
20.24
21.26
20.61
48.18
53.67
45.36
26.44
3.04
1.27
50.92
28.91
12.18
5.56
29.61
16.52
13.91
6.34
56.99
35.53
19.47
11.22
26.91
14.37
51.86
29.59
55.66
38.18
19.68
10.56
47.93
26.95
11.47
5.24
27.98
14.01
4.48
1.98
15.23
7.60
27.38
16.74
16.74
7.62
28.78
15.88
32.34
28.52
29.58
14.36
99.28
97.85
33.62
19.77
32.59
17.01
18.28
8.73
31.66
15.86
55.92
33.75
19.93
11.10
13.20
5.33
0.00
0.00
0.00
0.00
1.48
2.44
2.27
2.73
2.64
3.68
1.71
2.05
1.79
3.05
2.47
3.14
5.90
7.79
2.89
5.19
0.73
1.32
5.23
7.44
0.25
0.44
0.00
0.00
3.65
4.82
0.52
0.60
2.99
3.49
2.95
3.86
0.77
0.92
4.35
6.01
0.00
0.00
2.32
2.96
0.00
0.00
1.57
2.70
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.69
3.49
10.44
11.17
5.29
8.12
0.34
0.37
0.49
0.78
0.00
0.00
0.00
0.00
4.85
5.57
0.72
1.33
1.07
1.51
0.07
0.08
0.00
0.00
1.74
3.50
1.50
2.04
1.87
3.37
0.00
0.00
0.00
0.00
0.53
0.62
5.44
7.06
1.26
1.70
0.63
0.91
0.84
1.09
0.08
0.09
0.00
0.00
0.58
1.84
2.56
3.79
0.35
0.49
0.00
0.00
3.45
4.55
0.08
0.14
0.00
0.00
0.27
0.34
-46.56
-25.68
-45.13
-31.77
-40.72
-28.50
-48.51
-33.37
-34.90
-45.41
-54.50
-39.67
-44.59
-31.80
-35.89
-29.18
-26.79
-35.39
-31.61
-37.35
-30.59
-35.86
-68.39
-44.73
-40.31
-34.77
-37.80
-48.21
-34.56
-20.84
6
India
Indonesia
Iran, Islamic
Republic of
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem.
People's Rep.
Korea,
Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia,
Republic of
Malaysia
Malta
Mexico
Moldova,
Republic of
Mongolia
Montenegro
Morocco
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
1607.8
932.0
380.4
230.7
380.4
230.8
53.1
27.9
15.4
9.3
27.0
16.9
215.0
142.3
4.1
2.4
365.1
236.8
11.7
6.3
141.9
74.0
22.9
15.2
38.4
31.3
295.6
195.2
3.1
1.9
57.2
23.3
9.2
5.7
9.9
6.5
9.0
5.2
27.2
16.3
12.9
8.0
5.8
3.1
4.9
3.3
141.9
79.4
4.1
1.6
400.4
199.0
5.0
3.5
9.2
6.9
1.6
1.0
37.5
24.5
24.07
29.73
25.69
30.76
21.36
23.94
13.18
18.15
24.05
25.92
18.58
21.97
23.89
25.25
12.55
15.66
17.88
20.20
16.98
23.61
8.40
12.27
63.74
68.80
0.16
0.13
14.18
15.65
38.03
44.76
9.89
18.57
22.42
28.84
25.15
30.82
23.46
29.60
12.05
14.78
22.54
29.72
10.91
14.07
26.21
29.96
8.65
11.59
4.82
9.65
10.65
15.67
32.88
34.22
25.76
27.93
36.18
41.99
18.35
20.61
5.26
7.16
5.81
7.45
6.76
8.98
1.38
2.03
14.90
19.65
12.47
15.42
14.60
17.68
8.67
11.45
26.15
31.95
8.32
12.11
4.71
7.88
2.20
2.57
0.00
0.00
20.92
25.17
10.93
13.62
7.05
13.42
10.54
13.60
19.64
24.63
7.32
9.79
10.02
12.97
16.08
21.96
17.55
26.70
15.24
17.88
13.80
19.13
5.99
12.28
4.25
6.66
16.07
18.94
8.32
11.04
1.35
1.66
9.16
11.05
26.99
33.97
41.51
45.37
43.26
45.38
27.12
32.77
25.59
32.83
18.82
14.18
27.01
27.36
32.51
46.19
33.33
35.37
21.92
25.31
72.59
64.77
15.74
19.40
66.39
66.57
40.70
45.09
24.00
26.92
61.59
51.59
18.42
21.86
22.17
25.97
16.07
24.27
19.75
20.45
29.43
28.87
16.45
24.79
32.50
37.28
52.66
54.88
3.30
6.53
51.23
50.70
31.42
35.90
39.56
41.85
29.81
39.31
32.38
37.01
38.96
21.68
24.34
12.84
23.78
14.57
48.82
28.98
32.74
17.76
24.93
12.39
32.26
26.84
43.83
23.24
21.87
11.47
45.52
26.58
8.85
5.82
17.47
8.19
2.34
0.89
22.31
11.58
25.72
12.82
21.47
16.42
34.86
17.42
30.27
15.03
46.45
24.93
39.42
20.50
30.17
16.98
54.36
33.30
22.49
10.50
24.81
14.25
85.43
70.34
29.17
18.51
17.06
7.75
15.06
6.32
31.39
15.37
25.17
12.26
3.27
5.22
2.40
3.24
4.63
6.80
0.00
0.00
2.72
3.85
0.90
1.43
2.11
2.71
2.35
3.31
0.59
0.76
3.62
6.78
1.22
2.03
0.26
0.32
0.00
0.00
1.23
1.68
1.31
1.87
0.00
0.00
1.92
2.67
2.74
3.52
0.00
0.00
1.64
2.74
1.74
2.42
0.72
1.14
1.13
1.40
0.08
0.15
0.06
0.15
3.65
6.36
1.78
2.12
2.82
3.13
0.47
0.64
14.93
19.07
1.45
2.24
0.26
0.35
0.21
0.35
9.49
18.07
0.00
0.00
24.30
34.61
0.13
0.17
0.09
0.15
0.17
0.27
3.65
5.61
4.23
7.22
0.59
0.72
31.11
32.40
0.67
0.83
0.00
0.00
0.00
0.00
11.84
15.61
0.02
0.02
6.70
11.42
17.11
28.56
0.04
0.05
0.00
0.00
2.43
2.97
0.00
0.00
0.41
1.05
1.05
2.10
0.80
1.08
8.49
9.73
0.81
1.03
0.00
0.00
-42.03
-39.34
-39.32
-47.52
-39.77
-37.30
-33.84
-41.24
-35.13
-46.64
-47.88
-33.54
-18.43
-33.95
-37.51
-59.27
-37.67
-33.83
-41.96
-40.08
-38.35
-46.16
-32.74
-44.04
-62.17
-50.30
-29.06
-24.89
-35.80
-34.74
7
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands
Antilles
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian
Federation
Saudi Arabia
Senegal
Serbia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
14.2
10.5
26.5
18.6
3.9
2.8
16.0
11.1
105.2
61.0
7.8
2.4
23.7
13.8
3.8
2.2
207.1
134.3
37.0
21.1
55.2
34.9
169.8
117.8
14.5
6.4
8.7
5.3
35.4
18.9
67.7
42.5
99.3
61.3
24.4
14.4
71.7
23.7
56.1
35.0
864.1
583.6
232.4
123.7
5.9
3.7
20.8
13.8
142.2
57.9
16.5
10.6
7.2
4.5
236.5
133.8
147.1
83.8
22.1
14.2
50.07
48.60
56.81
58.22
7.06
7.05
71.92
74.25
16.90
20.28
1.24
2.78
10.35
13.25
37.16
45.32
60.21
66.62
19.74
26.49
4.07
4.92
44.79
45.46
7.39
12.32
23.98
29.09
15.67
21.30
19.12
22.52
26.83
29.08
17.30
21.42
2.89
6.65
27.42
32.73
24.67
30.95
11.63
16.67
39.68
45.44
32.94
37.80
1.85
3.44
21.46
25.22
24.45
28.74
16.67
15.83
16.94
21.48
30.73
34.69
1.24
1.30
2.87
3.17
0.11
0.12
2.98
3.36
16.12
22.72
0.00
0.00
8.66
11.70
9.72
12.85
3.53
4.36
14.55
20.35
3.78
4.64
4.88
5.58
7.89
13.93
8.51
10.93
7.04
10.30
15.49
19.15
15.08
19.98
7.56
10.52
1.69
3.95
10.66
14.01
7.13
8.34
9.23
13.45
8.28
10.19
13.13
15.88
5.84
11.15
16.69
21.24
11.76
14.88
8.28
11.52
13.20
18.18
7.06
8.58
40.08
46.33
25.29
26.26
14.02
16.98
11.72
14.16
29.54
31.44
19.36
14.50
37.64
47.12
16.37
20.49
25.21
21.15
46.24
39.47
74.11
79.30
33.91
39.09
11.36
21.56
28.03
37.40
28.96
34.69
34.55
40.68
31.08
30.39
37.66
44.15
78.15
68.72
37.11
37.11
43.31
40.29
39.62
46.19
22.71
29.04
32.20
34.27
19.44
29.17
42.87
40.76
27.98
35.39
54.24
55.54
31.65
32.96
30.88
39.37
8.43
3.57
8.06
4.13
28.00
12.18
10.44
4.69
32.62
18.39
78.49
79.75
38.63
20.79
34.56
18.21
7.76
3.73
17.61
10.79
15.27
7.53
14.93
7.80
73.18
51.87
39.49
22.58
46.78
31.22
29.25
15.38
21.18
12.72
35.08
20.44
15.17
14.28
22.14
12.53
22.75
17.46
39.08
22.87
28.43
14.05
20.06
9.90
72.78
56.00
17.56
10.91
33.37
17.84
15.52
9.07
34.43
21.71
28.57
13.84
0.18
0.19
1.19
1.38
22.72
26.01
2.82
3.36
4.83
7.16
0.00
0.00
4.28
6.38
2.13
3.06
0.01
0.01
1.45
2.31
0.11
0.17
1.31
1.87
0.17
0.32
0.00
0.00
1.55
2.49
1.59
2.25
5.84
7.82
2.25
3.26
0.00
0.00
1.72
2.39
2.12
2.95
0.39
0.74
0.42
0.66
1.67
2.15
0.00
0.00
1.42
1.87
1.85
2.39
2.52
3.88
3.04
4.48
0.05
0.06
0.00
0.00
5.79
6.83
28.09
37.67
0.12
0.17
0.00
0.00
0.91
2.98
0.44
0.76
0.05
0.07
3.28
4.15
0.41
0.59
2.66
3.46
0.17
0.20
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.15
0.21
2.12
6.40
0.94
1.24
0.01
0.01
0.05
0.09
0.48
0.62
0.00
0.00
0.10
0.25
0.00
0.00
0.58
0.75
2.77
4.16
0.75
1.19
2.71
3.45
-25.94
-29.76
-28.36
-30.55
-42.06
-69.34
-41.85
-40.88
-35.14
-43.05
-36.80
-30.63
-56.05
-39.61
-46.50
-37.13
-38.29
-40.77
-66.91
-37.67
-32.47
-46.77
-36.95
-33.72
-59.31
-35.53
-36.69
-43.43
-43.01
-35.71
8
Sudan
Sweden
Switzerland
Syrian Arab
Republic
Tajikistan
Tanzania, United
Republic of
Thailand
Togo
Trinidad and
Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab
Emirates
United Kingdom
United States of
America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
All countries
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
BAU
WWS
21.6
14.1
53.7
38.1
31.2
19.9
21.7
12.7
5.1
4.2
35.0
25.3
249.8
154.8
3.3
2.1
16.5
7.0
22.0
14.8
124.3
81.1
45.6
31.4
174.6
120.9
176.0
113.0
225.1
129.3
2310.3
1311.7
8.0
4.7
81.6
58.2
121.9
63.1
133.1
93.2
12.8
7.6
14.0
10.5
17.2
12.8
19,400
11,971
37.67
41.90
23.89
28.46
27.79
31.40
15.27
19.42
14.67
13.62
52.24
51.83
10.11
12.05
57.64
65.80
6.90
11.85
20.99
22.46
23.91
22.65
1.24
1.39
29.62
31.80
4.14
4.94
29.88
35.83
16.44
21.08
16.22
20.55
39.42
36.95
8.87
12.31
23.66
23.21
9.39
11.52
52.30
50.49
50.91
49.46
21.64
25.52
14.98
18.42
16.50
19.60
18.75
23.34
4.85
6.46
7.80
7.32
1.49
1.60
11.31
14.16
8.79
11.30
1.51
2.75
16.03
18.98
13.68
16.74
41.77
50.42
9.84
12.28
5.46
6.59
14.15
19.76
14.83
20.78
11.11
14.77
11.10
12.81
6.05
9.15
5.23
5.87
1.37
1.79
2.60
2.69
7.95
8.53
9.66
12.48
19.89
24.44
35.63
38.13
21.32
25.72
37.25
43.37
26.02
28.18
25.01
28.52
51.70
53.94
6.79
9.11
70.04
69.58
37.42
40.33
36.89
42.11
22.14
12.90
42.74
43.29
56.07
70.16
25.92
25.75
28.24
29.06
30.33
39.58
28.09
26.59
50.76
57.73
50.07
60.19
27.37
29.02
38.82
42.77
18.21
19.64
37.97
40.83
24.94
11.93
22.76
12.34
30.12
16.84
34.35
18.37
6.57
2.84
8.72
3.76
21.27
12.38
26.25
12.97
21.55
15.82
19.63
10.68
18.06
8.97
15.76
11.83
15.36
9.52
32.78
15.91
28.60
16.52
38.42
25.79
40.49
22.27
8.54
6.33
34.27
20.69
19.24
8.56
33.32
17.58
4.48
1.90
4.58
1.87
27.01
15.82
1.51
2.05
1.22
1.47
1.24
1.70
4.63
6.52
15.34
18.57
7.51
8.56
5.47
7.25
0.00
0.00
0.00
0.00
5.92
7.56
6.33
8.12
1.37
1.99
2.44
3.11
0.00
0.00
0.60
0.93
1.25
1.85
1.66
2.52
4.99
6.63
0.06
0.12
1.80
2.17
19.55
27.15
1.07
1.27
16.99
18.98
2.06
2.91
1.00
1.25
0.00
0.00
0.78
1.00
3.66
5.87
29.60
29.47
5.03
5.74
0.13
0.22
0.52
0.83
0.00
0.00
0.00
0.00
1.12
1.41
17.72
21.47
0.00
0.00
1.56
2.40
0.85
1.21
0.82
1.44
0.18
0.31
7.86
10.70
0.00
0.00
0.00
0.00
9.00
12.94
0.73
0.87
1.35
1.52
1.66
2.43
-34.89
-29.16
-36.08
-41.76
-17.37
-27.70
-38.03
-36.94
-57.56
-32.76
-34.75
-31.19
-30.77
-35.82
-42.56
-43.22
-41.30
-28.72
-48.26
-30.01
-40.96
-25.04
-25.71
-38.29
BAU values are extrapolated from IEA (2015) data for 2012 to 2050 as follows: EIA’s International Energy
Outlook (IEO) projects energy use by end-use sector, fuel, and world region out to 2040 (EIA, 2015). This is
extended to 2075 using a ten-year moving linear extrapolation. EIA sectors and fuels are then mapped to IEA
sectors and fuels, and each country’s 2012 energy consumption by sector and fuel is scaled by the ratio of
EIA’s 2050/2012 energy consumption by sector and fuel for each region. The transportation load includes,
among other loads, energy produced in each country for international transportation and shipping. 2050 WWS
values are estimated from 2050 BAU values assuming electrification of end-uses and effects of additional
energy-efficiency measures. See Delucchi et al. (2016) for details.
In 2012, the 139-country all-purpose, end-use load was ~11.95 TW. Of this, 2.4 TW
(20.1%) was electricity demand. Under the BAU trajectory, all-purpose end-use load may
9
grow to 19.4 TW in 2050. Conversion to WWS by 2050 reduces the 139-country load by
~38.3%, to 12.0 TW (Table 1), with the greatest percentage reduction in transportation.
While electricity use increases with WWS, conventional fuel use decreases to zero. The
increase in electricity is much less than the decrease in energy in the gas, liquid, and solid
fuels that the electricity replaces, because of (a) the high energy-to-work conversion
efficiency of electricity used for heating and electric motors and (b) because WWS
eliminates the energy needed to mine, transport, and refine coal, oil, gas, biofuels,
bioenergy, and uranium. Also, (c) the use of WWS electricity to produce hydrogen for fuel
cell vehicles, while less efficient than is the use of WWS electricity to run BEVs, is more
efficient and cleaner than burning liquid fuels for vehicles (Jacobson et al., 2005; Jacobson
and Delucchi, 2011). On the other hand, burning electrolytic hydrogen is slightly less
efficient but cleaner than is burning fossil fuels for direct heating. Table 1 takes account of
this. These three factors decrease 139-country demand ~31.4% with WWS. The remaining
6.9% reduction is due to end-use energy efficiency measures beyond BAU measures.
The percent decrease in load upon conversion to WWS in Table 1 is greater in some
countries than in others. The reason is that the transportation-energy share of total energy is
greater in some countries than in others. This trend is shown in Table 1, where countries
with a higher fraction of load in the transportation sector exhibit a larger efficiency gain.
4. Numbers of Electric Power Generators Needed and Land-Use Implications
Table 2 summarizes the number of WWS power plants or devices needed to power the sum
of all 139 countries in 2050 for all purposes assuming the end use power requirements in
Table 1 and the percent mixes of end-use power generation by country in Table 3. Table 2
accounts for power losses during energy transmission and distribution, generator
maintenance, and competition among wind turbines for limited kinetic energy (array losses).
Table 2. Number, capacity, footprint area, and spacing area of WWS power plants or devices needed to
provide total annually averaged end-use all-purpose load over all 139 countries examined. Derivations for
individual countries are in Delucchi et al. (2016).
a
Energy Technology
Annual power
Onshore wind
Offshore wind
Wave device
Geothermal plant
Hydropower plant c
Tidal turbine
Res. roof PV
Com/gov roof PV d
Solar PV plant d
Utility CSP plant d
Total for annual power
New land annual powere
Rated
power
one
plant
or
device
(MW)
5
5
0.75
100
1300
1
0.005
0.1
50
100
Percent
of 2050
allpurpose
load
met by
plant/de
vice
Name-plate
capacity,
existing
plus new
plants or
devices
(GW)
Percent
nameplate
capacity
already
installed
2014
19.76
12.74
0.38
0.73
4.31
0.063
6.51
7.01
40.81
7.67
100.00
6,422
3,812
199
97
1,036
31
3,937
4,279
24,432
1,574
45,818
5.65
0.23
0.00
13.03
100.00
1.74
1.01
1.39
0.30
0.36
3.49
Number of
new plants
or devices
needed for
139
countries
1,211,858
760,708
265,409
840
0
30,093
779,427,788
42,195,440
487,154
15,679
824,394,970
Percent of
139-country
land area for
footprint of
new plants
or devices
Percent of
139country
area for
spacing of
new plants
or devices
0.000013
0.000008
0.000116
0.000241
0.000000
0.000007
0.014299
0.015482
0.179980
0.038434
0.249
0.219
0.67493
0.42366
0.00000
0.00000
0.00000
0.00009
0.00000
0.00000
0.00000
0.00000
1.099
0.675
b
10
For peaking/storage
Additional CSP f
Solar thermal f
Geothermal heat f
Total all
Total new lande
100
50
50
4.60%
944
5,136
70
51,968
0.00
0.63
100.00
3.27
9,442
102,068
0
824,506,479
0.023144
0.006089
0.000000
0.278
0.248
0.000
0.000
0.000
1.099
0.675
The total number of each device is the sum among all countries. The number of devices in each country is the
end-use load in 2050 in each country to be supplied by WWS (Table 1) multiplied by the fraction of load
satisfied by each WWS device in each country (Table 3) and divided by the annual power output from each
device. The annual output by device equals the rated power in the first column (same for all countries)
multiplied by the country-specific annual capacity factor of the device, diminished by transmission,
distribution, maintenance, and array losses. The capacity factors, given in Delucchi et al. (2016), before
transmission, distribution, and maintenance losses for onshore and offshore wind turbines at 100-m hub height
in 2050, are calculated country by country from global model simulations of winds and wind power (Figure 3)
that account for competition among wind turbines for available kinetic energy based on the approximate
number of turbines needed per country as determined iteratively from Tables 2 and 3. Wind array losses due to
competition among turbines for the same energy are calculated here to be ~8.5%. The 2050 139-country mean
onshore and offshore wind capacity factors calculated in this manner after transmission, distribution,
maintenance, and array losses are 37.1% and ~40.2%, respectively. Short- and moderate distance transmission,
distribution, and maintenance losses for all energy sources treated here, except rooftop PV, are assumed to be
5-10%. Rooftop PV losses are assumed to be 1-2%. The plans assume 38 (30-45)% of onshore wind and solar
power and 20 (15-25)% of offshore wind power is subject to long-distance transmission with line lengths of
1400 (1200-1600) km and 120 (80-160) km, respectively. Line losses are 4 (3-5)% per 1000 km plus 1.5 (1.31.8)% of power in the station equipment. Footprint and spacing areas are calculated from the spreadsheets in
Delucchi et al. (2016). Footprint is the area on the top surface of soil covered by an energy technology, thus
does not include underground structures.
a
Total end-use power demand in 2050 with 100% WWS is from Table 1.
b
Total land area for each country is given in Delucchi et al. (2016). 139-country land area is 119,725,384 km2.
The world land area is 510,072,000 km2.
c
The average capacity factors of hydropower plants are assumed to increase from their current values to 49.9%,
except for Tajikistan and Paraguay, which are assumed to increase to 40% (see text).
d
The solar PV panels used for this calculation are Sun Power E20 panels. For footprint calculations, the CSP
mirror sizes are set to those at Ivanpah. CSP is assumed to have storage with a maximum charge to
discharge rate (storage size to generator size ratio) of 2.62:1. The capacity factors used for residential PV,
commercial/government rooftop PV, utility scale PV, and CSP are calculated here country-by-country
from the 3-D global model, which is also used to calculate the solar resource (Figure 5), and are given in
Delucchi et al. (2016). For utility solar PV plants, nominal “spacing” between panels is included in the
plant footprint area.
e
The footprint area requiring new land equals the sum of the footprint areas for new onshore wind, geothermal,
hydropower, and utility solar PV. Offshore wind, wave and tidal generators are in water
and thus do not require new land. Similarly, rooftop solar PV does not use new land because the rooftops
already exist. Only onshore wind requires new land for spacing area. Spacing area is for onshore and
offshore wind is calculated as 42D2, where D=rotor diameter. The 5-MW Senvion (RePower) turbine
assumed here has D=126 m.
The other energy sources either are in water or on rooftops, or do not use new land for spacing. Note that the
spacing area for onshore wind can be used for multiple purposes, such as open space, agriculture, grazing,
etc.
f
The installed capacities for peaking power/storage are estimated from Jacobson et al. (2015b). Additional CSP
is CSP plus storage needed beyond that for annual power generation to firm the grid across all countries.
Additional solar thermal and geothermal are used for soil heat storage. Other types of storage are also used
in Jacobson et al. (2015b).
Rooftop PV in Table 2 is divided into residential (5-kW systems on average) and
commercial/government (100-kW systems on average). Rooftop PV can be placed on
11
existing rooftops or on elevated canopies above parking lots, highways, and structures
without taking up additional undeveloped land. Table 4 summarizes projected 2050 rooftop
areas by country usable for solar PV on residential and commercial/government buildings,
carports, garages, parking structures, and parking lot canopies. The rooftop areas in Table 4
are used to calculate potential rooftop generation, which in turn limits the penetration of PV
on residential and commercial/government buildings in Table 3. Utility-scale PV power
plants are sized, on average, relatively small (50 MW) to allow optimal placement in
available locations. While utility-scale PV can operate in any country because it can take
advantage of both direct and diffuse solar radiation, CSP is assumed to be viable only in
countries with significant direct solar radiation, and its penetration in each country is limited
to less than its technical potential.
Onshore wind is available to some extent in every country but assumed to be viable in high
penetrations primarily in countries with good wind resources (Section 5.1). Offshore wind is
assumed to be viable in any country with either ocean or lake coastline (Section 5.1). Wind
and solar are the only two sources of electric power with sufficient resource to power the
world independently on their own. Averaged over the 139 countries, wind (~32.5%) and
solar (62.0%) are the largest generators of annually averaged end-use electric power under
these plans. The ratio of wind to solar end-use power is 0.52:1.
Under the roadmaps, the 2050 nameplate capacity of hydropower in each country is
assumed to be exactly the same as in 2014. However, existing dams in most countries are
assumed to run more efficiently for producing peaking power, thus the capacity factor of
dams is assumed to increase (Section 5.4). Geothermal, tidal, and wave energy expansions
are limited in each country by their technical potentials (Sections 5.3 and 5.5).
Table 2 indicates that 3.5% of the summed nameplate capacity required for a 100% WWS
system for 2050 all-purpose, annually-averaged power in the 139 countries was already
installed as of the end of 2014. Figure 1 shows that the countries closest to 100% 2050 allpurpose WWS power as of the end of 2014 are Norway (55%), Paraguay (53%), Iceland
(38%), Tajikistan (36%), Portugal (21%), Sweden (21%), and Costa Rica (18%). The
United States (3.7%) ranks 57th and China (3.3%) ranks 65th.
Figure 1. Countries ranked in order of how close they are at the end of 2014 to reaching 100% WWS power
for all purposes in 2050. The first number is nameplate capacity needed in 2050 (GW); percentages are of
2050 WWS installed capacity (summed over all WWS technologies) needed that is already installed. The 139country total needed is 45.82 TW and installed is 3.49% of this.
12
13
Table 2 also lists needed installed capacities of 1) additional CSP with storage, 2) solar
thermal collectors, and 3) existing geothermal. These collectors are needed to provide
electricity or heat that is stored then used later to provide peaking power and to account for
power losses into and out of such storage (Section 6).
Table 3. Percent of annually averaged 2050 country-specific all-purpose end-use load (not installed capacity)
in a WWS world from Table 1 proposed here to be met by the given electric power generator. All rows add up
to 100%.
Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei Darussalam
Bulgaria
Cambodia
Cameroon
Canada
Chile
China
Chinese Taipei
Colombia
Congo
Congo, Dem. Republic
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Onshore
wind
1.50
1.25
8.00
30.00
18.50
30.00
27.00
45.00
1.00
15.00
45.00
10.00
29.20
25.00
10.50
30.00
0.85
5.00
7.00
30.00
15.00
37.50
25.00
16.00
2.00
25.00
10.00
10.00
3.00
22.40
30.00
22.93
20.00
25.00
28.00
22.00
35.00
20.00
10.00
8.50
60.00
16.00
41.00
30.00
15.00
18.00
Offshore
wind
Wave
Geothermal
Hydroelectric
Tidal
Res
PV
Comm/
gov PV
Utility
PV
CSP
0.28
0.00
1.00
20.00
0.00
6.20
0.00
0.00
8.00
0.00
0.00
18.00
0.30
0.00
1.20
0.00
17.00
11.50
0.00
7.90
1.50
21.00
10.00
12.60
37.50
14.10
12.00
0.00
1.10
7.00
0.00
15.00
13.00
0.00
57.00
3.00
1.00
0.25
2.00
0.80
20.66
0.00
41.00
25.00
20.00
4.00
1.20
0.04
0.20
0.52
0.00
5.00
0.00
0.00
0.20
0.11
0.00
0.01
0.30
0.00
0.04
0.00
0.16
0.00
0.00
0.63
0.50
2.00
1.00
0.04
0.12
0.90
1.00
0.00
1.00
0.50
1.00
2.00
1.00
0.00
3.00
1.00
1.80
0.20
0.20
0.20
2.00
0.00
0.09
1.25
2.00
0.60
0.00
0.00
0.00
1.05
0.64
0.40
0.00
0.00
0.00
0.00
0.02
0.00
0.00
14.76
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.87
3.09
0.05
26.61
0.00
0.00
0.00
26.44
0.00
0.00
0.00
0.00
0.00
0.00
9.83
0.33
0.00
31.75
0.00
0.00
4.08
0.00
0.02
0.00
0.00
27.96
0.21
2.74
6.62
17.68
4.43
13.82
4.93
0.00
0.27
0.02
0.15
0.02
3.21
23.40
0.00
11.25
0.00
7.35
9.25
5.27
16.14
6.54
4.24
0.91
16.54
8.50
3.92
21.52
3.62
9.99
0.31
0.00
2.10
0.03
4.36
10.23
1.34
8.20
0.00
0.12
2.88
5.49
5.87
2.79
29.28
0.09
0.01
0.05
0.02
0.00
0.14
0.00
0.00
0.02
0.09
0.00
0.00
0.04
0.00
0.01
0.00
0.01
0.06
0.02
0.05
0.04
0.20
0.05
0.01
0.01
0.38
0.13
0.00
0.14
0.04
0.18
0.11
0.15
0.00
0.11
0.09
0.56
0.01
0.08
1.47
0.35
0.00
0.02
0.16
0.15
0.05
8.90
10.93
43.68
9.48
6.02
5.65
6.03
8.14
3.84
19.50
0.90
5.36
14.25
23.63
8.07
6.52
8.66
8.01
1.84
30.69
11.88
1.70
6.24
4.28
1.73
11.04
32.89
7.95
20.14
13.32
2.85
15.60
15.47
4.86
1.88
28.45
29.66
13.75
20.99
62.50
0.93
21.10
0.29
12.24
7.02
6.15
11.33
12.11
32.62
10.86
6.47
7.00
6.54
10.48
6.27
7.77
1.69
5.59
6.93
10.12
11.10
10.14
11.95
10.06
5.18
14.33
6.48
1.97
8.10
5.31
3.63
7.37
25.65
2.04
20.55
8.41
5.72
9.82
16.70
7.81
1.94
23.60
16.33
9.47
11.90
20.53
1.52
6.09
0.65
11.47
8.62
8.61
48.74
60.46
1.21
11.45
30.69
31.20
46.41
24.44
60.67
52.27
51.37
60.89
38.97
18.27
40.68
38.34
40.12
55.37
78.62
2.15
44.33
17.61
34.98
48.47
27.49
19.68
9.83
51.10
1.10
44.61
49.27
29.23
23.68
60.22
8.04
2.67
5.09
39.98
9.89
1.00
14.42
31.85
11.46
13.24
44.42
33.31
0.00
15.00
10.50
10.00
20.00
10.00
0.20
7.00
20.00
5.00
1.00
0.00
10.00
5.00
5.00
15.00
10.00
10.00
0.00
5.00
15.00
0.00
5.00
9.00
0.00
5.00
0.00
25.00
5.00
0.10
1.00
5.00
10.00
0.00
0.00
5.00
0.00
15.00
5.00
5.00
0.00
18.00
0.00
0.75
0.00
0.00
14
Germany
Ghana
Gibraltar
Greece
Guatemala
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. People's Rep.
Korea, Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia, Republic of
Malaysia
Malta
Mexico
Moldova, Republic of
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
35.00
21.10
0.03
30.00
7.00
30.00
25.00
0.25
1.70
39.64
17.00
6.30
11.00
25.00
46.00
6.00
11.00
10.00
4.50
30.00
46.50
21.00
25.00
3.50
15.00
5.00
15.00
35.00
10.00
26.50
15.00
22.00
40.00
14.00
1.00
25.00
45.00
38.00
10.00
22.50
25.00
10.00
14.00
15.00
5.00
2.00
30.00
10.00
20.00
14.00
18.00
2.50
30.00
3.00
25.00
5.00
43.00
35.00
3.50
17.00
4.00
4.40
4.00
3.00
11.00
7.50
10.00
0.00
6.00
3.20
10.00
2.50
0.00
37.00
1.00
0.90
20.00
6.00
0.00
6.50
7.00
12.50
12.00
0.00
6.80
0.00
14.50
8.00
3.50
50.00
0.00
0.00
8.90
10.00
7.90
0.00
0.00
15.00
5.00
2.00
12.00
3.25
0.00
60.00
8.00
13.25
2.00
0.00
10.00
3.80
2.25
5.00
0.00
0.00
10.00
29.00
15.00
7.90
0.08
0.50
0.10
1.00
0.40
1.00
1.50
0.21
0.00
2.00
0.07
2.00
0.09
0.02
1.80
0.14
0.45
2.00
1.00
0.00
0.20
0.30
0.70
0.10
0.00
0.18
0.00
0.60
0.25
0.95
0.10
0.00
0.00
0.50
1.25
0.40
0.00
0.00
2.00
0.60
2.00
0.20
2.00
0.00
0.06
1.30
1.00
1.00
0.05
0.55
0.50
0.08
1.00
0.00
1.00
5.00
0.06
1.00
0.20
0.01
0.00
0.00
2.37
23.59
0.00
11.08
0.00
2.13
23.20
0.03
3.82
0.00
0.00
0.00
0.00
0.63
0.00
0.56
0.00
0.00
10.69
0.00
0.00
36.97
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.34
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
13.09
18.27
0.00
0.00
0.00
0.00
0.00
0.00
6.70
12.13
0.16
0.62
0.00
0.87
8.27
0.00
7.90
5.74
1.06
5.82
0.00
0.17
28.75
2.40
1.14
2.20
4.51
1.27
0.02
5.03
0.48
4.70
0.12
1.53
2.66
7.98
0.45
10.21
0.00
26.96
12.03
2.12
0.00
0.73
0.54
8.15
2.99
0.00
3.12
1.07
0.00
31.48
2.67
10.38
8.47
6.14
3.18
0.03
0.00
19.13
2.37
0.76
68.26
0.00
3.08
12.73
67.11
10.09
4.14
0.48
15.44
0.00
0.00
0.03
0.03
0.16
0.03
0.25
0.09
0.01
0.00
0.40
0.02
0.03
0.00
0.00
0.07
0.01
0.01
0.21
0.23
0.01
0.01
0.02
0.79
0.13
0.00
0.01
0.00
0.05
0.04
0.04
0.01
0.00
0.00
0.02
0.13
0.01
0.00
0.00
0.21
0.03
2.34
0.26
0.24
0.00
0.00
0.11
0.36
0.21
0.00
0.41
0.02
0.00
0.77
0.00
0.05
0.29
0.00
0.85
0.01
6.75
10.44
0.44
16.72
26.77
33.97
20.34
3.77
3.56
0.00
7.45
10.24
2.91
12.89
2.78
17.13
6.04
19.34
8.69
11.08
2.95
17.25
2.15
2.47
3.50
1.60
9.84
0.75
5.22
6.66
2.09
6.23
6.62
4.75
4.93
12.10
4.62
3.23
4.69
9.91
15.96
24.50
4.02
13.76
1.88
4.08
3.26
28.13
11.40
0.42
1.76
14.42
13.40
20.18
25.53
37.08
5.69
8.65
1.36
6.48
7.47
0.89
10.78
12.33
7.52
7.86
4.40
5.17
0.00
10.27
10.16
2.66
7.69
3.40
16.16
7.44
14.82
13.38
10.77
4.71
8.63
0.67
5.86
3.37
3.66
7.14
1.38
10.21
9.31
2.78
5.75
9.40
12.49
9.44
16.39
5.36
3.77
7.72
8.82
5.62
11.30
4.75
4.48
1.91
3.77
4.36
11.85
11.74
0.83
2.30
7.84
12.67
9.47
15.95
20.32
11.78
11.29
3.19
33.80
47.94
94.08
22.37
11.14
5.20
13.31
66.36
87.26
0.01
48.06
46.31
60.63
38.99
7.67
39.54
66.49
33.16
58.95
33.02
37.60
25.44
50.21
74.25
30.95
54.74
41.06
35.69
59.17
38.03
29.28
65.48
35.83
56.35
68.26
19.74
43.95
55.00
28.89
45.47
31.71
28.26
60.60
53.58
31.11
80.74
15.55
21.18
36.04
5.54
58.62
54.82
24.43
0.24
13.68
6.04
9.82
9.40
77.33
0.00
0.25
0.04
4.70
10.00
10.00
7.50
15.00
0.00
0.00
11.50
10.00
18.00
10.90
0.00
20.00
2.00
0.00
2.00
15.00
0.00
7.00
0.00
1.25
0.00
28.00
0.00
0.00
5.00
15.00
0.00
0.00
0.00
0.00
5.00
13.00
0.00
0.00
0.00
5.00
5.00
5.00
5.00
10.00
0.00
0.00
0.00
5.00
20.00
0.00
15.00
15.00
0.00
0.00
2.00
0.00
0.00
2.75
6.50
15
Romania
Russian Federation
Saudi Arabia
Senegal
Serbia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania, United Republic
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
World average
24.40
48.80
11.00
20.00
25.00
0.10
35.00
30.00
20.00
25.70
20.00
12.00
55.00
15.00
35.00
20.00
18.55
11.00
21.50
1.00
23.00
16.00
16.00
25.00
4.00
20.00
30.92
30.00
4.00
15.80
0.01
4.00
20.00
21.50
19.76
22.00
22.00
0.00
5.00
0.00
0.48
0.00
2.80
7.00
10.00
22.00
7.35
19.00
0.00
1.50
0.00
7.00
5.40
2.00
48.00
4.00
0.05
0.00
30.00
4.00
65.00
17.50
13.50
0.00
20.00
30.00
5.00
0.00
0.00
12.74
0.05
0.50
0.15
1.00
0.00
0.03
0.00
0.08
0.15
0.50
0.70
0.50
0.70
0.00
0.00
0.00
0.50
0.16
0.20
0.40
0.60
0.50
0.00
0.20
0.10
0.80
0.37
2.00
0.00
1.00
1.90
2.00
0.00
0.00
0.38
0.26
0.08
0.00
0.00
0.00
6.10
0.00
1.99
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.07
0.00
0.00
0.00
0.82
0.00
0.00
0.00
0.00
0.45
0.00
0.00
0.00
0.00
1.19
0.77
0.00
0.73
9.80
4.34
0.00
0.00
8.07
0.00
7.54
12.27
0.51
8.57
5.70
7.98
21.61
34.76
5.94
49.61
1.11
1.13
1.55
0.00
0.22
14.59
0.00
2.26
0.00
0.65
3.02
16.30
1.49
12.00
7.68
0.00
10.77
2.93
4.31
0.01
0.01
0.01
0.09
0.00
0.00
0.00
0.01
0.01
0.29
0.04
0.03
0.06
0.00
0.01
0.00
0.49
0.01
0.04
0.04
0.04
0.02
0.00
0.01
0.01
2.17
0.01
0.08
0.00
0.02
0.01
0.10
0.00
0.00
0.06
1.86
0.86
3.80
21.84
2.82
4.33
2.68
2.76
1.82
12.44
21.38
24.91
0.85
5.22
13.88
19.63
9.91
3.99
13.96
4.20
6.02
12.82
1.70
1.71
0.92
1.28
9.47
8.90
4.11
8.07
9.84
27.28
15.28
7.61
6.51
6.30
1.57
5.07
10.69
5.85
4.20
4.54
4.08
2.98
11.09
14.40
15.85
1.14
9.35
6.80
9.46
4.29
5.12
4.41
4.07
6.59
11.95
3.31
2.41
1.53
3.49
8.67
12.40
2.42
6.99
5.76
8.29
9.11
7.22
7.01
35.33
21.84
44.97
31.38
58.26
84.76
50.24
46.01
57.53
20.37
15.77
11.38
1.62
35.67
29.87
1.31
48.15
68.12
51.35
41.29
54.53
35.25
78.99
38.41
79.45
6.60
22.30
16.82
87.99
25.88
30.85
39.14
26.07
36.74
40.81
0.00
0.00
35.00
10.00
0.00
0.00
0.00
0.00
10.00
10.98
0.00
20.00
0.00
0.00
7.00
0.00
10.00
5.00
5.00
1.00
5.00
8.00
0.00
0.00
10.00
0.00
7.30
0.00
0.00
10.25
13.95
13.00
18.00
24.00
7.67
Figure 2 shows the additional footprint and spacing areas required from Table 2 to replace
the 139-country all-purpose energy infrastructure with WWS by 2050. Footprint area is the
physical area on the top surface of the ground or water needed for each energy device.
Spacing area is the area between some devices, such as wind, tidal, and wave turbines,
needed to minimize interference of the wake of one turbine with downwind turbines.
Only onshore wind, geothermal, additional hydropower (none of which is proposed here),
utility PV plants, and CSP plants require new footprint on land. Rooftop PV does not take
up new land. Table 2 indicates that the total new land footprint required for the plans,
averaged over the 139 countries is ~0.25% of the land area of the countries, mostly for
utility PV and CSP plants. This does not account for the decrease in footprint from
eliminating the current energy infrastructure, which includes the footprint for mining,
transporting, and refining fossil fuels and uranium and for growing, transporting, and
refining biofuels and bioenergy.
The only spacing over land needed for the WWS system is between onshore wind turbines
and requires ~0.68% of the 139-country land area.
16
For several reasons, the footprint or spacing area of additional transmission lines is
neglected in this study. Transmission systems have virtually no footprint on the ground
because transmission towers are four metal supports connected to small foundations,
allowing grass to grow under the towers. Further, the rights-of-way under transmission lines
can typically accommodate many uses; more than can the rights-of-way under gas and oil
pipelines and other conventional infrastructure that new transmission lines will replace.
Finally, in the WWS roadmaps, as much additional transmission capacity as possible will be
placed along existing pathways but with enhanced lines.
Figure 2. Footprint plus spacing areas required from Table 2, beyond existing 2014 resources, to repower the
139 countries for all purposes in 2050. The dots do not indicate the actual location of energy farms, just their
relative spacing areas. After the name of each resource the thousands of square kilometers of footprint plus
spacing is listed. The land area of all 139 countries is 119,725,384 km2. For hydropower, the new footprint
plus spacing area is zero since no new installations are proposed. For tidal + wave and geothermal, the new
spacing areas are so small they are difficult to distinguish on the map. For rooftop PV, the circle represents the
new rooftop area needed.
90
Rooftop PV-35.4
Onshore wind-808
Geothermal-0.29
0
Hydro-0
Tidal+wave-0.15
Offshore wind-507
Utility PV+CSP-296
-90
-180
-90
0
90
180
5. Resource Availability
This section evaluates whether the 139 countries have sufficient wind, solar, geothermal,
and hydropower resources to supply each country’s all-purpose power in 2050.
5.1. Wind
Figure 3 shows three-dimensional computer model estimates, derived for this study, of the
world annually averaged wind speed and capacity factor at the 100-m hub height above the
topographical surface of modern wind turbines. The figures assume no extraction of kinetic
energy by wind turbines since they only indicate where winds are strong. The figure also
compares near-surface modeled wind speeds with QuikSCAT data over the oceans,
suggesting model predictions and data are similar at that height and giving confidence in the
100-m values.
17
Locations of strong onshore wind resources include the Great Plains of the U.S. and Canada,
the Sahara desert, the Gobi desert, much of Australia, the south of Argentina, South Africa,
and northern Europe among other locations. Strong offshore wind resources occur off the
east and west coasts of North America, over the Great Lakes, the North Sea, the west coast
of Europe and the east coast of Asia, offshore of Peru and Argentina, Australia, South
Africa, India, Saudi Arabia, and west Africa.
Our estimates of the nameplate capacity of onshore and offshore wind to be installed in each
country (Tables 2 and 3) are limited by the country’s power demand and technical potential
available for onshore (NREL, 2012a) and offshore (Arent et al., 2012) turbines. Only 3.6%
of the onshore technical potential and 26.5% of the near-shore offshore technical potential
are proposed for use in 2050. Table 2 indicates that the 2050 WWS roadmaps require
~0.67% of the 139-country onshore land area and 0.42% of the 139-country onshoreequivalent land area sited offshore for wind-turbine spacing to power 32.5% of all-purpose
annually averaged 139-country power in 2015.
Figure 3. (a) QuikSCAT 10-m above ground level (AGL) wind speed at 1.5o x 1.5o resolution (JPL, 2010), (b)
GATOR-GCMOM (Jacobson, 2010b) 4-year-average modeled annual 15-m AGL wind speed at 2.5o W-E x
2.0o S-N resolution, (c) Same as (b) but at 100 m AGL, (d) Same as (c) but showing capacity factors assuming
a Senvion (RePower) 5 MW turbine with 126-m rotor diameter. In all cases, wind speeds are determined
before accounting for competition among wind turbines for the same kinetic energy.
a) Quickscat 2004-2008 wind speed 10 m AGL (m/s)
90
12
b) Annual wind speed 15 m AGL (m/s) (global: 6.7; land: 4.8; sea: 7.5)
90
12
10
10
8
8
0
6
0
6
4
4
2
2
0
-90
-180
-90
0
90
180
-180
c) Annual wind speed (m/s) 100 m AGL (global: 8.9; land: 7.9; sea: 9.3)
90
0
-90
-90
0
90
180
d) Annual capacity factor (--) 100 m AGL (global: 0.436; land: 0.34; sea: 0.47)
90
0.7
14
0.6
12
0.5
10
8
0
0.4
0
6
0.3
4
0.2
0.1
2
0
-90
-180
-90
0
90
180
-180
a) Quickscat 2004-2008 wind speed 10 m AGL (m/s)
90
0
-90
12
-90
0
90
180
Wind speed 15 m AGL (m/s) (global: 6.2; land: 4.4; sea: 7.0)
90
12
10
10
8
0
6
8
0
6
4
4
2
0
-90
-180
-90
0
90
180
2
-90
-180
-90
0
90
180
18
Annual wind speed (m/s) 100 m AGL (global: 8.1; land: 7.0; sea: 8.5)
90
14
Wind power capacity factor 100 m AGL (global:0.46; land:0.35; sea:0.51)
90
0.8
12
0.7
10
0.6
0.5
8
0
6
0
0.4
0.3
4
0.2
2
0
-90
-180
-90
0
90
180
0.1
0
-90
-180
-90
0
90
180
As of the end of 2014, 3.6% of the proposed 2050 onshore plus offshore wind power
nameplate capacity of 10.2 TW among the 139 countries has been installed. Figure 4
indicates that China, the United States, and Germany have installed the greatest capacity of
onshore wind, whereas the United Kingdom, Denmark, and Germany have installed the
most offshore wind.
Figure 4. Installed onshore and offshore wind power by country as of the end of 2014. Capacity is determined
first from GWEC (2015) year-end values for 2014, followed by IEA (2014b) capacity estimates for 2014, then
IEA (2015) capacity estimates for 2011.
19
5.2. Solar
20
Figure 5 shows annually averaged modeled solar irradiance worldwide accounting for sun
angles, day/night, and clouds. The best solar resources are broadly between 40 oN and 40 oS.
The new land area in 2050 required for non-rooftop solar under the plan here is equivalent
to ~0.25% of the 139-country land area (Table 2).
Figure 5. Modeled annually averaged downward direct plus diffuse solar irradiance at the ground
(kWh/m2/day) worldwide. The model used is GATOR-GCMOM (Jacobson, 2010), which simulates clouds,
aerosols gases, weather, radiation fields, and variations in surface albedo over time. The model is run with
horizontal resolution of 2.5o W-E x 2.0o S-N.
90
Annual down solar irradiance at ground (kWh/m2/d) (global: 4.4; land: 4.3; sea: 4.5)
7
6
5
4
0
3
2
1
0
-90
-180
-90
0
90
180
of 1.5o W-E x 1.5o S-N.
90
Annual downward solar irradiance at ground (kWh/m2/d) (global: 4.57; land: 4.42)
7
6
5
4
0
3
2
1
0
-90
-180
-90
0
90
180
Table 4 provides estimates of each country’s maximum rooftop PV nameplate capacity. The
proposed capacity for each country, summed in Table 2, is limited by the values in Table 4.
Rooftops considered include those on residential, commercial, and governmental buildings,
21
and garages, carports, parking lots, and parking structures associated with these buildings.
Commercial and governmental buildings include all non-residential buildings except
manufacturing, industrial, and military buildings. Commercial buildings include schools.
The total residential rooftop area suitable for PV in each country in 2050 is calculated by
extrapolating the fraction of 2050 population living in urban versus rural areas linearly but
with upper limits from 2005-2014 urban fraction data (World Bank, 2015c). Projected 2050
population in each country is then divided between rural and urban population. Population
in each case is then multiplied by floor area per capita by country (assumed the same for
rural and urban homes) from Entranze Data Tool (2015) for European countries, IEA (2005)
for a few additional countries, and IEA (2014a) for remaining regions of the world. The
result is finally multiplied by the utilization factor (UF), which is the ratio of the usable
rooftop area to ground floor area. For rural areas in each country, UF=0.2. Eiffert (2003)
estimates UF=0.4 for rooftops and 0.15 for facades, but for single-family rural residential
homes, shading is assumed here to reduce the UF to 0.2. For urban areas, UF=0.4 is
assumed, but the urban area population is divided by the number of floors in each urban
complex to account for the fact that urban buildings house more people per unit ground
floor area. The number of floors is estimated by country in Europe from Entranze Data Tool
(2015) as the number of dwellings per multi-family building divided by an estimated four
dwellings on the bottom floor of a building. This gives the average number of floors in an
urban area ranging from 2 to 5 for these countries. We assume 3 floors per urban dwelling in
most other countries. Potential solar PV installed capacity is then calculated as the installed
capacity of a Sunpower E20 435 W panel multiplied by the suitable rooftop area and
divided by panel area.
The total commercial rooftop area suitable for PV for European countries in 2050 is
calculated as the product of the estimated 2050 country population, the average commercial
ground floor area per capita (Entranze Data Tool, 2015), and a UF=0.4 (Eiffert, 2003).
Scaling the European value to the GDP/capita of countries to that of European countries
gives the average commercial ground floor area per capita in other countries. Potential solar
PV installed capacity is then the installed capacity of a Sunpower E20 435 W panel
multiplied by suitable rooftop area and divided by panel area.
The potential rooftop or canopy area over parking spaces in each country is computed by
multiplying the number of passenger cars per person (World Bank, 2014) by the average
parking space per car (30 m2, Dulac, 2013) in the country. Given that 1) some of these
parking spaces will be in residential garages that have already been included in the
residential rooftop PV calculation, and 2) some parking spaces will not necessarily have a
roof (e.g. basement parking spaces), a utilization factor of 0.5 is applied to the estimate for
parking area suitable for PV. With these assumptions, the PV capacity on parking-space
rooftops is ~15% of the maximum capacity on residential rooftops and ~9% of the
maximum capacity on residential-plus-commercial rooftops.
One issue is the extent to which snow cover may reduce power output. If snow is not
removed from panels, panel output may decrease by ~5-15% in the annual average.
However, this appears more than compensated for by the fact that at -45 C, for example, a
22
PV system provides 29% more power than its rating suggests (Northern Alberta Institute of
Technology, 2016; Dodge and Thompson, 2016). We conservatively assume these two
factors offset each other so don’t modify the number of panels needed due to snow cover.
Based on the foregoing analysis, 2050 residential rooftop areas (including garages and
carports) are estimated to support up to 7.5 TWdc-peak of installed power among the 139
countries. The plans here propose to install 52.6% of this potential. In 2050,
commercial/government rooftop areas (including parking lots and parking structures) are
estimated to support 7.7 TWdc-peak of installed power. The country plans here propose to
cover 55.4% of installable power, with low-latitude and high GDP-per-capita countries
expected to adopt PV the fastest.
Table 4. Rooftop areas suitable for PV panels, potential capacity of suitable rooftop areas, and proposed
installed capacity for both residential and commercial/government buildings, by country. See Delucchi et al.
(2016) for calculations.
Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei Darussalam
Bulgaria
Cambodia
Cameroon
Canada
Chile
China
Chinese Taipei
Colombia
Congo
Congo, Dem. Republic
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Rooftop
area
suitable
for PVs in
2012
(km2)
12
127
105
216
14
179
54
53
5
800
32
73
54
69
20
7
1,064
4
13
82
79
267
79
5,606
58
240
21
348
25
82
15
40
11
39
Residential rooftop PV
Potential
Proposed
capacity of
installed
suitable
capacity
area in
in 2050
2050
(MWdc(MWdc-peak)
peak)
2,903
1,386
30,261
27,235
25,043
20,052
51,729
36,721
3,413
1,310
42,756
25,726
12,905
11,615
12,754
5,536
1,237
1,113
191,184
47,287
7,713
2,526
17,469
15,668
12,826
2,518
16,533
7,876
4,823
2,182
1,609
964
254,250
161,801
957
862
3,033
1,554
19,536
8,341
18,772
4,493
63,749
25,604
18,795
13,992
1,339,997
741,819
13,779
9,291
57,379
20,888
5,027
1,949
83,115
13,017
6,058
3,663
19,502
5,566
3,644
1,582
9,622
7,034
2,577
1,753
9,322
8,390
Percent
of
potential
capacity
installed
48
90
80
71
38
60
90
43
90
25
33
90
20
48
45
60
64
90
51
43
24
40
74
55
67
36
39
16
60
29
43
73
68
90
Commercial/government rooftop PV
Rooftop
Potential
Proposed
Percent
area
capacity of
installed
of
suitable suitable area
capacity in
potential
for PVs
in 2050
2050
capacity
in 2012
(MWdc-peak)
(MWdc-peak)
installed
(km2)
8
3,694
1,763
48
92
33,519
30,167
90
72
18,704
14,976
80
137
59,289
42,088
71
7
3,663
1,406
38
91
52,953
31,862
60
25
14,003
12,603
90
36
16,421
7,128
43
5
2,020
1,818
90
305
76,137
18,831
25
25
14,368
4,706
33
32
18,216
16,337
90
18
6,233
1,223
20
24
7,076
3,371
48
10
6,638
3,002
45
6
2,504
1,500
60
647
350,644
223,145
64
3
1,201
1,081
90
15
8,540
4,375
51
28
9,121
3,894
43
36
10,243
2,452
24
128
73,860
29,665
40
56
24,396
18,163
74
4,030
1,663,410
920,859
55
90
28,843
19,448
67
114
38,326
13,952
36
14
3,920
1,520
39
77
21,287
3,334
16
13
6,180
3,737
60
40
12,315
3,515
29
13
7,322
3,178
43
23
6,059
4,429
73
5
2,783
1,892
68
24
14,968
13,471
90
23
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Gibraltar
Greece
Guatemala
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. People's Rep.
Korea, Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia, Republic of
Malaysia
Malta
Mexico
Moldova, Republic of
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Zealand
40
56
97
472
27
30
4
746
16
567
7
18
579
89
0
112
107
57
58
25
37
1
2,819
929
284
172
40
75
373
17
492
31
112
185
85
123
5
10
43
7
12
31
17
7
10
121
2
622
12
12
3
124
154
239
5
165
138
2
30
9,559
13,503
23,138
112,909
6,357
7,208
907
178,297
3,904
135,417
1,560
4,394
138,452
21,372
27
26,689
25,637
13,725
13,819
5,902
8,790
339
673,791
222,086
67,779
41,002
9,574
17,984
89,090
4,055
117,539
7,431
26,889
44,121
20,277
29,476
1,278
2,476
10,383
1,617
2,754
7,484
4,118
1,605
2,338
28,894
414
148,685
2,779
2,967
635
29,737
36,754
57,021
1,121
39,523
32,915
497
7,252
2,415
7,888
13,819
62,876
2,843
1,691
243
39,352
779
121,875
985
1,685
124,607
5,576
24
14,915
10,915
4,148
4,724
5,312
3,563
0
334,584
114,376
31,060
17,071
2,210
13,068
46,551
2,085
105,785
3,148
12,574
12,802
3,800
26,529
400
1,727
3,252
402
1,270
4,766
1,231
1,439
1,198
18,340
373
107,746
967
1,243
288
11,867
7,948
21,737
528
8,753
8,938
447
2,847
25
58
60
56
45
23
27
22
20
90
63
38
90
26
90
56
43
30
34
90
41
0
50
52
46
42
23
73
52
51
90
42
47
29
19
90
31
70
31
25
46
64
30
90
51
63
90
72
35
42
45
40
22
38
47
22
27
90
39
18
28
40
254
10
9
2
208
15
217
8
9
234
49
0
27
36
11
16
23
22
1
3,270
663
164
87
21
32
183
6
292
16
80
74
22
129
3
12
14
5
7
24
10
3
6
125
2
383
5
10
2
66
39
101
4
49
60
1
17
9,885
11,202
12,743
77,788
3,604
2,368
1,489
51,490
8,811
126,886
1,916
6,158
133,003
15,277
55
17,202
11,805
3,037
5,340
6,886
12,766
648
929,179
220,434
61,796
24,455
11,715
16,959
109,839
3,107
180,977
7,225
42,972
22,066
6,300
70,029
1,232
5,647
7,536
2,972
5,384
10,456
5,468
1,480
3,318
76,035
794
201,316
3,229
3,458
1,045
26,466
12,939
26,312
1,324
12,861
33,486
460
9,719
2,498
6,544
7,611
43,318
1,612
556
399
11,365
1,759
114,197
1,210
2,362
119,702
3,986
49
9,613
5,026
918
1,826
6,197
5,174
0
461,402
113,525
28,318
10,182
2,704
12,324
57,393
1,597
162,880
3,061
20,094
6,403
1,181
63,026
386
3,938
2,360
740
2,482
6,659
1,635
1,327
1,700
48,263
715
145,885
1,123
1,448
473
10,561
2,798
10,030
623
2,848
9,093
414
3,816
25
58
60
56
45
23
27
22
20
90
63
38
90
26
90
56
43
30
34
90
41
0
50
52
46
42
23
73
52
51
90
42
47
29
19
90
31
70
31
25
46
64
30
90
51
63
90
72
35
42
45
40
22
38
47
22
27
90
39
24
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian Federation
Saudi Arabia
Senegal
Serbia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania, United Republic
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
World total or average
33
891
21
15
961
22
41
154
606
111
55
7
42
408
112
68
21
52
21
8
107
439
95
256
53
33
103
66
164
200
40
6
37
429
21
150
22
199
3,723
14
125
169
362
150
94
69
31,317
7,901
213,023
4,963
3,570
229,606
5,186
9,820
36,828
144,952
26,472
13,157
1,631
9,982
97,588
26,840
16,144
5,016
12,348
5,058
1,887
25,570
105,006
22,623
61,157
12,636
7,839
24,559
15,804
39,161
47,699
9,652
1,454
8,822
102,572
5,054
35,928
5,173
47,668
889,928
3,342
29,763
40,331
86,617
35,848
22,397
16,505
7,485,596
3,063
79,181
877
2,629
84,665
3,717
4,805
21,244
71,846
23,825
6,807
1,468
4,055
34,023
20,587
4,051
2,273
11,113
1,873
775
12,157
55,946
13,551
18,045
2,827
7,055
8,269
4,682
11,707
29,097
1,652
1,309
4,281
53,723
2,798
13,218
4,656
12,944
589,909
2,014
12,814
23,823
41,022
9,629
7,573
4,452
3,936,928
39
37
18
74
37
72
49
58
50
90
52
90
41
35
77
25
45
90
37
41
48
53
60
30
22
90
34
30
30
61
17
90
49
52
55
37
90
27
66
60
43
59
47
27
34
27
52.6
11
655
18
13
433
12
15
72
301
85
27
10
60
322
106
25
17
36
14
4
100
155
52
117
30
26
39
19
65
165
12
3
23
244
20
84
23
222
1,507
8
73
91
177
39
45
21
19,070
3,328
219,343
9,744
4,666
124,878
4,904
4,607
23,008
79,416
54,819
17,173
3,817
33,853
177,851
35,757
7,902
10,425
11,966
8,562
2,790
41,901
93,548
15,243
38,926
16,922
14,047
12,028
7,615
16,949
61,101
3,050
1,408
9,661
95,645
9,868
50,581
8,582
129,603
814,757
4,653
17,546
34,928
50,681
10,891
13,350
15,653
7,727,148
1,290
81,530
1,723
3,436
46,047
3,514
2,254
13,272
39,363
49,337
8,886
3,435
13,751
62,005
27,426
1,983
4,724
10,770
3,171
1,146
19,921
49,841
9,130
11,486
3,785
12,643
4,050
2,256
5,067
37,273
522
1,267
4,688
50,094
5,463
18,609
7,724
35,192
540,080
2,804
7,554
20,631
24,003
2,925
4,514
4,222
4,279,033
39
37
18
74
37
72
49
58
50
90
52
90
41
35
77
25
45
90
37
41
48
53
60
30
22
90
34
30
30
61
17
90
49
52
55
37
90
27
66
60
43
59
47
27
34
27
55.4
Utility-scale PV potential is determined with the NREL Global Solar Opportunity Tool
(NREL, 2012b), which gives the utility PV potential (in GW of rated capacity) by country
for different resource thresholds. We define the utility-scale PV potential as the potential
calculated from the tool in locations exceeding 4 kWh/m2/day.
As of the end of 2014, 0.30% of the proposed 2050 PV (residential rooftop,
commercial/government rooftop, and utility scale) capacity and 0.36% of the CSP capacity
among the 139 countries from Table 2 has been installed. Figure 6 indicates that Germany,
25
China, Japan, and Italy have installed the most PV. Spain, the United States, and India have
installed the most CSP.
Figure 6. (a) Installed residential, commercial/government, plus utility PV by country and (b) installed CSP by
country as of the end of 2014. Total PV is determined first from IEA-PVPS (2015) and IEA (2014b); the ratios
of residential : commercial/government : utility PV for 20 European countries and global averages used on the
remaining countries are from EPIA (2014). CSP by country includes operational plants and plants under
construction that broke ground before 2015 (CSP World, 2015; NREL, 2015).
26
5.3. Geothermal
Geothermal heat from volcanos, geysers, hot springs, conduction from the interior of the
Earth, and solar radiation absorbed by the ground can be used to generate electricity or
produce heat, depending on the temperature of the resource. All countries can extract heat
from the ground for direct heating or use in heat pumps.
As of the end of 2014, 12.586 GW of geothermal has been installed for electric power and
70.338 GW has been installed for heat worldwide. The United States, Philippines, and
Indonesia lead electric power installations, whereas China, the United States, and Sweden
lead heat installations (Figure 7). The installed geothermal for electricity represents 13.0%
of the nameplate capacity of geothermal needed for electric power generation under the
plans proposed here (Table 2). The geothermal for heat already installed is 100% of the
nameplate capacity of geothermal needed for heat storage under these plans (Table 2).
Figure 7. Installed geothermal power used for electricity and heat by country in 2014 (Lund and Boyd, 2015;
Bertani, 2015; REN21, 2015).
27
28
The average capacity factor of installed geothermal for electricity worldwide based on 2012
data is ~70.3% (IEA, 2015). However, this is not a technical or economic limit, and in a
100% WWS system the capacity factor for geothermal could be higher than this. Therefore,
the roadmaps here assume that the capacity factor of geothermal will increase to 90.5% by
2050. They call for a 139-country-total of 96.6 GW of installed geothermal for electricity,
producing 87.0 GW of delivered power in 2050.
5.4. Hydropower
In 2012, conventional (small and large) hydropower provided ~16.5% of the world electric
power supply (IEA, 2014a). 2014 installations of hydropower (excluding pumped
hydroelectric) were ~1.036 TW (Figure 8). Given the world-averaged capacity factor for
hydropower of ~41.8% in 2012 (IEA, 2014a), this implies hydropower delivered electricity
in 2014 of ~433.0 GW (3,794 TWh/yr).
Figure 8 shows the distribution of installed conventional hydropower by country. China, the
United States, Brazil, and Canada lead in installations. However, the countries of the world
with the greatest percentage of their electric power production from hydropower in 2012
include, in order: Albania (100%), Paraguay (100%), Montenegro (99.9%), Zambia
(99.7%), Tajikistan (99.6%), Democratic Republic of the Congo (99.6%), Nepal (99.5%),
Ethiopia (98.7%), Namibia (97.8%), Norway (96.7%), and the Kyrgyz Republic (93.5%)
(World Bank, 2015a). Thus, 7 countries already produce 99-100% of their electricity from
WWS hydropower. Further, 22 countries produce more than 70% of all their electricity from
hydropower and 36, of which 28 are developing countries, produce more than 50%.
Figure 8. Installed conventional hydropower by country in 2014 (IEA, 2014b; IHA, 2015).
29
30
Under the roadmaps proposed here, conventional hydropower will supply ~4.4% (516.4
GW) of the 139-country 2050 end-use power demand for all purposes (Table 2). However,
no new hydropower dams are proposed for installation. Instead, the capacity factor of
hydropower will be increased from a 139-country average of ~41.8% to 50% by 2050.
Increasing the capacity factor is feasible because existing dams currently produce less than
their maximum capacity, mainly since many other dispatchable sources of electricity exist in
the current energy system, greatly reducing the need for hydropower to balance supply and
demand. Also, in some cases, hydropower is not used to its full extent because other
priorities affect water use, and in a 100% WWS system, these other priorities will remain.
Whereas, increasing hydropower capacity factors should be possible, if it is not, additional
hydropower capacity can be obtained by powering presently non-powered dams. The U.S.,
for example, has over 80,000 dams that are not powered at present. Although only a small
fraction of these dams can feasibly be powered, DOE (2012) estimates that the potential
amounts to ~12 GW of capacity in the contiguous 48 states.
5.5. Tidal and Wave
Worldwide by the end of 2014, a total of ~534 MW of ocean devices (mostly tidal barrages)
has been installed. This capacity is mostly from two large plants in South Korea and France
and smaller plants in Canada and the United Kingdom (Figure 9).
Figure 9. Installed ocean power by country in 2014 (IEA 2014b). Ocean power includes tidal rise and fall,
ocean and tidal currents, wave power, ocean thermal energy conversion, and salinity gradients. Nearly all the
existing capacity in the figure arises from tidal barrages.
31
Under the roadmaps here, tidal is proposed to contribute ~0.068%, or ~8.03 GW, of the 139country end-use delivered power in 2050 (Table 2). This requires a nameplate capacity of
~32.6 GW installed of which ~1.6% has been installed as of the end of 2014. The needed
nameplate capacity is much less than the estimated world technical potential of ~556 GW
installed (1200 TWh/yr or 137 GW delivered power) (Marine Renewables Canada, 2013).
Some countries with significant tidal potential include Australia (3.8 GW nameplate
capacity), Canada (171 GW), France (16 GW), Ireland (107 GW), Japan (3 GW), United
Kingdom (8 GW), and the United States (116 GW) (Marine Renewables Canada, 2013).
Wave power is proposed here to contribute ~0.72%, or ~85.3 GW, of the 139-country enduse power demand in 2050 (Table 2). This requires a nameplate installation of ~372 GW,
which is much less than the world technical potential for the 139 countries considered of
~4.362 GW installed (8,850 TWh/yr, or 1010 GW delivered) (Marine Renewables Canada,
2015 but assuming 70% exclusion zones). Some of countries with significant wave potential
include Australia (192 GW installed), Canada (275 GW), France (14 GW), Japan (13 GW),
Ireland (3 GW), Norway (59 GW), United Kingdom (6 GW), and the United States (263
GW) (Marine Renewables Canada, 2013).
6. Matching Electric Power Supply with Demand
An important requirement for 100% WWS roadmaps is that the grid remains reliable. To
that end, Jacobson et al. (2015b) developed and applied a grid integration model to
determine the quantities and costs of storage devices needed to ensure that a 100% WWS
system developed for each of the 48 contiguous U.S. states, when integrated across all such
states, could match load without loss every 30 s for 6 years (2050-2055) while accounting
for the variability and uncertainty of WWS resources.
Wind and solar time-series are derived from 3-D GATOR-GCMOM global model
simulations that accounted for extreme events and competition among wind turbines for
kinetic energy and the feedback of extracted solar radiation to roof and surface
temperatures. Solutions are obtained by prioritizing storage for excess heat (in soil and
water) and electricity (in ice, water, phase-change material tied to CSP, pumped hydro, and
hydrogen), using hydropower only as a last resort, and using demand response to shave
periods of excess demand over supply. Additional simulations show that grid reliability is
maintained even without demand response by increasing electricity generation, but at a
slightly higher cost.
No stationary storage batteries, biomass, nuclear power, or natural gas are needed in these
roadmaps. Frequency regulation of the grid is provided by ramping up/down hydropower,
stored CSP or pumped hydro; ramping down other WWS generators and storing the
electricity in heat, cold, or hydrogen instead of curtailment; and using demand response.
Multiple low-cost stable solutions to the grid integration problem across the 48 contiguous
U.S. states were obtained, suggesting that maintaining grid reliability upon 100%
conversion to WWS in that region is a solvable problem. The mean U.S.-averaged levelized
cost of energy in that study, accounting for storage transmission, distribution, maintenance,
32
and array losses, is ~10.6 ¢/kWh for electricity and ~11.4 ¢/kWh for all energy in 2013
USD.
Here, current and future full social costs (including capital, land, operating, maintenance,
storage, fuel, transmission, and externality costs) of WWS electric power generators versus
non-WWS conventional fuel generators are estimated. These costs include the costs of CSP
storage, existing hydroelectric power used for storage, solar collectors used to collect heat
for storage, and all transmission/distribution costs. They do not include costs of pumped
hydro storage, underground storage in rocks, heat and cold storage in water and ice, or the
costs of hydrogen fuel cells. Such costs are estimated here as < 0.8 ¢/kWh from Jacobson et
al. (2015b) but will be estimated more precisely as part of a separate study.
7. Costs of Electric Power Generation
In this section, current and future full social costs (including capital, land, operating,
maintenance, storage, fuel, transmission, and externality costs) of WWS electric power
generators versus non-WWS conventional fuel generators are estimated. Beyond the cost
associated with CSP storage included here, these costs do not include the costs of storage
necessary to keep the grid stable, which are being quantified in Jacobson et al. (2016b). The
estimates here are based on current cost data and trend projections for individual generator
types. The estimates are only a rough approximation of costs in a future optimized
renewable energy system.
Table 5 presents 2013 and 2050 139-country weighted average estimates of fully annualized
levelized business costs of electric power generation for conventional fuels and WWS
technologies. The table indicates that the 2013 business costs of hydropower, onshore wind,
utility-scale solar PV, and solar thermal for heat are already similar to or less than the costs
of natural gas combined cycle. Residential and commercial PV, offshore wind, tidal, and
wave are more expensive. However, residential rooftop PV costs are given as if PV is
purchased for an individual household; a common business model today involves multiple
households entering a contract together with a solar provider to decrease the average cost.
By 2050, the costs of all WWS technologies are expected to drop, most significantly for
offshore wind, tidal, wave, rooftop PV, CSP, and utility PV, whereas conventional fuel costs
are expected to rise. Because WWS technologies have zero fuel costs, the drop in their costs
over time is due primarily to technology improvements. WWS costs are expected to decline
also due to less expensive manufacturing and streamlined project deployment from
increased economies of scale. Conventional fuels, on the other hand, face rising costs over
time due to higher labor and transport costs for mining, transporting, and processing fuels
continuously over the lifetime of fossil-fuel plants.
Table 5. Approximate fully annualized, unsubsidized 2013 and 2050 U.S.-averaged costs of delivered
electricity, including generation, short- and long-distance transmission, distribution, and storage, but not
including external costs, for conventional fuels and WWS power (2013 USD/MWh-delivered).
Technology
Advanced pulverized coal
Advanced pulverized coal w/CC
IGCC coal
Technology year 2013
LCHB
81.5
112.3
93.0
HCLB
104.0
157.5
120.7
Average
92.8
134.9
106.9
Technology year 2050
LCHB
77.4
97.9
83.3
HCLB
101.2
137.5
108.6
Average
89.3
117.7
96.0
33
IGCC coal w/CC
Diesel generator (for steam turbine)
Gas combustion turbine
Combined cycle conventional
Combined cycle advanced
Combined cycle advanced w/CC
Fuel cell (using natural gas)
Microturbine (using natural gas)
Nuclear, APWR
Nuclear, SMR
Distributed gen. (using natural gas)
Municipal solid waste
Biomass direct
Geothermal
Hydropower
On-shore wind
Off-shore wind
CSP no storage
CSP with storage
PV utility crystalline tracking
PV utility crystalline fixed
PV utility thin-film tracking
PV utility thin-film fixed
PV commercial rooftop
PV residential rooftop
Wave power
Tidal power
Solar thermal for heat ($/kWh-th)
138.3
185.5
182.7
81.2
n.a.
n.a.
120.7
122.6
80.9
92.9
n.a.
202.3
130.9
86.3
61.1
74.1
108.9
124.3
79.8
71.1
75.3
70.5
74.5
95.4
130.4
263.2
139.9
56.7
214.9
245.3
386.2
93.9
n.a.
n.a.
189.1
145.0
120.1
120.7
n.a.
256.7
167.5
127.4
83.6
100.8
198.0
191.6
113.3
94.7
104.1
92.7
104.1
142.4
193.4
598.0
296.4
67.0
176.6
215.4
284.5
87.6
n.a.
n.a.
154.9
133.8
100.5
106.8
n.a.
229.5
149.2
106.9
72.4
87.5
153.5
158.0
96.6
82.9
89.7
81.6
89.3
118.9
161.9
430.6
218.2
61.9
95.5
247.3
186.9
103.9
95.2
111.2
132.6
151.7
72.6
78.5
246.9
179.1
104.8
79.2
54.2
66.1
100.8
90.1
63.2
64.5
59.9
64.0
59.3
83.5
113.1
254.7
133.8
52.3
134.2
372.1
374.0
133.0
117.2
140.2
199.3
190.7
103.6
99.4
383.4
213.1
126.4
108.6
72.3
88.4
179.6
141.2
91.6
85.3
78.4
83.5
78.4
122.1
164.1
585.4
288.4
62.6
114.9
309.7
280.5
118.5
106.2
125.7
166.0
171.2
88.1
89.0
315.2
196.1
115.6
93.9
63.3
77.3
140.2
115.7
77.4
74.9
69.2
73.8
68.9
102.8
138.6
420.1
211.1
57.5
LCHB = low cost, high benefits case; HCLB = high cost, low benefits case; n.a = not available. The
methodology for calculating the costs is described in Jacobson et al. (2015a).
For the year 2050 100% WWS scenario, costs are shown for WWS technologies; for the year 2050 BAU case,
costs of WWS are slightly different. The costs assume $0.0115 (0.011-0.012)/kWh for standard (but not
extra-long-distance) transmission for all technologies except rooftop solar PV (to which no transmission cost
is assigned) and $0.0257 (0.025-0.0264)/kWh for distribution for all technologies. Transmission and
distribution losses are accounted for in the energy available.
CC = carbon capture; IGCC = integrated gasification combined cycle; AWPR = advanced pressurized-water
reactor; SMR = small modular reactor; PV = photovoltaics.
CSP w/storage assumes a maximum charge to discharge rate (storage size to generator size ratio) of 2.62:1.
Solar thermal for heat assumes $3,600-$4,000 per 3.716 m2 collector and 0.7 kW-th/m2 maximum power
(Jacobson et al., 2015a).
Table 5 does not include externality costs. These are estimated as follows. The 2050 139country air pollution cost (Table 7) plus global climate cost (Table 8) per unit energy
(converted to kWh) produced in all sectors in all countries in the 2050 BAU case (Table 1)
corresponds to a mean 2050 externality cost (in 2013 USD) due to conventional fuels of
~$0.24 (0.080-0.63)/kWh, with $0.14 (0.024-0.42)/kWh due to air pollution impacts and the
rest due to climate impacts. The mean air pollution cost is in the $0.014-$0.17/kWh range
from Buonocore et al. (2015). Externality costs arise due to air pollution morbidity and
mortality and global warming damage (e.g. coastline losses, fishery losses, agricultural
losses, heat stress mortality and morbidity, famine, drought, wildfires, and severe weather)
due to conventional fuels. When externality costs are added to the business costs of
conventional fuels, all WWS technologies cost less than conventional technologies in 2050.
Table 6 provides the mean value of the 2013 and 2050 LCOEs weighted among all
conventional generators (BAU cases) and WWS generators (WWS case) by country. The
34
table also gives the 2050 energy, health, and global climate cost savings per person. The
electric power cost of WWS in 2050 is not directly comparable with the BAU electric power
cost, because the latter does not integrate transportation, heating/cooling, or industry energy
costs. Conventional vehicle fuel costs, for example, are a factor of 4-5 higher than those of
electric vehicles, yet the cost of BAU electricity cost in 2050 does not include the
transportation cost, whereas the WWS electricity cost does.
The 2050 LCOEs, weighted among all generators and countries in the BAU and WWS
cases, are 9.7 and 8.1¢/kWh, respectively (Table S6). The difference gives a net WWS
energy business cost savings in 2050 per capita of $143/yr ($2013 USD). Adding 0.8 ¢/kWh
for additional storage (Section 6) gives a WWS business cost of 8.9 ¢/kWh, still providing
~$70/yr per capita savings relative to BAU.
In addition, WWS will save $2,632/yr in health costs plus $1,930/yr in global climate costs.
The total up-front capital cost of the 2050 WWS system (for both average annual power and
peaking storage in Table 2) for the 139 countries is ~$102 trillion for the 50.3 TW of
installed capacity needed (~$2.04 million/MW).
Table 6. Mean values of the levelized cost of energy (LCOE) for conventional fuels (BAU) in 2013 and 2050
and for WWS fuels in 2050. The LCOE estimates do not include externality costs. The 2013 and 2050 values
are used to calculate energy cost savings per person per year in each country (see footnotes). Health and
climate cost savings per person per year are derived from data in Section 8. All costs are in 2013 USD.
Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei Darussalam
Bulgaria
Cambodia
Cameroon
Canada
(a)
2013
LCOE of
BAU
(¢/kWh)
(b)
2050
LCOE of
BAU
(¢/kWh)
(c)
2050
LCOE of
WWS
(¢/kWh)
(d)
2050
Average
electricity
cost
savings per
person per
year ($/person/yr)
7.24
8.74
7.68
8.65
8.91
10.24
8.86
8.55
8.75
8.77
8.76
10.46
8.80
8.51
9.67
10.68
8.11
8.77
10.50
8.91
7.71
8.60
6.33
11.79
7.93
10.33
8.98
10.01
8.64
11.11
11.84
11.70
11.82
10.11
11.85
10.12
8.66
9.60
7.38
11.86
9.36
11.58
7.81
7.93
6.68
7.33
14.07
10.43
6.00
8.66
6.38
7.97
7.22
7.06
6.50
7.16
6.36
9.03
6.45
7.49
8.13
7.04
5.80
10.12
5.90
10.07
62
149
-12
71
153
358
397
208
1,367
26
663
484
10
18
201
111
34
1,243
863
13
16
-16
(e)
2050
Average air
quality
health cost
savings per
person per
year due to
WWS
($/person/yr)
1,774
1,112
1,904
1,329
3,409
762
4,235
3,810
2,275
1,623
10,744
4,491
1,382
442
2,254
1,111
495
245
6,146
593
1,646
2,571
(f)
2050
Average
climate cost
savings to
world per
person per
year due to
WWS
($/person/yr)
818
1,618
340
1,837
676
5,871
4,162
2,341
6,739
130
4,030
5,003
123
573
4,096
896
923
8,257
4,457
110
108
6,109
(g)
2050
Average
energy +
health +
world climate
cost savings
due to WWS
($/person/yr)
2,998
3,379
3,915
3,559
5,221
7,053
9,005
6,598
10,973
4,156
16,020
10,274
5,740
1,461
7,035
2,551
1,584
9,725
11,907
1,552
5,051
8,887
35
Chile
China
Chinese Taipei
Colombia
Congo
Congo, Dem. Republic
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Gibraltar
Greece
Guatemala
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. People's Rep.
Korea, Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia, Republic of
Malaysia
Malta
9.42
10.23
10.23
7.88
7.83
7.25
9.05
8.38
8.76
5.27
9.02
10.90
12.31
8.90
8.12
8.73
10.56
8.80
11.04
7.28
9.85
10.45
8.09
7.58
11.10
7.01
10.67
10.18
10.44
8.82
8.49
10.13
10.42
9.68
10.31
9.89
8.69
7.12
9.61
9.90
9.68
9.18
9.22
8.75
10.18
9.82
8.53
10.19
10.61
8.75
7.40
8.27
8.68
8.75
9.16
9.04
9.92
9.52
8.78
9.57
9.22
9.22
7.48
8.47
6.36
8.23
10.25
9.03
6.90
11.92
9.58
11.49
10.86
8.86
11.42
10.57
11.85
9.94
6.39
9.03
9.01
9.32
7.57
10.32
7.15
10.66
10.42
9.87
11.01
9.70
10.24
10.21
8.38
9.64
10.56
11.56
9.36
10.71
10.50
10.76
11.86
10.00
11.82
9.52
9.89
7.66
9.96
9.55
11.84
6.62
9.29
11.57
11.84
10.80
11.59
8.97
10.62
11.84
9.35
7.53
10.15
6.66
9.48
5.16
9.89
7.07
6.63
9.98
8.91
6.11
13.33
10.48
9.19
8.27
9.35
11.47
8.74
7.70
10.13
10.49
7.42
7.36
8.69
6.57
5.70
8.47
9.27
10.55
8.29
6.49
5.02
9.83
7.60
7.72
6.70
7.89
11.84
7.99
6.61
10.40
7.66
8.31
8.56
8.53
7.62
6.38
7.96
6.37
6.91
7.04
7.67
8.42
9.09
5.82
7.68
6.77
6.90
201
289
234
30
5
3
1
21
386
-32
408
463
61
33
16
158
38
2
504
0
129
67
56
75
412
10
17
331
13
2
22
1,207
361
-218
60
71
235
22
56
294
470
48
378
127
246
7
15
1,027
271
2,152
28
424
260
231
334
853
267
430
737
1,556
5,237
5,216
232
914
177
138
270
4,838
600
3,734
4,930
4,674
335
260
1,630
167
571
7,337
386
6,251
3,136
1,091
3,357
5,098
2,028
8,167
3,532
180
116
69
8,257
5,354
998
2,940
557
1,486
1,077
1,562
2,386
3,456
179
2,531
606
3,820
235
582
2,909
901
2,041
1,062
12,256
1,233
638
12,032
5,848
2,412
439
4,336
2,302
3,823
6,553
784
122
10
650
83
2,654
2,147
2,749
5,901
3,599
797
862
777
509
27
10,902
12
5,226
2,462
421
808
5,296
121
8,139
3,684
245
79
312
3,165
2,431
2,798
726
773
3,052
1,088
2,897
3,300
2,868
1,007
5,806
1,000
6,913
94
1,417
7,089
2,833
13,008
393
2,390
2,629
2,748
2,315
7,403
2,767
2,667
3,251
4,275
9,827
11,373
1,158
1,964
1,508
848
866
7,994
2,881
6,720
12,061
8,594
1,329
1,287
3,567
843
2,705
20,108
1,851
12,101
5,940
1,916
5,291
11,072
5,185
16,505
8,263
594
581
497
12,353
9,295
3,642
5,252
1,655
5,960
3,155
4,572
6,295
7,198
1,360
9,189
2,379
10,968
809
3,820
11,374
4,635
17,392
2,291
15,097
5,048
3,875
14,418
13,661
5,781
3,595
8,112
36
Mexico
Moldova, Republic of
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian Federation
Saudi Arabia
Senegal
Serbia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania, United Republic
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
World total or average
9.12
8.66
10.59
9.12
9.76
7.24
7.83
7.29
7.24
9.93
7.59
9.43
10.24
8.44
7.39
8.75
8.40
8.12
7.24
8.09
10.32
10.88
10.52
8.75
9.72
9.10
6.11
8.63
9.87
8.81
10.17
9.97
10.68
10.67
8.41
7.61
9.62
8.84
8.63
7.26
8.03
9.30
7.73
8.75
8.80
9.15
8.75
10.31
8.75
10.23
10.17
8.50
8.54
7.72
8.71
8.75
7.24
8.19
9.67
10.88
11.51
9.71
8.12
10.43
6.33
7.79
6.39
6.33
11.28
9.18
9.00
11.65
10.69
6.59
11.84
10.02
8.84
6.33
8.81
10.53
9.93
10.60
11.84
9.13
10.10
8.27
11.22
8.89
11.74
9.23
8.76
9.55
10.66
9.48
7.69
8.26
7.60
11.40
6.39
9.10
11.10
7.78
11.84
11.84
9.99
11.84
9.31
11.84
10.64
9.95
8.58
10.67
8.06
9.71
11.84
6.34
7.23
9.70
8.41
7.66
7.44
8.07
8.40
8.43
9.08
7.62
6.56
11.52
7.08
9.43
9.08
6.40
7.73
7.43
6.98
7.67
7.27
8.91
11.99
10.16
9.96
7.16
8.50
10.04
6.29
8.58
6.34
7.23
6.70
6.63
7.27
8.54
10.22
8.75
9.21
6.20
8.47
7.51
7.26
6.73
6.26
8.42
7.56
7.51
6.49
9.63
6.76
13.49
8.77
9.54
5.90
7.60
9.21
9.16
7.03
6.65
8.10
204
282
95
150
71
-6
0
-7
2
166
73
124
35
12
100
464
33
73
9
18
11
146
98
1,193
175
464
406
14
481
892
281
378
495
244
13
1
121
271
70
-8
6
392
5
703
225
100
242
210
1,394
-7
363
35
152
76
49
9
2
17
143
655
5,266
1,158
274
949
115
1,464
1,210
1,211
4,313
350
339
97
5,779
4,500
9,092
2,561
123
465
443
153
5,209
3,321
1,456
6,757
9,862
1,709
2,119
5,398
1,040
4,846
4,025
1,469
3,345
692
2,223
5,323
2,718
541
1,114
186
1,983
913
800
1,052
1,547
3,146
8,131
2,034
3,182
1,358
1,052
1,669
227
916
837
547
317
2,632
1,573
1,067
1,443
2,275
621
27
72
761
46
4,599
5,872
3,111
317
103
5,825
5,470
282
966
282
877
271
4,849
2,611
17,054
2,088
8,289
6,444
137
3,234
1,015
3,431
4,841
4,528
2,283
289
78
2,481
2,757
953
115
54
2,347
52
23,862
1,106
1,609
4,085
4,489
11,527
3,244
6,186
958
1,466
2,741
760
257
33
179
1,930
2,582
9,279
3,153
2,726
2,472
698
3,150
2,768
3,537
9,264
6,364
3,589
542
10,595
10,323
17,678
5,388
1,197
1,097
1,579
541
11,298
6,640
19,660
9,289
19,851
8,926
7,623
9,811
2,850
9,432
9,895
7,215
6,285
1,308
5,634
8,188
5,751
2,434
2,254
668
5,338
4,559
25,487
2,968
3,710
7,761
14,968
15,245
6,689
7,907
2,348
4,033
3,125
2,505
3,583
1,434
1,527
5,836
37
a) The 2013 LCOE cost for conventional fuels in each country combines the estimated distribution of
conventional and WWS generators in 2013 with 2013 mean LCOEs for each generator from Table 5.
Costs include all-distance transmission, pipelines, and distribution, but they exclude externalities.
b) Same as (a), but for a 2050 BAU case (Supplemental Information) and 2050 LCOEs for each generator
from Table 5. The 2050 BAU case includes significant existing WWS (mostly hydropower) plus future
increases in WWS and energy efficiency.
c) The 2050 LCOE of WWS in the country combines the 2050 distribution of WWS generators from Table 3
with the 2050 mean LCOEs for each WWS generator from Table 5. The LCOE accounts for all-distance
transmission and distribution (footnotes to Tables 2 and 5).
d) The total cost of electricity use in the electricity sector in the BAU case (the product of electricity use and
the LCOE) less the annualized cost of the assumed efficiency improvements and less the total cost in the
electricity sector in the WWS case, all divided by 2050 population. (See Delucchi et al., 2016 for details.)
e) Total cost of air pollution per year in the country from Table 7 divided by the 2050 population of the
country.
f) Total climate cost per year to the world due to country’s emissions (Table 8) divided by the 2050 population
of the country.
g) The sum of columns (d), (e), and (f).
8. Air Pollution and Global Warming Damage Costs Eliminated by WWS
Conversion to a 100% WWS energy infrastructure in the 139 countries eliminates energyrelated air pollution mortality and morbidity and the associated health costs, along with
energy-related climate change costs for individual countries and the world. This section
discusses these topics.
8.A. Air Pollution Cost Reductions due to WWS
The benefits of reducing air pollution mortality and its costs in each U.S. country can be
quantified as follows.
First, the premature human mortality rate worldwide due to cardiovascular disease,
respiratory disease, and complications from asthma arising from air pollution has been
estimated previously by combining computer model estimates of human exposure to
particulate matter (PM2.5) and ozone (O3) with the relative risk of mortality from these
chemicals and population. Results suggest that an estimated 4-7 million people currently
perish prematurely each year worldwide from outdoor plus indoor air pollution (e.g.,
Shindell et al., 2012; Anenberg et al., 2012; WHO, 2014a,b; OECD, 2014). These
mortalities represent ~0.7-1.2% of the 570 million deaths/year worldwide in 2015. Here,
modeled concentrations of PM2.5 and O3 in each of 139 countries are combined with the
relative risk of mortality as a function of concentration and with population in a healtheffects equation (e.g., Jacobson, 2010a) to estimate low, medium, and high mortalities due
to PM2.5 and O3 by country. Results are then extrapolated forward to 2050 while accounting
for efficiencies that occur under the BAU scenario.
Figure 10 shows the results. Premature mortalities in 2014, summed over the 139 countries
are estimated for PM2.5 to be ~4.28 (1.19-7.56) million/yr, and those for O3, ~279,000
(140,000-417,000)/yr. The sum is ~4.56 (1.33-7.98) million premature mortalities/yr for
PM2.5 plus O3, which is in the range of the previous literature estimates.
Figure 10. Modeled worldwide (all countries, including the 139 discussed in this paper) (a) PM2.5 and (b) O3
premature mortalities in 2014 as estimated with GATOR-GCMOM (Jacobson, 2010a), a 3-dimensional global
computer model.
38
a) Mean premature mortalities/yr due to PM2.5 (4.28 million)
90
40000
b) Mean premature mortalities/yr due to O3 (279,000)
90
4000
35000
30000
3000
25000
20000
0
0
2000
15000
10000
1000
5000
0
-90
-180
-90
0
90
0
-90
180
-180
-90
0
90
180
Premature mortality due to PM2.5 (global: 5,700,000)
90
30000
25000
20000
0
15000
10000
5000
0
-90
-180
-90
0
90
180
Premature mortality due to O3 (global: 478,000)
90
5000
4000
3000
0
2000
1000
0
-90
-180
-90
0
90
180
Table 7 shows estimated air pollution mortality avoided by country in 2050 due to
conversion to WWS, projected forward from 2014 with the methodology detailed in
Delucchi et al. (2016). This method projects future pollution from current levels with an
estimated annual rate of pollution change that considers increasing emission controls and
more sources over time. The number of mortalities in 2050 then accounts for the growth of
population by country and a nonlinear relationship between exposure and population. The
39
resulting number of 2050 air pollution mortalities avoided in the 139 countries due to WWS
is estimated at 3.3 (0.8-7.0) million/yr.
Table 7. Avoided air pollution PM2.5 plus ozone premature mortalities by country in 2050 and mean avoided
costs (in 2013 USD) from mortalities and morbidities.
Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei Darussalam
Bulgaria
Cambodia
Cameroon
Canada
Chile
China
Chinese Taipei
Colombia
Congo
Congo, Dem. Republic
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Gibraltar
2050 High
avoided
premature
mortalities/yr
1,197
16,035
43,242
19,230
2,736
4,759
6,139
8,298
1,016
264,472
15,715
8,900
33,188
3,811
2,193
1,021
36,341
24
5,665
9,036
45,570
22,214
6,910
1,349,179
13,773
4,753
4,736
72,332
281
8,660
3,430
1,495
817
9,628
5,128
1,592
2,154
83,713
460
9,097
1,481
163,820
6,059
45,497
1,061
3,569
71,015
50,372
41
2050 Mean
avoided
premature
mortalities/yr
538
7,214
18,694
8,155
1,223
1,988
2,707
3,715
494
123,340
7,125
3,881
16,812
1,606
969
439
15,305
10
2,527
3,911
22,149
9,598
2,981
624,262
6,164
1,986
2,049
31,482
120
3,603
1,514
659
365
4,267
2,238
701
913
38,708
204
4,205
671
70,820
2,697
19,875
446
1,597
31,132
25,133
18
2050 Low
avoided
premature
mortalities/yr
140
1,817
4,486
1,751
297
430
637
887
122
29,114
1,621
863
4,188
344
236
102
3,342
2
600
910
5,310
2,188
652
145,129
1,464
428
472
7,199
29
760
362
167
90
974
504
182
206
9,474
53
973
153
15,764
600
4,574
96
387
7,044
6,169
4
2014 Mean
avoided cost
($2013 mil./yr)
5,011
49,107
87,356
71,121
10,031
22,115
31,848
42,715
4,203
405,959
83,146
44,386
30,566
7,073
8,773
3,190
129,030
156
28,586
13,243
57,450
105,750
30,168
6,827,270
105,154
13,034
8,773
25,559
836
10,017
18,693
5,498
5,198
42,106
26,056
4,587
5,494
224,770
1,031
6,500
6,324
107,380
30,128
218,812
3,524
12,707
364,686
81,601
229
2050 Mean
avoided cost
as percent of
2050 GDP
3.7
3.9
11.5
3.1
8.8
1.1
5.6
5.3
5.5
15.2
14.5
6.3
24.8
2.9
4.9
3.5
1.2
0.2
8.8
5.4
20.3
3.9
2.8
8.0
2.9
0.9
6.2
6.6
0.5
3.1
5.8
1.5
3.3
9.2
6.3
1.2
1.1
7.3
0.9
12.0
14.9
8.3
9.2
4.7
3.0
9.2
6.8
19.3
9.7
40
Greece
Guatemala
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. People's Rep.
Korea, Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia, Republic of
Malaysia
Malta
Mexico
Moldova, Republic of
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian Federation
Saudi Arabia
8,668
2,000
2,070
604
7,913
11,294
70
1,586,512
61,331
64,875
28,542
1,927
5,561
45,774
262
66,619
3,645
14,842
15,196
19,725
27,371
354
1,526
3,862
3,190
2,183
2,175
5,380
642
1,072
4,247
266
24,913
4,038
1,585
35
18,808
13,580
59,875
1,065
43,866
14,933
36
352
372
900,528
3,760
12,147
341,378
169
1,846
6,293
11,351
40,913
7,933
543
22,892
229,017
15,938
3,852
875
913
257
3,677
5,023
31
767,247
26,207
29,850
13,116
821
2,526
20,246
118
28,833
1,650
6,528
6,450
8,743
12,095
250
723
1,704
1,469
991
970
2,479
277
474
1,849
120
10,925
1,851
695
15
8,485
5,660
26,143
468
20,502
6,487
15
148
159
472,188
1,649
6,011
170,517
73
784
2,641
4,855
18,348
3,487
271
10,184
104,097
7,598
947
226
238
61
873
1,154
8
186,459
5,987
7,280
3,283
187
618
4,992
33
6,481
410
1,522
1,453
1,958
2,690
143
180
408
338
247
249
570
61
118
464
31
2,698
428
166
3
2,080
1,272
5,920
113
4,840
1,435
3
34
37
121,680
383
1,481
42,461
18
173
562
1,159
4,172
817
68
2,351
23,877
1,855
35,446
4,141
1,550
894
50,967
45,453
350
4,869,508
177,617
148,638
60,634
9,892
25,831
212,281
636
271,346
6,817
84,946
16,661
15,683
126,177
1,347
7,886
8,751
18,923
5,122
6,936
33,546
4,216
4,802
18,831
1,717
96,808
11,906
5,027
158
39,866
6,785
105,520
2,602
55,668
77,228
141
1,765
700
2,325,773
22,349
49,114
744,932
598
4,114
16,364
26,336
167,133
32,987
3,727
122,040
1,076,798
68,775
7.5
1.1
2.2
0.6
7.7
11.7
1.4
11.5
2.0
8.2
6.6
2.0
4.2
5.8
0.9
5.2
4.3
4.1
2.9
10.6
4.9
4.5
3.2
5.6
13.4
6.5
2.1
11.8
4.3
4.3
0.8
3.7
1.5
20.1
3.7
0.4
5.7
3.1
10.7
5.7
14.2
5.6
0.7
0.4
0.6
32.4
4.4
23.7
16.9
0.3
2.4
1.8
0.8
11.2
6.7
1.4
8.6
16.9
3.7
41
Senegal
Serbia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania, United Republic
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
All-country sum/average
48,919
6,974
1,284
5,675
1,484
25,913
38,371
5,957
138,407
9,343
3,498
13,131
6,619
12,597
39,392
20,323
196
4,670
42,948
4,236
71,096
3,134
47,788
100,438
1,063
19,015
2,872
47,863
43,190
15,687
10,695
7,042,494
27,011
3,083
563
2,520
658
11,129
16,922
2,568
66,746
4,107
1,539
5,932
2,984
5,313
17,309
10,373
83
2,090
19,160
1,901
32,196
1,621
20,475
44,367
448
8,589
1,211
21,284
20,522
6,673
4,565
3,329,772
7,204
736
145
578
159
2,505
4,052
601
15,879
925
379
1,485
720
1,195
4,010
2,590
18
527
4,779
455
7,400
415
4,450
11,386
97
2,050
276
5,055
4,932
1,582
1,072
800,710
57,728
31,678
8,952
23,957
6,428
72,565
175,601
17,417
215,954
48,361
19,828
18,214
13,517
12,460
138,033
15,149
819
12,819
156,174
20,788
273,004
16,312
226,397
573,984
3,677
58,598
9,143
101,839
38,322
20,990
7,993
23,166,414
32.2
9.3
0.7
9.7
7.7
5.5
5.7
2.4
21.3
7.0
3.0
5.5
7.0
2.6
5.4
20.3
1.5
4.3
4.0
4.8
19.9
3.7
4.8
1.5
2.7
5.8
0.7
5.3
14.6
5.4
5.9
7.2
High, medium, and low estimates of premature mortalities in each country in 2050 are estimated by combining
computer-modeled changes in PM2.5 and ozone during 2014 due to anthropogenic sources in each country
(Figure 10) with low, medium, and high relative risks and country population, as in Jacobson (2010a). 2014
values are then extrapolated forward to 2050 as described in the text. Human exposure is based on dailyaveraged PM2.5 exposure and 8-hr maximum ozone each day. Relative risks for long-term health impacts of
PM2.5 and ozone are as in Jacobson (2010a). However, the relative risks of PM2.5 from Pope et al. (2002) are
applied to all ages as in Lepeule et al. (2012) rather than to those over 30 years old as in Pope et al. (2002).
The threshold for PM2.5 is zero but concentrations below 8 µg/m3 are down-weighted as in Jacobson (2010a).
The low ambient concentration threshold for ozone premature mortality is assumed to be 35 ppbv.
Air pollution costs are estimated by multiplying the value of statistical life (VSL) in each country by the low,
medium, and high number of excess mortalities due to PM2.5 and ozone. Estimates of the VSL are calculated as
in Delucchi et al. (2016). Values for the U.S. are projected to 2050 based on GDP per capita projections (on a
PPP basis) for the U.S. then scaled for each country as a nonlinear function of GDP per capita relative to the
U.S. Multipliers are then used to account for morbidity and non-health impacts of air pollution.
Cost of air pollution. The total damage cost of air pollution due to conventional fuels (fossil
fuel and biofuel combustion and evaporative emissions) in a country is the sum of mortality
costs, morbidity costs, and non-health costs such as lost visibility and agricultural output in
the country. The mortality cost equals the number of mortalities in the country multiplied by
the value of statistical life (VSL). The methodology for determining the VSL by country is
provided in the footnote to Table 7. The morbidity plus non-health cost per country is
estimated as the mortality cost multiplied by the ratio of the value of total air-pollution
42
damages (mortality plus morbidity plus other damages) to mortality costs alone. The result
of the calculation is that the 139-country cost of air pollution in 2050 is ~$23.2 ($4.0-$70.8)
trillion/yr in 2013 USD, equivalent to ~7.2 (1.2-22) percent of the 2050 139-country gross
domestic product on a purchasing power parity (PPP) basis.
8.B. Global-Warming Damage Costs Eliminated by 100% WWS in Each Country
This section provides estimates of two kinds of climate change costs due to greenhouse gas
(GHG) emissions from energy use (Table 8). GHG emissions are defined here to include
emissions of carbon dioxide, other greenhouse gases, and air pollution particles that cause
global warming, converted to equivalent carbon dioxide. A 100% WWS system in each
country will eliminate such damages. The cost calculated is the cost of climate change
impacts to the world attributable to emissions of GHGs from each country.
Costs of climate change include coastal flood and real estate damage costs, agricultural loss
costs, energy-sector costs, water costs, health costs due to heat stress and heat stroke,
influenza and malaria costs, famine costs, ocean acidification costs, increased drought and
wildfire costs, severe weather costs, and increased air pollution health costs. These costs are
partly offset by fewer extreme cold events and associated reductions in illnesses and
mortalities and gains in agriculture in some regions. Net costs due to global-warmingrelevant emissions are embodied in the social cost of carbon dioxide. The range of the 2050
social cost of carbon from recent papers is $500 (282-1,063)/metric tonne-CO2e in 2013
USD (Jacobson et al., 2015a). This range is used to derive the costs in Table 8.
Table 8. Percent of 2013 world CO2 emissions by country (GCP, 2014) and low, medium, and high estimates
of avoided 2050 global climate-change costs due to converting each country to 100% WWS for all purposes.
All costs are in 2013 USD.
Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei Darussalam
Bulgaria
Cambodia
Cameroon
Canada
Chile
China
2013
Percent of world
CO2 emissions
0.014
0.419
0.092
0.577
0.012
1.000
0.184
0.154
0.073
0.191
0.183
0.290
0.016
0.054
0.094
0.015
1.413
0.031
0.122
0.014
0.022
1.476
0.262
29.265
2050 avoided global climate cost ($2013 bil./yr)
Low
Medium
High
4.9
152.1
33.2
209.3
4.2
362.6
66.6
55.9
26.5
69.1
66.4
105.3
5.8
19.5
33.9
5.5
512.2
11.2
44.1
5.2
8.1
534.9
95.0
10608.0
2.3
71.4
15.6
98.3
2.0
170.3
31.3
26.2
12.4
32.5
31.2
49.4
2.7
9.2
15.9
2.6
240.6
5.3
20.7
2.4
3.8
251.3
44.6
4983.5
1.3
40.3
8.8
55.4
1.1
96.0
17.6
14.8
7.0
18.3
17.6
27.9
1.5
5.2
9.0
1.5
135.7
3.0
11.7
1.4
2.1
141.7
25.2
2809.5
43
Chinese Taipei
Colombia
Congo
Congo, Dem. Republic
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Gibraltar
Greece
Guatemala
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. People's Rep.
Korea, Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia, Republic of
Malaysia
Malta
Mexico
Moldova, Republic of
0.776
0.259
0.007
0.009
0.023
0.018
0.060
0.116
0.022
0.296
0.118
0.064
0.107
0.629
0.018
0.002
0.055
0.020
0.148
1.009
0.008
0.018
2.225
0.029
0.001
0.217
0.033
0.006
0.024
0.115
0.121
0.006
7.059
1.448
1.793
0.360
0.108
0.210
1.034
0.021
3.655
0.066
0.903
0.039
0.224
1.805
0.025
0.295
0.019
0.022
0.064
0.175
0.038
0.031
0.032
0.672
0.008
1.366
0.014
281.2
93.9
2.5
3.2
8.4
6.5
21.8
41.9
8.1
107.3
42.7
23.2
38.7
228.1
6.7
0.7
20.0
7.2
53.6
365.7
2.9
6.5
806.6
10.4
0.5
78.7
12.0
2.3
8.6
41.6
43.9
2.1
2558.7
525.0
649.9
130.4
39.1
76.1
375.0
7.6
1325.0
23.9
327.2
14.1
81.3
654.4
9.0
107.0
6.9
7.9
23.3
63.6
13.7
11.4
11.7
243.7
2.7
495.3
5.1
132.1
44.1
1.2
1.5
3.9
3.1
10.3
19.7
3.8
50.4
20.1
10.9
18.2
107.2
3.1
0.3
9.4
3.4
25.2
171.8
1.4
3.1
378.9
4.9
0.2
37.0
5.6
1.1
4.0
19.5
20.6
1.0
1202.0
246.6
305.3
61.3
18.3
35.7
176.2
3.6
622.5
11.2
153.7
6.6
38.2
307.4
4.2
50.2
3.2
3.7
10.9
29.9
6.5
5.3
5.5
114.5
1.3
232.7
2.4
74.5
24.9
0.7
0.8
2.2
1.7
5.8
11.1
2.2
28.4
11.3
6.2
10.2
60.4
1.8
0.2
5.3
1.9
14.2
96.8
0.8
1.7
213.6
2.7
0.1
20.8
3.2
0.6
2.3
11.0
11.6
0.6
677.7
139.0
172.1
34.5
10.3
20.1
99.3
2.0
350.9
6.3
86.7
3.7
21.5
173.3
2.4
28.3
1.8
2.1
6.2
16.8
3.6
3.0
3.1
64.5
0.7
131.2
1.4
44
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian Federation
Saudi Arabia
Senegal
Serbia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania, United Republic
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
World total or average
0.037
0.008
0.153
0.009
0.030
0.010
0.013
0.484
0.014
0.095
0.013
0.244
0.170
0.174
0.482
0.028
0.015
0.190
0.274
0.914
0.152
0.256
0.221
5.315
1.523
0.022
0.111
0.051
0.100
0.045
1.314
0.704
0.043
0.045
0.132
0.118
0.188
0.008
0.021
0.959
0.005
0.143
0.079
0.954
0.158
0.885
0.543
1.355
15.350
0.020
0.302
0.648
0.496
0.069
0.007
0.027
99.747
13.3
2.8
55.5
3.4
11.0
3.5
4.5
175.3
5.0
34.4
4.9
88.4
61.6
62.9
174.9
10.0
5.3
68.9
99.2
331.2
55.2
92.9
80.3
1926.5
552.1
7.9
40.4
18.6
36.1
16.5
476.1
255.0
15.5
16.1
48.0
42.8
68.3
3.0
7.7
347.7
1.8
52.0
28.7
345.7
57.4
320.8
196.8
491.3
5564.1
7.1
109.5
234.9
179.9
25.0
2.7
9.6
36,156
6.3
1.3
26.1
1.6
5.2
1.6
2.1
82.4
2.4
16.2
2.3
41.5
28.9
29.5
82.2
4.7
2.5
32.4
46.6
155.6
25.9
43.6
37.7
905.1
259.4
3.7
19.0
8.7
17.0
7.7
223.7
119.8
7.3
7.6
22.5
20.1
32.1
1.4
3.6
163.3
0.9
24.4
13.5
162.4
27.0
150.7
92.4
230.8
2614.0
3.3
51.5
110.3
84.5
11.8
1.3
4.5
16,986
3.5
0.7
14.7
0.9
2.9
0.9
1.2
46.4
1.3
9.1
1.3
23.4
16.3
16.7
46.3
2.6
1.4
18.3
26.3
87.7
14.6
24.6
21.3
510.2
146.2
2.1
10.7
4.9
9.6
4.4
126.1
67.5
4.1
4.3
12.7
11.3
18.1
0.8
2.0
92.1
0.5
13.8
7.6
91.6
15.2
85.0
52.1
130.1
1473.6
1.9
29.0
62.2
47.7
6.6
0.7
2.5
9,576
45
Table 8 indicates that the sum of the 139-country greenhouse gas and particle emissions
may cause, in 2050, $17 (9.6-36) trillion/year in climate damage to the world. Thus, the
global climate cost savings per person, averaged among these countries, to reducing all
climate-relevant emissions through a 100% WWS system, is ~$1,930/person/year (in 2013
USD) (Table 6).
9. Impacts of WWS on Jobs and Earnings in the Energy Power Sector.
This section provides estimates of job and revenue creation and loss due to implementing
WWS electricity. The analysis does not include the job changes in industries outside of
electric power generation, such as in the manufacture of electric vehicles, fuel cells or
electricity storage because of the additional complexity required and greater uncertainty as
to where those jobs will be located.
9.A. JEDI Job Creation Analysis
Changes in jobs and total earnings are estimated here first with the Jobs and Economic
Development Impact (JEDI) models (NREL, 2013). These are economic input-output
models with several assumptions and uncertainties (e.g. Linowes, 2012). They incorporate
three levels of impacts: 1) project development and onsite labor impacts; 2) local revenue
and supply chain impacts; and 3) induced impacts. Jobs and revenue are reported for two
phases of development: 1) the construction period and 2) operating years.
Scenarios for WWS powered electricity generation are run for each country assuming that
the WWS electricity sector is fully developed by 2050. The calculations account for only
new WWS jobs associated with new WWS generator capacity as identified in Table 2 and
corresponding new transmission lines. As construction jobs are temporary in nature, JEDI
models report construction job creation as full-time equivalents (FTE, equal to 2,080 hours
of work per year). Here, the job calculations assume that 1/35th of the WWS infrastructure is
built each year from 2015 to 2050.
The number of jobs associated with new transmission lines assumes 80% of new lines will
be 500 kV high-voltage direct current (HVDC) lines and 20% 230 kV alternating current
(AC) lines. Total line length is simplistically assumed to equal five times the circular radius
of a country. The transmission line JEDI model is used to calculate construction FTE jobs
and annual operations jobs for the 230 kV AC lines for each country. For HVDC lines, the
actual average numbers of construction FTE jobs and annual operation jobs among five
proposed projects in the U.S. (Clean Line Energy Partners, 2015) are multiplied by the ratio
of JEDI-model predicted number of jobs in a given country to that of the U.S. assuming 500
kV HVDC lines.
Table 9. Estimated new 35-year construction jobs, new 35-year operation jobs, 35-year construction plus
operation jobs minus jobs lost, annual earnings corresponding to new construction and operation jobs, and net
earnings from new construction plus operation jobs minus jobs lost (current jobs plus future jobs lost due to
not growing fossil-fuel infrastructure), by country, due to converting to 100% WWS, based on the number of
new generators needed of each type for annual average power and peaking/storage (Table 2). Earnings include
wages, services, and supply-chain impacts. Monetary values are in 2013 USD.
Country
35-year
construction
jobs
35-year
operation
jobs
Job losses in
fossil-fuel
and nuclear
35-year net
construction
plus
Annual
earning
s from
Earning
s from
new 40-
Net earnings
from new
construct-ion
46
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei Darussalam
Bulgaria
Cambodia
Cameroon
Canada
Chile
China
Chinese Taipei
Colombia
Congo
Congo, Dem. Republic
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Eritrea
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Gibraltar
Greece
Guatemala
Haiti
7,092
138,731
34,416
121,752
6,850
165,104
78,678
22,978
13,941
133,831
97,664
139,632
7,961
17,991
11,186
6,339
829,831
5,759
43,424
14,180
17,372
281,468
87,461
7,216,242
350,005
67,437
4,141
67,994
8,102
21,696
22,461
20,709
5,438
78,967
15,915
15,397
25,661
210,778
6,416
2,282
4,374
100,492
33,326
335,579
7,126
9,227
636,234
24,976
3,004
34,414
22,012
6,554
6,667
131,154
16,035
115,734
6,959
260,039
111,239
21,807
18,818
95,988
130,410
229,705
6,811
11,995
10,399
7,628
769,969
7,171
55,485
7,472
15,996
473,592
119,136
6,531,759
173,235
74,298
3,051
66,196
4,695
18,888
31,749
18,759
5,950
83,147
31,919
7,409
18,025
172,310
3,455
1,172
6,232
57,271
65,641
333,154
10,206
9,110
920,226
22,804
3,247
29,390
10,999
3,371
energy
industries
operation
jobs created
minus jobs
lost
7,258
342,715
276,754
184,828
3,720
251,580
45,216
100,757
48,055
158,232
40,536
46,817
27,737
47,774
9,303
7,026
930,732
35,536
27,969
41,070
64,546
627,112
113,412
3,915,817
91,975
170,306
55,339
216,959
10,072
101,997
18,154
19,785
2,985
41,410
39,005
12,862
68,515
288,007
9,485
8,233
8,239
391,955
44,095
191,713
40,959
8,361
261,444
59,948
2,055
28,378
44,490
23,384
6,500
(72,830)
(226,302)
52,658
10,088
173,562
144,702
(55,972)
(15,295)
71,588
187,537
322,520
(12,965)
(17,788)
12,282
6,941
669,068
(22,605)
70,940
(19,419)
(31,178)
127,949
93,185
9,832,184
431,264
(28,570)
(48,147)
(82,769)
2,725
(61,413)
36,056
19,684
8,402
120,703
8,829
9,945
(24,829)
95,081
386
(4,779)
2,368
(234,192)
54,871
477,020
(23,627)
9,976
1,295,016
(12,168)
4,196
35,426
(11,479)
(13,460)
new 40year
constru
ction
jobs
(bil $/
yr)
0.52
7.24
1.30
8.30
0.43
15.55
8.07
2.28
0.92
4.00
9.90
13.72
0.18
0.65
0.80
0.35
54.33
0.98
4.20
0.43
0.45
26.15
7.20
663.01
74.30
3.40
0.15
1.12
0.43
0.59
2.49
1.34
0.78
6.29
1.61
0.77
1.19
9.50
0.26
0.05
0.33
2.05
3.16
31.13
0.43
0.57
64.87
0.74
0.34
2.50
0.84
0.14
year
operatio
n jobs
(bil
$/yr)
plus operation
jobs minus
jobs lost
(bil $/yr)
0.49
6.84
0.61
7.89
0.44
24.49
11.42
2.16
1.25
2.87
13.22
22.56
0.15
0.43
0.74
0.42
50.41
1.22
5.36
0.23
0.42
43.99
9.81
600.13
36.77
3.74
0.11
1.09
0.25
0.51
3.52
1.21
0.85
6.62
3.22
0.37
0.84
7.77
0.14
0.02
0.47
1.17
6.22
30.91
0.62
0.56
93.83
0.68
0.36
2.14
0.42
0.07
0.48
-3.80
-8.56
3.59
0.64
16.35
14.85
-5.54
-1.01
2.14
19.01
31.68
-0.29
-0.64
0.88
0.39
43.81
-3.86
6.85
-0.59
-0.82
11.89
7.67
903.36
91.55
-1.44
-1.71
-1.36
0.15
-1.67
4.00
1.27
1.20
9.61
0.89
0.50
-1.15
4.29
0.02
-0.10
0.18
-4.79
5.20
44.26
-1.44
0.61
132.04
-0.36
0.47
2.58
-0.44
-0.29
47
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. People's Rep.
Korea, Republic of
Kosovo
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Lithuania
Luxembourg
Macedonia, Republic of
Malaysia
Malta
Mexico
Moldova, Republic of
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian Federation
Saudi Arabia
Senegal
Serbia
Singapore
11,059
69,941
58,386
1,105
1,985,687
555,664
437,393
58,296
11,309
41,713
389,696
5,947
666,203
11,894
139,143
38,478
63,571
564,620
4,317
43,330
12,928
13,888
12,272
29,690
16,026
11,250
6,894
203,649
4,501
381,308
7,692
15,351
1,913
53,425
21,316
48,267
6,251
30,007
120,726
6,076
17,428
6,493
308,175
12,156
70,080
303,174
13,290
6,973
45,323
145,215
113,266
20,485
54,600
67,105
779,145
210,373
9,658
34,577
148,370
7,626
98,173
61,510
3,264
1,677,554
493,704
510,769
56,062
17,043
35,461
534,661
4,690
821,088
10,802
192,336
22,996
95,646
708,979
3,940
64,022
11,628
23,322
15,061
36,531
25,715
14,153
6,885
249,291
7,902
271,106
7,862
18,387
2,316
48,034
17,214
42,510
8,437
24,599
204,319
7,105
22,589
3,864
259,542
32,822
141,165
235,610
14,963
5,981
32,783
46,663
90,909
17,804
78,332
87,829
1,331,472
293,350
6,682
40,184
184,974
18,494
21,374
27,063
3,769
2,221,582
841,563
743,454
326,682
12,186
19,147
151,227
5,618
238,182
11,729
239,190
143,630
42,094
177,682
5,505
274,425
9,278
17,685
10,079
198,627
15,658
3,845
4,688
246,378
2,540
691,818
6,085
21,305
2,833
34,756
117,515
122,226
42,380
72,275
125,545
5,825
32,859
10,824
1,078,316
205,142
144,132
368,451
11,698
30,191
71,076
112,329
93,287
28,069
289,419
78,931
1,451,618
980,824
27,169
28,561
63,855
191
146,739
92,833
600
1,441,659
207,805
204,708
(212,324)
16,166
58,028
773,130
5,018
1,249,110
10,967
92,290
(82,156)
117,123
1,095,917
2,752
(167,073)
15,278
19,524
17,255
(132,405)
26,084
21,557
9,091
206,563
9,863
(39,405)
9,469
12,433
1,396
66,703
(78,986)
(31,449)
(27,692)
(17,669)
199,500
7,357
7,157
(467)
(510,599)
(160,164)
67,113
170,333
16,555
(17,237)
7,030
79,549
110,888
10,220
(156,487)
76,002
658,999
(477,102)
(10,829)
46,201
269,488
0.34
9.52
4.16
0.11
96.91
28.86
17.37
2.19
1.20
3.48
33.70
0.25
49.85
0.41
16.89
1.00
1.39
48.49
0.18
3.96
0.53
1.66
0.50
1.63
2.09
1.85
0.57
16.92
0.65
26.48
0.38
0.85
0.17
2.03
0.40
1.64
0.27
0.80
12.63
0.45
1.82
0.24
12.14
1.59
4.43
10.92
0.84
0.29
2.16
6.18
8.14
1.54
7.31
7.09
66.12
14.99
0.23
2.91
26.71
0.24
13.37
4.38
0.32
81.87
25.64
20.28
2.10
1.82
2.96
46.24
0.20
61.44
0.37
23.35
0.60
2.10
60.88
0.17
5.85
0.47
2.78
0.62
2.00
3.35
2.33
0.57
20.71
1.15
18.83
0.39
1.02
0.21
1.82
0.32
1.45
0.37
0.66
21.37
0.52
2.36
0.14
10.22
4.28
8.92
8.49
0.95
0.25
1.57
1.99
6.53
1.34
10.49
9.29
112.99
20.91
0.16
3.38
33.30
0.01
19.98
6.61
0.06
70.36
10.79
8.13
-7.97
1.72
4.85
66.86
0.21
93.47
0.38
11.20
-2.14
2.57
94.11
0.12
-15.28
0.62
2.33
0.71
-7.25
3.40
3.55
0.75
17.16
1.43
-2.74
0.47
0.69
0.13
2.53
-1.48
-1.07
-1.20
-0.47
20.87
0.54
0.75
-0.02
-20.11
-20.91
4.24
6.13
1.05
-0.71
0.34
3.38
7.96
0.77
-20.95
8.03
55.92
-34.00
-0.26
3.89
48.51
48
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania, United Republic
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
World total or average
25,566
10,969
270,876
161,072
31,286
34,879
23,810
49,081
24,841
6,971
57,332
355,969
5,872
13,549
32,685
190,817
82,970
228,089
252,329
130,902
2,275,361
7,809
169,106
109,399
180,239
20,924
20,538
22,000
24,962,913
29,148
20,328
352,057
159,741
21,282
19,185
50,246
75,716
26,500
4,628
49,387
386,432
5,140
25,114
34,319
187,229
106,978
272,339
371,624
218,483
2,755,045
8,701
174,023
142,992
151,508
13,793
16,464
20,238
26,436,734
15,378
8,658
288,040
117,846
50,233
105,863
69,731
27,137
56,363
5,579
182,105
338,204
30,039
69,781
39,186
91,310
118,030
144,257
349,693
229,081
2,396,289
12,801
141,537
332,766
251,552
48,615
73,361
80,127
27,670,862
39,336
22,639
334,893
202,967
2,335
(51,799)
4,325
97,661
(5,021)
6,020
(75,385)
404,197
(19,027)
(31,119)
27,818
286,736
71,919
356,171
274,260
120,305
2,634,117
3,708
201,592
(80,375)
80,196
(13,898)
(36,359)
(37,889)
23,728,786
1.94
0.86
13.56
13.73
1.63
1.03
2.45
5.86
0.71
0.26
1.42
21.89
0.12
1.09
1.55
12.02
7.62
15.03
20.62
12.22
273.40
0.50
8.84
6.34
6.94
0.47
0.60
0.48
2044
2.21
1.60
17.62
13.62
1.11
0.57
5.17
9.04
0.76
0.17
1.23
23.76
0.10
2.02
1.62
11.80
9.83
17.95
30.37
20.40
331.04
0.55
9.10
8.28
5.84
0.31
0.48
0.44
2212
2.98
1.78
16.76
17.30
0.12
-1.53
0.44
11.65
-0.14
0.22
-1.87
24.85
-0.38
-2.50
1.32
18.06
6.61
23.48
22.41
11.23
316.51
0.24
10.54
-4.66
3.09
-0.31
-1.06
-0.82
2250
Table 9 indicates that 100% conversion to WWS across 139 countries may create ~25.0
million new 35-year construction jobs and ~26.4 million new 35-year operation and
maintenance jobs for the WWS generators and transmission proposed. These employment
numbers do not include all external jobs created in areas such as research and
development, storage development, and local economy improvement.
Table 10 provides a summary among 139 countries of job losses in the oil, gas, coal,
nuclear, and bioenergy industries. Job loss is calculated as the product of jobs per unit
energy in each employment category and total energy use. Total energy use is the product of
energy use in 2012 from IEA World Energy Balances, by country, and the ratio of energy
use in the target year to energy use in 2012 (from IEO projections by region, extrapolated
past 2040, and mapped to individual countries). Jobs per unit energy are calculated as the
product of jobs per unit energy unit in the U.S. in 2012, the fraction of conventional-fuel
jobs lost due to converting to WWS (Table 10), a multiplier for jobs associated with the jobs
lost but not counted elsewhere, and country-specific adjustment factors accounting for the
relationship between jobs per unit energy and GDP per capita and total energy use.
Calculations are detailed in Delucchi et al. (2016).
The fraction of fossil-fuel jobs lost in each job sector (Table 10), accounts for the retention
of some jobs for non-energy uses of fossil fuels (e.g., some petroleum products will still be
used as lubricants, asphalt, petrochemical feedstock, and petroleum coke) and transportation
of goods other than fossil fuels.
49
Job losses include construction jobs lost from not building future fossil, nuclear, and biopower plants because WWS plants are built instead. Job losses from not replacing existing
conventional plants are not treated to be consistent with the fact that jobs created by
replacing WWS plants with other WWS plants are not treated.
The shift to WWS is estimated to result in the loss of ~27.7 million jobs in the current fossil
fuel, biofuel, and nuclear industries in the 139 countries. The job loss represents ~1% of the
total workforce in the 139 countries.
Table 10. Estimated 139-country job losses due to eliminating energy generation and use from the fossil fuel
and nuclear sectors. Also shown is the percent of total jobs in the sector that are lost. Not all fossil-fuel jobs are
lost due to non-energy uses of petroleum, such as lubricants, asphalt, petrochemical feedstock, and petroleum
coke. For transportation sectors, the jobs lost are those due to transporting fossil fuels; the jobs not lost are
those for transporting other goods.
Energy sector
Jobs lost in
sector
Percent of
jobs in
sector that
are lost
87
97
100
87
87
87
93
0
30
50
100
100
100
100
10
52
23
8
100
Oil and gas extraction
2,272,000
Coal mining
987,000
Uranium mining
110,500
Support for oil and gas
3,412,000
Oil and gas pipeline construction
1,543,000
Mining & oil/gas machinery
1,101,000
Petroleum refining
561,000
Asphalt paving and roofing materials
0
Gas stations with stores
1,544,000
Other gas stations
361,000
Fossil electric power generation utilities
884,000
Fossil electric power generation non-utilities
154,000
Nuclear and other power generation
1,038,000
Natural gas distribution
1,033,000
Auto oil change shops/other repair
51,900
Rail transportation of fossil fuels
649,000
Water transportation of fossil fuels
211,600
Truck transportation of fossil fuels
758,700
Bioenergy except electricity
7,089,000
Total current jobs lost
23,762,000
h
Jobs lost from not growing fossil fuels
4,650,000
All jobs lost
28,412,000
i
Total labor force
2.87 billion
Jobs lost as percent of labor force
0.99%
a
See Delucchi et al. (2016) for detailed calculations and referencing.
b
Jobs lost from not growing fossil fuels are additional construction and operation jobs that would have accrued
by 2050 if BAU instead of WWS continued.
50
c
The total labor force in each country is obtained from World Bank (2015b).
Subtracting the number of jobs lost across the 139 countries from the number of jobs created
gives a net of ~23.7 million 35-year jobs created due to WWS. Although all countries
together are expected to gain jobs, some countries, particularly those that currently extract
significant fossil fuels (e.g., Kuwait, Iraq, Nigeria, Qatar, Saudi Arabia, Sudan, Venezuela,
and Yemen) may experience net job loss in the energy production sector. However, such
job loss in many of those countries can potentially be made up in the manufacture and
service of storage technologies, hydrogen technologies, electric vehicles, electric heating
and cooling appliances, and industrial heating equipment, although such job creation
numbers were not determined here.
The direct and indirect earnings from producing WWS electricity amount to ~$2.04
trillion/year during the construction stage and ~$2.21 trillion/yr during the operation stage.
The annual earnings lost from the fossil-fuel industries total ~$2.00 trillion/yr giving a net
gain in annual earnings of ~$2.25 trillion/yr.
10. Timeline for Implementing the Roadmaps and Impacts on Carbon Dioxide
Figure 11 shows the mean proposed timeline for the complete transformation of the energy
infrastructures of the 139 countries considered here. The timeline assumes 100% WWS by
2050, with 80% WWS by 2030. To meet this timeline, rapid transitions are needed in each
technology sector. Whereas, much new infrastructure can be installed upon retirement of
existing infrastructure or devices, other transitions will require aggressive policies (Section
11) to meet the timeline. Below is a list of proposed transformation timelines for individual
sectors.
Figure 11. Mean change in 139-country end-use power demand for all purposes (electricity, transportation,
heating/cooling, industry, agriculture/fishing/forestry, and other) and its supply by conventional fuels and
WWS generators over time based on the country roadmaps proposed here. Total power demand decreases
upon conversion to WWS due to the efficiency of electricity over combustion and end-use energy efficiency
measures. The percentages next to each WWS source are the final (2050) estimated percent supply of end-use
power by the source. The 100% demarcation in 2050 indicates that 100% of all-purpose power is provided by
WWS technologies by 2050, and the power demand by that time has decreased.
51
Development of super grids and smart grids: As soon as possible, countries should develop
long-term power-transmission-and-distribution systems to provide “smart” management of
energy demand and supply at all scales, from local to international (e.g., Smith et al., 2013;
Blarke and Jenkins, 2013; Elliott, 2013).
Power plants: by 2020, no more construction of new coal, nuclear, natural gas, or biomass
fired power plants; all new power plants built are WWS. This is feasible because few power
plants are built annually, and most WWS electric power generator technologies are already
cost competitive.
Heating, drying, and cooking in the residential and commercial sectors: by 2020, all new
devices and machines are powered by electricity. This is feasible because the electric
versions of these products are already available, and all sectors can use electricity without
adaptation (the devices can be plugged in or installed).
Large-scale waterborne freight transport: by 2020-2025, all new ships are electrified
and/or use electrolytic hydrogen, all new port operations are electrified, and port retroelectrification is well underway. This should be feasible for relatively large ships and ports
because large ports are centralized and few ships are built each year. Policies may be needed
to incentivize the early retirement of ships that do not naturally retire before 2050.
Rail and bus transport: by 2025, all new trains and buses are electrified. This requires
changing the supporting energy-delivery infrastructure and the manufacture method of
52
transportation equipment. However, relatively few producers of buses and trains exist, and
the supporting energy infrastructure is concentrated in cities.
Off-road transport, small-scale marine: by 2025 to 2030, all new production is electrified.
Heavy-duty truck transport: by 2025 to 2030, all new heavy-duty trucks and buses are
electrified or use electrolytic hydrogen. It may take 10-15 years for manufacturers to retool
and for enough of the supporting energy-delivery infrastructure to be put in place.
Light-duty on-road transport: by 2025-2030, all new light-duty onroad vehicles are
electrified. Manufacturers need time to retool, but more importantly, several years are
needed to get the energy-delivery infrastructure in place for a 100% WWS transportation
fleet.
Short-haul aircraft: by 2035, all new small, short-range aircraft are battery- or electrolytichydrogen powered. Changing the design and manufacture of airplanes and the design and
operation of airports are the main limiting factors to a more rapid transition.
Long-haul aircraft: by 2040, all remaining new aircraft use electrolytic cryogenic hydrogen
(Jacobson and Delucchi, 2011, Section A.2.7) with electricity power for idling, taxiing, and
internal power. The limiting factors to a faster transition are the time and social changes
required to redesign aircraft and airports.
During the transition, conventional fuels and existing WWS technologies are needed to
produce the remaining WWS infrastructure. However, much of the conventional energy
would be used in any case to produce conventional power plants and automobiles if the
plans proposed here were not implemented. Further, as the fraction of WWS energy
increases, conventional energy generation will decrease, ultimately to zero, at which point
all new WWS devices will be produced with existing WWS. In sum, the creation of WWS
infrastructure may result in a temporary increase in emissions before they are ultimately
reduced to zero.
Impacts on carbon dioxide: Figure 12 illustrates the impact on global carbon dioxide levels
of the aggressive goals proposed here (80% WWS by 2030 and 100% by 2050) as well as of
a less aggressive WWS scenario that provides 80% WWS by 2050 and 100% by 2100. Both
scenarios reduce CO2 levels below those today. The 100% by 2050 scenario reduces CO2 to
just below 350 ppmv by 2100, a level last seen around 1987. The 100% by 2100 scenario
reduces CO2 to ~370 ppmv by 2100. All IPCC (2000) emission scenarios similarly result in
CO2 levels much higher than in WWS scenarios through 2100, ranging from 460 to 800
ppmv. Such scenarios are certain to drive temperatures up further. WWS scenarios will
stabilize and ultimately reduce temperatures.
The WWS plans proposed here will also eliminate emissions of energy-related black carbon,
the second-leading cause of global warming after carbon dioxide, and of energy-related
methane, the third-leading cause, as well as tropospheric ozone precursors, carbon
monoxide, and nitrous oxide from energy. As such the aggressive worldwide conversion to
53
WWS proposed here will avoid exploding levels of CO2 and other global warming
contaminants, potentially avoiding catastrophic climate change.
Figure 12. Comparison of historic (1751-2014) observed CO2 mixing ratios (ppmv) from the Siple ice core
(Neftel et al., 1994) and the Mauna Loa Observatory (Tans and Keeling, 2015) with GATOR-GCMOM model
results (Jacobson, 2005) for the same period plus model projections from 2015-2100 for five
Intergovernmental Panel on Climate Change (IPCC) scenarios (IPCC, 2000) and three WWS cases: an
unobtainable 100% WWS by 2015 case; a 80% WWS by 2030 and 100% by 2050 case (from Figure 11), and a
less-aggressive 80% by 2050 and 100% by 2100 case.
800
750
700
650
CO2 (ppmv)
600
550
800
750
Data
Model-100% WWS by 2015 (no FF emissions)
Model-80% WWS by 2030; 100% by 2050
Model-80% WWS by 2050; 100% by 2100
Model-IPCC A1B
Model-IPCC A2
Model-IPCC B1
Model-IPCC-B2
Model-IPCC A1F1
700
650
600
550
500
500
450
450
400
400
350
350
300
300
250
1750
1800
1850
1900
1950
Year
2000
2050
250
2100
The model is set up as in Jacobson (2005) with two columns (one atmospheric box over 38 ocean layers plus
one atmospheric box over land). It treats full ocean chemistry in all layers, vertical ocean diffusion with
canonical diffusion coefficients, ocean removal of calcium carbonate for rock formation, gas-ocean transfer,
and emissions from fossil fuels. It also accounts for photosynthesis, plant and soil respiration, and removal of
carbon dioxide from the air by weathering. Fossil-fuel emissions from 1751-1958 are from Boden et al. (2011);
from 1959-2014 are from Le Quere et al. (2015), and for 2015 onward from the WWS scenarios scaled from
2014 emission and from the individual IPCC scenarios. Land use change emissions per year are 300 Tg-C/yr
for 1751-1849; from Houghton (2015) for 1850-1958; from Le Quere et al. (2015) for 1959-2014; from the
IPCC (2000) A1B scenario for the WWS cases for 2015-2100; and from the individual IPCC scenarios for the
remaining cases. The net carbon sink over land from 1751-2100 is calculated from the time-dependent
photosynthesis, respiration, and weathering processes mentioned.
11. Recommended First Steps
54
The policy pathways necessary to transform the 139 countries treated here to 100% WWS
will differ by country, depending largely on the willingness of the government and people in
each country to affect rapid change. This study does not advocate specific policy measures
for any country. Instead, it provides a set of policy options that each country can consider.
The list is by no means complete. Within each section, the policy options are listed roughly
in order of proposed priority.
12.1. Energy Efficiency Measures
• Expand clean, renewable energy standards and energy efficiency standards.
•
Incentivize conversion from natural gas water and air heaters to electric heat pumps
(air and ground-source) and rooftop solar thermal hot water pre-heaters.
•
Promote, though municipal financing, incentives, and rebates for implementing
energy efficiency measures in buildings and other infrastructure. Efficiency
measures include, but are not limited to, using LED lighting; evaporative cooling;
ductless heating and air conditioning; adding energy-storing materials to walls and
floors to modulate temperature changes; water-cooled heat exchanging; night
ventilation cooling; combined space and water heating; improved data center design;
improved air flow management; advanced lighting controls; variable refrigerant
flow; improved wall, floor, ceiling, and pipe insulation; double- and triple-paned
windows; and passive solar heating. Additional measures include sealing windows,
doors, and fireplaces; and monitoring/auditing building energy use to identify wasted
energy.
•
Revise building codes to incorporate “green building standards” based on best
practices for building design, construction, and energy use.
•
Incentivize landlord investment in energy efficiency. Allow owners of multi-family
buildings to take a property tax exemption for energy efficiency improvements in
their buildings that provide benefits to their tenants.
•
Create energy performance rating systems with minimum performance requirements
to assess energy efficiency levels and pinpoint areas of improvement.
•
Create a green building tax credit program for the corporate sector.
12.2. Energy Supply Measures
• Increase Renewable Portfolio Standards (RPSs).
•
Extend or create state WWS production tax credits.
•
Streamline the permit approval process for large-scale WWS power generators and highcapacity transmission lines. Work with local and regional governments to manage
55
zoning and permitting issues within existing planning efforts or pre-approve sites to
reduce the cost and uncertainty of projects and expedite their physical build-out.
•
Streamline the small-scale solar and wind installation permitting process. Create
common codes, fee structures, and filing procedures across a country.
•
Lock in fossil fuel and nuclear power plants to retire under enforceable
commitments. Implement taxes on emissions by current utilities to encourage their
phase-out.
•
Incentivize clean-energy backup emergency power systems rather
diesel/gasoline generators at both the household and community levels.
•
Incentivize home or community energy storage (through garage electric battery
systems, for example) that accompanies rooftop solar to mitigate problems
associated with grid power losses.
than
12.3. Utility Planning and Incentive Structures
• Incentivize community seasonal heat storage underground using the Drake Landing
solar community as an example.
•
Incentive the development of utility-scale grid electric power storage, such as in
CSP, pumped hydropower, and more efficient hydropower.
•
Require utilities to use demand response grid management to reduce the need for
short-term energy backup on the grid.
•
Incentivize the use of excess WWS electricity to produce hydrogen to help manage
the grid.
•
Develop programs to use EV batteries, after the end of their useful life in vehicles,
for local, short-term storage and balancing.
•
Implement virtual net metering (VNM) for small-scale energy systems.
12.4. Transportation
• Promote more public transit by increasing its availability and providing
compensation to commuters for not purchasing parking passes.
•
Increase safe biking and walking infrastructure, such as dedicated bike lanes,
sidewalks, crosswalks, timed walk signals, etc.
•
Adopt legislation mandating BEVs for short- and medium distance government
transportation and use incentives and rebates to encourage the transition of
commercial and personal vehicles to BEVS.
56
•
Use incentives or mandates to stimulate the growth of fleets of electric and/or
hydrogen fuel cell/electric hybrid buses starting with a few and gradually growing
the fleets. Additionally, incentivize electric and hydrogen fuel cell ferries, riverboats,
and other local shipping.
•
Adopt zero-emission standards for all new on-road and off-road vehicles, with 100%
of new production required to be zero-emission by 2030.
•
Ease the permitting process for installing electric charging stations in public parking
lots, hotels, suburban metro stations, on streets, and in residential and commercial
garages.
•
Set up time-of-use electricity rates to encourage charging at night.
•
Incentivize the electrification of freight rail and shift freight from trucks to rail.
12.5. Industrial Processes
• Provide financial incentives for industry to convert to electricity and electrolytic
hydrogen for high temperature and manufacturing processes.
•
Provide financial incentives to encourage industries to use WWS electric power
generation for on-site electric power (private) generation.
12. Summary
Roadmaps are presented for converting the energy systems for all purposes (electricity,
transportation, heating/cooling, industry, and agriculture/forestry/fishing) of 139 countries
into clean and sustainable ones powered by wind, water, and sunlight (WWS).
For each country, the study estimates 2050 BAU power demand from current data, converts
the supply for each load sector to WWS supply, and proposes a mix of WWS generators
within each the country that can match the projected 2050 all-sector power demand. The
conversion from BAU combustion to WWS electricity for all purposes is calculated to
reduce 139-country-averaged end-use load by ~38.3%, with ~82% of this due to the higher
work to energy ratio of electricity over combustion and eliminating the need for mining,
transport, and refining of conventional fuels, and the rest due to end-use energy efficiency
improvements.
Remaining all-purpose annually averaged end-use 2050 load over the 139 countries is
proposed to be met with ~1.2 million new onshore 5-MW wind turbines (providing 19.8%
of 139-country power for all purposes), 761,000 off-shore 5-MW wind turbines (12.7%),
487,000 50-MW utility-scale solar-PV power plants (40.8%), 15,700 100-MW utility-scale
CSP power plants with storage (7.7%), 779 million 5-kW residential rooftop PV systems
(6.5%), 42.2 million 100-kW commercial/government rooftop systems (7.0%), 840 100MW geothermal plants (0.73%), 265,000 0.75-MW wave devices (0.38%), 30,100 1-MW
tidal turbines (0.06%), and 0 new hydropower plants. The capacity factor of existing
57
hydropower plants will increase slightly so that hydropower supplies 4.3% of all-purpose
power. Another estimated 9,400 100-MW CSP plants with storage and 102,000 50-MW
solar thermal collectors for heat generation and storage will be needed to help stabilize the
grid. This is just one possible mix of generators.
The additional footprint on land for WWS devices is equivalent to about 0.25% of the 139country land area, mostly for utility scale PV. This does not account for land gained from
eliminating the current energy infrastructure. An additional on-land spacing area of about
0.68% for the 139 countries is required for onshore wind, but this area can be used for
multiple purposes, such as open space, agricultural land, or grazing land.
The 2013 LCOE for hydropower, onshore wind, utility-scale solar, and solar thermal for
heat is already similar to or less than the LCOE for natural gas combined-cycle power
plants. Rooftop PV, offshore wind, tidal, and wave presently have higher LCOEs. However,
by 2050 the LCOE for all WWS technologies is expected to drop, most significantly for
offshore wind, tidal, wave, rooftop PV, CSP, and utility PV, whereas conventional fuel costs
are expected to rise.
The 139-country roadmaps are anticipated to create 26.4 million 35-year construction jobs
and 23.7 million 35-year operation jobs for the energy facilities alone, the combination of
which would outweigh by ~23.7 million the 27.7 million jobs lost in the conventional
energy sector.
The 139-country roadmaps will eliminate ~4.6 (1.3-8.0) million premature air pollution
mortalities per year today and 3.3 (0.8-7.0) million/yr in 2050, avoiding ~$23.2 ($4.0-$70.8)
trillion/year in 2050 air-pollution damage costs (2013 USD).
Converting will further eliminate ~$17 (9.6-36) trillion/year in 2050 global warming costs
(2013 USD) due to 139-country greenhouse-gas and particle emissions.
These plans will result in the average person in 2050 saving $143/year in fuel costs
compared with conventional fuels, ~2,632/year (14 ¢/kWh) in air-pollution damage costs,
and $1,930/year (10 ¢/kWh) in climate costs (2013 USD).
Many uncertainties in the analysis here are captured in broad ranges of energy, health, and
climate costs given. However, these ranges may miss costs due to limits on supplies caused
by wars or political/social opposition to the roadmaps. As such, the estimates should be
reviewed periodically.
The timeline for conversion is proposed as follows: 80% of all energy to be WWS by 2030
and 100% by 2050. As of the end of 2014, three countries -- Norway (55%), Paraguay
(53%), and Iceland (38%) – have installed more than 35% of their projected all-purpose
2050 needed nameplate capacity of WWS energy. The world average conversion to date is
3.6%.
58
The major benefits of a conversion are the near-elimination of air pollution morbidity and
mortality and global warming, net job creation, energy-price stability, reduced international
conflict over energy because each country will largely be energy independent, increased
access to distributed energy and reduced energy poverty to the 4 billion people worldwide
who currently collect their own energy and burn it (2.7 billion people) or who have no
access to energy (1.3 billion people), and reduced risks of large-scale system disruptions
through power outage or terrorism because much of the world power supply will be
decentralized. Finally, the aggressive worldwide conversion to WWS proposed here will
avoid exploding levels of CO2 and catastrophic climate change and allow CO2 levels to
reach 350 ppmv by 2100.
The study finds that the conversion to WWS is technically and economically feasible. The
main barriers are still social and political.
Acknowledgments
We would like to thank The Solutions Project for partial funding of three students. We
would like to thank Karl Burkart for developing the timeline graphic for this paper. The
spreadsheets for this paper can be found in Delucchi et al. (2016).
References
Anenberg, S.C., D. Shindell, M. Amann, G. Faluvegi, et al., 2012. Global air quality and health co-benefits of
mitigating near-term climate change through methane and black carbon emission controls, Environmental
Health Perspectives, 120, 831-839.
Arent, D., P. Sullivan, D. Heimiller, A. Lopez, K. Eurek, J. Badger, H.E. Jorgensen, and M. Kelly, 2015, 2012,
Improved offshore wind resource assessment in global climate stabilization scenarios, NREL/TP-6A2055049, http://www.nrel.gov/docs/fy13osti/55049.pdf, Accessed June 11, 2015.
Bertani, R., 2015. Geothermal Power Generation in the World 2010-2014 Update Report, World Geothermal
Congress 2015 Conference Proceedings, Melbourne, Australia, 19-25 April 2015.
Beyond Zero Emissions, Zero Carbon Australia Stationary Energy Plan, Melbourne Energy Institute,
University of Melbourne, July, 2010. http://media.bze.org.au/ZCA2020_Stationary_Energy_Report_v1.pdf,
Accessed May 2, 2015.
Blarke, M. B., and B. M. Jenkins, 2013. SuperGrid or SmartGrid: Competing Strategies for large-scale
integration of intermittent renewables?, Energy Policy 58: 381-390.
Boden, T., B. Andres, and G. Marland, 2011. Global CO2 emissions from fossil-fuel burning, cement
manufacture, and gas flaring: 1751-2011, http://cdiac.ornl.gov/ftp/ndp030/global.1751_2011.ems, Accessed
November 27, 2015.
Buonocore, J.J., P. Luckow, G. Norris, J.D. Spengler, B. Biewald, J. Fisher, and J.I. Levy, Health and climate
benefits of different energy efficiency and renewable energy choices, Nat. Clim. Change,
doe:10.1039/NCLIMATE2771, 2015.
Clean Line Energy Partners, 2015, Projects, http://www.cleanlineenergy.com/projects, Accessed Sept. 20,
2015.
Connolly, D., and B.V. Mathiesen, 2014, A technical and economic analysis of one potential pathway to a
100% renewable energy system, Intl. J. Sustainable Energy Planning & Management, 1, 7-28.
CSP World, 2015. CSP world map. http://www.cspworld.org/cspworldmap. Accessed July 22, 2015.
DDDP (Deep Decarbonization Pathways Project), 2015, Pathways to deep decarbonization,
http://deepdecarbonization.org/wp-content/uploads/2015/12/DDPP_2015_REPORT.pdf,
Accessed
December 13, 2015.
Delucchi, M.A., Jacobson, M.Z., 2011. Providing all global energy with wind, water, and solar power, Part II:
Reliability, System and Transmission Costs, and Policies. Energy Policy, 39, 1170-1190,
doi:10.1016/j.enpol.2010.11.045.
Delucchi, M.A., M.Z. Jacobson, Z.A.F. Bauer, S. Goodman, and W. Chapman, 2016. Spreadsheets for 139country
100%
wind,
water,
and
solar
roadmaps,
59
http://web.stanford.edu/group/efmh/jacobson/Articles/I/WWS-50-USState-plans.html. Accessed April 18,
2016.
Dodge, D., and D. Thompson, 2016. Shining a light on solar energy myths,
http://www.pembina.org/blog/shining-a-light-on-solar-energy-myths, Accessed Feb. 10, 2016.
DOE (Department of Energy, U.S.), 2012. An Assessment of Energy Potential at Non-Powered Dams in the
United
states,
http://nhaap.ornl.gov/system/files/NHAAP_NPD_FY11_Final_Report.pdf.
Accessed
December 17, 2012.
Dulac, J., 2013. Global Land Transport Infrastructure Requirements, International Energy Agency,
https://www.iea.org/publications/freepublications/publication/TransportInfrastructureInsights_FINAL_WEB
.pdf, Accessed September 9, 2015.
Dvorak, M., C.L. Archer, and M.Z. Jacobson, 2010. California offshore wind energy potential, Renewable
Energy, 35, 1244-1254, doi:10.1016/j.renene.2009.11.022.
ECF (European Climate Foundation), 2010, Roadmap 2050: A Practical Guide to a Prosperous, Low-Carbon
Europe, http://www.roadmap2050.eu/project/roadmap-2050, Accessed May 2, 2015.
EIA
(Energy
Information
Agency),
2015.
International
Energy
Outlook,
2014,
http://www.eia.gov/forecasts/ieo/, Accessed November 17, 2015.
Eiffert, P., 2003. Non-technical barriers to the commercialization of PV power systems in the built
environment, NREL/TP-550-31976, http://www.nrel.gov/docs/fy03osti/31976.pdf, Accessed September 8,
2015.
Elliott, D., 2013. Emergence of European supergrids – Essay on strategy issues, Energy Strategy Reviews 1:
171-173.
ELTE/EENA (ELTE University sustainable energy research group/Environmental education network
association), 2014, Hungarian sustainable energy vision, http://inforse.dk/europe/VisionHU.htm, Accessed
May 2, 2015.
Entranze
Data
Tool,
2015.
.Intelligent
Energy
Europe
Program,
European
Union.
http://www.entranze.enerdata.eu/, Accessed September 8, 2015.
EPIA (European Photovoltaic Industry Association) 2014. Global Market Outlook for Photovoltaics 20142020,
http://www.epia.org/fileadmin/user_upload/Publications/44_epia_gmo_report_ver_17_mr.pdf,
Accessed 27 July 2015.
EREC (European Renewable Energy Council), 2010, RE-Thinking 2050: A 100% Renewable Energy Vision
for the European Union,
http://www.erec.org/fileadmin/erec_docs/Documents/Publications/ReThinking2050_full%20version_final.p
df, Accessed May 2, 2015.
GCP (Global Carbon Project), Global Carbon Budget 2014 spreadsheet, Territorial Emissions,
http://www.globalcarbonproject.org/carbonbudget/14/data.htm, Accessed August 12, 2015.
GWEC (Global Wind Energy Council), 2015. Global wind report annual market update 2014,
http://www.gwec.net/wp-content/uploads/2015/03/GWEC_Global_Wind_2014_Report_LR.pdf, Accessed
July 15, 2015.
Hooker-Stroud, A., P. James, T. Kellner, and P Allen, 2015. Understanding the challenges and opportunities in
managing hourly variability in a 100% renewable energy system for the UK, Carbon Management,
doi:10.1080/17583004.2015.1024955.
Houghton, R.A., 2015. Annual net flux of carbon to the atmosphere from land-use change: 1850-2005,
http://cdiac.ornl.gov/trends/landuse/houghton/1850-2005.txt, Accessed November 27, 2015.
IEA
(International
Energy
Agency),
2005,
Energy
use
in
the
new
millennium,
https://www.iea.org/publications/freepublications/publication/millennium.pdf, Accessed Sept. 8, 2015.
IEA
(International
Energy
Agency),
2014a.
2014
Key
world
energy
statistics,
http://www.iea.org/publications/freepublications/publication/keyworld2014.pdf, Accessed August 1, 2015.
IEA (International Energy Agency), 2014b, Renewable energy medium-term market report 2014,
https://www.iea.org/Textbase/npsum/MTrenew2014sum.pdf, Accessed December 11, 2015.
IEA (International Energy Agency), 2015, Statistics, http://www.iea.org/statistics/statisticssearch/, Accessed
June 10, 2015.
IEA-PVPS (International Energy Agency-Photovoltaic Power Systems Programme) 2015, PVPS Annual
report 2014, http://iea-pvps.org/index.php?id=6&eID=dam_frontend_push&docID=2557, Accessed 25 July
2015.
IHA (International Hydropower Association), 2015, Country profiles, http://www.hydropower.org/countryprofiles, Accessed August 1, 2015.
IPCC (Intergovernmental Panel on Climate Change), 2000. Special Report on Emission Scenarios (SRES),
http://sres.ciesin.org/final_data.html, Accessed November 19, 2015.
Jacobson, M.Z., 2005. Studying ocean acidification with conservative, stable numerical schemes for
nonequilibrium air-ocean exchange and ocean equilibrium chemistry, J. Geophys. Res., 110, D07302,
doi:10.1029/2004JD005220.
60
Jacobson, M.Z., W.G. Colella, and D.M. Golden, 2005. Cleaning the air and improving health with hydrogen
fuel cell vehicles, Science, 308, 1901-1905.
Jacobson, M.Z., Y.J. Kaufmann, Y. Rudich, 2007, Examining feedbacks of aerosols to urban climate with a
model that treats 3-D clouds with aerosol inclusions, J. Geophys. Res., 112,
D24205,
doi:10.1029/2007JD008922.
Jacobson, M.Z., 2009. Review of solutions to global warming, air pollution, and energy security, Energy &
Environmental Science, 2, 148-173, doi:10.1039/b809990c.
Jacobson, M.Z., and M.A. Delucchi, November 2009. A path to sustainable energy by 2030, Scientific
American.
Jacobson, M.Z., 2010a. Enhancement of local air pollution by urban CO2 domes, Environmental Science, and
Technology, 44, 2497-2502.
Jacobson, M.Z., 2010b, Short-term effects of controlling fossil-fuel soot, biofuel soot and gases, and methane
on climate, Arctic ice, and air pollution health, J. Geophys. Res., 115, D14209, doi:10.1029/2009JD013795.
Jacobson, M.Z., and M.A. Delucchi, 2011. Providing all Global Energy with Wind, Water, and Solar Power,
Part I: Technologies, Energy Resources, Quantities and Areas of Infrastructure, and Materials, Energy
Policy, 39, 1154-1169, doi:10.1016/j.enpol.2010.11.040.
Jacobson, M. Z., 2012. Air Pollution and Global Warming: History, Science, and Solutions, Second Edition,
Cambridge University Press, Cambridge, 375 pp.
Jacobson, M.Z., R.W. Howarth, M.A. Delucchi, S.R. Scobies, J.M. Barth, M.J. Dvorak, M. Klevze, H.
Katkhuda, B. Miranda, N.A. Chowdhury, R. Jones, L. Plano, and A.R. Ingraffea, 2013. Examining the
feasibility of converting New York State’s all-purpose energy infrastructure to one using wind, water, and
sunlight, Energy Policy, 57, 585-601.
Jacobson, M.Z., M.A. Delucchi, A.R. Ingraffea, R.W. Howarth, G. Bazouin, B. Bridgeland, K. Burkhart, M.
Chang, N. Chowdhury, R. Cook, G. Escher, M. Galka, L. Han, C. Heavey, A. Hernandez, D.F. Jacobson,
D.S. Jacobson, B. Miranda, G. Novotny, M. Pellat, P. Quach, A. Romano, D. Stewart, L. Vogel, S. Wang,
H. Wang, L. Willman, T. Yeskoo, 2014. A roadmap for repowering California for all purposes with wind,
water, and sunlight, Energy, 73, 875-889, doi:10.1016/j.energy.2014.06.099.
Jacobson, M.Z., M.A. Delucchi, G. Bazouin, Z.A.F. Bauer, C.C. Heavey, E. Fisher, S. B. Morris, D.J.Y.
Piekutowski, T.A. Vencill, T.W. Yeskoo, 2015a, 100% clean and renewable wind, water, sunlight (WWS)
all-sector energy roadmaps for the 50 United States, Energy and Environmental Sciences, 8, 2093-2117,
doi:10.1039/C5EE01283J.
Jacobson, M.Z., M.A. Delucchi, M.A. Cameron, and B.A. Frew, 2015b. A low-cost solution to the grid
reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes, Proc. Nat.
Acad. Sci., 112, doi: 10.1073/pnas.1510028112.
Jacobson, M.Z., M.A. Delucchi, G. Bazouin, M.J. Dvorak, R. Arghandeh, Z. A.F. Bauer, A. Cotte, G.M.T.H.
de Moor, E.G. Goldner, C. Heier, R.T. Holmes, S.A. Hughes, L. Jin, M. Kapadia, C. Menon, S.A.
Mullendore, E.M. Paris, G.A. Provost, A.R. Romano, C. Srivastava, T.A. Vencill, N.S. Whitney, and T.W.
Yeskoo, 2016a, A 100% wind, water, sunlight (WWS) all-sector energy plan for Washington State,
Renewable Energy, 86, 75-88.
Jacobson, M.Z., C. Clack, M.A. Delucchi, M.A. Cameron, 2016b, 100% grid reliability in 20 regions of the
world with 100% penetration of intermittent wind, water, and solar for all purposes, in preparation.
JPL
(Jet
Propulsion
Laboratory),
2010,
Dataset
Discovery,
http://podaac.jpl.nasa.gov/datasetlist?ids=Platform&values=QUIKSCAT, Processed by D. Whitt and M.
Dvorak, Accessed February 23, 2012.
Lepeule, J., F. Laden, D. Dockery, and J. Schwartz, 2012. Chronic exposure to fine particles and mortality: An
extended follow-up of the Harvard six cities study from 1974 to 2009, Environmental Health Perspectives,
120, 965-970.
LeQuere et al., 2015, Global carbon budget 2014, Earth Syst. Sci. Data 7, 47-85, 2015. Linowes, L., 2012. Wind Benefit Inflation: JEDI (NREL) Model Needs Reality Check.
http://www.masterresource.org/2012/12/wind-benefit-inflation-jedi-nrel-2/, Accessed Dec. 16, 2012.
Lund, J. W. and T.L. Boyd, 2015. Direct Utilization of Geothermal Energy 2015 Worldwide Review, World
Geothermal Congress 2015 Conference Proceedings, Melbourne, Australia, 19-25 April 2015.
Marine Renewables Canada, 2013. Marine renewable energy in Canada and the global context,
http://www.marinerenewables.ca/wp-content/uploads/2012/11/State-of-the-Canadian-MRE-Sector20131.pdf, Accessed August 1, 2015.
Mathiesen, B.V., H. Lund, D. Connolly, H. Wenzel, P.A. Ostergaard, B. Moller, S. Nielsen, I. Ridjan, P.
Kamoe, K. Sperling, F.K. Hvelplund, 2015, Smart energy systems for coherent 100% renewable energy and
transport solutions, Applied Energy, 145, 139-154.
Neftel, A., H. Friedli, E. Moor, H. Lotscher, H. Oeschger, U. Siegenthaler, and B. Stauffer, 1994. Historical
CO2 record from the Siple station ice core, http://cdiac.ornl.gov/ftp/trends/co2/siple2.013, Accessed
November 27, 2015.
61
Negawatt Association, 2015. The Negawatt 2050 scenario, http://www.negawatt.org/english-presentationp149.html, Accessed July 31, 2015.
NREL (National Renewable Energy Laboratory), 2012a. Wind resources by class and country at 50 m,
http://en.openei.org/datasets/dataset/wind-resources-by-class-and-country-at-50m, Accessed June 11, 2015.
NREL (National Renewable Energy Laboratory), 2012b. Global Solar Opportunity Tool,
http://maps.nrel.gov/global_re_opportunity#, Accessed September 9, 2015.
NREL (National Renewable Energy Laboratory), 2013, Jobs and Economic Development Impact Models
(JEDI). http://www.nrel.gov/analysis/jedi/download.html. Accessed October 27, 2013.
NREL (National Renewable Energy Laboratory), 2015, Concentrating Solar Power Projects by Country.
http://www.nrel.gov/csp/solarpaces/by_country.cfm. Accessed July 22, 2015.
Northern Alberta Institute of Technology, 2015. Solar photovoltaic reference array report,
http://www.nait.ca/docs/NAIT_Reference_Array_Report_March_31_2015.pdf, Accessed February 10,
2016.
OECD (2014), The Cost of Air Pollution: Health Impacts of Road Transport, OECD Publishing, Paris.
DOI: http://dx.doi.org/10.1787/9789264210448-en.
Parsons-Brinckerhoff, Powering the Future: Mapping our Low-Carbon Path to 2050, Dec. (2009).
https://www.pbworld.com/pdfs/powering_the_future/pb_ptf_full_report.pdf, Accessed May 2, 2015.
Pope, C.A. III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston, 2002. Lung
cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution, Journal Amer.
Med. Assoc., 287, 1132-1141.
Price-Waterhouse-Coopers, 100% Renewable Electricity: A roadmap to 2050 for Europe and North Africa,
2010, www.pwcwebcast.co.uk/dpliv_mu/100_percent_renewable_electricity.pdf, Accessed May 2, 2015.
REN21 (Renewable Energy Policy Network for the 21st Century), 2015, Renewables 2015 Global Status
Report, http://www.ren21.net/wp-content/uploads/2015/07/REN12-GSR2015_Onlinebook_low_nolinks.pdf,
Accessed 1 August 2015.
Shindell, D., J.C.I. Kuylenstierna, E. Vignati, R. van Dingenen, M. Amann et al., 2012. Simultaneously
mitigating near-term climate change and improving human health and food security, Science, 335, 183-189.
Smith, J. C., et al., 2013. Transmission planning for wind energy in the United States and Europe: status and
prospects, WIREs Energy Environ 2: 1-13.
Tans,
P.,
and
R.F.
Keeling,
2015.
Trends
in
atmospheric
carbon
dioxide,
http://www.esrl.noaa.gov/gmd/ccgg/trends/#mlo_full, Accessed November 27, 2015.
Teske, S., S. Sawyer, O. Schafer, T. Pregger, S. Simon, and T. Naegler, 2015, Energy revolution. A sustainable
world
energy
outlook
2015,
http://www.greenpeace.org/international/Global/international/publications/climate/2015/Energy-Revolution2015-Full.pdf, Accessed December 13, 2015.
WHO (World Health Organization), 2014a. 7 million premature deaths annually linked to air pollution,
http://www.who.int/mediacentre/news/releases/2014/air-pollution/en/, Accessed June 15, 2015.
WHO (World Health Organization), 2014b, Global Health Observatory Data Repository,
http://apps.who.int/gho/data/node.main.156 Accessed 2 July 2015.
World
Bank,
2014,
Infrastructure,
Passenger
cars
(Per
1000
people),
http://data.worldbank.org/indicator/IS.VEH.PCAR.P3, Accessed Fall 2014
World
Bank,
2015a,
Electricity
production
from
hydroelectric
sources,
http://data.worldbank.org/indicator/EG.ELC.HYRO.ZS, Accessed August 1, 2015.
World Bank, 2015b, Labor force, total, http://data.worldbank.org/indicator/SL.TLF.TOTL.IN/countries,
Accessed June 17, 2015.
World Bank, 2015c, Urban population (% of total), http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS,
Accessed September 8, 2015.
Zero
Carbon
Britain,
2013,
Zero
Carbon
Britain:
Rethinking
the
future,
http://zerocarbonbritain.org/images/pdfs/ZCBrtflo-res.pdf, Accessed May 2, 2015.
62