How do financial development and institutional quality influence greenhouse gases in the Next-11 economies?

Abstract

The sustainable development goals of many nations are challenged by the need to reduce emissions, improve ecological integrity, and address the impacts on climatic alteration. The study examines how the financial innovation and institution quality effect environmental sustainability in NEXT-11 countries between 1995 and 2023. This study analyzes how financial growth and institutional quality affects CO₂, N₂O, and CH₄ emissions in Next-11 nations, filling research gaps through advanced econometrics and policy-focused findings. Based on the second-generation Panel ARDL method and further with robustness checks of DOLS and FMOLS, this research found that the financial innovation contributes to mitigating carbon emissions but affects other types of gases (N₂O and CH₄) more complexly. The financial development decreases CO₂ emissions; it raises N₂O emissions, though slightly, and reduces CH₄ emissions, so financial development does exert different impacts on different sectors. Conversely, we found that increases in institutional strength consistently had positive effects on environmental outcomes. Reductions in CO₂, N₂O, and CH₄ emissions were associated with increased governance and regulatory quality. Results reveal the significance of both financial innovation and institution quality in determining the environmental destiny of emerging economies. When economic growth is linked to strong institutions, it plays a deterrent role in emissions, highlighting the significance of sustainable finance and good governance for environmental degradation.

Introduction

Human-induced production of greenhouse gases (GHGs) constitutes the most important cause of global warming and climate change. These changes in climate conditions are responsible for extreme weather phenomena, including floods, hurricanes, droughts, wildfires, as well as severe heatwaves (Umar and Safi, 2023). The consequences of change in climate for global health are broad and massively harmful to public health. However, as the environment deteriorates, the negative effects on food, safety, water security, health, and economic stability (Hayat et al., 2023). Emissions also drive the depletion of the ozone layer, rising sea level and melting glaciers. There are a number of health problems associated with the environmental degradation caused by this phenomenon, including respiratory diseases, strokes, cancer and heart disease. It also leads to increased infant mortality rate and a decline in life expectancy (Majeed and Ozturk, 2020).

The agenda of UN (2030) for Sustainable Development Goals (SDGs) highlights the immediate need for action to traditional emissions scenarios. In like manner, the Paris Climate Change Conference in 2015 continues to highlight the need for sustaining ecological integrity and limiting GHG emissions. A major aim of the conference was to achieve a universal commitment to a maximum global warming, seeking for the increase above pre-industrial levels to endure below 2 °C with 1.5 °C as the ideal limit UN (2015). The IPCC (Intergovernmental Panel on Climate Change) projects to maintain global warming below 1.5 °C from pre-industrial levels, GHG emissions need to fall 45% by 2030 and get to 0% by 2050 from 2010 levels (IPCC, 2018). The measures to limit CO₂ emissions are still not sufficient—even if many promises to reduce emissions have already been made. According to the UNEP (United Nations Environment Program), current global emissions have already exceeded 27 and 38% of the emission reductions that are required if global temperature increases are to remain within 2 °C and 1.5 °C when associated to pre-industrial levels (UNEP, 2019).

Concern regarding climate change at the recent COP27 in Egypt, with a big shout-out for the world to intensify global actions to achieve the target emissions cuts. In return, nations have made a sequence of policy commitments with the aim of controlling global GHG production. Climate finance, with other core approaches like technology transfer and capacity development assistance and climate finance, there is a new focus on financial support (UNEP, 2022). But there is still a need for academics and researchers, collaborating with national and international environmental organizations, to map the barriers blocking the transition to decarbonized societies and thereby to increased quality of life.

Researches emphasize the heterogeneous environmental effects induced by financial development. An efficient financial system can also result in access to capital to invest and optimal utilization of resources, thus enhancing investment and the overall performance of the economy, and, in turn, pushing pressure on energy consumption and the environment (Khan et al., 2021c). Nevertheless, a strong financial system may facilitate new technologies production and the utilization of energy-efficient techniques for production and, in this way, aid in mitigating environmental destruction (Sharif et al., 2020; Ulucak et al., 2020). Moreover, the capital markets and financial markets may be important in financing R&D for renewable energy, hosting foreign firms with the technical know-how to transfer green tech to the home countries (Khan et al., 2021a; Ahmed et al., 2021b).

Institutional quality matters in shaping ecological performance directly and indirectly. A strong institution influences its capacity to manage public finances, maintain law and order, fight corruption and limit political intervention by the military (Danish and Ulucak, 2020). Therefore, institutions have a vital role in encouraging environment sustainability, suggesting that countries can attain high economic growth and income levels with increasing improvements to environment quality (Hassan et al., 2020). It follows that the excellence of the institution is crucial for the mitigation of environment quality fand the advancement of the pursuit of SDGs. Numerous researchers have discovered the environment influence by the financial sector, with diverse results. Some investigation, such as that by (Amri 2018; Tahir et al., 2021; Tsaurai 2019; Mazhar et al., 2022; Zafar et al., 2021), focuses the negative function of financial sector in environment degradation. In compare, studies by (Sahoo et al., 2021; Halliru et al., 2020; Majeed and Mazhar 2020; Kirikkaleli and Adebayo 2021) claim that the financial sector can have a progressive impression on environmental outcomes. Additionally, some searches, like those by Destek and Sarkodie (2019), advise that the financial sector may have an insignificant influence on environmental changes.

Figure 1 shows trajectories CO₂, N₂O, and CH₄ emissions together with financial development from 1995 to 2023. Industrialization, urbanization, and population growth have ambitious emissions upward, while some positive trends are emerging. For instance, a slight deterioration in CO₂ emissions after 2020 replicates the global slowdown affected by the COVID-19 pandemic, while steadying in N₂O emissions in Fig. 2, post-2021 may designate better agricultural practices or environmental measures. In Fig. 3, CH₄ (methane) emissions show a steady upward trend from 1995 to 2023, increasing from approximately 2.03 to around 2.18. The growth is gradual with slight fluctuations, reaching a modest peak just before 2023. The influence of financial development in Fig. 4 is also observable, with a dejection in the late 1990s linked to the Asian financial misadventure, monitored by a retrieval driven by globalization and progressions in technology. Next-11 nations Egypt, Bangladesh, South Korea, Indonesia, Iran, Mexico, the Philippines, Nigeria, the Philippines, Pakistan, Vietnam, and Turkey are eleven emerging economies, and these nations have an extraordinary capability to emerge as the globe’s biggest economy in the 21st century. Fast-growing populations, rising disposable income levels, and an expanded consumer base, in spite of geographical, cultural, and political differences, are other factors these nations have in common that contribute to a positive outlook for their economies (Al Onaizi and Gadhoum, 2017).

Fig. 1

Trend of CO₂ emissions.

Fig. 2

Trend of N₂O emissions.

Fig. 3

Trend of CH₄ emissions.

Fig. 4

Trend of financial development.

Research problem is that developing nations pursue fast financial growth, concerns about environment sustainability have become increasingly crucial. While financial advancement can facilitate to expanded industrial activity, utilization of energy, and subsequently greater GHG emissions. Many present research studies have narrowly focused on carbon dioxide (CO₂) emissions, neglecting other critical pollutants like (N₂O) nitrous oxide and (CH₄) methane, which are significantly more potent in terms of global warming potential (GWP). Moreover, the significance of organizational quality on the environmental damage remains insufficiently explored. This is especially relevant within the framework of the Next-11 countries, where institutional structures vary widely and financial systems are evolving rapidly. Therefore, there is a pressing need to understand how financial growth and institutional quality to influence the emission of a broader range of GHGs in these dynamic economies. The principal aim of this study is to examine the financial development influence GHG emissions, specifically carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O), in the Next-11 economies. The study also aims to analyze the direct impact of institutional quality in shaping environmental outcomes. Thereby identifying the conditions under which financial growth becomes more environmentally sustainable. By comparing the responses of different GHGs to financial and institutional variables, the study seeks to provide an exhaustive comprehension of environmental impacts in diverse economic and governance contexts.

This study broadens the analytical scope to include nitrous oxide (N₂O) and methane (CH₄) gases with significantly higher GWP. This comprehensive approach provides a deeper comprehension of how economic and financial activities drive environmental deterioration. Additionally, the study emphasizes the essential influence of institutional integrity in influencing environmental outcomes. Given the varied institutional structures and rapidly evolving financial systems in the Next-11 economies, this research offers timely insights into how strong governance and regulatory regimes can alleviate the environmental effects of financial development and innovation. The findings are expected to offer meaningful policy guidance for governments and financial institutions aiming to equilibrate economic growth with long-term sustainable environmental practices. By uncovering the mechanisms through which financial and institutional dynamics affect different types pertaining to GHG emissions, this study contributes to the formulation of more targeted and effective sustainability strategies for emerging economies. This study presents multiple key contributions to the current body of literature. First, it extends the environmental finance literature by analyzing the influence of financial development and institutional quality on a broader spectrum of GHGs CO₂, CH₄, and N₂O unlike the majority of prior research that focus solely on CO₂ emissions. Second, it offering profound understanding of how governance structures exert impact over the environmental outcomes of financial growth in emerging economies. Second, by focusing on the Next-11 countries over the period 1995–2023, the study addresses a critical empirical gap related to rapidly developing but under-researched economies. Third, the use of proficient econometric methodologies, including cross-sectional dependence estimates, cross-sectionally augmented IPS (CIPS) unit root, ARDL, FMOLS, and DOLS ensures methodological rigor, providing robust evidence on long-term and causal relationships that can inform effective, context-specific policy design.

Literature review

The previous literature is distributed into two major parts. The first part interrogates the association between financial growth and degradation of environment, and focuses on how financial system growth can impact ecological results in good and in harmful ways. The second part examines the institutional quality-environment degradation nexus, suggesting the significant contribution of effective institutions in addressing the environmental problems and promoting sustainability. As a whole, these parts give a snapshot of how fiscal and institutional factors are combated against to impact environmental considerations.

Financial development and environmental degradation

The SDGs of the majority of nations face important obstacles in dealing with emissions, environment sustainability as well as alleviating the adverse impact of climatic alteration (Asif et al., 2024). Also, Shahbaz et al. (2018) emphasize that a properly operating financial system may be vital in stimulating economic growth, consumption and production. But this economic development can also increase the burden on the environment. Solaymani and Montes (2024) argued that financial development and effective governance significantly influences the lowering of CO₂ emissions. Yu et al. (2024) shows that in developed economies, strong financial systems tend to decrease the CO2 intensity, but needn’t do so in developing economies, where strong financial systems tend to increase CO₂ intensity. According to Asif et al. (2024), the advancement of the financial sector has a strong and favorable influence on promoting environmental sustainability. Xu et al. (2022) analyzed the Indian economy from 1971 to 2008 to investigate the connection between carbon emissions and financial advancement. It was discovered that one indicator of financial progress, domestic credit to private sector, was positively linked to environmental degradation. Similarly, Ali et al. (2023) observed the connection among CO₂ emissions and Financial progress in Europe, specifically focusing on domestic funding to the private business. Using the FMOLS model and cointegration tests, they found that expansion pertaining to the financial sector has a negative effect on environmental sustainability (Uche and Effiom, 2021).

Khan et al. (2022) applied system-GMM to 29 Chinese provinces and analyzed the influence of GDP increase on CO₂ emissions. It emerged that financial depth, assessed by the ratio of deposits and loans to gross domestic product, was positively related with higher CO₂ emissions. Likewise, Ruza and Caro-Carretero (2022) investigated the consequences of financial growth on CO₂ emissions in several industry sectors; they discovered that transport, oil, and gas emerged with the highest positive elasticities with respect to financial growth. In addition, Yu et al. (2023), financial development, such as the domestic credit to the private sector as a percentage of GDP, positively influence environment degradation as well. Renzhi and Baek (2020) discovered an inverted U-shaped association between the CO₂ emission and accessibility to financial institutions, hereby financial institutions’ access has an average increase, while the critical point is that the access of financial institutions in the first place can increase the CO₂ emission.

Wang et al. (2022) demonstrate that greater financial expansion results in more CO₂ emissions in G7 countries, since a developed financial system offers the firms more avenue for external financing, which can raise the emissions. A similar trend was reported by Ahmad et al. (2020) on Belt and Road nations. In less developed countries, Nasir et ali. (2019) found that financial institutional efficiency could increase CO₂ emission and that deeper financial markets could lower emission in some ASEAN countries. This may indicate that financial institutions and markets in emission reduction differ in different places. Le, Le, and Taghizadeh-Hesary (2020) also conclude that easier access to financial institutions in Asia increases CO₂ emissions, as it increases credit availability for individuals and firms. In contrast, Nasreen et al. (2017) determined that financial stability diminishes the CO₂ emissions in the South Asian economies, and, hence, stable financial institutions are important to mitigate the climate change.

Institutional quality and environmental degradation

Recent research conducted by Imam et al. (2024) underscore the significance of institution quality for environmental degradation. They also stress that countries with better governed systems are more capable of lowering emissions, hinting at the idea that effective institutions are necessary in the promotion of sustainable environmental practices. Xaisongkham and Liu 2024 contends that strong institutional variables such as good governance and compliance with the rule of law are paramount to CO₂ emissions reduction and improving environment quality especially in developing countries. Omri and Saidi (2022) emphasize that institutional quality plays a critical role for long-term environmental sustainability. In developing nations, which also frequently struggle with governance, weak institutions also stand in the way of concerted efforts to reduce emissions. Khan and Rana (2021) examined the effect of institution quality on CO₂ emissions utilizing a data set from 41 Asian nations over the duration 1996–2015. They found that reliable political and economic institutions lead to a reduction in environment degradation. In a further development from this, Sah (2021) emphasized the need to enhance institutional arrangements on issues as the quality of governance, anticorruption rules, regulatory behavior and legal changes for the case of institutions to be successful to manage environmental challenges. Danish and Ulucak (2020) maintained that several dimensions of institution quality (corruption control and democracy) have a beneficial effect over environment quality.

Similarly, Ntow-Gyamfi et al. (2020) discovered that a strong institutional structure can offset the adverse impacts of financial growth on CO₂ emissions over a long time. It is essential to acknowledge that improvement of institution quality is essential for sustained growth, especially in South Asia (Hunjra et al., 2020). Additionally, Ahmed et al. (2020) made institutions quality, financial development and trade openness as the important components for promoting the environmental quality. Liu et al. (2020) propose that building better governance structures, upgrading regulatory frameworks can emerge as pivotal in the successful operationalization of climatic change mitigation policies, which can go a long way in reducing carbon emissions. Bilgili et al. (2020) discovered the quality of governance is important in decreasing CO₂ emissions. Good governance leads to the implementation of environmental laws and support clean technology investments. Ali et al. (2019) called for developing-country institutions to be robust and functioning effectively. As long as institutions are working effectively, they create the regulations and laws that lead to the mitigation of carbon. Hassan et al. (2020) pointed out a bad institutional quality that leads to deterioration of environmental quality in Pakistan. According to Wawrzyniak and Doryn (2020), countries with poor institutions experience declining rates of CO₂ emission growth over time. This indicates that in developing countries, attention should be given to reinforce the institutional setting from where efforts on addressing environmental degradation are initiated.

Based on the literature which we above review, we conclude gap in prior work. This study examines the influence of financial growth and the quality of institutions on CO₂, N₂O, and CH₄ emissions in Next-11 countries (1995–2023), offering novel insights by analyzing multiple GHGs rather than just CO₂. It addresses gaps in prior research by emphasizing the significance of institutional quality and financial development in developing economies, utilizing advanced econometric methodologies (cross-section dependency tests, unit root CIPS test, ARDL, FMOLS, DOLS) to ensure robust long-term and causal relationships. The findings aim to guide policymakers in balancing sustainable growth with effective GHG reduction strategies, tailored to the unique challenges of rapidly developing nations.

Methodology and data

Construction of the model

The financial sector is listed as one most influential factor in explaining environmental quality in the Next-11 countries. The model is given as follows (Eqs. 1, 2 and 3) based on previous studies.

$${{\rm{CO}}}_{2}={\rm{f}}\,\left({\rm{FD}},{\rm{GDP}},{\rm{KOFGI}},{\rm{GDS}},{\rm{IQ}}\right)$$

(1)

$${{\rm{N}}}_{2}{\rm{O}}={\rm{f}}\,\left({\rm{FD}},{\rm{GDP}},{\rm{KOFGI}},{\rm{GDS}},{\rm{IQ}}\right)$$

(2)

$${{\rm{CH}}}_{4}={\rm{f}}\,({\rm{FD}},{\rm{GDP}},{\rm{KOFGI}},{\rm{GDS}},{\rm{IQ}})$$

(3)

Where, dependent indicators are “CO₂ is carbon dioxide emissions”, “N₂O is nitrous oxide emissions”, “CH₄ is methane emissions”, and independent variables are “FD is financial development”, “GDP is gross domestic product”, “KOFGI is KOF globalization index”, “GDS is gross domestic savings” and “IQ is institutional quality”.

The Fig. 5 presents a theoretical framework connecting economic, institutional, and environmental variables. Existing literature demonstrates that GDP and Gross Domestic Savings drive environmental degradation through increased industrial activity and energy use (Shahbaz et al., 2017), supporting the Environment Kuznets Curve hypothesis (Dinda, 2004). Globalization (KOF) shows dual effects—while it can increase emissions through expanded trade and Foreign direct investment (FDI) (Leitão, 2020), it may also enable cleaner technology transfer (Al-Mulali et al., 2015). These relationships highlight the complex interplay between economic development and environmental outcomes. Institutions quality plays a critical interaction role in the economic growth-emissions nexus. Strong institutions enforce environmental regulations and promote green technologies, reducing GHGs (Bhattacharya et al., 2017; Tamazian and Rao, 2010), while Unstable institutions intensify pollution due to inadequate regulation (Lau et al., 2014). This creates bidirectional causality—economic factors drive emissions, but institutional effectiveness determines the environmental impact.

Fig. 5

Framework of the model.

For the empirical evaluation, all chosen variables have undergone log transformation. Using a log-log econometric model offers several benefits, including generating efficient and consistent estimates while addressing econometric challenges like heteroscedasticity and multicollinearity. Equations (1, 2, and 3) are revised and presented as Eqs. (4, 5, and 6).

$${{LnCO}}_{2{\rm{it}}}=\,{\propto }_{0}+{{\alpha }_{1}{LnFD}}_{{\rm{it}}}+{{\alpha }_{2}{LnGDP}}_{{\rm{it}}}+{{\alpha }_{3}{LnKOFGI}}_{{\rm{it}}}+{\alpha }_{4}{{LnGDS}}_{{\rm{it}}}+{{\alpha }_{5}{IQ}}_{{\rm{it}}}+{\mu }_{{\rm{it}}}$$

(4)

$${L{{nN}}_{2}O}_{{\rm{it}}}=\,{\propto }_{0}+{{\alpha }_{1}{LnFD}}_{{\rm{it}}}+{{\alpha }_{2}{LnGDP}}_{{\rm{it}}}+{{\alpha }_{3}{LnKOFGI}}_{{\rm{it}}}+{{\alpha }_{4}{LnGDS}}_{{\rm{it}}}+{{\alpha }_{5}{IQ}}_{{\rm{it}}}+{\mu }_{{\rm{it}}}$$

(5)

$${{LnCH}}_{4{\rm{it}}}=\,{\propto }_{0}+{\alpha }_{1}{{LnFD}}_{{\rm{it}}}+{\alpha }_{2}{{LnGDP}}_{{\rm{it}}}+{\alpha }_{3}{{LnKOFGI}}_{{\rm{it}}}+{\alpha }_{4}{{LnGDS}}_{{\rm{it}}}+{{\alpha }_{5}{IQ}}_{{\rm{it}}}+{\mu }_{{\rm{it}}}$$

(6)

In the model, i indicates the number of countries, while the subscript t indicates the study’s time period (1995–2023). The term $\propto$₀ denotes the constant, and μ_it denotes the error term. The coefficient α₁ describes the linear influence of financial development on the dependent variables, while α₂ measures the effect of GDP per capita on the dependent variables, with expected signs being positive and negative, respectively. Coefficients α₃, α₄, and α₅ reflect the influence of the KOF globalization index, gross domestic savings, and institutional factors on the dependent variables CO₂, N₂O, and CH₄. The expected signs for these coefficients may vary, potentially being positive or negative. However, a positive influence is more plausible, given that these economies are in a developmental phase where financial sector growth often drives production activities without fully accounting for environmental externalities.

Methodological issues

Estimation framework

The estimation framework is predicated on seven stages. This section discusses each step in detail of following section (see Fig. 6).

Fig. 6

Estimation framework.

Cross-sectional dependency test

Cross-sectional dependency (CSD) is a common concern in cross-country studies, as nations are interconnected through shared borders, trade agreements, and cross-border movements of capital, goods, services, and people. These interlinkages often amplify cross-sectional dependencies. Neglecting CSD may result in biased and unreliable outcomes. To address this issue, many assessments, including the “Breusch-Pagan LM, Pesaran scaled LM, and Pesaran CD tests” are employed to examine the presence of CSD. The LM test, initially introduced by Breusch and Pagan (1980), is expressed mathematically through Eqs. (7) and (8).

$$LMBP=T\mathop{\sum }\limits_{i=1}^{N-1}\mathop{\sum }\limits_{j=i+1}^{N}{{P}}_{ij}^{2}$$

(7)

The LM test becomes less appropriate when the quantity of cross-sectional units (n) tends towards infinity. To mitigate this issue, Pesaran (2004) introduced a scaled variant of the LM test, which is represented as follows:

$$BPs=\sqrt{\frac{1}{N(N-1)}}\mathop{\sum }\limits_{i=1}^{N-1}\mathop{\sum }\limits_{j=i+1}^{N}(T{{P}}_{ij}^{2}-1)$$

(8)

Unit root test

After establishing the presence of cross-sectional dependence, the stationarity of the series is assessed. Given that the selected countries exhibit cross-sectional dependence; first-generation stationarity tests are deemed inappropriate. Instead, second-generation tests, specifically the IM Pesaran and Shin tests developed by Pesaran (2007), are utilized. These tests account for cross-sectional dependence and effectively determine the order of integration, yielding more reliable results. This study employs the “cross-sectionally augmented IPS (CIPS)” test, represented by Eq. (9).

$$\Delta Yit=\gamma it+xiYi,t-1+\lambda iT+\mathop{\sum }\limits_{k=1}^{n}{\pi }_{ik}\varDelta Yi,t-k+\mu it$$

(9)

The subscripts i and t represent the constant and time trend, respectively.

Slope homogeneity test

After analyzing the unit root test, it is crucial to assess slope homogeneity, as differences among nations can arise due to variations in demographic, economic, and socio-economic structures. To achieve this, the slope homogeneity test proposed by Hashem Pesaran et al. (2008) is employed. The corresponding test equations are outlined below:

$${\Delta }SH={(N)}^{\frac{1}{2}}{(2K)}^{-\frac{1}{2}}\left(\frac{1}{n}\tilde{S}-k\right)$$

(10)

$${\Delta }ASH={(N)}^{\frac{1}{2}}\left(2K{\left(\frac{T-k-1}{T-1}\right)}^{-\frac{1}{2}}\left(\right.\frac{1}{n}\tilde{S}-k\right)$$

(11)

Panel cointegration test

The Pedroni and Westerlund cointegration tests are so common for testing a long-run equilibrium relationship in panel data. To test for the stationarity of residuals as well as the impacts of cross-sectional heterogeneity, Pedroni (1999, 2004) employs both within-dimension and between-dimension statistics. In contrast, Westerlund (2007) uses an error-correction model to test for adjustment dynamics directly and is consistent to cross-sectional dependence. Collectively, these approaches offer a flexible way of detecting cointegration in data with various properties.

Panel auto regressive distributed lag (ARDL)/PMG

The Panel autoregressive distributed lag (ARDL)/PMG technique suggested by Pesaran and Smith (1995) is used before the analysis of the long run. This proposition is supported by long run ARDL Eqs. 12, 13 & 14 of all the three models.

Model 1:

$$\begin{array}{c}\Delta LnC{O}_{2it}=\alpha +\mathop{\sum }\limits_{i=1}^{{a}_{1}}{\eta }_{1}\Delta LnF{D}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{2}}{\eta }_{2}\Delta LnGD{P}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{3}}{\eta }_{3}\Delta LnKOFG{I}_{it-i}+\\ \mathop{\sum }\limits_{i=0}^{{a}_{4}}{\eta }_{4}\Delta LnGD{S}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{5}}{\eta }_{5}\Delta I{Q}_{it-i}+{\mu }_{it}\end{array}$$

(12)

Model 2:

$$\begin{array}{c}\Delta Ln{N}_{2}{O}_{it}=\alpha +\mathop{\sum }\limits_{i=1}^{{a}_{1}}{\eta }_{1}\Delta LnF{D}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{2}}{\eta }_{2}\Delta LnGD{P}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{3}}{\eta }_{3}\Delta LnKOFG{I}_{it-i}+\\ \mathop{\sum }\limits_{i=0}^{{a}_{4}}{\eta }_{4}\Delta LnGD{S}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{5}}{\eta }_{5}\Delta I{Q}_{it-i}+{\mu }_{it}\end{array}$$

(13)

Model 3:

$$\begin{array}{c}\varDelta LnC{H}_{4it}=\alpha +\mathop{\sum }\limits_{i=1}^{{a}_{1}}{\eta }_{1}\varDelta LnF{D}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{2}}{\eta }_{2}\varDelta LnGD{P}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{3}}{\eta }_{3}\varDelta LnKOFG{I}_{it-i}+\\ \mathop{\sum }\limits_{i=0}^{{a}_{4}}{\eta }_{4}\varDelta LnGD{S}_{it-i}+\mathop{\sum }\limits_{i=0}^{{a}_{5}}{\eta }_{5}\varDelta I{Q}_{it-i}+{\mu }_{it}\end{array}$$

(14)

Panel causality test

The Heterogeneous panel causality test developed by Dumitrescu and Hurlin (2012) is a technique of examining causality relationship in panel data allowing for different causality across units (country). In contrast to the traditional causality tests, this methodology considers that potential causality effect may vary across the entities. Therefore, the null hypothesis states that there is no causality among the units, and the alternative is that there is causality in at least one of the units. This method is especially suited when modeling complicated situations at the cross-country level where the causal relationship between financial development and carbon emissions may vary across panel entities.

The description of data and variables

This research focuses on analyzing environmental factors in the Next-11 countries over the period 1995–2023. The study includes all Next-11 nations: Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, the Philippines, South Korea, Türkiye, and Vietnam. Data for the selected variables were sourced from the World Development Indicators online database. Additionally, the annual series for the KOF Globalization Index was obtained from the KOF Swiss Economic Institute’s online database, while the Economic Freedom Index was retrieved from the Heritage Foundation in (Table 1).

Table 1 Description of variables.

In this research, there are three outcome variables carbon dioxide emissions (CO₂), nitrous oxide emissions (N₂O), and methane emissions (CH₄) are defined as “carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring”, “a measure of annual emissions of nitrous oxide (N₂O), one of the six Kyoto GHGs, from the agriculture, energy, waste, and industrial sectors. The measure is standardized to carbon dioxide equivalent values using the GWP factors of IPCC’s 5th Assessment Report (AR5)”, and “a measure of annual emissions of methane (CH₄), one of the six Kyoto GHGs, from the agriculture, energy, waste, and industrial sector. The measure is standardized to carbon dioxide equivalent values using the GWP factors of AR5”.

The effect of financial development measured by domestic credit to private sector percentage of GDP. “It’s refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a claim for repayment”. Growth of economy assessed using GDP per capita. “GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars”. “The KOF Globalisation Index measures the economic, social and political dimensions of globalisation. Globalisation in these fields has been on the rise since the 1970s, receiving a particular boost after the end of the Cold War”.

The variable Gross domestic savings measured by percentage of GDP. “Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption)”. Economic freedom index using as the proxy of institutional quality, reflects the fundamental right of individuals to have control over their labor and property. In societies that embrace economic freedom, people are empowered to operate, generate, utilize, and allocate resources in accordance with their choices. Such societies ensure that governments minimize interference, allowing the free movement of labor, goods and capital while limiting restrictions to those necessary for safeguarding and upholding individual liberties.

The descriptive analysis indicates that the standard deviations of the dependent variables CO₂, N₂O, and CH₄ are 0.372, 0.146, and 0.258, respectively, while their mean values are 5.275, 0.131, and 2.114. These variables range from minimum values of 4.219, −0.218, and 1.63 to maximum values of 5.804, 0.513, and 2.656. Additionally, for the primary independent variables, financial development and institutions quality, the average values are 1.519 and 56.397, with standard deviations of 0.35 and 8.172. The minimum values recorded for these variables are—1.352 and 34.5, while the maximum values are 2.246 and 74.6, as presented in Table 2. To address missing data in our panel analysis, we employ the average value imputation method. This approach replaces missing observations with the mean value of available data points for each variable, ensuring a balanced dataset while minimizing potential bias in the estimation process.

Table 2 Descriptive statistics.

This study is distinguished by its comprehensive multivariate framework, which incorporates various explanatory variables. However, this approach can potentially result in significant interdependence issues, such as multicollinearity. To address this concern, multicollinearity among the regressor is examined through the use of a variance inflation factor (VIF) and correlation matrix. The findings from these multicollinearity and correlation analyses are presented in Table 3.

Table 3 Pairwise correlations and multicollinearity test.

The correlation reveals a weak-willed relationship among every pair of independent indicators, with correlation coefficients well below 5. This indicates that multicollinearity, or significant interdependencies, is unlikely to be a concern in the analysis. These findings are further supported by the tolerance and VIF values for every independent indicator, which are greater than 0.2 and less than 5, respectively, confirming the minimal risk of multicollinearity as indicated by the correlation matrix.

Results and discussions

Cross-sectional dependency Test

In Table 4 the cross-sectional dependence tests “Pesaran scaled LM, Breusch-Pagan LM, and Pesaran CD” powerfully reject the null hypothesis of no dependence (p = 0.000) for all three models, so showing a strong correlation between residuals across units. This suggests that missing variables, spatial spillovers, or concealed common shocks simultaneously affecting several cross-sections.

Table 4 Cross-section dependency test.

Unit root test results

The outcomes of the CIPS unit root tests are presented in Table 5, which reveal that financial development, the KOF Globalization Index, and N₂O emissions are stationary at level, while the remaining indicators become stationary at first difference. This indicates a mix of integration orders some indicators are I(0), and others are I(1). Considering this mix, along with indication of CSD, the Panel ARDL method is compatible for analyzing the data in this research.

Table 5 Panel unit root test.

Slope homogeneity test

The slope homogeneity test results, presented in Table 6, reveal that the null hypothesis of “homogeneous slope coefficients” is rejected. Testing for slope homogeneity is crucial in large panel datasets, as assuming homogeneous slope coefficients when they are heterogeneous can lead to biased outcomes. To assess the robustness of the study’s conclusions about slope homogeneity, the mean group estimate method is utilized.

Table 6 Slope homogeneity test.

Panel cointegration test results

Table 7 presents the results of the Pedroni and Westerlund cointegration tests. The findings provide strong evidence that the null hypothesis of no cointegration is rejected for both tests at the 1% significance level.

Table 7 Panel cointegration test.

These outcomes further endorse the presence of a long-term elastic link between the dependent variable carbon dioxide emissions, nitrogen oxide emissions, methane emissions, nitrogen oxide emissions and the independent variables, including financial development, GDP per capita, the KOF globalization, and institutions quality, in the Next-11 countries.

Panel Auto regressive distributed lag (ARDL)/PMG

The findings, presented in Table 8, reveal that all the selected indicators significantly effect CO₂ emissions, N₂O emissions, and CH₄ emissions. Financial development is observed to exert an adverse impact on CO₂ emissions and CH₄ emissions, while its impact on N₂O emissions is positive. Specifically, the estimated parameters suggest that a 1% rise in financial development results in a 0.20% reduction in CO₂ emissions, a 0.12% increase in N₂O emissions, and a 0.27% decline in CH₄ emissions. These findings align with the work of Yu et al. (2024) and Ren et al. (2023), as well as Habiba and Xinbang (2022), who highlight the environmental benefits of financial progress through better access to funding for sustainable solutions. This observation aligns with Shukuru and Politaeva (2025), who describe the trade-offs linking economic growth and the environment outcomes in agriculture. Similarly, Liu et al. (2014) highlight the dual nature of financial development, where it supports both green initiatives and activities that are more emission-intensive, such as large-scale farming.

Table 8 Panel ARDL/PMG Long-run estimation results.

The estimated coefficients illustrate a 1% rise in GDP results in a 0.56% rise in CO₂ emissions, a 0.21% growth in N₂O emissions, and a 0.21% reduction in CH₄ emissions. These outcomes are equivalent with the research of Tezcan (2024), Wang et al. (2022), and Acheampong et al. (2020). Economic growth appears to drive increases in CO₂ and N₂O emissions, probable as a result of greater industrial procedures, energy utilization, and agricultural practices. Conversely, the decrease in CH₄ emissions may result from advancements in waste management, cleaner technologies, or changes in energy production methods. The coefficients reveal that a 1% rise in the KOF globalization index regulates to a 0.26% increase in CO₂ emissions, a 0.07% rise in N₂O emissions, and a 0.46% growth in CH₄ emissions, which aligns with the results of Abbas et al. (2024), Sultana et al. (2023), and Wu et al. (2022). Globalization tends to raise CO₂, N₂O, and CH₄ emissions by boosting industrial production, energy consumption, transportation, and expanding agriculture and livestock operations. Although it drives economic growth, it further contributes to greater environmental strain.

The coefficients show that a 1% rise in gross domestic savings results in a 0.32% increase in CO₂ emissions, a 0.13% reduction in N₂O emissions, and a 0.09% rise in CH₄ emissions, which is aligned with the research by Ren et al. (2023), Cheng et al. (2023), and Mohamed (2020). Larger savings can generate higher CO₂ and CH₄ emissions through advocacy capital investment in industry and energy. The reduction of N₂O emissions may be an indication of abandonment of agriculture or adoption of more sustainable farming practices. The coefficients suggest that 1% progress in institutional quality leads to 0.32, 0.13, and 0.09% decrease in CO₂, N₂O, and CH₄ emissions, which aligns with the research of Borozan (2024), Xaisongkham and Liu (2024), and Ali et al. (2019). These studies emphasize that well-functioning institutions are key to enforcing environmental laws, promoting renewable energy, and overseeing agricultural practices in ways that limit emissions. The decline in CO₂, N₂O, and CH₄ emissions is largely due to effective governance, which drives the execution of sustainability policies, improved agricultural methods, and the adoption of cleaner technologies.

Robustness checks

Once the relationships between the n variables have been tested using the Panel ARDL model, it is essential to evaluate the robustness of the analysis in order to confirm the consistency of the results. Sensitivity analyses help to verify that the findings are not simply consequences of the choice of estimation technique, but are rather faithful to the underlying economic relationships. We perform several robustness checks, including employing some alternative estimation methods (FMOLS and DOLS) and cointegration tests.

The advantage of FMOLS are that it can tackle the issues of Endogeneity, Serial cor relation, and cross-sectional (or which is panel data) heterogeneity that commonly arise in the panel data model. Through the adjustments of such biases, FMOLS offers less biased and more consistent estimates of long-run relationships among variables. It also handles non-stationarity in the data, so that its estimates are stable even if series are integrated. These characteristics render the FMOLS an appropriate way to evaluate the robustness of the findings obtained using other estimation methods as applied in the Panel ARDL model (Phillips and Hansen, 1990).

The study uses FMOLS approach to confirm the results of the Panel ARDL model in Table 9. The results indicate that there exists an inverse relationship between financial development and the CO₂ and CH₄ emissions but a positive effect on N₂O emissions.

Table 9 Robustness with panel dynamic OLS methods.

Table 10 presents the outcomes with the Dynamic OLS (DOLS) method to check the rationality of results of Panel ARDL model. Findings: the outcomes indicate that financial development has a significant and negative effect on the CO₂ and CH₄ emissions, but a significant and positive effect on the N₂O emissions.

Table 10 Robustness with panel fully modified OLS methods.

Heterogeneity checks

Heterogeneous analysis offers to study different sequence and different behavior of Next-11 countries.

Table 11 presents the outcomes of a country-specific analysis, where the data is disaggregated to examine each country individually. The findings indicate that the influences of financial development, institutions quality, and other control variables vary across countries. In some cases, these variables are statistically significant, while in others, they are not. This variation highlights the existence of heterogeneity between nations in the panel dataset, suggesting that national-level differences influence how these factors impact environmental outcomes.

Table 11 Heterogeneous analysis country-wise.

Panel causality test

To explore the causal relationship among input and output indicators, causality analysis is essential. Therefore, we apply the panel causality approach.

The causality analysis outcomes are given in Table 12, from which it can be noted that the dependent indicators CO₂ emissions, N₂O emissions and CH₄ emissions are generally found to be affected by financial advancement. This assumption suggests that if the financial system grows, it may drive further industrial activity and energy usage, which ultimately results in higher emissions. It is the deployment of financial resources that will make the difference in this tense relationship potential to result in positive or negative environmental effects. To reduce environmental costs arising from financial expansion, policy makers should make efforts to align financial development with environmentally friendly activities. Financial development influences CO₂, N₂O, and CH₄ emissions through three key channels. First, increased economic activity boosts energy consumption and industrial output, raising fossil fuel-based emissions (Shahbaz et al., 2017). Second, credit expansion funds pollution-intensive sectors like manufacturing and agriculture, escalating N₂O (fertilizers) and CH₄ (livestock) emissions (Boutabba, 2014). Third, FDI may transfer “dirty” industries to developing nations under weak regulations (Pazienza, 2015). Conversely, mature financial systems can reduce emissions by funding green technologies (Tamazian and Rao, 2010). Institutional quality affects CO₂, N₂O, and CH₄ emissions through three primary channels. First, regulatory enforcement reduces emissions by implementing environmental policies and pollution controls (Bhattacharya et al., 2017). Second, transparency and accountability minimize corruption in resource-intensive sectors, curbing excessive N₂O (agriculture) and CH₄ (waste/energy) outputs (Lau et al., 2014). Third, Investment in green technology is incentivized through stable governance, lowering fossil fuel dependence (Tamazian and Rao, 2010). Weak institutions, conversely, exacerbate emissions via lax oversight and unchecked industrial growth (Leitão, 2020).

Table 12 Panel causality results.

Conclusion and policy implications

Conclusions

This research empirically analyses the influence of financial development on GHG emissions in the Next-11 countries during 1995–2023. The analysis is carried out by using a second generation panel time-series methodology. It uses to test cross-sectional dependence between panel units and using Pedroni and Westerlund test to examined cointegration between the variables. To address the issue of long-run relationships, the authors estimate long-run relationships using the “ARDL/PMG” approach and also conduct robustness tests with both DOLS and FMOLS. The consistent results across ARDL, FMOLS, and DOLS despite their distinct methodologies confirm the robustness of our findings. While ARDL provides flexible short- and long-run estimates, FMOLS and DOLS offer specialized corrections for Endogeneity in cointegrated systems. Their convergence on similar long-run relationships suggests these effects reflect true economic patterns rather than methodological artifacts, strengthening confidence in our conclusions about key variables.

The outcomes of the research indicate that financial development results in the decrease of CO₂ emissions, while the influence of financial development on other GHGs, such as N₂O and CH₄, is more complicated. Particularly, financial development reduces CO₂ emissions but increases N₂O emissions and decreases CH₄ emissions, indicating that the influence of financial expansion is heterogeneous among sectors. However, positive environmental outcomes were observed to make more institutional quality enhancing actions. Enforcement and regulatory environments seem to mitigate the CO₂, N₂O, and CH₄ emissions. These findings suggest the significance of financial development as well as institutions quality on the environmental path of emerging countries. Robust institutions will ultimately reduce emission effectively, as sound financial sector growth could play a crucial role for emission reductions, so sustainable financial practices and institutionally effective governance would be indispensable for pollution reduction.

Policy implications

In consideration of the outcomes of the research, the policy recommendations underscore the need to synchronize financial development with sustainable environmental practices in NEXT-11 economies. One of the measures that can help do this is encouraging green finance in NEXT-11 nations, which means driving investments to projects that are beneficial to the environment. Governments have an essential role in helping to enable this transition by creating economic incentives, such as green bonds, subsidies and tax measures. Green bonds, for one thing, can steer investors toward renewable energy sources and energy-efficient technologies, as can financial incentives, such as subsidies and tax incentives that spur the adoption of more expensive clean technologies. These financing mechanisms not only encourage in decreasing emissions, but also promote the growth of sustainable industries. To make these efforts really effective, governments need to also have a focus on improving institutional quality. This will be achieved through improving governance, increasing transparency and the enforcement of stringent environmental standards that induce industries to lower their carbon intensity. By building better institutions, NEXT-11 economies can bypass the environmental breakdown that frequently occurs with rapid industrialization and prevent economic growth from coming at the expense of the planet. Good governance will also help steer industries into the use of cleaner technologies and incorporating sustainability into their business model.

Furthermore, the promotion of public-private collaboration for the development of innovation in the field of cleaning technological products and in the field of sustainable infrastructures is essential. Governments and the private sector can work together to develop and grow such new green solutions, sharing risks and resources along the way. This partnership could help ramp up production or adoption of renewable energy and efficient infrastructure that is necessary to reduce emissions in sectors such as transport, construction and manufacturing. Lastly, regional cooperation in facilitating mutual environmental policies and shared knowledge for addressing common issues, for example, climate change and air pollution, is paramount. Issues on some occasions are not confined only within borders and there is a need for a mutual cooperation of the country. Through sharing successful strategies and best practices, and supporting cooperative environmental programs, countries will be able to more effectively react to these global challenges and draw on the experience of others. In closing combining sustainable financial management with good governance and international cooperation can foster sustained economic expansion, while averting environment destruction and decline in GHG emissions.

Limitation of the research

This research explores the connection between financial development, institutional quality, and GHG emissions in the Next-11 economies. However, its limitations must be carefully considered. The research focuses exclusively on these eleven emerging markets, each of which has distinct economic structures and political environments. Because these nations are at varying stages of industrialization, their environmental challenges and financial systems differ substantially from both less developed countries and advanced economies. This narrow focus means the findings may not extend to other regions. Developed nations with sophisticated financial markets, for instance, might demonstrate entirely different patterns between financial growth and emissions. Similarly, developed countries with fewer robust institutions may also see different dynamics. Researchers in the specific context of the Next-11 countries should view these findings not as though they apply everywhere but rather in line with their situation. Future research could look at these links throughout a broader range of economies to raise the adaptability of the results.

Additionally, the study focuses on GHGs CO₂, N₂O, and CH₄, but these constitute but a minor segment of the broader environment challenges. There are further crucial environmental concerns, like as water pollution, land degradation, deforestation and the loss of biodiversity, which could also be influenced by financial development and quality of institutions. For example, financial evolution might lead to increased industrial waste, water contamination, or the overuse of natural resources, yet these potential impacts were not examined in this study. By focusing solely on GHGs, the research may provide an incomplete picture of how financial and institutional changes affect the environment more broadly. A more comprehensive approach that includes other environmental factors would likely offer a clearer understanding of the full range of impacts that financial and institutional development can have on sustainability.