Can environmental technologies and banking development generate green growth? Empirical evidence from 22 most polluted economies

The sixth assessment report by the Intergovernmental Panel on Climate Change (IPCC, 2023) demonstrates that the earth´s surface temperature has increased by 1.1°C since 2014. The report projects that global warming will reach 1.5°C by the early 2030s, emphasizing the imperative for urgent reduction of greenhouse gas emissions and the associated climate risks. These alarming realities serve reinforce the determination of certain countries to take action in the field of energy. Their strategy is predicated on three fundamental pillars: reduction of fossil fuel consumption, enhancement of energy efficiency, and acceleration of the deployment of renewable energies (Zhang, Li, & Chen, 2023). This strategic approach, underpinned by the concept of green growth, is gaining significant traction among political decision makers, environmental experts, professionals and researchers, who recognize its potential to facilitate the achievement of sustainable development goals.

The process of transitioning from a conventional economic structure to a green one, involving the formulation of effective policies and strategies, is referred to as 'green growth' (Economic Social Commission for Asia and the Pacific (ESCAP, 2006). The concept under discussion is not merely concerned with the enhancement of the environment; it is also concerned with the achievement of sustainable economic development through efficient utilization of capital and natural resources. This concept is a coherent and harmonious design for the growth of any nation (Li, Zhang, & Hong, 2020; Ullah, Zhongyi, Ullah, & Mumtaz, 2024). In this regard, recent literature has focused primarily on the technological transformation of the traditional economic structure to achieve green status (Wang et al., 2024). To explore the potential for technological change to engender environmental improvements, Aghion, Dechez Le prêtre, Hémous, Martin, and Van Reenen (2016) emphasized the capacity of environmental taxes and patents to stimulate technological advancement, which in turn will promote the utilization of renewable sources and mitigate carbon emissions. China, the United States, India, Russia, Japan and Iran are the six primary polluting economies responsible for almost 60% of global carbon dioxide emissions (CO2) in 2020 (World Bank, 2021). Therefore, green technological innovation, in conjunction with other factors such as renewable energy sources and the development of the financial sector, is of pivotal importance in explaining green growth (Amuakwa-Mensah & Näsström, 2022).

In addition to technological innovations, the growth of the financial sector has strengthened sustainable economic development. The nexus between finance and green productivity has emerged as a pivotal factor for achieving this objective. In this context, Green Bank serves as a case in point, demonstrating that financial institutions must consider the environmental impact of any financial service they offer. Consequently, conventional financial institutions must ensure that their financial resources are allocated towards environmentally sustainable projects and eco-friendly production practices. The issue of global warming and climate change has recently become of paramount importance, thereby giving rise to the concept of green growth. Consequently, there is mounting pressure on stakeholders, notably banking and financial institutions, to adopt green policies that will assist in achieving desired green growth (Liu, Zhang, & Wang, 2023).

This study investigates the relationship between environmental technologies, banking sector development and green growth in highly polluting economies. To this end, this study undertakes a comprehensive study of 22 countries over the period 2004–2023, utilizing the PMG-ARDL technique. This technique facilitates the confirmation of the robustness of the results obtained, as well as the validation and consistency of the relationships in both the short and long terms via cointegration approaches. The subsequent sections of this paper present a review of the relevant literature and specify the econometric model adopted. The subsequent section presents the interpretation of the results and conclusions.

In the contemporary context, environmental degradation has emerged as a significant impediment to sustainable development, underscoring the urgent need to establish a suitable model as a fundamental cornerstone. Advancements in environmental technology have played a pivotal role in both environmental and economic terms. Consequently, it is imperative to develop novel technologies aimed at curtailing greenhouse gas emissions without compromising energy production and consumption. This could enhance energy efficiency, augment the generation of electricity from renewable sources, and capture and sequester carbon emissions resulting from the combustion of fossil fuels. In this context, the green economy has emerged as a key driver of global economic transformation, although the levels of effort vary considerably from region to region. The green economy has demonstrated remarkable resilience and adaptability, underpinned by substantial investment in clean technologies and growing demand for sustainable solutions. For the period 2020–2024, it exhibited an average annual growth of 13 to 14%, significantly outpacing that of global Gross Domestic Product (GDP) (2.5–3%), which was confronted with structural and cyclical challenges that curbed its growth (persistent inflation, geopolitical tensions, and high debt levels in developing countries).

An examination of the world’s two primary polluting nations reveals that the rate of green growth in China and the USA significantly surpasses that of their respective economies. However, China, the world’s largest emitter of pollutants, has recently emerged as a global leader in clean energy investment, with plans to allocate over 500 billion USD by 2023 towards the development of sectors such as renewable, electric vehicles, battery ecosystems and smart grids (World Bank, 2021).

Recent analyses indicate that the growth of the US green economy systematically out performs that of the total economy, with an average gap of four percentage points over the past five years. This phenomenon is indicative of the structural dynamism that characterizes the green sector. Their investments in the green economy reached unprecedented levels between 2022 and 2024, driven by ambitious policies and strong industrial dynamics (American Clean Power Association, ACP, 2025).

As previously demonstrated by Álvarez-Herránza, Cantos, Belsalobre Lorente, and Shahbaz (2017a), these findings are corroborated by OECD countries. The researchers examined the correlation between energy innovation and CO2 emissions in 28 countries in the region from 1990 to 2014. The analysis was conducted based on the Kuznets Environmental Curve (CEK) hypothesis. Their econometric analysis, based on the generalized least squares (GLS) method, led to two important conclusions. First, innovation in the energy field has been shown to make a significant contribution to improving environmental quality in these countries by reducing CO2 emissions. Second, the growth in Gross Domestic Product per capita appears to be associated with environmental degradation. Álvarez-Herránz, Balsalobre-Lorente, Cantos, and Shahbaz (2017b) conducted an in-depth study of the impact of several factors, such as energy innovation, GDP and renewable energy consumption, on emissions in 17 OECD countries during the years 1990–2012. These findings indicate that economic growth has had an exacerbating effect on environmental degradation; however, innovation in the field of energy has been shown to reduce the damage caused.

Nonetheless, advancements in energy technology are imperative for combating global warming. In this context, Koçak and Ulucak (2019) examined the impact of R&D spending in the energy sector on carbon dioxide emissions in 19 high-income OECD countries during the years 2003–2015. Using a dynamic panel model, the researchers conducted an empirical analysis. These findings indicate that investment in research and development for energy efficiency and fossil fuels exerts an escalating influence on carbon dioxide (CO2) emissions. Conversely, no substantial correlation has been identified between R&D expenditure on renewable energies and carbon dioxide emissions. It is also noteworthy that R&D investment in electricity and storage contributed to a reduction in CO2 emissions. Furthermore, a time series is established to assess the impact of energy innovation on CO2 emissions.

Solarin, Tiwari, and Bello (2019) conducted a study examined the effect of energy innovation on environmental quality in the United States during the years 1974–2016. This study incorporates GDP and immigration as variables of interest. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology “Stochastic Impacts by Regression on Population, Affluence, and Technology” (STIRPAT) method, their study concluded that energy innovation has a substantial impact on environmental quality. Conversely, (GDP) exacerbates the situation, while immigration has been found to have no substantial impact. Within this framework, research reveals analogous findings regarding the impact of technological innovation (Xu & Lin, 2018). For instance, Yu and Du (2019) investigated the impact of innovation on carbon dioxide emissions using data from Chinese provincial expert groups for the period 1997–2015. The conclusion drawn by these authors conclude that innovation plays a significant role in reducing carbon dioxide emissions. Demir, Cergibozan, and Ari (2020) analyzed the impact of technological innovation on CO2 emissions in Turkey over the period 1971–2013. The researchers considered GDP, human capital, financial development, nuclear power and urban population as variables of interest and utilized ARDL bounds and threshold cointegration tests as econometric techniques. The findings substantiate the notion that technological innovation exerts a substantial influence on the enhancement of environmental quality in these nations.

Encouraging green innovation and regulating emissions through carbon pricing are two essential pillars of policies to combat climate change. Using the STIRPAT method, Hashmi and Alam (2019) conducted a comprehensive analysis of the impact of environmental regulations and innovations on reducing carbon emissions in OECD countries between 1999 and 2014. The findings indicate that a 1% rise in national eco-patents within the OECD results in a 0.017% decrease in carbon dioxide emissions, whereas a 1% increase in per capita environmental tax revenues leads to a 0.03% reduction in carbon dioxide emissions. Wen, Chen, Jingke, and Chen (2020) utilized panel data from 30 Chinese provinces from 2000 to 2015 to estimate the spatial distribution and analyze the carbon emission factors driving innovation. The empirical results confirm the significant spatial dependence and clustering characteristics of construction carbon emissions at the provincial level.

In their seminal study, Erdogan, Yıldırım, Çağrı Yıldırım, and Gedikli (2020) examined the impact of innovation on carbon emissions in fourteen G20 countries between 1991 and 1997 (Argentina, Brazil, Canada, France, Germany, India, Indonesia, Japan, Mexico, South Africa, South Korea, Turkey, United Kingdom and United States). The findings of this study contradict the Kuznet curve hypothesis (KCH). In the long term, innovation has no statistically significant effect on the energy and transport sectors. Furthermore, an increase in innovation in the industrial sector has been shown to result in a reduction in carbon emissions, whereas an increase in the construction sector has been demonstrated to increase these emissions. The coefficients were estimated by utilizing caution exchange capacity (CEC) and Algebraic Multigrid (AMG) methodologies.

Utilizing a spatial econometric model, Chen et al. (2020a, b) studied the impact of technological innovation on CO2 emissions in 96 countries between 1996 and 2018. Initially, the analysis revealed a substantial spatial correlation between carbon dioxide emissions and R&D intensity across nations, underscoring the efficacy of econometric models in this context. Second, this study found that technological innovation had no significant effect on mitigating global carbon dioxide emissions. However, high-tech, high-income innovations in CO2-intensive countries can significantly reduce CO2 emissions in neighboring countries. Conversely, high R&D intensity in other countries has been observed to result in increase in carbon dioxide emissions. Finally, it is evident that the greater the degree of globalization in a given country, the more obvious the impact of technological innovation on carbon dioxide emissions.

Secondary research posits that the financial sector, notably the banking sector, plays a pivotal role in accelerating and supporting climate change. Indeed, financial institutions wield considerable financial leverage, thereby directly impacting projects and industries that contribute to greenhouse gas emissions. The Initiative Finance Climat (ICF, 2021) revealed that banks financed $2.7 trillion in carbon emissions between 2016 and 2020, despite international commitments to limit global warming. In addition, the non-governmental organization Bank Track (2020) reveals that the world’s 60 largest banks have invested more than $3,800 billion in fossil fuel projects since the Paris Agreement was adopted in 2015.

Concurrently, numerous banking institutions are coming to terms with the fiscal risks inherent to climate change and the transition to a low-carbon economy. The integration of environmental, social and governance criteria into financial institutions lending decisions has become more prevalent. These criteria empower financial institutions to evaluate the risks associated with loans and ascertain that borrowing companies adhere to elevated environmental, social responsibility and governance standards. In the context of financial analysis, banking institutions have been observed to undertake a comprehensive assessment of corporate entities, encompassing the evaluation of their environmental policies and identification of risks associated with climate change (Oil Change International, 2024). Furthermore, these financial institutions may undertake a review to ascertain whether the company in question is committed to reducing greenhouse gas emissions, adopting sustainable production practices, or implementing measures to adapt to climate change.

With regard to social criteria, banks can analyze a company’s human resource management practices, including respect for workers' rights, diversity and inclusion, and safe working conditions (Lin, Wu, Chen, & Wang, 2020). Banks can also assess a company’s governance structure, level of transparency and ethics in its operations, and compliance with current regulations (Gallego-Álvarez & Pucheta-Martinez, 2019).

The Bank of England (BoE) has been at the forefront of climate risk assessments in the financial sector. In 2021, it launched climate stress tests for major banks and insurance companies, covering over 90% of United Kingdom banking assets, to assess their resilience to different climate scenarios. These tests aim to better understand the potential impact of climate change on the financial system and to encourage institutions to adopt more sustainable practices. Nevertheless, the People’s Bank of China (PBOC) has intensified its efforts to promote green and sustainable financing. In 2021, it initiated a pilot green loan program, offering favorable financing terms for environmental projects and clean technologies, with a total of 300 billion yuan (approximately €39 billion) allocated to the program. The objective of this initiative is to support China’s transition to a low-carbon economy by facilitating access to capital for companies engaged in sustainable activities (PBOC, 2021). Concurrently, the Federal Reserve (Fed) in the United States has intensified efforts to evaluate climate risks. The International Monetary Fund (IMF) has also intensified its endeavors to incorporate climate-related concerns into its operations. In 2021, it initiated a new initiative, the Climate Change Dashboard, with the objective of monitoring climate-related economic and financial indicators in more than 190 countries. The dashboard was developed to assist member countries in tracking their progress in reducing carbon emissions and evaluating the economic ramifications of climate policies.

These initiatives illustrate that central banks and international financial institutions are taking proactive measures to address climate change. By incorporating climate risks into their strategic frameworks and promoting environmentally sustainable financial practices, these entities facilitate a more expeditious and efficacious transition to a sustainable economic model. These actions seek not only to stabilize the economy in the short term but also to ensure long-term economic resilience in the face of the challenges posed by climate change (Oil Change International, 2024).

This study focuses exclusively on examining the impact of banking sector development and environmental technology on green growth in highly polluting economies. However, subsequent studies should investigate other factors related to human development. Furthermore, the findings of this study can be extrapolated to other economies and regions in the future.

Future studies should incorporate the supply- and demand-side determinants of green growth into the analysis. Financial innovation must be recognized as a significant determinant of green growth and consequently its incorporation into future analyses is essential. This study used panel linear autoregressive distributed lag (ARDL) approaches for empirical analysis. Incorporating asymmetric methods into future analyses is a potential avenue for future research.

It is imperative to examine the interplay among environmental technologies, banking sector development and green growth. A comprehensive understanding of the interplay between investment in environmental innovation and financial dynamics is paramount in elucidating their collective potential as catalysts for green economic growth that is environmentally sustainable. The present study examines the complex relationships between these dimensions using key indicators such as environmental technology patents, bank deposits, market capitalization and the share of trade in GDP to highlight the potential synergies between these factors in guiding policymakers towards integrated and effective policies for a green and sustainable economy.

The objective of this study is to substantiate the existence of long-term relationships between the aforementioned variables by considering their dynamic and structural interdependencies. Utilizing a sophisticated econometric approach encompassing stationarity and cointegration tests in conjunction with the implementation of a PMG-ARDL model, this study seeks to discern the underlying connections and comprehend the manner in which economic dynamics affect the transition to sustainable green growth.

The selection of the study interval (2004–2023) for the model was informed by the observation that, over the past two decades, the issue of global warming and climate change has assumed central importance, giving rise to the concept of green growth. Evidently the banking and financial sectors are subjected to mounting pressure to adopt environmentally sustainable policies and practices. These practices have the potential to serve as pivotal instruments in achieving green growth, while concurrently facilitating the provision of environmentally sustainable banking and financial services (Liu et al., 2022).

The following 22 countries were identified as the most significant contributors to global pollution on a worldwide scale: Bangladesh, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, India, Indonesia, Iran, Italy, Japan, Mexico, Pakistan, Russia, Spain, Switzerland, Turkey, the United Kingdom and the United States. These countries are notable for their varied approaches to the management of carbon dioxide emissions. Over the period considered, these countries accounted for more than 50% of total CO2 emissions, real GDP and global final energy consumption. The elevated levels of greenhouse gases and concomitant environmental challenges experienced by these nations substantiate their commitment to technological innovation.

Utilizing a comprehensive array of robust economic and environmental data from multiple sources, this research employs an advanced econometric approach to elucidate the dynamics of green transition and the factors that propel it (Table 1).

Descriptive analysis revealed a wide range of variations and consistency in the variables studied (Table 2). The mean value of green growth (GG) is 33.39, with a median value of 12.79, suggesting an asymmetric distribution characterized by extreme values. A similar observation can be made regarding bank deposits (DMBA) and market capitalization (SMC). In both cases, the existence of entities with significantly higher financial levels is indicated by the differences between the average and the median. In the context of both resident (RPA) and non-resident (NRPA) patent applications, the proximity between the average and median values indicates a relatively symmetrical distribution.

The dispersion, measured by the standard deviation, confirmed high heterogeneity for some variables. The variable (GG) demonstrates a standard deviation of approximately 73.62, reflecting a substantial discrepancy between countries regarding green growth performance. Financial variables such as (DMBA) (40,80) and (SMC) (56,19) also demonstrate significant variations, indicating substantial structural differences in the levels of access to finance and financial markets. By contrast, patent variables such as Environment-related technologies (ET) (3.88) and (RPA) (0.99) exhibit a more moderate dispersion, suggesting a degree of homogeneity in environmental innovation.

Analysis of asymmetry (skewness) and flattening (kurtosis) revealed abnormal distributions for several variables. The variables describing green growth (skewness = 3.67, kurtosis = 17.07) and market capitalization (skewness = 3.96, kurtosis = 34.07) demonstrate marked asymmetry in the right and leptokurtic distributions, characterized by elevated peaks and extreme values. The econometric results obtained reflect a high concentration of low performance for the majority of observations but also the existence of exceptionally high values. Conversely, the variables describing patents (RPA and NRPA) demonstrate low asymmetry and flattening that approaches a normal distribution, suggesting relatively balanced distributions.

Finally, the descriptive statistics reveal high heterogeneity and marked asymmetries for some variables, notably green growth (GG) and market capitalization (SMC), which highlight significant disparities between the entities studied. These observations necessitate comprehensive econometric analysis to manage extreme values and account for data heterogeneity.

Econometric modeling employs a recent estimation method that allows for heterogeneity in the adjustment dynamics of variables towards long-term relationships. In this context, the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) estimator proposed by Pesaran et al. (1999) can be used to analyze short- and long-term relationships between variables. The model is predicated on the assumption that the model constant, short-term coefficients and error variances may vary from one country to another, whereas the long-term coefficients are constrained to be uniform across all countries. In light of the divergent impacts of vulnerability to financial crises and external shocks, stabilization policies and monetary policy, short-term adjustment is permitted to be country-specific, as evidenced in this case.

The following hypotheses underpin this research:

1. Are environmental patents and patent applications (both resident and non-resident) conducive to long-term green growth?

2. Does the development of the banking sector, as measured by bank deposits (DMBA) and market capitalization (SMC), play a key role in ecological transition?

3. Does trade (Trade) support environmental innovation and green growth policies?

Presentation of the empirical model

The model is expressed as follows:

p and q are the lagged orders of the dependent and explanatory variables respectively. $εit$ represents random error.

The correlation matrix provides information on the linear relationships between the variables studied (Table 3). The results demonstrate that the green growth (GG) variable exhibits a positive and statistically significant correlation with bank deposits (DMBA) of 0.328, indicating that higher bank deposits could be associated with improved green growth, although the strength of this relationship is moderate. A similar yet weaker relationship was observed between green growth (GG) and market capitalization (SMC) with a correlation of 0.140, suggesting that developed financial markets play a positive, albeit limited, role in promoting green growth.

The correlation between environmental patents (ET) and green growth (GG) is negative, with a coefficient of −0.155. This finding suggests that the number of environmental patents in a country does not necessarily lead to immediate green growth. A similar trend was observed in the context of both resident (RPA) and non-resident (NRPA) patent applications. The respective correlations were significantly positive (0.636 for RPA and 0.565 for NRPA). However, there are differences in their intensities, which underscore the significance of both local and international innovation in the transition to a green economy.

A significant negative correlation between (Trade) and (GG) is indicated (−0.268), suggesting that elevated levels of trade openness may be associated with diminished performance in terms of green growth in certain instances. This may be indicative of reliance on less sustainable economic activities in open economies. However, this relationship merits further exploration to identify potential causalities. Furthermore, correlation analysis reveals intriguing associations, particularly between innovation (as measured by patents) and financial development.

As illustrated in Table 4, the results of the stationarity tests for Levin, Lin and Chu (LLC), Im, Pesaran and Shin (IPS) and ADF-Fisher Chi-square. The variables (SMC), (ET), and (NRPA) are found to be stationary at level (I(0)). By contrast, the remaining variables (GG), (DMBA), (RPA), and (Trade) are integrated in order one (I(1)). To avoid bias in the estimation of relationships between these variables, an appropriate econometric approach, such as an integration model or cointegration test, may be necessary. In conclusion, the results of the stationality tests (LLC, IPS, ADF) indicate that the variables are integrated of order one or zero (I(1) or I(0)), thereby validating the implementation of an ARDL model.

The ADF statistic yielded a t-statistic value of −5.503476, which was significant at a very high level (p = 0.0000). This finding indicated that the model residues were stationary (Table 5). This finding suggests that despite the individual non-stationarity of the variables studied, they exhibit a stable long-term relationship, with some variables being integrated of order one.

The residual variance (1180.528) and HAC variance (162.0815) provided information on the quality of the residue estimation and the robustness of the results (see Table 5). A lower HAC variance suggests better correction of the potential time series errors. Consequently, it can be concluded that the Kao cointegration test confirms a significant long-term relationship between the variables (GG, SMC, DMBA, RPA, NRPA, ET, Trade), thus validating the relevance of the model.

The optimal delay number analysis demonstrates that the ARDL model (1, 2, 2, 2, 2, 2) is the optimal model for estimation (Table 6). The Akaike Information Criterion (AIC) of 9.030563 was found to be inferior to other tested models, suggesting that this specification minimizes information loss while maintaining a satisfactory balance between model accuracy and economy. The Bayesian Information Criterion (BIC) and the Hannan-Quinn Criterion (HQ) also confirm that this model offers a suitable delay structure to capture the time dynamics between variables without overloading the model.

The long-term results of the PMG-ARDL model provide significant insights into the determinants of green growth (GG). The coefficients of the explanatory variables provide the following results (see Table 7). The coefficient of stock market capitalization of listed domestic companies (SMC) is negative (−0.0549) and significant at the 1% level. This shows that in the long term, an increase in market capitalization slightly reduces green growth. This may be interpreted as an indication that stock markets, which often focus on carbon-intensive industries, do not necessarily support green transition. However, the outstanding deposits of commercial banks (DMBA) are negative (−0.2184) and significant at the 1% level. Bank deposits appeared to have an inverse effect on green growth. This may indicate that banks in the most polluted economies primarily fund projects that do not support sustainability objectives. Recent analyses have indicated that sustainable finance (green finance) falls short of its stated objectives and commitments. (Londeix, 2019) has been demonstrated that the measure is ineffective in supporting truly sustainable projects. Furthermore, this would be counterproductive, as demonstrated by the validation of the negative sign found above. Investments in green technologies may yield lower returns primarily because of the inherent risks and uncertainties associated with such investments (Wang et al., 2024).

The number of resident patent applications (RPA) was positive (0.000218) and significant at the 1% level. This indicates that local innovation in environmental technologies positively contributes to green growth, although the effect is relatively small. Conversely, the coefficient of patent applications by non-residents (NRPA) was 0.000366. The figure, while low, is of some significance at the 1% level. This demonstrates that foreign innovation has a greater impact on green growth than does local innovation. It is therefore important to highlight the importance of international green technology diffusion in polluted economies. Despite its seemingly negligible size, the figure is of some importance given that it constitutes 1% of the total. This observation underscores the notion that foreign innovation exerts a more substantial influence on green growth than domestic innovation does. Consequently, it is imperative to underscore the significance of international diffusion of green technologies in polluted economies.

Conversely, patents related to environmental technologies (ET) have a strongly positive significance (2.3216) and are significant at the 1% level, implying that a 1% increase in environmental innovation increases green growth by 2.3216%. This suggests that advances in green technologies are the key to stimulating green growth. The present findings are consistent with those of Talebzadeh Hosseini and Garibay (2022), who report that environmental technology is a crucial determinant of green growth in polluted economies. This finding is further corroborated by the results of Wang et al. (2024).The findings of these studies indicate that the implementation of environmental technology has the capacity to stimulate green growth, with this effect being achieved through the effective management of both production-based and demand-based pollution emissions. Environmental technology has been demonstrated to have a substantial impact on the enhancement of green growth, with the enhancement of energy efficiency being a key contributing factor.

Finally, the international trade variable (Trade) has a positive coefficient (0.1724) that is significant at the 1% level (Table 7). This finding lends credence to the notion that trade openness fosters green growth by facilitating increased access to sustainable technologies and products. The findings of the present study were corroborated by the research conducted by Huang and Liu (2022) and Li et al. (2022a, b). Huang and Liu (2022) demonstrated that trade has a significant impact on technological progress, which, in turn, fosters green growth. Li et al. (2022a, b) posited that trade activities exert three distinct effects on green growth: namely technology structure and scale.

Short-term relationships are characterized by a more complex dynamic, particularly in the presence of delays in the explanatory variables. The short-term effect of the Stock Market Capitalization (SMC) variable is examined and it is found that an immediate change in (SMC) has a negative impact of −0.2473, which is significant at the 1% level. Conversely, the delay has a positive effect of 0.3139, which is also significant at the 1% level. This result reflects a dynamic in which initial stock market investment may not immediately stimulate green growth but rather produce deferred returns. The present study supports the findings of Zhou et al. (2022) and Amuakwa-Mensah and Näsström (2022) in terms of the association between the banking sector and green growth.

Regarding the deposit money bank assets (DMBA) variable, the results indicate a positive (0.1488) and significant effect at the 10% threshold, which is an immediate effect of bank deposits. However, a negative effect was also indicated for its delay (−0.0983, significant at 1%), which may be indicative of a more complex relationship between the variables. This duality indicates that financial resources must be allocated prudently to green projects; otherwise, there is a risk of decelerating ecological transition. As Li and Liao (2020) demonstrate, analogous results were obtained in the case of China. It has been asserted that the development of the banking sector is conducive to the promotion of green growth, a notion supported by the mobilization of savings, diversification of risks, and allocation of green resources.

With regard to environmental patents (ET), there is an immediate negative effect of −3,086 and a significant effect at the 1% level, but a positive effect with a delay of 2,095, which is also significant at the 1% level. This may be related to the time required to translate innovation into measurable impact. Trade openness (Trade) has an immediate positive effect (0.3248, significant at 1%) and a modest delayed effect (0.1343, significant at 1%), showing that the benefits of trade for green growth are quickly evident.

Air pollution has reached a critical point in major urban areas. Forest areas have been extensively damaged, and biodiversity has been threatened by the disappearance of animals and plants. Natural resources have declined and there has been an increase in environmental disasters. Global environmental hazards, such as greenhouse gases, ozone layer and acid rain, require concerted action by nations. Examples of environmental degradation presented here are unequivocally attributable to human activity. The necessity for a balance between economic development and environmental protection is of global significance. It is imperative to acknowledge the urgent need to address climate change and raise global awareness of its devastating impact.

This study’s findings, derived from empirical analyses of the relationship between environmental technologies, banking sector development and green growth, confirm significant links between these disparate areas. Empirical evidence from the applied PMG-ARDL model suggests that public policies must encourage local and foreign technological innovations to drive green growth. It is imperative to promote international collaboration and adoption of green technologies through technology transfer programs. The financial sector plays a pivotal role by integrating environmental, social and governance criteria into its operations. Consequently, it is necessary for financial institutions to allocate a greater proportion of their lending towards environmentally sustainable projects to optimize the beneficial impact of their financing. It is imperative to reorient bank funds and financial markets towards sustainable industries through the implementation of tax incentives or environmental regulations. Furthermore, international trade and investment have been identified as playing a critical role in ecological transition. Therefore, it is crucial to promote trade agreements based on sustainable development to maximize the benefits of trade.

The findings of the present study demonstrate that an integrated approach in which the banking sector supports innovation in environmental technologies and promotes green growth can serve as a significant catalyst for the transition to a sustainable economy. Consequently, public policies should aspire to foster synergies among these disparate sectors by promoting innovation, investment in green technologies and sustainable financing. Policymakers and economic actors must collaborate in fostering these interconnections to expedite the transition to a greener, more inclusive economic paradigm. It is important to recognize that efforts to protect the environment should not be viewed as hindrances to progress. The objective should not be merely to reduce production, but rather to transition towards a paradigm of production that is inherently different and to address the adverse externalities engendered by the pursuit of growth.