The Earth is approaching 1.5°C global warming, air pollution kills over 7 million people yearly, and limited fossil fuel resources portend social instability. Rapid solutions are needed. We provide Green New Deal roadmaps for all three problems for 143 countries, representing 99.7% of world’s CO 2 emissions. The roadmaps call for countries to move all energy to 100% clean, renewable wind-water-solar (WWS) energy, efficiency, and storage no later than 2050 with at least 80% by 2030. We find that countries and regions avoid blackouts despite WWS variability. Worldwide, WWS reduces energy needs by 57.1%, energy costs from $17.7 to $6.8 trillion/year (61%), and social (private plus health plus climate) costs from $76.1 to $6.8 trillion/year (91%) at a capital cost of ∼$73 trillion. WWS creates 28.6 million more long-term, full-time jobs than are lost and needs only 0.17% and 0.48% of land for footprint and space, respectively. Thus, WWS needs less energy, costs less, and creates more jobs than current energy.

Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries. The roadmaps call for a 100% transition of all-purpose business-as-usual (BAU) energy to wind-water-solar (WWS) energy, efficiency, and storage by 2050 with at least 80% by 2030. Our studies on grid stability find that the countries, grouped into 24 regions, can match demand exactly from 2050 to 2052 with 100% WWS supply and storage. We also derive new cost metrics. Worldwide, WWS energy reduces end-use energy by 57.1%, aggregate private energy costs from $17.7 to $6.8 trillion/year (61%), and aggregate social (private plus health plus climate) costs from $76.1 to $6.8 trillion/year (91%) at a present value capital cost of ∼$73 trillion. WWS energy creates 28.6 million more long-term, full-time jobs than BAU energy and needs only ∼0.17% and ∼0.48% of land for new footprint and spacing, respectively. Thus, WWS requires less energy, costs less, and creates more jobs than does BAU.

This paper provides GND energy roadmaps for transitioning 143 countries, representing more than 99.7% of global fossil fuel COemissions, to 100% WWS energy for all energy purposes (which include electricity, transportation, building heating and cooling, industry, agriculture, forestry, fishing, and the military; Note S28 ). The proposed transition timeline is no less than 80% WWS energy by 2030 and 100% by no later than 2050 ( Figure S1 ) worldwide. The paper also provides analyses of grid stability for 24 world regions encompassing the 143 countries ( Table 1 ). Because the 100% clean, renewable, and zero-emission energy goals of the present study are the same as those of the US GND, but with an adjusted timeline, the present study can help to evaluate the costs and feasibility of the energy component of not only the US GND but also the GNDs of 142 other countries. The US GND contains additional proposed legislation related to jobs, health care, education, and social justice.The present study does not fully evaluate the costs or merits of these other components. However, because the energy transitions outlined here benefit air-pollution health, climate, and jobs, this work partly addresses some of these components. In this study, we evaluate results considering both private and social costs in terms of (1) the cost per unit end-use energy and (2) the cost aggregated over all end-use energy (“aggregate” cost). New cost metrics are provided. At the end, we discuss uncertainties and sensitivities as well as differences between the present study and two recent studies that argue that using 100% renewables for electricity is not feasible at low cost.

In 2009, Jacobson and Delucchicalculated that transitioning the world’s all-purpose energy to 100% WWS energy by 2030 could be technically and economically feasible, but for social and political reasons, a complete transition by 2030 was unlikely and could take up to a couple of decades longer. Subsequent roadmapsproposed an 80% transition by 2030 and a 100% transition by no later than 2050 (e.g., Figure S1 ). The energy portion of the Green New Deal (GND) proposed in the US Congressand earlier versions of itadopted Jacobson and Delucchi’s “technically and economically feasible” 2030 deadline and “100% clean, renewable, and zero-emission energy sources” goal.

Matching demand with supply at low cost among 139 countries within 20 world regions with 100 percent intermittent wind, water, and sunlight (WWS) for all purposes.

Third, most analyses look at the cost per unit energy rather than the aggregate energy cost per year. This problem is significant because a WWS system uses much less end-use energy than does a BAU system.

Second, many studies have not compared the cost of WWS energy with that of BAU energy. As such, determining the magnitude of the benefit of one over the other is difficult. Differences between WWS and BAU energy are masked even more when a private-cost analysis, which ignores health and climate costs, is performed instead of a social-cost analysis.

First, social (economic) costs are private market costs plus external costs not accounted for in market costs or prices. In the present context, the most relevant external costs are those due to (1) air-pollution mortality, morbidity, and non-health damage and (2) global warming damage. A social-cost analysis is more useful to policy makers than is an analysis that considers only private costs because the former gives policy makers a more complete picture of the impacts of policies that affect climate change and air pollution than does the latter.

Among the studies that find that 100% renewable energy is cost effective, many have been of limited use to policy makers because they considered only private cost and not social cost, did not compare business-as-usual (BAU) with wind-water-solar (WWS) energy, and considered only cost per unit energy and not the aggregate (summed) cost over all end-use energy used.

In an effort to solve these problems, studies among at least 11 independent research groups have found that transitioning to 100% renewable energy in one or all energy sectors, while keeping the electricity and/or heat grids stable at a reasonable cost, is possible.The reviews of Brown et al.and Diesendorf and Ellistonfurther find that critiques of 100% renewable systems are misplaced. The latter study, for example, concludes, “the main critiques published in scholarly articles and books contain factual errors, questionable assumptions, important omissions, internal inconsistencies, exaggerations of limitations and irrelevant arguments.”

Matching demand with supply at low cost among 139 countries within 20 world regions with 100 percent intermittent wind, water, and sunlight (WWS) for all purposes.

The world is beginning to transition to clean, renewable energy for all energy purposes. However, to avoid 1.5°C global warming, we must stop at least 80% of all energy and non-energy fossil fuels and biofuel emissions by 2030and stop 100% no later than 2050.Air pollution from these same sources kills 4–9 million people each year ( Figure 1 ),and this damage will continue unless the sources of air pollution are eliminated. Finally, if the use of fossil fuels is not curtailed rapidly, rising demand for increasingly scarce fossil energy will lead to economic, social, and political instability, enhancing international conflict.

2016 and projected 2050 all-cause indoor plus outdoor air-pollution mortalities per year in 24 world regions encompassing 143 countries (see Table 1 for a list of countries in each region). We obtained 2016 data by multiplying country-specific indoor plus outdoor air-pollution deaths per 100,000 people from the World Health Organizationby 2016 country population. 2050 estimates were obtained with Equation S35 in Note S39 . BAU energy is estimated to be responsible for 90% of the mortalities in this figure (most of the rest are from open biomass burning, wildfires, and dust). See Table S15 for a breakdown of 2016 world air-pollution deaths by cause.

Matching demand with supply at low cost among 139 countries within 20 world regions with 100 percent intermittent wind, water, and sunlight (WWS) for all purposes.

Matching demand with supply at low cost among 139 countries within 20 world regions with 100 percent intermittent wind, water, and sunlight (WWS) for all purposes.

Results and Discussion

Table 2 Reduced End-Use Demand upon a Transition from BAU to WWS Energy Scenario Total End-Use Demand Percentage of Total 2050 Change in Demand Total 2050 Change in Demand Due to Switching to WWS Residential Commercial Industrial Transport Agriculture, Forestry, and Fishing Military and Other Due to Higher WWS Work/Energy Ratio Due to Eliminating Upstream Emissions with WWS Due to Greater Efficiency with WWS Than with BAU BAU 2016 12,628 GW 21.1% 8.13% 38.4% 28.7% 2.1% 1.5% – – – – BAU 2050 20,255 GW 19.1% 7.80% 37.4% 32.3% 1.9% 1.5% – – – – WWS-A 2050 a a Case WWS-A eliminates the energy used for mining, transporting, and refining fossil fuels and uranium and increases energy efficiency beyond that of BAU energy (change all values for extra efficiency in Table S1 to current values from unity), but it does not change the work-output-to-energy-input ratio relative to that of BAU energy. It assumes that the efficiency of electrification is the same as that of fossil fuels (leave the electricity-to-fuel ratio = 1 for all fuels in all sectors in Table S1 ). 15,932 GW 20.2% 8.50% 34.9% 32.6% 2.2% 1.6% 0% −13.7% −7.6% −21.3% WWS-B 2050 b b Case WWS-B is the same as WWS-A, except that it includes the higher work-output-to-energy-input ratio of electric vehicles and hydrogen-fuel-cell vehicles powered by WWS energy over internal-combustion vehicles (reduce the electricity-to-fuel ratios from 1 to their current values for oil, natural gas, biofuels, and waste in the transportation sector and for oil in the agriculture, forestry, and fishing sector, military sector, and other sectors in Table S1 ). 11,968 GW 27.0% 11.3% 46.4% 11.8% 1.6% 1.9% −21.7% −12.4% −6.8% −40.9% WWS-C 2050 c c Case WWS-C is the same as WWS-B, except that it accounts for the higher work-output-to-energy-input ratio of high-temperature industrial processes with WWS energy (reduce the electricity-to-fuel ratios from 1 to their current values for oil, natural gas, coal, biofuels, and waste in the industrial sector in Table S1 ). 11,294 GW 28.6% 12.0% 43.2% 12.5% 1.7% 2.0% −25.1% −12.3% −6.8% −44.2% WWS-D 2050 d d Case WWS-D is the same as WWS-C, except that it accounts for the higher work-output-to-energy-input ratio of heat pumps over internal-combustion heating for low-temperature heat (reduce the electricity-to-fuel ratios from 1 to their current values for all remaining values below 1 in Table S1 : namely, oil, natural gas, coal, biofuels, and waste in the residential and commercial sectors; heat for sale in all sectors; natural gas, coal, biofuels, and waste in the agriculture, forestry, and fishing sector, military sector, and other sectors). 8,693 GW 17.7% 10.5% 52.0% 16.2% 1.7% 1.8% −38.3% −12.1% −6.6% −57.1% This table shows annually averaged end-use power demand for 2016 BAU, 2050 BAU, and 2050 100% WWS energy by sector, summed among the 143 countries in Table 1 . The last column shows the total percent reduction in 2050 BAU end-use power demand due to switching from BAU to WWS energy, including the effects of reduced energy use caused by the higher work-output-to-energy-input ratio of electricity over combustion; eliminating energy used for mining, transporting, and/or refining coal, oil, natural gas, biofuels, bioenergy, and uranium; and assumed policy-driven increases in end-use energy efficiency beyond those in the BAU case. Four 2050 WWS cases are shown: WWS-A, WWS-B, WWS-C, and WWS-D. The result indicates that, of the 38.3% demand reduction due to the higher work-output-to-energy-input ratio of electricity over combustion, 21.7, 3.4, and 13.2 percentage points are due to the efficiency of WWS transportation, the efficiency of WWS electricity for industrial heat, and the efficiency of heat pumps, respectively. Table S2 shows rows “BAU 2050” and “WWS-D 2050” by country. Note S28 defines sectors. We first projected 2016 end-use BAU energy in multiple energy sectors in 143 countries to 2050 ( Note S3 ). 2050 BAU end-use energy loads were then electrified, the electricity for which was provided by WWS energy ( Notes S4–S12 ). Table 2 and Figure S1 indicate that transitioning from BAU to WWS energy in 143 countries reduces 2050 annual average demand for end-use power (defined in Note S3 ) by 57.1% (case WWS-D in Table 2 ). Of this, 38.3 percentage points are due to the efficiency of using WWS electricity over combustion; 12.1 percentage points are due to eliminating energy in the mining, transporting, and refining of fossil fuels; and 6.6 percentage points are due to improvements in end-use energy efficiency and reduced energy use beyond those in the BAU case. Of the 38.3% reduction due to the efficiency advantage of WWS electricity, 21.7 percentage points are due to the efficiency advantage of WWS transportation, 3.4 percentage points are due to the efficiency advantage of WWS electricity for industrial heat, and 13.2 percentage points are due to the efficiency advantage of heat pumps.

Initial estimates of nameplate capacities needed to meet annual average load were then derived for each of the 143 countries ( Note S13 ). The 143 countries were subsequently grouped into 24 world regions ( Table 1 ). LOADMATCH was next run from 2050 to 2052 with 30 s timesteps to match all-sector demand with supply in each region. For each region, the initial inputs were adjusted for each simulation until a zero-load-loss solution was found among all timesteps, typically within ten simulation attempts. After one successful simulation, we ran the model another 4–20 simulations, with further adjustments, to find additional lower-cost solutions. Thus, multiple zero-load loss solutions were obtained for each region, but only the lowest-cost solution is presented here. Tables S20 and S21 provide the final generator nameplate capacities and capacity factors, respectively, in each region. Table S11 provides the final storage characteristics.

Table 3 Nameplate Capacities Needed by Generator Type for 100% WWS Energy Energy Technology (A) Nameplate Capacity of One Plant or Device (B) 2050 All-Purpose Annual Average Demand Met by Plant or Device (C) Initial Nameplate Capacity: Existing plus New Plants or Devices to Meet Annual Average Demand (D) Final Nameplate Capacity: Existing plus New Plants or Devices to Meet Time-Dependent Demand (E) Percentage of Final Nameplate Capacity Already Installed by 2018 (F) Final Numbers of New Plants or Devices Needed for 143 Countries Annual Average Power Onshore wind turbine 5 MW 30.50% 8,251 GW 11,976 GW 4.76% 2,281,019 Offshore wind turbine 5 MW 14.51% 3,841 GW 3,606 GW 0.68% 716,252 Wave device 0.75 MW 0.34% 156 GW 156 GW 0.0001% 208,313 Geothermal electricity 100 MW 0.92% 97 GW 97 GW 13.67% 837 Hydropower plant a a No increase in the number of dams or in the peak discharge rate of hydropower is assumed. 1,300 MW 5.72% 1,109 GW 1,109 GW 100.0% 0 Tidal turbine 1 MW 0.08% 31 GW 31 GW 1.76% 30,075 Residential rooftop PV 0.005 MW 11.14% 5,082 GW 2,776 GW 3.44% 536,080,000 Commercial or governmental rooftop PV b b 4 Jacobson M.Z.

Delucchi M.A.

Bauer Z.A.F.

Goodman S.C.

Chapman W.E.

Cameron M.A.

Bozonnat C.

Chobadi L.

Clonts H.A.

Enevoldsen P.

et al. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world. The solar PV panels used for this calculation were SunPower E20 panels. A CSP plant is assumed to have storage with a maximum charge-discharge rate (ratio of storage size to generator size) of 2.62:1. See the footnotes in Table S7 of Jacobson et al. 0.1 MW 13.84% 6,705 GW 5,121 GW 1.87% 50,250,000 Utility PV plant b b 4 Jacobson M.Z.

Delucchi M.A.

Bauer Z.A.F.

Goodman S.C.

Chapman W.E.

Cameron M.A.

Bozonnat C.

Chobadi L.

Clonts H.A.

Enevoldsen P.

et al. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world. The solar PV panels used for this calculation were SunPower E20 panels. A CSP plant is assumed to have storage with a maximum charge-discharge rate (ratio of storage size to generator size) of 2.62:1. See the footnotes in Table S7 of Jacobson et al. 50 MW 19.03% 8,234 GW 13,691 GW 2.09% 268,090 Utility CSP plant b b 4 Jacobson M.Z.

Delucchi M.A.

Bauer Z.A.F.

Goodman S.C.

Chapman W.E.

Cameron M.A.

Bozonnat C.

Chobadi L.

Clonts H.A.

Enevoldsen P.

et al. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world. The solar PV panels used for this calculation were SunPower E20 panels. A CSP plant is assumed to have storage with a maximum charge-discharge rate (ratio of storage size to generator size) of 2.62:1. See the footnotes in Table S7 of Jacobson et al. 100 MW 3.93% 634 GW 1,262 GW 0.43% 12,565 Total for average power – 100.00% 34,138 GW 39,842 GW 5.53% 610,045,000 For Peaking and Storage Additional CSP c c Additional CSP is the estimated CSP plus storage beyond that for annual average power generation needed to provide peaking power to stabilize the grid. Additional solar thermal and existing geothermal heat are used for direct heat or heat storage in soil. “Geothermal heat” is existing geothermal heat, which is assumed not to change in the future (hence the same values in columns C and D). 100 MW 2.36% 381 GW 0 GW 0% 0 Solar thermal heat c c Additional CSP is the estimated CSP plus storage beyond that for annual average power generation needed to provide peaking power to stabilize the grid. Additional solar thermal and existing geothermal heat are used for direct heat or heat storage in soil. “Geothermal heat” is existing geothermal heat, which is assumed not to change in the future (hence the same values in columns C and D). 50 MW – 2,573 GW 632 GW 72.6% 3,468 Geothermal heat c c Additional CSP is the estimated CSP plus storage beyond that for annual average power generation needed to provide peaking power to stabilize the grid. Additional solar thermal and existing geothermal heat are used for direct heat or heat storage in soil. “Geothermal heat” is existing geothermal heat, which is assumed not to change in the future (hence the same values in columns C and D). 50 MW – 70.3 GW 70.3 GW 100.00% 0 Total peaking and storage – 2.36% 3,024 GW 702 GW 75.31% 3,468 Total All – – 37,163 GW 40,544 GW 6.74% 610,049,000 This table shows the estimated (C) initial nameplate capacities (meeting the annual average all-purpose end-use power demand) and final (D) nameplate capacities (meeting time-dependent demand) of WWS generators, summed among 143 countries in 24 regions, needed to supply 100% of all-purpose energy with WWS energy. Also shown are (B) the 143-country-averaged percent end-use demand estimated to be supplied by the initial nameplate capacity of each generator (values for individual countries are given in Table S5 ), (E) the percentage of final 2050 nameplate capacity of each generator already installed in 2018, and (F) the final numbers of new devices of specified sizes still needed. All values are summed over 143 countries in 24 regions. “Annual average power” is annual average all-purpose energy demand divided by the number of seconds per year. The nameplate capacity of each device (A) is assumed to be the same for all countries. The percentage of annual average power demand met by each device type (B) is a demand-weighted average among the mixes given for 143 countries in Table S5 before time-dependent demand calculations are performed with LOADMATCH. The “initial” nameplate capacity (C) is equal to the total end-use demand (B) multiplied by the percentage of demand satisfied by the device and then divided by the capacity factor of the device. This initial nameplate capacity (meeting average annual demand) for each grid region is used at the start of LOADMATCH simulations. The “For Peaking and Storage” section of (C) is the initial estimate of additional CSP installations and solar thermal heat generators for the start of the LOADMATCH simulations. Column (D) shows the 143-country final nameplate capacities needed to match load after the LOADMATCH simulations for each of the 24 grid regions. Table S20 gives the final nameplate capacities for each region. Columns (D) and (E) show the fraction of final nameplate capacity already installed as of the end of 2018 and the remaining number of devices of size specified in (A) still needed, respectively. Table 3 indicates that only 9% more generator nameplate capacity is needed, in the 143-country average, to meet time-dependent load than to meet annually averaged load. Storage is also needed to meet time-dependent load ( Table S11 ).

Figure 2 3-Year LOADMATCH Results for Two World Regions Show full caption Time-series comparison, from 2050 to 2052 for two world regions, of modeled (first row) total WWS power generation versus total load plus losses plus changes in storage plus shedding; (second row) same as first row but for a window of days 400–500 during the 3-year period; (third row) a breakdown of WWS power generation by source during the window; and (fourth row) a breakdown showing inflexible load; flexible electricity, heat, and cold load; flexible hydrogen load; losses in and out of storage; transmission and distribution losses; changes in storage; and shedding. The model was run at 30-s resolution. Results are shown hourly. No load loss occurred during any 30-s interval. Figure S4 shows results for all 24 world regions. Figure 2 shows the full 3-year time series of WWS power generation versus load plus losses plus changes in storage plus shedding for two world regions. Figure S4 shows the same but for all 24 world regions. Both figures also show a distribution of WWS power generation and of load plus losses plus changes in storage plus shedding for 100 days during each time series. The figures demonstrate no load loss at any time in any region.

Figure 3 Energy Private Costs, Capital Costs, and Loads by World Region Show full caption (A) Low, mean, and high modeled levelized private costs (averaged between today and 2050 in 2013 USD) of converting 24 world regions encompassing 143 countries to 100% WWS energy for all energy purposes. (B) Annual average all-purpose end-use loads and present values (2013 USD) of mean capital costs for a 100%-WWS-energy system. See Table S22 for low and high values of levelized cost. The 2050–2052 WWS mean social cost per unit all-sector energy, when weighted by generation among all 24 regions, is 8.96 ¢/kWh-all-energy (USD 2013) ( Figure 3 A and Tables S22 and S23 ). However, Figure 3 A shows that the individual regional averages range from 6.5 ¢/kWh-all-energy (Iceland) to 13.1 ¢/kWh-all-energy (Israel). The largest portion of cost is the cost of generation, which includes capital, operation, maintenance, and decommissioning costs ( Table S14 ). In descending order, the next-largest costs are of transmission and distribution; electricity storage; hydrogen production, compression, and storage; and thermal energy storage.

Figure 3 B indicates that the overall net present value of the capital cost of transitioning all energy sectors of 143 countries to 100% WWS energy while keeping the grid stable is about $72.8 trillion (USD 2013). Individual regional costs range from $2.6 billion for Iceland to $16.6 trillion for the China region. The cost for the US is about $7.8 trillion, and that for Europe is about $6.2 trillion. These capital costs pay themselves off over time by electricity and heat sales.

Figure 4 Summary of Private and Social Costs of WWS and BAU Energy Show full caption (A) Levelized private and social costs per kWh of energy produced by region in a BAU-energy world versus a WWS-energy world. BAU costs include energy, health, and climate costs. WWS costs include only energy costs because energy external costs are approximately zero. Energy costs are averaged between today and 2050 because the WWS-energy system will be built out during this period. (B) Same as (A) but with the annual aggregate cost per year, obtained by multiplication of the cost per unit energy in (A) by the end-use energy consumption per year in the BAU or WWS case (from Table S2 ). See Table S22 for low and high annual aggregate costs of WWS energy per year. (C) The WWS-to-BAU aggregate social cost ratio and its three component factors: the WWS-to-BAU ratio of cost per unit energy (obtained from A), the ratio of private cost of BAU energy to social cost (obtained from B), and the WWS-to-BAU ratio of end-use load (e.g., from Table S2 but for each region). 90% of all air-pollution mortalities are ascribed to BAU energy. Most of the rest are ascribed to open biomass burning, wildfires, and dust. The mean and range in aggregate health cost, summed over all regions, is $30 ($17.9–$52.7) trillion/year. That in aggregate climate costs is $28.4 ($16.0–$60.5) trillion/year. All costs are in 2013 USD. Table 4 Summary of Private and Social Costs over 143 Countries Private and Social Costs Value (A) Private cost per unit BAU energy a a This is the electricity-sector cost of BAU energy per unit energy. It is assumed to equal the all-energy cost of BAU energy per unit energy. The cost per unit WWS energy is for all energy, which is almost all electricity (plus a small amount of direct heat). 9.99 ¢/kWh (B) Health cost per unit BAU energy 16.9 ¢/kWh (C) Climate cost per unit BAU energy 16.0 ¢/kWh (D) Social cost per unit BAU energy (A + B + C) 42.9 ¢/kWh (E) Private and social cost per unit WWS energy a a This is the electricity-sector cost of BAU energy per unit energy. It is assumed to equal the all-energy cost of BAU energy per unit energy. The cost per unit WWS energy is for all energy, which is almost all electricity (plus a small amount of direct heat). 8.96 ¢/kWh (F) End-use power demand of BAU energy b b Multiply GW by 8,760 h/year to obtain GWh/year. 20,255 GW (G) End-use power demand of WWS energy b b Multiply GW by 8,760 h/year to obtain GWh/year. 8,693 GW (H) Aggregate annual private cost of BAU energy in the electricity sector (A × F) $17.7 trillion/year (I) Health cost of BAU energy (B × F) $30.0 trillion/year (J) Climate cost of BAU energy (C × F) $28.4 trillion/year (K) Social cost of BAU energy (D × F) $76.1 trillion/year (L) Private and social costs of WWS energy (E × G) $6.82 trillion/year (M) WWS-to-BAU ratio of private cost per kWh (R WWS:BAU-E ) (E/A) 0.90 (N) Ratio of private cost of BAU energy (kWh) to social cost of BAU energy (kWh) (R BAU-S:E ) (A/D) 0.23 (O) Ratio of WWS energy used (kWh) to BAU energy used (kWh) (R WWS:BAU-C ) (G/F) 0.43 WWS-to-BAU ratio of aggregate social cost (R ASC ) (M × N × O) 0.09 WWS-to-BAU ratio of aggregate private cost (R APC ) (M × O) 0.39 WWS-to-BAU ratio of social cost per unit energy (R SCE ) (M × N) 0.21 This table shows the 2050 mean social costs per unit WWS versus BAU energy for 143 countries (24 world regions), as well as the WWS-to-BAU ratio of aggregate social cost and the components of its derivation ( Equation 5 ). Figure 4 and Table 4 present results from our main cost metrics. Multiplying the private cost per unit energy in Figure 4 A by the end-use energy consumed per year (or by the annual average power) in the WWS and BAU cases gives the aggregate annual private energy cost in each case, shown in Figure 4 B. Among 143 countries, the aggregate annual private energy cost is $6.8 trillion/year in the WWS case and $17.7 trillion/year in the BAU case. The main (but not only) reason for this difference is the 57.1% lower end-use energy consumption in the WWS case ( Tables 2 and 4 ).

What’s more, the aggregate annual social cost across all regions worldwide is $76.1 trillion/year in the BAU case but only $6.8 trillion/year in the WWS case ( Table 4 and Figure 4 B). Thus, the WWS-to-BAU aggregate annual social cost ratio is 9% ( Table 4 ). In other words, the aggregate annual social cost (energy plus health plus climate costs) of WWS energy is only 9% that of a BAU system each year. Figure 4 C shows the aggregate social cost ratio and its components for all 24 world regions. The ratio varies from 3.9% for the Philippines to 24.9% for Iceland. The smallest benefit of a transition occurs in Iceland simply because Iceland has already transitioned much of its energy, so its air pollution and climate emissions are already low. Thus, it sees less remaining benefit of converting than other regions.

We assumed here that the BAU cost per unit all energy equals the BAU cost per unit electricity given the lack of data on the BAU cost per unit non-electrical energy. Because the aggregate annual social and private costs in the WWS cases for all world regions are an order of magnitude lower than those in the BAU cases, we believe that assumption makes no difference to the conclusion found here, namely that WWS energy is much less expensive than BAU energy, given that the conclusion would still hold even if the assumption were off by a factor of, say, eight.

Figure 3 A indicates that the 2050 cost of WWS energy per unit energy is relatively low for large regions (e.g., Canada, Russia, Africa, China, Europe, and the US) and for small countries with good WWS resources (e.g., Iceland and New Zealand). Larger land areas permit greater geographical dispersion of wind and solar energy. Connecting these dispersed resources via the regional grid reduces overall intermittency. These regions also have a good balance of solar and wind power, which are complementary in nature seasonally. Finally, the larger regions have some existing hydropower that can provide peaking power. Iceland has substantial hydropower, geothermal, and wind power.

Costs are highest in small countries with high population densities (Taiwan, Cuba, South Korea, Mauritius, and Israel). Nevertheless, the 2050 private cost of WWS energy per year in all five regions is 43%–65% that of BAU energy, indicating that a transition to WWS energy reduces costs even under the least favorable circumstances.

Land-use impacts are represented here by footprint and spacing areas required by WWS technologies. Footprint is the physical area on the top surface of soil or water needed for each energy device. New land footprint is created only for solar photovoltaic (PV) plants, concentrated solar power (CSP) plants, onshore wind turbines, geothermal plants, and solar thermal plants. Rooftop PV does not take up new land. Spacing is the area between some devices—such as wind turbines, wave devices, and tidal turbines—needed to minimize interference of the wake of one device with downstream devices. Spacing area can be used for multiple purposes, including rangeland, ranching land, industrial land (e.g., installing solar PV panels), open space, or open water. The only spacing area over land needed in a 100% WWS world is between onshore wind turbines.

31 World Bank

Agricultural land. The total new land areas for footprint and spacing with 100% WWS energy are about 0.17% and 0.48%, respectively, for a total of 0.65% of the 143-country land area ( Note S44 Table S26 , and Figure S6 ). This is equivalent to about 1.85 times California’s land area for virtually all world energy. In comparison, about 37.4% of the world’s land was agricultural land in 2016, and 2.5% was urban area in 2010.The footprint needed for WWS energy is almost all for utility PV and CSP plants. Some of the utility PV can fit on the spacing area that wind occupies, illustrating the dual use of the wind land.

Finally, a transition could increase the net number of long-term, full-time jobs. Such jobs arise as a result of energy generation, transmission, and storage. Note S45 describes how changes in jobs are determined. The calculation accounts for direct jobs, indirect jobs, and induced jobs. Direct jobs are jobs for project development, onsite construction, onsite operation, and onsite maintenance of the electricity-generating facility. Indirect jobs are revenue and supply-chain jobs. They include jobs associated with construction material and component suppliers, analysts and attorneys who assess project feasibility and negotiate agreements, banks financing the project, all equipment manufacturers, and manufacturers of blades and replacement parts. The number of indirect manufacturing jobs is 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, for example. Job changes due to changes in energy prices are not included. Changes in energy pricing could trigger changes in factor allocations among capital, energy input, and labor and thus changes in job numbers.

Results here indicate that a transition could create about 28.6 million more long-term, full-time jobs than lost among the 143 countries ( Table S28 and Note S45 ). Net job gains occurred in 21 out of 24 world regions. Net losses occurred in regions heavily dependent on fossil fuels, namely Canada, Russia, and parts of Africa. However, additional jobs in those and other regions could result from the need to build more electrical appliances, vehicles, and machines and to increase building energy efficiency, and these jobs were not considered here.

In the US, the estimated aggregate private and social costs of BAU energy are $2.1 and $5.9 trillion/year, respectively, whereas those of WWS energy are both $0.77 trillion/year. Thus, WWS energy decreases the aggregate private cost by 64% and aggregate social cost by 87%. The social-cost reduction arises from eliminating about 63,000 US air-pollution deaths per year (in 2050) and corresponding illnesses as well as eliminating the US energy contribution to global warming.

29 US House of Representatives

H.Res. 109 – Recognizing the duty of the federal government to create a Green New Deal. The US transition to 100% WWS energy is estimated to cost a mean of $7.8 trillion in net-present-value capital but create 3.1 million net long-term full-time US jobs ( Table S28 ) and use only 0.22% of the country’s land for footprint and 0.86% for spacing ( Table S26 ). As such, a complete US transition, as also called for by the US GND,will reduce aggregate energy costs each year, reduce health-care costs and mortality, reduce climate damage, and create jobs.

Uncertainties and Sensitivities The results here contain uncertainties. Some include uncertainties arising from inconsistencies between load and resource datasets, the timing of generator and storage downtime, assuming perfect transmission, not modeling transmission congestion, not modeling frequency regulation, and projecting future energy use. Note S46 discusses these issues as well as several sensitivity tests performed here to examine uncertainties in more detail. These include cost sensitivities due to changes in the fraction of thermal loads subject to district heating and underground thermal energy storage, to changes in hydrogen storage, and to changes in demand response. One particular concern is whether the simulations here captured the variability of energy demand and wind and solar supply, including during extreme weather events. However, GATOR-GCMOM (gas, aerosol, transport, radiation, general circulation, mesoscale, and ocean model) accounts for extreme weather events because it models the variability of weather everywhere worldwide at a 30 s time resolution on the basis of physical principles. It also accounts for competition among wind turbines for available kinetic energy and the resulting feedback of such turbines to weather. Zero-load-loss results were found here every 30 s for 3 years, thus accounting for extreme weather events, in 24 vastly different world regions, each with different WWS supplies. 24 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. Another uncertainty arises from our assumption of a perfectly interconnected transmission system. Whereas the study accounts for transmission and distribution costs and losses, it assumes that electricity can flow to where it is needed without bottlenecks. This concern applies to only about half the regions examined given that 11 regions (Iceland, Cuba, Jamaica, Haiti and the Dominican Republic, Israel, Japan, Mauritius, New Zealand, the Philippines, South Korea, and Taiwan) have or could have, because of their small size, well-connected transmission and distribution systems. Stable, low-cost systems were found here for all those regions. As such, there is no reason to think that the US, for example, broken up into multiple isolated or moderately interconnected regions rather than one completely interconnected region can’t also maintain a low-cost, stable 100% WWS grid. In fact, many of the dozens of earlier cited papers that have examined 100% renewable grids have treated transmission spatially and have found low-cost solutions. Aghahosseini et al.,for example, found stable, low-cost, time-dependent electric grid solutions when North and South America were run on 100% renewables, and transmission flows were modeled explicitly among multiple lines. Although the present paper sacrifices spatial resolution needed to treat transmission explicitly, it treats time resolution (30 s) higher than other studies. 15 Jacobson M.Z.

Delucchi M.A.

Cameron M.A.

Frew B.A. Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. Finally, although the impact of transmission congestion on reliability is not modeled explicitly, Jacobson et al.ran sensitivity tests (see their Figure S13) to check how different fractions of wind and solar power subject to long-distance transmission might affect cost. The result was that, if congestion is an issue at the baseline level of long-distance transmission, increasing the transmission capacity will relieve congestion with only a modest increase in cost. Many remaining uncertainties are captured by the use of low, mean, and high costs of energy, air-pollution damage, and climate damage. Table S14 , for example, shows low, mean, and high estimates of capital cost, operation and maintenance cost, decommissioning cost, energy generator lifetimes, and transmission, distribution, and downtime losses assumed here. Table S22 and Figure 3 provide the resulting low, mean, and high levelized private costs of energy per unit energy and private aggregate costs of energy for each world region. Table S18 provides the low, mean, and high estimated social costs of carbon, and Table S16 provides the parameters needed for calculating low, mean, and high air-pollution costs. Table S17 provides the resulting low, mean, and high air-pollution and climate costs per unit energy by country.

Comparison with Studies Critical of 100% Renewables 32 Shaner M.R.

Davis S.J.

Lewis N.S.

Caldeira K. Geophysical constraints on the reliability of solar and wind power in the United States. 4 Jacobson M.Z.

Delucchi M.A.

Bauer Z.A.F.

Goodman S.C.

Chapman W.E.

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Bozonnat C.

Chobadi L.

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et al. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world. , 5 Jacobson M.Z.

Delucchi M.A. A path to sustainable energy by 2030. Scientific American. , 6 Lund H.

Mathiesen B.V. Energy system analysis of 100% renewable energy systems: the case of Denmark in years 2030 and 2050. , 7 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. , 8 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. , 9 Mathiesen B.V.

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Karlsson K. 100% renewable energy systems, climate mitigation, and economic growth. , 10 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. , 11 Steinke F.

Wolfrum P.

Hoffmann C. Grid vs. storage in a 100% renewable Europe. , 12 Connolly D.

Mathiesen B.V. Technical and economic analysis of one potential pathway to a 100% renewable energy system. , 13 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. , 14 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. , 15 Jacobson M.Z.

Delucchi M.A.

Cameron M.A.

Frew B.A. Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. , 16 Mathiesen B.V.

Lund H.

Connolly D.

Wenzel H.

Ostergaard P.Z.

Moller B.

Nielsen S.

Ridjan I.

Karnoe P.

Sperling K.

Hvelplund F.K. Smart energy systems for coherent 100% renewable energy and transport solutions. , 17 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. , 18 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. , 19 Blakers A.

Lu B.

Socks M. 100% renewable electricity in Australia. , 20 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. , 21 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. , 22 Sadiqa A.

Gulagi A.

Breyer C. Energy transition roadmap towards 100% renewable energy and role of storage technologies for Pakistan by 2050. , 23 Liu H.

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Greiner M. Cost-optimal design of a simplified highly renewable Chinese network. , 24 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. , 25 Bogdanov D.

Farfan J.

Sadovskaia K.

Aghahosseini A.

Child M.

Gulagi A.

Oyewo A.S.

de Souza Noel Simas Barbosa L.

Breyer C. Radical transformation pathway towards sustainable electricity via evolutionary steps. , 26 Hansen K.

Breyer C.

Lund H. Status and perspectives on 100% renewable energy systems. , 27 Brown T.W.

Bischof-Niemz T.

Blok K.

Breyer C.

Lund H.

Mathiesen B.V. Response to ‘Burden of proof: a comprehensive review of the feasibility of 100% renewable electricity systems.’. , 28 Diesendorf M.

Elliston B. The feasibility of 100% renewable electricity systems: a response to critics. Two recent studies argue that 100% renewables is not a low-cost solution. One studystates that 80% of current US demand can be met by solar and wind power interconnected by either a US-wide transmission grid or 12 h of electrical storage but that more than 80% requires “costly” excess storage or solar or wind nameplate capacity. The present study and numerous papers among 11 independent research groupscontradict these findings. 32 Shaner M.R.

Davis S.J.

Lewis N.S.

Caldeira K. Geophysical constraints on the reliability of solar and wind power in the United States. First, the previous studydid not consider electrification of transportation, building heating, or industrial heat. Electrification of such loads not only reduces end-use demand substantially, as shown here, but also reduces the daily and seasonal variability of electric loads while creating more flexible loads that are subject to demand response. For example, current US electricity demand has a summer peak due to a high summer demand for air conditioning. Winter demand for building heating is currently provided mostly by natural gas and fuel oil, so it results in less winter electricity demand. Although replacing such heat with electric heat pumps increases winter electrical load (but by much less than the energy in the fuel it replaces as a result of the high coefficient of performance of heat pumps), the electrification of winter heating evens out seasonal (between summer and winter) electrical loads substantially as a result of the high summer electrical load. On top of that, vehicles are used daily, so electrification of transportation results in a relatively even (throughout the year) distribution of additional electric load, further reducing the summer-winter electric-load imbalance. Because electric cars are charged mostly at night (particularly with tiered electrical rates that are lowest at night), such electrification also evens out day versus night electrical loads in comparison with the present grid. 32 Shaner M.R.

Davis S.J.

Lewis N.S.

Caldeira K. Geophysical constraints on the reliability of solar and wind power in the United States. Not only did this previous studyassume an unrealistic load distribution, but it also did not treat demand response, district heating, seasonal heat and cold storage, existing hydropower storage, or hydrogen production and storage for transportation. As a result, it shed excess wind and solar power instead of storing that energy in seasonal or daily thermal energy storage or hydrogen. In the present study, seasonal underground thermal energy storage is applied to the fraction of a region’s thermal energy that is subject to district heating ( Table S9 ). In addition, hydrogen is used for fuel cells for a portion of transportation, namely for long-distance heavy transport. 32 Shaner M.R.

Davis S.J.

Lewis N.S.

Caldeira K. Geophysical constraints on the reliability of solar and wind power in the United States. By not treating naturally rechargeable existing hydropower storage, the previous studyalso limited its ability to fill in gaps in supply during key winter hours, when some of its shortfalls occurred. The present study treats these processes and finds low-cost solutions with 100% WWS energy and storage not only in the US but also in 24 world regions. 33 Sepulveda N.A.

Jenkins J.D.

deSisternes F.J.

Lester R.K. The role of firm low-carbon electricity resources in deep decarbonization of power generation. A second studyused an optimization model that treats electricity from renewables, nuclear energy, natural gas with carbon capture, and biomass and battery storage in an effort to examine grid stability in two US regions. Simulations were run for 1 year with a 1-h time resolution. The model did not electrify transportation, building heating, or industrial heating; did not treat district heating or seasonal underground thermal energy storage; did not treat demand response or hydrogen production or storage; and did not treat concentrated solar power with storage, pumped hydropower storage, or hydropower storage. These processes are all treated here. 2 and air-pollution emissions and costs due to such delays. It also assumed that carbon capture reduces 90% of CO 2 emissions, but that assumption ignores the upstream emissions from natural gas mining and transport and the fact that a natural gas plant with carbon-capture equipment requires 25%–50% more energy, and thus results in additional emissions, than the same plant without capture. 34 Jacobson M.Z.

Kaufmann Y.J.

Rudich Y. Examining feedbacks of aerosols to urban climate with a model that treats 3-D clouds with aerosol inclusions. 2 emissions, carbon capture could result in a net emission reduction of only 10%–30% over a 20- to 100-year time frame. 35 Jacobson M.Z. The health and climate impacts of carbon capture and direct air capture. That study also did not consider the health or climate costs of the combustion sources, the delays between planning and operation of nuclear plants or plants using natural gas with carbon capture, or the resulting background-grid COand air-pollution emissions and costs due to such delays. It also assumed that carbon capture reduces 90% of COemissions, but that assumption ignores the upstream emissions from natural gas mining and transport and the fact that a natural gas plant with carbon-capture equipment requires 25%–50% more energy, and thus results in additional emissions, than the same plant without capture.Thus, instead of reducing 90% of COemissions, carbon capture could result in a net emission reduction of only 10%–30% over a 20- to 100-year time frame. 36 Lazard

Levelized cost of energy and levelized cost of storage 2019. 35 Jacobson M.Z. The health and climate impacts of carbon capture and direct air capture. Moreover, that study substantially underestimated the private energy costs of nuclear power and natural gas with carbon capture. The nuclear capital cost in its mid-range case was 50% below the mean estimated nuclear capital cost from Lazard.Its mid-range cost of natural gas with carbon capture was only $1,720/kW. However, the cost of the carbon-capture equipment alone for the only US power plant with carbon capture, the W.A. Thompson coal plant in Texas, was $1 billion or $4,200/kW. 33 Sepulveda N.A.

Jenkins J.D.

deSisternes F.J.

Lester R.K. The role of firm low-carbon electricity resources in deep decarbonization of power generation. 2 consistently lower[s] the cost of decarbonizing electricity generation” was not shown. As calculated here, a transition to 100% WWS energy should reduce private and social costs substantially over those incurred by BAU energy without the need for nuclear power, fossil fuels with carbon capture, or bioenergy. In sum, this previous studynot only biased nuclear and natural gas costs but also underestimated emissions and ignored many process that facilitate matching renewable supply with demand. Thus, its conclusion that “including nuclear power and natural gas plants that capture COconsistently lower[s] the cost of decarbonizing electricity generation” was not shown. As calculated here, a transition to 100% WWS energy should reduce private and social costs substantially over those incurred by BAU energy without the need for nuclear power, fossil fuels with carbon capture, or bioenergy. 37 Hand M.M.

Baldwin S.

DeMeo E.

Reilly J.M.

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Porro G.

Meshek M.

Sandor D. Volume 1: Exploration of High-Penetration Renewable Electricity Futures. 38 Williams, J.H., Haley, B., Kahrl, F., Moore, J., Jones, A.D., Torn, M.S., and McJeon, H. (2014). Pathways to deep decarbonization in the United States. The U.S. report of the Deep Decarbonization Pathways Project of the Sustainable Development Solutions Network and the Institute for Sustainable Development and International Relations. (Energy and Environmental Economics Inc., Lawrence Berkeley National Laboratory, and Pacific Northwest National Laboratory). 39 MacDonald A.E.

Clack C.T.

Alexander A.

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Xie Y. Future cost-competitive electricity systems and their impact on US CO 2 emissions. Finally, several additional studies have examined high penetrations of renewables. None of these studies examined scenarios with 100% renewables or disputed the possibility of using 100% renewables. One studyfound that each region of the US could be powered with at least 90% renewable electricity and storage while matching power demand with supply hourly during a year. Renewable curtailment at 90% penetration was only 7%. The study did not examine 100% scenarios or scenarios in which all sectors were electrified. Two other studies similarly found that reducing US energyor electricitygreenhouse gas emissions 80% below 1990 levels by 2050 is technically feasible and that multiple alternative pathways for achieving those reductions exist. Neither study examined 100% scenarios.