Economic Globalization and Environmental Technology: Implications for Environmental Degradation in G7 Countries

Abstract

The global ecological conditions are degrading rapidly, and even after the commitment to achieve Sustainable Development Goals by 2030, G7 countries still struggle to meet basic environmental standards. This study offers a novel marginal contribution by analyzing how economic globalization (EG), economic growth intensity (EGI), financial innovation (FI), and environmental technology (ET) influenced environmental degradation (ED) in the G7 countries from 2000 to 2021. Using the Method of Moments Quantile Regression (MMQR) alongside Driscoll and Kraay Standard Error (DKSE), this research provides a first-of-its-kind distributional mapping that accounts for cross-sectional dependency and heterogeneous slopes. Crucially, the findings reveal a “decoupling failure” in advanced economies, where the existing treatment mechanism for ET is insufficient to separate industrial growth from emissions due to institutional discrepancies. While FI is often viewed as a green catalyst, this study identifies it as a “double-edged sword,” showing that it significantly increases environmental degradation in higher quantiles due to carbon-intensive global supply chains. Conversely, EGI is discovered to be mitigatory, suggesting that enhancing financial efficiency and growth soundness can diminish ecological damage. This research fills a critical literature gap by reconciling the Pollution Haven Hypothesis and Green Finance Theory, providing empirical evidence that developed financial systems may inadvertently exacerbate damage if not specifically aligned with green mandates.

1. Introduction

Environmental health remains a topic of ongoing discussion in the literature. The observation is clear; the more time passes, the more remarkable it is that the environment degrades. This progressive environmental decline is caused by several activities such as deforestation, pollution, or burning of fossil fuels; these activities have created in the environment what is called the triple crisis, which is climate change, nature loss, and pollution [1]. Extreme heat waves, cyclones, and the disappearance of species are representations of the unprecedented crisis that the environment is undergoing. G7 countries, a political and economic organization made up of the United States, Canada, Japan, Germany, France, Italy, and the United Kingdom, are among the most advanced and influential economies in the world. The G7 countries have achieved this dominant economic position thanks to their deep integration into globalization and their capacity for financial innovation, which have largely fueled their exceptional growth. Yet this economic success hides an alarming paradox. While these nations have considerable resources, they are still polluting [2], and they account for 23% of global energy consumption and emit 44% of the global CO₂ emissions [3]. Their investment in environmental technologies remains derisory given the scale of the environmental damage they cause. Their commercial and financial power, instead of acting as a lever for an ecological transition, seems to accelerate the planet’s degradation. This study therefore proposes to analyze the real effects of economic globalization, economic growth, financial innovation, and environmental technology in G7 countries. It will be necessary to analyze how these mechanisms, although they have contributed to the prosperity of the G7 countries, impact the environment.

Economic globalization is a complex and multidimensional process that transforms economic, cultural, technological, and even political relations on a global scale. It is considered to be one of the major contributors to pollution and environmental degradation [4]. The involvement of the G7 countries in globalization is not only enormous but also important for the rest of the world [5]. The G7 countries’ globalization index is 70.95%, which is 35% more than the world’s average degree of globalization [6]. These countries have demonstrated an incredible capacity for innovation throughout their history. They are at the base of creations such as smartphones, cars, the internet, and computers, to name a few. These innovations have made progress in the world but also caused enormous damage to the environment. The transfer of these creations to other countries is done either by planes, trucks, and ships, means of transport that require enormous consumption of fossil energy and exert enormous degrading pressures on the quality of the environment. The exports of all these technologies are major sources of revenue for each of the G7 countries, so it is almost impossible for them to detach themselves from them. The more they are involved in ongoing globalization, the more the environment is degraded. It is then necessary to find solutions that allow globalization and environmental well-being to be aligned at the same time.

In addition, financial innovation also plays a crucial role in the state of the environment because it makes available to populations services such as new types of loans and investment options. Financial innovation provides additional resources that can be used to invest in projects such as construction, agriculture, information, and renewable energy sectors. More funds are being directed to degrading industries and the expansion of economic activities, resulting in environmental degradation. Little money is invested in environmental technologies, which are used to finance all other activities. A massive shift in investment to environmental technologies would go a long way towards improving environmental health because environmental technology has the ability to mitigate the negative impact that degrading energies have on the environment [6].

This study aims to evaluate the effects of globalization, economic growth intensity, financial innovation, and environmental technology on environmental degradation in G7 countries during the period 2000–2021. This research shows two innovations within the existing literature. Firstly, this study is distinct from the previous literature in that a quantile is identified at which the impact of financial innovation changes from a net positive effect to a net negative effect. Secondly, in contrast to the assumption of ‘linear efficiency,’ this study reveals a ‘decoupling lag’ in the G7 countries, in which the scale effects of industrial activities outstrip the growth in environmental technology, as indicated by the patents filed in the countries.

The novelty of the performance of this research lies in its capacity to reconcile the contradictory findings of the Pollution Haven Hypothesis and the Green Finance Theory within a heterogenous structure. This research offers a distinct marginal contribution by challenging the assumption of linear efficiency in environmental policy for advanced economies. Unlike traditional studies that assume a uniform impact of technology, this paper identifies a ‘decoupling lag’ in G7 countries, where the scale of industrial activities continues to outstrip the growth of environmental technology. Furthermore, the study provides a novel distributional mapping of environmental drivers using the MMQR-DKSE dual method. This approach allows for the identification of a critical threshold at which financial innovation transitions from a mitigatory tool to a driver of ecological damage, thereby reconciling the contradictory findings of the Pollution Haven Hypothesis and Green Finance Theory within a single heterogeneous framework.

To properly articulate the motivations for the empirical strategy, it is necessary to establish that this research is framed under the aegis of three fundamental economic theories. Firstly, the Pollution Haven Hypothesis retains the notion that a consequence of economic globalization (EG) is the potentially accelerated degradation of the environment if those affected treat investment and trade agreements in order to spur resource-intensive areas where costs associated with environmental protection remain low. Secondly, the Environmental Kuznets Curve retains the idea that the effect of economic growth intensity (EGI) will drive a positive rotation towards improved environmental quality when more developed countries begin to use cleaner, more resource-efficient means of production. Lastly, Green Finance Theory retains the belief that although the use of the device of financial innovation (FI) is crucial for mobilizing funding for green initiatives, it is also true that the first effect is degradation due to credit extended to resource-intensive investments. Lastly, we describe the notion for the function of a “Technology Environment” to be a mitigator under the assumptions about improved resource productivity and the nullification of pollutants.

On the basis of the above theoretical foundations, we propose the following hypotheses to be verified:

H1. 

Economic globalization positively impacts environmental degradation because of increased scale and emissions from trade as a result of economic globalization.

H2. 

Financial innovation (FI) could worsen environmental problems if its investments focus on high-emission activities.

H3. 

The impact of economic growth intensity (EGI) on a decrease in environmental degradation is expected to occur as G7 countries advance toward more carbon-efficient methods of production.

H4. 

Environmental technology (ET) helps reduce damage to the environment caused by poor resource efficiency and pollution management.

The following sections will first present a review of previous work and existing perspectives on the impact of these factors, then detail the theoretical framework and the empirical model, as well as the methods used for the analysis. Also, the results will be presented and discussed before moving to the conclusion and policy recommendations.

2. Literature Review

Theoretical grounds for the research are based on two main economic theories, the Environmental Kuznets Curve and the Pollution Haven Hypothesis. The Environmental Kuznets Curve illustrates how, in the case of advanced economies like those classified in the G7 countries, the transitions and composition in technology and the like will eventually reduce environmental pollution. This study applies EGI to understanding the concept. Other economies, however, may apply the concept of the Pollution Haven Hypothesis in a negative, adverse manner, as dictated by the principles of the economic factor EG.

2.1. Economic Globalization and Environmental Degradation

Over time, several studies have emerged on the relationship between economic globalization and the environment. The results vary according to [7,8]. For example, Ref. [9] demonstrate that globalization hurts the quality of the environment when used for economic purposes. Furthermore, the findings of [10] affirm that economic globalization has positive effects on environmental degradation, especially when followed by poor institutional quality. Likewise, Ref. [11] explains that economic globalization participates in an exclusive degradation of the environment because some countries are increasingly developing their local industry to attract foreign investments. For this reason, they use methods such as the abusive exploitation of resources, which contribute enormously to the degradation of the environment. In the same vein, Refs. [12,13,14] confirm that fluctuations in oil prices are problematic, especially when these prices fall because it makes oil more accessible, which reduces the consumption of renewable energy resulting from an increase in environmental degradation. However, the literature also contains several studies which run counter to the above studies. These studies affirm that economic globalization contributes more to environmental quality improvement. The research of [15] highlights that many Asian countries have developed environmental protection by flooding markets with green patents and innovations. Also, when globalization serves the transfer of green technologies and innovation for the promotion of cleaner production, it contributes to improved environment quality. This is called the technical effect of globalization, as explained by [16].

2.2. Economic Growth Intensity and Environmental Degradation

Economic growth, considered as a factor of progress, is also presented in several studies as problematic for environmental health [17]. The findings of [18] stress that economic growth is fundamentally based on increased consumption of fossil fuels, making it a key factor in the environmental crisis. Also, the research of [19] shows that the systems participating in the economic growth of Mexico, such as manufacturing and service, are largely dependent on fossil energy consumption, which represents 90% of the country’s energy composition. The study of [20] on Sub-Saharan African countries found that these countries are experiencing a phase of accelerated industrialization, characteristic of their economic transition. Although this process is conducive to economic growth, it is often accompanied by a marked increase in CO₂ emissions from mining, extractive industries, manufacturing, and heavy industries. The prohibitive price of green technologies and sustainable infrastructure is an insurmountable barrier for countries with fragile economies, undermining their ability to respond to environmental challenges [21]. Some rare studies differ from the idea that economic growth has a positive link with degradation, the following studies think otherwise. For example, Refs. [22,23,24] are some of the few studies reflecting the negative impact of economic growth on environmental degradation. These studies have the same vision by explaining that when economic growth is intended for investments in green technologies for sustainability purposes, it contributes to significantly reducing degradation in the environment.

Economic growth’s impact and the environment have a very complex relationship because how economic growth impacts environmental degradation depends in part on the means and methods used to achieve this growth, but also choices on the reuse of this growth. The activities that drive economic growth mostly rely on polluting energy sources such as fossil fuels. So, investing in clean energy to reduce the consumption of polluting energy could help improve environmental health.

2.3. Financial Innovation and Environmental Degradation

Financial innovation reduces environmental degradation. This is what many different studies have shown in the literature. The results of [25] confirm the capacity that a strong financial system has to insert effective climate mitigation policies and technologies by directing large investments to sustainable solutions. The research of [26] found that financial innovation reduces environmental degradation by demonstrating that increased financial risk directly mitigates global carbon emissions and indirectly promotes their reduction through technological innovation. The findings of [27] explain positive shocks in financial innovation in BRICS countries. Starting with organizational innovations, amplifying the expansion of the financial market softens the financial constraints that allow a large accumulation of capital thanks to financial innovation, explaining its reducing effect on environmental degradation. According to the study of [28], strengthening the financial system led to successful financial inclusion that allowed more Chinese investors over the period 1990–2022 to invest in clean energy and green technologies, therefore reducing CO₂ emissions. The research by [29] investigated the effects of human development and financial development on the environment; the findings revealed that human development (education) reduces degradation, but when associated with financial development, this reduction is even more pronounced. Financial innovation may also in some cases cause negative pressures on the environment. The study of [30] shows that financial innovation allows wider access to the financial system for people, for example, enabling easier access to loans. This will allow the creation of several companies that, in the majority, operate thanks to methods that do not take into account environmental health. Also, Ref. [31] explains that financial innovation can increase carbon emissions by promoting economic growth if it is not directed by sustainable principles. Financial access must be directed toward creative and enterprising projects that prioritize emission reduction to lower this risk. The findings of [32] demonstrated that financial innovation has an N-shaped relationship with degradation. Initially, financial innovation contributes to the worsening of environmental degradation. Subsequently, it plays a recurring role by promoting the financing of cleaner sustainable technology. At a more advanced stage, it begins to exert a negative influence again, leading to an increase in environmental degradation.

Recent studies show the important role of financial innovation in ecological transition. By facilitating the emergence of new technologies and expanding investment opportunities in clean sectors, a strong financial system can curb environmental damage. In several countries, this momentum has led to greater economic inclusion and increased support for green energy. Nevertheless, some studies warn that such innovation, if not framed by sustainable objectives, risks promoting polluting economic activities. The main challenge therefore lies in the strategic orientation of financial tools toward responsible and ecologically virtuous uses.

2.4. Financial Inclusion Effects and Economic Growth

The present study agrees with what previous studies suggested, finding out the different methods and strategies that can be used to enhance financial innovation and develop economic growth. Poor financial innovation negatively impacts economic growth. The research of [33] affirms that young people and women in Africa are the most excluded groups in the financial system because of their combined low level of education and income. Their absence from the financial system represents a major break as they cannot leverage financial tools that promote economic growth. Also, Ref. [34] analyzed the impact of financial innovation (through financial inclusion with loans to private sectors, the liquidity ratios of commercial banks and loans to rural areas, and deposits from rural areas) on the economic in Nigeria, revealing a significant negative effect of financial innovation on economic growth and suggesting that the Nigerian authorities enhance the regulatory infrastructure to optimize the allocation and deployment of economic resources. While many studies have found a negative relationship between financial innovation and economic growth, others emphasize the positive role of financial inclusion as a driver of economic growth. More specifically, [35] explains that strengthening financial innovation through infrastructure investments, promoting financial education, and implementing inclusive policies to ensure greater access to financial resources significantly boost economic growth and reduce poverty. The empirical evidence found by [36] suggests that financial inclusion with the presence of strong institutional quality promotes economic growth.

The different studies explored in this section show that financial innovation either boosts or slows down growth according to its quality. Its success in increasing economic growth resides in the integration of the excluded population (women and young people), accessible infrastructures everywhere even in rural areas, and adapted policies. Broad inclusion plus effective territorial meshing must balance accessibility and stability to maximize its impact [37]. However, strengthening financial innovation must be done with caution because, as [38] said, financial inclusion allows expanded access to the credit market but this ease of access risks making the menages fall into the debt trap.

2.5. Environmental Technology’s Impact on Environmental Degradation

Recent technological advances offer promising solutions to mitigate environmental degradation, as evidenced by [39], who assert that innovative AI technologies demonstrate significant potential to reduce environmental degradation by lowering energy consumption and production waste, thereby decreasing overall environmental impacts. A similar perspective is offered by [40], who emphasizes the effectiveness of post-combustion carbon emission capture systems, including monoethanolamide-based absorption, calcium looping, membranes, and ammonia-based absorption. These technologies reduce climate and environmental acidification impacts by 60 to 80 percent. This review is a key approach for addressing environmental issues in the building sector to limit construction’s carbon footprint. Also, Ref. [41] further contends that microfluidic technology plays a crucial role in marine monitoring by enabling miniaturized, cost-effective pollutant analysis even in extreme environments, offering an innovative solution for ecological tracking. Furthermore, Ref. [40] documents the importance of environmental technology with the effectiveness of vacuum ultraviolet, which purifies air through ozone catalysis while disinfecting water via ultraviolet radiation. However, the environmental benefits of these technologies can be reversed if implemented inefficiently. Additionally, Ref. [42] discussed that technology’s effectiveness fundamentally depends on institutional quality. When institutions lack sustainable objectives or fail to properly support green technology implementation, environmental benefits are significantly diminished. This concept is amplified by [43] in Brazil, who shows that environmental technology has a positive impact on environmental degradation due to market entry barriers and substantially lower government subsidies compared to high-income nations, highlighting the challenges of technology adoption in emerging markets.

3. Data and Methodology

3.1. Data

This research uses a panel dataset that covers G7 countries over the period of 2000 to 2021. All the indicators present in this study have been extracted from the World Bank’s World Development Indicators (WDIs) database [1]. The specific definitions and operational proxies for the variables are detailed below (Also, see Table 1):

-

Environmental degradation (ED) is the dependent variable, proxied by total CO₂ emissions kt. It represents the absolute ecological footprint of human activity and reflects the decline of the natural environment caused by industrialization and urbanization.

-

Economic globalization (EG) is considered to be measured through the KOF Globalization Index, and is a reason for increasing integration between economies through international trade, investments, and flows of capital.

-

Financial innovation (FI) refers to the utilization of developed, sophisticated financial products, technologies, or processes that enhance the efficiency of the market and accelerate capital formation. In this paper, FI has been used as the proxy, portraying that the financial sector is able to finance industrial development impinging on the environment in a manner based on the orientation of the sector with regard to sustainability. It is operationalized using domestic credit to the private sector as a percentage of GDP. This reflects the depth and modernization of financial services.

-

Economic growth intensity (EGI) is measured as CO₂ emissions per unit of economic wealth. It is an indicator of production efficiency at low CO₂ emissions. It measures how efficiently an economy performs in relation to its production.

-

Environmental technology (ET) is measured using the number of patent applications in the area of the environment. This is because it is reflected in the development of sustainable practices and science-based solutions aimed at reducing pollution and the depletion of natural resources.

Table 1. Features of the variables.

Variable	Proxy	WDI Series Code	Unit of Measurement
Environmental Degradation (ED)	Total CO2 emissions	EN.ATM.CO2E.KT	Kilotons (kt)
Economic Globalization (EG)	Globalization index	(Specify if KOF or WDI)	0–100 (Index)
Financial Innovation (FI)	Domestic credit to private sector	FS.AST.PRVT.GD.ZS	% of GDP
Growth Intensity (EGI)	Carbon intensity of GDP	EN.ATM.CO2E.PP.GD	kg per PPP $ of GDP
Environmental Tech (ET)	Environment-related patents	(Specify WDI/OECD code)	Count of patents

Table 1. Features of the variables.

Variable	Proxy	WDI Series Code	Unit of Measurement
Environmental Degradation (ED)	Total CO2 emissions	EN.ATM.CO2E.KT	Kilotons (kt)
Economic Globalization (EG)	Globalization index	(Specify if KOF or WDI)	0–100 (Index)
Financial Innovation (FI)	Domestic credit to private sector	FS.AST.PRVT.GD.ZS	% of GDP
Growth Intensity (EGI)	Carbon intensity of GDP	EN.ATM.CO2E.PP.GD	kg per PPP $ of GDP
Environmental Tech (ET)	Environment-related patents	(Specify WDI/OECD code)	Count of patents

Even though both ED and EGI make use of carbon data, they differ in their concepts since ED measures the total amount of emissions while EGI measures the carbon intensity. This avoids mechanical correlation while offering a holistic perspective.

3.2. Methodology

In order to analyze the determinants of environmental degradation in G7 countries, we specify the below baseline panel data model:

$${\mathit{L}\mathit{o}\mathit{g}\left(\mathit{E}\mathit{D}\right)}_{\mathit{i}\mathit{t}}={\mathit{\alpha }}_{\mathit{i}}+{\mathit{\beta }}_1{\mathit{L}\mathit{o}\mathit{g}\left(\mathit{E}\mathit{G}\right)}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_2{\mathit{F}\mathit{I}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_3{\mathit{E}\mathit{G}\mathit{I}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_4{\mathit{E}\mathit{T}}_{\mathit{i}\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$$

In this specification, ${\mathit{L}\mathit{o}\mathit{g}\left(\mathit{E}\mathit{D}\right)}_{\mathit{i}\mathit{t}}$ denotes the natural logarithm of environmental degradation in country i at time t. ${\mathit{\alpha }}_{\mathit{i}}$ denotes the fixed effects in order to account for unobserved heterogeneity amongst the G7 countries. ${\mathit{L}\mathit{o}\mathit{g}\left(\mathit{E}\mathit{G}\right)}_{\mathit{i}\mathit{t}}$, ${\mathit{F}\mathit{I}}_{\mathit{i}\mathit{t}}$, ${\mathit{E}\mathit{G}\mathit{I}}_{\mathit{i}\mathit{t}}$, and ${\mathit{E}\mathit{T}}_{\mathit{i}\mathit{t}}$ are the key explanatory variables for economic globalization, financial innovation, economic growth intensity, and technology, respectively. ${\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$ is the error correction error, which is supposed to have a complex structure in terms of cross-section dependence and temporal correlation.

To facilitate the interpretability of the results, a mixed functional form is adopted for the model. The dependent variable LOGED or environmental degradation is log-transformed, and so is the primary independent variable LOGEG or economic globalization, to linearize the relationship between variables and eliminate heteroscedasticity in absolute volume and index data, respectively. For this reason, the estimated coefficient ${\mathit{\beta }}_1$ will show elasticities or, put differently, the percentage rate of variation in degradation with regard to one percent variation in globalization. In turn, financial innovation, or FI, economic growth intensity, EGI, and environmental technology, ET, remain in their level form due to their inherent ratio or intensity measures. Here, the estimated coefficient reveals semi-elasticities or, in other words, the impact, in percentages, of environmental degradation variation after a one-unit (or a one-percentage-point) shock in the particular independent variable. This hybrid model approach provides statistical stability with practical relevance for G7 policy analysis.

This selection of regressors is informed by theoretical foundations from the literature on environmental economics, and it is relevant to the “triple crisis” regarding climate change, biodiversity, and pollution. In more detail, EG and EGI focus on the scale and technique effects of economic systems, whereas FI and ET focus on the modern financial and technological capabilities and their impact on ecological systems. Including these structural factors, which are quite distinct from one another, helps to reduce the omitted variable bias and ensures a parsimonious modeling of the data.

The empirical approach involves a strict sequence of the following steps: (1) the CD test for cross-sectional dependency in order to have robust results for the model, (2) CIPS and CADF unit root tests for stationarity, (3) the Westerlund cointegration test for the measure of the long-run relationship, and, finally, (4) the MMQR and DKSE methods for the estimation.

This research begins with an analysis of the fundamental characteristics of the time series in the panel. Particular attention is paid to the evaluation of the interdependencies between the different cross-sectional units of the panel. For this purpose, the method proposed by [44] is used to detect possible cross-correlation between the observations. The cross-dependency test (CD test) is essential, as it guides the selection of the most appropriate stationarity test. When significant dependencies between variables are identified, it is necessary to use second-degree unit root tests, which are more robust than classic methods. However, in the absence of a strong correlation, first-degree techniques are valid.

$$\mathit{C}\mathit{D}=\sqrt{{\displaystyle \frac{2\mathit{T}}{\mathit{N}\left(\mathit{N}-1\right)}}}\left(\sum _{\mathit{i}=1}^{\mathit{N}-1}\sum _{\mathit{j}=\mathit{i}+1}^{\mathit{N}}{\widehat{\mathit{\rho }}}_{\mathit{i}\mathit{j}}\right)$$

${\widehat{\mathit{\rho }}}_{\mathit{i}\mathit{j}}$ presents the pair-wise correlation coefficient of the residuals, N is the number of cross-sectional units (N = 7), and T shows the number of time periods (T = 22).

In this study, the CADF (Cross-sectionally Augmented Dickey–Fuller) and CIPS (Cross-sectional Im, Pesaran, and Shin), respectively, developed by [45,46], were used to analyze the stationarity of the series. These advanced methods have the advantage of effectively correcting biases related to interconnections between units in the panel. The CADF regression is defined as follows:

$$∆{\mathit{y}}_{\mathit{i}\mathit{t}}={\mathit{\alpha }}_{\mathit{i}}+{\mathit{\rho }}_{\mathit{i}}{\mathit{y}}_{\mathit{i},\mathit{t}-1}+{\mathit{\gamma }}_{\mathit{i}}{\overline{\mathit{y}}}_{\mathit{t}-1}+\sum _{\mathit{j}=0}^{\mathit{P}}{\mathit{\delta }}_{\mathit{i}\mathit{j}}∆{\overline{\mathit{y}}}_{\mathit{t}-\mathit{j}}+\sum _{\mathit{j}=1}^{\mathit{P}}{\mathit{\lambda }}_{\mathit{i}\mathit{j}}∆{\mathit{y}}_{\mathit{i},\mathit{t}-\mathit{j}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{j}}$$

The CIPS statistic is the average of the individual CADF t-statistics:

$$\mathit{C}\mathit{I}\mathit{P}\mathit{S}\left(\mathit{N},\mathit{T}\right)={\displaystyle \frac{1}{\mathit{N}}}\sum _{\mathit{i}=1}^{\mathit{N}}{\mathit{t}}_{\mathit{i}}(\mathit{N},\mathit{T})$$

where ${\mathit{t}}_{\mathit{i}}(\mathit{N},\mathit{T})$ shows the t-statistic of ${\mathit{\rho }}_{\mathit{i}}$ in the CADF regression.

Subsequently, the study focuses on identifying the key determinants of the model, verifying the presence of long-term relationships (cointegration) using the Westerlund cointegration test, and also verifying the individual heterogeneity with the heterogeneity slope test, which is applied to assess the possible low dependence between cross-sectional units. The Westerlund cointegration test shows the long-run linkage between the variables by using the group mean (${\mathit{G}}_{\mathit{\tau }}$) and panes statistics (${\mathit{P}}_{\mathit{\tau }}$).

$${\mathit{G}}_{\mathit{\tau }}={\displaystyle \frac{1}{\mathit{N}}}\sum _{\mathit{i}=1}^{\mathit{N}}{\displaystyle \frac{{\widehat{\mathit{\alpha }}}_{\mathit{i}}}{\mathit{S}\mathit{E}\left({\widehat{\mathit{\alpha }}}_{\mathit{i}}\right)}}$$

${\widehat{\mathit{\alpha }}}_{\mathit{i}}$ is the error correction term and the null hypothesis is ‘no cointegration’.

To check if the coefficients change across countries, ${\widetilde{∆}}_{\mathit{a}\mathit{d}\mathit{j}}$ is used in the slope heterogeneity test:

$$\widetilde{∆}=\sqrt{\mathit{N}}\left({\displaystyle \frac{{\mathit{N}}^{-1}\widehat{\mathit{S}}-\mathit{K}}{\sqrt{2\mathit{K}}}}\right)$$

where $\widehat{\mathit{S}}$ presents the modified Swamy statistic and K is the number of regressors^.

The presence of significant cross-sectional dependence, validated by the CD tests, requires the application of econometric methods resilient to such interdependencies. Since the slope heterogeneity test confirms that relationships differ across G7 countries, we utilize the Method of Moments Quantile Regression (MMQR) to capture such diverse impacts along different environmental degradation quantiles. MMQR is particularly effective because it allows for individual effects to shift the whole distribution, not just the mean. The MMQR estimator captures the impact across different quantiles ($\mathit{\tau }$):

$${\mathit{Q}}_{\mathit{y}}\left(\mathit{\tau }⋮\mathit{X}\right)=\mathit{\alpha }+\acute{\mathit{X}}\mathit{\beta }+\left(\mathit{\delta }+\acute{\mathit{X}}\mathit{\gamma }\right)\mathit{q}\left(\mathit{\tau }\right)$$

where X shows the vector of independent variables and $\mathit{q}\left(\mathit{\tau }\right)$ presents the $\mathit{\tau }$-th quantile of the instrumented distribution.

To ensure that our results are statistically valid under confirmed cross-sectional dependence, we further employ the Driscoll–Kraay Standard Error (DKSE) estimator. The approach of the DKSE is meant for estimation in the presence of spatial and temporal dependencies, obtaining a class of covariance matrix estimators robust to both. Thus, the application of this dual methodological framework provides an opportunity to analyze heterogenic and dynamic effects of the independent variables while correcting for complex error structures identified during the diagnostic phase.

To manage the cross-sectional dependence and autocorrelation, the DKSE covariance matrix is calculated as follows:

$$\mathit{V}\left(\widehat{\mathit{\beta }}\right)={\left(\acute{\mathit{X}}\mathit{X}\right)}^{-1}{\widehat{\mathit{S}}}_{\mathit{T}}{\left(\acute{\mathit{X}}\mathit{X}\right)}^{-1}$$

where ${\widehat{\mathit{S}}}_{\mathit{T}}$ is the weighted sum of the cross-sectional products of the residuals.

4. Empirical Findings

The outcome for the cross-sectional dependency test appears in

Table 2. Cross-sectional dependency test.

Test	Statistic	p-Value
Pesaran	11.58 ***	0.00
Fisher	37.53 *	0.07
Frees	2.53 ***	0.00

Note: *** and * indicate that the estimated parameters are significant at the 1% and 10% significance levels, respectively.

. The test investigates whether innovations or shock from one country spill over and influence other countries present in the sample. Interestingly, both Pesaran and Frees tests reject the null hypothesis of Cross-Sectional Independence at the 1% significance level. This confirms the existence of transversal or cross-sectional dependence. However, the outcome for the third test, which uses the Fischer test, yields an insignificant result, with the p-value being 0.07. This tends to establish that there exists marginally greater dependence on this dimension. Taken collectively, there exists an interdependence between environmental degradation and its controlling factors for the G7 nations, owing to extensive economic ties.

The stationarity of the variables was investigated using both the CIPS and CADF unit root tests, which account for cross-sectional dependence. The results are reported in

Table 3. Unit root tests (CADF and CIPS).

Variable	CIPS	CADF	Integration
ED	−3.01 ***	−7.24 ***	I(0)
EG	−2.71 *	−4.51 ***	I(0)
FI	−2.43 **	−2.75 ***	I(0)
EGI	−2.09 *	−1.58 *	I(0)
ET	−2.17 ***	−2.26 **	I(0)

Note: ***, **, and * indicate that the estimated parameters are significant at the 1%, 5%, and 10% significance levels, respectively.

and suggest that all variables are stationary. It means that they are integrated of order zero, or I(0). This level stationarity lays a sound foundation for the econometric analysis.

The results of the cointegration test, aimed at evaluating long-term equilibrium relationships between the variables, are presented in

Table 4. Cointegration test.

Test	Statistic	p-Value
Westerlund	6.73 ***	0.00
Modified Dickey–Fuller	3.85 ***	0.00
Dickey–Fuller	−3.36 ***	0.00
Augmented Dickey–Fuller	6.71 ***	0.00
Unadjusted modified Dickey–Fuller	−2.07 *	0.09
Unadjusted Dickey–Fuller	−8.55 ***	0.00

Note: *** and * indicate that the estimated parameters are significant at the 1% and 10% significance levels, respectively.

. Westerlund’s test, as well as those of Dickey–Fuller (standard, modified, and augmented), indicate the existence of cointegration at the 1% and 10% levels. This means that there is a stable long-term relationship between environmental degradation and its explanatory variables financial innovation, economic growth intensity, economic globalization, and environmental technology. These findings justify the use of panel cointegration methods, given the structural interdependence between the variables studied, beyond short-term fluctuations.

The heterogeneity slope test, presented in

Table 5. Heterogeneity slope test.

Δ	p-Value	Δ Adj	p-Value
−2.96 ***	0.00	−3.03 ***	0.00

Note: *** indicates that the estimated parameters are significant at the 1% significance level.

, makes it possible to check if the relationships between variables differ according to the countries within the panel. Two sets of statistics, adjusted and unadjusted, reveal significance at the 1% threshold, thus rejecting the null hypothesis of homogeneity. These results indicate that the impact of independent variables (EG, EGI, FI, and ET) on ED varies across countries. Such heterogeneity can be attributed to country-specific factors, such as national priorities for sustainable development, levels of industrialization, differences in energy policies, and institutional quality. This finding highlights the importance of adopting differentiated policy strategies better suited to the specific contexts of each country, rather than imposing uniform policies on the entire group.

The results of this research are divided into two parts. The first part is those from the MMQR model, and the second part shows the results from the DKSE model. All these analyses allow for a deeper understanding of the link between ED and EG, EGI, FI, and ET in G7 countries, while taking into account the heterogeneity between countries and cross-sectional dependencies. The Method of Moments Quantile Regression (MMQR) enables a more complex analysis of how independent variables affect environmental damage throughout the distribution as a whole. The comprehensive results, with coefficients and their standard errors in parentheses, can be found in

Table 6. MMQR model.

Quantile	LOG(EG)	FI	EGI	ET	Intercept
5th	0.20 (0.15)	0.60 (0.42)	0.02 (0.15)	−0.08 (0.26)	9.23 (0.11)
25th	0.07 (0.11)	0.21 (0.16)	0.00 (0.21)	0.01 (0.18)	13.52 *** (0.00)
50th	0.06 * (0.05)	0.17 *** (0.00)	−0.01 ** (0.04)	0.01 ** (0.04)	13.89 *** (0.00)
75th	0.04 ** (0.02)	0.14 *** (0.00)	−0.01 *** (0.00)	0.02 *** (0.00)	14.28 *** (0.00)
95th	0.03 (0.12)	0.08 (0.16)	−0.01 ** (0.04)	0.03 ** (0.02)	14.86 *** (0.00)

Note: ***, **, and * indicate that the estimated parameters are significant at the 1%, 5%, and 10% significance levels, respectively. The values in parenthesis present the p-value.

below. This specific distributional mapping of environmental drivers represents a novel contribution to the environmental economics of advanced economies.

Firstly, the impact of economic globalization (EG) varies across different levels of environmental degradation, as shown by the quantile regression results. At lower quantiles (5th and 25th), the EG coefficient is positive (0.20 and 0.07, respectively) but statistically nonsignificant. This is explained by the fact that countries had a limited effect on environmental degradation. In countries where environmental degradation is low, economic globalization does not have a significant or consistent effect on degradation. However, in the upper quantiles (50th, 75th, and 95th), environmental degradation is more pronounced by 0.06, 0.04, and 0.03, respectively, implying that economic globalization contributes modestly but significantly to environmental degradation in countries where it is already more severe. The downward trend in coefficients across higher quantiles also suggests that while globalization has a degrading impact, its marginal effect decreases as environmental degradation worsens. This could be due to stricter environmental regulations, technology transfers or improved institutional capacities in the most affected countries. These results are in line with the existing literature, which posits that globalization can both exacerbate [9,10,11] and mitigate environmental problems [12,13,14].

The significant positive coefficients at the middle and upper quantiles, 0.17 and 0.14, respectively, show that FI increases environmental degradation. This suggests that in G7 countries, the finances are still tilted more toward supporting high-emission activities or consumption patterns rather than weighing out the benefits from green financing initiatives. The positive coefficient of financial innovation in the MMQR and DKSE equations indicates that the current progress in financial innovation for the G7 countries is positively related to environmental damage. This indicates that “financial modernization has mainly enabled consumption-driven growth and the expansion of industry, rather than using these advances to decarbonize the economy.” The studies of [30,31,32] perfectly support our results and arguments.

Moreover, the results of the influence of EGI on environmental degradation in G7 countries show us that at the lower quantiles (5th and 25th), the effect of EGI is, respectively, slightly positive, 0.02 and 0.00, without being significant. At the lower levels of ED, EGI has a very negligible impact on degradation. These findings go against the study of [20], which suggests that the first levels of economic growth are followed by consequent negative pressures on the environment. On the other hand, from the 50th to 95th quantiles, the coefficients become negative and significant. These results indicate that EGI tends to reduce environmental degradation in contexts when it is higher. In other words, in G7 countries, the moderating effects of EGI on ED become more noticeable when levels of degradation are already high. This could reflect improved efficiency of environmental policies or a move toward more sustainable growth patterns in developed economies with high economic intensity. These results and ideas explained are in accordance with the studies of [22,23,24].

Finally, the most important innovative outcome of this current research is that the ‘Technology–Environment’ segment of the G7 countries is not very efficient. As hypothesized in standard economic theory, patent growth, understood as the ‘ET’ variable, should instigate instant emissions reduction; however, the results showed a positive coefficient of 0.01 at the 10% significance level. This may be interpreted as having a ‘rebound effect’; in other words, there is a significant ‘institutional misalignment’. The relatively small positive coefficient in the case of environmental technology suggests that the G7 countries are at a leadership level in regard to innovation but that until now the absolute level of the environmental and related technologies has not been significant enough, such that the growth process can be decoupled from emissions. The first results where ET reduces ED go hand to hand with the studies of [39,40] that explain the ability of these technologies to capture, absorb, and transform pollution into reusable resources or less harmful substances. Then, the contribution of ET to environmental degradation in higher quantiles results from institutional choices oriented primarily toward economic growth, leading to the detriment of environmental protection, as explained by [42].

A prominent innovation of this study is the empirical validation of the ‘rebound effect’ within the G7’s technological framework. While standard economic theory suggests that patent growth (ET) should trigger an immediate reduction in emissions, our results reveal a counterintuitive positive coefficient of 0.01 at the 10% significance level. This suggests a significant institutional misalignment where environmental technologies are utilized primarily as compliance instruments rather than transformative solutions, failing to decouple growth from emissions. This finding shifts the discourse from the quantity of innovation to the directional efficiency of technological implementation in advanced economies.

Table 7. DKSE (Driscoll–Kraay Standard Error) method.

	Coefficient	p-Value
LOG(EG)	0.07 *	0.09
FI	0.21 ***	0.00
EGI	−0.01 **	0.04
ET	0.01 *	0.05
Intercept	13.45 ***	0.00

Note: ***, **, and * indicate that the estimated parameters are significant at the 1%, 5%, and 10% significance levels, respectively.

shares the results of the Driscoll–Kraay Standard Error model. The results of the DKSE model offer additional insights that both complement and reinforce the conclusions drawn from the MMQR estimations. As indicated in the model’s structure, DKSE accounts for dynamic interactions while addressing common panel data issues such as cross-sectional dependency and autocorrelation. This enhances the reliability of the coefficients’ estimates in assessing drivers of environmental degradation in G7 countries.

A unit increase in economic globalization (LOGEG) results in an increase of 0.07% in environment degradation, confirming the existence of a positive, yet inelastic, association. This result also aligns with the studies of [10,11]. A unit increase in the ratio of financial innovation (FI), in turn, results in the highest association with an increase of 0.21% in degradation. This supports the MMQR’s findings that unbridled financial innovation may exacerbate environmental deterioration by encouraging speculative activities with negative ecological effects or by fostering high-consumption investment patterns. It emphasizes how crucial it is to incorporate green finance concepts into G7 economies’ innovation-driven systems. This observation supports the arguments made by [30,31], which state that few people intend to invest in ecological projects once they obtain the required funds from financial services. This lack of orientation towards sustainable investments contributes to environmental degradation.

On the other hand, an improvement of one unit in economic growth intensity (EGI) results in an improvement of 0.01% in degradation, reflecting the efficiency factor in the G7 economic processes. These findings provide a nuanced perspective: economic growth, which is originally linked to degradation [18], may help to lessen environmental harm when it is effectively pursued with investments in green projects or technologies [22,23,24]. We found that the results in the MMQR model also pointed out that, especially in developed economies, controlled and decarbonized economic growth has the potential to reduce ED. Remarkably, ET produces a positive coefficient of 0.01 at the 10% significance level. Despite being counterintuitive, this result supports previous MMQR findings that the G7 countries’ current technologies may not be enough to counteract the pollution caused by industrial activities. It might also be a result of slow implementation, lax enforcement of regulations, or potentially that these technologies are more frequently employed as compliance instruments rather than as game-changing environmental remedies [42]. These findings urge thorough integration of ET in core industrial and energy systems, backed by aligned policies and incentives.

In order to further substantiate the effectiveness of the MMQR approach, we carried out a joint test of the equality of the coefficients across the quantiles (Wald Test). The test rejected the null hypothesis of slope equality ($X^2$ = 34.12, p-Value < 0.001), confirming that the drivers of environmental degradation in G7 countries, specifically financial innovation and growth intensity, have significantly different impacts depending on the existing severity of ecological damage. This, in effect, implies that the OLS regression would have been inadequate in explaining the environmental factors.

5. Conclusions

This research addresses the different environmental challenges that continue to grow in the G7 countries. In this study, MMQR and DKSE were used, models that allow the exploration of heterogenic and dynamic effects of LOG(EG), EGI, FI, and ET on environmental degradation LOG(ED). The results offer new perspectives on how advanced economies, despite their capacity for innovation and political leadership, continue to face important environmental challenges rooted in structural economic choices. As expected, FI showed the most permanent and constant effect on ED through the models, especially in the highest quantiles where environmental damage is already severe. This confirms the concerns that G7 financial systems remain largely unconnected to sustainability goals and continue to favor high-emission sectors that maximize profits. Similarly, LOG(EG), although it offers access to technologies and international cooperation, also contributes to the increase in LOG(ED), probably because of increased consumption patterns and carbon-intensive production chains. On the other hand, EGI has demonstrated a negative impact on ED, suggesting that qualitative and energy-efficient growth models can reduce environmental pressures. This is encouraging, especially for the G7 countries that have already begun structural transitions towards low-carbon economies. However, the weak but positive effect of ET on LOG(ED) highlights its limited scalability or misalignment with broader environmental goals, calling for deeper political integration and behavioral changes.

Policy Recommendations

The results support a multidimensional policy response. Since FI accelerates degradation most in instances where ecosystem pressure is greatest, a required green credit ratio must be adopted by central banks and Financial Regulators in G7 countries. This is a measure that forces financial institutions to first finance decarbonization initiatives and not carbon-fed sectors, focusing on the top countries in the given percentile that are currently funded by FI and therefore exhibit the greatest degradation. Also, ET does not contribute to offsetting industrial pollution in G7 countries; the Ministries of Industry and Technology need to focus on performance-related incentives rather than overall R&D subsidies. This is to ensure that there is no ‘rebound effect’ occurring because of technological progress, causing consumption levels to rise.

As economic globalization (EG) is always linked to intensified environmental pressures, the Trade Ministries need to establish carbon border adjustment mechanisms or ‘eco-tariffs’ to cope with the situation. The purpose of this mechanism is to focus on the carbon-intensive value chains explored within the study and make global trade take into account the environmental costs. Additionally, the economic growth intensity (EGI) demonstrates the potential to decrease the level of degradation; it is recommended for the National Governments to apply circular economy frameworks as the major engine of the GDP. In this context, the ‘misdirected’ character of the current growth is covered.

The marginal contribution of this study extends to the policy domain by advocating for a shift from general R&D subsidies to performance-related incentives. Because financial innovation (FI) is identified as a ‘double-edged sword’ that exacerbates degradation at higher quantiles, we propose a mandatory ‘green credit ratio’ for G7 central banks. This targeted intervention addresses the ‘misdirected’ character of current growth by ensuring that financial modernization supports decarbonization rather than carbon-intensive consumption patterns.