
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg

39230811
34785
10.1007/s11356-024-34785-7
Research Article
Financial inclusion and environmental pollution in sub-Saharan Africa: moderating effects of economic growth and renewable energy
http://orcid.org/0009-0004-6254-1109
Said Rabie r.said@latrobe.edu.au

https://ror.org/01rxfrp27 grid.1018.8 0000 0001 2342 0938 La Trobe Business School, La Trobe University, Melbourne, Australia
Responsible Editor: Eyup Dogan

4 9 2024
4 9 2024
2024
31 43 5534655360
22 2 2024
19 8 2024
© The Author(s) 2024
2024
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A thriving literature exists about the role of financial inclusion in socio-economic development. Nevertheless, the environmental effects of financial inclusion are largely unknown in the literature, especially in sub-Saharan African countries. Therefore, this study explores the association between financial inclusion and CO2 emissions utilizing data from 23 sub-Saharan Africa for the period 2004–2019. Based on different estimation methods such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), canonical correlation regression (CCR), and an instrumental variable generalized-method of moment (IV-GMM), the results show that financial inclusion is responsible for a substantial increase in CO2 emissions. In addition, financial inclusion moderates economic growth, resulting in higher CO2 emissions. Alternatively, financial inclusion moderates renewable energy use to lower CO2 emissions. The outcomes also verify the presence of the Environmental Kuznets Curve hypothesis (EKC). This study proposes uniting financial inclusion and environmental policies as a strategy for reducing CO2 emissions in sub-Saharan Africa.

Keywords

CO2 emissions
Financial inclusion
Renewable energy
Economic growth
Sub-Saharan Africa
Panel data
La Trobe UniversityOpen Access funding enabled and organized by CAUL and its Member Institutions

issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Climate change is today’s most contentious environmental matter, which has gained the attention of policymakers (Amin et al. 2022; Said and Acheampong 2024). The increasing emphasis on greenhouse gases in the atmosphere has serious consequences for both economic and human development (Tamazian et al. 2009; Shahbaz et al. 2022; Hussain et al. 2023). In spite of attempts to diminish CO2 emissions, international emissions have been rising. According to the World Bank (2023), worldwide CO2 emissions reached to more than 34 million kilotons (kt) in 2019 compared to around 29 million (kt) in 2009. This enormous increase in CO2 emissions contradicts the Paris consent on climate change to alleviate CO2 emissions.

It is undeniable that economic growth improves living standards, but it is also harmful to the environment (Acheampong 2019; Mehmood 2022; Naseem et al. 2024; Işık et al. 2024). Following the work of Grossman and Krueger (1995), numerous studies have assessed the link between economic growth and environmental pollution, notably recognized as the Environmental Kuznets curve (EKC). The EKC hypothesis recommends that economic growth and the environment have an inverted U-shape relationship. Thus, during the early phases of growth, environmental pollution increases, but after a certain threshold of growth, it decreases. Empirical findings on the EKC hypothesis have been inconsistent. Some authors such as Farhani and Ozturk (2015), Acheampong (2019), Tenaw and Beyene (2021), and Naseem et al. (2024) state that CO2 emissions are continuously rising with economic growth; thus, the EKC hypothesis is not supportable in this case. In contrast, other works have maintained the presence of the EKC postulate (see, e.g., Stern 2004; Tamazian et al. 2009; Omri et al. 2015; Sapkota and Bastola 2017; Aydoğan and Vardar 2020; Jebabli et al. 2023; Raza and Tang 2024).

Theoretically, the impact of financial inclusion on the environment remains debatable. Some authors are with the standpoint that financial inclusion enhances the quality of the environment through diminishing CO2 emissions. Jensen (1996), for example, argues that the financial sector plays a considerable role in lowering energy emissions through encouraging technological advancements in the energy supply to minimize environmental pollution. In addition, as Tamazian et al. (2009) point out, financial services are the source of greater levels of research and development, which ultimately benefit the environment. Other scholars, however, argue that financial development can have detrimental effects on environmental quality, which contradicts these desirable impacts of financial services on the environmental quality. Sadorsky (2011), as an instance, argues financial development leads to more energy consumption and environmental degradation related to energy-intensive consumer items like refrigerators, cars, and big houses. Furthermore, financial inclusion could boost CO2 emissions through stimulating industrial manufacturing and utilizing energy-intensive consumable items (Le et al. 2020).

Despite renewable energy’s favorable environmental effects, its role in reducing CO2 emissions remains controversial. Some scholars support the opinion that renewable energy upgrades the environment. For example, Huang et al. (2021) contend that renewable energy use has a beneficial influence on the environment by creating a minimal carbon footprint in comparison with fossil fuels consumption. They also contend that coal use results in 3.6 pounds of CO2 E/kWh in comparison with 0.04 pounds produced through wind. Moreover, renewable energy leads to the increasing demand for energy use driven by economic growth (Rasoulinezhad et al. 2020). Contrarily, other group of scholars claim that renewable energy worsens the environment by rising CO2 emissions. Nguyen and Kakinaka (2019) find that renewable energy exerts a positive influence on CO2 emissions in developing nations. The authors stress that in developing countries, a lack of modern and affordable forms of energy decreases economic productivity and income-generating opportunities, so they cannot afford to promote cleaner energy. Furthermore, renewable energy projects may need highly sophisticated technology and relatively high costs, which can be quite burdensome for national budgets.

Till date, few papers have assessed the effect of financial inclusion on CO2 emissions and provided the contradictions in the literature; Acheampong (2019), Saidi and Omri (2020), and Said and Acheampong (2024) contend that further empirical studies should be conducted to resolve these inconsistent findings. Thus, investigations of financial inclusion and the environment are still in the early phases, and additional studies are needed to adequately comprehend how financial inclusion affects the environment. In addition, little research has been conducted to assess how financial inclusion moderates economic growth and renewable energy to influence on CO2 emissions. As a final point, scarcely is shown about the influence of financial inclusion on CO2 emissions in sub-Saharan Africa. Based on literature disagreements, this paper explores the direct and indirect impacts of financial inclusion on CO2 emissions in 23 sub-Saharan African nations between 2004 and 2019.

This study focuses on the sub-Saharan African region since the region is the lowest contributor to worldwide CO2 emissions. However, sub-Saharan Africa has experienced a continuous growth in CO2 emissions over the past two decades. For example, according to the World Bank (2023), CO2 emissions increased from 608,000 (kt) in 2004 to 789,000 (kt) in 2014 and further to about 824,000 (kt) in 2019 (see Fig. 1).Fig. 1 CO2 emissions (kt)-sub-Saharan Africa.

Source: The World Bank (2023)

The region has also experienced a significant improvement in financial inclusion. Despite improvements in the percentage of adults with bank accounts in sub-Saharan Africa between 2011 and 2021, only 55% of adults in this region had bank accounts in 2021. In this region, account ownership does not only lag behind high-income countries and the world average but also behind other developing nations (see Fig. 2). Therefore, it is crucial to understand how financial inclusion impacts CO2 emissions in sub-Saharan Africa to develop climate change policies and for sustainable development in the region.Fig. 2 Percentage of the adult population with an account at a financial institution.

Source: Global Findex database 2022

As of now, very few studies have simultaneously incorporated financial inclusion, economic growth, and renewable energy in their model. Therefore, this study evaluates whether financial inclusion, economic growth, and renewable energy simultaneously affect CO2 emissions. In addition, it examines whether renewable energy and economic growth activities moderate the relationship between financial inclusion and environmental pollution, which has largely been neglected in the previous studies. Consequently, this paper addresses three issues that are lacking in the past research. (1) How does financial inclusion affect the environment in the sub-Saharan African region? (2) Does economic growth moderate the connection between financial inclusion and CO2 emissions? (3) Do renewable energy sources moderate the linkage between financial inclusion CO2 emissions in the region?

This study extends and contributes to knowledge by the following ways. First, it examines the direct relationship between financial inclusion and CO2 emissions for 23 countries in sub-Saharan Africa between 2004 and 2019. Second, it illustrates indirect effects of financial inclusion on CO2 emissions. Hence, in contrast to previous studies, the present study extends the literature by looking at how financial inclusion moderates the impact of economic growth and renewable energy on CO2 emissions. As a final point, to accomplish consistent and robust empirical findings, this study utilizes different techniques such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), canonical correlation regression (CCR), and an instrumental variable generalized-method of moment (IV-GMM).

Here are the remaining sections of this study. The “Related prior studies and hypothesis development” section presents a review of the existing literature. A description of the research methodology and variables is given in the “Methodology and data” section. The “Estimation strategy” section presents the estimation strategy. The “Results and discussions” Section summarises the main findings, while the “Conclusions, policy implications, and future outlook” section concludes with policy implications.

Related prior studies and hypothesis development

Financial inclusion and CO2 emissions

The principle of financial inclusion is to ensure that financial products and services are accessible to all individuals and businesses. The World Bank acknowledged that fulfilling this principle would contribute to the accomplishment of the 17 United Nations Sustainable Development Goals (SDGs) (World Bank 2018). There are currently few studies on the relationship between financial inclusion and the environment, with conflicting theoretical perspectives and empirical results about how financial inclusion influences the quality of the environment (Le et al. 2020; Shahbaz et al. 2022; Said and Acheampong 2024). According to theoretical standpoint, financial inclusion could both negatively and positively influence the environment. By improving financial inclusion, individuals would be more likely to be able to access financial products and to use energy-consuming appliances such as refrigerators, automobiles, and coolers (Said et al. 2023; Le et al. 2020; Frankel and Romer 2017), leading to a higher level of CO2 emissions. In addition, through fostering inclusive financial systems, more economic activity occurs, which increases the demand for non-renewable energy sources and increases the amount of CO2 emitted into the atmosphere (Tao et al. 2022; Said and Acheampong 2024; Said 2024). However, according to the credit-innovation theory, financial inclusion has the potential to reduce CO2 emissions through green technology development. The development of green technologies is widely recognized as a key component of CO2 emission reduction (Du et al. 2019; Said and Acheampong 2024). Yang et al. (2024) support this point of view by stating that financial inclusion will reduce CO2 emissions by promoting green innovation, environmental procedures, and the use of clean energy.

As with theoretical ambiguity, empirical studies have also found inconsistent results regarding the impact of financial inclusion on CO2 emissions. As presented in Table 1, while some empirical studies suggest that financial inclusion increases CO2 emissions, other empirical studies suggest the opposite. For instance, Le et al. (2020) point out financial inclusion leads to higher CO2 emissions in the case of Asia. In addition, Mehmood (2022) indicates that financial inclusion exacerbates CO2 emissions in South Asia. Additionally, Ahmad et al. (2022) assess the influence of financial inclusion on environmental pollution in the ASEAN region from 2000 through 2019. The results indicate that financial inclusion brings out environmental degradation in the ASEAN region. In particular, they indicate that a 1% increase in financial inclusion results in a 0.42% increase in environmental degradation. Using a sample of Belt and Road Initiative (BRI) countries, Cai and Wei (2023) report that financial inclusion is associated with greater CO2 emissions. They find that 1% rise in financial inclusion increases CO2 emissions by 0.158%. The same conclusion is also reached by Mukalayi and Inglesi-Lotz (2023) who point out that financial inclusion contributes to the decline in environmental quality in Africa between 1990 and 2019. However, Du et al. (2022) contend that financial inclusion negatively impacts CO2 emissions, indicating that boosting financial inclusion is beneficial for the environment. Hussain et al. (2023), based on the pooled mean group estimation technique (PMG), explores the association between financial inclusion and CO2 emissions in a large sample of Asian economies. The results indicate that financial inclusion negatively impacts CO2 emissions over the long-term. Zhou et al. (2023) examine the effect of financial inclusion on carbon emissions using 30 Chinese provinces for the period 2011–2020. The empirical findings exhibit that financial inclusion results in a significant decrease in CO2 emissions, suggesting that financial access contributes to the environmental quality. Employing the AMG estimator and the Driscoll–Kraay panel regression approach, Prempeh et al. (2023) reveal that the development of the banking sector improves environmental quality in western African countries between 1990 and 2019. In a more recent study, Prempeh (2024) indicates that a higher level of financial development and the use of renewable energy are linked with lower levels of CO2 emissions in 10 West African countries. In addition, Said and Acheampong (2024) indicate that financial inclusion contributes significantly to decarbonization in the MENA region. According to the proposed relationship in the literature, we hold the following hypothesis:H1. Financial inclusion increases CO2 emissions in sub-Saharan Africa.

Table 1 Literature review-financial inclusion-CO2 emissions relationship

Authors	Period	Countries	Methodology	Results	
Le et al. (2020)	2004–2014	31 Asian countries	Hoechle (2007) model	FI increases CO2 emissions	
Mehmood (2022)	1990–2017	4 South Asian countries	ARDL approach	FI increases CO2 emissions	
Ozturk and Ullah (2022)	2007–2019	42 BRI countries	GMM	FI increases CO2 emissions	
Liu et al. (2022)	1995–2019	China	ARDL	FI decreases CO2 emissions	
Shahbaz et al. (2022)	2011–2017	China	GLS	FI decreases CO2 emissions	
Jebli and Hakimi (2023)	2004–2019	Top 10 technological countries	ARDL approach	FI increases CO2 emissions	
Singh et al. (2023)	2008–2018	India	ARDL approach	FI increases CO2 emissions	
Sharif et al. (2023)	1990–2019	ASEAN countries	ARDL approach	FI decreases CO2 emissions	
Hussain et al. (2023)	2004–2020	102 countries	ARDL approach	No linear relationship	
Mukalayi and Inglesi-Lotz (2023)	1990–2019	Sub-Saharan Africa	Fixed effects estimation technique	FI increases CO2 emissions	
Zhou et al. (2023)	2011–2020	China	Two-way fixed effect panel estimation	FI decreases CO2 emissions	
Prempeh et al. (2023)	1990–2019	11 West African states	AMG estimator	FI decreases CO2 emissions	
Prempeh et al. (2023)	1990–2019	10 West African states	Driscoll-Kraay panel regression	FI decreases CO2 emissions	
Said and Acheampong (2024)	2004–2019	MENA region	FMOLS, DOLS, and CCR	FI decreases CO2 emissions	
Yang et al. (2024)	2011–2020	China	Spatial econometric models	FI decreases CO2 emissions	
FI, financial inclusion; ARDL, autoregressive distributed lag model approach; GMM, generalized method of moments approach; GLS, generalized least squares approach; FMOLS, fully modified ordinary least square approach; DOLS, dynamic ordinary least square approach; CCR, canonical cointegrating regressions approach; AMG, augmented mean group approach

Moderating role of renewable energy in financial inclusion-CO2 emissions nexus

It is indispensable to conduct research about renewable energy and the environment, as it provides policymakers with additional insight into achieving Sustainable Development Goals (SDGs), specifically Goals 7 (Affordable and Clean Energy) and 13 (Climate Action). As renewable energy has a lower carbon footprint, it may become an important tool for reducing CO2 emissions. Several countries are developing policies to raise the production of renewable energy. According Fahim et al. (2023a), ASEAN nations have increased their renewable energy share through investments in geothermal, solar wind, and hydroelectric sources. ASEAN countries have developed regulations and incentives to encourage businesses and individuals to use renewable energy in the future (Fahim et al. 2023b). Renewable energy sources have been expanded to speed up the transmission to an environmentally friendly economy.

Considering the importance of the renewable energy in environmental sustainability, a number of studies have explored the relationship between renewable energy and CO2 emissions. Overall, the empirical research on the renewable energy-CO2 emissions linkage can be categorised into several segments. The first one asserts that renewable energy has a detrimental influence on CO2 emissions. For instance, Aydoğan and Vardar (2020) investigate the impact of renewable energy on CO2 emissions for a large sample of emerging countries. The results point out that renewable energy decreases CO2 emissions. Wang et al. (2021) assess the relationship between renewable energy and CO2 emissions in ten developing nations. The empirical findings show that renewable energy consumption leads to a reduction in CO2 emissions. In addition, Mirziyoyeva and Salahodjaev (2022) reveal that renewable energy improves the environmental quality in top carbon-intense countries in the world. The results suggest that a 1% increase in renewable energy consumption leads to a reduction of 0.98% in CO2 emissions. Recently, Said and Acheampong (2024) examine the connection between renewable energy and environmental pollution in 11 MENA countries. The outcomes reveal that renewable energy reduces the carbon emissions in the region, thereby improving environmental quality.

The second segment of empirical studies argues that renewable energy consumption worsens environmental quality. Bulut (2017) analyzes the influence of renewable energy on Turkey’s environment. It has been shown that renewable energy use enhances CO2 emissions. By other words, renewable energy negatively impacts Turkish environmental quality. The same finding can also be found in Nguyen and Kakinaka (2019), who argue that renewable energy worsens environmental quality over time.

The last segment of studies reports that renewable energy exerts negligible impacts on CO2 emissions. Twumasi (2017), for instance, evaluates the influence of renewable energy on CO2 emissions in United States of America. Empirical findings indicate that renewable energy use wields no influence on CO2 emissions. Similarly, Boontome et al. (2017) contend that renewable energy has an inconsiderable influence on environmental quality in Thailand for the period 1971–2013. Nathaniel and Iheonu (2019) examine the association between renewable energy and environmental pollution in sub-Saharan African countries. The empirical findings indicate that renewable energy consumption has an unimportant influence on CO2 emissions. Similarly, Saidi and Omri (2020) point out that there is no link between CO2 emissions and renewable energy consumption in 15 major renewable energy nations from 1990 through 2014. Lastly, Pata et al. (2023) indicate that renewable energy does exert any impact on the environment in six ASEAN countries from 1995 to 2018. It is apparent from this review that the wide variety of empirical models used in these studies has resulted in inconclusive results.

Despite the direct role of renewable energy consumption in CO2 emissions, it can indirectly influence CO2 emissions by influencing the impact of financial inclusion on CO2 emissions. For instance, the use of renewable energy plays an important role in promoting financial inclusion (Işık et al. 2024). An environment-friendly energy policy attracts the attention of financial investors. In addition, as the financial sector improves, companies become more attractive to investing in energy, which in turn increases energy demand and creates more trust in the financial system (Işık et al. 2024). Many empirical studies have supported that renewable energy consumption matters in enhancing financial inclusion (Islam et al. 2013; Furuoka 2015; Anton and Nucu 2020). These discussions and evidence show that renewable energy can condition the effect of financial inclusion to influence CO2 emissions. It is, however, rare to find empirical evidence that demonstrate how renewable energy moderates the relationship between financial inclusion and CO2 emissions; hence, this insures additional investigation. Therefore, we formulate the second hypothesis below:H2: Renewable energy consumption moderates the relationship between financial inclusion and CO2 emissions.

Moderating role of economic growth in financial inclusion-CO2 emissions nexus

Over the past few decades, the empirical link between economic growth and CO2 emissions has been heavily explored. This relationship is closely linked with testing the validity of the environmental Kuznets curve (EKC) hypothesis. Numerous present studies on this relationship have contended that the level of environmental pollution and economic growth follows the inverted U-shaped relationship. This inverted U-shaped linkage is identified as EKC in the literature. Since the 1990s, that relationship has been investigated after Grossman and Krueger (1995) presented empirical evidence suggesting that economic growth contributes to a gradually deteriorating environment in its early stages. However, after a certain threshold of economic growth, it contributes to an enhancement in the environmental conditions. Subsequent to this seminal study, numerous studies have investigated the link between growth and environmental pollution after Grossman and Krueger’s (1995) theory and demonstrate contradictory conclusions. For instance, Sapkota and Bastola (2017) test the validity EKC hypothesis between economic growth and environmental pollution in Latin American region over the period 1980–2010. Results indicate the presence of the EKC hypothesis in the region. Omri et al. (2015) also validates the existence of EKC hypothesis in MENA countries from 1990 to 2011. By contrast, numerous studies have different perspective regarding the link between economic growth and environmental pollution. Pablo-Romero and De Jesús (2016) test the EKC hypothesis between energy consumption and CO2 emissions in Latin America. The results indicate that EKC is not supported for the region. Acheampong (2019) indicates that EKC is not valid using data from 46 sub-Saharan African countries from 2000 to 2015. Similar results are also found in empirical studies such as Stern and Common (2001), Stern (2004), and Acheampong et al. (2020).

Although the evidence that economic growth is associated with CO2 emissions, economic growth indirectly affects CO2 emissions through its impact on financial inclusion. According to Sarma and Pais (2011), economic growth contributes to higher financial development, which in turn has an impact on financial inclusion. The authors indicate that financial inclusion is considerably affected by income, inequality, literacy, urbanization, and physical infrastructure in a country. In addition, Wang and Guan (2017) and Van et al. (2021) contend that an individual’s income and education play a crucial role in determining the level of financial inclusion in a country. Synthesizing the above evidence, we formulate the third hypothesis:H3: Economic growth moderates the link between financial inclusion and CO2 emissions.

Gaps in the literature

Few studies have explored the influence of financial inclusion, economic growth, renewable energy, and CO2 emissions across different regions. The link between financial inclusion, economic growth, renewable energy use, and CO2 emissions varies from study to another. A difference in geographical location, economic development level, approach, selected variables, or econometrics techniques can also affect the results of research. To the author’s knowledge, none of these studies has explored this relationship in the case of sub-Saharan Africa. In the last two decades, Sub-Saharan countries have experienced improvements in terms of economic development, financial systems, and renewable energy consumption; therefore, examining this relationship would be worthwhile, as it would offer more insightful implications for these countries. Further, a number of recent studies investigated the role of finance in the renewable energy-CO2 emissions (see, e.g., Wang et al. 2022a); however, the roles of renewable energy and economic growth in moderating the influence of financial inclusion on environmental pollution is unknown. Aiming to address this issue, this paper explores the moderating roles of renewable energy and economic growth in the linkage between financial inclusion and environmental pollution in 23 nations in sub-Saharan Africa.

Methodology and data

Empirical model

This paper explores the moderating role of economic growth and renewable energy on financial inclusion-CO2 emissions nexus in a sample of 23 sub-Saharan countries from 2004 to 2019.1 To achieve this objective, this study considers three models. In the main effect model Eq. (1), CO2 emission is a function of financial inclusion (FI), economic growth (GDPPC), squared of economic growth (GDPPC)2, renewable energy consumption (RE), and industrialization (INDUS). As per existing literature (Shahbaz et al. 2022; Said and Acheampong, 2023), this study investigates the impact of financial inclusion on CO2 emissions using the empirical estimation shown in Eq. (1).1 ln CO2PCit=θ1+β1lnREit+β2lnGDPPCit+β3(lnGDPPC)2it+β4lnINDUSit+β5lnFIit+μt+εit

To probe the moderating roles of financial inclusion and economic growth and financial inclusion and renewable energy on CO2 emissions, Eqs. (2) and (3) extend Eq. (1) to consider the interaction terms of financial inclusion and economic growth (lnFI × lnGDPPC) and of financial inclusion and renewable energy (lnFI × lnRE). Equations (2) and (3) are written as follows:2 lnCO2PCit=θ1+β1lnREit+β2lnGDPPCit+β3(lnGDPPC)2it+β4lnINDUSit+β5lnFIit+α1(lnFI∗lnGDPPC)it+μt+εit

3 lnCO2PCit=θ1+β1lnREit+β2lnGDPPCit+β3(lnGDPPC)2it+β4lnINDUSit+β5lnFIit+α2(lnFI∗lnRE)it+μt+εit

where i = 1……..23 and t = 2004……0.2019; β1…….β5 are the coefficients to be estimated; α1 and α2 capture the indirect effect of financial inclusion; θ1 represents the fixed country effect,μt is time fixed effects, and ε is the white noise.

Table 2 displays descriptive statistics between the variables. CO2 emissions per capita increase on average by 0.19% with a standard deviation of 0.65%. In addition, economic growth in sub-Saharan Africa averages 7.94%, while renewable energy usage averages 4.10%. Regarding financial inclusion indicators, ATMs in sub-Saharan Africa have an average number of 2.83%, while bank branches have an average number of 2.15%. Table 2 Descriptive statistics (Logarithm form)

Variables	Mean	SD	Min	Max	
lnCO2 PC	0.19	0.65	 − 3.91	2.15	
lnATM	2.83	2.99	 − 3.22	4.50	
lnBB	2.15	2.40	 − 0.92	4.01	
lnODCB	3.52	3.49	1.38	5.26	
lnOLCB	3.15	2.96	 − 0.24	4.90	
lnRE	4.10	3.38	 − 0.34	4.56	
lnGDPPC	7.94	8.08	5.65	9.74	
lnINDUS	3.23	2.29	2.14	4.13	

Table 3 shows that there is a strong positive correlation between financial inclusion variables (lnATM, lnBB, and lnOLCB) and CO2 per capita (lnCO2PC). In addition, it has been reported a strong negative connection between renewable energy (lnRE) and lnCO2PC. Finally, the results show that the economic growth (lnGDPPC) is found to be positively and highly related with lnCO2PC. Table 3 Correlations

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	
lnCO2 PC (1)	1.00								
lnATM (2)	0.75	1.00							
lnBB (3)	0.58	0.74	1.00						
lnODCB (4)	0.41	0.65	0.61	1.00					
lnOLCB (5)	0.63	0.77	0.52	0.80	1.00				
lnRE (6)	 − 0.77	 − 0.83	 − 0.73	 − 0.68	 − 0.74	1.00			
lnGDPPC (7)	0.79	0.82	0.84	0.61	0.55	 − 0.82	1.00		
lnINDUS (8)	 − 0.02	 − 0.01	 − 0.20	 − 0.11	 − 0.06	 − 0.07	 − 0.03	1.00	
Values in bold indicate a strong relationship between the two variables

Description of variables

Financial inclusion index (lnFI)

To calculate a composite financial inclusion index (FI), four measures are considered: (1) number of ATMs per 10,000 adults, (2) number of commercial bank branches per 10,000 adults, (3) outstanding deposits from commercial banks (% of GDP), and (4) outstanding loans from commercial banks (% of GDP). Due to the different units and scales of these measures, it is necessary to transform them into normalized variables before aggregating them into a composite index (FI). Normalization using standardized Z-score approach is conducted as follows:Z-score=Xi-X¯α

where Xi denotes the raw score; X¯ is the group average, and α is the standard deviation. Then, conduct a principal component analysis (PCA) on the normalized indicators.

CO2 emissions per capita (lnCO2 PC)

The variable CO2 emission is expressed in metric tons per capita. In the literature, CO2 emissions per capita have been widely used to assess environmental pollution (Boontome et al. 2017; Acheampong et al. 2020; Shahbaz et al. 2022; Said and Acheampong 2024). CO2 emissions are the primary cause of global warming.

Renewable energy (lnRE)

Switching to renewable energy is an important factor in improving the environment. It has been found that renewable energy consumption and CO2 emissions are negatively correlated regardless of the sample and methodology used (see, e.g., Charfeddine and Kahia 2019; Chen et al. 2019; Sharif et al. 2019; Yao et al. 2019), other evidence suggests that renewable energy has a positive or even neutral effect on CO2 emissions (Apergis et al. 2010; Menyah and Wolde-Rufael 2010; Saidi and Omri 2020).

Economic growth (lnGDPPC)

Economic growth is represented by real GDP per capita. By including per capita GDP and its square into the analysis, one can establish the EKC hypothesis, which assumes that CO2 emissions and per capita GDP are inversely related (Inverted U-shaped). Otherwise, it is U-shaped if GDP per capita is negative and significant, and its square term is positive and significant. The validity of the EKC hypothesis is contradictory (see, e.g., Stern 2004; Omri et al. 2015).

Industry (lnINDUS)

Industry affects CO2 emissions both positively and negatively. The impact of industrialization on CO2 emissions has been shown to be positive because it wields higher environmental pollution (Liu and Bae 2018). Alternatively, the development of the tertiary industry and the rationalization of the industrial structure lead to a reduction in environmental pollution (Du et al. 2019; Wang et al. 2022b).

Estimation strategy

Cross-section dependence test

Previous research shows that panel data can have heterogeneity and cross-section dependence (CD) issues. The effects of CD must be investigated as they can sometimes produce erroneous results (Danish et al. 2018). Hence, this study computes cross-sectional dependence using Pesaran’s CD test (Pesaran 2004). De Hoyos and Sarafidis (2006) suggested that disregarded common shocks can affect the cross-sectional dependence of cross-country panels. In other words, if cross-sectional dependence is present in data but not considered in the analysis, it will cause inconsistent standard errors (Driscoll and Kraay 1998). The results reported in Table 4 indicate that null hypothesis is not rejected in all tests, which confirms the residuals are cross-sectionally independent under fixed effect and random effect specifications. Table 4 Results of the cross-sectional dependence tests

Test	Statistics	Prob	Statistics	Prob	
Equation (1)	Fixed effect		Random effect		
Pesaran’s test	 − 0.949	0.343	 − 0.555	0.579	
Friedman’s test	6.708	0.999	9.531	0.990	
Equation (2)	
Pesaran’s test	 − 0.985	0.325	 − 0.477	0.634	
Friedman’s test	6.708	0.999	10.398	0.982	
Equation (3)	
Pesaran’s test	 − 0.996	0.319	 − 0.551	0.582	
Friedman’s test	6.708	0.999	9.503	0.990	

Panel unit root test

Testing the stationarity of the variables is a crucial process in any study. The validation of cross-sectional independence led me to employ the unit root test recommended by Im et al. (2003). Table 5 shows that all variables have a unit root. As soon as the first difference between all variables is taken, stationarity is implemented, indicating that all variables are integrated of order 1. In order to estimate variables empirically, it is necessary to establish their stationary state. Table 5 Results of panel unit root tests

Series	Level		First difference		
Variable	W-t-bar	P-value	W-t-bar	P-value	
lnCO2 PC	0.901	0.816	 − 5.396***	0.000	
lnFI	 − 0.586	0.279	 − 5.047***	0.000	
lnRE	1.620	0.947	 − 5.070***	0.000	
lnGDPPC	 − 0.876	0.191	 − 3.027***	0.001	
lnINDUS	0.091	0.536	 − 8.124***	0.000	
***P < 0.01

Panel co-integration test

Once all the series are stationary and integrated at order 1, it needs to be checked whether they cointegrate. This study uses the Pedroni panel co-integration test (Pedroni 2004).2 Pedroni test takes into account the heterogeneity through parameters that may differ between individuals. Under the null hypothesis, the test is based on the absence of co-integration. In contrast, under the alternative hypothesis, there is a co-integration relationship between the variables for each panel (Dinda and Coondoo 2006). Based on the results in Table 6, the test statistically rejects the null hypothesis of no co-integration and supports the alternative hypothesis regarding co-integration between variables in Eqs. (1), (2), and (3). Table 6 Results of panel cointegration tests

	Cointegration test	Statistic	P-value	
Equation (1)	
Pedroni test	
Ho: no cointegration–Ha: all panels are co-integrated			
1	Modified Phillips–Perron t	5.229***	0.000	
2	Phillips–Perron t	 − 2.771***	0.003	
3	Augmented Dickey–Fuller t	 − 2.669***	0.004	
Equation (2)	
Pedroni test	
Ho: no cointegration– Ha: all panels are cointegrated			
1	Modified Phillips–Perron t	6.153***	0.000	
2	Phillips–Perron t	 − 3.295***	0.001	
3	Augmented Dickey–Fuller t	 − 2.812***	0.003	
Equation (3)	
Pedroni test	
Ho: no cointegration–Ha: all panels are cointegrated			
1	Modified Phillips–Perron t	6.332***	0.000	
2	Phillips–Perron t	 − 2.356***	0.009	
3	Augmented Dickey–Fuller t	 − 2.272***	0.012	
***P < 0.01

Estimation of long-run coefficients

This study utilizes different estimation techniques to investigate the linkage between financial inclusion and environmental pollution. DOLS is used since it is a parametric estimator that addresses autocorrelation (Liguo et al. 2022). However, FMOLS approach takes into consideration the heterogeneity in the cointegration link. Additionally, it rectifies the endogeneity of the explanatory indicators and the serial link inherent in the dynamic panels (Pedroni 2001). CCR employs a stationary conversion approach to reduce the relation between stochastic regressor errors and cointegration equations (Christiansen 2015). To check the robustness of the outcomes in presence of possible endogeneity between financial inclusion and environmental quality, this paper also utilizes the instrumental variable generalized method of moment (IV-GMM). The purpose of this study is to examine the effect of financial inclusion on CO2 emissions, so it is essential to have an instrument for financial inclusion index. This study employs the lags of the financial inclusion index as the instruments for financial inclusion index.3 To test the validity of the instruments, Kleibergen-Paap rk LM is used to examine whether the endogenous regressor is well identified by the instruments. Under the null hypothesis, the model is under-identified. However, the rejection of the null indicates that the model is identified. This paper also uses Cragg-Donald Wald F statistic test to examine whether the excluded instruments are correlated with the endogenous regressors. Under the null hypothesis, the model is weak. In contrast, the rejection of the null indicates the model is well-specified. Finally, Hansen J test of over-identifying restrictions is used. The null hypothesis of this test indicates that the instruments are valid instruments, i.e., uncorrelated with the error term. However, the rejection of this test indicates that the instruments are not valid, i.e., correlated with the error term.

Results and discussions

Table 7 reports the panel results of the influence of financial inclusion on CO2 emissions. The empirical findings indicate that renewable energy is negatively and significantly correlated with CO2 emissions at a 1% significance level in all specifications, reflecting the potentially favorable influence of renewable energy on the environment. A 1% increase in renewable energy leads to a reduction in CO2 emissions per capita within a range of 0.394–0.414%. This outcome agrees with the previous literature, which indicates that renewable energy decreases CO2 emissions (see, e.g., Charfeddine and Kahia 2019; Abbasi et al. 2021; Said et al. 2022; Said and Acheampong 2024). However, the results are not in line with Twumasi (2017), Saidi and Omri (2020), Pata et al. (2023), and Mukhtarov (2024), which show that renewable energy consumption exerts insignificant influence on CO2 emissions. The coefficients of GDP per capita and its square term, respectively, have significant positive and negative effects on CO2 emissions. This indicates that income has an inverted-U shape relationship with pollution and, thus, supports environmental Kuznets curve (EKC) hypothesis for sub-Saharan Africa. It implies that the level of per capita CO2 emissions initially increases with per capita GDP and, after a certain level of growth, any decrease in per-capita GDP increases the per-capita CO2 emissions. This outcome does not agree with the work of Acheampong (2019) that shows that the EKC postulate is not supported in sub-Saharan Africa. However, the results are in line with the empirical work of Sapkota and Bastola (2017) which demonstrates that the EKC hypothesis is valid in Latin America. Furthermore, industry exerts a significant positive effect on CO2 emissions in all specifications. A 1% increase in industry increases per capita CO2 emissions within a range of 0.282–0.347%. This outcome agrees with the works of Le et al. (2020) and Dong et al. (2022) which indicate that industrialization contributes to higher environmental pollution. The results further indicate that financial inclusion positively related with CO2 emissions in all regressions. A 1% rise in the financial inclusion index increases CO2 emissions per capita within a range of 0.104–0.121%. The results support the empirical studies of Le et al. (2020), Singh et al. (2023), Fareed et al. (2022), and Said et al. (2023) which indicate that financial inclusion worsens the environmental quality. However, the results do not align with the findings of Shahbaz et al. (2022), Du et al. (2022), and Said and Acheampong (2024) which contend that financial inclusion has a beneficial impact on the environment. Table 7 Financial inclusion and CO2 emissions

	Model 1	Model 2	Model 3	Model 4	
Variable	DOLS	FMOLS	CCR	IV-GMM	
lnRE	 − 0.406*** (0.109)	 − 0.394*** (0.062)	 − 0.394*** (0.064)	 − 0.414*** (0.051)	
lnGDPPC	3.712*** (0.886)	3.781*** (0.492)	3.782*** (0.510)	3.551*** (0.347)	
lnGDPPC2	 − 0.185*** (0.062)	 − 0.189*** (0.035)	 − 0.189*** (0.036)	 − 0.177*** (0.024)	
lnINDUS	0.347** (0.138)	0.283*** (0.078)	0.282*** (0.080)	0.344*** (0.046)	
lnFI	0.114** (0.057)	0.104*** (0.032)	0.104*** (0.033)	0.121*** (0.036)	
Constant	 − 17.471*** (2.839)	 − 17.591*** (1.583)	 − 17.594*** (1.636)	 − 16.760*** (1.125)	
Kleibergen-Paap rk LM statistic-P value				0.000	
Cragg-Donald Wald F statistic-P value				1742.749	
Hansen J statistic-P value				0.594	
Number of observations	342	344	344	301	
Number of countries	23	23	23	23	
Under-identification is the Kleibergen–Paap rk test; weak identification test is Cragg-Donald Wald F statistic; over-identification test is the Hansen J statistic. **P < 0.05, ***P < 0.01

Lastly, the Hansen J test does not reject the over-identifying restrictions, affirming the validity of the instruments. Furthermore, the Kleibergen–Paap rk test and Cragg-Donald Wald F test confirm that these specifications are properly identified and do not suffer from under- and weak-identification problems.

Table 8 reports the panel results of the influence of financial inclusion on CO2 emissions in the presence of the interaction term of financial inclusion and economic growth. The results are at large similar to those presented in Table 7. Renewable energy decreases CO2 emissions per capita, industry increases CO2 emissions, and EKC hypothesis is valid between economic growth and environmental pollution. On the contrary, financial inclusion exerts a significant negative effect on CO2 emissions. As shown in Table 8, the interaction term enters with a positive sign, significant at the 5% level, suggesting a complementary relationship between finance and economic growth to worsen environmental quality. Financial inclusion could promote strong economic growth and encourage industrial activities, which leads to increasing CO2 emissions. A 1% increase in FI* GDPPC increased CO2 emissions with a range of 0.133–0.194%. Table 8 Interaction effect of financial inclusion and economic growth on CO2 emissions

	Model 1	Model 2	Model 3	Model 4	
Variable	DOLS	FMOLS	CCR	IV-GMM	
lnRE	 − 0.394*** (0.066)	 − 0.383*** (0.045)	 − 0.383*** (0.046)	 − 0.412*** (0.044)	
lnGDPPC	4.532*** (0.561)	4.603*** (0.375)	4.602*** (0.387)	4.099*** (0.421)	
lnGDPPC2	 − 0.244*** (0.040)	 − 0.249*** (0.026)	 − 0.249*** (0.027)	 − 0.216*** (0.030)	
lnINDUS	0.324*** (0.084)	0.299*** (0.057)	0.299*** (0.058)	0.342*** (0.043)	
lnFI	 − 1.298*** (0.288)	 − 1.266*** (0.193)	 − 1.267*** (0.199)	 − 0.854* (0.459)	
Ln(FI *GDPPC)	0.194*** (0.039)	0.188*** (0.026)	0.188*** (0.027)	0.133** (0.062)	
Constant	 − 20.354*** (1.818)	 − 20.585*** (1.217)	 − 20.585*** (1.257)	 − 18.686*** (1.409)	
Kleibergen-Paap rk LM statistic-P value				0.000	
Cragg-Donald Wald F statistic-P value				41.042	
Hansen J statistic-P value				0.536	
Number of observations	342	344	344	301	
Number of countries	23	23	23	23	
Under-identification is the Kleibergen–Paap rk test; weak identification test is Cragg-Donald Wald F statistic; over-identification test is the Hansen J statistic. *P < 0.10, **P < 0.05, ***P < 0.01

Table 9 provides similar results to Table 7 in relation to renewable energy, economic growth, industry, and financial inclusion impact on CO2 emissions. It is observed from Table 9 that the interaction term of financial inclusion and renewable energy wields a negative influence on CO2 emissions. This result suggests that financial inclusion complements renewable energy to reduce the CO2 emissions. Hence, financial inclusion assures efficacy in renewable energy, thereby lessening CO2 emissions. A 1% increase in FI* RE significantly reduced CO2 emissions within a range of 0.282–0.336%. Table 9 Interaction effect of financial inclusion and renewable energy on CO2 emissions

	Model 1	Model 2	Model 3	Model 4	
Variable	DOLS	FMOLS	CCR	IV-GMM	
lnRE	 − 0.284*** (0.094)	 − 0.275*** (0.041)	 − 0.275*** (0.042)	 − 0.286*** (0.050)	
lnGDPPC	4.229*** (0.727)	4.278*** (0.317)	4.277*** (0.328)	4.147*** (0.425)	
lnGDPPC2	 − 0.224*** (0.051)	 − 0.227*** (0.022)	 − 0.227*** (0.023)	 − 0.221*** (0.030)	
lnINDUS	0.404*** (0.113)	0.353*** (0.050)	0.352*** (0.051)	0.423*** (0.053)	
lnFI	1.258*** (0.293)	1.240*** (0.130)	1.240*** (0.134)	1.447*** (0.373)	
Ln(FI *RE)	 − 0.288*** (0.073)	 − 0.283*** (0.032)	 − 0.282*** (0.033)	 − 0.336*** (0.092)	
Constant	 − 19.965*** (2.375)	 − 20.030*** (1.042)	 − 20.027*** (1.074)	 − 19.641*** (1.496)	
Kleibergen-Paap rk LM statistic-P value				0.000	
Cragg-Donald Wald F statistic-P value				41.907	
Hansen J statistic-P value				0.359	
Number of observations	342	344	344	301	
Number of countries	23	23	23	23	
under-identification is the Kleibergen–Paap rk test; weak identification test is Cragg-Donald Wald F statistic; over-identification test is the Hansen J statistic. ***P < 0.01

Finally, the Hansen J test does not reject the over-identifying restrictions affirms the validity of the instruments. In addition, The Kleibergen–Paap rk test and Cragg-Donald Wald F test suggest that this regression is well specified.

Conclusions, policy implications, and future outlook

The present study may provide the first empirical analysis of the association between financial inclusion, economic growth, renewable energy, and CO2 emissions for 23 sub-Saharan African economies over the period 2004–2019. As estimation approaches, DOLS, FMOLS, and CCR are employed, as well as the IV-GMM estimator to determine the reliability of the results. The results of this research can be outlined in the following manner:

First, the findings demonstrate that financial inclusion exerts a significant positive effect on CO2 emissions in sub-Saharan Africa over the investigated period. The results indicate that a 1% increase in the financial inclusion index increases CO2 emissions per capita within a range of 0.104–0.121%. On the contrary, the results indicate that renewable energy has a significant negative impact on CO2 emissions. Specifically, a 1% increase in renewable energy use can reduce CO2 emissions within a range of 0.394–0.414%. The results also validate the EKC hypothesis in sub-Saharan Africa.

Second, the results indicate that the financial inclusion moderates economic growth to increase the CO2 emissions. However, our empirical findings show that financial inclusion moderates renewable energy to reduce CO2 emissions in the region. While this paper establishes that financial inclusion directly deteriorates environmental quality, it indirectly improves the quality of the environment by promoting renewable energy use.

These findings have three main implications for policies for attaining SDG 7 in sub-Saharan Africa. This study calls on policymakers to enact measures that warrant the stability and effectiveness of the financial system. Finding the adverse environmental influence of financial inclusion definitely does not mean lowering financial inclusion. Instead, policymakers should strive to improve the environmental impact of financial inclusion. Consistent with the recommendation of Le et al. (2020) and Said et al. (2023), for financial inclusion to drive SDG 7, policy makers should support access to financial services initiatives consistent with environmental policies in region-wide countries. Secondly, financial inclusion indirectly increases CO2 emissions by spurring economic growth in sub-Saharan nations. It is important for policy makers to encourage investment in sustainable economic sectors. Finally, financial inclusion indirectly reduces CO2 emissions by promoting renewable energy in the region; policymakers should promote renewable energy use through policy support, public awareness, and investments in research and development. Moreover, tax incentives should be offered to encourage the use of renewable energy in these nations. Thus, this will spur the production of clean and modern energy (Mentel et al. 2022).

Despite contributing to the relevant research area, the current work has some limitations. Firstly, this study uses CO2 emissions as the only proxy for the environmental pollution. Nevertheless, additional proxies of environmental pollution, such as sulfur dioxide (SO2) and nitrogen dioxide (NO2), can be used to ensure robustness. Furthermore, a narrow definition of financial inclusion was constructed in this study. Various aspects should be included to create a financial inclusion index, such as credit, savings, payments, and insurance.

While this study makes a valuable contribution, some further research is still needed. There are numerous ways to expand this research. Future research could look at the relationship between economic growth, financial inclusion, and CO2 emissions in other developing regions. Second, future studies can explore the impact of financial inclusion, economic growth, and renewable energy on ecological footprint, which generally describes environmental degradation. Lastly, a comparative study that concentrates on the degree of a nation’s economic development may help to clarify the impact of financial accessibility and economic expansion on CO2 emissions.

Appendix

See Table 10Table 10 Sample composition

Country name	Observation period	
Angola	[2004–2019]	
Botswana	[2004–2019]	
Burundi	[2004–2016]	
Cabo Verde	[2004–2019]	
Cameroon	[2004–2019]	
Comoros	[2004–2019]	
Eswatini	[2004–2019]	
Ghana	[2008–2019]	
Guinea	[2004–2019]	
Guinea-Bissau	[2010–2019]	
Kenya	[2004–2019]	
Lesotho	[2004–2019]	
Madagascar	[2004–2019]	
Mauritius	[2004–2019]	
Mozambique	[2004–2019]	
Namibia	[2006–2019]	
Nigeria	[2004–2019]	
Rwanda	[2004–2019]	
Seychelles	[2004–2019]	
South Africa	[2004–2019]	
Uganda	[2004–2019]	
Zambia	[2004–2019]	
Zimbabwe	[2009–2019]	

Author contribution

I declare that I am the only author of this manuscript and agreed with the content of the manuscript.

Funding

Open Access funding enabled and organized by CAUL and its Member Institutions.

Availability of data and materials

Data are available upon request.

Declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Competing interests

The author declares no competing interests.

1 See Table 10 in the Appendix for the list of sub-Saharan African countries included in the analysis.

2 Pesaran’s (2004) CD test is chosen as it is more appropriate for unbalanced panels.

3 This study uses the first and second lags of financial inclusion as instruments of financial inclusion index.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The work described has not been published before, and it is not under consideration for publication anywhere else.
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References

Abbasi KR Adedoyin FF Abbas J Hussain K The impact of energy depletion and renewable energy on CO2 emissions in Thailand: fresh evidence from the novel dynamic ARDL simulation Renew Energy 2021 180 1439 1450 10.1016/j.renene.2021.08.078
Abbasi KR, Adedoyin FF, Abbas J, Hussain K (2021) The impact of energy depletion and renewable energy on CO2 emissions in Thailand: fresh evidence from the novel dynamic ARDL simulation. Renew Energy 180:1439–1450. 10.1016/j.renene.2021.08.078
Acheampong AO Modelling for insight: does financial development improve environmental quality? Energy Econ 2019 83 156 179 10.1016/j.eneco.2019.06.025
Acheampong AO (2019) Modelling for insight: does financial development improve environmental quality? Energy Econ 83:156–179. 10.1016/j.eneco.2019.06.025
Ahmad S Khan D Magda R Assessing the influence of financial inclusion on environmental degradation in the ASEAN region through the panel PMG-ARDL approach Sustainability 2022 14 12 7058 10.3390/su14127058
Ahmad S, Khan D, Magda R (2022) Assessing the influence of financial inclusion on environmental degradation in the ASEAN region through the panel PMG-ARDL approach. Sustainability 14(12):7058. 10.3390/su14127058
Amin N, Song H, Khan ZA (2022) Dynamic linkages of financial inclusion, modernization, and environmental sustainability in South Asia: a panel data analysis. Environ Sci Pollut Res 1–9. 10.1007/s11356-021-16648-7
Anton SG Nucu AEA The effect of financial development on renewable energy consumption. A panel data approach Renew Energy 2020 147 330 338 10.1016/j.renene.2019.09.005
Anton SG, Nucu AEA (2020) The effect of financial development on renewable energy consumption. A panel data approach. Renew Energy 147:330–338. 10.1016/j.renene.2019.09.005
Apergis N Payne JE Menyah K Wolde-Rufael Y On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth Ecol Econ 2010 69 11 2255 2260 10.1016/j.ecolecon.2010.06.014
Apergis N, Payne JE, Menyah K, Wolde-Rufael Y (2010) On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecol Econ 69(11):2255–2260. 10.1016/j.ecolecon.2010.06.014
Aydoğan B Vardar G Evaluating the role of renewable energy, economic growth and agriculture on CO2 emission in E7 countries Int J Sustain Energ 2020 39 4 335 348 10.1080/14786451.2019.1686380
Aydoğan B, Vardar G (2020) Evaluating the role of renewable energy, economic growth and agriculture on CO2 emission in E7 countries. Int J Sustain Energ 39(4):335–348. 10.1080/14786451.2019.1686380
Boontome P Therdyothin A Chontanawat J Investigating the causal relationship between non-renewable and renewable energy consumption, CO2 emissions and economic growth in Thailand Energy Procedia 2017 138 925 930 10.1016/j.egypro.2017.10.141
Boontome P, Therdyothin A, Chontanawat J (2017) Investigating the causal relationship between non-renewable and renewable energy consumption, CO2 emissions and economic growth in Thailand. Energy Procedia 138:925–930. 10.1016/j.egypro.2017.10.141
Bulut U The impacts of non-renewable and renewable energy on CO2 emissions in Turkey Environ Sci Pollut Res 2017 24 15416 15426 10.1007/s11356-017-9175-2
Bulut U (2017) The impacts of non-renewable and renewable energy on CO2 emissions in Turkey. Environ Sci Pollut Res 24:15416–15426. 10.1007/s11356-017-9175-2
Cai X Wei C Does financial inclusion and renewable energy impede environmental quality: empirical evidence from BRI countries Renew Energy 2023 209 481 490 10.1016/j.renene.2023.04.009
Cai X, Wei C (2023) Does financial inclusion and renewable energy impede environmental quality: empirical evidence from BRI countries. Renew Energy 209:481–490. 10.1016/j.renene.2023.04.009
Charfeddine L Kahia M Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: a panel vector autoregressive (PVAR) analysis Renew Energy 2019 139 198 213 10.1016/j.renene.2019.01.010
Charfeddine L, Kahia M (2019) Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: a panel vector autoregressive (PVAR) analysis. Renew Energy 139:198–213. 10.1016/j.renene.2019.01.010
Chen Y Wang Z Zhong Z CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China Renew Energy 2019 131 208 216 10.1016/j.renene.2018.07.047
Chen Y, Wang Z, Zhong Z (2019) CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China. Renew Energy 131:208–216. 10.1016/j.renene.2018.07.047
Christiansen B (2015) Comparative political and economic perspectives on the MENA Region. IGI Global
Danish Zhang B Wang Z Wang B Energy production, economic growth and CO 2 emission: evidence from Pakistan Nat Hazards 2018 90 27 50 10.1007/s11069-017-3031-z
Danish, Zhang B, Wang Z, Wang B (2018) Energy production, economic growth and CO 2 emission: evidence from Pakistan. Nat Hazards 90:27–50. 10.1007/s11069-017-3031-z
De Hoyos RE Sarafidis V Testing for cross-sectional dependence in panel-data models Stand Genomic Sci 2006 6 4 482 496 10.1177/1536867X060060040
De Hoyos RE, Sarafidis V (2006) Testing for cross-sectional dependence in panel-data models. Stand Genomic Sci 6(4):482–496. 10.1177/1536867X060060040
Dinda S Coondoo D Income and emission: a panel data-based cointegration analysis Ecol Econ 2006 57 2 167 181 10.1016/j.ecolecon.2005.03.028
Dinda S, Coondoo D (2006) Income and emission: a panel data-based cointegration analysis. Ecol Econ 57(2):167–181. 10.1016/j.ecolecon.2005.03.028
Dong K Taghizadeh-Hesary F Zhao J How inclusive financial development eradicates energy poverty in China? The role of technological innovation Energy Econ 2022 109 106007 10.1016/j.eneco.2022.106007
Dong K, Taghizadeh-Hesary F, Zhao J (2022) How inclusive financial development eradicates energy poverty in China? The role of technological innovation. Energy Econ 109:106007. 10.1016/j.eneco.2022.106007
Driscoll JC Kraay AC Consistent covariance matrix estimation with spatially dependent panel data Rev Econ Stat 1998 80 4 549 560 10.1162/003465398557825
Driscoll JC, Kraay AC (1998) Consistent covariance matrix estimation with spatially dependent panel data. Rev Econ Stat 80(4):549–560. 10.1162/003465398557825
Du K Li P Yan Z Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data Technol Forecast Soc Chang 2019 146 297 303 10.1016/j.techfore.2019.06.010
Du K, Li P, Yan Z (2019) Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technol Forecast Soc Chang 146:297–303. 10.1016/j.techfore.2019.06.010
Du Q Wu N Zhang F Lei Y Saeed A Impact of financial inclusion and human capital on environmental quality: evidence from emerging economies Environ Sci Pollut Res 2022 29 22 33033 33045 10.1007/s11356-021-17945-x
Du Q, Wu N, Zhang F, Lei Y, Saeed A (2022) Impact of financial inclusion and human capital on environmental quality: evidence from emerging economies. Environ Sci Pollut Res 29(22):33033–33045. 10.1007/s11356-021-17945-x
Fahim KE De Silva LC Hussain F Shezan SA Yassin H An evaluation of ASEAN renewable energy path to carbon neutrality Sustainability 2023 15 8 6961 10.3390/su15086961
Fahim KE, De Silva LC, Hussain F, Shezan SA, Yassin H (2023a) An evaluation of ASEAN renewable energy path to carbon neutrality. Sustainability 15(8):6961. 10.3390/su15086961
Fahim KE Silva LCD Hussain F Yassin H A state-of-the-art review on optimization methods and techniques for economic load dispatch with photovoltaic systems: progress, challenges, and recommendations Sustainability 2023 15 15 11837 10.3390/su151511837
Fahim KE, Silva LCD, Hussain F, Yassin H (2023b) A state-of-the-art review on optimization methods and techniques for economic load dispatch with photovoltaic systems: progress, challenges, and recommendations. Sustainability 15(15):11837. 10.3390/su151511837
Fareed Z Rehman MA Adebayo TS Wang Y Ahmad M Shahzad F Financial inclusion and the environmental deterioration in Eurozone: the moderating role of innovation activity Technol Soc 2022 69 101961 10.1016/j.techsoc.2022.101961
Fareed Z, Rehman MA, Adebayo TS, Wang Y, Ahmad M, Shahzad F (2022) Financial inclusion and the environmental deterioration in Eurozone: the moderating role of innovation activity. Technol Soc 69:101961. 10.1016/j.techsoc.2022.101961
Farhani S Ozturk I Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia Environ Sci Pollut Res 2015 22 15663 15676 10.1007/s11356-015-4767-1
Farhani S, Ozturk I (2015) Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ Sci Pollut Res 22:15663–15676. 10.1007/s11356-015-4767-1
Frankel JA, Romer D (2017) Does trade cause growth? In: Global trade, Routledge, pp 255–276
Furuoka F Financial development and energy consumption: evidence from a heterogeneous panel of Asian countries Renew Sustain Energy Rev 2015 52 430 444 10.1016/j.rser.2015.07.120
Furuoka F (2015) Financial development and energy consumption: evidence from a heterogeneous panel of Asian countries. Renew Sustain Energy Rev 52:430–444
Grossman GM Krueger AB Economic growth and the environment Q J Econ 1995 110 2 353 377 10.2307/2118443
Grossman GM, Krueger AB (1995) Economic growth and the environment. Q J Econ 110(2):353–377. 10.2307/2118443
Huang Y Kuldasheva Z Salahodjaev R Renewable energy and CO2 emissions: empirical evidence from major energy-consuming countries Energies 2021 14 22 7504 10.3390/en14227504
Huang Y, Kuldasheva Z, Salahodjaev R (2021) Renewable energy and CO2 emissions: empirical evidence from major energy-consuming countries. Energies 14(22):7504. 10.3390/en14227504
Hussain S Akbar M Gul R Shahzad SJH Naifar N Relationship between financial inclusion and carbon emissions: International evidence Heliyon 2023 9 6 e16472 10.1016/j.heliyon.2023.e16472 37274701
Hussain S, Akbar M, Gul R, Shahzad SJH, Naifar N (2023) Relationship between financial inclusion and carbon emissions: International evidence. Heliyon 9(6):e16472. 10.1016/j.heliyon.2023.e1647237274701
Im KS Pesaran MH Shin Y Testing for unit roots in heterogeneous panels J Econ 2003 115 1 53 74 10.1016/S0304-4076(03)00092-7
Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115(1):53–74. 10.1016/S0304-4076(03)00092-7
Işık C Bulut U Ongan S Islam H Irfan M Exploring how economic growth, renewable energy, internet usage, and mineral rents influence CO2 emissions: a panel quantile regression analysis for 27 OECD countries Resour Policy 2024 92 105025 10.1016/j.resourpol.2024.105025
Işık C, Bulut U, Ongan S, Islam H, Irfan M (2024) Exploring how economic growth, renewable energy, internet usage, and mineral rents influence CO2 emissions: a panel quantile regression analysis for 27 OECD countries. Resour Policy 92:105025. 10.1016/j.resourpol.2024.105025
Islam F Shahbaz M Ahmed AU Alam MM Financial development and energy consumption nexus in Malaysia: a multivariate time series analysis Econ Model 2013 30 435 441 10.1016/j.econmod.2012.09.033
Islam F, Shahbaz M, Ahmed AU, Alam MM (2013) Financial development and energy consumption nexus in Malaysia: a multivariate time series analysis. Econ Model 30:435–441
Jebabli I Lahiani A Mefteh-Wali S Quantile connectedness between CO2 emissions and economic growth in G7 countries Resour Policy 2023 81 103348 10.1016/j.resourpol.2023.103348
Jebabli I, Lahiani A, Mefteh-Wali S (2023) Quantile connectedness between CO2 emissions and economic growth in G7 countries. Resour Policy 81:103348. 10.1016/j.resourpol.2023.103348
Jebli MB Hakimi A How do financial inclusion and renewable energy collaborate with Environmental quality? Evidence for top ten countries in technological advancement Environ Sci Pollut Res 2023 30 11 31755 31767 10.1007/s11356-022-24430-6
Jebli MB, Hakimi A (2023) How do financial inclusion and renewable energy collaborate with Environmental quality? Evidence for top ten countries in technological advancement. Environ Sci Pollut Res 30(11):31755–31767. 10.1007/s11356-022-24430-6
Jensen A Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival Can J Fish Aquat Sci 1996 53 4 820 822 10.1139/f95-233
Jensen A (1996) Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival. Can J Fish Aquat Sci 53(4):820–822. 10.1139/f95-233
Le T-H Le H-C Taghizadeh-Hesary F Does financial inclusion impact CO2 emissions? Evidence from Asia Finance Res Lett 2020 34 101451 10.1016/j.frl.2020.101451
Le T-H, Le H-C, Taghizadeh-Hesary F (2020) Does financial inclusion impact CO2 emissions? Evidence from Asia. Finance Res Lett 34:101451. 10.1016/j.frl.2020.101451
Liguo X Ahmad M Khattak SI Impact of innovation in marine energy generation, distribution, or transmission-related technologies on carbon dioxide emissions in the United States Renew Sustain Energy Rev 2022 159 112225 10.1016/j.rser.2022.112225
Liguo X, Ahmad M, Khattak SI (2022) Impact of innovation in marine energy generation, distribution, or transmission-related technologies on carbon dioxide emissions in the United States. Renew Sustain Energy Rev 159:112225. 10.1016/j.rser.2022.112225
Liu X Bae J Urbanization and industrialization impact of CO2 emissions in China J Clean Prod 2018 172 178 186 10.1016/j.jclepro.2017.10.156
Liu X, Bae J (2018) Urbanization and industrialization impact of CO2 emissions in China. J Clean Prod 172:178–186. 10.1016/j.jclepro.2017.10.156
Liu N, Hong C, Sohail MT (2022) Does financial inclusion and education limit CO2 emissions in China? A new perspective. Environ Sci Pollut Res 1–8. 10.1007/s11356-021-17032-1
Mehmood U Examining the role of financial inclusion towards CO2 emissions: presenting the role of renewable energy and globalization in the context of EKC Environ Sci Pollut Res 2022 29 11 15946 15954 10.1007/s11356-021-16898-5
Mehmood U (2022) Examining the role of financial inclusion towards CO2 emissions: presenting the role of renewable energy and globalization in the context of EKC. Environ Sci Pollut Res 29(11):15946–15954. 10.1007/s11356-021-16898-5
Mentel U Wolanin E Eshov M Salahodjaev R Industrialization and CO2 emissions in Sub-Saharan Africa: the mitigating role of renewable electricity Energies 2022 15 3 946 10.3390/en15030946
Mentel U, Wolanin E, Eshov M, Salahodjaev R (2022) Industrialization and CO2 emissions in Sub-Saharan Africa: the mitigating role of renewable electricity. Energies 15(3):946. 10.3390/en15030946
Menyah K Wolde-Rufael Y CO2 emissions, nuclear energy, renewable energy and economic growth in the US Energy Policy 2010 38 6 2911 2915 10.1016/j.enpol.2010.01.024
Menyah K, Wolde-Rufael Y (2010) CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6):2911–2915. 10.1016/j.enpol.2010.01.024
Mirziyoyeva Z Salahodjaev R Renewable energy and CO2 emissions intensity in the top carbon intense countries Renew Energy 2022 192 507 512 10.1016/j.renene.2022.04.137
Mirziyoyeva Z, Salahodjaev R (2022) Renewable energy and CO2 emissions intensity in the top carbon intense countries. Renew Energy 192:507–512. 10.1016/j.renene.2022.04.137
Mukalayi NM Inglesi-Lotz R Digital financial inclusion and energy and environment: global positioning of sub-Saharan African countries Renew Sustain Energy Rev 2023 173 113069 10.1016/j.rser.2022.113069
Mukalayi NM, Inglesi-Lotz R (2023) Digital financial inclusion and energy and environment: global positioning of sub-Saharan African countries. Renew Sustain Energy Rev 173:113069. 10.1016/j.rser.2022.113069
Mukhtarov S Do renewable energy and total factor productivity eliminate CO2 emissions in Turkey? Environ Econ Policy Stud 2024 26 2 307 324 10.1007/s10018-023-00377-x
Mukhtarov S (2024) Do renewable energy and total factor productivity eliminate CO2 emissions in Turkey? Environ Econ Policy Stud 26(2):307–324. 10.1007/s10018-023-00377-x
Naseem S Hu X Sarfraz M Mohsin M Strategic assessment of energy resources, economic growth, and CO2 emissions in G-20 countries for a sustainable future Energ Strat Rev 2024 52 101301 10.1016/j.esr.2024.101301
Naseem S, Hu X, Sarfraz M, Mohsin M (2024) Strategic assessment of energy resources, economic growth, and CO2 emissions in G-20 countries for a sustainable future. Energ Strat Rev 52:101301. 10.1016/j.esr.2024.101301
Nathaniel SP Iheonu CO Carbon dioxide abatement in Africa: the role of renewable and non-renewable energy consumption Sci Total Environ 2019 679 337 345 10.1016/j.scitotenv.2019.05.011 31085413
Nathaniel SP, Iheonu CO (2019) Carbon dioxide abatement in Africa: the role of renewable and non-renewable energy consumption. Sci Total Environ 679:337–345. 10.1016/j.scitotenv.2019.05.01131085413
Nguyen KH Kakinaka M Renewable energy consumption, carbon emissions, and development stages: some evidence from panel cointegration analysis Renew Energy 2019 132 1049 1057 10.1016/j.renene.2018.08.069
Nguyen KH, Kakinaka M (2019) Renewable energy consumption, carbon emissions, and development stages: some evidence from panel cointegration analysis. Renew Energy 132:1049–1057. 10.1016/j.renene.2018.08.069
Omri A Daly S Rault C Chaibi A Financial development, environmental quality, trade and economic growth: what causes what in MENA countries Energy Econ 2015 48 242 252 10.1016/j.eneco.2015.01.008
Omri A, Daly S, Rault C, Chaibi A (2015) Financial development, environmental quality, trade and economic growth: what causes what in MENA countries. Energy Econ 48:242–252. 10.1016/j.eneco.2015.01.008
Ozturk I Ullah S Does digital financial inclusion matter for economic growth and environmental sustainability in OBRI economies? An empirical analysis Resour Conserv Recycl 2022 185 106489 10.1016/j.resconrec.2022.106489
Ozturk I, Ullah S (2022) Does digital financial inclusion matter for economic growth and environmental sustainability in OBRI economies? An empirical analysis. Resour Conserv Recycl 185:106489. 10.1016/j.resconrec.2022.106489
Pablo-Romero MdP De Jesús J Economic growth and energy consumption: the energy-environmental Kuznets curve for Latin America and the Caribbean Renew Sustain Energy Rev 2016 60 1343 1350 10.1016/j.rser.2016.03.029
Pablo-Romero MdP, De Jesús J (2016) Economic growth and energy consumption: the energy-environmental Kuznets curve for Latin America and the Caribbean. Renew Sustain Energy Rev 60:1343–1350. 10.1016/j.rser.2016.03.029
Pata UK Dam MM Kaya F How effective are renewable energy, tourism, trade openness, and foreign direct investment on CO2 emissions? An EKC analysis for ASEAN countries Environ Sci Pollut Res 2023 30 6 14821 14837 10.1007/s11356-022-23160-z
Pata UK, Dam MM, Kaya F (2023) How effective are renewable energy, tourism, trade openness, and foreign direct investment on CO2 emissions? An EKC analysis for ASEAN countries. Environ Sci Pollut Res 30(6):14821–14837. 10.1007/s11356-022-23160-z
Pedroni P Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis Economet Theor 2004 20 3 597 625 10.1017/S0266466604203073
Pedroni P (2004) Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Economet Theor 20(3):597–625. 10.1017/S0266466604203073
Pedroni P (2001) Fully modified OLS for heterogeneous cointegrated panels. In: Baltagi BH, Fomby TB, Carter HR (ed) Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, Leeds 15:93–130. 10.1016/S0731-9053(00)15004-2
Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Available at SSRN 572504. 10.2139/ssrn.572504
Prempeh KB The role of economic growth, financial development, globalization, renewable energy and industrialization in reducing environmental degradation in the economic community of West African States Cogent Econ Finance 2024 12 1 2308675 10.1080/23322039.2024.2308675
Prempeh KB (2024) The role of economic growth, financial development, globalization, renewable energy and industrialization in reducing environmental degradation in the economic community of West African States. Cogent Econ Finance 12(1):2308675. 10.1080/23322039.2024.2308675
Prempeh KB Yeboah SA Danso FK Frimpong JM Banking sector development and environmental degradation in the Economic Community of West African States: do technology effects matter? Future Bus J 2023 9 1 106 10.1186/s43093-023-00286-1
Prempeh KB, Yeboah SA, Danso FK, Frimpong JM (2023) Banking sector development and environmental degradation in the Economic Community of West African States: do technology effects matter? Future Bus J 9(1):106. 10.1186/s43093-023-00286-1
Rasoulinezhad E Taghizadeh-Hesary F Taghizadeh-Hesary F How is mortality affected by fossil fuel consumption, CO2 emissions and economic factors in CIS region? Energies 2020 13 9 2255 10.3390/en13092255
Rasoulinezhad E, Taghizadeh-Hesary F, Taghizadeh-Hesary F (2020) How is mortality affected by fossil fuel consumption, CO2 emissions and economic factors in CIS region? Energies 13(9):2255. 10.3390/en13092255
Raza MY Tang S Nuclear energy, economic growth and CO2 emissions in Pakistan: evidence from extended STRIPAT model Nucl Eng Technol 2024 10.1016/j.net.2024.02.006
Raza MY, Tang S (2024) Nuclear energy, economic growth and CO2 emissions in Pakistan: evidence from extended STRIPAT model. Nucl Eng Technol. 10.1016/j.net.2024.02.006
Sadorsky P Financial development and energy consumption in Central and Eastern European frontier economies Energy Policy 2011 39 2 999 1006 10.1016/j.enpol.2010.11.034
Sadorsky P (2011) Financial development and energy consumption in Central and Eastern European frontier economies. Energy Policy 39(2):999–1006. 10.1016/j.enpol.2010.11.034
Said R Acheampong AO Financial inclusion and energy poverty reduction in sub-Saharan Africa Util Policy 2023 82 101567 10.1016/j.jup.2023.101567
Said R, Acheampong AO (2023) Financial inclusion and energy poverty reduction in sub-Saharan Africa. Util Policy 82:101567. 10.1016/j.jup.2023.101567
Said R Bhatti MI Hunjra AI Toward understanding renewable energy and sustainable development in developing and developed economies: a review Energies 2022 15 15 5349 10.3390/en15155349
Said R, Bhatti MI, Hunjra AI (2022) Toward understanding renewable energy and sustainable development in developing and developed economies: a review. Energies 15(15):5349. 10.3390/en15155349
Said R, Acheampong AO (2024) Achieving carbon-neutrality in MENA countries: does financial inclusion matter?. J Environ Dev 10704965231225780. 10.1177/10704965231225780
Said R, Bhatti I, Mancuso J (2023) The impact of financial inclusion on CO2 emissions. Available at SSRN: https://ssrn.com/abstract=4543250 or.10.2139/ssrn.4543250
Saidi K Omri A The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries Environ Res 2020 186 109567 10.1016/j.envres.2020.109567 32361260
Saidi K, Omri A (2020) The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries. Environ Res 186:109567. 10.1016/j.envres.2020.10956732361260
Said R (2024) The effects of education and financial development on energy poverty reduction in latin America. The Journal of Environment & Development 10704965241246708. 10.1177/10704965241246707
Sapkota P Bastola U Foreign direct investment, income, and environmental pollution in developing countries: panel data analysis of Latin America Energy Econ 2017 64 206 212 10.1016/j.eneco.2017.04.001
Sapkota P, Bastola U (2017) Foreign direct investment, income, and environmental pollution in developing countries: panel data analysis of Latin America. Energy Econ 64:206–212. 10.1016/j.eneco.2017.04.001
Sarma M Pais J Financial inclusion and development J Int Dev 2011 23 5 613 628 10.1002/jid.1698
Sarma M, Pais J (2011) Financial inclusion and development. J Int Dev 23(5):613–628. 10.1002/jid.1698
Shahbaz M Li J Dong X Dong K How financial inclusion affects the collaborative reduction of pollutant and carbon emissions: the case of China Energy Econ 2022 107 105847 10.1016/j.eneco.2022.105847
Shahbaz M, Li J, Dong X, Dong K (2022) How financial inclusion affects the collaborative reduction of pollutant and carbon emissions: the case of China. Energy Econ 107:105847. 10.1016/j.eneco.2022.105847
Sharif A Raza SA Ozturk I Afshan S The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations Renew Energy 2019 133 685 691 10.1016/j.renene.2018.10.052
Sharif A, Raza SA, Ozturk I, Afshan S (2019) The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations. Renew Energy 133:685–691. 10.1016/j.renene.2018.10.052
Sharif A, Mehmood U, Tariq S, Haq ZU (2023) The role of financial inclusion and globalization toward a sustainable economy in ASEAN countries: evidence from advance panel estimations. Environ Dev Sustain 1–18. 10.1007/s10668-023-03145-9
Singh AB Tandon P Jasuja D Does financial inclusion spur carbon emissions in India: an ARDL approach Manag Environ Qual: An Int J 2023 34 2 511 534 10.1108/MEQ-04-2022-0102
Singh AB, Tandon P, Jasuja D (2023) Does financial inclusion spur carbon emissions in India: an ARDL approach. Manag Environ Qual: An Int J 34(2):511–534. 10.1108/MEQ-04-2022-0102
Stern DI The rise and fall of the environmental Kuznets curve World Dev 2004 32 8 1419 1439 10.1016/j.worlddev.2004.03.004
Stern DI (2004) The rise and fall of the environmental Kuznets curve. World Dev 32(8):1419–1439. 10.1016/j.worlddev.2004.03.004
Stern DI Common MS Is there an environmental Kuznets curve for sulfur? J Environ Econ Manag 2001 41 2 162 178 10.1006/jeem.2000.1132
Stern DI, Common MS (2001) Is there an environmental Kuznets curve for sulfur? J Environ Econ Manag 41(2):162–178. 10.1006/jeem.2000.1132
Tamazian A Chousa JP Vadlamannati KC Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries Energy Policy 2009 37 1 246 253 10.1016/j.enpol.2008.08.025
Tamazian A, Chousa JP, Vadlamannati KC (2009) Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries. Energy Policy 37(1):246–253. 10.1016/j.enpol.2008.08.025
Tao R Su C-W Naqvi B Rizvi SKA Can Fintech development pave the way for a transition towards low-carbon economy: a global perspective Technol Forecast Soc Chang 2022 174 121278 10.1016/j.techfore.2021.121278
Tao R, Su C-W, Naqvi B, Rizvi SKA (2022) Can Fintech development pave the way for a transition towards low-carbon economy: a global perspective. Technol Forecast Soc Chang 174:121278. 10.1016/j.techfore.2021.121278
Tenaw D Beyene AD Environmental sustainability and economic development in sub-Saharan Africa: a modified EKC hypothesis Renew Sustain Energy Rev 2021 143 110897 10.1016/j.rser.2021.110897
Tenaw D, Beyene AD (2021) Environmental sustainability and economic development in sub-Saharan Africa: a modified EKC hypothesis. Renew Sustain Energy Rev 143:110897. 10.1016/j.rser.2021.110897
Twumasi Y Relationship between CO2 emissions and renewable energy production in the United States of America Arch Curr Res Int 2017 7 1 1 12 10.9734/ACRI/2017/30483
Twumasi Y (2017) Relationship between CO2 emissions and renewable energy production in the United States of America. Arch Curr Res Int 7(1):1–12. 10.9734/ACRI/2017/30483
Van LT-H Vo AT Nguyen NT Vo DH Financial inclusion and economic growth: an international evidence Emerg Mark Financ Trade 2021 57 1 239 263 10.1080/1540496X.2019.1697672
Van LT-H, Vo AT, Nguyen NT, Vo DH (2021) Financial inclusion and economic growth: an international evidence. Emerg Mark Financ Trade 57(1):239–263. 10.1080/1540496X.2019.1697672
Wang X Guan J Financial inclusion: measurement, spatial effects and influencing factors Appl Econ 2017 49 18 1751 1762 10.1080/00036846.2016.1226488
Wang X, Guan J (2017) Financial inclusion: measurement, spatial effects and influencing factors. Appl Econ 49(18):1751–1762. 10.1080/00036846.2016.1226488
Wang Z Jebli MB Madaleno M Doğan B Shahzad U Does export product quality and renewable energy induce carbon dioxide emissions: evidence from leading complex and renewable energy economies Renew Energy 2021 171 360 370 10.1016/j.renene.2021.02.066
Wang Z, Jebli MB, Madaleno M, Doğan B, Shahzad U (2021) Does export product quality and renewable energy induce carbon dioxide emissions: evidence from leading complex and renewable energy economies. Renew Energy 171:360–370. 10.1016/j.renene.2021.02.066
Wang X Wang X Ren X Wen F Can digital financial inclusion affect CO2 emissions of China at the prefecture level? Evidence from a spatial econometric approach Energy Econ 2022 109 105966 10.1016/j.eneco.2022.105966
Wang X, Wang X, Ren X, Wen F (2022a) Can digital financial inclusion affect CO2 emissions of China at the prefecture level? Evidence from a spatial econometric approach. Energy Econ 109:105966. 10.1016/j.eneco.2022.105966
Wang Z Pham TLH Sun K Wang B Bui Q Hashemizadeh A The moderating role of financial development in the renewable energy consumption-CO2 emissions linkage: the case study of Next-11 countries Energy 2022 254 124386 10.1016/j.energy.2022.124386
Wang Z, Pham TLH, Sun K, Wang B, Bui Q, Hashemizadeh A (2022b) The moderating role of financial development in the renewable energy consumption-CO2 emissions linkage: the case study of Next-11 countries. Energy 254:124386. 10.1016/j.energy.2022.124386
World Bank (2018) World Bank financial inclusion is a key enabler to reducing poverty and boosting prosperity World Bank, Washington DC (2018). Retrieved from http://www.worldbank.org/en/topic/financialinclusion/overview. Google Scholar
World Bank (2023) World development indicators. InEN.ATM,CO2E.PC.https://data.worldbank.org/indicator/
Yang A, Yang M, Zhang F, Kassim AAM, Wang P (2024) Has digital financial inclusion curbed carbon emissions intensity? Considering technological innovation and green consumption in China. J Knowl Econ 1–30. 10.1007/s13132-024-01902-3
Yao S Zhang S Zhang X Renewable energy, carbon emission and economic growth: a revised environmental Kuznets Curve perspective J Clean Prod 2019 235 1338 1352 10.1016/j.jclepro.2019.07.069
Yao S, Zhang S, Zhang X (2019) Renewable energy, carbon emission and economic growth: a revised environmental Kuznets Curve perspective. J Clean Prod 235:1338–1352. 10.1016/j.jclepro.2019.07.069
Zhou Y Zhang C Li Z The impact of digital financial inclusion on household carbon emissions: evidence from China J Econ Struct 2023 12 1 2 10.1186/s40008-023-00296-w
Zhou Y, Zhang C, Li Z (2023) The impact of digital financial inclusion on household carbon emissions: evidence from China. J Econ Struct 12(1):2. 10.1186/s40008-023-00296-w
