The Symmetric and Asymmetric Effect of Defense Expenditures, Financial Liberalization, Health Expenditures on Sustainable Development

This study examines the short run, long run, and causal relationships among financial liberalization, healthcare expenditures, and defense expenditures on sustainable development in Pakistan covering the period from 1971 to 2017. The paper also explored the asymmetric relationships among the target variables. To explore these relationships, ARDL and NARDL Models are utilized. Additionally, advanced econometric techniques such as Maki cointegration and quasi-GLS unit root are used to take multiple structural breaks into account. Maki cointegration results show a stable long run relationship between the underlying variables. The findings of ARDL suggest a positive effect of financial liberalization and health expenditures while the negative effect of military expenditures on sustainable development. NARDL estimates suggest strong asymmetry as sustainability responds to positive (negative) shocks in militarization, health expenditures, and financial liberalization differently. The Toda-Yamamoto causality test shows that any policy to target health expenditures and financial liberalization significantly alters sustainable development and vice versa. For robustness checks, FMOLS and alternative proxy of sustainable development are used. The key findings posited the need to shift military expenditures to health expenditures and financial markets to achieve sustainable development goals in Pakistan.

1 Introduction

The drastic inferences of climate change to the ecosystems and human lives have remained an alarming situation for the ecologists, policymakers, and general public. The Earth’s surface is producing rampant atmospheric heat which is substantially contributed by Nitrous Oxide, Fluorinated gases, Methane, Carbon dioxide (CO₂), and other greenhouse gases (Singh et al., 2020). The rapid escalation in economic activities, energy demand, population, and other human activities are responsible for environmental degradation around the globe (Jahanger et al., 2021). The industrial boom in various countries largely compromised environmental quality leading to health problems, natural resource depletion, and land erosion. If sustainable development initiatives are not considered seriously, humanity will face a dark and dangerous future (Ulucak et al., 2019). Since maintaining and preserving sustainable performance is the foremost concern of the world (Pervaiz et al., 2021), current empirical studies are thriving to identify certain factors that reduce environmental issues without compromising economic growth (Doğan et al., 2020; Doğan et al., 2021; Koseoglu et al., 2022; Xia et al., 2022).

According to Global Climate Risk Index, Pakistan is the fifth-most vulnerable country to climate change (Eckstein et al., 2019). Over the last decade, Pakistan has faced 152 extreme weather events and lost around 10,000 lives with an economic loss worth USD 3.8 billion due to environmental disasters (Ahmed T. et al., 2020; Ullah et al., 2021). Researchers have identified several determinants of pollutant emissions in Pakistan, including energy consumption, financial development, globalization, foreign direct investment (FDI), urbanization, industrial growth, and international tourism (Ali et al., 2019; Khan et al., 2019; Godil et al., 2020; Munir and Ameer, 2020; Ali et al., 2021).

In the last few years, significant debate among economists and environmentalists has emerged against defense expenditures. Countries spending a substantial amount of their national income on military defense are facing economic deterioration, income inequality, and environmental degradation (Alptekin and Levine, 2012; Raza et al., 2017; Ahmed Z. et al., 2020). Testing nuclear weapons, maintaining heavy machinery and active armed operations intensify militarization leading to an increase in fuel consumption and thermal radiation (Solarin et al., 2018; Qayyum et al., 2021) Thus, militarization is one of the most ecologically destructive human activities and a serious threat to national sustainability (Gokmenoglu et al., 2021).

Since their independence from British Rule, India and Pakistan have been arch-enemies. To date, both neighboring countries have violated several ceasefire agreements, been involved in numerous border skirmishes, and four full-fledged armed conflicts (Amir-ud-Din et al., 2020). Their continuous arms race has compelled Pakistan to spend a substantial portion of its gross domestic product (GDP) on defense which could have been utilized on economic, social, and environmental development (Jalil et al., 2016; Hussain, 2019; Raju and Ahmed, 2019). Certain efforts are made by previous studies to empirically link militarization with economic growth (Alptekin and Levine, 2012; Karadam et al., 2017; Saba and Ngepah, 2019), environmental degradation (Ahmed S. et al., 2020; Gokmenoglu et al., 2021), industrialization (Saba and Ngepah, 2020) and some social development indicators (Doğan et al., 2018; Biswas et al., 2019; Coutts et al., 2019). Nonetheless, there is a dearth of empirical literature on the relationship between military expenditures and sustainable performance, especially in the context of Pakistan.

On the other hand, a liberalized financial sector is crucial for the economic development of Pakistan (Adeel-Farooq et al., 2017; Naveed and Mahmood, 2019). In the early 1990s, Pakistan recognized the importance of an efficient financial mechanism and introduced diverse financial reforms under structural adjustment programs (SAP) to mitigate the distortion in the financial markets (Ashraf et al., 2022). Excessive control over interest and exchange rates may restrict savings, discourage investments, increase the margin of financial intermediation, increase financial markets segmentation, and retard the efficient allocation of resources which eventually lead to financial instability (Bumann et al., 2013; Akinsola and Odhiambo, 2017). Financial liberalization reduces informational asymmetries and enhances FDI cash flows leading to accelerated economic development (Tamazian et al., 2009; Kim et al., 2010). Besides the increase in economic activities by the liberalized financial system, it has an inevitable effect on the environment. Although there are studies on the relationship between financial development and environmental quality (Jalil and Feridun, 2011; Shah et al., 2019; Zakaria and Bibi, 2019), the link between financial liberalization¹ and the environment is underexplored (Hua and Boateng, 2015). Accordingly, we attempt to fill the gap in the literature by investigating the impact of financial liberalization on the sustainable performance of a country.

Climate change and severe levels of greenhouse gas (GHG) emissions are a serious threat to public health. Accordingly, prior studies reveal that environmental degradation mainly due to GHG emissions increases healthcare expenditures (Alimi et al., 2020; Anwar et al., 2021). Most of the studies have focused on the cause-effect from CO₂ emissions to health expenditures, recent evidence has also revealed that health expenditures play a vital role in economic development and restricting environmental degradation (Chaabouni and Zghidi, 2016; Wang et al., 2019). The developing countries are facing a dual-sword challenging situation where they are dealing with both economic and environmental concerns. Unfortunately, these economies are not allocating an adequate level of budget for the healthcare expenditures compared to the GHGs they are emitting (Usman et al., 2019). The situation of the healthcare system is not satisfactory in Pakistan. Both adult and infant mortality rates in Pakistan are very high as compared to other developing countries with similar economic growth patterns (Saleem et al., 2021). Even amid the health crisis, Pakistan allocate more budget to military expenditures compared to health expenditures (Siddiqa, 2020). Thus, it is important to investigate if the health spending of Pakistan is linked to its sustainable growth. Since an increase in healthcare services alleviates poverty, boosts productivity and GDP (Rahman et al., 2018; Raghupathi and Raghupathi, 2020), we believe that healthcare expenditures improve the sustainable performance of an economy.

Along with symmetric effects, researchers have also evaluated the asymmetric effect of financial liberalization, health expenditures, and militarization on economic growth and environmental degradation (Chen et al., 2020; Ullah et al., 2020; Ullah et al., 2021; Zeeshan et al., 2021). Positive and negative shocks in the target variables may respond differently to sustainable development. Thus, this study provides insight into postulation whether there is a positive-positive, negative-negative, or linear relationship between health expenditures, financial liberalization, militarization, and sustainable development. Assuming linearity among underlying variables may produce biased policy implications.

The main objective of the study is to assess the symmetric and asymmetric effect of military expenditures, financial liberalization, and health expenditures on sustainable development in both the short and long run. This study is the first attempt to investigate the underlying relationship, especially in the context of Pakistan, as most of the previous studies have analyzed these variables with individual dimensions of sustainable development, i.e., economic growth (Bumann et al., 2013; Chaabouni et al., 2016; Adeel-Farooq et al., 2017; Ahmed S. et al., 2020), social indicators (Töngür and Elveren, 2017; Biswas et al., 2019; Coutts et al., 2019; Owumi and Eboh, 2021), and environmental quality (Jalil and Feridun, 2011; Wang et al., 2019; Gokmenoglu et al., 2021).

Second, along with extensively used techniques such as autoregressive distributed lag (ARDL) bound test (Pesaran et al., 2001) for cointegration, Augmented Dickey-Fuller (Dickey and Fuller, 1979), and Phillips Perron (Phillips and Perron, 1988) for unit root testing, we have applied advanced econometric techniques including quasi-generalized least squares (quasi-GLS) (Carrion-i-Silvestre et al., 2009), and Maki (2012) cointegration to account for possible structural breaks. Third, the short run and long run symmetric and asymmetric effects are evaluated using ARDL and NARDL. The NARDL approach produces valid estimates compared to Markov-Switching and smooth transition ECM, especially for a small sample size (Chen et al., 2020; Ullah et al., 2020). For robustness checks, the fully modified ordinary least squares (FMOLS) (Phillips and Hansen, 1990) is utilized. Lastly, Toda and Yamamoto’s (1995) causality test is utilized for the causal relationship between the target variables which accounts for the structural breaks in the series.

The remainder of this paper is structured as follows. Section 2 offers a detailed review of the prior relevant literature. Section 3 explains the data and methodology. A detailed description of econometric techniques is provided in Section 4. Empirical results are reported in Section 5 and discussion in Section 6. Lastly, Section 7 concludes the study with policy implications.

2 Literature Review

In the wake of biodiversity degradation, air pollution, illiteracy, gender inequalities, health risks, and poverty, United Nations (UN) is thriving to establish global strategies to achieve sustainable development (Griggs et al., 2013). Sustainable development is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (Brundtland, 1987). It identifies the need for environmentally sound and inclusive growth to mitigate poverty and develop shared fortune for the global population. The three important components of sustainable development include social inclusion, environmental stewardship, and social inclusion (Muralikrishna and Manickam, 2017).

Despite the attention drawn by policymakers, there is a dearth of literature on the determinants of sustainable development. Koirala and Pradhan (2020) studied the determinants of sustainable development in 12 Asian countries over the period 1990–2014 using fixed- and random-effect estimators. The authors revealed that national resource rent and inflation rate are negatively associated with sustainable development while there is a positive effect of per capita income and financial development. Similarly, Kaimuri and Kosimbei (2017) found a negative effect of energy efficiency, unemployment, and household consumption per capita but the insignificant effect of trade, real GDP, and resource productivity on sustainable development in Kenya. Hess (2010) established a positive association of financial development, the share of natural resources in exports, working-age population, and human development with sustainable development in developing countries. Based on the data of 72 developing and 40 developed countries, Güney (2019) asserted a positive effect of renewable energy consumption on sustainable development.

We further extend the sustainable development literature by investigating the role of financial liberalization, defense expenditures, and health expenditures. Although the empirical evidence on the relationship between these three variables and sustainable development is not established, their causal, short run, and long run relationships are analyzed with economic, social, and environmental indicators. For instance, Adeel-Farooq et al. (2017) analyzed the effect of financial liberalization on economic growth in Pakistan over the period 1985–2014. The authors found that financial liberalization has a positive impact on Pakistan’s economic growth only in the long run. Similar results were asserted by Naveed and Mahmood (2019) in the long run but a negative effect in the short run using multivariate cointegration technique and error-correction mechanism. While considering the non-linear relationship between financial liberalization and economic crisis in 28 transition economies, Hartwell (2017) argued that financial openness increases the probability of a crisis until the economy reaches a higher level of liberalization. Similarly, Chen et al. (2020) found the asymmetric effect of financial development (principally constitutes financial liberalization) on economic growth in Kenya using NARDL.

Although a wide range of studies investigates the impact of financial liberalization on economic growth, its relation with social and environmental indicators is underexplored. Kim et al. (2021) examined the effect of financial liberalization on income inequality in both developing and developed countries from the year 1989–2011. The authors revealed that financial openness alleviates poverty and income inequality, especially in the presence of weak democratic structures. Hua and Boateng (2015) investigated the long run association between financial openness and CO₂ emissions across 167 countries over the period 1970–2007. Especially in the Northern economies, the authors found a negative effect of financial liberalization on CO₂ emissions. Boufateh and Saadaoui (2020) considered 22 African economies to assess the asymmetric financial development shocks on CO₂ emissions using the non-linear panel ARDL-PMG Model. The authors asserted that positive financial development shocks help African economies to curb air pollution. Nonetheless, there are few studies related to asymmetric effects of financial development and no previous study, to the best of our knowledge, explored the effect of financial liberalization on sustainable development.

The trade-off between military spending and socio-economic indicators such as education, health, income inequality explains the reason for studying militarization (Coutts et al., 2019; Biscione and Caruso, 2021; Vallejo-Rosero et al., 2021). This gun-vs-butter tradeoff is also relevant for sustainable development. The evidence in previous literature is quite mixed related to the role of military expenditures. Using a balanced panel of 35 African countries over the period 1990–2015, Saba and Ngepah (2019) suggest a feedback causality between defense expenditures and industrialization. On the other hand, based on the Wavelet approach, Khalid and Habimana (2021) purported that military expenditures do not promote economic growth in the long run in Turkey. Similar results were asserted by Ahmed S. et al. (2020) in the context of Myanmar. Tao et al. (2020) also found the crowding-out effect of militarization on sustainable economic growth in Romania. There is also a strand of literature that revealed between military expenditures and economic growth in middle eastern countries and Turkey (Karadam et al., 2017), in South Africa (Phiri, 2019), in Pakistan and India (Ullah et al., 2021), and in top defense expenders (Hatemi-J et al., 2018).

Studies also hold militarization accountable for uneven income distribution and compromising the quality of the natural environment. Some theoretical evidence in this regard can be derived from the “treadmill of destruction” theory and the “guns vs. butter” Model. Grounded on the “treadmill of destruction” theory, Clark et al. (2010) argued that militarization is positively associated with energy consumption and exacerbates ecological degradation irrespective of whether the military is involved in conflicts or not. Similarly, studies support the “guns vs. butter” Model that military expenditures crowd out social welfare resulting in socio-environmental issues. For instance, in the long run, Raza et al. (2017) found a positive impact of military expenditures on income inequality in Pakistan. Doğan et al. (2018) also examined the effect of military expenditures on income inequality in North American countries. The authors revealed an inverted U-shaped relationship between military expenditures and income inequality. Gokmenoglu et al. (2021) investigated that military expenditures impede air quality and alleviate environmental degradation in Turkey based on FMOLS and Todo-Yamamoto causality test.

Similarly, Ahmed et al. (2020c) also analyzed the effect of military spending on environmental degradation and economic growth in Pakistan using cointegration and bootstrap causality. Their findings suggest that military spending is negatively associated with economic growth while positively related to ecological footprints. Biswas et al. (2019) employed panel data of 76 countries from 2000 to 2014 and argued that defense expenditures make no contribution to human development and only marginally contribute to GDP. In both the short run and long run, Ullah et al. (2021) found an asymmetric relationship between military expenditures and CO₂ emissions using NARDL. In light of the aforementioned evidence, it can be postulated that positive (negative) shocks in military expenditures impede (improve) sustainable development in Pakistan.

Our third target variable is healthcare expenditures. Since there is inconclusive evidence related to the role of healthcare spending for economic, social, and environmental sustainability, two distinct hypotheses exist. The first hypothesis argues that health is a basic necessity due to which government intervention in the healthcare sector is essential. However, the second hypothesis considers healthcare as a luxury good that should be left to market forces. Accordingly, previous studies found both unidirectional and bidirectional causality between health expenditures and economic growth (Chaabouni and Abednnadher, 2014; Chaabouni and Zghidi, 2016). Wang et al. (2019) examined the short run and long run relationships between healthcare expenditures, CO₂ emissions, and economic growth using autoregressive distributed lag (ARDL) in Pakistan and found significant estimates. The authors also reveal bidirectional Granger causality between the underlying variables. Although the nexus between health expenditures and sustainable development is underexplored, Khan S. A. R. et al. (2020) investigated that a higher level of healthcare expenditures undermine economic growth in the presence of low labor productivity and poor environmental performance in Southeast Asian countries.

Pervaiz et al. (2021) also investigated the long run relationship between health expenditures and CO₂ emissions using FMOLS and DOLS techniques in BRICS countries. The authors argued that air pollution negatively affects human health leading to an upsurge in health expenditures. Using ARDL techniques, similar relationships are found by researchers in MENA (Yazdi and Khanalizadeh, 2017) and ASEAN (Haseeb et al., 2019). However, Moosa and Pham (2019) argued that the association between environmental degradation and health expenditures varies across countries based on their per capita income. Although the antecedents of health expenditures are empirically examined in the previous studies, there is a dearth of literature related to the impact of health expenditures on sustainable development. Additionally, few studies explored the asymmetric links among healthcare expenditures, economic growth, and environmental degradation (Khan A. et al., 2020; Fan et al., 2021; Mujtaba and Ashfaq, 2021). Adequate government funding in healthcare systems may help countries to achieve SDGs by training the health workforce and enhancing health literacy (Liaropoulos and Goranitis, 2015; Chotchoungchatchai et al., 2020). Thus, we posited that positive (negative) shocks in healthcare expenditures improve (curb) sustainable development.

3 Data and Methodology

The underlying variables of the study include financial liberalization, military expenditures, health expenditures, and sustainable development. The current study uses the data for Pakistan over the period from 1971 to 2017. In order to monitor and assess sustainable development, a wide range of indices reflecting economic, social, and environmental dimensions are developed (Bilbao-Ubillos, 2013; Estoque and Murayama, 2014; Strezov et al., 2017; Hickel, 2020). Nonetheless, a consensus is not yet developed on a single index acceptable among political and scientific communities. It is believed that there is a dearth of the clear route through which sustainable development can be achieved (Wilson et al., 2007; Nourry, 2008). After a thorough analysis of various sustainable development indices, Nourry (2008) argued that no indicator can give a comprehensive insight into sustainability.

Based on the fundamental dimensions of sustainable development, we have assessed the economic aspect with GDP, the social aspect with life expectancy (Bilas et al., 2014), and the environmental aspect with ecological footprints (Moffatt, 2000; Siche et al., 2008). The index is developed using principal component analysis (PCA) to address the multicollinearity issue. Without losing the original information, PCA diminishes a large sum of correlated values into smaller uncorrelated values called components by incorporating their variances (Jolliffe, 1986). Nonetheless, for robustness checks, we have used an additional proxy, i.e., adjusted net saving (ANS). Previous studies have employed this measurement to assess sustainable development or green growth (Hess, 2010; Koirala and Pradhan, 2020; Ahmed et al., 2021). Adjusted net saving can be measured using following formula:

$ANS=\frac{\left(GNS-DPC+CEE-RDN-DCD\right)}{GNI}(1)$

where GNS is gross national saving, DPC is depreciation of produced capital, CEE is current (non-fixed capital) expenditure on education, RDN is rent from the depletion of natural capital, damages from CO₂ emissions, and GNI is gross national income.

Previous literature has developed the de jure and de facto measures of financial liberalization. For developing countries, studies find de factor measures more appropriate (Yao et al., 2018). For the de facto measure, a composite index based on Broad Money to GDP, domestic credit to the private sector (as a percentage of GDP), gross domestic savings to GDP, and FDI inflows. The data of all variables including military expenditures are retrieved from the World Development Indicators (WDI, 2017) except health expenditures and ecological footprints. The data of health expenditure is collected from the Pakistan Bureau of statistics while ecological footprints from Global Footprints Network (2019).

The following Model (Eq. 2) is developed to investigate the effect of military expenditures, health expenditures, and financial liberalization on sustainable development:

$\ln {\text{SD}}_t={\beta }_0+{\beta }_1\ln {\text{FL}}_t+{\beta }_2\ln {\text{HE}}_t+{\beta }_3\ln {\text{ME}}_t+{\mu }_t(2)$

where SD, sustainable development; FL, financial liberalization; HE, health expenditures; ME, military expenditures, and µ_t = error term.

Additionally, our study aims to examine the asymmetries between our target variables. By employing the non-linear ARDL Model by Shin et al. (2014), we investigate whether positive (negative) shocks in militarization, healthcare expenditures, and financial liberalization affect sustainable development in the short and long run. Following previously used empirical approaches the non-linear Model is developed below:

$\ln S{D}_t= {\beta }_0+{\beta }_t^{+}\ln F{L}_t^{+}+{\beta }_t^{-}\ln F{L}_t^{-}+{\beta }_t^{+}\ln H{E}_t^{+}+{\beta }_t^{-}\ln H{E}_t^{-}+{\beta }_t^{+}\ln M{E}_t^{+}+{\beta }_t^{-}\ln M{E}_t^{-}+ {\mu }_t(3)$

where β⁺ and β^- are the asymmetric parameters and $F{L}_t^{+},H{E}_t^{+}, and M{E}_i^{+}$ are the partial sum process of positive changes in financial liberalization, human capital, and military expenditures;i.e.,  POS  = $lnF{L}_t^{+}= {\displaystyle \sum }_{j=1}^t\Delta lnF{L}_t^{+}= {\displaystyle \sum }_{j=1}^t\text{max}\left(\Delta lnF{L}_j, 0\right)$; $lnH{E}_t^{+}= {\displaystyle \sum }_{j=1}^t\Delta lnH{E}_t^{+}= {\displaystyle \sum }_{j=1}^t\text{max}\left(\Delta lnH{E}_j, 0\right)$;  and $lnM{E}_t^{+}= {\displaystyle \sum }_{j=1}^t\Delta lnM{E}_t^{+}= {\displaystyle \sum }_{j=1}^t\text{max}\left(\Delta lnM{E}_j, 0\right)$,  while $F{L}_t^{-},H{E}_t^{-}, and M{E}_i^{-}$ are the partial sum process of negative changes in financial liberalization, human capital, and military expenditures;  i.e.,  NEG  = $lnF{L}_t^{-}= {\displaystyle \sum }_{j=1}^t\Delta lnF{L}_t^{-}= {\displaystyle \sum }_{j=1}^t\text{min}\left(\Delta lnF{L}_j, 0\right)$; $lnH{E}_t^{-}= {\displaystyle \sum }_{j=1}^t\Delta lnH{E}_t^{-}= {\displaystyle \sum }_{j=1}^t\text{min}\left(\Delta lnH{E}_j, 0\right)$; and $lnM{E}_t^{-}= {\displaystyle \sum }_{j=1}^t\Delta lnM{E}_t^{-}= {\displaystyle \sum }_{j=1}^t\text{min}\left(\Delta lnM{E}_j, 0\right)$.

4 Econometric Techniques

Our preliminary analysis includes the testing of the unit root. Initially, Augmented Dickey and Fuller (1979) and Phillips and Perron (1988) tests are analyzed to ensure the stationarity of series at the order I (0) or I (1). However, the stationarity of underlying variables is also assessed using Carrion-i-Silvestre et al. (2009) unit root test. The conventional unit root tests (i.e., ADF or PP) do not take structural breaks into account which lose the power and size of the test, leading to spurious empirical results (Hecq and Urbain, 1993). Carrion-i-Silvestre et al. (2009) unit root test allows up to five structural breaks in both slope and level. The algorithm of Bai and Perron (2003) is utilized by Carrion-i-Silvestre et al. (2009) to estimate structural breaks. Additionally, it incorporated the quasi-GLS detrending technique of Elliott et al. (1992) that allows asymptotic power functions.

In order to identify long run parameters or equilibrium between our underlying variables, Gregory and Hansen (1996) and Hatemi-j (2008) developed cointegration tests with structural breaks but the test of Maki (2012) performed better to deal with unknown multiple structural breaks. Likewise Carrion-i-Silvestre et al. (2009), Maki (2012) cointegration also provides up to five structural breaks stemming from the data. We utilized the regime shift approach that allows for structural breaks in levels and regressors. A wide range of studies has used this approach in economic and finance literature (Doğan, 2018; Rafindadi and Usman, 2019). Thus, we believe that Maki’s (2012) approach efficiently tests the cointegration relationship between sustainable development and its determinants. Four Models are developed by Maki (2012) to perform the test, i.e., Model 0 includes a break in intercept and no trend, Model 1 is related to a break in intercept, coefficients, and no trend, Model 2 includes a break in intercept, coefficients, and with a trend, last Model 3) includes break in intercept, coefficient, and trend. These Models can be expressed as:

$\text{Model}0:z_t= \mu + {\displaystyle \sum }_{i=1}^k{\mu }_i{D}_{i,t}+{\beta }^{\prime }{y}_t+{\mu }_{t,1}(4)$

$\text{Model}1:z_t= \mu + {\displaystyle \sum }_{i=1}^k{\mu }_i{D}_{i,t}+{\beta }^{\prime }{y}_t+{\displaystyle \sum }_{i=1}^k{\beta }_i{y}_t{D}_{i,t}+ {\mu }_{t,2}(5)$

$\text{Model}2:z_t= \mu + {\displaystyle \sum }_{i=1}^k{\mu }_i{D}_{i,t}+\gamma t+{\beta }^{\prime }{y}_t+{\displaystyle \sum }_{i=1}^k{\beta }_i{y}_t{D}_{i,t}+ {\mu }_{t,3}(6)$

$\text{Model}3:z_t= \mu + {\displaystyle \sum }_{i=1}^k{\mu }_i{D}_{i,t}+\gamma t+{\displaystyle \sum }_{i=1}^k{\gamma }_{i}t{D}_{i,t}+{\beta }^{\prime }{y}_t+{\displaystyle \sum }_{i=1}^k{\beta }_i{y}_t{D}_{i,t}+ {\mu }_{t,4}(7)$

where D_t, is the dummy variable, D_t = 1 if t > T_bi, and 0 if otherwise. T_bi represents the break years in the series. µ_t,1, µ_t,2, µ_t,3, µ_t,4 are the error terms for Eqs 4–7, which are identically and independently distributed with zero means. The null hypothesis states no cointegration among underlying variables.

Since the study is also interested in short-run effects along with long run effects using ARDL, the bounding testing approach consistent with previous studies is applied (Chen et al., 2020; Ullah et al., 2020; Baloch et al., 2021). Subject to the identification of valid lag order, the ARDL approach is able to mitigate serial correlation, omitted variables, and endogeneity bias (Pesaran et al., 2001). The short-run and long run coefficients are estimated after ensuring the cointegration. To use the bound testing approach, Eq. 2 is rewritten as an ARDL version of the Vector Error Correction Model (VECM):

$\Delta lnS{D}_t= {\beta }_0+ {\beta }_1^{\prime }\Delta lnS{D}_{t-k}+{\displaystyle \sum }_{k=0}^p{\beta }_1^{\prime }\Delta lnF{L}_{t-k}+ {\displaystyle \sum }_{k=0}^p{\beta }_2^{\prime }\Delta lnH{E}_{t-k}+{\displaystyle \sum }_{k=0}^p{\beta }_3^{\prime }\Delta lnM{E}_{t-k}+ {\gamma }_{1}lnF{L}_{t-1}+ {\gamma }_{2}lnH{E}_{t-1}+ {\gamma }_{3}lnM{E}_{t-1}+ \theta EC{T}_{t-1}+ {\epsilon }_t(8)$

where Δ is the difference operator, β₀ is the drift component, ε_t is the estimated error term, β₁, β₂, and β₃ are the short-run coefficients while γ₁, γ₂, and γ₃ denote long-run parameters. θECT_t-1 is the error correction term that signifies the long-run convergence and speed of adjustment to the equilibrium. The null hypothesis for Eq. 8 tests the presence of no cointegration (i.e., H₀ = γ₁ = γ₂ = γ₃ = 0) whereas the alternative hypothesis specific the presence of cointegration (i.e., H₁: γ₁ ≠ γ₂ ≠ γ₃ ≠ 0) among the underlying variables. The Wald F-statistics value is estimated with the concerned critical values following previous studies (Pesaran et al., 2001; Nkoro and Uko, 2016). The F-statistic value less than the lower bound critical value shows no cointegration while F-statistics above the upper bound critical value indicates the presence of cointegration. The F-statistic value between lower and upper bound critical values indicates that the test results are inconclusive. For the lag selection criterion, the Akaike information criterion (AIC) is selected based on previous studies (Chen et al., 2020; Baloch et al., 2021).

After ensuring the validity of the Models using diagnostic tests, we transformed Eq. 2 into ARDL (p, q) with the long run regression Model to derive a NARDL Model as shown below:

$S{D}_t= {\displaystyle \sum }_{j=1}^{p-1}{\lambda }_{j}S{D}_{t-j}+{\displaystyle \sum }_{j=0}^{q-1}\left({\Phi }_j^{+}lnF{L}_{t-j}^{+}+{\Phi }_j^{-}lnF{L}_{t-j}^{-}+{\Phi }_j^{+}lnH{E}_{i-j}^{+}+{\Phi }_j^{-}lnH{E}_{t-j}^{-}+{\Phi }_j^{+}lnM{E}_{t-j}^{+}+{\Phi }_j^{-}lnM{E}_{t-j}^{-}\right)+ {\epsilon }_t(9)$

where λ denotes the autoregressive parameters, ${\Phi }^{+}$ and ${\Phi }^{-}$ represents the distributed asymmetric lag parameters while ε is the error term, distributed with zero mean and constant variance. The unrestricted error terms for the NARDL Model (ECM) is shown below:

$\Delta S{D}_t= \rho S{D}_{t-1}+ {\theta }^{+}F{L}_{t-1}^{+}+ {\theta }^{-}F{L}_{t-1}^{-}+ {\theta }^{+}H{E}_{t-1}^{+}+ {\theta }^{-}H{E}_{t-1}^{-}+ {\theta }^{+}M{E}_{t-1}^{+}+ {\theta }^{-}M{E}_{t-1}^{-} {\displaystyle \sum }_{j=1}^{p-1}{\lambda }_{j}S{D}_{t-j}+{\displaystyle \sum }_{j=0}^{q-1}\left({\pi }_j^{+}lnF{L}_{t-j}^{+}+{\pi }_j^{-}lnF{L}_{t-j}^{-}+{\pi }_j^{+}lnH{E}_{i-j}^{+}+{\pi }_j^{-}lnH{E}_{t-j}^{-}+{\pi }_j^{+}lnM{E}_{t-j}^{+}+{\pi }_j^{-}lnM{E}_{t-j}^{-}\right)+ \mu (10)$

where $\rho = {\displaystyle \sum }_{j=1}^p{\lambda }_{j-1},{\theta }^{+}= {\displaystyle \sum }_{j=0}^q{\Phi }_j^{+},{\displaystyle \sum }_{j=0}^q{\Phi }_j^{-}, {\lambda }_j= -{\displaystyle \sum }_{i=j+1}^p{\lambda }_j$, for all j = 1, 2, 3, ., p-1; ${\pi }_j^{+}=-{\displaystyle \sum }_{i=j+1}^q{\Phi }_j^{+}$ for j = 1, 2, 3, ., q-1; ${\pi }_j^{-}=-{\displaystyle \sum }_{i=j+1}^q{\Phi }_j^{-}$ for j = 1, 2, 3, ., q-1. Using the ordinary least square (OLS) one-step procedure, the long run and short-run coefficients are estimated with Eq. 8. The long run coefficients are retrieved as β⁺ = −θ⁺/ρ and β⁻ = −θ⁻ whereas the short-run coefficients are estimated by the first differences variables. Lastly, the recursively asymmetric responses of sustainable development to a unit change in negative and positive shocks in financial liberalization, healthcare expenditures, and militarization are computed as follows:

$m_h^{+}={\displaystyle \sum }_{j=0}^h\frac{\partial S{D}_{t+j}}{\partial F{L}_t^{+}};m_h^{-}={\displaystyle \sum }_{j=0}^h\frac{\partial S{D}_{t+j}}{\partial F{L}_t^{-}};{\displaystyle \sum }_{j=0}^h\frac{\partial S{D}_{t+j}}{\partial H{E}_t^{+}};m_h^{-}={\displaystyle \sum }_{j=0}^h\frac{\partial S{D}_{t+j}}{\partial H{E}_t^{-}};{\displaystyle \sum }_{j=0}^h\frac{\partial S{D}_{t+j}}{\partial M{E}_t^{+}};m_h^{-}={\displaystyle \sum }_{j=0}^h\frac{\partial S{D}_{t+j}}{\partial M{E}_t^{-}}\text{for}h=\mathrm{0,1,2},\dots (11)$

Both long run and short run asymmetric effects are incorporated into ECM non-linear equations. Over the short-run, null hypothesis of symmetric adjustment can be tested using Wald test as: π⁺ = π⁻ for all j = 1,2,3, … ,q-1, and for long run as: θ⁺ = θ⁻. The decomposition of financial liberalization, health expenditures, and militarization in its negative and positive partial sums may provide complex interdependencies to evaluate non-linear cointegration. The NARDL Model of Shin et al. (2014) has the ability to efficiently disentangle the interactions between financial liberalization, health expenditures, militarization, and sustainable development by incorporating the asymmetric response of underlying variables toward sustainable development over time. To test the null hypothesis of ρ = 0 against the alternative hypothesis of ρ < 0 for detecting cointegration, the F_PSS statistic of Pesaran et al. (2001) and t_BDM of Banerjee et al. (1998). The cointegration can be detected when the value (F_PSS) is above the upper bound. On the other hand, the test will be inconclusive if the value is between upper and lower bound.

After ensuring the long run relationship among variables, the long run coefficients are further estimated using the fully modified ordinary least squares (FMOLS) method developed by Phillips and Hansen (1990) and dynamic OLS (Stock and Watson, 1993) for robustness checks. This technique has the advantage over others in dealing with serial correlation issues, endogeneity, and sample bias (Narayan and Narayan, 2005). Following FMOLS Model is estimated for the long run relationships:

$X_t={\beta }_0+{\beta }_1{Y}_t+{\beta }_t\text{t}=\mathrm{1,2,3},\dots ,\text{n}(12)$

where X_t is an I (1) variable and Y_t is a (k × 1) vector of I (1) regressors.

Lastly, the possible causal relationships among the variables are investigated using Toda and Yamamoto (1995). It is a modified version of the Granger causality and produces consistent and robust causality Wald test statistic even when the order of integration in a time-series is I (0), I, (1), or a mix of these orders. Basically, it is constructed on the vector regressive (VAR) structure (k + d_max) where d_max is the optimum order of integration, and k is the optimum order in the VAR system. Eqs 13–16 are specified to study the causal relationships using Toda and Yamamoto (1995):

$\text{ln}S{D}_t={\gamma }_0+{\displaystyle \sum }_{i=1}^k{\gamma }_{i1}\text{ln}S{D}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\gamma }_{i2}\text{ln}S{D}_{t-j}+{\displaystyle \sum }_{i=1}^k{\beta }_{i1}\text{ln}M{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\beta }_{i2}\text{ln}M{E}_{t-j}+ {\displaystyle \sum }_{i=1}^k{\mu }_{i1}\text{ln}F{L}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\mu }_{i2}\text{ln}F{L}_{t-j}+ {\displaystyle \sum }_{i=1}^k{\alpha }_{i1}\text{ln}H{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\alpha }_{i2}\text{ln}H{E}_{t-j}+ {\upsilon }_{it}(13)$

$\text{ln}M{E}_t={\beta }_0+{\displaystyle \sum }_{i=1}^k{\beta }_{i1}\text{ln}M{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\beta }_{i2}\text{ln}M{E}_{t-j}+{\displaystyle \sum }_{i=1}^k{\gamma }_{i1}\text{ln}S{D}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\gamma }_{i2}\text{ln}S{D}_{t-j}+ {\displaystyle \sum }_{i=1}^k{\mu }_{i1}\text{ln}F{L}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\mu }_{i2}\text{ln}F{L}_{t-j}+ {\displaystyle \sum }_{i=1}^k{\alpha }_{i1}\text{ln}H{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\alpha }_{i2}\text{ln}H{E}_{t-j}+ {\upsilon }_{2t}(14)$

$\text{ln}F{L}_t={\mu }_0+ {\displaystyle \sum }_{i=1}^k{\mu }_{i1}\text{ln}F{L}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\mu }_{i2}\text{ln}F{L}_{t-j}+{\displaystyle \sum }_{i=1}^k{\gamma }_{i1}\text{ln}S{D}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\gamma }_{i2}\text{ln}S{D}_{t-j}+{\displaystyle \sum }_{i=1}^k{\beta }_{i1}\text{ln}M{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\beta }_{i2}\text{ln}M{E}_{t-j}+ {\displaystyle \sum }_{i=1}^k{\alpha }_{i1}\text{ln}H{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\alpha }_{i2}\text{ln}H{E}_{t-j}+ {\upsilon }_{2t}(15)$

$\text{ln}H{E}_t={\alpha }_0+ {\displaystyle \sum }_{i=1}^k{\alpha }_{i1}\text{ln}H{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\alpha }_{i2}\text{ln}H{E}_{t-j}+{\displaystyle \sum }_{i=1}^k{\gamma }_{i1}\text{ln}S{D}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\gamma }_{i2}\text{ln}S{D}_{t-j}+{\displaystyle \sum }_{i=1}^k{\beta }_{i1}\text{ln}M{E}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\beta }_{i2}\text{ln}M{E}_{t-j}+ {\displaystyle \sum }_{i=1}^k{\mu }_{i1}\text{ln}F{L}_{t-1}+{\displaystyle \sum }_{j=k+1}^{dmax}{\mu }_{i2}\text{ln}F{L}_{t-j}+ {\upsilon }_{3t}(16)$

5 Empirical Results

The descriptive statistics of the variables are given in Table 1. The mean values, standard deviation, minimum, maximum, skewness, kurtosis, and Shapiro-Wilk statistics are given in the Table. Since our number of observations is less than 50, the Shapiro-Wilk test is more appropriate for testing normality (Mishra et al., 2019). Additionally, the study proceeds with the deterministic properties of these parameters.

TABLE 1

TABLE 1. Descriptive statistics.

Initially, ADF and PP unit root tests are applied to ensure if the series is stationary at level or first difference. The findings reported in Table 2 show that all variables are stationary either at I (0) or I (1). None of the variable series is integrated at the order I (2). In addition to PP and ADF tests, the Carrion-i-Silvestre et al. (2009) unit root test is adopted to investigate the integration orders of the variables under the existence of multiple structural breaks. Table 3 represents the results of the Carrion-i-Silvestre et al. (2009) unit root test. According to unit root test results, the null hypothesis of there is a unit root under multiple structural breaks in the series can be rejected when we take the first differences of the variables. All variables in Eq. 1 are stationary at their first differences under multiple structural breaks meaning that all variables are integrated of order one, I (1).

TABLE 2

TABLE 2. Unit root tests (without structural breaks).

TABLE 3

TABLE 3. The quasi-GLS based Unit Root Tests under Multiple Structural Breaks.

The existence of the long run equilibrium relationship among variables under multiple structural breaks is investigated by Maki’s (2012) cointegration test and ARDL bound testing test. Table 4 shows bound testing cointegration F-statistics values for both ARDL and NARDL. To avoid the classical assumptions’ violation, different tests are utilized to selected optimum lags. Starting from high lag order, lags are decided based on AIC. The F-test values denote the existence of long run cointegration among underlying variables. The critical values developed by Pesaran et al. (2001) and Narayan (2005) are utilized to compare the F-statistics. Since both F-test values are above upper bound critical values (5% for all Models), the null hypothesis of no cointegration can be rejected. Accordingly, we suggest a long run relationship among sustainable development, health expenditures, military expenditures, and financial liberalization. The ECT_t-1 further confirms the existence of the long run relationships (see Table 6). Besides bound testing, the results of Maki (2012) cointegration test are reported in Table 5. The test also indicates that there is a long run equilibrium relationship among target variables in Pakistan when structural breaks are taken into consideration in all Models of Maki (2012). Our findings suggest that financial liberalization, health, and military expenditures are long run determinants of sustainable development for the case of Pakistan over the period 1971–2017.

TABLE 4

TABLE 4. Cointegration bound test results for the baseline equation.

TABLE 5

TABLE 5. Maki (2012) Cointegration test under multiple structural breaks.

After the confirmation of cointegration among variables, the ARDL and NARDL Models are utilized for the estimation of short run and long run coefficients (see Table 6). The results of linear estimations confirm the negative effect of militarization on sustainable development in both the short and long run. This evidence suggests the “tread of destruction” and the crowding-out effect of militarization in Pakistan consistent with previous studies (Ahmed Z. et al., 2020; Gokmenoglu et al., 2021; Ullah et al., 2021). Our findings also suggest no significant effect of health expenditures in the short run but a positive effect on sustainable development in the long run. Although the effect of health expenditures is not observed on sustainable development in previous studies, our study postulated that an increase in health expenditures can help Pakistan to achieve its SDGs goals (Liaropoulos and Goranitis, 2015; Chotchoungchatchai et al., 2020).

TABLE 6

TABLE 6. Short run and long run estimates based on the selected linear and non-linear ARDL Model.

The linear ARDL estimates also show a significant and positive effect of financial liberalization on sustainable development in both the short and long run. It is purported that financial liberalization does not hamper environmental quality (Hua and Boateng, 2015) and is a good mechanism for sustainable economic growth. Finally, the ECM coefficient value is significant and negative (−0.326), suggesting that a deviation from the long run equilibrium level of sustainable development in 1 year is corrected by 33% in the subsequent year. Some interesting insights are provided by non-linear ARDL estimations.

The findings show that positive shocks in military expenditures impede sustainability in the short-run and the effect gets stronger in the long run as the coefficient value increase from −0.04 to −0.89. Nonetheless, the negative shock in militarization improves sustainable development in the long run only. Results are consistent with the prior literature that there is an asymmetric effect of militarization on economic or environmental factors (Hatemi-J et al., 2018; Amir-ud-Din et al., 2020; Ullah et al., 2021). In the context of health expenditures, the evidence strongly suggests the non-linear relationship between health expenditures and sustainable development. The effect and negative in short-run only when negative shocks in health expenditures occur. On the other hand, the positive effect on sustainability can be observed in the long run only when there are positive shocks in health expenditures. In tandem with the postulations of Fan et al. (2018), we suggest more budget allocation to the healthcare sector to promote sustainable economic department and reduction of militarization’s crowding effect. Finally, the results of NARDL show that positive (negative) shocks in financial liberalization improve (impede) sustainable development in both the short and long run. However, the effect is stronger in the long run.

5.1 Robustness Checks

For robustness checks, we have utilized adjusted net saving (ANS) as an alternative proxy of sustainable development. However, the results largely remain the same except for militarization (see Table 7). Using ANS as a proxy, we find no significant effect of positive (negative) shocks of militarization in the short run. Additionally, in the long run, negative shocks in military expenditures improve green growth but positive shocks play no role in influencing sustainability. Despite the little variation, the findings suggest asymmetry among the underlying variables.

TABLE 7

TABLE 7. Short run and long run estimates based on the selected linear and non-linear ARDL Model.

The long run coefficients of the variables are also estimated by the FMOLS approach (see Table 8). Our findings suggest that health expenditure and financial liberalization have significant and positive impacts on sustainable development in long run for the case of Pakistan. When health expenditure and financial liberalization increase by 1%, the sustainable development of Pakistan increases by 0.070 and 0.056% in long run, respectively. Our findings also reveal that military expenditure has a significant and negative impact on sustainable development in the long run. If military expenditure increases by 1%, sustainable development decreases by 0.199% for in Pakistan. Estimated long run coefficients suggest that health expenditure and financial liberalization contribute to the sustainable development of Pakistan while military expenditure impedes sustainability.

TABLE 8

TABLE 8. Estimation of Long Run Coefficients by FMOLS approach.

The causal relationships among variables are investigated by Toda and Yamamoto’s (1995) causality test. Table 9 represents the results of the Toda and Yamamoto (1995) causality test. Causality results suggest that unidirectional relationships are running from health expenditure and financial liberalization to sustainable development. When there is a change in health expenditure and financial liberalization, there is a change in the sustainable development of Pakistan. Moreover, we can conclude that sustainable development in Pakistan is health expenditure and financial liberalization driven. There is also a unidirectional causality running from financial liberalization to military expenditure meaning that changes in financial liberalization cause changes in military expenditure of Pakistan.

TABLE 9

TABLE 9. Toda and Yamamoto (1995) Causality test results.

6 Discussion

The findings of our study are in accordance with the theoretical propositions. Although it is the first attempt to empirically test the military expenditures with sustainable development, the detrimental effects of militarization are consistent with the results of Gokmenoglu et al. (2021) and Ahmed Z. et al. (2020). In tandem with the “treadmill of destruction” theory, we argue that military mobility, training, weapon testing, and other activities increase the energy demands, pushing regimes to fulfill these demands through GHG emitting resources (Clark et al., 2010). Therefore, imprudent spending on military activities is against the UN’s 2030 agenda for sustainable development. Countries need to rethink their opportunity cost and size of defense spending that could instead be used to directly stimulate green growth (Tian et al., 2020).

Especially in the context of Pakistan, the government is allocating more budget to militarization leaving less financial resources for other productive sectors. For instance, the defense spending of Pakistan was around 3.60% of GDP (in the year 2016) which is greater than the military expenditures of some stable economies such as India (2.5%), China (1.92%), and the United States (3.2%). Our results can also be supported by the postulation of Korkmaz (2015) that a high level of militarization crowd out investment in health, infrastructure, and human capital which impede economic development. On the other hand, our results suggest increasing healthcare expenditures for the long-term sustainability of Pakistan.

Unfortunately, the Pakistani government is ambiguously allocating resources. Even during the peak of the COVID-19 pandemic, the government of Pakistan allocated more budget to defense (USD 7.85 billion) and a very repressive level of budget for the healthcare sector (USD 151 million) for the financial year 2020–2021 (Siddiqa, 2020). Our results are not consistent with Pervaiz et al. (2021) who found a negative effect of health expenditures on air quality. Especially for Pakistan, we are strongly in favor of increasing healthcare expenditures to spur sustainable development. Currently, the “out-of-pocket” healthcare expenditures are very high, elevating the vulnerability of poor households to health shocks. In order to achieve green growth by 2030, Pakistan needs to shift its military expenditures to health expenditures (Brollo and Hanedar, 2021).

In accordance with health expenditures, our findings also support financial liberalization as a strong mechanism to improve sustainable growth. To some extent, our findings can be supported by the results of Adeel-Farooq et al. (2017) and Hua and Boateng (2015) that economic growth coupled with environmental quality can be spurred by financial liberalization. More financial openness in a country like Pakistan can act as a strategic tool to achieve sustainability as powers can be swung to financial markets from military and bureaucracy, fetching energy-efficient eco-friendly technologies to the country. Some support can also be derived from ecological modernization theory (York and Rosa, 2003) that economic growth of a developing country can be stimulated by increasing healthcare expenditures and financial liberalization without ruining the environmental quality.

7 Conclusion and Policy Implications

The effect of financial liberalization, health expenditures, and military expenditures on sustainable development remained underexplored in previous studies. In an attempt to fill the theoretical and econometric gap, our study estimates short run and long run relationships among these variables using ARDL and NARDL approaches. Additionally, Maki cointegration under multiple structural breaks. Our results from Maki cointegration reveal the stable long run relationship between financial liberalization, health expenditures, military expenditures, and sustainable development. The findings of ARDL and NARDL assert the positive effect of financial liberalization and health expenditures while the negative effect of military expenditures on sustainable development in Pakistan. Additionally, NARDL suggests strong asymmetry among target variables. Based on the causality test, it can be also be purported that any policy to target health expenditures and financial liberalization will significantly affect the sustainable performance of Pakistan.

Grounded on the study’s findings, certain policy implications can be retrieved. Pakistan should not ignore the pollution-promoting facet of militarization and switch its defense expenditures to health and production-driven sectors. Owing to the internal conflicts and political instability in Pakistan, combat expenditures can be retained. However, it is high time to reduce non-combat expenditures to avoid macroeconomic, health, and environmental shocks. Additionally, investment in healthcare is an indirect investment in productive human capital which is one of the main drivers of sustainable development. Since our findings suggest that healthcare is a basic necessity and not a mere luxury for the sustainability of Pakistan, encouraging public-private partnerships will explore new avenues of investment in the healthcare sector. Lastly, more financial openness will help the economy to grow along with the reduction of environmental sustainability. Although the financial markets of South Asia are more liberalized after the post-reform period, military involvement in political affairs has hampered the democratic quality in Pakistan. Unless the detrimental effect of militarization is not scaled down, Pakistan will remain on the verge of an environmental catastrophe.

The study has certain limitations. First, it explores the underlying relationship in the context of Pakistan only. Second, the time span is limited to the year 2017. Some devastating events such as the coronavirus pandemic and the Russia-Ukraine war substantially changed the economic and political dynamics of the countries. These events are major structural breaks and may significantly change our estimated results. Third, only three variables are considered to investigate their effect on sustainable development. There is a fundamental role of green technology, clean energy, and eco-innovation to achieve carbon neutrality targets which should be incorporated in future frameworks.

Data Availability Statement

The data for this study was extracted from secondary sources which are publicly available. However, the data can be provided on demand.

Author Contributions

All authors equally contributed to conception and design, acquisition of data, analysis and interpretation of data. They also drafted the article for important intellectual content. All authors approved final version to be published and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

¹Despite common practice to treat financial liberalization and financial development analogous, the concepts are not identical. Financial development represents improvement and progress of the financial structure while financial liberalization denotes dismantling of barriers in the access and provision of financial services.

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