Do Digital Adaptation, Energy Transition, Export Diversification, and Income Inequality Accelerate towards Load Capacity Factors across the Globe?

Abstract

To limit global warming to 1.5 °C, it is imperative to accelerate the global energy transition. This transition is crucial for solving the climate issue and building a more sustainable future. Therefore, within the loaded capacity curve (LCC) theory framework, this study investigates the effects of digital adaptation, energy transition, export diversification, and income inequality on the load capacity factor (LCF). This study also attempts to investigate the integration effects of digital adaptation and energy transition, and digital adaptation and export diversification, on LCF. Furthermore, we explored how income inequality influences the LCF in economies. For this study, 112 countries were selected based on the data availability. Panel data from 2010 to 2021 were analyzed using the STATA software 13 application utilizing a two-step system generalized method of moments (GMM) approach. First, interestingly, our finding shows that digital adaptation and income significantly affect the LCF. An increase in income increases the LCF among the middle-income group of countries. Therefore, LCC is confirmed in this research. Surprisingly, energy transition, export diversification, and foreign direct investment negatively impact the LCF in the base model. Second, the impact of integrating digital adaptation and energy transition has a positive effect on LCF. Third, a negative correlation was observed between the interaction of export diversification and digital adaptation with the LCF. Fourth, a positive correlation was observed between the interaction of renewable energy and digital adaptation with the LCF. Finally, this study explores the impact of the energy transition, export diversification, and income inequality on the LCF with reference to the Organization of Petroleum Exporting Countries (OPEC). The result shows a negative effect between export diversification and LCF among OPECs at a 10% significance level. To improve the quality of our planet, policymakers must understand the forces causing climate change. By adopting a comprehensive perspective, the study aims to understand how these interrelated factors collaboratively influence the LCF thoroughly. Additionally, this research seeks to provide valuable insights related to energy transition, digital adaptation, and export diversification to policymakers, researchers, and stakeholders regarding possible avenues for cultivating a more joyful and sustainable global community.

1. Introduction

An unprecedented increase in the Earth’s surface temperature presents a serious risk to both the environment and human health. About 75% of greenhouse gas emissions are carbon dioxide (CO₂) emissions, which play a significant role in determining global warming and other climatic extremes such as more frequent hot spells, heat waves, precipitation, degraded agriculture and environments, powerful stifling storms, and shrinking arctic snow shield, which are threatening the planet’s biological networks and environment [1]. According to a study by the World Meteorological Organization, 2023 marked the hottest year on capture, with an overall near-surface temperature of 1.45 °C (with a ±0.12 °C range of error) worldwide over the before industrialization baseline. In chronology, it was the hottest decade ever [1].

These difficulties are making an extensive renovation of the current energy system necessary since the global energy sector is the primary source of CO₂. The move away from fossil fuels and towards a more sustainable energy mix that includes renewable energy sources like solar and wind power is known as the energy transition. This shift is imperative to solve the climate issue and build a more sustainable future. Global participants committed to reducing climate change at COP26 [2,3]. The Glasgow Climate Pact highlights the need to reduce nonrenewable energy sources and boost the share of renewable energy in the worldwide energy mix. Because of this, studies on reaching net zero and developing a renewable energy economy have lately accelerated [4,5,6]. Numerous research works have examined the effects of the Kyoto Protocol on the use of renewable, nonrenewable, and total energy, along with income, oil price, carbon emissions, trade openness, financial development, foreign direct investment, human capital, governance, and other factors [7,8,9,10,11,12,13]. A few key issues in the debate related to the energy transition, net zero, and green economic transformation remain unresolved despite the substantial research into renewable energy consumption to support the energy transition and enhance energy efficiency by adopting digital technology, export diversification, and its implications towards the LCF. Therefore, this study answers the following research questions: (1) What is the impact of digital adaptation, energy transition, export diversification, and income inequality acceleration on LCFs? (2) What is the role of the integration effects of digital adaptation on energy transition and export diversification in LCFs? (3) Is there any variation in LCFs among high-, medium-, and low-income countries? (4) Do digital adaptation, energy transition, export diversification, and income inequality affect the LCF in PECs?

Using the environmental Kuznets curve (EKC) hypothesis, several studies have examined the demand side of natural resources, including energy, biodiversity, and environmental quality. These studies have measured environmental quality using indicators such as carbon emissions (CO₂) and ecological footprint [14,15,16,17,18,19,20,21,22,23,24]. Additionally, a study conducted by Sarabdeen et al. [25] employed the Happy Planet Index (HPI) as a novel environmental quality metric. The HPI assesses how well nations are doing at providing a high quality of life for their citizens while utilizing resources sustainably. HPI is a composite of three metrics: ecological footprint, life expectancy, and self-reported life satisfaction. However, the supply side of the availability of natural resources, biocapacity, and load capacity received less attention. The ability of the Earth to generate valuable resources and absorb trash is known as biocapacity.

The load capacity factor (LCF) was first examined empirically by Pata [26] after being introduced by Siche et al. [27]. The LCF is used to fulfill the demand for natural resources. It is recommended that this ratio be greater than one to ensure environmental sustainability. Spending on energy technologies and R&D increases the LCF when an economy reaches a particular income level. Following the loaded capacity curve (LCC) hypothesis, both ecological footprint and biocapacity are influenced by income. An increase in income accelerates the LCC. According to several studies, technological innovation and dissemination improve environmental quality, supporting the LCC hypothesis [28,29,30]. The goal of the Kyoto Protocol also emphasizes technology sharing between developed and developing nations per their respective needs to enhance environmental quality.

Considering the existing research, we examine how the LCC hypothesis is applied to the material footprint between 2010 to 2021 concerning digital adaptation, energy transition, export diversification, and income inequality. In this study, we take material footprint as the LCF and frontier technology readiness index (FTRI), renewable energy transition, and export diversification as the indicators of green innovation in 112 countries.

Material footprint is a consumption-based indicator of resource use. It includes the distribution of raw materials collected for economical use to the demand that follows [31]. Materials are listed as a connection between the start of a production chain where raw materials are extracted from the natural environment and the end where the good or service is consumed in the material footprint. A life cycle analysis demonstrates how resources are collected worldwide and used in a specific location. Ecological backpacks were referred to as “material footprints” by Giljum et al. [32]. Material footprint is an indicator of resource consumption and optimization of the entire value chain of products and ingredients. Therefore, in this study, we take material footprint as the LCF. An increase in digital adaptation, renewable energy transition, export diversification, and income would increase the LCF.

FTRI represents the capacity, acceptance, and adoption of frontier technologies of economies, and FTRI has ranked 158 economies. The index comprises five building blocks: ICT deployment, skills, research and development, industry activities (the ability of the local industry to manufacture advanced technology and export digital services), and finance. A score of 1 indicates the best performance, and 0 indicates the lowest performance [33].

Moreover, we considered renewable energy and export diversity in our model. The term “renewable energy” describes the shift from fossil fuels to green energy because of technological advancements. The process of reallocating financial resources and investments into potential product and service categories that a nation exports to foreign markets to boost its economy’s robust, sustained growth and boost its competitiveness is known as export diversification. A large number of studies have focused more on investigating the association between export diversification and economic growth, export diversification and resource consumption [34,35,36], and resource consumption and environmental depreciation [37,38,39,40,41]. The new technologies have contributed to the best possible exploitation of natural resources by accelerating production processes, increasing levels of production, and reducing costs. In return, increases in the overall efficiency of production lead to increased long-term economic growth, competitiveness, and exports [33]. However, there is scant literature on the moderation effect of technology and export diversification on environmental quality using the LCC approach.

In addition, income inequality hinders efforts to reduce pollution and shift to renewable energy. Moreover, income inequality makes society more susceptible to a lack of social cohesiveness and mistrust, which makes its people less concerned about protecting the environment [42,43]. Therefore, we have included income inequality in this study to investigate more effective and sustainable policies in line with the argument of Vo et al. [44].

Foreign direct investment (FDI) is included in our model, as it brings innovation into the host country. However, there are no consistent findings in previous studies. A study by Padhan and Bhat [45] states that while FDI raises the ecological footprint of the N11 countries (Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines. South Korea, Turkey, and Vietnam), the connection between FDI and ecological footprint is negatively moderated by green innovation. In addition, the employment rate which serves as a proxy for the growth of a nation’s human capital and domestic bank lending to the private sector (as a percentage of GDP) were considered as control variables in this research.

The main results of this study show that the LCF is significantly affected by digital adaptation and income. The LCF increases with income among middle-income countries. Thus, the LCC is confirmed. The LCF is negatively impacted by energy transition, export diversification, and foreign direct investment. Additionally, integrating digital adaptation and energy transition has a favorable impact on the LCF. However, a negative integrating impact was found between digital adaptation and export diversification and the LCF. The objectives of this research were achieved using the GMM approach. System GMM considers biases of the traditional static model estimation, the mutual causal relationship between independent and dependent variables, and indignity. Data were collected from 112 countries from valid resources such as the World Development Indicators database, the International Monetary Fund Database, and the United Nations Conference on Trade and Development (UNCTAD).

The following are some ways that this study adds to the body of literature: (i) The literature hardly ever uses the load capacity factor as a measure of environmental quality. To the best of our knowledge, this study is the first to investigate the LCC theory across a significant number of nations. (ii) This study aimed to gain a thorough understanding of how these interrelated factors collaboratively influence the LCF by adopting a comprehensive perspective. (iii) This study explored how income inequality influences the LCF. (iv) Regarding the results of this research, special reference to OPEC is imperative for academicians, policymakers, and environmentally concerned authorities to guide in policy making. (v) Additionally, the research seeks to provide valuable insights to policymakers, researchers, and stakeholders regarding possible openings for fostering a more enthusiastic and sustainable global community.

The literature review, research techniques, findings, conclusion, and policy implications make up the remaining sections of the study.

2. Literature Review and Hypothesis Development

There are existing studies on the interconnected relationships between key factors, such as digital adaptation, energy transition, export diversification, and income inequality and their collective influence on the overall well-being of societies on a global scale. We have separated this section under subheadings to ensure a thorough reading.

2.1. Digital Adaptation, Energy Transition, and LCF

Numerous studies have examined the demand side of natural resources, such as energy, biodiversity, and environmental quality, using the environmental Kuznets curve (EKC) theory. Ecological footprint and CO₂ have been employed as environmental quality indicators [16,17,18,19,20,21,23,24,27]. Moreover, Sarabdeen et al. [25] reported that the Happy Planet Index represented a new approach for gauging environmental quality. On the other hand, there was less discussion of the supply side’s biocapacity or natural resource availability.

The debates around the relationships between load capacity variables and energy transition, export diversification, and income inequality have not been resolved by prior research. Regretfully, contradictory comparisons are still found in new research. To be clear, Khan et al. [46] acknowledged that renewable energy raises the LCF in both G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and E7 (China, India, Brazil, Mexico, Russia, Indonesia, and Turkey) nations using estimates from the CS-ARDL technique. Similar findings were provided by Adebayo and Kirikkaleli [47] for Spain, Iorember et al. [48] for South Africa, Dogan and Pata [49] for the G7 countries, Obiakor et al. [50] for West Africa, Liu et al. [51] for the ASEAN economies, and Uche et al. [52] for the E7 countries. Adebayo and Samour [53] reported that the LCF of Brazil, Russia, India, China, and South Africa (BRICS) rises because of renewable energy. Likewise, Alola et al. [54], Guloglu, Caglar et al. [55], Pata et al. [56], Samour et al. [57], and Ullah et al. [58] extended affirmative parallels in the situations of BRICS, India, OECD countries, Germany, and BRICS-T economies, respectively.

Reaching remarkable growth rates requires the use of energy resources. Since many energy sources today are nonrenewable, environmental degradation is anticipated to rise with industrialization [59]. On the other hand, raising the budgetary allotment for energy efficiency research and development may foster innovation, technology, and energy efficiency, all of which can stimulate energy savings and raise the LCF. Enhancing energy efficiency can also result in the formation of the LCC by reducing the negative environmental effects of economic growth. Similarly, as alternative energy improves environmental quality, supporting budgets for nuclear and renewable energy research and development can also boost the LCF [60]. In addition, the use of clean energy can lessen the usage of conventional energy and the detrimental ecological effects of expansion, which can result in the formation of the LCC and/or EKC.

Recent studies have used the LCF to describe environmental quality because it gives an appropriate evaluation of environmental quality [61]. It was found by Dogan and Pata [49] that development in the G7 countries eventually boots LCF when R&D, ICT, and renewable energy are present. The LCF, however, has a negative association with early growth levels. Similarly, whereas many researchers have examined how different variables affect the LCF, relatively few have investigated the LCC theory.

Under the framework of the LCC hypothesis, Dai et al. [61] investigated the correlation between environmental quality, green energy, and human capital employing data from the Association of Southeast Asian Nations (ASEAN) from 1986 to 2018. In light of the LCC hypothesis, this study looked at the relationship between environmental quality, green energy, and human capital. The findings show that, within the framework of ASEAN, the LCC is valid. Thus, raising income levels will help ASEAN achieve its goals of growing the LCF and improving environmental quality. It has been discovered that human capital improves environmental quality and increases the LCF. Likewise, it has been discovered that green energy increases the LCF. Environmental degradation is accelerated by the globalization of the economy and human density.

The effects of nuclear energy research and development, energy efficiency, renewable energy, and financial globalization on the LCF are evaluated by Jin et al. [62] using the ARDL method for Germany from 1974 to 2018. The findings demonstrate how improving ecological quality through R&D in green energy and energy efficiency advances the LCF. However, compared to nuclear energy research and development, energy efficiency, and renewable energy, the beneficial effects on the LCF are shown to be less significant. Expanding the LCF and financial globalization slows down ecological damage. The U-shaped link between economic growth and the LCF provides more evidence in favor of the load capacity curve.

Although many studies on the beneficial effects of renewable energy on the LCF have been carried out, there are still significant differences in viewpoints. In this case, Pata and Isik [63] showed via the ARDL approach that whereas renewable energy has no noticeable effect in Japan, it improves the LCF in the United States. Huilan et al. [64] extended this by using the completely modified ordinary least squares procedure. According to the report, renewable energy in Mexico reduces the LCF. Similarly, Pata and Samour [63] showed that the LCF is dampened by renewable energy, especially in France. Supporting this claim, Xu et al. [5] found that renewable energy did not increase the LCF in Brazil based on estimations from the ARDL method. Renewable energy could not raise the LCF in South Korea [65]. There are notable differences in opinion among researchers who have already studied the relationship. Similarly, studies have failed to recognize the significance of digital adaptation and energy transition in the LCC. Therefore, the current study hypothesized as follows.

Hypothesis 1.

Digital adaptation improves the LCF in economies.

Hypothesis 2.

Energy transition improves the LCF in economies.

2.2. Export Diversification (EXD) and LCF

Researchers and policymakers are under pressure to identify the elements that promote resource conservation, export diversification, and environmental preservation because of the growing threats to the environment, resource depletion, and wasteful use of natural resources. Research on the effects of trade openness and international trade on environmental management by Zhou and Zhou [66]; trade diversification, intellectual capital, and the transition to renewable energy on resource management for BRICS by Sun et al. [67]; and export diversification with resource consumption by Saleem et al. [34] for the South Asian Association for Regional Cooperation (SAARC), by Shahzad et al. [36] for new industrial countries, and by Sharma et al. [35] for BRICS are just a few of the many diverse topics being researched. However, little research has been performed on trade diversification and environmental preservation. Islam et al. [68] found that beneficial aspects of trade diversification reduce the ecological impact over time. Furthermore, over time, a positive shock from technological advancements greatly lowers environmental pollution in the US economy. Zafar et al. [37] investigated how 22 remittance-receiving nations’ resource consumption and environmental degradation were affected by the diversification of their remittance exports. Based on the estimations, it was shown that export diversification had a negative correlation with both ecosystem degradation and resource depletion since it stabilized the use of natural resources.

Mania [38] applied the EKC theory, a sample of 98 rich and developing nations, and the nexus between export diversification (EXD) and CO₂ emissions, using data from 1995 to 2013. The results indicate that increasing EXD raises CO₂ emissions in developing nations. However, it was discovered that the EXD helped lower CO₂ emissions for the established economies. The results point to emerging nations having a comparatively larger scale effect, whereas technology or composition effects are comparatively more prominent. According to Apergis et al. [39], EXD reduced environmental deterioration in 19 developed economies from 1962 to 2010. Liu et al. [41] investigated EXD including both market and product concerning environmental pollution in the three East Asia nations of South Korea, China, and Japan between 1990 and 2013. According to their research, EXD helps to slow down environmental deterioration. It is challenging to reconcile the sample’s results with the body of research, which mostly points to a positive (negative) link between EXD and emissions for both established and developing economies because it includes two developed nations (South Korea and Japan) and one developing country (China). Research on energy economies, energy demand, EXD, and extended and intense margins by Shahzad et al. [69] has also discovered similar conclusions. Overall, they discovered that EXD lowers energy consumption. This would reduce the harm to the environment indirectly. Moreover, Bashir et al. [70] examined how EXD affected the energy and carbon intensities of OECD nations. The analysis discovers a strong case for reducing energy intensity across all EXD parameters. Wang et al. [71] discovered that EXD was connected to an increase in CO₂ emissions, which is in contradiction to many study findings. Although the study emphasizes the importance of ecological innovation, it also shows that when ecological innovations rise over time, the detrimental effects of EXD diminish. However, there is no consistency in the results. Moreover, as we discussed earlier in this paper, scant research focuses on the LCF. Therefore, in this study, we included EXD as one of the green innovation factors that contribute to the LCF. Then, we derived a hypothesis.

Hypothesis 3.

There is a direct relationship between EXD and the LCF.

2.3. Income and LCF

Income inequality hinders environmental pollution and renewable energy adoption. According to some scholars, Churchill et al. [42], Asongu and Odhiambo [43], and Berthe and Elie [72], wealth disparity makes society more susceptible to a lack of social cohesiveness and mistrust, which makes its members less attentive to environmental preservation. Additionally, because nonrenewable energy is less costly than renewable energy, consumers with lower incomes would use nonrenewable energy more frequently [73]. According to Uzar [74], in an unequal society, the affluent control the policymaking process and have the power to frustrate incentives and subsidies that would encourage the use of and investment in renewable energy sources. This demonstrates that the energy transition agenda would be aided by effective policies that favor income redistribution since income inequality is a major obstacle to the energy transition.

Growing financial disparity erodes social cohesiveness by upsetting interpersonal peace and trust, which ultimately lowers trust and debilitates society [72]. For environmental use, both the rich and the poor will choose to act independently [74]. According to Boyce [73], unequal wealth distribution leads to a lack of social cohesiveness and collective action, which is a sign of an environmentally insensitive society. Sadly, the degradation of the ecosystem is a result of low-income individuals using the environment to meet their fundamental needs. Laurent [75] found that significant deforestation occurs in Madagascar and Haiti anytime low-income individuals depend on the forest for their subsistence needs. This behavior is a result of their preference for short-term gains above the long-term effects of their current purchasing decisions [72,74]. According to Fredriksson et al. [76], elite groups in these cultures may engage in more activities that are environmentally harmful even while they benefit them because of poor environmental legislation. Moreover, institutions have a critical role in regulating environmental legislation, wealth distribution, and energy use [77]. According to Uzar [74], wealth inequality is caused by under-privileged institutions and distorts investment outcomes. A bottleneck in renewable energy expenditures can result in the decision to switch to nonrenewable energy. Unproductive financial processes could indicate a lack of funding for alternative energy sources. A further possibility of the EKC’s theoretical stance is that high-income populations ultimately have a greater concern for post-materialist concerns, such as the environment [78]. According to Vo et al. [44], income disparity and equitable growth policies are strongly correlated with clean energy adoption. LCC states that more income would increase the environmental quality and LCF. Therefore, this study intends to investigate the link between different income groups and the LCF. We developed the hypothesis as below.

Hypothesis 4.

There is a direct relationship between income and the LCF.

2.4. Foreign Direct Investment (FDI) and LCF

Empirical research by Padhan and Bhat [45] demonstrates that FDI raises the ecological footprint, which lowers environmental quality. Consequently, research demonstrates that FDI contributes to pollution-free zones in BRICS and Next-11 nations. On the other hand, the interaction between FDI and ecological footprint is negatively moderated by green innovation. That indicates that the pollution halo theory is supported by the combined effects of green innovation and FDI. Furthermore, the ecological footprint is reduced using renewable energy, but in BRICS and Next-11 countries, industrialization and economic expansion are making the environment worse.

The globalization of finance has the potential for changing ecological quality. According to Ulukak et al. [79], globalization of finance is quantified in terms of FDI, portfolio investments, stocks of foreign assets, and various laws and limits on these elements. It has the potential to either help or worsen environmental quality since foreign companies can introduce efficient technology, which can encourage local companies to upgrade their technology to remain competitive in the commercial sector and result in increased energy savings and decreased pollution. Conversely, a nation’s compassionate environmental regulations may encourage the entry of polluting technology, creating the illusion of a pollution refuge [80]. Green project funding may also be impacted by the level of financial globalization, as well as any ensuing restrictions and hurdles to international investment.

Research by Ulucak et al. [79] in emerging nations and Shahzad et al. [81] in China reveals that there is a positive association between the globalization of finance and ecological footprint. In the meantime, a 1% increase in financial globalization raises the LCF by 1.409%, demonstrating that Germany’s ecological quality increased because of financial globalization between 1974 and 2018 [62]. Since the globalization of finance (FDI) brings green innovation into host countries, we take FDI as another green innovation variable in this study.

Hypothesis 5.

There is a direct relationship between FDI and the LCF.

3. Methods

This section discusses the sample, data definition, empirical models, and estimation strategy used in the model.

3.1. Sample

This study used balanced panel data from 2010 to 2021 for 112 countries based on data availability, as enclosed in

Table 1. List of Countries.

Afghanistan	Cambodia	Germany	Libya	Nigeria	Singapore
Albania	Cameroon	Ghana	Latvia	New Zealand	Slovakia
Argentina	Chile	Greece	Lebanon	Nicaragua	Slovenia
Armenia	China	Guatemala	Lithuania	North Macedonia	Sri Lanka
Austria	Colombia	Guinea	Luxembourg	Norway	Sudan
Bahrain	Costa Rica	Haiti	Madagascar	Pakistan	Switzerland
Bangladesh	Cote d’Ivoire	Honduras	Malawi	Panama	Tanzania
Azerbaijan	Croatia	Hungary	Malaysia	Paraguay	Thailand
Belarus	Cyprus	India	Mali	Peru	Togo
Belgium	Denmark	Indonesia Iran Iraq Ireland	Malta	Philippines	Turkey
Benin	Dominican Republic	Israel	Mauritania	Poland	Uganda
Bolivia	Ecuador	Italy	Mexico	Portugal	Ukraine United Arab Emirates
Bosnia and Herzegovina	Egypt		Moldova	Qatar	United Kingdom
Botswana	El Salvador	Jamaica	Mongolia	Russian Federation	United States of America
Brazil	Estonia	Japan	Morocco	Rwanda	Vietnam
Bulgaria	Finland	Jordan	Mozambique	Saudi Arabia	Zambia
Burkina Faso	France Gabon	Kazakhstan	Myanmar	Serbia	Zimbabwe
Burundi	Georgia	Kenya Kuwait	Nepal	Sierra Leone

. The data were collected from valid resources such as the World Economic Indicators database and UNCAT.

3.2. Data Definition and Data Sources

Table 2. Variables Definition and Data Sources.

Variables	Explanation	Indicator	Source
Material Footprint is Proxy for LCF	The total amount of raw materials extracted to meet final consumption demands. “It is one indication of the pressures placed on the environment to support economic growth and to satisfy the material needs of people”	Indicator of environmental quality	UNEP Global Material Flows Database
Energy Transition (REN)	Traditional fossil fuel to renewable energy transition	Indicator of green innovation.	UNCTAD
Frontier Technology Readiness Index (FTRI)	FTRI indicates how prepared countries are to adopt and adapt frontier technologies. It combines data on information and communications technology (ICT) deployment, labor skills, research and development (R&D), industrial capacity, and availability of finance	Digital Adaptation, Indicator of green innovation.	UNCTAD
Trade Diversification (EXD)	International Trade Diversification Index (weighted average of exports and imports)	Indicator of green innovation	UNCTAD
Foreign Direct Investment (FDI)	Foreign direct investment, net inflows (% of GDP)	Indicator of green innovation	WB-WDI
Income Group	The groups are: “low income”, USD 1135 or less; “middle income”, USD 1136 to USD 13,845; and “high income”, USD 13,846 or more	Income inequality	WB-WDI
Domestic Credit to Private Sector by Banks (DCPS)	Domestic credit to private sector by banks (% of the GDP)	Financial development Indicator	WB-WDI
Employment (EMP)	Employment to population ratio, 15+, total (%) (modeled ILO estimate)	Human development indicator	WB-WDI

presents definitions of the variables and data sources used in the models.

3.3. Empirical Models and Estimation Strategy

3.3.1. Base Model

We developed an appropriate framework to investigate the impact of digital adaptation, energy transition, export diversification, income inequality, and foreign direct investment on the LCF. To ensure the validity of the regression, other control factors that may affect the LCF, such as banks’ domestic credit to the private sector and employment, must be considered. The relationship between the LCF and the selected variables is constructed as follows in the base model:

$${LCF}_{it}=f\left({REN}_{it},{FTIRI}_{it},{EXD}_{it},{{FDI}_{it},DCPSD}_{it},{EMP}_{it,}{INCO}_{it}\right)\dots \dots \left(Basemodel\right)$$

where subscripts i and t stand for country and time, respectively, and LCF stands for load capacity factor, which represents the dependent variable of this study. REN, FTIRI, EXD, INCO, and FDI represent the explanatory variables. DCPS and EMP are control variables.

The system GMM model is used to estimate Equations (1) to (4). System GMM considers biases of the traditional static model estimation, the mutual causal relationship between independent and dependent variables, and indignity. In models 1–4, the income group represents a dummy variable with only 1 and 0 values. The high-income group of countries is represented by 1, otherwise 0, α₀ illustrates the constant, and ε_it refers to the random disturbance terms. The other variables’ definitions are the same as in the base model. The natural logarithm system GMM model of this study is specified as follows:

lnLCF_it = α₀ + β₁lnLCF_it−1 + β₂lnREN_it + β₃lnFTIRI_it + β₄ lnEXD_it + β₅ lnDCPS_it + β₆lnEMP_it + β₇ FDI_it + β₈ INCO_it + ε_it

(1)

3.3.2. Moderating Effects

Based on previous studies, green innovation influences environmental quality (LCF). However, this research requires further investigation on how green innovation affects the hindering influences of green innovation issues on the LCF and to evaluate whether the improvement of green innovation level affects this relationship. Therefore, this study inspires us to develop the following econometric models to examine the mediating effects as in models 2–4.

lnLCF_it = α₀ + β₁lnLCF_it−1 + β₂lnREN_it + β₃lnFTIRI_it + β₄lnEXD_it + β₅lnDCPS_it + β₆lnEMP_it + β₇ FDI_it + β₈INCO_it + β₉ (lnREN ∗ lnFTRI) + εit

(2)

lnLCF_it = α₀ + β₁lnLCF_it−1 + β₂lnREN_it + β₃lnFTIRI_it + β₄lnEXD_it + β₅lnDCPS_it + β₆lnEMP_it + β₇FDI_it + β₈INCM_it + β₉(lnREN ∗ lnFTRI) + β₁₀(lnEXD_it ∗ lnFTRI_it) + ε_it

(3)

lnLCF_it = α₀ + β₁lnLCF_it−1 + β₂lnREN_it + β₃lnFTIRI_it + β₄lnEXD_it + β₅lnDCPS_it + β₆lnEMP_it + β₇FDI_it + β₈INCO_it + β₉(lnREN ∗ lnFTRI) + β₁₀(lnEXD_it ∗ lnFTRI_it) + β₁₂(INCOM_it ∗ lnFTRI_it) + εit

(4)

Finally, the study tries to answer the following question: Do digital adaptation, energy transition, export diversification, and income inequality affect the LCF in PECs?

3.4. Descriptive Statistics of the Variable

Table 3. Descriptive statistics of the variable.

Variable	Obs	Mean	Std. Dev.	Min	Max
lnLCF	1390	16.648	1.789	12.266	22.278
lnREN	1375	2.839	1.602	−4.605	4.535
lnFTRI	1373	−0.917	0.671	−2.526	0
lnEXD	1392	−0.475	0.286	−1.474	−0.0640
lnDCPS	1392	3.632	1.179	−5.357	6.262
lnEMP	1392	4.021	0.203	3.434	4.472
FDI	1392	5.062	17.653	−117.375	280.146
INCO high	1392	0.311	0.463	0	1
INCO med	1392	0.540	0.499	0	1
INCO low	1392	0.149	0.356	0	1

Authors’ calculation using STAT 13.

below lists the descriptive statistics of all the variables including the number of observations of the variables (obs.), the mean value of the variables (Mean), the standard deviation (Std. Dev.), the maximum and minimum values of the variables (Min and Max, respectively). The correlation coefficients shown in

Table 4. Pairwise correlations.

Variable	lnREN	lnFTRI	lnEXD	lnDCPS	lnEMP	FDI	INCO High	INCO Med	INCO Low
lnREN	1.0000
lnFTRI	−0.3819	1.0000
lnEXD	0.1109	−0.7041	1.0000
lnDCPS	−0.2272	0.6518	−0.4592	1.0000
lnEMP	0.1020	−0.0721	0.0504	0.0077	1.0000
FDI	−0.0388	0.0627	−0.0125	0.0833	0.0325	1.0000
INCO high	−0.3086	0.6480	−0.5833	0.4534	0.0703	0.1288	1.0000
INCO med	0.0350	−0.1509	0.3009	−0.0876	−0.2388	−0.1128	−0.7390	1.0000
INCO low	0.3592	−0.6434	0.3432	−0.4759	0.2482	−0.0095	−0.2694	−0.4498	1.0000

Authors’ calculation using STAT 13.

are less than 0.8, indicating that there is no high collinearity.

4. Empirical Results and Discussion

The two-step system GMM approach used in the study has the benefit of being able to address heterogeneity, bias from missing variables, measurement error, and other issues related to indignity. In addition, the study examines the validity of the instrumental variables (IVs) and the autocorrelation characteristics of the difference in the random disturbance terms using the Arellano–Bond, Sargan, and Hansen tests. While the Hansen and Sargan tests demand a p-value of more than 0.1, the Arellano–Bond test requires AR (1) to be less than 0.1 and AR (2) to be greater than 0.1.

Table 5. Results of System GMM models.

Variable	Model 1	Model 2	Model 3	Model 4
lnLCF (−1)	0.5890 *** (0.000)	0.5782 *** (0.000)	0.6166 *** (0.000)	0.6083 *** (0.000)
lnREN	−0.1118 *** (0.000)	−0.0740 ** (0.021)	−0.1001 ** (0.012)	−0.0806 ** (0.042)
lnFTRI	0.2017 *** (0.004)
lnEXD	−1.4086 *** (0.000)	−1.5048 *** (0.000)	−1.5178 *** (0.000)	−1.2273 *** (0.000)
FDI	−0.0020 *** (0.000)	−0.0020 *** (0.000)	−0.0017 *** (0.000)	−0.0019 *** (0.000)
lnDCPS	0.0230 (0.488)	0.0297 (0.356)	0.02666 (0.383)	0.0342 (0.265)
lnEMP	0.1211 (0.465)	0.1783 (0.288)	0.1246 (0.425)	0.0822 (0.619)
INCO high	−0.0726 (0.668)	−0.0377 (0.827)	−0.0630 (0.705)
INCO med	0.2546 ** (0.020)	0.2682 **(0.015)	0.2783 ** (0.012)
lnREN ∗ lnFTRI		0.0469 *** (0.009)	0.0081(0.743)	0.0530 ** (0.046)
lnEXD ∗ lnFTRI			−0.4802 * (0.075)	−0.0189 (0.937)
INCO ∗ lnFTRI				0.6677 ** (0.017)
Constant	6.0212 *** (0.000)	5.7353 *** (0.000)	5.4078 *** (0.000)	5.9715 *** (0.000)
AR (1)	−4.60 (0.000)	−4.61 (0.000)	−4.55 (0.000)	−4.65 (0.000)
AR (2)	1.58 (0.115)	1.63 (0.102)	1.55 (0.121)	1.41 (0.160)
Sargan test	6.26 (0.282)	8.29 (0141)	8.38 (0.136)	6.58 (0.254)
Hansen test	5.86 (0.320)	6.58 (0.254)	7.73 (0.172)	6.49 (0.261)
Number of Obs	1246	1246	1246	1246

Authors’ calculation using STAT 13. Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels.

demonstrates that every test result for the base and moderation effects models was successful. All the models passed the robust tests.

4.1. The Natural Logarithm System GMM Base Model Outcomes

We analyzed the link between digital adaptation, energy transition, export diversification, income inequality, and foreign investment in the LCF as presented in Formula (1), which points out that lnLCF (−1) has a positive impact that is significant at 1% and illustrates that digital adaptation has a notably positive influence on the load capacity factor (LCF). It means that a 1% increase in digital adaptation would increase the LCF by 0.2017%, which supports Hypothesis 1. In the digital era, technological improvement has occurred globally. Lack of technological improvement has greatly reduced the clean environment as well as the sustainable economic growth of the countries. Hence, it is crucial to improve green technology development, technological structure, and technological efficiency to promote the LCF of countries. This finding is supported by the findings of Melnykovych et al. [82]; Ramzan et al. [83]; and Sarabdeen et al. [25]. They found that innovative green technology makes a major effort to enhance environmental sustainability.

Moreover, income has a positive relationship with the LCF, as per the LCC framework. It means an increase in income by 1% would increase the LCF by 0.2546%, which shows that the LCC is confirmed among the moderate-income group countries. It confirms that emerging economies are trying to adopt, expand, and develop technology while their incomes increase. It would increase the LCF of the countries in line with the LCC theory. The findings of Dai et al. [61] confirmed that the LCC is valid within the framework of ASEAN. Thus, raising income levels will help ASEAN achieve its goals of growing the LCF and improving environmental quality. It has been discovered that green energy increases the LCF.

However, surprisingly, energy transition, export diversification, and foreign direct investment negatively affect the LCF at 1% significance levels. A 1% increase in energy transition, export diversification, and foreign direct investment would reduce the LCF by −0.1118%, −1.4086%, and 0.0020%, respectively. A possible explanation for the negative relationship between energy transition and the LCF might be that most of the sample countries are developing and poor. The process of early stages of renewable energy infrastructure development in these countries tends to emit pollutants and deteriorate the environmental quality. For example, lead and cadmium are two harmful substances that are used in large quantities during the manufacturing of solar panels. Steel and concrete are used in the construction of wind turbines, which are energy-intensive and emit significant amounts of carbon dioxide when they are produced. This finding is in line with the findings of Sarabdeen et al. [25]. They found that the green energy transition led to a reduction in the Happy Planet Index among selected PECs.

Further statistical tests revealed that export diversification has a negative relationship with the LCF. It could be argued that the negative results were because export diversification, the strategies of redistribution of financial assets, and investment are not in favor of promising areas of products and services that a country exports to international markets to enhance the economy’s resilient, sustainable growth and increase the country’s competitiveness. This outcome is aligned with the studies of Mania [38] and Wang et al. [71]. Mania [38] discovered that EXD was connected to an increase in CO₂ emissions among 98 countries. Furthermore, the study of Wang et al. [71] emphasized that EXD was connected to an increase in CO₂ emissions. But it also shows that when ecological innovations rise over time, the detrimental effects of EXD diminish. According to the study of Apergis et al. [39], EXD reduces environmental deterioration in 19 developed economies. Shahzad et al. [69] have also found similar findings. Overall, they discovered that EXD lowers energy consumption and reduces CO₂. However, a study by Liu et al. [41] found mixed results between EXD and environmental pollution in three Asian countries. They found that South Korea and Japan are in favor of EXD and environmental pollution, but not China.

It is noteworthy that FDI has a negative relationship with the LCF. It shows that FDI does not have as much influence in bringing green technology into foreign countries as expected. FDI might focus on labor-intensive products rather than high-tech-intensive products of the host country. This finding is in line with the findings of Dai et al. [61] and Jin et al. [62]. They found that there is a negative relationship between global financing presented by FDI and environmental deterioration for the ASEAN and German economies, respectively. Green innovation, on the other hand, adversely moderates the link between FDI and ecological footprint, even when FDI increases the ecological footprint of the N-11 nations [45].

4.2. Moderation Affects Outcomes

In this research, we investigated the moderating effect between energy transition and digital adaptation, export diversification and digital adaptation, and income inequality and digital adaptation on the LCF by two-way moderation effects as presented in Formulas (2)–(4), which point out that lnLCF (−1) has a positive impact that is significant at 1% and illustrates that technology has a notably positive influence on the load capacity factor (LCF). Interestingly, a significant interaction of energy transition and digital adaptation (lnREN ∗ lnFTRI) on the dependent variable (LCF) was observed at 1% and 5% levels in model 2 and model 4, respectively. Increasing the interaction of energy transition and digital adaptation by 1% would increase the LCF by 0469%, and 0.0530% in models 2 and 4, respectively. An explanation for the positive relationship between energy transition and technology for the LCF might be that renewable energy is not alone in making significant changes in the LCF. When we interact with renewable energy and technology can make a difference. It may be further said that adopting new technology would improve energy efficiency and new strategies, all of which can encourage energy savings and increase the LCF. By lessening the adverse environmental repercussions of economic expansion, increasing energy efficiency can also lead to the establishment of the LCC. This finding is aligned with the studies of Sharif et al. [59] and Pata and Samour [60].

Additionally, it is important to note that there was also a positive significant interaction of income and digital adaptation (lnINC ∗ lnFTRI) for the dependent variable (LCF) was observed at the 5% level in model 4. Increasing the interaction of income and digital adaptation by 1% would increase the LCF by 0.6677%. In addition, the interaction of income and digital adaptation and the interaction of energy transition and digital adaptation are complementary goods in model 4.

Further, the statistical tests revealed that a negative correlation was observed between the interaction of export diversification and digital adaptation (lnEXD ∗ lnFTRI) with the LCF at the 10% level in model 3, with no statistically significant differences in models 2 and 4. Increasing the interaction of export diversification and digital adaptation by 1% would decrease the LCF by 0.4802% in model 3. This finding is in line with the findings of Bashir et al. [70] and Wang et al. [71]. However, it contrasts with the studies of Apergis et al. [39], Liu et al. [41], and Shahzad et al. [69]. They found increasing export diversity improves the environmental quality or LCF in the studied countries.

In model 3, the interaction of energy transition and digital adaptation and the interaction of export diversification and digital adaptation are substitute goods. Energy transition and digital adaptation seem to be linked in a way that causes most nations to compromise on export diversification and digital adaptation. Many nations will focus on energy transition and cutting-edge technologies to reduce temperature increases to 1.5 °C while achieving sustainable growth.

In model 4, we investigated the moderating effects between energy transition and digital adaptation, export diversification and digital adaptation, and income inequality and digital adaptation for the LCF. In this model, energy transition with digital adaptation and income inequality with digital adaptation have contributed favorably to the LCF. Here, the existence of the LCC is proved in this study.

Surprisingly, the statistical results show there is a negative relationship between FDI and the LCF in all four models. This finding is similar to the study of Gyamfi et al. [84] which found that FDI leads to environmental decline in BRICS. Foreign companies were expected to introduce efficient technology, resulting in increased energy savings and reduced pollution by encouraging local companies to upgrade their technology. Environmental regulations that do not encourage the entry of polluting technologies may be the reason for this. This outcome contrasts with the studies by Shahzad et al. [81] and Ulucak et al. [79]. They found a positive relationship between FDI and the LCF in China and emerging nations, respectively.

Finally, we were interested in exploring the interaction effect on PECs, as their income is mainly derived from fossil fuels. Saudi Arabia and the United Arab Emirates, among other PECs, are taking export diversification initiatives. However, the statistical analyses show that there is a negative relationship between the interaction of export diversification and digital adaptation (lnEXD ∗ lnFTRI) and the dependent variable (LCF) at a 10% significance level for PECs. An increase in export diversification and digital adaptation (lnEXD ∗ lnFTRI) by 1% would lead to a reduction in the LCF by −2.92524% (

Table 6. Results of System GMM model for OPEC.

Variable	All	OPEC
lnLCF (−1)	0.6078557 *** (0.000)	0.5801601 ** (0.010)
lnREN	−0.1259337 *** (0.000)	−0.2832684 (0.309)
lnFTRI	0.2026261 *** (0.004)
lnEXD	−1.171722 *** (0.000)	−2.92524 * (0.096)
lnDCPS	0.0345931 (0.286)	−0.1720048 (0.547)
lnEMP	0.0167152 (0.921)	−1.09357 (0.743)
FDI	−0.0020049 *** (0.000)	−0.0050481 (0.939)
inco ∗ lnFTRI	0.648754(0.017)	2.133377(0.492)
Constant	6.413833 *** (0.000)	11.54148 (0.278)
AR (1)	−4.65 (0.000)	−1.77 (0.076)
AR (2)	1.42 (0.155)	0.14 (0.887)
Sargan test	6.15 (0.292)	0.887 (0.367)
Hansen test	6.19 (0.288)	1.09 (0.955)
Number of Obs	1246	95

Authors’ calculation using STAT 13. Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels.

). The results show that there is a drastic impact on PECs as their main source of income from traditional fossil energy.

Taken together, these results suggest that there is an association between digital adaptation and income with the LCF. The moderation effects of energy transition and digital adaptation and income inequality and digital adaptation have contributed favorably to the LCF. However, export diversification and energy transition become substitute goods. Moreover, FDI harms the LCF. Lastly, there is a negative relationship between the interaction of export diversification and digital adaptation for the LCF for PECs.

5. Conclusions and Policy Implications

Based on the analysis in the previous section, we come to the following discussion. Digital adaptation has a notably positive influence on the LCF, and income has a positive relationship with the LCF. However, energy transition, export diversification, and foreign direct investment negatively affect the LCF. There are a lot of new green technologies being developed right now that could have a big impact on how industry, the environment, and energy consumption interact (Liu et al. [85]). These technologies are being developed with the intention of either developing new environmentally sustainable technology or enhancing the functionality of already existing technology. By installing these technologies, the risk of pollution, environmental harm, and resource consumption throughout related life cycle activities may be avoided or significantly reduced [86]. It could be argued that the early stage of development of technological infrastructure could emit more pollution [25].

Moreover, a significant interaction of energy transition and digital adaptation (lnREN ∗ lnFTRI) for the LCF was observed. To reduce energy consumption and increase energy efficiency while sustaining green economic growth, technological advancements are essential. Countries can concentrate on developing more green technology for the energy transition. It is also possible to set some useful objectives in this area and measure the performance of these objectives regularly to ensure that these objectives are achieved. The market for alternative energy products can also be expanded by carbon taxation, increasing taxes on fossil fuels, and offering tax relief for using green energy.

In addition, income and digital adaptation (lnINC ∗ lnFTRI) have a significant positive interaction with the LCF. Globally, governments and businesses have committed significant funds to research and development (R&D). When income increases, countries increase their R&D, which leads to improvement in the LCF. In the long run, high-income populations tend to care more about post-materialist topics, such as the environment and green finance [78].

Further, the correlation between FDI and LCF is inverse. The government may expedite foreign investments in environmentally friendly inventions in their economy and establish regulations and processes regarding environmental issues. The reduction of taxes on investments in green energy production and technology by policymakers is expected to stimulate the development of alternative technologies.

Finally, increasing export diversification and digital adaptation decreases the LCF. The significance of ecological innovation gradually decreases the negative consequences of EXD as ecological innovations grow (Bashir et al. [70]). Increasing green and effective technology would help to increase the LCF in the long term. It is possible to create plans for households to increase the usage of green energy in the home by acquiring certain green energy goods. In this sense, it may be possible to provide certain subsidies and to motivate the banking industry to fund the purchase of environmentally friendly goods. Therefore, more land-use regulations, more agricultural research, and improved technology could improve biocapacity and contribute to closing the resource gap between resources produced and resources needed. In light of the study’s findings, this research offers crucial insights into the effects of cutting-edge technology, energy transitions, and export diversification on the quality of the environment worldwide.

Limitations

Naturally, this study has a few shortcomings. For example, this research focuses on 112 countries only. Second, panel data analysis may be used in future research to compare groupings or areas like PECs with NICs. Third, the LCF indicator in this study is derived from the most current material footprint data available. Other environmental indicators may be included in future research, extending the study period to include the most recent years. The Happy Planet Index may also be used. Additional research can concentrate on sectoral analysis of smaller-scale ideas. To obtain the desired outcomes, the continuing energy performance certification (EPC) initiative deserves greater praise and should be pushed further.