Assess the Economic and Environmental Impacts of the Energy Transition in Selected Asian Economies

Abstract

Energy transition and green innovation have appeared as new hopes for environmental impact due to human activity, which has destroyed biodiversity and increased environmental degradation. Therefore, developed and emerging economies are focusing on green innovation and energy transition to tackle the environmental impact. Thus, this study was initiated to provoke a meaningful relationship between energy transition, economic growth, trade, green innovation, and good governance to measure the role of concerning factors in achieving environmental sustainability. For this objective, dynamic econometric approaches such as cointegration, heteroskedastic OLS estimation using GMM (HOLS-GMM), AMG, and Driscoll–Kraay were implemented to estimate the Asian dataset between 1990 and 2022. The result indicates that concerning factors have a significant influence on environmental impact. The findings specify that a 1% rise in the energy transition and green innovation will influence the environment by 0.0517% and 3.051%, respectively. Further, AMG and Driscoll–Kraay validate the findings of HOLS-GMM. The robust tests indicate that the factors, which are concerning, significantly impact environmental sustainability. Consequently, the energy transition, trade, and green innovation significantly contribute to attaining ecological sustainability in the long term, and the Sustainable Development Theory prevails in the economy. Thus, innovative policy implications, including energy transition, green innovation, trade, and economic growth, are required to make Asia prominent in achieving environmental sustainability via implementing sustainable and green technologies and clean energy sources.

1. Introduction

Climate change has appeared as a significant worldwide issue, impacting human existence and economic progress directly and indirectly. It directly impacts human health, food security, and air quality [1,2]. Climate change poses immediate health hazards through severe weather phenomena, prolonged periods of high temperatures, and the spread of diseases transmitted by vectors. It indirectly impacts human health by causing the water and air quality to worsen, which can result in heart and respiratory problems and increase susceptibility to several diseases. It indirectly impacts food security by causing food costs to rise, decreasing the nutritional value of food, and increasing the likelihood of food deficits and malnutrition, especially between disadvantaged groups [3]. Therefore, energy transition has an important role in climate change. Energy transition is the process of gradually replacing accessible energy systems that depend on fossil fuels with cleaner and more sustainable alternatives [4,5]. It encompasses a profound overhaul of the methods by which energy is generated, used, and controlled, with the objective of tackling climate change, minimizing environmental consequences, and advancing a more stable and robust energy future [6]. The energy transition is propelled by a confluence of elements, encompassing emissions concerns, technical progress, governmental and regulatory structures, market forces, and public consciousness [7]. In order to expedite the transition towards a more sustainable and low-carbon energy system that adequately addresses the energy requirements of current and future generations, it is imperative to foster cooperation and active involvement among governments, businesses, communities, and individuals [8].

The main goal of energy transition is to decrease the release of greenhouse gases, namely carbon dioxide (CO₂), by substituting fossil fuels with energy sources that have low or no carbon content [9]. The proposed strategy entails augmenting the proportion of sustainable energy sources, such as solar, wind, hydro, and geothermal power, while gradually eliminating or reducing the reliance on coal, oil, and natural gas [10]. Renewable energy transition refers to the process of incorporating renewable energy sources into the current energy substructure, which encompasses power grids, transportation networks, and heating and cooling systems [11]. This necessitates the advancement of renewable energy technology, enhancement of grid flexibility and storage capacities, and adoption of decentralized energy solutions. The energy transition agenda focuses on enhancing energy efficiency in several sectors, such as buildings, transportation, and industrial processes [12]. The main objective is to improve energy efficacy, minimize inefficiencies, and boost energy output, thus decreasing total energy consumption and the necessity for supplementary energy production [13]. Energy transition often entails the electrification of several sectors, including transportation and heating, in order to decrease dependence on fossil fuels. The aforementioned include the extensive implementation of electric vehicles, electric heating systems, and the electrification of industrial operations [14]. The objective of the energy transition is to guarantee ubiquitous availability of cost-effective, dependable, and environmentally friendly energy services. Energy poverty alleviation refers to the provision of clean energy solutions to marginalized groups and communities, especially in emerging nations, while simultaneously tackling social and economic disparities [15].

The environmental results of free trade might shift, either emphatically or adversely, based on criteria such as estimate, strategy, and substance [16]. Additionally, trade has an impact on the environment by forming economic development. The early stage of economic advancement is regularly connected to negative environmental results, mainly attributed to the scale impact emerging from the increment in vitality utilization. It may be possible to surrender positive natural future results of composition and innovation. The marvel of scale impact outlines a coordinated relationship between pollution emissions, financial growth, and energy use. The prioritizing of economic extension over contamination control amid the beginning stages of advancement can be recognized as the primary cause for this wonder. Thus, as improvement progresses, economic development leads to an expanded requirement for a clean and safe environment to accomplish a better living standard. To achieve this, the composition impact is the term utilized to portray the substitution of smudged fabricating forms with clean generation forms, or the benefits sector. Additionally, there is an expanding requirement for ecologically inviting advances within the afterward stage of development. Subsequently, the utilization of this innovation leads to positive environmental outcomes. In outline, the increment in economic development is capable of the corruption of the environment amid the early phases of advancement, whereas at the same time contributing to natural advancement in afterward stages. The Environmental Kuznets Curve (EKC) hypothesis delineates a quadratic effect [17]. Later observational considerations have been conducted to explore and approve the idea of EKC.

The present study evaluates the impacts of green innovations (GI), trade openness (TR), economic development, and good governance on low-carbon emissions in selected Asian nations. One of the key determinants influencing greenhouse gas emissions is international commerce, a significant economic activity that stimulates economic growth and facilitates the exchange of commodities and services. Conversely, a rise in trade caused a large spike in energy consumption and the exploitation of other resources, therefore exerting immense pressure on the resilience of the ecosystem. Given the detrimental effects of trade, it can be argued that establishing a green economy could help mitigate environmental degradation and attain carbon neutrality [18]. Green trade is a vital determinant of fast green economic growth as it enhances the economic development of a country, decreases greenhouse gas emissions, expands industrial production processes, improves the efficiency of energy sources, and increases trade volume through trade liberalization and global integration [19]. However, the worldwide expansion of an environmentally sustainable economy is challenging to accomplish without the international exchange of environmentally favorable products. Predictions indicate that the use of these products would greatly enhance environmental characteristics.

Furthermore, green innovation (GI) can also contribute to the promotion of green development. Without the implementation of green technological advancements, the progress of green growth becomes increasingly ineffective [20]. Furthermore, these developments not only stimulate the development of more affordable and environmentally friendly technologies but also reduce the cost of ecological sustainability. Furthermore, it enhances production efficiency and promotes the conservation of natural resources by mitigating CO₂ emissions. In addition to promoting ecological sustainability and macroeconomic efficiency, the green energy sources are the primary drivers of green economic development [21]. In addition to progress in green energy and wastewater treatment, green innovation mechanisms encompass clean and sustainable food production and other sectors considered significant catalysts for economic development and environmental sustainability [22]. Additionally, the promotion of sustainable development is aided by the assistance of technology and the accelerated advancement of energy-saving technology research and development [23]. Furthermore, the use of green technologies alleviates the load on the nation’s stability of expenditures and diminishes reliance on imported fossil fuels [24].

Furthermore, this study assesses the significance of good governance (GG) in promoting green economic development and reducing carbon emissions in selected Asian economies. Major nations, environmentalists, and policymakers widely concur with the necessity of developing new policy guidelines to tackle the ecological complexities caused by environmental degradation. Therefore, it can be inferred from the aforementioned discussions that the issues pertaining to the environment and sustainable development are garnering significant global attention [25]. Considerable global endeavors are underway to modify economic and industrial frameworks in order to foster environmentally sustainable and environmentally friendly economic growth. For a period of time, research on the primary factors contributing to global warming has fascinated scholars and politicians. Good governance can simultaneously promote economic growth and environmental sustainability. Therefore, it is important to implement specific ways that guarantee and protect environmental sustainability during the long-term growth process in rising economies. The present study focuses on selected Asian economies due to their remarkable expansion experienced in recent decades. Nevertheless, the current economic and population growth patterns in the majority of these economies have placed increasingly burdensome demands on the environment and natural resources. The current study has concentrated on the necessity to transition into a development trajectory that steers clear of submitting to ecologically detrimental infrastructure and resulting in a lasting impact of expensive environmental harm and depletion of resources.

The present work addresses a knowledge gap by including asymmetric short- and long-term associations of the chosen explanatory variables to analyze their positive and negative impacts on the accomplishments of green growth (GG) and low-carbon emissions in the selected Asian countries. To achieve this, we utilized dynamic econometric approaches such as cointegration, HOLS-GMM, AMG, and Driscoll–Kraay.

The remaining part of this study comprises the following sections: Section 2 demonstrates a detailed analysis of the review, while Section 3 explains the methodology and materials used. Section 4 presents the outcomes and examines the discoveries. Lastly, Section 5 provides the closing remarks for the work.

2. Review of Literature

2.1. Energy Transition and Environmental Impact

H₁. 

Does energy transition affect the environment?

With the rapid acceleration of global carbon reduction efforts and the intensification of energy transitions, conventional power systems are transforming into clean, low-carbon, reliable, and adaptable ones. The utilization of renewable energy as an innovative energy source is not only reducing reliance on fossil fuels but also efficiently tackling pollution concerns. This paradigm change has garnered major international attention from scholars. One perspective is that energy transition is an essential strategy to tackle worldwide climate change and environmental concerns [26]. Mainly dependent on fossil fuels, the conventional energy system has led to significant releases of carbon dioxide and other greenhouse gases, therefore hastening global climate change and environmental deterioration [27]. Concurrently, the conventional energy framework has difficulties relating to energy security and geopolitical concerns. Consequently, the successful attainment of energy transition through the advancement of clean and renewable energy sources is an essential approach to decrease greenhouse gas emissions, safeguard the ecological environment, and alleviate energy-related hazards. Previous research conducted by Doytch et al. [28] examined the correlation between renewable energy production and the ecological environment in OECD nations. Their findings confirmed the effectiveness of renewable energy in decreasing carbon intensity and footprints. According to Bhuiyan et al. [29], carbon emissions and air pollution have significantly decreased because of the substitution of renewable energy sources like wind and solar for fossil fuels and the integration of renewables into the grid transformation process. Consistently, these research studies have confirmed the substantial impact of energy transition on carbon reduction and ecological conservation. However, the pursuit of sustainable growth requires the implementation of energy transition. First and foremost, the widespread use of clean and renewable energy sources can decrease dependence on conventional energy sources, therefore improving energy security [30]. Bui Minh et al. [31] asserted that the employment of renewable energy sources has the potential to lessen dependence on fossil fuels and significantly enhance the energy security of the nation. Furthermore, the use of clean and renewable energy sources has the aptitude to foster economic growth, enhance energy efficiency, and mitigate environmental deterioration [32]. In their study, Ivanoski et al. [33] examined the effect of investment in renewable energy on energy efficacy and climate risk. They highlighted that investing in renewable energy can significantly enhance energy efficacy and mitigate the frequency of climate catastrophes. Furthermore, clean and renewable energy sources play a substantial role in improving the overall well-being and advancing society’s development [34]. However, some academics maintain a contrasting viewpoint, arguing that the shift to renewable energy may not have a beneficial effect on environmental sustainability and could even worsen environmental deterioration. Hu et al. [35] revealed non-linear correlations between the employment of renewable energy sources and emissions, indicating diverse effects and underscoring that renewables may not decrease carbon emissions in areas with fast increasing electricity use. Chen et al. [36] contended that the shift towards renewable energy may worsen environmental deterioration rather than effectively reducing emissions. Collectively, these studies highlight the complex connection between the shift in electricity generation and environmental sustainability. The reasons emphasize the need to investigate this connection, despite contradictory results that expose its intricacy and emphasize the difficulties in attaining sustainable environmental results during the shift to renewable energy.

A study conducted by Shah et al. [37] examined the association between industrial energy patterns and environmental sustainability in recently industrialized economies. The authors suggested that ongoing advancements in green technologies can assist policymakers in mitigating changes in climate and global warming. Wang et al. [38] conducted a comprehensive analysis of the environmental and energy performance of family enterprises. Their findings indicate that advancements in environmental technology have enabled the industrial sector to effectively address climate change by transitioning to sustainable energy sources. A study conducted by Hailiang et al. [39] performed an analysis of energy patterns in industrialized economies and revealed that the existence of renewable energy sources is essential for attaining a balanced state of economic and environmental sustainability. Ma et al. [40] investigated the importance of mandatory carbon disclosure rules and environmental agenda in demonstrating the criticality of consistent environmental policies in diminishing dependence on fossil fuels and encouraging the adoption of renewable energy sources to mitigate greenhouse gas emissions, improve energy efficiency, and protect environmental quality.

2.2. Trade and Environmental Impact

The focus of several influential studies has been investigating the relationship between wealth and pollution emissions. These studies have suggested that trade liberalization is accountable for increased emissions because of a large-scale influence while also providing opportunities for reducing emissions through composition and/or technique effects. The EKC theory assumes that there exists a causal association between higher income levels and an initial rise in pollution emissions during the initial phases of economic development. Simultaneously, it suggests that higher income levels also contribute to environmental enhancement during the subsequent stages of growth. A non-linear correlation between income and pollutant emissions is influenced by trade liberalization [41]. Subsequently, academic literature pertaining to the environment shifted its focus towards investigating the effect of trade openness on the release of pollutants.

Table 1. Parameter details.

Acronyms	Employed as	Measurement Unit	Source
EI	Environmental Impact	CO2 emissions (metric tons per capita)	WDI
ET	Energy Transition	% of the total renewable energy consumption	WDI
EG	Economic Growth	GDP growth (annual %)	WDI
TR	Trade	Trade (% of GDP)	WDI
GI	Green Innovation	Environmental-related patents and residents	WDI
GG	Good Governance	Role of law, no violence, government effectiveness, and regulatory quality are employed to generate index	WDI

Note: WDI; World Bank Development Indicators.

shows a thorough impression of the relevant literature. Chen et al. [42] conducted a study to analyze the elements of different pollutant emissions in a panel comprising both OECD and non-OECD nations. The investigators employed three methodologies, namely Ordinary Least Squares (OLS), Fixed Effects (FE), and Generalized Method of Movement (GMM). Udeagha et al. [43] conducted a study in Turkey to explore EKC theory, particularly centered on the incorporation of outside exchange within the show of per capita carbon outflows. The experimental proof for the EKC hypothesis was set up by the analyst, who moreover reported the positive impacts of vitality utilization and outside exchange on per capita carbon outflows. Moreover, the ponder illustrates a Granger causal affiliation between vitality utilization and pay with respect to carbon outflows per capita, in spite of the fact that no such association is recognized within the setting of remote commerce. Mahmood et al. [44] did a Granger causality analysis on a sample of nine newly developed nations from 1971 to 2007. Musah et al. [45] conducted an analysis spanning the years 1990 to 2013, encompassing a comprehensive sample of 23 European nations. The research uncovered a direct relationship between economic growth and urban development and carbon emissions. However, it was observed that trade openness had a reducing impact on carbon emissions. Furthermore, research has established that specific types of renewable electricity generation exhibit positive environmental consequences, but the remainder of the sources exhibit minimal effects. Fan et al. [46] conducted a longitudinal study to examine the consistent impacts of energy usage, trade openness, and affluence on carbon emissions in four recently industrialized countries from 1970 to 2013. The research results suggest a highly significant and positive correlation between energy use and carbon emissions. Conversely, this study also reveals that trade openness and income exhibit negative relationships with carbon emissions. In the Granger causality investigation, the researchers identified a one-way causal relationship to find the influence of trade openness and energy utilization on carbon emissions. Furthermore, it has been seen that trade openness exerts an impact on both energy use and economic growth. Park et al. [47] did a study that investigated the determinants of carbon emissions in Tunisia and Morocco throughout the period spanning from 1971 to 2013. The results obtained from the time series and panel studies demonstrate a direct correlation between foreign direct investment, trade openness, and capital and carbon emissions in both sovereign nations. The panel causal analysis revealed a bi-directional Granger causality link between income and carbon emissions, as well as between foreign direct investment (FDI) and carbon emissions.

Rahman et al. [48] performed a study to examine the influence of trade openness on carbon emissions across a sample of 105 countries throughout the period from 1980 to 2014. Business openness’ influence on Tunisia’s carbon emissions is minimal. The panel study’s findings also indicate a beneficial association between income, trade openness, and carbon emissions. The EKC idea was investigated by Mustascu et al. [49] in the context of Saudi Arabia, from 1970 to 2016. The EKC theory was validated, revealing a negative correlation between trade and carbon emissions. Dauda et al. [50] did a think about looking at the determinants of per capita carbon outflows and EKC speculation in Egypt amid the period crossing from 1990 to 2014. The EKC speculation was inspected within Egypt, uncovering that the impact of trade openness could have been more inconsequential in this situation. Moreover, the effect of REC and remote coordinate speculation on per capita carbon outflows appeared to be both positive and negative by the analysts. Various scholars have examined the determinants of vitality utilization and carbon outflows inside the Tunisian economy, specifically focusing on the impact of exchange openness. The study by Aytun et al. [51] is an example of this phenomenon since it reveals a bidirectional Granger causation relationship between economic growth and energy use over a prolonged duration. A study was undertaken by Abid et al. [52] between 1971 and 2012, which aimed to investigate the correlation between energy consumption in transport and carbon emissions in the transport sector. The researchers’ findings unveiled a unidirectional Granger causation in the short term. Furthermore, they discovered various other occurrences of Granger causality between energy consumption connected to transportation, carbon emissions, infrastructure, and the value contributed to transportation. Ref. [53] conducted a study in Tunisia, as well as in a panel of Middle East and North African (MENA) states and several individual countries, and their findings provided empirical support for the existence of EKC theory. In Tunisia, the EKC hypothesis needed to be supported, and instead, a consistent and linear correlation between income and carbon emissions was discovered. The prevalence of the EKC hypothesis in the link between income and SO₂ emissions in Tunisia from 1961 to 2004 was validated by Shang et al. [54]. In their study, Chhabra et al. [55] aimed to examine and validate EKC theory regarding the association between carbon emissions and wealth in Tunisia between 1971 and 2010. Furthermore, it has been asserted that there is a direct correlation between trade openness and energy use, characterized by a low degree of responsiveness. Furthermore, research has indicated that energy consumption has a significant role in the generation of carbon emissions. The study conducted by Chhabra et al. [55] investigated the hypothesis of EKC and studied the relationship between carbon emissions and economic growth, revealing a U-shaped link. Furthermore, a significant inverse relationship was identified between the degree of corruption control and the magnitude of carbon emissions.

H₂. 

Does trade affect the environment?

Several scholars have conducted analogous investigations on relatively distinct topics, resulting in similar outcomes. The research undertaken by Aneja et al. [56] to evaluate the impact of trade liberalization on the economic development of Pakistan. Mosikari et al. [57] did a study that investigated the influence of trade liberalization on India’s economic development. The researchers analyzed several explanatory variables associated with the dependent variables, including trade liberalization, FDI, inflation, gross fixed capital formation, capital stock, human capital, and real GDP growth rate. The Johansson co-integration method was employed to assess the association among these variables. Feng et al. [58] employed the ARDL approach to estimate the relationship. Omoke et al. [59] posit that a robust and positive association exists between trade liberalization, capital formation, and human capital in connection to GDP and FDI. Conversely, inflation exerts a detrimental influence on the rate of GDP expansion. Kumari et al. [60] state that capital stock positively impacts the GDP growth rate, but FDI has negatively influenced real GDP. Rasoanomenjanahary et al. [61] created an inquiry about what appeared to be a solid and significant relationship between exchange openness, human capital, and agrarian division development. Analysts utilized the ARDL Model to assess the persevering relationship between the factors and explore this relationship. When they come about, they propose that there exists a favorable and persevering affiliation between exchange openness, human capital, and rural development. Moreover, applying the Granger Causality Test revealed a critical causal affiliation between trade openness, human capital, physical capital, and genuine rural net residential items. Several researchers, such as Burange et al. [62], have utilized similar titles to inspect the correlation between FDI, openness, and economic growth. The researchers have employed yearly data from emerging countries for over thirty years. The Error Correction Model was applied by Intisar et al. [63], while other publications utilized the Fixed-Effect Regression Model. The results suggest that openness significantly impacts growth, but foreign direct investment is negligible. Aperegdina et al. [64] revealed their key findings in their seminal paper. Trade liberalization is advantageous for the economic growth of developing countries, but foreign direct investment does not significantly impact economic progress. According to [65], the presence of FDI and trade facilitates the growth of Pakistan’s real sector economy. Usman et al. [66] conducted a study wherein they noticed a significant and positive association between the interest rate, FDI, and exchange rate alongside the growth of Pakistan’s GDP. These studies repeatedly demonstrate that a wide range of situations influence the choice to participate in FDI, and many factors likewise influence the influx of FDI. Thanh et al. [67] conducted a study to examine the association between certain elements, namely trade openness, Pakistan’s export (to OEC), exchange rate, Pakistan’s total export, and political conditions, and their impact on FDI. The study’s results demonstrated a definitive causal association between the variables under investigation. The research conducted by Rehman et al. [68] has established a significant relationship between trade openness and FDI inflow. Moreover, a significant positive association was identified between trade openness and FDI inflow. According to Ismehene et al. [69], capital inflow is subject to various factors, including the exchange rate, GDP, and political instability. Elfaki et al. [70] conducted a study wherein they found a noteworthy and favorable correlation between gross fixed capital formation, liberalization, and the economic growth of Pakistan. Pan et al. [71] conducted a study that demonstrated that the influx of FDI in Pakistan has negative implications for the nation’s current account. In Pakistan, the association between openness, industry value added, and economic growth was investigated in a study undertaken by Mester et al. [72]. The researchers applied OLS and co-integration approaches to analyze annual time series data. The findings of the review revealed a substantial and favorable association between trade openness and economic growth. Islam et al. [73] investigated the persevering impacts of exchange openness on financial development. The analysts utilized an annual dataset covering a period of around forty years. In their individual examination, the analysts inspected a few autonomous components, specifically human capital, physical capital, and exchange openness. The subordinate variable, on the other hand, was the GDP development rate. The data were analyzed using the ARDL Model, cointegration, and dynamic ordinary least squares (DOLS) techniques. The discoveries of them uncovered that a significant extent of the factors displayed factual centrality. The study conducted by Nosheen et al. [74] revealed a noteworthy reciprocal relationship between trade openness and the economic development of Pakistan. A favorable and statistically significant link was established by Liu et al. [75] between physical capital and human capital and economic growth in their study. In contrast, a decline in trade openness and a statistically significant correlation were seen.

2.3. Green Innovation and Environmental Impact

The relationship between carbon outflows and green innovation has been a subject of significant discussion for a long time, essentially driven by the expanding consideration given to climate change. Studies about green advancement sometimes battle to recognize between the components and properties that characterize development. The development assessment is commonly conducted by considering numerous perspectives, counting consumption in investigation and advancement (R&D), the number of licenses held, and the money-related worth of recently presented items. In this way, we think about respecting these components as non-specific progressions. The influence of by and large development on carbon outflows can show in either a hindering, advantageous, or unbiased way. Past studies have illustrated that advancement has a critical inhibitory impact on noticeable economies’ total carbon dioxide outflows. The studies conducted by Luo et al. [76] have demonstrated a significant decrease in carbon emissions within the G20 nations due to innovative measures. The effect of R&D investments on reducing carbon emissions in OECD nations has been noted by Zhang et al. [77]. Nevertheless, the magnitude of this decrease exhibits significant variation among different nations. In a study conducted by Ahmed et al. [78], it was observed that innovation had a mitigating effect on carbon emissions, particularly in G6 countries, across 18 advanced and emerging nations. The influence of innovation on BRICS countries was examined by Kuang et al. [79], who observed a moderating effect specifically on Brazil’s carbon emissions. Abbas et al. [80] conducted a comprehensive investigation of the European Union, the United States, and China in their paper. Their study suggests that allocating resources towards research and development has yielded positive outcomes in mitigating carbon emissions within industrialized countries. The study by Liu et al. [81] uncovered a negative relationship between trade-induced specialized development and carbon emanations within the Asian locale. Green development, as characterized by the OECD, includes the purposefulness or inadvertent headway or considerable advancement of assorted components, counting items, forms, showcasing techniques, organizational frameworks, and organization structures. The elemental point of this activity is to lighten the negative results connected to natural risks, outflows of poisons, and the utilization of assets and vitality. Green development and common advancement are fundamentally separated based on the concept of double externality. According to Wang et al. [82], green development can supply favorable externalities in connection to the dispersal of data and the natural results. Integrating cleaner-prepared development inside the system of green advancement holds a guarantee for progressing the proficiency of asset and vitality utilization, subsequently lessening the dependence on fossil powers such as coal and oil. Subsequently, this plays a key role in moderating carbon dioxide emissions. Tests have shown that end-of-pipe advancement is viable in dispensing or changing toxins that are transmitted amid the generation handle, diminishing the coordinated discharge of carbon, sulfur dioxide (SO₂), and other toxins. Fan et al. [83] assert that the prime goal of green product innovation is to effectively respond to the environmental requirements over the whole lifespan of a product, with the purpose of providing environmental benefits during its entire lifecycle. Hence, green innovation demonstrates a more significant suppressive effect on carbon emissions. In their empirical analysis, Nan et al. [84] examined OECD nations and found a positive association between a 1% increase in environmentally beneficial patents and a 0.017% reduction in carbon emissions. Tian et al. [85] found that the simultaneous use of ecological patents and trademarks led to a reduction in CO₂ emissions in OECD nations. The research by Li et al. [86] has shown that the progress in renewable energy technology has led to a reduction in carbon emissions. Xu et al. [87] conducted a study wherein they investigated the top 20 nations engaged in the exportation of refined oil. Their findings revealed that ecological innovation has a detrimental effect on carbon emissions. Ahmed et al. [78] conducted a study on an oil company situated in Northern Cyprus. Ecological innovation was classified into three distinct categories: investment in environmental protection, staff training, and R&D. The research findings suggest that allocating resources towards environmental investment yielded significant long-term outcomes in terms of mitigating carbon emissions. Conversely, the implementation of R&D initiatives and employee training programs has shown a more immediate impact on reducing carbon emissions.

3. Data and Methodology

To heighten the environmental impact, this study consists of energy transition, economics, trade, green innovation, and good governance. For this purpose, a panel dataset of selected Asian economies such as Afghanistan, Bhutan, Brunei Darussalam, China, Hong Kong, Bangladesh, India, Indonesia, Japan, the Maldives, Mongolia, Myanmar, Nepal, Pakistan, Thailand, Timor-Leste, Vietnam, and Cambodia were assimilated. In this study, the novel econometric method HOLS-GMM approach has been employed. Aydin et al. [88] disclose that the transition from fossil fuels to renewable energy is spreading and has a significant influence on the environment. Further, Razzaq et al. [89] examine that the influence of GDP on environmental factors totally depends on the governance level. The use of green innovation, such as eco-innovations, has the potential to effectively mitigate environmental strain, particularly when integrated into the broader energy transition.

Several hypotheses, like the EKC hypothesis (pollute now–grow later concept), and carbon curse theory, aim to elucidate the mechanisms that influence environmental quality. However, Wang et al. [90] revealed that Sustainable Development Theory (SDT) is more suitable in this perspective to explore the coordination between environmental impact and concerning factors. Because SDT consolidated economic prosperity, social equity, and environmental sustainability, advocating for strategies and practices that meet present basic requirements without conciliating the capability of future generations to meet their own requirements.

This theoretical perspective advocates for the mitigation of environmental effects through the promotion of sustainable resource management, pollution control, and conservation activities. This statement underscores the need to shift towards renewable energy sources and energy-efficient technology as strategies to reduce carbon emissions and tackle the issues presented by climate change. Furthermore, the SDT promotes the adoption of trade practices that are just and impartial, among others, to strike a balance between the protection of society and the environment and economic growth. Furthermore, the theory offers a critical analysis of the exclusive dependence on GDP as a metric for assessing advancement, advocating for more comprehensive indicators that consider social and environmental variables. It emphasizes the significance of effective governance in fostering transparent, accountable, and inclusive decision-making procedures that incorporate environmental factors. The empirical specification serves as the basis and analytical framework for the examination, drawing upon a pre-established practical model. The variables are summarized in Table 1.

Table 1 presents data that demonstrate the utilization of carbon emissions as the environmental effect, represented by the variable “EI”. Gross domestic product (GDP) is commonly denoted as “EG”, with EG representing economic growth. Moreover, the use of environmental-related patents serves as a sign of green innovation, as indicated by the acronym “GI”. Furthermore, the research utilized an index that is predicated upon five key elements, including the function of law, absence of violence, effectiveness of government, control of corruption, and regulatory quality.

To provide a concise summary of the primary characteristics of study components, this research utilized descriptive statistics. The components considered in this study are central tendency, variation from the mean, and dispersion of data. Jarque–Bera reveals that the quality of fitness is influenced by the balance and tail of the data, which tend to favor normal distribution.

Table 2. Descriptive statistics.

Predator	Mean	Min	Max	SD	Skewness	Kurtosis	JB
EI	0.175	−3.04	3.493	1.487	0.010	2.030	0.000
ET	2.527	−4.605	4.600	2.179	−1.493	4.544	6.421
EG	1.611	−3.738	3.629	0.808	−2.249	0.000	0.000
TR	4.32	0.695	6.777	0.846	0.235	3.030	0.000
GI	6.024	0.000	14.171	2.947	1.011	3.739	0.000
GV	−0.484	−7.643	0.904	1.16	−1.592	7.389	6.601

displays the results.

Table 2 demonstrates that the measures of central tendency (mean, mini, and maxi) support the research, while the standard deviation values are within the recommended range (±2), apart from GI. The general rule value range includes various symmetries and tails of data, such as skewness of ±3 and kurtosis of ±10. Therefore, the goodness of fit is demonstrated as the skewness and kurtosis values fall within the acceptable range, suggesting a normal distribution. Therefore, the results indicate that the descriptive statistical analysis supports this study.

In accordance with the aforementioned objectives, the subsequent economic model is developed. In the first model, we explore the role of energy transition, economic growth, trade, green innovation, and good governance on environmental impact. The economic functional Model 1 is reported in Equation (1).

$$E{I}_{it}=f(E{T}_{it},E{G}_{it},T{R}_{it},G{I}_{it},G{G}_{it})$$

(1)

In the second model, we explore the role of environmental impact, economic growth, trade, green innovation, and good governance on energy transition; the functional form of the second model is reported in Equation (2).

$$E{T}_{it}=f(E{I}_{it},E{G}_{it},T{R}_{it},G{I}_{it},G{G}_{it})$$

(2)

While in the third model, study explores the influence of environmental impact, energy transition, trade, green innovation, and good governance on economic growth, the 3rd model in functional form is reported in Equation (3).

$$E{G}_{it}=f(E{T}_{it},E{I}_{it},T{R}_{it},G{I}_{it},G{G}_{it})$$

(3)

Methodology

Initially, we inquired about centers on incline homogeneity. A incline with high homogeneity appears to have consistent slants or drops from one conclusion to the next, while a slant with destitute homogeneity has more variety in slant points, with both soak and gentle ranges scattered unevenly. Blomquist et al. [91] introduces a methodology to reveal the slope homogeneity, mathematically expressed as follows:

$$\begin{array}{l}\Delta =\sqrt{S}\left({\displaystyle \frac{S^{-1}F\%-L}{\sqrt{2L}}}\right)and\\ \Delta adj=\sqrt{S}\left({\displaystyle \frac{S^{-1}F\%-L}{\sqrt{\frac{2L(M-L-1)}{M+1}}}}\right)\end{array}$$

(4)

However, it is important to assess cross-sectional dependency when working with panel datasets, as it is conceivable for different subgroups within the dataset to exhibit correlation or dependence on one another [92].

$$CD=\sqrt{{\displaystyle \frac{2M}{S(S-1)}}}\left({\displaystyle \sum _{z=1}^{S-1}{\displaystyle \sum _{x=z+1}^S{\widehat{\rho }}_{zx}}}\right)\sim S(0,1)$$

(5)

While in the presence of second-generation unit root is more efficient as compared to the conventional unit root tests because it beautifully incorporated the cross-sectional dependence and stationery accurately at the same time [93], as displayed in Equations (6) and (7).

$$CIPS={\displaystyle \frac{1}{S}}{\displaystyle \sum _{Z=1}^S{m}_z}(S,M)$$

(6)

$$\Delta B_{zm}={\phi }_z+{\zeta }_Z{B}_{z,m-1}+\delta z{\overline{B}}_{m-1}+{\displaystyle \sum _{x=0}^q{\delta }_{zx}}{\overline{B}}_{m-1}+{\displaystyle \sum _{x=1}^q{\lambda }_{zx}}\Delta B_{z,m-1}+{\epsilon }_{zm}$$

(7)

Additionally, for the long-term affiliation among the study factors when there is cross-sectional dependency prevailing, Westerlund is a more suitable technique as compared to other cointegration tests [94].

$$\begin{gathered} E_m=1/S{\displaystyle {\sum }_{z=1}^S{{\alpha }^{\prime }}_z}/FR({{\alpha }^{\prime }}_z) \\ {E}_a=1/S{\displaystyle {\sum }_{z=1}^{S}M{{\alpha }^{\prime }}_z}/({{\alpha }^{\prime }}_z(1) \\ {P}_m={{\alpha }^{\prime }}_z/FR({\alpha }^{\prime }) \\ {P}_a=M{\alpha }^{\prime } \end{gathered}$$

(8)

where the value of group statistics is shown as $E_a$ and $E_m$, and panel statistics are represented by $P_m$ and $P_a$. While the null hypothesis posits the absence of co-integration, the alternative hypothesis suggests the presence of a long-term relationship between the variables.

Given the ever-changing nature of the data included in the study and the fact that their present conduct is influenced by their previous behavior, it is necessary to utilize a dynamic panel model. Hence, the inherent variability of the model renders conventional Ordinary Least Squares (OLS) estimators ineffective, as they may exhibit bias and inconsistency owing to the association between the unobserved panel-level effects and the lagged dependent variable [39]. Hence, the fixed/random effect models employed for panel data fail to address the econometric challenges that are inherent in dynamic models. Arellano and Bond et al. [95] A novel GMM estimator is developed for the dynamic panel model to address the problems of endogeneity, which leads to biased results, and unobserved heterogeneity within banks, which is challenging to precisely measure. Their proposal involved including more instruments in the dynamic panel model and using other transformations. Subsequently, Arellano and Bover [96] and Blundell and Bond [97] suggested enhancing the Arellano and Bond estimator by placing further limitations on the initial conditions. This extension enables the incorporation of more instruments to enhance efficiency. This approach integrates the first difference in equations with equations at the level where the first differences in the variables are treated as instruments. A system of two equations (system GMM) is generated, consisting of one original equation and one altered equation.

The phenomenon of heteroskedasticity and autocorrelation is efficiently incorporated using the heteroskedastic OLS-GMM (HOLS-GMM). The HOLS-GMM estimation strategy empowers the estimation of parameters whereas bookkeeping for measurable concerns, consequently guaranteeing the consistency and effectiveness of the achieved coefficients.

4. Results and Estimations

The estimation starts with the presentation of the study factor trend in each country included in the estimation, as depicted in Figure 1.

The data in Figure 1 recognized that EI, ET, EG, TR, GI, and GG have an impact on their individual nations with a blue line, which demonstrates each nation’s reaction included within the think about.

This study employs the pairwise association to measure the correlation among the study factors, along with the variance inflation factor (VIF). The payoff is reported in

Table 3. Pairwise correlation and VIF.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	St.d	VIF	1/VIF
(1) EI	1.000						1.487
(2) ET	−0.576 *	1.000					2.179	1.52	0.656
	(0.000)
(3) EG	−0.071	0.029	1.000				0.808	1.30	0.769
	(0.055)	(0.441)
(4) TR	0.147 *	−0.279 *	−0.438 *	1.000			0.846	1.74	0.575
	(0.000)	(0.000)	(0.000)
(5) GI	0.256 *	−0.181 *	0.000	−0.126 *	1.000		2.047	1.11	0.899
	(0.000)	(0.000)	(0.994)	(0.001)
(6) GG	0.562 *	−0.578 *	−0.052	0.461 *	0.159 *	1.000	1.16	1.86	0.538
	(0.000)	(0.000)	(0.162)	(0.000)	(0.000)
Mean VIF							1.51

* p < 0.1.

.

The data in Table 3 uncover that there is a critical converse relationship between ET and EG but a positive affiliation with TR. In any case, there are no outstanding connections between EG and other variables. There are striking converse connections between TR, GI, and ET. On the other hand, as it were, GI and GV appear to have a measurably noteworthy positive association. Critical associations between GV, TR, and ET are found. A cruel VIF of 1.51 shows moo multicollinearity among the factors, and VIF values comfortably drop underneath the conventional threshold of (±5). This is often the result of utilizing VIF to analyze multicollinearity.

Further, this study employs cross-section dependency (CSD) to affirm the correlation among the subgroup of dataset and second-generation unit root to measure the stationery in the presence of CSD [92]. The results are displayed in

Table 4. CSD and second-generation unit root.

	CSD		CIPS		PSADF
Variables	CSD	p-Value	I (0)	I (1)	I (0)	I (1)
EI	38.27	0.000	−0.316	−3.084 ***	−1.753	−3.102 ***
ET	28.10	0.000	−1.575	−4.712 ***	−1.700	−3.070 ***
EG	21.30	0.000	−3.315 ***	−5.537 ***	−2.986 ***	−4.977 ***
TR	7.752	0.000	−1.396	−3.656 ***	−1.228	−2.471 ***
GI	19.65	0.000	−1.902	−3.792 ***	−1.352	−3.053 ***
GG	00.46	0.646	−1.984	−5.676 ***	−1.962	−3.960 ***

*** p < 0.01.

.

Table 4 comprises the comes about of second-generation unit root and cross-sectional reliance. CSD values speak to the degree of cross-sectional reliance. Noteworthy cross-sectional dependence is obvious in EI, ET, EG, TR, and GI, as demonstrated by their comparatively tall CSD values. Moreover, unit root tests utilizing the CIPS and PSADF methods appear to show that each variable shows non-stationary behavior, demonstrated by an integration arrangement of 1 (I (1)), autonomous of cross-sectional reliance.

Additionally, ref. [91] declares that in the panel dataset uniformity should be the same in all subgroups; otherwise, results will be compromised. The results of slope heterogeneity are presented in

Table 5. Slope heterogeneity.

Variables		$\tilde{\Delta }$		$\hat{\Delta }$ Adj
		Statistics	p-Value	Statistics	p-Value
Model 1	EI	16.590	0.257	19.663	0.000
Model 2	ET	12.631	0.152	14.970	0.000
Model 3	EG	0.774	0.439	0.917	0.359

.

Table 5 offers data on incline heterogeneity in a few models utilizing GDP, ET, and EI factors. To decide whether there are factually noteworthy contrasts within the relationship between free and subordinate factors between distinctive bunches or circumstances, the insights $\tilde{\Delta }$ and $\hat{\Delta }$ Adj appeared along with their related probabilities. The $\tilde{\Delta }$ Adj measurements appear solid to prove slant homogeneity for models involving ET and EI, recommending that there are critical bunch or condition-specific contrasts within the intelligent between these factors and the dependent variable. On the other hand, for the GDP variable, the $\tilde{\Delta }$ and $\tilde{\Delta }$ Adj measurements demonstrate that there is no considerable proof to negate slant homogeneity, showing a consistent interface between bunches and circumstances.

The Westerlund cointegration method is a significant advancement as it can be incorporated to involve variations in slope [98]. Furthermore, the test accounts for cross-sectional dependence. The results are reported in

Table 6. Westerlund cointegration.

	Dependent Variable	Cointegrating Vector	Statistic	p-Value
Model 1	EI	Variance ratio	2.1865 ***	0.0144
Model 2	ET	Variance ratio	1.5972 **	0.0551
Model 3	EG	Variance ratio	−2.9665 ***	0.0015

*** p < 0.01, ** p < 0.05

.

In Model 1, all the components concerned have long-term cointegration with financial effects, demonstrating that ET, EG, TR, GI, and GG have a long-term alliance with the EI. However, Model 2, where Et is subordinate and all others are on the inverse side, indicates a significant long-term association with the vitality movement. In any case, especially in Model 3, where EG is a subordinate figure and all others display as autonomous, appears to have a long-term connection with financial development. Subsequently, it may be concluded that all three models have indicated that these variables are altogether taking part long-term. Studies similar to Usman et al. [99,100] employed this method to deal with cross-sectional dependency in the subsistence of stationarity.

Finally, think about utilizing HOLS-GMM, which could be a measurable approach that altogether incorporated the endogeneity, serial relationship, or heteroscedasticity within the dataset. We utilized this technique to look at the effects of changes in the natural effect, vitality move, financial development, exchange designs, green development programs, and administration laws on long-term connection. The results are given in

Table 7. HOLS-GMM.

	(EI)	(ET)	(EG)	(ET*EG)	(EI*EG)	(EI*ET)
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
ET	−0.0517 ***
	(0.00353)
EG	−0.676 ***
	(0.203)
TR	−0.317 **		0.329 ***	−0.570 ***		0.457 ***
	(0.153)		(0.0598)	(0.160)		(0.0768)
GI	3.0506 ***	1.9306 ***	2.9507 *	3.5506 ***	1.0306 ***
	(4.9107)	(2.5607)	(1.7907)	(6.6807)	(2.2907)
GG	1.638 ***	−0.731 ***	−0.165 **	2.891 ***	−1.476 ***	−0.465 ***
	(0.147)	(0.0916)	(0.0669)	(0.199)	(0.0934)	(0.0887)
EI		−0.283 ***	−0.0304 **
		(0.0215)	(0.0137)
EG		−0.0221 ***
		(0.00813)
TR		−0.0036 ***			−0.00150 **
		(0.000663)			(0.000623)
ET			0.0856 ***
			(0.0283)
ET*EG				−0.00069 **
				(0.000351)
EI*EG					−0.00718 *
					(0.00435)
EI*ET						0.117 **
						(0.0555)
GI						0.0281 *
						(0.0158)
Constant	6.951 ***	3.785 ***	0.0111	5.354 ***	2.772 ***	−0.902 **
	(0.684)	(0.116)	(0.293)	(0.740)	(0.109)	(0.357)
Observations	584	726	584	660	726	413
R-squared	0.533	0.684	0.167	0.377	0.493	0.210

Robust standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

.

Table 7 reveals that in Model 1, there is a vital reverse relationship between energy transition and environmental effect. This proposes that as energy transition develops, there is a propensity for natural effects to diminish. Economic development too illustrates an inconvenient effect on the environment. By the by, the steady term proceeds to have a measurably noteworthy effect. In Model 2, the relationship between economic development and environmental impact remains negative, and the model’s capacity to clarify the relationship makes strides essentially, as demonstrated by an R-squared esteem of around 68.4%.

According to Model 3, variable trade incorporates an outstanding negative effect on environmental effects. This proposes that a rise in exchange seems to possibly lead to a diminishment in environmental impact. On the other hand, Green Advancement features a favorable effect on maintained alliances, recommending that it can help construct long-term associations. In Model 4, the relationship between energy transition and exchange includes a negative effect on natural effect, demonstrating that the combined impact of both factors diminishes natural effect. In any case, the useful impact of green development on natural effects remains consistent.

Model 5 uncovers a critical antagonistic effect on environmental maintainability when considering the interaction between economic development and energy transition. This highlights the complicated relationship between financial advancement and the move toward cleaner energy sources. Moreover, the relationship between economic development and green advancement moreover has an unfavorable effect on natural maintainability. At long last, Model 6 illustrates an eminent and significant relationship between vitality movement and green development, emphasizing the conceivable collaborative impact of both variables in advancing environmental affect. All things considered, the progressing negative relationship between economic extension and energy transition shows the challenges in accomplishing an adjustment between financial development and natural concerns.

In Model 4, we observe a negative relationship between ET and EG on environmental impact (EI). Also, the result of the interaction term (ET*EG) is negative, which shows a positive effect for energy transition and economic growth on environmental sustainability. This is because Energy Transition (ET) has a high coefficient (−0.0517), meaning that lower ET scores are associated with lower environmental impacts as economies shift towards cleaner forms of energy supply. Trade increases EG, a positive variable in Model 4 (+0.0856), as tradition VAT is negative for EG, and economic growth with trade raises the environmental impact. However, the interaction term ET*EG has a negative coefficient (−0.00069), demonstrating that cleaner energy policies only have a positive impact in an environmentally inefficient sector when also trade is carried out. This implies that the advantages of energy transition in reducing environmental impact may be reduced to some extent if trade patterns lead to increasing emissions, but it can compensate for negative trade effects. The letters ET and TR are used as independent variables here, following the convention. In line with econometric literature (i.e., interaction terms in regression enable us to consider the joint effect of multiple factors, hence making it possible to capture more complicated relationships).

Model 5 only analyzes the interactive effect between EG and ET. The significant negative effect of economic growth on environmental sustainability with energy transition conditioning is exhibited in the negative coefficients. The negative sign of the coefficient (−0.00718) for the interaction term EG*ET implies that economic growth and energy transition act to reduce environmental degradation jointly. In other words, not even the synergy between economic growth and energy transition can save us from crises such as this one for what economic growth costs to give its results, which normally have impacts (emissions or resource use feeds) on the environment. In instances where former control variables (EG) are turned into independent variables (specifically in interaction terms), it should be critically discussed why so. Nevertheless, the problem here is how to assess economic growth not in isolation but alongside transition to a different structure of energy use. I refer to the econometric literature for a discussion of interaction terms that can be used to explore joint effects. This approach can be justified by the Generalized Method of Moments (GMM) or system GMM frameworks, in which terms of interaction between growth and other variables are inserted to understand their compounded impacts on the environment.

The association of energy transition with environmental impact (EI), which contributes to environmental sustainability, is investigated in this study as shown in Model 6. The interaction between energy transition and green growth programs exerts a positive influence on environmental sustainability; appropriately enough, the ET*EI variable offers a largely significant positive coefficient (+0.117). This gorgeously illustrates the value of green development strategies in complementing the benefits of energy transition. This dimension contributes to the negative values, which indicate that economic development without greenness challenges sustainability.

In both cases, EG and ET are the independent variables; their joint effect is assessed to know how they interact with each other. Using dependent variables that had already been used in Equation (4) (e.g., GG) as independent variables was uncommon, unless these have been lagged or are part of a cointegration relationship with other factors; this is to avoid multicollinearity and to capture more nuanced relationships, as supported by the work of Kripfganz et al. [101], who use GMM techniques, and Acheampong et al. [102], on VAR models. Lagging or interaction modeling: A way to consider time dependencies.

In some models, ET and EG are independent variables because the interaction between them shows how they affect environmental sustainability together. A frequent approach to accounting for past influences on current outcomes is to include lagged dependent variables (e.g., the HOLS-GMM), and then the lagged variables have some roles, such as reducing the autocorrelation problem and describing how previous values of a dependent variable influence the current case.

The GMM estimation is convenient in dynamic panel data models when there are endogeneity, serial correlation, and heteroscedasticity issues. This material is a staple in GMM analysis, where we often include lagged dependent variables as instruments to avoid endogeneity. Assuming a dependent variable as an independent one is perfectly fine in cases like lags and interaction terms. This choice of modeling is also inspired by the literature on HOLS-GMM and dynamic panel models [95], both in helping deal with endogeneity as well as for correctly capturing the dynamics of variables over time.

To summarize, the models recommend that there is a negative relationship between energy transition and environmental impact, as well as economic growth. Trade development incorporates a hindering effect on long-term connection, but the presentation of environmentally inviting advancements contains a useful effect on it. The correlation between energy transition and exchange diminishes the durability of bonding. Moreover, the interaction between financial development and energy transition, as well as between economic development and green progress, has a prohibitive impact on long-term commitment. On the other hand, a favorable association between the vitality movement and green advancement suggests the plausibility of an advantageous collaboration in advancing long-lasting organizations. These discoveries emphasize the perplexing economic challenges related to overseeing development, trade, advancement, and environmental concerns in keeping up long-term connections.

Discussion

Environmental concerns are a pressing issue because human activity destroys the Earth’s ecosystems, leading to environmental degradation and appearing to be a big threat to biodiversity. Therefore, it is mandatory to consider strategies to appease the energy transition, economic growth, trade, green innovation, and good governance to reduce the environmental impact. Hence, the current study incorporated a panel dataset of (1990–2022) to constitute the correlation between study factors and environmental impact.

It is acknowledged that energy transition has a multifaceted influence on acclamation environmental impact. Chien et al. [103] accent the incorporation of energy, green innovation, and good governance to decline the environmental impact and to achieve sustainability. While Ullah et al. [104] highlight the noteworthiness of economic soundness, innovation, and government solidity in executing vitality move methodologies to meet environmental objectives. It is detailed that a 1% increment in vitality movement will decrease the natural effect by 0.0517%. The discoveries of current consider are upheld by the work of [105,106,107].

Economic growth is considered the core indicator of enhancing environmental degradation due to increased production and human activity [108]. Economic expansion has a crucial yet intricate influence on the environment, encompassing both the worsening of environmental degradation and the promotion of sustainable solutions [109]. Although development is regularly gone with by more noteworthy industrialization, urbanization, and utilization, which can result in contamination, territory misfortune, and asset consumption, it moreover encourages ventures in cleaner innovation, renewable vitality, and preservation activities. Upgraded financial success can develop increased natural awareness and reinforce backing for arrangements that advocate supportability [110]. The results indicate that a 1% increase in economic growth will influence the environmental impact by 0.676. The study results align with previous studies like [111,112,113].

The effect of trade on the environment is affected by an extent of economic, social, and environmental issues. Universal exchange can moderate environment disintegration, depending on the characteristics of the exchanged items, strategies of generation, and administrative frameworks [114]. Trade can contribute to environmental corruption through different implications. The extension of exchange regularly comes about in raised levels of generation and utilization, which in turn increments the request for common assets and energy-intensive items [115]. Subsequently, this will lead to increased extraction of crude materials, deforestation, living space pulverization, and the emanation of nursery gases. Moreover, trade can empower the movement of pollution-intensive businesses to nations with less rigid environmental controls, coming about in what is known as the “contamination safe house” impact [116]. The findings of [117,118,119] are in the line of findings of the current study.

The impact of green innovation on the environment is significant, providing transformative solutions to reduce deterioration and promote sustainability [120]. Green innovation facilitates businesses in decreasing their carbon footprint, minimizing waste, and conserving resources by promoting the advancement and acceptance of cleaner technology and practices [121]. Moreover, it encourages the move towards a circular economy, in which assets are utilized with more noteworthy effectiveness and squander is minimized through the forms of reusing and reuse. Green development envelops more than fair mechanical breakthroughs [122]. It also includes changes in business structures, consumer behavior, and governmental frameworks. This encourages the adoption of sustainable practices across different industries. The work of [123,124,125] is consistent with this study findings.

The impact of good governance on the environment is significant, as it shapes policies and laws that promote sustainability and reduce deterioration [126]. Good governance ensures environmental protection by implementing efficient management practices, promoting openness, and encouraging active participation [127]. This cultivates responsibility and guarantees compliance with set-up systems. Moreover, it moves forward the capacity of teaching to handle errands, making it simpler for them to arrange and work together to attain coordinated natural administration [128]. In addition, by maintaining the principles of legality and guaranteeing the availability of legal remedies, effective governance prevents environmental infractions and encourages the implementation of regulations. The work of [129,130,131] supports the finding of this work.

The pressing necessity to shift towards cleaner, renewable energy sources is highlighted by environmental degradation caused by unsustainable practices and pollution. Economic development can at the same time encourage and block this move; whereas improved riches may outfit the implies for contributing to renewable vitality and cultivating advancement, it can moreover support reliance on fossil fuels and businesses that hurt the environment [132]. Trade plays a vital part within the exchange of innovation and speculation, and its effect on energy transition can either speed up or ruin advance, depending on the kind of merchandise being sold and the administrative systems in place [133]. Green innovation is crucial for facilitating the shift towards renewable energy sources. It involves the creation and implementation of environmentally friendly technologies and methods to decrease carbon emissions and improve energy efficiency [134]. Effective governance is essential for establishing a favorable atmosphere for the shift towards sustainable energy.

Unsustainable hones and contamination can cause natural weakening, which in turn can prevent financial development by draining characteristic assets, raising wellbeing costs, and exasperating environments. On the opposite, moving to more ecologically inviting vitality sources and executing economical hones can advance financial development through the foundation of modern divisions, the creation of occupations, and the diminishment of long-term environmental costs [135]. Trade is a vital factor in helping economic growth as it enables the exchange of commodities, services, and technologies, which in turn stimulate innovation and productivity. Moreover, the usage and utilization of environmental neighborly headways, such as clean innovation and feasible hones, can contribute to economic extension by advancing expanded effectiveness, bringing down fabricating costs, and producing new roads for showcase development. Great administration, which is characterized by clear and open rules, productive strategies of authorization, and dependable organizations, sets up the premise for long-lasting economic advancement by ensuring solidity, empowering belief in ventures, and supporting mindful administration of assets [136].

5. Conclusions and Policy Implications

The objective of this study was to investigate the potential benefits of enforcing environmental legislation in facilitating a sustainable and optimized energy transition within the nation. Additionally, HOLS-GMM was utilized to capture the impact of the considerable factors in three different models. HOLS-GMM uncovered that components are related to characterizing long-term alliances with natural affect, good governance, and financial development. Moreover, AMG and Driscoll Kraay agree that they all have a critical impact on natural effects. By doing thorough data analysis and employing rigorous modeling mechanisms, we have derived significant findings that are relevant for policymakers, industries, and society at large. The results of our study demonstrate that environmental control significantly contributes to enhancing the energy transition performance in selected Asian economies. Implementing more stringent environmental regulations and measures can incentivize firms to embrace cleaner and more sustainable energy practices, therefore supporting the nation’s endeavors to combat climate change and decrease greenhouse gas emissions. The findings emphasize the need to implement strong and focused environmental legislation that is designed to tackle particular regional and industry issues.

5.1. Policy Implications

Considering these discoveries, arrangements relating to the environment are created at the administrative and regulatory levels.

• To lessen the impact on the environment, comprehensive approaches that bolster vitality move, empower green advancement, and move forward great administration hones ought to be put into put. Advancing venture in clean vitality advances counting hydropower, solar-based, and wind vitality to rush the move away from fossil powers and diminish nursery gas emanations. supporting R&D ventures that goad development in economical hones and clean vitality innovation, progressing natural maintainability within the handle.
• Adopt laws that energize speculation in green innovations, renewable vitality sources, and ecologically inviting frameworks to cultivate maintainable financial development. This will incorporate awards, charge breaks, and endowments for companies that utilize eco-friendly strategies. To reduce the inconvenient impacts of financial development on the environment, natural laws and authorization laws ought to be fortified. This may involve controlling contamination releases, defending characteristic zones, and applying sanctions for natural infractions. Empower ecologically neighborly hones of generation and utilization to cut down on squander yield, asset utilization, and contamination. To diminish their natural effect, firms ought to be energized to execute circular economic thoughts, such as reusing and reuse.
• To ensure that exchange arrangements bolster feasible development and natural preservation, incorporate natural issues in exchange ascension. This could incorporate empowering eco-labeling, setting natural guidelines, and supporting the sharing of eco-friendly hones and advances. Empower companies to execute eco-friendly supply chain techniques that diminish their effect on the environment at each organization of the fabricating and conveyance handle. This could include utilizing energy-efficient transportation strategies, cutting back on squander generation, and obtaining crude materials dependable. To address the negative natural externalities connected to exchange, natural rules and requirement strategies ought to be fortified. This may involve controlling contamination releases, shielding characteristic regions, and applying sanctions for natural infractions. Request natural affect assessments for ventures, investments, and exchange understandings to look at potential natural repercussions and decide countermeasures. This will ensure that exchange operations are carried out in a way that minimizes natural harm.
• Financing bolsters, awards, and money-related motivations ought to be made accessible for the headway of green innovation and feasible hones. This may advance inventiveness and hurry the selection of eco-friendly innovations. Set up an enactment that energizes companies to contribute to green advances and grasp feasible hones. This may incorporate ordered natural measures, charge breaks, and item endowments for ecologically inviting products. Energize participation between the open segment, private segment, scholarly teaching, and gracious society to develop green advancement and agreeably handle natural issues. Public-private organizations for feasible advancement, agreeable investigating ventures, and innovation exchange activities can all be a portion of this.
• Make speculations to fortify natural administration organizations’ capabilities so they can make and carry out effective rules, approaches, and requirement frameworks. Empower openness and inclusivity within the advancement and application of natural policies by cultivating straightforwardness within the decision-making forms. Give frameworks of responsibility to create open hirelings and government offices liable for their natural stewardship. Energize citizen cooperation in environmental decision-making forms so that individuals can impact the creation and execution of environmental activities and approaches.

5.2. Study Limitations and Future Study Directions

This study has certain limitations. Initially, only energy variables and two economic parameters were scrutinized to assess environmental impacts. Additional factors, like green finance, corporate internal environmental policies, and GDP growth, also influence the environment. Prospective authors are advised to incorporate relevant elements influencing the environment. This study gathers data on the influence of EG, REC, energy consumption, GG, and trade on EI exclusively from Asian countries, a confined region characterized by distinct natural resources, environmental quality, and economic conditions. This study’s findings cannot be extrapolated to all countries, which may serve as a focal point for future research.