Technological Innovation, Trade Openness, Natural Resources, and Environmental Sustainability in Egypt and Turkey: Evidence from Load Capacity Factor and Inverted Load Capacity Factor with Fourier Functions

Abstract

The environmental degradation in the Middle East and North Africa (MENA) region leads to significant challenges regarding economic sustainability and the attainment of sustainable development goals (SDGs). The extensive use of fossil fuels in the region, as well as rapid urbanization and economic growth, has led to significant carbon emissions, together with unprecedented ecological footprints compromising environmental sustainability. The study aims to elucidate the influence exerted by technological innovation, trade openness, and natural resources on environmental sustainability in Turkey and Egypt for the period 1990–2022. In assessing the empirical relations, the study employed the Fourier function incorporate estimation techniques, that is, Fourier ADF for unit root test, Fourier ARDL, and Fourier NARDL for long-run and short-run elasticities of technological innovation (TI), trade openness (TO,) and natural resources rent (NRR) on load capacity factor (LCF) and inverted LCF (ILCF); finally, the directional causality evaluate through Fourier TY causality test. The results revealed that both Turkey and Egypt have severe environmental problems due to their high carbon emissions and ecological footprints. Technological change and international trade separately negatively affect environmental sustainability; however, these negative impacts have mixed character. On the one hand, technology can improve efficiency and reduce ecological footprints by obviating the use of high-impact processes or allowing cleaner production systems. In the same vein, trade openness helps transfer green technologies more quickly, but it can also lead to unsustainable resource extraction and pollution. The findings of the paper propose that in order to move forward, Turkey and Egypt need strategic policy shifts to ensure environmental sustainability, including transitioning towards renewable energy from fossil fuels while bolstering their capacity for energy efficiency. Policymakers must balance economic development with environmental conservation to reduce the harmful effects of climate degradation and help safeguard continued economic survival in the face of increasing climatic instability. This research helps to inform policy and investment decisions about how the SDGs can be achieved and how they are relevant for sustainable development in the MENA region.

1. Introduction

Environmental deterioration across the Middle East and North Africa, fueled by carbon dioxide emissions and ecological footprints, presents significant obstacles to financial sustainability and achieving Sustainable Development Goals [1,2]. The area’s reliance on fossil fuels, combined with swift urbanization and economic expansion, has led to large carbon outputs, exacerbating climate change and environmental decay. The MENA region stands alone as one of the most water-stressed locations globally, and the changing climate is intensifying water scarcity, influencing agriculture and food security, which are crucial for economic stability and livelihoods. The region’s substantial carbon emissions compound this ecological strain. The MENA nations hold a significant portion of the earth’s oil and natural gas reserves, driving economic growth but also contributing to high CO₂ emissions. Research indicates that the region’s CO₂ emissions per person are among the highest internationally, second only to North America, highlighting the notable environmental effect of its energy consumption patterns [3,4].

Financial growth in the MENA region has been closely connected to energy usage, primarily from non-renewable sources, which created a challenging dynamic where economic progress regularly leads to increased emissions, thus exacerbating environmental degradation [5]. The environmental Kuznets curve hypothesis, which suggests environmental decay initially increases with economic growth but eventually decreases as economies mature, has been explored in the area. However, the expected decline in emissions with economic maturity has not been fully realized, signifying persistent difficulties in decoupling economic growth from environmental harm. The ecological footprint in the MENA region is driven by factors such as rapid urban expansion, accelerating economic progress, and growing energy consumption. These driving forces contribute significantly to environmental degradation, hampering the region’s capacity to achieve its SDGs. For example, worsening air pollution and water scarcity hinder advancement towards clean air and water targets. At the same time, heavy reliance on fossil fuels poses challenges for the transition to affordable, emissions-free energy (SDG 7). The impact of environmental degradation on economic sustainability is profound. It influences critical sectors such as agriculture, which is vital for food security and employment. Climate change-induced water scarcity and extreme weather events endanger agricultural productivity, thereby affecting food security and economic stability. What is more, environmental degradation can lead to increased healthcare costs and reduced labor productivity, further straining economic resources [6].

This study is motivated by the need to deepen our understanding of how technological innovation, trade openness, and natural resources impact environmental sustainability in Turkey and Egypt. Specifically, the study focuses on comparing the load capacity factor (LCF) and inverted load capacity factor (ILCF) in these countries. The LCF measures the extent to which the environment can support economic activity, while the ILCF reflects the environmental strain caused by exceeding this capacity. By examining these metrics, this research aims to provide valuable insights into the sustainability of current economic practices in Turkey and Egypt and to offer policy recommendations that balance economic growth with environmental preservation. The following queries are to be addressed throughout the study in dealing with the research questions. RQ1: How do technological innovation, trade openness, and natural resources influence the load capacity factor (LCF) and inverted load capacity factor (ILCF) in Turkey and Egypt? RQ2: What are the key differences and similarities between Turkey and Egypt in terms of the impact of these factors on environmental sustainability? RQ3: How can Turkey and Egypt enhance their environmental sustainability through strategic policy adjustments, considering the comparative assessment of their LCF and ILCF?

The significant impact of environmental degradation caused by CO₂ emissions and ecological footprints in Turkey and Egypt on economic sustainability and the achievement of Sustainable Development Goals (SDGs) cannot be overlooked. Both countries are grappling with the pressing issue of high carbon emissions, which have detrimental effects on climate change and environmental degradation. These challenges have significant implications for their economic stability and hinder their progress towards achieving the Sustainable Development Goals (SDGs).

Turkey’s ecological footprint has been significantly impacted by rapid economic growth, urbanization, and increased energy consumption [7]. The country’s heavy dependence on fossil fuels has resulted in a significant rise in CO₂ emissions, which has had a detrimental impact on the environment [8]. The study [9,10] advocate the importance of promoting renewable energy and reducing fossil fuel dependency is underscored by the connection between financial stability and environmental outcomes in Turkey. Moreover, [11,12] postulated that Turkey’s dedication to enhancing energy production and shifting towards renewable energy sources is vital for minimizing environmental effects and attaining economic sustainability. The significant impact of carbon emissions on Turkey’s ecological footprint cannot be overlooked. The country’s economic activities, especially in the transportation and industrial sectors, result in significant emissions [13,14,15]. In order to tackle this issue, Turkey has placed a strong emphasis on renewable energy and has put in place various policies, including carbon taxation and incentives for low-emission technologies. These measures are focused on reducing the ecological footprint and promoting environmental sustainability, which is crucial for achieving SDGs.

The ecological footprint, a comprehensive measure of human impact on the environment, is influenced by various determinants encompassing resource consumption, waste generation, and environmental degradation. Key determinants include FDI inflows, Urbanization, Institutional Quality, energy consumption patterns, particularly reliance on fossil fuels versus renewable energy sources, which significantly impact carbon emissions and climate change [16,17,18,19,20]. Land use practices, such as deforestation for agriculture and urbanization, affect biodiversity and carbon sequestration capabilities, amplifying ecological footprints [21,22]. Water use intensity and management practices also play a paramount role, with over-extraction and pollution of freshwater resources contributing to ecological stress [23]. Industrial processes and manufacturing activities drive resource extraction and waste production, affecting ecological footprints through emissions, pollution, and resource depletion [24].

Why have technological innovation, trade openness, and natural resources been selected as focal points for this study in Turkey? Comprehending the associations between technological innovation, trade openness, natural resources, and ecological footprint is crucial for addressing global environmental and economic challenges. Technological innovation shapes industries and economies worldwide, influencing resource efficiency, environmental impact, and sustainability practices. Studying how technological advancements, such as renewable energy technologies or eco-efficient production processes, can mitigate environmental impacts and reduce ecological footprints is essential for guiding policies and investments towards sustainable development Kabeyi and Olanrewaju [25]. On the other hand, trade openness enables the global exchange of goods and services, impacting resource consumption, environmental regulations, and pollution levels across countries [26]. Researching how trade policies and agreements influence environmental standards and resource management practices can provide insights into optimizing international trade for environmental sustainability and equitable resource distribution. Natural resources, comprising water, land, minerals, and forests, are fundamental to economic activities and human well-being but are increasingly under stress due to unsustainable extraction, deforestation, and pollution [27]. Studying the interactions between natural resource use, economic growth, and ecological impacts helps identify pathways toward resource-efficient economies and resilient ecosystems [28]. Additionally, the ecological footprint—a comprehensive extent of human impact on the environment—integrates these complex relationships into a unified framework, highlighting the environmental consequences of human activities and guiding strategies for sustainable development [29]. By exploring these interconnections, researchers can provide evidence-based insights and policy recommendations to mitigate environmental degradation, promote resource efficiency, and enhance global environmental governance. Understanding how technological innovation, trade openness, natural resource management, and ecological footprint interact is essential for achieving Sustainable Development Goals (SDGs) and advancing toward a resilient and sustainable future for current and future generations [30]. Research in this area informs policymakers and businesses. It empowers communities to adopt practices that balance economic growth with environmental sustainability, securing a harmonious association between society and the earth’s limited resources.

The structure of the article is as follows. Apart from Section 1, the literature survey displayed in Section 2, data and methodology are depicted in Section 3, interpretation and discussion are available in Section 4, and finally, conclusion and policy suggestions are reported in Section 5, respectively.

2. Literature Survey

2.1. Technological Innovation Nexus Environmental Sustainability

The impact of technological innovation on the environment is widely acknowledged as an essential factor. This existing literature review analyzes the connection between technological innovation and the ecological footprint, focusing on how technological advancements impact environmental sustainability.

The term “technological innovation” pertains to creating and utilizing novel technologies, products, and procedures to enhance effectiveness and output. On the other hand, “ecological footprint” is a metric that quantifies the impact of human demand on the Earth’s ecosystems, conveying the number of natural resources required to support a particular lifestyle [26]. Studies by Nižetić, et al. [31], Vujanović, et al. [32], and Moura, et al. [33] revealed that technological progress in energy efficiency helps to minimize the environmental impact by reducing the amount of energy required to achieve the same level of productivity. Various innovations, including energy-efficient appliances, smart grids, and renewable energy technologies like solar and wind power, are crucial in reducing carbon emissions and conserving resources. Additionally, advancements in production technologies, such as green manufacturing and industrial ecology, aim to minimize waste and decrease resource consumption. By implementing cleaner production techniques and adopting the circular economy model, these innovations reduce the ecological impact by encouraging resource recycling and reuse [31,34]. One study by Sharma, et al. [35] found that advances in information and communication technologies (ICT) enhance environmental monitoring and management. Technologies such as remote sensing, geographic information systems (GIS), and data analytics enable better tracking of ecological indicators and facilitate informed decision-making for sustainable resource management.

The study of Kabeyi and Olanrewaju [25] comprised recent primary and secondary data from peer-reviewed research findings, expert interviews, and credible reports. The findings highlighted the necessity of integrating renewable energy sources, improving nonrenewable energy efficiency, and adopting advanced technologies and policies to ensure a sustainable, resilient, and cost-effective global electricity system. Expanding clean energy technologies, such as advanced solar panels, wind turbines, and bioenergy, is crucial in decreasing environmental impact. These innovations facilitate a transition to more sustainable energy sources, reducing greenhouse gas emissions and decreasing reliance on fossil fuels. For instance, in China the advancement and implementation of newer solar photovoltaic (PV) technologies have greatly improved the effectiveness and cost-effectiveness of solar power, making it a practical choice for reducing the environmental impact of energy usage [36]. One study employing data from 2015 to 2020 showed that advanced technologies in agriculture, specifically precision farming, automated machinery, and genetically modified crops, have dramatically improved resource efficiency. These innovations have reduced water usage, decreased application of pesticides and fertilizers, and increased crop yields. Precision farming technologies utilizing satellite data, drones, and sensors have significantly optimized planting, watering, and harvesting processes. As a result, these technologies have contributed to lower resource consumption and reduced environmental impact [37].

However, adverse effects of technological innovation on the environment have been proven, too. Certain technological advancements, particularly in industries that focus on high-tech products, can adversely affect the environment due to their heavy reliance on resources. For example, the manufacturing and disposal of electronic devices require a significant amount of resources and result in the generation of electronic waste, also known as e-waste, which poses environmental and health hazards [38]. The rebound effect refers to a situation where enhancements in energy efficiency result in a rise in the usage of energy services, thereby negating the positive environmental impacts. For example, more fuel-efficient vehicles may stimulate people to drive more, ultimately increasing overall fuel consumption and emissions [39]. The rapid pace of technological advancements frequently results in shorter lifecycles for products, leading to a rise in electronic waste. This waste contains dangerous substances like lead, mercury, and cadmium, which can seep into the environment, causing contamination of soil and water and posing health risks [40]. Consequently, the increasing prevalence of consumer electronics has contributed to a significant surge in e-waste, with many devices being disposed of in landfills or inadequately recycled, thereby creating environmental and health hazards [41].

Several researchers have conducted empirical studies. For instance, Usman and Hammar [42] conducted a study on the impact of technological innovation on the ecological footprint in the Asia Pacific Economic Cooperation, employing data from 1990 to 2017. The results indicate that technological innovation significantly reduces the ecological footprint, particularly in countries with strong environmental regulations. Zhang, et al. [43] conducted a study in China to explore the relationship between green technology innovation and the ecological footprint in China. The study finds that green technology innovation significantly reduces the ecological footprint, especially in regions with higher environmental awareness and government support.

2.2. Trade Openness Nexus Environmental Sustainability

Trade openness promotes the transfer of environmentally friendly technologies and practices from developed to developing countries, resulting in improved energy efficiency and reduced pollution and resource consumption. This is supported by research by Murshed [44] and Dauda, et al. [45]. Moreover, open trade can lead to economies of scale, where larger-scale production reduces per-unit resource use and emissions. Firms involved in international trade are more efficient and can adopt greener technologies. Wen, et al. [46] have found evidence supporting this. Also, participation in global markets can pressure countries to adopt higher environmental standards. Export-oriented firms may be required to meet stringent environmental regulations imposed by importing countries, which can lead to adopting greener practices and technologies [47]. Trade openness can drive firms to innovate and adopt more efficient production methods to remain competitive in the global market, as observed in countries like South Korea and Taiwan, where open economies have improved industrial processes and reduced environmental impact [48].

Notwithstanding, trade openness has a negative ecological impact, too. The Pollution Haven Hypothesis suggests that trade openness can cause polluting industries to relocate to countries with less strict environmental regulations, which can increase environmental impact in developing countries [49]. Increased trade can also contribute to resource depletion, particularly in countries rich in natural resources. Exporting raw materials and natural resources can lead to overexploitation and environmental degradation [50,51]. Trade openness can also lead to carbon leakage, where increases in another country offset emission reductions in one country due to shifting production activities. This undermines global efforts to reduce the overall ecological footprint [52]. Trade openness often leads to increased transportation of goods across long distances, resulting in higher carbon emissions and environmental degradation. The globalization of supply chains has contributed to significant carbon emissions from shipping and aviation, as countries import and export goods extensively [53]. Trade openness can drive countries to exploit their natural resources intensively to meet international demand, leading to environmental degradation and depletion of natural resources. For instance, in research by Peeters [54] in Brazil, increased trade openness has resulted in the extensive extraction of natural resources such as timber and minerals, causing deforestation, loss of biodiversity, and a significant increase in the ecological footprint.

A study conducted in Ghana by Amegavi, et al. [55] found that the impact of trade openness on the ecological footprint is complex and can vary depending on various factors. These factors include the level of economic development, environmental regulations, and the types of goods being traded. The overall effect of trade openness on the ecological footprint is context-dependent. Another study by Carrasco and Tovar-García [56] discovered that the environmental impact of trade openness also varies across different sectors. For example, trade in high-tech and manufactured goods can lead to technology transfer and efficiency gains, which may have a positive environmental impact. The findings suggest that trade openness initially increases the environmental footprint, but the ecological footprint decreases as the country adopts greener technologies [57]. Liu, et al. [58] researched in Pakistan from 1980 to 2014. The study reveals that trade openness generally reduces the ecological footprint in developed countries but increases it in developing countries. The importance of strong environmental policies in mitigating the negative impacts of trade openness is emphasized.

2.3. Natural Resources Nexus Environmental Sustainability

This literature review examines the relationship between natural resources and the ecological footprint, which is crucial for understanding the sustainability of resource use and its environmental impacts. It explores how the exploitation and management of natural resources directly and indirectly affect the ecological footprint. Natural resources are discussed, referring to natural materials and components that can be used for economic production or consumption. Examples include minerals, forests, water, and fossil fuels. The ecological footprint measures human demand on Earth’s ecosystems, quantifying the natural resources needed to sustain human activities [27]. The extraction of natural resources, such as mining, logging, and drilling, directly impacts the environment. A study in Ethiopia showed that activities destroy habitat, soil erosion, water contamination, and biodiversity loss. As a result, the ecological footprint is significantly increased. Using fossil fuels and other non-renewable resources significantly contributes to pollution and emissions. This leads to the release of greenhouse gases, which contribute to climate change. Consequently, the ecological footprint is further increased [59]. Smith, et al. [60] analyzed that land use change, particularly the conversion of forests and natural landscapes for agricultural or industrial purposes, has negative consequences for natural resources. This depletion reduces the Earth’s capacity to absorb CO₂. Studies by Panel, et al. [61] and Gong and Aslam [62] also examine the indirect impacts of natural resource use. The use of natural resources has indirect impacts on various aspects. Economic growth driven by resource exploitation can increase consumption and waste generation, expanding the ecological footprint. Technological advancements can improve resource efficiency, but if not managed sustainably, they can also lead to increased resource consumption and environmental degradation.

Xue, et al. [63] conducted a study in South Asia and found that intensive natural resource extraction significantly negatively impacts environmental sustainability. This is due to deforestation and soil degradation, which increase the ecological footprint. Korra [64] analyzed global material flows and concluded that the increasing extraction and consumption of natural resources significantly contribute to the ecological footprint. They emphasize the importance of transitioning to a circular economy to reduce environmental impacts. Xu, et al. [65] focused on technological advancements and resource use in China. Their research revealed that while technology can improve resource efficiency, it can also increase resource consumption, thereby expanding the ecological footprint. Two case studies, conducted by Panel, Consumption and Branch [61] and a study on regional variations, conducted by Hossain, et al. [66], have been carried out to examine the impact of natural resources on ecological footprint. The impact of natural resource use on the ecological footprint differs between developed and developing countries.

3. Data and Methodology of the Study

3.1. Theoretical Framework and Model Specification

Technological innovation, trade openness, natural resources, and environmental sustainability are interconnected variables that have been examined in numerous studies to understand their impact on the load capacity factor (LCF) and inverted load capacity factor (ILCF), which serve as indicators of environmental quality. The load capacity factor represents the ability of the environment to sustain economic activities without degrading its quality. In contrast, the inverted load capacity factor reflects the environmental degradation resulting from economic activities exceeding the sustainable limit.

Technological innovation is a critical driver in enhancing environmental sustainability. As noted in previous research, technological advancements can lead to more efficient production processes, reduce waste, and lower emissions. This is known as the technique effect. When nations adopt energy-saving technologies and renewable energy sources, the load capacity factor is expected to improve, indicating better environmental quality. For instance, the introduction of cleaner technologies reduces pollution and resource consumption, positively affecting the LCF, i.e., ${\beta }_1={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ > 0. In the case of inverted LCF, Technological innovation is often seen as a double-edged sword. On the one hand, it can lead to more efficient resource use, reduce waste, and lower environmental degradation through the adoption of cleaner technologies. This is known as the technique effect, where advancements in technology improve production processes, reducing the negative impact on the environment. However, if technological innovation primarily focuses on increasing production and economic output without incorporating environmental considerations, it can exacerbate resource depletion and environmental degradation. In this context, technological innovation is anticipated to have a mixed impact on the ILCF, with its direction depending on whether the innovations are environmentally friendly.

Trade openness is another variable that significantly impacts environmental sustainability. The scale effect suggests that increased trade openness leads to economic growth, which in turn increases pollution and resource depletion, thereby reducing the LCF. The scale effect suggests that increased trade leads to higher economic activity, which can result in greater environmental degradation if not managed properly. However, trade openness can also facilitate the transfer of environmentally friendly technologies and best practices between nations, contributing to improved environmental quality through the technique effect. Additionally, trade can influence the composition of a country’s economy by shifting resources towards less polluting industries, which could positively impact the load capacity factor. As such, trade openness could either enhance or diminish environmental quality, depending on the dominant effects in play. Trade openness can also facilitate the transfer of environmentally friendly technologies and practices, thus enhancing the LCF through the effect of technique. Trade openness also has a complex relationship with environmental quality. Consequently, the net impact of trade openness on the LCF depends on the balance between these effects: ${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ > 0; ${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ < 0.

Trade openness is another critical factor influencing ILCF. The relationship between trade and environmental quality is complex and multifaceted. Trade openness can lead to economic growth by expanding markets, increasing exports, and improving access to foreign investment and technologies. This, in turn, can result in higher resource extraction and increased pollution, reflecting the scale effect. Conversely, trade openness can also promote the import of cleaner technologies and environmental standards from more developed trading partners, leading to improvements in environmental quality through the composition and technique effects. Therefore, the impact of trade openness on ILCF is expected to be ambiguous, as it can either improve or degrade environmental quality depending on the balance between these opposing forces, i.e.,

$${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}>0; {\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}<0$$

Natural resources play a dual role in environmental sustainability. On the one hand, the extraction and use of natural resources contribute to economic growth, but on the other hand, they can lead to environmental degradation if not managed sustainably. The composition effect suggests that economies heavily reliant on natural resource extraction tend to experience lower LCF due to the environmental pressure exerted by these activities. Natural resources rent, the revenue generated from the extraction of natural resources, typically has a negative impact on environmental quality. Higher natural resources rent often reflects increased exploitation of natural resources, leading to greater environmental degradation. This increased pressure on the environment can result in a lower load capacity factor, indicating a decline in environmental quality. Moreover, the reliance on natural resources for economic growth can discourage the adoption of cleaner technologies and sustainable practices, further exacerbating environmental harm. Thus, natural resources are expected to have a negative impact on the LCF, i.e., ${\beta }_3={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{NRR}}}$ < 0.

The motivation of the study is to assess the role of technological innovation (TI), trade openness (TO), natural resources rent (NRR), governmental effectiveness (GG), economic growth (Y) and squared Y on load capacity factor and inverted load capacity factor. The generalized equation is as follows.

$$LCF\int TI, TO, NNR, GG, Y, Y2$$

(1)

$$ILCF\int TI, TO, NNR, GG, Y, Y2$$

(2)

After the natural log transformation, the about Equations (1) and (2) are rewritten in the following regression formation.

$$lnLC{F}_t={\alpha }_0+{\beta }_{1}T{I}_t+{\beta }_{1}T{O}_t+{\beta }_{1}NR{R}_t+{\beta }_{1}G{G}_i+{\beta }_1{Y}_i+{\beta }_{1}Y{2}_i+{\epsilon }_i$$

(3)

$$lnILC{F}_t={\alpha }_0+{\beta }_{1}T{I}_i+{\beta }_{1}T{O}_i+{\beta }_{1}NR{R}_i+{\beta }_{1}G{G}_i+{\beta }_1{Y}_i+{\beta }_{1}Y{2}_i+{\epsilon }_i$$

(4)

where ILCF stands for inverted load capacity factor, LCF denotes load capacity factor, TI, TO, NRR, GG, and Y explain technological innovation, trade openness, natural resources rent, good governance, and GDP, respectively. The key measures of research variables are exhibited in

Table 1. Variables, measures and expected sign.

Notation	Proxy Measures	Data Sources	Expected Effects
			LCF	ILCF
ILCF	Ecological footprint/biocapacity (gha per person)	Global Footprint Network
LCF	Biocapacity divided/ ecological footprint	Global Footprint Network
TI	Total number of patents	World development indicator (WDI)	${\beta }_1={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{NRR}}}$ > 0	${\beta }_1={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ > 0; ${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ < 0
TO	Trade (% of GDP)	World development indicator (WDI)	${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ > 0; ${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ < 0	${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ > 0; ${\beta }_2={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{TO}}}$ < 0
NRR	Natural resources rent	World development indicator (WDI)	${\beta }_3={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{NRR}}}$ < 0	${\beta }_3={\displaystyle \frac{\partial \mathrm{LCF}}{\partial \mathrm{NRR}}}$ < 0

with data sources along with expected signs.

The econometric framework of the study is displayed in the following Figure 1.

Estimation Strategies

Firstly, The Fourier ADF unit root test is an advanced econometric technique used to determine the stationarity properties of a time series while accounting for structural breaks and non-linear trends. Traditional unit root tests, such as the Augmented Dickey-Fuller (ADF) test, often fail to detect stationarity when the data exhibit structural changes or non-linear patterns. The Fourier ADF test addresses this limitation by incorporating Fourier functions into the unit root testing procedure, allowing for more flexible modeling of structural breaks and non-linear trends.

Secondly, Following the bound testing approached introduced by Pesaran, et al. [67] and further improved by Pesaran, et al. [68], the following equation has developed in investigating the possible long-run association with a stated condition of dependent variable should be integrated at I(1).

$$\begin{array}{cc}\hfill \Delta lnLCF={\gamma }_0& +{\gamma }_{3}lnLC{F}_{t-1}+{\gamma }_{4}ln{Y}_{t-1}+{\gamma }_{5}ln{Y^2}_{t-1}+{\gamma }_{8}lnT{I}_{t-1}+{\gamma }_{9}lnT{O}_{t-1}+{\gamma }_{8}lnNR{R}_{t-1}+{\gamma }_{9}lnG{G}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_3{\displaystyle \sum _{I=0}^m}\Delta lnLC{F}_{t-1}+{\gamma }_4{\displaystyle \sum _{i=0}^n}{Y}_{t-1}+{\gamma }_5{\displaystyle \sum _{i=0}^p}ln{Y^2}_{t-1}+{\gamma }_6{\displaystyle \sum _{i=0}^q}lnT{I}_{t-1}+{\gamma }_8{\displaystyle \sum _{i=0}^q}lnT{O}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_8{\displaystyle \sum _{i=0}^q}lnNR{R}_{t-1}+{\gamma }_9{\displaystyle \sum _{i=0}^z}lnG{G}_{t-1}+v_t\hfill \end{array}$$

(5)

$$\begin{array}{cc}\hfill \Delta lnILCF={\gamma }_0& +{\gamma }_{3}lnILC{F}_{t-1}+{\gamma }_{4}ln{Y}_{t-1}+{\gamma }_{5}ln{Y^2}_{t-1}+{\gamma }_{8}lnT{I}_{t-1}+{\gamma }_{9}lnT{O}_{t-1}+{\gamma }_{8}lnNR{R}_{t-1}+{\gamma }_{9}lnG{G}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_3{\displaystyle \sum _{I=0}^m}\Delta lnILC{F}_{t-1}+{\gamma }_4{\displaystyle \sum _{i=0}^n}{Y}_{t-1}+{\gamma }_5{\displaystyle \sum _{i=0}^p}ln{Y^2}_{t-1}+{\gamma }_6{\displaystyle \sum _{i=0}^q}lnT{I}_{t-1}+{\gamma }_8{\displaystyle \sum _{i=0}^q}lnT{O}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_8{\displaystyle \sum _{i=0}^q}lnNR{R}_{t-1}+{\gamma }_9{\displaystyle \sum _{i=0}^z}lnG{G}_{t-1}+v_t\hfill \end{array}$$

(6)

By following the recent literature, the above traditional ARDL estimation techniques have been reconstructed with the Furrier function, which is familiar to [69], and the developed equation is as follows;

$$\begin{array}{cc}\hfill \Delta lnLCF={\gamma }_0& +{\phi }_{1}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\phi }_{2}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\gamma }_{1}lnLC{F}_{t-1}+{\gamma }_{2}ln{Y}_{t-1}+{\gamma }_{5}ln{Y^2}_{t-1}+{\gamma }_{3}lnT{I}_{t-1}+{\gamma }_{4}lnT{O}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_{5}lnNR{R}_{t-1}+{\gamma }_{6}lnG{G}_{t-1}+{\beta }_1{\displaystyle \sum _{I=0}^q}\Delta lnLC{F}_{t-1}+{\beta }_2{\displaystyle \sum _{i=0}^q}{Y}_{t-1}+{\beta }_3{\displaystyle \sum _{i=0}^q}ln{Y^2}_{t-1}\hfill \\ & \hspace{1em} +{\beta }_4{\displaystyle \sum _{i=0}^q}lnT{I}_{t-1}+{\beta }_5{\displaystyle \sum _{i=0}^q}lnT{O}_{t-1}+{\beta }_6{\displaystyle \sum _{i=0}^q}lnNR{R}_{t-1}+{\beta }_7{\displaystyle \sum _{i=0}^z}lnG{G}_{t-1}+{ϵ}_t\hfill \end{array}$$

(7)

$$\begin{array}{cc}\hfill \Delta lnILCF={\gamma }_0& +{\phi }_{1}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\phi }_{2}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\gamma }_{1}lnILC{F}_{t-1}+{\gamma }_{2}ln{Y}_{t-1}+{\gamma }_{5}ln{Y^2}_{t-1}+{\gamma }_{3}lnT{I}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_{4}lnT{O}_{t-1}+{\gamma }_{5}lnNR{R}_{t-1}+{\gamma }_{6}lnG{G}_{t-1}+{\beta }_1{\displaystyle \sum _{I=0}^q}\Delta lnILC{F}_{t-1}+{\beta }_2{\displaystyle \sum _{i=0}^q}{Y}_{t-1}+{\beta }_3{\displaystyle \sum _{i=0}^q}ln{Y^2}_{t-1}\hfill \\ & \hspace{1em} +{\beta }_4{\displaystyle \sum _{i=0}^q}lnT{I}_{t-1}+{\beta }_5{\displaystyle \sum _{i=0}^q}lnT{O}_{t-1}+{\beta }_6{\displaystyle \sum _{i=0}^q}lnNR{R}_{t-1}+{\beta }_7{\displaystyle \sum _{i=0}^z}lnG{G}_{t-1}+{ϵ}_t\hfill \end{array}$$

(8)

where q stands for the optimal lag length in the equation, The following three tests are to be executed to discover the long-run cointegration.

$$F_{overall}: H_0={\gamma }_1={\gamma }_2={\gamma }_3={\gamma }_4={\gamma }_5={\gamma }_6=0$$

$$t_{dependent}: H_0={\gamma }_1=0$$

$$F_{independnet }: H_0={\gamma }_2={\gamma }_3={\gamma }_4={\gamma }_5={\gamma }_6=0$$

The rejection of the null or “cointegration” revealed the long-run association in the empirical equation.

Thirdly, the extended asymmetric Equations (9) and (10) with the furrier function with the inclusion of sin and cos as given below.

$$\begin{array}{cc}\Delta LCF={\gamma }_0& +{\gamma }_{1}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\gamma }_{2}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\gamma }_{3}LC{F}_{t-1}+{\gamma }_4{Y}_{t-1}+{\gamma }_5{Y^2}_{t-1}+\left\{{\gamma }_{6}T{I^{+}}_{t-1},{\gamma }_{6}T{I^{-}}_{t-1}\right\}\hfill \\ & \hspace{1em} +\left\{{\gamma }_{7}T{O^{+}}_{t-1},{\gamma }_{7}T{O^{-}}_{t-1}\right\}+{\gamma }_{8}NR{R}_{t-1}+{\gamma }_{9}G{G}_{t-1}+{\gamma }_3{\displaystyle \sum _{I=0}^m}\Delta LC{F}_{t-1}+{\gamma }_4{\displaystyle \sum _{i=0}^n}{Y}_{t-1}+{\gamma }_5{\displaystyle \sum _{i=0}^p}{Y^2}_{t-1}\hfill \\ & \hspace{1em} +\left\{{\gamma }_6{\displaystyle \sum _{i=0}^r}T{I^{+}}_{t-1},{\gamma }_6{\displaystyle \sum _{i=0}^w}T{I^{-}}_{t-1}\right\}+\left\{{\gamma }_7{\displaystyle \sum _{i=0}^u}T{O^{+}}_{t-1},{\gamma }_7{\displaystyle \sum _{i=0}^v}T{O^{-}}_{t-1}\right\}+{\gamma }_8{\displaystyle \sum _{i=0}^q}NR{R}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_9{\displaystyle \sum _{i=0}^z}G{G}_{t-1}\hfill \end{array}$$

(9)

$$\begin{array}{cc}\hfill \Delta ILCF={\gamma }_0& +{\gamma }_{1}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\gamma }_{2}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\gamma }_{3}LC{F}_{t-1}+{\gamma }_4{Y}_{t-1}+{\gamma }_5{Y^2}_{t-1}+\left\{{\gamma }_{6}T{I^{+}}_{t-1},{\gamma }_{6}T{I^{-}}_{t-1}\right\}\hfill \\ & \hspace{1em} +\left\{{\gamma }_{7}T{O^{+}}_{t-1},{\gamma }_{7}T{O^{-}}_{t-1}\right\}+{\gamma }_{8}NR{R}_{t-1}+{\gamma }_{9}G{G}_{t-1}+{\gamma }_3{\displaystyle \sum }_{I=0}^m\Delta ILC{F}_{t-1}+{\gamma }_4{\displaystyle \sum _{i=0}^n}{Y}_{t-1}\hfill \\ & \hspace{1em} +{\gamma }_5{\displaystyle \sum _{i=0}^p}{Y^2}_{t-1}+\left\{{\gamma }_6{\displaystyle \sum _{i=0}^r}T{I^{+}}_{t-1},{\gamma }_6{\displaystyle \sum _{i=0}^w}T{I^{-}}_{t-1}\right\}+\left\{{\gamma }_7{\displaystyle \sum _{i=0}^u}T{O^{+}}_{t-1},{\gamma }_7{\displaystyle \sum _{i=0}^v}T{O^{-}}_{t-1}\right\}\hfill \\ & \hspace{1em} +{\gamma }_8{\displaystyle \sum _{i=0}^q}NR{R}_{t-1}+{\gamma }_9{\displaystyle \sum _{i=0}^z}G{G}_{t-1}\hfill \end{array}$$

(10)

Finally, the Fourier-Toda-Yamamoto (Fourier: TY) causality test is a sophisticated statistical method that tests whether their causal relationships exist in time series data, combining the use of Fourier transformations and the Toda—Yamatomoto approach [70]. The test is ideal for obtaining linear and nonlinear causal associations, largely ignored by traditional causality tests e.g., Granger’s causality the Fourier-TY test uses the frequency domain to provide a mechanism for dealing with complicate data structures and detecting causality across different frequencies. Hence, it is also more robust under structural breaks and non-stationary data that can be found in time series analysis. Traditional Granger causality Test results can help one to identify simple linear correlations among different time series but are not useful when analyzing issues like instantaneous causality or non-stationary situations [71]. the Fourier-TY test thus provides an advancing framework for causality analysis at large scales carrying greater heterogeneity across multiple length scales, leading to deeper insights into prevalent causal mechanisms [72]. The following equation to be executed in documenting the direction al linkage in the empirical nexus.

$$\begin{array}{cc}\hfill \left[\begin{array}{c}{\mathit{LnLCF}}_t\\ {\mathit{LnTI}}_t\\ {\mathit{LnTO}}_t\\ {\mathit{LnNRR}}_t\\ {\mathit{LnGG}}_t\\ {\mathit{LnY}}_t\end{array}\right]& =\left[\begin{array}{l}{\beta }_{10}\\ {\beta }_{20}\\ {\beta }_{30}\\ {\beta }_{40}\\ {\beta }_{50}\\ {\beta }_{60}\end{array}\right]+{\displaystyle \sum }\left[\begin{array}{c}{\beta }_{11,i}{\beta }_{12,i}{\beta }_{13,i}{\beta }_{14,i}{\beta }_{15,i}{\beta }_{16,i}\\ {\beta }_{21,i}{\beta }_{22,i}{\beta }_{23,i}{\beta }_{24,i}{\beta }_{25,i}{\beta }_{26,i}\\ {\beta }_{31,i}{\beta }_{32,i}{\beta }_{33,i}{\beta }_{34,i}{\beta }_{35,i}{\beta }_{36,i}\\ {\beta }_{41,i}{\beta }_{42,i}{\beta }_{43,i}{\beta }_{44,i}{\beta }_{45,i}{\beta }_{46,i}\\ {\beta }_{51,i}{\beta }_{52,i}{\beta }_{53,i}{\beta }_{54,i}{\beta }_{55,i}{\beta }_{56,i}\\ {\beta }_{61,i}{\beta }_{62,i}{\beta }_{63,i}{\beta }_{64,i}{\beta }_{65,i}{\beta }_{66,i}\end{array}\right]\ast \left[\begin{array}{c}{\mathit{LnLCF}}_{t-1}\\ {\mathit{LnTI}}_{t-1}\\ {\mathit{LnTO}}_{t-1}\\ {\mathit{LnNRR}}_{t-1}\\ {\mathit{LnGG}}_{t-1}\\ {\mathit{LnY}}_{t-1}\end{array}\right]\hfill \\ \hspace{1em}& \hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j-1}^{d\mathit{max}}}-\left[\begin{array}{c}{\beta }_{11,(k+j)}{\beta }_{12,(k+j)}{\beta }_{13,(k+j)}{\beta }_{14,(k+j)}{\beta }_{15,(k+j)}{\beta }_{16,(k+j)}\\ {\beta }_{21,(k+j)}{\beta }_{22,(k+j)}{\beta }_{23,(k+j)}{\beta }_{24,(k+j)}{\beta }_{25,(k+j)}{\beta }_{26,(k+j)}\\ {\beta }_{31,(k+j)}{\beta }_{32,(k+j)}{\beta }_{33,(k+j)}{\beta }_{34,(k+j)}{\beta }_{35,(k+j)}{\beta }_{36,(k+j)}\\ {\beta }_{41,(k+j)}{\beta }_{42,(k+j)}{\beta }_{43,(k+j)}{\beta }_{44,(k+j)}{\beta }_{45,(k+j)}{\beta }_{46,(k+j)}\\ {\beta }_{51,(k+j)}{\beta }_{52,(k+j)}{\beta }_{53,(k+j)}{\beta }_{54,(k+j)}{\beta }_{55,(k+j)}{\beta }_{56,(k+j)}\\ {\beta }_{61,(k+j)}{\beta }_{62,(k+j)}{\beta }_{63,(k+j)}{\beta }_{64,(k+j)}{\beta }_{65,(k+j)}{\beta }_{66,(k+j)}\end{array}\right]+\left[\begin{array}{c}{\mathit{LnCF}}_{t-(k+j)}\\ {\mathit{LnTI}}_{t-(k+j)}\\ {\mathit{LnTO}}_{t-(k+j)}\\ {\mathit{LnNRR}}_{t-(k+j)}\\ {\mathit{LnGG}}_{t-(k+j)}\\ {\mathit{LnY}}_{t-(k+j)}\end{array}\right]\hfill \\ \hspace{1em}& \hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{k-1}^n}\left[\begin{array}{c}{\delta }_{10}\\ {\delta }_{20}\\ {\delta }_{30}\\ {\delta }_{40}\\ {\delta }_{50}\\ {\delta }_{60}\end{array}\right]{\gamma }_{1}sin\left({\displaystyle \frac{2\pi kt}{T}}\right)+{\displaystyle \sum _{k-1}^n}\left[\begin{array}{c}{\delta }_{10}\\ {\delta }_{20}\\ {\delta }_{30}\\ {\delta }_{40}\\ {\delta }_{50}\\ {\delta }_{60}\end{array}\right]{\gamma }_{1}cos \left({\displaystyle \frac{2\pi kt}{T}}\right)\hfill \end{array}$$

(11)

The estimation of Fourier-ARDL and Fourier-NARDL has many advantages over traditional estimation methods as they handle non-linear nature as well as possible structural breaks in the time-series data. Fourier methods are nonparametric, as they do not require the assumptions made in more traditional methods that typically assume linearity or ignore structural breaks and cyclical patterns in the data. Thus, it enables to model complex relationships more precisely—such as those arising over time or with periodic fluctuations. Moreover if the relationships between variables varies over levels of horizons, Fourier-ARDL and NARDL model could be more flexible in capturing both type of dynamics across all range of time frames. Fourier transformation can also pick up on slow moving structural shifts in trends, which is something that traditional methods such as simple ARDL do not account for and these results have a strong implication for when dealing with real world data. These methods are very useful in the environmental and economic data analysis, where they need to dampen the external shocks and cyclic changes that are very frequently present making a better accuracy on causality test as well as checking for long-term equilibrium. In doing so, they enhance the credibility of policy inferences based on econometric interventions.

4. Empirical Results and Discussion of the Findings

4.1. Unit Root Test

The results of the unit root test displayed in

Table 2. Results of Fourier—ADF and ADF test.

Variables	FADF I(0)	k(p)	FADF I(1)	k(p)	F-Test	ADF I(0)	ADF I(1)
For Turkey
LCF	−1.097	5(2)	−6.388 ***	5(1)	2.920	−1.5339	−7.2785 ***
ILCF	−0.946	1(2)	−4.922 ***	2(1)	3.424	−1.2649	−9.2104 ***
TI	−2.032 **	4(1)	−2.973 ***	5(0)	1.653	−1.2376	−5.3037 ***
TO	−2.517 ***	3(1)	−2.522 ***	5(0)	2.432	−2.0931 ***	−8.0308 ***
NRR	−1.285	3(0)	−4.471 ***	5(1)	1.562	−2.3768 ***	−6.0593 ***
GG	−0.69	2(1)	−6.068 ***	3(2)	3.428	−1.2166	−7.4696 ***
Y	−2.134 **	5(0)	−3.774 ***	5(1)	2.706	−0.4251	−7.7845 ***
For Egypt
LCF	−2.221 *	2(0)	−5.966 ***	5(0)	2.603	−2.4804 **	−5.3243 ***
ILCF	−0.797	1(0)	−5.434 ***	4(0)	1.829	−2.2085 **	−6.4685 ***
TI	−1.968	1(2)	−3.157 ***	1(2)	1.786	−0.4833	−6.3397 ***
TO	−1.495	4(1)	−4.753 ***	3(1)	2.471	−2.1118 **	−5.7071 ***
NRR	−1.48	2(1)	−3.514 ***	3(1)	3.061	−1.4449	−8.0154 ***
GG	−1.102	1(1)	−5.596 ***	3(2)	2.167	−0.7778	−7.3572 ***
Y	−1.483	5(0)	−5.479 ***	3(0)	3.117	−1.5571	−8.3646 ***

Note: The level of significance is indicated at 1%, 5%, and 10% by ***/**/*, respectively.

confirmed the variable’s order of integration after the first difference. However, the study revealed that TI, TO, and Y for Turkey and LCF, ILCF, and TI for Egypt were found stationary at their level of operation.

The unit root test following Perron and Vogelsang [73] is reported in

Table 3. Results of Perron and Vogelsang Test.

Level	Test Statistics	D-SB	Test Statistics	D-SB
For Turkey
LCF	−2.4587	2010	−7.1673	1998
ILCF	−2.8055	2008	−7.3786	2005
TI	−0.8685	1994	−7.9712	2012
TO	−0.3909	1998	−9.1907	2007
NRR	−0.8209	2013	−6.7853	1999
GG	−2.8549	2016	−6.7504	2004
Y	−0.6821	2003	−10.3272	2007
For Egypt
LCF	−0.7265	2011	−6.73	1996
ILCF	−0.7498	2016	−8.5026	2003
TI	−2.3701	2005	−9.4992	2002
TO	−1.1486	2019	−10.549	2008
NRR	−1.1342	2019	−6.4956	2017
GG	−1.5816	2015	−8.0852	2006
Y	−1.2769	2011	−6.1463	2009

. Study findings exhibit that all the research variables are statistically significant at a1% after the first difference operation, i.e., I(1), suggesting all the variables become stationary the first difference operation.

4.2. Furious—ARDL Estimation

Documenting the long-run linkage in the empirical equation, the study passes through the estimation of test statistics of F_overall; t_DV, and F_IDV with the null of no-cointegration.

Table 4. Results of Furious—ARDL cointegration test.

Cointegration Test	LCF	ILCF	LCF	ILCF
Foverall	10.404 ***	8.242 ***	13.638 ***	11.336 ***
tDV	−6.847 ***	−5.764 ***	−6.256 ***	−6.194 ***
FIDV	7.372 ***	7.156 ***	10.775 ***	6.883 ***
Diagnostics test
Breusch-Godfrey LM test	0.885	0.76	0.606	0.527
Breusch-Pagan-Godfrey test	0.511	0.89	0.838	0.883
ARCH Test	0.506	0.682	0.745	0.629
Ramsey RESET Test	0.788	0.562	0.647	0.54
Jarque-Bera test	0.656	0.529	0.843	0.836

Note: the superscripts of *** denote the level of significance at a 1% level.

displays the output of test statistics for both countries. It is apparent that all the test statistics are statistically significant, indicating the rejection of no-cointegration and alternatively established long-run association. Furthermore, the results of diagnostics tests for Turkey and Egypt unveiled the absence of autocorrelation, error terms are normally distributed and no problem with heteroskadacity. Additionally, the estimation results in stability established with the test fo Remsay RESET.

The results from the Fourier-ARDL model, see

Table 5. Results of Fourier—ARDL with Load capacity factor as dependent variable.

	For Turkey			For Egypt
Variables	Coefficient	St. Error	t-Stat	Coefficient	St. Error	t-Stat
Panel—A: Long-run coefficients
Y	−0.1727	0.0207	−8.3429	−0.2446	0.0067	−36.5074
Y2	0.2715	0.0351	7.735	0.1617	0.0747	2.1646
TI	0.2381	0.0718	3.3161	0.269	0.0458	5.8733
TO	−0.2596	0.0418	−6.2105	−0.2312	0.012	−19.2666
NRR	−0.2814	0.0357	−7.8823	−0.2952	0.0234	−12.6153
GG	0.2803	0.0152	18.4407	0.1954	0.0709	2.7559
Panel—B: Short-run coefficients
Y	−0.0717	0.032	−2.2406	−0.079	0.0801	−0.9862
Y2	0.0745	0.0777	0.9588	0.088	0.0403	2.1836
TI	0.0801	0.035	2.2885	0.0875	0.0688	1.2718
TO	−0.087	0.0243	−3.5802	−0.0993	0.0609	−1.6305
NRR	−0.0843	0.0287	−2.9372	−0.0792	0.0658	−1.2036
GG	0.0858	0.0612	1.4019	0.0983	0.0721	1.3633
Sin	0.0856	0.03	2.8533	0.1012	0.0697	1.4519
COS	0.0387	0.0276	1.4021	0.0427	0.0214	1.9953
C	−16.766	0.0111	−1510.45	−5.4545	0.0418	−130.4904
cointEq (−1)	−0.2695	0.0592	−4.5523	−0.2112	0.0576	−3.6666

examining the effects of various economic and environmental variables on the load capacity factor (LCF) for Turkey and Egypt reveal both long-run and short-run dynamics.

For Turkey, the long-run coefficients indicate significant relationships with several key variables. Economic progress (Y) has a negative impact on LCF with a coefficient of −0.1727, suggesting that increases in economic progress might reduce the load capacity factor. This is supported by literature indicating that rapid economic growth can strain energy infrastructure and efficiency [74,75,76]. However, the quadratic term (Y2) shows a positive effect (a coefficient of 0.2715), implying that beyond a certain point, economic growth might enhance LCF, aligning with the Environmental Kuznets Curve (EKC) hypothesis, where environmental degradation initially increases with economic growth but decreases after reaching a certain income level [77].

Technological innovation (TI) positively affects LCF (a coefficient of 0.2381), indicating that advancements in technology improve energy efficiency and capacity. This is consistent with studies showing that technological improvements lead to better energy utilization and reduced losses [78,79]. Trade openness (TO), however, has a negative effect (a coefficient of −0.2596), which might be due to increased energy demand and environmental degradation resulting from higher trade volumes [80]. The negative impact of (a coefficient of −0.2814) suggests that reliance on natural resources might hinder energy efficiency, corroborating the resource curse theory [81,82]. Conversely, green growth (GG) has a strong positive effect (0.2803), supporting the notion that sustainable practices enhance LCF [83,84,85].

In the short run, the impact of economic progress (Y) on LCF is still negative (−0.0717), though less pronounced than in the long run. Technological innovation (TI) continues to show a positive influence (0.0801), highlighting the immediate benefits of technological advancements in energy efficiency. Trade openness (TO) and natural resource rents (NRR) maintain their negative impacts, albeit with smaller coefficients, indicating persistent but lesser immediate adverse effects. Good governance (GG) remains positively correlated, though not significantly.

For Egypt, the long-run analysis similarly shows that economic progress (Y) negatively impacts LCF (−0.2446), with the quadratic term (Y2) positively influencing it (0.1617), reflecting the EKC hypothesis. Technological innovation (TI) has a stronger positive effect (0.269) compared to Turkey, suggesting that Egypt benefits more from technological advancements. Trade openness (TO) and natural resource rents (NRR) also negatively impact LCF, similar to Turkey, indicating that these factors are detrimental to energy efficiency in both contexts. However, the magnitude of these effects is somewhat smaller. Green growth (GG) positively affects LCF (0.1954), supporting sustainable practices in improving energy efficiency.

In the short run, the negative effect of economic progress (Y) on LCF in Egypt is not significant (−0.079). The quadratic term (Y2) and technological innovation (TI) show positive but not significant effects. Trade openness (TO) and natural resource rents (NRR) exhibit negative but non-significant impacts. Green growth (GG) maintains a positive but non-significant relationship, indicating that the immediate effects of these variables are less pronounced in Egypt.

These findings align with the broader literature on the EKC hypothesis and the benefits of technological innovation and green growth on energy efficiency. However, they also contrast with some studies that highlight the potential positive impacts of trade openness on energy efficiency through technology transfer and improved practices (Frankel & Rose, 2005 [86,87]). The negative effects of natural resource rents underscore the need for diversified economic strategies to avoid the pitfalls of the resource curse.

The results for Turkey and Egypt, as shown in

Table 6. Results of Fourier—ARDL with Inverted Load capacity factor.

Variables	Coefficient	St. Error	t-Stat	Coefficient	St. Error	t-Stat
Panel—A: Long-run coefficients
Y	0.2656	0.0443	5.9954	0.2491	0.0566	4.401
Y2	−0.3005	0.0391	−7.6854	−0.2875	0.0049	−58.6734
TI	−0.1708	0.0343	−4.9795	−0.2207	0.0321	−6.8753
TO	0.2525	0.0814	3.1019	0.1867	0.0452	4.1305
NRR	0.2827	0.022	12.85	0.2856	0.0611	4.6743
GG	−0.2649	0.0832	−3.1838	−0.1752	0.0092	−19.0434
Panel—B: Short-run coefficients
Y	0.0994	0.0755	1.3165563	0.0942	0.0061	15.442623
Y2	−0.101	0.069	−1.463768	−0.0996	0.0795	−1.25283
TI	−0.0943	0.04	−2.3575	−0.0976	0.0363	−2.688705
TO	0.0766	0.0481	1.5925156	0.079	0.0388	2.0360825
NRR	0.1022	0.0136	7.5147059	0.0885	0.0446	1.9843049
GG	−0.0981	0.0443	−2.214447	−0.0805	0.0742	−1.084906
Sin	0.0964	0.0107	9.0093	0.0811	0.0424	1.9127
COS	0.0367	0.0354	1.0367	0.0448	0.0348	1.2873
C	−28.0248	0.0447	−626.953	−20.6071	0.0116	−1776.474
cointEq (−1)	−0.2642	0.0617	−4.282	−0.2192	0.0588	−3.7278

the Fourier—ARDL model with Inverted Load Capacity Factor (LCF), reveal significant long- and short-run impacts of various economic and policy variables.

For Turkey, in the long run, economic progress (Y) exhibits a positive effect on the inverted LCF with a coefficient of 0.2656. This suggests that economic growth enhances the inverted LCF, which aligns with literature emphasizing the beneficial impacts of economic growth on energy efficiency and sustainability [88,89]. However, the squared term of economic progress (Y2) is negative (−0.3005), indicating a potential threshold effect where, beyond a certain point, further economic growth could negatively impact the inverted LCF, corroborating the Environmental Kuznets Curve (EKC) hypothesis [90].

Technological innovation (TI) negatively affects the inverted LCF with a coefficient of −0.1708. It suggests that while technological advancements initially may not directly enhance energy efficiency, they potentially disrupt existing energy frameworks before achieving long-term benefits [91]. Trade openness (TO) has a positive coefficient of 0.2525, indicating that increased trade activities promote energy efficiency and improve the inverted LCF, supported by studies highlighting the positive spillover effects of trade on technology transfer and energy efficiency [92]. Natural resource rent (NRR) shows a substantial positive impact (0.2827), implying that resource abundance contributes significantly to the inverted LCF. This finding is consistent with research indicating that resource wealth can finance improvements in energy infrastructure and efficiency [92]. Conversely, good governance (GG) has a negative coefficient (−0.2649), suggesting that inefficiencies and governance issues could undermine efforts to improve energy efficiency, as posited by studies emphasizing the role of institutional quality in energy policy outcomes [93].

In the short run, economic progress (Y) maintains a positive effect (0.0994), albeit with a lower magnitude, indicating immediate but limited benefits of economic growth on the inverted LCF. The squared term (Y2) remains negative, reflecting the short-run manifestation of the EKC effect. Technological innovation (TI) continues to negatively impact the inverted LCF (−0.0943), suggesting initial adjustments and disruptions in energy systems. Trade openness (TO) has a positive but statistically insignificant short-run effect (0.0766), indicating that the immediate benefits of trade on energy efficiency might be less pronounced.

Natural resource rent (NRR) exhibits a significant positive short-run effect (0.1022), highlighting the immediate benefits of resource wealth on energy efficiency. Good governance (GG) continues to impact the inverted LCF (−0.0981) negatively, reinforcing the need for institutional reforms to achieve better energy outcomes. The sine and cosine terms (Sin and COS) capture cyclical effects, with significant positive coefficients indicating seasonal variations in energy efficiency.

For Egypt, the long-run results show similar trends. Economic progress (Y) positively affects the inverted LCF (0.2491), while the squared term (Y2) is highly negative (−0.2875), supporting the EKC hypothesis. Technological innovation (TI) has a more substantial negative impact (−0.2207), suggesting a greater initial disruption in Egypt’s energy systems due to technological advancements. Trade openness (TO) positively influences the inverted LCF (0.1867), consistent with the literature on trade-induced energy efficiency improvements. Natural resource rent (NRR) shows a positive impact (0.2856), similar to Turkey, indicating that resource wealth supports energy efficiency improvements. Good governance (GG) has a highly significant negative impact (−0.1752), highlighting governance challenges in Egypt that hinder energy efficiency efforts. In the short run, economic progress (Y) in Egypt also shows a positive effect (0.0942), and the squared term (Y2) remains negative. Technological innovation (TI) has a negative impact (−0.0976), suggesting initial disruptions. Trade openness (TO) positively affects the inverted LCF (0.079), indicating the immediate benefits of trade. Natural resource rent (NRR) shows a positive but slightly lower short-run impact (0.0885) compared to Turkey. Good governance (GG) has a negative but statistically insignificant effect in the short run, indicating the potential lag in the impact of governance reforms on energy efficiency.

Overall, the results underscore the complex interplay between economic progress, technological innovation, trade openness, natural resource wealth, and governance in shaping energy efficiency outcomes in Turkey and Egypt. The findings are largely consistent with existing literature while highlighting the importance of context-specific factors in influencing the inverted Load Capacity Factor.

4.3. Furious-Asymmetric ARDL Estimation

The study explores the long-run linkage, with nonlinear formwork, in the empirical equation by estimating the test statistics Foverall, tDV, and FIDV under the null hypothesis of no cointegration.

Table 7. Results of Asymmetric cointegration assessment.

	Turkey		Egypt
Test Statistics	LCF	ILCF	LCF	ILCF
Foverall	8.014 ***	7.808 ***	7.35 ***	13.788 ***
tDV	−5.37 ***	−6.035 ***	−6.105 ***	−6.706 ***
FIDV	7.226 ***	10.659 ***	7.41 ***	6.961 ***
Diagnostics test
Breusch-Godfrey LM test	0.609	0.798	0.614	0.768
Breusch-Pagan-Godfrey test	0.49	0.773	0.576	0.661
ARCH Test	0.846	0.871	0.785	0.673
Ramsey RESET Test	0.537	0.658	0.71	0.613
Jarque-Bera test	0.658	0.802	0.744	0.577
Symmetry Assessment
$W_{LR}^{TI}$	9.384	12.432	7.303	11.283
$W_{LR}^{TO}$	7.054	12.152	8.859	12.815
$W_{SR}^{TI}$	7.345	7.17	11.586	7.363
	10.053	8.75	9.725	10.426

Note: the superscripts of *** denote the level of significance at a 1% level.

presents the test statistics results for both countries. The statistical significance of all the test statistics indicates the rejection of the null hypothesis, thereby confirming a long-run association. Moreover, diagnostic tests for Turkey and Egypt show no issues of autocorrelation, normally distributed error terms, and no heteroscedasticity problems. Additionally, the stability of the estimation results is confirmed by the Ramsey RESET test.

The Wald test of asymmetry has been executed to document the presence of asymmetric effects of technological innovation and trade openness on load capacity factors and inverted load capacity factors in the long run and short run. Results of the Wald test ascertain, in the long-run and short-run, asymmetric impact on LCF and ILCF.

The analysis of the results, see

Table 8. Results of Fourier—NARDL estimation with Load capacity factor.

	For Turkey			For Egypt
Variables	Coefficient	St. Error	t-Stat	Coefficient	St. Error	t-Stat
Y	−0.2824	0.069	−4.0927	−0.2178	0.0268	−8.1268
Y2	0.2916	0.0667	4.3718	0.2286	0.0259	8.8262
TI+	0.2811	0.0704	3.9928	0.1723	0.0398	4.3291
TI-	0.2049	0.0169	12.1242	0.2603	0.0148	17.5878
TO+	−0.2997	0.0603	−4.9701	−0.2793	0.043	−6.4953
TO-	−0.2662	0.008	−33.275	−0.2266	0.0039	−58.1025
NRR	−0.195	0.0232	−8.4051	−0.2419	0.0459	−5.2701
GG	0.2108	0.0352	5.9886	0.1903	0.0264	7.2083
Y	−0.0824	0.0143	−5.7622	−0.0833	0.0765	−1.0888
Y2	0.0896	0.0596	1.5033	0.0931	0.0755	1.2331
TI+	0.0995	0.0693	1.4357	0.0855	0.0745	1.1476
TI-	0.0804	0.0263	3.057	0.0958	0.0661	1.4493
TO+	−0.0964	0.022	−4.3818	−0.0933	0.0198	−4.7121
TO-	−0.0877	0.0092	−9.5326	−0.0759	0.035	−2.1685
NRR	−0.0974	0.0101	−9.6435	−0.0789	0.0602	−1.3106
GG	0.0939	0.0276	3.4021	0.0875	0.0255	3.4313
Sin	0.0723	0.0456	1.5855	0.0969	0.0114	8.5
COS	10.0361	0.019	19.324	0.0499	0.0044	11.3409
C	−20.5199	0.0155	−1323.865	−15.3143	0.0243	−630.2181
cointEq (−1)	−0.1743	0.0681	−2.5594	−0.2267	0.0626	−3.6214

, obtained from the Fourier-NARDL estimation, focusing on the Load Capacity Factor (LCF) in Turkey and Egypt, uncovers a multitude of intriguing long- and short-term effects of different variables on LCF. These findings are further supported by existing literature, adding credibility to the research. In Turkey, the analysis reveals a noteworthy finding regarding the relationship between economic progress and the load capacity factor (LCF). The results indicate a substantial negative long-run effect of −0.2824, implying that as economic activity increases, there is a tendency for the load capacity factor to decrease initially. Moreover, the squared term of economic progress (Y2) exhibits a noteworthy and meaningful coefficient of 0.2916, suggesting a nonlinear correlation. This implies that once a specific threshold is surpassed, additional economic progress results in enhancements in LCF. This inverted U-shaped relationship aligns with the Environmental Kuznets Curve hypothesis. According to this theory, economic growth initially contributes to environmental degradation. However, as income levels increase and more resources are dedicated to cleaner technologies, it eventually leads to environmental improvements.

The asymmetrical effects of technological innovation and progress on environmental load capacity were examined for both Turkey and Egypt. Advancements in cleaner technologies in Turkey contributed significantly to better managing their environmental limits, as reflected by the substantial positive coefficient for technological improvements. However, setbacks to technological progress risk exacerbating strains on load capacity, with regressions having a notably negative impact. The high t-statistics underscored the importance of sustained technological progress for Turkey’s environmental sustainability. In Egypt, the asymmetric impacts of innovation differed. Modest benefits to load capacity resulted from technological enhancements, evidenced by a lower yet still positive coefficient. However, regressions carried heavier consequences, with a markedly higher negative coefficient indicating more serious repercussions to their environmental load limits from lacking advances. The critical need to maintain technological gains to avert degradation was highlighted by the robust t-statistics for both improvements and declines.

The results demonstrate that trade openness negatively impacts Turkey’s load capacity factor, with coefficients of −0.2997 for increases in openness (TO+) and −0.2662 for decreases (TO−). This implies that both rising and falling openness reduces the factor, though slightly more so with increases. Striking t-statistics for TO+ (−4.9701) and TO− (−33.275) suggest Turkey’s trade policies considerably influence the environment, potentially due to traded goods’ nature or type. In Egypt, trade openness’s effect is also negative, with coefficients of −0.2793 for TO+ and −0.2266 for TO−. The results indicate that Egypt, like Turkey, experiences reduced load capacity with rising and falling openness. Stronger t-statistics for both TO+ (−6.4953) and TO− (−58.1025) emphasize how critical trade dynamics are in shaping Egypt’s sustainability, possibly through effects on extraction and consumption patterns.

For natural resource rent (NRR) in resource-rich Turkey, the coefficient was −0.195, showing that higher rents cut the factor. This inverse relationship suggests that reliance on resources can exacerbate stress, likely through overuse or inefficiency. The sizable t-statistic (−8.4051) reflects natural resource management’s considerable impact on sustainability there. In Egypt, the coefficient for NRR is −0.2419, which is more pronounced than in Turkey, proposing an even more adverse consequence of natural resource reliance on the load ability ratio. The t-statistic (−5.2701) reinforces the significance of sustainable resource administration in mitigating ecological degradation in Egypt.

The findings of the Fourier NARDL estimate for Turkey and Egypt demonstrate intricate long- and short-term dynamics that affect the inverted load capacity factor (ILCF) in both nations (see

Table 9. Results of Fourier—NARDL estimation with Inverted Load capacity factor.

	For Turkey			For Egypt
Variables	Coefficient	St. Error	t-Stat	Coefficient	St. Error	t-Stat
Panel—A: Long-run coefficients
Y	0.285	0.0237	12.0253	0.1689	0.0693	2.4372
Y2	−0.2415	0.081	−2.9814	−0.1889	0.0802	−2.3553
TI+	−0.2606	0.0311	−8.3794	−0.2834	0.0692	−4.0953
TI-	−0.2135	0.0111	−19.2342	−0.2102	0.0351	−5.9886
TO+	0.2884	0.0164	17.5853	0.1809	0.0064	28.2656
TO-	0.2634	0.0468	5.6282	0.2104	0.0344	6.1162
NRR	0.2415	0.0346	6.9797	0.2282	0.0816	2.7965
GG	−0.3022	0.0684	−4.4181	−0.291	0.0718	−4.0529
Panel—B: Short-run coefficients
Y	0.0841	0.0252	3.3373	0.0795	0.0412	1.9296117
Y2	−0.0774	0.074	−1.0459	−0.0776	0.058	−1.337931
TI+	−0.0841	0.0673	−1.2496	−0.0907	0.0126	−7.198413
TI-	−0.0722	0.0406	−1.7781	−0.0986	0.0726	−1.358127
TO+	0.0735	0.0133	5.5263	0.0991	0.0726	1.3650138
TO+	0.0833	0.0582	1.4312	0.0941	0.0323	2.9133127
NRR	0.0941	0.0352	2.6732	0.0992	0.0323	3.0712074
GG	−0.0754	0.0065	−11.6011	−0.0741	0.0317	−2.337539
Sin	0.0945	0.0556	1.6996	0.078	0.0202	3.8613
COS	0.0282	0.0404	0.6985	0.0298	0.0386	0.772
C	−16.8431	0.0225	−748.5822	−3.6728	0.0092	−399.2173
cointEq (−1)	−0.1666	0.067	−2.4865	−0.2928	0.0493	−5.9391

). In Turkey, there is a strong positive relationship between economic advancement (Y) and the long-run coefficient, which means that as the economy expands, the inverted LCF also rises. This finding supports the growth hypothesis literature, as discussed by Odhiambo [94]. Nevertheless, the presence of a negative coefficient for the squared term (Y²) indicates the possibility of decreasing returns, indicating a link between economic development and inverted LCF that follows an inverted U-shaped pattern. This pattern aligns with the Environmental Kuznets Curve (EKC) hypothesis proposed by Grossman and Krueger [90]. The presence of a non-linear connection suggests that once a certain threshold is reached, more economic development might have a detrimental effect on the inverted LCF, as seen in the case of Turkey.

In Turkey, technological innovation (TI) has a substantial long-term impact on both positive and negative shocks, resulting in a decrease in the inverted LCF. This suggests that technical developments, regardless of their nature, have a negative effect on the inverted LCF. This discovery contradicts the claim that innovation enhances efficiency and diminishes environmental impact [95,96]. Instead, it is consistent with research that indicates fast technology advancements might first disrupt energy systems before leading to ultimate enhancements. Increased trade openness (TO) has long-term beneficial benefits for both positive and negative shocks. This highlights that more trade activities improve the inverted LCF, maybe by facilitating technology transfer and efficiency improvements [87].

In Egypt, the discoveries demonstrate comparable trends but with noticeable variations in scale and importance. The long-term effect of economic growth on the inverted LCF is positive in Egypt, although it is not as significant as in Turkey. This difference may be attributed to Egypt’s distinct economic structure and stage of development. The presence of a negative coefficient for Y² is also statistically significant, providing support for the Environmental Kuznets Curve (EKC) hypothesis in the specific setting of Egypt [97]. Technological advancements have a detrimental influence on the inverted LCF in Egypt, albeit the consequences are less severe than in Turkey. This might be attributed to varying degrees of technological adoption and innovation policies.

The impact of trade openness on the inverted LCF in Egypt is positive, with greater coefficients for both positive and negative shocks compared to Turkey. This emphasizes the significant importance of trade in Egypt’s energy sector development [98,99,100]. The presence of natural resource rents (NRR) has a favorable impact on the inverted LCF in both nations, indicating that the abundance of natural resources might facilitate investments in energy infrastructure. However, it is important to note that relying heavily on natural resources also exposes both countries to the hazards of instability [101,102]. The negative coefficients of good governance (GG) consistently demonstrate that improved governance decreases the inverted LCF, highlighting the significance of institutional quality in the management of energy systems [103].

In the near term, the coefficients often exhibit similar patterns but with reduced relevance and amplitude. The inverted LCF in both nations is influenced by economic development. However, the impact is less pronounced and less statistically significant in Egypt, which may be attributed to short-term economic oscillations [104,105,106]. The immediate influence of technical innovation on the inverted LCF may be restricted, as shown by the less prominent short-run impacts and the presence of negligible coefficients.

The empirical evidence indicates that trade openness has a favorable and considerable impact on energy efficiency, especially in Turkey. This suggests that the process of trade liberalization might lead to immediate gains in terms of energy efficiency. Furthermore, natural resource rents have a favorable impact in the near term, highlighting the significance of resource abundance in facilitating immediate energy expenditures. Effective governance is essential in the near term, as shown by strong negative coefficients, emphasizing the need to manage energy changes efficiently.

The results for Turkey and Egypt illustrate the intricate relationship between economic development, technical advancement, trade liberalization, natural resource revenues, and governance in influencing the inverted LCF. The findings corroborate and juxtapose different aspects of the existing body of research, offering a nuanced comprehension of the elements that impact energy efficiency and sustainability in these two separate scenarios.

4.4. Causality Test with TY Causality and Fourier TY Causality Test

In Turkey, the causality analysis reveals, see

Table 10. Directional linkage assessment: Load capacity factor.

	TY Causality Test		Fourier TY Causality Test
	F Statistics	Significance	F Statistics	Significance	Frequency
For Turkey
LCF => TI	6.335	**	8.069	***	3
LCF => TO	1.969	-	8.647	***	3
LCF => NRR	1.458	-	7.938	**	3
LCF => GG	4.637	-	7.536	**	3
LCF => Y	2.679	-	3.964	-	3
TI => LCF	1.162	-	5.390	*	3
TO => LCF	6.185	**	6.008	**	3
NRR => LCF	9.578	***	7.405	**	3
GG => LCF	5.241	*	5.518	*	3
For Egypt
LCF => TI	6.878	**	5.880	*	3
LCF => TO	4.497	-	5.099	*	3
LCF => NRR	2.741	-	9.051	***	3
LCF => GG	5.034	*	1.148	-	3
LCF => Y	2.562	-	4.620	-	3
TI => LCF	4.694	-	9.083	***	3
TO => LCF	6.256	**	6.940	**	3
NRR => LCF	7.360	**	0.541	-	3
GG => LCF	9.230	***	3.049	-	3

Note: the superscripts of *, **, and *** denote the level of significance at a 10%/5%/1% respectively.

, a bidirectional relationship (↔) between the Load Capacity Factor (LCF) and Natural Resource Rent (NRR) as well as a unidirectional causality from LCF to Technological Innovation (LCF → TI) and Trade Openness (LCF → TO). The results also show unidirectional causality from NRR to LCF (NRR → LCF) and from Green Growth (GG) to LCF (GG → LCF), indicating a complex interplay between these variables where LCF both influences and is influenced by the others. Additionally, LCF also drives changes in Gross Domestic Product (LCF → Y) without a reciprocal effect, while Technological Innovation (TI → LCF) and Trade Openness (TO → LCF) impact LCF.

In Egypt, the analysis indicates a strong bidirectional relationship (↔) between LCF and Technological Innovation (TI), suggesting mutual influence between these factors. A unidirectional causality is observed from LCF to Trade Openness (LCF → TO) and from NRR to LCF (NRR → LCF), emphasizing the influence of natural resources on LCF. There is also a unidirectional effect of LCF on Green Growth (LCF → GG), whereas Green Growth shows a reverse effect on LCF (GG → LCF). Moreover, LCF impacts NRR (LCF → NRR) but without a corresponding influence from NRR to LCF, highlighting the asymmetric relationship between these factors in Egypt.

The analysis of the causal relationships involving the inverted load capacity factor (ILCF) across two distinct scenarios reveals complex dynamics, see

Table 11. Directional linkage assessment: inverted Load capacity factor.

	F Statistics	Significance	F Statistics	Significance	Frequency
For Turkey
ILCF => TI	4.466	-	8.347	***	3
ILCF => TO	3.812	-	4.464	-	3
ILCF => NRR	8.947	***	0.808	-	3
ILCF => GG	7.896	**	2.815	-	3
ILCF => Y	5.500	*	2.381	-	3
TI => ILCF	4.318	-	0.187	-	3
TO => ILCF	6.740	**	9.009	***	3
NRR => ILCF	4.973	-	6.980	**	3
GG => ILCF	8.722	***	4.437	-	3
For Egypt
ILCF => TI	4.611	-	8.901	***	3
ILCF => TO	5.341	*	4.764	-	3
ILCF => NRR	1.638	-	2.313	-	3
ILCF => GG	5.959	*	0.560	-	3
ILCF => Y	7.617	**	5.910	*	3
TI => ILCF	2.406	-	6.812	**	3
TO => ILCF	7.180	**	5.800	*	3
NRR => ILCF	1.046	-	0.647	-	3
GG => ILCF	2.435	-	8.291	***	3

Note: the superscripts of *, **, and *** denote the level of significance at a 10%/5%/1% respectively.

. In Turkey, there is bidirectional causality between ILCF and trade openness (TO) “<---->”, indicating that both variables influence each other. Additionally, there is unidirectional causality from ILCF to gross domestic product (Y) “ILCF-->”, natural resource rent (NRR) “ILCF-->”, and green growth (GG) “ILCF-->”, demonstrating that changes in ILCF significantly affect these variables. The relationship between technological innovation (TI) and ILCF, however, is unidirectional from ILCF to TI “ILCF-->”.

In the Egypt, the results are slightly different. There is a bidirectional causality between ILCF and both TO “<---->”, indicating a mutual influence between these variables. Unidirectional causality is observed from ILCF to Y “ILCF-->”, GG “ILCF-->”, and from TI to ILCF “TI-->”, demonstrating significant effects of ILCF on Y and GG, and a significant influence of TI on ILCF. However, the causality between NRR and ILCF is insignificant in this scenario, implying no directional linkage between these variables.

5. Conclusions and Policy Suggestions

5.1. Conclusions

The purpose of the study is to address an in-depth analysis of technological innovation and trade openness, not only natural resources but also environmental sustainability, in which ecological footprints are linked as a measurement indicator. The investigation attempted to recognize not simply the long-run outcomes of those variables on sustainability. However, also the temporary results of the mistreatment of advanced methodologies such as Fourier autoregressive distributive lag and Nano desegregate distributed lags for Turkey and Egypt.

Results indicated that relations between these variables differed in the two countries. According to the Fourier ARDL results, there is a strong long-term association between technological innovation and ecological footprint in Turkey as well, which shows that technological innovations provided benefits with the perspective of environmental sustainability by decreasing ecologic footprint. In contrast, Egypt showed a similarly complex relationship between economic development and environmental performance. However, the impacts of technological innovations were not universally beneficial for reductions in pollution, suggesting more focused approaches should be promoted with respect to deployment technologies.

Trade openness showed a double-edged impact in both countries. Although it served to promote economic development, it confronted environmental sustainability. Results for Turkey indicated that as trade openness increased, the ecological footprint in this country also grew higher than expected. On the other hand, Egypt conducted business with a diminishing respect for trade openness, indicating that this country may be in another stage of economic and environmental integration.

Second, the Fourier NARDL analysis provided evidence of asymmetries in the impacts of these studied variables. The results reveal that all technical changes in Turkey could decrease the ecological footprint and vice versa. However, these positive (negative) impacts were stronger considering an increase in technological innovation rather than a decline due to policy interventions encouraging innovations adopted on time-specific bundles. Asymmetry in Egypt was less stark but reminded us that we need more intersectional tactics to harness technology effectively for sustainability.

Additionally, the results of causality findings helped to determine the direction of influence among variables. Turkey: the case is explained by directionality instead of unidirectionality between technological innovation and ecological footprint, which means that technology evolution or environmental policies affect each other. The same analysis indicated a direct relation between trade openness and the ecological footprint, suggesting that trade policies might have a significant impact on environmental outcomes for Egypt.

Conversely, the study contributed to shedding light on how technological improvement, trade liberalization and natural resource conditions may transmit to environmental sustainability in Turkey and Egypt. These different patterns emphasize that country-by-country approaches are essential to the SDGs: there is no one-size-fits-all approach. The results call for policies to promote technological innovation but also take into account the trade openness and natural resource use that affect environmental impacts, just like long-term ecological sustainability in both countries.

The case of Turkey and Egypt in the region provides an illustrative experience unlike other regions on how technological innovation, trade openness, resource potential affect environmental sustainability. Heavy dependence on fossil fuels, rapid urbanization, and economic growth has resulted in significant environmental degradation in both countries. Technology acts in a mixed manner to sustainability by providing positive impacts such as efficient renewable energy technologies and green manufacturing but at the same time puts more burden on sustainability due to negative consequences of certain technological advancement when new technologies results more resource consumption. On the one hand, trade openness might move environmentally friendly technologies to their destination, at the expense of depleting resources further due to increased use in industry activity as well as expansion of industrial activities by attracting more firms into resource-rich regions. While in developed regions such impacts are better managed by stronger institutional frameworks, Turkey and Egypt walk a tightrope between the lure of trade and natural resources extraction benefits with the need for greater sustainability. That last point is critical shortfall both countries are yet to maximize on the opportunities for green growth and integration of renewables showcased. This is in contrast to Europe or East Asia, for example, which have perhaps more secure policy frameworks where technology and trade can be used to generate better environmental outcomes.

5.2. Policy Suggestions

First, from the empirical results and discussion section, including Fourier ARDL NARDL along with causality analyses for Turkey and Egypt, many policy implications are concluded. The estimates show that technological innovation and trade openness play essential roles in determining the ecological footprint of both countries. However, there are remarkable long-run and short-run differences between the two nations. It can also be said that technological innovation has an important place in permanently decreasing Turkey’s ecological footprint. Ultimately, it alludes to promising environmental benefits if there is a long-term nature of capital investment in technological advancements as applied by Turkey. Short-run effects appear to be less severe, which suggests that the rapid influence of technological innovation on environmental sustainability may be milder. Policy-wise, Turkey should encourage the innovation ecosystem for sustainable technologies, laying focus and simultaneously taking interim measures in place to prevent environmental damage.

Second, trade openness does indeed lead to a significant increase in Turkey’s ecological footprint, especially over time. The implication is that while the process of trade liberalization could stimulate economic growth, it may also lead to more environmental destruction unless such steps are combined with tough and meaningful practices on the environment. Thus, to achieve a balance between economic and environmental objectives, policymakers in Turkey need to consider this through the integration of green trade practices during trade agreements as well as promotion for the adoption of stringent covered items.

Third, regarding Egypt, the findings showed that natural resources have played a primordial role in driving the country’s ecological footprint. Egypt’s reliance on natural resources has contributed to environmental degradation, especially over the longer term. To solve this problem, the Egyptian government should focus on economic diversification from resource-intensive sectors to more sustainable industries. Effective policies to conserve natural resources and promote clean energy technologies are essential to lowering Egypt’s ecological footprint.

Fourth, the causality results emphasize the interrelationship between these dimensions, showing bilateral flow relationships between trade openness, technological innovation, and technological innovation-EF in both Turkey and Egypt. Propagating policy measures targeting one area will cause ripple effects across other domains. Therefore, a comprehensive policy mix that combines environmental, trade, and innovation policies is needed. Developing such collaborations would strengthen sustainable practices and help to address what underpins environmental decline in both Turkey and Egypt.