Innovation-Led Environmental Sustainability in Vietnam—Towards a Green Future

Abstract

The motivation of the study is to assess the role of education, technological innovation, financial innovation, and clean energy consumption trade openness on environmental sustainability in Vietnam. The study implemented symmetric and asymmetric frameworks to document explanatory variables’ coefficients on ES. Study findings suggest that education, innovation, and clean energy prompt environmental sustainability by lowering the carbon emission and ecological imbalance in the long and short run. Regarding the asymmetric assessment, the standard Wald test confirmed the asymmetric association in the long run and short run. For directional causality, the study implemented the TY–Fourier causality test. It revealed bidirectional causality between technological innovation and environmental sustainability. In contrast, unidirectional causality ran from education to ES and FDI to ES. Policy recommendations have been derived from the empirical findings of both symmetric and asymmetric investigations. These recommendations highlight the importance of prioritizing investments in education and technological innovation to enhance environmental sustainability. Moreover, promoting clean energy technologies and encouraging financial innovations can serve as additional catalysts for advancing sustainable practices. The implications of the policy as mentioned above offers valuable insights for policymakers and stakeholders in their endeavor to develop strategies to achieve environmental sustainability in Vietnam. While the findings contribute to our understanding of the determinants of ES in Vietnam, it is important to note that the study’s scope is limited to the examined determinants. Other factors not included in the study may also have a significant role.

1. Introduction

Given the escalating severity of climate change and environmental degradation, environmental sustainability has recently surfaced as a crucial worldwide priority [1,2,3]. As a result of the concerning rise in greenhouse gas emissions, the exhaustion of natural resources, and the decline of biodiversity, it is imperative that we fully grasp the importance of environmental sustainability and its impact on the welfare of current and future generations. The degradation of the environment has a direct impact on human health. Exposure to hazardous substances, air and water pollution, and the disruption of ecosystems are all factors that contribute to a range of health issues, including respiratory diseases, waterborne illnesses, and the propagation of infectious diseases. Implementing environmental sustainability measures, such as reducing pollution, promoting renewable energy, and enhancing waste management, can significantly enhance public health outcomes. Several studies have indicated that environmental sustainability has a beneficial impact on mitigating disease burden and improving overall health [4,5]. Contrary to conventional belief, environmental sustainability and economic development are not mutually exclusive. Indeed, they are intricately interconnected. Sustainable practices and innovations have the potential to fuel economic growth, job creation, and technological advancements. Adopting renewable energy sources, allocating resources towards sustainable infrastructure, and incorporating environmentally conscious business practices can yield substantial economic advantages in the long run [6,7,8,9,10,11]. Research consistently underscores the potential of green industries, including renewable energy, ecotourism, and sustainable agriculture, to create economic prospects and foster inclusive growth [8,12,13,14,15]. Environmental sustainability is an essential requirement that cannot be overemphasized due to the importance of environmental sustainability, as evidenced by scientific research and literature, as it plays a crucial role in mitigating climate change, preserving ecosystems, ensuring resource security, safeguarding human health, and promoting socioeconomic development. Achieving sustainability requires collaborative efforts, inventive approaches, and the integration of sustainable principles across all societal domains, prioritizing environmental concerns [16,17,18].

The emission of greenhouse gases from the combustion of fossil fuels, deforestation, and industrial activities has caused an increase in global temperatures and severe weather phenomena. Mitigating climate change through environmental sustainability requires the reduction of greenhouse gas emissions, adopting renewable energy sources, and implementing sustainable practices across sectors. Several studies have highlighted the crucial necessity of limiting global warming to avert catastrophic impacts on ecosystems, communities, and economies [19,20,21]. Ecosystems furnish essential services, such as unpolluted air, water, sustenance, and pharmaceuticals, that are vital for sustaining life on our planet. Nonetheless, unsustainable human activities, including but not limited to habitat devastation, pollution, and overexploitation of natural resources, have led to the swift deterioration of ecosystems and the depletion of biodiversity. Scientific research underscores the importance of environmental sustainability in the preservation of ecosystems and the protection of biodiversity. Healthy ecosystems are crucial in regulating the climate, cycling nutrients, and maintaining ecological balance. These benefits ultimately translate to improved food security, water availability, and overall well-being for human societies. As the global population grows, so does the demand for natural resources; nonetheless, several resources, such as potable water, fossil fuels, and minerals, are dwindling at an alarming rate due to their limited availability. Ensuring resource security through environmental sustainability requires promoting responsible consumption, efficient resource management, and developing circular economies [22]. To avert resource scarcity, conflicts, and socioeconomic disruptions in the future, it is crucial to transition toward sustainable resource management [23,24].

Referring to the existing literature dealing with environmental sustainably and environmental degradation, a growing number of studies have been executed to reveal the key macro fundamentals that are critically associated with environmental degradation, such as financial development, foreign direct investment, clean energy inclusion, remittances, urbanization, and globalization [7,25,26,27,28,29,30,31,32,33]. An interesting finding is that the literature has yet to offer conclusive pieces of evidence, suggesting the geographical position and economic structure of the economy have experienced different influences in the process of environmental changes. The present study has considered financial innovation, technological innovation, clean energy, education, and trade in the environmental equation, sustainably. Existing literature focuses on the nexus of innovation-led environmental sustainability. It postulates that numerous environmental issues we face today can only be resolved through innovation [34,35]. Both financial and technological innovation can be utilized to promote sustainability in various industries [36]. In recent years, financial innovations such as ecological bonds and impact investing have garnered popularity, which aims to finance projects that promote sustainability by connecting investors with environmentally conscious businesses and public institutions [28]. By providing these sustainable businesses with financial assistance, they can operate more efficiently without negatively impacting the environment. Technological advances such as renewable energy sources significantly contribute to environmental sustainability. The use of solar panels and wind turbines for power generation has increased dramatically in recent years. Due to their lower carbon emissions compared to gas-powered vehicles, electric vehicles are acquiring popularity [37,38,39,40]. Agriculture innovations like vertical farming enable crops to be cultivated on much less land and water. Innovators are also designing refuse recycling systems that prevent pollution from infiltrating our oceans and other natural habitats. Innovative solutions provide a path to long-term environmental sustainability in all societal sectors, including industry, finance, and everyday life [41].

As a case study, Vietnam has been considered for empirical investigation. Vietnam has experienced significant economic impacts due to environmental degradation. Rapid industrialization and urbanization have increased pollution, negatively impacting public health and the economy [42]. The impact of environmental degradation on tourism is one of the most notable effects of this phenomenon. Vietnam’s natural grandeur is one of the country’s greatest tourist attractions. However, pollution has made many areas less appealing to visitors, leading to decreased revenue for businesses that rely on tourism and wasted opportunities for employment creation. The impact on agriculture is another economic consequence. As a result of soil contamination and water pollution, producers are confronted with reduced crop yields or crop failures. Moreover, environmental calamities caused by deforestation, such as floods and landslides, can cause infrastructure damage costing billions of dollars to restore and disrupting supply chains that negatively impact business operations [7,43]. These economic effects emphasize the need for immediate action against environmental degradation before it causes irreversible damage. Vietnam, a land of great cultural legacy and breathtaking natural wonders, faces environmental challenges due to rapid industrialization and urbanization. Despite great challenges, Vietnam is making important progress toward environmental sustainability through innovative initiatives. Vietnam is walking towards a more sustainable future by utilizing the latest technological innovations and implementing policies that look ahead. Vietnam is adopting sustainable energy sources to lessen its dependence on fossil fuels and fight against climate change. Great strides have been taken in advancing solar and wind energy projects. Vietnam’s land and sky have blessed us with a prime location and ample sunshine, allowing us to excel in harnessing the sun’s power. To promote the development of renewable energy infrastructure, the Vietnamese government has implemented favorable policies, such as feed-in tariffs. As a result, the tribe’s solar power capacity has increased significantly, making it a regional leader in solar energy production. Vietnam strongly advocates for inventive agricultural methods, acknowledging the importance of sustainable agriculture for preserving food security and the environment. The tribe has adopted nature-friendly traditions like hydroponics, natural farming, and meticulous agriculture. These ways lessen harmful pesticides and fertilizers, decrease soil erosion, and increase water usage [6,44]. Furthermore, Vietnam is embracing intelligent agricultural technologies, such as Internet of Things (IoT) tools and data analysis, to enhance efficiency and minimize environmental effects. With great emphasis on the circular economy, Vietnam is taking strong measures to tackle waste management concerns. The Vietnamese council has set strict laws to reduce waste and encourage recycling. Furthermore, creative waste-to-energy endeavors that transform waste into electricity or other advantageous resources have gained momentum. Vietnam aims to lessen the amount of garbage in landfills and endorse a sustainable method of waste control by applying the principles of decrease, reuse, and recycling.

By taking into account the perused motivation and empirical model, the present study has intended to address the following research questions: First, how does technological innovation contribute to environmental sustainability in Vietnam? Second, what is the impact of financial innovation on environmental sustainability in Vietnam? Third, how does clean energy consumption influence environmental sustainability in Vietnam? The research questions aim to examine the distinct roles and interconnections of technological innovation, financial innovation, consumption of renewable energy, and trade openness in promoting environmental sustainability in Vietnam. Additionally, the study endeavors to attain a comprehensive understanding of the various factors that contribute to environmental sustainability. Furthermore, it seeks to provide valuable insights that can inform and guide policy-making and decision-making processes in Vietnam’s ongoing efforts to achieve a greener and more sustainable future. Technological innovation helps to ensure environmental sustainability. Specifically, when technical advances are achieved, such as the creation of cleaner and more efficient technologies, it is envisaged that the environmental effect will be decreased, including fewer carbon emissions and enhanced ecological balance. As a result, the hypothesis for this research topic is as follows:

Hypothesis 1 (H1).

Technological innovation improves environmental sustainability.

Environmental sustainability is influenced by financial innovation. Financial innovations, such as the implementation of green financing systems and sustainable investing practices, are speculated to offer the required funds and incentives for ecologically friendly projects and activities. As a result, the hypothesis for this research topic is as follows:

Hypothesis 2 (H2).

Financial innovation has a favorable impact on environmental sustainability.

Clean energy usage is intended to benefit environmental sustainability. It is expected that, when clean energy sources, such as renewable energy, are used to replace fossil fuels, carbon emissions will decrease and ecological balance will improve. As a result, the hypothesis for this research topic is as follows:

Hypothesis 3 (H3).

Consumption of clean energy has a favorable impact on environmental sustainability.

These hypotheses provide the basis for empirical testing and analysis, with the goal of providing insights into the links and effects of technical innovation, financial innovation, and renewable energy usage on environmental sustainability.

This study contributes to the current knowledge on the determinants of environmental sustainability by analyzing the interrelatedness and correlations among financial innovation, technological innovation, clean energy, education, FDI, and environmental sustainability in Vietnam. The major contributions of the study are as follows: First, the study employs a comprehensive methodology to examine the interdependence between financial innovation, technical innovation, clean energy, education, FDI, and environmental sustainability. Upon considering these aspects collectively, the research offers a comprehensive analysis of the intricate interconnections and their combined impact on environmental sustainability in Vietnam. Second, although a burgeoning body of literature exists on environmental sustainability and its determinants, this study is specifically centered on the Vietnamese context. Vietnam’s unique socioeconomic and environmental characteristics render it a compelling subject for examining the challenges associated with achieving environmental sustainability in a rapidly developing country. The study’s results contribute to the limited literature on Vietnam and provide valuable insights for policymakers and stakeholders. Third, the study holds significant policy implications as it reveals the interdependencies among financial innovation, technical innovation, clean energy, education, FDI, and environmental sustainability. This study’s outcomes can assist policymakers and practitioners in Vietnam in formulating efficacious policies and interventions to enhance environmental sustainability. The study’s recommendations could potentially facilitate the development of policies that endorse financial and technical innovation, entice foreign direct investment, augment education and skill enhancement, and encourage the adoption of clean energy technology, ultimately resulting in improved environmental sustainability outcomes. Furthermore, the study fills a research gap by examining the limited literature on the correlation between financial innovation, technical innovation, clean energy, education, FDI, and environmental sustainability in Vietnam. This research contributes to a comprehensive understanding of the factors that impact environmental sustainability within the country by examining these connections and their collective impact. It establishes the trajectory for subsequent research and is a foundation for future investigations within this domain.

The remaining structure of the study is as follows: Section 2 deals with the overview of environmental performance along with the different initiatives initiated and implemented by the nation with the intention of environmental correction. The relevant literature survey is displayed in Section 3. The theoretical development and empirical model justification are displayed in Section 4. The data and methodology of the study are reported in Section 5. The results and their interpretation are available in Section 6. Section 6 discusses the study findings, and the conclusion and policy suggestions for future development can be found in Section 7.

2. Environmental Performances in Vietnam

In recent years, environmental changes have become increasingly apparent in Vietnam, a rapidly developing and dynamic nation in Southeast Asia. Vietnam is faced with various environmental challenges, including but not limited to the impacts of climate change, pollution, and deforestation. The government has taken noteworthy measures towards enhancing the environment and promoting sustainable development. This article employs statistical analysis to scrutinize environmental changes in Vietnam. It highlights endeavors aimed at achieving a more ecologically sound and sustainable future.

Vietnam exhibits a high degree of sensitivity towards the impacts of climate change, such as elevated mean temperatures, altered precipitation regimes, and an increased frequency of severe weather incidents. According to statistics provided by the Ministry of Natural Resources and Environment, there has been a gradual increase in average temperatures in Vietnam over the past 60 years, with a rise of 0.5 °C per decade. The agricultural seasons have shifted, resulting in altered water availability and an increased probability of floods and droughts. Vietnam’s significant deforestation issue has been caused by illegal logging, land conversion, and agricultural expansion. The proportion of land area occupied by trees in the country has decreased from 43% during the 1940s to less than 30%. Deforestation has direct consequences such as biodiversity loss, soil erosion, and reduced carbon sequestration capacity. These effects further worsen the impact of climate change.

Furthermore, the escalation of industry and urbanization in Vietnam has increased air and water pollution. In urban centers, air quality has declined due to industrial pollutants, vehicular emissions, and agricultural practices. Water pollution from sewage overflows, agricultural runoff, and industrial waste has adversely affected rivers, lakes, and coasts, damaging aquatic ecosystems and human health. Vietnam’s National Climate Change Strategy prioritizes adaptation and resilience in response to climate change, representing a significant environmental development effort. Incorporating climate-resilient infrastructure, improving early warning systems, and promoting climate-smart agricultural techniques are integral components of the strategy. Furthermore, the government has implemented initiatives to fortify coastal regions, protect vulnerable populations, and mitigate the risks of catastrophes. Vietnam has established protected areas and implemented reforestation initiatives in recognition of the crucial role of trees in conserving biodiversity and sequestering carbon dioxide. The national target for forestation is set at 42%, to be achieved by the year 2020. The Payments for Forest Environmental Services (PFES) programs offer monetary incentives to forest landowners and community members in exchange for their efforts to preserve and promote the well-being of forest ecosystems.

Vietnam has endeavored to curtail air and water pollution by implementing more stringent environmental regulations, and similar initiatives have been pursued concerning waste disposal. The government has implemented various measures, including the establishment of wastewater treatment facilities, initiatives aimed at promoting cleaner output across sectors, and the imposition of car emission limits. Initiatives such as “Clean Up the World” strive to educate the general public on the importance of appropriate waste management, recycling, and mitigating plastic pollution. The Vietnamese government is advocating for promoting renewable energy sources to decrease reliance on non-renewable energy sources. The government’s energy policy aims to enhance the proportion of renewable energy sources in the overall energy blend. In pursuit of achieving a 20% renewable energy generation target by 2030, significant investments have been allocated toward wind and solar power projects. The promotion of investments has been facilitated by implementing feed-in tariffs and other tax incentives.

Climate change, deforestation, and pollution significantly affect Vietnam’s environmental degradation. Nevertheless, the government has demonstrated its commitment to environmental progress through various initiatives. Vietnam is endeavoring to achieve a sustainable and eco-friendly future through various initiatives, including reforestation, pollution mitigation, and advancing renewable energy. Achieving long-term environmental sustainability in Vietnam necessitates continuous endeavors supported by evidence-based decision making and the enthusiastic involvement of all concerned parties. Vietnam can become a model leader in the environmental protection movement, which can be achieved by safeguarding its natural resources, adapting to the impacts of climate change, and promoting sustainable activities.

Vietnam has experienced significant environmental degradation in recent years, negatively affecting its people, economy, and environment. Vietnam has one of the greatest deforestation rates globally, with an estimated loss of 2% of its forests annually, resulting in soil erosion, biodiversity loss, and increased susceptibility to natural disasters like floods and landslides [45]. Vietnam’s rivers and coastal waters are extensively polluted due to industrial and agricultural activities and untreated effluent. The pollution has resulted in declining fish populations and health issues for those living near contaminated water sources. Vietnam’s main cities, especially Hanoi and Ho Chi Minh City, have some of the world’s highest levels of air pollution, predominantly due to industrial emissions and vehicle exhaust [46]. Pollution has been associated with respiratory and other health concerns. Vietnam’s intensive agricultural practices have degraded the soil, diminished its fertility, and increased its susceptibility to erosion.

Vietnam’s air quality has been a major concern, particularly in urban areas. According to the Air Quality Index (AQI), the average air quality in Hanoi and Ho Chi Minh City in 2020 was “unhealthy for sensitive groups”, with AQI values ranging from 100 to 200. However, due to the COVID-19 pandemic, the air quality in Vietnam in 2020 and 2021 improved significantly, with AQI values ranging from 50 to 100, which is considered moderate air quality. Water bodies have also been under threat from pollution. According to a 2020 report by the Ministry of Natural Resources and Environment, only 34% of industrial zones in Vietnam meet environmental standards, and many wastewater treatment plants are outdated or poorly maintained. However, the report also noted the percentage of industrial parks with wastewater. Overall, Vietnam has committed to improving its environmental performance, but there is still a long way to go. The government and businesses must work together to ensure sustainable development, and individuals must also play their part in reducing their environmental impact. See Figure 1 exhibiting the trend in REC over the period.

Vietnam has experienced a surge in carbon emissions in recent years, attributed to its swift economic expansion and industrialization (see Figure 2). The nation heavily depends on fossil fuels, specifically coal, to generate electricity, significantly contributing to its carbon emissions. Vietnam has acknowledged the significance of decreasing carbon emissions. It has established ambitious objectives to alleviate the impact of climate change.

Vietnam has made significant progress in promoting the consumption of renewable energy. The government has implemented policies and incentives to attract investments in renewable energy projects, focusing on solar and wind power. The proportion of renewable energy in Vietnam’s energy composition has been progressively rising. Large-scale solar and wind projects have been implemented nationwide, contributing to expanding renewable energy consumption. Vietnam has established objectives to enhance the proportion of renewable energy in its energy composition and diminish greenhouse gas discharges. The nation’s objective is to attain a 10% share of renewable energy in the overall primary energy supply by 2030, followed by a 20% share by 2050. The entity in question has committed to decrease greenhouse gas emissions by 8% by 2030, per its Nationally Determined Contributions (NDCs) under the Paris Agreement [47].

The Vietnamese government recognizes the seriousness of environmental deterioration and has taken measures to counteract it. One of the key measures is the application of laws and regulations that aim to minimize pollution levels in areas such as agriculture, manufacturing, and transportation. Vietnam’s Ministry of Natural Resources and Environment (MONRE) is responsible for environmental preservation efforts which involve monitoring air and water quality, waste management techniques, and other environmental issues. In addition, the organization works closely with local governments to guarantee compliance with national rules. Significantly, the government invests in sustainable energy sources such as solar and wind power. In recent years, substantial investments have been made in hydroelectric facilities, which offer a clean power source for a nation noted for its rivers. Despite these attempts, the Vietnamese government continues to struggle against environmental deterioration. In certain locations, enforcement remains inadequate due to corruption or a lack of resources necessary for successful implementation at all levels. Despite not being ideal, the policies enacted by MONRE on behalf of the Vietnamese people, who deserve sustainable progress without sacrificing the environment, have made encouraging strides toward resolving significant environmental degradation challenges.

3. Literature Survey

3.1. Technological Innovation, Financial Innovation, and Energy Consumption

Technical innovation is the creation of new technologies or the improvement of existing ones. Almost every aspect of our life has altered due to technological advancements, from cell phones and computers to sustainable energy sources like solar panels and wind turbines. Those improved our quality of life and increased our independence. One of the most significant technological advancements contributing to environmental protection is using renewable energy sources like solar and wind power. By using these forms of energy, we become less dependent on finite resources like coal and oil, which greatly contribute to climate change and air pollution [47]. Systems for smart home automation are yet another significant example of how technological advancements have significantly improved sustainability. Homeowners may utilize these systems to monitor their energy use and ensure it is done as effectively as possible. As a result, power costs are reduced, and the carbon footprint is reduced. Since they are eco-friendly, green technologies like electric vehicles (EVs) are also gaining popularity. Unlike conventional gas-powered automobiles, EVs do not emit hazardous pollutants into the air, producing many greenhouse gases contributing to global warming.

An empirical study by Cheng et al. [48] revealed that technology innovation directly reduces CO₂ emissions, but this effect is asymmetric and heterogeneous. Additionally, based on China, Lin and Zhu [49] observed that the intensive emission of CO₂ has accelerated technological innovation for renewable energy, which means that innovation is responding actively to climate change. Again, enterprises and governments both have a role in promoting innovation through R&D investment. However, in Malaysia, Yii and Geetha [50] found a negative correlation between technology innovation and CO₂ emissions in the short term. However, there is no relationship in the long run. In the case of RCEP and non-RCEP countries, Zhao et al. [51] found that neither financial risk nor technological innovation significantly affected CO₂ emissions in RCEP countries. In contrast, they affected CO₂ emissions negatively in non-RCEP countries. A study by Chen and Lee [52] revealed that CO₂ emissions are not significantly mitigated by technological innovation. However, group-based studies have found that technological innovations can significantly reduce CO₂ emissions in neighboring countries. In contrast, R&D intensity in other countries increases them, and the greater the level of globalization in a country, the more obvious the effect technological innovation has on reducing CO₂. On the other hand, a recent study by Adebayo et al. [53] mentioned that renewable energy reduces CO₂. In contrast, trade openness, technological innovation, and economic growth increase it. As a result, policymakers in Portugal should encourage investment in renewable energy sources, create restrictive laws, and enhance energy innovation to reduce CO₂ emissions in the medium and long run. Similarly, it is also noted by Wen et al. [54] that the ratio of technical equipment to technical personnel has a significant impact on the reduction of construction carbon emissions [55]. Additionally, Adebayo et al. [56] found that increasing technology adoption, exporting high-tech products, and consuming electric power cause CO₂ emissions to rise in BRICS economies.

In the case of China and India, Fan and Hossain [57] stated that economic growth in China is affected by technological innovation, trade openness, and CO₂ emissions on a long-term basis; however, for India, these variables have no effect. However, from the study of Godil et al. [58], the authors concluded that transport greenhouse gas emissions (CO₂)₄ are negatively impacted by both renewable energy consumption and innovation. Additionally, it is confirmed by Lin and Zhu [59] that renewable energy technological innovation reduces CO₂ emissions significantly. Ali et al. [60] performed an empirical study based on Malaysia; during the study period, TI was found to have a negative, but insignificant, relationship with environmental pollution in Malaysia. Cristina De Stefano et al. [61] performed empirical research based on the automaker’s industry, showing that clean technology innovations have significantly reduced CO₂ emissions from vehicles.

Existing literature demonstrates how financial innovation may benefit the environment. To achieve the objectives of the Paris Agreement on climate change, it is critical to incentivize private investment in low-carbon and climate-resilient infrastructure, according to a new World Bank report. The research discovered that “green bonds,” or bonds intended to support environmentally friendly initiatives, might be a crucial source of private funding for climate-related projects. The market for green bonds has expanded rapidly in recent years. They were sold for US$171 billion in 2017, an increase from US$7 billion in 2012. The research also discovered that other financial tools, such as carbon credits and energy efficiency loans, may aid in securing private funding for climate-related initiatives. For instance, carbon credits may incentivize companies to reduce their emissions. At the same time, energy efficiency loans can assist firms in investing in energy-saving equipment. The research suggests that financial innovation to support environmental sustainability may succeed. The appropriate laws and regulations might attract billions of dollars in private funding for climate-friendly initiatives all around the globe.

BRIC [62] has investigated the nexus between innovation (financial and technological innovation)-led environmental quality for the period by employing AGM and CCEMG. The study documented a positive and statistically significant link between innovation and environmental quality; precisely, technological and financial innovation significantly declines carbon emissions, especially in the long run. The author of [63] examines various approaches and technologies that can be utilized to promote sustainable finance and provides an overview of the current state of green finance. The authors contend that financial innovation can be crucial in promoting sustainable development by promoting green investments, financing low-carbon technologies, and nurturing environmental consciousness among financial institutions and investors. As evidenced in China, the author of [64] investigates the connection between financial innovation and environmental sustainability in China. The authors conclude that financial innovation positively affects environmental sustainability, especially in energy conservation and emission reduction. The study also suggests that regulatory policies and incentives can be utilized to promote sustainable financial practices. The study of [65] examines the relationship between financial innovation and environmental sustainability. This research investigates the potential for carbon markets to promote environmental sustainability via financial innovation. The authors contend that carbon markets can aid in reducing greenhouse gas emissions by incentivizing businesses to invest in low-carbon technologies and practices. However, the study also observes that carbon markets can be complex and require cautious regulation to achieve environmental objectives. This study [66] examines recent developments in environmental finance, including green bonds and impact investing. The study contends that these financial instruments can play a significant role in advancing sustainable development by establishing new sources of capital for green investments and nurturing environmental consciousness among investors and financial institutions.

Existing literature indicates that financial innovation can positively affect environmental sustainability by promoting green investments, financing low-carbon technologies, creating economic incentives for sustainable practices, and nurturing environmental awareness. Nevertheless, prudent regulation and supervision may be required to ensure financial innovation is consistent with environmental sustainability objectives. The financial industry has shown a growing interest in environmental preservation in recent years, largely because sustainable development presents the industry with threats and possibilities. On the one hand, environmental problems like climate change may lead to financial instability since they can harm infrastructure and disrupt the economy. Conversely, financial institutions may benefit financially by investing in sustainable development. More and more evidence is emerging to support the notion that sustainable development may be profitable. Many studies have shown that businesses that perform well in environmental, social, and governance (ESG) often outperform their competitors because organizations that endure often function more efficiently, spend less money, and can better withstand shocks, implying that environmental sustainability presents both dangers and possibilities for financial organizations. The correlation between education and environmental sustainability has garnered considerable attention in scholarly literature as societies strive to tackle pressing environmental issues. This literature review analyzes significant studies and findings that underscore the crucial function of education in advancing environmental sustainability. This review underscores the importance of education in promoting favorable environmental outcomes. It does so by analyzing the correlation between education and environmental consciousness, the modification of behavior, the formulation of policies, and the fostering of sustainable values [62,64].

Several studies emphasize the significance of education in promoting environmental awareness and knowledge [44,67]. The research conducted by [68] indicates that environmental education programs positively impact students’ understanding of ecological systems, natural resource management, and sustainability principles. The research by [69] indicates that environmental education can enhance individuals’ connection with nature by promoting pro-environmental attitudes and increasing environmental awareness. The role of education is pivotal in motivating sustainable behavior change [70]. Moreover, ref. [71] has reported that environmental education initiatives have effectively promoted sustainable behaviors, including but not limited to pollution reduction, energy conservation, and sustainable transportation. The studies above showcase education’s importance in enabling individuals to make well-informed decisions and embrace environmentally sustainable practices.

Education has an impact on both environmental policy and development. The research conducted by [72] emphasizes the importance of education in fostering environmental citizenship and enabling individuals to engage in policy-making procedures. Education empowers individuals to champion sustainable policies, engage in decision-making processes, and demand government accountability regarding environmental stewardship by promoting environmental literacy. The acquisition of knowledge and skills through education plays a crucial role in shaping values and attitudes related to sustainability. Based on the findings of [73,74], it can be inferred that environmental education initiatives play a significant role in fostering values such as respect for the environment, social accountability, and intergenerational fairness. These values form the basis of a sustainability-focused worldview and guide individuals’ decisions and actions throughout their lives.

Existing literature dealing with education and environmental sustainability has emphasized the pivotal function of education in heightening awareness, shaping policy formulation, fostering sustainable values, and advancing Education for Sustainable Development (ESD) [75,76,77,78]. Education catalyzes positive environmental outcomes by equipping individuals with the necessary knowledge, skills, and attitudes to tackle environmental challenges and foster a sustainable future. By integrating environmental education and ESD in formal and informal settings, societies can foster a sustainable culture and empower individuals to serve as proactive agents of change within their communities. ESD has become a pivotal framework for integrating sustainability principles into educational systems. According to UNESCO’s research in 2014, there is a significant emphasis on the transformative potential of ESD. This educational approach aims to foster the necessary knowledge, skills, values, and attitudes in individuals to enable them to make meaningful contributions toward building a sustainable society. Research studies have shown that the implementation of Education for Sustainable Development (ESD) has the potential to cultivate a mindset focused on sustainable development among students and educators.

Environmental education can be conducted in both formal and informal settings. Formal education, encompassing elementary, secondary, and tertiary institutions, provides structured opportunities for integrating environmental content into curricula. Informal education, encompassing nature centers, museums, and community-based programs, complement formal education by fostering experiential learning, establishing a connection between individuals and nature, and advocating for environmental stewardship [79]. Recent research by Liu et al. [80] performed an empirical study on China. It noted that education played a significant role in China’s CO₂ reduction. Moreover, CO₂ emissions were also negatively impacted by the GDP and population, while R&D activities positively impacted them. Therefore, based on the results, the authors suggest that global warming can be mitigated through financial inclusion and education. More evidence can be found in the study of Li and Ullah [81], where the empirical study revealed that positive economic and educational improvements in BRICS have reduced CO₂ emissions. In contrast, negative education improvements have increased them. Zaman et al. [82] suggested that, in China, the reduction of CO₂ emissions can be achieved through education expenditures, an increase in female employment, and the use of renewable energy as a percentage of total energy use.

Another recent study by Eyuboglu and Uzar [83] investigated the linkage between education and carbon emission, and, according to the results, cointegration exists between the variables, and higher education negatively impacts CO₂ emissions. An empirical study by Sarwar et al. [84] showed that the coefficients for education and carbon emissions are insignificant in the short run but significant and positive in the long run, meaning that education reforms and climate change mitigation policies will take time to impact the economy. Further evidence can be found in the study of Umaroh [85], where the author confirmed that education initially increased CO₂ emissions. However, eventually, it reduced CO₂ emissions in the short run but not in the long run. Additionally, Zafar et al. [86] stated that emissions are exacerbated by economic growth and education, resulting in environmental degradation as it increases carbon emissions.

However, Zhu et al. [87] found positive and negative impacts on CO₂ emission based on the higher education scale and higher education quality. The study found that further progress in higher education may increase the positive effect on carbon emissions per capita, especially when technology surpasses the threshold. When income exceeds the threshold, the positive effect of higher education quality on carbon emissions per capita may be moderated by its further growth. Therefore, the authors suggested that carbon emissions per capita may be reduced by continuously improving higher education quality. Alkhateeb et al. [88] conducted a study in Saudi Arabia and found that primary education does not affect CO₂ emissions, secondary education negatively affects CO₂ emissions, and energy consumption positively affects CO₂ emissions.

3.2. Research Gap in the Existing Literature and Ways to Address the Issue

Firstly, it is worth noting that there appears to be a lack of research assessing the impact of innovation on promoting environmental sustainability in Vietnam. This is despite the existence of literature on both environmental sustainability and innovation. The existing gap could be addressed by researching the role of technology and non-technological innovation in environmental sustainability in Vietnam. This would provide a better understanding of the country’s challenges and potential opportunities.

Secondly, it is plausible that the literature may not adequately address the extent to which financial innovation influences environmental sustainability in Vietnam. A comprehensive understanding of the financial aspect of environmental sustainability in Vietnam can be achieved by examining the role of innovative financing mechanisms such as green bonds, carbon pricing systems, and sustainable investment frameworks in facilitating and expanding green projects.

Thirdly, it is worth noting that there exists a potential void in the existing body of research about the impact of innovation-driven environmental sustainability endeavors in Vietnam on broader socio-economic patterns. A comprehensive understanding of the potential benefits and challenges associated with Vietnam’s shift towards a sustainable future can be achieved by examining the influence of innovation-driven sustainability initiatives on economic growth, employment opportunities, social equity, and public health.

3.3. Theoretical Development and Justification of the Study

The study highlights the significance of innovation in promoting environmental sustainability in Vietnam. This section provides a theoretical framework and rationale for the chosen title, highlighting the significance of innovation toward a more sustainable future. First, the advancement of technology plays a crucial role in promoting environmental sustainability. The adoption and development of innovative technologies have the potential to aid in mitigating climate change, lessening environmental impact, and enhancing resource efficiency. The works of [89] indicate a favorable association between technological advancement and environmental efficacy. The adoption of innovative technologies has the potential to aid Vietnam in tackling environmental concerns, including pollution, deforestation, and greenhouse gas emissions. The utilization of clean energy is a crucial component of promoting environmental sustainability. The adoption of renewable energy sources, such as solar, wind, and hydroelectric power, has the potential to significantly decrease carbon emissions and alleviate the impact of climate change. The significance of innovation cannot be overstated in the advancement and proliferation of renewable energy technologies. Sovacool [90] underscores the importance of innovation in expediting the shift toward renewable energy. Advocating for innovative solutions in the production and distribution of renewable energy in Vietnam has the potential to foster a more environmentally conscious and sustainable future.

Thirdly, innovation encompasses technological advancements and innovative environmental policy and administration approaches. Promoting environmental sustainability can be facilitated by implementing innovative policy frameworks, including market-based mechanisms, green financing, and circular economy strategies. Refs. [91,92] emphasize the significance of policy innovation in promoting sustainable development. Incorporating innovative policy strategies that incentivize sustainable practices, encourage green investments, and support circular economy initiatives can potentially facilitate the transition towards a more environmentally conscious future in Vietnam. Education aimed at promoting environmental sustainability is an indispensable factor. Incorporating innovative methodologies in environmental education, such as experiential learning, interdisciplinary collaboration, and digital technologies, can augment students’ understanding of environmental issues and enable them to act as agents of change. Ref [93] emphasize the potential for transformative sustainability through innovative educational practices. By integrating innovative techniques into environmental education in Vietnam, it is possible to foster a cohort of environmentally aware and resourceful individuals who can contribute to a more sustainable future. It is increasingly recognized that economic growth and environmental sustainability are not mutually exclusive. Through the promotion of resource efficiency, green entrepreneurship, and sustainable business models, innovation-led economic development has the potential to facilitate sustainable growth. Ref. [94] emphasizes the potential of innovation to disassociate economic expansion from environmental deterioration. The adoption of innovation across diverse industries, including manufacturing, agriculture, and services, has the potential to facilitate sustainable economic development while simultaneously mitigating environmental impact, having the potential to lead the way towards a more environmentally friendly and sustainable future by adopting technological advancements, encouraging the shift towards renewable energy, implementing innovative environmental policies, promoting education for sustainability, and driving economic development through innovation.

4. Data and Methodology of the Study

4.1. Model Specification

The motivation of the study is to evaluate the impact of technological innovation, financial innovation, clean energy, and trade.

$${\mathrm{E}\mathrm{D}}_{{\mathrm{C}\mathrm{O}}_2 \& \mathrm{E}\mathrm{F}}\int \mathrm{T}\mathrm{I}, \mathrm{F}\mathrm{I}, \mathrm{C}\mathrm{E}$$

(1)

where ED, CO₂, EF, TI, FI, and CE stand for environmental degradation, which is measured by carbon emission (CO₂), ecological footprint (EF), technological innovation, financial innovation, and clean energy, respectively, following existing literature dealing environmental degradation; see, for instance, Equation (1), which has been extended with the inclusion of two control variables, namely, foreign direct investment and education. The revised Equation (2) is as follows:

$${\mathrm{E}\mathrm{D}}_{{\mathrm{C}\mathrm{O}}_2 \& \mathrm{E}\mathrm{F}}\int \mathrm{T}\mathrm{I}, \mathrm{F}\mathrm{I}, \mathrm{C}\mathrm{E}, \mathrm{F}\mathrm{D}\mathrm{I}, \mathrm{E}\mathrm{D}\mathrm{U}$$

(2)

After the transformation of research units with natural log, the above Equation (2) can be displayed in the following regression manner for coefficient determination; see Equations (3) and (4).

$${\mathrm{E}\mathrm{D}}_{{\mathrm{C}\mathrm{O}}_2}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{T}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{F}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{C}\mathrm{E}}_{\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}}$$

(3)

$${\mathrm{E}\mathrm{D}}_{\mathrm{E}\mathrm{F}}={\mathsf{\alpha }}_0+{\mathsf{\gamma }}_1{\mathrm{T}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{F}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\gamma }}_3{\mathrm{C}\mathrm{E}}_{\mathrm{t}}+{\mathsf{\gamma }}_4{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\gamma }}_5{\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}}$$

(4)

The coefficients of ${\beta }_1-{\beta }_5$ and ${\gamma }_1-{\gamma }_5$ explained the magnfititutes of TI, FI, CE, TO, and EDU on environmental degradation, measured by carbon emission and ecological footprint.

4.2. Empirical Model Justification

Taking into account the above Equations (3) and (4), it is anticipated that there is a positive linkage between environmental quality and technological innovation, particularly for the sign of coefficients to be negative and statistically significant, i.e., ${\beta }_1\&{\gamma }_1:1<\frac{{\mathrm{E}\mathrm{D}}_{{\mathrm{C}\mathrm{O}}_2\&\mathrm{E}\mathrm{F}}}{\mathrm{T}\mathrm{I}}$. Existing literature advocates inventing new technologies and solutions that decrease pollution, preserve natural resources, and promote sustainable development. Moreover, technological innovation may assist in reducing environmental deterioration through advances in renewable energy technologies, such as solar, wind, and hydropower, for example, which may decrease greenhouse gas emissions and air pollution. On the other hand, new technology might introduce new environmental concerns, such as e-waste from electronic gadgets or pollution from the production process [7,95,96].

Financial innovation may encourage sustainable development by increasing access to funding for green initiatives such as renewable energy and sustainable agriculture. On the other hand, financial innovation may generate incentives for unsustainable behaviors, such as finance for fossil fuel projects or unsustainable agricultural techniques. Thus, it is anticipated that there will be a positive statistically significant linkage to environmental development and financial innovation, or, alternatively, that the sign of the FI coefficients will be negative in both cases, i.e., ${\beta }_2\&{\gamma }_2:1<\frac{{\mathrm{E}\mathrm{D}}_{{\mathrm{C}\mathrm{O}}_2\&\mathrm{E}\mathrm{F}}}{\mathrm{F}\mathrm{I}}$. Numerous researchers have investigated the relationship between financial innovation and its effect on carbon emissions and ecological footprint [62]. Some contend that innovative financial products such as green bonds, ESG funds, and renewable energy investment schemes can direct capital toward sustainable industries while decreasing investments in polluting activities. Other studies suggest that these instruments may contribute to “greenwashing” practices, in which businesses employ superficially eco-friendly strategies without enhancing their environmental performance. In addition, some researchers advocate for a greater focus on how financial innovations impact marginalized communities and developing nations, as they are disproportionately impacted by climate change.

By lowering greenhouse gas emissions and air pollution, renewable energy may assist in minimizing environmental deterioration. On the other hand, renewable energy may have severe environmental consequences, such as the possibility of land-use disputes and repercussions on animal habitats. Renewable energy sources, including solar, wind, hydroelectric, and biomass, are known to generate fewer greenhouse gas emissions when compared to fossil fuels. The shift from energy systems reliant on fossil fuels to those based on renewable sources holds the potential to decrease carbon emissions significantly. Alternatively, negative and statistically significant coefficients are anticipated in both cases. ${\beta }_3\&{\gamma }_3:1<\frac{{\mathrm{E}\mathrm{D}}_{{\mathrm{C}\mathrm{O}}_2\&\mathrm{E}\mathrm{F}}}{\mathrm{F}\mathrm{I}}.$Research findings, a worldwide shift towards utilizing 100% clean and sustainable energy sources can eradicate 80% of global carbon dioxide emissions by the year 2050. Furthermore, a study conducted by Creutzig et al. [97] illustrates that implementing renewable energy technologies can significantly reduce emissions in various sectors, including electrical generation, transportation, and heating. The augmented utilization of sustainable energy directly replaces carbon-intensive fossil-fuel-based energy sources. It encourages the decarburization of related sectors, leading to an overall reduction in carbon emissions. Clean energy sources have a lower ecological impact when compared to fossil-fuel-based energy systems. The extraction and combustion of fossil fuels contribute to environmental degradation, including but not limited to air and water pollution and habitat devastation. On the contrary, clean energy technologies exhibit negligible environmental impacts during their operational phase, characterized by significantly curtailed emissions and resource utilization. According to Sovacool [90], an analysis was conducted on the environmental impacts of different energy sources throughout their life cycle. The study concluded that renewable energy technologies possess comparatively smaller ecological footprints than fossil fuels. Solar and wind energy, for example, have relatively low land-use requirements and do not necessitate the extraction or transportation of finite resources. Furthermore, the research conducted by Hertwich et al. [98] indicates that the extensive implementation of renewable energy sources can aid in mitigating various environmental stressors, including but not limited to freshwater and mineral resource consumption, land use, and hazardous emissions. Renewable energy technologies aid in mitigating climate change, preserving natural resources, and safeguarding ecosystems by decreasing carbon emissions and minimizing ecological footprints.

4.3. Estimation Strategy

For documenting the variable’s stationary attributes, the present study has executed several unit root tests following Dickey and Fuller [99], Phillips and Perron [100], Ng and Perron [101], and Kwiatkowski et al. [102]. Moreover, the unit root with structural break has been tested by employing the unit root framework initiated by [103].

Following Bayer and Hanck [104], the combination of the computed significance level (p-value) of the individual cointegration test in this article is in Fisher’s formula as follows (see Equations (5) and (6)):

EG − JOH = −2[ln(P_EG) + (P_JOH)]

(5)

EG − JOH − BO − BDM = −2[ln (P_EG) + (P_JOH) + (P_BO) + (P_BDM)]

(6)

The possible p-values of several individual cointegration tests to be extracted from Engle and Granger [105], Johansen [106], Peter Boswijk [107], and Banerjee et al. [108] are P_EG, P_JOH, P_BO, and P_BDM, respectively. To obtain evidence regarding the long-run association, the calculated F-stat has to be greater than the critical value proposed by Bayer and Hanck; [104] is the rejection of the null hypothesis of “no cointegration”.

The augmented autoregressive distributed lag (AARDL) model is a powerful time-series data predictor, an extension of the ARDL model that allows for time-varying coefficients and various delays of the dependent variable [109]. The AARDL model may be estimated using maximum likelihood techniques, and time-series predictions can be made using the calculated values. The AARDL model excels at predicting data with a substantial seasonal component because it can consider both seasonality and autocorrelation in the data. Take a time series with considerable yearly seasonality, for instance. The AARDL model would enable the coefficients to change over time to accommodate seasonality fluctuations in the data. Forecasts for time series would be more accurate as a consequence. Moreover, data with various seasonality patterns may be anticipated using the AARDL model. Consider a time series with seasonality on an annual and monthly scale, for example. To provide more accurate time-series predictions, the AARDL model might be used to estimate discrete coefficients for each kind of seasonality. The AARDL model is an effective tool for predicting time-series data, and it is particularly well-suited for predicting data with a substantial seasonal component.

Considering the bound testing approach, the above Equations (2) and (3) are displayed in a conditional ARDL specification; see Equations (7) and (8).

$$\begin{gathered} {{∆\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{O}}_2}_{\mathrm{t}}={\mathsf{\alpha }}_0+\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\mu }}_1{{∆\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{O}}_2}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_2{∆\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_3∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{I}}_{\mathrm{t}-\mathrm{i}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_4∆\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_5∆\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_6∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{i}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathsf{\gamma }}_0{\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{O}}_2}_{\mathrm{t}-1}+{\mathsf{\gamma }}_1{\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_2{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{I}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_3\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}-1} \\ +{\mathsf{\gamma }}_4{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_5{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-1}+{\mathsf{\omega }}_{1\mathrm{t}} \end{gathered}$$

(7)

$$\begin{gathered} {∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{F}}_{\mathrm{t}}={\mathsf{\alpha }}_0+\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\mu }}_1{∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{F}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_2{∆\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_3∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_4∆\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_5∆\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\mu }}_6∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+{\mathsf{\gamma }}_0{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{F}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_1{\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{t}-1} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathsf{\gamma }}_2{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{I}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_3\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_4{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_5{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-1}+{\mathsf{\omega }}_{1\mathrm{t}} \end{gathered}$$

(8)

where the coefficients of ${\mu }_1\mathrm{t}\mathrm{o}{\mu }_6$ and ${\gamma }_1-{\gamma }_5$ explained the magnitudes of explanatory variables on environmental sustainability measured by carbon emission and ecological footprint in their respective equation. $∆$ stands for first difference operation. The long-run cointegration in the empirical equation can be assessed with the null hypothesis of no-cointegration by comparing the critical value offered by [110] and McNown, Sam, and Goh [109].

The following error correction equation (see Equations (9) and (10)) will be implemented to explore the short-run coefficients.

$$\begin{array}{cc}{{∆\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{O}}_2}_{\mathrm{t}}={\mathsf{\alpha }}_2+\hfill & \sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\beta }}_1{{∆\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{O}}_2}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_2{∆\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_3∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}\hfill \\ & +\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_6∆\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_6∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_7∆\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-\mathrm{i}}+\mathsf{\rho }{\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}\hfill \\ & +{\mathsf{\omega }}_{1\mathrm{t}}\hfill \end{array}$$

(9)

$$\begin{gathered} {∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{F}}_{\mathrm{t}}={\mathsf{\alpha }}_2+\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\beta }}_1{∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{F}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_2{∆\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_3∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_6∆\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_6∆\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}+\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathsf{\beta }}_7∆\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-\mathrm{i}}+\mathsf{\rho }{\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\omega }}_{1\mathrm{t}} \end{gathered}$$

(10)

The nonlinear autoregressive distributed lag (ARDL) model extends the conventional linear autoregressive distributed lag (NARDL) model. The NARDL model may consider nonlinear interactions between the dependent variable and the lagged regressors, in contrast to the linear ARDL model. The short-run and long-run impacts of the explanatory factors on the dependent variable may also be estimated using the NARDL model. Estimates of the explanatory factors’ short- and long-term impacts on the dependent variable are then obtained by combining the two equations. The NARDL model has several advantages over other estimating techniques. First, it does not need specialized software and is simple to implement. Second, it may be used for non-stationary economic data, often. Its last contribution is its flexible manner in representing nonlinear interactions between variables [111,112].

Following Shin et al. [113], the partial sum asymmetry cointegration equation can now be obtained by inserting positive and negative shocks of the explanatory variable in the standard symmetric Equation (11) and the new nonlinear ARDL as follows:

$$\begin{gathered} {∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}}_{\mathrm{t},{\mathrm{C}\mathrm{O}}_2\&\mathrm{E}\mathrm{F}}={\mathsf{\alpha }}_0+\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\mu }}_1{∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}}_{\mathrm{t}-1,{\mathrm{C}\mathrm{O}}_2\&\mathrm{E}\mathrm{F}}+\sum _{\mathrm{i}=0}^{\mathrm{m}}{\mathsf{\mu }}_2^{+}{∆\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{T}\mathrm{I}\right)}_{\mathrm{t}-\mathrm{i}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+\sum _{\mathrm{i}=0}^{\mathrm{k}}{\mathsf{\mu }}_2^{-}{∆\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{T}\mathrm{I}\right)}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{r}}{\mathsf{\mu }}_3^{+}{∆\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{F}\mathrm{I}\right)}_{\mathrm{t}-\mathrm{i}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+\sum _{\mathrm{i}=0}^{\mathrm{r}}{\mathsf{\mu }}_3^{-}{∆\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{F}\mathrm{I}\right)}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{P}}{\mathsf{\mu }}_4^{+}{∆\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{C}\mathrm{E}\right)}_{\mathrm{t}-\mathrm{i}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+\sum _{\mathrm{i}=0}^{\mathrm{P}}{\mathsf{\mu }}_4^{-}{∆\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{C}\mathrm{E}\right)}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{q}}{\mathsf{\mu }}_5{∆\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=0}^{\mathrm{q}}{\mathsf{\mu }}_6{∆\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{D}\mathrm{U}}_{\mathrm{t}-\mathrm{i}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathsf{\gamma }}_0{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{D}}_{\mathrm{t}-1}+{\mathsf{\gamma }}_1^{+}{\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{T}\mathrm{I}\right)}_{\mathrm{t}-1}+{\mathsf{\gamma }}_1^{-}{\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{T}\mathrm{I}\right)}_{\mathrm{t}-1} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\mathsf{\gamma }}_2^{+}{\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{F}\mathrm{I}\right)}_{\mathrm{t}-1}+{\mathsf{\gamma }}_2^{-}{\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{F}\mathrm{I}\right)}_{\mathrm{t}-1}+{\mathsf{\gamma }}_3^{+}{\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{C}\mathrm{E}\right)}_{\mathrm{t}-1} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathsf{\gamma }}_3^{-}{\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{C}\mathrm{E}\right)}_{\mathrm{t}-1}+{\mathsf{\gamma }}_4{\mathrm{l}\mathrm{n}\left(\mathrm{E}\mathrm{D}\mathrm{U}\right)}_{\mathrm{t}-1}+{\mathsf{\gamma }}_5{\mathrm{l}\mathrm{n}\left(\mathrm{F}\mathrm{D}\mathrm{I}\right)}_{\mathrm{t}-1}{+\mathsf{\omega }}_{\mathrm{t}} \end{gathered}$$

(11)

The asymmetric variables of financial innovation, technological innovation, and clean energy can be derived by implementing the following partial sum equation; see Equations (12)–(14), respectively.

$$\left\{\begin{array}{c}{\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{T}\mathrm{I}\right)}_{\mathrm{t}}=\sum _{\mathrm{k}=1}^{\mathrm{t}}{\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{k}}^{+}=\sum _{\mathrm{K}=1}^{\mathrm{T}}\mathrm{M}\mathrm{A}\mathrm{X}\left({∆\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{k}},0\right)\\ {\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{T}\mathrm{I}\right)}_{\mathrm{t}}=\sum _{\mathrm{k}=1}^{\mathrm{t}}{\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{k}}^{-}=\sum _{\mathrm{K}=1}^{\mathrm{T}}\mathrm{M}\mathrm{I}\mathrm{N}\left({∆\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}}_{\mathrm{k}},0\right)\end{array}\right.$$

(12)

$$\left\{\begin{array}{c}{\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{F}\mathrm{I}\right)}_{\mathrm{t}}=\sum _{\mathrm{k}=1}^{\mathrm{t}}{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{I}}_{\mathrm{k}}^{+}=\sum _{\mathrm{K}=1}^{\mathrm{T}}\mathrm{M}\mathrm{A}\mathrm{X}\left({∆\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{I}}_{\mathrm{k}},0\right)\\ {\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{F}\mathrm{I}\right)}_{\mathrm{t}}=\sum _{\mathrm{k}=1}^{\mathrm{t}}{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{I}}_{\mathrm{k}}^{-}=\sum _{\mathrm{K}=1}^{\mathrm{T}}\mathrm{M}\mathrm{I}\mathrm{N}\left({∆\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{I}}_{\mathrm{k}},0\right)\end{array}\right.$$

(13)

$$\left\{\begin{array}{c}{\mathrm{P}\mathrm{O}\mathrm{S}\left(\mathrm{C}\mathrm{E}\right)}_{\mathrm{t}}=\sum _{\mathrm{k}=1}^{\mathrm{t}}{\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{E}}_{\mathrm{k}}^{+}=\sum _{\mathrm{K}=1}^{\mathrm{T}}\mathrm{M}\mathrm{A}\mathrm{X}\left({∆\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{E}}_{\mathrm{k}},0\right)\\ {\mathrm{N}\mathrm{E}\mathrm{G}\left(\mathrm{C}\mathrm{E}\right)}_{\mathrm{t}}=\sum _{\mathrm{k}=1}^{\mathrm{t}}{\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{E}}_{\mathrm{k}}^{-}=\sum _{\mathrm{K}=1}^{\mathrm{T}}\mathrm{M}\mathrm{I}\mathrm{N}\left({∆\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{E}}_{\mathrm{k}},0\right)\end{array}\right.$$

(14)

In Equation (11), m, n, r, p, and q denote the optimal lag length for model estimation. A standard Wald test to be performed to ascertain the long-run asymmetric effect with the null hypothesis of symmetry; see Equations (15) and (16):

$${\mathrm{H}}_0:{\mathsf{\gamma }}_1^{+}={\mathsf{\gamma }}_1^{-}={\mathsf{\gamma }}_2^{+}={\mathsf{\gamma }}_2^{-}={\mathsf{\gamma }}_3^{+}={\mathsf{\gamma }}_3^{-}={\mathsf{\gamma }}_4^{+}={\mathsf{\gamma }}_4^{-}=0$$

(15)

Against the alternative hypothesis of asymmetry:

$${\mathrm{H}}_1:{\mathsf{\gamma }}_1^{+}\ne {\mathsf{\gamma }}_1^{-}\ne {\mathsf{\gamma }}_2^{+}\ne {\mathsf{\gamma }}_2^{-}\ne {\mathsf{\gamma }}_3^{+}\ne {\mathsf{\gamma }}_3^{-}\ne {\mathsf{\gamma }}_4^{+}\ne {\mathsf{\gamma }}_4^{-}\ne 0$$

(16)

The rejection of the null hypothesis confirms the existence of asymmetrical effects from financial development, trade openness, foreign direct investment, and inflation on the economic growth of Bangladesh in the long run. The long-run elasticity can be computed, for ${\mathrm{T}\mathrm{I}}^{+}=\frac{-{\mathsf{\gamma }}_1^{+}}{{\mathsf{\gamma }}_0}$; ${\mathrm{T}\mathrm{I}}^{-}=\frac{-{\mathsf{\gamma }}_1^{-}}{{\mathsf{\gamma }}_0}$; ${\mathrm{F}\mathrm{I}}^{+}=\frac{-{\mathsf{\gamma }}_2^{+}}{{\mathsf{\gamma }}_0}$; ${\mathrm{F}\mathrm{I}}^{-}=\frac{-{\mathsf{\gamma }}_2^{-}}{{\mathsf{\gamma }}_0}$; ${\mathrm{C}\mathrm{E}}^{+}=\frac{-{\mathsf{\gamma }}_3^{+}}{{\mathsf{\gamma }}_0}$; and ${\mathrm{C}\mathrm{E}}^{-}=\frac{-{\mathsf{\gamma }}_3^{-}}{{\mathsf{\gamma }}_0}$.

To investigate the existence of the long-run asymmetric relationship, Shin proposed a bound test, a joint test of all lagged levels of regressors. Wald F-test is utilized to test the null hypothesis that has no asymmetric relationship: ${\mathrm{H}}_0:{{\mathsf{\gamma }}_0=\mathsf{\gamma }}_1^{+}={\mathsf{\gamma }}_1^{-}={\mathsf{\gamma }}_2^{+}={\mathsf{\gamma }}_2^{-}={\mathsf{\gamma }}_3^{+}={\mathsf{\gamma }}_3^{-}=0$.

Against the alternative hypothesis: ${\mathrm{H}}_1:{{\mathsf{\gamma }}_0=\mathsf{\gamma }}_1^{+}={\mathsf{\gamma }}_1^{-}={\mathsf{\gamma }}_2^{+}={\mathsf{\gamma }}_2^{-}={\mathsf{\gamma }}_3^{+}={\mathsf{\gamma }}_3^{-}\ne 0$.

5. Empirical Model Estimation and Interpretation

The study implemented a static test following [99,100,102,114] to document the variable’s order of integration for robust econometrical estimation.

Table 1. Results of unit root test.

At Level					First Difference
Variable	ADF	GF-DLS	PP	KPSS	Variables	ADF	GF-DLS	PP	KPSS
Panel A: Conventional Unit root test
CO2	−2.4042	−2.0385	−1.2126	0.8732 ***	CO	−6.9243 ***	−5.6579 ***	−8.5408 ***	0.0198
EF	−0.3624	−0.7061	−1.1837	0.6456 ***	EF	−8.3393 ***	−8.9472 ***	−8.4434 ***	0.0208
TI	−1.7684	−1.1311	−0.6687	0.8498 ***	TI	−7.3656 ***	−7.6597 ***	−9.2171 ***	0.0192
FI	−1.8437	−1.3705	−1.4588	0.7882 ***	FI	−8.2728 ***	−9.2303 ***	−9.396 ***	0.0205
CE	−0.3216	−0.2554	−0.8351	0.6297 ***	CE	−7.633 ***	−6.38 ***	−8.5678 ***	0.0186
EDU	−1.9264	−1.4848	−0.6059	0.6295 ***	EDU	−7.7712 ***	−8.9789 ***	−7.4112 ***	0.0185
FDI	−1.4267	−2.5233	−2.0226	0.5654 ***	FDI	−5.7839 ***	−7.5467 ***	−7.6817 ***	0.0213
Panel B: Ng-Perron unit root test
	MZa	MZt	MSB	MPT	Variables	MZa	MZt	MSB	MPT
CO2	−1.8987	−0.9589	0.3689	7.5523	CO2	−18.858	−4.1192	0.136	4.6012
EF	−2.6426	−1.1105	0.2823	8.3441	EF	−20.754	−4.1986	0.1511	3.1897
TI	−2.3965	−1.0219	0.2412	7.4968	TI	−24.497	−3.8082	0.1566	3.4917
FI	−2.5726	−1.4121	0.3382	8.361	FI	−19.875	−5.4378	0.1456	3.4817
CE	−1.9617	−1.5745	0.2465	8.2786	CE	−18.974	−4.1651	0.1597	4.772
EDU	−1.7421	−0.7019	0.2958	7.7555	EDU	−19.32	−5.1594	0.1462	4.0747
FDI	−2.0269	−1.2859	0.2471	7.928	FDI	−25.265	−5.4774	0.1298	5.1464
Panel C: Results of Narayan unit root test with a structural break
	Model 1				Model 2
	T-statistic		Time Break		T-statistic			Time Break
CO2	−2.7686		1998:2002		−2.9543			2000:2015
EF	−2.7098		2004:2000		−2.0563			2000:2015
TI	−2.8683		2007:2010		−2.6312			2006:2001
FI	−3.223		1999:2015		−2.5651			2004:2006
CE	−3.0864		2009:2019		−1.7008			2008:2005
EDU	−2.4138		2004:2010		−2.577			2008:2008
FDI	−3.1011		1998:2016		−3.0351			1997:2000
	First difference
CO2‛	−8.4734 ***		1997:2017		−5.6586 ***			2000:2015
EF‛	−5.8691 ***		1995:2017		−6.4499 ***			1997:2004
TI‛	−4.621 ***		2002:2004		−7.1044 ***			2001:2010
FI‛	−5.788 ***		2000:2014		−7.0295 ***			1998:2001
CE‛	−5.6249 ***		1996:2006		−9.2601 ***			2009:2001
EDU‛	−7.7578 ***		1997:2000		−9.3632 ***			2003:2007
FDI‛	−5.1831 ***		2009:2002		−6.6623 ***			2001:2010

Note: the superscripts of *** denote the level of significance at a1% level.

displays the results of the stationary test with the indication of statistical significance and suggests all variables become stationary after the first difference, i.e., I(1). Notably, none of the research’s variables become stationary after the second difference, which is appreciated in the advanced estimation.

The long-run association between the explained and explanatory variables was evaluated by executing the novel cointegration test familiarized by [104] without a break period and [115] with a break year. The cointegration test results are displayed in

Table 2. Results of Bayer–Hancked and Makki cointegration test.

Panel A: Bayer–Hanck Combined Cointegration without Structural Break
Test	Model 1	Model 2
EG-JOH	14.815	24.118
EG-JOH-BO-BDM	14.476	39.585
Panel B: Maki Cointegration with Structural Break
	Model 1	Model 2
Level shift with trend	−9.0286 [1991:2004:2016]	−7.1868 [2002:2008:2012]
Regime shifts	−13.0048 [1992:2003:2007]	−10.3548 [1990:2009:2012]
Regime shifts with trend	−16.3914 [1996:2008:2015]	−10.8395 [1996:2003:2007]

, including Panel A for Bayer–Hancked and Panel B for the Makki cointegration test, respectively. Referring to the test statistics, the study established a long-run linkage between TI, FI, CE, EDU, FDI, and ED.

In the following,

Table 3. Long-run cointegration test: symmetric and asymmetric assessment.

Long-Run Cointegration	Foverall		tDV		FIDV
ARDL	9.783 ***		−7.062 ***		6.364 ***
NARDL	11.305 ***		−6.004 ***		7.459 ***
Critical value	1%		5%		10%
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
Pesaran, Shin, and Smith [82]	5.095	6.77	3.673	5.002	3.087	4.277
Narayan [96]	−3.96	−5.13	−3.41	−4.52	−3.13	−4.21
Sam, McNown, and Goh [92]	3.58	5.91	2.46	4.18	2	3.47

Note: The superscripts *** stands for a 1% level of significnce.

reports the results extracted through the execution of a long-run test offered by Pesaran et al. [110], Narayan [116], and Goh et al. [117]. The study revealed all the test statistics became significant at a 1% level, establishing the presence of long-run cointegration in both symmetric and asymmetric environments. Once the long run was revealed, the study moved to assess the coefficients magnitudes on ED in the long-run and short-run horizon under the linear and nonlinear framework.

Table 4. The effects of TI, FI, CE, TO, and FD on ED (measured by CO₂) are shown.

	Symmetric			Asymmetric
Model: ED|TI, FI, CE, EDU, FDI				Model: ED|TI+, TI−, FI+, FI−, CE+, CE−, EDU, FDI
Panel A: Long-run coefficients
TI	−0.2181 ***	0.0293	−7.4432	TI+	−0.0993 **	0.0397	−2.5015
FI	−0.1013 ***	0.0164	−6.142	TI	−0.5893 ***	0.0354	−16.6289
REC	−0.2307 ***	0.0151	−15.2305	FI	−0.0587 ***	0.0032	−18.2241
EDU	−0.0723 ***	0.0107	−6.7231	FI	−0.0367 ***	0.0071	−5.155
FDI	−0.1091 ***	0.0082	−13.2723	CE	−0.0303 ***	0.0027	−10.9461
				CE	−0.0236 ***	0.0028	−8.2996
				FDI	−0.1023 ***	0.0096	−10.6223
				EDU	−0.0738 ***	0.0068	−10.7417
Panel B: Short-run coefficients
∆ED(−1) *	−0.0567 *	0.0406	−1.3947	TI	0.0014 *	0.0007	1.8072
∆TI	−0.0123	0.0186	0.6641	TI	−0.0236 ***	0.0028	−8.2996
∆FI	−0.0157 ***	0.0011	−14.2168	FI	−0.0005	0.0007	−0.6464
∆REC	0.0013 *	0.0008	1.5942	FI	0.0171 **	0.0035	4.8695
∆EDU	0.0019	0.0048	0.4022	CE	−0.0111 *	0.0039	−2.8461
C	8.7781 ***	1.7521	5.0098	CE	−0.0616 ***	0.0096	−6.4166
				EDU	−0.0186 **	0.0052	−3.5357
				FDI	−0.0001	0.0013	−0.1105
COINTEQ *	−0.3567 ***	0.0057	−61.6115		−0.2993 ***	0.0281	−13.122
Symmetry test and diagnostic test
$W_{LR}^{TI}$					11.591
$W_{SR}^{TI}$					5.223
$W_{LR}^{FI}$					7.909
$W_{SR}^{FI}$					9.085
$W_{LR}^{CE}$					10.128
$W_{SR}^{CE}$					5.781
$x_{Auto}^2$	0.798				0.854
$x_{Het}^2$	0.812				0.656
$x_{Nor}^2$	0.637				0.64
$x_{RESET}^2$	0.509				0.841

Note: The superscripts of ***/**/* denotes a level of significant at 1%, 5% and 10% respectively.

exhibits the results of long-run and short-run coefficients derived from AARDL. The coefficient of technological innovation (TI) was revealed to be negative and statistically significant at 1% in the long and short run (a coefficient of ${TI}_{LR}=-0.2181\mathrm{a}\mathrm{n}\mathrm{d}{\mathrm{T}\mathrm{I}}_{\mathrm{S}\mathrm{R}}=-0.0567$). Precisely, a 10% change in TI will result in environmental development by reducing CO₂ by 2.181% and 0.567% in the long run and short run. Our study finding aligns with the existing literature, for instance [83,96,118,119,120]. The literature postulated that the possible channel through TI prompts environmental sustainability by creating new technology and solutions that lessen the environmental burden by decreasing pollution, preserving natural resources, and advancing sustainable development. For instance, renewable energy sources like solar, wind, and hydropower may mitigate greenhouse gas emissions and air pollution. E-waste from electronic gadgets and factory pollution are only two examples of how the advent of new technology can wreak havoc on the natural world.

Financial innovation has exhibited beneficial effects on environmental sustainability; in both the symmetric and asymmetric investigation, FI supports in reducing carbon emission and ecological amplification. More precisely, according to the symmetric assessment, a 10% change in FI would increase carbon contention by 1.013% and 0.123% in the long and short run. Furthermore, the asymmetric coefficients revealed that the positive (negative) innovations in FI have a negative statistically significant linkage to CO₂ in the long run. Particularly, a 1% positive (negative) shock in FI will result in the decline (acceleration) of CO₂ emission by 0.0587% (0.0367%) in the long run. The positive FI shock on CO₂ becomes statistically insignificant in the short run. In contrast, the negative shocks appear to be controlling forces in managing CO₂ emissions. Our study findings are sported by the literature offered by [46,62,121].

As anticipated, the study findings revealed a positive connection to environmental sustainability: clean energy promotion in the economy assists in controlling the present level of carbon emission in the long run (coefficients of −0.2307) and short run (a coefficient of −0.0157). Inferring the CE elasticities, with a 10% change in CE, will reduce CO₂ by 2.307% in the long run and by 0.157% in the short run. Asymmetric evaluation exposed a negative tie between positive (negative) shocks in CE and CO₂ in the long and short run; however, the long-run coefficients have been exposed more intensely than the short-run coefficients. Precisely, a 10% increase (decrease) in CE consumption will result in the control (augmentation) of environmental degradation through CO₂ emission by 0.303% (0.236%). Moreover, the short-run asymmetric coefficients explained that CE consumption accelerated the environmental development of CO₂ increase (decreases) by −0.0111% (−0.0616%) due to a 1% asymmetric shock in CE. The results of the beneficial effects of CE on environmental development due to clean energy are supported by the literature offered by [7,8,10,122,123,124].

Referring to the elasticity of control variables, and education and foreign direct investment, the study revealed that investment in education prompts environmental sustainability and FDI inflows amplify the intense environmental degradation in the long and short run. Notably, all the coefficients are statistically significant at a 1% level.

The estimated model has passed several diagnostic tests, namely, autocorrelation, normality, heteroskadacity, and the Remsey RESET test, for confirming the robustness and efficiency in empirical mode construction and estimation. Panel C displayed residual diagnostic test results and confirmed that the estimated model is free from serial correlation, normally distributed residuals, and internal consistency among the research variables.

The study established a beneficial role of TI on ED in the process of ecological improvement both in the long-run and short-run assessment; see

Table 5. Results of the effects of TI, FI, CE, EDU, and FDI on ED: measured by ecological footprint.

Model: EF|TI, FI, CE, EDU, FDI				Model: EF|TI+, TI−, FI+, FI−, CE+, CE−, EDU, FDI
Panel A: Long-run coefficients
TI	−0.0748	0.0054	−13.8098	TI+	−0.1776	0.0081	−22.0202
FI	−0.1036	0.0039	−26.2759	TI−	−0.1544	0.0059	−26.0597
REC	−0.1156	0.0056	−20.2997	FI+	−0.1442	0.0091	−15.76162
EDU	−0.1397	0.0108	−12.8747	FI−	−0.0297	0.0051	−5.9348
FDI				REC+	−0.0853	0.0075	−11.3479
				REC−	−0.0389	0.0081	−4.8474
				FDI	−0.0385	0.0091	−4.1922
				EDU	−0.0304	0.0031	−9.678526
Panel B: Short-run coefficients
▲TI	−0.0491	0.0081	−6.0493	▲TI+	−0.0289	0.0088	−3.284
▲FI	−0.0257	0.0034	−7.5588	▲TI−	−0.0458	0.0082	−5.5853
▲CE	−0.0207	0.0099	−2.0909	▲FI+	−0.0387	0.0059	−6.5593
▲EDU	0.0431	0.0043	10.0232	▲FI−	−0.0496	0.0021	−23.619
▲FDI				▲CE+	−0.0275	0.0035	−7.8571
				▲CE−	−0.0432	0.005	−8.64
				▲FDI	0.0581	0.0034	17.0882
				▲EDU	0.0693	0.0116	5.9741
ect.	−0.55294	0.1568	−3.52638		−0.37258	0.003941	−94.5384
Panel C: Symmetry and residual diagnostic test
				$W_{LR}^{TI}$	11.833
				$W_{SR}^{TI}$	7.29
				$W_{LR}^{FI}$	11.616
				$W_{SR}^{FI}$	8.7
				$W_{LR}^{CE}$	4.262
				$W_{SR}^{CE}$	3.719
$x_{Auto}^2$	0.669				0.706
$x_{Het}^2$	0.553				0.722
$x_{Nor}^2$	0.836				0.803
$x_{RESET}^2$	0.571				0.734

. More precisely, a 10% change in TI can contribute to environmental development by controlling ecological sustainability in the long run by 0.748% and in the short run by 0.491%, respectively. Our study finding aligns with the literature offered by [125,126,127,128]. In terms of asymmetric coefficients of TI on ED, according to the study finding, the positive (negative) shock established a negative statistically significant linkage to the ecological footprint in the long run (coefficients of ${TI}_{-0.1776}^{+};{TI}_{-0.1544}^{+}$) and short run (a coefficient of ${TI}_{-0.0289}^{+};{TI}_{-0.0458}^{+}$). It is apparent that TI has a contributory effect on environmental correction in the long and short run. Particularly, the magnitudes of long-run coefficients have a significant influence compared to the short-run coefficients. The literature explained that advanced monitoring and tracking technologies could assist in identifying areas where ecosystem degradation may occur and allow for prompt intervention. Furthermore, technological solutions that assist in the recovery of natural systems can support innovative conservation strategies, such as the restoration of degraded ecosystems, such as mangroves and wetlands [129].

The elasticity of financial innovation under the symmetric (a coefficient of ${FI}_{-0.1036}^{LR}\mathrm{a}\mathrm{n}\mathrm{d}{FI}_{-0.0257}^{SR}$) and asymmetric framework (coefficients of ${FI}_{-0.1442}^{LR+};{FI}_{-0.0297}^{LR-}\mathrm{a}\mathrm{n}\mathrm{d}{FI}_{-0.0387}^{SR+};{FI}_{-0.0496}^{SR-}$) was shown to be negative and statistically significant at a 1% level in the long- and short-run evaluation. Study findings postulate that innovativeness in the financial system assists in ensuring environmental efficiency and mitigating the adverse effects of the ecological footprint. Particularly, a 10% change in FI in the financial system will result in environmental improvement through ecological stability by 1.036% in the long run and by 0.257% in the short run. In comparison, the asymmetric shocks explained that the positive (negative) innovation in FI decreases (increases) the ecological development by 1.442% (0.297%) in the long run and by 0.387% (0.496%) in the short run. The existing literature has reported a similar line of findings; see, for instance [130,131,132].

Regarding renewable energy’s role in ecological correction, the study established a positive linkage between including renewable energy sources and ecological development. Precisely, a 1% progress in REC in the economy will result in the correction of ecological degradation in the long run (short run) by 0.1397% (0.0207%). The asymmetric estimation established a negative and statistically significant association between positive (negative) innovations in REC with EF in the long-run and short-run assessment. In particular, a 10% positive (negative) variation in REC might amplify the ecological development (degradation) by 0.853% (0.389%) in the long run. At the same time, the short-run asymmetric coefficients explained that the environmental performance could be improved (deteriorated) by 0.0275% (0.0432%) due to a 1% increase (decrease) in the REC in the short run. Furthermore, the test statistics of the standard Wald test established an asymmetric linkage between REC and EF both in the long-run and short-run evaluation. Our findings align with existing literature, for instance [133,134,135].

The coefficients of the control variable, i.e., trade openness and education, on the ecological footprint were present under the symmetric and asymmetric evaluation. The study established that investment in education caused rapid ecological development while trade openness degraded the overall environmental quality. The conclusion is valid for both the symmetric and asymmetric assessments. The coefficients of the error correction term (ect-1) were negative and statistically significant at a 1% level, suggesting the speed of disequilibrium correction due to variation in the short-run shocks in explanatory variables. As far as estimation efficiency and robustness are concerned, the empirical models have passed through several diagnostic tests such as serial correlation, heteroskadacity, the ARCH test, and the Remsey test, and the test statistics showed that the constructed empirical models are free from autocorrelation, residuals are normally distributed, and there is efficiency in coefficient estimations.

Table 6. Results of Fourier–TY causality test: symmetric.

0	ED	TI	FI	CE	EDU	FDI	Causalities
Panel A: ED measured by carbon emission
CO2		13.416 ***	5.033 *	6.462 **	3.643	6.953 **	FI CO2; CE→CO2; FDI→CO2; CO2←→TI; FI→TI; EDU←→TI; FDI→TI; FI→CE; CO2→EDU; FI→EDU; FDI→EDU; CE→FDI
TI	6.761 **		6.074 **	3.817	6.867 **	4.25 *
FI	2.054	3.068		1.443	0.376	0.822
CE	0.529	3.781	5.744 *		2.362	0.951
EDU	5.467 *	5.582 *	5.645 *	2.387		4.146 *
FDI	2.69	2.274	3.302	5.385 *	0.726
Panel B: ED measured by ecological footprint
ED		4.811 *	5.033 *	4.469 *	0.335	4.806 *	TI←→EF; FI→EF; CE←→EF; FDI←→EF; CE→TI; EDU←→TI; FDI→TI; TI→FI; FDI→FI; EDU←→CE; FDI←→CE; EF→EDU; FDI←→EDU;
TI	6.73 **		0.214	6.776 **	5.949 *	4.791 *
FI	3.881	6.715 **		0.994	0.877	6.284 **
CE	5.476 *	3.645	1.901		5.374 *	4.802 *
EDU	5.376 *	4.115 *	3.067	5.032 *		4.993 *
FDI	5.69 *	3.103	1.788	7.073 **	6.219 **

Note: “→ and “←→” explained the unidirectional and bidirectional causal link; the superscripts of ***, **, and * denote the significance level at 1%, 5%, and 10%, respectively.

displayed the results of the causal linkage between environmental degradation and explanatory variables, including Panel A for CO₂ and Panel –B for the ecological footprint as a proxy of ED. Study findings established that a feedback hypothesis can explain the causal association between CO₂ and technological innovation [CO₂←→TI]; ecological footprint and technological innovation [EF←→TI]; clean energy and ecological footprint [CE←→EF]; and trade openness and ecological footprint [TO←→EF]. Moreover, unidirectional effects were found between financial innovation and CO₂ [FI→CO₂], clean energy and CO₂ [CE→CO₂], and trade openness and CO₂ [TO→CO₂].

The results of the asymmetric causality test are displayed in

Table 7. Results of TY causality test: asymmetric framework.

	ES	TI+	TI−	FI+	FI−	CE+	CE−	FDI	EDU
Panel A: Environmental sustainability measured by CO2
ES	-	6.7419 ***	7.5826 ***	0.9108	5.9693 **	1.9211	4.5703 *	4.8128 *	4.775 *
TI+	1.5359	-	5.5661 **	0.8671	6.1808 ***	7.3993 ***	6.2088 ***	6.1815 ***	5.1796 **
TI−	6.6827 ***	0.7579	-	5.7994 **	4.3911 *	5.7573 **	7.1613 ***	3.8852 *	6.5962 ***
FI+	0.5176	1.155	5.2989 **	-	1.9533	1.4042	2.4339	7.936 ***	6.8531 ***
FI−	2.2181	4.3001 *	4.751 *	3.2006	-	3.5745 *	3.7951 *	0.9144	0.2465
CE+	4.3988 *	5.853 **	3.4373	3.0796	0.329	-	5.9535 **	3.3949	1.8804
CE−	0.6903	6.7378 ***	0.9427	5.7528 **	1.5598	6.5467 ***	-	3.3682	2.5663
FDI	5.3506 **	7.7819 ***	2.4717	3.536 *	0.1567	7.443 ***	3.0037	-	4.959 *
EDU	7.3585 ***	4.8307 *	7.7313 ***	5.3544 **	1.7294	0.7644	5.7838 **	2.536	-
Panel B: Environmental sustainability measured by ecological footprint
ES	-	6.9932 ***	7.6963 ***	5.6839 **	5.4891 **	6.7035 ***	3.5618 *	3.8231 *	7.0076 ***
TI+	4.8353 *	-	3.7531 *	3.9801 *	0.8566	7.2144 ***	5.0529 **	5.0638 **	0.6783
TI−	4.7022 *	5.9307 **	-	2.7987	6.2543 ***	2.6806	0.1269	2.1163	4.5456 *
FI+	0.0837	7.591 ***	2.9073	-	7.4715 ***	0.0772	3.9781 *	2.1207	0.2533
FI−	5.9561 **	1.3467	5.0376 **	5.0204 **	-	0.8501	3.3874	4.6272 *	4.2668 *
CE+	1.6464	1.6238	7.3523 ***	6.9675 ***	6.4949 ***	-	6.5234 ***	3.0099	1.6781
CE−	3.1237	3.8114 *	6.9168 ***	2.3026	5.73 **	5.8903 **	-	5.3828 **	7.6026 ***
FDI	1.1724	7.68 ***	4.5903 *	4.945 *	3.3448	6.4386 ***	7.3131 ***	-	4.1768 *
EDU	0.002	0.4166	2.4919	1.8251	5.0028 **	0.9098	1.6334	6.3893 ***	-

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

, of Panel A for CO₂ and Panel B for the ecological footprint as a measure of environmental sustainability. Referring to the test statistics, it is apparent that a unidirectional linkage was found between positive shocks in TI and ES [TI⁺→ES], negative shocks in FI and ES [FI⁻→ES], ES and clean energy [ES→CE], and negative shocks in CE and ES [CE⁻ →ES]. Furthermore, a feedback hypothesis can be disclosed between a negative shock in TI and ES [TI⁻ ←→ES], foreign direct investment and ES [FDI←→ES], and education and ES [EDU←→ES].

Referring to the results displayed in Panel B, a unidirectional causality runs from the positive shock in financial innovation, FDI, and education to ecological footprint [FI⁺→ES, FDI→ES; and EDU→ES]. Moreover, a bidirectional association was revealed between the asymmetric shocks in TI and ecological footprint [TI⁺→←ES; TI⁻←→ES], negative variation in financial innovation and ecological footprint [FI⁻→ES], and positive variation in clean energy and ecological footprint [CE⁺→ES].

6. Discussion

For the nexus between TI and ED, the study documented a negative and statistically significant linkage between them, the results of long-run and short-run coefficients derived from AARDL. The coefficient of technological innovation (TI) was revealed to be negative and statistically significant at 1% in the long and short run (a coefficient of ${TI}_{LR}=-0.2181\mathrm{a}\mathrm{n}\mathrm{d}{TI}_{SR}=-0.0567$). Precisely, a 10% change in TI will result in environmental development by reducing CO₂ by 2.181% and 0.567% in the long run and short run. Our study finding aligns with the existing literature, for instance [83,96,118,119,120]. TI fosters environmental sustainability by reducing carbon emissions and ecological stability. The existing literature supports the findings, for instance [25,28,119,136,137]. Innovations in technology can aid in lowering environmental pollution levels. In their respective studies, Rej, Bandyopadhyay, Das, Hossain, Islam, Bera and Yeediballi [25], and Qamruzzaman and Kler [138] advocated that innovations in renewable energy sources such as solar, wind, and hydropower, for instance, can supplant fossil-fuel-based energy generation, thereby reducing greenhouse gas emissions and air pollution. In addition, advanced water treatment technologies can be used to remediate polluted water and mitigate the effects of water pollution. Moreover, Temesgen Hordofa, Minh Vu, Maneengam, Mughal, The Cong, and Liying [18,122], in the respective studies, support that innovative technologies can be used to increase the effectiveness of resource utilization, decrease waste production, and promote the sustainable use of natural resources. For instance, precision agriculture technologies can reduce fertilizer and pesticide use, conserve water, and promote sustainable food production. Similarly, 3D printing can reduce the consumption of natural resources in the manufacturing industry by producing products with less material. Technology innovation can also be utilized to preserve and defend ecological systems. The study of Chishti and Sinha [62] demonstrated that advanced surveillance and tracking technologies can assist in identifying areas where ecosystem degradation may occur and facilitate prompt intervention. In addition, innovative conservation strategies, such as the restoration of degraded ecosystems such as mangroves and wetlands, can be supported by technological solutions that assist in recovering natural systems [28]. Environmental awareness and education can be enhanced by technological innovation. For example, mobile applications and online platforms can disseminate information about environmental issues and provide resources to support environmental conservation efforts. In addition, augmented and virtual reality technologies can provide immersive and engaging experiences that aid environmental education [75,76]. In the case of China, Cheng, McCarl, and Fei [20], and Udeagha and Ngepah [30] demonstrated that technological innovation can contribute to environmental enhancement and ecological development via pollution reduction, sustainable resource utilization, ecological conservation, and environmental education and awareness. By adopting and promoting innovative technologies, societies can meet their requirements more effectively while preserving the environment for future generations.

Financial innovation has exhibited beneficial effects on environmental sustainability; in both the symmetric and asymmetric investigation, FI supports the reduction of carbon emission and ecological amplification. More precisely, according to the symmetric assessment, a 10% change in FI would increase carbon contention by 1.013% and 0.123% in the long and short run. Furthermore, the asymmetric coefficients revealed that the positive (negative) innovations in FI have a negative statistically significant linkage to CO₂ in the long run. Particularly, a 1% positive (negative) shock in FI will result in the decline (acceleration) of CO₂ emission by 0.0587% (0.0367%) in the long run. The positive FI shock on CO₂ becomes statistically insignificant in the short run. In contrast, the negative shocks appear to be controlling forces in managing CO₂ emissions. Our study findings are supported by the literature offered by [46,62,121]. The study established the contributory effects of financial innovation in achieving environmental sustainability through reducing carbon emissions and ecological stability both in the symmetric and asymmetric framework. FI’s negative effects on environmental degradation align with the existing literature [46,121,139]. Financial innovation has emerged as a critical enabler of environmental sustainability due to its ability to access new capital sources and channel them toward sustainable initiatives. The study of Chishti and Sinha [62], and Liang and Qamruzzaman [140] demonstrated that innovative financial instruments, such as green bonds, impact investing, and carbon trading, have allowed investors to support environmentally responsible businesses while earning a return on their investments. These tools finance renewable energy, waste management, water conservation, and other eco-friendly initiatives [56,95]. For BRIC, literature offered by Aldieri, Carlucci, Vinci, and Yigitcanlar [34], and Shahbaz, Nasir, and Roubaud [112] explained that they incentivize businesses to implement sustainable practices by providing reduced borrowing costs or other benefits. In addition, the development of blockchain technology facilitates greater transparency in supply chains. It assists businesses in monitoring their environmental impact. As financial innovation develops, it will promote environmental sustainability by facilitating investment in game-changing technologies that reduce our reliance on fossil fuels and safeguard natural resources for future generations [28].

As anticipated, the study findings revealed a positive connection to environmental sustainability: clean energy promotion in the economy assists in controlling the present level of carbon emission in the long run (coefficients of −0.2307) and short run (a coefficient of −0.0157). Inferring the CE elasticities, with a 10% change in CE, will reduce CO₂ by 2.307% in the long run and by 0.157% in the short run. The asymmetric evaluation exposed a negative tie between positive (negative) shocks in CE and CO₂ in the long and short run; however, the long-run coefficients have been exposed more intensely than the short-run coefficients. Precisely, a 10% increase (decrease) in CE consumption will result in the control (augmentation) of environmental degradation through CO₂ emission by 0.303% (0.236%). Moreover, the short-run asymmetric coefficients explained that CE consumption accelerated the environmental development of CO₂ increase (decrease) by −0.0111% (−0.0616%) due to a 1% asymmetric shock in CE. The results of the beneficial effects of CE on environmental development due to clean energy are supported by the literature offered by [7,8,10,122,123,124]. According to the study estimation, clean energy inclusion in the energy mix directly contributes to environmental correction and improvement in the long run in Vietnam. Our study findings are supported by the existing literature, such as [45,133,135,141,142,143,144,145,146]. The utilization of clean energy is of the utmost importance for environmental sustainability as it aids in the reduction of carbon emissions and promotes ecological growth. Vietnam has heavily depended on fossil fuels, specifically coal, to meet its energy needs. The adoption of renewable energy sources, such as solar, wind, and hydropower, has the potential to decrease carbon emissions significantly [7]. Vietnam has the potential to decrease its greenhouse gas emissions. The study of Jiakui, Abbas, Najam, Liu, and Abbas [12] supports worldwide endeavors to alleviate climate change by substituting coal-fired power facilities with renewable energy installations. Implementing renewable energy infrastructure expansion and discontinuing fossil fuel power facilities can aid Vietnam in accomplishing its carbon reduction objectives and fulfilling its global climate commitments [147].

Implementing clean energy is imperative for rural electrification and the facilitation of reliable electricity access in the remote areas of Vietnam [34,37,64]. Empirical literature in the study of Aldieri, Carlucci, Vinci and Yigitcanlar [34], Lin and Zhu [59], Silvestre and Ţîrcă [66], Bekun et al. [148], and Lyman et al. [149] advocated that renewable energy solutions that operate independently of the main power grid, such as solar household systems and mini-grids, have the potential to offer clean and economical electricity to communities that are not connected to the grid. Vietnam has the potential to improve the living standards of its rural population, stimulate economic growth, and decrease dependence on conventional biomass fuels that have detrimental environmental and health effects, which can be achieved by extending the reach of renewable energy technologies in rural regions [5,150]. When implemented with meticulous planning and environmental considerations, clean energy initiatives can contribute to ecological development and biodiversity conservation [43]. Hydropower projects must incorporate sustainable design principles to minimize their impact on river ecosystems, including implementing fish-friendly turbines and effective river flow management. The study of Mongo, Belaïd, and Ramdani [36] revealed that wind and solar power installations can be strategically positioned and managed to reduce disruption to wildlife habitats and ensure a harmonious coexistence with the surrounding ecological landscapes. By integrating ecological considerations in renewable energy development, Vietnam can safeguard its rich biodiversity and ecosystem services while promoting sustainable energy production.

Regarding FDI’s role in environmental sustainability, the study finding has portrayed a beneficial effect: foreign capital contribution amplifies the environmental correction through the channel of carbon emission and ecological improvement in technological and energy efficiency. The existing literature supports our study; see [150,151,152,153,154]. Empirical studies such as Karim, Qamruzzaman, and Jahan [9], Ju, Andriamahery, Qamruzzaman and Kor [27], Wei, Mohsin, and Zhang [152], and Rehman and Noman [153] show that foreign direct investment (FDI) has played a crucial role in supporting Vietnam’s efforts toward carbon reduction and ecological development. Ju, Andriamahery, Qamruzzaman, and Kor [27] revealed that foreign direct investment has facilitated the transition towards renewable energy sources, construction of eco-friendly infrastructure, and implementation of stringent environmental regulations, thereby contributing to the country’s environmental sustainability. To fully capitalize on the benefits of foreign direct investment (FDI) while minimizing the associated risks, it is imperative that we implement strict monitoring protocols, foster capacity development, and maintain a harmonious balance between economic and environmental objectives [150]. With continued commitment from the government, co-operation from investors, and participation from local populations, FDI can significantly contribute to Vietnam’s objectives of environmental improvement and sustainable development.

Additionally, FDI has facilitated the implementation of cleaner and more efficient manufacturing methods by the investments made by multinational companies (MNCs) in clean energy production, electronics, and manufacturing, which have contributed to reducing pollution [39,112]. These funds have facilitated the dissemination of expertise, which serves as an incentive. In the study of Hakimi and Hamdi [120], the renewable energy sector has garnered significant foreign direct investment, made by multinational corporations in wind and solar power projects, which have facilitated the country’s transition towards a low-carbon economic model. As a result of these expenditures, there has been a reduction in greenhouse gas emissions and an increase in renewable energy capacity [8,9]. Vietnam’s renewable energy industry has grown substantially, which bodes well for the nation’s economy and the environment. FDI has also contributed to the advancement of sustainable infrastructure development in Vietnam. Multinational corporations have implemented environmentally conscious procedures in infrastructure investments [4,78]. This holds particularly true within the transportation, urban development, and waste management industries. Investments in public transit systems, such as electric buses and metro systems, have reduced carbon emissions related to transportation. Investments in facilities have improved waste management methods and reduced pollution levels [9,11,129,155].

7. Conclusions and Policy Suggestions

The motivation of the study is to assess the critical role of innovation measured by technological innovation and financial innovation and clean energy inclusion in the energy mix for ensuring environmental stability in Vietnam from 1995 to 2021. The study considered several econometric techniques, including novel Bayer–Hancked and Makki cointegration for long-run assessment, the linear and nonlinear ARDL approach in documenting the coefficients of explanatory variables, and directional casualty tested through the TY causality framework. The key findings of the study are as follows: First, the stationary test revealed that all the variables become stationary after the first difference. Second, long-run cointegration had been established by employing the Bayer–Hancked and Makki cointegration. Third, the ARDL and NARDL assessment disclosed a negative and statistically significant link between innovation and the proxy of environmental sustainably, implying that innovation led to environmental progress through the reduction of CO₂ and ecological correction. Additionally, the asymmetric evaluation divulged an asymmetric association between TI, FI, CE, and environmental sustainability, which is validated in both models.

Technological innovation drives the development and adoption of greener and more efficient technologies, reducing carbon emissions and ecological footprints. Several sectors in Vietnam, such as energy, transportation, and manufacturing, have adopted technological advancements. The author of [147] conducted a study highlighting technological innovation’s impact on carbon emissions in Vietnam’s energy sector. By incorporating advanced technologies, such as energy-efficient appliances, renewable energy systems, and intelligent infrastructure, it is possible to significantly decrease carbon emissions through enhanced energy efficiency and a transition to more sustainable energy sources. Electric vehicles (EVs) and other technological advancements possess the potential to curtail carbon emissions within the transportation sector. As per the research conducted by [149], the extensive adoption of EVs in Vietnam has the potential to curtail the transportation industry’s carbon emissions significantly. Furthermore, the implementation of advanced technology in manufacturing processes has the potential to decrease carbon emissions and mitigate ecological footprints. Using more healthful production methods and incorporating environmentally friendly materials can lead to reduced emissions and lower resource consumption.

The essential nature of financial innovation in enabling investments in renewable energy initiatives and aiding the shift toward a low-carbon economy cannot be overstated. Financial innovation in Vietnam could involve the development of novel financial instruments, investment mechanisms, and policies aimed at promoting investments in renewable energy. Sun, Guan, Razzaq, Shahzad, and Binh An [155]’s research underscores the importance of financial innovation in promoting the advancement of renewable energy in Vietnam. Implementing feed-in tariffs, green bonds, and other inventive financing mechanisms has facilitated the execution of renewable energy projects and stimulated investments from the private sector. Financial innovation has the potential to enhance energy efficiency. Innovative financial mechanisms, such as energy performance contracts and green financing, can be utilized to encourage investments in energy-efficient technologies and practices. This can reduce carbon emissions and ecological footprints, as stated by Jan et al. [156].

Using renewable energy sources, namely, solar, wind, and hydropower, can effectively aid Vietnam in its endeavors to curtail carbon emissions and minimize ecological footprints. Clean energy technologies have a lower environmental impact and produce fewer greenhouse gas emissions when compared to energy sources that are based on fossil fuels. Bekun, Gyamfi, Onifade, and Agboola [148]’s research underscores the favorable effects of implementing renewable energy sources in Vietnam, particularly in mitigating carbon emissions. The swift proliferation of solar and wind energy initiatives has significantly contributed to the decarburization of the electricity industry. Furthermore, the implementation of renewable energy technologies has the potential to mitigate the environmental impact resulting from resource extraction and environmental degradation. Hydropower projects, such as those discussed by Tran Liang and Qamruzzaman [140] et al. (2020), have the potential to offer renewable energy sources and decrease the reliance on fossil-fuel-based power generation. Vietnam’s endeavors to curtail carbon emissions and mitigate ecological footprints rely on technological innovation, financial innovation, and the incorporation of renewable energy. The utilization of sustainable energy sources results in a reduction of carbon emissions and ecological footprints in a straightforward manner. The interdependence and complementarity of these elements are crucial in promoting sustainable development and mitigating climate change impacts. Vietnam has the potential to progress towards a more environmentally friendly and sustainable future by incorporating these innovative solutions and encouraging their widespread implementation.

Vietnam’s environmental degradation is a significant problem with severe economic repercussions. The contamination of air, water, and soil endangers the health of citizens. It diminishes the output of vital industries, such as agriculture and tourism. While the government has taken some measures to address this issue, additional action is required on both the local and national levels. Businesses must employ sustainable practices that prioritize environmental protection alongside profit maximization objectives. There should also be a greater emphasis on education programs to raise citizens’ awareness of environmental issues. If we do not act immediately, future generations will inherit a drastically altered environment. We must immediately mitigate the effects of environmental degradation in Vietnam and create a greener, more sustainable future for all its citizens. Based on study findings, the study has developed the following policy suggestions to improve environmental sustainability with the effective commercialization of technological innovation, trade openness, renewable energy, and FDI. First, Vietnam can foster technological innovation by funding R&D and fostering co-operation between the executive branch, the private sector, and academic institutions. New technologies and solutions may be developed to address issues with waste management, air pollution, and water pollution. The government may provide financial incentives, including tax breaks and subsidies, research and development (R&D) financing, and assistance for public-private partnerships (PPPs) in environmental technology. Second, Vietnam may encourage trade openness by lowering tariffs and non-tariff obstacles to trade in products and services that protect the environment. This may aid in encouraging green technology and goods while opening doors for export-focused green firms. The government may also collaborate on environmental challenges through partnerships and bilateral and multilateral agreements.

Third, Vietnam may encourage the use of renewable energy by increasing the proportion of these sources in its energy mix and offering financial incentives for establishing renewable energy projects. In addition to enhancing energy security, this can help cut greenhouse gas emissions. In addition, the government may use tools like feed-in tariffs, tax rebates, and loan guarantees to create a favorable regulatory environment for renewable energy and encourage private sector participation in renewable energy projects. Four, Vietnam can draw FDI for projects and sectors that are environmentally sustainable by offering a welcoming regulatory and investment environment. The establishment of environmental standards and rewards for companies that use environmentally friendly methods are other ways in which the government may promote FDI that is environmentally conscious. Moreover, the government may use FDI to encourage knowledge exchange and technology transfer in environmental technologies and solutions. Vietnam may implement various measures to encourage environmental growth via technical advancement, trade opening, renewable energy, and FDI. These measures may assist the nation’s long-term economic and social growth while reducing environmental degradation and promoting sustainable development.

This study has offered valuable insights into the role of innovation in promoting environmental sustainability in Vietnam. However, several research avenues can enhance our understanding of this topic and guide future sustainability efforts. The following avenues offer potential future research directions:

Further studies could explore the socioeconomic implications of innovation-led environmental sustainability initiatives in Vietnam. This may involve analyzing the employment opportunities that arise from adopting sustainable technologies, evaluating the cost-effectiveness of sustainability measures driven by innovation, and examining the impact on different social groups. By comprehending the wider societal impacts, policymakers and stakeholders are empowered to formulate strategies that tackle environmental issues and foster social equity and inclusive development.

Future research should explore innovative policy approaches and governance mechanisms that effectively promote environmental sustainability driven by innovation in Vietnam. This task may involve assessing the effectiveness of current policy frameworks, such as those related to green finance, renewable energy targets, and circular economy initiatives. Furthermore, a thorough analysis of the regulatory and institutional frameworks that support the transfer of technology, protection of intellectual property rights, and dissemination of innovation can offer valuable perspectives on the policy landscape required to ensure Vietnam’s sustainable development.