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Article

Transitioning All Energy in 74 Metropolitan Areas, Including 30 Megacities, to 100% Clean and Renewable Wind, Water, and Sunlight (WWS)

by
Mark Z. Jacobson
*,
Anna-Katharina von Krauland
,
Zachary F.M. Burton
,
Stephen J. Coughlin
,
Caitlin Jaeggli
,
Daniel Nelli
,
Alexander J. H. Nelson
,
Yanbo Shu
,
Miles Smith
,
Chor Tan
,
Connery D. Wood
and
Kelyn D. Wood
Atmosphere/Energy Program, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Energies 2020, 13(18), 4934; https://doi.org/10.3390/en13184934
Submission received: 28 August 2020 / Revised: 16 September 2020 / Accepted: 16 September 2020 / Published: 20 September 2020
(This article belongs to the Special Issue Accelerating Renewable Energy Transition Post Major World Events)
Browse Figure
Versions Notes

Abstract

:
To date, roadmaps and policies for transitioning from fossil fuels to clean, renewable energy have been developed for nations, provinces, states, cities, and towns in order to address air pollution, global warming, and energy insecurity. However, neither roadmaps nor policies have been developed for large metropolitan areas (aggregations of towns and cities), including megacities (metropolitan areas with populations above 10 million). This study bridges that gap by developing roadmaps to transition 74 metropolitan areas worldwide, including 30 megacities, to 100% wind, water, and sunlight (WWS) energy and storage for all energy sectors by no later than 2050, with at least 80% by 2030. Among all metropolitan areas examined, the full transition may reduce 2050 annual energy costs by 61.1% (from $2.2 to $0.86 trillion/yr in 2013 USD) and social costs (energy plus air pollution plus climate costs) by 89.6% (from $8.3 to $0.86 trillion/yr). The large energy cost reduction is due to the 57.1% lower end-used energy requirements and the 9% lower cost per unit energy with WWS. The air pollution cost reduction of ~$2.6 (1.5–4.6) trillion/yr is due mostly to the saving of 408,000 (322,000–506,000) lives/yr with WWS. Global climate cost savings due to WWS are ~$3.5 (2.0–7.5) trillion/yr (2013 USD). The transition may also create ~1.4 million more long-term, full-time jobs than lost. Thus, moving to 100% clean, renewable energy and storage for all purposes in metropolitan areas can result in significant economic, health, climate, and job benefits.

Graphical Abstract

1. Introduction

Megacities and metacities are defined as metropolitan areas with populations above 10 and 20 million, respectively [1]. A metropolitan area (or metropolis) is a “major city together with its suburbs and nearby cities, towns, and environs over which the major city exercises a commanding economic and social influence” [2]. An area must have a population of at least 100,000, with at least 50,000 in the urban portion, to be considered a metropolitan area [2].
In 1950, the only megacities in the world were the New York–Newark and Tokyo metropolitan areas [1]. By 2020, this count had risen to 34, including nine metacities [3]. The largest of these were Tokyo (37.4 million), Delhi (30.3 million), Shanghai (27.1 million), and São Paulo (22.0 million) [3]. Based on current trends from [3], the number of megacities is expected to grow substantially by 2050. Furthermore, the physical expansion of megacities has been rapid. For example, between 2000 and 2009, the urban extent of Beijing quadrupled [4] and those of Delhi and Los Angeles increased by ~80% and ~23%, respectively [5]. Such increases in urban extent, even before considering concomitant changes in emissions, had notable impacts on air pollution and meteorology [4,5].
The addition of people to a megacity increases overall energy consumption, thereby increasing overall megacity emissions. However, this increase in energy consumption is not necessarily linear. For example, Facchini et al. [6] found that per capita energy consumption decreased with increasing population density according to a power law characterized by a −3/4 scaling. Emissions from energy consumption also depend on emission control technologies and the efficiencies of appliances and machines used.
Addressing climate and pollution problems requires policies implemented at national, regional, and local levels. To that point, by late 2020, 165 U.S. cities and towns [7] and at least 250 international cities [8] had passed resolutions or ordinances committing to being powered by 100% renewable electricity. Fifty of these international cities had committed to 100% renewables in more than one sector (e.g., transport, building heating/cooling, etc., in addition to electricity) [8].
Several previous studies have analyzed or reviewed some of the components necessary to transition individual cities or islands to clean, renewable energy (e.g., [9,10,11,12,13,14,15,16]). Many studies have also examined the ability of 100% or near 100% renewable energy to keep the electric power grid stable in one or multiple countries (e.g., [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48]).
However, to date, no roadmap has been developed for a metropolitan area, let alone a megacity, to transition all energy sectors (electricity, transport, building heating/cooling, industry, agriculture/forestry/fishing, and the military) to 100% clean, renewable energy and storage. Moreover, the benefits to air pollution health and climate resulting from a transition to 100% clean, renewable energy remain wholly unexplored for metropolitan areas.
The goal of this paper is to provide such roadmaps for 74 metropolitan areas worldwide (including 30 megacities) to transition to 100% clean, renewable energy for all purposes by no later than 2050 (and ideally sooner), with at least 80% transition by 2030. These cities, collectively, represent about 9% of the projected 2050 world population. This paper builds upon a previous study that developed roadmaps for 53 individual towns and cities (rather than metropolitan areas) solely in North America [15].
The roadmaps here assume that all energy sectors will use electricity or direct heat. All electricity and direct heat will be generated with 100% wind, water, and sunlight (WWS). Some electricity will be used to produce hydrogen, primarily for transportation. The direct heat will come from either solar or geothermal sources. Electric heat pumps will provide remaining low-temperature heat. Less total energy will be needed because electrification of all energy sectors will lower energy demand. Because several WWS generators are intermittent, some electricity, heat, cold, and hydrogen will need to be stored. Finally, transmission lines will be needed to transmit electricity short and long distances.
Each roadmap provides a clean, renewable energy scenario for a metropolitan area to meet annual average all-purpose energy loads in 2050. This study assumes that each metropolitan area is connected to the heat and electricity grids of the country or region wherein the metropolitan area resides. The study does not attempt to quantify the energy mix needed, the storage needed, or the additional transmission/distribution needed to meet minute-by-minute energy demand. However, the study does provide an estimate of the additional levelized cost of energy (LCOE) needed to match such demand continuously; thus, it accounts for the cost of WWS supply, storage, and transmission/distribution needed to keep the grid stable. This estimate is based on results from a previous study focused on meeting demand with supply and storage in 24 world regions encompassing 143 countries [33]. All metropolitan areas examined here reside in one of those countries.

2. Methodology

This section describes the methodology for developing year 2050 roadmaps to transition each metropolitan area to 100% WWS among all energy sectors. First, country-specific data were obtained from [33], who developed all-sector energy roadmaps for 143 countries. Such data included 2016 business-as-usual (BAU) end-use energy consumption data for all energy sectors (residential, commercial, transport, industrial, agriculture/forestry/fishing, and military), for each energy type (oil, natural gas, coal, electricity, waste heat, solar and geothermal heat, and biofuels and waste), for each of 143 countries [49]. These data were projected for each country, sector, and fuel type from 2016 to 2040 using “BAU reference scenario” projections for the same sectors and fuel types for 16 world regions from [50]. The projections for a given country were assigned from those of the region in which the country resided. The reference scenario is one of moderate economic growth and accounts for policies in different countries, population growth, economic and energy growth, the growth of some renewable energy, modest energy efficiency measures, and reduced energy use. Consumption of each fuel type in each sector in each country was then extrapolated in [33] from 2040 to 2050 using a 10-year moving linear extrapolation.
In [33], the 2050 BAU energy for each fuel type in each sector and country was then transitioned to 2050 WWS electricity and heat. WWS electricity generators included onshore and offshore wind turbines, rooftop and utility-scale solar photovoltaics (PV), concentrated solar power (CSP) plants, tidal and wave devices, geothermal electric power plants, and existing hydroelectric power plants (no new reservoirs were assumed). WWS heat generators included solar and geothermal heat.
Thus, for example, the source of building heat was converted from fossil fuels or bioenergy to air- and ground-source heat pumps running on WWS electricity and direct solar thermal or geothermal heat. Building cooling was also provided by heat pumps.
Fossil fuel and biofuel vehicles were transitioned primarily into battery electric (BE) vehicles and some hydrogen fuel cell (HFC) vehicles, where the hydrogen in that case was produced using WWS electricity (i.e., green hydrogen). BE vehicles were assumed to dominate short- and long-distance light-duty ground transportation, construction machines, agricultural equipment, short- and moderate-distance trains (except where powered by electric rails or overhead wires), ferries, speedboats, short-distance ships, and short-haul aircraft traveling under 1500 km. HFC vehicles were assumed to make up all long-distance, heavy payload transport by road, rail, water, and air.
High-temperature industrial processes were electrified with electric arc furnaces, induction furnaces, resistance furnaces, dielectric heaters, and electron beam heaters.
Next, in each country, a mix of WWS resources was estimated to meet the all-sector annual-average end-use energy demand. The mix was determined after a WWS resource analysis was performed for each country. Air pollution and climate damage in 2050 were estimated for each country, and the social cost benefits of reducing such damage with WWS were then calculated. Energy costs between BAU and WWS were also calculated, as were the required land areas and changes in the number of jobs.
In [33], country populations were projected to 2050 with data from [51]. Here, metropolitan area populations are extrapolated linearly to 2050 from 2000–2020 population data [3]. Here, the ratio of metropolitan area-to-country population in 2050 is then used to scale down all country energy data from [33] to the metropolitan area level. While this is a simplistic method that does not account for the fact that urban energy consumption decreases with increasing population density [6], the country-level analysis that it relies on is detailed. In addition, urban energy consumption includes energy used to transport people, goods, heat, and electricity to and from urban areas, which is not accounted for if energy consumption is scaled by urban population. As such, the overestimate of urban energy consumption based on linear population scaling may be at least partly offset by the underestimate of energy consumption due to transport to and from a metropolitan area.

3. Results and Discussion

3.1. Resulting End-Use Demand and Nameplate Capacities of New Generators

Table 1 provides the resulting BAU and WWS end-use power demand (load) in 2050 for each metropolitan area. In 2016, the 74 metropolitan area all-purpose, end-use load was ~1730 GW (15,100 TWh/yr). Under BAU, the all-purpose end-use load is estimated to increase to ~2540 GW in 2050 (Table 1). A move to 100% WWS by 2050 reduces the 74-metropolitan-area end-use load by ~57.1%, down to ~1090 GW (~9550 TWh/yr) (Table 1), with the largest percentage reduction (37.5%) due to the efficiency of WWS heat pumps, battery electric vehicles, hydrogen fuel cell vehicles, and industrial heat, when compared with their business-as-usual equivalents. An additional 12.9% reduction is due to eliminating the energy needed to extract, transport, and refine fossil fuels and uranium. The remaining 6.7% reduction is due to end-use energy efficiency improvements and reduced energy use beyond those under BAU. Megacities with the greatest all-purpose end-use WWS load needed in 2050 include Shanghai (80.7 GW), Tokyo (70.1 GW), Beijing (63.0 GW), New York City (45.5 GW), Seoul (36.3 GW), Shenzhen (35.6 GW), and Moscow (35.2 GW).
Table 2 summarizes the 2050 percent of annual-average end-use load summed among all metropolitan areas (from Table 1) to be met by each energy generator type. It also provides the new plus existing nameplate capacities of each generator type needed to meet such load, the nameplate capacities of existing generators of each type, and the capital cost of the new nameplate capacities, all summed among the metropolitan areas.
The average mix of generators in Table 2 is the end-use-load-weighted average mix of generators in each metropolitan area, obtained from Table 3. The total new plus existing nameplate capacity is the sum of values among each metropolitan area (Table 4). Nameplate capacities for each area are determined from the end-use WWS load for the metropolitan area (Table 1), the mix of generators for the area (Table 3), and the product of the capacity factor and the transmission and distribution efficiency of each generator type in each area (Table 5).
WWS generators are not constrained to exist within a metropolitan area due to land and renewable resource limitations in such areas. Nonetheless, all rooftop PV is proposed to exist within each metropolitan area. Table 6 provides estimated 2050 metropolitan area residential and commercial/government rooftop areas suitable for PV. It also shows the potential PV nameplate capacity in each area and the proposed installed nameplate capacity for each area (which is consistent with values in Table 4). Rooftop PV areas include existing plus new building roof areas plus elevated canopy areas above parking lots, highways, and structures. Table 6 indicates that only 22.3% of potential residential rooftop PV and 58.9% of potential commercial/government rooftop PV nameplate capacities are proposed for installation among all metropolitan areas. As such, rooftop area is not a limiting factor in transitioning to 100% WWS in these roadmaps.
Unlike PV, concentrated solar power is viable only in countries with significant direct sunlight. Thus, CSP penetration is limited to metropolitan areas in countries exposed to significant sunlight. As such, no CSP is proposed for use in Russia, Canada, Norway, Germany, Switzerland, Ukraine, or Mongolia.
Onshore wind is available in every country. Offshore wind, wave, and tidal power are assumed to be available only in metropolitan areas located in countries with ocean or substantial lake coastlines. Thus, for example, no offshore wind is available in Ethiopia, Nigeria, Uzbekistan, Mongolia, Austria, or Switzerland.
Table 2 indicates that ~8% of the 2050 nameplate capacity required for a 100% all-purpose WWS system among all metropolitan areas was already installed as of 2018 end. Table 2 also provides the nameplate capacities of new plus existing generators needed to meet annual average all-purpose energy demand in each metropolitan area. In most areas, additional generators, storage, transmission lines, and distribution lines are needed to keep the electricity and heat grids stable continuously due to the intermittency of WWS generators. The estimated costs of such equipment are accounted for in the following section.

3.2. Energy Costs

Table 7 shows the BAU levelized costs of energy in 2015 and projected for 2050, in each metropolitan area. The LCOEs include those of keeping the BAU electricity and heat grids stable. The LCOEs were derived for the electric power sector only, but are assumed, for simplicity, to equal the LCOEs for all BAU energy. Because of the large (57.1%) reduction in end-use energy that occurs upon converting from BAU to WWS (Table 1), the uncertainty in the LCOE of non-electricity versus electricity BAU energy is small, so makes no difference in the conclusions drawn here.
Table 7 also shows the capital cost of the WWS infrastructure needed to meet annual average end-use power demand (load), the 2050 WWS LCOE needed to meet annual average load, and the 2050 WWS LCOE needed to meet continuous load (thus to keep the electric and heat grids stable). Footnote (e) of Table 7 describes the methodology for deriving the LCOE of WWS needed to meet continuous load. Finally, Table 7 provides the private and social cost savings of using WWS instead of BAU energy.
The total capital cost of all WWS infrastructure needed to meet annual average power for all metropolitan areas is $7.25 trillion (Table 7, 2013 USD) for 3903 GW of new WWS generators (Table 2). This results in a capital cost of ~$1.86 million per MW. Shanghai requires the greatest capital input ($513 billion), followed by Tokyo ($443 billion), then Beijing ($401 billion).
The LCOE accounts for capital, land, operating, maintenance, fuel, short- and long-distance transmission, distribution, and decommissioning costs. Table 7 indicates that the mean BAU LCOE in 2013 USD increased only ~1.9% between 2015 and 2050 (from 9.72 to 9.9 ¢/kWh), increasing in many locations but decreasing in others. However, the 2050 WWS LCOE (9.0 ¢/kWh) for meeting continuous end-use load, averaged over all metropolitan areas, was about 9.1% less than the 2050 BAU LCOE (9.9 ¢/kWh) due to the projected drop in WWS generator cost due to both economies of scale and improvements in WWS technologies.
The 2050 LCOE needed to meet continuous load with WWS (9.0 ¢/kWh) was about 18% higher than that required for meeting annual average load with WWS (7.6 ¢/kWh) (Footnote (e) of Table 7). The difference (1.4 ¢/kWh) is similar to the 1.35 ¢/kWh difference found among 139 countries in [32] and the 1.39 ¢/kWh difference found among 143 countries in [33]. The higher cost of meeting continuous load than annual average load with WWS was due to (1) the need to overbuild WWS to meet continuous load, (2) the need for more electricity, heat, cold, and hydrogen storage to meet continuous load, and (3) the need for more transmission and distribution lines to meet continuous load.
Combining the 57.1% lower energy requirement (Table 1) with the 9% lower LCOE (Table 7) in the WWS case gives a 61.1% lower annual energy cost ($0.86 instead of $2.2 trillion/yr, in 2013 USD) with WWS (Table 8). This energy cost savings translates to a benefit of ~$1500 per person per year in 2050 (Table 7). The annual health and climate cost savings per person due to converting to WWS are even larger, an average of $2500 and $4300 per person per year, respectively (Table 7). The average energy plus air pollution health plus climate cost (i.e., the total social cost) savings of WWS over BAU is thus $8200 per person per year (Table 7), or $7.4 trillion/yr among all people in all metropolitan areas (Table 8).
The social cost savings is greatest in locations with high CO2 emissions per capita. Social costs here do not include the insurance cost against nuclear accidents, the costs of conflicts over fossil fuel resources, groundwater pollution costs, lower land values due to mining and drilling operations, or costs of road repair due to road transport of fossil fuel extraction equipment and the fuels themselves.

3.3. Air Pollution Cost Reductions due to WWS

Air pollution contributes to death from heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory tract infection, lung cancer, and asthma. Common types of COPD are chronic bronchitis and emphysema. Common types of lower respiratory tract infections are the flu, bronchitis, and pneumonia [52]. In 2016, 56.9 million people died worldwide from all causes [53]. Air pollution may cause between 24% and 45% of the deaths for each of five out of the six leading causes of death [53]. About 4.5 million people died prematurely from outdoor air pollution and 7.1 million died from indoor plus outdoor air pollution in 2016 [53]. Thus, about 12.5% of all deaths worldwide in 2016 were due to indoor plus outdoor air pollution, making it the second leading cause of death after heart disease. The authors of [33] estimated that, in the 143 countries examined, 6.8 million people died prematurely due to air pollution in 2016, and 5.3 million may die prematurely per year in 2050. The reduction is due to some BAU improvements in emission control technologies.
Scaling the 2050 individual country numbers from [33] by population to each metropolitan area gives approximately 408,000 (322,000–506,000) premature deaths per year in these areas in the BAU case (or avoided deaths in the WWS case) (Table 9). The greatest numbers of premature deaths occur in Delhi (45,200/yr), Shanghai (38,500/yr), Beijing (30,100/yr), Lagos (25,600/yr), Dhaka (23,700/yr), and Mumbai (23,300/yr).
The damage cost due to air pollution from fossil fuel and biofuel burning and evaporative emissions in a metropolitan area is the sum of mortality, morbidity, and non-health costs. Non-health costs include costs from lower visibility and agricultural losses. Mortality, morbidity, and non-health costs are estimated as in [33]. The avoided air pollution cost among all metropolitan areas due to transitioning to 100% WWS is ~$2.6 ($1.5–$4.6) trillion/yr (Table 8), or ~11.5 (6.5–20.5) ¢/kWh-BAU-all-energy (Table 9), which translates to a mean of $2500/yr per person (2013 USD) (Table 7).

3.4. Global-Warming Damage Costs Eliminated

Damage arising from global warming includes damage from higher sea levels (coastal infrastructure losses), reduced crop yields for certain crops, more intense hurricanes, more droughts and floods, more wildfires and air pollution, more migration due to crop losses and famine, more heat stress and heat stroke, more malaria and dengue fever, fishery and coral reef losses, and greater air cooling requirements, among other impacts. These costs are partly offset by fewer extreme cold events and concomitant decreases in illness and mortality, and the increase in agricultural output in some regions.
The damage caused by carbon dioxide equivalent (CO2e) emissions to the global economy through their impacts on climate is quantified with the social cost of carbon (SCC). The SCC is usually expressed in cost per metric tonne-CO2e emissions. The SCC from several recent studies is estimated for 2050 as ~$500 (282–1060)/metric tonne-CO2e in 2013 USD [33]. Multiplying the SCC by estimated 2050 CO2e emissions in each metropolitan area suggests that BAU emissions from the metropolitan areas here may cause $3.5 (2.0–7.5) trillion/yr in climate losses to the world by 2050 (Table 8), or 15.8 (8.9–33.7) ¢/kWh-BAU-all-energy (Table 9), which translates to ~$4300/yr per person (in 2013 USD) (Table 7). Transitioning to 100% WWS will avoid these costs.

3.5. Impacts of WWS on Job Creation and Loss

Governments are concerned about changes in employment upon transitioning their energy economies to entirely clean, renewable energy ones. Here, the numbers of long-term, full-time jobs created and lost are estimated for each metropolitan area. Job changes may not necessarily occur in the metropolitan area itself, but at least in the state, province, or country wherein the metropolitan area resides.
The calculation is done starting with the 2050 country job production and loss numbers from [33], determined for 143 individual countries for meeting annual average load and, separately, for 24 world regions encompassing the 143 countries, for meeting continuous load. That study relied substantially on results from NREL Jobs and Economic Development Impact (JEDI) Models [56]. Job production and loss for individual countries (after removing jobs created for producing generators beyond those needed to meet annual average load) were scaled by population to job production and loss for individual megacities. Those numbers were then scaled further by the LCOE needed to meet continuous load (Column (e) of Table 7) to that needed to meet annual average load (Column (d) of Table 7). This ratio mostly exceeds unity but is less than unity for some countries or regions (e.g., in Canada, Iceland, New Zealand, and Russia) where the initial number of generators estimated to meet annual average load was too high compared with what was needed to meet continuous load [33]. When the ratio exceeds unity, the additional jobs are for installing and operating additional electricity and heat generators; additional electricity, heat, cold, and hydrogen storage equipment; and additional transmission and distribution lines needed to meet continuous load rather than annual average load.
Jobs created include onsite (direct) jobs, local revenue and supply chain (indirect) jobs, and induced jobs. Indirect jobs include jobs associated with construction material and component suppliers, analysis and attorneys who assess project feasibility and negotiate agreements, banks financing the project, all equipment manufacturers, and manufacturers of blades and replacement parts. Indirect manufacturing jobs are included in the number of construction jobs. Induced jobs result from the reinvestment and spending of earnings from direct and indirect jobs. They include jobs resulting from increased business at local restaurants, hotels, and retail stores, and for childcare providers.
Table 10 suggests that a 100% conversion to WWS across the metropolitan areas may create ~2.3 million new long-term, full-time construction jobs and ~2.3 million new plus existing long-term, full-time operation plus maintenance jobs, totaling ~4.6 million new plus existing long-term, full-time jobs for WWS generators and transmission.
Job losses due to a transition to WWS will include losses of jobs to extract, transport, and process fossil fuels, bioenergy, and uranium. Job losses will also occur in the BAU electricity generation industry and in the manufacturing of appliances that use combustion fuels. Finally, jobs will be lost upon ceasing the construction of BAU electricity generation plants, petroleum refineries, and oil and gas pipelines.
Overall, shifting to 100% WWS is estimated to result in ~3.2 million jobs lost in the fossil fuel, bioenergy, and nuclear industries by 2050 (Table 10). Subtracting jobs lost from jobs created gives a net of ~1.4 million long-term, full-time jobs created among the metropolitan areas due to replacing fossil fuel, bioenergy, and nuclear generation among all sectors with WWS generation and transmission (Table 10). Job earnings show a net gain of ~$110 billion/yr (2013 USD) (Table 10).
Metropolitan areas in countries with significant fossil extraction may experience net job losses in the energy production sector. Several such metropolitan areas include Abidjan, Addis Ababa, Baghdad, Calgary, Caracas, Edmonton, Kinshasa, Lagos, Moscow, Oslo, Tehran, Toronto, and Yangon. These losses may be offset by the manufacturing, servicing, and exporting of machines and appliances associated with WWS energy (e.g., electric vehicles, fuel cell vehicles, electric heat pump air and water heaters, electric heat pump dryers, induction cooktops, etc.). Neither those jobs produced nor the jobs lost producing the equivalent machines and appliances replacing them were included in the job calculations here.

4. Conclusions

Transitioning 74 metropolitan areas, including 30 megacities, to 100% wind, water, and solar energy and storage for all energy purposes has the potential to prevent ~408,000 (322,000–506,000) premature air-pollution mortalities/yr in 2050. This, along with non-mortality impacts, avoids a 2050 air pollution cost of ~$2.6 (1.5–4.6) trillion/yr, or a mean of 11.5 ¢/kWh-BAU-all-energy (2013 USD). Transitioning also avoids ~$3.5 (2.0–7.5) trillion/yr (a mean of 15.8 ¢/kWh-BAU-all-energy) in 2050 global warming costs, ~$1.35 trillion/yr (a mean of 0.9 ¢/kWh-BAU-all-energy) in 2050 energy costs, and energy plus health and climate costs of ~$7.4 trillion/yr (a mean of 27.3 ¢/kWh-BAU-all-energy). These translate to ~$1500/person/yr in energy cost savings and ~$6700/person/yr in health plus climate cost savings. Finally, transitioning creates ~1.4 million more new long-term, full-time jobs than lost and stabilizes energy prices.
Due to the current severity of air pollution, global warming, and energy insecurity problems worldwide, a transition to 100% WWS should occur no later (and ideally earlier) than 2050, with at least 80% by 2030 [32,33]. Although a natural transition is currently occurring due to decreases in WWS generation and storage costs, such a timeline can be met only with aggressive policies.
Because metropolitan areas consist of a core city surrounded by other towns and cities, effective policies in a metropolitan area are best instituted if the cities and towns making up the area act in a unified manner rather than in piecemeal fashion. In many countries, each town and city in the area must pass its own resolutions and ordinances; nonetheless, such resolutions and ordinances can be proposed in sync or at least with consistent goals. Sometimes, the competition among towns and cities in a metropolitan area can increase the aggressiveness of policies adopted among these entities. Given that transitioning to 100% WWS for all energy purposes presents minimal downside, metropolitan areas and their constituent towns and cities have significant motivation to transition.

Author Contributions

Conceptualization: M.Z.J. methodology: M.Z.J. coordination: A.-K.v.K. data gathering and analysis: M.Z.J., Z.F.M.B., S.J.C., C.J., C.T., D.N., Y.S., and K.D.W. writing: M.Z.J. editing: Z.F.M.B., S.J.C., C.J., A.-K.v.K., A.J.H.N., D.N., M.S., Y.S., C.T., and K.D.W. visualization: A.J.H.N., C.D.W., and M.S. All authors have read and agree to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We would like to thank Chengliang Fan, Julia M. Leal, and Sabrina Mengrani for assistance with the study. The spreadsheet containing the derivation of all data can be found here: https://web.stanford.edu/group/efmh/jacobson/Articles/I/143Countries-Megacities.xlsx. The roadmaps described here are summarized in infographic maps that area available here: https://sites.google.com/stanford.edu/wws-roadmaps/home.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BAUBusiness-as-Usual
BEBattery Electric
CO2eCarbon Dioxide Equivalent
COPDChronic Obstructive Pulmonary Disorder
CSPConcentrated Solar Power
EIAEnergy Information Administration
GWGigawatt
HFCHydrogen Fuel Cell
JEDIJobs and Economic Development Impact
LCOELevelized Cost of Energy
LCOEALCOE to meet annual average load
LCOECLCOE to meet continuous load
MWMegawatt
NRELNational Renewable Energy Laboratory
PVPhotovoltaic
SCCSocial Cost of Carbon
TWTerawatt
TWhTerawatt-hour
USDUnited States Dollars
WWSWind, Water, and Sunlight (or Solar)

References

  1. UN-HABITAT. The State of the World’s Cities Report 2006/2007; United Nations Human Settlements Programme (UN-HABITAT): London, UK, 2006. [Google Scholar]
  2. Encyclopaedia Britannica. Metropolitan Area. Available online: https://www.britannica.com/topic/metropolitan-area (accessed on 11 August 2020).
  3. Macrotrends. Metropolitan Area Populations. Available online: https://www.macrotrends.net/cities/largest-cities-by-population (accessed on 30 July 2020).
  4. Jacobson, M.Z.; Nghiem, S.V.; Sorichetta, A.; Whitney, N. Ring of impact from the mega-urbanization of Beijing between 2000 and 2009. J. Geophys. Res. Atmos. 2015, 120, 5740–5756. [Google Scholar] [CrossRef]
  5. Jacobson, M.Z.; Nghiem, S.V.; Sorichetta, A. Short-term impacts of the mega-urbanizations of New Delhi and Los Angeles between 2000 and 2009. J. Geophys. Res. Atmos. 2019, 124, 35–56. [Google Scholar] [CrossRef] [Green Version]
  6. Facchini, A.; Kennedy, C.; Steward, I.; Mele, R. The energy metabolism of megacities. Appl. Energy 2017, 186, 86–95. [Google Scholar] [CrossRef]
  7. Sierra Club. Check out Where We Are Ready for 100%. Available online: https://www.sierraclub.org/ready-for-100/map?show=committed (accessed on 14 September 2020).
  8. REN21 (Renewable Energy Policy Network for the 21st Century). Renewables in Cities: 2019 Global Status Report. Available online: https://www.ren21.net/wp-content/uploads/2019/05/REC-2019-GSR_Full_Report_web.pdf (accessed on 5 August 2020).
  9. Agar, B.; Renner, M. Is 100 percent renewable energy in cities possible? In State of the World; Island Press: Washington, DC, USA, 2016; pp. 161–170. [Google Scholar]
  10. Calvillo, C.F.; Sanchez-Miralles, A.; Villar, J. Energy management and planning in smart cities. Renew. Sustain. Energy Rev. 2016, 55, 273–287. [Google Scholar] [CrossRef] [Green Version]
  11. Park, E.; Kwon, S. Towards a sustainable island: Independent optimal renewable power generation systems at Gadeokdo Island in South Korea. Sustain. Cities Soc. 2016, 23, 114–118. [Google Scholar] [CrossRef]
  12. Bibri, S.E.; Krogstie, J. Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustain. Cities Soc. 2017, 31, 183–212. [Google Scholar] [CrossRef]
  13. Newman, P. The rise and rise of renewable cities. Renew. Energy Environ. Sustain. 2017, 2, 10. [Google Scholar] [CrossRef] [Green Version]
  14. Dahal, K.; Juhola, S.; Niemela, J. The role of renewable energy policies for carbon neutrality in Helsinki Metropolitan area. Sustain. Cities Soc. 2018, 40, 222–232. [Google Scholar] [CrossRef] [Green Version]
  15. Jacobson, M.Z.; Cameron, M.A.; Hennessy, E.M.; Petkov, I.; Meyer, C.B.; Gambhir, T.K.; Maki, A.T.; Pfleeger, K.; Clonts, H.; McEvoy, A.L.; et al. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 53 towns and cities in North America. Sustain. Cities Soc. 2018, 42, 22–37. [Google Scholar] [CrossRef]
  16. Thellufsen, J.Z.; Lund, H.; Sorknaes, P.; Ostergaard, P.A.; Chang, M.; Drysdale, D.; Nielsen, S.; Djorup, S.R.; Sperling, K. Smart energy cities in a 100% renewable energy context. Renew. Sustain. Energy Rev. 2020, 129, 109922. [Google Scholar] [CrossRef]
  17. Lund, H.; Mathiesen, B.V. Energy system analysis of 100% renewable energy systems—The case of Denmark in years 2030 and 2050. Energy 2009, 34, 524–531. [Google Scholar] [CrossRef]
  18. Mason, I.G.; Page, S.C.; Williamson, A.G. A 100% renewable energy generation system for New Zealand utilizing hydro, wind, geothermal, and biomass resources. Energy Policy 2010, 38, 3973–3984. [Google Scholar] [CrossRef]
  19. Connolly, D.; Lund, H.; Mathiesen, B.V.; Leahy, M. The first step to a 100% renewable energy-system for Ireland. Appl. Energy 2011, 88, 502–507. [Google Scholar] [CrossRef]
  20. Connolly, D.; Lund, H.; Mathiesen, B.V. Smart energy Europe: The technical and economic impact of one potential 100% renewable energy scenario for the European Union. Renew. Sustain. Energy Rev. 2016, 60, 1634–1653. [Google Scholar] [CrossRef]
  21. Hart, E.K.; Jacobson, M.Z. A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables. Renew. Energy 2011, 36, 2278–2286. [Google Scholar] [CrossRef]
  22. Hart, E.K.; Jacobson, M.Z. The carbon abatement potential of high penetration intermittent renewables. Energy Environ. Sci. 2012, 5, 6592–6601. [Google Scholar] [CrossRef]
  23. Mathiesen, B.V.; Lund, H.; Karlsson, K. 100% renewable energy systems, climate mitigation, and economic growth. Appl. Energy 2011, 88, 488–501. [Google Scholar] [CrossRef]
  24. Mathiesen, B.V.; Lund, H.; Connolly, D.; Wenzel, H.; Ostergaard, P.Z.; Moller, B.; Nielsen, S.; Ridjan, I.; Karnoe, P.; Sperling, K.; et al. Smart energy systems for coherent 100% renewable energy and transport solutions. Appl. Energy 2015, 145, 139–154. [Google Scholar] [CrossRef]
  25. Elliston, B.; Diesendorf, M.; MacGill, I. Simulations of scenarios with 100% renewable electricity in the Australian national electricity market. Energy Policy 2012, 45, 606–613. [Google Scholar] [CrossRef]
  26. Elliston, B.; MacGill, I.; Diesendorf, M. Comparing least cost scenarios for 100% renewable electricity with low emission fossil fuel scenarios in the Australian national electricity market. Renew. Energy 2014, 66, 196–204. [Google Scholar] [CrossRef]
  27. Rasmussen, M.G.; Andresen, G.B.; Greiner, M. Storage and balancing synergies in a fully or highly renewable pan-European power system. Energy Policy 2012, 51, 642–651. [Google Scholar] [CrossRef]
  28. Budischak, C.; Sewell, D.; Thompson, H.; Mach, L.; Veron, D.E.; Kempton, W. Cost-minimized combinations of wind power, solar power, and electrochemical storage, powering the grid up to 99.9% of the time. J. Power Sources 2013, 225, 60–74. [Google Scholar] [CrossRef] [Green Version]
  29. Steinke, F.; Wolfrum, P.; Hoffmann, C. Grid vs. storage in a 100% renewable Europe. Renew. Energy 2013, 50, 826–832. [Google Scholar] [CrossRef]
  30. Becker, S.; Frew, B.A.; Andresen, G.B.; Zeyer, T.; Schramm, S.; Greiner, M.; Jacobson, M.Z. Features of a fully renewable U.S. electricity-system: Optimized mixes of wind and solar PV and transmission grid extensions. Energy 2014, 72, 443–458. [Google Scholar] [CrossRef] [Green Version]
  31. Jacobson, M.Z.; Delucchi, M.A.; Cameron, M.A.; Frew, B.A. A low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. Proc. Natl. Acad. Sci. USA 2015, 112, 15060–15065. [Google Scholar] [CrossRef] [Green Version]
  32. Jacobson, M.Z.; Delucchi, M.A.; Cameron, M.A.; Mathiesen, B.V. Matching demand with supply at low cost among 139 countries within 20 world regions with 100% intermittent wind, water, and sunlight (WWS) for all purposes. Renew. Energy 2018, 123, 236–248. [Google Scholar] [CrossRef]
  33. Jacobson, M.Z.; Delucchi, M.A.; Cameron, M.A.; Coughlin, S.J.; Hay, C.A.; Manogaran, I.P.; Shu, Y.; von Krauland, A.K. Impacts of green new deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries. One Earth 2019, 1, 449–463. [Google Scholar] [CrossRef]
  34. Aghahosseini, A.; Bogdanov, D.; Barbosa, L.S.N.S.; Breyer, C. Analyzing the feasibility of powering the Americas with renewable energy and inter-regional grid interconnections by 2030. Renew. Sustain. Energy Rev. 2019, 105, 187–205. [Google Scholar] [CrossRef]
  35. Bogdanov, D.; Breyer, C. North-east Asian super grid for 100% renewable energy supply: Optimal mix of energy technologies for electricity, gas, and heat supply options. Energy Convers. Manag. 2016, 112, 176–190. [Google Scholar] [CrossRef]
  36. Child, M.; Breyer, C. Vision and initial feasibility analysis of a recarbonized Finnish energy system for 2050. Renew. Sustain. Energy Rev. 2016, 66, 517–536. [Google Scholar] [CrossRef]
  37. Barbosa, L.S.N.S.; Bogdanov, D.; Vainikka, P.; Breyer, C. Hydro, wind, and solar power as a base for a 100% renewable energy supply for South and Central America. PLoS ONE 2017, 12, e0173820. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Blakers, A.; Lu, B.; Stocks, M. 100% renewable electricity in Australia. Energy 2017, 133, 471–482. [Google Scholar] [CrossRef]
  39. Gulagi, A.; Choudhary, P.; Bogdanov, D.; Breyer, C. Electricity system based on 100% renewable for India and SAARC. PLoS ONE 2017, 12, e0180611. [Google Scholar] [CrossRef] [Green Version]
  40. Gulagi, A.; Bogdanov, D.; Fasihi, M.; Breyer, C. Can Australia power the energy-hungry Asia with renewable energy? Sustainability 2017, 9, 233. [Google Scholar] [CrossRef] [Green Version]
  41. Lu, B.; Blakers, A.; Stocks, M. 90–100% renewable electricity for the south west interconnected system of western Australia. Energy 2017, 122, 663–674. [Google Scholar] [CrossRef]
  42. Esteban, M.; Portugal-Pereira, J.; Mclellan, B.C.; Bricker, J.; Farzaneh, H.; Djalikova, N.; Ishihara, K.N.; Takagi, H.; Roeber, V. 100% renewable energy system in Japan: Smoothening and ancillary services. Appl. Energy 2018, 224, 698–707. [Google Scholar] [CrossRef] [Green Version]
  43. Liu, H.; Andresen, G.B.; Greiner, M. Cost-optimal design of a simplified highly renewable Chinese electricity network. Energy 2018, 147, 534–546. [Google Scholar] [CrossRef] [Green Version]
  44. Sadiqa, A.; Gulagi, A.; Breyer, C. Energy transition roadmap towards 100% renewable energy and role of storage technologies for Pakistan by 2050. Energy 2018, 147, 518–533. [Google Scholar] [CrossRef]
  45. Zapata, S.; Casteneda, M.; Jiminez, M.; Aristizabel, A.J.; Franco, C.J.; Dyner, I. Long-term effects of 100% renewable generation on the Colombian power market. Sustain. Energy Technol. Assess. 2018, 30, 183–191. [Google Scholar] [CrossRef]
  46. Bogdanov, D.; Toktarova, A.; Breyer, C. Transition towards 100% renewable power and heat supply for energy intensive economics and severe continental climate conditions: Case for Kazakhstan. Appl. Energy 2019, 253, 113606. [Google Scholar] [CrossRef]
  47. Kiwan, S.; Al-Gharibeh, E. Jordan toward a 100% renewable electricity system. Renew. Energy 2020, 147, 423–436. [Google Scholar] [CrossRef]
  48. Chen, A.A.; Stephens, A.J.; Koon Koon, R.; Ashtine, M.; Koon, K.M.K. 100% renewable for a small island: Jamaica as an example. Renew. Sustain. Energy Rev. 2020, 121, 109671. [Google Scholar] [CrossRef]
  49. IEA (International Energy Agency). World Energy Statistics 2018; OECD Publishing: Paris, France, 2019. [Google Scholar]
  50. EIA (Energy Information Administration). U.S. International Energy Outlook 2016. DOE/EIA-0484. Available online: http://www.eia.gov/forecasts/ieo/pdf/0484(2016).pdf (accessed on 17 September 2020).
  51. Wikipedia. List of Countries by Past and Estimated Future Population. Available online: https://en.wikipedia.org/wiki/List_of_countries_by_past_and_estimated_future_population (accessed on 17 September 2020).
  52. WHO (World Health Organization). Health Statistics and Information Systems. Available online: https://www.who.int/healthinfo/global_burden_disease/estimates/en/ (accessed on 17 September 2020).
  53. WHO (World Health Organization). Global Health Observatory (GHO) Data. Available online: https://www.who.int/gho/phe/outdoor_air_pollution/en/ (accessed on 17 September 2020).
  54. European Commission EDGAR: Fossil CO2 Emissions of all Countries. 2019. Available online: https://edgar.jrc.ec.europa.eu/overview.php?v=booklet2018 (accessed on 17 September 2020).
  55. Jacobson, M.Z.; Delucchi, M.A.; Bazouin, G.; Bauer, Z.A.F.; Heavey, C.C.; Fisher, E.; Morris, S.B.; Piekutowski, D.J.Y.; Vencill, T.A.; Yeskoo, T.W. 100% clean and renewable wind, water, sunlight (WWS) all-sector energy roadmaps for the 50 United States. Energy Environ. Sci. 2015, 8, 2093–2117. [Google Scholar] [CrossRef]
  56. NREL (U.S. Department of Energy National Renewable Energy Laboratory). JEDI: Jobs & Economic Development Impact Models. Available online: https://www.nrel.gov/analysis/jedi (accessed on 17 September 2020).
Table
Table 1. Business-as-usual (BAU) and wind, water, and sunlight (WWS) end-use energy load by sector and city. First row for each city: Estimated 2050 total annually averaged end-use load (GW) and percent of the total load by sector if conventional fossil fuel, nuclear, and biofuel use continue from today to 2050 under a BAU trajectory. Second row for each city: 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 four columns show the percent reductions in total 2050 BAU load due to switching from BAU to WWS, including the effects of (a) energy use reduction due to the higher work-to-energy ratio of electricity over combustion, (b) eliminating energy use for the upstream mining, transporting, and/or refining of coal, oil, gas, biofuels, bioenergy, and uranium, and (c) policy-driven increases in end-use efficiency and demand reduction beyond those in the BAU case.
Table 1. Business-as-usual (BAU) and wind, water, and sunlight (WWS) end-use energy load by sector and city. First row for each city: Estimated 2050 total annually averaged end-use load (GW) and percent of the total load by sector if conventional fossil fuel, nuclear, and biofuel use continue from today to 2050 under a BAU trajectory. Second row for each city: 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 four columns show the percent reductions in total 2050 BAU load due to switching from BAU to WWS, including the effects of (a) energy use reduction due to the higher work-to-energy ratio of electricity over combustion, (b) eliminating energy use for the upstream mining, transporting, and/or refining of coal, oil, gas, biofuels, bioenergy, and uranium, and (c) policy-driven increases in end-use efficiency and demand reduction beyond those in the BAU case.
Metropolitan AreaScenario2050
Total End-Use Load (GW)		Percent of End-Use Load due to Residential BuildingsPercent of End-Use Load due to Commercial and Govern-Ment Build-IngsPercent of End-Use Load due to Indus-TryPercent of End-Use Load due to Trans-Port (a)
Percent Change in End-Use Load with WWS due to Higher Work:
Energy Ratio 		(b)
Percent Change in End-Use Load with WWS due to Eliminating Energy in Mining, Transporting, Refining		(c)
Percent Change in End-Use Load with WWS due to Effic-iency Beyond BAU		Overall
Percent Change in End-Use Load with WWS
Abidjan, Côte d’IvoireBAU3.94344.413.413.726.7----
WWS1.29231.716.331.510.4−57.4−1.7−8.2−67.2
Addis Ababa, EthiopiaBAU2.37483.12.44.68.9----
WWS0.53568.44.816.59.4−66.8−0.2−10.4−77.5
Ankara, TurkeyBAU12.96821.013.132.929.7----
WWS5.68519.416.645.815.3−39.1−9.9−7.1−56.2
Auckland, New ZealandBAU14.67110.511.539.833.7----
WWS7.96412.615.253.714.9−33.5−5.2−7.0−45.7
Baghdad, IraqBAU9.44518.41.233.941.5----
WWS3.61725.92.435.725.9−40.5−14.7−6.5−61.7
Bangkok, ThailandBAU64.1487.97.339.641.9----
WWS29.6469.311.558.918.2−36.3−11.6−5.9−53.8
Beijing, ChinaBAU139.6516.64.047.528.0----
WWS63.04915.85.064.510.4−32.6−16.0−6.2−54.9
Berlin, GermanyBAU19.81224.316.030.529.2----
WWS8.39219.219.243.218.4−41.7−8.4−7.6−57.6
Bogotá, ColombiaBAU25.78714.74.634.638.4----
WWS9.44617.58.446.010.4−42.1−15.3−6.0−63.4
Bologna, ItalyBAU3.31124.413.224.336.1----
WWS1.26719.220.335.623.5−42.2−11.6−8.0−61.7
Bucharest, RomaniaBAU4.22631.99.131.725.0----
WWS1.61126.312.443.616.2−44.7−9.9−7.3−61.9
Buenos Aires, ArgentinaBAU58.40820.56.431.637.2----
WWS20.47120.511.449.216.6−39.1−18.5−7.3−65.0
Cairo, EgyptBAU43.04320.77.226.841.4----
WWS19.27727.712.538.718.3−35.6−11.4−8.3−55.2
Calgary, CanadaBAU24.30613.411.546.325.5----
WWS9.10717.318.244.017.7−33.3−23.2−6.0−62.5
Cape Town, South AfricaBAU35.13813.86.244.332.1----
WWS15.89913.58.058.017.7−37.2−11.9−5.7−54.8
Caracas, VenezuelaBAU7.0158.75.249.436.6----
WWS2.74912.18.756.622.5−37.2−19.0−4.7−60.8
Casablanca, MoroccoBAU4.80018.18.718.747.1----
WWS2.03019.99.637.027.1−49.4−0.9−7.3−57.7
Chicago, USABAU53.28814.315.030.837.3----
WWS21.73618.120.038.020.6−40.1−12.2−6.9−59.2
Delhi, IndiaBAU57.32921.64.139.727.5----
WWS28.99916.74.259.512.1−36.6−5.8−7.0−49.4
Dhaka, BangladeshBAU11.04538.42.130.425.2----
WWS4.53726.23.259.88.2−40.4−9.9−8.7−58.9
Dubai, United Arab EmiratesBAU60.5586.85.441.943.7----
WWS32.5339.57.960.119.4−38.0−2.6−5.7−46.3
Edmonton, CanadaBAU22.31313.411.546.325.5----
WWS8.36017.318.244.017.7−33.3−23.2−6.0−62.5
Guayaquil, EcuadorBAU5.30911.26.720.054.8----
WWS2.01515.110.134.734.5−50.7−5.0−6.3−62.0
Hanoi, VietnamBAU13.59124.34.543.127.1----
WWS7.50118.54.864.311.7−36.6−1.0−7.2−44.8
Havana, CubaBAU3.20918.35.044.020.0----
WWS1.79420.76.858.410.4−33.1−4.1−6.9−44.1
Ho Chi Minh City, VietnamBAU22.33324.34.543.127.1----
WWS12.32618.54.864.311.7−36.6−1.0−7.2−44.8
Houston, USABAU55.56014.315.030.837.3----
WWS22.66318.120.038.020.6−40.1−12.2−6.9−59.2
Ibiza, SpainBAU0.24015.612.429.440.1----
WWS0.09518.319.434.825.5−39.8−13.6−6.9−60.3
Istanbul, TurkeyBAU41.17521.013.132.929.7----
WWS18.05219.416.645.815.3−39.1−9.9−7.1−56.2
Jakarta, IndonesiaBAU19.58327.64.730.036.3----
WWS7.98821.08.249.021.0−46.7−5.7−6.8−59.2
Karachi, PakistanBAU17.75538.13.527.130.0----
WWS7.19825.94.752.614.7−45.8−5.5−8.1−59.5
Kiev, UkraineBAU11.70635.710.532.518.2----
WWS4.60730.813.642.510.9−41.6−11.0−8.0−60.6
Kinshasa, CongoBAU13.22848.00.94.146.9----
WWS3.25941.90.711.810.4−65.8−1.6−8.0−75.4
Kyoto, JapanBAU5.03116.020.035.127.4----
WWS2.40417.323.242.616.1−34.5−10.1−7.6−52.2
Lagos, NigeriaBAU16.93163.43.412.120.9----
WWS4.16849.74.725.220.2−62.7−4.3−8.4−75.4
Lima, PeruBAU20.07312.85.129.750.7----
WWS7.52513.49.149.326.7−42.3−13.9−6.3−62.5
London, United KingdomBAU40.83426.913.025.133.6----
WWS15.51724.619.631.723.1−44.3−9.5−8.2−62.0
Los Angeles, USABAU73.80314.315.030.837.3----
WWS30.10418.120.038.020.6−40.1−12.2−6.9−59.2
Madrid, SpainBAU28.67815.612.429.440.1----
WWS11.39718.319.434.825.5−39.8−13.6−6.9−60.3
Mexico City, MexicoBAU58.15412.64.938.639.2----
WWS24.17914.96.950.322.0−38.9−13.5−6.0−58.4
Montevideo, UruguayBAU5.78816.17.538.334.1----
WWS2.99017.110.355.115.6−37.5−4.4−6.5−48.3
Montreal, CanadaBAU53.41113.411.546.325.5----
WWS20.01117.318.244.017.7−33.3−23.2−6.0−62.5
Moscow, RussiaBAU110.7823.88.139.327.5----
WWS35.17825.212.845.115.6−41.3−21.1−5.9−68.2
Mumbai, IndiaBAU29.57021.64.139.727.5----
WWS14.95716.74.259.512.1−36.6−5.8−7.0−49.4
Nairobi, KenyaBAU4.52156.21.110.631.5----
WWS1.29139.93.030.326.4−62.0−0.8−8.7−71.5
New York City, USABAU111.5814.315.030.837.3----
WWS45.51518.120.038.020.6−40.1−12.2−6.9−59.2
Oslo, NorwayBAU13.73817.513.245.822.2----
WWS5.91327.221.638.611.3−24.0−25.2−7.8−57.0
Palma, SpainBAU2.13515.612.429.440.1----
WWS0.84918.319.434.825.5−39.8−13.6−6.9−60.3
Paris, FranceBAU46.64326.917.522.130.6----
WWS20.83125.323.329.719.8−40.6−5.9−8.9−55.3
Perth, AustraliaBAU22.59610.511.843.232.5----
WWS9.85712.819.548.318.4−34.0−16.1−6.3−56.4
Philadelphia, USABAU36.05514.315.030.837.3----
WWS14.70718.120.038.020.6−40.1−12.2−6.9−59.2
Phoenix, USABAU37.75614.315.030.837.3----
WWS15.40118.120.038.020.6−40.1−12.2−6.9−59.2
Pyongyang, North KoreaBAU2.5941.30.058.57.8----
WWS1.5290.40.078.53.2−34.8−1.8−4.6−41.1
Quezon City, PhilippinesBAU2.52217.011.824.645.4----
WWS1.12417.815.441.124.3−44.9−3.2−7.4−55.4
Rio de Janeiro, BrazilBAU64.8118.65.142.739.2----
WWS30.49610.68.159.418.2−37.0−10.5−5.5−52.9
Rome, ItalyBAU18.11524.413.224.336.1----
WWS6.93219.220.335.623.5−42.2−11.6−8.0−61.7
San Jose, USABAU11.90114.315.030.837.3----
WWS4.85418.120.038.020.6−40.1−12.2−6.9−59.2
San José, Costa RicaBAU2.78512.410.719.854.8----
WWS1.27317.616.835.010.4−45.5−1.5−7.3−54.3
Santiago, ChileBAU30.25414.510.337.936.5----
WWS15.21913.212.358.915.0−33.9−8.6−7.2−49.7
Sao Paulo, BrazilBAU67.7038.65.142.739.2----
WWS31.85710.68.159.418.2−37.0−10.5−5.5−52.9
Seoul, South KoreaBAU74.14210.915.642.628.8----
WWS36.3268.621.555.013.0−33.0−10.7−7.3−51.0
Shanghai, ChinaBAU178.8216.64.047.528.0----
WWS80.73215.85.064.510.4−32.6−16.0−6.2−54.9
Shenzhen, ChinaBAU78.87716.64.047.528.0----
WWS35.61115.85.064.510.4−32.6−16.0−6.2−54.9
Sydney, AustraliaBAU49.74210.511.843.232.5----
WWS21.69912.819.548.318.4−34.0−16.1−6.3−56.4
Tashkent, UzbekistanBAU4.72440.38.625.610.1----
WWS1.66426.47.944.54.9−46.3−9.2−9.3−64.8
Tehran, IranBAU52.48622.54.937.630.6----
WWS21.44017.35.957.314.6−39.5−12.5−7.2−59.2
Tokyo, JapanBAU146.6016.020.035.127.4----
WWS70.05317.323.242.616.1−34.5−10.1−7.6−52.2
Toronto, CanadaBAU84.97513.411.546.325.5----
WWS31.83717.318.244.017.7−33.3−23.2−6.0−62.5
Ulaanbaatar, MongoliaBAU5.62122.77.032.622.6----
WWS2.20019.54.551.413.7−53.6−3.5−3.7−60.9
Vancouver, CanadaBAU34.91213.411.546.325.5----
WWS13.08017.318.244.017.7−33.3−23.2−6.0−62.5
Vienna, AustriaBAU16.44221.49.830.536.9----
WWS7.02918.813.144.722.3−38.4−12.1−6.8−57.3
Yangon, MyanmarBAU4.09949.92.815.816.9----
WWS1.28233.24.033.710.4−54.6−5.4−8.7−68.7
Yixing, ChinaBAU2.59916.64.047.528.0----
WWS1.17315.85.064.510.4−32.6−16.0−6.2−54.9
Zurich, SwitzerlandBAU8.29627.118.018.035.6----
WWS3.94825.420.827.425.5−40.6−3.3−8.6−52.4
All metropolitan areasBAU2542.417.29.537.732.4----
WWS1089.917.113.150.016.5−37.5−12.9−6.7−57.1
Annually averaged end-use loads (GW) can be converted to energy per year units (TWh/yr) by multiplying the loads by 8760 h/year and dividing the result by 1000 GW/TW. BAU annually averaged end-use load in each sector for each metropolitan area is calculated as the country value from [33] multiplied by the city-to-country population ratio. The load reductions due to electrification are calculated as the country values from [33].
Table
Table 2. (a) Percent of 2050 all-purpose end-use load met by each energy generator, averaged among all metropolitan areas. (b) Estimated nameplate capacities of WWS generators needed to meet annual average all-purpose energy demand. (c) Nameplate capacities of WWS generators existing in 2018. (d) Percent of 2050 needed nameplate capacity existing in 2018, which equals Column (c) divided by Column (b). (e) Capital cost of new generators needed to meet annual average power by 2050.
Table 2. (a) Percent of 2050 all-purpose end-use load met by each energy generator, averaged among all metropolitan areas. (b) Estimated nameplate capacities of WWS generators needed to meet annual average all-purpose energy demand. (c) Nameplate capacities of WWS generators existing in 2018. (d) Percent of 2050 needed nameplate capacity existing in 2018, which equals Column (c) divided by Column (b). (e) Capital cost of new generators needed to meet annual average power by 2050.
Energy Technology(a)
Percent of 2050 All-Purpose Annual Average Demand Met by Plant or
Device		(b)
Nameplate Capacity, Existing Plus New Plants or Devices Needed to Meet 2050 Annual Average Demand (GW)		(c)
Nameplate Capacity of Generators Existing as of 2018 (GW)		(d)
Percent of 2050 Needed Nameplate Capacity Already Installed 2018		(e)
Total Average Capital Cost of New Generators Needed by 2050 (2013 USD Billion)
Onshore wind29.14105670.46.661234
Offshore wind14.714442.30.53811
Wave device0.5028.30.0220.08114
Geothermal electricity0.7510.12.120.731
Hydropower plant f7.91192.7192.71000
Tidal turbine0.125.90.122.0121
Res. roof PV10.8160613.92.291730
Com./gov. roof PV g13.5681713.91.701627
Utility PV plant g18.92101141.54.111382
Utility CSP plant g3.58700.901.28300
Total10042413387.977250
All values are summed or averaged over all metropolitan areas. “Annual average power” is annual average all-purpose energy demand divided by the number of hours per year. The percent of annual-average power demand met by each device type, shown in Column (a), is a demand-weighted average among the mixes given for all metropolitan areas. f No increase in the number of dams or in the peak discharge rate of hydropower is assumed. g The solar PV panels used for this calculation are SunPower E20 panels. CSP is assumed to have storage with a maximum charge to discharge rate (storage size to generator size ratio) of 2.62:1. See the footnotes to Table S7 of [31] for more details.
Table
Table 3. Percent of the annually averaged 2050 metropolitan area all-purpose end-use WWS load (not nameplate capacity) in Table 1 to be met with the given type of electric power generator. Each row sum to 100%.
Table 3. Percent of the annually averaged 2050 metropolitan area all-purpose end-use WWS load (not nameplate capacity) in Table 1 to be met with the given type of electric power generator. Each row sum to 100%.
Metropolitan AreaOnshore
Wind		Offshore
Wind		WaveGeo-ThermalHydro-ElectricTidalRes. PVCom./Gov. PVUtility PVCSP
Abidjan, Côte d’Ivoire35.3810.990.930.006.670.059.4220.9410.994.62
Addis Ababa, Ethiopia35.940.000.007.118.830.007.9117.5718.454.20
Ankara, Turkey35.482.080.001.3315.650.027.8117.3516.134.15
Auckland, New Zealand32.4210.380.779.3213.830.258.909.9710.383.79
Baghdad, Iraq40.830.910.000.004.490.008.9819.9620.044.78
Bangkok, Thailand3.7518.810.000.081.310.0122.2815.6533.184.93
Beijing, China34.8414.290.050.076.300.0212.2513.2214.294.68
Berlin, Germany41.0520.420.100.021.350.018.188.4620.420.00
Bogotá, Colombia34.428.830.810.0018.300.387.5716.838.834.03
Bologna, Italy37.3113.970.350.987.790.0211.979.1013.974.54
Bucharest, Romania37.4512.030.000.4416.320.0110.3111.4212.030.00
Buenos Aires, Argentina38.779.950.001.407.900.028.5318.959.954.53
Cairo, Egypt42.1010.800.000.001.520.019.2620.5810.804.92
Calgary, Canada32.748.870.732.6323.600.297.6014.678.870.00
Cape Town, South Africa42.1913.701.000.000.300.0111.7412.4313.704.93
Caracas, Venezuela35.919.220.180.0015.800.027.9017.569.224.20
Casablanca, Morocco41.0110.530.970.003.060.039.0220.0510.534.80
Chicago, USA31.4416.420.960.573.900.0110.9514.6016.424.73
Delhi, India36.926.230.060.022.120.0212.0315.8621.854.89
Dhaka, Bangladesh7.087.120.620.000.320.1023.189.6846.964.95
Dubai, United Arab Emirates5.9012.260.000.000.000.017.053.9565.835.00
Edmonton, Canada32.748.870.732.6323.600.297.6014.678.870.00
Guayaquil, Ecuador33.383.090.790.3320.230.567.3416.3214.053.90
Hanoi, Vietnam0.7225.190.570.008.070.0121.5914.0825.194.57
Havana, Cuba42.1210.811.000.000.350.139.2720.5910.814.93
Ho Chi Minh City, Vietnam0.7225.190.570.008.070.0121.5914.0825.194.57
Houston, USA31.4416.420.960.573.900.0110.9514.6016.424.73
Ibiza, Spain37.3611.920.880.0611.340.3310.2111.6111.924.37
Istanbul, Turkey35.482.080.001.3315.650.027.8117.3516.134.15
Jakarta, Indonesia15.7915.280.944.451.340.0313.1029.1115.284.66
Karachi, Pakistan23.9910.550.240.003.650.0015.2316.5524.984.81
Kiev, Ukraine42.4115.210.000.005.730.0213.048.3915.210.00
Kinshasa, Congo41.4310.630.930.006.910.099.1120.2510.630.00
Kyoto, Japan10.2431.550.930.695.840.2811.787.1431.550.00
Lagos, Nigeria13.630.000.300.001.290.0114.3731.9433.544.92
Lima, Peru33.560.020.796.6214.040.057.3816.4117.213.93
London, United Kingdom20.2932.660.960.000.932.815.414.2732.660.00
Los Angeles, USA31.4416.420.960.573.900.0110.9514.6016.424.73
Madrid, Spain37.3611.920.880.0611.340.3310.2111.6111.924.37
Mexico City, Mexico39.4410.120.932.963.840.028.6819.2810.124.61
Montevideo, Uruguay36.559.380.860.0013.570.078.0417.879.384.27
Montreal, Canada32.748.870.732.6323.600.297.6014.678.870.00
Moscow, Russia40.2513.060.500.178.960.0311.1911.8813.060.90
Mumbai, India36.926.230.060.022.120.0212.0315.8621.854.89
Nairobi, Kenya35.469.100.8412.923.270.037.8017.349.104.15
New York City, USA31.4416.420.960.573.900.0110.9514.6016.424.73
Oslo, Norway13.715.560.310.0068.830.394.770.875.560.00
Palma, Spain37.3611.920.880.0611.340.3310.2111.6111.924.37
Paris, France40.2813.150.920.037.510.2011.2711.6513.151.83
Perth, Australia26.9515.090.960.363.990.129.4315.7222.644.73
Philadelphia, USA31.4416.420.960.573.900.0110.9514.6016.424.73
Phoenix, USA31.4416.420.960.573.900.0110.9514.6016.424.73
Pyongyang, North Korea36.1313.740.000.0017.551.7311.784.9113.740.43
Quezon City, Philippines7.6815.130.6011.243.950.2712.9728.8215.134.20
Rio de Janeiro, Brazil35.539.120.840.0016.040.027.8217.379.124.16
Rome, Italy37.3113.970.350.987.790.0211.979.1013.974.54
San Jose, USA31.4416.420.960.573.900.0110.9514.6016.424.73
San José, Costa Rica21.465.510.5124.7424.430.134.7210.495.512.51
Santiago, Chile36.7110.060.873.809.410.068.6216.1310.064.29
São Paulo, Brazil35.539.120.840.0016.040.027.8217.379.124.16
Seoul, South Korea4.4437.180.000.001.980.158.905.2737.184.89
Shanghai, China34.8414.290.050.076.300.0212.2513.2214.294.68
Shenzhen, China34.8414.290.050.076.300.0212.2513.2214.294.68
Sydney, Australia26.9515.090.960.363.990.129.4315.7222.644.73
Tashkent, Uzbekistan40.940.000.000.004.230.009.0120.0221.024.79
Tehran, Iran28.9212.560.000.002.680.0014.6714.6321.674.87
Tokyo, Japan10.2431.550.930.695.840.2811.787.1431.550.00
Toronto, Canada32.748.870.732.6323.600.297.6014.678.870.00
Ulaanbaatar, Mongolia44.870.000.000.000.280.009.8721.9423.030.00
Vancouver, Canada32.748.870.732.6323.600.297.6014.678.870.00
Vienna, Austria36.180.000.000.0019.590.0010.559.0524.620.00
Yangon, Myanmar37.679.670.890.0010.650.338.2918.429.674.41
Yixing, China34.8414.290.050.076.300.0212.2513.2214.294.68
Zurich, Switzerland26.910.000.000.0039.310.006.5611.9015.320.00
All metropolitan areas29.1414.710.500.757.910.1210.8113.5618.923.58
Table
Table 4. Existing plus new nameplate capacities (MW) needed for each WWS electricity generation source in each metropolitan area to meet 2050 metropolitan area all-purpose end-use WWS load in the annual average. These capacities are determined by taking the product of end-use WWS load (Table 1) and the fraction of load met by each generator (Table 3), all divided by the product of the capacity factor and transmission/distribution efficiency (Table 5).
Table 4. Existing plus new nameplate capacities (MW) needed for each WWS electricity generation source in each metropolitan area to meet 2050 metropolitan area all-purpose end-use WWS load in the annual average. These capacities are determined by taking the product of end-use WWS load (Table 1) and the fraction of load met by each generator (Table 3), all divided by the product of the capacity factor and transmission/distribution efficiency (Table 5).
Metropolitan AreaOnshore
Wind		Offshore
Wind		WaveGeother-MalHydro-ElectricTidalRes. PVCom./Gov. PVUtility PVCSP
Abidjan, Côte d’Ivoire3154552100020036881501785129
Addis Ababa, Ethiopia5600051114025956458449
Ankara, Turkey6043356010221696244359934890497
Auckland, New Zealand66371985187907242491401850054647656
Baghdad, Iraq4946233004471185944034113378
Bangkok, Thailand533017,4970298461033,19622,97648,4312772
Beijing, China55,74124,8512315186305539,95046,29445,1985840
Berlin, Germany8598409461224123980496393200
Bogotá, Colombia22,296323957603782161435095555004807
Bologna, Italy138450535152181818696857119
Bucharest, Romania1656518095841938120910360
Buenos Aires, Argentina21,19150570362367820784118,94692041596
Cairo, Egypt21,2015899006558816018,69294191661
Calgary, Canada7870199224330048561203523843041210
Cape Town, South Africa17,053517351101046942410,36010,8941516
Caracas, Venezuela8319106447011683133129121524271
Casablanca, Morocco2262542770145394321761091192
Chicago, USA24,29610,1317641511852810,62015,77115,8591229
Delhi, India28,4265060131913962220,60627,42632,1132749
Dhaka, Bangladesh8721919232034226645282213,282543
Dubai, United Arab Emirates48959232000710,123579093,3262749
Edmonton, Canada7225182922327644581103234773937830
Guayaquil, Ecuador47153565589125268015051295138
Hanoi, Vietnam1785886344013714799251669152638
Havana, Cuba230860751014107801748897159
Ho Chi Minh City, Vietnam293967256602252613,132848815,0381049
Houston, USA25,33210,5637961571931811,07316,44316,5351281
Ibiza, Spain10528302515466619
Istanbul, Turkey19,19011310324688818775819,03015,5271579
Jakarta, Indonesia8967464531044724312555012,2536423756
Karachi, Pakistan5030206715606552690578739905763
Kiev, Ukraine506616970058543411259036340
Kinshasa, Congo11,97025073070638171939429522530
Kyoto, Japan73519071722030130147495636070
Lagos, Nigeria2405010401261366379838352466
Lima, Peru17,3729232607231815262557235980534
London, United Kingdom827412,445517032619925027492929,2090
Los Angeles, USA33,64914,031105820925651114,70921,84321,9651702
Madrid, Spain12,5103386377929521746442789972761016
Mexico City, Mexico39,2538745193795422331810,53523,81012,1282144
Montevideo, Uruguay362766320008799109925891268224
Montreal, Canada17,294437853466010,671264774118,52590550
Moscow, Russia37,30611,3077437571055322,61529,74024,293699
Mumbai, India14,66226106757201110,62814,14616,5641418
Nairobi, Kenya17624109322210125611244653109
New York City, USA50,87521,214159931638791722,23933,02433,2092573
Oslo, Norway20737867208904102180443619540
Palma, Spain9312522812201348058854276
Paris, France21,52065657596343618513,47516,10314,424836
Perth, Australia8456353730742848534902839610,946857
Philadelphia, USA16,439685551710212535718610,67110,731831
Phoenix, USA17,214717854110713126752511,17411,237870
Pyongyang, North Korea1720671006581321072498120915
Quezon City, Philippines3505985415910114733160483791
Rio de Janeiro, Brazil75,54684252015010,9532212,15226,68713,9072473
Rome, Italy757627631918311946447438064688650
San Jose, USA54262263171344142237235223542274
San José, Costa Rica155539351383681828862832957
Santiago, Chile14,3963685403708315943630112,69165511074
São Paulo, Brazil78,91688012105011,4422312,69427,87814,5282583
Seoul, South Korea504642,63400151923416,67010,67168,8523508
Shanghai, China71,37431,8202966611,0507151,15459,27757,8757478
Shenzhen, China31,48314,0361312948743122,56426,14725,5283299
Sydney, Australia18,614778567693186611610,79018,48224,0951886
Tashkent, Uzbekistan1731000153074718631653152
Tehran, Iran16,3146619011296416,49317,42121,2111939
Tokyo, Japan21,42155,5614999575876586642,95727,860105,1140
Toronto, Canada27,5146965850105016,97742012,31629,47314,4060
Ulaanbaatar, Mongolia27920001501243321225840
Vancouver, Canada11,30428613494326975173506012,10959190
Vienna, Austria7380000298504074411192250
Yangon, Myanmar22694071060355236281395720128
Yixing, China1037462411611743862841109
Zurich, Switzerland3212000342101460309133120
All metropolitan areas1056,442443,91528,26010,149192,6775945605,936816,7481010,51670,192
Table
Table 5. 2050 product of capacity factor and transmission/distribution efficiency for each energy generating technology and metropolitan area. Values are derived for each country in [33]. Capacity factors for onshore and offshore wind account for the competition among wind turbines for limited kinetic energy (array losses). A “--” indicates no installed generators.
Table 5. 2050 product of capacity factor and transmission/distribution efficiency for each energy generating technology and metropolitan area. Values are derived for each country in [33]. Capacity factors for onshore and offshore wind account for the competition among wind turbines for limited kinetic energy (array losses). A “--” indicates no installed generators.
Metropolitan AreaOnshore
Wind		Offshore
Wind		WaveGeo-ThermalHydro-ElectricTidalRes. PVCom./Gov. PVUtility PVCSP
Abidjan, Côte d’Ivoire0.1450.2570.121--0.4320.2120.1770.1800.1810.462
Addis Ababa, Ethiopia0.343----0.7430.413--0.1630.1670.1690.455
Ankara, Turkey0.3340.332--0.7390.4100.2020.1820.1650.1880.475
Auckland, New Zealand0.3890.4160.3260.8180.4540.2240.1760.1590.1780.461
Baghdad, Iraq0.2990.142----0.3630.1790.1750.1640.1760.457
Bangkok, Thailand0.2080.319--0.8260.4590.2260.1990.2020.2030.527
Beijing, China0.3940.3630.1290.8290.4600.2270.1930.1800.1990.505
Berlin, Germany0.4010.4190.1310.8450.4690.2310.1720.1430.184--
Bogotá, Colombia0.1460.2580.133--0.4570.2250.1640.1660.1670.471
Bologna, Italy0.3410.3500.1260.8140.4520.2230.1860.1660.2070.485
Bucharest, Romania0.3640.374--0.8110.4500.2220.1770.1520.1870.463
Buenos Aires, Argentina0.3740.403--0.7920.4400.2160.2230.2050.2210.582
Cairo, Egypt0.3830.353----0.4470.2200.2190.2120.2210.572
Calgary, Canada0.3790.4050.2750.7970.4430.2180.1970.1580.196--
Cape Town, South Africa0.3930.4210.310--0.4600.2260.1980.1910.2000.518
Caracas, Venezuela0.1190.2380.104--0.3720.1830.1630.1660.1660.426
Casablanca, Morocco0.3680.3940.256--0.4300.2120.1940.1870.1960.507
Chicago, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
Delhi, India0.3770.3570.1230.7930.4400.2170.1690.1680.1970.516
Dhaka, Bangladesh0.3680.1680.120--0.4300.2120.1580.1560.1600.413
Dubai, United Arab Emirates0.3920.432------0.2320.2270.2220.2290.592
Edmonton, Canada0.3790.4050.2750.7970.4430.2180.1970.1580.196--
Guayaquil, Ecuador0.1430.1750.2890.8050.4470.2200.2170.2180.2190.568
Hanoi, Vietnam0.3050.3210.123--0.4420.2170.2030.2040.2060.537
Havana, Cuba0.3270.3200.351--0.4370.2150.2130.2110.2160.557
Ho Chi Minh City, Vietnam0.3050.3210.123--0.4420.2170.2030.2040.2060.537
Houston, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
Ibiza, Spain0.3400.4010.2670.7880.4380.2160.1810.1680.1870.490
Istanbul, Turkey0.3340.332--0.7390.4100.2020.1820.1650.1880.475
Jakarta, Indonesia0.1410.2630.2430.7940.4410.2170.1890.1900.1900.493
Karachi, Pakistan0.3430.3670.112--0.4010.1970.1590.1510.1820.453
Kiev, Ukraine0.3860.413----0.4510.2220.1760.1490.193--
Kinshasa, Congo0.1130.1380.099--0.3540.1740.1530.1540.154--
Kyoto, Japan0.3350.3980.1310.8410.4670.2300.1920.1800.210--
Lagos, Nigeria0.236--0.118--0.4240.2090.1640.1670.1670.440
Lima, Peru0.1450.1780.2580.8200.4560.2240.2120.2160.2170.553
London, United Kingdom0.3800.4070.289--0.4450.2190.1670.1340.174--
Los Angeles, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
Madrid, Spain0.3400.4010.2670.7880.4380.2160.1810.1680.1870.490
Mexico City, Mexico0.2430.2800.1160.7490.4160.2050.1990.1960.2020.520
Montevideo, Uruguay0.3010.4230.129--0.4620.2270.2190.2060.2210.572
Montreal, Canada0.3790.4050.2750.7970.4430.2180.1970.1580.196--
Moscow, Russia0.3800.4060.2370.7980.4430.2180.1740.1410.1890.455
Mumbai, India0.3770.3570.1230.7930.4400.2170.1690.1680.1970.516
Nairobi, Kenya0.2600.2870.1170.7520.4170.2050.1790.1800.1800.489
New York City, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
Oslo, Norway0.3910.4190.254--0.4570.2250.1560.1170.168--
Palma, Spain0.3400.4010.2670.7880.4380.2160.1810.1680.1870.490
Paris, France0.3900.4170.2530.8200.4560.2240.1740.1510.1900.455
Perth, Australia0.3140.4210.3070.8360.4640.2290.1900.1850.2040.544
Philadelphia, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
Phoenix, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
Pyongyang, North Korea0.3210.313----0.4080.2010.1680.1510.1740.439
Quezon City, Philippines0.2470.2840.1230.7940.4410.2170.1990.2020.2030.520
Rio de Janeiro, Brazil0.1430.3300.127--0.4470.2200.1960.1980.2000.512
Rome, Italy0.3410.3500.1260.8140.4520.2230.1860.1660.2070.485
San Jose, USA0.2810.3520.2720.8250.4580.2250.2240.2010.2250.837
San José, Costa Rica0.1760.1780.1280.8220.4560.2250.2090.2120.2130.559
Santiago, Chile0.3880.4150.3270.8160.4530.2230.2080.1930.2340.608
São Paulo, Brazil0.1430.3300.127--0.4470.2200.1960.1980.2000.512
Seoul, South Korea0.3200.317----0.4720.2330.1940.1790.1960.507
Shanghai, China0.3940.3630.1290.8290.4600.2270.1930.1800.1990.505
Shenzhen, China0.3940.3630.1290.8290.4600.2270.1930.1800.1990.505
Sydney, Australia0.3140.4210.3070.8360.4640.2290.1900.1850.2040.544
Tashkent, Uzbekistan0.394------0.460--0.2010.1790.2120.524
Tehran, Iran0.3800.407--0.8000.4440.2190.1910.1800.2190.538
Tokyo, Japan0.3350.3980.1310.8410.4670.2300.1920.1800.210--
Toronto, Canada0.3790.4050.2750.7970.4430.2180.1970.1580.196--
Ulaanbaatar, Mongolia0.354------0.413--0.1750.1500.196--
Vancouver, Canada0.3790.4050.2750.7970.4430.2180.1970.1580.196--
Vienna, Austria0.345----0.8310.461--0.1820.1550.188--
Yangon, Myanmar0.2130.3040.108--0.3850.1890.1690.1690.1720.442
Yixing, China0.3940.3630.1290.8290.4600.2270.1930.1800.1990.505
Zurich, Switzerland0.331------0.454--0.1780.1520.183--
All metropolitan areas0.3010.3610.1950.8010.4470.2210.1940.1810.2040.556
Table
Table 6. Year 2050 (a,e) rooftop areas suitable for photovoltaics (PV) panels, (b,f) potential nameplate capacities of suitable rooftop areas, (c,g) proposed nameplate capacities for 2050, and (d,h) percent of potential capacity to be installed (proposed capacity divided by potential capacity) for both residential and commercial/government buildings.
Table 6. Year 2050 (a,e) rooftop areas suitable for photovoltaics (PV) panels, (b,f) potential nameplate capacities of suitable rooftop areas, (c,g) proposed nameplate capacities for 2050, and (d,h) percent of potential capacity to be installed (proposed capacity divided by potential capacity) for both residential and commercial/government buildings.
Metropolitan AreaResidential Rooftop PVCommercial/Government Rooftop PV
(a)
Rooftop Area Suitable for PV in 2050
(km2)		(b)
Potential Nameplate Capacity of Suitable Area in 2050 (MWdc-peak)		(c)
Proposed Nameplate Capacity in 2050 (MWdc-peak)		(d)
Percent of Potential Capacity to Be Installed		(e)
Rooftop Area Suitable for PV in 2050
(km2)		(f)
Potential Nameplate Capacity of Suitable Area in 2050 (MWdc-peak)		(g)
Proposed Nameplate Capacity in 2050 (MWdc-peak)		(h)
Percent of Potential Capacity to Be Installed
Abidjan, Côte d’Ivoire122.229,2376882.425.56089150124.6
Addis Ababa, Ethiopia114.927,4722590.98.1194456429.0
Ankara, Turkey7117,034244314.34911,727599351.1
Auckland, New Zealand378810401845.6286677500575.0
Baghdad, Iraq11527,58718596.76415,401440328.6
Bangkok, Thailand32277,11233,19643.011727,96122,97682.2
Beijing, China41899,91839,95040.025460,79246,29476.2
Berlin, Germany255948398066.9276493496376.4
Bogotá, Colombia31074,13543505.911627,789955534.4
Bologna, Italy11258181831.7493369674.5
Bucharest, Romania16379793824.781866120964.8
Buenos Aires, Argentina21651,651784115.215035,87718,94652.8
Cairo, Egypt452108,13881607.515938,12218,69249.0
Calgary, Canada235551352363.54410,611843079.5
Cape Town, South Africa10224,333942438.75212,49310,36082.9
Caracas, Venezuela5012,060133111.0204684291262.2
Casablanca, Morocco4911,7699438.0215112217642.6
Chicago, USA19145,70310,62023.213131,42715,77150.2
Delhi, India600143,48120,60614.415937,99927,42672.2
Dhaka, Bangladesh19947,703664513.9296849282241.2
Dubai, United Arab Emirates6415,27010,12366.3327577579076.4
Edmonton, Canada215096323463.5419741773979.5
Guayaquil, Ecuador9021,5426803.2296921150521.8
Hanoi, Vietnam11126,434799230.2276423516680.4
Havana, Cuba31751978010.4153563174849.1
Ho Chi Minh City, Vietnam18243,43713,13230.24410,554848880.4
Houston, USA19947,65211,07323.213732,76716,44350.2
Ibiza, Spain11945427.80886675.1
Istanbul, Turkey22654,086775814.315637,23719,03051.1
Jakarta, Indonesia27265,09655508.58620,59112,25359.5
Karachi, Pakistan23355,741690512.46214,743787353.4
Kiev, Ukraine245693341159.9204902259052.8
Kinshasa, Congo553132,15719391.517942,849429510.0
Kyoto, Japan102313147463.75129895673.7
Lagos, Nigeria30773,41036635.08119,446798341.1
Lima, Peru29670,77326253.711727,934572320.5
London, United Kingdom348098502762.15813,796492935.7
Los Angeles, USA26563,29814,70923.218243,52621,84350.2
Madrid, Spain9723,175644227.84410,523789975.1
Mexico City, Mexico35785,47210,53512.317942,80423,81055.6
Montevideo, Uruguay225235109921.0133184258981.3
Montreal, Canada5112,198774163.59723,31718,52579.5
Moscow, Russia13331,91622,61570.924658,84829,74050.5
Mumbai, India30974,00610,62814.48219,59914,14672.2
Nairobi, Kenya15136,1785611.6245684124421.9
New York City, USA40095,70222,23923.227565,80933,02450.2
Oslo, Norway122962180460.92354464368.0
Palma, Spain7172548027.8378358875.1
Paris, France10024,03213,47556.18821,08016,10376.4
Perth, Australia9622,843490221.55713,713839661.2
Philadelphia, USA12930,923718623.28921,26410,67150.2
Phoenix, USA13532,382752523.29322,26711,17450.2
Pyongyang, North Korea194586107223.46135249836.8
Quezon City, Philippines5914,1527335.2153536160445.3
Rio de Janeiro, Brazil40396,38812,15212.617842,46426,68762.8
Rome, Italy5914,121447431.7215105380674.5
San Jose, USA4310,207237223.2297019352250.2
San José, Costa Rica2151262885.69212662829.6
Santiago, Chile10424,978630125.26916,40012,69177.4
São Paulo, Brazil421100,68812,69412.618544,35827,87862.8
Seoul, South Korea10725,61416,67065.15914,14210,67175.5
Shanghai, China535127,94251,15440.032577,84159,27776.2
Shenzhen, China23656,43522,56440.014434,33626,14776.2
Sydney, Australia21050,28510,79021.512630,18718,48261.2
Tashkent, Uzbekistan29688174710.9153473186353.7
Tehran, Iran14634,94416,49347.28820,97517,42183.1
Tokyo, Japan28267,38942,95763.715837,81727,86073.7
Toronto, Canada8119,40612,31663.515537,09629,47379.5
Ulaanbaatar, Mongolia337894124315.7317355321243.7
Vancouver, Canada337973506063.56415,24112,10979.5
Vienna, Austria276559407462.1225314411177.4
Yangon, Myanmar11427,2246282.3256096139522.9
Yixing, China8186074340.05113186276.2
Zurich, Switzerland204696146031.1173987309177.5
All metropolitan areas11,3552715,925605,93622.357971386,473816,748-
Table
Table 7. (a) Mean year 2050 capital cost estimate for new generators to meet annual average WWS electric power demand after electrification of all energy sectors (this does not include the additional generators beyond those needed for annual average power in Table 2). (b) Mean values of the private levelized cost of energy (LCOE) for conventional fuels (BAU) in the electricity sector in 2015, which is assumed to be the LCOE for all BAU energy. LCOE estimates do not include externality costs and are assumed to account for meeting continuous load (thus accounting for grid stability). (c) Same as (b), but for 2050. (d) Same as (b), but for WWS averaged over all energy sectors in 2050 and for generators to meet annual average load. (e) Same as (b) but for WWS averaged over all energy sectors in 2050 and for generators, storage, transmission/distribution, and demand response to meet continuous load (thus accounting for grid stability). (f) Mean private energy cost savings per person per year due to switching from BAU to WWS in all energy sectors in 2050. (g) Mean estimates by metropolitan area of 2050 air pollution health and non-health cost savings per person per year due to switching to WWS. (h) Mean estimates of climate cost savings to the world per person per year due to switching to WWS. (i) Mean estimates of private energy plus health plus climate cost savings per person per year in the metropolitan area due to switching to 100% WWS. All costs are in 2013 USD.
Table 7. (a) Mean year 2050 capital cost estimate for new generators to meet annual average WWS electric power demand after electrification of all energy sectors (this does not include the additional generators beyond those needed for annual average power in Table 2). (b) Mean values of the private levelized cost of energy (LCOE) for conventional fuels (BAU) in the electricity sector in 2015, which is assumed to be the LCOE for all BAU energy. LCOE estimates do not include externality costs and are assumed to account for meeting continuous load (thus accounting for grid stability). (c) Same as (b), but for 2050. (d) Same as (b), but for WWS averaged over all energy sectors in 2050 and for generators to meet annual average load. (e) Same as (b) but for WWS averaged over all energy sectors in 2050 and for generators, storage, transmission/distribution, and demand response to meet continuous load (thus accounting for grid stability). (f) Mean private energy cost savings per person per year due to switching from BAU to WWS in all energy sectors in 2050. (g) Mean estimates by metropolitan area of 2050 air pollution health and non-health cost savings per person per year due to switching to WWS. (h) Mean estimates of climate cost savings to the world per person per year due to switching to WWS. (i) Mean estimates of private energy plus health plus climate cost savings per person per year in the metropolitan area due to switching to 100% WWS. All costs are in 2013 USD.
Metropolitan Area(a)
2050 Capital Cost of New WWS Electricity Generators Needed to Meet Annual Average Load ($bil)		(b)
2015 LCOE of BAU for Meeting Continuous Load (¢/kWh-All-Energy)		(c)
2050 LCOE of BAU Energy for Meeting Continuous Load (¢/kWh-All-Energy)		(d)
2050 LCOE of WWS for Meeting Annual avg. Load (¢/kWh-All-Energy)		(e)
2050 LCOE of WWS for Meeting Continuous Load (¢/kWh-All-Energy)		(f)
2050 Mean Private Energy Cost Savings due to Switch-ing All Energy to WWS ($/person/yr)		(g)
2050 Mean Air Pollution Damage Cost Savings to Metro Area due to Switching all Energy to WWS ($/per-son/yr)		(h)
2050 Mean Climate Cost Savings to World due to Switch-ing All Energy to WWS
($/per-son/yr)		(i)
2050 Mean Energy + Health + Climate Cost Savings due to Switching to WWS
($/per-son/yr)
Abidjan, Côte d’Ivoire12.18.6111.055.846.6236423394003102
Addis Ababa, Ethiopia3.87.896.917.398.1712778064971
Ankara, Turkey35.29.409.947.408.77862203328145708
Auckland, New Zealand45.79.839.207.937.77271689259069513
Baghdad, Iraq28.58.6511.517.859.22658144038815980
Bangkok, Thailand262.19.5311.417.4410.8021503720488610,756
Beijing, China400.710.249.277.968.3618687273556214,704
Berlin, Germany44.711.8510.857.958.7032132789736313,365
Bogotá, Colombia79.48.248.105.307.67659115712383054
Bologna, Italy7.610.5111.067.538.282445274938899083
Bucharest, Romania9.410.359.697.528.2715207023347312,015
Buenos Aires, Argentina118.28.6210.318.6611.041720164336447007
Cairo, Egypt119.18.7111.498.669.44863242422255512
Calgary, Canada48.08.878.247.797.04482491811,88017,622
Cape Town, South Africa102.210.789.698.499.272254214811,22815,630
Caracas, Venezuela25.77.798.375.898.261033110433655503
Casablanca, Morocco13.210.4110.408.539.30584107317383395
Chicago, USA138.310.2810.437.819.3331641757725512,176
Delhi, India216.010.519.687.6110.07444472719067076
Dhaka, Bangladesh52.08.7911.805.919.2821118654122487
Dubai, United Arab Emirates209.18.7711.897.038.408470127414,95624,699
Edmonton, Canada44.18.878.247.797.04482491811,88017,622
Guayaquil, Ecuador14.58.149.136.038.4063368017393052
Hanoi, Vietnam62.08.789.208.0411.41379168223964457
Havana, Cuba12.09.1811.988.2612.01726366031717558
Ho Chi Minh City, Vietnam101.98.789.208.0411.41379168223964457
Houston, USA144.210.2810.437.819.3331641757725512,176
Ibiza, Spain0.511.2410.847.768.522056151435507121
Istanbul, Turkey111.79.409.947.408.77862203328145708
Jakarta, Indonesia75.910.1010.406.9910.35741296619895697
Karachi, Pakistan63.98.4510.077.688.9639324528263671
Kiev, Ukraine29.410.559.558.158.9017274885473711,349
Kinshasa, Congo38.57.958.955.436.2032718146812823
Kyoto, Japan15.29.9010.787.819.7618622183643710,482
Lagos, Nigeria44.28.4810.886.347.1254943852805213
Lima, Peru54.78.299.236.118.49668186314063938
London, United Kingdom99.011.3311.167.988.732256192735247707
Los Angeles, USA191.610.2810.437.819.3331641757725512,176
Madrid, Spain65.311.2410.847.768.522056151435507121
Mexico City, Mexico179.69.3011.107.319.521336148233916209
Montevideo, Uruguay16.59.959.117.559.931012134218424195
Montreal, Canada105.58.878.247.797.04482491811,88017,622
Moscow, Russia234.79.1610.217.957.734568468210,32119,571
Mumbai, India111.410.519.687.6110.07444472719067076
Nairobi, Kenya9.411.7510.657.388.153805913121283
New York City, USA289.610.2810.437.819.3331641757725512,176
Oslo, Norway13.07.446.616.587.3328671386625110,504
Palma, Spain4.911.2410.847.768.522056151435507121
Paris, France132.010.719.397.978.721737147532066418
Perth, Australia66.710.5010.347.479.044144106611,56916,779
Philadelphia, USA93.610.2810.437.819.3331641757725512,176
Phoenix, USA98.010.2810.437.819.3331641757725512,176
Pyongyang, North Korea9.88.067.397.477.87176271017064592
Quezon City, Philippines9.110.5610.597.959.3529735159714784
Rio de Janeiro, Brazil235.98.868.536.018.38915120918413965
Rome, Italy41.510.5111.067.538.282445274938899083
San Jose, USA30.910.2810.437.819.3331641757725512,176
San José, Costa Rica6.89.448.246.819.0151697912462741
Santiago, Chile84.89.879.538.5010.871279178146077668
São Paulo, Brazil246.48.868.536.018.38915120918413965
Seoul, South Korea265.210.3110.146.8211.872766215411,31416,234
Shanghai, China513.110.249.277.968.3618687273556214,704
Shenzhen, China226.310.249.277.968.3618687273556214,704
Sydney, Australia146.710.5010.347.479.044144106611,56916,779
Tashkent, Uzbekistan11.18.5210.657.588.851005173920994844
Tehran, Iran154.68.7211.578.239.6029211530736011,811
Tokyo, Japan443.39.9010.787.819.7618622183643710,482
Toronto, Canada167.88.878.247.797.04482491811,88017,622
Ulaanbaatar, Mongolia17.110.779.867.587.9811613768722412,152
Vancouver, Canada69.08.878.247.797.04482491811,88017,622
Vienna, Austria40.59.248.676.537.2831782422635311,953
Yangon, Myanmar10.37.908.606.9310.2924124964903228
Yixing, China7.510.249.277.968.3618687273556214,704
Zurich, Switzerland18.28.917.796.337.081786170336027090
All metropolitan areas72509.729.907.609.001508246742708245
All costs are in 2013 USD. (a) Capital costs are only for new electricity generators needed to meet annual average WWS load (Table 1). (b) The 2015 LCOE cost of retail electricity in the BAU case in each metropolitan area combines the percentage mix of conventional electricity generators by country in which the metropolitan area resides in 2015 with contemporary mean LCOEs for each BAU generator in the country, taken from [33]. Such BAU costs include all-distance transmission, distribution, and pipeline costs, but they exclude health and climate costs. This LCOE is assumed to apply to all BAU sectors. (c) Same as (b), but for the 2050 BAU case and using 2050 LCOEs for each generator as derived in [33]. The 2050 BAU case includes some existing WWS (mostly hydropower) plus future increases in WWS electricity in the BAU case, as well as energy efficiency measures. The cost of keeping the grid stable in the BAU case is conservatively assumed to be made possible by BAU generators, and this is accounted for in the BAU costs. This LCOE is assumed to apply to all BAU sectors. (d) The 2050 LCOE of WWS for meeting load in the annual average is found by combining the 2050 mix of WWS generators among all energy sectors by metropolitan area from Table 3 with the 2050 mean LCOEs for each WWS generator by country from [33]. (e) The 2050 LCOE of WWS for meeting continuous load is the sum of the LCOE from meeting annual average load plus the difference in cost between meeting continuous and annual average load in each metropolitan region. This difference is determined from data in [33]. That study calculated the LCOEs to meet annual average load (LCOEA) with 100% WWS in 143 countries and the LCOEs to meet continuous load (LCOEC) with 100% WWS in 24 world regions encompassing the 143 countries. The LCOEAs for each country were first averaged (weighted by end-use WWS load) to find average LCOEAs for each region. The difference between the LCOEC and LCOEA for each region was then assigned to each country in the region. The difference for each metropolitan area was then assigned as the difference in the country that the metropolitan area resided in. This difference was then added to the LCOEA from Table 7, Column (d) to obtain the value in Column (e). Note that, for some regions (Canada, Iceland, New Zealand, and Russia), the cost of keeping the grid stable was less than the estimated cost of meeting annual average load. The reason is that the number of generators estimated to meet annual average load was a rough estimate. However, the WWS resource strength (usually wind) in these countries was stronger when calculated with a weather prediction model used to predict continuous WWS supply than when estimated for determining the number of generators to meet annual average power in [33]. (f) The 2050 mean private energy cost savings per capita per year due to switching from BAU to WWS retail electricity is calculated as the cost of all energy use in the BAU case (the product of BAU end-use power from Table 1, 8760 h per year, and the 2050 BAU LCOE from Column (c) of Table 7) less the WWS private energy cost (which is the product of WWS end-use power from Table 1, 8760 h per year, and the 2050 WWS LCOE from Column (e) of Table 7), all divided by 2050 population from Table 9. (g) This column equals the total air pollution cost per year for the metropolitan area from Table 8 divided by the 2050 metropolitan area population. (h) This column equals the total climate cost per year to the world due to the metropolitan area’s emissions (from Table 8) divided by the 2050 metropolitan area population. (i) This column equals the sum of Columns (f), (g), and (h).
Table
Table 8. (a) BAU annual energy cost, (b) BAU annual air pollution cost (from mortalities, morbidities, and non-air pollution effects) due to energy, (c) BAU annual climate cost due to energy, (d) BAU annual total social (energy+air pollution+climate) cost, (e) WWS annual social cost (= energy cost), and percent reduction in (f) energy cost and (g) social cost due to transitioning from WWS to BAU. All costs are in 2013 USD.
Table 8. (a) BAU annual energy cost, (b) BAU annual air pollution cost (from mortalities, morbidities, and non-air pollution effects) due to energy, (c) BAU annual climate cost due to energy, (d) BAU annual total social (energy+air pollution+climate) cost, (e) WWS annual social cost (= energy cost), and percent reduction in (f) energy cost and (g) social cost due to transitioning from WWS to BAU. All costs are in 2013 USD.
Metropolitan Area(a)
2050 BAU Energy Cost ($bil/yr)		(b)
2050 BAU Air Pollution Cost ($bil/yr)		(c)
2050 BAU Climate cost ($bil/yr)		(d)
2050 BAU Total Social Cost
(a + b + c) ($bil/yr)		(e)
2050 WWS Energy and Total Social Cost ($bil/yr)		(f)
Percent Change in Energy Cost due to WWS (e − a)/a		(g)
Percent Change in Total Social Cost due to WWS (e − d)/d
Abidjan, Côte d’Ivoire3.819.73.426.90.7−80.4−97.2
Addis Ababa, Ethiopia1.46.50.58.50.4−73.4−95.5
Ankara, Turkey11.316.322.650.24.4−61.3−91.3
Auckland, New Zealand11.82.113.927.85.4−54.1−80.5
Baghdad, Iraq9.514.438.962.92.9−69.3−95.4
Bangkok, Thailand64.162.482.0208.628.1−56.3−86.6
Beijing, China113.4261.7200.1575.246.2−59.3−92.0
Berlin, Germany18.810.828.558.16.4−66.0−89.0
Bogotá, Colombia18.321.022.561.76.3−65.3−89.7
Bologna, Italy3.22.63.69.40.9−71.3−90.2
Bucharest, Romania3.611.25.520.31.2−67.5−94.3
Buenos Aires, Argentina52.831.569.9154.119.8−62.5−87.2
Cairo, Egypt43.377.070.7191.015.9−63.2−91.7
Calgary, Canada17.52.329.449.25.6−68.0−88.6
Cape Town, South Africa29.816.184.3130.312.9−56.7−90.1
Caracas, Venezuela5.13.410.318.82.0−61.3−89.4
Casablanca, Morocco4.45.08.117.41.7−62.2−90.5
Chicago, USA48.717.270.9136.817.8−63.5−87.0
Delhi, India48.6245.198.8392.625.6−47.4−93.5
Dhaka, Bangladesh11.468.515.195.03.7−67.7−96.1
Dubai, United Arab Emirates63.15.969.1138.123.9−62.0−82.7
Edmonton, Canada16.12.127.045.15.2−68.0−88.6
Guayaquil, Ecuador4.23.07.614.81.5−65.1−90.0
Hanoi, Vietnam11.015.421.948.27.5−31.6−84.5
Havana, Cuba3.47.56.517.31.9−44.0−89.1
Ho Chi Minh City, Vietnam18.025.336.079.212.3−31.6−84.5
Houston, USA50.817.974.0142.618.5−63.5−87.0
Ibiza, Spain0.20.10.30.60.1−68.8−88.4
Istanbul, Turkey35.851.871.7159.413.9−61.3−91.3
Jakarta, Indonesia17.842.428.488.77.2−59.4−91.8
Karachi, Pakistan15.762.521.199.25.6−63.9−94.3
Kiev, Ukraine9.817.517.044.43.6−63.3−91.9
Kinshasa, Congo10.447.717.975.91.8−82.9−97.7
Kyoto, Japan4.83.29.317.22.1−56.8−88.1
Lagos, Nigeria16.1108.26.9131.32.6−83.9−98.0
Lima, Peru16.229.622.468.25.6−65.5−91.8
London, United Kingdom39.924.043.8107.711.9−70.3−89.0
Los Angeles, USA67.423.898.2189.524.6−63.5−87.0
Madrid, Spain27.213.832.373.48.5−68.8−88.4
Mexico City, Mexico56.640.492.4189.320.2−64.4−89.3
Montevideo, Uruguay4.62.73.711.02.6−43.7−76.3
Montreal, Canada38.55.064.5108.112.3−68.0−88.6
Moscow, Russia99.177.2170.1346.423.8−76.0−93.1
Mumbai, India25.1126.451.0202.513.2−47.4−93.5
Nairobi, Kenya4.25.12.712.00.9−78.1−92.3
New York City, USA102.036.0148.5286.537.2−63.5−87.0
Oslo, Norway8.02.09.119.13.8−52.3−80.1
Palma, Spain2.01.02.45.50.6−68.8−88.4
Paris, France38.419.141.598.915.9−58.5−83.9
Perth, Australia20.53.335.459.17.8−61.9−86.8
Philadelphia, USA32.911.648.092.612.0−63.5−87.0
Phoenix, USA34.512.250.396.912.6−63.5−87.0
Pyongyang, North Korea1.79.66.117.31.1−37.3−93.9
Quezon City, Philippines2.316.84.623.80.9−60.7−96.1
Rio de Janeiro, Brazil48.434.452.5135.322.4−53.8−83.5
Rome, Italy17.514.119.951.55.0−71.3−90.2
San Jose, USA10.93.815.830.64.0−63.5−87.0
San José, Costa Rica2.01.92.46.31.0−50.0−84.2
Santiago, Chile25.315.038.879.114.5−42.6−81.7
São Paulo, Brazil50.636.054.8141.423.4−53.8−83.5
Seoul, South Korea65.921.9114.9202.637.8−42.6−81.4
Shanghai, China145.2335.1256.2736.559.1−59.3−92.0
Shenzhen, China64.1147.8113.0324.926.1−59.3−92.0
Sydney, Australia45.17.277.8130.117.2−61.9−86.8
Tashkent, Uzbekistan4.45.46.516.31.3−70.7−92.1
Tehran, Iran53.218.488.6160.218.0−66.1−88.7
Tokyo, Japan138.592.1271.6502.259.9−56.8−88.1
Toronto, Canada61.37.9102.7171.919.6−68.0−88.6
Ulaanbaatar, Mongolia4.910.820.636.31.5−68.3−95.8
Vancouver, Canada25.23.342.270.68.1−68.0−88.6
Vienna, Austria12.56.116.034.64.5−64.1−87.0
Yangon, Myanmar3.120.03.927.01.2−62.5−95.7
Yixing, China2.14.93.710.70.9−59.3−92.0
Zurich, Switzerland5.73.16.515.22.4−56.8−83.9
All metropolitan areas2205255435298288859−61.1−89.6
The 2050 BAU annual energy cost is the 2050 BAU LCOE from Table 7 multiplied by the 2050 BAU end-used load from Table 1 and 8760 h per year. The 2050 BAU air pollution cost per year is the 2050 air pollution cost from energy in the country each metropolitan area resides in, from [33], multiplied by the metropolitan area-to-country population ratio. The 2050 BAU climate cost per year is derived by multiplying the 2050 climate cost to the world from energy emissions in the country that each metropolitan area in, from [33], multiplied by the metropolitan area-to-country population ratio. The climate cost due to the country’s emissions assume a 2050 mid-value of the social cost of carbon (SCC) from Table S18 of [33] of $500/tonne-CO2e.
Table
Table 9. (a) Year 2050 estimated population by metropolitan area (bold indicates a megacity, whose population exceeds 10 million). Year 2050 (b) high, (c) mean, and (d) low avoided air pollution premature mortalities by metropolitan area due to transitioning to 100% WWS. (e) Mean avoided air pollution cost (from avoided mortalities, morbidities, and non-air pollution effects) per unit BAU-energy from all sectors due to converting each metropolitan area to 100% WWS for all energy purposes. (f) 2017 estimated percent of global energy-related carbon-dioxide-equivalent (CO2e) emissions due to the metropolitan area. 2050. (g) 2050 mean avoided climate-change costs to the world per unit BAU-energy from all sectors due to converting each metropolitan area to 100% WWS for all energy purposes. All costs are in 2013 USD.
Table 9. (a) Year 2050 estimated population by metropolitan area (bold indicates a megacity, whose population exceeds 10 million). Year 2050 (b) high, (c) mean, and (d) low avoided air pollution premature mortalities by metropolitan area due to transitioning to 100% WWS. (e) Mean avoided air pollution cost (from avoided mortalities, morbidities, and non-air pollution effects) per unit BAU-energy from all sectors due to converting each metropolitan area to 100% WWS for all energy purposes. (f) 2017 estimated percent of global energy-related carbon-dioxide-equivalent (CO2e) emissions due to the metropolitan area. 2050. (g) 2050 mean avoided climate-change costs to the world per unit BAU-energy from all sectors due to converting each metropolitan area to 100% WWS for all energy purposes. All costs are in 2013 USD.
Metropolitan Area(a)
2050 Population		(b)
2050
High
Avoided Premature
Mortalities/yr		(c)
2050
Mean Avoided Premature Mortalities/yr		(d)
2050
Low
Avoided Premature Mortalities/yr		(e)
2050
Mean Avoided Air Pollution Cost ¢/kWh-BAU-All-Energy		(f)
2017
Percent of Global CO2 Emissions		(g)
2050
Mean Avoided
Climate Change
Cost ¢/kWh-BAU-All-Energy
Abidjan, Côte d’Ivoire8431,50389227658644157.10.0089.8
Addis Ababa, Ethiopia8325,96252664556391431.30.0012.5
Ankara, Turkey8030,10527162265189314.40.09519.9
Auckland, New Zealand2356,9942992011251.60.04710.8
Baghdad, Iraq10,024,20127402227187817.50.09947.0
Bangkok, Thailand16,781,43111,1838582624311.10.18814.6
Beijing, China35,979,01437,97330,09222,26121.40.83716.4
Berlin, Germany3867,994139410317566.20.12016.4
Bogotá, Colombia18,135,9324965377528829.30.0679.9
Bologna, Italy935,4353712752018.90.01512.5
Bucharest, Romania1592,9181969115473830.20.02014.9
Buenos Aires, Argentina19,168,0446001435331656.20.21013.7
Cairo, Egypt31,755,77919,05714,58812,20520.40.16618.7
Calgary, Canada2472,0043292261451.10.10313.8
Cape Town, South Africa7508,6573416275722275.20.19827.4
Caracas, Venezuela3053,6427135504365.50.03116.7
Casablanca, Morocco4651,7241491114590711.90.01919.2
Chicago, USA9776,493201614509853.70.33015.2
Delhi, India51,860,32852,85545,22637,21348.80.21419.7
Dhaka, Bangladesh36,712,29626,92523,66520,64170.80.03515.6
Dubai, United Arab Emirates4620,0005814673891.10.17613.0
Edmonton, Canada2269,2843022081331.10.09513.8
Guayaquil, Ecuador4363,8558735944146.40.02316.3
Hanoi, Vietnam9136,63044093628295312.90.05018.4
Havana, Cuba2038,2212147107958326.50.01923.0
Ho Chi Minh City, Vietnam15,013,38472445961485312.90.08218.4
Houston, USA10,193,4002102151210273.70.34415.2
Ibiza, Spain76,253181285.50.00112.9
Istanbul, Turkey25,497,66786247193601114.40.30319.9
Jakarta, Indonesia14,300,69888547105547924.70.06516.6
Karachi, Pakistan25,485,19321,16118,00115,07840.20.04813.5
Kiev, Ukraine3592,43737212871214417.10.06116.6
Kinshasa, Congo26,264,11914,34512,39910,45841.10.04215.4
Kyoto, Japan1448,3075193672577.20.05021.2
Lagos, Nigeria24,681,41929,67225,61821,46473.00.0164.7
Lima, Peru15,903,09369955652449616.90.06712.7
London, United Kingdom12,432,1593368241517466.70.18512.2
Los Angeles, USA13,540,3362792200813643.70.45715.2
Madrid, Spain9107,7222117149010115.50.13612.9
Mexico City, Mexico27,241,8956798552545027.90.26118.1
Montevideo, Uruguay1997,0465623862675.30.0117.3
Montreal, Canada5432,0877244983191.10.22713.8
Moscow, Russia16,482,52510,832831463958.00.74317.5
Mumbai, India26,749,08327,26223,32719,19448.80.11119.7
Nairobi, Kenya8671,96825122180182112.90.0066.8
New York City, USA20,472,0164222303720633.70.69015.2
Oslo, Norway1452,4252451661011.70.0387.5
Palma, Spain678,064158111755.50.01012.9
Paris, France12,933,8032669195214324.70.17510.1
Perth, Australia3055,7484713202171.60.11817.9
Philadelphia, USA6614,93813649816673.70.22315.2
Phoenix, USA6926,976142810276983.70.23415.2
Pyongyang, North Korea3547,97657224960408042.30.01426.6
Quezon City, Philippines4776,17342243519272676.00.01121.0
Rio de Janeiro, Brazil28,484,1717457541840306.10.1509.2
Rome, Italy5118,6662033150510988.90.08412.5
San Jose, USA2183,4044503242203.70.07415.2
San José, Costa Rica1946,8354243232417.80.0079.9
Santiago, Chile8421,9012493178912815.70.10914.6
São Paulo, Brazil29,755,0507789565942106.10.1579.2
Seoul, South Korea10,151,8773099210415753.40.44017.7
Shanghai, China46,069,71048,62338,53228,50521.41.07216.4
Shenzhen, China20,321,31921,44816,99612,57321.40.47316.4
Sydney, Australia6726,77910367054771.60.26017.9
Tashkent, Uzbekistan3101,3841362102578513.00.02315.7
Tehran, Iran12,037,0893149258321284.00.22519.3
Tokyo, Japan42,200,30415,11210,69974937.21.45021.2
Toronto, Canada8642,21111527925081.10.36213.8
Ulaanbaatar, Mongolia2858,56920251713142221.90.04741.9
Vancouver, Canada3550,6334733252091.10.14913.8
Vienna, Austria2517,2277885844064.20.06711.1
Yangon, Myanmar7995,35065545704491155.60.00910.9
Yixing, China669,58670756041421.40.01616.4
Zurich, Switzerland1800,2433772691874.20.0278.9
All metropolitan areas892,969,664506,188408,270322,35811.513.1015.8
Metropolitan area populations between 2000 and 2020 were obtained from [3]. The full trend was then extrapolated to 2050. Avoided air pollution mortalities are calculated from country values determined in [33], then scaled by the metropolitan area-to-country population ratio. Mean ¢/kWh-BAU-all-energy equals the mean avoided annual air pollution cost from Table 8 divided by the total (all-sector) BAU end-use energy in 2050 (which equals the annual-average end-use BAU power demand from Table 1 multiplied by 8760 h/year). CO2e emissions are estimated from country energy-related CO2 emissions [54] scaled by population to give metropolitan area emissions, then adjusted for non-CO2 climate-relevant emissions, as described in [55]. Emissions are then projected to 2050 as in [33]. The avoided climate cost per unit energy is the annual mean BAU climate cost from Table 8 divided by the kWh of all energy consumed per year in the metropolitan area in the BAU case from Table 1.
Table
Table 10. Estimated numbers of 2050 new long-term, full-time (a) construction and (b) operation jobs produced due to converting to 100% WWS. (c) Job losses due to the transition. (d) Long-term, full-time construction plus operation jobs produced minus jobs lost. Annual earnings corresponding to new (e) construction and (f) operation jobs produced. (g) Net earnings from new construction plus operation jobs produced minus jobs lost due to converting to 100% WWS. Costs are in 2013 USD.
Table 10. Estimated numbers of 2050 new long-term, full-time (a) construction and (b) operation jobs produced due to converting to 100% WWS. (c) Job losses due to the transition. (d) Long-term, full-time construction plus operation jobs produced minus jobs lost. Annual earnings corresponding to new (e) construction and (f) operation jobs produced. (g) Net earnings from new construction plus operation jobs produced minus jobs lost due to converting to 100% WWS. Costs are in 2013 USD.
Metropolitan Area(a)
New Long-Term, Full-Time Construction Jobs		(b)
New Plus Existing Long-Term, Full-Time Operation Jobs		(c)
Job Losses in Fossil-Fuel, Biofuel, and Nuclear Energy Industries		(d)
Net Jobs: Long-Term, Full-Time Net Construction Plus Operation Jobs Created Minus Jobs Lost		(e)
Annual Earnings from New Construction Jobs ($bil/yr)		(f)
Annual Earnings from New and Existing Operation Jobs
($bil/yr)		(g)
Net Annual Earnings from New Construction Plus New + Existing Operation Jobs Minus Jobs Lost
($bil/yr)
Abidjan, Côte d’Ivoire5667540623,194−12,1200.160.15−0.34
Addis Ababa, Ethiopia2032186712,037−81380.040.04−0.17
Ankara, Turkey12,89011,907910515,6920.810.750.99
Auckland, New Zealand12,74511,99418,14365961.291.220.67
Baghdad, Iraq10,77010,02464,321−43,5270.610.56−2.45
Bangkok, Thailand107,220103,81788,152122,8846.806.597.80
Beijing, China93,76188,75176,805105,7077.366.978.30
Berlin, Germany14,07515,47215,54614,0011.431.581.43
Bogotá, Colombia34,86134,58360,63688091.691.680.43
Bologna, Italy22612259230522150.200.200.19
Bucharest, Romania3221294252848800.290.270.08
Buenos Aires, Argentina43,27337,86366,61714,5192.732.390.92
Cairo, Egypt38,84733,94461,85910,9331.801.570.51
Calgary, Canada11,524999635,471−13,9521.100.96−1.33
Cape Town, South Africa31,56230,37044,88717,0441.611.540.87
Caracas, Venezuela10,43410,57521,180−1710.560.56−0.01
Casablanca, Morocco51984476462650480.210.180.20
Chicago, USA33,88334,33450,97617,2404.234.292.15
Delhi, India77,22171,59777,47771,3413.663.403.39
Dhaka, Bangladesh31,35033,78025,90039,2300.951.021.18
Dubai, United Arab Emirates61,02192,554108,10445,4718.5412.956.36
Edmonton, Canada10,579917632,563−12,8081.010.88−1.22
Guayaquil, Ecuador7006747814,522−380.310.330.00
Hanoi, Vietnam28,31327,11821,47133,9591.101.051.32
Havana, Cuba56045010431263020.340.300.38
Ho Chi Minh City, Vietnam46,52444,56035,28155,8021.811.732.17
Houston, USA35,32835,79853,15017,9764.414.472.25
Ibiza, Spain1711661731640.010.010.01
Istanbul, Turkey40,93037,80928,91249,8262.572.373.13
Jakarta, Indonesia31,02226,19039,04518,1671.611.360.94
Karachi, Pakistan26,23623,73334,92815,0400.890.800.51
Kiev, Ukraine10,189965411,42284200.540.510.45
Kinshasa, Congo19,46119,012148,562−110,0890.710.69−3.99
Kyoto, Japan51396198342879080.400.480.61
Lagos, Nigeria17,45214,95462,911−30,5040.680.58−1.18
Lima, Peru24,96726,05633,18717,8361.151.200.82
London, United Kingdom27,72739,45640,42626,7582.613.712.52
Los Angeles, USA46,92847,55270,60223,8785.865.942.98
Madrid, Spain20,42319,82620,72019,5291.741.691.67
Mexico City, Mexico58,73555,27083,59230,4133.753.521.94
Montevideo, Uruguay79757301952357540.480.440.35
Montreal, Canada25,32321,96577,947−30,6582.422.10−2.93
Moscow, Russia56,54250,701160,801−53,5594.844.34−4.59
Mumbai, India39,83036,92939,96236,7971.891.751.75
Nairobi, Kenya4331387919,307−11,0970.120.10−0.30
New York City, USA70,95271,895106,74536,1028.868.984.51
Oslo, Norway5015644753,418−41,9560.650.84−5.44
Palma, Spain15211476154314540.130.130.12
Paris, France37,43134,34736,06135,7173.453.163.29
Perth, Australia20,72021,25036,02859422.032.080.58
Philadelphia, USA22,92623,23134,49111,6652.862.901.46
Phoenix, USA24,00724,32736,11812,2153.003.041.53
Pyongyang, North Korea46584929526943180.110.120.11
Quezon City, Philippines35662826358528070.150.120.12
Rio de Janeiro, Brazil81,89183,96296,05969,7944.514.623.84
Rome, Italy12,37412,36412,61512,1231.071.071.05
San Jose, USA7567766811,38538500.950.960.48
San José, Costa Rica32163228343430090.160.170.15
Santiago, Chile31,20327,38737,56921,0212.342.051.58
São Paulo, Brazil85,54587,708100,34572,9084.714.834.02
Seoul, South Korea105,988147,89744,311209,57410.6914.9121.13
Shanghai, China120,058113,64298,346135,3549.438.9310.63
Shenzhen, China52,95750,12743,38059,7044.163.944.69
Sydney, Australia45,61246,77879,31013,0804.464.571.28
Tashkent, Uzbekistan458641088600940.210.190.00
Tehran, Iran49,37045,38588,67160843.062.820.38
Tokyo, Japan149,729180,58399,883230,42911.6114.0017.87
Toronto, Canada40,28734,946124,009−48,7763.853.34−4.66
Ulaanbaatar, Mongolia7334611018,290−48460.400.33−0.26
Vancouver, Canada16,55214,35750,949−20,0401.581.37−1.92
Vienna, Austria14,40715,06815,07314,4031.461.531.46
Yangon, Myanmar5723534814,750−36800.190.18−0.13
Yixing, China17451652142919670.140.130.15
Zurich, Switzerland68576702635972000.800.790.84
All metropolitan areas2274,3482310,0463187,3981396,996174184110
A temporary construction job is a full-time equivalent (FTE) job (one that provides 2080 h per year of work) required for building infrastructure for one year. A long-term construction job is defined as the number of consecutive temporary one-year construction jobs for L years to replace 1/L of the total nameplate capacity of an energy device every year, all divided by L years, where L is the average facility life. By way of example, suppose 40 GW of nameplate capacity of an energy technology must be installed over 40 years, which is also the lifetime of the technology. Also, suppose the installation of 1 MW creates 40 one-year construction jobs (direct, indirect, and induced jobs). In that case, 1 GW of wind is installed each year and 40,000 one-year construction jobs are required each year. Thus, over 40 years, 1.6 million one-year jobs are required. This is equivalent to 40,000 40-year jobs. After the technology life of 40 years, 40,000 more one-year jobs are needed continuously each year in the future. As such, the 40,000 construction jobs are long-term jobs. Long-term operation jobs are full-time jobs that last as long as the energy facility lasts and that are needed to manage, operate, and maintain an energy generation facility. In a 100% WWS system, long-term jobs are effectively indefinite because, once a plant is decommissioned, another one must be built to replace it. The new plant requires additional construction and operation jobs. Monetary values are in 2013 USD. Calculations are based on individual country job and monetary changes from [33] (after removing jobs created due to generators beyond those needed to meet annual average load). The calculated number, for each country that a metropolitan area resides in, is scaled by the 2050 metropolitan area-to-country population ratio and by the LCOE that results from keeping the grid stable (Table 7, Column (e)) to the LCOE that results from meeting annual average load (Table 7, Column (d)). The job change numbers are across all energy sectors. Construction jobs are for new WWS devices only. Operation jobs are for new and existing devices. The jobs created account for new jobs in the electricity, heat, cold, and hydrogen generation, storage, and transmission (including high-voltage direct current transmission) industries. By accounting for the LCOE ratio of keeping the grid stable to meeting annual average load, the job change numbers also attempt to account for jobs created for building additional electricity and heat generators beyond those needed to meet annual average load; electricity, heat, cold, and hydrogen storage; and additional transmission and distribution lines. They do not account for changes in the numbers of jobs due to the production of electric appliances and machines or due to increasing building energy efficiency. Job losses are due to eliminating jobs for mining, transporting, processing, and using fossil fuels, biofuels, and uranium. Fossil-fuel jobs due to non-energy uses of petroleum (e.g., lubricants, asphalt, petrochemical feedstock, and petroleum coke) are retained. For transportation sectors, the jobs lost are those due to transporting fossil fuels (e.g., through truck, train, barge, ship, or pipeline). The jobs not lost are solely those for transporting other goods. The table does not account for jobs lost in the manufacture of combustion appliances, including the manufacture of automobiles, ships, or industrial machines.

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Jacobson, M.Z.; von Krauland, A.-K.; Burton, Z.F.M.; Coughlin, S.J.; Jaeggli, C.; Nelli, D.; Nelson, A.J.H.; Shu, Y.; Smith, M.; Tan, C.; et al. Transitioning All Energy in 74 Metropolitan Areas, Including 30 Megacities, to 100% Clean and Renewable Wind, Water, and Sunlight (WWS). Energies 2020, 13, 4934. https://doi.org/10.3390/en13184934

AMA Style

Jacobson MZ, von Krauland A-K, Burton ZFM, Coughlin SJ, Jaeggli C, Nelli D, Nelson AJH, Shu Y, Smith M, Tan C, et al. Transitioning All Energy in 74 Metropolitan Areas, Including 30 Megacities, to 100% Clean and Renewable Wind, Water, and Sunlight (WWS). Energies. 2020; 13(18):4934. https://doi.org/10.3390/en13184934

Chicago/Turabian Style

Jacobson, Mark Z., Anna-Katharina von Krauland, Zachary F.M. Burton, Stephen J. Coughlin, Caitlin Jaeggli, Daniel Nelli, Alexander J. H. Nelson, Yanbo Shu, Miles Smith, Chor Tan, and et al. 2020. "Transitioning All Energy in 74 Metropolitan Areas, Including 30 Megacities, to 100% Clean and Renewable Wind, Water, and Sunlight (WWS)" Energies 13, no. 18: 4934. https://doi.org/10.3390/en13184934

APA Style

Jacobson, M. Z., von Krauland, A. -K., Burton, Z. F. M., Coughlin, S. J., Jaeggli, C., Nelli, D., Nelson, A. J. H., Shu, Y., Smith, M., Tan, C., Wood, C. D., & Wood, K. D. (2020). Transitioning All Energy in 74 Metropolitan Areas, Including 30 Megacities, to 100% Clean and Renewable Wind, Water, and Sunlight (WWS). Energies, 13(18), 4934. https://doi.org/10.3390/en13184934

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