Resources Policy 92 (2024) 105013

Contents lists available at ScienceDirect

Resources Policy
journal homepage: www.elsevier.com/locate/resourpol

The role of green finance, environmental benefits, fintech development,

and natural resource management in advancing sustainability
Zheng Zhou a, Ka Yin Chau b, Amena Sibghatullah c, Massoud Moslehpour d, e, *,
Nguyen Hoang Tien f, g, Khajimuratov Nizomjon Shukurullaevich h
a
Zhejiang Business College, China
b
Faculty of Business City University of Macau, Taipa, Macau
c
Faculty of Business and Management, Universiti Teknologi Mara, UiTM, Puncak Alam, Selangor, Malaysia
d
Department of Business Administration, Asia University, Taichung, Taiwan
e
Department of Management, California State University, San Bernardino, United States
f
WSB Merito University in Gdansk, Poland
g
Ho Chi Minh City University of Industry and Trade, Vietnam
h
Head of Department of Financial Analysis and Audit, Tashkent State University of Economics, Uzbekistan

A R T I C L E I N F O A B S T R A C T

Keywords: Green finance, generally considered as financial support for green growth, significantly reduces greenhouse gas
Green finance and air pollutant emissions. Green finance in agriculture, green buildings and other green projects should in­
Environmental benefits crease the economic development of a country. Rapid advances in digital technology are revolutionizing the
Fintech
financial landscape. The rise of fintech has the potential to make financial systems more efficient and competitive
Natural resources
and broaden financial inclusion. With greater technological complexity, however, fintech also brings potential
systemic risks. Thus, using a panel dataset from the G7 and E7 countries, this investigation explores the interplay
between green finance, environmental benefits, total natural resource management, fintech and economic sus­
tainability. The panel dataset is subject to rigorous estimation techniques, including quantile regression and the
generalized method of moments (GMM) for long-run estimation. The cointegrations among the variables are
validated using the Johansen Fisher panel test. In the long-run estimation, the quantile regression shows sig­
nificant influences of environmental benefits and total natural resource management. The GMM strengthens the
analysis due to its effectiveness and reliability in the presence of heteroskedasticity. The outcomes of the GMM
technique support the negative and significant impact of fintech, green finance and total natural resource
management on GDP, while environmental benefits are significantly positively linked with GDP. The study
enhances our understanding of the nexus between environmental factors and economic sustainability. Therefore,
it has value in terms of its practical implications, offering policymakers insights for enhancing economic sus­
tainability. Despite the significant contribution of the study, the limitations are acknowledged, which provide
future researchers opportunities to expand on the topic.

1. Introduction persistent reliance on fossil fuels to fulfil energy demands (Udeagha and
Muchapondwa, 2023). To resolve these environmental issues, we need
By providing ever growing opportunities for trade, employment and to manage resources sustainability through green finance, environ­
development, globalization has integrated economies worldwide, but mental benefits, fintech development and natural resource management
ultimately tends to raise various environmental threats. This global (see Fig. 1).
phenomenon poses several challenges regarding the environment, Some of the most developed economies in the world are in the G7
including climate change, natural disasters and changes to ecosystem (Germany, the United Kingdom, Canada, the United States, Italy, France
(Amin et al., 2023). The primary cause of environmental concern is the and Japan). These nations’ environmental circumstances have become

* Corresponding author. Department of Business Administration, Asia University, Taiwan.

E-mail addresses: 00817@zjbc.edu.cn (Z. Zhou), gavinchau@cityu.mo (K.Y. Chau), amena@uitm.edu.my (A. Sibghatullah), writetodrm@gmail.com
(M. Moslehpour), vietnameu@gmail.com (N.H. Tien), n.xajimuratov@tsue.uz (K. Nizomjon Shukurullaevich).

https://doi.org/10.1016/j.resourpol.2024.105013
Received 13 January 2024; Received in revised form 5 April 2024; Accepted 17 April 2024
Available online 25 April 2024
0301-4207/© 2024 Elsevier Ltd. All rights reserved.
Z. Zhou et al. Resources Policy 92 (2024) 105013

worse in tandem with their extremely rapid economic growth, raising the contradictory findings make the discussion interesting. According to
serious concerns among many stakeholders. For the countries of the E7 studies, fintech increases the economic growth of countries where the
(Turkey, India, Mexico, China, Russia, Brazil and Indonesia), a financial sector is more developed (Feyen et al., 2023; Haftu, 2019). This
commitment to energy transition and carbon neutrality by 2050 is a indicates that fintech can be used as an effective driver of the economy
significant goal. However, the effort is not strong enough to force a move in countries which have already achieved financial advancement.
to cleaner, more environmentally friendly energy sources that may However, in emerging economies, factors such as financial literacy,
lower carbon emissions, as conventional and non-renewable energy financial infrastructure and regulation might hinder growth. The fintech
sources still account for the majority of the E7 energy consumption. sector is relatively small compared to traditional financial institutions,
Green finance and fintech development both play significant roles in however its rapid expansion in riskier areas is notable. Since it is capable
the environmental quality of G7 and E7 countries. The G7 nations are of bringing financial instability, it can be a threat as well as an oppor­
committed to using fintech to increase energy efficiency, while the tunity (Daud et al., 2022;Pierri and Timmer, 2020). Fintech is known to
governments of the E7 countries may be able to leverage the advantages mitigate financial risk by promoting decentralization and transparency,
of environmental benefits to advance their energy efficiency and carbon but the same technology is vulnerable to cybersecurity risks, market
reduction initiatives. Green finance techniques contribute to environ­ volatility and contagious behaviour among consumers and financial
mental remediation and strengthen policymakers’ beliefs in the G7 and institutions which can jeopardize financial stability.
E7 nations’ ability to achieve green economic growth (Wu et al., 2021). Green finance has a role in balancing economic growth and envi­
Responsible investment in green finance has environmental benefits, ronmental sustainability, but is subject to debate. The literature is
reflecting the significant influence of ecosystem, and can enhance the divided into two strands, presenting its negative or positive effect on
efficiency of technological innovations which preserve a pure and clean economic growth. The majority of evidence is in the category which
environment. supports its positive effects (Singh and Mishra, 2022), however there is
Both governmental and commercial initiatives aimed at promoting evidence which encourages scholars to delve further into the relation­
environmental cleanliness are linked to green financing. Both pollution ship in various contextual settings. Thus, this paper investigates the
and climate change have negative effects on human life, and there are ways in which fintech and green finance might support the E7 and G7
long-term advantages and societal benefits of responsibly allocating objectives of reaching carbon neutrality without compromising eco­
resources. Fintech makes the effective control of environmental hazards nomic growth, taking into account the environmental benefits and
possible by combining blockchain technology with big data analytics natural resource management. More recourses should be set aside for
(Tao et al., 2022). Fintech plays a dominant role in reducing the cost of studies of the efficient use of green finance solutions, controlling for
financial services, enhancing financial inclusion and reducing carbon environmental risks. This research addresses this aim by determining the
emissions (Tao et al., 2022). However, using natural resources could role of growth through fintech which ultimately supports economies to
make it harder to achieve environmental objectives. It is essential to achieve smooth transitions with low levels of greenhouse gas and carbon
convert to renewable energy generation to reduce carbon dioxide emissions.
emissions and promote development sustainability. In order to meet the The study proceeds in the following way. Section 2 offers a thorough
demand for energy, various well-reputed technologies, such as solar analysis of previous research. The study design and data collection
power and wind, could play a dominant role without producing emis­ procedures are discussed in Section 3. The experimental results are
sions. The E7 and G7 countries could encourage the use of fintech presented in Section 4 and critically analysed in Section 5. The meth­
through environmental action initiatives. This process could stimulate odology aims to reveal the nuanced relationship between green finance,
environmental goals through the reduction of fossil fuel use, which is environmental benefits, fintech development and natural resource
ultimately linked to natural resources (Li et al., 2024). management in furthering sustainability in the E7 and G7 countries. By
Other than environmental sustainability, the impact of green finan­ outlining the ways in which sustainable development can be achieved,
cial technology on economic growth is the subject of various studies, and the research hopes to further the academic discourse and have an

Fig. 1. Economic complexity and environmental sustainability.

Source: Science Direct

2
Z. Zhou et al. Resources Policy 92 (2024) 105013

influence in the E7 and G7 countries. the principal component analysis method. The study reports a signifi­
cant impact of green finance on improving economic development in the
2. Literature review region, and the instrumental role of green finance in ensuring environ­
mental quality by reducing industrial waste and other pollutants.
2.1. Theoretical background However, the findings have limited scope because of their exclusive
focus on the Chinese provinces. Nevertheless, the study makes a valu­
The theoretical literature on the circular economy (CE) offers a able contribution to the discourse concerning economic sustainability.
robust theoretical background for the present study’s research frame­ Abuatwan (2023) investigates the impact of green finance on the sus­
work. Several scholars have contributed to the development of the tainability performance of banks in Palestine. The study highlights the
concept of CE, leading to the popularization of various concepts, such as social, economic and environmental aspects of green finance and its
limits to growth (Meadows et al., 2018), “cradle-to-cradle” (Stahel and impact on the sustainable performance of the banking sector. The study
Reday-Mulvey, 1981), and the performance economy (Stahel, 2010). makes a significant contribution by highlighting the importance of green
The theoretical conception of CE reached its peak with the seminal finance to the strategic framework of the financial sector, which could
contribution of MacArthur (2013). CE aims to shift the economic model lead to great environmental benefits and economic outcomes. However,
from its traditional focus on linear production and introduce innovative the study has a limited focus on Palestine. Yang (2023) highlights the
strategies to enhance economic growth without compromising envi­ impact of green finance on economic development in China, reaffirming
ronmental protection. CE encourages the integration of modern tech­ its role in revolutionizing economic growth in the country. Several other
nology to accelerate economic sustainability (MacArthur, 2013). empirical studies affirm the significant impact of green finance on eco­
Despite the several theoretical conceptions of CE, its key tenets nomic sustainability (Afzal et al., 2022; Chunyu et al., 2022; Sachs et al.,
remain focused on restoration and regeneration. The CE model endorses 2019). Therefore, the present study proposes the hypothesis.
the consumption of renewable energy, the eradication of toxic envi­
H1. Green finance has a significant impact on economic sustainability.
ronmental pollutants, and reduced waste generation. Scholars use the
concept of CE to promote the principles of reusing and remarketing
2.3. Environmental benefits and economic sustainability
products, with the aim of enhancing quality of life and ensuring
ecological balance (Stahel, 2013). Thus, CE presents a robust research
Recent empirical studies focus on the environmental benefits of a
framework through which to analyse the determinants of economic
circular economy and its subsequent impact on the economic sustain­
sustainability. This research uses the theoretical foundation of CE to
ability of countries. The principles of recycling and reusing, the key
study the impact of natural resource management on economic sus­
tenets of the CE model, have been hailed as indicators of environmental
tainability. The theoretical background of CE is appropriate for studying
benefits (Minunno et al., 2020). Ahmad et al. (2021) highlight how the
the impact of green finance on economic sustainability, and the key
transformation of organizational environmental policies can address
tenets of CE align with the present study’s research objectives. CE fa­
issues related to pollution and control the advancement of polluting
cilitates the analysis of the impact of the independent variables on
factors, stimulated by consistent stakeholder pressure and an emphasis
economic sustainability in the E7 and G7 nations.
on eco-friendly practice. Ahmad et al. (2021) investigate the impact of
environmental sustainability on the economic performance of the
2.2. Green finance and economic sustainability
manufacturing sector in Pakistan, and report a significant impact.
Gatimbu et al. (2018) observe organizations’ focus on the re-evaluation
Connecting the economy and the environment, green finance is a fine
of their impact on the environment as a consequence of global concerns
example of a financial breakthrough which, unlike traditional finance,
related to climate change and resource scarcity. The study investigates
has a major focus on activities that benefit the environment (Banelienė
the correlation between environmental efficiency and the profitability of
and Strazdas, 2023). Its prime motivation is to put restrictions on the
small-scale organizations in Kenya, and concludes that small-scale or­
resources which supply polluting businesses. Scholars scrutinize the
ganizations require motivation in the form of subsidies and other ben­
practical implications of green finance from various perspectives and,
efits to exercise environmental protection policies. Danso et al. (2019)
while most focus on its effect on the environment, studies also explore its
also find environmental sustainability to be a key determinant of the
macro-economic implications on industrial output, investment and
performance outcomes of firms in Ghana. Arena et al. (2020) regard
corporate efficiency (Ahmad Radzi and Abdul Hadi, 2023; Chen et al.,
environmental benefits as crucial to enhancing the economic sustain­
2023).
ability of urban wastewater reuse projects. Ye et al. (2023) investigate
Green finance entails the integration of environmental-protection
the impact of energy efficiency, and find a significant environmental
policies and investment in ecologically friendly activities. It encour­
benefit of economic sustainability in China. Based on the findings of
ages financial decisions which promote the protection of the natural
these empirical studies, this study predicts a substantial role of envi­
environment and the eradication of environmental deterioration from
ronmental benefits in driving economic sustainability in E7 and G7
business operations (Zhang et al., 2022). Several studies highlight the
nations, and proposes the hypothesis.
role of financial institutions in promoting economic sustainability.
Usman et al. (2022) find that financial development in countries rich in H2. Environmental benefits have a significant impact on economic
resources can reduce their ecological footprint. Nenavath and Mishra sustainability.
(2023) investigate the impact of green finance on sustainable economic
growth in India, collecting data from Indian states, and using panel 2.4. Fintech development and economic sustainability
regression analysis. They report that green finance is particularly helpful
in ensuring high quality economic development, and highlight the role Fintech covers various digital technologies and forms of assistance
of green finance in improving the structure and effectiveness of the along with mobile applications to accommodate individuals, organiza­
financial sector. They report a robust impact of green finance on tions and entrepreneurs, helping them to manage their financial oper­
improving environmental quality in the region, with valuable implica­ ations efficiently (Hatmanu and Cautisanu, 2023). By 2030, it is
tions for improving existing Indian environmental protection policies. estimated that the potential economic output from eco-friendly growth
However, the scope of the study is limited to the Indian states and cannot models might reach 12 trillion dollars. Fintech helps firms reduce their
be applied to other demographics. Zhou et al. (2022) investigate the waste and shifts the focus of shareholders to direct their holdings toward
impact of green finance on economic development and environmental eco-friendly products (Udeagha and Muchapondwa, 2023).
quality in China, collecting relevant data from 30 provinces, and using Nenavath and Mishra (2023) report a significant impact of fintech on

3
Z. Zhou et al. Resources Policy 92 (2024) 105013

boosting the sustainable economy in India. However, the findings are proxies for the variables are chosen. The explained variable, economic
limited to that context. Nonetheless, the findings help explain the role of sustainability, is measured using the common indicator of economic
fintech innovation in accelerating the sustainable economy in such growth, gross domestic product. Fintech is gauged using net financial
resource-rich countries, and offer significant guidance for other such accounts. The second explanatory variable, environmental benefits, is
countries to integrate fintech innovation into their national economic assessed by considering carbon emissions. Low levels of carbon emission
policies. The integration of fintech is in line with the basic principles of indicate environmental benefits. Green finance is measured using green
the CE framework. Ni et al. (2023) focus on another emerging economy, investment. Lastly, natural resource management is incorporated into
China. They highlight the role of fintech in promoting regional green the model as the predictor, and measured using natural resource rents.
innovation in the digitalized economy. The findings indicate that fintech Population density is taken as a control variable. Data is sourced from
represents a unique opportunity for green innovative initiatives, which reputable databases including the World Bank (WDI, 2023) and the
can help restore the natural environment and boost economic progress. Organization for Economic Cooperation and Development (OECD,
The study focuses on the combined effects of fintech and environmental 2023).
regulation in reducing the harmful impacts of industrial activities.
Nonetheless, the study has an exclusive focus on China and therefore its 3.1. Quantile regression
findings cannot be generalized to other global economies.
Chueca Vergara and Ferruz Agudo (2021) analyse the correlation This investigation focuses on a sample of multiple countries, specif­
between fintech and sustainability, and highlight the potential connec­ ically the G7 and E7 economies. The sample is diverse, which indicates
tion between them, taking data from European economies. They report that the countries have varying levels of economic, political, financial
that fintech has the potential to support sustainable finance and help and social development. While some countries in the sample are regar­
achieve sustainability goals in the region. The study does not focus ded as advanced, the E7 comprises emerging economies. Thus, this
exclusively on economic sustainability in Europe. Deng et al. (2019) find diverse sample requires a suitable approach for data analysis. Uddin
fintech to be a powerful tool of sustainable development in China. Based et al. (2017) note that it is imperative to account for variations in the
on these findings, the following hypothesis is proposed. levels of development between countries, as these differences can lead to
H3. Fintech development has a significant impact on economic unreliable results if a standard least squares regression is applied. Robust
sustainability. results are not achievable as the assumption of a normal distribution of
the residuals does not hold in these datasets. Careful evaluation of the
limitations of standard regression techniques leads us to adopt quantile
2.5. Natural Resource management and economic sustainability
regression which, several researchers note, outperforms standard
regression techniques (Uddin et al., 2017; Zhu et al., 2016). Even in the
Natural resources are the foundation of life and economic develop­
ment, and their scarcity can pose a serious challenge to world econo­ presence of heavy-tailed distributions and outliers, quantile regression
provides reliable outcomes (Uddin et al., 2017; Zhu et al., 2016).
mies. The economies of both developed and developing nations are
Another strength of this approach is that it is able to calculate coefficient
vulnerable to varying extents. Singh et al. (2023) see the efficient uti­
estimates at various quantiles. All of which provides sufficient support
lization and management of natural resources as integral components of
for the selection of quantile regression for the present study as a robust
public policy. Several studies see natural resources, both exhaustible and
estimation technique. The following equation gives the conditional
inexhaustible, as crucial determinants of economic development (Cho­
quantile of yi given xi :.
pra et al., 2022; Haseeb et al., 2021; Hayat and Tahir, 2021; Sharma
et al., 2021). Aslan and Altinoz (2021) reveal the significant impact of Qyi ( τ | xi ) = xTi βτ
natural resources on economic growth in Asian, European and American
economies. While studies report the significance of both exhaustible and
3.2. Generalized method of moments (GMM)
inexhaustible resources on economic development, scholars favour
renewable sources for their potential to promote economic sustainability
This investigation expands its analysis by employing another statis­
(Awosusi et al., 2022). Surya et al. (2021) investigate the role of
tical test, the generalized method of moments (GMM), initially intro­
renewable energy source utilization from the perspective of natural
duced by Griliches and Hausman (1986) to cater for potential issues in
resource management and report that the use of renewable energy
panel data, which consists of time and cross-sections. Given the dataset
sources leads to sustainable development. Shah et al. (2022) argue that
of G7 and E7 countries, the GMM technique is best suited because of its
natural resources require efficient management because of their sub­
robustness, which has given it increased prominence in the literature.
stantial role in economic sustainability. Lee et al. (2021) see the man­
Previous researchers, particularly Farooq et al. (2021), discuss the
agement of natural resources as vital to economic sustainability by
endogeneity issues which can emerge from economic research, as eco­
limiting the ecological footprint and taking the scarcity of natural re­
nomic factors are not always perfectly exogenous. Endogeneity concerns
sources into consideration. Zahoor et al. (2022) argue that the sustain­
are mitigated by the GMM technique. Another benefit, explained by
able management of natural resources is significant because it entails an
Twerefou et al. (2017), is that GMM provides better estimations than the
efficient and responsible use of natural resources, putting the needs of
two-stage least squares (TSLS) and instrumental variable (IV) tech­
future generations at stake. Thus, based on these study findings, the
niques, because GMM uses previous values of endogenous variables as
present study formulates the following hypothesis for the correlation
instruments. Furthermore, GMM is able to provide accurate results even
between natural resource management and economic sustainability.
in the presence of heteroskedastic residuals, making it a more credible
H4. Natural resource management has a significant impact on eco­ approach than TSLS or IV methods (Twerefou et al., 2017; Wooldridge,
nomic sustainability. 2001).

3. Methodology and empirical model 4. Results

In this study, we carry out an investigation into the dynamic inter­ A statistical summary of the dataset is given in Table 1, below. For
play among green finance, environmental benefits, fintech develop­ each variable, a total of 350 observations are obtained, and the table
ment, natural resource management and economic sustainability using gives details of the central tendency measures and normal distribution of
two techniques. A panel dataset is gathered for G7 and E7 countries, the indicators. The Jarque-Bera (JB) test statistic for GDP is 1180, which
from prominent databases, over the period 1999 to 2022. Appropriate is high, and the low probability value suggests that the variable is not

4
Z. Zhou et al. Resources Policy 92 (2024) 105013

Table 1
Statistical summary.
GDP FINT EB GF PD TNR

Mean 3.3E+12 − 1.6E+10 1524926 8.68E+09 147.9667 2.863718

Median 1.86E+12 − 7.2E+09 536754.4 3.75E+09 121.6973 1.215967
Maximum 2.54E+13 4.42E+11 10944686 1.62E+11 473.4187 21.5027
Minimum 9.54E+10 − 8.3E+11 192651.4 − 7.1E+10 3.363434 0.012489
Std. Dev. 4.35E+12 1.69E+11 2252386 2.22E+10 124.8109 4.103815
Skewness 2.705952 − 1.95806 2.581446 3.06199 0.762624 2.24755
Kurtosis 10.18586 10.08525 9.281704 19.7597 2.648904 8.102913
Jarque-Bera 1180.161 955.7448 964.1808 4643.197 35.72408 674.4158
Probability 5.39E-257 2.90E-208 4.27E-210 0 1.75E-08 3.57E-147
Sum 1.16E+15 − 5.7E+12 5.34E+08 3.04E+12 51788.33 1002.301
Sum Sq. Dev. 6.60E+27 9.91E+24 1.77E+15 1.72E+23 5436637 5877.614
Observations 350 350 350 350 350 350

Notes: GDP = gross domestic product, FINT = fintech, EB = environmental benefits, GF = green finance, PD = population density, TNR = total natural resources.

normally distributed. Similarly, fintech (FINT) adheres to a non-normal

Table 3
distribution with a large JB value and small p-value. The descriptive
Residual cross-sectional dependence test.
analysis also shows that green finance (GF), environmental benefits
(EB), population density (PD) and total natural resources (TNR) are not Test Statistic d.f. Prob.
normally distributed. Breusch-Pagan LM 343.3376 91 5.86E-31
Following the summary of the data, the cointegration test is used to Pesaran scaled LM 18.70451 4.55E-78
evaluate whether there are cointegration associations among the vari­ Pesaran CD 13.37758 8.18E-41

ables in the panel data. Table 2 presents the outcomes of the Johansen
Fisher panel test, including the trace statistics, eigenvalues and p-values.
We gauge the results against the null hypothesis, presuming the absence Table 4
of cointegration. The findings validate the presence of cointegration. Quantile regression (Median).
The results lead to a refutation of the null hypothesis for all cointegra­ Variable Coefficient Std. Error t-Statistic Prob.
tion equations at a 1% significance level. Consequently, the long-run C − 3.5E+10 2.05E+10 − 1.72 0.09
associations among the variables is proven, suggesting that the vari­ GDP(-1) 1.03329 0.009979 103.54 0.00
ables undergo changes together in the long-run (Marimuthu et al., FINT − 0.21654 0.448391 − 0.48 0.63
2021). FINT(-1) 0.24864 0.424226 0.59 0.56
EB 604561.4 277596.1 2.18 0.03
A robust analysis of panel data requires careful consideration of
EB(-1) − 567708 289090.3 − 1.96 0.05
cross-sectional dependence, which implies that the units in similar cross- GF 0.610572 1.723839 0.35 0.72
sections are related to each other. Cross-sectional dependence can occur GF(-1) − 0.59105 1.811026 − 0.33 0.74
due to unobserved factors which affect all units. Common factors TNR 3.31E+10 1.66E+10 1.99 0.05
responsible for causing cross-sectional dependence include oil prices TNR(-1) − 2.8E+10 1.59E+10 − 1.73 0.08

and global prices (Henningsen and Henningsen, 2019). Before pro­ Pseudo R-squared 0.926149 Mean dependent var 3.37E+12
ceeding to panel data analysis, we perform the diagnostic tests. The Adjusted R-squared 0.924111 S.D. dependent var 4.4E+12
S.E. of regression 2.84E+11 Objective 2.84E+13
findings given in Table 3, below, show that there is no sufficient indi­
Quantile dependent var 1.9E+12 Restr. objective 3.85E+14
cation to reject the null hypothesis of no cross-sectional dependence. Sparsity 3.99E+11 Quasi-LR statistic 7153.623
Table 4, below, presents the associations among the variables and the Prob(Quasi-LR stat) 0
effects of the explanatory variables, FINT, EB, GF and TNR, on GDP. The
Notes: GDP = gross domestic product, FINT = fintech, EB = environmental
adjusted R-squared values shown in the table demonstrate that the benefits, GF = green finance, PD = population density, TNR = total natural
model has a high predictive power, as 92% of the variance in GDP is resources.
explained by the predictors. The lag of the GDP shows that past values of
GDP have a positive influence on present values. With a p-value of 0.00,
value of 0.03 confirms the significance of the association at a 5% sig­
the relationship between lagged and present values of GDP is significant
nificance level using the quantile regression approach. This shows the
at a 1% significance level. On the contrary, the findings fail to support an
importance of addressing environmental degradation to support GDP.
association between FINT and GDP, indicating a negative but insignifi­
The lagged values of EB are negatively linked to GDP. GF has a positive
cant influence of FINT on GDP. Lagged values of FINT are found to be
impact on GDP, but the p-value of 0.72 indicates the lack of statistical
positively associated with GDP, but this association is also insignificant
significance in the association. A similarly insignificant association is
with a p-value of 0.56. EB exerts a positive influence on GDP, and the p-
found between GF(-1) and GDP. This insignificant relationship can be
explained by looking at the synthesized literature, where numerous
Table 2 studies scrutinize the role of green finance in balancing economic and
Johannsen Fisher panel cointegration test. environmental sustainability. The literature is broadly categorized into
two schools of thought. The first suggests a negative effect of green
Hypothesized Fisher Stat.* Fisher Stat.*
financing on economic growth, while the other argues that green finance
No. of CE(s) (from trace Prob. (from max-eigen Prob. increases economic growth (Haiyang, 2017). Interestingly, the latter
test) test)
None 579.6848 2.27E- 305.5043 1.98E-48
perspective has gained more support in the literature than the former
104 (Dabyltayeva and Rakhymzhan, 2019; Wang et al., 2019). Secondly,
At most 1 336.692 1.17E-54 20J.3644 3.78E-28 most literature claims that financial development innovation can affect
At most 2 171.4702 1.49E-22 94.57789 3.76E-09 economic growth, however, with excessive credit growth and novel
At most 3 98.21817 9.83E-10 65.93994 6.73E-05
financial products, financial instability can be instigated, which un­
At most 4 57.38009 0.00087 51.23754 0.004691
At most 5 44.20539 0.026511 44.20539 0.026511 dermines the dynamics of growth. Studies also claim that fintech

5
Z. Zhou et al. Resources Policy 92 (2024) 105013

promotes economic development through financial intermediation and Table 6

services, but not every fintech type can benefit the economy. Scholars Panel cross-section heteroskedasticity LR test.
using panel data claim that fintech can sometimes be unfavourable to Likelihood ratio Value Df Probability
economies, and the negative effect gets stronger in economies where
272.57 14 0.00
there is a high level of income (Bu et al., 2023). Lastly, TNR is found to
play a positive role in GDP improvement, with a p-value of 0.05, sta­ LR test summary:
Value df
tistically supporting the association.
The estimation using the GMM approach offers further insights into Restricted LogL − 9325.57 326
the relationships between the variables. A negative association is re­ Unrestricted LogL − 9189.29 326
ported between FINT and GDP, and with a p-value of 0.00, meaning the
significant impact of FINT on GDP is supported. GF is also found to exert
a significant and negative influence on GDP. Again, this negative asso­ the dynamic panel isolation model and quantile regression model, are
ciation can be justified by the claim that fintech can have a negative used to analyse the data. The economic sustainability of the targeted
impact on a country’s economic growth, because its diversification shifts countries is measured by their GDP. The findings from both models give
funds from the real economy to fintech platforms. This means that the valuable insights into the determinants of economic sustainability.
capital of banking institutions, through financial hoarding, might Based on the literature review of relevant studies, the present study
experience low growth which results in low credit supply. The entire proposes a significant correlation between green finance and economic
scenario can give birth to a financial crisis, leaving economic growth sustainability, and this hypothesis is supported by the findings from both
stagnant due to cutbacks in investment and production. Studies support models. The emergence of green finance as a significant determinant of
the claim that fintech weakens the positive links between bank deposit economic sustainability has valuable implications. Green finance can be
growth, capitalization and bank liquidity (Cevik, 2023). This indicates used to allocate sufficient investment to renewable energy projects.
that fintech’s association with economic growth can be heterogenous in Countries can use green finance investments as a way to revolutionize
different contextual settings. For example, some regions might have their patterns of energy consumption and accelerate their journeys to­
advanced levels of fintech development, due to which they experience wards achieving economic sustainability. Green finance represents a
high economic growth because fintech not only increases the employ­ unique opportunity for major economies to establish energy-efficient
ment ratio but also the investment ratio. Meanwhile, in regions where models aimed at moderating energy consumption. The present study
fintech development has not reached an advanced level, the impact on affirms the role of green finance in empowering countries to achieve
the economy is less. This indicates that fintech’s effect depends on financial, social and economic sustainability. Thus, green finance can be
various factors, and its growth can help countries extend its spillover used to promote green and sustainable urban infrastructure. The
effect to economic development (Rizova, 2023; Singh and Mishra, increasing global concerns regarding the harmful impact of traditional
2022). economic models make it crucial for major economies to invest in the
TNR is negatively associated with GDP, indicating that increasing the development of green finance. This would help them achieve not only
use of natural resources leads to lower GDP. This association is signifi­ unprecedented economic sustainability but also restore the ecological
cant at a 1% significance level. Table 5, below, shows a positive impact balance, previously disturbed due to their unsustainable activities. This
of EB on GDP, which is significant at a 1% significance level. The GMM integration of green finance would encourage business organizations to
model holds good predictive power, with 68% of the variability in GDP minimize their waste generation and emission of pollutants into the
being accounted for by the explanatory variables. The model confirms a environment. Above all, the findings of this study put great emphasis on
significant impact of FINT, GF, TNR and EB on GDP. the financial sector developing robust financial strategies aimed at
The assessment of the constant variance of residuals is done through financing sustainable initiatives. Policymakers in financial institutions
the heteroskedasticity LR test, as demonstrated in Table 6. A p-value of should prioritize the promotion of green and sustainable initiatives.
0.00 is obtained showing that the panel model is affected by hetero­ Thus, green finance can facilitate the protection of the ecological bal­
skedasticity, and the null hypothesis of constant variance cannot be ance and simultaneously enhance the economic outcomes of a region by
supported. Nonetheless, the GMM estimator is regarded as efficient promoting guided management of available resources. Green finance
under heteroskedasticity (Wooldridge, 2001). can help countries provide financial support to sustainable business
practices and achieve economic sustainability. World economies,
5. Discussion therefore, need to focus on promoting green practice in the financial
sector and introduce innovative financial policies to support sustainable
The present study investigates the impact of green finance, fintech economic practice. The study also hypothesizes a significant correlation
development, environmental benefits, and natural resource manage­ between environmental benefits and economic sustainability. The
ment on economic sustainability in the E7 and G7 nations. Two models, findings from both models support this second hypothesis, which im­
plies that the emphasis of a country on securing environmental benefits
can help it introduce innovative technology and strategies focused on
Table 5 accelerating economic sustainability. Moreover, economic policies
Panel generalized method of moments. aimed at environmental benefits can help a country abide by circular
Variable Coefficient Std. Error t-Statistic Prob. economy principles and establish sustainable economic development.
FINT − 10.6397 0.824641 − 12.9022 0.00
Environmental benefits can help countries face the challenges posed by
GF − 70.5918 13.60924 − 5.18705 0.00 traditional models of economic development in terms of environmental
TNR − 2.15E+11 3.30E+10 − 6.52348 0.00 degradation and climate change.
EB 1224330 61141.09 20.02466 0.00 The present study posits a significant impact of fintech development
C 1.88E+12 1.88E+11 10.01575 0.00
on economic sustainability through the formulation of H3. Both models
R-squared 0.68719 Mean dependent var 3.37E+12 indicate a significant impact of fintech development on economic sus­
Adjusted R-squared 0.683409 S.D. dependent var 4.40E+12 tainability, and thus fintech development can be regarded as a beneficial
S.E. of regression 2.48E+12 Sum squared resid 2.03E+27
Durbin-Watson stat 0.509489 J-statistic 259.1512
tool for promoting sustainability initiatives. This finding highlights the
Instrument rank 6 Prob(J-statistic) 0 significant role of financial institutions in accelerating the economic
sustainability of a region. Fintech development has the potential to
Notes: FINT = fintech, EB = environmental benefits, GF = green finance, TNR =
empower small entrepreneurial firms to design robust strategies for risk
total natural resources.

6
Z. Zhou et al. Resources Policy 92 (2024) 105013

mitigation. Fintech platforms can be used to enhance the financial lit­ The findings of this study highlight the need for world economies to
eracy of citizens and educate them in terms of effective resource allo­ emphasize fintech development and natural resource management in
cation for achieving economic sustainability. The findings of this study their economic and environmental protection policies. Thus, world
support the results of Nenavath and Mishra (2023), who find a signifi­ economies need to invest in fintech development and environmental
cant role of green finance and fintech in promoting sustainable eco­ protection to achieve economic sustainability goals. Governments across
nomic growth. The present study suggests a significant correlation the world should develop robust guidance campaigns to promote
between natural resource management and economic sustainability, various green initiatives to help industries adopt sustainable practices
through H4. The findings from both the dynamic panel isolation model and prioritize ecological balance over traditional models of economic
and quantile regression support this hypothesis and, thus, natural development. These implications can serve as guidelines for the re-
resource management emerges as a substantial strategy for achieving evaluation of existing environmental protection policies for industries
economic sustainability in E7 and G7 economies. The effective man­ in E7 and G7 countries.
agement of natural resources can minimize the risks of resource scarcity
and encourage countries to consume resources more moderately. 6.2. Limitations and future directions
Another principle of effective natural resource management is the
preference for using inexhaustible over exhaustible resources, to ensure The present study has several limitations, which need to be delin­
the conservation of natural resources. The consumption of inexhaustible eated to present directions for future studies in the field of economic
resources can help a country avoid resource scarcity and dependency on sustainability. The first limitation relates to the methodological choices.
resources imported from other countries. An et al. (2023) also highlight The study relies on a secondary research design, making it challenging to
the substantial socio-economic benefits of natural resource management gather data relating to green finance and fintech development in E7 and
and its potential for enhancing sustainable development. G7 economies. Future studies could adopt a primary research approach
to collecting data. In addition, the findings focus on E7 and G7 countries,
6. Conclusion and therefore may not be applicable to other economies. Future studies
could collect data from other regions, such as the ASEAN region.
The present study analyses the role of green finance, fintech devel­ Moreover, future studies could focus on other determinants of economic
opment, natural resource management and environmental benefits in sustainability, such as environmental regulation and renewable energy
achieving economic sustainability in E7 and G7 economies. The dy­ resources.
namic panel isolation model and quantile regression model are used for
the analysis of secondary data. Green finance emerges as a robust in­ CRediT authorship contribution statement
strument for enhancing economic sustainability in global economies.
Fintech is reported to be an innovative financial tool for boosting eco­ Zheng Zhou: Data curation, Conceptualization. Ka Yin Chau:
nomic sustainability. The study affirms the efficacy of natural resource Methodology, Investigation. Amena Sibghatullah: Software, Re­
management as a beneficial strategy for encouraging citizens in major sources. Massoud Moslehpour: Supervision. Nguyen Hoang Tien:
economies to ensure moderate consumption of natural resources and Writing – original draft, Visualization. Khajimuratov Nizomjon Shu­
conserve resources for future generations. kurullaevich: Writing – review & editing.

6.1. Implications of the study

Declaration of competing interest
The findings of this study enhance existing studies and make sub­
The authors declare that they have no known competing financial
stantial contributions to the understanding of ways to achieve economic
interests or personal relationships that could have appeared to influence
sustainability in E7 and G7 countries. The study is based on the theo­
the work reported in this paper.
retical conception of CE and validates its main tenets by applying them
to the analysis of the determinants of economic sustainability in these
Data availability
regions. The theoretical contributions are significant, because of the
focus on the economic sustainability of major economies. The E7 and G7
Data will be made available on request.
economies have seen enormous growth in terms of economic develop­
ment. However, this growth has resulted in immense environmental
Acknowledgements
deterioration. By highlighting the role of fintech in economic sustain­
ability, this study supports technological innovation as a beneficial in­
This work was supported by a grant from the Teacher’s Professional
vestment for promoting sustainable economic growth in a country. In
Development. Project for Domestic visiting scholars from Universities of
addition, the study advances the role of technology in promoting eco­
Zhejiang Provincial Department of Education.“Research on the influ­
nomic sustainability and makes a substantial contribution to the existing
ence of fintech on the efficiency of financial institutions in China”(No.
literature by advancing the understanding of the effectiveness of natural
FX2021143).This work was supported by a grant from Zhejiang Business
resource management for promoting economic sustainability.
College(No.2021RCKY04).The corresponding author would like to
In addition to its significant theoretical implications, the study has
thank Asia University.
valuable practical implications. The findings imply the need to establish
platforms that promote fintech development. Local governments and
media can play active roles in disseminating the potential of fintech to References
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