Navigating the asymmetric influence of financial inclusion on environmental sustainability: Dynamic role of energy consumption and human capital

Abstract

Due to the adoption of more financial inclusion (FI), energy utilization with sustainability became a challenge for world economies. Our research examines whether FI symmetrically and asymmetrically affects environmental sustainability in Pakistan. Six proxies are indexed for the FI data through principal component analysis (PCA). In the present research explanatory variables are, energy usage, industrialization, urbanization, and human capital during from 1975 to 2018. Our study engaged the Stochastic Impact by Regression on Population, Affluence, and Technology Approach (STIRPAT). Additionally, the econometric strategy is adopted for the empirical analysis to acquire the symmetric and asymmetric outcomes. The empirical result validates the asymmetric association of FI and carbon dioxide (CO₂) emanations in short and long lags. Besides, fossil energy utilization, industrialization, and urbanization smoothen the path of environmental pollution. However, human capital significantly aids in reducing carbon pollution in the short and long terms. The policy makers can practically implement the research to utilize FI effectively to improve environmental sustainability and develop policies that discourage fossil energy utilization. Moreover, we pointed out the alarming situation of dealing with harmful emissions from urbanization and industrialization.

Keywords

Financial inclusion fossil energy consumption human capital urbanization industrialization

Introduction

The idea of financial inclusion (FI) was raised in the early 1990s when the term financial exclusion focused on the inadequate number of bank branches, with less accessibility to bank loans becoming a considerable hindrance in the financial sector development.¹ In the past, financial exclusion highlighted the obstacles to achieving the primary financial facilities from the perspective of both producers (suppliers) and consumers (demand).² The concept of FI works in parallel with society's demand and supply of credits and loans.³ That's why poverty is a significant factor in the growth of FI because if the majority of the citizens of an economy are beneath the poverty line, they do not hold a sufficient amount of financial savings to deposit in the banks.⁴ In the same way, if a country's economic position is gradually enhanced, then the contribution of investments and the pace of financial activities result in low-slung demand and supply for FI. In such a situation, the saving habit shifts from poor people to only wealthy individuals causing the push upwards in the financial demand and supply.^2,5,6

State Bank of Pakistan defines FI as “the ease of entrance to financial services for firms and individuals to provide quality payments, promote saving habits, provide accessible credits and insurance facilities with the great level of dignity and fairness”.⁷ FI, in simple words, provides the comfort of financial services in terms of more banks, insurance, and leasing facilities to firms and individuals to promote saving habits.⁸ Furthermore, FI stabilizes individuals and businesses in the economy by providing stress-free availability to financial services, insurance, and credit facilities. FI supports the challenges in terms of economic growth by reducing the distances between the debtors and investors with a high level of transparency.⁹ The government of Pakistan stressed the “National Financial Inclusion Strategy” SBP ⁷ plan in the year 2018, which provides easy access to loans in various sectors such as energy, agriculture, and small and medium enterprises.

Over the previous few decades, FI and environmental management through energy consumption have become a highly debatable concern for policy makers and researchers.^8,10–16 However, the role of FI in environmental sustainability is theoretically discussed from both perspectives, that is, positive and negative dimensions.^12,17–19 The enhancement of FI in some economies brings cleaner technologies and easy access to renewable energy technologies that are environmentally friendly.^12,20 The government provides interest-free loans and subsidies through FI to spread the networks of cleaner technologies. On the other hand, increased FI degrades the environmental sustainability in several economies.⁹ The ease of loans, credit, and leasing habits gives more individuals access to air conditions, automobiles, electronic appliances, and industrial pollution that requires more energy needs and produces carbon dioxide (CO₂) emanations.^17,18,21,22

In many developing nations, the primary source of energy usage is based on fossil energy resources such as natural gas, and oil that produce hazardous environmental emanations.^23,24 Furthermore, the International Energy Agency (IEA)²⁵ declared that the world climate is already suffering from CO₂, methane, nitrous, and other related emissions. Conforming to the Economic Survey of Pakistan (ESP),²⁶ climate vulnerability in Pakistan is already at stake due to the fossil energy convention from manufacturing, transport, energy production, and household activities. The percentage fractions stated by Environment Protection Agency (EPA)²⁷ for the greenhouse gases (GHGs) in Pakistan are CO₂discharge by fossil petroleum use and industrial fossil emissions (65%), carbon emissions through agricultural activities 11%, methane gas 16%, nitrous oxide productions 6%, and other F-gases encompassed of 2%. That's why the government of Pakistan is seriously concerned about a cleaner environment and has launched several programs.²³

In parallel with other factors, industrialization plays a tremendous role in economic development and environmental management. The industrial division is the fundamental component for improving the economic activities of any economy. Moreover, industrialization divulges a multiplier surge in the economy of the nation. Pakistan's manufacturing sector adds 13.59% to the national income.²⁶ Industrialization carries the highest percentage ratio of carbon production that suffers environmental sustainability.²⁸ Among the previous year's evaluation, Pakistan's economy was based on agricultural growth that is now shifting toward an industrial-led economy. Eventually, industrial development requires more energy production and utilization and yields more carbon discharges, seriously affecting environmental performance.²⁹ Industrial development requires heavy consumption of the country's natural resources (energy, financial, human, etc.). That is why industrialization and urbanization can raise economic improvement and a badly polluted environment. Urbanization and industrialization state a cause-and-effect relationship. Urbanization is a global sensation that moves more than 50% of the population into urban areas.³⁰ Either developed economies, the developing nations are highly inclined toward urbanization. The law permits every person to move independently in the whole country, which causes urbanization. Most unemployed individuals migrate from rural areas to urban locations, do jobs, and set up new business activities that consume more energy sources and badly pollute the environment.

Furthermore, despite the tremendous role human capital plays as a backbone in achieving environmental sustainability. Human capital enables more educated individuals to exact knowledge about a cleaner and more sustainable environment.³¹ Over the past decade, human capital has become a debatable topic for policy makers, academics, and researchers.^32–38 However, there is an utmost need to grab more human capital in terms of skilled workers in the manufacturing sector and household individuals that are more concerned about the reduction in conventional energy sources and motivated toward environment-friendly solutions.³⁹ Human capital can be made more beneficial by skilled workers, energy-related training, competency, knowledge, education, and skills that give environment-friendly productions.³¹

The present research aims to investigate the symmetric and asymmetric role of FI on the environmental sustainability of Pakistan. Further, the explanatory variables are energy consumption, industrialization, urbanization, and human capital. Despite the prior literary studies on FI in Pakistan are highly concerned about the banking and economic sector.^{3,5,6,10,40,41} While a few studies just focused on provincial-level analysis for FI and environmental sustainability.^17,18,42 Thus estimation of FI, economic growth, and environmental sustainability for a single province does not clear the actual picture of the related effects; that's why there is an utmost need to analyze the national level data to get the problem's big picture. So, the issues mentioned above are highly motivated to grab in depth about the FI and environmental sustainability with energy consumption for the entire nation data.

Furthermore, the present study underwrites the literature review in several ways in the above research scenario. First, our research focused on the symmetric dynamic estimates and asymmetric estimations of FI with CO₂ discharges. Second, the present study inspected the aggregate data for Pakistan apart from previous research that only focused on provincial data estimates. Further, a principal component analysis (PCA) index is developed to evaluate six proxies of FI. Third, a modern methodology belonging to Shin et al.⁴³ is investigated to grab the asymmetric dynamics of FI. Fourth, apart from the traditional unit test, we applied the structural break analysis to get the structural break years. Fifth, our research formulated practical implications for government personnel and policy makers.

Literature review

The present research highlights FI’s symmetric and asymmetric effect on CO₂ emanation under explanatory variables such as energy utilization, human capital, industrialization, and urbanization. The literature review critically evaluates the variables under discussion from global perspectives and develops deep insights into the current study.

Theoretical background

The dynamic link between FI and CO₂ discharges has become word of mouth and a highly debatable topic for researchers and policy makers in global economies.^2,19,44 That is why FI's impacts became a challenge and opened a new discussion on Sustainable Development Goals (SDGs). For instance, FI insists the residents of a country get more loans for luxury life items such as modern cars, washing machines, and electrical appliances that ultimately produce higher carbon emanations in the environment.^8,45 Furthermore, in developing countries, enhancing financial ease boosts energy demand in the industrial, transportation, manufacturing, and various other sectors that create a trade-off between economy and environment.^23,24 The financial ease creates easy access to fossil energy consumptions such as financial leasing from banks for automobile loans, small business loans, air conditions, industrial machinery loans and producing more environmental carbon emissions.⁹

Conforming to the research by Jensen,⁴⁶ FI badly affects the economies that are inherently based on fossil energy consumption for electricity production and energy needs. Such economies face twofold outcomes, on one hand, they import petroleum oil from other countries and pay heavy duties for energy imports that badly affect the economic conditions of that economy.⁴⁶ On the other hand, in economies with primary consumption of petroleum oil and fossil, energy consumption badly degrades the environment at the rate of increasing carbon emissions.⁴⁷ Conversely, the financial ease tremendously contributes to reducing CO₂ emanations by adopting green technologies that produce cleaner technologies in the country.⁴⁸ The government interventions for improvements and renewable energy motivate the nationals toward renewable energy sources that are ultimately achieved through FI.^20,49

FI and CO₂ emanations

The dynamic impact of FI and CO₂pollution has become an extremely debatable topic these days. That's the reason that several authors inspected the linkage between FI and their trade-off with environment concerns like performance and sustainability.^{2,4,11,19,44,50}

Le et al.⁹ research focuses on the link between carbon emissions and FI for a panel of 31 Asian kingdoms from 2004 to 2014 under a cross-sectional dependence approach. The outcomes revealed FI, energy consumption, and industrialization became frontrunners in environmental degradation. Besides, the panel findings are less reliable than the individual country findings. Qin et al.⁴⁴ evaluated the association between FI and CO₂ emanations for the E7 countries from 2004 to 2016 under the quantized regression technique with Kao and Johansen panel technique. The results determined that FI escalates carbon emanations while renewable energy strides toward cleaner production. Zaidi et al.⁸ inspected the dynamics of FI and CO₂discharge for the Organization of Economic Co-operation and Development (OECD) economies under the cross-sectional dependence autoregressive distributive lag (CS-ARDL) technique with the dynamic common correlated effects estimators (DCCE). The findings validated a significant linkage between FI and carbon products for the OECD countries.

Furthermore, the research inspected by Amin et al.¹⁹ estimated the connection between FI, foreign direct investment (FDI), gross domestic product (GDP), and carbon production from 1998 to 2019 among the South Asian economies. The conclusion for carbon emissions exhibited positive results with FI. Moreover, FDI and GDP also surge environmental degradation while trade openness aids long-run consequences for environmental improvement. Thus, most of the prior studies applied panel data analysis techniques to investigate the effects of FI on environmental sustainability, which seems unsuitable for studying a group of countries. That's why our research grabs attention toward the single country empirical analysis with the most updated econometric approach for FI and environmental sustainability.

Human capital and carbon emanations

The dynamic action of human capital advocates an economy toward environmental sustainability through cleaner productions and cleaner societies. Globally, the connection between human capital and environmental sustainability became an arguable topic for the researchers and policy makers such as Ullah et al., Bano et al., and Mahmood et al.^17,18,31,51 in Pakistan, Ahmed et al. and Hao et al.^34,35,37 in G7 economies, Ahmed and Wang³² in India, Zafar et al.⁵² belongs to the United States, Ahmed et al. and Sarkodie et al.^34,35,53 for China, Saleem³⁶ for Brazil Russia India China South Africa (BRICS) economies, Wang et al.^13–16 for a panel of 208 countries, and Haini⁵⁴ for Association of Southeast Asian Nations (ASEAN) countries. However, mixed findings are obtained by the prior research for the group and individual economies analysis.

The research conducted by Jena et al.⁵⁵ focused on the top Asian pollution emitters (China, Japan, and India). The research specifically estimated the human capital, energy consumption, and CO₂emissions using the panel data estimates from 1980 to 2016. The findings suggested a negative link between human capital and environmental pollution for top Asian emitters. Further, Bano et al. ³¹ asserted the connection between Pakistan's human capital, energy consumption, and CO₂production from 1971 to 2014 under the autoregressive distributive lag (ARDL) and granger causality approach. The findings proved that human capital negatively affected CO₂ emanations in the long term. Hao et al. ³⁷ explored the dynamic impact of human capital on the green environment for G7 countries from 1991 to 2017 using the cross-sectional dependence technique. The outcome supports that human capital aids in achieving a green environment. Ahmed and Wang ³² examined the role of human capital and economic growth with CO₂ emissions for India for the period 1971 to 2014 under ARDL estimation. The results determined the inverse linkage between human capital and environmental sustainability.

In the same way, Saleem ³⁶ investigated the involvement of human capital, energy usage, and economic growth in carbon emissions for BRICS countries from 1991 to 2014 using panel data estimations. The conclusion indicated a negative connection between human capital and the environment. Additionally, Wang et al. ^13–16 investigated the panel of 208 countries for the link among renewable energy, human capital, and environmental pollution using the generalized method of movement. The findings showed a negative association between human capital, renewable energy, and environmental degradation. Furthermore, several studies proved the negative link between human capital and CO₂emanations, including Sarkodie et al. ⁵³ in China, Haini ⁵⁴ in ASEAN countries, and Mahmood et al. ⁵¹ in Pakistan.

Industrialization and carbon emanations

Industrialization supports an essential part of economic growth and highly pollutes the environment. An ample of researchers and policy makers are critically engrossed in the effects of industrialization on environmental sustainability Ahmad and Zhao, Wang and Su, Gu et al., and Meng et al.^28,56–58 for China, Mahmood et al. ²⁹ for Saudi Arabia, Pata ⁵⁹ for Turkey, and Hanif et al.^5,6 for Pakistan.

Ahmad and Zhao ²⁸ scrutinized the connection between urbanization and industrialization with carbon emanations for the period from 2000 to 2016 for the 30 provinces of China through an augmented mean group (AMG) estimator. The outcomes exposed that both urbanization and industrialization positively affect environmental sustainability. Further, the explanation determined that fossil energy consumption points toward environmental degradation. In addition, Mahmood et al. ²⁹ explored the dynamic of industrialization with carbon dioxide discharges in Saudi Arabia from 1968 to 2014 using the asymmetric cointegration technique. The results proved the asymmetric relationship between industrialization and carbon emissions. More industrialization badly affects the environment. Further, Ali et al. ⁶⁰ investigated the link between industrialization and carbon discharges in Pakistan from 1981 to 2017 under the ARDL model with vector error correction term (ECT). The results advocate that industrialization determined a significantly positive correlation with Pakistan's CO₂discharges in the short and long terms. Moreover, Le et al. ⁹ analyzed the significant concept of industrialization and carbon emissions and originated a positive association between environmental degradation and industrial pollution in Asian countries from 2004 to 2014. Thus, historical analysis of previous research recommends that industrialization is crucial to discuss environmental sustainability through the dynamic econometric methods.

Urbanization and carbon emanations

The rapid increase in the country's population causes urbanization that requires more resources such as industries, employment opportunities, energy sources, automobiles, and educational institutes, and ultimately pollutes the environment. Hence, a relationship between urbanization and carbon emissions attained greater attention through literary studies for the group and individual economies. The research conducted by Dong ⁶¹ for 120 economies, Ahmad et al. ⁶² intended for 24 OECD republics, Arshad et al. ⁶³ for Asian regions, Ahmed et al. ^34,35 used for G7 kingdoms, Saida and Kais ⁶⁴ for sub-Saharan African economies, You and Lv ⁶⁵ for the panel of 83 countries, Ahmad et al.^66,67 for China, Aung et al. ⁶⁸ for Myanmar, Ali et al. and Dong ^30,69 for Pakistan, Mahmood et al. ²⁹ for Saudi Arabia, and Pata ⁵⁹ for Turkey.

Furthermore, Ali et al. ³⁰ determined the influence of urbanization, and economic development, on the CO₂ releases from 1972 to 2014 for Pakistan by the ARDL cointegration method. The results designated a positive link between urbanization and carbon emission. Ahmad and Zhao ²⁸ considered the dynamics of urbanization with environmental sustainability in China from 2000 to 2016 through AMG estimation. The findings support a positive linkage between the environment and urbanization. Mahmood et al. ²⁹ investigated the association between urbanization and carbon emanations in Saudi Arabia from 1968 to 2014. The results stated a positive connection between urbanization and the environment. Furthermore, Pata ⁵⁹ inspected the positive bond between urbanization and carbon discharges from 1974 to 2013 in Turkey. Ahmad et al. ⁶² administered a link between urbanization and CO₂ pollution in OECD nations from 1993 to 2014. The outcome validated a positive association between urbanization and carbon production in the OECD countries. In addition¹⁵ governed the connection between environmental pollution and urbanization for the 53-panel countries using the nonlinear panel data technique. The results suggested a positive linkage between urbanization and environmental degradation.

Energy utilization and carbon emanations

Fossil energy consumption plays a tremendous role in the deterioration of environmental sustainability. An ample of researchers and policy makers critically analyzed the drawbacks of fossil energy consumption. Research conducted by Tanveer et al. and Lin et al.^{23,24,70–72} in Pakistan, Wang and Su, Gu et al., and Mujtaba and Jena^56,57,73 for China, Ahmad and Du ⁷⁴ for Iran, Salman et al. ⁷⁵ for a panel of South Korean, Indonesia, as well as Thailand, Zakaria and Bibi and Sattar et al.^76–78 in South Asian economies, Hussain et al. ⁷⁹ for Belt and Road Initiative (BRI) economies, Ahmad et al. and Akalpler and Hove^80,81 for the Indian economy, Raza et al. and Udom et al.^40,82 for Malaysia, Shahbaz et al. ⁸³ for Japan, Marques, Marques⁸⁴ in OECD world countries, Khurram et al. ⁸⁵ in Kuwait, Mujtaba et al. ⁸⁶ for a panel of five regions, and Munir et al. ⁸⁷ estimated for ASEAN nations.

Tanveer et al. ^23,24 focused on the central issue of symmetric and asymmetric shocks for fossil energy utilization in Pakistan from 1985 to 2018 under the ARDL and nonlinear autoregressive distributive lag (NARDL) approach. The research found that fossil energy usage badly pollutes the manufacturing sector of Pakistan. In parallel, Lin and Ahmad ⁷⁰ assessed Pakistan's total fossil energy utilization (gas, coal, and oil) using the logarithm means divisions index under the usual business scenario from 1971 to 2011. The findings advocated that the entire range of fossil energy consumption damages the environment. Moreover, in developing countries like Pakistan, primary energy consumption is based on fossil fuels like petroleum. Further, Mujtaba and Jena⁷³ investigated the impact of oil prices and energy consumption on China's CO₂emanations from 1986 to 2014 by the NARDL approach. The consequences support positive energy usage and carbon production.

The research conducted by Acheampong et al. ⁸⁸ evaluated the connection of fossil energy sources with CO₂ gas discharge for the sub-Saharan African economies. They found a positive link among them from 1980 to 2015 under the fixed and random effects estimations approach. Ahmad and Du ⁷⁴ governed a positive connection between energy use and CO₂discharges in Iran from 1971 to 2011. Additionally, Sattar et al. ^77,78 determined the role of energy consumption in environmental sustainability for BRI economies. They found that energy consumption is the main culprit in environmental pollution. Hence, much of the research has proved the positive association between fossil energy usage and CO₂ by Zhang and Wang ⁸⁹ intended for Pakistan, Shahbaz et al. ⁹⁰ on behalf of the MENA economies, and finally, Muhammad et al. ⁴⁰ estimated for the Malaysian economy.

The literature review summarized that energy consumption, urbanization, industrialization, and FI play a prominent role in degrading the environment. Furthermore, slight attention is grabbed to the effects of FI on environmental sustainability in Pakistan. However, much devotion is grasped to Pakistan's banking sector and FI. Our research advocated filling the space by estimating the symmetric and asymmetric association between FI and environmental sustainability in Pakistan.

Models specifications, data sources, and research methodology

The research applies the motivating force behind FI and CO₂production through the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. For empirical inspection, the dynamics of the linear (symmetric) approach are evaluated through the ARDL. Despite asymmetric association is determined through the NARDL approach. The STIRPAT model is an extension of the IPAT model initiated by Ehrlich and Holdren.⁹¹ Besides, the IPAT model indicates the influence, population, affluence, and technology. The IPAT approach gives influences socioeconomic linkage with environmental sustainability. However, the broad range STIRPAT approach formulated by Dietz and Rosa ⁹² provides a stochastic association of environmental sustainability with the affluence of population and technology.

I_{t} = δ P_{t}^{k} A_{t}^{l} T_{t}^{m} e_{t}

(1)

where equation (1) δ donates the statistical intercept value, k, l, m indicates the coefficient values, and t expresses the period of the model. Furthermore, I represent the environmental factor, that is, CO₂ production. P shows the population that is the urbanization factor in our study, A is the affluence that is energy affluence, and T donates the technology of the industries. According to (York et al., 2003),⁹³ STIRPAT intuition holds many advantages over the IPAT technique. First, it can be applied to the time series data, panel, and cross-sectional dependence frameworks. Second, it involves the stochastic approach that supports the verification of the hypothesis. Third, it encompasses the environmental sustainability drivers of population, affluence, and technology. Several past studies applied the STIRPAT approach to environmental factors.^94,95 Moreover, the STIRPAT model is also feasible for linking FI and other environmental drivers such as urbanization, human capital, and industrialization.⁹

To gain in-depth understanding of the dynamic associations between factors. The appropriate variables for Pakistan are CO₂ emanations, FI, industrialization, urbanization, and human capital. The core independent variables of the study are FI, urbanization, and fossil energy utilization, while CO₂ radiation is the dependent variable. For FI, fives proxies are combined with the PCA factor analysis technique.⁹⁶ It is quite beneficial to use the PCA that covers a large number of variables under a single index.⁹⁷ Furthermore, the dynamic estimations indicate the degree of the movement of the variables not only for the current period but also for covering the data from the previous years. On the other hand, static regression only estimates the current year's data. That is the fact our study provides more information by the dynamic regression estimates. The research articulated the time series data from 1975 to 2018 for Pakistan. Concerning the data sources, CO₂ radiations, industrialization, and urbanization are obtained from the World Bank database,⁹⁸ primary energy utilization grabbed from British Petroleum Statistics,⁹⁹ human capital data from Penn World Table PWT 10.0, and the FI data is collected from the International Monetary Fund database. The trends of the selected variables are graphically expressed in Figures A1–A5 presented in the Appendix. Moreover, Table 1 expresses the variable's descriptions, signs, data sources, and measuring units.

Table 1.

Variable, notations, descriptions, and data origins.

Variable	Notations	Descriptions	Source	Expected sign
Carbon Emission	CRBN	CO₂ emissions (metric tons per capita)	WDI
Human Capital	HUC	Human Capital Index	PWT 10.0	Negative
Industrialization	INDUS	Industry (including construction), value addition (annual % growth)	WDI	Positive
Urbanization	URBAN	Urban population growth (annual %)	WDI	Positive
Energy Consumption	ENRC	Primary energy: Consumption	BPS	Positive
Financial Inclusion	FINC1	Private credits by deposit money banks to GDP (%)	IMF	Positive
Financial Inclusion	FINC2	Deposit money banks’ assets to GDP (%)	IMF	Positive
Financial Inclusion	FINC3	Deposit money bank assets to deposit money bank assets and central bank assets (%)	IMF	Positive
Financial Inclusion	FINC4	Liquid liabilities to GDP (%)	IMF	Positive
Financial Inclusion	FINC5	Central bank assets to GDP (%)	IMF	Positive
Financial Inclusion	FINC6	Financial system deposits to GDP (%)	IMF	Positive

CO₂: carbon dioxide; GDP: gross domestic product.

Econometric strategy with mathematical modeling

The basic mathematical model is represented in equation (2) for the econometric estimations.

C R B N = f (F I N C, H U C, I N D U S, U R B N, E N R C)

(2)

CRBN donates the CO₂ productions that work as dependent variables in the equation. While FINC indicates the FI index, HUC represents the human capital, INDUS shows industrialization, URBN expresses urbanization, and ENRC illustrates primary energy utilization.

C R B N_{t} = ω_{o} + ω_{1} F I N C_{t} + ω_{2} H U C_{t} + ω_{3} I N D U S_{t} + ω_{4} U R B N_{t} + ω_{5} E N R C_{t} + μ_{t}

(3)

where ω_o indicates the intercept value and (ω_{1 −}ω₅) represents the coefficient values and μ shows the statistical residual term in equation (3).

Unit root statistical analysis

The ARDL begins with the validation of cointegration among the variables. The unit root statistical test can validate the ordering of cointegration that the variables showed stationary either at level I (0) or at a statistical value of first difference I (1). However, conforming to the criterion by Pesaran et al. ,¹⁰⁰ the model pertains to stationary values at the level or at the first variance and may be of mixed command of cointegration. While the stationary value at the second difference is termed an invalid model.

ARDL cointegration bound testing approach

The ARDL bounds approach inspects the dynamic linkage of variables by estimating the cointegration under the long-term and short-term dynamics. However, the static regressions only focus on the current period regression values. In fact, the ARDL method is quite beneficial for the reasons below. First, it provides the cointegration values that work on the different lag year data apart from the conventional methods that only concern the current year values. Second, it is convenient for small sets of data ranges. Third, it is quite flexible and works for both at level or first difference stationary values and mixed sequence values. Fourth, the interpretation and estimation of the ARDL equation belong to a single equation that is easy to understand. Fifth, it analyzes the unbiased assessments for long and short-run parameters. Sixth, it appropriately addresses the endogeneity and autocorrelations issues. Focusing on the studies by ,Akalpler and Hove and Abbasi and Riaz^81,101 the ARDL equation (4) is given as

\begin{aligned} Δ C R B N_{t} = ω_{o} + \sum_{i = 1}^{l} ω_{1 i} Δ C R B N_{t - 1} + \sum_{i = 0}^{p} ω_{2 i} Δ F I N C_{t - i} + \sum_{i = 0}^{q} ω_{3 i} Δ H U C_{t - i} + \sum_{i = 0}^{r} ω_{4 i} I N D U S_{t - i} \\ + \sum_{i = 0}^{s} ω_{5 i} Δ U R B N_{t - i} + \sum_{i = 0}^{u} ω_{6 i} Δ E N R C_{t - i} + D_{1} C R B N_{t - 1} + D_{2} F I N C_{t - 1} + D_{3} H U C_{t - 1} \\ + D_{4} I N D U S_{t - 1} + D_{5} U R B N_{t - 1} + D_{6} E N R C_{t - 1} + μ_{t} \end{aligned}

(4)

where CRBN, FINC, HUC, INDUS, URBN, and ENRC are the desired variables of the study.

\begin{aligned} Δ C R B N_{t} = ω_{o} + \sum_{i = 1}^{l} ω_{1} Δ C R B N_{t - i} + \sum_{i = 0}^{p} {ω_{2} Δ F I N C}_{t - i} + \sum_{i = 0}^{q} ω_{3} Δ H U C_{t - i} + \sum_{i = 0}^{r} ω_{4} Δ I N D U S_{t - i} \\ + \sum_{i = 0}^{s} ω_{5} Δ U R B N_{t - i} + \sum_{i = 0}^{u} ω_{6 i} Δ E N R C_{t - i} + η E C T - 1 + v t_{t} \end{aligned}

(5)

C R B N_{t} = D_{1} + D_{2}^{+} F I N C_{t}^{+} + D_{2}^{-} F I N C_{t}^{-} + D_{3} X_{t} + μ_{t}

(6)

The basic level nonlinear mathematical model is specified in equation (5). $D$ ⁺₂ expresses energy utilization effects on CO₂ discharge in the long term in equation (6), which is predictable to be a positive value. Additionally, $D$ ⁻₂ in equations (7) and (8) indicated the sinking effects among energy utilization and CO₂ emanation.

D_{2}^{+} F I N C_{t}^{+} = \sum_{j = 1}^{t} Δ F I N C_{J}^{+} = \sum_{j = 1}^{t} max (Δ F I N C_{j}, 0)

(7)

D_{2}^{-} F I N C_{t}^{-} = \sum_{j = 1}^{t} Δ F I N C_{J}^{-} = \sum_{j = 1}^{t} min (Δ F I N C_{j}, 0)

(8)

The core focus of the math model is to evaluate how energy exploitation symmetrically or asymmetrically influences CO₂ production. Shin et al. ⁴³ hosted NARDL formulation with extensive work on the ARDL. The asymmetric finding through NARDL splits the data into two dimensions, that is, positive and negative.^13–16 In the present research, FI is segregated into positive and negative shocks that are mentioned as

\begin{aligned} Δ C RB N_{t} = ω_{0} + \sum_{i = 1}^{l} ω_{1 i} Δ C R B N_{t - 1} + \sum_{i = 0}^{p} ω_{2 i}^{+} Δ F I N C_{t - 1}^{+} + \sum_{i = 0}^{p} ω_{2 i}^{-} Δ F I N C_{t - 1}^{-} + \sum_{i = 0}^{q} ω_{3 i} Δ H U C_{t - i} + \\ \sum_{i = 0}^{r} ω_{4 i} Δ I N D U S_{t - i} + \sum_{i = 0}^{s} ω_{5 i} Δ U R B N_{t - i} + \sum_{i = 0}^{s} ω_{6 i} Δ E N R C_{t - i} + D_{1} C R B N_{t - 1} + D_{2}^{+} F I N C_{t - 1}^{+} + \\ D_{2}^{-} F I N C_{t - 1}^{-} + D_{3} H U C_{t - 1} + D_{4} I N D U S_{t - 1} + D_{5} U R B N_{t - 1} + D_{6} E N R C_{t - 1} + μ_{t} \end{aligned}

(9)

The mathematical equation (9) develops the shockwaves of positive and negative for FI on the CO₂ discharges for Pakistan. The long-term and short-run asymmetries are measured by

D

₂ ⁺ and

D

₂⁻, ω₂⁺ and ω₂⁻ correspondingly through the below given hypotheses model:

H₀: $D$ ₂⁺ = $D$ ₂⁻ = 0

H_{0} : \sum_{i = 0}^{p 1} ω^{+} 2_{i} = \sum_{i = 0}^{p 2} ω_{2 i}^{-}

For all statistical calculated values i = 0,….,p.

Research analysis and discussions

Research analysis and discussions section of the article briefly explains the research analysis and discussions. Table 2 gives information about the probability statistics, SDs, mean, maximum, minimum, mode, and median statistics, along with kurtosis and skewness values. The outcomes for the description of the variable indicate the top mean, maximum, and median values for industrialization while the lowest and negative values for CO₂. Concerning the correlation statistical matrix, CO₂expressed a positive linkage with FI, human capital, and carbon showed a positive association, while energy consumption stated a positive relationship with carbon discharges.

Table 2.

Descriptive statistics estimates and correlation matrix.

	CRBN	FINC	HUC	INDUS	URBAN	ENRC
Mean	−0.4842	1.2477	0.4113	2.4030	1.2289	0.3136
Median	−0.3985	1.2459	0.3914	2.4095	1.2727	0.4280
Maximum	−0.0183	2.0560	0.5876	3.1121	1.5051	1.2463
Minimum	−1.1032	−0.0032	0.2077	−2.8267	0.9797	−0.9459
SD	0.2927	0.5367	0.1423	0.4529	0.1831	0.6548
Skewness	−0.6151	−0.2911	0.0477	−3.4059	−0.0435	−0.4269
Kurtosis	2.4284	2.0768	1.3806	19.255	1.4863	1.9970
Jarque–Bera	3.3740	2.1831	4.8243	569.481	4.2143	3.1809
Probability	0.1850	0.3355	0.0896	0.0000	0.1215	0.2038
Sum	−21.308	54.902	18.098	105.73	54.071	13.799
Sum Sq. Dev.	3.6856	12.387	0.8718	8.8211	1.4422	18.438
Observations	44	44	44	44	44	44

Source: Authors own visualization by E-views.

Correlation matrix and PCA

The correlation matrix gives the association of the variables with the variable itself. For the present research, six proxies are indexed for the FI data through PCA. Therefore, referred by Hussain, ⁹⁷ correlation matrix for the FI (PCA variables) is separately presented while the general correlation matrix is given in Table 3.

Table 3.

Correlation matrix for financial inclusion (FI) variables.

	FIN1	FIN2	FIN3	FIN4	FIN5	FIN6
FIN1	1
FIN2	0.28454	1
FIN3	0.39759	0.76601	1
FIN4	0.50333	0.91759	0.81685	1
FIN5	−0.30650	−0.35844	−0.86677	−0.47663	1
FIN6	0.46731	0.91277	0.81755	0.98564	−0.48122	1
General correlation matrix
	CRBN	FINC	HUC	INDUS1	URBAN	ENRC
CRBN	1
FINC	0.8646	1
HUC	0.9205	0.9136	1
INDUS	−0.2284	−0.1677	−0.2562	1
URBAN	−0.9141	−0.8817	−0.9834	0.2715	1
ENRC	0.9871	0.8813	0.9588	−0.2476	−0.9583	1

Source: Authors own visualization by E-views.

Unit root analysis

The systematic procedure for the ARDL technical analysis, that takes starts from the stationary values statistics. The stationary statistical estimates are governed through the Phillips–Perron (PP), along with the augmented Dickey–Fuller (ADF) estimations. The validity criterion for the stationary values is that the entire range of variables focused must be stationary at no different level I (0), or maybe on the first difference I (1) math values. The stationary values can hold the mixed order of the integration, that is, with level and first difference statistical math figures; however, no second-order I (2) cointegration figure is accepted. For the unit root statistical analysis dynamics are shown in Table 4 with the first difference stationary math values; however, industrialization verifies stationary at level. The unit root estimates seem white sound. In addition the conventional unit-roots techniques that offer spurious outcomes, the present research implemented the Zivot and Andrews ¹⁰² structural breaks models to get the peak values for break years.^13–16 The structural breaks for the studied variables and the years are explained below.

Table 4.

Unit root statistics and Zivot-Andrews (ZA) structure of breaks.

Variables	Level		First difference		Level		First difference		Decision
Variables	ADF	PP	ADF	PP	ZA	Break	ZA	Break
CRBN	−0.008	−0.167	−5.287*	−5.315*	−3.881	2008	−6.004*	1989	I (1)
FINC	−1.539	−1.582	−5.914*	−5.932*	−4.021	2010	−7.956*	1992	I (1)
HUC	−1.426	−0.886	−3.596*	−3.544*	−3.464	2001	−6.223**	2001	I (1)
INDUS	−4.817*	−4.867*	−7.854	−9.670	−6.791	2009	−8.710**	2011	I (0)
URBAN	−0.876	−0.289	−3.666*	−3.366*	−4.810	1999	−5.475*	1998	I (1)
ENRC	3.654	1.689	−7.855*	−7.733*	−2.776	2008	−8.254	2003	I (1)

Note: The significance values indicated one steric, double steric, and triple steric for 10%, 5%, and 1%, respectively.

Source: Authors own visualization by E-views.

ADF: augmented Dickey–Fuller.

Bounds estimation

In the stepwise approach, the second point in the ARDL estimation is bounded test analysis that validates the presence of long-term cointegration amongst the specific variables. The F-bounds statistical values analyzed the mathematical models in Table 5. The econometric rule requires that the F-statistical numerical value must be higher in comparison with the lower and upper critical bounds estimate math figures suggested by Narayan.¹⁰³ The outcome of bounds analysis validates the occurrence of long-term cointegration for the present research models. The findings from the bounds test showed white noise math estimates.

Table 5.

F-bounding estimation: ARDL.

		Null hypothesis: No linkage at levels
		Significant levels	I (0) bounds	I (1) bounds
F-statistics	5.721	10%	2.45	3.52
k	5	5%	2.86	4.01
		2.5%	3.25	4.49
		1%	3.74	5.06

Source: Authors own visualization by E-views.

ARDL: autoregressive distributive lag.

Long-term, short-run, stability, and diagnostic evaluations

After the verification of the cointegration amongst the models of the study, we are further conducting the short, long, stable, and diagnostic test analysis in Table 6. Conforming to the long-term and short-term outcomes, surprisingly, our research grabbed a significant and positive link between FI and carbon production for individual long and short-term results. Our study consequences are similar to the prior research by Le et al. ⁹ for the Asian regions and Qin et al. ⁴⁴ among E7 countries. The reason for the positive association between FI and carbon production in E7 countries is the fact that most financial inclusion motivates to use of the technologies and equipment that produce high CO₂. Moreover, the alignment of Le et al. ⁹ states that FI surges environmental pollution in the Asian region because in that financial ease creates more utilization of energy sources. FI in Pakistan promotes more fossil combustion in the form of automobiles and electrical appliances that badly pollutes the environment.

Table 6.

Long and short term, stability, and diagnostics verifications (ARDL).

Long-term dynamics
Variables	Coefficients	Std. errors	t-statistics	Prob.
FINC	0.0808**	0.0332	2.4292	0.0202
HUC	−0.3169	0.2995	−1.0583	0.2970
INDUS	0.0148*	0.0158	0.9373	0.3548
URBAN	0.7509**	0.2185	3.4366	0.0015
ENRC	0.6601**	0.0397	16.6092	0.0000
C	−1.6123	0.3655	−4.4103	0.0001
Short-term dynamics
Variables	Coefficients	Std. errors	t-statistics	Prob.
D(FINC)	0.0549**	0.0218	2.5171	0.0164
D(HUC)	−0.2152	0.2022	−1.0642	0.2943
D(INDUS)	0.0100*	0.0102	0.9832	0.3320
D(URBAN)	0.5098**	0.1621	3.1454	0.0033
D(ENRC)	0.4482**	0.0737	6.0797	0.0000
ECT (−1)	−0.6790	0.1116	−6.0795	0.0000
Model stability statistics
R-square	0.991	Adjusted R-square		0.990
F-statistics	707.16	Durbin–Watson statistics		1.952
Model diagnostic statistics
Histogram normality	0.582	Serial LM test		0.697
Hetero test	0.199	Ramsey RESET test		0.436
CUSUM	Stable	CUSUM square		Stable

Note: Single steric for 1%, double steric for 5%, and triple steric for 10% significant values.

Source: Authors own visualization by E-views.

ARDL: autoregressive distributive lag; CUSUM: cumulative sum.

Fantastically, human capital expressed a negative link in the short and long term with carbon radiations in the environment for Pakistan. Currently, the economic policy of Pakistan should not focus on the bad impact of FI on environmental sustainability through FI. However, the government of Pakistan should formulate certain policies to care about environmental degradation in respect of FI. Moreover, the findings are on the same track as those¹⁰⁴ for Indonesia and³¹ for Pakistan. Our outcome suggests that human capital improves environmental sustainability activities through more educated individuals in society. Indonesia also showed negative human capital and environmental results due to the same historical location as Pakistan. Furthermore, Indonesia is highly concerned about educating individuals that are motivated to achieve a cleaner environment. Despite this, the research by Haini ⁵⁴ claimed that human capital increases environmental Pollution for the ASEAN. The contradiction in finding with our research is due to many reasons. First, the study focused on a panel of economies like ASEAN instead our research determined an individual country for the sample of the study. Second, the research time span is different from panel estimators. Third, geographical directions in the ASEAN countries focused on the southeast countries while Pakistan belongs to the South Asian region. So, in place of such factors, our research contradicts with Haini.⁵⁴

In terms of industrialization, that surges CO₂production in the long and short terms. The consequences are like to those Ahmad and Zhao ²⁸ in China, Mahmood et al. ²⁹ in Saudi Arabia, and further Ali et al. ⁶⁰ trends for Pakistan. In the present time like other developing nations, Pakistan's economic policy is still based on the conventional industrial production process that consumes fossil energy and produces hazardous emissions. A lack of certain alternatives leads to environment-friendly productions. Additionally, the findings explain that the studied countries lie in the same geographical region and utilize fossil energy consumption for manufacturing purposes. Most Asian countries run industrial activities using conventional technologies that input oil, natural gas, and coal. Urbanization and carbon discharges governed a significant positive association both in short and long estimations. The outcome matched with Ahmed et al. ^34,35 in China, Saida and Kais ⁶⁴ in African countries, and Ali et al. ³⁰ in Pakistan. The outcome asserts that rapid urbanization and industrialization badly pollute the environment. For China that is heavily based on fossil energy usage for manufacturing plants, it causes hazardous emissions to the environment. African countries yet use conventional technologies for production. The same is the position of Pakistan which uses worn-out technologies in industries that badly deteriorate environmental sustainability. Moreover, there is a lack of proper planning for rapid urbanization in Pakistan. In Pakistan, many housing societies are developed by cutting the forest and agriculture land by divesting many natural resources and habitat loss. In the same way, urbanization happens in Pakistan in a horizontal direction that destroys a lot of land and agricultural habitats further it causes climate change, less rain, and droughts.

The research conducted by Zhang and Wang ⁸⁹ examined the negative linkage between urbanization and environmental pollution. The result explains that urbanization causes a cleaner environment that increases in urban population is highly concerned about environmental and ecological sustainability. The converse finding is due to the different periods and data analysis strategies for Pakistan. However, the fact is that Pakistan is still highly based on fossil energy consumption such as petroleum oil, coal, and natural gas. Further, most of the population burns wood for household purposes.

Interestingly, the estimation of fossil energy utilization determined a significant increasing trend with the environment both in short and long-term dynamics. The outcome coordinated similar outcomes with Ahmad and Du ⁷⁴ in Iran, Meng et al. and Hamid et al.^58,105 amidst China, and Salman et al. ⁷⁵ in a panel of Thailand, South Korea, and Indonesia. The conclusion of energy utilization recommends burning fossil energy sources producing harmful radiation in the environment. Pakistan has natural reserves of coal and natural gas for households and commercial needs. Furthermore, Pakistan imports fossil oil for energy requirements such as transportation, energy production, and industrial requirements. Pakistan has not seriously focused on alternative energy sources. However, the massive use of fossil energy degrades the environment badly. Thus, Pakistan should re-evaluate the energy mix and work for cleaner productions. At the end of model validation, the ECT for the studied ARDL model gives a negative and significant mathematical figure that is another validation of long-term cointegration.

Moreover, the stability analyses along with diagnostic estimates are mentioned at the end of Table 6. The goodness of the fit model is verified through the R² math value (0.991), and the adjusted R² figure (0.990). For the diagnostic statistical investigation, various criterion tests are applied for the normality of the model histogram normality test is performed, hetero test, for autocorrelation we analyzed the serial correlation test, and cumulative sum (CUSUM) statistics with a cumulative sum of square (CUSUMSQ) statistics dynamical charts are shown in Figures 1 and 2. The entire range of diagnostic tests showed that the model outcomes white sound math figures.

Figure 1.

CO₂CUSUM (ARDL).

Figure 2.

CUSUMSQ (ARDL).

NARDL dynamic estimates

The manipulation of asymmetric investigation is performed through the advanced technique of the NARDL recommended by Shin et al.⁴³ The bounding test cointegration statistical figures are offered in Table 7. The bigger f bounding value from the upper and lower critical bound values proposed by Narayan ¹⁰³ proves a long-run connection among the variables of the study.

Table 7.

F-bounds estimation: NARDL.

		Null hypotheses: No at-level relations
		Significant values	I (0) bounds	I (1) bounds
F-statistics	5.7296	10%	2.45	3.52
k	6	5%	2.86	4.01
		2.5%	3.25	4.49
		1%	3.74	5.06

Source: Authors own visualization by E-views.

NARDL: nonlinear autoregressive distributive lag.

Long and short-term outcomes of NARDL

Our research analyzed the asymmetric association of FI (FINC⁺, FINCˉ) with carbon radiations for Pakistan presented in Table 8. The positive shocks of FI FINC⁺ determined positive and most significant outcomes in the direction of long and short-term estimates. The negative effects of FI FINCˉ asserted positive and significant outcomes for the short and long-term estimations. The finding validates the asymmetric linkage of FI to environmental sustainability by explaining the fact that increasing or decreasing FI badly affects the environment. The FI results are quite similar to symmetric outcomes and further, the findings are harmonized with Zaidi et al. ⁸ aimed at OECD economies, and Mehmood ⁴ panel intended for Asian economies. The results express that FI causes environmental detraction at a higher rate in Pakistan and many Asian nations. The reason is the utilization of fossil energy and time-worn technologies. Amazingly, the asymmetric outcomes are highly similar to our symmetric results in terms of industrialization, fossil energy usage, and urbanization, indicating a positive association with CO₂ emanations in either the short or long term. Furthermore, the estimation of asymmetric analysis of the human capital and environment indicated significant negative connections in the long- and short-term dimensions that are similar to our symmetric findings.

Table 8.

Long and short run, stability, and diagnostics verification (NARDL).

Long-run dynamics
Variables	Coefficients	Std. errors	t-statistics	Prob.
FINC⁺	0.0915*	0.0415	2.2010	0.0358
FINCˉ	0.1868***	0.0569	3.2816	0.0027
HUC	−0.5406	0.2435	−2.2203	0.0344
INDUS	0.0332**	0.0187	1.7760	0.0862
URBAN	0.4050**	0.2630	1.5399	0.1344
ENRC	0.7300***	0.0889	8.2059	0.0000
C	−1.0163	0.5109	−1.9889	0.0562
Short-run dynamics
Variables	Coefficients	Std. errors	t-statistics	Prob.
D(FINC⁺)	0.0776*	0.0332	2.3357	0.0266
D(FINCˉ)	0.0812**	0.0482	1.6816	0.1034
D(HUC)	−1.3725	0.5553	−2.4713	0.0196
D(INDUS)	0.0072*	0.0095	0.7611	0.4527
D(URBAN)	−0.3093	0.3384	−0.9140	0.3682
D(ENRC)	0.4053**	0.1143	3.5432	0.0014
ECT (−1)	−0.8487	0.1491	−5.6920	0.0000
Model stability statistics
R-square	0.994	Adjusted R-square		0.991
F-statistics	409.68	Durbin–Watson statistics		2.010
Model diagnostic statistics
Histogram normality	0.682	Serial LM test		0.866
Hetero test	0.350	Ramsey RESET test		0.152
CUSUM	Stable	CUSUM square		Stable

Note: Single steric for 1%, double steric for 5%, and triple steric for 10% significant values.

Source: Authors own visualization by E-views.

CUSUM: cumulative sum; NARDL: nonlinear autoregressive distributive lag.

Moving forward, the ECT is given in Table 8, which validates the presence of long dynamical cointegration amongst the asymmetric model. The ECT statistical figure shows a negative and significant value that is the endorsement of the long-term relationship of the studied model. Additionally, the stability statistics are given at the end of Table 6, which shows the various criteria and goodness of fit for the math model with an R² value (0.994) and adjusted R figure (0.991), and Durbin–Watson statistic figure (2.101). Furthermore, various diagnostic estimates are inspected in our study, like serial correlation test is checked through the LM test, the Breusch–Pagan test is analyzed to verify the heteroskedasticity, the Ramsey RESET test is articulated to find the entire model specifications, and the histogram test is applied to get the normality values of the model. The diagnostic analysis states that the models give accurate statistics and lead in the right direction. Moreover, the stability graphs like CUSUM and CUSUM square indicate the stability of the asymmetric model as shown in Figures 3 and 4. Besides many other tests, the Wald statistical estimate is applied for the verification of the long-term and short-run cointegration for the asymmetrical association of the variables under discussion. Table 8 provides the Wald test statistical value that gives the accepted range of figures. However, the multiplier graphical representation as shown in Figure 5 presents the validity of the asymmetric relationship among the variables.

Figure 3.

CUSUM (NARDL).

Figure 4.

CUSUMSQ (NARDL).

Figure 5.

Multiplier graph.

Wald test estimates

The Wald test is specifically performed for the verification of long cointegration about the asymmetric analysis. The decision is devised on the standard that the long-run probability value must be significant and the short-run probability shows insignificant statistical value. Table 9 presents Wald test results.

Table 9.

Nonlinear estimation for Wald analysis.

Test	F-statistic	Probability	Decision
WALD_{Long Run}	9.5651	0.0674	Long-term asymmetrical link survives
WALD_{Short Run}	0.1368	0.7135	Short-term asymmetrical link survives

Source: Authors own visualization by E-views.

Multiplier diagram

Concluding remarks with policies

The current research aimed at grabbing the attention of the asymmetric influence of FI on environmental sustainability under energy utilization, industrialization, urbanization, and human capital from 1975 to 2018 for Pakistan. For the estimation of FI, we employed PCA for the six proxies of FI. The symmetric finding of our research asserts that the directions of long and short-run FI indicated a significant positive link with CO₂radiation in Pakistan. The consequence of FI advocates that in Pakistan, financial ease lead the economy toward more consumption of timeworn and conventional technologies such as fossil energy production, automobiles, and heavy use of electrical appliances that cause environmental degradation. The financial loans provide the nation with easy access to more automobiles, air conditioners, refrigerators, and other appliances that produce chlorofluorocarbons (CFCs) and deplete the ozone layer that degrades the environment. Moreover, the leasing facility creates a high increase in the number of personnel vehicles and luxury lifestyles.

Urbanization and industrialization are a phenomenon that occurs simultaneously. In our research, both urbanization and industrialization expressed significant positive findings in the long and short term for Pakistan under symmetrical and asymmetric analysis. Rapid urbanization has tremendous effects on a country's national resources, industrialization, labor forces, and economic growth. Pakistan is facing rapid urbanization; according to the economic survey report 2018, the urbanization growth is 36.38%. More urbanization results in more industrialization to provide job opportunities that eventually degrade environmental sustainability. Pakistan is still working on conventional methods for manufacturing that causes environmental pollution.

With symmetric and asymmetric findings, fossil energy utilization determined a significant positive role in degrading the environment in terms of the short and long run. Pakistan is part of those countries that import energy requirements from other countries, which has dual effects on the economy. On one hand, it imports fossil energy sources for daily needs and burdens economic growth. On the other hand, fossil energy utilization causes bad effects on the environmental sustainability of the economy. The developed and developing nations are facing the hazardous results of fossil energy use and are highly inclined toward alternative energy sources. Unluckily, due to political and economic instability, Pakistan remained in the planning process only and was not yet confined to adopting modern energy sources.

Human capital indicated negative outcomes for symmetric and asymmetric analysis in the short and long term for Pakistan. The findings explain that according to the national bureau of statistics, Pakistan holds the world's fifth highest population with the world's ninth labor force. Pakistan possesses 27% of youth bulge aged from 15 to 27 years. The government of Pakistan aims to improve the literacy rate by fourth SDGs and launching “Quality of Education” factors for attaining environmental sustainability. Furthermore, Pakistan developed the human development index and ranked 154th among 189 countries. Human capital works for the reduction of environmental degradation by applying modern ways of sustainable production.

The asymmetric results of an increase in FI (FINC⁺) designated a significance and positive link with carbon radiations in the long term. Moreover, a decrease in FI (FINCˉ) also expressed a significantly positive outcome in the long run that proves the asymmetric connection with carbon production in long-term findings. The findings describe that an increase or decrease in FI affects environmental sustainability. In a developing economy like Pakistan, timeworn technologies, harsh usage of fossil energy sources, and increasing financial credits move the economy toward environmental degradation.

Practical implications of research

The policy makers should introduce some environment-friendly strategies related to FI to reduce fossil energy consumption. Moreover, proper policies should be required in Pakistan for electricity consumption and working hours in Pakistan. Usually, the markets and shops remained open in Pakistan late at night which needs more lighting in the markets that eventually possess a heavy burden on national energy sources. Additionally, the government can provide subsidies for renewable energy sources to improve the environment. Furthermore, policy makers should discourage the utilization of fossil energy utilization and implement heavy taxes the carbon-emitting technologies. Besides fossil energy sources, alternative energy sources should be promoted in the country that provides environment-friendly production. Additionally, the research scientifically and practically contributes to FI policies formulation and implementation at the national level.

Many individuals in the economy harshly use the energy sources that put heavy pressure on national energy sources, ultimately putting a burden on economic growth. Therefore, policy makers should implement heavy taxes on petroleum oil consumption to a certain limit per individual in a periodic manner.

Government officials and policy makers should focus on balancing urban and rural populations. The government of Pakistan should provide equal resources and opportunities for rural and urban residents to avoid unnecessary migrations and traveling.

Policy makers should promote educational activities, sustainability training, and skilled staff to attain sustainable environmental goals.

Government personnel should discourage credit habits that result in more energy consumption. However, the government of Pakistan should launch subsidy plans for promoting environment-friendly appliances, especially in the case of transportation electrical vehicles or hybrid vehicles becoming of the utmost need in the current scenario of Pakistan.

Future perspectives of research

The present research defines some future directions to extend the present work in modern dimensions. First, research may be carried out through the quartile on regression technique to study the FI role in quarterly data sets. Second, research can be extended through the partial least square equations model to evaluate the secondary data. Third, future research can focus the sector-wise FI results on environmental sustainability. Moreover, a range of environmental proxies can be examined to get a clearer picture of environmental pollution.

Footnotes

Author's contribution

Dr Arsalan Tanveer: Conceptualization, complete writing, mathematical equations, data analysis, and econometric analysis. Professor Dr Huaming Song: Conceptualization, supervision, and analysis verification. Assistant Professor Dr Muhammad Faheem: Results verification, mathematical equations, and analysis verification. Dr Abdul Daud: Results support and references verification. Assistant Professor Dr Noreen Safdar: Mathematics equations and references verification.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

It is declared that our manuscript is only submitted in Energy and Environment.

Agreement to publish

All the authors agreed to submit and publish the manuscript in Energy and Environment.

Consent to participate

Not applicable.

ORCID iDs

Arsalan Tanveer

Huaming Song

Data availability statement

Data are grabbed through an online database and will be available on demand.

Arsalan Tanveer is a PhD candidate in School of Economics and Management at Nanjing University of Science and Technology. His primary research interests focus on multicriteria decision-making, renewable energy technologies, climate change, evaluation theory and method. Publications on these topics appear in Renewable Energy, Environmental Science & Pollution Research, Soft Computing, among others.

Huaming Song is presently working as a professor at the School of Economics and Management, Nanjing University of Science and Technology, China. He has focused on researches in several areas such as industrial economy, efficiency evaluation, financial industry, and energy economy. He has several papers published in the SSCI and SCI journal, and supervises international PhD scholars.

Muhammad Faheem is currently professor in School of Economics at Bahauddin Zakariya University, Multan, Pakistan. He has focused on researches in several areas such as industrial economy, efficiency evaluation, financial industry, and energy economy. He has several papers published in the SSCI and SCI journal, and supervises PhD scholars.

Abdul Daud is a PhD candidate in School of Economics and Management at Nanjing University of Science and Technology. His primary research interests focus on multicriteria decision-making, renewable energy technologies, climate change, evaluation theory and method. Publications on these topics appear in Renewable Energy, Environmental Science & Pollution Research, Soft Computing, Materials Today: Proceedings, among others.

Noreen Safdar is currently professor in Department of Economics at The Women University, Multan, Pakistan. He has focused on researches in several areas such as industrial economy, efficiency evaluation, financial industry, and energy economy. He has several papers published in the SSCI and SCI journal, and supervises PhD scholars.

Appendix

References

European Commission. Directorate-General for Employment, Social Affairs and Inclusion. Towards a common operational European definition of over-indebtedness. Publications Office, 2008. https://data.europa.eu/doi/10.2767/17772.

Liu

Xie

Hafeez

, et al.

The trade-off between economic performance and environmental quality: does financial inclusion matter for emerging Asian economies?

Environ Sci Pollut Res 2022; 29: 29746–29755. https://doi.org/10.1007/s11356-021-17755-1

Zulfiqar

Chaudhary

Aslam

. Financial inclusion and its implications for inclusive growth in Pakistan. Pak Econ Soc Rev 2016; 54: 297–325.

Mehmood

. Examining the role of financial inclusion towards CO2 emissions : presenting the role of renewable energy and globalization in the context of EKC. Environ Sci Polluti Res 2022; 29: 15946–15954. https://doi.org/10.1007/s11356-021-16898-5.

Hanif

Sharif

Ramzan

. Does islamic banking augment financial inclusion in Pakistan ? A reinforcement analysis. Rev Econ Dev Stud 2020; 6: 739–758.

Hanif

Baig

, et al. A correlative analysis of modern logistics industry to developing economy using the VAR model: a case of Pakistan. J Adv Transp 2020: 1–10. https://doi.org/10.1155/2020/8861914

SBP. State Bank of Pakistan Financial Inclusion. National Financial Inclusion Strategy website 2018, (accessed 12 March 2022). Retrieved from: http://www.sbp.org.pk/FSR/2018/Chp-7.pdf.

Zaidi

SAH

Hussain

Uz Zaman

. Dynamic linkages between financial inclusion and carbon emissions: evidence from selected OECD countries. Resources, Environ Sustain 2021; 4: 100022–100029.

Taghizadeh-Hesary

. Does financial inclusion impact CO2 emissions? Evidence from Asia. Finance Res Lett 2020; 34: 101451.

10.

Kemal

. Mobile banking in the government-to-person payment sector for financial inclusion in Pakistan. Inf Technol Dev 2019; 25: 475–502.

11.

Renzhi

Baek

. Can financial inclusion be an effective mitigation measure? Evidence from panel data analysis of the environmental Kuznets curve. Finance Res Lett 2020; 37: 101725–101733.

12.

Shahbaz

Dong

, et al. How financial inclusion affects the collaborative reduction of pollutant and carbon emissions: the case of China. Energy Econ 2022; 107: 105847–105863.

13.

Wang

. Uncovering the impact of income inequality and population aging on carbon emission efficiency: an empirical analysis of 139 countries. Sci Total Environ 2023; 857: 159508–159519.

14.

Wang

. Trade protectionism jeopardizes carbon neutrality – decoupling and breakpoints roles of trade openness. Sustain Prod Consum 2023; 35: 201–215.

15.

Wang

Yang

. Does income inequality reshape the environmental Kuznets curve (EKC) hypothesis? A nonlinear panel data analysis. Environ Res 2023; 216: 114575–114590.

16.

Wang

Zhang

. Revisiting the environmental kuznets curve hypothesis in 208 counties: the roles of trade openness, human capital, renewable energy and natural resource rent. Environ Res 2023; 216: 114637–114656.

17.

Ullah

Mohsin

Saboor

, et al. Financial inclusion, socioeconomic disaster risks and sustainable mountain development : empirical evidence from the Karakoram valleys of Pakistan. Sustain MDPI 2020; 9737: 1–26.

18.

Ullah

Ozturk

Usman

, et al. On the asymmetric effects of premature deindustrialization on CO2 emissions : evidence from Pakistan. Environ Sci Pollut Res 2020; 27: 13692–13702.

19.

Amin

Song

Khan

. Dynamic linkages of financial inclusion, modernization, and environmental sustainability in South Asia: a panel data analysis. Environ Sci Pollut Res 2022; 29: 16588–16596.

20.

Abdul

Wenqi

Tanveer

. Environmental stewardship : analyzing the dynamic impact of renewable energy, foreign remittances, and globalization index on China’ s CO2 emissions. Renew Energy 2022; 201: 418–425.

21.

Tanveer

Song

Faheem

, et al.

The paradigms of transport energy consumption and technological innovation as a panacea for sustainable environment : is there any asymmetric association ?

Environ Sci Pollut Res 2022; 6: 9193–9202.

22.

Tanveer

Song

Faheem

, et al. Validation of environmental Philips curve in Pakistan : a fresh insight through ARDL technique. Environ Sci Pollut Res 2022; 29: 25060–25077.

23.

Tanveer

Anwer

Umar

. The impact of environmental sustainability and financial resources utilization on firm financial performance : the mediating role of leadership styles. Ann Soc Sci Perspective 2021; 2: 389–407.

24.

Tanveer

Song

Muhammad

, et al. Unveiling the asymmetric impact of energy consumption on environmental mitigation in the manufacturing sector of Pakistan. Environ Sci Pollut Res 2021; 28: 64586–64605.

25.

IEA. International Energy Agency. CO2 emission from Fuels Combustions: Highlights. IEA statistics, International energy agency, OECD, Paris 2019, (accessed 19 December 2019). Retrieved from: https://www.iea.org/reports/world-energy-outlook-2019.

26.

ESP. Bureau of Statistics Pakistan. Retrieved , from Economic Survey of Pakistan website 2020, (accessed 20 December 2020). Retrieved from: www.finance.gov.pk>survey_1819.

27.

EPA. EPA. December 20, 2020, United States Environmnetal Protection Agency website 2020, (accessed 20 December 2020). Retrieved from: https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data.

28.

Ahmad

Zhao

. Empirics on linkages among industrialization, urbanization, energy consumption, CO2 emissions and economic growth: a heterogeneous panel study of China. Environ Sci Pollut Res 2018; 25: 30617–30632.

29.

Mahmood

Alkhateeb

TTY

Furqan

. Industrialization, urbanization and CO2 emissions in Saudi Arabia: asymmetry analysis. Energy Rep 2020; 6: 1553–1560.

30.

Ali

Bakhsh

Yasin

. Impact of urbanization on CO2 emissions in emerging economy : evidence from Pakistan. Sustain Cities Soc 2019; 48: 101553.

31.

Bano

Zhao

Ahmad

, et al. Identifying the impacts of human capital on carbon emissions in Pakistan. J Cleaner Prod 2018; 183: 1082–1092.

32.

Ahmed

Wang

. Investigating the impact of human capital on the ecological footprint in India: an empirical analysis. Environ Sci Pollut Res 2019; 26: 26782–26796.

33.

Pata

Caglar

. Investigating the EKC hypothesis with renewable energy consumption, human capital, globalization and trade openness for China: evidence from augmented ARDL approach with a structural break. Energy 2020; 216: 119220–119236.

34.

Ahmed

Asghar

Malik

, et al. Moving towards a sustainable environment: the dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China. Resour Policy 2020; 67: 101677–101688.

35.

Ahmed

Zafar

Ali Sajid

. Linking urbanization, human capital, and the ecological footprint in G7 countries: an empirical analysis. Sustain Cities Soc 2020; 55: 102064–102075.

36.

Saleem

Shujah-ur-Rahman

. The impact of human capital and biocapacity on environment: environmental quality measure through ecological footprint and greenhouse gases. J Pollut Effects Control 2019; 7: 1–13.

37.

Hao

Umar

Khan

, et al.

Green growth and low carbon emission in G7 countries: how critical the network of environmental taxes, renewable energy and human capital is?

Sci Total Environ 2021; 752: 141853–141863.

38.

Rahman

Nepal

Alam

. Impacts of human capital, exports, economic growth and energy consumption on CO2 emissions of a cross-sectionally dependent panel: evidence from the newly industrialized countries (NICs). Environ Sci Policy 2021; 121: 24–36.

39.

Yao

Ivanovski

Inekwe

, et al. Human capital and CO2 emissions in the long run. Energy Econ 2020; 91: 104907.

40.

Raza

Tang

Rubab

, et al. Determining the nexus between financial inclusion and economic development in Pakistan. J Money Laund Control 2019; 22: 195–209. https://doi.org/10.1108/JMLC-12-2017-0068.

41.

Ramzan

Amin

Abbas

. How does corporate social responsibility affect financial performance, financial stability, and financial inclusion in the banking sector ? Evidence from Pakistan. Res Int Bus Finance 2021; 55: 101314–101323.

42.

Chien

Ngo

, et al. Vertical financial disparity, energy prices and emission reduction : empirical insights from Pakistan. J Environ Manag 2021; 294: 112946.

43.

Shin

Greenwood-nimmo

. Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: Sickles R and Horrace W (eds) Festschrift in honor of Peter Schmidt. New York, NY: Springer, 2014.

44.

Qin

Raheem

Murshed

, et al. Does financial inclusion limit carbon dioxide emissions ? analyzing the role of globalization and renewable electricity output. Sustain Dev 2021; 29: 1138–1154. https://doi.org/10.1002/sd.2208.

45.

Tamazian

Chousa

J P

Vadlamannati

K C

. Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries. Energy Policy 2009; 37: 246–253.

46.

Jensen

. Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival. Can J Fish Aquat Sci 1996; 53: 820–822.

47.

Frankel

Romer

. Does trade cause growth? Am Econ Rev 1999; 89: 379–399.

48.

Usama

Solarin

Salahuddin

. The prominence of renewable and non-renewable electricity generation on the environmental Kuznets curve: a case study of Ethiopia. Energy 2020; 211: 118665–118673.

49.

Abdul

Wenqi

Tanveer

. Prioritization of renewable energy source for electricity generation through AHP-VIKOR integrated methodology. Renew Energy 2022; 184: 1018–1032.

50.

Ullah

Ali

Shah

, et al. Environmental concerns of financial inclusion and economic policy uncertainty in the era of globalization: evidence from low & high globalized OECD economies. Environ Sci Pollut Res 2022; 29: 36773–36787. https://doi.org/10.1007/s11356-022-18758-2

51.

Mahmood

Wang

Hassan

. Renewable energy, economic growth, human capital, and CO2 emission : an empirical analysis. Environ Sci Pollut Res 2019; 26: 20619–20630.

52.

Zafar

Zaidi

SAH

Khan

, et al. The impact of natural resources, human capital, and foreign direct investment on the ecological footprint: the case of the United States. Resour Policy 2019; 63: 101428–101438.

53.

Sarkodie

Adams

Owusu

, et al. Mitigating degradation and emissions in China: the role of environmental sustainability, human capital and renewable energy. Sci Total Environ 2020; 719: 137530–137544.

54.

Haini

. Examining the impact of ICT, human capital and carbon emissions: evidence from the ASEAN economies. Int Econ 2021; 166: 116–125.

55.

Jena

Mujtaba

Joshi

DPP

, et al. Exploring the nature of EKC hypothesis in Asia’s top emitters: role of human capital, renewable and non-renewable energy consumption. Environ Sci Pollut Res 2022; 29: 88557–88576.

56.

Wang

. The effects of urbanization and industrialization on decoupling economic growth from carbon emission – A case study of China. Sustain Cities Soc 2019; 51: 101758–101766.

57.

Zhao

Yan

, et al. Energy technological progress, energy consumption, and CO2 emissions : empirical evidence from China. J Cleaner Prod 2019; 236: 117666.

58.

Meng

Guo

. The dynamic linkage among urbanisation, industrialisation and carbon emissions in China: insights from spatiotemporal effect. Sci Total Environ 2021; 760: 144042.

59.

Pata

. The effect of urbanization and industrialization on carbon emissions in Turkey: evidence from ARDL bounds testing procedure. Environ Sci Pollut Res 2018; 25: 7740–7747.

60.

Ali

Sadiq

Kumail

, et al. A cointegration analysis of structural change, international tourism and energy consumption on CO2 emission in Pakistan. Curr Issues Tour 2020; 23: 3001–3015. https://doi.org/10.1080/13683500.2020.1804338.

61.

Dong

. How renewable energy consumption lower global CO2 emissions ? Evidence from countries with different income levels. World Econ (Wiley Online Library) 2020; 2017: 1665–1698.

62.

Ahmad

Khattak

Rahman

, et al. Innovation, foreign direct investment (FDI), and the energy – pollution – growth nexus in OECD region : a simultaneous equation modeling approach. Environ Ecol Stat 2020; 2: 203–232.

63.

Arshad

Robaina

Shahbaz

, et al. The effects of deforestation and urbanization on sustainable growth in Asian countries. Environ Sci Pollut Res 2020; 27: 10065–10086.

64.

Saida

Kais

. Environmental pollution, health expenditure and economic growth and in the Sub-Saharan Africa countries: panel ARDL approach. Sustain Cities Soc 2018; 41: 833–840.

65.

You

. Spillover effects of economic globalization on CO2 emissions: a spatial panel approach. Energy Econ 2018; 73: 248–257.

66.

Ahmad

Khan

Rahman

, et al. Does financial development asymmetrically affect CO2 emissions in China ? an application of the nonlinear autoregressive distributed lag (NARDL) model. Carbon Manag 2019; 9: 631–644.

67.

Ahmad

Zhao

. Revealing stylized empirical interactions among construction sector, urbanization, energy consumption, economic growth and CO2 emissions in China. Sci Total Environ 2019; 657: 1085–1098.

68.

Aung

Saboori

Rasoulinezhad

. Economic growth and environmental pollution in Myanmar : an analysis of environmental Kuznets curve. Environ Sci Pollut Res 2017; 24: 20487–20501. https://doi.org/10.1007/s11356-017-9567-3

69.

Mohsin

Abbas

Zhang

, et al. Integrated effect of energy consumption, economic development, and population growth on CO2 based environmental degradation : a case of transport sector. Environ Sci Pollut Res 2019; 26: 32824–32835.

70.

Lin

Ahmad

. Analysis of energy related carbon dioxide emission and reduction potential in Pakistan. J Cleaner Prod 2017; 143: 278–287.

71.

Lin

Raza

. Analysis of energy related CO2 emissions in Pakistan. J Cleaner Prod 2019; 219: 981–993.

72.

Lin

Yousaf

. Energy substitution effect on transport sector of Pakistan : a trans-log production function approach. J Cleaner Prod 2020; 251: 119606–119621.

73.

Mujtaba

Jena

. Analyzing asymmetric impact of economic growth, energy use, FDI inflows, and oil prices on CO2 emissions through NARDL approach. Environ Sci Pollut Res 2021; 28: 30873–30886.

74.

Ahmad

. Effects of energy production and CO2 emissions on economic growth in Iran: ARDL approach. Energy 2017; 123: 521–537.

75.

Salman

Long

Dauda

, et al. The impact of institutional quality on economic growth and carbon emissions: evidence from Indonesia, South Korea and Thailand. J Cleaner Prod 2019; 241: 118331.

76.

Zakaria

Bibi

. Financial development and environment in South Asia: the role of institutional quality. Environ Sci Pollut Res 2019; 26: 7926–7937.

77.

Sattar

Hussain

Ilyas

. An impact evaluation of belt and road initiative (BRI) on environmental degradation. Sage Open 2022; 12: 21582440221078836. https://doi.org/10.1177/21582440221078836.

78.

Sattar

Tolassa

Hussain

, et al. Environmental effects of China’s overseas direct investment in South Asia. Sage Open 2022; 12: 21582440221078301. https://doi.org/10.1177/21582440221078301.

79.

Hussain

Sattar

, et al. Evaluating the impact of energy and environment on economic growth in BRI countries. Energy Environ 2022; 1: 1–16.

80.

Ahmad

Zhao

Shahbaz

, et al. Carbon emissions, energy consumption and economic growth: an aggregate and disaggregate analysis of the Indian economy. Energy Policy 2016; 96: 131–143.

81.

Akalpler

Hove

. Carbon emissions, energy use, real GDP per capita and trade matrix in the Indian economy-an ARDL approach. Energy 2019; 168: 1081–1093.

82.

Udom

Adebola

Yorucu

, et al. Modeling natural gas consumption, capital formation, globalization, CO2 emissions and economic growth nexus in Malaysia : fresh evidence from combined cointegration and causality analysis. Energy Strategy Rev 2020; 31: 100526–100537.

83.

Shahbaz

Jawad

Shahzad

, et al. Is globalization detrimental to CO2 emissions in Japan ? New threshold analysis. Environ Model Access Springer Nature 2017; 23: 557–568.

84.

Marques

. Rediscovering the EKC hypothesis for the 20 highest CO2 emitters among OECD countries by level of globalization. Int Econ Elesvier 2020; 164: 36–47.

85.

Wasti

SKA

Zaidi

. An empirical investigation between CO2 emission, energy consumption, trade liberalization and economic growth: a case of Kuwait. J Build Eng 2020; 28: 101104–101110. https://doi.org/10.1016/j.jobe.2019.101104.

86.

Mujtaba

Jena

Mishra

, et al. Do economic growth, energy consumption and population damage the environmental quality? Evidence from five regions using the nonlinear ARDL approach. Environ Chall 2022; 8: 100554–100569.

87.

Munir

Lean

Smyth

. CO2 Emissions, energy consumption and economic growth in the ASEAN-5 countries: a cross-sectional dependence approach. Energy Econ 2020; 85: 104571–104581. https://doi.org/10.1016/j.eneco.2019.104571.

88.

Acheampong

Adams

Boateng

. Do globalization and renewable energy contribute to carbon emissions mitigation in Sub-Saharan Africa? Sci Total Environ 2019; 677: 436–446.

89.

Zhang

Wang

. Energy production, economic growth and CO2 emission : evidence from Pakistan. Nat Hazards 2017; 90: 27–50.

90.

Shahbaz

Balsalobre-Lorente

Sinha

. Foreign direct investment–CO2 emissions nexus in Middle East and North African countries: importance of biomass energy consumption. J Cleaner Prod 2019; 217: 603–614.

91.

Ehrlich

Holdren

. Impact of population growth. Obstet Gynecol Survey 1971; 26: 767–771.

92.

Dietz

Rosa

. Rethinking the environmental impacts of population. Hum Ecol Rev 1994; 1: 277–300.

93.

York

Rosa

E A

Dietz

. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological economics 2003; 46: 351–365.

94.

Nathaniel

Abdul

Khan

. The nexus between urbanization, renewable energy, trade, and ecological footprint in ASEAN countries. J Cleaner Prod 2020; 272: 122709–122718.

95.

Anser

Apergis

Syed

, et al. Exploring a new perspective of sustainable development drive through environmental Phillips curve in the case of the BRICST countries. Environ Sci Pollut Res 2021; 28: 48112–48122.

96.

Ahmed

Nathaniel

Shahbaz

. The criticality of information and communication technology and human capital in environmental sustainability: evidence from Latin American and Caribbean countries. J Cleaner Prod 2021; 286: 125529–125539.

97.

Hussain

. Evaluating the impact of air transportation, railway transportation, and trade openness on inbound and outbound tourism in BRI countries. J Air Transp Manag 2023; 106: 102307–102318.

98.

WDI. World Bank Developmnet Indicators 2020, https://doi.org/World Development Indicators.

99.

BPS. British Petroleum Energy Statistical Review 2020, (accessed 1 February 2022). Retrieved from: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html.

100.

Pesaran

Shin

Smith

. Bounds testing approaches to the analysis. Econ Methods Appl 2001; 326: 289–326.

101.

Abbasi

Riaz

. CO2 emissions and financial development in an emerging economy : an augmented VAR approach. Energy Policy 2016; 90: 102–114.

102.

Zivot

Andrews

DWK

. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat 1992; 10: 251–270.

103.

Narayan

. The saving and investment nexus for China: evidence from cointegration tests. Appl Econ 2005; 37: 1979–1990.

104.

Bashir

Husni Thamrin

Farhan

, et al. The causality between human capital, energy consumption, CO2 emissions, and economic growth: empirical evidence from Indonesia. Int J Energy Econ Policy 2019; 9: 98–104.

105.

Hamid

Jena

Mukhopdhyay

. Should China prefer more foreign direct investment inflows to environmental change? J Public Aff 2022; 22: e2466.

106.

IMF. International Monetary Fund 2022 (accessed 15 March 2022). Retrieved from: https://data.imf.org/.

107.

PWT. Penn World Table Version, 9, 2020, https://doi.org/10.15141/S5Q94M.

Navigating the asymmetric influence of financial inclusion on environmental sustainability: Dynamic role of energy consumption and human capital

Abstract

Keywords

Introduction

Literature review

Theoretical background

FI and CO2 emanations

Human capital and carbon emanations

Industrialization and carbon emanations

Urbanization and carbon emanations

Energy utilization and carbon emanations

Models specifications, data sources, and research methodology

Econometric strategy with mathematical modeling

Unit root statistical analysis

ARDL cointegration bound testing approach

Research analysis and discussions

Correlation matrix and PCA

Unit root analysis

Bounds estimation

Long-term, short-run, stability, and diagnostic evaluations

NARDL dynamic estimates

Long and short-term outcomes of NARDL

Wald test estimates

Multiplier diagram

Concluding remarks with policies

Practical implications of research

Future perspectives of research

Footnotes

Author's contribution

Declaration of conflicting interests

Funding

Ethical approval

Agreement to publish

Consent to participate

ORCID iDs

Data availability statement

Appendix

References

FI and CO₂ emanations