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Article Contents

Introduction, 1 installed capacity and application of solar energy worldwide, 2 the role of solar energy in sustainable development, 3 the perspective of solar energy, 4 conclusions, conflict of interest statement.

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Solar energy technology and its roles in sustainable development

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Ali O M Maka, Jamal M Alabid, Solar energy technology and its roles in sustainable development, Clean Energy , Volume 6, Issue 3, June 2022, Pages 476–483, https://doi.org/10.1093/ce/zkac023

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Solar energy is environmentally friendly technology, a great energy supply and one of the most significant renewable and green energy sources. It plays a substantial role in achieving sustainable development energy solutions. Therefore, the massive amount of solar energy attainable daily makes it a very attractive resource for generating electricity. Both technologies, applications of concentrated solar power or solar photovoltaics, are always under continuous development to fulfil our energy needs. Hence, a large installed capacity of solar energy applications worldwide, in the same context, supports the energy sector and meets the employment market to gain sufficient development. This paper highlights solar energy applications and their role in sustainable development and considers renewable energy’s overall employment potential. Thus, it provides insights and analysis on solar energy sustainability, including environmental and economic development. Furthermore, it has identified the contributions of solar energy applications in sustainable development by providing energy needs, creating jobs opportunities and enhancing environmental protection. Finally, the perspective of solar energy technology is drawn up in the application of the energy sector and affords a vision of future development in this domain.

With reference to the recommendations of the UN, the Climate Change Conference, COP26, was held in Glasgow , UK, in 2021. They reached an agreement through the representatives of the 197 countries, where they concurred to move towards reducing dependency on coal and fossil-fuel sources. Furthermore, the conference stated ‘the various opportunities for governments to prioritize health and equity in the international climate movement and sustainable development agenda’. Also, one of the testaments is the necessity to ‘create energy systems that protect and improve climate and health’ [ 1 , 2 ].

The Paris Climate Accords is a worldwide agreement on climate change signed in 2015, which addressed the mitigation of climate change, adaptation and finance. Consequently, the representatives of 196 countries concurred to decrease their greenhouse gas emissions [ 3 ]. The Paris Agreement is essential for present and future generations to attain a more secure and stable environment. In essence, the Paris Agreement has been about safeguarding people from such an uncertain and progressively dangerous environment and ensuring everyone can have the right to live in a healthy, pollutant-free environment without the negative impacts of climate change [ 3 , 4 ].

In recent decades, there has been an increase in demand for cleaner energy resources. Based on that, decision-makers of all countries have drawn up plans that depend on renewable sources through a long-term strategy. Thus, such plans reduce the reliance of dependence on traditional energy sources and substitute traditional energy sources with alternative energy technology. As a result, the global community is starting to shift towards utilizing sustainable energy sources and reducing dependence on traditional fossil fuels as a source of energy [ 5 , 6 ].

In 2015, the UN adopted the sustainable development goals (SDGs) and recognized them as international legislation, which demands a global effort to end poverty, safeguard the environment and guarantee that by 2030, humanity lives in prosperity and peace. Consequently, progress needs to be balanced among economic, social and environmental sustainability models [ 7 ].

Many national and international regulations have been established to control the gas emissions and pollutants that impact the environment [ 8 ]. However, the negative effects of increased carbon in the atmosphere have grown in the last 10 years. Production and use of fossil fuels emit methane (CH 4 ), carbon dioxide (CO 2 ) and carbon monoxide (CO), which are the most significant contributors to environmental emissions on our planet. Additionally, coal and oil, including gasoline, coal, oil and methane, are commonly used in energy for transport or for generating electricity. Therefore, burning these fossil fuel s is deemed the largest emitter when used for electricity generation, transport, etc. However, these energy resources are considered depleted energy sources being consumed to an unsustainable degree [ 9–11 ].

Energy is an essential need for the existence and growth of human communities. Consequently, the need for energy has increased gradually as human civilization has progressed. Additionally, in the past few decades, the rapid rise of the world’s population and its reliance on technological developments have increased energy demands. Furthermore, green technology sources play an important role in sustainably providing energy supplies, especially in mitigating climate change [ 5 , 6 , 8 ].

Currently, fossil fuels remain dominant and will continue to be the primary source of large-scale energy for the foreseeable future; however, renewable energy should play a vital role in the future of global energy. The global energy system is undergoing a movement towards more sustainable sources of energy [ 12 , 13 ].

Power generation by fossil-fuel resources has peaked, whilst solar energy is predicted to be at the vanguard of energy generation in the near future. Moreover, it is predicted that by 2050, the generation of solar energy will have increased to 48% due to economic and industrial growth [ 13 , 14 ].

In recent years, it has become increasingly obvious that the globe must decrease greenhouse gas emissions by 2050, ideally towards net zero, if we are to fulfil the Paris Agreement’s goal to reduce global temperature increases [ 3 , 4 ]. The net-zero emissions complement the scenario of sustainable development assessment by 2050. According to the agreed scenario of sustainable development, many industrialized economies must achieve net-zero emissions by 2050. However, the net-zero emissions 2050 brought the first detailed International Energy Agency (IEA) modelling of what strategy will be required over the next 10 years to achieve net-zero carbon emissions worldwide by 2050 [ 15–17 ].

The global statistics of greenhouse gas emissions have been identified; in 2019, there was a 1% decrease in CO 2 emissions from the power industry; that figure dropped by 7% in 2020 due to the COVID-19 crisis, thus indicating a drop in coal-fired energy generation that is being squeezed by decreasing energy needs, growth of renewables and the shift away from fossil fuels. As a result, in 2020, the energy industry was expected to generate ~13 Gt CO 2 , representing ~40% of total world energy sector emissions related to CO 2 . The annual electricity generation stepped back to pre-crisis levels by 2021, although due to a changing ‘fuel mix’, the CO 2 emissions in the power sector will grow just a little before remaining roughly steady until 2030 [ 15 ].

Therefore, based on the information mentioned above, the advantages of solar energy technology are a renewable and clean energy source that is plentiful, cheaper costs, less maintenance and environmentally friendly, to name but a few. The significance of this paper is to highlight solar energy applications to ensure sustainable development; thus, it is vital to researchers, engineers and customers alike. The article’s primary aim is to raise public awareness and disseminate the culture of solar energy usage in daily life, since moving forward, it is the best. The scope of this paper is as follows. Section 1 represents a summary of the introduction. Section 2 represents a summary of installed capacity and the application of solar energy worldwide. Section 3 presents the role of solar energy in the sustainable development and employment of renewable energy. Section 4 represents the perspective of solar energy. Finally, Section 5 outlines the conclusions and recommendations for future work.

1.1 Installed capacity of solar energy

The history of solar energy can be traced back to the seventh century when mirrors with solar power were used. In 1893, the photovoltaic (PV) effect was discovered; after many decades, scientists developed this technology for electricity generation [ 18 ]. Based on that, after many years of research and development from scientists worldwide, solar energy technology is classified into two key applications: solar thermal and solar PV.

PV systems convert the Sun’s energy into electricity by utilizing solar panels. These PV devices have quickly become the cheapest option for new electricity generation in numerous world locations due to their ubiquitous deployment. For example, during the period from 2010 to 2018, the cost of generating electricity by solar PV plants decreased by 77%. However, solar PV installed capacity progress expanded 100-fold between 2005 and 2018. Consequently, solar PV has emerged as a key component in the low-carbon sustainable energy system required to provide access to affordable and dependable electricity, assisting in fulfilling the Paris climate agreement and in achieving the 2030 SDG targets [ 19 ].

The installed capacity of solar energy worldwide has been rapidly increased to meet energy demands. The installed capacity of PV technology from 2010 to 2020 increased from 40 334 to 709 674 MW, whereas the installed capacity of concentrated solar power (CSP) applications, which was 1266 MW in 2010, after 10 years had increased to 6479 MW. Therefore, solar PV technology has more deployed installations than CSP applications. So, the stand-alone solar PV and large-scale grid-connected PV plants are widely used worldwide and used in space applications. Fig. 1 represents the installation of solar energy worldwide.

Installation capacity of solar energy worldwide [ 20 ].

1.2 Application of solar energy

Energy can be obtained directly from the Sun—so-called solar energy. Globally, there has been growth in solar energy applications, as it can be used to generate electricity, desalinate water and generate heat, etc. The taxonomy of applications of solar energy is as follows: (i) PVs and (ii) CSP. Fig. 2 details the taxonomy of solar energy applications.

The taxonomy of solar energy applications.

Solar cells are devices that convert sunlight directly into electricity; typical semiconductor materials are utilized to form a PV solar cell device. These materials’ characteristics are based on atoms with four electrons in their outer orbit or shell. Semiconductor materials are from the periodic table’s group ‘IV’ or a mixture of groups ‘IV’ and ‘II’, the latter known as ‘II–VI’ semiconductors [ 21 ]. Additionally, a periodic table mixture of elements from groups ‘III’ and ‘V’ can create ‘III–V’ materials [ 22 ].

PV devices, sometimes called solar cells, are electronic devices that convert sunlight into electrical power. PVs are also one of the rapidly growing renewable-energy technologies of today. It is therefore anticipated to play a significant role in the long-term world electricity-generating mixture moving forward.

Solar PV systems can be incorporated to supply electricity on a commercial level or installed in smaller clusters for mini-grids or individual usage. Utilizing PV modules to power mini-grids is a great way to offer electricity to those who do not live close to power-transmission lines, especially in developing countries with abundant solar energy resources. In the most recent decade, the cost of producing PV modules has dropped drastically, giving them not only accessibility but sometimes making them the least expensive energy form. PV arrays have a 30-year lifetime and come in various shades based on the type of material utilized in their production.

The most typical method for solar PV desalination technology that is used for desalinating sea or salty water is electrodialysis (ED). Therefore, solar PV modules are directly connected to the desalination process. This technique employs the direct-current electricity to remove salt from the sea or salty water.

The technology of PV–thermal (PV–T) comprises conventional solar PV modules coupled with a thermal collector mounted on the rear side of the PV module to pre-heat domestic hot water. Accordingly, this enables a larger portion of the incident solar energy on the collector to be converted into beneficial electrical and thermal energy.

A zero-energy building is a building that is designed for zero net energy emissions and emits no carbon dioxide. Building-integrated PV (BIPV) technology is coupled with solar energy sources and devices in buildings that are utilized to supply energy needs. Thus, building-integrated PVs utilizing thermal energy (BIPV/T) incorporate creative technologies such as solar cooling [ 23 ].

A PV water-pumping system is typically used to pump water in rural, isolated and desert areas. The system consists of PV modules to power a water pump to the location of water need. The water-pumping rate depends on many factors such as pumping head, solar intensity, etc.

A PV-powered cathodic protection (CP) system is designed to supply a CP system to control the corrosion of a metal surface. This technique is based on the impressive current acquired from PV solar energy systems and is utilized for burying pipelines, tanks, concrete structures, etc.

Concentrated PV (CPV) technology uses either the refractive or the reflective concentrators to increase sunlight to PV cells [ 24 , 25 ]. High-efficiency solar cells are usually used, consisting of many layers of semiconductor materials that stack on top of each other. This technology has an efficiency of >47%. In addition, the devices produce electricity and the heat can be used for other purposes [ 26 , 27 ].

For CSP systems, the solar rays are concentrated using mirrors in this application. These rays will heat a fluid, resulting in steam used to power a turbine and generate electricity. Large-scale power stations employ CSP to generate electricity. A field of mirrors typically redirect rays to a tall thin tower in a CSP power station. Thus, numerous large flat heliostats (mirrors) are used to track the Sun and concentrate its light onto a receiver in power tower systems, sometimes known as central receivers. The hot fluid could be utilized right away to produce steam or stored for later usage. Another of the great benefits of a CSP power station is that it may be built with molten salts to store heat and generate electricity outside of daylight hours.

Mirrored dishes are used in dish engine systems to focus and concentrate sunlight onto a receiver. The dish assembly tracks the Sun’s movement to capture as much solar energy as possible. The engine includes thin tubes that work outside the four-piston cylinders and it opens into the cylinders containing hydrogen or helium gas. The pistons are driven by the expanding gas. Finally, the pistons drive an electric generator by turning a crankshaft.

A further water-treatment technique, using reverse osmosis, depends on the solar-thermal and using solar concentrated power through the parabolic trough technique. The desalination employs CSP technology that utilizes hybrid integration and thermal storage allows continuous operation and is a cost-effective solution. Solar thermal can be used for domestic purposes such as a dryer. In some countries or societies, the so-called food dehydration is traditionally used to preserve some food materials such as meats, fruits and vegetables.

Sustainable energy development is defined as the development of the energy sector in terms of energy generating, distributing and utilizing that are based on sustainability rules [ 28 ]. Energy systems will significantly impact the environment in both developed and developing countries. Consequently, the global sustainable energy system must optimize efficiency and reduce emissions [ 29 ].

The sustainable development scenario is built based on the economic perspective. It also examines what activities will be required to meet shared long-term climate benefits, clean air and energy access targets. The short-term details are based on the IEA’s sustainable recovery strategy, which aims to promote economies and employment through developing a cleaner and more reliable energy infrastructure [ 15 ]. In addition, sustainable development includes utilizing renewable-energy applications, smart-grid technologies, energy security, and energy pricing, and having a sound energy policy [ 29 ].

The demand-side response can help meet the flexibility requirements in electricity systems by moving demand over time. As a result, the integration of renewable technologies for helping facilitate the peak demand is reduced, system stability is maintained, and total costs and CO 2 emissions are reduced. The demand-side response is currently used mostly in Europe and North America, where it is primarily aimed at huge commercial and industrial electricity customers [ 15 ].

International standards are an essential component of high-quality infrastructure. Establishing legislative convergence, increasing competition and supporting innovation will allow participants to take part in a global world PV market [ 30 ]. Numerous additional countries might benefit from more actively engaging in developing global solar PV standards. The leading countries in solar PV manufacturing and deployment have embraced global standards for PV systems and highly contributed to clean-energy development. Additional assistance and capacity-building to enhance quality infrastructure in developing economies might also help support wider implementation and compliance with international solar PV standards. Thus, support can bring legal requirements and frameworks into consistency and give additional impetus for the trade of secure and high-quality solar PV products [ 19 ].

Continuous trade-led dissemination of solar PV and other renewable technologies will strengthen the national infrastructure. For instance, off-grid solar energy alternatives, such as stand-alone systems and mini-grids, could be easily deployed to assist healthcare facilities in improving their degree of services and powering portable testing sites and vaccination coolers. In addition to helping in the immediate medical crisis, trade-led solar PV adoption could aid in the improving economy from the COVID-19 outbreak, not least by providing jobs in the renewable-energy sector, which are estimated to reach >40 million by 2050 [ 19 ].

The framework for energy sustainability development, by the application of solar energy, is one way to achieve that goal. With the large availability of solar energy resources for PV and CSP energy applications, we can move towards energy sustainability. Fig. 3 illustrates plans for solar energy sustainability.

Framework for solar energy applications in energy sustainability.

The environmental consideration of such applications, including an aspect of the environmental conditions, operating conditions, etc., have been assessed. It is clean, friendly to the environment and also energy-saving. Moreover, this technology has no removable parts, low maintenance procedures and longevity.

Economic and social development are considered by offering job opportunities to the community and providing cheaper energy options. It can also improve people’s income; in turn, living standards will be enhanced. Therefore, energy is paramount, considered to be the most vital element of human life, society’s progress and economic development.

As efforts are made to increase the energy transition towards sustainable energy systems, it is anticipated that the next decade will see a continued booming of solar energy and all clean-energy technology. Scholars worldwide consider research and innovation to be substantial drivers to enhance the potency of such solar application technology.

2.1 Employment from renewable energy

The employment market has also boomed with the deployment of renewable-energy technology. Renewable-energy technology applications have created >12 million jobs worldwide. The solar PV application came as the pioneer, which created >3 million jobs. At the same time, while the solar thermal applications (solar heating and cooling) created >819 000 jobs, the CSP attained >31 000 jobs [ 20 ].

According to the reports, although top markets such as the USA, the EU and China had the highest investment in renewables jobs, other Asian countries have emerged as players in the solar PV panel manufacturers’ industry [ 31 ].

Solar energy employment has offered more employment than other renewable sources. For example, in the developing countries, there was a growth in employment chances in solar applications that powered ‘micro-enterprises’. Hence, it has been significant in eliminating poverty, which is considered the key goal of sustainable energy development. Therefore, solar energy plays a critical part in fulfilling the sustainability targets for a better plant and environment [ 31 , 32 ]. Fig. 4 illustrates distributions of world renewable-energy employment.

World renewable-energy employment [ 20 ].

The world distribution of PV jobs is disseminated across the continents as follows. There was 70% employment in PV applications available in Asia, while 10% is available in North America, 10% available in South America and 10% availability in Europe. Table 1 details the top 10 countries that have relevant jobs in Asia, North America, South America and Europe.

List of the top 10 countries that created jobs in solar PV applications [ 19 , 33 ]

Solar energy investments can meet energy targets and environmental protection by reducing carbon emissions while having no detrimental influence on the country’s development [ 32 , 34 ]. In countries located in the ‘Sunbelt’, there is huge potential for solar energy, where there is a year-round abundance of solar global horizontal irradiation. Consequently, these countries, including the Middle East, Australia, North Africa, China, the USA and Southern Africa, to name a few, have a lot of potential for solar energy technology. The average yearly solar intensity is >2800 kWh/m 2 and the average daily solar intensity is >7.5 kWh/m 2 . Fig. 5 illustrates the optimum areas for global solar irradiation.

World global solar irradiation map [ 35 ].

The distribution of solar radiation and its intensity are two important factors that influence the efficiency of solar PV technology and these two parameters vary among different countries. Therefore, it is essential to realize that some solar energy is wasted since it is not utilized. On the other hand, solar radiation is abundant in several countries, especially in developing ones, which makes it invaluable [ 36 , 37 ].

Worldwide, the PV industry has benefited recently from globalization, which has allowed huge improvements in economies of scale, while vertical integration has created strong value chains: as manufacturers source materials from an increasing number of suppliers, prices have dropped while quality has been maintained. Furthermore, the worldwide incorporated PV solar device market is growing fast, creating opportunities enabling solar energy firms to benefit from significant government help with underwriting, subsides, beneficial trading licences and training of a competent workforce, while the increased rivalry has reinforced the motivation to continue investing in research and development, both public and private [ 19 , 33 ].

The global outbreak of COVID-19 has impacted ‘cross-border supply chains’ and those investors working in the renewable-energy sector. As a result, more diversity of solar PV supply-chain processes may be required in the future to enhance long-term flexibility versus exogenous shocks [ 19 , 33 ].

It is vital to establish a well-functioning quality infrastructure to expand the distribution of solar PV technologies beyond borders and make it easier for new enterprises to enter solar PV value chains. In addition, a strong quality infrastructure system is a significant instrument for assisting local firms in meeting the demands of trade markets. Furthermore, high-quality infrastructure can help reduce associated risks with the worldwide PV project value chain, such as underperforming, inefficient and failing goods, limiting the development, improvement and export of these technologies. Governments worldwide are, at various levels, creating quality infrastructure, including the usage of metrology i.e. the science of measurement and its application, regulations, testing procedures, accreditation, certification and market monitoring [ 33 , 38 ].

The perspective is based on a continuous process of technological advancement and learning. Its speed is determined by its deployment, which varies depending on the scenario [ 39 , 40 ]. The expense trends support policy preferences for low-carbon energy sources, particularly in increased energy-alteration scenarios. Emerging technologies are introduced and implemented as quickly as they ever have been before in energy history [ 15 , 33 ].

The CSP stations have been in use since the early 1980s and are currently found all over the world. The CSP power stations in the USA currently produce >800 MW of electricity yearly, which is sufficient to power ~500 000 houses. New CSP heat-transfer fluids being developed can function at ~1288 o C, which is greater than existing fluids, to improve the efficiency of CSP systems and, as a result, to lower the cost of energy generated using this technology. Thus, as a result, CSP is considered to have a bright future, with the ability to offer large-scale renewable energy that can supplement and soon replace traditional electricity-production technologies [ 41 ]. The DESERTEC project has drawn out the possibility of CSP in the Sahara Desert regions. When completed, this investment project will have the world’s biggest energy-generation capacity through the CSP plant, which aims to transport energy from North Africa to Europe [ 42 , 43 ].

The costs of manufacturing materials for PV devices have recently decreased, which is predicted to compensate for the requirements and increase the globe’s electricity demand [ 44 ]. Solar energy is a renewable, clean and environmentally friendly source of energy. Therefore, solar PV application techniques should be widely utilized. Although PV technology has always been under development for a variety of purposes, the fact that PV solar cells convert the radiant energy from the Sun directly into electrical power means it can be applied in space and in terrestrial applications [ 38 , 45 ].

In one way or another, the whole renewable-energy sector has a benefit over other energy industries. A long-term energy development plan needs an energy source that is inexhaustible, virtually accessible and simple to gather. The Sun rises over the horizon every day around the globe and leaves behind ~108–1018 kWh of energy; consequently, it is more than humanity will ever require to fulfil its desire for electricity [ 46 ].

The technology that converts solar radiation into electricity is well known and utilizes PV cells, which are already in use worldwide. In addition, various solar PV technologies are available today, including hybrid solar cells, inorganic solar cells and organic solar cells. So far, solar PV devices made from silicon have led the solar market; however, these PVs have certain drawbacks, such as expenditure of material, time-consuming production, etc. It is important to mention here the operational challenges of solar energy in that it does not work at night, has less output in cloudy weather and does not work in sandstorm conditions. PV battery storage is widely used to reduce the challenges to gain high reliability. Therefore, attempts have been made to find alternative materials to address these constraints. Currently, this domination is challenged by the evolution of the emerging generation of solar PV devices based on perovskite, organic and organic/inorganic hybrid materials.

This paper highlights the significance of sustainable energy development. Solar energy would help steady energy prices and give numerous social, environmental and economic benefits. This has been indicated by solar energy’s contribution to achieving sustainable development through meeting energy demands, creating jobs and protecting the environment. Hence, a paramount critical component of long-term sustainability should be investigated. Based on the current condition of fossil-fuel resources, which are deemed to be depleting energy sources, finding an innovative technique to deploy clean-energy technology is both essential and expected. Notwithstanding, solar energy has yet to reach maturity in development, especially CSP technology. Also, with growing developments in PV systems, there has been a huge rise in demand for PV technology applications all over the globe. Further work needs to be undertaken to develop energy sustainably and consider other clean energy resources. Moreover, a comprehensive experimental and validation process for such applications is required to develop cleaner energy sources to decarbonize our planet.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Wind power: an important source in energy systems.

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• Adnan Durakovic, World’s Largest, Most Powerful Wind Turbine Stands Complete. Available online: https://www.offshorewind.biz/2021/11/12/worlds-largest-most-powerful-wind-turbine-stands-complete/ (accessed on 28 November 2021).
• IRENA. Renewable Power Generation Costs in 2020 ; International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2021; ISBN 978-92-9260-348-9. [ Google Scholar ]
• Share of Wind Power Coverage in Denmark from 2009 to 2020. Available online: https://www.statista.com/statistics/991055/share-of-wind-energy-coverage-in-denmark/ (accessed on 28 November 2021).

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Chen, Z. Wind Power: An Important Source in Energy Systems. Wind 2021 , 1 , 90-91. https://doi.org/10.3390/wind1010006

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Examining the Relationship Between Leaders' Power Use, Followers' Motivational Outlooks, and Followers' Work Intentions

Taylor peyton.

1 School of Hospitality Administration, Boston University, Boston, MA, United States

2 Valencore Consulting, Cambridge, MA, United States

Drea Zigarmi

3 The Ken Blanchard Companies, Escondido, CA, United States

4 University of San Diego, San Diego, CA, United States

Susan N. Fowler

From the foundation of self-determination theory and existing literature on forms of power, we empirically explored relationships between followers' perceptions of their leader's use of various forms of power, followers' self-reported motivational outlooks, and followers' favorable work intentions. Using survey data collected from two studies of working professionals, we apply path analysis and hierarchical multiple regression to analyze variance among constructs of interest. We found that followers' perceptions of hard power use by their leaders (i.e., reward, coercive, and legitimate power) was often related to higher levels of sub-optimal motivation in followers (i.e., amotivation, external regulation, and introjected regulation). However, followers who perceived their leaders used soft power (i.e., expert, referent, and informational power) often experienced higher levels of optimal motivation (i.e., identified regulation and intrinsic motivation), but further investigation of soft power use is warranted. The quality of followers' motivational outlooks was also related to intentions to perform favorably for their organizations.

Introduction

This study merges two fields of investigation: forms of leadership power stemming from empirical research on the psychology of power over the last five decades, and motivational outlooks from research on self-determination theory (SDT) over the last 40 years. Researchers in both areas have called for greater in-depth exploration of the relationship between leadership and motivation in organizational settings (eg., Elias, 2008 ; Stone et al., 2009 ; Meyer et al., 2010 ; Randolph and Kemery, 2011 ; Anderson and Brion, 2014 ).

Much of the research on the psychology of power is “still largely removed from the complexities and confounds of behavior in organizational settings” (Anderson and Brion, 2014 , p. 85). While laboratory studies have certain strong advantages, future research will need to grapple with the dynamics of interpersonal and psychological interactions, and related implications for organizational life (Anderson and Brion, 2014 ).

Podsakoff and Schriesheim ( 1985 ) called for investigations of the independent contributions different power bases make to explain variance in criterion variables relevant to subordinate outcomes. Also, Elias ( 2008 ) identified a need for more research on specific criteria that facilitate leaders' decisions regarding the kind of power they should exercise. In response to these calls and other apparent gaps in the literature, studies began to appear. For example, Mossholder et al. ( 1998 ) found that subordinates' perceptions of procedural justice fully mediated the relationship between ratings of their supervisors' use of five forms of social power, and job satisfaction and organizational commitment. Ward ( 1998 ) also studied subordinates' perceptions of their managers, and found that for four of eight aspects of psychological work climate, managerial power bases interacted with subordinates' manifest needs (achievement, dominance, autonomy, and affiliation). Notably, managers' use of personal power (expert and referent) had the biggest impact on psychological climate, especially when personal power use also occurred with reward power use (Ward, 1998 ). Politis ( 2005 ) examined relationships between five forms of managerial power and credibility, with employee knowledge acquisition attributes. Politis ( 2005 ) uncovered a positive relationship between expert power and knowledge acquisition attributes including negotiation, control, and personal traits; also, the study found that greater use of coercive and referent power related to lower levels of knowledge sharing and knowledge acquisition. Additionally, Pierro et al. ( 2012 ) discovered how supervisors' and subordinates' need for cognitive closure related to the efficacy and application of various social power bases, specifically regarding: employee preference for soft or hard power, subordinates' performance, and other organizational outcomes. Pierro et al. ( 2013 ) found positive relationships between transformational/charismatic leadership and subordinates' inclinations to comply with soft power, which was also indicative of higher levels of affective organizational commitment.

While the above studies demonstrate some of the work on power bases as they relate to criterion variables in accordance with calls from Podsakoff and Schriesheim ( 1985 ) and others, very few studies examine the connection between motivation and forms of power use, and SDT has not yet been comprehensively applied in these investigations. Power is an undeniable aspect of leadership, and we agree with other authors (i.e., Aguinis et al., 1994 ; Randolph and Kemery, 2011 ) who maintain that there is not enough is known about the degree to which employee perceptions of their managers' use of various forms of power is correlated with various forms of employee motivation.

Scholars in the field of SDT have made similar calls for more in-depth research connecting leadership behavior to motivation in organizational settings (e.g., Deci and Ryan, 2002 ; Stone et al., 2009 ). Some SDT researchers have requested a closer examination of how leadership qualities and interpersonal styles of managers influence their followers' tendencies to align their personal goals with organizational goals (i.e., Gagné and Deci, 2005 ). Other SDT researchers call for the examination of how leader behaviors can foster increased levels of intrinsic motivation in followers (e.g., Jungert et al., 2013 ). Still other SDT authors ask for research on how leaders might optimize employee engagement in organizational settings (Dysvik and Kuvaas, 2010 ).

A preponderance of the SDT literature and its core principles has been applied in settings other than business (Deci and Ryan, 2002 ; Ryan and Deci, 2017 ). While some business leaders may have a cursory awareness of the fundamental concepts of SDT (such as the basic psychological needs of autonomy, relatedness, and competence), few understand how to successfully support employees' needs in the face of organizational pressures for performance and output (Stone et al., 2009 ). Much of the present organizational psychology depends upon the “carrot and the stick” strategy for motivation and command-and-control methods for performance and behavior (Stone et al., 2009 ; Fowler, 2014 ). Here we explore the impact of various kinds of leadership power on non-power holders in organizations. We are interested in gaining insight into how a non-power holder's motivational outlooks may relate to their leaders' use of power in the workplace.

Purpose and Organization of the Study

This study aims to answer calls from researchers in both fields of investigation by empirically examining the relationship between leaders' use of various forms of power, followers' motivational outlooks, and followers' work intentions in organizations. Specifically, we propose to answer Podsakoff and Schriesheim ( 1985 ) call “to assess the independent contribution of each of the power bases to the variance explained in subordinate criterion variables” (p. 406). This study, is also directed toward the third research avenue suggested by “to consider putting future research efforts into those who are powerless” (Strum and Antonakis, 2015 , p. 157). In response, we focus on the intrapersonal experiences of followers who operate under their leaders' power. We examine the degree to which the perceived use of six forms of leader power might explain variance in follower motivation (i.e., its sub-forms), and follower work intentions. We consider existing literature on power and SDT to hypothesize that leaders' use of different kinds and combinations of power is connected to various motivational outlooks and work intentions in the non-power holder.

Review Of The Literature On Leader Power, Employee Motivation, And Employee Work Intention

Leader power.

Power typically entails a condition in which some individuals have control over resources and some do not. The term power is most commonly defined as “the asymmetric control over valued resources” (Anderson and Brion, 2014 , p. 69). Power relationships are inherently social and exist only in relation to others; parties with low power rely on parties with high power to obtain rewards and avoid punishment (Vince, 2014 ).

For leaders to be effective they must be able to shape the behavior of others (Elias, 2008 ). Forms of leader power that can be used to shape others behavior are embedded in people's psyches (Vince, 2014 ) through the structural features of today's organizations (Pfeffer, 1992 ; Clegg et al., 2006 ; Vince, 2014 ). Before the turn of this century, much of the literature concerned with leader power has been sociological or philosophical in origin and character (Elias, 2008 ; Anderson and Brion, 2014 ) and has traditionally dealt with the “social structure of corporations” (Clegg et al., 2006 , p. 387). The structural perspective of power (Pfeffer, 1992 ) has given rise to the foundation of systems of authority and the formation of legitimate social power relationships (e.g., Pfeffer, 1992 , 2011 ; DuBrin, 2009 ; Haugaard and Clegg, 2012 ; Lunenberg, 2012 ; Vince, 2014 ).

Leaders in the workplace may reinforce their power through their own demeanor and behavior, but they are ordained with their power by the organizational context (e.g., the authority to control resources, followers assigned to work under them), which includes higher-ranking small groups or strategic decision makers (Anderson and Brion, 2014 ). The antecedents for the bases for power stem from the social structure, the cultural patterned behavior of groups, and other practices within organizations (Lukes, 1974 , 2005 ). Much of the early literature on power was concerned with the character, skills and personality of the designated leader, with the organization's structures, policies, procedures, and with forms of hierarchy that authorize a leader's power (Foucault, 1982 ).

The bases of power (French and Raven's, 1959 ; Raven, 1965 ), which will be described next, define forms of power observed from human and contextual factors, whereas the psychology of power is concerned with how people perceive and experience the power bases, either when they hold the power themselves, or when they are under the power of others. Since the beginning of the twenty-first century, there has been a rise in the number of empirical studies on the psychology of power, as researchers have recently begun to explore psychological perceptions of power to better understand how power use affects individuals in an organizational context (Anderson and Brion, 2014 ). There has been a shift away from studying power as it resides in structures, policies, and procedures, and a shift toward studying individual perceptions that are held by power/non-power holders when various uses of power occur. Recent work on individual perceptions includes, for example: studies concerned with why power facilitates self-interested behavior (DeCelles et al., 2012 ); how people who are primed with high- vs. low-power tend to adopt the visual perspective of others, adjust to other people's points of view, and feel empathy for others (Galinsky et al., 2006 ); and how power influences people's thinking while resolving moral dilemmas (Lammers and Stapel, 2009 ). Additionally, we agree with other researchers (e.g., Farmer and Aguinis, 2005 ; Dambe and Moorad, 2008 ) who have noted that most studies on power have predominantly focused on the power holder and not on either the mutuality of the power holder and the non-power holder, or on the perspective of the individual non-power holder.

Various Bases of Power

French and Raven's ( 1959 ) and Raven ( 1965 ) presented five conceptual forms of leader power which have been the basis of 50 years of research (Elias, 2008 ). These five forms of leader power—expert, referent, reward, legitimate, and coercive power—have remained relatively constant over time, even though there have been controversial issues, such as response bias possibilities, concept overlap, and single-item measurement (cf. Podsakoff and Schriesheim, 1985 ). To date, French and Raven's ( 1959 ) five forms of power are frequently used for the study of power in organizations.

Subsequently, Raven refined the five concepts of power from the earlier 1959 publication by adding a sixth major form of power, informational power, and further differentiated types of legitimate power into legitimate reciprocity, legitimate equity, and legitimate dependence (Raven, 1993 ). Additionally, coercive power was divided into personal coercive power and impersonal coercive power (Raven et al., 1998 ), and reward power was separated into personal reward power and impersonal reward power (Raven et al., 1998 ). These changes were made in an effort to overcome some of the concerns raised by Podsakoff and Schriesheim ( 1985 ) and Yukl and Tracy ( 1992 ), among others (Raven et al., 1998 ).

Alternatively, some researchers have classified varying forms of power into two clusters, i.e., soft and hard power, based on the amount of perceived freedom employees have in responding to the types of power used by their managers (e.g., Raven et al., 1998 ; Pierro et al., 2008 ; Randolph and Kemery, 2011 ). Expert power, informational power, and referent power are referred to by these and other authors as soft forms of power, while coercive, reward, and legitimate power have been classified as hard forms of power. Hard types of power require higher levels of non-power holder compliance and result in lower levels of autonomy.

Expert power depends on perceptions of the follower regarding the influencer's superior knowledge (Raven et al., 1998 ). The strength of this power turns upon the amount of expertise or knowledge the follower attributes to the influencer on a specific topic (Podsakoff and Schriesheim, 1985 ). Informational power refers to the influencer's perceived capacity to provide a rationale to the follower regarding why the follower should change his or her beliefs or behaviors (Raven et al., 1998 ). Referent power is dependent on the follower's perceived personal identification with the influencer (Raven et al., 1998 ). The basis of this power stems from the extent to which the follower's personal self-identity is made better through interaction with the influencer or the desire of the follower to be like, or associated with, the influencer (Podsakoff and Schriesheim, 1985 ).

Coercive power is defined as the perceived ability of the leader to penalize the targets if they do not adhere to requested outcomes (Raven et al., 1998 ). The potency of coercive power lies in the perceived extent of the punishment possible, and its use often correlates with increased negative affect between leader and follower (Podsakoff and Schriesheim, 1985 ). Reward power originates from a perceived possibility of monetary or non-monetary compensation (Raven et al., 1998 ). The intensity of reward power heightens with an increase in the rewards possible, and with its relative attractiveness to the receiver (Podsakoff and Schriesheim, 1985 ). Legitimate power originates from the subordinate's perceived understanding of the leader's right to influence (Raven et al., 1998 ). The potency of legitimate power arises from the internalized values the follower has concerning the authority or right of the leader to be the leader (Podsakoff and Schriesheim, 1985 ).

Correlates and Outcomes of Power

The nature of power is inherently a double-edged sword in which some people have prerogatives and others do not. This realization has given rise to research on self-interested behavior and the misuse of various forms of power, the subsequent examination of psychological explanations (e.g., Galinsky et al., 2006 ; Lammers and Stapel, 2009 ; DeCelles et al., 2012 ), and resultant possible solutions, such as empowerment (e.g., Conger and Kanungo, 1988 ; Randolph and Kemery, 2011 ).

Research studies in the last two decades reveal that the use of various forms of power correlates with various desirable and undesirable organizational and individual outcomes. For example, greater use of soft kinds of power (expert, referent, and informational power) are connected to higher levels of organizational citizenship behavior, empowerment, organizational commitment, and job satisfaction (e.g., Podsakoff and Schriesheim, 1985 ; Elias, 2008 ; Randolph and Kemery, 2011 ), whereas the use of hard forms of power (coercive, reward, and legitimate) are related to greater absenteeism, lower productivity, lower self-confidence levels, and burnout (Podsakoff and Schriesheim, 1985 ; Elias, 2008 ; Randolph and Kemery, 2011 ).

Employee Motivation and Self-Determination Theory

The concept of motivation in this study originates from the fundamental tenets of SDT, which have been researched and confirmed in the past five decades. This theory holds that individuals are volitional, able to initiate behaviors (Deci and Ryan, 1985 , 2002 ) and that individuals thrive when their psychological needs are satisfied (Deci and Ryan, 1985 , 2002 ). SDT purports that the individual cognitively process their experience which results in self-direction through flexible psychological structures that allow individuals to direct action toward the achievement of desired ends (Ryan and Deci, 2017 ).

Additionally, SDT researchers (e.g., Deci and Ryan, 1985 ; Ryan and Deci, 2000 ) maintain that an individual's actions are self-determined when they are chosen and supported by personally defined boundaries rather than being coerced, pressured, or induced through incentives. The term self-determination connotes a sense of self-management or self-regulation that, over time, brings with it goal direction, energy, persistence, and intention (Ryan and Deci, 2000 , 2017 ). In other words, individuals can understand why they behave the way they do. SDT purports that individuals can understand the causality of their actions, develop causality orientations (implicit and explicit, Deci and Ryan, 1985 ), and regulate their future behaviors to be congruent with such orientations.

SDT emphasizes that individuals' psychological needs for autonomy, relatedness, and competence must be fulfilled (Deci and Ryan, 1985 , 2002 ). Rather than focusing upon the lessening of physiological drives of sex, hunger, thirst, and pain avoidance, lasting human motivation originates from intrinsic needs for integration and growth (Deci and Ryan, 1985 ). Given that there is a spectrum of needs, frequent interactions with the environment allow for the fulfillment of basic psychological needs for thriving and flourishing, that encompasses more than physiological satiation (Deci and Ryan, 2002 ). The basic psychological needs of autonomy, relatedness, and competence, often designated as “psychological nutriments,” are as mandatory as physiological nourishment for human psychological development and well-being (Ryan and Deci, 2000 , p. 75). These basic psychological needs can give rise to various forms of motivational regulation and their associated motivational outlooks.

SDT defines two broad categories of motivational regulation: controlled regulation and autonomous regulation. Controlled regulation entails participation in an activity for instrumental reasons, rather than for reasons of pleasure or being interested in the activity for the sake of the activity itself (Gagné and Deci, 2005 ; Meyer et al., 2010 ). Autonomous regulation is designated as a person's participation in an activity for its own sake, because it is pleasurable or because it is of interest (Gagné and Deci, 2005 ; Meyer et al., 2010 ). Within controlled and autonomous regulation, SDT postulates various sub-categories or motivational outlooks: external, introjected, identified, and intrinsic. In this paper, we use the terms for motivation, such as motivational outlooks or forms regulation offered by Gagné et al. ( 2015 ).

An external motivational outlook is driven by desired rewards or punishment avoidance (i.e., controlled regulation). An introjected motivational outlook is connected to ego enhancement or to the avoidance of guilt or shame (i.e., controlled regulation). External and introjected motivational outlooks are classified as controlled regulation, in that they originate from instrumental outcomes or external conditions (Gagné and Deci, 2005 ).

An identified motivational outlook is a state in which the individual participates in activities to be congruent with valued personal goals (i.e., autonomous regulation). The identified motivational outlook usually stems from willful actions that adhere to stated values. If, after reflection, the individual believes he/she has chosen at will to engage in an activity because it is congruent with his/her fundamental needs and values, a sense of autonomy is obtained (Ryan and Deci, 2000 ; Gagné and Deci, 2005 ). And finally, an intrinsic motivational outlook is a state in which a personal sense of self is expressed by the individual when participating in an activity (i.e., autonomous regulation) (Gagné and Deci, 2005 ; Meyer et al., 2010 ). Identified or intrinsic motivational outlooks are categorized as autonomous regulation.

Apart from the motivational regulations, a state of amotivation, or disinterest, can occur when people lack the volition to act—or act passively—toward a specific outcome. Amotivation may exist because of forces beyond the individual's control. This feeling of helplessness may stem from uncontrollable or unpredictable environmental factors, or it could happen because the individual was overwhelmed by thoughts and feelings from within, such as anger, rage, resignation, or despair (Deci and Ryan, 1985 ). It is possible that employees may carry out their tasks mindlessly and without purpose or care, with little regard for their performance. External and introjected regulation are different from amotivation because, in the former, the motivation of the individual expresses a modicum of volition for some specific outcome.

We use the language “sub-optimal” and “optimal” to broadly refer to clusters of motivational outlooks or states, and the distinction is based on each state's support of sustainable, long-term human flourishing. Sub-optimal motivational outlooks include amotivation, external regulation, and introjected regulation; they are classified together as sub-optimal, in that an individual's energy toward a given task is approached from a lack of interest, or from a psychological place other than positive interest or value-congruent reasons. Thus, employee performance originating from any of the sub-optimal motivational outlooks, over the long haul, will either be characterized by a lack of effort or will likely not be sustainable. Alternatively, we classify identified and intrinsic motivational outlooks (i.e., autonomous regulation) as optimal, because they involve greater fulfillment of basic psychological needs, and therefore employee efforts stemming from optimal motivation are more likely to be sustainable.

Work Intentions

In keeping with the SDT, an individual's energy for “volitional, intentional behavior originates from underlying personal needs for autonomy, relatedness, and competence” (Deci, 1980 , p. 23). Here an intention is defined as a mental image of the behavior an individual plans to manifest (Bagozzi, 1992 ). Several studies have revealed that intentions are an important concept in the attitude-intention-behavior chain (e.g., Ajzen and Fishbein, 1980 ; Armitage and Connor, 2001 ). Studies testing social cognitive appraisal theory, for instance, have predicted work satisfaction from self-efficacy, positive affect, and work conditions (Duffy and Lent, 2009 ), have examined the relationship between control coping and employee withdrawal during organizational change (Fugate et al., 2011 ), have identified the relationship between appraisal/coping variables and stressful encounter outcomes (Folkman et al., 1986 ), and have uncovered relationships between consumers' behavioral intentions to use services in the future with consumer expectations, perceived quality, and satisfaction (Gotlieb et al., 1994 ).

In the fields of health and social psychology, various meta-analyses have demonstrated strong relationships between intentions and behavior (e.g., Cooke and Sheeran, 2004 ; Gollwitzer and Paschal, 2006 ; Webb and Sheeran, 2006 ). We chose to use the concept of work intentions as outcome variables because they are stronger predictors of employee behavior. Three meta-analyses conducted over the last 40 years have established that intentions are better predictors of employee behavior than outcome variables, such as organizational commitment and job satisfaction (e.g., Steel and Ovalle, 1984 ; Tett and Meyer, 1993 ; Podsakoff et al., 2007 ).

This research explores possible relationships between followers' perceptions of their leader's use of different kinds and combinations of power, various types of motivational outlooks in followers, and five work intentions held by followers. See Figure 1 for our conceptual model.

Overall conceptual model.

Very little empirical testing of the relationships between power and motivation exists in the literature, so the main impetus for our hypotheses is theoretical. Our underlying logic regarding followers' motivational outlooks assumes that the non-power holders' basic psychological need for autonomy, relatedness, and competence will be met or not met, facilitated are not facilitated, through the leader's use of various forms of power, hard or soft. The theoretical justification for these hypotheses lies in the followers' experience of the quality of: choice or autonomy given by the leader, relatedness cultivated by the leader, and competence experienced in relationship to the leader.

Specifically, we point to the research on human choice. Building on the work of SDT researchers, Patall et al. ( 2008 ) included 41 studies in a meta-analysis examining the effects of choice on intrinsic motivation. The authors concluded that intrinsic motivation was stronger when choice was given, and when rewards were not given. According to SDT, when a person's basic psychological needs (autonomy, relatedness, competence) are met, they thrive and behave in more self-determined (or optimally motivated) ways. Giving a person a choice relates to their experience of autonomy, one of the three basic psychological needs. Definitions of various forms of power (i.e., Podsakoff and Schriesheim, 1985 ; Raven et al., 1998 ; Pierro et al., 2008 ) suggest that coercive power, reward power, and legitimate (i.e., hard) power provide limited opportunity for the person under these types of power to exercise choice, compared to expert, referent, and informational (i.e., soft) power. If a leader's hard power limits follower choice much more than soft power, we anticipate followers' basic psychological needs and motivation will be affected accordingly.

Also, we found two empirical studies that examined different forms of power and motivation. First, Elangovan and Xie ( 1999 ), reported many positive relationships between subordinates' levels of internal motivation and forms of power being used by their supervisors, but effects were notably stronger for subordinates with low self-esteem or external locus of control. Second, Pierro et al. ( 2008 ) reported hard power compliance positively correlated with extrinsic motivation and negatively correlated with intrinsic motivation, whereas soft power compliance was positively correlated with intrinsic motivation. Pierro et al. ( 2008 ) and Elangovan and Xie ( 1999 ) both did not frame their studies primarily in SDT, so neither study included measures of motivation that captured the various subscales of motivation comprehensively defined by SDT.

Given the small number of studies exploring the relationship between leader power and subordinate motivation, more research is needed. Thus, applying conclusions from the research on human choice, from SDT, and from the above literature testing French and Raven's power bases in relationship to motivation, we propose:

• Hypothesis 1a : Leaders' use of various kinds of hard power will positively correlate with followers' sub-optimal motivation (i.e., amotivation, external regulation, introjected regulation).
• Hypothesis 1b : Leaders' use of various kinds of hard power will negatively correlate with, or not correlate with, followers' optimal motivation (i.e., identified regulation, intrinsic motivation).
• Hypothesis 1c : Leaders' use of various kinds of soft power will negatively correlate with, or not correlate with, followers' sub-optimal motivation (i.e., amotivation, external regulation, introjected regulation).
• Hypothesis 1d : Leaders' use of various kinds of soft power will positively correlate with followers' optimal motivation (i.e., identified regulation, intrinsic motivation).

Furthermore, leaders' use of power and followers' motivation may also relate to followers' work intentions. Zigarmi et al. ( 2016 ) examined employee locus of control, forms of motivational regulation, harmonious and obsessive passion, and desirable work intentions. While the direct connection between motivational regulation variables and work intentions in Zigarmi et al. ( 2016 ) was not estimated in their structural model, their correlation matrix showed: strong positive relationships between autonomous motivation and all five work intentions, small-to-medium negative relationships between amotivation and all work intentions, and small positive relationships between controlled motivation and three of five work intentions. Such relationships are in accordance with the assumptions of SDT, which purport that optimal motivation relates to human thriving.

Additionally, Zigarmi et al. ( 2015 ) found slight-to-moderate positive correlations between employees' perceptions of their leaders' use of expert and referent (i.e., soft) power and all favorable work intentions. In the same study, coercive and legitimate (i.e., hard) power were negatively and somewhat weakly correlated with five of ten possible work intentions, whereas reward power was somewhat positively correlated to all five work intentions. In their structural model that included positive and negative affect as mediators, Zigarmi et al. ( 2015 ) found that—except for expert power which had a negative, direct path to intent to perform—reward power and expert power each positively and directly related to two of five favorable work intentions. From that work, we observe variability in employees' intentions to perform well for their organization, relative to the kind of power their leaders use.

Considering the above, regarding the relationship between power, motivation, and work intentions, we propose:

• Hypothesis 2a : Followers' sub-optimal motivation (i.e., amotivation, external regulation, introjected regulation) will negatively correlate with, or not correlate with, their work intentions.
• Hypothesis 2b : Followers' optimal motivation (i.e., identified regulation, intrinsic motivation) will positively correlate with their work intentions.
• Hypothesis 3a : Followers' motivational outlooks will partially mediate perceptions of leaders' use of various kinds of power and followers' work intentions.
• Hypothesis 4a : Followers of leaders who use multiple kinds of hard power at high levels (as compared to leaders who use lower levels of all kinds of hard power) will report higher levels of amotivation, external regulation, and introjected regulation, lower levels of (or no difference in levels of) identified regulation and intrinsic motivation, and lower levels of (or no difference in levels of) work intentions.
• Hypothesis 4b : Followers of leaders who use multiple kinds of soft power at high levels (as compared to leaders who use lower levels of all kinds of soft power) will report higher levels of identified regulation and intrinsic motivation, lower levels of (or no difference in levels of) amotivation, external regulation, and introjected regulation, and higher levels of work intentions.

Hypothesis 3a was written parsimoniously to address all substantive constructs of interest to us, as our approach to partial mediation analyses will be exploratory. Thus, any non-significant relationships we uncover from testing Hypotheses 1a − 1d and 2a − 2b will naturally affect the possibility to test partial mediation proposed by Hypothesis 3a.

In summary, we hypothesize that a leader's increased use of harder forms of power will be related to decreased quality of their followers' forms of motivation, and that less optimal motivation in employees will relate to lower levels of work intentions. Also, the more optimal forms of motivation in employees should correlate with higher levels of work intentions. While various studies we cited above provide some support for the connection between followers' work intentions relative to their motivation, and to their leaders' use of power, we have found no empirical study yet that has examined these factors together.

We conducted two studies to test our hypotheses. Study 1 involved a sample of respondents from a single organization, while Study 2 collected a larger sample of employees working across many organizations. Study 2 was conducted to determine if the findings from Study 1 could be replicated. Both studies were approved by the Research Ethics Committee of The Ken Blanchard Companies.

Participants for Study 1

Three-hundred seventy employees from a training and consulting organization in Southern California were invited to participate in Study 1. The sample for analysis included 229 employees, or a 62% response rate. Seventy percent of respondents were female, 78% were White/Caucasian, 22% were managers, and 60% reported being born in 1961 or later. Thirty percent had graduate degrees, 44% were college graduates, and 26% had some college education or less. Organizational tenure varied; 30% said they had been with their organization for 5 years or less, 21% reported a tenure of 6–10 years, 34% reported a tenure of 11–20 years, and 15% said they had worked for their organization for 21 years or more.

Procedures for Study 1

Participants from a single organization were invited through email to complete an online survey. Data for this study were gathered as part of a voluntary, anonymous, annual survey conducted by the company's human resources department.

In addition to demographic information, participants were asked to respond to subscales measuring their manager's use of power and the kinds of motivational outlooks they personally experience at work.

Managerial use of power was measured through the Interpersonal Power Inventory (IPI) from Raven et al. ( 1998 ). The IPI presents 11 subscales representing various power bases and is an extension of the original six power bases proposed by French and Raven's ( 1959 ) and subsequently Raven ( 1965 ). The IPI asks respondents to think of a time when they complied with their supervisor's request despite initially being reluctant to do so, then presents 33 items asking for their reason for compliance to be rated on a 7-point response scale (1 = definitely not a reason , 7 = definitely a reason ). To ensure parsimony and practicality in the interpretation of results, Study 1 combined the IPI's eleven subscales of power to measure the original six power bases: reward power, coercive power, legitimate power, expert power, referent power, and informational power. The reward and coercive power subscales each had six items, the legitimate power subscale included nine items, and the expert, referent, and informational power subscales were each made up of three items. Example items follow for each kind of power subscale used in this work: “My supervisor could help me receive special benefits” (reward power), “My supervisor may have been cold and distant if I did not do as requested” (coercive power), “I understood that my supervisor really needed my help on this” (legitimate power), “My supervisor probably knew more about the job than I did” (expert power), “I looked up to my supervisor and generally modeled my work accordingly” (referent power), and “My supervisor gave me good reasons for changing how I did the job” (informational power). Alpha coefficients for the power subscales in Study 1 ranged from 0.87 to 0.96.

Employee workplace motivation was measured using the Multidimensional Work Motivation Scale (MWMS). This 19-item work scale has been validated in seven languages (see Gagné et al., 2015 ). Participants were asked to respond to the item “why do you or would you put efforts into your current job” and were given a 7-point rating scale (1 = not at all , 7 = completely-entirely ) to indicate the degree to which each survey item represented their reasons for expending the effort to become involved with their job. The MWMS includes six subscales for workplace motivation: amotivation, external-social, external-material, introjected, identified, and intrinsic. For this study, researchers combined the external-social and external-material subscales to create a total score for external regulation, such that we included five dimensions for motivation. Three, six, four, three, and three items, respectively, made up our five forms of motivational outlooks: amotivation, external regulation, introjected regulation, identified regulation, and intrinsic motivation subscales. An example item for motivation is “Because the work I do is interesting” (intrinsic motivation).

For Study 1, alpha coefficients for amotivation and introjected regulation were below 0.70, and respective results should, therefore, be interpreted with caution. Gagné et al. ( 2015 ) tested the psychometric properties of all MWMS subscales in various cultural contexts and cited borderline or inadequate reliability (≤0.70) for the introjected regulation subscale in three of seven samples. However, Gagné et al. cited adequate reliability (>0.70) for the amotivation subscale in all samples, so issues with the measurement of amotivation were surprising. The reliabilities of all other motivation subscales for Study 1 were adequate, ranging from 0.80 to 0.90.

Also, in Study 1 there was a strong positive skew of the amotivation subscale, which was initially problematic for analysis; for the three items for this subscale, 81–89% of respondents indicated that they were “not at all” experiencing amotivation at work. To work with the data, we dichotomized the amotivation items so that those who were experiencing no amotivation at all were coded in a separate category from respondents reporting any level of amotivation at work, and subscale average total scores were calculated from these variables prior to analysis. This treatment of the data was beneficial because it lessened the strength of the positive skew of the amotivation variable to be used in subsequent regression analyses.

Employee work intentions data were collected from the short form of the Work Intentions Inventory (WII) (Nimon and Zigarmi, 2015 ), and this inventory was included within the surveys used for Studies 1 and 2. Over the past 4 years, two versions of the work intentions inventory have been developed. The initial WII long form (Zigarmi et al., 2012 ) contained 25 items while the WII short form included 15 items. In each WII version, 5 types of work intentions are represented (Nimon and Zigarmi, 2015 ): intent to use discretionary effort, intent to perform at a higher than average level, intent to endorse, intent to stay with the organization, and intent to use organizational citizenship behavior. The WII has demonstrated construct and content validity for the five work intentions subscales and has repeatedly displayed appropriate internal consistency and factorial structure (Nimon and Zigarmi, 2015 ).

The WII offers a six-point Likert-type response scale format to capture the respondent's extent of intention experienced, ranging from 1 = no extent to 6 = the fullest extent . For the WII short form used in this study, each of the five work intentions subscales were measured with three items. Example items include: “I intend to take home work when I know it will make me more effective the next day” (intent to use discretionary effort), “I intend to exert the energy it takes to do my job well” (intent to perform), “I intend to talk positively about this organization to my friends and family” (intent to endorse), “I intend to continue to work here because I believe it is the best decision for me” (intent to stay), and “I intend to watch out for the welfare of others at work” (intent to use OCB). In this work, reliabilities for the five intentions subscales ranged from 0.77 to 0.93 in Study 1.

Demographics

Age and gender were included in this study because these employee characteristics may relate to motivational outlooks at work (cf. Gagné et al., 2015 ). Both demographics questions used in Study 1 were part of a pre-existing pool of items regularly launched by the participating organization, so researchers were unable to alter their format. Age in Study 1 was measured using a single survey item that asked for respondents to indicate the year they were born by selecting among ranges of years. Some categories of age had too few respondents for analyses to be stable, so empirical criteria were used to dichotomize the age variable (born in 1960 or earlier vs. born in 1961 or later) in preparation for analysis. Gender was measured by asking respondents if they were male or female.

Participants for Study 2

An invitation to participate in the study was sent electronically to a listserv of ~40,000 employees across the United States. The sample of participants included 1,103 employees of various organizations, or a 3% response rate. Females made up 58% of the respondents. Data were not available on participants' ethnic or educational backgrounds, but 67% percent were from organizations operating within the United States. Thirty-three percent of respondents were non-managers, with an average age of 49. Forty-one percent had been with their current organization for more than 10 years, 55% had been in their current position for 4 years or less, and 70% had been reporting to their current supervisor for 4 years or less. Thirty-three percent worked for organizations with 500 or fewer employees, and 20% were from organizations with more than 20,000 employees.

Procedures for Study 2

Access to the listserv was granted by an international training and management company that works with organizations from various industries. Respondents were granted access to the company's white papers as an incentive for their participation.

For the second study, we used the same measures for power, motivation, and work intentions as we did in the first study (i.e., the IPI, MWMS, and WII). In Study 2, alpha reliabilities for the power subscales ranged from 0.84 to 0.93, and alpha reliabilities for the motivation subscales ranged from 0.70 to 0.89—notable improvements from reliability issues observed from these subscales in Study 1. Specifically, in Study 2, the reliability of amotivation and introjected regulation was acceptable at 0.85 and 0.70, respectively. Also, in Study 2 a lower percentage of respondents reported being “not at all” amotivated at work (i.e., 67–72% for the three amotivation items compared to 81–89% in Study 1), so for Study 2 the amotivation subscale was not dichotomized and was instead calculated from the amotivation items in their original, continuous format. In Study 2, alpha reliabilities for the work intentions subscales ranged from 0.73 to 0.95.

To measure respondent demographics, age and gender were again included in Study 2. In this launch, we had the option to redesign the age variable to better capture variability in respondents; thus, instead of the categorical, generationally-based scale for age used in Study 1, in Study 2 respondents were asked to type their age in years. No additional data manipulation was conducted on our age variable in the second study prior to analysis because it was analyzed as a continuous variable in our regression analyses in Study 2.

Study 2 used a larger sample spanning organizations across North America, and provided improved reliability coefficients for amotivation and introjected regulation compared to Study 1.

Evaluating Common Method Bias

We aimed to examine intrapersonal psychological phenomena for this work, so we collected self-report data. Self-report data is most appropriate for learning about respondents' inner experiences and perceptions, which were of focal interest here. Specifically, Chan ( 2009 ) highlighted the valuable insight self-report data may provide for researchers aiming to investigate potential affective, cognitive, and motivational processes implicated with individuals' response patterns. Some researchers have expressed concern about the possibility of relationship inflation among substantive constructs in self-report studies incorporating one source of data only (Spector, 2006 ), and the potential for such constructs to vary due to significant common method bias effects (Podsakoff and Organ, 1986 ; Podsakoff et al., 2003 ). We implemented the following approaches to minimize the likelihood of having problems with common method bias. First, we used measures featuring different kinds of response scales, which helps prevent participants from being overly consistent or automatic in their answers. Specifically, participants were asked to rate on a 7-point scale their perceived reasons underlying their manager's behavior, and then they were asked to reflect on their job experiences using a 6-point and 7-point scale, each involving different response anchors. Second, in separate sections of the survey, we varied the instructions and referent for phenomena being rated (i.e., participants were asked to shift from rating their manager's behavior prior to rating their own motivations and intentions on the job).

To additionally probe for the negative effects of common method bias, we applied Harman's single factor procedure (Podsakoff and Organ, 1986 ; Podsakoff et al., 2003 ). While this technique for evaluating issues with common method variance may be limited, if only one factor accounts for the majority of variance in the data, that could indicate problems attributable to common method bias (Podsakoff et al., 2003 ). In both studies, we ran Harman's procedure using exploratory factor analysis. Four and three factors were evident in Study 1 and Study 2, respectively, whereby the first factor accounted for 21.7% (Study 1) and 24.1% (Study 2) of the variance. We also, in both studies, used the marker variable approach suggested by Williams et al. ( 2010 ), which involves testing five nested models. In both studies, we found: (1) our data fit the Method-U model better than the Method-C model (i.e., our marker was differentially associated with our variables of interest), and (2) our data did not fit significantly better for the Method-R model than the Method-U model, which indicated that common method bias was not a problem. Harman's procedure and the Williams et al. ( 2010 ) both provided evidence that issues with common method bias were not evident in either study.

Path Analysis Approach

We used Mplus 7.2 and maximum likelihood estimation (MLR) to run competing multivariate models to test our conceptual model involving leader power use, followers' motivational outlooks, and followers' work intentions (i.e., Hypotheses 1a−1d, 2a−2b). Initially we attempted to conduct the full analysis as structural equation models by using latent variables for all substantive constructs, but for the smaller dataset in Study 1, we had too many parameters of interest to estimate, relative to our available statistical power. We therefore modeled both samples using path analysis, which only included two latent variables, one for soft power and one for hard power, and all other key variables were observed scale scores.

At the measurement model level, hard power as a latent variable was calculated from three mean scale scores: reward power, coercive power, and legitimate power. Our latent variable for soft power was calculated from the following three mean scale scores: expert, referent, informational power. Measurement model fit for the power variables was not adequate initially, but model modification indices showed that coercive power also should cross-load onto soft power. Upon making this modification, model fit improved (Δχ 2 [1] = 40.28, p < 0.001) and was well-fitting to the data (χ 2 [7] = 57.527, CFI = 0.902, SRMR = 0.060, RMSEA = 0.182). The cross-loading of coercive power onto soft power was −0.544 in Study 1.

Because we anticipated certain kinds of motivational outlooks would be highly related, we allowed them to correlate: i.e., external and introjected regulation were correlated, and so were identified and intrinsic motivation. In accordance with theory and previous research conducted on work intentions, we also specified correlations among the five work intentions. All path models controlled for respondent age and gender.

Overall model fit was evaluated using the following indices: the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). Specifically, we retained models demonstrating CFI values >0.90, RMSEA values < 0.06, and SRMR values < 0.08 (Hu and Bentler, 1999 ; Hooper et al., 2008 ). Chi-square difference testing compared the fit of nested models for full and partial mediation. In each study, path analysis began by running a full mediation model in accordance with our overall conceptual model (Model 1), and that was followed by running variations of partial mediation models, whereby: starting with the full mediation model, five direct paths were added from hard power to each of the work intentions (Model 2); then starting again with the full mediation model, five direct paths were added from soft power to every work intention (Model 3); then all significant direct paths from Model 2 and Model 3 were noted so they could be added collectively to the full mediation model (Model 4); and then Model 4 was examined for non-significant direct paths so they could be removed for subsequent partial mediation model testing (Models 5–6).

Study 1 Path Analysis Results

Table ​ Table1 1 provides Study 1 variables' means, standard deviations, correlations, and alpha reliabilities. In Study 1, Models 1–3 were run as described above. Then, in Model 4, a partial mediation model was run by adding 6 direct paths: hard power to intent to perform, soft power to intent to perform, hard power to intent to endorse, soft power to intent to endorse, hard power to intent to use OCBs, and soft power to intent to use OCBs. Compared to Model 1, Models 2–4 fit the data better (Model 2: Δχ 2 [5] = 11.90, p < 0.05; Model 3: Δχ 2 [5] = 21.58, p < 0.001; Model 4: Δχ 2 [6] = 21.72, p < 0.01). Model 5 removed the non-significant direct paths found in Model 4, and only included 2 direct paths: soft power to intent to endorse, and soft power to intent to use OCBs. Model 5 was compared to Model 4 and did not fit the data better (Δχ 2 [4] = 8.72, p > 0.05). Results from chi-square significance testing to compare nested models is included in Table 2 . Model 4, shown in Figure 2 , fit the data best.

Study 1—scale score means, standard deviations, reliabilities, and correlations.

Cronbach's alpha estimates are in parentheses on the diagonal. Pairwise deletion, ns = 216–228. Gender coded as 1 = male, 2 = female. Age coded as 1 = born 1961 or later, 2 = born 1960 or earlier .

Chi-square significance testing for comparison of structural equation model fit.

Model 4, Final Model, Study 1 ( n = 215).

All standardized path coefficients for Model 4 are shown in Figure 2 , and respective endogenous variables' r 2 values were as follows: 0.25 for amotivation, 0.23 for external regulation, 0.072 for introjected regulation, 0.11 for identified regulation, 0.18 for intrinsic motivation, 0.23 for intent to use discretionary effort, 0.42 for intent to perform, 0.39 for intent to endorse, 0.35 for intent to stay, 0.37 for intent to use OCBs. Of the above listed r 2 values, only introjected regulation was not significant ( p > 0.05). Table 3 reports significant indirect effects.

Summary of significant, specific indirect effects for Model 4 in Study 1.

Only significant indirect effects are shown, p < 0.05 .

Study 2 Path Analysis Results

Variables' means, standard deviations, correlations, and alpha reliabilities for Study 2 are shown in Table 4 . Similar to Study 1, in Study 2 power variables' measurement model fit to the data was improved by allowing coercive power to load onto soft power (Δχ 2 [1] = 263.72, p < 0.001), resulting in a well-fitting measurement model for power (χ 2 [7] = 42.442, CFI = 0.981, SRMR = 0.025, RMSEA = 0.068). In Study 2, coercive power loaded onto soft power at −0.662. We ran structural Models 1–3 in the same way as previously presented in Study 1.

Study 2—scale score means, standard deviations, reliabilities, and correlations.

Cronbach's alpha estimates are in parentheses on the diagonal. Pairwise deletion, ns = 1,095–1,103. Gender coded as 1 = male, 2 = female. Age was a continuous variable with lower values representing younger ages .

Model 4 was a partial mediation model with the following 8 direct paths added: hard power to intent to use discretionary effort, soft power to intent to use discretionary effort, soft power to intent to perform, hard power to intent to endorse, soft power to intent to endorse, hard power to intent to stay, soft power to intent to stay, soft power to intent to use OCBs. Relative to Model 1, Models 2–4 fit the data better (Model 2: Δχ 2 [5] = 12.05, p < 0.05; Model 3: Δχ 2 [5] = 37.91, p < 0.001; Model 4: Δχ 2 [8] = 41.89, p < 0.001). Removing the non-significant direct paths from Model 4, Model 5 included 4 direct paths: soft power to intent to perform, soft power to intent to endorse, soft power to intent to stay, soft power to intent to use OCBs. Model 5 did not fit the data significantly better than Model 4 (Δχ 2 [4] = 8.78, p > 0.05). Applying the same logic, Model 6 included only 2 direct paths: soft power to intent to endorse, soft power to intent to stay. Model 6 did not show better fit to the data than Model 4 (Δχ 2 [6] = 11.19, p > 0.05). See Table 5 for model comparison results from chi-square significance testing, and Figure 3 for Model 4, which was best-fitting to the data.

Model 4, Final Model, Study 2 ( n = 1,039).

For Model 4, standardized path coefficients are presented in Figure 3 . For that final model, endogenous variables' r 2 values were: 0.18 for amotivation, 0.33 for external regulation, 0.13 for introjected regulation, 0.20 for identified regulation, 0.21 for intrinsic motivation, 0.20 for intent to use discretionary effort, 0.35 for intent Ω to perform, 0.32 for intent to endorse, 0.30 for intent to stay, 0.27 for intent to use OCBs. Of the above listed r 2 values, all were significant ( p < 0.001). Significant indirect effects are shown in Table 6 .

Summary of significant, specific indirect effects for Model 4 in Study 2.

Studies 1 And 2: Interpretation Of Final Models

Hypothesis 1a was mostly supported by both studies' final models, leaders' use of hard power was strongly and positively correlated with followers' amotivation and external regulation (sub-optimal motivational outlooks). For the path from hard power to amotivation, in Study 1 β = 0.42, p < 0.05, and in Study 2, β = 0.39, p < 0.05. Similarly, hard power was related to external regulation: Study 1 β = 0.54, p < 0.05, and in Study 2, β = 0.70, p < 0.05. A significant path between hard power and introjected regulation was only found in Study 2, however, possibly due to having more statistical power in that sample (β = 0.25, p < 0.05). Followers who perceived greater hard power use by their leaders were more likely to hold higher levels of sub-motivational outlooks at work.

Hypothesis lb was supported by the final model of Study 1, which found no significant relationships between leaders' use of hard power and identified or intrinsic motivation. In Study 2, this hypothesis was supported in that there were negative relationships between leaders' use of hard power and identified (β = −0.15, p < 0.05) and intrinsic (β = −0.20, p < 0.05) motivation. Thus, followers with leaders exerting higher levels of hard power use were somewhat more likely to report lower levels of optimal motivation.

In both studies, Hypothesis 1c was supported. Leaders' use of soft power was significantly and negatively related to followers' amotivation (β = −0.59, p < 0.05 in Study 1, and β = −0.53, p < 0.05 in Study 2) and to followers' external regulation (β = −0.20, p < 0.05 in Study 1, and β = −0.36, p < 0.05 in Study 2). In both studies, soft power was not significantly related to followers' introjected regulation. Followers who viewed higher amounts of soft power use by their leaders were more likely to report lower levels of amotivation and lower levels of external regulation.

Hypothesis 1d was supported by both studies, as soft power use by leaders positively correlated with followers' optimal motivational outlooks. Leaders' use of soft power was significantly related to followers' identified regulation (β = 0.29, p < 0.05 in Study 1, and β = 0.49, p < 0.05 in Study 2) and to followers' intrinsic motivation (β = 0.34, p < 0.05 in Study 1, and β = 0.53, p < 0.05 in Study 2). Followers who perceived higher soft power use from their leaders were more likely to report higher levels of optimal motivation.

Evidence supporting Hypothesis 2a was found in both studies, as followers' sub-optimal motivational outlooks (i.e., amotivation, external regulation, introjected regulation) either negatively correlated with, or did not correlate with, their work intentions. Four of five paths from amotivation to work intentions were negative and significant (i.e., for intent to perform β = −0.25, p < 0.05 in Study 1, and β = −0.21 p < 0.05 in Study 2; for intent to endorse β = −0.28, p < 0.05 in Study 1, and β = −0.17 p < 0.05 in Study 2; for intent to stay β = −0.32, p < 0.05 in Study 1, and β = −0.13, p < 0.05 in Study 2; for intent to use OCBs β = −0.30, p < 0.05 in Study 1, and β = −0.22, p < 0.05 in Study 2). Therefore, followers experiencing greater levels of amotivation were more likely to score lower on four of the five work intentions. Paths between amotivation and intent to use discretionary effort were not significant in either study. External regulation and introjected regulation were not significantly related to any of the work intentions in either study.

Hypothesis 2b was supported by both studies in eight of ten possible paths: for the most part, followers' optimal motivational outlooks (i.e., identified, intrinsic motivation) positively and significantly correlated with their work intentions. Identified regulation was positively related to the all five work intentions in Study 1 (βs ranged from 0.18 to 0.53, p < 0.05) and in Study 2 (βs ranged from 0.09 to 0.45, p < 0.05). Positive and significant paths were also found from intrinsic motivation to intent to use discretionary effort (β = 0.27, p < 0.05 in Study 1, and β = 0.19, p < 0.05 in Study 2), to intent to endorse (β = 0.15, p < 0.05 in Study 1, and β = 0.19 p < 0.05 in Study 2) and to intent to stay (β = 0.30, p < 0.05 in Study 1, and β = 0.24 p < 0.05 in Study 2). Taken together, except for intent to perform and intent to use OCBs, followers with higher levels of optimal motivation were more likely to report favorable levels of work intentions.

In both studies, partial mediation models fit the data better than complete mediation models, thereby somewhat supporting Hypothesis 3a . Finally, regarding direct paths from power to work intentions, followers who perceived their leaders used higher levels of soft power were more likely to intend to endorse their organizations (β = 0.25, p < 0.05 in Study 1, and β = 0.20, p < 0.05 in Study 2) and to intend to use OCBs (β = 0.21, p < 0.05 in Study 1, and β = 0.07, p < 0.05 in Study 2). In Study 2 only, followers working under leaders who used soft power reported increased intentions to stay with their organization (β = 0.27, p < 0.05).

Regression Approach

As a follow-up analysis to supplement our path modeling, we used hierarchical multiple regression analysis in SPSS version 22, which tested Hypotheses 4a and 4b to explain variance in the five motivational outlooks and the five work intentions. Specifically, we were interested in evaluating the potential effects of followers' experiences of their leaders' use of multiple kinds of hard or soft power in combination with one another.

In preparation for this analysis, in each study's dataset, we transformed power use so it could be meaningfully aggregated to indicate followers' perceptions of the degree of each kind of power was being used by their leader. As the subscale for the power measures ranged from 1 (to no extent) to 6 (to the fullest extent), we examined the labels of the response scales as well as the distributions of responses in each study, to determine that values of 4.5 or higher would allow for the meaningful dichotomization of followers' perceptions of their managers. That is, for every power variable, we re-coded followers with ratings of 4.49 and lower (i.e., the lower half of the “to a great extent” anchor and including all responses through “to no extent”) as 1s, and we re-coded followers with power ratings of 4.5 and higher (i.e., the upper half of the “to a great extent” anchor and including responses indicating “to a very great extent” and “to the fullest extent”) as 2s. Then, for each respondent, we summed the recoded scores on all hard power variables (coercive, reward, legitimate), to create a single variable that indicated the degree of hard power used by their leader, and aggregating across all types of hard power. Thus, scores for this new sum combination variable for hard power ranged from 3 to 6, with 3 indicating that a follower's leader uses low levels of all kinds of hard power, and 6 indicating that a follower's leader uses high levels of all kinds of hard power. The same transformation was done for soft power, prior to regression analysis.

In the regression analyses, respondent age and gender were entered into Step 1, and the two sum combination variables for hard and soft power were then also entered into Step 2. The mean imputation procedure within SPSS was conducted for cases with missing data.

Studies 1 And 2: Regression Results And Interpretation

Follow-up regression results are shown in Table 7 for Study 1, and in Table 8 for Study 2. Hypothesis 4a was supported in both studies, as followers of leaders who used multiple kinds of hard power at high levels demonstrated higher levels of amotivation (β = 0.16, p < 0.05 in Study 1, and β = 0.18, p < 0.05 in Study 2), external regulation (β = 0.41, p < 0.05 in Study 1, and β = 0.44, p < 0.05 in Study 2), and introjected regulation (β = 0.15, p < 0.05 in Study 1, and β = 0.23, p < 0.05 in Study 2). When leaders engaged in using many kinds of hard power, sub-optimal motivation levels in followers were more pronounced. Additionally, Hypothesis 4a was also confirmed in both studies as no significant relationships were found between followers' perceptions of their leaders' use of multiple kinds of hard power and follower's optimal motivational outlooks, or followers' work intentions.

Study 1—combinations of power regression models, controlling for age and gender ( n = 229).

β value shows asterisk if p < 0.05. Higher values for age represent older respondents .

Study 2—combinations of power regression models, controlling for age and gender ( n = 1,103).

In accordance with Hypothesis 4b , followers of leaders who used many kinds of soft power at high levels reported higher levels of identified (β = 0.15, p < 0.05 in Study 1, and β = 0.26, p < 0.05 in Study 2), and intrinsic motivation (β = 0.19, p < 0.05 in Study 1, and β = 0.31, p < 0.05 in Study 2). Followers with leaders engaging in multiple types of soft power also demonstrated significantly lower amounts of amotivation (β = −0.31, p < 0.05 in Study 1, and β = −0.22, p < 0.05 in Study 2), no significant change in external regulation (in both studies), and in Study 2 only, somewhat significantly higher levels of introjected regulation (β = 0.12, p < 0.05; note, however, the Study 1 β looked similar despite not being significant, which may be due to statistical power). Except for intent to use discretionary effort in Study 1, followers who perceived their leaders used many kinds of soft power were more likely to report favorable levels of work intentions (βs ranged from 0.19 to 0.31, p < 0.05 in Study 1, and βs ranged from 0.18 to 0.29, p < 0.05 in Study 2). See Table 9 for a summary of all hypotheses and results for both studies.

Summary of hypotheses and results for studies 1 and 2.

This work calls attention to the types of power that, when used by leaders, are more likely to relate to optimal or sub-optimal motivational outlooks in followers, and varying levels of followers' work intentions. Our findings highlight the benefits of when leaders use soft power with their followers, and the psychological costs to followers when leaders operate from hard power. This study demonstrates SDT is a relevant lens for understanding the psychology of followers working under power, and suggests the value of SDT in organizations.

Importantly, followers who experience their leaders using hard power will be much more likely to have sub-optimal motivational outlooks, particularly amotivation and external regulation. Furthermore, a highly notable finding was that leaders' use of multiple kinds of hard power together will correlate with all types of sub-optimal motivational outlooks in followers, and the strength of this relationship for external regulation is important for practice. This finding aligns with SDT literature and previous research that has provided evidence for negative outcomes associated with the use of hard power in organizations (Podsakoff and Schriesheim, 1985 ; Elias, 2008 ; Randolph and Kemery, 2011 ).

In some cases, followers who perceive their leaders use hard power may also suffer from a slight decline in optimal motivation. Results differed between our studies in the relationship between leaders' hard power use and followers' optimal motivation (i.e., Study 1 found no significant relationships, whereas Study 2 uncovered small negative, significant relationships). We wonder if the findings of Study 2 have more generalizability than Study 1, given that Study 2 was a large sample comprising followers from various organizations, whereas Study 1 was a small sample of followers from a single organization. The observed differences could be due to statistical power, or it is possible that employees from the same organization may collectively experience other factors that could mitigate the psychological sting of hard power. In general, we wonder if the latter may be the case when followers under their leaders' hard power have learned that their leaders still value them overall, as indicated by other cultural norms (e.g., one-on-one lunch outings, expressing the deeper meaning of the work, caring conversations, being granted extra flexibility in their work schedules). Or, perhaps in organizational cultures that normalize autocratic rule from leaders (e.g., military settings), employees may expect to often experience hard power and therefore be less personally affected by it.

Additionally, followers who perceive soft power use from their leaders will be notably more likely to experience optimal motivational outlooks. Followers working with leaders who use higher amounts of soft power may benefit from feeling lower levels of sub-optimal motivation, specifically for amotivation and external regulation. Additionally, when leaders use many kinds of soft power at once, followers' motivational outlooks may benefit by being more optimal, and less characterized by amotivation. The potential compounding psychological effect felt by followers who had leaders using multiple kinds of power was evident in this study, and is in alignment with SDT. Mainly, leaders who exercise many kinds of hard power at once (or many kinds of soft power at once), may be more strongly depleting or enriching followers' basic psychological needs of autonomy, relatedness, and competence than leaders who only use one kind of power.

The quality of followers' motivational outlooks also appears to be connected to followers' intentions to engage in positive work outcomes for their organizations. Amotivation in followers tends to decrease followers' intentions to perform, endorse their organizations, stay with their organizations, and use OCBs, although followers' intentions for discretionary effort may remain unaffected by experiences of amotivation. The other two kinds of followers' sub-optimal motivational outlooks, external regulation and introjected regulation, are not related to followers' work intentions. Conversely, optimal motivational outlooks in followers are very often related to followers' favorable work intentions. These findings strongly support assumptions of SDT, as it is expected that followers who are more optimally motivated will have their basic psychological needs met, and therefore they will have greater capacity to function from a place of strength and resilience at work.

Furthermore, leaders' use of multiple kinds of soft power will relate to the increased likelihood that their followers will intend to work positively for their organization. This relationship was observed in all work intentions variables across both studies, except for in Study 1 where combinations of soft power use did not correlate with followers' intent to use discretionary effort. This may suggest that employees in Study 1, our organizational sample, collectively have other reasons besides soft power to exert strong effort in their work (e.g., recognition, appreciation, teamwork mentality). Also, the final model in Study 2 had a positive and significant direct path between leaders' use of soft power and followers' intentions to stay with their organization, whereas Study 1 did not. Again, the differences in findings could be attributable to statistical power, or something else may be explaining Study 1 followers' variability in their intentions to stay. Qualitatively, we know that employees in the organization used in Study 1 often have high tenure, so it could be that followers' intention to stay with that organization is less related to leader soft power, and perhaps more related to other benefits the company offers.

Overall, the magnitude of the relationships between followers' optimal motivational outlooks and work intentions is relevant for practice; these findings suggest that leading in a manner that encourages others to form and maintain identified and intrinsic motivational outlooks is not only a practice that sounds ideal in principle, but indeed it is a practice that could add great value to workplace outcomes.

Taken together, for leaders who wish to promote sustainable and healthy kinds of motivation in their followers, the above suggests that using soft power is a superior approach to using hard power. Considering the basic tenants SDT, our findings may suggest that leaders' use of hard power will likely disrupt followers' psychological flourishing in the areas of autonomy, relatedness, and competence—and thereby will impair followers' motivation, and their intentions to perform favorably on the job. Thus, we raise the question: are organizational leaders who rule from hard power ultimately undermining themselves?

Limitations and Recommendations for Future Research

Our findings regarding the introjected motivational outlook were generally inconclusive, so we recommend additional research in that area. It should be noted that Gagné ( 2016 ), in a paper presented at the SDT Conference in June of 2016, said that different sub-forms of introjection may be at work in the larger concept of introjection. Gagné offered that the definition of introjection may be incomplete, such that it may include shame and guilt as subconstructs. The nascent state of introjection as an academic concept may be affecting its measurement in our studies and, therefore, may be shaping our results accordingly. It may be worthwhile to investigate whether introjected motivational outlooks may be connected to other context-specific factors besides managerial use of power, or whether perhaps introjected motivational outlooks may instead be more strongly connected to individual personality differences or social axioms held by employees.

Effect decomposition analyses for the path models specifying hard and soft power uncovered total indirect effects between hard power and work intentions, through all motivational outlook variables. Specifically, regarding indirect effects flowing through motivational outlooks, in both studies, employees with managers using higher amounts of hard power were somewhat less likely to report favorable levels of work intentions. The opposite, and slightly stronger, indirect effect was found for employees with managers exercising higher levels of soft power; these employees reported greater intentions to work favorably. Therefore, managerial use of power was related to more than employees' motivational outlooks; productive levels of work intentions were also related to power use. These findings are in keeping with Zigarmi et al. ( 2016 ), whose research revealed correlations between amotivation and work intentions ( r s ranged from −0.14 to −0.31), controlled regulation and work intentions ( r s ranged from not significant to 0.13), and autonomous regulation and work intentions ( r s ranged from 0.42 to 0.56). Future research could investigate causal relationships between perceived leader power, employee motivation, and intentions, or other aspects of organizational life that may be influenced by managerial use of certain kinds of power.

Low reliabilities of the amotivation and introjected regulation subscales in Study 1 indicate that conclusions drawn from Study 1 regarding those variables should be made with caution. Also, although demographic effects on motivation differed somewhat between studies, we are optimistic that comparing findings across studies and controlling for demographic differences strengthened our conclusions.

Both studies were convenience samples, so findings may not generalize to broader populations. Additionally, because data were cross-sectional, this study does not provide evidence for the directionality of relationships observed. Thus, future research could examine potential cause-and-effect relationships between leader use of power and forms of employee motivation using longitudinal data. Another potential limitation to this study is the use of single-source, self-report measures. Future research could include objective, observable outcomes to investigate how employee motivation and the use of various forms of leader power might impact organizational performance metrics.

Conclusion and Practical Implications

Overall, we found that leaders' use of hard power relates to higher levels of sub-optimal motivational outlooks in followers, while leaders' use of soft power is connected to higher levels of optimal motivational outlooks in followers. Followers' motivational outlooks were also related to their intentions to perform favorably for their organizations.

As this work provides insight into the relationship between leaders' use of power and less optimal kinds of follower motivation, we encourage managers in the field to consider how their use of coercive, legitimate, and reward power may be adversely connected to the daily quality of follower motivation. Said differently, leaders who often resort to hard types of power should proceed with caution, because they may unknowingly be undermining their own efforts to inspire an engaging and productive workplace that encourages autonomous regulation. Finally, we encourage both power and non-power holders to carefully consider how any use of power relates to the daily quality of follower motivation.

Ethics Statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Namely, the Research Ethics Committee at the Ken Blanchard Companies approved the ethics of the study's design and procedures.

Informed consent was obtained from all individual participants included in the study using the text provided to participants in the electronic survey's beginning instructions, as shown verbatim in italics below. The following informed consent text was adapted from a version of survey instruction text approved by the IRB through the University of San Diego for a 2012 study with similar instructions. For this study, the adapted instruction text was approved by the Research Ethics Committee at the Ken Blanchard Companies. We did not provide participants with a printed paper version of an informed consent form, due to the electronic format of the survey. However, participants indicated their voluntary agreement to participate by reading the below instructions and clicking forward into the survey. In that text, we made clear the researcher's promise to keep data anonymous and to only report individual data in aggregate form, and that participants could quit at any time without penalty.

“ The purpose of this questionnaire is to assess the extent to which certain leader behaviors impact employee work intentions. You are being asked to participate in a survey that will take about 15–20 min to complete. Completion of this survey involves no foreseeable risks. Your participation is voluntary and you may stop at any time with no penalty. No one will see your individual responses other than the researchers. Any data will be reported on a group basis only. You give your consent to participate in the study by completing this survey. If you have any questions please contact [email protected].”

For background, individuals invited to the survey had previously opted-in to receive electronic survey invitations of this kind, as their emails were being housed by a consulting company's national listserv database. This listserv and process has been used by The Ken Blanchard Companies to conduct survey research for the last 15 years, particularly in the areas of engagement and work passion for companies all over the globe. Protecting survey respondent confidentiality and ensuring participant psychological well-being has been a necessary precondition required by participating companies and individuals.

Author Contributions

TP led the data cleaning, data analysis, results write-up, and table/figure creation for both studies and is the corresponding author. DZ designed both studies, oversaw data collection, and wrote the literature review and discussion section. SF contributed to the literature review and discussion section, and all authors reviewed the piece prior to submission.

Conflict of Interest Statement

DZ is affiliated to the Ken Blanchard Companies through an advisory role, but he is not an employee of Blanchard. SF is Principal of Out of the Box Learning, Inc. (i.e., self-employed, author). TP is Principal of Valencore, LLC (i.e., at the time of this study she was self-employed). DZ and SF have both been employed by the University of San Diego for 20 years as adjuncts in the Master's of Executive Leadership program, where they teach two courses. TP is currently employed by Boston University's School of Hospitality Administration as a full-time faculty member.

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• Published: 08 December 2022

Spatial energy density of large-scale electricity generation from power sources worldwide

• Jonas Kristiansen Nøland 1 ,
• Juliette Auxepaules 1 , 2   na1 ,
• Antoine Rousset 1 , 2   na1 ,
• Benjamin Perney 1 , 2   na1 &
• Guillaume Falletti 1 , 2   na1  

Scientific Reports volume  12 , Article number:  21280 ( 2022 ) Cite this article

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• Electrical and electronic engineering
• Energy infrastructure

This paper introduces the annual energy density concept for electric power generation, which is proposed as an informative metric to capture the impacts on the environmental footprint. Our investigation covers a wide range of sources classified by rated power and compares different regions to establish typical spatial flows of energy and evaluate the corresponding scalability to meet future net-zero emission (NZE) goals. Our analysis is conducted based on publicly available information pertaining to different regions and remote satellite image data. The results of our systematic analysis indicate that the spatial extent of electric power generation toward 2050 will increase approximately sixfold, from approximately 0.5% to nearly 3.0% of the world’s land area, based on International Energy Agency (IEA) NZE 2050 targets. We investigate the worldwide energy density for ten types of power generation facilities, two involving nonrenewable sources (i.e., nuclear power and natural gas) and eight involving renewable sources (i.e., hydropower, concentrated solar power (CSP), solar photovoltaic (PV) power, onshore wind power, geothermal power, offshore wind power, tidal power, and wave power). In total, our study covers 870 electric power plants worldwide, where not only the energy density but also the resulting land or sea area requirements to power the world are estimated. Based on the provided meta-analysis results, this paper challenges the common notion that solar power is the most energy-dense renewable fuel source by demonstrating that hydropower supersedes solar power in terms of land use in certain regions of the world, depending on the topography.

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Introduction.

Due the to rising energy needs and changing energy mix, the spatial extent of the area required for electricity generation has recently received increasing attention 1 , 2 . In 2015, Smil 1 provided quantitative estimates in terms of the order of magnitude of the mean power density of renewable flows, which captures the spatial concentration of power. These estimates are summarized in Table 1 and recalculated considering the concept of the annual energy density, which is introduced in this paper as a metric explaining the annual electric energy that can be generated for a given amount of regulated site area of the power plant.

van Zalk and Behrens 3 quantitatively improved Smil’s 1 mean power density estimates and performed a meta-analysis of 54 eligible studies of nonrenewable and renewable power generation facilities. However, the data provided were exclusively limited to the US. This paper goes much further by establishing a comprehensive population study of 870 power generation facilities worldwide to reveal significant geographical variations and includes other renewable sources, such as wave, tidal, and hybrid renewable offshore parks, to utilize natural marine resources 4 . We challenge the current consensus that solar energy exhibits the highest power density among renewable technologies 5 . It is revealed that there exist significant differences between different regions, depending on the topography and solar irradiation intensity 6 , impacting the affordability 7 . A detailed meta-analysis of electric power generation from bioenergy is beyond the scope of this paper. A thorough population study of the biomass energy density for electric power generation ( \(n = 63\) ) has already been provided 3 . Even though bioenergy constitutes a dilute resource, it could be utilized involving surplus biomass originating from other production processes, thus reducing environmental impacts 8 , 9 .

Spatial power density evaluation is a topic of relevance to the field of life cycle assessment (LCA). In power generation LCA, not only is the power plant itself considered but also the land used for the mining of energy fuel sources, minerals, construction materials, waste handling, and plant decommissioning 10 . This process considers the fuel cycle and land use intensity throughout the entire lifetime of a facility, including direct and indirect impacts 11 . Earlier, this method was applied in the entire US to estimate the footprint of different sources needed to produce a given amount of energy 12 . Moreover, a more recent detailed-level analysis of natural gas land use types in Texas has been published 13 . In general, energy sprawl has been found to be the key driver of land use increase for energy development in the US 14 . Other environmental concerns included pollution-related impacts such as greenhouse gas emissions, freshwater ecotoxicity, eutrophication, and particulate-matter exposure, in which renewable energies were particularly beneficial 15 . In present LCA studies, the plant-side power density of different types of facilities worldwide has not been closely studied.

In general, it is challenging to develop a common definition of the energy density and the spatial extent of different power sources. These quantities also vary over time, e.g., the power density of photovoltaic (PV) generation decreases one order of magnitude from noon to the annual average level. Therefore, an average power density is often defined, while this paper proposes the annual energy density independent of temporal variations. Our work also presents a transparent framework for the definition of the land requirements of power generation. For example, the land requirements of hydropower generation can vary to a high degree, depending on whether one considers the catchment area or only the footprint of the reservoir. Similarly, nuclear power generation must include the exclusion zone, and natural gas power plants must consider the land occupation of pipelines and mining.

Power generation facilities exert a myriad of other important environmental impacts on the local environment 16 that are not considered herein. The implications of our investigation of the annual energy density include not only the resulting land requirements but also the spatial extent of the power infrastructure needed to generate energy. Another factor is that the land use of different power sources tends to be perceived differently 17 , as hydropower and solar power are associated with a significantly lower negative perception than that associated with wind power. Solar power can be utilized in combination with agricultural land to potentially maximize the benefits of a given land use 18 . Moreover, distributed solar power generation on residential rooftops utilizes existing available surfaces to harvest energy 19 . Even though building-integrated solar power generation to a certain extent can solve the problem of land use by utilizing existing surfaces 19 , 20 , solar power is generated at the low-voltage end of the power grid but can still help reduce local energy needs. Nevertheless, this technology is not perceived as a large-scale power generation solution due to its distributed nature. However, this approach can reduce the future demand for electricity in residential applications, which can liberate capacity from large-scale generation.

There are also known ways to significantly improve the power density of renewable sources 23 . However, this paper focuses on real-world power generation facilities (or at least considers prototypes in the case of wave and tidal power generation), suggesting that low-technology readiness level (TRL) technologies are beyond the research scope. Another example of facilities not considered in this paper is run-of-river hydropower plants, which occupy less land. However, the power capabilities of these hydropower plants may be simultaneously restricted in all regions to the minimum stream flow, which challenges the supply security. Large water reservoirs inherently occupy more space but override this drawback by providing firm dispatchable power to the electricity grid and supporting the energy security. As indicated in Table 2 , the relative share of fossil fuels in primary energy and electricity use remains very high and must be significantly shifted to meet net-zero emission (NZE) goals toward 2050 22 . The impacts on the US alone have been presented in a 2021 Princeton University report indicating a significant increase in land requirements needed to achieve a zero-carbon US economy 24 . This paper focuses on the worldwide energy transition, thereby analyzing and projecting the energy density and spatial extent of the global electric power generation fleet.

The present paper is organized as follows: first, the mathematical relationships used for the calculations are described in the “ Methods ” section. Then, an analysis of the two above nonrenewable sources is presented. The above eight renewable sources are evaluated in the following section. Power and energy densities are summarized and compared in terms of their implications for land and sea requirements, and finally, conclusions are outlined.

This section presents the data collection process and provides the mathematical relationships needed to perform the calculations and analyze the data presented in this paper.

Data collection

The process to obtain data was as follows: the individual pieces of information gathered for each power generation facility included in this paper were retrieved from publicly available information (e.g., Global Energy Observatory, Wikipedia, power producer nameplate sheets, etc.), including the rated power ( \(P_g\) ) and energy production ( \(E_g\) ). The land use requirement ( \(A_g\) ) for each facility was estimated from satellite images using the Google Earth Engine for spatially obtained measurements customized from high-resolution satellite data 25 . Sporadically, the total regulated area was also published. As there are alternate ways to define land requirements, Table 3 provides an overview of the lower and upper estimates found in the literature. Our chosen definitions of the spatial extent of the different power sources are bold and will be described in detail in later sections in this paper. All collected data are depicted in scatter plots in this article, and the values are provided in Supplementary Information files S1 , separated by each power source. Two examples of area estimation are depicted in Fig. 1 , illustrating the land use requirements for nuclear and geothermal power plants.

Example cases of the use of the Google Earth Engine 25 , version 7.3.2.5776 ( https://earth.google.com/web/ ), to estimate the land area requirement for power generation facilities. ( a ) Nuclear power plant (in Nogent-sur-Seine, France). ( b ) Geothermal power plant (in Krafla, Iceland).

Mathematical framework

Equations ( 1 )–( 3 ) cover the capacity factor and mean power density of consumption and generation, and their approximate energy balancing.

The energy densities of consumption and generation can be determined from their respective mean power densities according to Eq. ( 4 ). Both metrics can be used to obtain the land use fraction to generate the needed energy over the total land area, as formulated in Eq. ( 5 ). Both of these surface areas are defined in Eq. ( 6 ).

When electricity is generated, the actual power flow is higher, depending on the efficiency, as formulated in Eq. ( 7 ). This indicates that the mean specific power is influenced not only by the capacity factor but also by the electric energy conversion efficiency.

Nonrenewable resources

This section covers the two nonrenewable sources considered for electricity generation in this paper. While nuclear power can contribute to the net-zero climate target, natural gas is not a carbon-free resource without carbon capture and storage (CCS).

Natural gas

Natural gas is an energy source that usually comprises a mix of methane, ethane, and propane, with methane as the major component. Electricity is generated via thermal power plants, which account for approximately one-third of the total natural gas energy use. In 2010, approximately 80% of the world’s electricity was produced in thermal power generation facilities.

A total of 26 natural gas electricity-generating power plants are shown in Fig. 2 . Statistical analysis was performed to determine key data metrics. The case study includes the largest power plants worldwide in terms of the installed rated power. Figure 2 a shows a scatter plot of the obtained capacity factors, and some factor values were very low, as these power plants were merely used for peak shaving, while the capacity factors for other power plants were closer to the typical value of 85%. The accumulated capacity factor of the worldwide natural gas fleet for 2020 was 39.5%, based on an overall rated power of 1830 GW and a total generation of 6333 TWh 35 . As shown in Fig. 2 a, this value is slightly lower than the mean and median of the population we studied. As the capacity factor influences the power density, outliers could reduce the mean annual energy density of natural gas. A scatter plot of the land requirement was fitted against the rated power rating, as shown in Fig. 2 b. Even though most of the points deviate from the linear fit, the trend is visible. They scale similarly in terms of their land requirement per power plant rating, and the land use consistently increased with the rated power. Finally, the scatter plot in Fig. 2 c shows that the data exhibit a high standard deviation in terms of the power and energy density. Comparisons against van Zalk and Behrens 3 are included, which is generally lower since their meta-analysis considered studies that took into account land use from the whole value chain of natural gas.

The overall results for the natural gas combined-cycle power plants are presented in Table 4 . We observe that the overall power and energy density mean values of the entire population are relatively high (23.634 \({{\text {TWh/km}}^2}\) or 2696.17 \({\text{W/m}}^{2}\) ). It should be noted that these numbers consider only the power plant surface. When considering the rest of the fuel cycle, including natural gas production pads, underground fuel extraction is perceived not to entail high land requirements 10 . However, according to a recent study involving all the fuel cycle stages, the land requirement for production sites was approximately 1.44 times higher than that of the power plants themselves 13 . Table 5 provides the relative contribution to each of the stages, where several were determined insignificant, and thus, will be neglected in this paper. As indicated in Table 5 , the highest contributor to land use is the delivery infrastructure of natural gas, which significantly reduces the final power and energy density values obtained. In this paper, we incorporated this effect by reviewing natural gas pipeline infrastructure estimates globally. The calculation steps needed are provided in Table 6 , which starts with the worldwide energy use and electricity generation from natural gas. Then, the total pipeline length and its land requirement are obtained before the power and energy densities, including pipelines, are calculated. We could observe that the obtained end results were approximately seven to fifteen times lower than the previous mean value computed with only the surface of natural gas power plants. Notably, pipelines were not negligible, reflecting the implications of the initial assumptions we made, where the surface of pipelines was not considered. Considering that most of the natural gas pipelines worldwide are buried underground 36 , we hereafter use the upper estimate for the mean annual energy density in Table 6 , where the underground safety distance applies 37 , i.e., we only consider the land use that cannot be used for other purposes. Nevertheless, this assumption neglects that stricter safety distances apply to pipelines in the vicinity of buildings 37 and overlooks the pipeline’s right-of-way agreement distance, where certain activities are prohibited 38 , 39 .

Scatter plots of 26 large- and medium-sized natural gas combined-cycle electric power plant facilities in the world (13 in the US, 5 in Japan, 4 in Russia, 2 in the United Arab Emirates, and 2 in Vietnam). Typical, mean, median, and fitted values are also included. The subplots are as follows: ( a ) capacity factor ( \(C_g\) ); ( b ) land use ( \(A_g\) ); and ( c ) annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ) excl. pipelines with van Zalk and Behrens 3 as a reference.

Nuclear power

The power and energy density of a nuclear power plant differ from those of a natural gas power plant, as there exist a unique additional land requirement, and the volume of nuclear fuel is significantly smaller. Indeed, as shown in Fig. 3 a, a nuclear power plant requires a security zone that greatly contributes to increasing its space requirements. There occurs a protective barrier at the boundary where security personnel are located for control purposes. The estimated surface was obtained from Google Earth using the boundary defined based on the main safety radius, as this was clearly defined at the first barrier. A circular area is shown as an example of the safety surface of a nuclear power plant in Fig. 3 b.

Scatter plots for the 159 nuclear power plants located in 32 countries considered in this study are shown in Fig. 4 . To support the veracity of our findings, we performed an approximated capacity factor calculation of the worldwide nuclear power generation fleet in operation in 2020. Collectively, with a rated power of 393 GW, they generated 2553 TWh in total 41 . With the use of Eq. ( 1 ), we determined an accumulated capacity factor ( \(C_g\) ) of 74.1% for the whole fleet. Compared to the mean results of our population study, this value was of the same order of magnitude, with a deviation of only \(+\) 6.9% ( \(C_g\) was 81%). Table 7 highlights that the energy density increases threefold when excluding the safety area. The mean annual energy density ( \(\varepsilon _g\) ) for the population was 6.703 \({{\text {TWh/km}}^2}\) , including the safety surface, approximately twice the value of 3.333 \({{\text { TWh/km}}^2}\) , as determined based on the land use per unit of electricity in a recent LCA study of power generation (within one standard deviation of our results). The LCA study was based on a different methodology with certain assumptions and data inputs to establish a generic case for a nuclear power plant 10 , 26 . The output highly depends on the assumptions made for associating the land use of waste storage or whether it is not stored above ground and can be neglected. Deep geological repository sites are expected as soon as 2023 10 . Regarding fuel extraction from 2016 to 2020, a significant portion was derived from underground mines (32%), while in situ leaching accounted for the majority (55%) of the total fuel extraction, whereas the most land-intensive open-pit mining occupied the minority (14%) of the total fuel extraction 10 .

Security measures at a nuclear power plant to protect people and the environment in accordance with the United States Nuclear Regulatory Commission (USNRC). ( a ) Side view. ( b ) View from above indicating the safety radius including an exclusion area and a barrier space allocated in case of possible accidents.

Scatter plots of the 159 nuclear power plants worldwide, 56 in Europe (1 in the Netherlands, 2 in Belgium, 19 in France, 6 in UK, 7 in Germany, 5 in Spain, 1 in Armenia, 2 in the Czech Republic, 2 in Finland, 1 in Hungary, 1 in Romania, 2 in Slovakia, 1 in Slovenia, 3 in Sweden, and 3 in Switzerland), 14 in the CIS (10 in Russia and 4 in Ukraine), 2 in the Middle East (1 in the United Arab Emirates and 1 in Iran), 1 in Africa (South Africa), 4 in Latin America (2 in Argentina, 1 in Brazil, and 1 in Mexico), 59 in North America (55 in the US and 4 in Canada), and 23 in Asia Pacific (4 in South Korea, 2 in Taiwan, 5 in Japan, 1 in the Philippines, 4 in China, 7 in India, and 2 in Pakistan). Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ).

Renewable resources

The majority of this paper is dedicated to renewable sources, which will be the focus of this section, divided into separate subsections.

Geothermal power

The results obtained for geothermal power generation were retrieved from a population study of 8 power plants located in Iceland and the US, which are two of the three most prominent countries (including Indonesia) in terms of geothermal energy produced. To determine the land required by geothermal power plants, we used the perimeter delimited by power plant wells, and we then estimated the surface area within this perimeter by using a satellite view in the Google Earth environment. The final results are shown in three scatter plots in Fig. 5 . Based on our analysis, some power plants were close in terms of the calculated power and energy densities compared to those of van Zalk and Behrens 3 . However, our population included a larger portion of high-temperature generation facilities (temperature threshold \(\ge\) 250 \(^{\circ }C\) ) known to exhibit higher power densities, causing the mean power density to be approximately 58% higher than that of van Zalk and Behrens 3 .

Case study of 8 geothermal power plants, 3 in Iceland and 5 in the United States. Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ) with van Zalk and Behrens 3 as a reference.

In this study, we particularly focused on large-to-small hydropower plants with a dam and a reservoir and not run-of-river hydropower plants. Land use definitions could vary between the catchment area (i.e., source area, \(A_s\) ) and the reservoir surface area (i.e., generation area, \(A_g\) ). Figure 6 shows the difference between these two definitions. In LCA, it is common to consider the inundated land area (ILA) of water surfaces while neglecting the construction of associated infrastructure 42 . However, the water body surface before dam construction is typically subtracted from the highest regulated water level, which yields a slightly lower ILA than the reservoir surface area considered in this paper.

In the literature, the mean power densities of hydropower plants could reach as low as \(\le\) 1 \({{\text {W/m}}^2}\) . The reason could be that the studied power plants exhibited a particularly low drop height, which could strongly influence the power and energy outputs. Another reason could be that the assumed area for the power density calculation was the catchment area. The ratio \(A_s/A_g\) is a key metric to evaluate the difference between the source power and energy density ( \(p_s\) and \(\varepsilon _s\) ) and the generation power and energy density ( \(p_g\) and \(\varepsilon _g\) ), as formulated in Eq. ( 8 ) and originating from the concept of scaling.

As shown in Fig. 6 a, catchment areas ( \(A_s\) ) are locations in low-lying regions where water from higher areas accumulates into a single water body.

Reservoir-based hydropower facilities are built with their key conversion components underground, which hides them from the natural surface in their vicinity. This is the main argument when one considers the reservoir surface itself for power and energy density calculations. Contrary to Fig. 6 b, there could occur hydropower plants with more than one reservoir occupying additional areas in which a very large amount of water could be stored. Therefore, we must carefully consider all reservoir surfaces to accurately determine \(A_g\) in density calculations. For example, the hydropower plant in Saurdal, Norway, contains nine reservoirs to be considered in the calculation. It is also important to note that one reservoir could, in many instances, feed several hydropower plants, and therefore, they could share the assumed environmental footprint based on the solidarity principle, scaled by their respective energy output in energy density calculations.

In our worldwide population study of hydropower generation, the catchment area ( \(A_s\) ) was obtained for 256 power plants, while 459 power plants were investigated based on the reservoir surface ( \(A_g\) ). Figure 7 shows scatter plots of the obtained and calculated data, where Fig. 7 a–c show the capacity factor, land use of reservoirs ( \(A_g\) ) and power and energy density based on the considered land use, respectively. In many instances, hydropower dominates the power system, so in these cases, the load variation of the power system fed by hydropower more notably influences the capacity factor rather than representing a physical property of the power plant itself. Therefore, Fig. 7 a shows that \(C_g\) greatly varied. There was also a slight correlation between the land use and the rated capacity of hydropower plants, as shown in Fig. 7 b. Wide variations in the power and energy density were observed, as shown in Fig. 7 c, which are further quantified in Table 8 to reveal the differences between the various regions worldwide. The Asia Pacific region exhibited the highest density, at more than double that of Europe, which approached the overall density worldwide. Nevertheless, Europe exhibited outliers due to topography differences, and Norway was significantly denser than Europe as a whole.

As shown in Fig. 7 d, we could observe why using the reservoir area ( \(A_g\) ) yielded a much higher power density than that based on the catchment area ( \(A_s\) ). Indeed, with our calculations, the ratio between the values based on these two surfaces reached 3040. Moreover, considering the median, we could obtain a factor of 208.

Illustration of the surface areas needed for hydroelectric power generation. ( a ) Catchment area (i.e., source area, \(A_s\) ) used to collect the water. ( b ) Reservoir-allocated surface area ( \(A_g\) ) of the Hohenwarte II pumped-storage power plant (taken by Vattenfall on October 19, 2011, https://www.flickr.com/photos/vattenfall/6779452824 , published under the courtesy of the CC BY-NC-ND 2.0 license, https://creativecommons.org/licenses/by-nc-nd/2.0/ ).

Scatter plots of 451 hydropower plants in the world, 71 in Norway, 68 in Europe, 21 in Middle East, 38 in the CIS, 98 in Asia Pacific, 12 in Africa, 34 in Latin America, and 26 in North America. Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ). ( d ) Catchment-to-reservoir area ratio.

It is unfortunate that hydropower, even though it often exhibits a very high power and energy density among renewables (depending on the region), is not as easy to scale up as other renewable sources. However, there remains a high potential for the retrofitting of existing dams, which is an emerging solution to introduce more hydropower in existing occupied reservoirs.

The above is a very important consideration, as it has been previously emphasized that dams exert a significant environmental impact. Indeed, an international survey of the 60,000 largest dams globally indicated that only one-third of all dams was used for hydropower production 43 . Even in Europe, Poland exhibited a significant unused hydroelectric potential 44 . Moreover, there are 90,000 dams in the US and only approximately 2500 of these dams generate power, i.e., \(\sim\) 3%. While most of these nonpowered dams do not store enough water or are too remote to be suitable for hydropower development, thousands could be retrofitted to generate electricity, according to the US Department of Energy (DOE). In a 2016 report 45 , the agency found that retrofitting existing dams could add as much as 12,000 MW of generation capacity to the grid. Developing these dams could, therefore, yield a double effect, greatly reducing the environmental impact by utilizing existing constructions and increasing electricity production with hydraulic hydropower technology.

According to known hydropower data, the world’s production reached 4418 TWh in 2020 22 . Assuming that only one-third of the total dams is used, that we can use 100% of these dams to generate hydraulic energy and that they, on average, can be linearly scaled, we can obtain 13,254 TWh of electricity generation. This corresponds to approximately 50% of the global electricity generation in 2020. However, this result is utopian, as the International Energy Agency’s (IEA’s) prediction assumed only a 91.6% increase in hydropower toward 2050, which accounts for only 63.9% of our estimated potential. Nevertheless, hydropower generation could significantly contribute to the global energy mix by 2050.

Solar power

Large solar power plants are either photovoltaic (PV) or concentrated solar power (CSP) plants, where the latter tends to exhibit a higher energy density.

CSP plants were studied via a scatter plot of 27 large CSPs worldwide, as shown in Fig. 8 . It should be noted that CSP farms usually attain a capacity factor between 15% and 30% 46 , even though expected industry values can reach as high as 50%. One standard deviation of the mean of the population depicted in Fig. 8 a covers values ranging from 14.1% to 35.6%, which agrees with earlier reporting 46 . Even though the land use of CSP plants could be linearly fitted with respect to the rated capacity, a high deviation exists, as shown in Fig. 8 b. This is also reflected in the power and energy density scatter plot of Fig. 8 c, where our analysis yielded higher densities than those of van Zalk and Behrens 3 , which involved a more limited population study ( \(n = 2\) ).

Case study of 27 concentrated solar power (CSP) plants, 17 in the United States, 7 in Spain, 1 in India, 1 in Chile, and 1 in South Africa. Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ) with van Zalk and Behrens 3 as a reference.

Solar PV systems can be used in residential applications. Here, rooftop PV systems were selected as a reference for utility-scale onshore and offshore solar PV farms. Residential PV values were obtained from a recent study of up to 40 countries 5 . The rooftop PV data contained no installation specifications but rather provided numbers based on the total installed rated power in these countries (i.e., accumulated power and energy densities). Rooftop PV technology solves the challenge of land use by utilizing existing surfaces on buildings. However, this technology is limited in scalability, as building surfaces are limited. Table 9 provides the geographical differences in residential PV applications, which are assumed to exhibit a similar land use relative to that of utility-scale PV farms. It is clear that Africa exhibits the highest PV potential, even though Capelan studied only one country in this region 5 .

Offshore solar PV farms provide several advantages over onshore installations. First, the water temperature approaches the equilibrium temperature of the solar panels. Second, large water bodies benefit from maximum sunlight and a clean environment. Finally, offshore PV technology allows the development of unexploited seas and can thus prevent possible space competition on land. However, this presupposes that offshore PV farms do not impact oceans as profoundly and negatively as do on-ground land installations. Nevertheless, offshore installations need more infrastructure to collect power and transfer it to land, which is associated with its own environmental footprint. Offshore PV farms remain less developed than onshore PV farms. However, an increasing number of floating solar plants are being built, but there is still a lack of data for these installations. This study was based on six offshore plants, while the onshore population involved eleven PV farms (Fig. 9 ).

In the scatter plots in Fig. 9 , we distinguished the following three types of PV installations: utility-scale onshore and offshore installations and rooftop PV (residential) installations. Regarding utility-scale onshore and offshore PV farms, this study did not consider countries but actual large-scale solar farms. In general, larger PV farms are significantly more scalable in terms of their power and energy density, as highlighted in Fig. 9 c. Even though onshore and offshore utility-scale PV farms are distinguished in the scatter plots, they are grouped in statistical analysis as they provide comparable energy densities.

Scatter plots of onshore and offshore utility-scale PV farms and residential PV farms, namely, 11 onshore PV farms ( \(n= 11\) ), including 1 in Peru, 1 in South Korea, 1 in Japan, 1 in Taiwan, 1 in the Philippines, 1 in the Netherlands, 1 in Belgium, 1 in the UK, 1 in Germany, 1 in France, and 1 in Spain, and 6 offshore PV farms ( \(n= 6\) ), including 2 in France, 1 in China, 1 in India, 1 in Japan, and 1 in Singapore. Cumulative residential PV installations in 39 countries ( \(n= 39\) ), including 23 in Europe, 7 in Asia Pacific, 2 in the Middle East, 2 in Latin America, 2 in North America, and 1 in the CIS. Cumulative data retrieved from estimations 5 . Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ) with van Zalk and Behrens 3 as a reference.

Wind power exhibits, naturally, a relatively low capacity factor influencing its power and energy density. Wind turbines must also be distributed spatially to prevent negatively influencing each other’s performance, causing a lack of energy concentration. This section first evaluates the accumulated capacity factor of the global wind power fleet, as listed in Table 10 . The global capacity factor in 2018 was 25.6%, while it reached 26.1% in 2019 24 , and 24.6% in 2020 24 . On average, the rated power of wind farms is available only one-fourth of the time, yielding the above accumulated energy.

Regarding the power and energy densities, it is slightly more complicated to gather precise data. The total land use of a wind power plant comprises the area within the perimeter surrounding all turbines. A detailed investigation was conducted of each wind farm to identify the accurate amount of regulated land required for wind turbines ( \(A_g\) ). We were limited to Google Earth-obtained measurements. Our overall population study was based on 173 wind farms located in 19 countries, including 162 onshore wind farms, 14 single-string wind farms, and 11 offshore wind farms. The key data obtained for the different continents are listed in Table 11 , which summarizes the onshore wind farm findings. The scatter plots in Fig. 10 also include offshore wind farms for comparison.

Onshore wind installations were divided into the regular type and single-string topology, where the latter is a unique onshore case. In the string-based farm topology, no other wind turbines can disturb the operation of the installed wind turbines since they are arranged in a single row and not in columns, which leads to an improved performance. It is also ideal when geographical areas do not allow widely distributed wind farms, as they occupy only long, narrow areas (e.g., atop a mountain, along an ocean, etc.). Nevertheless, the main bottleneck for string-configured wind farms is that they are spatially not scalable because they do not allow turbine distribution along both directions horizontally, i.e., also along the wind direction.

We found that string-type wind farms attained a higher spatial energy density due to their inherently small spatial area regulated for installation. The estimated area is equivalent to a thin and straight corridor with the same width as the rotor diameter and projected along the entire string of turbines. Even though thin corridors led to a nearly tenfold reduction in land requirements, it remained an order of magnitude larger than the direct land use associated with the wind turbine towers and their corresponding infrastructure. According to the United Nations Economic Commission for Europe (UNECE) 10 , 26 , based on the direct land impact, a local annual energy density of approximately 2.50 \({{\text {TWh/km}}^2}\) was estimated. However, including indirect impacts and the total area needed, 0.01 \({{\text {TWh/km}}^2}\) was found for the regular wind farm topology 26 . The National Renewable Energy Laboratory (NREL) distinguishes between the directly impacted area and the total regulated area in their classification 31 . From the perspective of the total area, the straight corridor we assumed in this paper could be justified. Inevitably, a permanent service road and a temporary road occur on each side of the string of turbines 31 . We presumed that undisturbed land did not occur within this corridor. Nevertheless, the estimated width of the string corridor is a sensitive assumption even though it is found to scale well with the rotor diameter. The exact scaling could vary among different string-based wind farms, but we found a proportionality to the diameter that could provide a fairly satisfactory approximation. Via dimensional analysis, it is beneficial to consider a large rotor diameter to enhance the energy density. Size effects could reduce the environmental impact per TWh of production. It has been found this can be reduced by 14% for every doubling in the rated power (i.e., a larger rotor diameter increases power more than the land area needed), making the case for wind farms comprising larger turbines 48 .

Worldwide study of 173 wind farms ( \(n = 173\) ), where 148 are onshore regular farms (4 in Africa, 3 in Latin America, 77 in North America, 53 in Europe, and 11 in Asia Pacific), 14 are single-string farms, and 11 are offshore farms. Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ) with van Zalk and Behrens 3 as a reference.

According to the NREL 31 , the average spatial power density of regular land-based wind farms in the US is approximately 3 \({{\text {W/m}}^2}\) , which occurs within one standard deviation of our data. It is also acknowledged that at high scales of deployment, the power density is reduced. In our data, a higher mean power density of offshore wind turbines (3.89 \({{\text {W/m}}^2}\) ) was determined than that of onshore facilities (2.12 \({{\text {W/m}}^2}\) ), as shown in Fig. 10 c. This occurs because the areas are windier, and therefore, the capacity factor for offshore wind turbines is slightly higher (also found for the accumulated \(C_g\) values in Table 10 ). However, offshore wind farms need more grid infrastructure to collect and transport energy from sea to land, where long-distance direct current grids are more relevant. Additionally, higher maintenance needs and a lower mobility at sea suggests higher operational costs. For these reasons, offshore wind is still not very widely developed today, but it remains very promising for the future, and the price has already significantly decreased in recent years.

Offshore wind technology occupies space, but it needs sea area, not land area. When studying the occupied space of conventional offshore turbines, it is important to emphasize that they cannot be positioned just anywhere in the ocean. A useful area must be clearly defined. To not excessively occupy territorial waters, the shortest distance from the coastline for larger offshore windfarm projects is often 10 km 49 , where winds are stronger and more consistent. People living on the coast tend to not want to see large wind turbines, which is referred to as the not in my backyard (NIMBY) phenomenon. Table 12 provides positioning data available for 112 operative and 53 planned offshore windfarms. The mean distance from the shore in planned projects is approximately 30 km with a standard deviation of approximately 29 km. Based on these data, considering the abovementioned limitations near the coast, one could assume a majority of offshore windfarms to be placed within a corridor between 10 km 60 km from the shore. This is also the stricter limitation for conventional turbines sensitive to the sea depth. In the conservative estimate in this paper, offshore turbines were located within a coastal corridor approximately 50 km wide, between 10 km and 60 km from the coast. In addition to this conservative constraint, all the unavailable areas restricting this generic corridor are usually not easy to identify when focusing on a worldwide assessment not limited to certain regions. To illustrate the implications of the conservative estimates of useful areas, Fig. 11 shows two examples: Norway and Africa. According to Fig. 11 a, Norway exhibited a total useful area of \({131{,}601}\,\,{\text{km}^2}\) , which could be validated considering that Norway has a perimeter of 2632 km where the 50-km corridor applies. Similarly, Africa exhibited a total useful area of \({1{,}279{,}351}\,\,{\text{km}^2}\) , corresponding to a perimeter of 25,587 km.

Illustrative examples to estimate the minimum available sea area (conservative case) for offshore wind turbines considering a corridor of just 50 km (more cases are provided in Table 13 ). They were created with the use of the Google Earth Engine 25 , version 7.3.2.5776 ( https://earth.google.com/web/ ). ( a ) Norway. ( b ) Africa.

To reveal the full potential of the spatial installation of offshore wind turbines, including new technologies such as floating wind farms, another more optimistic corridor limited by the exclusive economic zone (EEZ) should instead be considered 50 , which is a maritime area in which a coastal state has sovereign and economic rights to explore and use natural resources. Indeed, the EEZ area is a close estimate of the maximum useful area for offshore wind technology. In some countries, this area extends to a maximum of 200 nautical miles (NM) (370.42 km) from the coastline, before entering into international territory. A first-order approximation of the 100% energy from offshore wind scenario was made based on the described lower and upper estimates of the available sea area for the different countries and regions, as summarized in Table 13 . Belgium exhibited a required area that was more than six times larger than the available area based on the conservative estimate. This occurred because Belgium has a short coastline directly impacting the coastline of the UK. France also faces challenges in meeting all its energy needs from offshore wind, but if approximately two-thirds of the 50-km corridor could be utilized, these needs could be met. However, this could challenge use of the coastline for other purposes, e.g., the maritime traffic could be disturbed.

Tidal power

Case study of 12 tidal power plants worldwide ( \(n = 12\) ), 2 in the CIS (Russia), 1 in North America (Canada), 5 in Europa (1 in France and 4 in the UK), and 4 in Asia Pacific (1 in China, 1 in India, and 2 in South Korea). Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ).

Tidal energy is not yet widely used, so there are few farms to collect data on. The results obtained for the tidal energy density are retrieved from a case study of 12 farms located in seven countries. The details are described in the caption of Fig. 12 . The scatter plots in Fig. 12 include the capacity factor, pool land use, and power and energy density. Concerning the energy density, we found the same problem as that for hydropower, where two different surfaces could be considered. Either the area of the entire infrastructure (i.e., river, dam, and pool) or that of the pool only. In all calculations, the lower area estimate of the pool was considered. We found a mean power density of 2.84 \({{\text {W/m}}^2}\) , or an annual energy density of 0.025 \({{\text {TWh/km}}^2}\) . In fact, few power density studies of tidal energy have been conducted thus far. For comparison, another study found an average power density of 3 \({{\text {W/m}}^2}\) via calculation 33 , equivalent to an annual energy density of 0.026 \({{\text {TWh/km}}^2}\) , which verifies the accuracy of our findings. Other studies only consider the swept area of the tidal turbine 51 , 52 , 53 , where estimates of the tidal stream power density become incomparable to this work.

Several sources estimated that the technically exploitable tidal power worldwide, in near-shore areas, is approximately 1 TW 54 , which corresponds to the production of roughly 8760 TWh per year. The world’s electricity consumption in 2020 was 22,492 TWh. Tidal energy could, therefore, only satisfy approximately 37% of the global electricity demand. As the world’s primary energy needs in 2020 reached 160,318 TWh, the exploitable tidal potential could only meet 5% of the demand. Even under the best-case scenario, if all the tidal energy were exploited, this would not meet the total consumption. This is a limitation in tidal power scaling, which should be regarded as a supplemental energy source.

Wave energy provides a very high untapped potential, as approximetaly 10% of the world’s electricity consumption could be covered by wave generation, corresponding to a technically exploitable potential of 1400 TWh per year. However, several constraints slow the development of wave energy. First, the difficult environment (e.g., storms) could quickly damage power conversion equipment. Moreover, this technology exhibits a relatively high levelized cost of energy (LCOE). In addition, acceptability and social perception could pose problems (e.g., disturbance of fauna, marine traffic, and fishing). These constraints are the reasons why wave energy is not yet very widely developed. The population study in this paper was based on prototypes of different technologies already installed. We also considered planned future wave power projects to be developed in the coming years (e.g., the Pelamis converter, CETO converter, Waveswing system, Wave dragon, etc.). Our population study consequently considered the available data obtained from three prototype designs and eight future wave power projects. Figure 13 shows three scatter plots of the available data, including the expected capacity factor, allocated land use, and estimated annual energy density or mean specific power.

Case study of 11 prototype or designed wave power plants worldwide ( \(n = 11\) ), 1 in Scotland, 2 in Portugal, 2 involving Wave hub, 4 involving Wave dragon, 1 involving AWS, and 1 involving the Wave dragon wave generator. Typical, mean, median, and fitted values are also included. ( a ) Capacity factor ( \(C_g\) ). ( b ) Land use ( \(A_g\) ). ( c ) Annual energy density ( \(\varepsilon _g\) ) or mean specific power ( \(p_g\) ).

Hybrid renewable power

Even though currently underdeveloped, hybrid renewable solutions combining solar PV and wind installations are promising for the future. They have been extensively studied, but just a few have already been installed 4 , 55 . Three hybrid parks were studied herein: Haringvliet Zuid (Netherlands), Parc Cynog (UK), and Port Augusta (Australia). These farms contain two parts: one part containing the hybrid solution, where PV panels are installed at the foot of wind turbines, and one part containing only wind turbines. We were mainly interested in the hybrid part of these projects. The area of the hybrid part was estimated using the Google Earth environment, and the final results are listed in Table 14 . Notably, the hybrid solution revealed a significant increase in the hybrid annual energy densities. Wind power exhibits a structurally lower power density than that of solar PV technology. For this reason, the mean hybrid energy density of the three farms was approximately 53% higher than that of solar PV technology alone. Furthermore, the cost of these installations was reduced as the same grid connection could be used for both sources. Hybrid renewable parks are relevant in regions where land or sea areas with natural resources are scarce.

Analyzing the obtained energy densities for land and sea use

This section finalizes the analysis of the nonrenewable and renewable resources investigated in the two previous sections. Here, we considered the energy consumption today and the predicted electricity needs in the future to obtain estimates of the needed surface area for the global generation mix.

In grouping the worldwide energy consumption into specific regions, we could establish the local annual energy density of the energy consumption, as listed in Table 15 . These values could then be used to estimate the land and sea requirements of the different energy sources in the region to match the specific consumption pattern. Since the annual energy density of consumption is based on the primary energy use, this value could be adopted as the upper estimate of the potential electric energy use in the different regions. This is based on the assumption that deep decarbonization causes an increase in the electrification level of the energy supply. However, this transition also significantly increases the efficiency of energy use 30 . Primary energy is, therefore, used as an upper estimate of the future all-purpose electric energy demand.

Similar to the power consumption, the annual energy densities of the power sources are summarized in Table 16 as aggregated values, including respective standard deviations. The same values are plotted on linear and logarithmic scales in Fig. 14 . The obtained aggregated data establish metrics of each source used in land use estimation hereafter.

The mean values in Table 17 of the annual energy density in this work were compared to other results reported in the literature. Even though there were significant deviations from our population studies, our mean results remained on the same order as that of the findings in other studies. One outlier was that the mean value of natural gas obtained from van Zalk and Behrens 1 was overestimated, occurring outside one standard deviation in our work. Moreover, the solar CSP and PV values obtained from the UNECE 10 and OWID 26 were slightly below one standard deviation of our mean values.

Overall result of the average values and standard deviations of the annual energy density ( \(e_g\) ) or mean specific power ( \(p_g\) ) for the different energy sources considered based on the output data provided in Table 16 (rooftop solar PV technology is excluded). ( a ) Linear scale. ( b ) Logarithmic scale. The biomass numbers are based on the meta-analysis of van Zalk and Behrens 3 .

Table 18 provides the spatial requirements to achieve 100% primary energy in the different regions based on the various power sources examined in this paper. In general, it could be observed that nuclear power needed the smallest amount of space in each region, while biomass needed the largest amount of land. In the world as a whole (land areas only), the land use ranged from 96.154% land to as low as 0.016%. All the investigated sources were distributed within this window. Please note that regarding marine energy, such as offshore wind, tidal, and wave energy, the needed area was calculated in terms of land areas for the sake of comparability to that of the other sources (i.e., their minimum or maximum available areas were not considered). It is also important to note that the estimations were based on only the annual energy densities of the sources. The estimates did not consider that some sources faced limitations regarding the available areas that are strictly region-specific (e.g., hydropower).

To facilitate an even more detailed-level assessment of the land and sea requirements for electric power generation, Table 19 lists the needed electricity mix toward 2050 to reach the NZE target based on the predicted growth in energy use 22 . This mix is based on the NZE normative scenario of the IEA, which provides a narrow but assumed achievable pathway to net zero emissions by 2050. The NZE roadmap assumes slightly below a threefold increase in worldwide electric energy use by 2050. Examples of electrification predictions indicate that 60% of all car sales will comprise electric vehicles (EVs) by 2030 and that 50% of sold heavy trucks will comprise electric vehicles by 2035. Moreover, electricity is expected to achieve net zero emissions in advanced economies by 2035 and globally by 2040.

The resulting land and sea use needed to achieve the IEA NZE predictions listed in Table 19 are given in Table 20 . With the use of the mean energy density values in Table 16 , the needed land and sea uses were calculated for the different sources ( \(A_g\) ) via Eq. ( 6 ). Each spatial component was summed to determine the total area of land needed, including and excluding sea use. It was demonstrated that we are moving toward a world where much more land is needed to meet the electricity supply demand. This occurs not only because the electric power needs are growing but also because the energy mix will comprise sources with inherently lower energy densities, which will cause the aggregated energy density of generation to reach half by 2050. A sixfold increase will occur in the spatial extent of power generation, from approximately 0.5% of land areas used for electric generation in 2020 to nearly 3.0% of land areas in 2050 (i.e., 430 million hectares of land). The world will be electrified by requiring an area roughly equal to the entire European Union (EU), which is one and a half times the size of India. The major contributor to increasing land use will be related to power generation from biomass, which is clearly seen in Fig. 15 . Moreover, onshore wind power also drives up the spatial extent of electricity generation toward 2050. The estimated total of 3% of global land for electric generation in 2050 is significant. Even expanding cities are perceived to need much space. Nevertheless, they take up only 1% of global land 57 . Similarly, van Zalk and Behrens found a nearly fourfold increase in land use in the US from 2020 to 2050 based on the NREL 80% renewable energy scenario 3 , 58 .

When predicting land requirements, there are limitations to be mentioned. An additional challenge in the integration of more renewable sources is not only the land requirements but also the fact that power generation can be distributed across a larger area resulting in a higher need for infrastructure for electric power collection and further transmission. There is also an intermittency problem of renewable sources to meet the power adequacy requirements of the load. Residential users could act more flexibly, but there is an interest in industries relying on large amounts of electrical power to operate at a high capacity factor of consumption ( \(C_c\) ). Solutions to address this issue entail the introduction of a higher share of the ramping capability of the firm dispatchable power into the system 59 or to realize large-scale energy storage 60 . Depending on the backup power solution or energy storage solution, these elements will occupy additional land and materials. Suppose the intermittency of renewables could be predicted more reliably 61 . In this case, this could reduce the amount of backup power needed to call on the demand. Recent research proposed ways to include the cost of storage in the cost of wind energy production 62 . Here, the spatial needs and additional infrastructure for these buffering services could also be included. In the case of storage such as hydrogen, the round-trip efficiency in a conservative estimate could reach as low as 35% 60 . In the extreme case, large portions of the power delivered by intermittent renewables must be fully buffered. This could generate rippling effects that could result in highly inefficient electricity generation. The equivalent annual energy density for this system could become even lower while the area required continued to increase. Finally, another layer on top as a result of increasing the share of clean electricity is that we would greatly expand the transmission capacity, at least triple the size toward 2050 according to IEA energy mix predictions.

Filled area plots of the changes in the worldwide energy mix composition according to the IEA NZE scenario 22 . ( a ) Electricity generation mix (adopted from Table 19 ). ( b ) Predicted land and sea use requirements (adopted from Table 20 ).

This paper revealed that the land and sea requirements for future power generation facilities are currently projected to significantly change by 2050. The obtained annual energy densities for 870 real-world power sources were used to estimate the environmental footprint of the future energy mix. A sixfold increase in the spatial extent of the worldwide power generation resulted not only result from the fact that new renewable energies are more challenging to harvest than the existing mix of sources but also from the fact that global electrification will experience a threefold increase by 2050.

Our paper provided evidence that, in a worldwide sense, hydropower is the most energy-dense renewable source. However, this is not the case when one considers certain regions, e.g., where the topography does not favor hydropower generation or in areas where the performance of solar power is much higher than the global average. It must also be emphasized that hydropower exhibited the highest standard deviation among the investigated sources. The standard deviation of the annual energy density ranged from 0 to 1.67 \({{\text {TWh/km}}^2}\) . The upper standard deviation of hydropower was very close to the lower standard deviation of nuclear power, at 1.88 \({{\text {TWh/km}}^2}\) , but far higher than that of the natural gas population.

Contrary to conventional wisdom, our work also demonstrated that nuclear power exhibits a higher annual generation density than that of natural gas power plants, considering the land occupation of pipelines and mining to feed gas-fired power plants. In this paper, the generation density of a nuclear power plant included the safety surface in addition to the nuclear power plant itself.

While biomass is by far the most dilute renewable energy source, this paper found through a population study of 148 specimens that onshore wind farms are the second most dilute source for power generation based on the assumptions of the spatial extent outlined in this paper. Even though our paper confirmed the order of magnitude in earlier studies, the limitations of the assumptions should be stated. Our calculations considered the total site area where wind farms are distributed. There occur empty areas between wind turbines that could be utilized for grazing, agriculture, and recreation. When the occupied area of wind power only considers tower footprints and access roads, the specific power could easily increase by at least an order of magnitude 26 . However, this does not fully represent the high spacing between the distributed sources of a wind farm and the low scalability in space-limited regions. High land requirements also generate significant implications for the need for materials and infrastructure to collect energy from wind turbines. There exist potential indirect effects on wildlife and degradation of the quality of landscapes 64 , and the visual footprint is significant throughout the entire area. The wind power performance could be enhanced in the future via technological improvements such as wake steering, which could be used to enhance the annual energy production of spaced turbines in wind farms 65 .

Data availability

All collected data in this study are included in the published article (and Supplementary Information files).

Abbreviations

Electrical energy conversion efficiency of generation facility, [%] or [pu]

Annual energy density (spatially) of the generation and consumption, \([{\text{TWh/km}}^{2}]\) , \([{\text{GWh/km}}^{2}]\) , or \([{\text{J/m}}^2]\)

Required area of the generation facility and energy source, [ \({\text{km}^{2}}\) ] or \([{\text{m}^{2}}]\)

Minimum and maximum available areas for power generation and the total area, \([{\text{km}^{2}}]\) or \([{\text{m}^{2}}]\)

Mean and deviation values of the dataset

Capacity factor or load factor of power generation and consumption, [%] or [pu]

Electrical energy generated and consumed, [TWh] or [J]

Median and maximum values of the dataset

Number of samples in the population

Mean power density (spatially) of the generation facility, energy source, and consumption, [ \({\text{W/m}}^{2}\) ]

Power of the source and rated electrical power of generation and consumption, [MW] or [W]

Total time of the entire year, \(\approx\) 8765.8 h

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Acknowledgements

The authors would like to acknowledge the assistance of proof-readers, academic formatting specialists, and editors at the Nature Research Editing Service. The work would not have been possible without the support of the internship exchange program offered by the Grenoble Institute of Technology (Grenoble INP). In addition, the authors would also like to thank Senior Engineer John Arild Wiggen at the Department of Electric Power Engineering (IEL), NTNU, for his valuable assistance in the collection of data during the project.

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Department of Electric Power Engineering (IEL), Norwegian University of Science and Technology (NTNU), O. S. Bragstads plass 2E, 7034, Trondheim, Norway

Jonas Kristiansen Nøland, Juliette Auxepaules, Antoine Rousset, Benjamin Perney & Guillaume Falletti

Grenoble Institute of Technology (Grenoble INP), Grenoble, 38031, France

Juliette Auxepaules, Antoine Rousset, Benjamin Perney & Guillaume Falletti

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J.K.N. contributed to the study conceptualization and methodology and managed the research project. J.A., A.R., B.J., and G.F. gathered all the data, processed these data, and initially interpreted the results. J.K.N. performed final processing of all the results, set up the comparative analysis process, and led the writing and preparation of the final manuscript. All authors have read and approved the final version.

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Nøland, J.K., Auxepaules, J., Rousset, A. et al. Spatial energy density of large-scale electricity generation from power sources worldwide. Sci Rep 12 , 21280 (2022). https://doi.org/10.1038/s41598-022-25341-9

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Year-long fallout over retracted trans youth paper highlights new research era

Fallout from the retracted paper ultimately led to one editor’s resignation.

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By Anil Oza

Sept. 18, 2024

Sharon Begley Science Reporting Fellow

Last year, a Springer Nature journal published a study surveying 1,700 parents of adolescents and young adults with gender dysphoria. Just a few months later, the study was retracted because there had been no formal process for those parents to consent to the study.

But the story didn’t end there. Ongoing fallout from the paper and its retraction has opened up an internal rift among academic editors and journal staff that led to one editor’s resignation, as first reported by Retraction Watch .

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The controversy, outside experts say, is a microcosm of an important shift in how academic circles think about research on trans people and other marginalized communities. Advocacy from members of those communities, including Black and Indigenous scientists as well as disabled scientists, aims to ensure that the subjects of research play a central role in shaping it.

“The entire point of any research related to transgender and gender diverse people should be to improve the health of that population,” said Alex Keuroghlian, the director of the National LGBTQIA+ Health Education Center. “Any research about a community should be led by that community, or at least robustly include community voice.”

The ripple effect of a retraction 

The retracted article, “ Rapid Onset Gender Dysphoria: Parent Reports on 1655 Possible Cases ,” was published in March 2023 in the Springer Nature journal Archives of Sexual Behavior. Rapid onset gender dysphoria is a much-criticized theory that, as the paper explains, suggests social contagion leads adolescents to “falsely believe that they are transgender, and that they must undergo social and medical gender transition to resolve their issues.”

Rapid onset gender dysphoria is not an accepted medical diagnosis, and experts dispute the hypothesis, pointing to surveys of transgender and gender diverse people that show they have often understood themselves as transgender for years before they began telling other people.

The study surveyed parents of trans youth who found and contacted the website ParentsofROGDKids.com, which the study’s authors note is a self-selecting group of parents “unlikely to be supportive about their children’s transgender status.” According to the parents, their children’s mental health deteriorated after transitioning. The backlash to the study was swift, with researchers arguing that the study did not obtain proper consent before or after data were collected. For those reasons, it was ultimately retracted a few months later, in June 2023. 

“Yeah, there are certain things that could have been done better,” Michael Bailey, a psychologist at Northwestern University who co-authored the retracted study, told STAT. “[But], all research is imperfect.”

In the months after the retraction, Chris Ferguson, a psychologist at Stetson University and an editor at a different Springer Nature journal, Current Psychology, accepted a manuscript by Bailey that argued retractions are “increasingly a vehicle for scientific censorship,” as reported by  RetractionWatch . 

“This was a special issue on retractions, good and bad,” Ferguson told STAT. “I hoped Bailey’s article would open some insight into what it’s like to experience retraction, particularly one which may be motivated more by politics than by science.” Then, last month, the editors-in-chief of Current Psychology informed Bailey that they had decided to rescind the acceptance of the paper, prompting Ferguson to resign. “I couldn’t support a censorious effort, which mainly came from on high at Springer, not the editorial board,” Ferguson said. 

When asked about the process behind the initial retraction and the fallout of the Current Psychology article, Chris Graf, the director of research integrity at Springer Nature, said that the publishing company’s position on research on the trans community “is the same as our approach to all research, that is, we support the publication of methodologically-sound research, conducted following established ethical standards, in which the conclusions are supported by the methods and data.”

The initial study is emblematic of an older genre of research that does not include representation by the community it is researching, according to experts on trans health.

“It’s not coincidental that a study like this would be published in [ The Archives of Sexual Behavior ],” says Jules Gill-Peterson, a historian at Johns Hopkins University. “This journal has a very long history of publishing incredibly moralizing, but totally peer-reviewed, in some cases, federally or privately funded research that basically studied small minority populations, treated them as mentally ill, and then prescribed forms of therapeutic intervention, if not to cure them, at least to modify their behavior, to make them more socially compliant.” 

As examples, Gill-Peterson specifically cited a 1971 paper in the journal describing trans people as “individuals with extreme psychopathology,” another 1971 study on conversion therapy for trans people, and a 1984 paper that “speculates wildly based on a tiny sample that Black trans men are often psychotic because, in the view of the psychiatrists, Black women are so masculine in the U.S. that it would require a break with reality to still want to transition.” The editor-in-chief of Archives of Sexual Behavior and Springer Nature did not respond to a request for comment on the journal’s history. 

Trans health experts say that biased studies can lend credibility to legislation limiting access to gender-affirming care. The paper to first popularize the idea of rapid onset gender dysphoria was amended with a correction after its initial publication in 2018 , and yet “it subsequently has taken on this huge life,” Gill-Peterson said, including being mentioned in several bills to limit access to gender affirming care and in publications like the New York Times . The retracted 2023 study, too, has been cited in government documents in several states, including Maryland and Missouri . 

A changing climate for trans research

In this highly politicized environment, journals are grappling with their responsibilities toward how marginalized groups are represented. 

“The scientific publishing industry cannot prevent a sociopolitical group from misusing science and misrepresenting science,” said Isabel Goldman, a former editor at Cell and DEI lead at Cell Press, who helped develop guidelines for reporting sex- and gender-based analyses at Elsevier. “But what I think we can do, and what I feel we actually have an ethical responsibility to do as stewards of the scientific literature, is when we see the science being misused is to say something.”

The trans rights movement has prompted a number of changes at journals and other scientific institutions. Goldman edited a special issue of Cell on sex and gender, and the new guidelines on reporting on sex- and gender-based analyses were implemented at more than 2,000 Elsevier journals. The guidelines call for collecting data on both gender identity and “sex assigned at birth.” The National Institutes of Health has also changed the language it uses to describe sex and gender. Since the agency’s guidelines on sex as a biological variable was introduced, its language has become more complex. Keuroghlian recently published the first editorial on transgender health research in the prestigious journal, Science, in its 150-year history . 

“The good news is that the generation of researchers and scholars coming up really understands and prioritizes community engaged research and scholarship,” Keuroghlian said. “So we’re seeing that this will be less and less of a problem in the years ahead, as the culture within academia shifts towards respect for community voices.”

While research on trans communities is changing, Gill-Peterson also raises a more fundamental question about the purpose of research. “Why do we think people’s decision making and power, their right to control their own body, depends on peer-reviewed scholarship?” she asks. “I doubt most people think that their sense of the right to their own body flowed originally from a peer-reviewed scientific journal. I think most people would say that that’s something that belongs to them inexorably.”

About the reporting

STAT’s investigation is based on interviews with nearly 100 people around the country, including incarcerated patients and grieving families, prison officials, and legal and medical experts. Reporter Nicholas Florko also filed more than 225 public records requests and combed through thousands of pages of legal filings to tell these stories. His analysis of deaths in custody is based on a special data use agreement between STAT and the Department of Justice.

You can read more about the reporting for this project and the methodology behind our calculations.

The series is the culmination of a reporting fellowship sponsored by the Association of Health Care Journalists and supported by The Commonwealth Fund.

Anil Oza is STAT’s 2024-2025 Sharon Begley Science Reporting Fellow.

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Second judge refuses to grant home release to Sean ‘Diddy’ Combs

Sean “Diddy” Combs was headed to jail Tuesday to await trial in his federal sex trafficking case, after a magistrate ordered him to be held without bail in a case that accuses him of presiding over a sordid empire of sexual crimes. AP explains.

Sean “Diddy” Combs has been ordered held without bail in his federal sex trafficking case. U.S. Magistrate Judge Robyn Tarnofsky made the decision Tuesday after hearing lengthy arguments from prosecutors and Combs’ lawyers. Prosecutors wanted the music mogul held without bail. (AP video/Joe Frederick)

Sean “Diddy” Combs has been indicted on federal sex trafficking and racketeering charges. A former federal prosecutor explains why more defendants could still be named in the case, and why it may have taken time for authorities to make an arrest. (Sept. 17)

In this courtroom sketch, Sean Combs, seated right, looks at his attorney, Marc Agnifilo, left, as he delivers his bail argument as Combs’ family in the gallery, background, raise their hands indicating to Judge Tarnofsky that they are in attendance, to bolster the defense attorney’s bail argument, Tuesday, Sept. 17, 2024, in Manhattan Federal Court in New York. (Elizabeth Williams via AP)

FILE -Sean ‘Diddy’ Combs participates in “The Four” panel during the FOX Television Critics Association Winter Press Tour in Pasadena, Calif., Jan. 4, 2018. (Photo by Richard Shotwell/Invision/AP, File)

U.S. Attorney Damian Williams speaks about federal sex trafficking and racketeering charges against Sean “Diddy” Combs during a news conference, Tuesday, Sept. 17, 2024, in New York. (AP Photo/Pamela Smith)

In this courtroom sketch, Sean Combs, center, is flanked by his defense attorney Marc Agnifilo, left, and Teny Garagos, in Manhattan Federal Court, Tuesday, Sept. 17, 2024, in New York. (Elizabeth Williams via AP)

From left, King Combs, Quincy Brown and Justin Dior Combs arrive at Manhattan federal court, Tuesday, Sept. 17, 2024, in New York. (AP Photo/Seth Wenig)

Marc Agnifilo, attorney for Sean “Diddy” Combs, arrives at Manhattan federal court, Tuesday, Sept. 17, 2024, in New York. (AP Photo/Seth Wenig)

FILE - Sean “Diddy” Combs appears at the premiere of “Can’t Stop, Won’t Stop: A Bad Boy Story” on June 21, 2017, in Beverly Hills, Calif. (AP Photo by Chris Pizzello/Invision/AP, File)

Marc Agnifilo, attorney for Sean “Diddy” Combs, arrives at Manhattan federal court, Tuesday, Sept. 17, 2024, in New York. (AP Photo/Eduardo Munoz Alvarez)

From right, Justin Dior Combs, Quincy Brown and King Combs, arrive at Manhattan federal court, Tuesday, Sept. 17, 2024, in New York. (AP Photo/Seth Wenig)

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As it happened : Sean ‘Diddy’ Combs arrested and charged with sex trafficking.

NEW YORK (AP) — A second judge refused to grant bail to Sean ‘Diddy’ Combs on Wednesday, saying the government had proved “by clear and convincing evidence” that no amount of bail could guarantee the hip-hop mogul won’t tamper with witnesses.

U.S. District Judge Andrew L. Carter handed down the ruling after prosecutors and defense lawyers presented strenuous arguments for and against a $50 million bail package that would allow Combs to be released to home detention with GPS monitoring and strict limitations on who could visit him.

Combs, 54, pleaded not guilty Tuesday after an indictment accused him of using his “power and prestige” to induce female victims and male sex workers into drugged-up, elaborately produced sexual performances dubbed “Freak Offs” that Combs arranged, participated in and often recorded. The events would sometimes last days, the indictment said.

The indictment alleges he coerced and abused women for years, with the help of a network of associates and employees, while using blackmail and violent acts including kidnapping, arson and physical beatings to keep victims from speaking out.

Combs has been in federal custody since his arrest Monday night at a Manhattan hotel.

Arguing to keep him locked up, prosecutor Emily Johnson told U.S. District Judge Andrew L. Carter that the once-celebrated rapper has a long history of intimidating both accusers and witnesses to his alleged abuse. She cited text messages from women who said Combs forced them into “Freak Offs” and then threatened to leak videos of them engaging in sex acts.

AP AUDIO: Sean ‘Diddy’ Combs’ indictment alleges he used power to build empire of sexual crime

AP correspondent Julie Walker reports Sean “Diddy” Combs is trying to get out of jail on bail after being charged with sex trafficking and conspiracy.

Johnson said Combs’ defense team was “minimizing and horrifically understating” Combs’ propensity for violence, taking issue with his lawyer’s portrayal of a 2016 assault at a Los Angeles hotel as a lovers’ quarrel. Security video of the event showed Combs hitting his then-girlfriend, the R&B singer Cassie , in a hotel hallway.

Johnson seized on a text message from a woman who said Combs dragged her down a hallway by her hair. According to Johnson, the woman told the rapper: “I’m not a rag doll, I’m someone’s child.”

Combs is a “danger to the community and poses a serious risk to the integrity” of his case, Johnson argued.

Federal Magistrate Robyn F. Tarnofsky initially ruled that Combs was too dangerous to be freed. But Combs’ attorney, Marc Agnifilo, submitted a letter to Carter on Wednesday asking again for bail under conditions that would allow him to leave the Metropolitan Detention Center, the lockup on the Brooklyn waterfront where he was taken after his arraignment.

The jail, which has around 1,200 inmates, is the subject of frequent complaints from lawyers and some judges that it is overcrowded, violent and neglected.

Combs’ Florida house is on Star Island, a man-made dollop of land in Biscayne Bay, reachable only by a causeway or boat. It is among the most expensive places to live in the United States. Combs’ request echoes that of a long line of wealthy defendants who have offered to pay multimillion-dollar bails in exchange for home detention in luxurious surroundings.

If he were to be granted bail, Combs would have to stay in that house while awaiting trial, his lawyers offered. Visits would be restricted to family, property caretakers and friends who are not considered co-conspirators.

“I am feeling confident. We’re going to go get Mr. Combs out of jail,” Agnifilo said on his way into court Wednesday. He said Combs is “doing great, he’s focused and he’s ready for his hearing.”

Many of the accusations in the indictment parallel allegations contained in a November lawsuit filed by Cassie, whose legal name is Casandra Ventura. The suit was settled the following day, but its allegations have followed Combs since.

The AP does not typically name people who say they have been sexually abused unless they come forward publicly, as Ventura did.

Without naming Ventura but clearly referring to her, Agnifilo argued at Tuesday’s arraignment that the entire criminal case is an outgrowth of one long-term, troubled-but-consensual relationship that faltered amid infidelity. The “Freak Offs,” he contended, were an expansion of that relationship, and not coercive.

Prosecutors portrayed the scope as larger. They said they had interviewed more than 50 victims and witnesses.

Like many aging hip-hop figures, Bad Boy Records founder Combs had established a gentler public image. The father of seven children was a respected international businessman, whose annual “White Party” in the Hamptons was once a must-have invitation for the jet-setting elite.

But prosecutors said he used the same companies, people and methods he used to build his business and cultural power to facilitate his crimes. They said they would prove it with financial and travel records, electronic communications and videos of the “Freak Offs.”

In March, authorities raided Combs’ luxurious homes in Los Angeles and Miami, seizing narcotics, videos and more than 1,000 bottles of baby oil and lubricant, according to prosecutors. They said agents also seized firearms and ammunition, including three AR-15s with defaced serial numbers.

A conviction on every charge in the indictment would require a mandatory 15 years in prison with the possibility of a life sentence.

This story has been edited to correct the spelling of Cassie’s legal first name: Casandra, not Cassandra.

Dalton reported from Los Angeles.