Asymmetric Effects of Fiscal Policy and Foreign Direct Investment Inflows on CO₂ Emissions—An Application of Nonlinear ARDL

Abstract

Research on the impact of fiscal policy and foreign direct investment (FDI) on environmental quality has yielded conflicting results on their effects on carbon dioxide emissions. To further explore the asymmetric influences of these two critical factors on environmental quality, we employed a nonlinear ARDL approach to examine how fiscal policy (GOEX), FDI inflows, and other drivers of CO₂ emissions, such as trade openness, financial development, and economic growth, have affected environmental quality in Vietnam from 1990 to 2022. Our findings indicate that a positive shock in GOEX results in decreased emissions, whereas a negative shock in GOEX leads to increased emissions, challenging previous research that suggests that higher expenditures typically harm the environment. We also observe that positive changes in FDI result in higher CO₂ emissions, whereas negative FDI shifts have no significant impact. Additionally, our study reveals that trade openness improves environmental conditions, whereas economic growth and financial development contribute to increased CO₂ emissions. The responses of CO₂ emissions to the asymmetric effects of fiscal policy, FDI inflows, and other determinants in the short term last in the long term. These insights are valuable for policymakers in developing environmental sustainability strategies to mitigate climate change by addressing fiscal policies and other determinants of CO₂ emissions.

1. Introduction

The global issue of climate change demands swift action to shift economies towards a low-carbon future. According to the Intergovernmental Panel on Climate Change report [1], global temperatures are projected to rise by 1.5 °C between 2030 and 2050, with further increases likely if greenhouse gas emissions are not reduced. There is a strong consensus among economists and scientists that global warming and climate change pose significant threats to economic growth, people’s livelihoods, and human survival [2,3,4,5].

As emissions rise globally, research on their causes, especially on the role of macroeconomic policies, is expanding. Fiscal policies, which are crucial for managing aggregate demand, economic growth, income, and inflation, are increasingly being scrutinized for their environmental impacts. These policies could worsen and mitigate CO₂ emissions. In a theoretical analysis of environmental fiscal reforms in India, Chaturvedi, et al. [6] determined that environmental fiscal reforms offer a more efficient and cost-effective approach to improving environmental conditions compared to conventional regulatory measures. Few studies have focused on government spending as a tool for environmental management [7,8], while others have linked significant public expenditure to environmental harm [9,10,11,12]. Some studies have reported ambiguous results on the effects of fiscal policies on environmental quality [13,14], with limited investigations on the specific impacts of expansionary and contractionary fiscal policies on emissions.

Thus, it is crucial to explain the impact of fiscal policy instruments on environmental quality. Government spending can influence carbon emission sources, thereby negatively affecting the environment [15]. Lopez and Palacios [16] discussed the mechanisms by which government expenditure affects environmental quality. Health and education spending can increase consumer income and potentially enhance environmental quality through income effects. Conversely, substantial government consumption can improve administrative and environmental controls and strengthen institutions that enhance environmental quality. Therefore, government spending has a positive effect on environmental pollution. However, López, et al. [7] also shows that fiscal policy mechanisms include technique, income, and composition effects, potentially suggesting the favorable ecological effects of fiscal policy. The technique effect enhances labor efficiency, which is linked to higher amounts of health and education spending, thus reducing pollution. The income effect denotes an increased demand for environmental quality with increasing income level. The composition effect involves public spending that promotes human capital-related economic activities that are less environmentally harmful than those linked to physical capital.

The impact of foreign direct investment (FDI) on carbon dioxide emissions is complex, as illustrated by the “Pollution Haven” and “Pollution Halo” hypotheses [17]. The “Pollution Haven” hypothesis suggests that FDI from developed countries relocates to developing nations with lax environmental regulations, increasing CO2 emissions. This follows comparative advantage theory, in which stricter regulations in developed countries raise production costs, prompting companies to move pollution-intensive operations to regions with looser standards. Conversely, the “Pollution Halo” hypothesis argues that FDI can improve environmental quality by introducing advanced technologies and management practices that reduce emissions. Proponents claim that FDI using cleaner technologies fosters eco-friendly growth and mitigates carbon emissions.

The World Bank identifies Vietnam as the country most impacted by air pollution among ten nations worldwide [18], with environmental deterioration emerging as a critical concern [19]. As a rapidly growing Southeast Asian economy striving to achieve a high–middle-income status and become a modern industrialized nation by 2035, Vietnam faces challenges in balancing economic growth with environmental conservation [20]. The country’s shift from a market economy to industrialization has negatively affected its environment and natural resources. Accelerated economic expansion, urban development, and industrial growth have led to increased energy use and increased environmental strain. The power sector’s reliance on fossil fuels contributes to approximately two-thirds of the nation’s greenhouse gas emissions [18].

Since 1990, Vietnam’s fiscal policy has supported national development through strategies such as economic revitalization, the State Budget Law, a Medium-Term Expenditure Framework, and tax reforms such as reducing corporate income tax rates and introducing value-added tax. The Public Debt Management Law (2013) and Public Investment Law (2016) aim to enhance public debt management, fiscal sustainability, and investment transparency. Vietnam has invested in infrastructure, education, and healthcare to boost its economic growth. Additionally, tax reforms and incentives have been introduced to attract foreign investment, while maintaining a low public debt-to-GDP ratio [21].

Research suggests that the effects of fiscal policy on environmental quality and FDI inflows vary according to pollutant type, region, and income level, necessitating further empirical studies to understand the relationship between fiscal policy, FDI inflows, and the environment. This study examines the dynamic connections between fiscal policy through government expenditure, FDI inflows, and CO₂ emissions in Vietnam from 1990 to 2022. Furthermore, negative and positive shocks to Vietnam’s government expenditure can lead to lower and higher CO₂ emissions, respectively. These findings are not in line with previous research that has linked increased spending to environmental degradation. Additionally, an increase in FDI correlates with decreased CO₂ emissions, whereas a reduction in FDI has no significant effect on emission levels. Furthermore, trade openness improves environmental quality by enhancing energy efficiency, while economic growth and financial development are associated with higher CO₂ emissions.

This study provides novel insights into the complex and non-uniform impacts of fiscal policy and foreign direct investment (FDI) on environmental quality in Vietnam. The asymmetric influence of fiscal policies and FDI on CO₂ emissions varies significantly according to the nature of the policy and the economic context of Vietnam. Specifically, expansionary fiscal policies, marked by positive government spending shocks, tend to elevate emissions, whereas contractionary fiscal policies, characterized by negative spending shocks, produce mixed effects that depend on the prevailing economic structure and regulatory framework. Moreover, the research highlights the asymmetric effects of FDI inflows: positive FDI shocks, such as investments in cleaner technologies and sustainable practices, can accelerate the transition towards a low-carbon economy, whereas negative FDI shocks have no statistical effect. Additionally, this study employs a nonlinear autoregressive distributed lag (ARDL) approach to assess these effects on Vietnam’s emerging economy. The nonlinear ARDL method offers several advantages. It captures both positive and negative variable effects simultaneously, performs well with limited sample sizes, accommodates variables integrated at different orders, and provides robust results through dynamic error correction. In contrast to conventional linear models that assume a uniform and symmetric impact of policy variables on environmental quality, the nonlinear ARDL approach employed in this study distinguishes between the effects of positive and negative shocks. This distinction enables a more refined analysis of the interplay between fiscal policy, FDI, and other determinants of CO₂ emissions in Vietnam, thereby providing deeper insights into policy implications and uniquely contributing to the literature on sustainable economic development.

The remainder of this paper is organized as follows. Section 2 reviews the relevant literature. The third section elaborates on the data sources, variable estimation techniques, and methodological approaches. Section 4 presents the results and analyses. Section 5 presents the conclusions and offers policy recommendations.

2. Literature Review

2.1. Fiscal Policy and Environmental Quality

Environmental and energy economists have shown a growing interest in the link between fiscal tools and environmental quality, although empirical research is still limited. Fiscal measures can increase and decrease CO₂ emissions, thereby influencing environmental quality. Few studies have analyzed government expenditure as a fiscal tool for managing environmental quality [7,8]. López, et al. [7] identified four ways that fiscal spending impacts atmospheric pollution: “scale, composition, technique, and income” effects. The scale effect pertains to environmental pressure from rapid economic growth. The composition effect involves shifting from physical capital-intensive inputs to less polluting human capital-intensive inputs. This technique results in more efficient labor through improved practices. The income effect reflects the desire for a better environment as the income increases. Chaturvedi, et al. [6] conducted a theoretical analysis of environmental fiscal reforms (EFR) in India and concluded that EFR could achieve more efficient and cost-effective environmental improvements than traditional regulations.

Joshi and Beck [22] focused on factors affecting carbon emissions, such as economic growth, political and economic freedom, population growth, urbanization, and energy use. Halkos and Paizanos [23] discovered no significant effect on CO₂ emissions but found a notable reduction in SO₂ emissions from government expenditures in 77 economies (1980–2000), with SO₂’s impact shifting from negative to positive as income levels rose. Studies by [16,23] neglect the asymmetric relationship between fiscal policy and CO₂ emissions. Gerlagh, et al. [24] found that vehicle registration taxes reduced carbon emissions in the automotive sector of 15 EU countries from 2001 to 2010. Current research has inadequately addressed the complex relationships between fiscal policies and CO₂ emissions, neglecting both the positive and negative aspects.

Katircioglu and Katircioglu [10] examined Turkey’s fiscal policy impact on environmental quality from 1960 to 2013 and concluded that fiscal measures reduce carbon emissions and are crucial for environmental management. Similarly, Yuelan, et al. [12] studied China’s fiscal policy tools and environmental degradation from 1980 to 2016, and found that fiscal policies significantly worsened long-term environmental quality, implying that expansionary fiscal measures harmed the environment. One limitation of these studies is the assumption of a symmetrical relationship between fiscal policy instruments and environmental quality.

Mughal, et al. [25] revealed conflicting results among ASEAN countries regarding the asymmetric impacts of fiscal policies on CO₂ emissions. In the long run, expansionary fiscal policy (positive government spending shocks) increased CO₂ emissions in Indonesia, the Philippines, and Thailand but had no significant effects in Malaysia or Singapore. Contractionary fiscal policy (negative government spending shocks) decreased CO₂ emissions in Indonesia, the Philippines, and Singapore but increased emissions in Malaysia. These divergent outcomes suggest that the environmental impacts of fiscal policies vary considerably across the ASEAN nations. In the short term, the effects of fiscal policies on CO₂ emissions differ among countries. Expansionary fiscal policies positively influenced emissions in all ASEAN economies except Malaysia. Contractionary fiscal policies negatively impacted emissions only in Malaysia and the Philippines. These contrasting short- and long-term effects across countries highlight the complex relationship between fiscal policies and environmental quality in the ASEAN region.

Given these conflicting results, there is a clear need for further research on the ecological impact of fiscal policies. Future studies should investigate the underlying factors contributing to these divergent outcomes across countries, such as differences in economic structures, the energy mix, and environmental regulations. Additionally, research should explore how specific attributes of government spending in each country can affect emissions. More insight into Vietnam would be valuable for designing more effective and targeted fiscal policies to improve environmental quality in ASEAN and other countries with similar economic and environmental backgrounds.

2.2. FDI and Environmental Quality

The “Pollution Haven” hypothesis introduced by [26] suggests that economic globalization drives industrialized nations to transfer high-pollution industries to developing countries with lax environmental regulations. This aligns with comparative advantage theory [27,28], which argues that stringent environmental regulations in developed countries increase production costs, leading manufacturers to relocate pollution-intensive operations to nations with looser regulations, a phenomenon also termed the “industrial flight hypothesis” [29,30,31,32,33]. Birdsall and Wheeler [34] proposed the “Pollution Halo” hypothesis, suggesting that FDI improves host countries’ environmental quality through advanced technologies and management experience, which was confirmed by [35,36,37,38] in South Africa from 1990 to 2018. However, Dong, et al. [39] observed heterogeneous technical spillover effects of FDI across Chinese provinces, indicating the coexistence of “Pollution Haven” and “Pollution Halo” effects. Xin-gang, et al. [40] attributed the “Pollution Haven” in China to the energy rebound effect from FDI-induced energy efficiency improvements. The overall impact of FDI on China’s carbon emissions remains uncertain, because these studies do not address the scale and industrial structure effects of FDI.

Foreign direct investment (FDI) has been hailed as a potential catalyst for environmental improvements in host countries. The transfer of advanced technologies and management practices from multinational enterprises can facilitate the adoption of cleaner production processes and energy-efficient techniques [41]. Moreover, FDI inflows can stimulate economic growth, which may lead to an increased demand for environmental quality and stricter environmental regulations [42]. Consequently, FDI could potentially reduce CO₂ emissions and improve environmental quality in recipient countries, especially in the long run [43]. Conversely, FDI may exacerbate environmental degradation through the Pollution Haven hypothesis, whereby multinational corporations relocate their polluting activities to countries with lax environmental regulations [44]. Furthermore, the scale effect of FDI-induced economic growth could outweigh the technique effect, leading to an overall increase in CO₂ emissions [45].

Previous research on foreign direct investment (FDI) has affected environmental quality in Southeast Asia, with a focus on nonlinear relationships. For example, increased FDI generally corresponds with higher CO₂ emissions and environmental degradation in most ASEAN countries [46]. Several studies identified U-shaped or inverted U-shaped relationships between economic growth, FDI, and environmental indicators, indicating that environmental impacts may shift as economies develop [47]. Studies in Singapore have reported no significant relationship between FDI and emissions [48]. Prior studies also discuss several contextual factors affecting the FDI–environment relationship, including the economic development stage, policy frameworks, and technological innovation [49]. Sector-specific research, particularly from Vietnam, has indicated varying environmental impacts across economic sectors [50]. FDI in the energy sector could correspond to increased pollution, while FDI in manufacturing and construction has shown less environmental impact despite high investment levels.

Given the contrasting potential impacts of FDI on environmental quality, there is a compelling need to investigate the asymmetric effects of FDI on CO₂ emissions. Negative and positive shocks or fluctuations in FDI inflows may have different effects on a country’s environmental performance. Negative FDI shocks, such as the divestment or downsizing of foreign firms, could lead to job losses, reduced economic activity, and potentially weaker environmental regulations, exacerbating CO₂ emissions [51]. In the context of Vietnam’s transitional economy, regulatory frameworks are still in the process of development, and political priorities can significantly influence policy enforcement [52]. Consequently, negative FDI shocks, such as divestment or downsizing by foreign firms, can lead to more severe adverse effects, including substantial job losses and reduced economic momentum, while also weakening the capacity to enforce environmental regulations. These factors create vulnerabilities that potentially exacerbate CO₂ emissions, particularly in sectors in which institutional oversight is still maturing. Conversely, positive FDI shocks, such as increased foreign investment in cleaner technologies and sustainable practices, may facilitate the transition to a low-carbon economy [53]. Moreover, Vietnam’s strategic focus on industrial upgrading and environmental sustainability has been central to its development agenda [54]. Positive FDI shocks, characterized by investments in cleaner technologies and sustainable practices, are amplified further in this context. Supportive political initiatives and comprehensive economic reforms have fostered an environment that not only attracts such beneficial investments but also promotes technological innovation and improved environmental outcomes, thereby facilitating a smoother transition towards sustainable growth [55]. By explicitly accounting for these asymmetries, policymakers can devise targeted strategies to mitigate the adverse environmental consequences of negative FDI shocks while capitalizing on the potential benefits of positive FDI inflows [56]. Such an asymmetric analysis is crucial for achieving sustainable economic development while safeguarding environmental quality.

2.3. Other Factors Responsible for Carbon Emissions

2.3.1. Financial Development and Carbon Emissions

The literature has found that financial development has a direct or indirect impact on carbon. Some studies suggest that financial development reduces the cost of credit, facilitating loans [57] and consequently boosting industrial output and development. This positive association with energy consumption is supported by [58], indicating that a developed financial sector stimulates energy use and carbon emissions [59,60]. Sadorsky [61] found that in Central and Eastern Europe’s frontier economies, financial development increased credit availability for energy-intensive products, such as houses, cars, and appliances, leading to higher energy use and carbon emissions. The mechanism behind this is that increased access to credit encourages the consumption of energy-intensive goods, thereby elevating overall energy demand and associated emissions. Conversely, Nasir, et al. [62] argued that financial development reduces intermediary costs and risks for investors in clean energy projects and enhances R&D investment in low-carbon technologies, which is crucial for reducing carbon emissions. In this context, financial institutions play a pivotal role by lowering barriers for green investments and channeling funds into energy-efficient technologies and renewable energy sources, which ultimately help mitigate the environmental impacts of traditional energy consumption. However, some scholars question whether financial development can reduce carbon dioxide emissions [63]. Therefore, the relationship between financial development and carbon emissions requires an empirical investigation across various contexts and variables.

2.3.2. Trade Openness and Carbon Emissions

Trade openness and development not only attract foreign direct investment and enhance R&D capabilities [64] but also boost energy efficiency and reduce carbon emissions [65]. They have improved their environmental governance [66], per capita carbon emissions, and elevated global environmental standards [67]. Moreover, international trade facilitates the spread of eco-friendly products and services, helping nations transition to low-carbon economies and tackle global carbon reduction [68]. In China, trade is crucial for economic growth, advancing pollution control and environmental management techniques [69]. The underlying mechanisms have been multifaceted in previous studies. On one hand, trade openness forces domestic firms to upgrade technology and improve efficiency to compete in global markets, thereby reducing their carbon intensity [70]. However, exposure to international environmental standards encourages the adoption of cleaner production practices [71]. Additionally, trade enables the diffusion of green technologies and eco-friendly products, creating a market pull for sustainable innovations [72]. However, the relationship between trade openness and carbon emissions has been debated, with studies showing mixed results. Wang, et al. [73] found that trade openness achieves carbon neutrality in wealthier countries, but not in poorer ones, echoing [74] on how income influences emissions. Lopez [75] noted that trade increases pollution and resource use owing to its scale effect, supporting the environmental Kuznets hypothesis. The impact of trade liberalization varies by a country’s income and economic structure, with [76] concluding that it reduces emissions only in high-income nations.

2.3.3. Economic Growth and Carbon Emissions

Economic growth positively affects CO₂ emissions across countries, as evidenced by numerous empirical studies that use diverse methodologies. This relationship has been identified in Nigeria [77], OECD countries [78], Egypt [79], South Africa [80], Turkey [81], and China [82] using estimators such as ARDL, FMOLS, and DOLS, with data spanning several decades. Raheem and Ogebe [83] and Zmami and Ben-Salha [84] found that economic growth increased CO₂ emissions in various countries and regions, such as 20 African, MINT, GCC, G7, and APEC countries, using different econometric approaches, including ARDL, STIRPAT, and DSUR, for different time periods ranging from 1980 to 2018.

3. Model and Methodology

Based on previous studies on the determinants of CO₂ emissions [11,85], we modeled CO₂ emissions as a function of the variables under consideration as follows:

$${\mathrm{C}\mathrm{O}}_2=\mathrm{f}(\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E},\mathrm{C}\mathrm{R}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{T},\mathrm{F}\mathrm{D}\mathrm{I},\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X},\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{C})$$

(1)

where CO₂, TRADE, CREDIT, FDI, GOEX, and GDPPC represent carbon dioxide emissions, trade openness, financial development, foreign direct investment inflows, fiscal policy, and economic growth, respectively. We claim that these variables are decisive production factors and the major drivers of CO₂ emissions. Variables in time-series data undergo standardization, and natural logarithms are employed to generate accurate estimates [86]. The period of 1990–2022 was selected based on the availability of complete data for all relevant variables. The dataset comprised 33 annual observations from 1990 to 2022, which is consistent with prior research in the field. For instance, Mujtaba and Jena [17] analyzed data from 1986 to 2014, and [85] used annual data from 1990 to 2021 to study environmental and economic dynamics. These examples demonstrate the methodological feasibility of obtaining meaningful results with datasets of similar sizes, supporting the suitability of our data for the applied econometric framework.

Table 1. Variable descriptions.

Symbol	Variables	Definition	Sources
CO2	Per capita carbon dioxide	Kt of CO2 emissions	WDI
TRADE	Trade openness	The sum of import and export (% of GDP)	WDI
CREDIT	Financial development	The volume of domestic credit to private sector (% of GDP)	WDI
FDI	Foreign direct investment inflow	Foreign direct investment inflows (% of GDP)	WDI
GOEX	Fiscal policy	Government expenditure (% of GDP)	WDI
GDPPC	Economic development	GDP per capita (constant USD 2010)	WDI

provides a detailed description of the study’s variables.

This study employs the augmented Dickey–Fuller (ADF) [87] and Phillips–Perron (PP) [88] tests to examine unit roots. To assess nonlinearity, this study utilized the BDS test, which was published as [89]. The logarithmically transformed Equation (1), incorporating both positive and negative changes in fiscal policy and foreign direct investment inflows while controlling for other determinants of carbon emissions, can be expressed as

$${\mathrm{C}\mathrm{O}}_2={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1^{+}{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{+}+{\mathsf{\beta }}_2^{-}{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{-}+{\mathsf{\beta }}_3^{+}{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{+}+{\mathsf{\beta }}_4^{-}{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{-}+{\mathsf{\beta }}_5{\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{C}\mathrm{R}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{T}}_{\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{C}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(2)

In this equation, the intercept is represented by $\mathsf{\alpha }$, the coefficients of the variables are denoted by $\mathsf{\beta }$, and $\mathsf{\epsilon }$ signifies the error term at time t. This study divides fiscal policy and foreign direct investment inflows into two separate components: a positive partial sum and a negative partial sum. From these, four new time series are generated as follows:

$${\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{+}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}∆{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{+}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}\mathrm{m}\mathrm{a}\mathrm{x}(∆{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{+},0)$$

(3)

$${\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{-}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}∆{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{-}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}\mathrm{m}\mathrm{i}\mathrm{n}(∆{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{-},0)$$

(4)

$${\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{+}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}∆{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{+}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}\mathrm{m}\mathrm{a}\mathrm{x}(∆{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{+},0)$$

(5)

$${\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{-}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}∆{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{-}={\sum }_{\mathrm{n}=1}^{\mathrm{t}}\mathrm{m}\mathrm{i}\mathrm{n}(∆{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{-},0)$$

(6)

The nonlinear autoregressive distributed lag (NARDL) framework in Equation (2) can be written as

$$\begin{gathered} {∆\mathrm{C}\mathrm{O}}_{2,\mathrm{t}}={\mathsf{\alpha }}_0+{\sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\theta }}_{\mathrm{k}}{∆\mathrm{C}\mathrm{O}}_{2,\mathrm{t}-\mathrm{k}}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{\sigma }}_{\mathrm{k}}^{+}{∆\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-\mathrm{k}}^{+}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{\tau }}_{\mathrm{k}}^{-}{∆\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-\mathrm{k}}^{-}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\cap }_{\mathrm{k}}^{+}{∆\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{k}}^{+}+ \\ {\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\varnothing }}_{\mathrm{k}}^{-}{∆\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{k}}^{-}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\aleph }}_{\mathrm{k}}{∆\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}-\mathrm{k}}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\forall }_{\mathrm{k}}{∆\mathrm{C}\mathrm{R}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{T}}_{\mathrm{t}-\mathrm{k}}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\aleph }}_{\mathrm{k}}{∆\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{C}}_{\mathrm{t}-\mathrm{k}}+{\mathsf{\omega }}_1{\mathrm{C}\mathrm{O}}_{2,\mathrm{t}-1}+ \\ {\mathsf{\omega }}_2{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-1}^{+}+{\mathsf{\omega }}_3{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-1}^{-}+{\mathsf{\omega }}_4{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-1}^{+}+{\mathsf{\omega }}_5{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-1}^{-}+{\mathsf{\omega }}_6{\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}-1}+{\mathsf{\omega }}_7{\mathrm{C}\mathrm{R}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\omega }}_8{\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{C}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(7)

The asymmetric influence of variables on carbon emissions was investigated using the nonlinear ARDL approach for several reasons: (i) it integrates nonlinear asymmetry and cointegration within a single equation; (ii) the NARDL model assesses how positive and negative changes in decomposed variables affect the dependent variable; (iii) the ARDL bounds testing approach is well suited for small samples due to its methodological flexibility and efficiency. Unlike traditional cointegration methods, it accommodates both I(0) and I(1) variables without requiring large datasets. Its unrestricted error correction mechanism (UECM) integrates short- and long-run dynamics in a single equation, reducing omitted variable bias. Additionally, ARDL relies on ordinary least squares (OLS), which remains effective in small samples. The asymptotic critical values provided by [90] further mitigate small-sample biases, making ARDL a robust tool for examining long-run relationships in limited data environments; (iv) it offers flexibility by not requiring variables to be integrated in the same order; (v) it functions as a dynamic error-correction representation, providing robust empirical results even with limited sample sizes.

The long-run symmetry (${\mathsf{\omega }}_4$ = ${\mathsf{\omega }}_5$ for GOEX and ${\mathsf{\omega }}_2$ = ${\mathsf{\omega }}_3$ for FDI) and asymmetry (${\mathsf{\omega }}_4$ ≠ ${\mathsf{\omega }}_5$ for GOEX and ${\mathsf{\omega }}_2$ ≠ ${\mathsf{\omega }}_3$ for FDI) are tested using the standard Wald test.

Regarding the long-term asymmetric relationship between FDI and carbon emissions, a statistically significant positive coefficient (${\mathsf{\omega }}_2$ > 0) indicates that an increase in FDI (positive shocks in the partial sum of FDI) leads to higher carbon emissions. Conversely, a statistically significant negative coefficient (${\mathsf{\omega }}_3$ < 0) suggests that a decrease in FDI (negative shocks in the partial sum of FDI) results in a substantial rise in carbon emissions. Similarly, for the long-term asymmetric relationship between GOEX and carbon emissions, a statistically significant positive coefficient (${\mathsf{\omega }}_4$ > 0) implies that an expansionary fiscal policy (positive shocks in the partial sum of FDI) increases carbon emissions. In contrast, a statistically significant negative coefficient (${\mathsf{\omega }}_5$ < 0) indicates that a contractionary fiscal policy (negative shocks in the partial sum of FDI) significantly raises carbon emissions.

To calculate short-term NARDL elasticities by incorporating an error correction mechanism, researchers can employ the following equation:

$$\begin{gathered} {∆\mathrm{C}\mathrm{O}}_{2,\mathrm{t}}={\mathsf{\alpha }}_0+{\sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\theta }}_{\mathrm{k}}{∆\mathrm{C}\mathrm{O}}_{2,\mathrm{t}-\mathrm{k}}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{\sigma }}_{\mathrm{k}}^{+}{∆\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-\mathrm{k}}^{+}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{\tau }}_{\mathrm{k}}^{-}{∆\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-\mathrm{k}}^{-}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\cap }_{\mathrm{k}}^{+}{∆\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{k}}^{+}+ \\ {\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\varnothing }}_{\mathrm{k}}^{-}{∆\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-\mathrm{k}}^{-}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\aleph }}_{\mathrm{k}}{∆\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}-\mathrm{k}}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\forall }_{\mathrm{k}}{∆\mathrm{C}\mathrm{R}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{T}}_{\mathrm{t}-\mathrm{k}}+{\sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\aleph }}_{\mathrm{k}}{∆\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{C}}_{\mathrm{t}-\mathrm{k}}+\mathrm{\varnothing }{\mathrm{E}\mathrm{C}\mathrm{M}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(8)

In Equation (8), $\varnothing$ represents the error correction term, which also shows the long-run equilibrium speed of adjustment after the shock in the short run. Also, the short-run symmetry (${\mathsf{\sigma }}_{\mathrm{k}}^{+}$ =${\mathsf{\tau }}_{\mathrm{k}}^{-}$ for GOEX and ${\cap }_{\mathrm{k}}^{+}$ = ${\mathrm{\varnothing }}_{\mathrm{k}}^{-}$ for FDI) and asymmetry (${\mathsf{\sigma }}_{\mathrm{k}}^{+}$ ≠${\mathsf{\tau }}_{\mathrm{k}}^{-}$ for GOEX and ${\cap }_{\mathrm{k}}^{+}$ ≠ ${\mathrm{\varnothing }}_{\mathrm{k}}^{-}$ for FDI) are tested by using the standard Wald test.

Following the verification of a long-term relationship, the dynamic multiplier effect is evaluated. The asymmetric cumulative dynamic multipliers’ impacts on CO₂ results from a single unit change in ${\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{+}$, ${\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}}^{-}$, ${\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{+},$ and ${\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}}^{-}$ and can be determined using the following methods:

$${\mathrm{m}}_{\mathrm{h}}^{+}={\sum }_{\mathrm{j}=0}^{\mathrm{h}}{\displaystyle \frac{\mathsf{\delta }{\mathrm{C}\mathrm{O}2}_{\mathrm{t}+\mathrm{j}}}{\mathsf{\delta }{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-1}^{+}}},{\mathrm{m}}_{\mathrm{h}}^{-}={\sum }_{\mathrm{j}=0}^{\mathrm{h}}{\displaystyle \frac{\mathsf{\delta }{\mathrm{C}\mathrm{O}2}_{\mathrm{t}+\mathrm{j}}}{\mathsf{\delta }{\mathrm{G}\mathrm{O}\mathrm{E}\mathrm{X}}_{\mathrm{t}-1}^{-}}},{\mathrm{m}}_{\mathrm{h}}^{+}={\sum }_{\mathrm{j}=0}^{\mathrm{h}}{\displaystyle \frac{\mathsf{\delta }{\mathrm{C}\mathrm{O}2}_{\mathrm{t}+\mathrm{j}}}{\mathsf{\delta }{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-1}^{+}}},{\mathrm{m}}_{\mathrm{h}}^{-}={\sum }_{\mathrm{j}=0}^{\mathrm{h}}{\displaystyle \frac{\mathsf{\delta }{\mathrm{C}\mathrm{O}2}_{\mathrm{t}+\mathrm{j}}}{\mathsf{\delta }{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{t}-1}^{-}}},\mathrm{h}=\mathrm{0,1},2\dots$$

(9)

where as $\mathrm{h}$ → ∞, then ${\mathrm{m}}_{\mathrm{h}}^{+}$→ ${\mathsf{\beta }}_1^{+}$ and ${\mathrm{m}}_{\mathrm{h}}^{-}$ → ${\mathsf{\beta }}_1^{-}$ for GOEX together with ${\mathrm{m}}_{\mathrm{h}}^{+}$→ ${\mathsf{\beta }}_3^{+}$ and ${\mathrm{m}}_{\mathrm{h}}^{-}$ → ${\mathsf{\beta }}_4^{-}$ for FDI. One should note that ${\mathsf{\beta }}_1^{+}={\displaystyle \frac{{\mathsf{\sigma }}_{\mathrm{k}}^{+}}{{\mathsf{\theta }}_{\mathrm{k}}}}$, ${\mathsf{\beta }}_2^{-}={\displaystyle \frac{{\mathsf{\tau }}_{\mathrm{k}}^{-}}{{\mathsf{\theta }}_{\mathrm{k}}}}$, ${\mathsf{\beta }}_3^{+}={\displaystyle \frac{{\cap }_{\mathrm{k}}^{+}}{{\mathsf{\theta }}_{\mathrm{k}}}}$, and ${\mathsf{\beta }}_4^{-}={\displaystyle \frac{{\mathrm{\varnothing }}_{\mathrm{k}}^{-}}{{\mathsf{\theta }}_{\mathrm{k}}}}$. The calculated dynamic multipliers allow for the observation of system-impacting shocks, revealing the dynamic process of adjustment both towards and away from a new equilibrium state.

Table 2. Descriptive statistics.

Variables	Mean	Median	Maximum	Minimum	Std. Dev.	Skewness	Kurtosis	Jarque–Bera	Prob.
CO2	11.427	11.460	13.027	9.869	0.943	−0.074	1.912	1.657	0.437
TRADE	4.778	4.830	5.229	4.193	0.293	−0.515	2.290	2.151	0.341
CREDIT	3.900	4.180	4.836	2.500	0.774	−0.527	1.759	3.646	0.162
FDI	1.631	1.569	2.480	1.023	0.352	0.792	2.768	3.520	0.172
GOEX	23.376	23.323	24.245	22.561	0.561	0.125	1.539	3.020	0.221
GDPPC	7.390	7.417	8.205	6.512	0.514	−0.089	1.824	1.945	0.378

shows the descriptive statistics that evaluate the performance of the underlying variables during the study period of 1990–2022, focusing on their central tendencies and dispersion. GOEX exhibits the highest mean value, followed by CO₂, GDPPC, TRADE, CREDIT, and FDI. Negative skewness was observed for all variables except FDI and GOEX. The 33 observations in each series demonstrated a normal distribution. As indicated by the standard deviation, CO₂ showed the highest volatility, followed by CREDIT, GOEX, GDPPC, FDI, and TRADE. This indicated a significant level of variability among the examined variables.

4. Results and Discussion

4.1. Preliminary Analysis

This study examines the uneven impact of fiscal policy tools and foreign direct investment inflows on environmental conditions in Vietnam.

Table 3. Unit root test.

Variables	ADF (p-Value)				PP (p-Value)
	Intercept		Intercept and Trend		Intercept		Intercept and Trend
Level
CO2	0.078	0.959	−2.114	0.519	0.110	0.962	−2.228	0.459
TRADE	−0.683	0.837	−2.270	0.437	−0.497	0.879	−2.231	0.457
CREDIT	−1.706	0.419	−0.961	0.936	−1.706	0.419	−1.023	0.926
FDI	−3.057 **	0.041	−3.342 *	0.078	−2.807 *	0.069	−2.969	0.156
GOEX	0.324	0.976	−3.763 **	0.036	0.194	0.968	−1.814	0.674
GDPPC	−1.330	0.602	−3.758 **	0.034	−2.257	0.192	−1.280	0.875
First differences
CO2	−5.501 ***	0.000	−5.395 ***	0.001	−5.542 ***	0.000	−5.429 ***	0.001
TRADE	−7.665 ***	0.000	−7.522 ***	0.000	−7.510 ***	0.000	−7.523 ***	0.000
CREDIT	−4.800 ***	0.001	−5.036 ***	0.002	−4.794 ***	0.001	−5.036 ***	0.002
FDI	−4.204 ***	0.003	−4.129 **	0.014	−4.099 ***	0.003	−4.007 **	0.019
GOEX	−4.424 ***	0.001	−4.364 ***	0.008	−4.507 ***	0.001	−4.456 ***	0.007
GDPPC	−4.887 ***	0.000	−5.026 ***	0.002	−3.671 **	0.010	−4.181 **	0.013

Note: *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively.

presents the results of the ADF and PP tests. The statistical findings from these tests reveal that all variables exhibit stationarity at either I(0) or I(1), with no variables showing stationarity at I(2).

Before employing the NARDL model, it is essential to examine the dataset for nonlinearity. To accomplish this, a nonlinear method known as BDS, introduced in [89], was applied.

Table 4. BDS test for nonlinearity results.

Series	Dimension 2	Dimension 3	Dimension 4	Dimension 5	Dimension 6
CO2	0.195 ***	0.327 ***	0.423 ***	0.488 ***	0.540 ***
TRADE	0.161 ***	0.285 ***	0.384 ***	0.456 ***	0.505 ***
CREDIT	0.202 ***	0.339 ***	0.433 ***	0.495 ***	0.535 ***
FDI	0.103 ***	0.160 ***	0.181 ***	0.200 ***	0.206 ***
GOEX	0.191 ***	0.317 ***	0.399 ***	0.454 ***	0.491 ***
GDPPC	0.199 ***	0.336 ***	0.431 ***	0.499 ***	0.549 ***

Note: *** indicates significance at the 1% level.

presents the results of the BDS test, indicating that all the variables exhibit nonlinear characteristics. Consequently, the NARDL model was deemed appropriate for further analysis.

4.2. Results of NARDL Bound Test

This study employed a NARDL bound methodology to establish long-term connections between variables. The results of the NARDL bound cointegration tests are presented in

Table 5. NARDL bound test.

F-Bounds Test		Null Hypothesis: No Levels Relationship
Test Statistic	Value	Signif.	I(0)	I(1)
F-statistic	3.90 **	10%	2.03	3.13
k	7	5%	2.32	3.50
		2.50%	2.60	3.84
		1%	2.96	4.26

Note: ** indicates significance at the 5% level.

. The findings reveal that the calculated F-statistics are significant at the 5% level, confirming the existence of a long-term cointegration relationship among the examined variables. This study distinguishes itself from previous research by demonstrating a long-run cointegration link between fiscal policy tools and CO₂ emissions within a nonlinear framework.

4.3. Long-Run Results

Another method for determining the existence of a long-term relationship is to examine the value of the cointegrating equation ECT_t−1 [90] among the variables under study. The error correction term (ECT_t−1) coefficient is both negative and statistically significant, indicating a substantial convergence towards a long-term equilibrium. Consequently, any deviation from the long-term equilibrium among variables is corrected in each period, ultimately resulting in the restoration of a stable, long-term equilibrium state.

Table 6. NARDL coefficient estimates.

Long-run estimation
Variable	Coefficient	Std. Error	t-Statistic	Prob.
FDI+	−0.124 ***	0.029	−4.294	0.005
FDI−	−0.125	0.088	−1.418	0.206
GOEX+	−1.275 **	0.354	−3.604	0.011
GOEX−	5.627 **	2.022	2.783	0.032
TRADE	−0.487 **	0.162	−3.009	0.024
CREDIT	0.357 **	0.112	3.188	0.019
GDPPC	3.197 ***	0.362	8.823	0.000
Short-run estimation
Variable	Coefficient	Std. Error	t-Statistic	Prob.
Δ (FDI+)	−0.128 **	0.041	−3.158	0.020
Δ (FDI−)	0.018	0.070	0.259	0.804
Δ (GOEX+)	−0.719 **	0.273	−2.632	0.039
Δ (GOEX−)	7.678 ***	1.164	6.597	0.001
Δ (TRADE)	−0.608 ***	0.113	−5.358	0.002
Δ (CREDIT)	0.491 ***	0.106	4.648	0.004
Δ (GDPPC)	1.209 *	0.494	2.446	0.050
C	−23.875 ***	3.378	−7.068	0.000
ECM (−1)	−2.456 ***	0.347	−7.088	0.000
R-squared	0.882	Adjusted R-squared	0.736
F-statistic	6.054	Prob (F-statistic)	0.001

Note: *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively.

presents the results for the asymmetric ARDL model. Long-term findings reveal that a positive change in FDI significantly affects CO₂ emissions. Specifically, a positive shock of 1% in FDI leads to a 0.124% improvement in the environmental quality. While a negative shift in FDI shows an unfavorable impact on environmental quality through an increase in carbon emissions, this effect is not statistically significant. Foreign direct investment has played a crucial role in improving the environmental quality and reducing carbon emissions in Vietnam through several channels. Multinational electronics and semiconductor companies have introduced advanced energy-efficient technologies to their manufacturing plants in Vietnam, contributing to cleaner production processes and reduced emissions [91]. Global apparel and footwear brands impose stricter environmental standards on their suppliers, prompting local firms to adopt sustainable practices [92]. For example, in the first nine months of 2024, FDI into Vietnam reached USD 24.8 billion, led by high-value industries, such as electronics, auto components, semiconductors, and green technology [93]. Furthermore, foreign investors in the renewable energy sector have increased environmental awareness and invested in wind farms and solar power plants, contributing to Vietnam’s transition towards a greener energy mix [94,95]. These outcomes agree with [96], who argue that, in the long run, a positive shock in FDI inflows has a harmful and substantial effect on CO₂ emissions in Nigeria, indicating that such a shock impedes CO₂ emissions. The research conducted by [97] aligns with our results, indicating that in the immediate aftermath, a 1% increase in FDI correlates with a 0.015% decrease in energy transformation quality, suggesting initial negative environmental consequences. Nevertheless, over an extended period, a significant 0.042% enhancement in energy transformation quality implies that FDI-driven projects ultimately contribute positively to sustainable energy practices.

This study deviates from the findings of [98], who demonstrate that a 1% increase in FDI results in a 0.1% increase in CO₂ emissions per capita. This divergence can be attributed to our study employing positive shocks in FDI as a factor that positively affects CO₂ emissions. According to our analysis, positive FDI shocks can decrease CO₂ emissions in Vietnam by facilitating technology transfers from multinational corporations. These investments have introduced advanced, environmentally friendly processes that enable Vietnamese firms to adopt energy-efficient, low-carbon practices. For instance, Samsung’s investment has led to emission reduction through the adoption of such technologies [99]. Similarly, increased FDI in renewable energy projects, such as the Bac Lieu wind farm, a joint venture between the Vietnamese government and Philippines-based companies, has reduced fossil fuel reliance and lowered power sector emissions [100].

In terms of practical implications for public policy, these findings underscore the importance of designing targeted policies that incentivize positive FDI inflows, while mitigating the risks associated with negative shocks. Specifically, policymakers should consider implementing measures, such as tax incentives, streamlined approval processes, and robust environmental standards, to attract green investments. Additionally, strengthening regulatory oversight can help ensure that any adverse effects of negative FDI shocks are promptly addressed, thereby safeguarding environmental quality. Such policy interventions not only support sustainable economic development but also enhance the overall resilience of Vietnam’s environmental regulatory framework.

The long-term effects of the changes in government spending show that a 1% increase in government expenditure results in a 1.275% reduction in pollution, whereas a 1% decrease leads to a 5.627% increase. This contrasts with previous studies by [11,12,23], which indicate that higher government spending harms environmental quality. In Vietnam, recent substantial growth in government operational expenses, outpacing revenue growth primarily driven by expanded social welfare programs, higher wages and benefits, and increased debt interest payments, explain the findings. The wage component now constitutes approximately 20% of the total expenditure, mostly because the base salary increases. Investments in education and healthcare have grown significantly, outpacing the average expenditure increase and increasing their budget shares to 19% and 9.5%, respectively [101]. This aligns with [7], who argued that changes in spending on public goods could significantly reduce pollution levels. As a typical case, Japan’s economy benefits from increasing government expenditures, leading to a 0.352% reduction in CO₂ emissions [11]. The Japanese government is likely to focus on investments in economic projects that employ advanced eco-friendly technologies to reduce pollution. This indicates that Japanese spending is mainly on human capital activities, such as health and education, which generate lower carbon emissions than physical infrastructure. This supports the notion that, while consumption-driven expenditures can cause environmental pollution, spending on health and education may prompt the government to enhance environmental quality through income-related channels [7,8]. These findings imply that fiscal policies are effective tools to improve environmental quality and address climate change in Vietnam.

A negative shock in fiscal policy, characterized by a reduction in government spending, can lead to higher emissions in Vietnam for several reasons. First, a contraction in government expenditure may result in lower investments in environmentally friendly infrastructure, renewable energy projects, and energy-efficient technologies. Foreign direct investment has played a crucial role in the development of Vietnam’s renewable energy sector, particularly in wind and solar power projects, which could discourage such investments, hindering the transition towards a greener energy mix and leading to a higher reliance on fossil fuels, which are major contributors to emissions [94,102]. Second, cuts in public spending on education and healthcare can adversely affect environmental quality [103]. As noted by [101], investments in these sectors have grown significantly in Vietnam, raising their budget shares, with education promoting environmental awareness and healthcare expenditure, improving overall well-being, and potentially reducing the harmful effects of pollution. A negative fiscal shock might undermine these investments, consequently weakening the government’s ability to effectively address environmental concerns. Furthermore, a fiscal contraction could slow economic growth and reduce household incomes, potentially leading to a greater reliance on less expensive but more polluting energy sources [104], such as coal or biomass, which is particularly relevant in Vietnam, where a significant portion of the population still relies on traditional energy sources [95].

The long-term findings suggest that TRADE coefficients have a considerable negative effect on carbon dioxide emissions. This indicates that a 1% shift in the TRADE coefficient results in an approximately 0.487% enhancement in environmental quality. This result suggests that trade liberalization could play a crucial role in enhancing energy efficiency and promoting energy-saving technologies, thereby reducing the emission of pollutants, such as CO₂ [105]. The estimated long-term coefficient of CREDIT is positive and statistically significant at the 5% level, suggesting that a 1% growth in economic activity leads to a 0.357% increase in CO₂ emissions. This indicates that economic expansion contributes to environmental deterioration over time. This suggests that financial development enables the provision of credit for energy-intensive goods such as homes, automobiles, and appliances, which increases energy usage and carbon emissions [61]. For comparison, FDI tends to reduce carbon emissions because multinational corporations often bring advanced green technologies and adhere to international environmental standards that promote energy efficiency (e.g., [61]). In contrast, domestic credit typically supports local firms, which may rely on more conventional, energy-intensive production methods, owing to less stringent environmental regulations and fewer incentives to invest in sustainable practices (see [62]). Consequently, the difference in financing sources contributes to the contrasting effects on carbon emissions.

Moreover, economic growth has a positive impact on CO₂ emissions, with a 1% increase in GDPPC resulting in a 3.197% decline in environmental quality. The increase in CO₂ emissions due to economic growth is primarily attributed to increased energy consumption, industrialization, and urbanization. As economies grow, they tend to rely more heavily on fossil fuels for energy, thereby releasing substantial amounts of CO₂. Research has demonstrated that increased industrial activity and urbanization accompanying economic growth also contribute to higher emissions, owing to greater energy and transportation demands [80,106,107].

4.4. Short-Run Findings

As reported in Table 6, a 1% increase in foreign direct investment (FDI) leads to a 0.128% decrease in CO₂ emissions in the short term, while negative changes in FDI show no significant impact on environmental quality. Additionally, positive shifts in government expenditure (GOEX) demonstrate significantly decreasing effects on CO₂ emissions, whereas a negative shift in government expenditure can lead to environmental degradation. Specifically, a 1% positive shock in the partial sum of government expenditure decreases carbon emissions by 0.719%, whereas a 1% negative shock increases emissions by 7.678%. Trade openness (TRADE) has a negative and significant effect on CO₂ emissions, with a 1% increase in trade openness improving environmental quality by 0.608%. Financial development (CREDIT) has a positive and significant impact on CO₂ emissions, as a 1% increase in this factor increases CO₂ emissions levels by 0.491%. Economic growth (GDP) significantly contributes to increased CO₂ emissions in the short run, with a 1% rise in GDP resulting in a 1.209% decline in environmental quality. Therefore, these short-term asymmetric responses persist over the long term. The nature of the linkages persists consistently in both the short and the long run.

4.5. Stability Diagnostic Test

Various diagnostic assessments were conducted to evaluate NARDL outcomes, and the comprehensive results are presented in

Table 7. Results of diagnostic tests.

Diagnostic Tests	F-Statistic	p-Value
Model misspecification: RESET test	0.376	0.566
Serial correlation: Breusch–Godfrey LM test	3.340	0.140
Heteroskedasticity test: Breusch–Pagan–Godfrey	0.486	0.902
Heteroskedasticity test: ARCH	0.779	0.385
Normality test: Jarque–Bera test	2.147	0.342
Long-run asymmetries based on Wald test	F-statistics	Prob.
FDI	0.552	0.486
GOEX	5.053 *	0.066
Short-run asymmetries based on Wald test	F-statistics	Prob.
FDI	0.663	0.425
GOEX	1.327	0.262

Note: * indicates a 10% significance level.

. The Breusch–Godfrey LM test was employed to detect serial correlations among variables. The table indicates that the LM test shows no first-order serial correlations. The RESET test confirmed the absence of model misspecification issues. Heteroscedasticity was examined using the Breusch–Pagan–Godfrey test and the ARCH test, with insignificant probability F-statistic values, suggesting that the null hypothesis of homoscedasticity cannot be rejected. The Jarque–Bera test for normal distribution demonstrates that the variables follow a normal distribution. Finally, the Wald test was used to examine the nonlinear behavior of the model. The Wald test results indicate long-run asymmetries in the model for government expenditures.

Stability techniques were used to evaluate the structural stability of the models. Figure 1 illustrates the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) stability tests, as introduced by Brown, et al. [108]. The results show that the statistics are significant at the 5% level, indicating that the model coefficients are structurally stable. Although an ECM coefficient of less than −1 may raise concerns about rapid adjustments and potential oscillatory behavior around the equilibrium, the stability of our model has been thoroughly validated through CUSUM and CUSUMSQ tests, as illustrated in Figure 1. These results confirm that the model remains stable despite the unusual ECM value, which likely reflects strong adjustment dynamics rather than structural issues.

Figure 2 illustrates the dynamic multiplier plots used to evaluate the asymmetries resulting from separating the GOEX and FDI into positive and negative shocks. The findings indicate an asymmetric adjustment of government expenditure (GOEX) and foreign direct investment inflows (FDI) towards equilibrium in response to positive and negative shocks over the long term. Additionally, an expansionary fiscal policy leads to a reduction in CO₂ emissions. Conversely, a contractionary fiscal policy resulted in a slight decrease in emissions during the forecast period.

5. Conclusions and Policy Implications

The impact of fiscal policy on environmental quality can be beneficial or harmful, depending on public sector spending, causing asymmetric effects on CO₂ emissions. Furthermore, mixed results have been found in previous studies on the decisive role of FDI. Against this backdrop, this study examines the asymmetric influence of fiscal policy and FDI on Vietnam’s environmental pollution (1990–2022) using the NARDL approach. The results indicate that the NARDL models statistically confirm a long-term causal relationship among variables, with a significant negative cointegrating parameter indicating both short- and long-term equilibrium effects. Based on the asymmetric ARDL estimation, an analysis of the long-run impacts of alterations in government expenditures reveals that a positive shock in GOEX results in decreased emissions, whereas a negative disruption in GOEX leads to increased emissions. Increases in foreign direct investment (FDI) can significantly decrease CO₂ emissions because FDI targets energy-saving industries and eco-friendly technologies. Conversely, decreases in FDI have a statistically insignificant effect on reducing CO₂ emissions, indicating that an increase in pollution is not strongly linked to an FDI decline. Trade openness enhances environmental quality, possibly by improving energy efficiency, whereas economic growth and financial development increase CO₂ emissions because of higher energy consumption, industrialization, and urbanization.

Given these findings, Vietnam and other countries with identical economic backgrounds could adopt strategic measures to improve environmental quality by reducing carbon emissions. First, adopting fiscal policy is essential, with increased government investment in public goods such as healthcare and education, which have smaller carbon footprints than heavy industry, thus reducing emissions while fostering economic growth towards sustainability. Second, Vietnam should design policies to draw FDI into green technologies and low-energy sectors by offering incentives for clean energy initiatives and enforcing strict regulations for high-polluting industries. Third, enhancing trade openness can aid in acquiring eco-friendly technologies and adopting international energy efficiency standards by revising trade policies to favor low-carbon imports and exports. Fourth, financial development should prioritize green investments and sustainable financial mechanisms, including green bonds and sustainability-linked loans, to channel funds into emission-reducing projects. Finally, economic growth must shift towards green sectors, encouraging investments in clean energy and low-carbon technologies, thereby uncoupling economic progress from energy consumption and CO₂ emissions.

This analysis elucidates the asymmetric impact of fiscal policy, FDI inflows, and other variables on CO₂ emissions in Vietnam. However, certain limitations of this study must be considered in subsequent studies. Extending the nonlinear ARDL approach to a panel of countries should improve the generalizability of the findings and facilitate cross-country comparisons, thus yielding more robust conclusions relevant to various economic structures and policy contexts. Furthermore, integrating disaggregated data at regional or sectoral levels may reveal potential heterogeneities within Vietnam, providing more nuanced insights and facilitating customized policy recommendations. Although this study examined the overall influence of fiscal policy on environmental quality, a more detailed analysis of its impact across different industries could offer valuable insights. Future research could break down government spending by sectors, such as infrastructure, manufacturing, and renewable energy, to better understand the distinct environmental effects of fiscal policies in each area.

Moreover, incorporating other explanatory variables, such as technical advancements, investments in renewable energy, and environmental regulations, could yield a more thorough understanding of the factors that affect environmental quality. Expanding the research to include additional environmental indicators beyond CO₂ emissions, such as air pollution, water quality, and deforestation, would provide a comprehensive evaluation of environmental impacts, thereby guiding policymakers on a wider array of issues. Furthermore, future research could delve deeper into examining the environmental impacts of specific forms of investment, distinguishing between external and internal investments, and carbon- and non-carbon-intensive ventures. This approach provides a more nuanced understanding of how different types of FDI contribute to emission dynamics and environmental quality. Addressing these potential enhancements could provide policymakers with more nuanced knowledge, facilitate the creation of effective and context-specific solutions to combat climate change, and advance sustainable development across diverse regions and industries.