Next Article in Journal
‘A Poor Prospect Indeed’: The State’s Disavowal of Child Abuse Victims in Youth Custody, 1960–1990
Previous Article in Journal
Cui Bono? Assessing Community Engagement in San Francisco Community Benefit Agreements
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Communication

Could 79 People Solarize the U.S. Electric Grid?

1
Department of Materials Science & Engineering and Department of Electrical & Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA
2
Department of Social Sciences, Michigan Technological University, Houghton, MI 49931, USA
*
Author to whom correspondence should be addressed.
Societies 2019, 9(1), 26; https://doi.org/10.3390/soc9010026
Submission received: 29 January 2019 / Revised: 13 March 2019 / Accepted: 20 March 2019 / Published: 26 March 2019

Abstract

:
Although wealth inequality has many established negatives, this study investigates a potential positive, unprecedented wealth concentration makes it possible for solutions to large and seemingly intractable problems to be deployed by convincing a relatively small number of individuals to invest. In order to probe this potential outcome of inequality, this study quantifies the number of people necessary to radically reduce the greenhouse gas emissions responsible for global climate destabilization from the U.S. electric grid, which is one of the largest sources of emissions. Specifically, this study determined that 1544 GW of solar photovoltaic (PV) technology must be deployed to eliminate the use of fossil fuels on the U.S. electric grid, if PV is conservatively deployed as a function of population density. The results showed that only 79 American multi-billionaires would need to invest in PV. This investment would still leave each investor with a billion dollars of liquid assets as well as substantial long-term profits from PV. The analysis also concluded that 79 people is a conservative upper estimate of those that would need to be convinced of the usefulness of moving to a solar U.S. grid and that this estimate is likely to decrease further in the future.

1. Introduction

By the 1990s in most publicly held companies, the compensation of the highest-paid employees (e.g., top executives) was virtually independent of their job performance [1,2]. Simultaneously, income inequality has risen as the value provided to society by these top earners has become divorced from their earnings.1 Despite the relatively extreme income inequality observed in the U.S., the distribution of wealth is far more concentrated [5,6,7]. In 2017, the distribution of wealth has become clearly unequal, with the lower-income half of the American population owning about 1.1% of the total wealth, while the wealthiest 1% possessing about 35.5% of the wealth [8]. It is well known that much of this wealth is inherited and that the transmission of capital down the generations is an extremely important determinant of an individual’s or a household’s wealth [9,10,11,12,13]. For all of these extremely wealthy people, income is dominated not by their efforts (work), but by their investments (e.g., rental income, capital gains, dividends, interest, etc.). As, for example, the higher one’s income the greater the share of that income is dominated by capital gains [14]. In addition, U.S. tax law for capital gains has an effective tax rate of less than half of those whose income is based on labor [15] and decreases in taxes are evident as the rich become super rich [16]. This tax policy was one of the factors that has further concentrated wealth over time.
The majority of the literature negatively views wealth inequality as studies show that wealth inequality leads to high social costs [17]. Here, this literature will be summarized to put the potential benefit of inequality discussed in this paper into context. Inequality does not generate optimal outcomes for society if the incentives rest on rents [18]. Inequality can cause individuals to divert effort to securing favor from the wealthy because they possess the preponderance of capital, resulting in resource misallocation, corruption, nepotism, and the expected sub-optimal economic, social, and environmental consequences [17,18,19,20,21,22,23,24,25,26,27,28,29]. It has already been well-established that income inequality negatively affects economic growth in the future [19,20,21], in part because lower income households cannot stay healthy and accumulate physical and human capital [22,23] (e.g., poor, smart, young people cannot go on to college because of access to funding or perceived unacceptable debt burden, which reduces overall labor productivity [18]). This inequality also contributes to lower intergenerational mobility (e.g., less earnings mobility across the generations) [24] and increases the probability of political conflict [25]. Simultaneously, income inequality generally creates sub-optimal policy [26], which reduces the public good to help economic growth and creates an inequality of opportunities for the poor (e.g., access to education, credit, infrastructure, public decision making, etc.) [27]. In extreme situations, a preponderance of inequality can drive a global financial crisis [28] and policies that further inequality are formalized into law because of lobbying [29].
Although inequality has these established negatives, there is a positive to inequality that concentrates wealth for a few individuals, as follows: For solving large and capital-intensive problems, the number of individuals necessary to invest has grown smaller over time and may be approaching a point where the individuals could conceivably all know each other as they already preferentially associate with one another [30]. This association is important as it would make it possible for solutions to large and seemingly intractable problems to be deployed by convincing a relatively small number of individuals to invest within their social circle. In order to probe this potential benefit for inequality and wealth concentration, this study quantifies the number of billionaires necessary to reduce the greenhouse gas emissions responsible for climate destabilization in areas across the globe [31]. Specifically, it investigates elimination of carbon emissions from one of the largest sources—combustion of fossil fuels for the U.S. electric grid (e.g., the three grids made up of the Western interconnection, Eastern interconnection, and ERCOT (Electric Reliability Council of Texas), which are loosely connected, that service the continental U.S.) [32]. This study will determine the amount of solar photovoltaic (PV) technology that must be deployed to convert the U.S. electric grid, which is currently 1.2% solar [33], to eliminate all existing fossil fuels. Then, it will estimate the costs for that conversion and the number of billionaires that would be able to maintain billionaire status while still completing the conversion. The results will be presented and discussed in the context of leveraging wealth concentration by focusing excess capital from the wealthiest individuals to partially solve global climate destabilization from greenhouse gas (GHG) emissions that threaten everyone throughout all global societies.

2. Materials and Methods

Data on U.S. population and solar flux was collected and geolocated. Three databases were obtained to analyze the ratio of population-adjusted solar flux for each state, as follows: (1) A shapefile of the United States was obtained from the ArcGIS database [34], (2) a shapefile of normal direct solar irradiance was obtained from the National Renewable Energy Laboratory database [35], and (3) the population density throughout the U.S. was obtained from the U.S. Census [36]. The ratio of population-adjusted solar flux in each state was transcribed to a map utilizing ArcMap version 10.6.1. Population-adjusted solar flux is needed to determine a practical PV deployment density over the U.S. and prevent unnecessarily costly adaptations of the electric grid for the placement of large PV systems in high solar flux areas. Thus, the calculations here are based on providing solar power where the people are located, which has the benefits of distributed generation.
In order to obtain the average population-adjusted solar flux for the U.S., FUSA in kWh/m2/day, the following equation was developed here and used the fraction of solar flux multiplied by the population of each state for the whole country:
F U S A = s = 1 50 ( F s p s s = 1 50 ( F s p s ) ) F s   [ kWh / m 2 / day ]
where Fs is the average kWh/m2/day of solar irradiation in each state, s; and ps is the population of state, s. Thus, a ratio is determined of the population adjusted solar flux for each state in the large bracket. By summing this ratio, times the solar flux for the state, the average solar flux for the whole U.S. is determined.
The list of 2123 individuals that have more than a billion U.S. dollars in assets, maintained by Forbes [37], was first screened for Americans, which reduced the number to 568 individuals listed in Appendix A. In order to ensure that each individual maintained US$1 billion in wealth if they decided to invest in solarizing the U.S., the aggregate potential capital was calculated by:
C = n = 1 n = T c w n $ 1 b   [ US $ ]
where wn is the individual wealth for each person and Tc is the total number of individuals needed to meet a capital investment goal. So, for example, for all 568 Americans, C is equal to US$2511.7 billion.
U.S. Energy Information Administration data for 2017 [38] was used to calculate the energy needing to be resourced. The U.S. used 4.034 trillion kWh/year in 2017 in total, but fossil fuels, which all contribute to climate change, were only responsible for 2.536 trillion kWh/year [38]. So, this latter value was used. The National Renewable Energy Laboratory estimates [39] that the total installed system cost, which is one of the primary inputs used to compute the levelized cost of electricity (LCOE) [40], has declined to US$2.80 per direct current watts (Wdc) for residential systems, US$1.85 Wdc for commercial, US$1.03 Wdc for fixed-tilt utility-scale systems, and US$1.11 Wdc for one-axis tracking utility-scale systems. The value of US$1.03 Wdc for fixed-tilt utility-scale systems is used in the final analysis because previous learning rates [41,42,43,44,45] for the global PV industry has resulted in continuous and aggressive reduction in the costs of solar modules [46,47]. As of January 2019, the spot price of the lowest cost PV modules has dropped below US$0.20/W [48]. Technical improvements, like moving to black silicon, are expected to drop those costs further [49] and the International Renewable Energy Agency (IRENA) predicts that the prices will fall by 60% in the next decade [50]. Thus, it is within reason that the PV prices that have already been obtained in the U.S. on the large scale could be met on the small, distributed scale in the near future, with a massive scale up of the industry as analyzed here.

3. Results

The ratio of population-adjusted solar flux in each state is shown in Figure 1.
Using Equation (2) and the data shown in Figure 1, the FUSA was found to be 4.499 kWh/m2/day. Using the conservative assumption that solar PV is deployed as a function of population and not optimal solar flux, this 4.499 kWh/m2/day demands that 1544 GW of installed PV. This amount of PV would produce the 2.536x1012 kWh needed to replace all fossil fuels for electricity production within the U.S. At $1.03/Wp (per peak Watt), this would entail an investment of US$1.59 trillion. Historically, US$1.59 trillion would be considered a substantial sum of capital, however, as discussed in the above, a relatively small number of people (568) in America now control more than this quantity of capital. After assuming that each multi-billionaire in the U.S. would dedicate their wealth in excess of $1 billion to solarization, the cumulative sum of Equation (2) was calculated and is shown in Table 1.
As can be seen in Table 1, only 79 American multi-billionaires would need to invest to convert all of the U.S. to solar from fossil fuels. This investment would still leave each investor with a billion dollars to use in any way they please.

4. Discussion

The results of this study indicate that a relatively small number of America’s wealthiest individuals could completely convert the U.S. electric grid away from fossil fuels by replacing the remaining fossil fuel generation with solar. Seventy-nine Americans would need to give up some of their wealth to make this conversion possible and although they would each remain a billionaire, there are three areas that need to be discussed in the next three subsections, as follows:
  • Why might multi-billionaires want to voluntarily give up their wealth to solarize the U.S.?
  • What is the probability that multi-billionaires would be willing to make the required investment in solar even if they found the reasoning compelling?
  • What are the primarily limitations of the methodology and assumptions made here that resulted in such a low number of individuals needing to give up their wealth to radically remake the U.S. electric grid?

4.1. Why Would a Multi-Billionaire Want to Invest in Solarizing the U.S.?

Both global GHG emissions [31] and global atmospheric carbon dioxide (CO2) concentrations are increasing rapidly [51], which creates an enormous urgency to cut emissions [52]. The resultant climate change is well-established with a high confidence as are the negative impacts on natural and socio-economic systems [53,54] including the following:
(i)
Higher temperatures and heat waves that result in thousands of deaths from hyperthermia and are expected to increase [55,56,57],
(ii)
other adverse effects on human society and health [58],
(iii)
crop failures [59,60] that aggravate global hunger and food insecurity [61,62,63],
(iv)
electric power outages [64,65],
(v)
rising sea levels that cause low-lying coastal areas throughout the world to submerge gradually, as well as erosion of shorelines [66,67],
(vi)
increased risk of flooding [68] and saltwater intrusion [69], as well as severe storms that cause more damage to coastal environments [70],
(vii)
risks to forests [71,72,73,74],
(viii)
droughts [75] and
(ix)
fire [71,76,77].
These negative externalities have been shown to be due to human activities, with a confidence level of 95% (primarily combustion of fossil fuels, which is the dominant cause of global warming from 1951 to 2010) [78,79]. Climate change is widely viewed as one of the greatest challenges of our age [80] and GHG emissions from electricity generation is one of the largest contributors to the problem in the U.S. (in 2016 transportation surpassed electric generation for the first time) [32]. Some of the billionaires shown in Table 1 have already discussed what a large problem climate change is and have begun to contribute to energy solutions by investing in Breakthrough Energy Ventures, which is a billion-dollar fund backed by some of the world’s top entrepreneurs and investors, including the following: Jeff Bezos, Bill Gates, Mark Zuckerberg, and Michael Bloomberg [81]. Bill Gates, for example, has thought hard about not only the solution to climate change, but others as well [82]. Other multi-billionaires on the list, like Elon Musk, the Tesla founder, said sustainable energy solutions are technologically viable and have been working aggressively for their success [83]. In addition, many of the companies that American multibillionaires control have made substantial investments in solar; so, they are familiar with the technical and economic potential of the technology. For example, Larry Page and Sergey Brin’s Google officially hit its 100% renewable energy target in 2018 [84] and the Walton’s Walmart has made a public commitment to solar [85], with the second most onsite PV of any company in the world [86]. Despite this promise, there are a minority on the list in Table 1 who are heavily invested in fossil fuels and would find the transition more challenging. For these individuals, as the potential liability for climate change becomes more serious [87], they might also be convinced to convert for the good of the companies they helped develop. As of this writing, no lawsuits have been won to make a corporation that is a GHG emitter liable for emissions. There are, however, multiple such lawsuits pending and, as the potential liability is so large that it could easily bankrupt most companies, converting to renewable energy could be used as a hedge against this risk [87].
Alternatively, these individuals may be interested in solarizing the electrical grid using a distributed generation model, proposed here, by following the largely successful securitization of PV assets [88,89,90,91] due to the purely economic advantages of PV. PV is made even more profitable by the plethora of tax incentives available, which result in large economic returns on investment. First, the renewable energy tax credit allows the system owner to effectively reduce system costs by 30% [92] and the systems are eligible for MACRS (Modified Accelerated Cost Recovery System) 5-year accelerated depreciation. It should be noted here that these tax credit and depreciation factors were conservatively not used in the financial estimates made in the results to eliminate any risk due to policy changes at the U.S. federal level, which would make the estimates inaccurate. Using these mechanisms could make the PV investments discussed in the results substantially profitable for the investors. It is noted that this profitability would need to be weighed against other potential sources of profits for the multi-billionaires, as well as their personal stake in the moving society towards sustainability.
As of this writing, the federal investment tax credit is available at 30% through 2019 and steps down to 26% in 2020, 22% in 2021, and 10% for commercial and industrial systems thereafter [93]. Business owned systems are also eligible for MACRS 5-year accelerated depreciation. The 2017 tax law allows for 5 years of 100% bonus depreciation for systems installed after September 27, 2017 [94]. The term 100% bonus depreciation means that the whole project’s applicable tax depreciation is accelerated to the first year of the system’s commissioning [95]. This is especially significant for investors in higher income tax brackets, as they see comparatively more value because electricity expenses are paid with after-tax dollars—they are not tax deductible. Different states offer solar energy property tax incentives, providing various amounts of tax exemptions on residential, commercial, and industrial solar PV systems [96]. A final tax incentive opportunity is the creation of Opportunity Zones [97]. This is an investment vehicle that attempts to match economic need with private investment. Qualified opportunity zone property includes any qualified opportunity zone stock, any qualified opportunity zone partnership interest, and any qualified opportunity zone business property [97]. Solar PV systems are well within the defined qualified business property. First, it allows for the temporary deferral of including gross income for gains that are reinvested in a qualified opportunity fund [97]. Second, it allows for exclusion of up to 15% of the gain on the original investment, that is deferred by the investment in the qualified opportunity fund if held for seven years [97]. Third, the taxpayer may elect to exclude the post-acquisition gains on investments from gross income in qualified opportunity funds that are held for at least ten years [97]. As an added bonus, opportunity zone tax benefits can be layered on top of the Renewable Energy Investment Tax Credit and accelerated depreciation to make an even better investment.

4.2. Probability of Solar Investment

Many of those on the list in Table 1 are already familiar with solar and are investing in it. With the potential to be in a group of the elite that would be potentially credited with “saving the world”, there is a non-zero probability that convincing all of these 79 individuals to make the investment is possible. This hypothesis is further supported by the number of multi-billionaires pledging to give away much of their fortunes before they die. This is formally being done in the Giving Pledge, which is a commitment by the world’s wealthiest individuals and families to dedicate the majority of their wealth to giving back to the rest of society through philanthropy [94]. At the end of 2018, the pledge had 187 pledgers including several on the list in Table 1, including Warren Buffett, Larry Ellison, James Simons, George Kaiser, and George Lucas [98]. None of these pledges were factored into the analysis here. In academia, there has been an enormous debate raging about inequality [19,99,100,101,102,103,104,105,106,107,108] but there appears to be a potential consensus forming among the world’s economic elite that their wealth should be used for the betterment of society. Future work is needed to quantify these consensuses and the probability that a relatively small group would collaborate on such a major project. It should also be noted that some of those on the list (e.g., Charles (5) and David (6) Koch) are heavily invested in fossil fuel industries, as well as climate denial activities [109]). However, as noted above, if even a single GHG emissions liability case is won, all investors in fossil fuel industries would financially benefit from immediate renewable energy investment to mitigate climate change-related liability. In addition, all of the analysis presented here assumed conventional economics (e.g., no value was assigned to environmental externalities). However, as the costs of climate change continue to mount [54,110,111], the discipline of green economics [112,113,114] may gain prominence over conventional economics, which would have the effect of making solar PV even more economically profitable.

4.3. Limitations

This study has several limitations. First, this study assumed that there was more than enough non-shaded optimal surface area to allow for distributed generation with PV, but it did not explicitly calculate siting for the 1544 GW of PV necessary to replace all of fossil fuel electricity production in the U.S. The nuances of territory and siting at both the large scale for PV output [115], as well as DG benefits [116,117] and roof top [118,119,120,121,122] as well as façade [123] locations have been covered extensively. Here, the conservative assumption about locating the PV systems was based on a distributed generation model where the PV would be located following population density in each state across the U.S. There are far more than enough optimal locations (surface area) to install PV in each region to cover more than 100% of the entire U.S. electricity use (let alone the 63% needed here) [124]. A more granular analysis is left for future work. Second, this study did not look at past investments nor to future investments that would reduce the need for the full 1544 GW of PV. The calculations for the PV necessary to completely eliminate fossil fuels from U.S. electric generation are only for the new solar investments necessary. All previous investments and investments in other renewable energy technologies, like wind power, are not considered. It also did not attempt to quantify profitable investments in energy efficiency and conserving technologies (e.g., lighting [125], moving from resistive electricity-based heating to heat pumps [126], buildings [127], and electric motors and drives [128]). It is highly likely that there will continue to be investments in energy efficiency and other renewable energy technologies. Thus, it is highly likely that the value of PV needed, calculated here, is an overestimate. Determining the degree of that overestimate is left for future work. Third, this study assumed modern PV technology. Again, the learning rate in PV production and the efficiency of the technologies can be expected to continue to climb, thus reducing PV costs further [48,49]. This again was taken as a conservative assumption, the correction of which is left for future work.
This study only looked at the generation component of electricity and did not take into account load balancing, efficiency, storage, power quality factor, or transmission. With the solar slated to be put in place, the investment in storage and transmission and other technologies to maintain operation of the grid would be expected to be provided using the conventional utility models. There is recent evidence that this assumption is valid in Germany, where renewables have been able to cover 100% of power for the first time as of January 2018 [129]. Critics may demand that the 79 billionaires must also pay for storage to regulate the grid. This study does not consider this additional investment for the following complexities related to the structure of the U.S. electric utilities, that would both increase as well as decrease costs that will be briefly summarized here. First, only roughly 63% of the power sources on the U.S. grid would need to be converted to solar to replace the existing fossil fuels. The solar specifically investigated here is for use in distributed generation (DG—the assumption based on population density-based deployment). It is well established that DG can postpone investments in generation, transmission, and distribution as electrical power demand grows and, at a large enough scale, eliminate them [130]. DG also reduces transmission losses [131]. Elimination of these losses would result in cost savings of about 10–15% [132]. Other DG benefits include decreased pollution and greenhouse gas emissions [133] and their concomitant potential reductions in mortality by converting to solar [134]. For coal replacement in particular, these premature deaths prevented can be substantial to the point that they number more per year than the current total coal mining employment [135]. In addition, DG provides transmission congestion relief, increased reliability, and ancillary services [131,136]. The economic impacts of these details are highly dependent on the potential for changes in laws revolving around electric utilities and green economics and are left for future work to ensure a smooth transition from fossil fuel generation to solar.
Another limitation is addressing the variations in the PV power that exist in a high PV penetration scenario, like the one discussed in this study is due to (i) the night/day cycle, (ii) the yearly cycle, and (iii) fluctuating cloud conditions. Variations (i) and (ii) will be addressed by changes in the grid and investments by conventional utilities as more PV is deployed and storage becomes necessary. However, reason (iii) (of fluctuating cloud conditions and thus rapidly changing PV power) is the largest problem that needs to be addressed at high penetration rates immediately. However, cloud variations can be largely mitigated using the deployment recommended in this study (e.g., DG). Specifically, by deploying solar PV systems over a larger geographic area, any specific clouds have only a small effect on the overall grid. For example, if a network of PV installations is dispersed throughout a 100 km2 area, the tolerable acute penetration for PV will increase to 18.1% and if the area expands to 1000 km2, the limit for PV penetration is 35.8% [137]. It should be noted that, in the solar PV, penetration level is the real time percentage (not the overall percentage of PV electricity generation), which would of course be considerably less as peak sun hours are only available for a few hours each day. Effectively, this means that a PV penetration many times the current value could be tolerated from the grid before any changes are necessary. As an increasing penetration of PV is made, if it is deployed with DG strategies, the penetration level can get to about a third before significant changes have to be made. Some existing policies and pricing methods will help make these changes less challenging. In many cases, this will mean using existing techniques for load shedding, load temporal displacement, and the usage of more storage. For example, time of use metering (TOU), which currently favors using electricity at night, will be reversed so that using electricity in the middle of the day will be the least costly when PV is at full output. As the goal of elimination of fossil fuel production for the grid is approached, utilities would need to invest in storage and other technologies to ensure normal operation and they would do it following the same basis that they currently do to make capital investments for generation that would no longer be necessary. The details of this arrangement and the timeline are left for future work.
Overall, this study is overly conservative in the number of billionaires needed to solarize the U.S. because it made the assumption that solar would be distributed based on population density and that current PV prices would be used. There is an expectation of cost decreases based on deployment of known technologies, as well as the scale, as society approaches a sustainable future [138]. The following effects would be expected after 79 of the wealthiest Americans began to invest all but US$1 billion in conversion of the U.S. electric grid away from fossil fuels. First, the price of solar, after the first shock to supply by the rapidly increased demand, would be decreased. Similar drops would be expected in the balance of systems components (i.e., racking, electronics) as well as, eventually, storage. In addition, less PV would be necessary if it were strategically located in high solar flux areas in certain utility regions. Similarly, the growth of other renewable energy sources, like wind, which currently costs less than fossil fuel generation, is expected to continue and would also reduce the demand for solar. Likewise, with the surge in demand from the proposed solar replacement of all fossil-fuel generation in the U.S., the price per unit solar would be expected to drop considerably. At the same time, the concentration of wealth continues to increase in the U.S. [139,140,141], and globally (the richest 26 globally own more wealth than the bottom 50% of humanity [142]). All of these factors combine to mean less and less individuals will need to be convinced as time goes forward. For these reasons, it can be comfortably concluded that 79 is a conservative estimate on the number of American multi-billionaires that would need to be convinced of the usefulness of moving to a solar U.S. grid in order to make it a reality. Finally, this analysis can be expanded beyond the U.S. to globally reduce greenhouse gas emissions, while accounting for the life cycle of greenhouse gas emissions of various types [143], as well as the impact on emissions as a function of growth of PV [144].

5. Conclusions

Although wealth inequality has many established negatives, this study has shown the potential positive that, when solving large capital-intensive problems, the number of individuals that need to be convinced to act has become small and manageable. Here, we have investigated the potential to reduce greenhouse gas emissions, responsible for climate destabilization in areas across the globe, in the U.S. electric grid by first determining the amount of solar PV technology that must be deployed to eliminate all fossil fuels from the U.S. electric grid, the costs for that conversion, and the number of multi-billionaires that would be able to maintain billionaire status while still completing the conversion. The results show that only 79 American multi-billionaires are needed. The analysis also concludes that this is a conservative estimate on the number that would need to be convinced of the usefulness of moving to a solar U.S. grid and that upper estimate is likely to decrease even further in the future.

Author Contributions

Conceptualization, J.M.P.; Methodology, J.M.P.; Validation, E.P.; Formal Analysis, J.M.P. and E.P.; Investigation, J.M.P. and E.P.; Resources, J.M.P.; Data Curation, E.P.; Writing-Original Draft Preparation, J.M.P.; Writing-Review & Editing, J.M.P. and E.P.; Visualization, E.P.; Funding Acquisition, J.M.P.

Funding

This research was funded by the Witte Endowment.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

2018 RANKING REAL TIME RANKING
2018 World RankAmerican Multi-BillionairesWealth (B US$)Source of Wealth
#1Jeff Bezos112Amazon
#2Bill Gates90Microsoft
#3Warren Buffett84Berkshire Hathaway
#5Mark Zuckerberg71Facebook
#8Charles Koch60Koch Industries
#8David Koch60Koch Industries
#10Larry Ellison58.5software
#11Michael Bloomberg50Bloomberg LP
#12Larry Page48.8Google
#13Sergey Brin47.5Google
#14Jim Walton46.4Walmart
#15S. Robson Walton46.2Walmart
#16Alice Walton46Walmart
#21Sheldon Adelson38.5casinos
#22Steve Ballmer38.4Microsoft
#28Phil Knight29.6Nike
#34Jacqueline Mars23.6candy, pet food
#34John Mars23.6candy, pet food
#39Michael Dell22.7Dell computers
#44Paul Allen21.7Microsoft, investments
#47Thomas Peterffy20.3discount brokerage
#48Len Blavatnik20.2diversified
#52James Simons20hedge funds
#54Elon Musk19.9Tesla Motors
#58Laurene Powell Jobs18.8Apple, Disney
#67Ray Dalio17.7hedge funds
#73Carl Icahn16.8investments
#80Donald Bren16.3real estate
#83Abigail Johnson15.9money management
#83Lukas Walton15.9Walmart
#94Rupert Murdoch15newspapers, TV network
#100Harold Hamm14.1oil & gas
#102Steve Cohen14hedge funds
#102Dustin Moskovitz14Facebook
#106Charles Ergen13.4satellite TV
#106Eric Schmidt13.4Google
#108Philip Anschutz13investments
#108Jim Kennedy13media
#108Blair Parry-Okeden13media, automotive
#113Leonard Lauder12.9Estee Lauder
#117Stephen Schwarzman12.6investments
#121Donald Newhouse12.3media
#132Andrew Beal11.6banks, real estate
#134John Menard, Jr.11.5home improvement stores
#138David Tepper11hedge funds
#145Pierre Omidyar10.5eBay
#152Ronald Perelman9.8leveraged buyouts
#154Micky Arison9.7Carnival Cruises
#158Thomas Frist, Jr.9.6health care
#162Charles Schwab9.4discount brokerage
#164Herbert Kohler, Jr.9.3plumbing fixtures
#170Jan Koum9.1WhatsApp
#172James Goodnight9software
#172Ken Griffin9hedge funds
#178James Chambers8.7media, automotive
#178Katharine Rayner8.7media, automotive
#178Margaretta Taylor8.7media, automotive
#181Gordon Moore8.5Intel
#183Stanley Kroenke8.3sports, real estate
#186John Malone8.1cable television
#190Carl Cook8medical devices
#190David Geffen8movies, record labels
#190George Soros8hedge funds
#196Edward Johnson, III.7.9money management
#198David Duffield7.8business software
#198George Kaiser7.8oil & gas, banking
#198Patrick Soon-Shiong7.8pharmaceuticals
#205Stephen Ross7.6real estate
#207Pauline MacMillan Keinath7.4Cargill
#211Eli Broad7.3investments
#211Sun Hongbin7.3real estate
#211Christy Walton7.3Walmart
#217Shahid Khan7.2auto parts
#222John Doerr7.1venture capital
#242David Green6.8retail
#242Hank & Doug Meijer6.8supermarkets
#251Brian Acton6.6WhatsApp
#251Ann Walton Kroenke6.6Walmart
#261Leon Black6.5private equity
#261John Paulson6.5hedge funds
#265David Shaw6.4hedge funds
#265John A. Sobrato6.4real estate
#274Daniel Gilbert6.3Quicken Loans
#281Richard Kinder6.2pipelines
#281Robert Kraft6.2New England Patriots
#281Ralph Lauren6.2Ralph Lauren
#287Les Wexner6.1retail
#289Whitney MacMillan6Cargill
#296Marijke Mars5.9candy, pet food
#296Pamela Mars5.9candy, pet food
#296Valerie Mars5.9candy, pet food
#296Victoria Mars5.9candy, pet food
#305Nancy Walton Laurie5.8Walmart
#305Tom & Judy Love5.8retail & gas stations
#305Robert Rowling5.8hotels, investments
#305Dennis Washington5.8construction, mining
#315David Sun5.7computer hardware
#315John Tu5.7computer hardware
#321Jensen Huang5.6semiconductors
#321Charles Johnson5.6money management
#321Jerry Jones5.6Dallas Cowboys
#321Richard LeFrak5.6real estate
#321Steven Rales5.6manufacturing
#334Dannine Avara5.5pipelines
#334Scott Duncan5.5pipelines
#334Milane Frantz5.5pipelines
#334Diane Hendricks5.5roofing
#334Gabe Newell5.5videogames
#334Randa Williams5.5pipelines
#351Richard DeVos5.4Amway
#351George Roberts5.4private equity
#351Edward Roski, Jr.5.4real estate
#365Jim Davis5.3New Balance
#365David Filo5.3Yahoo
#365Henry Kravis5.3private equity
#372Israel Englander5.2hedge funds
#372Marian Ilitch5.2pizza, sports team
#372Bruce Kovner5.2hedge funds
#372George Lucas5.2Star Wars
#372Robert Rich, Jr.5.2frozen foods
#382Bernard Marcus5.1Home Depot
#382Fred Smith5.1FedEx
#382Ronda Stryker5.1medical equipment
#388Martha Ingram5book distribution, transportation
#388Karen Pritzker5hotels, investments
#404Robert Bass4.9oil, investments
#404Marc Benioff4.9business software
#404Charles Dolan4.9cable television
#404Ray Lee Hunt4.9oil, real estate
#404John Overdeck4.9hedge funds
#404Sumner Redstone4.9media
#404Reinhold Schmieding4.9medical devices
#404David Siegel4.9hedge funds
#404Sam Zell4.9real estate, private equity
#422Bubba Cathy4.8Chick-fil-A
#422Dan Cathy4.8Chick-fil-A
#422Rupert Johnson, Jr.4.8money management
#422Travis Kalanick4.8Uber
#422Trevor Rees-Jones4.8oil & gas
#422Jeff Skoll4.8eBay
#422Daniel Ziff4.8investments
#422Dirk Ziff4.8investments
#422Robert Ziff4.8investments
#441Stanley Druckenmiller4.7hedge funds
#441Ted Lerner4.7real estate
#441Gwendolyn Sontheim Meyer4.7Cargill
#441J. Christopher Reyes4.7food distribution
#441Jude Reyes4.7food distribution
#441Sheldon Solow4.7real estate
#456Jeremy Jacobs, Sr.4.6food service
#456Chris Larsen4.6cryptocurrency
#466Paul Tudor Jones, II.4.5hedge funds
#466John Sall4.5software
#466Leonard Stern4.5real estate
#480Tamara Gustavson4.4self-storage
#480John Morris4.4sporting goods retail
#480Robert Smith4.4private equity
#480Russ Weiner4.4energy drinks
#499Rocco Commisso4.3telecom
#499Tilman Fertitta4.3Houston Rockets, entertainment
#499Terrence Pegula4.3natural gas
#499Robert Pera4.3wireless networking gear
#499Gary Rollins4.3pest control
#499Randall Rollins4.3pest control
#499Alejandro Santo Domingo4.3beer
#499Andres Santo Domingo4.3beer
#499Roger Wang4.3retail
#514Stephen Bisciotti4.2staffing, Baltimore Ravens
#514Austen Cargill, II.4.2Cargill
#514James Cargill, II.4.2Cargill
#514Archie Aldis Emmerson4.2timberland, lumber mills
#514Marianne Liebmann4.2Cargill
#514Bobby Murphy4.2Snapchat
#514Igor Olenicoff4.2real estate
#514Walter Scott, Jr.4.2utilities, telecom
#514Clemmie Spangler, Jr.4.2investments
#527Arthur Blank4.1Home Depot
#527Jack Dangermond4.1mapping software
#527James Jannard4.1sunglasses
#527Isaac Perlmutter4.1Marvel comics
#527H. Ross Perot, Sr.4.1computer services, real estate
#527Thomas Pritzker4.1hotels, investments
#527Julian Robertson, Jr.4.1hedge funds
#527Evan Spiegel4.1Snapchat
#527Kelcy Warren4.1pipelines
#550Ben Ashkenazy4real estate
#550Dagmar Dolby4Dolby Laboratories
#550Dan Friedkin4Toyota dealerships
#550Ronald Lauder4Estee Lauder
#550Michael Moritz4venture capital
#550Richard Schulze4Best Buy
#550Jeff Sutton4real estate
#572Rick Caruso3.9real estate
#572Tom Gores3.9private equity
#572Stewart and Lynda Resnick3.9agriculture, water
#572Jerry Speyer3.9real estate
#572Harry Stine3.9agriculture
#572Steven Udvar-Hazy3.9aircraft leasing
#588Nathan Blecharczyk3.8Airbnb
#588Brian Chesky3.8Airbnb
#588Joe Gebbia3.8Airbnb
#588Jeff Greene3.8real estate, investments
#588Robert McNair3.8energy, sports
#588Ira Rennert3.8investments
#588Henry Samueli3.8semiconductors
#606Nick Caporella3.7beverages
#606Mark Cuban3.7online media
#606Ken Fisher3.7money management
#606H. Fisk Johnson3.7cleaning products
#606Imogene Powers Johnson3.7cleaning products
#606S. Curtis Johnson3.7cleaning products
#606Helen Johnson-Leipold3.7cleaning products
#606Winifred Johnson-Marquart3.7cleaning products
#606Michael Milken3.7investments
#629Jeffery Hildebrand3.6oil
#629Edward Johnson, IV.3.6money management
#629Elizabeth Johnson3.6money management
#629Peter Kellogg3.6investments
#629Rodger Riney3.6discount brokerage
#629Steven Spielberg3.6Movies
#629Anita Zucker3.6chemicals
#652Judy Faulkner3.5health IT
#652Joshua Harris3.5private equity
#652Douglas Leone3.5venture capital
#652Anthony Pritzker3.5hotels, investments
#652J.B. Pritzker3.5hotels, investments
#652Mitchell Rales3.5manufacturing, investments
#652Bernard Saul, II.3.5banking, real estate
#652Donald Sterling3.5real estate
#679Riley Bechtel3.4engineering, construction
#679Stephen Bechtel, Jr.3.4engineering, construction
#679Jimmy Haslam3.4gas stations, retail
#679Min Kao3.4navigation equipment
#679Steve Wynn3.4casinos, hotels
#703John Arnold3.3hedge funds
#703Sid Bass3.3oil, investments
#703John Brown3.3medical equipment
#703Charles Cohen3.3real estate
#703Rakesh Gangwal3.3airline
#703Reid Hoffman3.3LinkedIn
#703Amos Hostetter, Jr.3.3cable television
#703Ken Langone3.3investments
#703George Lindemann3.3investments
#703Mary Alice Dorrance Malone3.3Campbell Soup
#703Henry Nicholas, III.3.3semiconductors
#703Pat Stryker3.3medical equipment
#729Neil Bluhm3.2real estate
#729Andrew & Peggy Cherng3.2restaurants
#729Scott Cook3.2software
#729Leon G. Cooperman3.2hedge funds
#729John Paul DeJoria3.2hair products, tequila
#729Tom Golisano3.2payroll services
#729Daniel Loeb3.2hedge funds
#729Daniel Och3.2hedge funds
#729Marc Rowan3.2private equity
#729Haim Saban3.2TV network, investments
#729Lynn Schusterman3.2oil & gas, investments
#729Mark Shoen3.2U-Haul
#729Meg Whitman3.2eBay
#766John Catsimatidis3.1oil, real estate
#766Do Won & Jin Sook Chang3.1fashion retail
#766Barry Diller3.1online media
#766Jack Dorsey3.1Twitter, Square
#766Allan Goldman3.1real estate
#766Jane Goldman3.1real estate
#766Amy Goldman Fowler3.1real estate
#766Diane Kemper3.1real estate
#766James Leprino3.1cheese
#766Richard Sands3.1Food & Beverage
#766Donald Trump3.1television, real estate
#766Romesh T. Wadhwani3.1software
#791Clifford Asness3Investment Management
#791Tom Benson3New Orleans Saints
#791Jim Breyer3venture capital
#791Valentin Gapontsev3lasers
#791Johnelle Hunt3trucking
#791John Middleton3tobacco
#791Jorge Perez3real estate
#791Jean (Gigi) Pritzker3hotels, investments
#791Michael Rubin3online retail
#791Robert Sands3Food & Beverage
#791Herb Simon3real estate
#791Don Vultaggio3AriZona Beverages
#822Chuck Bundrant2.9fishing
#822Gerald Ford2.9banking
#822Joseph Grendys2.9poultry processing
#822Randal Kirk2.9pharmaceuticals
#822Jeff Rothschild2.9Facebook
#822Thomas Siebel2.9business software
#822Paul Singer2.9hedge funds
#822Jon Stryker2.9medical equipment
#822Vincent Viola2.9electronic trading
#859William Conway, Jr.2.8private equity
#859Daniel D’Aniello2.8private equity
#859Jim Davis2.8staffing & recruiting
#859Doris Fisher2.8Gap
#859John Fisher2.8Gap
#859Kieu Hoang2.8medical products
#859H. Wayne Huizenga2.8investments
#859Osman Kibar2.8biotech
#859Penny Pritzker2.8hotels, investments
#859David Rubenstein2.8private equity
#859Mark Walter2.8finance
#859William Wrigley, Jr.2.8chewing gum
#859Mortimer Zuckerman2.8real estate, media
#887Ray Davis2.7pipelines
#887Edward DeBartolo, Jr.2.7shopping centers
#887Bennett Dorrance2.7Campbell Soup
#887Don Hankey2.7auto loans
#887Reed Hastings2.7Netflix
#887James Irsay2.7Indianapolis Colts
#887Bob Parsons2.7web hosting
#887Phil Ruffin2.7casinos, real estate
#887Howard Schultz2.7Starbucks
#887E. Joe Shoen2.7U-Haul
#887Frank VanderSloot2.7nutrition and wellness products
#887Ty Warner2.7real estate, plush toys
#887Oprah Winfrey2.7TV shows
#924David Bonderman2.6private equity
#924Phillip Frost2.6pharmaceuticals
#924B. Wayne Hughes2.6self-storage
#924Stephen Mandel, Jr.2.6hedge funds
#924Sean Parker2.6Facebook
#924Jay Paul2.6real estate
#924Patrick Ryan2.6insurance
#924Thomas Secunda2.6Bloomberg LP
#924Warren Stephens2.6investment banking
#924Glen Taylor2.6printing
#924Jerry Yang2.6Yahoo
#965Edward Bass2.5oil, investments
#965Lee Bass2.5oil, investments
#965Bert Beveridge2.5vodka
#965George Bishop2.5oil & gas
#965Norman Braman2.5art, car dealerships
#965Kenneth Feld2.5circus, live entertainment
#965Noam Gottesman2.5hedge funds
#965Jonathan Gray2.5investments
#965John Henry2.5sports
#965Aerin Lauder2.5cosmetics
#965Jane Lauder2.5cosmetics
#965Jeffrey Lorberbaum2.5flooring
#965Joe Mansueto2.5investment research
#965C. Dean Metropoulos2.5investments
#965Arturo Moreno2.5billboards, Anaheim Angels
#965Richard Peery2.5real estate
#965Larry Robbins2.5hedge funds
#965Charles Simonyi2.5Microsoft
#965Mark Stevens2.5venture capital
#965Peter Thiel2.5Facebook, Palantir
#965Elaine Wynn2.5casinos, hotels
#965Denise York2.5San Francisco 49ers
#965David Zalik2.5financial technology
#1020George Argyros2.4real estate, investments
#1020John Arrillaga2.4real estate
#1020Peter Buck2.4Subway sandwich shops
#1020Drayton McLane, Jr.2.4Walmart, logistics
#1020Daniel Pritzker2.4hotels, investments
#1020John Pritzker2.4hotels, investments
#1020Eric Smidt2.4hardware stores
#1020Alexander Spanos2.4real estate, Los Angeles Chargers
#1070David Gottesman2.3investments
#1070Bill Haslam2.3truck stops
#1070W. Herbert Hunt2.3oil
#1070Bradley Jacobs2.3logistics
#1070Brad Kelley2.3tobacco
#1070Vinod Khosla2.3venture capital
#1070Clayton Mathile2.3pet food
#1070J. Joe Ricketts2.3TD Ameritrade
#1070Dan Snyder2.3Washington Redskins
#1070John Tyson2.3food processing
#1103Ron Baron2.2money management
#1103Timothy Boyle2.2Columbia Sportswear
#1103Chase Coleman, III.2.2hedge fund
#1103Jim Coulter2.2private equity
#1103Frank Fertitta, III.2.2casinos, mixed martial arts
#1103Lorenzo Fertitta2.2casinos, mixed martial arts
#1103Ernest Garcia, II.2.2used cars
#1103Stanley Hubbard2.2DirecTV
#1103Thomas Lee2.2private equity
#1103Eric Lefkofsky2.2Groupon
#1103Phillip T. (Terry) Ragon2.2health IT
#1103Stewart Rahr2.2drug distribution
#1103T. Denny Sanford2.2banking, credit cards
#1103Julio Mario Santo Domingo, III.2.2beer
#1103Ted Turner2.2cable television
#1103William Young2.2plastics
#1157Leslie Alexander2.1sports team
#1157Todd Christopher2.1hair care products
#1157Gordon Getty2.1Getty Oil
#1157Alec Gores2.1private equity
#1157Catherine Lozick2.1valve manufacturing
#1157David Murdock2.1Dole, real estate
#1157H. Ross Perot, Jr.2.1real estate
#1157Tor Peterson2.1commodities
#1157Kavitark Ram Shriram2.1venture capital, Google
#1157David Walentas2.1real estate
#1157Ronald Wanek2.1furniture
#1215S. Daniel Abraham2Slim-Fast
#1215Ron Burkle2supermarkets, investments
#1215James Clark2Netscape, investments
#1215Christopher Cline2coal
#1215Alexandra Daitch2Cargill
#1215Glenn Dubin2hedge funds
#1215Robert Duggan2pharmaceuticals
#1215Thomas Hagen2insurance
#1215Bruce Karsh2private equity
#1215Henry Laufer2hedge funds
#1215Jeffrey Lurie2Philadelphia Eagles
#1215Sarah MacMillan2Cargill
#1215Howard Marks2private equity
#1215Jonathan Nelson2private equity
#1215Peter Peterson2investments
#1215Antony Ressler2finance
#1215Rodney Sacks2energy drinks
#1215Brian Sheth2investments
#1215Lucy Stitzer2Cargill
#1215Katherine Tanner2Cargill
#1215Amy Wyss2medical equipment
#1215Jon Yarbrough2video games
#1215Charles Zegar2Bloomberg LP
#1284James Dinan1.9hedge funds
#1284Bill Gross1.9investments
#1284Jeffrey Gundlach1.9investments
#1284Jennifer Pritzker1.9hotels, investments
#1284Alan Trefler1.9software
#1284Evan Williams1.9Twitter
#1339Nicolas Berggruen1.8investments
#1339James France1.8Nascar, racing
#1339Stewart Horejsi1.8Berkshire Hathaway
#1339Hamilton James1.8investments
#1339John Kapoor1.8healthcare
#1339William Lauder1.8Estee Lauder
#1339Linda Pritzker1.8hotels, investments
#1339Brian Roberts1.8Comcast
#1339William Stone1.8software
#1394Herbert Allen, Jr.1.7investment banking
#1394John Farber1.7chemicals
#1394Robert Fisher1.7Gap
#1394William Fisher1.7Gap
#1394Timothy Headington1.7oil & gas, investments
#1394Jim Justice, II.1.7coal
#1394William Koch1.7oil, investments
#1394Marc Lasry1.7hedge funds
#1394David Lichtenstein1.7real estate
#1394Craig McCaw1.7telecom
#1394Miguel McKelvey1.7WeWork
#1394Vincent McMahon1.7Entertainment
#1394Gary Michelson1.7medical patents
#1394Jerry Moyes1.7transportation
#1394Charles Munger1.7Berkshire Hathaway
#1394Nelson Peltz1.7investments
#1394Roger Penske1.7cars
#1394Henry Swieca1.7hedge funds
#1394Todd Wagner1.7online media
#1477Bill Austin1.6hearing aids
#1477Louis Bacon1.6hedge funds
#1477William Berkley1.6insurance
#1477Aneel Bhusri1.6business software
#1477O. Francis Biondi1.6hedge funds
#1477David Booth1.6mutual funds
#1477Steve Conine1.6online retail
#1477Stephen Feinberg1.6private equity
#1477Paul Foster1.6oil refining
#1477Mario Gabelli1.6money management
#1477Christopher Goldsbury1.6salsa
#1477Brian Higgins1.6hedge funds
#1477Michael Jordan1.6Charlotte Hornets, endorsements
#1477Edward Lampert1.6Sears
#1477Thai Lee1.6IT provider
#1477Billy Joe (Red) McCombs1.6real estate, oil, cars, sports
#1477Manuel Moroun1.6transportation
#1477Sheryl Sandberg1.6Facebook
#1477Niraj Shah1.6online retail
#1477Ben Silbermann1.6Pinterest
#1477Thomas Steyer1.6hedge funds
#1477Charlotte Colket Weber1.6Campbell Soup
#1561Bill Alfond1.5shoes
#1561Susan Alfond1.5shoes
#1561Ted Alfond1.5shoes
#1561Carol Jenkins Barnett1.5Publix supermarkets
#1561Martha Ford1.5Ford Motor
#1561Richard Hayne1.5Urban Outfitters
#1561Seth Klarman1.5investments
#1561Eren Ozmen1.5aerospace
#1561Fatih Ozmen1.5aerospace
#1561Mark Pincus1.5online games
#1561Kevin Plank1.5Under Armour
#1561Nicholas Pritzker, II.1.5hotels, investments
#1561Fayez Sarofim1.5money management
#1561Kevin Systrom1.5Instagram
#1561Jim Thompson1.5logistics
#1561Jonathan Tisch1.5insurance, NFL team
#1561Kenneth Tuchman1.5outsourcing
#1650Herb Chambers1.4car dealerships
#1650John Edson1.4leisure craft
#1650David Einhorn1.4hedge funds
#1650Victor Fung1.4trading company
#1650Alan Gerry1.4cable television
#1650J. Tomilson Hill1.4investments
#1650George Joseph1.4insurance
#1650Michael Krasny1.4retail
#1650James Leininger1.4medical products
#1650Gary Magness1.4cable TV, investments
#1650Forrest Preston1.4health care
#1650Jerry Reinsdorf1.4sports teams
#1650Evgeny (Eugene) Shvidler1.4oil & gas, investments
#1650Peter Sperling1.4education
#1650Kenny Troutt1.4telecom
#1650Dan Wilks1.4natural gas
#1650Farris Wilks1.4natural gas
#1650Richard Yuengling, Jr.1.4beer
#1756Edmund Ansin1.3television
#1756Steve Case1.3AOL
#1756Darwin Deason1.3Xerox
#1756Jamie Dimon1.3banking
#1756Anne Gittinger1.3Nordstrom department stores
#1756Irwin Jacobs1.3semiconductors
#1756Mitchell Jacobson1.3industrial equipment
#1756Alexander Karp1.3software firm
#1756Sidney Kimmel1.3retail
#1756Rodney Lewis1.3natural gas
#1756Cargill MacMillan, III.1.3Cargill
#1756John MacMillan1.3Cargill
#1756Martha MacMillan1.3Cargill
#1756William MacMillan1.3Cargill
#1756Craig Newmark1.3Craigslist
#1756Bruce Nordstrom1.3Nordstrom department stores
#1756Alexander Rovt1.3fertilizer, real estate
#1756Leonard Schleifer1.3pharmaceuticals
#1756Wilma Tisch1.3diversified
#1756Jayshree Ullal1.3computer networking
#1756Stephen Winn1.3real estate services
#1867Marc Andreessen1.2venture capital investing
#1867Thomas Bailey1.2money management
#1867Charles Brandes1.2money management
#1867Henry Engelhardt1.2insurance
#1867Donald Foss1.2auto loans
#1867Robert Friedland1.2mining
#1867Donald Friese1.2manufacturing
#1867Ryan Graves1.2uber
#1867B. Wayne Hughes, Jr.1.2storage facilities
#1867Thomas James1.2finance
#1867Gail Miller1.2basketball, car dealers
#1867Michael Price1.2investments
#1867Lynsi Snyder1.2In-N-Out Burger
#1867Thomas Tull1.2movies
#1867Alfred West, Jr.1.2money management
#1999William Ackman1.1hedge funds
#1999J. Hyatt Brown1.1insurance
#1999Bharat Desai1.1IT consulting
#1999Joseph Edelman1.1hedge funds
#1999Paul Fireman1.1Reebok
#1999J. Christopher Flowers1.1investments
#1999Drew Houston1.1cloud storage service
#1999Richard Kayne1.1investments
#1999Isaac Larian1.1toys
#1999Frank Laukien1.1scientific equipment
#1999Nancy Lerner1.1banking, credit cards
#1999Norma Lerner1.1banking
#1999Randolph Lerner1.1banking, credit cards
#1999William Macaulay1.1energy investments
#1999John Martin1.1pharmaceuticals
#1999Andrea Reimann-Ciardelli1.1consumer goods
#1999Chris Sacca1.1venture capital investing
#1999Michael Steinhardt1.1hedge funds
#1999Laurie Tisch1.1insurance, NFL team
#1999Steven Tisch1.1insurance
#1999James Truchard1.1software
568 peopleBillions total in wealth3079.7

References

  1. Jensen, M.C.; Murphy, K.J. CEO Incentives—It’s Not How Much You Pay, But How. Harvard Business Review. 1 May 1990. Available online: https://hbr.org/1990/05/ceo-incentives-its-not-how-much-you-pay-but-how (accessed on 8 November 2018).
  2. Hesket, J. Is There an “Efficient Market” in CEO Compensation? 2005. Available online: http://hbswk.hbs.edu/item/is-there-an-efficient-market-in-ceo-compensation (accessed on 8 November 2018).
  3. Hembree, D. CEO Pay Skyrockets to 361 Times That of the Average Worker. Forbes. 2018. Available online: https://www.forbes.com/sites/dianahembree/2018/05/22/ceo-pay-skyrockets-to-361-times-that-of-the-average-worker/#16dcf48d776d (accessed on 8 November 2018).
  4. Lewis, A. Fraud, Failure and Bankruptcy Pay Well for CEOs. Markewatch, 2013. Available online: https://www.marketwatch.com/story/fraud-failure-and-bankruptcy-pay-well-for-ceos-2013-08-28 (accessed on 8 November 2018).
  5. Quadrini, V.; Rıos-Rull, J.V. Dimensions of inequality: Facts on the US distribution of earnings, income and wealth. Fed. Reserv. Bank Minneap. Q. Rev. 1997, 21, 3–21. [Google Scholar]
  6. Rodriguez, S.B.; Rios-Rull, J.V.; Diaz-Gimenez, J.; Quadrini, V. Updated facts on the US distributions of earnings, income, and wealth. Fed. Reserv. Bank Minneap. Q. Rev. 2002, 26, 2–36. [Google Scholar]
  7. Atkinson, A.B.; Bourguignon, F. (Eds.) Handbook of Income Distribution; Elsevier: Amsterdam, The Netherlands, 2014; Volume 2. [Google Scholar]
  8. Credit Suisse. U.S. Wealth Distribution in 2017 | Statistic. 2018. Available online: https://www.statista.com/statistics/203961/wealth-distribution-for-the-us/ (accessed on 8 November 2018).
  9. Becker, G.S.; Tomes, N. An equilibrium theory of the distribution of income and intergenerational mobility. J. Political Econ. 1979, 87, 1153–1189. [Google Scholar] [CrossRef]
  10. Becker, G.S.; Tomes, N. Human capital and the rise and fall of families. J. Labor Econ. 1986, 4 Pt 2, S1–S39. [Google Scholar] [CrossRef]
  11. Kotlikoff, L.J.; Summers, L.H. The role of intergenerational transfers in aggregate capital accumulation. J. Political Econ. 1981, 89, 706–732. [Google Scholar] [CrossRef]
  12. Mulligan, C.B. Parental Priorities and Economic Inequality; University of Chicago Press: Chicago, IL, USA, 1997. [Google Scholar]
  13. Hurd, M.D.; Smith, J.P. Anticipated and actual bequests. In Themes in the Economics of Aging; University of Chicago Press: Chicago, IL, USA, 2001; pp. 357–392. [Google Scholar]
  14. Frank, R. Where the Rich Make Their Income. CNBC. Available online: https://www.cnbc.com/2015/04/09/where-the-rich-make-their-income.html (accessed on 16 November 2018).
  15. Steverman, B. Why American Workers Pay Twice as Much in Taxes as Wealthy Investors. Bloomberg. 12 September 2017. Available online: https://www.bloomberg.com/news/features/2017-09-12/why-american-workers-pay-twice-as-much-in-taxes-as-wealthy-investors (accessed on 16 November 2018).
  16. Ingraham, C. As the Rich Become Super-Rich, they pay lower taxes. For real. The Washington Post. 4 June 2015. Available online: https://www.washingtonpost.com/news/wonk/wp/2015/06/04/as-the-rich-become-super-rich-they-pay-lower-taxes-for-real/ (accessed on 16 November 2018).
  17. Dabla-Norris, M.E.; Kochhar, M.K.; Suphaphiphat, M.N.; Ricka, M.F.; Tsounta, E. Causes and Consequences of Income Inequality: A Global Perspective; International Monetary Fund: Washington, DC, USA, 2015; ISBN 978-1-5135-4437-3. [Google Scholar]
  18. Stiglitz, J.E. The Price of Inequality: How Today’s Divided Society Endangers Our Future; W. W. Norton & Company: New York, NY, USA, 2012; ISBN 978-0-393-08869-4. [Google Scholar]
  19. Ostry, M.J.D.; Berg, M.A.; Tsangarides, M.C.G. Redistribution, Inequality, and Growth; International Monetary Fund: Washington, DC, USA, 2014; ISBN 978-1-4843-9704-6. [Google Scholar]
  20. Berg, A.; Ostry, J.D.; Zettelmeyer, J. What makes growth sustained? J. Dev. Econ. 2012, 98, 149–166. [Google Scholar] [CrossRef] [Green Version]
  21. Cingano, F. Trends in Income Inequality and its Impact on Economic Growth. OECD iLibrary 2014. [Google Scholar] [CrossRef]
  22. Aghion, P.; Caroli, E.; Garcia-Penalosa, C. Inequality and Economic Growth: The Perspective of the New Growth Theories. J. Econ. Lit. 1999, 37, 1615–1660. [Google Scholar] [CrossRef] [Green Version]
  23. Galor, O.; Moav, O. From Physical to Human Capital Accumulation: Inequality and the Process of Development. Rev. Econ. Stud. 2004, 71, 1001–1026. [Google Scholar] [CrossRef] [Green Version]
  24. Corak, M. Income Inequality, Equality of Opportunity, and Intergenerational Mobility. J. Econ. Perspect. 2013, 27, 79–102. [Google Scholar] [CrossRef] [Green Version]
  25. Lichbach, M.I. An Evaluation of “Does Economic Inequality Breed Political Conflict?” Studies. World Politics 1989, 41, 431–470. [Google Scholar] [CrossRef]
  26. Claessens, S.; Perotti, E. Finance and inequality: Channels and evidence. J. Comp. Econ. 2007, 35, 748–773. [Google Scholar] [CrossRef]
  27. Bourguignon, F.; Dessus, S. Equity and development: Political economy considerations. In No Growth without Equity? Walton, M.L., Ed.; Equity and Development; The World Bank: Santiago, Chile, 2009; ISBN 978-0-8213-7767-3. [Google Scholar]
  28. Rajan, R.G. Fault Lines: How Hidden Fractures Still Threaten the World Economy; Princeton University Press: Princeton, NJ, USA, 2011; ISBN 978-1-4008-3980-3. [Google Scholar]
  29. Acemoglu, D.; Naidu, S.; Restrepo, P.; Robinson, J.A. Democracy, public policy and inequality. Am. Political Sci. Rev. 2012, 106, 495–516. [Google Scholar]
  30. Corley, T.C. Rich Habits—The Daily Success Habits of Wealthy Individuals; Langdon Street Press: Minneapolis, MN, USA, 2010; ISBN 978-1-934938-93-5. [Google Scholar]
  31. Pachauri, R.K.; Allen, M.R.; Barros, V.R.; Broome, J.; Cramer, W.; Christ, R.; Church, J.A.; Clarke, L.; Dahe, Q.; Dasgupta, P.; et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Pachauri, R.K., Meyer, L., Eds.; IPCC: Geneva, Switzerland, 2014; ISBN 978-92-9169-143-2. [Google Scholar]
  32. U.S. EPA. Greenhouse Gas Inventory Data Explorer. Available online: https://www3.epa.gov/climatechange/ghgemissions/inventoryexplorer/#electricitygeneration/allgas/source/all (accessed on 13 March 2019).
  33. U.S. Energy Information Administration. What Is U.S. Electricity Generation by Energy Source?—FAQ—U.S. Energy Information Administration (EIA). Available online: https://www.eia.gov/tools/faqs/faq.php?id=427&t=3 (accessed on 18 December 2018).
  34. ArcGIS. States_Basic. 2012. Available online: https://www.arcgis.com/home/item.html?id=f7f805eb65eb4ab787a0a3e1116ca7e5 (accessed on 11 January 2019).
  35. NREL. Geospatial Data Science. 2012. Available online: https://www.nrel.gov/gis/data-solar.html (accessed on 11 January 2019).
  36. U.S. Census Bureau. National Population totals and Components of Change: 2010–2018. 2018. Available online: https://www.census.gov/data/tables/time-series/demo/popest/2010s-national-total.html#par_textimage (accessed on 22 January 2019).
  37. The World’s Billionaires. Forbes. Available online: https://www.forbes.com/billionaires/list/ (accessed on 12 October 2018).
  38. U.S. Energy Information Administration (EIA)—Total Energy Monthly Data. Available online: https://www.eia.gov/totalenergy/data/monthly/ (accessed on 18 December 2018).
  39. NREL Report Shows Utility-Scale Solar PV System Cost Fell Nearly 30% Last Year | NREL | News | NREL. Available online: https://www.nrel.gov/news/press/2017/nrel-report-utility-scale-solar-pv-system-cost-fell-last-year.html (accessed on 18 December 2018).
  40. Branker, K.; Pathak, M.J.M.; Pearce, J.M. A review of solar photovoltaic levelized cost of electricity. Renew. Sustain. Energy Rev. 2011, 15, 4470–4482. [Google Scholar] [CrossRef]
  41. Albrecht, J. The future role of photovoltaics: A learning curve versus portfolio perspective. Energy Policy 2007, 35, 2296–2304. [Google Scholar] [CrossRef]
  42. Yu, C.F.; van Sark, W.G.J.H.M.; Alsema, E.A. Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects. Renew. Sustain. Energy Rev. 2011, 15, 324–337. [Google Scholar] [CrossRef]
  43. Hong, S.; Chung, Y.; Woo, C. Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea. Energy 2015, 79, 80–89. [Google Scholar] [CrossRef]
  44. Trappey, A.J.C.; Trappey, C.V.; Tan, H.; Liu, P.H.Y.; Li, S.-J.; Lin, L.-C. The determinants of photovoltaic system costs: An evaluation using a hierarchical learning curve model. J. Clean. Prod. 2016, 112, 1709–1716. [Google Scholar] [CrossRef]
  45. Mauleón, I. Photovoltaic learning rate estimation: Issues and implications. Renew. Sustain. Energy Rev. 2016, 65, 507–524. [Google Scholar] [CrossRef]
  46. Feldman, D.; Barbose, G.; Margolis, R.; Wiser, R.; Darghout, N.; Goodrich, A. Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections; Technical Report No. DOE/GO-102012-3839; Lawrence Berkeley National Lab.: Berkeley, CA, USA, 2012.
  47. Barbose, G.L.; Darghouth, N.R.; Millstein, D.; LaCommare, K.; DiSanti, N.; Widiss, R. Tracking the Sun 10: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States. Available online: https://emp.lbl.gov/publications/tracking-sun-10-installed-price (accessed on 25 July 2018).
  48. PVinsights. Available online: http://pvinsights.com/ (accessed on 18 January 2019).
  49. Modanese, C.; Laine, H.S.; Pasanen, T.P.; Savin, H.; Pearce, J.M. Economic Advantages of Dry-Etched Black Silicon in Passivated Emitter Rear Cell (PERC) Photovoltaic Manufacturing. Energies 2018, 11, 2337. [Google Scholar] [CrossRef]
  50. Reuters. Solar Costs to Fall Further, Powering Global Demand-Irena. Available online: https://www.reuters.com/article/singapore-energy-solar/solar-costs-to-fall-further-powering-global-demand-irena-idUSL4N1MY2F8 (accessed on 5 March 2018).
  51. Earth’s CO2 Home Page. Available online: https://www.co2.earth/ (accessed on 21 January 2019).
  52. IPCC. Special Report:Global Warming of 1.5 °C. Available online: https://www.ipcc.ch/sr15/ (accessed on 21 January 2019).
  53. Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; et al. The next generation of scenarios for climate change research and assessment. Nature 2010, 463, 747–756. [Google Scholar] [CrossRef] [PubMed]
  54. Stern Review: The Economics of Climate Change (Miscellaneous) | ETDEWEB. Available online: https://www.osti.gov/etdeweb/biblio/20838308 (accessed on 18 January 2019).
  55. Dhainaut, J.-F.; Claessens, Y.-E.; Ginsburg, C.; Riou, B. Unprecedented heat-related deaths during the 2003 heat wave in Paris: Consequences on emergency departments. Crit. Care 2003, 8, 1. [Google Scholar] [CrossRef] [PubMed]
  56. Poumadère, M.; Mays, C.; Mer, S.L.; Blong, R. The 2003 Heat Wave in France: Dangerous Climate Change Here and Now. Risk Anal. 2005, 25, 1483–1494. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Fouillet, A.; Rey, G.; Laurent, F.; Pavillon, G.; Bellec, S.; Guihenneuc-Jouyaux, C.; Clavel, J.; Jougla, E.; Hémon, D. Excess mortality related to the August 2003 heat wave in France. Int. Arch. Occup. Environ. Health 2006, 80, 16–24. [Google Scholar] [CrossRef] [Green Version]
  58. D’Amato, G.; Cecchi, L. Effects of climate change on environmental factors in respiratory allergic diseases. Clin. Exp. Allergy 2008, 38, 1264–1274. [Google Scholar] [CrossRef]
  59. Challinor, A.J.; Simelton, E.S.; Fraser, E.D.G.; Hemming, D.; Collins, M. Increased crop failure due to climate change: Assessing adaptation options using models and socio-economic data for wheat in China. Environ. Res. Lett. 2010, 5, 034012. [Google Scholar] [CrossRef]
  60. Jones, P.G.; Thornton, P.K. The potential impacts of climate change on maize production in Africa and Latin America in 2055. Glob. Environ. Chang. 2003, 13, 51–59. [Google Scholar] [CrossRef]
  61. Parry, M.L.; Rosenzweig, C.; Iglesias, A.; Livermore, M.; Fischer, G. Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob. Environ. Chang. 2004, 14, 53–67. [Google Scholar] [CrossRef]
  62. Parry, M.; Rosenzweig, C.; Livermore, M. Climate change, global food supply and risk of hunger. Philos. Trans. R. Soc. B Biol. Sci. 2005, 360, 2125–2138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Schmidhuber, J.; Tubiello, F.N. Global food security under climate change. Proc. Natl. Acad. Sci. USA 2007, 104, 19703–19708. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Klinenberg, E. Are you ready for the next disaster? New York Times Magazine, 6 June 2008. [Google Scholar]
  65. Vine, E. Adaptation of California’s electricity sector to climate change. Clim. Chang. 2012, 111, 75–99. [Google Scholar] [CrossRef]
  66. Moorhead, K.K.; Brinson, M.M. Response of Wetlands to Rising Sea Level in the Lower Caostal Plain of North Carolina. Ecol. Appl. 1995, 5, 261–271. [Google Scholar] [CrossRef]
  67. Frihy, O.E. The Nile delta-Alexandria coast: Vulnerability to sea-level rise, consequences and adaptation. Mitig. Adapt. Strateg. Glob. Chang. 2003, 8, 115–138. [Google Scholar] [CrossRef]
  68. Nicholls, R.J.; Hoozemans, F.M.J.; Marchand, M. Increasing flood risk and wetland losses due to global sea-level rise: Regional and global analyses. Glob. Environ. Chang. 1999, 9, S69–S87. [Google Scholar] [CrossRef]
  69. Bobba, A.G. Numerical modelling of salt-water intrusion due to human activities and sea-level change in the Godavari Delta, India. Hydrol. Sci. J. 2002, 47, S67–S80. [Google Scholar] [CrossRef] [Green Version]
  70. Desantis, L.R.G.; Bhotika, S.; Williams, K.; Putz, F.E. Sea-level rise and drought interactions accelerate forest decline on the Gulf Coast of Florida, USA. Glob. Chang. Biol. 2007, 13, 2349–2360. [Google Scholar] [CrossRef]
  71. Dale, V.H.; Joyce, L.A.; McNulty, S.; Neilson, R.P.; Ayres, M.P.; Flannigan, M.D.; Hanson, P.J.; Irland, L.C.; Lugo, A.E.; Peterson, C.J.; et al. Climate Change and Forest DisturbancesClimate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. BioScience 2001, 51, 723–734. [Google Scholar] [CrossRef]
  72. Flannigan, M.D.; Stocks, B.J.; Wotton, B.M. Climate change and forest fires. Sci. Total Environ. 2000, 262, 221–229. [Google Scholar] [CrossRef]
  73. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H. (Ted); et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  74. Carnicer, J.; Coll, M.; Ninyerola, M.; Pons, X.; Sánchez, G.; Peñuelas, J. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl. Acad. Sci. USA 2011, 108, 1474–1478. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 2013, 3, 52–58. [Google Scholar] [CrossRef]
  76. Amiro, B.D.; Stocks, B.J.; Alexander, M.E.; Flannigan, M.D.; Wotton, B.M. Fire, climate change, carbon and fuel management in the Canadian boreal forest. Int. J. Wildland Fire 2001, 10, 405–413. [Google Scholar] [CrossRef]
  77. Flannigan, M.; Stocks, B.; Turetsky, M.; Wotton, M. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Chang. Biol. 2009, 15, 549–560. [Google Scholar] [CrossRef]
  78. UN News Centre. 2014. Available online: http://www.un.org/apps/news/story.asp?NewsID=47047#.VDLw1BaaXGU (accessed on 6 October 2014).
  79. IPCC Fifth Assessment Report. 2013. Available online: http://www.ipcc.ch/publications_and_data/publications_and_data_reports.shtml (accessed on 29 September 2014).
  80. What Are the 10 Biggest Global Challenges? WE Forum. Available online: https://www.weforum.org/agenda/2016/01/what-are-the-10-biggest-global-challenges/ (accessed on 21 January 2019).
  81. Friedman, Z. Why Bill Gates, Jeff Bezos, Mark Zuckerberg & Richard Branson Are Investing In These 2 Startups. Available online: https://www.forbes.com/sites/zackfriedman/2018/06/14/bill-gates-jeff-bezos-mark-zuckerberg-branson-startups/ (accessed on 21 January 2019).
  82. Climate Change and the 75% Problem | Bill Gates. Available online: https://www.gatesnotes.com/Energy/My-plan-for-fighting-climate-change (accessed on 21 January 2019).
  83. Pyper, J. Elon Musk: Humanity Is Pretending Fossil Fuels Have ‘No Probability of a Bad Outcome’. Available online: https://www.greentechmedia.com/articles/read/elon-musk-fossil-fuels-carbon-price-sxsw (accessed on 21 January 2019).
  84. Pyper, J. Google Officially Hits Its 100% Renewable Energy Target. Available online: https://www.greentechmedia.com/articles/read/google-officially-hits-100-renewable-energy-target (accessed on 21 January 2019).
  85. Ozment, D. Walmart’s Commitment to Solar. Available online: https://blog.walmart.com/_blog_/sustainability/20140509/walmarts-commitment-to-solar (accessed on 21 January 2019).
  86. Fehrenbacher, K. These Are the U.S. Companies with the Most Solar Power. Available online: http://fortune.com/2016/10/19/corporate-solar-target-walmart/ (accessed on 21 January 2019).
  87. Heidari, N.; Pearce, J.M. A review of greenhouse gas emission liabilities as the value of renewable energy for mitigating lawsuits for climate change related damages. Renew. Sustain. Energy Rev. 2016, 55, 899–908. [Google Scholar] [CrossRef]
  88. Lowder, T.; Mendelsohn, M. Potential of Securitization in Solar PV Finance; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2013.
  89. Alafita, T.; Pearce, J.M. Securitization of residential solar photovoltaic assets: Costs, risks and uncertainty. Energy Policy 2014, 67, 488–498. [Google Scholar] [CrossRef]
  90. Hyde, D.; Komor, P. Distributed PV and Securitization: Made for Each Other? Electr. J. 2014, 27, 63–70. [Google Scholar] [CrossRef]
  91. Pawłowski, M. Financing The Solar Energy Market Through The Use of Securitization–The Case of The United States. Copernic. J. Financ. Account. 2018, 7, 63–76. [Google Scholar] [CrossRef]
  92. 109th Congress. Energy Policy Act. 2005. Available online: https://www.congress.gov/109/plaws/publ58/PLAW-109publ58.pdf (accessed on 28 January 2019).
  93. Solar Energy Industries Association. Solar Investment Tax Credit. 2018. Available online: https://www.seia.org/initiatives/solar-investment-tax-credit-itc (accessed on 28 January 2019).
  94. 115th Congress. Tax Cuts and Jobs Act. 2017. Available online: https://www.congress.gov/115/bills/hr1/BILLS-115hr1enr.pdf (accessed on 28 January 2019).
  95. Internal Revenue Service. New Rules and Limitations for Depreciation and Expensing under the Tax Cuts and Jobs Act. 2018. Available online: https://www.irs.gov/newsroom/new-rules-and-limitations-for-depreciation-and-expensing-under-the-tax-cuts-and-jobs-act (accessed on 28 January 2019).
  96. Database of Incentives for Renewables and Efficiency. 2018. Available online: http://www.dsireusa.org/ (accessed on 28 January 2019).
  97. Internal Revenue Service. Treasury, IRS Issue Proposed Regulations on New Opportunity Zone Tax Incentive. 2018. Available online: https://www.irs.gov/newsroom/treasury-irs-issue-proposed-regulations-on-new-opportunity-zone-tax-incentive (accessed on 28 January 2019).
  98. Home—The Giving Pledge. Available online: https://givingpledge.org/ (accessed on 21 January 2019).
  99. Paul, G.S.; Verdier, T. Inequality, redistribution and growth: A challenge to the conventional political economy approach. Eur. Econ. Rev. 1996, 3–5, 719–728. [Google Scholar] [CrossRef]
  100. Lupu, N.; Pontusson, J. The Structure of Inequality and the Politics of Redistribution. Am. Political Sci. Rev. 2011, 105, 316–336. [Google Scholar] [CrossRef] [Green Version]
  101. Moene, K.O.; Wallerstein, M. Inequality, Social Insurance, and Redistribution. Am. Political Sci. Rev. 2001, 95, 859–874. [Google Scholar]
  102. Acemoglu, D.; Naidu, S.; Restrepo, P.; Robinson, J.A. Chapter 21—Democracy, Redistribution, and Inequality. In Handbook of Income Distribution; Atkinson, A.B., Bourguignon, F., Eds.; Elsevier: Amsterdam, The Netherlands, 2015; Volume 2, pp. 1885–1966. [Google Scholar]
  103. Evans, M.D.R.; Kelley, J. Strong Welfare States Do Not Intensify Public Support for Income Redistribution, but Even Reduce It among the Prosperous: A Multilevel Analysis of Public Opinion in 30 Countries. Societies 2018, 8, 105. [Google Scholar] [CrossRef]
  104. Fatke, M. Inequality Perceptions, Preferences Conducive to Redistribution, and the Conditioning Role of Social Position. Societies 2018, 8, 99. [Google Scholar] [CrossRef]
  105. VanHeuvelen, T.; Copas, K. The Intercohort Dynamics of Support for Redistribution in 54 Countries, 1985–2017. Societies 2018, 8, 69. [Google Scholar] [CrossRef]
  106. Mijs, J.J.B. Inequality Is a Problem of Inference: How People Solve the Social Puzzle of Unequal Outcomes. Societies 2018, 8, 64. [Google Scholar] [CrossRef]
  107. Ignácz, Z.S. The Remains of the Socialist Legacy: The Influence of Socialist Socialization on Attitudes toward Income Inequality. Societies 2018, 8, 62. [Google Scholar] [CrossRef]
  108. Berg, A.; Ostry, J.D.; Tsangarides, C.G.; Yakhshilikov, Y. Redistribution, inequality, and growth: New evidence. J. Econ. Growth 2018, 23, 259–305. [Google Scholar] [CrossRef]
  109. Mayer, J. In the withdrawal from the Paris Climate Agreement, the Koch Brothers’ campaign becomes overt. The New Yorker. 8 August 2017. Available online: http://www.newyorker.com/news/news-desk/in-the-withdrawal-from-the-paris-climate-agreement-the-koch-brothers-campaign-becomes-overt (accessed on 25 January 2019).
  110. Hsiang, S.; Kopp, R.; Jina, A.; Rising, J.; Delgado, M.; Mohan, S.; Rasmussen, D.J.; Muir-Wood, R.; Wilson, P.; Oppenheimer, M.; et al. Estimating economic damage from climate change in the United States. Science 2017, 356, 1362–1369. [Google Scholar] [CrossRef] [Green Version]
  111. Tol, R.S.J. The Economic Impacts of Climate Change. Rev. Environ. Econ. Policy 2018, 12, 4–25. [Google Scholar] [CrossRef]
  112. Pearce, D. Green economics. Environ. Values 1992, 1, 3–13. [Google Scholar] [CrossRef]
  113. Jackson, T. Prosperity without Growth: Economics for a Finite Planet; Routledge: Abingdon-on-Thames, UK, 2009; ISBN 978-1-136-54678-5. [Google Scholar]
  114. Cato, M.S. Green Economics: An Introduction to Theory, Policy and Practice; Routledge: Abingdon-on-Thames, UK, 2012; ISBN 978-1-136-56442-0. [Google Scholar]
  115. Nguyen, H.T.; Pearce, J.M. Estimating potential photovoltaic yield with r. sun and the open source Geographical Resources Analysis Support System. Sol. Energy 2010, 84, 831–843. [Google Scholar] [CrossRef]
  116. Wang, C.; Nehrir, M.H. Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE Trans. Power Syst. 2004, 19, 2068–2076. [Google Scholar] [CrossRef]
  117. Prommee, W.; Ongsakul, W. Optimal multi-distributed generation placement by adaptive weight particle swarm optimization. In Proceedings of the 2008 International Conference on Control, Automation and Systems, Seoul, Korea, 14–17 October 2008; pp. 1663–1668. [Google Scholar]
  118. Wiginton, L.K.; Nguyen, H.T.; Pearce, J.M. Quantifying rooftop solar photovoltaic potential for regional renewable energy policy. Comput. Environ. Urban Syst. 2010, 34, 345–357. [Google Scholar] [CrossRef]
  119. Strzalka, A.; Alam, N.; Duminil, E.; Coors, V.; Eicker, U. Large scale integration of photovoltaics in cities. Appl. Energy 2012, 93, 413–421. [Google Scholar] [CrossRef]
  120. Nguyen, H.T.; Pearce, J.M. Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Sol. Energy 2012, 86, 1245–1260. [Google Scholar] [CrossRef] [Green Version]
  121. Srećković, N.; Lukač, N.; Žalik, B.; Štumberger, G. Determining roof surfaces suitable for the installation of PV (photovoltaic) systems, based on LiDAR (Light Detection And Ranging) data, pyranometer measurements, and distribution network configuration. Energy 2016, 96, 404–414. [Google Scholar] [CrossRef]
  122. Vinco, S.; Bottaccioli, L.; Patti, E.; Acquaviva, A.; Macii, E.; Poncino, M. GIS-based optimal photovoltaic panel floorplanning for residential installations. In Proceedings of the 2018 Design, Automation Test in Europe Conference Exhibition (DATE), Dresden, Germany, 19–23 March 2018; pp. 437–442. [Google Scholar]
  123. Redweik, P.; Catita, C.; Brito, M. Solar energy potential on roofs and facades in an urban landscape. Sol. Energy 2013, 97, 332–341. [Google Scholar] [CrossRef]
  124. Ong, S.; Campbell, C.; Denholm, P.; Margolis, R.; Heath, G. Land-Use Requirements for Solar Power Plants in the United States; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2013.
  125. Dubois, M.-C.; Blomsterberg, Å. Energy saving potential and strategies for electric lighting in future North European, low energy office buildings: A literature review. Energy Build. 2011, 43, 2572–2582. [Google Scholar] [CrossRef]
  126. Hepbasli, A.; Kalinci, Y. A review of heat pump water heating systems. Renew. Sustain. Energy Rev. 2009, 13, 1211–1229. [Google Scholar] [CrossRef]
  127. Brown, R.U.S. Building-Sector Energy Efficiency Potential; LBNL: Berkeley, CA, USA, 2008.
  128. de Almeida, A.; Bertoldi, P.; Leonhard, W. Energy Efficiency Improvements in Electric Motors and Drives; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012; ISBN 978-3-642-60832-2. [Google Scholar]
  129. Amelang, S. Renewables cover about 100% of German Power Use for First Time Ever. 2018. Available online: https://www.cleanenergywire.org/news/renewables-cover-about-100-german-power-use-first-time-ever (accessed on 21 January 2019).
  130. Hoff, T.E.; Wenger, H.J.; Farmer, B.K. Distributed generation: An alternative to electric utility investments in system capacity. Energy Policy 1996, 24, 137–147. [Google Scholar] [CrossRef]
  131. Pepermans, G.; Driesen, J.; Haeseldonckx, D.; Belmans, R.; D’haeseleer, W. Distributed generation: Definition, benefits and issues. Energy Policy 2005, 33, 787–798. [Google Scholar] [CrossRef]
  132. Dondi, P.; Bayoumi, D.; Haederli, C.; Julian, D.; Suter, M. Network integration of distributed power generation. J. Power Sources 2002, 106, 1–9. [Google Scholar] [CrossRef]
  133. Strachan, N.; Farrell, A. Emissions from distributed vs. centralized generation: The importance of system performance. Energy Policy 2006, 34, 2677–2689. [Google Scholar] [CrossRef]
  134. Prehoda, E.W.; Pearce, J.M. Potential lives saved by replacing coal with solar photovoltaic electricity production in the U.S. Renew. Sustain. Energy Rev. 2017, 80, 710–715. [Google Scholar] [CrossRef]
  135. Pearce, J.M. Towards Quantifiable Metrics Warranting Industry-Wide Corporate Death Penalties. Soc. Sci. 2019, 8, 62. [Google Scholar] [CrossRef]
  136. Meyers, E.M.; Hu, M.G. Clean Distributed Generation: Policy Options to Promote Clean Air and Reliability. Electr. J. 2001, 14, 89–98. [Google Scholar]
  137. Jewell, W.T.; Unruh, T.D. Limits on cloud-induced fluctuation in photovoltaic generation. IEEE Trans. Energy Convers. 1990, 5, 8–14. [Google Scholar] [CrossRef]
  138. Pearce, J.M. Photovoltaics—A path to sustainable futures. Futures 2002, 34, 663–674. [Google Scholar] [CrossRef]
  139. Keister, L.A.; Moller, S. Wealth Inequality in the United States. Annu. Rev. Sociol. 2000, 26, 63–81. [Google Scholar] [CrossRef]
  140. Castañeda, A.; Díaz-Giménez, J.; Ríos-Rull, J. Accounting for the U.S. Earnings and Wealth Inequality. J. Political Econ. 2003, 111, 818–857. [Google Scholar] [CrossRef] [Green Version]
  141. Saez, E.; Zucman, G. Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data. Q. J. Econ. 2016, 131, 519–578. [Google Scholar] [CrossRef]
  142. Elliott, L. World’s 26 richest people own as much as poorest 50%, says Oxfam. The Guardian, 21 January 2019. [Google Scholar]
  143. Nugent, D.; Sovacool, B.K. Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey. Energy Policy 2014, 65, 229–244. [Google Scholar] [CrossRef]
  144. Kenny, R.; Law, C.; Pearce, J.M. Towards real energy economics: Energy policy driven by life-cycle carbon emission. Energy Policy 2010, 38, 1969–1978. [Google Scholar] [CrossRef] [Green Version]
1
In the U.S. in 2018, the average CEO of the S&P 500 Index firms was paid 361 times the wages earned by the average worker [3]. Interestingly, CEOs are often rewarded economically, even when the are objectively ineffective, with pay, bonuses, and “golden parachutes” worth on average $48 million (e.g., the roughly 40% who were the top 25 highest paid in 20 years whose companies failed, went bankrupt, paid millions of dollars in fines for fraud, or received taxpayer bailouts [4]). It should be pointed out that there is no evidence that even the 60% of top paid executives, who were not objective failures, were able to provide material value to their companies or society from the work they performed (e.g., analysis and decision making from themselves, not their subordinates) equivalent to the value of their remuneration.
Figure 1. Ratio of population-adjusted solar flux in each U.S. State.
Figure 1. Ratio of population-adjusted solar flux in each U.S. State.
Societies 09 00026 g001
Table 1. Cumulative American multi-billionaires necessary to reach investment goals of complete replacement of all of the fossil fuel-based electricity generation in the U.S. with solar.
Table 1. Cumulative American multi-billionaires necessary to reach investment goals of complete replacement of all of the fossil fuel-based electricity generation in the U.S. with solar.
Cumulative Number of InvestorsAmerican Multi-BillionairesWealth Total
-$1B [B US$]
Cumulative Excess Wealth [B US$]
1Jeff Bezos111111
2Bill Gates89200
3Warren Buffett83283
4Mark Zuckerberg70353
5Charles Koch59412
6David Koch59471
7Larry Ellison57.5528.5
8Michael Bloomberg49577.5
9Larry Page47.8625.3
10Sergey Brin46.5671.8
11Jim Walton45.4717.2
12S. Robson Walton45.2762.4
13Alice Walton45807.4
14Sheldon Adelson37.5844.9
15Steve Ballmer37.4882.3
16Phil Knight28.6910.9
17Jacqueline Mars22.6933.5
18John Mars22.6956.1
19Michael Dell21.7977.8
20Paul Allen20.7998.5
21Thomas Peterffy19.31017.8
22Len Blavatnik19.21037
23James Simons191056
24Elon Musk18.91074.9
25Laurene Powell Jobs17.81092.7
26Ray Dalio16.71109.4
27Carl Icahn15.81125.2
28Donald Bren15.31140.5
29Abigail Johnson14.91155.4
30Lukas Walton14.91170.3
31Rupert Murdoch141184.3
32Harold Hamm13.11197.4
33Steve Cohen131210.4
34Dustin Moskovitz131223.4
35Charles Ergen12.41235.8
36Eric Schmidt12.41248.2
37Philip Anschutz121260.2
38Jim Kennedy121272.2
39Blair Parry-Okeden121284.2
40Leonard Lauder11.91296.1
41Stephen Schwarzman11.61307.7
42Donald Newhouse11.31319
43Andrew Beal10.61329.6
44John Menard, Jr.10.51340.1
45David Tepper101350.1
46Pierre Omidyar9.51359.6
47Ronald Perelman8.81368.4
48Micky Arison8.71377.1
49Thomas Frist, Jr.8.61385.7
50Charles Schwab8.41394.1
51Herbert Kohler, Jr.8.31402.4
52Jan Koum8.11410.5
53James Goodnight81418.5
54Ken Griffin81426.5
55James Chambers7.71434.2
56Katharine Rayner7.71441.9
57Margaretta Taylor7.71449.6
58Gordon Moore7.51457.1
59Stanley Kroenke7.31464.4
60John Malone7.11471.5
61Carl Cook71478.5
62David Geffen71485.5
63George Soros71492.5
64Edward Johnson, III.6.91499.4
65David Duffield6.81506.2
66George Kaiser6.81513
67Patrick Soon-Shiong6.81519.8
68Stephen Ross6.61526.4
69Pauline MacMillan Keinath6.41532.8
70Eli Broad6.31539.1
71Sun Hongbin6.31545.4
72Christy Walton6.31551.7
73Shahid Khan6.21557.9
74John Doerr6.11564
75David Green5.81569.8
76Hank & Doug Meijer5.81575.6
77Brian Acton5.61581.2
78Ann Walton Kroenke5.61586.8
79Leon Black5.51592.3

Share and Cite

MDPI and ACS Style

Pearce, J.M.; Prehoda, E. Could 79 People Solarize the U.S. Electric Grid? Societies 2019, 9, 26. https://doi.org/10.3390/soc9010026

AMA Style

Pearce JM, Prehoda E. Could 79 People Solarize the U.S. Electric Grid? Societies. 2019; 9(1):26. https://doi.org/10.3390/soc9010026

Chicago/Turabian Style

Pearce, Joshua M., and Emily Prehoda. 2019. "Could 79 People Solarize the U.S. Electric Grid?" Societies 9, no. 1: 26. https://doi.org/10.3390/soc9010026

APA Style

Pearce, J. M., & Prehoda, E. (2019). Could 79 People Solarize the U.S. Electric Grid? Societies, 9(1), 26. https://doi.org/10.3390/soc9010026

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop