Empirical Evidence on the Impact of Technological Innovation and Human Capital on Improving and Enhancing Environmental Sustainability

Abstract

Conservation of the natural balance to hinder global warming is a contemporary task for policymakers. To this end, several policy tools have been proposed. Technological innovation, which increases productivity and aids in the development of eco-friendly technologies, and human capital, which fosters environmental awareness and provides knowledge of technology use, are among the policy options. Therefore, the primary aim of this study is to test whether human capital accumulation and technological innovation improve environmental sustainability in emerging countries by utilizing the recently proposed novel Cross-sectionally Augmented Autoregressive Distributed Lag method in ten of N-11 countries from 1996 to 2019. The empirical results suggest that economic development and human capital negatively impact environmental sustainability, proxied by the load capacity factor. In contrast, renewable energy has a positive impact on sustainable development. Lastly, empirical estimations using several technological innovation indicators uncover that technological innovation may have no systematic impact on the load capacity factor. Therefore, technological advances and human capital may not create the desired impact on environmental quality.

1. Introduction

Human capital, defined as the skill level of the labor increasing the effective unit of labor [1], and technological innovation, introduced as a dynamic process driven by market needs and inventive capacity [2], are vital in boosting economic performance in contemporary economic literature. Technological advances boost capital productivity, enabling higher outputs with existing resources, while human capital directly affects labor productivity. Indeed, human capital can serve as complementary capital, boosting economic performance through greater capital accumulation. Thus, decision-makers consider that higher technological development and human capital accumulation would improve economic performance [3,4,5]. Researchers have studied the impact of technology and human capital on economic performance, but their role in sustainable development remains less understood. The urgent need for environmental sustainability is highlighted by the visible impacts of global warming caused by greenhouse gas emissions (GHGs) (Figure 1). Despite the urgent significance of global warming and its associated challenges, there has been insufficient research and focus on the core reasons behind the consistent increase in emissions and the complex interactions among innovation, human capital, and environmental sustainability. This gap in understanding may be due to economic incentives, political motives, extractive institutions, and a limited grasp of the issue’s overall importance. Tackling these neglected factors is essential for ensuring environmental sustainability and overcoming barriers to technological advancement and sustainable change.

Most researchers have regarded technological innovation as the primary driver of increasing environmental performance [7]. Technological innovation holds considerable promise for decreasing GHGs and combating global warming by moving away from outdated technologies and fostering the development of sustainable energy sources [8]. This is because increasing GHGs and environmental deterioration are strongly linked with traditional resource utilization, such as oil and coal. Moreover, advances in environmental technologies could enable decision-makers to internalize environmental externalities without relying on interventionist instruments such as taxes and stringent restrictions [9]. Furthermore, technological development may improve environmental quality indirectly by encouraging more sustainable behavior among consumers and producers, increasing public environmental awareness, enabling close monitoring of government environmental policies, optimizing production and supply chain systems, and providing better environmental monitoring and assessment [10,11,12,13]. However, not all technological advances can support a sustainable environment. As is envisaged in the endogenous growth paradigm, technological innovation is a strong promoter of economic development [14], but this growth may come at an environmental cost if innovations increase resource consumption or pollution. For instance, the invention of coke marked a significant technological breakthrough, leading to a boom in iron and steel production [15]. However, the invention of coke has resulted in historically high coal utilization. Thus, GHG concentrations and environmental deterioration have increased.

Human capital accumulation helps prevent global warming by improving environmental quality. A nation with more human capital can rapidly adopt high-tech eco-friendly products, develop responsible consumption patterns like conserving energy and water, and use renewable energy in economic activities [7,16,17]. Furthermore, human capital influences FDI’s environmental impact. Lan et al. [18] found that FDI harms the environment in Chinese provinces with low human capital but supports it in high human capital provinces. Thus, human capital may hinder pollution acceptance.

Human capital accumulation is key for economic growth, as a nation’s output depends on workforce health and education [19]. Highly educated individuals drive technological innovation [20], boosting economic development and welfare. It can also enhance economic activity and resource demand. Additionally, human capital complements capital by enabling more efficient use of resources over time.

Since the late 1900s, emerging and developing economies have seen rising foreign direct investment, technological progress, financial growth, and productivity gains [8]. These developments have contributed to better economic performance. Figure 2 illustrates global economic growth trends, contrasting developed and emerging nations. Emerging economies have outpaced developed countries in growth, accounting for most of the world’s economic success. From 1980 to 2023, the average real growth rate in developed nations was 2.3%, nearly half of the 4.4% average in emerging economies [21]. However, this growth has not come without costs, as it has significantly increased carbon emissions (CO₂), a major driver of climate change and environmental damage [22]. Consequently, ecological decline and global warming present major challenges for emerging economies.

One can assert that establishing effective environmental policies is needed to mitigate the environmental pressure that may arise from the high economic performance of the N-11 countries. In light of these theoretical explanations and discussions, the present study aims to test the following research hypotheses for the ten N-11 countries.

H1. 

Technological Innovation supports Environmental Sustainability in the N-11 countries.

H2. 

Human Capital Accumulation supports Environmental Sustainability in the N-11 countries.

H3. 

Renewable Energy Consumption supports Environmental Sustainability in the N-11 countries.

H4. 

Economic Development supports Environmental Sustainability in the N-11 countries.

The existing literature pays less attention to testing the first and second hypotheses. To seek an answer to this research question, yearly data from 1996 to 2019 for ten N-11 countries are utilized, employing a cross-sectionally augmented autoregressive distributed lag (CS-ARDL) method that considers cross-section dependence (CD) and controls for possible endogeneity using a traditional ARDL framework.

2. Literature Review

Climate change, mainly driven by economic activities such as resource demand (e.g., energy, water, resources, etc.), agriculture, livestock, infrastructure development, financial development, industrial production, technological advancement efforts, trade, and manufacturing activities, has caused the rapid GHG concentration and environmental deterioration [23,24,25]. Therefore, reversing climate change and reducing environmental deterioration have become significant tasks and research topics for policymakers and researchers, respectively. In this regard, the number of studies has grown since the 1990s, and the seminal study by Grossman & Krueger [26] has been a leading edge in the literature, uncovering the dynamic interaction between economic development and environmental performance. Since then, economic development has become the basis of empirical estimations and models. In this context, the Environmental Kuznets Curve (EKC) has served as a theoretical basis for modeling the relationship between economic development and environmental interaction. It can be said that a significant part of the studies reveals a dynamic interaction between economic growth and environmental quality, depending on the level of economic development, see [27,28,29].

Moreover, it is frequently asserted that the global economy’s unsustainable energy structure has caused environmental deterioration alongside economic activities. Therefore, abandoning unsustainable energy sources such as oil and coal is required. In this regard, renewable energy can be a solid alternative for ensuring energy security, preventing energy crises, and powering economies [30]. Indeed, the significance of the energy transition based on renewable sources has been emphasized in SDG-7 [31]. Researchers have empirically tested the impact of renewable energy on achieving environmental sustainability in various studies. For instance, Bekun et al. [32] have investigated the role of renewable energy in ensuring a sustainable environment in 16 European Union countries and reported that renewable energy can help build a sustainable environment, while Sharif et al. [33] have inquired whether renewable energy can be an effective tool in reducing ecological footprint in Türkiye and found that renewable energy utilization can reduce ecological footprint. Anwar et al. [34] have revealed that the utilization of renewable energy can reduce CO₂ emissions in Asian countries. Karlilar et al. [35] have evidenced in the Organization for Economic Cooperation and Development (OECD) countries that renewable energy can significantly promote environmental development. Therefore, it can be said that researchers have predominantly found that renewable energy can be a crucial tool for reducing environmental pollution.

Additionally, recent studies have focused on another research question: How does human capital contribute to environmental pollution? Some of the prominent papers can be reported as follows. Lan et al. [18] have examined the impact of human capital on pollution emission intensity in China at the provincial level. They have evidence that human capital can help reduce environmental pollution. Similarly, Zafar et al. [17] have investigated the role of human capital on environmental sustainability in the BRICS sample and discovered that human capital can diminish the ecological footprint. Ahmed et al. [36] have analyzed the effect of human capital on environmental deterioration in China and empirically evidenced that human capital decreases the ecological footprint. Çakar et al. [37] have examined the impact of human capital on carbon emissions in 21 EU countries. The authors have determined that human capital harms the environment in high-growth, low-financial development, and low-human capital regimes. On the contrary, it decreases environmental degradation in low-growth, high-financial development, and high-human capital regimes. Yin et al. [38] have investigated the role of human capital accumulation in a set of developed and emerging countries and obtained mixed results. Hondroyiannis et al. [39] have examined the nexus between human capital and environmental quality in 19 OECD countries and found that human capital is associated with decreased CO₂ emissions. Li et al. [40] have examined the impact of human capital accumulation on environmental deterioration, and they have reported that human capital accumulation could increase the CO₂ emissions in Pakistan. Du et al. [41] have focused on revealing the dynamics of human capital accumulation and environmental sustainability in 18 emerging countries. They have reported that human capital accumulation enhances the CO₂ emissions. Adikari et al. [42] investigated the impact of human capital on CO₂ emissions in Sri Lanka and found that an increase in human capital has a positive effect on ecological quality. Aytun et al. [43] have examined how human capital spurs environmental sustainability in 19 emerging countries, and they have reported that human capital can reduce the ecological footprint. Gu et al. [44] have examined the impact of human capital on the ecological footprint in BRICST nations and found that human capital negatively affects environmental sustainability.

Similar to human capital, the environmental impacts of technological progress have recently garnered the attention of researchers, and relatively immature research bodies exist in this area. Omri & Hadj [8] have examined the impact of technological advances on environmental pollution in 23 emerging economies and determined that technological progress can mitigate CO₂ emissions. Esquivias et al. [12] have investigated the impacts of technological innovations on the CO₂ emissions in Asian emerging economies and uncovered that technological innovations increase ecological degradation. Mughal et al. [45] have investigated how technology promotes environmental sustainability in Asian countries and revealed that it can actually increase CO₂ emissions. Zhang et al. [46] have examined the impact of technological innovations on the ecological footprint in 41 Sub-Saharan African countries, yielding mixed results. Wang & Dong [47] have investigated whether technology can be an eco-friendlier economic activity in the OECD sample and reported mixed empirical inferences. Awosusi et al. [48] have attempted to unveil the interaction between technology and the environment in BRICS countries. The empirical evidence has implied that technological progress can increase the ecological footprint. Ahmad et al. [49] have examined how technological innovations affect sustainable development and the CO₂ emissions in China. They have detected that technological innovation supports economic growth without harming the environment. Kiani et al. [13] have investigated the nexus between technological innovations and environmental degradation in South and Southeast Asian countries, proving that technological innovations mitigate the ecological footprint. Sibt-e-Ali et al. [50] have examined the nexus between technological innovations and CO₂ emissions in the East and South Asian regions, finding that increased technological innovations enhance environmental quality. Avcı et al. [51] have investigated how green technological innovation and the environment interact in 15 countries. The empirical results show that green technological innovation can help mitigate CO₂ emissions.

As seen in the literature review, although understanding the determinants of environmental deterioration has been a significant research topic for researchers since the 1990s, no study has yet addressed the N-11 countries, and researchers have yet to reach a consensus on whether human capital and technological innovation support environmental efforts. Second, most of the former literature has utilized either single GHGs, such as CO₂, or the ecological footprint in measuring environmental deterioration. Such a measuring strategy is frequently criticized because it only considers the demand side of nature [52]. Therefore, a gap exists in the literature. The present study primarily examines the impact of human capital and technological advances on environmental quality in emerging countries, with significance for the following reasons. First, understanding how technological innovation and human capital accumulation impact environmental sustainability in emerging countries can help us comprehend the interplay between human capital, technological innovation, and environmental interactions. Having empirical evidence on the issue can help policymakers establish efficient human capital and technological innovation policies for addressing climate change. Thus, the empirical outcomes of the present study can help achieve the climate targets of the Sustainable Development Goals (SDGs) successfully. Second, the outcomes of the present studies could be significant for emerging countries and crucial for the global economy in combating global warming, which requires collective action. Therefore, understanding the nature of sustainable development in emerging countries is crucial. Third, the present study utilizes the load capacity factor (LCF) as a proxy for measuring environmental sustainability. Unlike traditional consumption-based indicators (i.e., CO₂, ecological footprint), LCF considers both the supply and demand sides of the ecology, enabling researchers to model environmental development comprehensively. Fourth, using the CS-ARDL method allows researchers to control for possible CD and endogeneity in models. Thus, robust empirical evidence can be obtained, and solid policy proposals can be made. Therefore, the present study combines those unique contributions to fill the gap in the former literature.

3. Theoretical Underpinnings, Model and Data

3.1. Theoretical Underpinnings

Understanding environmental sustainability has long been a complex challenge in sustainable development. Early studies significantly shaped the theoretical framework, while the work by Grossman & Krueger [26] marked a crucial milestone in empirical studies. They identified economic development as a key driver of environmental degradation. Since then, economic growth has become a central component of empirical models in sustainable development research. Furthermore, rising economic activities—such as consumption and production—have historically increased energy demand, which has impacted financial sustainability and contributed to higher GHGs [53]. Researchers assert that growing resource demands have exerted substantial pressure on traditional energy sources, making renewable energy a hopeful solution for energy shortages and environmental protection [54]. Consequently, researchers routinely include both traditional and renewable energies, building on the prominent studies by Cole et al. [55] and Richmond and Kauffman [56]. Some researchers argue that old-fashioned, energy-intensive technologies drive environmental harm, with hydrocarbon energy being predominant. They suggest a green growth paradigm, which aims to increase output while avoiding environmental damage, as a necessary shift [57]. Jacobs [58] highlights theories emphasizing technological innovation’s role in sustainable economic development. Technological advances can promote growth without environmental pressure, as per green growth theory [59], making their impact a key research focus.

Human capital accumulation has become increasingly important following the introduction of Endogenous Growth Theory, as demonstrated by the seminal studies of Romer [60] and Lucas [61]. Consequently, human capital accumulation has attracted significant interest from researchers. Early studies focused on examining the role of human capital in economic development by using either education or health as proxies for human capital accumulation [62]. However, researchers have recently begun to focus on the ecological impact of human capital accumulation.

Another key topic is how to model environmental performance in empirical research. Early studies used carbon emissions as a proxy because they represent the largest share of total GHGs, which was 73.7% in 2023 [63]. Consequently, most previous literature relied on carbon emissions to measure environmental performance. However, this approach has been criticized for focusing only on one aspect of environmental degradation, prompting researchers to seek more comprehensive indicators. The ecological footprint, which assesses environmental damage through multiple subcomponents, has gained prominence, leading the second wave of studies to adopt it as a model for environmental performance [64]. Nonetheless, recent research points out that the ecological footprint emphasizes only the environmental outcomes of economic activities and ignores environmental improvements. Such modeling might produce biased results because regions with strong environmental regeneration could appear less deteriorated, even if overall issues persist. To address this, Siche et al. [65] proposed the load capacity factor (LCF), calculated as Biocapacity divided by the Ecological Footprint. This approach incorporates both the supply side (biocapacity) and demand side (ecological footprint) of environmental assessment. Pata [66] was the first to use LCF as a proxy for environmental performance. Currently, the latest studies increasingly employ LCF as a sustainable development indicator due to its more nuanced and comprehensive structure.

3.2. Model and Data

Based on the arguments provided in the former section, to uncover how technology, human capital, economic development, and energy spur LCF, the following models have been utilized in emerging countries from 1996 to 2019:

Model 1 = LCF = f (Y, R, HC, T1)

(1)

Model 2 = LCF = f (Y, R, HC, T2)

(2)

Model 3 = LCF = f (Y, R, HC, T3)

(3)

Model 4 = LCF = f (Y, R, HC, T4)

(4)

where LCF indicates the load capacity factor estimated using the Biocapacity/Ecological Footprint metric, increases in LCF values suggest an improvement in environmental quality (and vice versa). As previously mentioned, traditional environmental pollution indicators like CO₂ and ecological footprint are often criticized because they focus only on the demand side of nature. This approach can lead to biased empirical estimates. However, the LCF proposed by Siche et al. [65] considers both sides of nature—supply and demand—by including biocapacity and ecological footprint in its calculations. This modeling strategy enables researchers to account for the complex interactions within nature. Therefore, LCF helps researchers monitor both sides of the environment and obtain more reliable empirical results [66].

Y denotes GDP per capita (Constant 2015 US$), R indicates renewable energy consumption (% of total final energy consumption), which represents the share of renewables in the total energy used by end users such as households, industry, and agriculture. HC stands for human capital accumulation, estimated based on years of schooling and returns to education. T1 (Patent Applications (Residents)), T2 (Patent Applications (Non-residents)), T3 (Total Patent Applications (Residents + Non-residents)), and T4 (Number of Scientific Articles) are used as proxies for technological innovation. LCF components have been obtained from the Global Footprint Network [67], while GDP per capita, renewable energy, and technological innovation proxies are sourced from the World Development Indicators database [68]. Lastly, HC data are retrieved from the Groningen Growth and Development Centre [69].

All data were converted into logarithmic values. Detailed information on the data characteristics and sources is provided in

Table 1. Statistical Properties of Data.

Variable	Mean	Min.	Max.	Std. Dev.	Obs.
LCF	−1.102	−5.438	0.061	1.331	240
Y	8.162	6.354	10.362	0.978	240
R	2.537	−0.916	4.137	1.432	240
HC	0.816	0.385	1.326	0.206	240
T1	6.495	2.773	12.053	2.274	240
T2	7.561	4.745	10.767	1.596	240
T3	8.236	5.030	12.297	1.665	240
T4	8.210	5.133	11.141	1.629	240

. The primary challenge in using data from 1996 to 2019 was balancing the dataset and data availability, since HC data ended in 2019, while proxies for technological innovation began in 1996 for the relevant sample. Additionally, to the best of our knowledge, the next 11 (N-11) countries—Bangladesh, Egypt, Indonesia, Iran, South Korea, Mexico, Pakistan, the Philippines, Türkiye, and Vietnam—are being used as a research sample for the first time. Nigeria could not be included due to a significant lack of data. Goldman Sachs reports that N-11 countries are emerging economies with the potential to become the largest in the 21st century because of their strong economic growth prospects [70]. Moreover, these countries exhibit some of the highest population growth rates [71]. Consequently, including N-11 countries in the study could have important implications for the literature. Additionally, these countries have ecological deficits, exerting negative pressure on the environment. Over the past two decades, CO₂ emissions in N-11 have gradually increased, representing 11% of global CO₂ emissions by 2022 [72,73,74].

Moreover, the statistical properties of the utilized dataset are reported in Table 1. According to the statistical indicators, negative values are observed in LCF and R properties. Therefore, one can infer that some of the LCF and R values are between 0 and 1, because taking the natural logarithm produces negative values. In addition, the highest mean belongs to the T3, while the lowest belongs to the LCF. The lowest minimum level is −5.438, which belongs to the LCF, while the highest is 6.354, which belongs to the Y. In addition, T3 has the highest maximum level of 12.297, while the LCF has the lowest maximum level of 0.061. The most volatile data is T1, while the most stable one is HC. Lastly, all data have equal observation numbers. Hence, the dataset is strongly balanced.

4. Empirical Method Framework

One can assert that the panel data estimations are frequently subjected to the CD. In this regard, the impact of a positive or negative shock in a given country can easily spill over to others with similar characteristics or common ground [75]. Therefore, a preliminary analysis is needed to determine proper panel data estimation methods. To test whether CD exists in the estimated models, in the first step of the empirical analysis, the Bias-Adjusted Lagrange Multiplier (LM_Adj) test proposed by Pesaran et al. [76] has been employed to uncover the CD properties of the estimated models. In the second step of the empirical analysis, to determine a proper empirical estimating strategy, delta tests proposed by Pesaran & Yamagata [77] are used to uncover whether slope coefficients are homogeneous. As the third step in the empirical analysis, the second-generation unit root test of Pesaran [78], the CIPS test, is employed to determine the data set’s level of integration to determine whether testing cointegration is needed in the estimated models.

After determining the integration order of the variables, the existence of cointegration in the examined models was investigated using the bootstrap-augmented cointegration test proposed by Persyn & Westerlund [79] in the fourth step of the empirical analysis. The CS-ARDL estimation method, developed by Chudik et al. [80], is employed to estimate long-run coefficients of the variables in the case of the existence of cointegration among the variables in the fifth and final step of the empirical methodology. CS-ARDL estimators permit researchers to have mixed integrational levels of the regressors, such as I(0) and I(1). Additionally, it enables researchers to obtain robust empirical estimates using heterogeneous slope coefficients and CD. Lastly, it addresses potential endogeneity issues using the traditional ARDL method for each cross-section [80]. The CS-ARDL procedure of the four models reported above can be estimated by following the procedure denoted in Equation (5):

$$\mathsf{\Delta }LCF={\theta }_{0i}+{\theta }_1+{{\sum }_{i=1}^s\mathsf{\Delta }{LCF}_{i,t-1}+{\theta }_2{\sum }_{i=0}^e\mathsf{\Delta }{X}_{it-1}+{\theta }_3{\sum }_{i=0}^r\mathsf{\Delta }\overline{CA}}_{i,t-1}+{\epsilon }_{1it}$$

(5)

where LCF represents the load capacity factor, s, e, and r show lags, X denotes the regressor set and $\overline{CA}$ denotes the cross-sectional average of the related variable. The coefficient of the lagged values of the dependent variable coefficient shows the error correction term. A summary of the empirical strategy is provided in Figure 3.

5. Empirical Results

The CD results are provided in

Table 2. Homogeneity and CSD Test Results.

Test	Model 1	Model 2	Model 3	Model 4
LMAdj	11.190 (0.000)	8.562 (0.000)	9.529 (0.000)	9.778 (0.000)
Delta	6.134 (0.000)	6.787 (0.000)	6.595 (0.000)	6.233 (0.000)
DeltaT	7.083 (0.000)	7.837 (0.000)	7.616 (0.000)	7.197 (0.000)

. According to the empirical results, the null hypothesis of cross-sectional independence is firmly rejected for the models used. Therefore, CD emerges in the predicted models. Therefore, second-generation panel data techniques must be utilized. Moreover, the delta test results evidence that the null hypothesis of slope homogeneity is firmly rejected. Therefore, slope coefficients are heterogeneous.

By considering the emergence of the CD, the CIPS test is used as a second-generation panel unit root method to determine the integration order of the variables. According to the results reported in

Table 3. CIPS Test Results.

Variable	Level	$\mathbf{First} \mathbf{Difference} ({∆{\mathit{\vartheta }}_{\mathit{t}} = \mathit{\vartheta }}_{\mathit{t}}-{\mathit{\vartheta }}_{\mathit{t}-1})$
LCF	−2.547	−5.196
Y	−2.648	−4.077
R	−1.316	−4.048
HC	−2.102	−2.867
T1	−2.370	−4.920
T2	−2.790	−5.060
T3	−2.550	−5.404
T4	−0.879	−5.059
Critical Values	10% = −2.73, 5% = −2.86, 1% = −3.1

Note: $\vartheta$ denotes the observation value (logarithmic) of the related data value at the year t.

, the null hypothesis of the unit root is accepted for the variables at the different significance levels. At the same time, they become stationary at their first difference. Therefore, all variables have I(1) integration orders; therefore, possible cointegration relations should be investigated in the tested models. To this end, a bootstrap-augmented cointegration test is used.

Table 4. Bootstrap-Augmented Cointegration Test Results.

Test	Model 1	Model 2	Model 3	Model 4
GTau	−3.766 (0.000)	−3.405 (0.000)	−3.556 (0.000)	−3.469 (0.000)

reports the bootstrap-augmented cointegration test results. The results reject the null hypothesis of no cointegration in all models. Therefore, all models have cointegration relations among the variables, and long-run coefficient estimations should be done.

Lastly, the long-run coefficient estimations are performed using the CS-ARDL method to account for CD and heterogeneity properties of the data, as well as to control for possible endogeneity. The empirical evidence reported in

Table 5. Long-Run Coefficient Estimation Results by Using the CS-ARDL Method.

Variable	Model 1	Model 2	Model 3	Model 4
Y	−1.329 (0.069)	−2.349 (0.081)	−1.352 (0.234)	−0.968 (0.065)
R	0.257 (0.156)	0.492 (0.005)	0.364 (0.005)	0.258 (0.023)
HC	−14.229 (0.042)	−25.215 (0.506)	−6.667 (0.048)	−7.472 (0.074)
T1	0.118 (0.366)	-	-	-
T2	-	0.114 (0.564)	-	-
T3	-	-	0.262 (0.227)	-
T4	-	-	-	0.391 (0.377)

denotes that economic growth (Y) has a negative and statistically significant impact on LCF in Models 1, 2, and 4, while it has a statistically insignificant impact in Model 3. In addition, renewable energy (R) has a positive and statistically significant impact on LCF in Models 2, 3, and 4, while it has a statistically insignificant impact in Model 1. Human capital (HC) has a negative and statistically significant impact on LCF in Models 1, 3, and 4, whereas it has a statistically insignificant impact in Model 2. Last, the empirical evidence denotes that technological innovation has a positive but statistically insignificant impact on LCF in all models. One can argue that it would be more rational to determine this by considering the majority of the empirical evidence.

Although N-11 countries can frequently be classified into similar groups by international organizations such as Goldman Sachs, they can vary significantly in socioeconomic aspects. Therefore, one may consider such empirical results as informative, rather than generalizing them to all sample groups. By taking this intuition into account, the empirical results can be summarized as follows:

• Economic growth may have a negative impact on LCF.
• Renewable energy may have a positive impact on LCF.
• Human capital may have a negative impact on LCF.
• Technological advances may have no systematic impact on LCF.

6. Discussion

The theoretical and methodological interpretation of the empirical results can be done as follows. It can be said that economic development reduces LCF in emerging countries. Therefore, economic development may have harmful consequences for environmental sustainability. As is known, human activities, including industrialization, deforestation, urbanization, etc., are among the main reasons for environmental deterioration [81]. In other words, economic activities boosting growth performance can result in environmental deterioration. In addition, the detrimental impact of economic performance on environmental sustainability could be related to the nature of economic development. Dang et al. [82] emphasize that some countries clearly show a trade-off between economic development and environmental sustainability. For instance, China has experienced a decade of strong economic growth at the expense of environmental sustainability, while Norway has managed to reduce PM2.5 levels while maintaining a strong economy. One can infer that developing countries, such as the N-11, may be more likely to incur high environmental costs during periods of strong economic performance. In addition, as emphasized in the EKC hypothesis, economic development can depend more on resource utilization in the initial phases of economic development. Therefore, resource utilization could enhance economic performance in developing countries, such as those in the N-11, as emphasized by the scale effect of the EKC hypothesis. Therefore, the detrimental impact of economic growth on environmental sustainability in the initial periods of economic development is consistent with theoretical expectations and confirms the empirical results obtained by Li et al. [27], Ulucak et al. [28], and Wang et al. [29].

Human capital accumulation has a negative impact on LCF in emerging countries. Therefore, increases in human capital accumulation can reduce the environmental sustainability level of these countries. This could be related to the more substantial economic growth impact than the environmental sustainability impact of human capital accumulation in emerging countries. Du et al. [41] suggest that economic growth is strongly linked to worker competence. Therefore, increasing human capital can enhance economic growth by improving the competence levels of workers. Therefore, the welfare impact of human capital can exceed its sustainability impact. Additionally, a threshold level of human capital may be required to enhance environmental sustainability, as suggested by the EKC hypothesis for economic development. Therefore, human capital can hinder sustainable development to some extent, depending on the level of human capital and the stage of development. Additionally, Li et al. [40] suggested that foreign direct investments are shifting from countries with strict environmental regulations to countries with lax ecological standards, low human capital levels, and, thus, cheaper labor. Therefore, increasing human capital in countries with high environmental standards can benefit environmental sustainability. However, although the level of human capital in emerging countries is increasing, it does not support environmental sustainability. This may be because the level of human capital is still below average compared to other countries or because environmental standards are lax. This empirical result on human capital accumulation is consistent with Li et al. [40], Du et al. [41] and Gu et al. [44], but it contradicts Zafar et al. [17], Lan et al. [18], Ahmed et al. [36], Hondroyiannis et al. [39], Adikari et al. [42] and Aytun et al. [43].

Renewable energy has a positive impact on the LCF level. Therefore, it moderates environmental deterioration in emerging countries and promotes sustainable development. This could be related to the nature of the renewable energy. As is known, renewable energy has a far lower harmful environmental impact than traditional energy sources. Therefore, the United Nations [31] emphasizes the importance of the increasing share of renewables in the global energy composition in reversing environmental deterioration. Moreover, the International Energy Agency [83] has stated that renewable energy is the primary means of keeping global temperatures below 1.5 °C and enables the decarbonization of electricity generation. Therefore, the moderating impact of renewable energy is consistent with theoretical expectations and confirms the empirical results reported by Bekun et al. [32], Sharif et al. [33], Anwar et al. [34], and Karlilar et al. [35].

The empirical findings of the present study imply that, by utilizing several technological proxies, there is no systematic interaction between technological innovation and LCF. Therefore, technological innovation may not be directly linked to environmental efforts in emerging countries, but it may indirectly impact environmental performance. The EKC hypothesis emphasizes that composition and technique effects can occur after passing the threshold level of economic development, and the impact of technological innovation may be weak before this threshold level due to a more substantial scale effect [84]. Therefore, it can be said that there may be a threshold dependence of technological innovation on the level of economic development. Moreover, the moderating impact of the technological innovation efforts may not occur if the required investments for renewable energy technologies are not made [85]. Considering the low level of renewable energy shares in the total energy mix and the insufficient momentum worldwide, including in developing countries [86] such as the N-11 countries, technological innovation may not directly create the desired impact on fostering environmental sustainability. Lastly, technological advances may not directly and systematically support environmental development; on the contrary, technological innovation can sometimes be detrimental to the environment. For instance, the invention of the famous steam engine was a significant technological innovation and a great milestone for economic welfare. However, the environmental consequences of the widespread use of steam engines have been detrimental due to increased coal utilization. Therefore, one may not introduce a systematic interaction between technology and the environment. This branch of the empirical results contradicts Omri et al. [8], Esquivias et al. [12], Kiani et al. [13], Mughal et al. [45], Awosusi et al. [48], Ahmad et al. [49], Sibt-e-Ali et al. [50] and Avcı et al. [51].

7. Conclusions

The impact of global warming has become increasingly severe in recent years. Unfortunately, due to extreme weather levels, most critical thresholds have already been passed. Therefore, an urgent response and road map to prevent climate change and reduce environmental pressure have become inevitable. To reverse environmental deterioration and shape a sustainable development path, the researchers and policymakers have focused on developing several policy alternatives. In this regard, Endogenous Growth and Green Growth Theories have opened a new phase of discussion in sustainable development literature. Within this context, technological innovations and human capital accumulation are among the policy alternatives. Therefore, the present study has primarily focused on uncovering the role of human capital accumulation and technological innovation in emerging countries. The empirical outcomes indicate that economic development and human capital accumulation reduce LCF, while the utilization of renewable energy enhances it. In addition, there is no systematic impact of technological innovation on LCF.

Considering the empirical outcomes, economic development has a reducing impact on LCF. Therefore, there is a need to harmonize economic development and environmental development in emerging economies to balance the negative impact of economic development on environmental sustainability. In this regard, powering economic activities by renewable energy sources instead of fossil resources could be more effective because renewable energy helps enhance environmental quality in the sample countries. Considering such empirical outcomes, sample countries should prioritize increasing the share of renewables in their total energy mix. In this regard, increasing governmental budgets and promoting private investments through tax exemptions and subsidies for related investments to enhance renewable energy deployments could be rational policies. Additionally, policymakers in sample countries may consider intervening in the use of fossil resources by employing more effective instruments. In this regard, either establishing environmental taxes (i.e., transport taxes, energy taxes, emission taxes, resource taxes, etc.) or increasing the existing taxes on non-ecofriendly practices could be another policy option. Last, increasing green investments (i.e., research and development expenditures for energy technologies) could be an effective policy option to increase the effectiveness of green technologies. Thus, supporting economic activities by powering economies through renewables can help achieve SDG-7 and -13.

Empirical outcomes indicate that human capital has a negative impact on LCF. Therefore, policymakers in emerging economies should not rely on human capital accumulation to prevent environmental deterioration. In this regard, policymakers in emerging countries should focus on uncovering the possible transmission mechanism between human capital accumulation and environmental quality to prevent possible negative interactions and establish harmony among them. To this end, policymakers should focus on the possible impact channels of human capital-related foreign direct investment characteristics that flow to emerging economies. Second, policymakers may focus on uncovering the possible threshold dependence between economic development and human capital accumulation. Thus, policymakers can determine whether the impact of human capital level depends on the level of development, and they can develop efficient human capital accumulation policies to support environmental efforts.

The empirical evidence, using several environmental proxies, suggests that technological advances may not have a systematic impact on the sustainable development performance of the sample countries. Such a phenomenon does not imply a need for a being to be oblivious to the technological advances in the sample countries. Policymakers should focus on establishing a monitoring mechanism to have better insights into the technological advances and sustainable development in the sample countries. Thus, the complex nature of technological innovation and sustainable development may be better understood, and the direct and indirect impacts of technological advances could be uncovered. Furthermore, technological proxies cover all aspects of possible scientific advances in sample countries. Therefore, policymakers should consider establishing innovative monitoring mechanisms to assess the impact of green innovations and technologies. Thus, better technology policies could be established in the sample countries.

The present study has several limitations. First, due to data constraints on human capital and technological innovation, the data utilized in this research are limited to the period from 1996 to 2019. Therefore, future studies may extend the study period as long as the number of data observations increases. Thus, more comprehensive inferences can be made. Second, due to the severe missing data problems, research and development expenditures data—a significant proxy for technological innovation—could not be included in the analysis. Future studies should consider extending the analysis by including related proxies when the required observation level is ensured. Third, present studies are limited to ten of the N-11 economies. Future studies may extend the analysis by including different income groups to understand whether income level could effectively affect human capital-environment interactions. Fourth, this study utilizes a linear-logarithmic model to estimate the impact of economic development on environmental sustainability. Future studies can adopt a quadratic or cubic framework to gain insights into the long-term interactions between economic development and environmental performance. Thus, state-of-the-art can be developed.