Assessing the Interplay of Financial Development, Human Capital, Democracy, and Industry 5.0 in Environmental Dynamics

Abstract

The anthropogenically induced ecological resource exploitation surpasses the Earth’s regenerative capacity and has resulted in ecological bankruptcy. Conceding that, the United Nations mandates environmental restoration by 2030. Against this backdrop, this study seeks to orchestrate a hybrid framework by modulating the Quintuple Helix Model into an Anthropomorphized Stochastic Quintuple Helix Model (ASQHM). This model introduces human behavior and allows for hypothesis testing. ASQHM stipulates that the propensity of espoused eco-innovation aimed at environmental restoration is contingent upon five composite helices: human capital, democracy, Industry 5.0, media, and pro-environmental human behavior. In addition, financial development has been deemed imperative to facilitate these variables, which were considered stakeholders in this study. To fill gaps in the literature, three variables, namely democracy, Industry 5.0, and pro-environmental human behavior (PEHB), are formed through principal component analysis. This panel data study employs the Generalized Methods of Moments model to compute the ASQHM for developed and less developed countries from 1995 to 2022. The results imply that the first helix (human capital) levitates environmental restoration in developed countries (DCs) but yields the opposite in less developed countries (LDCs). Democracy, Industry 5.0, and information and communication technology helices demonstrate a solicited negative relationship with ecological footprints in both panels, thus supplementing environmental restoration. The fifth helix, PEHB, escalates ecological footprints in DCs; however, it abets environmental restoration in LDCs. The postulated ASQHM “partially” works in DCs and LDCs, rejecting its hypothesized role in the former group while confirming it in the latter group. Astonishingly, DCs fall short of the requisite PEHB (fifth helix), and LDCs do not have the at-par human capital (first helix) to reduce ecological footprints, catalyze eco-innovation, and partake in the environmental restoration process. Despite slight discrepancies in both panels, these findings validate the effectiveness of this hybrid ASQHM as a decisive determinant of environmental restoration. Based on the findings, this study also suggests practical policies.

1. Introduction

Anthropogenic activities such as economic growth, globalization, and the proliferation of industrial sectors have altered the Earth’s ecosystems [1]. Since the advent of the Industrial Revolution, the unprecedented increase in carbon emissions (often termed as “great acceleration”) coupled with radioactive elements (product of nuclear proliferation) have changed the planet Earth’s mantle, crust, and atmosphere in a way that would be witnessed by generations to come, even after millions of years [2]. Some environmental economists have even declared this era the “Anthropocene Epoch”, where human activities have had an irreversible impact on climate, ecosystems, soil, water, atmosphere, biodiversity, acidification of oceans, and natural habitats [2,3]. Scientists have pronounced anthropogenic activities as the primary underlying reason for this environmental fiasco [4]. The environmental perils have overshadowed most macroeconomic challenges; however, researchers have not construed or developed a comprehensive mechanism to prevent ecological decay.

National Oceanic and Atmospheric Administration (2022) (https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report.html, (accessed on 4 May 2022)) reported that the global surface temperature has increased by 2 degrees Celsius, and the global sea level has risen by about 160 to 210 mm (6 to 8 inches) over the past century. According to the United Nations (2020) (https://www.climatecentre.org/450/un-climate-related-disasters-increase-more-than-80-over-last-four-decades/#:~:text=Crescent%20Climate%20Centre-,UN%3A%20Climate%2Drelated%20disasters%20increase%20more%20than,80%25%20over%20last%20four%20decades&text=Extreme%2Dweather%20events%20have%20increased,for%20Disaster%20Risk%20Reduction%20today, (accessed on 19 December 2022)), the Earth has witnessed an 80% increase in climate-induced disasters, causing catastrophic effects on ecosystems, natural habitats, coastal communities, supply chains, and economies. Conceding that, the United Nations has also declared this decade (2021–2030) a “Decade of Ecosystem Restoration”. Additionally, the Global Footprint Network (2023) (https://www.footprintnetwork.org/one-planet-prosperity/#:~:text=Human%20demand%20,(%E2%80%9CEcological%20Footprint%E2%80%9C,on%20August%202%20in%202023 (accessed on 11 July 2023)) emphasizes that anthropogenic activities have been consuming ecological resources at a rate that exceeds the Earth’s regenerative capacity, leading to ecological bankruptcy (Figure 1).

Amidst rampant environmental decay, the signatories of the Paris Agreement (2015) have pledged to retain the rise in temperature around 1.5 to 2 degrees Celsius by 2030 and attain carbon neutrality by 2050 [4,5]. Against this backdrop, developed and less developed countries have been striving to devise efficient, comprehensive, and feasible measures to control their greenhouse gas (GHG) emanations. However, despite mitigation efforts, ecological footprints and biocapacity increased by 1.5 percent and 0.4 percent, respectively, in 2022 [6]. The ecological deficit has been consistently worsening since the 1970s (Figure 2).

Owing to this orientation, researchers have proposed and empirically tested several aspects of environmental restoration to stipulate evidence-based suggestions to policymakers. However, no study has examined the integrated effect of financial development, human capital, democracy, information and communication technology (ICT), and Industry 5.0 on environmental restoration. This highlights the need for comprehensive research and action to conserve and protect ecological resources for present and future generations.

Startlingly, the Quintuple Helix Model (QHM) of innovation can potentially cater to environmental menaces. It brings all interdisciplinary stakeholders under one canopy. The QHM is the constituent of five helices: (i) academia, (ii) government, (iii) industry, (iv) media, and (v) natural environment [7,8]. The QHM initially perceived the environment as a binding and motivating factor for all stakeholders to drive innovation and become a knowledge economy. However, all stakeholders pursued their respective vested interests and failed to achieve the end goal—eco-innovation [9]. In addition, the QHM is a qualitative framework that looks good in theory but has practical implementation issues. In this study, we propose transforming the qualitative QHM into a quantitative stochastic model that is comprehensive, effective, and suitable for empirical hypothesis testing. To achieve this, we sought guidance from the “Stochastic Impacts by Regression on Population, Affluence, and Technology” (STIRPAT) model [10]. The postulated environmental restoration model is named the “Anthropomorphized Stochastic Quintuple Helix Model” (ASQHM). This novel approach can provide valuable insights into how these variables interact and project their impact on the environment. The stakeholders of ASQHM can potentially crack the environmental restoration code by diminishing the propensity of green greenhouse gas emissions and ecological footprints.

In this study, human capital is a metaphor for academia—the “first helix of ASQHM”. In this proposed model, human capital thinks and acts rationally and conducts advanced research to restore the environment. According to the World Economic Forum (2017), human capital is indispensable for environmental restoration as it can potentially counter the destructive impact of economic growth on the environment [11,12]. Countries can develop and implement eco-innovation, clean energy, and energy-efficient technologies provided they have human capital [12,13]. Human capital supports eco-friendly habits such as e-reading, e-communication, e-commerce, e-banking, and virtual meetings. These habits significantly reduce paper use, travel costs, energy consumption, commute time, and subsequent GHG emissions [13]. Conversely, considering economic growth and a clean environment mutually exclusive, emerging countries often prioritize economic development over environmental sustainability; consequently, human capital is found to exacerbate environmental degradation [14,15,16]. The QHM defines human capital as a driving force for research and development and eco-innovation [7,8]; nevertheless, not a single study has estimated the QHM empirically.

The democratic form of government constitutes the “second helix/stakeholder of ASQHM”. The government is hypothesized to devise pro-environmental policies and regulate markets for compliant products and technologies. The literature exhibits that democracy can promote economic growth, investment, and entrepreneurship; optimize resource use; and indirectly abet environmental mitigation efforts [17]. Ideally, democracy endorses civic liberty [18] where the public propagates environmental and ecological concerns and asks for compliance policies [19,20,21]. Since democratic governments are more responsive to public demands, they abide by international environmental pacts and pledges more vigorously [22,23]. However, in selected OECD nations, democracy does not perform in favor of the environment [24]. Democratic governments are observed to have implemented environmental policies, but institutional frameworks and corruption tend to hinder climate mitigation endeavors [25]. In developing countries, political and institutional systems and people have been victims of inertia and follow the status quo; however, it is time to change this attitude. On the other hand, the literature argues that democracies neglect the upcoming generations and are biased toward existing ones. This leads to a lack of long-term ecological solutions. Owing to these discrepancies, there is a dire need to ascertain the role of democracy in keeping the upsurging trend of ecological footprints on track, which is the subject of this paper.

Industry 5.0 is the “third helix” of ASQHM. In this proposed model, the industry will abide by environmentally compliant rules and regulations, implement green technology, and substitute a significant portion of its fossil fuel-ridden energy mix with renewable energy. The use of energy burgeons industrial activity in an economy [26]; on the flip side, it contaminates soil, water, and air quality and hence often leaves irreversible ecological impressions [27]. It is evident from the literature that the first industrial revolution fueled by nonrenewable energy has led to the onset of environmental devastation [28,29]. The recently emerged Fifth Industrial Revolution/Industry 5.0 is focused on addressing environmental challenges and implementing practical measures to achieve sustainable economic growth and a sustainable environment simultaneously. Embracing Industry 5.0 involves promoting green technology and using renewable energy sources, which helps to reduce carbon emissions [30]. Therefore, it is also likely to fulfill sustainable development goals/SDG 7 (affordable and clean energy), SDG 9 (industry, innovation, and infrastructure), SDG 12 (responsible consumption and production), and SDG 13 (climate action) [31]. It can also sustain a firm’s competitive edge without compromising environmental quality [32]. In developed economies, Industry 5.0 tends to accelerate technological competitiveness for they have apt education, advanced skill sets, trained personnel, digitization, and contemporary eco-friendly organizational strategies [31,33]. Industry 5.0 is also envisioned to aid in the urban development process, such as smart cities [34], through robotics and artificial intelligence [35]. The literature projects Industry 5.0 as environmentally friendly and human-friendly [36]; however, its direct impact on EFs is nevertheless unknown and calls for an investigation. There is no variable to quantify the impact of Industry 5.0 on the environment; therefore, empirical research on the said relationship is nonexistent. This study aims to fill this gap by forming and computing the Industry 5.0 variable.

Media represents the “fourth helix of ASQHM”. Media can potentially create environmental awareness. It has been proxied as information and communication technology (ICT), which can help improve communication among stakeholders [37,38], as it is the primary technology for communication, acquiring knowledge, conducting research, and making informed pro-environmental decisions [39,40]. Moreover, ICT is found to have pragmatically disseminated information and propagated awareness in the masses regarding every societal issue and economic problem, including environmental challenges [13,41]. However, the literature lacks pertinent research. Therefore, this study aims to inquire about the impact of ICT on environmental restoration, particularly within the ASQHM framework.

The most controversial “fifth helix” is the environment variable, which explicitly steers the novelty of this study. The transformation of qualitative QHM into a stochastic model implies the reshuffling of the environment variable to the left-hand side of the equation, whereas the vacant fifth helix on the right-hand side is constituted by introducing the astringently neglected, yet most indispensable, aspect of environmental research—human behavior. In the novel ASQHM, professionals and the general masses ought to practice pro-environmental behavior at home to sustain the efforts of the first four helices/stakeholders. Anthropogenic activities are the major contributor to greenhouse gas (GHG) emissions followed by natural instances [1]. Ironically, in the wake of scant behavioral data, the impact of human behavior on environmental decay is underexplored [42]. The rationale of human behavior and the decision-making process is quite challenging to estimate [43]. Additionally, behavioral anomalies such as the commons dilemma and free-rider problem are difficult to modify since these are deeply engraved and imprinted in social norms [44]. However, free and open access to and the overexploitation of natural resources is navigating toward ecological bankruptcy [45]. Therefore, this study aims to incorporate human behavior in the empirical ASQHM to emphasize its importance in environmental restoration.

Financial development is imperative for an economy as it augments economic growth, finances research and development, spurs investment, encourages savings, and ensures access to credit [46,47]. Financial development is said to partake in environmental restoration by increasing individuals’ and companies’ clean energy adoption capacities, assisting the proliferation of energy-efficient technologies, and consequently reducing CO₂ emissions [48,49,50]. Conversely, financial development boosts environmentally detrimental economic activities such as industrialization, transportation, energy consumption, infrastructure development, and overall production and consumption [47,51]. Therefore, following the aftermath of unhinged economic growth accompanied by excessive GHG emissions, research often deems financial development a precursor of environmental decay. However, the literature on the subject variable lacks consensus [46,47] and calls for further investigation. Despite these nuances, financial development is hypothesized as indispensable to finance and assist the environmental restoration process within the ASQHM.

As this is the decade (2021–2030) of ecosystem restoration, there is a dire need to construe a comprehensive framework to levitate environmental restoration. Several studies have empirically estimated the individual impacts of human capital, democracy, industry, human behavior, and financial development, but little is known about their composite bearing. However, the QHM has reckoned all of these variables crucial for eco-innovation. Therefore, this study proposes the Anthropomorphized Stochastic Quintuple Helix Model (ASQHM) to impart valuable insights into the comparative demeanor of subject stakeholders in developed countries (DCs) and less developed countries (LDCs). This research aims to find whether the hybrid ASQHM framework alongside financial development—being a facilitator and financer—will work in DCs and LDCs or not. Owing to cross-country income discrepancies, the study hypothesizes that this model efficaciously partakes in DCs’ environmental restoration process but partially assists in this process in LDCs.

Section 2 reviews the literature on financial development and the constituents of QHM. Section 3 postulates the theoretical framework of the study. The materials and methods to undertake empirical estimation are illustrated in Section 4, and the results are interpreted in Section 5. A thorough discussion of results, conclusions, and policy implications is presented in Section 6.

2. Literature Review

The literature on the original qualitative Quintuple Helix Model, five helices of the proposed Anthropomorphized Stochastic Quintuple Helix Model, and financial development is discussed below.

2.1. Quintuple Helix Model and Environment Nexus

The Quintuple Helix Model (QHM) is an extension of the Triple Helix Model of innovation. The Triple Helix Model initially comprised academia, government, and industry nexus and was deemed essential for innovation [52]. Later, the Quadruple Helix Model incorporated media and culture-based people as vital participants [53]. Afterward, to promote eco-innovation and address environmental concerns, the Quintuple Helix Model introduced the environment as a fifth helix [54].

However, the QHM prioritizes innovation for economic growth and neglects environmental restoration [55]. Furthermore, the QHM has failed to converge the interests of all stakeholders, which is accredited to a lack of trust, cohesion, anticipated support among stakeholders, information dissemination and knowledge sharing in the face of intellectual property theft, and the absence of unanimous leadership [56]. Additionally, during its implementation, the government is considered a mere financer and assumed to be unaware of the innovation process, hence overlooked [57]. However, in the presence of motivation, the QHM can perform better; nonetheless, the source of continuous motivation remains unknown [58]. Dankbaar thoroughly studied the QHM and suggested that, if the QHM accounts for human behavior, it can yield the desired outcome [59].

One of the main issues with the QHM is that it does not consider environmental restoration as the main locus of attention but rather holds it crucial to weave new ideas, keep up the streak of innovation, and pave the way to become a knowledge economy [60]. However, owing to the contemporary environmental fiasco, the natural environment should be doctored as a primary priority. The QHM has the potential to develop an interdisciplinary nexus where innovative, eco-friendly economies and societies can flourish. However, by far, all studies have qualitatively analyzed the working of the QHM and criticized and proposed a new set of rules to abide by and frameworks to adhere to [8,61] but have not estimated it empirically. A study concluded that the QHM burgeons eco-entrepreneurship and eco-innovation [60], espouses green technologies, enhances social consciousness, improves the natural factor and water endowment, and helps in global trade excellence and competitive advantage [9]. In contrast, Dankbaar [59] proclaimed that the helix model is allegedly flawed in its prevalent form. Therefore, there is a dire need to modify and empirically measure the QHM to underpin the loopholes that may emerge during its implementation. Conceding that, this study aimed at transforming the archaic QHM to a quantifiable stochastic model that can cater to empirical estimations and falsification tests. The model postulates that its five helices, namely human capital, democracy, Industry 5.0, ICT, and human behavior, coupled with financial development, are crucial determinants of eco-innovation and environmental restoration. The prudently modified model is named the “Anthropomorphized Stochastic Quintuple Helix Model (ASQHM)”.

2.2. Human Capital and Environmental (First Helix)

An empirical estimation of the first helix “academia” entails human capital. The literature renders human capital a crucial predictor for ecological conservation [62,63]. Researchers have introduced human capital in regression analysis to address the omitted variable bias [63]. Human capital ensures a smooth transition to green energy and carbon neutrality [13]. Human capital has the potential to gain new skills; invent and innovate new ideas, strategies, and technologies; carry out R&D; and foster sustainable urbanization [13,64]. Human capital also adopts eco-friendly practices that lower carbon emissions and per capita EFs [12]. A study in 19 middle-income countries employed the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) technique from 1980 to 2016 and depicted a statistically negative relationship between human capital and EFs [12]. In BRICS economies, green innovation, renewable energy, and human capital cumulatively curtailed the EFs [11]. Furthermore, human capital has explicitly reduced the ecological footprints of BRICS economies, China, and 122 pooled countries [14,64,65]. The recent literature has been projecting human capital’s impeccable impact on environmental restoration, for they are prone to be well aware of environmental issues and hence adopt pro-environment behavior [12], thoughtfully consume natural resources and energy [13], and recycle products [64]. Human capital is essential for addressing incessantly flaring environmental challenges [65].

On the flip side, educated individuals delve into practices that have a catastrophic impact on the environment [66,67]. For instance, they have been found to indulge in energy-intensive professions, use the Internet in their leisure time, and are likely to prefer comfort and automation (cars, dishwashers, and navigating through technology) over manual work [34,63]. Such practices can nullify the mitigation efforts. However, a very meager amount of research has investigated the relationship between human capital and EFs and tilted toward carbon emissions. Furthermore, there is no comparative study on DCs and LDCs. Moreover, these studies have employed “secondary education” as a proxy for human capital, which seems a shallow approach. Recently, the University of Groningen has developed a comprehensive human capital index. This study aims to regress this index to unveil profound insights into the subject variable’s effect on environmental restoration.

2.3. Democracy and Environmental Decay (Second Helix)

The second helix is government, which is estimated through the democracy variable as it can contribute to environmental restoration. One strand of the literature cites a statistically negative relationship between democracy and environmental decay [68,69]. As per the available manuscripts, democracy diminished the level of carbon emissions in 38 selected African economies [70], 17 Middle Eastern and North African countries “MENA Region” [71], and Sub-Saharan economies [72]. Eren [73] observed a pivotal positive correlation between democracy and environmental restoration in low- and medium-carbon-emitting countries. In the ASEAN group, democracy efficaciously promoted eco-innovation and green electricity to moderate the adverse impact of economic growth on environmental quality [74]. Similarly, BRICS economies can alleviate the deleterious effect of incessant economic growth and population bulge if their economic growth is coupled with renewable energy consumption and democracy [75]. However, the second strand of the literature refers to a positive relationship between the subject variables. A study of 26 OECD economies revealed that democracy undermined their ecological retrieval [24]. The direct form of democracy formulates policies in light of public whims, whereas representative democracy is found to exercise discretionary policies that favor money-minting groups who are incautious about the environment [24]. The empirical estimation of 144 countries indicated that democracies perform ineffectively in the presence of corruption, whereas they robustly relegate carbon emissions otherwise [76]. There are three problems in the current literature: Firstly, these studies lack consensus. Secondly, the impact of democracy on the recently coined comprehensive measure of environmental erosion—ecological footprints—is inadequately explored. Thirdly, these studies employ different forms of democracies, which could be one of the reasons for dichotomous inferences. Therefore, this paper aims to develop a composite variable of five rampantly practiced democracies to cater to all cross-sections.

2.4. Industry 5.0 and Environment Nexus (Third Helix)

The fossil fuel-backed First Industrial Revolution has preordained environmental devastation [28,29]. Hence, countries should deploy contemporary forms of industry. The advent of mechanization was termed the First Industrial Revolution (IR), followed by electrification (Second IR), automation (Third IR), and digitization (Fourth IR/Industry 4.0). However, the recent realization of environmental perils and the importance of “DE Carbonization”, particularly in the industrial sector, has marked the advent of Fifth IR—often termed Industry 5.0. However, Industry 5.0 is a newfangled phenomenon that is insufficiently explored. It primarily focuses on the well-being of humans and the environment through renewable energy consumption and eco-innovation [36].

2.4.1. Renewable Energy and Environment (Constituent of Industry 5.0)

Renewable energy coupled with technological innovations is a cost-effective and sustainable way to address the contemporary ecological menaces [77] and procure a sustainable environment and economic growth [78,79]. The use of renewable energy in BRICS economies has significantly lowered carbon emissions from 2000 to 2013 [80]. Studies in 15 emerging economies [81] and Japan [82] also validated these findings. Renewable energy consumption ensures a sustainable environment and energy efficiency [83,84]. In BRICS economies, between 1990 and 2018, economic complexity, foreign direct investment, and renewable energy consumption fostered environmental restoration [85]. In the Asia Pacific Economic Cooperation (APEC), renewable energy consumption slashed the incidence of EFs [83], and similar findings were revealed in Turkey [86] and BRICS countries [87]. However, the research on said nexus does not exist in middle-income and low-income countries. Similarly, the Industry 5.0 notion and its environmental implications are insufficiently explored. However, the literature suggests that renewable energy consumption and green technological innovations constitute Industry 5.0.

2.4.2. Technological Innovations’ Impact on Environment (Constituent of Industry 5.0)

Technological innovations have been metaphorically termed saviors against environmental perils [88]. They also help in producing renewable energy [89], acquiring energy efficiency, and carbon sequestration [90]. The use of prevalent smart technologies promotes increasing returns, ensuring that economic activity is undertaken with less detrimental environmental impact. Patents are predominantly used as a proxy for technological innovations in the sparsely available articles in the literature. A study based on the South African region revealed that technological innovations in the coal mining industry pertinently diminished the EFs from 1981 to 2017 [91]. In China, eco-innovation inhibited the EFs’ proliferation and helped in sustainable development [92]. Another study used CS-ARDL and Driscoll–Kraay techniques to show that combining renewable energy consumption with technological innovations substantially reduced EFs in the Belt and Road Initiative (BRI) countries [93]. However, in China and APEC economies, innovation has proven detrimental to the environment [83,88], whereas it failed to form any relationship with EFs in selected South Asian economies [94]. There is ample research on the constituent factions of Industry 5.0; however, the literature lacks a proxy to quantify the impact of Industry 5.0 on environmental restoration to validate its claims. Against this backdrop, this research aims to form a novel Industry 5.0 variable based on renewable energy, the Internet of Things (IoT), and technological innovations.

2.5. ICT and Environmental Node (Fourth Helix)

The culture- and media-based people constitute the fourth helix of ASQHM, and its empirical estimation can be performed by employing the indigenous mass utilization of information and communication technology (ICT) in a country. The use of ICT proliferates better communications channels, diminishes transaction costs, improves financial management, ensures a smooth flow of money in the economy, curtails transaction time, and helps businesses expand their horizons via e-commerce. ICT can potentially increase the per capita real income, produce human capital [95], and facilitate economic growth [96]. ICT exhibits a negative relationship with EFs and hence is helpful in curtailing EFs [97]. Conversely, research on emerging-7 (E-7) and great-7 (G-7) countries has linked ICT with excessive EFs. However, when it interacted with human capital, it was found to have decreased EFs in both groups [95]. Furthermore, in Pakistan, ICT, financial development, renewable energy consumption, green technology, and foreign direct investment have helped limit the disproportionate spread of EFs [98]. These inferences provide a reasonable rationale for investing in ICT [98,99]. Investment in ICT has considerably inhibited EFs in Saudi Arabia [40]. Moreover, in G-7 countries, ICT exports intensified EFs; however, ICT imports assisted in environmental restoration [97]. The comparative findings of a study involving 91 countries indicated that ICT diminished environmental destruction in developed nations, whereas it had the opposite effect in developing economies [65]. However, the role of ICT in ensuring communication [38], information spillover, and disbursement of knowledge for informed decision-making within the proposed ASQHM framework is yet to be seen.

2.6. Human Behavior and Environmental Restoration Nexus (Fifth Helix)

The impact of pro-environmental human behavior on the environmental restoration process is a work in progress. However, the potential probability of this connection is supported by psychological behavioral models. For instance, “Value Belief Norm theory” (VBN) studies investigate how individuals opt for eco-friendly behavior among all the available choices, by exercising their cognitive abilities. Conversely, the “Theory of Planned Behavior” (TPB) emphasizes that human beings make decisions based on the interaction of both “individual cognition” and “societal factors” and then behave rationally. To summarize, the former theory (VBN) has an intrinsic approach, whereas the latter (TPB) has an extrinsic approach in terms of projected behavior toward the environment [100]. Furthermore, the norm activation theory (NAT) accounts for three aspects of human behavior, namely normative and contextual behavior, awareness and understanding of a problem, and acknowledgment and realization of responsibility [101].

In enabling societies, individuals are prone to exhibit “cognitive behavior” that is collaborative, problem-solving, and pertinent to situations. When individuals perceive social trust as a form of cognition, they are more likely to promote local and global cooperation in order to engage in pro-environmental behavior [102,103]. Since the environment is a non-rival public good that is non-excludable, collective action is necessary to mitigate and halt further environmental destruction. If the community believes that all members are actively taking environmentally friendly measures, they can effectively reduce CO2 emissions and vice versa [104]. Furthermore, the “public trust theory” states that trust in government and other institutions is important for the effectiveness of environmental policies. When individuals trust that the government is concerned about anthropogenically induced ecological problems and devising green policies, they are more likely to support and comply with those policies [105]. Furthermore, the “values” theory states that religious beliefs can shape individuals’ values and attitudes toward the environment [106]. When mutual trust prevails in a society, people are likely to purchase eco-friendly products [102,107], take part in the implementation of green economic policies [104], spare time to recycle [108], save water, prefer public transport, conserve energy, and consequently witness low carbon emissions [107,109]. However, most of the research on human behavior relies on primary data from small cohorts. This study aims to explore secondary data to unfold how human behavior differs between developed countries (DCs) and less developed countries (LDCs) and to determine which income groups’ masses are actively participating in and complying with environmental restoration efforts.

2.7. Financial Development and Environment Facet

The impact of financial development (FD) on the environment is nevertheless unknown as it lacks consensus, which could be accredited to a lack of sound proxy [46]. If the financial sector is well developed, it can assist the government and private sector’s pro-environmental measures by ensuring easy access to credit [47]. Access to and the efficiency of financial institutions and financial markets accelerate economic growth that can fund green technologies [46]. Saqib et al. investigated the impact of financial development, renewable energy, and eco-innovation in the top ten countries having the highest EFs from 1990 to 2019. Their study exhibited a detrimental impression of FD on the green growth of these economies. However, eco-innovation and renewable energy helped attain environmental restoration [49]. Ngcobo and Wet [50] conducted a study on the South African region to explore the impact of financial development and economic growth on the production and supply of renewable energy by employing the ARDL (autoregressive distributed lag) technique. Their study inferred that FD and economic growth formed a statistically significant and positive relationship with renewable energy supply in the region. Similarly, financial development is found to have reduced CO₂ emissions in emerging economies [110]. Conversely, if the financial sector is less developed, it exacerbates carbon emissions and leads to environmental destruction [51]. The existing literature on FD demonstrates two problems. Firstly, it yields discrepancies that entail further empirical investigation [47]. Secondly, these studies employed different proxies [47]. Against this backdrop, the International Monetary Fund (IMF) has developed a comprehensive FD index that accounts for the depth, access, and efficiency of financial institutions and markets in a country [110]. However, little is known about its impact on environmental restoration. Therefore, to broaden the spectrum of ecological research, this study aims to regress the IMF’s FD index within postulated ASQHM.

This study is set to address the shortcomings of the existing literature by forming a composite democracy variable of five rampantly exercised democracies in order to cater to all the cross-sections. The literature pronounces Industry 5.0 as environmentally friendly and human-friendly; however, it lacks a proxy. Therefore, after seeking guidance from the literature, this study intends to develop an Industry 5.0 variable. Furthermore, pro-environmental human behavior (PEHB) is potentially the lost piece of the environmental restoration puzzle as the masses at large possess the power to sustain or undo the restoration endeavors of academia, industry, government, and media. To this end, a composite PEHB is formed to tweak, maneuver, and testify to its impact on the environment. Most of the studies apply inefficient proxies of human capital and financial development. Against this backdrop, this study will employ the University of Groningen’s human capital index and IMF’s financial development index to empirically estimate these variables and establish a consensus over the dichotomous and asymmetric roles of human capital and FD in environmental restoration. This is the first study that postulated the transformation of the QHM of eco-innovation into a quantitative model and empirically estimated the composite environmental imprint of financial development, human capital, Industry 5.0, democracy, media, and PEHB. It can help DCs and LDCs understand in which areas they are lagging and necessitate improvement. The ASQHM is imperative to spur eco-innovation that catalyzes ecological conservation and environmental restoration.

3. Theoretical Framework

The Quintuple Helix Model (QHM) stemmed from the Triple Helix Model that considered academia, industry, and government crucial for innovation [52]. In order to make this model more inclusive, the Quadruple Helix Model was developed to account for media- and culture-based people [53]. However, when environmental concerns spiked, researchers using the archaic QHM intuitively presumed that knowledge and policy-driven innovation ought to prioritize the ecological environment [54,55] and, therefore, introduced the environment as the fifth helix (Figure 3). The QHM is not suitable for practical implementation. However, it can be improved by incorporating pro-environmental human behavior [58] to witness its two-edged impact. Firstly, the improved model allows us to determine if these stakeholders have a streak to practice intrinsic pro-environmental human behavior (PEHB) when they are not in a professional setting or under observation. Secondly, it unveils whether the general masses/public practice PEHB and partake in green policies’ implementation and environmental restoration. The implementation of stringent green policies, along with an intrinsic motivation to carry out behavioral interventions, is required to actualize (i) SDGs of climate change mitigation and environmental protection; (ii) fulfill the UN’s environmental restoration pledge by 2030; and (iii) meet the Paris Agreement’s targets of 45% slack in carbon emissions by 2020 and net zero-carbon emissions by 2050.

3.1. Proposed Anthropomorphized Stochastic Quintuple Helix Model (ASQHM)

The QHM is primarily a model of innovation where the first four helices are placed to spur innovation for the fifth helix—environment. However, the environment does not fit here as it is not a stakeholder; rather, it is at stake (Equation (1)).

(Academia + Government + Industry + Media + Environment)

(1)

Against this backdrop, this study transforms the QHM into an empirically computable model to depict the environmental impact of these constituent stakeholders. The fifth helix (environment) is moved to the left-hand side of the equation, while the critically neglected human behavior secures the vacant position (Equation (2)).

Environment = f (Academia + Government + Industry + Media + Human Behavior + ε)

(2)

Therefore, the model is named the “Anthropomorphized Stochastic Quintuple Helix Model” (ASQHM). Stochastic refers to randomness or uncertainty in the outcomes. The model has five helices to foster eco-innovation followed by environmental restoration. However, there could be more variables that may partake in this process. Therefore, following the transformation of the IPAT (Environmental Impacts of Population, Affluence, and Technology) Model into the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) Model [111], the error term is incorporated to capture the randomness and omitted variable bias in the postulated ASQHM. Furthermore, the QHM considers the government as a financer, whereas, in ASQHM, the democratic forms of government will devise eco-friendly policies and regulate markets. During empirical estimation, financial development is considered to ensure easy access to credit and avoid potential omitted variable bias. The studies in the future can use various proxies of five helices and the environment to empirically regress this hybrid ASQHM, aiming to develop a consensus on the environmental impact of these components through time series, cross-sectional, and panel data analyses in the short and long run. The helices overlap because they function together in an integrated setting (Figure 4).

The natural capital is an impetus for a sustainable planet Earth [112]. However, its optimal exploitation for economic growth requires incessant human capital development [113]. The endogenous growth theory (1986) (https://www.sciencedirect.com/topics/economics-econometrics-and-finance/endogenous-growth-model, accessed on 4 May 2022) also emphasizes the significance of investment in human capital (K) and innovation to yield higher productivity (A) for sustainable economic growth (Y) in a country (Y = AK). The subsequent economic growth can either protect or devastate the ecological environment. However, Paul Romer modified this theory and secured the Nobel Prize in 2018. He stated, “Both the speed and nature of growth, (whether sustainable or based on resource depletion) will depend on today’s policy choices concerning infrastructure and technologies. The decarbonization cost in decades will be a function of the action and investment taken today” [114]. Therefore, incumbent governments are liable to devise and implement pro-environmental policies—the sooner the better.

Furthermore, eco-innovation based on renewable energy is inevitable for environmental restoration. It entails the development and deployment of Industry 5.0, which necessitates a vigorous change in the industrial sector and may partially stunt economic growth. Schumpeter (1976) (https://www.jstor.org/stable/1885348, accessed on 4 May 2022) defines it as creative destruction, i.e., “the process of industrial transformation where innovation incessantly revolutionizes the economic structure from within, continually destroying the old one, and creating a new one”. Hence, investment in Industry 5.0 will revolutionize the economic structure by replacing the nonrenewable energy-fueled economic activity with eco-friendly green technology in smart cities, businesses, industrial, transportation, and communication sectors. Additionally, the amalgamation of human psychology and economics forms a thriving cross-over—behavioral economics. It unveils innate and intrinsic human behavior and suggests maneuvering it for the greater good [115]. The “social norms” theory states that individuals’ behavior is influenced by the norms and expectations of their social group [116]. The “pro-social behavior” theory asserts that individuals are more likely to engage in behaviors that benefit others, including the environment [116]. Furthermore, the “public goods” theory indicates that when individuals trust that others will pay for the provision of public goods, such as clean air and water, they feel motivated to contribute themselves [117]. The rationale for all the constituents that are part of ASQHM is illustrated in Figure 5.

3.2. Hypothetical Prophecies

In the Anthropomorphized (humanized) Stochastic (quantifiable) Quintuple (five) Helix (hybrid spiral) Model (ASQHM), environmental restoration is contingent upon the environment-compliant functioning of five stakeholders (academia/human capital, Industry 5.0, government/democracy, media/ICT, and human behavior). As the name suggests, all stakeholders are intertwined, well knitted, and mutually affect each other [118]. In this framework, human capital is well informed about potential environmental degradation scenarios and has a high per capita income; hence, it can afford to make pro-environmental choices and lower the incidence of EFs. Moreover, human capital is assumed to pilot advanced green research to assist industries’ renewable energy-backed eco-innovation and decarbonization endeavors, whereas experts from industries visit universities to enlighten students [119], with on-ground practical knowledge (human capital–industry nexus). On the other hand, the government inhibits market failure and regulates the market [119] for advanced pro-environmental products to implement policies in letter and spirit. Industry 5.0 pays back to democracies by materializing green growth pledges and restoring public confidence (Industry–government nexus). In reciprocity, governments build public universities, ensure the provision of quality higher education, and provide research grants [120], particularly for environmental sciences (human capital–government nexus). Science parks are the joint ventures of industry, university, and government. There is a need to replicate that passion for a green environment. However, ICT enhances and expedites the entire process by fostering collaboration among all stakeholders, providing access to information, raising awareness, and facilitating informed decision-making based on environmentally friendly alternatives [120]. Furthermore, the prevalence of “social trust” and “confidence in government” will leave no room for suspicion among all these hybrid players.

4. Methodology and Data Description

The list of variables, data sources, measurement units, and expected signs of variables are mentioned in

Table 1. List of variables, data sources, and expected signs.

Variable	Symbol	Unit	Source	Expected Sign
Ecological Footprints	EFs	Global Hectares	GFN
Human Capital 1	HC	Index values	University of Groningen	±
Democracy	D	Index values	V-Dem	-
Renewable Energy	RE	% of total energy consumption	WDI	-
Patents	IND	No. of applications	WDI	-
Trademarks	IND	No. of applications	WDI	-
Individuals Using the Internet	IND	% of total population	WDI	-
Information and communication technology	ICT	Exports as % of total exports	WDI	-
Social Trust	PEHB	Survey-based	WVS	-
Religiosity	PEHB	Survey-based	WVS	-
Confidence in Government	PEHB	Survey-based	WVS	-
Confidence in Environmental Organizations	PEHB	Survey-based	WVS	-
Financial Development	FD	Index values	IMF	±
Economic Growth	GDP	GDP per capita current $US	WDI	+
Agriculture Value Added	AVA	% of total GDP	WDI	-
Urbanization	UR	urban population as % of the total population	WDI	±

GFN = Global Footprint Network, V-Dem = Varieties of Democracy, WDI = World Development Indicator, WVS = World Value Survey, IMF = International Monetary Fund, and GDP = Gross Domestic Product. ¹ The detailed formation of the human capital index: https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf (accessed on 25 April 2024).

. All the variables have annual frequency except human behavior. The data on human behavior were obtained from the World Value Survey (WVS), which publishes a new wave every five years. This study used the data of waves 3–7 ranging from 1995 to 2022. However, the study employed the moving averages method to convert five yearly human behavior data into annual frequency.

4.1. Methodology: Generalized Method of Moments (GMM)

The GMM estimator has been considered best when the dependent variable partially depends on past values that inhibit the ability of predictors to be exogenous (not correlated with error terms) and individual predictors are autocorrelated [121]. The advantage of GMM is that it accounts for unobserved country-specific effects, which overturns the probability of the model suffering from omitted variable bias (due to unobserved heterogeneity). Furthermore, to overcome the endogeneity, GMM utilizes the lag-dependent variable as an instrument to alleviate any probability of predictors’ correlation with the error term [122]. This study transformed the QHM into a stochastic model. The new ASQHM has the characteristics of uncertainty and randomness that cannot be fully captured through generic error terms. Additionally, ASQHM has endogeneity and unobserved heterogeneity; therefore, the GMM estimator appropriately accounts for these statistical problems by incorporating lag-dependent variables, lagged independent variables as instruments, and idiosyncratic error terms in the equation. In this study, the GMM model was used to tackle the theoretical endogeneity, which was determined through institutional variables like democracy in different forms. The software STATA 17 has been used to estimate the GMM models and perform Principal Component Analysis empirically. EViews 13 is used to estimate cross sectional dependence and causality tests.

4.1.1. Linear Dynamic Panel Data Generalized Methods of Moments (GMM) Model

Generic Equation

y_it = λy_i,t−1 + x^′_itβ + α_i + µ_it

(3)

where ε_it = α_i + µ_it.

Cross-sections: i = 1, 2, ……, N and time period: t = 1, 2, ……, T, y_it = dependent variables, y_i,t−1 = lag dependent variable, x^′_it = set of explanatory variables, which can either be strictly/weakly exogenous or endogenous; α_i = unobserved cross-sectional heterogeneity and conceded to be correlated with the set of regressors x^′_it and the lagged dependent variable incorporated as in dependent variable y_i,t−1. However, µ_it is the idiosyncratic error term and has to be serially uncorrelated.

This equation is transformed into a 1st differenced GMM equation to remove time-invariant cross-sectional effects [90], which is denoted as follows:

Δ y_it = λ Δy_i,t−1 + Δ x^′_itβ + Δε_it

(4)

Following Arellano and Bover (1995), forward orthogonal deviations were calculated to remove time-fixed effects by subtracting the mean from respective variables [122].

$$\widetilde{{\Delta }_{\mathrm{t}}} {\mathrm{y}}_{\mathrm{it}}=\mathsf{\lambda }\widetilde{{\Delta }_{\mathrm{t}}} {\mathrm{y}}_{\mathrm{i},\mathrm{t}-1}+\widetilde{{\Delta }_{\mathrm{t}}} {{\mathrm{x}}^{\prime }}_{\mathrm{it}}\mathsf{\beta }+\widetilde{{\Delta }_{\mathrm{t}}}{\mathsf{\epsilon }}_{\mathrm{it}}$$

(5)

where Δε_it = $\sqrt{\mathrm{T}-\mathrm{t}+1/\mathrm{T}-\mathrm{t}}{{\displaystyle \sum }}_{\mathrm{t}}^{\mathrm{i}}\left(\mathsf{\epsilon }\mathrm{it}-{\displaystyle \frac{1}{\mathrm{T}-\mathrm{t}+1}} {{\displaystyle \sum }}_{\mathrm{s}=0}^{\mathrm{T}-\mathrm{t}} \mathsf{\epsilon }\mathrm{i} \right)$ and ε_i = ε_i,t+s.

Stacked moment conditions (for the 1st differenced model) are as follows:

E[_Zi^D′ Δε_i] = 0

where Δε_i = (Δε_i2, Δε_i3, …, Δε_iT), and _Zi^D′ = (_Zyi^D, _Zxi^D) with GMM-type instruments are illustrated below. In the following notation, y_i = y_i,T−2:

$${{\mathbf{Z}}_{\mathrm{yi}}}^{\mathrm{D}}=\left(\begin{array}{ccc}yi0& 0& 0\\ 0& yi0& yi1\\ 0& 0& 0\end{array}\begin{array}{ccc}\dots 0& 0& \dots 0\\ \dots 0& 0& \dots 0\\ \dots yio& yi1& \dots yi\end{array}\right)\left(\begin{array}{c}t=2\\ t=3\\ t=T\end{array}\right)$$

And moment conditions for _Zxi^D are stacked similarly, where xi = x_i,T−2.

$${{\mathbf{Z}}_{\mathrm{xi}}}^{\mathrm{D}}=\left(\begin{array}{ccc}xi0& 0& 0\\ 0& xi0& xi1\\ 0& 0& 0\end{array}\begin{array}{ccc}\dots 0& 0& \dots 0\\ \dots 0& 0& \dots 0\\ \dots xio& xi1& \dots xi\end{array}\right)\left(\begin{array}{c}t=2\\ t=3\\ t=T\end{array}\right)$$

Putting the proposed variables in the GMM equation,

EF_it = EF_i,t−1 + HC_it+ D_it + IND_it + ICT_it + PEHB_it + FD_{it +} AVA_it + U_it + GDP_it + α_i + γ_t + µ_it

(6)

where EF = ecological footprint, HC = human capital, D = democracy, IND = Industry 5.0, ICT = information and communication technology, PEHB = pro-environmental human behavior, FD = financial development, AVA = agriculture value added, U = urbanization, GDP = economic growth, α_i = capturing unobserved cross-sectional heterogeneity, γ_t = capturing time-fixed effects, and µ_it = time-varying idiosyncratic error term; I = cross-sections and t = time.

Developed countries (DCs) had i = 32 and t = 28, whereas less developed countries (LDCs) had i = 18 and t = 28. This study followed the income classification of the World Bank to segregate the countries into developed and less developed groups. The confined number of DCs and LDCs was subject to data availability.

4.1.2. IPAT and STIRPAT Inspired Mathematical Expression of ASQHM

The “Stochastic Impact by Regression on Population, Affluence, and Technology” (STIRPAT) emerges from the IPAT model that explores the multiplicative environmental impact (I) of the population (P), affluence (A), and technology (T) [111]. Dietz and Rosa transformed the STIRPAT model into a stochastic model that allowed for hypothesis testing [123]. Similarly, the ASQHM in this study derived inspiration and sought guidance from the IPAT and STIRPAT models. The generic IPAT model is expressed as follows:

I_i,t = α^βP_i,t ^γA_i,t ^δT_i,t µ_i,t

(7)

where α is the intercept; β,γ, and δ are the exponents of the population (P), affluence (A), and technology (T), respectively; and µ embodies the error term.

Following the IPAT (Equation (7)) model, the ASQHM’s equation can be denoted as follows:

EF_i,t = α * EF_i,t−1 * ^λ1HC_i,t * ^λ2D_i,t * ^λ3IND_i,t * ^λ4ICT_i,t * ^λ5PEHB_i,t * ^λ6FD_i,t * ^λ7AVA_i,t * ^λ8U_i,t * ^λ9GDP_i,t * α_i * γ_t * µ_it

(8)

However, the STIRPAT model introduced the error term and reinstated the multiplication signs with addition. Therefore, the STIRPAT-inspired, empirical ASQHM takes the following form:

EF_i,t = λ_o + EF_i,t−1 + λ₁HC_i,t + λ₂D_i,t + λ₃IND_i,t + λ₄ICT_i,t + λ₅PEHB_i,t + λ₆FD_i,t + λ₇AVA_i,t + λ₈U_i + λ₉GDP_i,t + α_i + γ_t + µ_it

(9)

where λ₁, λ₂, λ₃ … λ₉ are the elasticities explanatory variables. Based on theoretical postulations, the expected impact of Equation (9) (λs) is listed as follows:

• ${\displaystyle \frac{\partial EF}{\partial HC}}$ < 0, (λ₁) = the higher level of human capital will be reflected in higher levels of R&D, eco-innovation, smart cities, smart jobs, and smart transportation, leading to lesser ecological footprints in both income groups. However, it can potentially yield the opposite inference;
• ${\displaystyle \frac{\partial EF}{\partial D}}$ < 0, (λ₂) = the persistently exercised democratic governments will reduce EFs by implementing eco-friendly policies and providing regulatory support in both subject groups;
• ${\displaystyle \frac{\partial EF}{\partial IND}}$ < 0, (λ₃) = The higher extent of eco-friendly Industry 5.0 will yield lower EFs in both panels;
• ${\displaystyle \frac{\partial EF}{\partial ICT}}$ < 0, (λ₄) = an increase in the use of ICT will ensure connectivity among all stakeholders and result in lower EFs in both groups;
• ${\displaystyle \frac{\partial EF}{\partial PEHB}}$ < 0, (λ₅) = in DCs and LDCs, the higher prevalence of PEHB will significantly reduce Efs;
• ${\displaystyle \frac{\partial EF}{\partial FD}}$ < 0, (λ₆) = the increase in financial development will reflect truncated EFs in both groups;
• ${\displaystyle \frac{\partial EF}{\partial AVA}}$ < 0, (λ₇) = an increase in efficient agriculture value added will significantly reduce EFs in both income groups;
• ${\displaystyle \frac{\partial EF}{\partial U}}$ > 0, (λ₈) = higher levels of urbanization are associated with higher EFs in LDCS; however, ${\displaystyle \frac{\partial EF}{\partial U}}$ < 0 is expected to lead toward smart cities, smart housing, smart jobs, smart transportation, and energy efficiency and hence lower EFs in DCs;
• ${\displaystyle \frac{\partial EF}{\partial GDP}}$ < 0, (λ₉) = higher GDP is expected to accelerate EFs in both panels.

4.1.3. Panel Dumitrescu and Hurlin (D-H) Test

It is better to check the causal relationship between long-term heterogeneous panel data series [124]. The panel Dumitrescu and Hurlin (D-H) non-causality test also accounts for cross-sectional dependence, which is usually suspected in panel data. D-H provides precise results even with small samples and balanced and unbalanced panels [82]. The D-H test takes the following functional form (Equation (10)):

$${\mathrm{Y}}_{\mathrm{it}}={\mathsf{\beta }}_{\mathrm{i}}+{{\displaystyle \sum }}_{k=1}^q {{\mathsf{\delta }}_{\mathrm{i}}}^{\mathrm{k}} {\mathrm{Y}}_{\mathrm{i},\mathrm{t}-\mathrm{k}}+{{\displaystyle \sum }}_{k=1}^q {{\mathsf{\eta }}_{\mathrm{i}}}^{\mathrm{k}} {\mathrm{X}}_{\mathrm{i},\mathrm{t}-\mathrm{k}}+{\mathsf{\mu }}_{\mathrm{it}}$$

(10)

where Y and X are characterized as stationary for all N and T. The parameters β_i and η_i = (η_i¹, η_i², η_i³, …, η_i^k) are deemed fixed over time. D-H’s null hypothesis is denoted below (Equation (11)).

H₀: η_i = 0 for all cross-sections

(11)

Furthermore, Wald statistics tests the H₀ and H₁ hypothesis using the following functional form (Equation (12)):

$${ }^{\mathrm{HNC}}{\mathrm{W}}_{\mathrm{N}.\mathrm{T}}={\mathrm{N}}^{-1} {{\displaystyle \sum }}_{i=1}^N {\mathrm{W}}_{\mathrm{i},\mathrm{T}}$$

(12)

where W_i,T symbolizes the individual cross-sectional test statistics.

4.2. Formation of Variables

This study attempted to fill the gaps in the literature by forming three novel variables, namely (1) democracy, (2) Industry 5.0, and (3) pro-environmental human behavior (PEHB). As the literature suggests, the outcomes of different types of democracy vary [125]. Therefore, it is better to merge the most frequently exercised forms of democracy to cater to all the heterogeneous cross-sectional cohorts. Industry 5.0 is termed human- and environmentally friendly [126]. Therefore, renewable energy consumption and technological innovations constitute the Industry 5.0 variable [36]. However, for empirical estimation, the literature only suggests “patents” as a proxy for eco-friendly technological innovations. However, this paper incorporated “trademarks”—another type of Intellectual Property Rights (IPR) that exhibits the presence of competitive markets/registration of new businesses in a country. Industry 5.0 is a resource-efficient approach that promotes eco-innovation and the development of intelligent machines. This way, human capital can be utilized for creative work rather than repetitive labor tasks [36]. Usman and Hammar [82] also employed patents and trademarks as a proxy for technological innovations. In addition, Industry 5.0. also entails the Internet of Things (IoT), which abets the integration of multiple technologies, sensors, computing, energy efficiency, and interoperability. IoT ensures the provision of services at any time and place. IoT also supports automation and digitization, which was the precursor of Industry 4.0/Fourth Industrial Revolution [127]. Therefore, the IoT is also a part of Industry 5.0, where it can surpass the impact of information and communication technology (ICT) and the Internet [128]. Furthermore, PEHB is a variable that measures the collective impact of social trust, religiosity, and confidence in government and environmental organizations on individuals’ tendency to adopt eco-friendly practices at work and at home. In the ASQHM framework, the PEHB variable plays a significant role in determining its impact on the environment.

4.3. Principal Component Analysis (PCA)

The variables of democracy, Industry 5.0, and PEHB were determined through PCA, and the results can be seen in Table 2, Table 3 and Table 4, respectively. This is an extensively used statistical technique that transforms multidimensional correlated data into a weighted index [129]. PCA condenses data and captures the maximum variance whereby the resultant series is free from correlation [130]. High correlations are prone to exhibit multicollinearity and are likely to compromise the efficiency of the empirical results. However, PCA is tailored to reduce the dimensionality of large datasets, making the regression and interpretation relatively easier [46]. Since all three variables had multidimensional subcategories, they exhibited correlations. PCA formed new one-dimensional variables (Table 2, Table 3 and Table 4). In this study, PCA was used to make an unequal weighted index, where every indicator was treated as per its contribution. The generic PCA equation is illustrated as follows:

Ultimate _{Index = Wa * CIa + Wb * CIb + …. + WmeCIm =} ^mΣ_{iWi * CIi}

(13)

where

• Ultimate _Index = conclusive composite index;
• CI_i = factor scores of respective constructing indicators;
• W_i = Allocated weights of each CI_i.

The respective apportioned weights are calculated as follows:

$$W_i={\displaystyle \frac{{\theta }_i}{{{\displaystyle \sum }}_i^n{\theta }_i }}\ast 100$$

where W_i symbolizes the apportioned weight assigned to the ith factor, and θ_i is the variance of the ith factor [46].

Table 2. PCA: formation of composite democracy variable.

Component	Democracy Indicators	DCs Proportion (Weights)	LDCs Proportion (Weights)
1	Electoral	0.9669	0.9411
2	Liberal	0.0152	0.0239
3	Participatory	0.0100	0.0199
4	Deliberative	0.0059	0.0087
5	Egalitarian	0.0021	0.0064

Source: estimated by authors.

Table 2. PCA: formation of composite democracy variable.

Component	Democracy Indicators	DCs Proportion (Weights)	LDCs Proportion (Weights)
1	Electoral	0.9669	0.9411
2	Liberal	0.0152	0.0239
3	Participatory	0.0100	0.0199
4	Deliberative	0.0059	0.0087
5	Egalitarian	0.0021	0.0064

Source: estimated by authors.

Table 3. PCA: formation of Industry 5.0 variable.

Component	Industry 5.0 Indicators	DCs Proportion (Weights)	LDCs Proportion (Weights)
1	Patents	0.7412	0.7582
2	Trademarks	0.1401	0.1926
3	Internet of Things	0.0859	0.0509
4	Renewable energy	0.0280	0.0189

Source: estimated by authors.

Table 3. PCA: formation of Industry 5.0 variable.

Component	Industry 5.0 Indicators	DCs Proportion (Weights)	LDCs Proportion (Weights)
1	Patents	0.7412	0.7582
2	Trademarks	0.1401	0.1926
3	Internet of Things	0.0859	0.0509
4	Renewable energy	0.0280	0.0189

Source: estimated by authors.

Table 4. PCA: formation of pro-environmental human behavior (PEHB) variable.

Component	PEHB Indicators	DCs Proportion (Weights)	LDCs Proportion (Weights)
1	Social trust	0.3926	0.3671
2	Religiosity	0.2424	0.2416
3	CIG *	0.1972	0.2327
4	CIEO **	0.1678	0.1586

Source: estimated by authors.* CIG = confidence in government and ** CIEO = confidence in environmental organizations.

Table 4. PCA: formation of pro-environmental human behavior (PEHB) variable.

Component	PEHB Indicators	DCs Proportion (Weights)	LDCs Proportion (Weights)
1	Social trust	0.3926	0.3671
2	Religiosity	0.2424	0.2416
3	CIG *	0.1972	0.2327
4	CIEO **	0.1678	0.1586

Source: estimated by authors.* CIG = confidence in government and ** CIEO = confidence in environmental organizations.

The acquired proportionate weights were multiplied with their respective indicators, and then weighted averages were taken to form the final one-dimensional composite variables of democracy, industry, and PEHB.

5. Results and Discussion

5.1. Interpretation of the Results of Developed Countries (DCs)

This study postulates and quantifies a hybrid ASQHM framework to impart valuable insights into the cumulative working of essential stakeholders of environmental restoration in DCs and LDCs. The summary statistics of DCs and LDCs are exhibited in

Table 5. Summary statistics (DCs).

	EF	LNHC	LND	LNIND	LNICT	LNPEHB	FD	AVA	URBAN
Mean	4.5098	1.4659	−0.4148	7.4761	0.5373	2.1572	0.5252	38.654	72.741
Median	4.1131	1.1327	−0.1875	7.2774	0.9880	2.2013	0.4719	43.102	75.094
Maximum	10.926	12.861	−0.0825	13.523	4.2306	2.5657	9.0000	85.487	95.515
Minimum	1.0312	0.6174	−2.6010	2.8749	−7.5442	1.2333	0.0000	2.6938	30.276
Std. Dev.	2.0258	2.0250	0.4931	1.9480	2.2092	0.2105	0.4771	20.901	13.738
Skewness	0.6682	5.2692	−2.5582	0.4509	−0.6926	−1.2845	6.3587	−0.0537	−0.8205
Kurtosis	2.9416	28.964	9.8347	3.0369	3.0574	5.2701	114.73	2.1060	3.2564
Jarque–Bera	65.171	28,594.2	2654.50	29.675	69.999	428.03	46,052.0	29.522	100.46
Probability	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Sum	3941.58	1281.23	−362.62	6534.1	469.64	1885.4	459.0767	33,783.6	6357.4
Sum Sq. Dev.	3582.77	3580.14	212.30	3312.8	4260.8	38.700	198.7509	381,378.5	16,478.5
Observations	874	874	874	874	874	874	874	874	874

Source: estimated by authors.

and

Table 6. Summary statistics (LDCs).

	EF	LNHC	LND	LNIND	LNICT	LNPEHB	FD	LNAVA	LNURB
Mean	1.7922	2.3108	0.4112	6.3593	4.1873	10.511	0.1850	47.324	49.480
Median	1.2146	2.2641	0.4020	6.3627	0.9733	10.739	0.1832	46.685	51.825
Maximum	7.7656	3.8490	0.8518	8.8498	38.761	16.868	1.0000	84.313	91.418
Minimum	0.0000	1.1303	0.0905	3.4293	−0.0262	3.7935	0.0000	10.916	13.827
Std. Dev.	1.4996	0.6297	0.1998	1.1934	7.0512	2.6238	0.1467	19.549	20.512
Skewness	1.8235	0.2857	0.4380	−0.0535	2.1329	0.0191	1.1242	−0.0610	0.2177
Kurtosis	6.2289	2.2492	2.2549	62.475	7.2353	2.2363	7.2584	1.9115	2.0804
Jarque–Bera	498.26	18.696	27.774	6.0167	758.85	12.278	486.99	25.192	21.739
Probability	0.0000	0.0000	0.0000	0.0493	0.0000	0.0021	0.0000	0.0000	0.0000
Sum	903.29	1164.6	207.27	3205.12	2110.4	5297.7	93.275	23,851.6	24,937.9
Sum Sq. Dev.	1131.2	199.50	20.081	716.336	25,009.4	3462.8	10.833	192,232.5	21,169.0
Observations	504	504	504	504	504	504	504	504	504

Source: estimated by authors.

, respectively. The Dumitrescu and Hurlin (D-H) causality tests and panel cross-sectional dependence results can be seen in

Table 7. Panel Dumitrescu and Hurlin (D-H) causality results.

Null Hypothesis	W-Statistics	Z-Statistics	Probability	Inference
DCs
$\mathrm{HC} \nLeftrightarrow$ Democracy	12.2890	6.84654	8.1200	$\mathrm{HC} \leftarrow$ D
$\mathrm{Democracy} \nLeftrightarrow$ HC	29.8353	25.9486	0.0000
$\mathrm{Democracy} \nLeftrightarrow$ Industry 5.0	3.61806	3.24082	0.0012	$\mathrm{D} \leftrightarrow$ IND
$\mathrm{Industry} 5.0 \nLeftrightarrow$ Democracy	6.05687	8.85608	0.0000
$\mathrm{Industry} 5.0 \nLeftrightarrow$ ICT	3.24697	2.38938	0.0170	$\mathrm{IND} \leftrightarrow$ ICT
$\mathrm{ICT} \nLeftrightarrow$ Industry 5.0	3.30291	2.51518	0.0119
$\mathrm{ICT} \nLeftrightarrow$ PEHB	7.80790	4.45130	9.0600	$\mathrm{ICT} \leftrightarrow$ PEHB
$\mathrm{PEHB} \nLeftrightarrow$ ICT	7.01676	3.34821	0.0008
LDCs
HC $\nLeftrightarrow$ Democracy	9.13039	11.9496	0.0000	$\mathrm{HC} \leftrightarrow$ D
$\mathrm{Democracy} \nLeftrightarrow$ HC	8.58475	11.0071	0.0000
$\mathrm{Democracy} \nLeftrightarrow$ Industry 5.0	6.64184	2.11911	0.0341	$\mathrm{D} \to$ IND
$\mathrm{Industry} 5.0 \nLeftrightarrow$ Democracy	8.40804	3.96606	7.0500
$\mathrm{Industry} 5.0 \nLeftrightarrow$ ICT	3.76866	2.67688	0.0071	$\mathrm{IND} \to$ ICT
$\mathrm{ICT} \nLeftrightarrow$ Industry 5.0	2.97647	1.31318	0.1891
$\mathrm{ICT} \nLeftrightarrow$ PEHB	2.90544	−1.78812	0.0738	$\mathrm{ICT} \to$ PEHB
$\mathrm{PEHB} \nLeftrightarrow$ ICT	5.86733	1.30919	0.1905

Source: estimated by authors.

and

Table 8. Panel cross-sectional dependence test.

Test	Statistic	d.f.	Prob.
(H0: No cross-section dependence in residuals)
DCs
Breusch–Pagan LM	2584.421	496	0.0000
Pesaran scaled LM	66.30743		0.0000
Pesaran CD	8.644968		0.0000
LDC
Breusch–Pagan LM	180.3621	153	0.0646
Pesaran scaled LM	1.564186		0.1178
Pesaran CD	3.068368		0.0022

Source: calculated by authors.

, respectively. As the constituents of ASQHM espouse one another, it is essential to assess their bidirectional linkages. The D-H panel data causality test was applied, revealing that in DCs, human capital does not foster democracy; however, the presence of democracy encourages the development of human capital and deployment of Industry 5.0. Analogously, Industry 5.0 depicts bidirectional relationships with democracy and ICT. Furthermore, ICT was found to have a bidirectional relationship with Industry 5.0 and PEHB in the DCs’ panel.

Table 9. GMM empirical estimation results of developed and less developed countries.

	Developed Countries		Less Developed Countries
Dependent Variable: EFs	Coefficient	t-Statistic	Coefficient	t-Statistic
EF (−1)	0.496506 a	0.052540	0.761760 a	106.5219
LNHC	−1.496516 a	0.563202	0.067458 a	3.936695
LND	−0.834851 a	0.301687	−0.046165 a	-3.640037
LNIND	−0.323182 a	0.105182	−0.024089 a	-4.998196
LNICT	−0.014459 b	0.006236	−0.001079 c	-2.112932
LNHB	0.389915 c	0.201516	−0.161746 a	-16.43732
LNGDP	0.054465 c	0.032682	0.371624 a	11.47177
LNAVA	0.028005 c	0.016352	0.041340 a	2.982425
LNURB	−0.802925	0.972866	−0.068198 b	-22.13565
FD	0.316818 b	0.134791	−0.021241 a	21.78402
Effect Specifications (DCs)
Root MSE	0.390917	Mean dependent var		0.176465
S.D. dependent var	0.476216	S.E. of regression		0.394680
Sum squared resid	80.53414	J-statistic		228.0345
Instrument rank	266	Prob(J-statistic)		0.895129
Effect Specifications (LDCs)
Root MSE	0.206732	Mean dependent var		−0.044798
S.D. dependent var	0.361954	S.E. of regression		0.209648
Sum squared resid	15.47125	J-statistic		274.2170
Instrument rank	280	Prob(J-statistic)		0.417149

Source: estimated by authors. The exponents ^a, ^b, and ^c depict the level of statistical significance at 1%, 5%, and 10%, respectively.

demonstrates the empirical estimation results. The GMM inference depicts that a percentage increase in human capital leads to a 1.4965-unit decrease in EFs in DCs. This coefficient is highly significant and bears the expected outcome, indicating that human capital in DCs has been conducting advanced research to reduce EFs and taking part in eco-innovation and environmental restoration [7]. This inference is consistent with previous findings [63,64,65]. According to a study [64], human capital is crucial to promoting environmental sustainability. Individuals in DCs are educated, aware of ecological issues, and engage in eco-friendly behaviors, including thoughtful consumption of natural resources such as energy and water [12], and therefore they are more likely to recycle and minimize waste [61,63]. Additionally, a positive shock in democracy demonstrates a gigantic decline (83.49%) in EFs. The magnitude and negative sign of this highly significant coefficient indicate that the inferred role of democracy in the environmental restoration endeavors of DCs is consistent with the literature. The democratic forms of government effectively devise and implement environmentally compliant policies, boost investment opportunities, and regulate markets for eco-innovation to reduce EFs and restore the environment [18]. Democratic governments are more receptive to public demands, enact eco-friendly policies, and uphold environmental protection commitments [24,131].

The combined impact of renewable energy, technological innovations, and IoT as an Industry 5.0 variable exhibits a solicited negative sign with EFs. The results depict that a 1% increase in the development and adoption of Industry 5.0 induces a 32.32% decrease in EFs and espouses environmental restoration in DCs. This inference validates that Industry 5.0 is eco-friendly and performs the assumed role in the ASQHM framework. The highly significant Industry 5.0 coefficient appositely demonstrates a negative relationship with EFs, which is consistent with the literature. Renewable energy consumption significantly reduces ecological destruction, as noted by Usman and Hammar [82], Ghosh [74], and Afshan and Yaqoob [91]. On the other hand, technological innovations also play a critical role in halting the proliferation of EFs in DCs. They spur eco-innovation, improve energy efficiency and resource conservation, foster sustainable development, and preserve the environment. This outcome endorses the findings of [15,89,132,133]. Similarly, the rampant use of ICT depicts a statistically negative relationship with environmental deterioration. The empirical inference suggests that a percentage increase in the use of ICT induces a 1.45% debility in EFs. The magnitude of the ICT coefficient is meager yet statistically significant (0.05), which is in line with its postulated role in the ASQHM and is also consistent with the literature. It can ensure incessant communication and knowledge dissemination amongst all stakeholders [97]. The positive influence of ICT on environmental quality magnifies when it interacts with human capital [96]. ICT helps individuals make informed decisions and opt for green alternatives, thus curbing the consequent pollution and EFs [38].

The coefficient of the pro-environmental human behavior (PEHB) variable exhibits an unsolicited positive relationship with EFs and defies its hypothesized role in the ASQHM. A 1% increase in the prevalence of PEHB in DCs leads to a 38.99% increase in EFs. When individuals engage in environmentally friendly behavior, they assume to have played their part; however, due to extravagant lifestyles, they end up increasing EFs, which is detrimental to environmental sustainability [106,134]. On the other hand, pro-environmental human behavior can reduce EFs, but it requires significant effort to sustain and often falls victim to common dilemmas [104]. The literature suggests that if pro-environmental efforts are costly, people may only engage in conservation practices [102].

Furthermore, in DCs, economic growth and environmental restoration are found to be mutually exclusive. A percentage increase in economic growth tends to upsurge EFs by 5.45% in DCs. When economic growth is based on harmful practices instead of clean energy and green technology, it harms the environment. DCs have been working to relegate their indigenous EFs, but their extravagant consumption and production patterns impede the process [87,134]. Nevertheless, when countries focus on environmental quality, it hampers their economic growth and vice versa [133]. Similarly, a positive shock in financial development leads to a 31.68% increase in EFs. This indicates that DCs’ financial development is not compliant with green policies and fosters unhinged economic growth instead. This inference is in line with the literature [46,135]. The transition from conventional energy to renewable energy requires a significant amount of funds and is likely to induce labor layoffs for a short period. Therefore, most countries avoid this change to sustain their economic growth, which results in environmental devastation in DCs [136]. Moreover, an increase in agriculture value added (AVA) escalates EFs since DC’s agrarian sector is capital-intensive and heavily relies on large crop yields; the overexploitation of soil, water bodies, and farmland; and the overproduction of meat and food, which ultimately exacerbate EFs [137]. However, urbanization does not establish any relationship with EFs, hence not contributing to DCs’ environmental restoration endeavors. These findings reject the postulated hypothesis of DCs, namely that all stakeholders of the ASQHM partake in the eco-innovation and environmental restoration process.

5.2. Interpretation of the Results of Less Developed Countries (LDCs)

The LDCs’ Dumitrescu and Hurlin (D-H) causality and GMM estimation results are illustrated in Table 7 and Table 9, respectively. The D-H causality test reveales that in LDCs, human capital and democracy have bi-directional causality, however, democracy forms a unidirectional relationship with Industry 5.0 to elevate eco-innovation and resource conservation. In addition, Industry 5.0 supports the ICT infrastructure which unidirectionally accelerates PEHB. Even though HC is an essential driver of environmental restoration in DCs, it yields the opposite inference in LDCs. A percentage increase in the highly significant human capital coefficient decreases ecological footprints by 6.75%. However, the inference is in line with the literature [17]. In some instances, human capital is likely to engage in energy-intensive jobs, and their energy consumption at home also tends to be higher than that of illiterate individuals, as they use the Internet in their leisure time, play games, surf, watch TV, and prefer automation over manual tasks. Therefore, human capital has high EFs at work and home. Such practices can nullify the mitigation efforts [138]. Furthermore, in LDCs, the indigenous human capital is not sufficiently competitive to engage in advanced green research and eco-innovation. If it is, they do not receive adequate resources and opportunities. However, this result negates the findings of [139,140]. When it comes to democracy, its coefficient is highly significant and appositely positive, but its magnitude indicates a meager reduction in EFs that is up to 4.62% in LDCs. Nevertheless, this inference is consistent with the literature [24,73,132]. In LDCs, democratic governments respond to public demands to secure their vote banks, strive to abide by international environmental protection pledges, and sign environment protection treaties to secure funds/loans/aid and strengthen bilateral and multilateral relations. In this way, democracies earn a good reputation at the international level [70], resulting in resource conservation and a reduction in greenhouse gas emissions and EFs.

An increase in Industry 5.0 reduces EFs by 2.41% in LDCs. Despite being an essential constituent of ASQHM, the highly significant Industry 5.0 coefficient depicts the desired negative sign. This reiterates that if LDCs consume renewable energy, use innovative green technologies, and adopt IoT in industrial sectors and smart cities, they can substantially decrease EFs, conserve ecological resources, halt habitat destruction, and restore the environment. This inference complies with the literature [90,92,136].

Similarly, the coefficient of ICT also demonstrates a negative relationship with EFs. A percentage increase in ICT use can potentially cut down EFs by 0.11%. ICT/media has the least impact on environmental restoration compared to other stakeholders. As per the literature, ICT propagates awareness, makes information accessible, and is a pertinent source of communication [97]. Therefore, within the hybrid ASQHM, ICT will tailor the informed pro-environmental decisions, which help save time, energy, and resources at work and home. The use of ICT is also found to increase real per capita income, nurture human capital, and facilitate economic growth [38,94]. One of the studies cites that ICT makes the actualization of SDGs easier [141]. However, this inference contradicts the findings of Khan et al. [64], who revealed that ICT has exacerbated environmental degradation in LDCs.

In contrast to the DCs, a percentage increase in the prevalence of pro-environmental human behavior (PEHB) reduces environmental damage by 16.18% in LDCs. Social trust and religiosity let people channel their intrinsic cognition and rational behavior, which improves the environment and reflects better air, water, and soil quality. Furthermore, religious people tend to take responsibility for environmental conservation, and they feel more connected to nature and accountable for their actions. Similarly, increasing social trust strengthens the social fabric of communities, which helps them sustain PEHB and attain environmental sustainability [43]. If a society has high mutual trust, individuals are more inclined to buy eco-friendly products, recycle, conserve water and energy, utilize public transportation, and minimize carbon emissions [44].

Furthermore, the coefficient of urbanization is statistically significant (0.05), intuitively positive but smaller in magnitude (0.0682), which implies that a percentage increase in urbanization contributes meagerly to the environmental restoration process. Urbanization tends to reduce EFs and promotes sustainable development, provided it levitates clean and green technology, develops environmentally compliant smart cities, effectively regulates transportation, and promotes eco-friendly jobs through research and development (R&D). In such a way, LDCs can reduce human demand on nature, which is indispensable for environmental restoration (46,49].

Additionally, a 1% increase in economic growth exacerbates EFs by 37.16%. This outcome aligns with the literature, as economic growth and environmental quality are referred to as mutually exclusive. In LDCs, the energy mix of production sectors leans heavily toward fossil fuel consumption. They are oblivious to the environmental impact of economic growth [133]. Likewise, emerging economies are accelerating economic growth at the expense of environmental quality. Owing to lax environmental regulations, they attract substantial foreign direct investment (FDI), establish subsidiaries of multinational companies, and unabashedly utilize fossil fuels in the production and transportation sectors [142].

Despite being in the neonatal stage, financial development in LDCs is found to support environmental restoration efforts. A percentage increase in financial development results in a 2.12% decrease in EFs. This inference negates the studies by [50,108] and endorses the research outcome of [46,49]. The access to and efficiency of financial institutions and markets can encourage economic growth that funds green technologies [46,110]. On the other hand, a percentage increase in agriculture value added exacerbates EFs by 4.12%. As LDCs are predominantly agrarian economies, they rely on agriculture-based primary industries. Despite the high demand for food, they have low per-hectare crop yields due to ineffective labor-intensive practices [143]. They overexploit natural resources, which goes against the notion of a sustainable environment.

6. Conclusions and Policy Implications

Heedless anthropogenic activities have resulted in unprecedented ecological footprints (EFs). To achieve incessant economic growth, countries have been overexploiting natural resources beyond the regenerative capacity of the Earth. As a result, the annual ecological budget is depleted by July, and then resources that belong to future generations begin to be exploited, leading to ecological bankruptcy. However, the existing studies have not developed a comprehensive framework of resource conservation to anchor a sustainable environment. Remarkably, the Quintuple Helix Model (QHM) of innovation can offer a perfect blend of multidimensional stakeholders/variables that has the potential to restore the environment, but this qualitative framework is highly underexplored. Conceding that, this study transforms and improves the archaic QHM, which consists of five helices—academia, government, industry, media, and the environment. The postulated Anthropomorphized Stochastic Quintuple Helix Model (ASQHM) is a multifaceted yet hybrid framework that qualifies for empirical regression analysis and falsification tests. For empirical testing, the qualitative QHM is first introduced with the sign of equality. Then, the fifth helix (environment) is moved to the left-hand side of the equation, and pro-environmental human behavior fills the vacant position. The novel ASQHM hypothesizes that environmental restoration is contingent upon and a function of five helices, namely (i) human capital/academia, (ii) democracy/government, (iii) Industry 5.0, (iv) ICT/media, and (v) pro-environmental human behavior. This is the first study that brings potential stakeholders under a canopy to attest to their cumulative environmental impression and stipulate evidence-based subject measures.

To fill the gaps in the literature, the composite democracy, Industry 5.0, and pro-environmental human behavior variables were formed by employing PCA. The model was estimated using Generalized Methods of Moments (GMM) for developed countries (DCs) and less developed countries (LDCs) for the period 1995–2022. All variables are statistically significant, confirming that the constituent stakeholders of ASQHM are crucial determinants of environmental restoration in both panels. The GMM results reveal that the first four helices—human capital, democracy, Industry 5.0, and media—significantly diminish ecological footprints (EFs) in DCs and restore the environment, hence performing their hypothesized role. However, the fifth helix—pro-environmental human behavior—is found to increase EFs. Masses in DCs are less connected, more secular, and rightfully lack confidence in governments because they are found to prioritize economic growth over a green environment. Among all stakeholders, democracy projects the highest effect on environmental restoration, followed by Industry 5.0, human capital, and ICT. This implies that democratic governments are in charge of green macroeconomic policy. Similarly, environmental quality can be improved if the conventional industrial sector adopts renewable energy and eco-innovation to transition to Industry 5.0 alongside human capital. However, the positive coefficients of financial development and PEHB indicate that governments and the masses in DCs do not prioritize eco-friendly measures. In DCs, individuals do not practice their intrinsic cognitive behavior at home (when they are outside of a professional setting or not under observation). The hybrid ASQHM seems to work in DCs except for the fifth helix (PEHB).

However, in less developed countries (LDCs), human capital is found to have exacerbated EFs because they are not at par with DCs’ human capital, lack resources to conduct research, innovate green technology, and do not fulfill their expected role within the ASQHM. Nevertheless, the rest of the helices—democracy, Industry 5.0, ICT, and pro-environmental human behavior (PEHB)—serve as significant catalysts for environmental restoration. The fifth helix, PEHB, considerably contributes to environmental restoration because communities are knitted and have greater social trust and religiosity; as a result, they value the environment. In LDCs, the population is naïve, leading them to trust incumbent governments and international environmental organizations with their pro-environmental commitments. However, financial development has the propensity to assist eco-friendly policies and make green alternatives accessible. In LDCs’ model, the coefficient of PEHB is highest in magnitude, followed by democracy, Industry, and ICT, indicating their relative importance in the practical implementation of ASQHM. The stakeholders/helices of the postulated hybrid ASQHM can levitate eco-innovation and restore the environment in LDCs, barring the first helix (human capital).

However, as for policy implications, following the Conference of Parties (COP 28) requisites, the incumbent governments must devote a specific financial budget for environmental restoration measures. Both income groups should prioritize renewable resource-based energy mix and green technology in the production, transportation, and smart city sectors. The government should enforce Intellectual Property Rights and price ceilings, raise awareness, and regulate markets to create an equilibrium between the supply of and demand for advanced pro-environmental products. Furthermore, there is a need to create environmentally friendly jobs and research opportunities to effectively utilize human capital. Both panels should elect democratic governments, for they adhere to eco-friendly policies. DCs should help LDCs adopt Industry 5.0 based on renewable energy and eco-friendly technological innovations via profound R&D, the dissemination of information, joint ventures, and technical support. Additionally, these countries should promote media to enlighten the public about environmental dangers, the importance of the natural environment, and resource conservation. Governments should ensure access to ICT for the masses to save energy, time, and resources, and to make informed eco-friendly decisions.

Individuals need continuous nudging and positive reinforcements to exercise pro-environmental behavior, including incentives, tax exemptions, and economical eco-friendly alternatives to curb the exponentially growing EFs. Similarly, incentives for research, public–private partnerships (PPPs), the subsidized dissemination of technology, and the diffusion of clean energy can promote eco-innovation. Lastly, DCs should envision and ensure environmentally compliant financial services and disburse discounted credit to environmentally conscious firms. However, LDCs need to improve the efficiency and depth of, as well as access to, financial markets and institutions. This can ensure easy access to credit and overall financial services for extending auxiliary to governments and the masses’ pro-environmental interventions.

In a nutshell, the ASQHM along with financial development can restore the environmental quality in both panels. It has the potential to unite all stakeholders, and the absence of any stakeholder will compromise the said process. This comprehensive framework offers unparalleled insights into the complex dynamics of human capital, democracy, Industry 5.0, ICT, and PEHB for eco-innovation-backed environmental restoration. Furthermore, future research can validate these findings by employing different proxies of helices in cross-sectional, time series, and panel data studies to project their short- and long-run elasticities.