Financial Flows and Green Growth: Investigating the Moderating Role of Energy Consumption and Energy Transition in Asian Economies

Abstract

The study investigates the influence of financial flows, measured using foreign direct investment, foreign trade (FT) and net borrowing, on green growth (GG). Energy consumption (EC) and energy transition are used as moderating variables. The theoretical framework draws support from the Pollution Halo Hypothesis and the Pollution Heaven Hypothesis. We use ordinary least squares and the system generalized method of moments for panel data, covering a time period of 28 years, from 1994 to 2021. The findings mainly suggest an adverse effect of financial flows on GG in Asia. Financial flows in the form of FT and net borrowing, although they support economic growth in Asian economies, pose environmental challenges. The findings also suggest a negative moderating role of EC in the relationship between financial flows and GG. The findings suggest that policymakers should align economic policies with environmental goals for a prosperous and sustainable ecosystem. Policies directing financial flows towards low carbon sectors and prioritizing improvements in energy efficiency can boost GG in Asian economies.

Keywords

Green growth financial flows energy consumption energy transition Asian economies

1. Introduction

Policymakers and regulators highly emphasize economic growth for the prosperity and development of nations. However, a paradigm shift has been observed, where greater emphasis is given to economic growth that contributes to ecological sustainability, which is called green growth (GG). GG aims to achieve economic growth while preserving environmental and natural resources (Liu & Chen, 2024). Climate change, caused by excessive pollution and emissions, poses a major challenge for Asian economies to achieve economic growth that is also eco-friendly.

Globally, carbon emissions constitute a major part of greenhouse gas emissions, and Asia is among the most adversely affected regions of the world, having 18,184.683 metric tons of carbon emissions, which are 158% higher than those recorded in the year 2000. In Asia, China, India and Japan are the top three countries with CO₂ emissions of 11,129.838, 2,763.338 and 934.785 metric tons, respectively (International Energy Agency, n.d.). A detailed snapshot of CO₂ emissions, energy transition (ET) and GG in Asia is provided in Appendices B–D.

GG simultaneously captures essential dimensions of economic growth and environmental conservation (OECD, 2011). The pace of GG in Asia is largely dependent on key financial flows, which include foreign direct investment (FDI), net borrowing or lending (B&L) and foreign trade (FT) (Phung, Hoang, et al., 2023). We study the influence of these financial flows on GG and also examine the moderating role of energy consumption (EC) and ET in Asia.

Financial flows in the form of FDI have a close linkage with GG in Asian economies. Empirically, FDI negatively affects GG, as FDI promotes economic growth, but does so at the cost of environmental damage (Chen et al., 2022). This argument is supported by recent evidence that FDI harms the environment if it is not supported by ET and strong environmental regulations (Deng et al., 2024). This supports recent evidence from South Asia, which suggests an increase in carbon emissions due to FDI and industrialization, whereas the use of green energy reduces environmental damage (Asif et al., 2024). Similarly, Ofori et al. (2023) suggest that the adverse effect of FDI on GG can be reduced through energy efficiency. Moreover, in the relationship between financial flows in the form of net B&L and economic growth and environmental sustainability, the evidence is mixed (Khodaparast Shirazi et al., 2020; Muhammad et al., 2021; Wang et al., 2021). However, in this regard, recent findings from Belt and Road Initiative (BRI) economies suggest that financial flows in the form of green finance promote GG; however, financial development can adversely affect GG due to the use of non-renewable energy (Saqib et al., 2024; Zhang et al., 2021).

Keeping in view the above-discussed findings, it is important to study the moderating role of EC and ET in the relationship between financial flows and GG. Shifting reliance towards renewable energy sources decreases dependence on fossil fuel-based energy sources, thereby increasing economic growth while protecting the environment (Shahbaz et al., 2019). A key problem for developing economies like those in Asia in attaining GG is excessive reliance on fossil fuels, which need to be replaced by clean energy sources (Zhang, Guo, et al., 2023). We use natural resource rents and forest area (FA) as control variables based on the critical role of these variables in GG (Hodzic et al., 2023; Shuchun & Alola, 2024).

The study contributes to the existing body of literature on GG in several ways. First, unlike earlier studies focused on single-channel analysis, this study examines the combined compatibility of FDI, FT and borrowing with GG in Asia. Second, the study adds value by testing the conditioning effect of EC and ET in the relationship between financial flows and GG in host economies. Particularly, the study shows that the EC channel is more prominent in explaining the effect of financial flows on GG, and that the ET channel has not become a significant channel. Third, the study theoretically contributes by suggesting a more dominant role of the Pollution Haven Hypothesis than the Pollution Halo Hypothesis in these economies, as financial flows are mainly targeted at achieving economic targets without serious consideration for ecological preservation. Finally, the study offers a policy guideline that financial flows are not fundamentally green; their impact on eco-friendly economic growth is dependent on the cleaner, regulated and appropriate use of financial flows in low-carbon domains.

The remaining study is organized as follows: Section 2 explains the literature and formulates hypotheses, Section 3 deals with materials and methods, Section 4 summarizes the results, and Section 5 offers the conclusion.

2. Literature Review

2.1. Financial Flows and Green Growth

In recent years, developed and developing economies have paid attention to the concept of GG, which integrates ecological sustainability into economic growth strategies. This cannot be achieved until policy interventions and sustainable projects are aligned with efficient resource governance (OECD, 2011; UNDP, 2023). Economies demand financial flows to balance economic advancement with environmental conservation. Financial flows represent the movement of monetary resources within and across borders to finance economic activities (Dyushu, 2025). Broadly, financial flows come from FDI, FT and net B&L. These sources contribute to generating the income of countries while investing in projects, production and consumption (Weimin & Chishti, 2021).

Debt financing is common in developing nations because these economies have restricted equity resources. Debt accumulation encourages energy-intensive projects in host countries, promoting economic expansion at the cost of environmental sustainability. However, developed economies have mature financial markets, relying on equity resources to finance clean-energy projects (Wartiainen et al., 2024).

The primary source of financial flows is FDI in developing Asian economies. Empirical studies conducted in Asian countries highlight that the impact of FDI on GG is mixed (Bhuiyan et al., 2023; Chen et al., 2022). These studies analyzed the halo mechanism and observed that FDI facilitates global financial integration, ensuring the movement of funds across countries for designing business infrastructure, creating a green economy and generating a balanced ecosystem while investing in renewable energy projects, including wind, solar and hydroelectric power and in clean technologies. Contrastingly, Tanveer et al. (2021), as well as Zhuo and Qamruzzaman (2022), highlight that developing countries are fossil-fuel-dependent and usually follow a haven approach to promote economic growth at the cost of environmental degradation. These inconsistencies lead to the first hypothesis of the study.

H₁: FDI affects the GG of Asian economies.

The second key source of financial flows is net B&L at the country level. Recent studies by Cheilas et al. (2024) and Miguel et al. (2024) found that borrowing and lending have the potential to significantly contribute to GG by financing eco-friendly projects. These projects follow the halo mechanism, encompassing energy-efficient technology, sustainable agriculture and eco-friendly urban planning, leading to a shift in the country towards a low-carbon economy.

Over the past 5 years, sustainable and green bonds have served as key sources of borrowing in Asian economies. Consequently, green bonds in emerging economies issued by corporations represented 80% of sustainable bonds in China and 46% of sustainable bonds in Japan. The official sector issued 59% of green bonds in China and 62% in Japan. This huge emphasis by Asian economies on green bonds aims to provide support to growth-oriented, innovative industries and promote environmentally friendly, sustainable growth (Chesini, 2024; OECD, 2025). On the contrary, Quang and Thao (2022) observed a negative relationship between green bonds and greenhouse gas emissions in ASEAN, indicating that green bonds have no pivotal role in the environmental sustainability of developing economies. However, Chesini (2024) highlights challenges, such as the diversion of funds from climate-friendly projects into carbon-intensive projects in developing countries under the haven hypothesis, underscoring the need to investigate how all forms of sovereign borrowing affect GG in Asian economies, thereby generating the second hypothesis of the study.

H₂: B&L affects GG.

The third important element of financial flows is FT, which involves the exchange of goods and services between countries, including eco-friendly advanced technologies. Zhang and Choi (2025) empirically observed a long-term equilibrium relationship between digital FT and total green factor productivity across 51 countries during 2005–2021. The results support the idea that eco-friendly technologies enhance the capacity of host industries to adopt cleaner and more sustainable practices and support the construction of green infrastructure projects, such as waste management facilities, water treatment plants and sustainable transportation systems, in line with the halo approach. As a result, FT is a crucial source of financial flows in reducing carbon emissions, improving resource efficiency, fostering the progress of green industries and generating a green economy under the halo hypothesis.

On the other hand, Liu et al. (2022) proposed another view regarding FT. They developed a threshold index for FT. The research outcome highlights that GG would be detrimental if economies go beyond the threshold index, resulting in reduced energy conservation and decreased environmental protection. This empirical evidence leads to the formulation of the third hypothesis of the study.

H₃: FT volume affects GG in Asian economies.

2.2. Financial Flows, Energy Consumption and Green Growth

The Pollution Haven Hypothesis suggests that developing countries attract capital investment in mining and extraction industries, oil refining and petrochemical plants, heavy transportation infrastructure, steel and cement manufacturing and desalination plants that demand more energy per unit and release maximum levels of CO₂ into the environment of recipient economies. As a result, the environment of host countries deteriorates and forms a ‘pollution haven’ around the deployed capital (Ozkan et al., 2023). Contrary to that, there is also a possibility that foreign investment may transfer environmentally friendly production practices to the host countries through the Halo approach. However, the impact of foreign capital on GG cannot be studied without considering the EC structure in host countries.

In Asia, especially in developing economies, the strategic goal of economic prosperity is to increase the standard of living by reducing poverty. These countries have little concern about environmental sustainability, prioritizing economic activities at the cost of ecological integrity. Tanveer et al. (2021) demonstrate that financing in energy-intensive industries may create new jobs, increase exports and escalate economic expansion, but may also undermine environmental quality. Moreover, Zhuo and Qamruzzaman (2022) empirically investigate the relationship among significant variables, including FDI, fuel consumption and environmental preservation in BRI participating economies. The findings indicate that the environment is being severely hampered due to the positive relationship between economic productivity and energy demand in BRI economies. Contrary to that, Si et al. (2024) observed that financial flows do not actively contribute to environmental pollution while investing in cleaner practices in host countries, especially developing ones. Therefore, EC may affect the strength and direction of the relationship between financial flows and GG and may act as a moderator in the study.

The moderating effect of EC is based on the intensity process, which suggests that deploying financial flows in fossil-fuel-intensive sectors, such as manufacturing, transportation and infrastructure, may increase economic growth, but at the cost of environmental degradation, which deteriorates GG. This argument is supported by the Pollution Haven Hypothesis, which suggests that external cash flows directed towards energy-intensive projects, although they increase economic output, leave ecologically harmful effects on the host country. Thus, EC not only plays the role of an additional input, but it also affects the way financial flows shape the GG pattern of Asian economies. Therefore, we propose the following hypotheses:

H_4a: EC moderates the relationship between FDI and GG.

H_4b: EC moderates the relationship between net B&L and GG.

H_4c: EC moderates the relationship between FT and GG.

2.3. Financial Flows, Energy Transition and Green Growth

The Pollution Halo Hypothesis suggests that investment in eco-friendly projects, including pollution-reducing technologies, sustainable infrastructure and renewable energy sources, prioritizes economic advancement alongside environmental sustainability, thereby promoting GG in recipient countries in Asia. However, the strategy of capital-exporting countries emphasizes controlling CO₂ emissions to prevent environmental degradation, shifting the recipient countries into a Pollution Halo effect (Phung, Rasoulinezhad, et al., 2023).

The Pollution Halo Hypothesis emphasizes foreign capital investment in practices that involve the global economic shift from intensive fossil fuel industries to eco-friendly infrastructure relying on less-carbon or zero-carbon emissions, resulting in GG and environmental stability (Si et al., 2024).

Ofori et al. (2023) studied the impact of FDI and energy efficiency on green economic interventions in Africa. The research findings indicate that energy efficiency has primarily contributed to the GG of African countries. Therefore, the ET towards clean energy may act as a moderator between FDI and inclusive GG. Similarly, Adejumo and Asongu (2020) document that FDI decreases CO₂ emissions, whereas domestic investment increases CO₂ emissions. One possible reason for this is that multinational organizations are often socially responsible and invest in energy-efficient projects that promote economic growth without degrading the environment. Li et al. (2022) discuss how developing countries are increasingly concerned with becoming environmentally stable economies. As a result, they selectively invest in opportunities that utilize renewable energy to achieve green development and follow a Halo mechanism. The following hypotheses of the study are generated under the Pollution Halo mechanism:

H_5a: ET moderates the relationship between FDI and GG.

H_5b: ET moderates the relationship between net B&L and GG.

H_5c: ET moderates the relationship between FT and GG.

2.4. Theoretical Underpinning

The conceptual framework of the study is derived from the Pollution Halo and Haven hypotheses to explore the influence of ET and EC in strengthening the relationship between financial flows and GG. Countries that follow the Haven Hypothesis and invest foreign funding in energy-intensive industries facilitate economic production at the expense of environmental stability. In contrast, the Halo approach facilitates clean-energy projects, fostering economic growth aligned with environmental quality. Based on this theoretical underpinning, Figure 1 depicts the conceptual model of the study.

3. Materials and Methods

3.1. Data and Sample

We use data from 26 Asian countries for the period spanning 1994–2021. The choice of the number of Asian economies and the time period is based on data availability. In order to ensure data quality, missing values are carefully removed, and countries having large data gaps are also excluded. In order to overcome biases and comparability issues, a balanced panel data set is used. We use the database of British Petroleum to collect data on EC and ET, and the World Bank Development Indicators database for the remaining variables.

3.2. Variable Measurement

GG is measured by subtracting per capita carbon emissions and per capita natural resource depletion from GDP per capita (Caetano et al., 2022). The environmental accounting framework, which requires adjustment for pollution and resource damage, supports the measurement of GG (Weber, 2018). In order to ensure measurement consistency and synchronization, all three elements of GG are measured on a per capita basis (Apergis & Payne, 2010). The natural logarithm of the three components of GG is also taken to normalize the data. Asian economies are facing a severe challenge of intensive natural resource utilization, which, although it results in economic growth, affects the environment, and this proxy truly captures this phenomenon.

FDI, FT and net B&L are used as proxies for financial flows, as these elements comprehensively measure external sources through which capital, trade goods and financing enter an economy. EC is measured using fossil fuels in exajoules, and ET is measured as the ratio of fossil fuel usage to renewable energy. ET captures the dependence of economies on fossil fuels relative to clean sources of energy. The control variables, natural resource rents and FA, represent the dependence of economies on natural resources and ecological capacity. Detailed variable measurement, along with empirical support, is provided in Table A1, Appendix A.

3.3. Estimation Method

To examine the effect of financial flows, measured through FDI, net B&L and FT, on GG, we estimate the following base model:

{G G}_{i t} = a_{0} + a_{1} ({F D I}_{i t}) + a_{2} ({B & L}_{i t}) + a_{3} ({F T}_{i t}) + a_{4} ({C O N T R O L S}_{i t}) + ε_{i t}

(1)

where i and t denote country and year, respectively.

Pooled ordinary least squares (OLS) is used as a base model to obtain preliminary insights into the relationship using a simple method before moving to advanced panel data techniques. System GMM is particularly suitable compared to fixed-effects, random-effects and panel-corrected standard-error methods, as GG is dynamic in nature, and the findings may suffer from endogeneity or reverse causality. By using instruments and lagged dependent variables, system GMM offers more reliable findings than static panel estimation methods.

4. Results and Discussions

4.1. Descriptive Statistics and Correlation Matrix

We present the descriptive statistics in Table 1. For GG, we document a mean of −0.070 and a standard deviation of 2.645 across the sample countries. Among the financial flow measures, net B&L has the highest mean of 4.880, with substantial variation (standard deviation = 4.798), followed by FDI with a mean of 4.520 and a standard deviation of 2.124. We observe more stable patterns of FT (mean = 4.229, standard deviation = 0.768). Among the moderating variables, EC has the highest standard deviation of 13.095, implying a diverse energy-usage pattern across Asian economies.

Table 1.

Descriptive Statistics.

Variable	Obs.	Mean	Std Dev.	Min	Max
EC	729	7.031	13.095	0.098	89.104
ET	729	0.118	0.313	0.000	3.798
GG	729	−0.070	2.645	−3.628	8.605
FDI	729	4.520	2.124	−4.129	10.143
B & L	729	4.880	4.798	0.000	15.066
FT	729	4.229	0.768	−3.863	6.081
NRR	729	0.997	2.763	−8.684	4.322
FA	729	2.065	2.015	−4.819	4.227

Note: Variables of descriptive statistics include energy consumption (EC), energy transition (ET), green growth (GG), foreign direct investment (FDI), net borrowing and lending (B & L), foreign trade (FT), natural resource rent (NRR) and forest area (FA).

We present the pairwise correlation results in Table 2. We find positive correlations of FDI, B&L and FA with GG at the 5% significance level. Meanwhile, EC and natural resource rents (NRR) are negatively correlated with GG at the 1% significance level. These are initial indications of the relationships between financial flows and control variables with GG.

Table 2.

Pairwise Correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) GG	1.000
(2) EC	−0.100*	1.000
(3) ET	0.040	−0.078*	1.000
(4) FDI	0.091*	0.087*	−0.124*	1.000
(5) B & L	0.451*	0.070	−0.101*	0.046	1.000
(6) FT	−0.040	0.099*	−0.013	0.390*	−0.048	1.000
(7) NRR	−0.968*	0.036	−0.045	−0.102*	−0.491*	−0.015	1.000
(8) FA	0.504*	0.161*	0.131*	−0.143*	0.424*	−0.052	−0.524*	1.00

Notes: Variables of pairwise correlations include energy consumption (EC), energy transition (ET), green growth (GG), foreign direct investment (FDI), net borrowing and lending (B & L), foreign trade (FT), natural resource rent (NRR) and forest area (FA). *Shows significance at p < 0.05.

Moreover, the Wooldridge test for autocorrelation (F (1,25) = 65.029, p < 0.001) suggests the issue of autocorrelation in the model. Similarly, the Breusch–Pagan/Cook–Weisberg test (χ²(1) = 134.41, p < 0.001) suggests the presence of the heteroskedasticity problem as well. Instrumental variables (2SLS) estimation is employed to identify the endogeneity problem. A p value of 0.2946 for the chi-square statistic suggests the issue of endogeneity for trade, whereas there is no such issue for any other variable.

4.2. Results of Ordinary Least Squares Estimations

Table 3 presents the regression results. We developed four distinct models. In Models 1–3, we include each variable of financial flows separately. Particularly, we include FDI in Model 1, B&L in Model 2 and FT in Model 3, respectively. In Model 4, we include all the variables together. We include NRR and FA as control variables in all model specifications. The table documents that financial flows, including FT and net B&L, have negative and statistically significant coefficients for GG, with coefficients ranging from −0.189 to −0.217 for FT and −0.017 to −0.020 for B&L, all significant at the 1% level.

Table 3.

Ordinary Least Squares (OLS) Estimations.

	Model (1) GG	Model (2) GG	Model (3) GG	Model (4) GG
FDI	−0.012 (0.011)			0.021 (0.021)
B & L			−0.017*** (0.005)	−0.020*** (0.005)
FT		−0.189*** (0.031)		−0.217*** (0.080)
NRR	−0.931*** (0.010)	−0.932*** (0.010)	−0.941*** (0.011)	−0.942*** (0.009)
FA	−0.010 (0.014)	−0.012 (0.013)	0.003 (0.014)	0.003 (0.015)
Constant	0.933*** (0.076)	1.688*** (0.141)	0.948*** (0.050)	1.782*** (0.287)
Observations	729	729	729	729
R ²	0.938	0.941	0.938	0.942
F-statistics(p value)	3,652.55 (0.000)	3,845.78 (0.000)	3,695.14 (0.000)	3,048.39 (0.000)

Notes: This table reports the impact of financial flows on green growth. Financial flows include foreign direct investment (FDI), borrowing and lending (B&L) and foreign trade (FT). Models 1–3 show individual effects, while Model 4 reports the joint effect. Natural resource rents (NRR) and forest area (FA) are control variables. Parentheses report robust standard errors. *** and * indicate significance at the 1%, 5% and 10% levels, respectively.

For all variables, winsorization at the 1% and 99% levels is employed to reduce the influence of extreme outliers arising from macroeconomic shocks, data-reporting inconsistencies and cross-country heterogeneity, consistent with prior empirical studies. The signs of the coefficients of FT and B&L are consistent with prior studies (Akam et al., 2022; Bombardini & Li, 2020; Farooq et al., 2023; Xu et al., 2022; Zhang, Yang, et al., 2023). In terms of economic impact, the findings advise that a 1% rise in FT activity or net borrowing by the government results in a significant decrease in GG. These findings support the second and third hypotheses, which claim that financial flows in the form of FT and B&L influence GG in Asian economies. From a practical perspective, the findings suggest that trade-driven growth in Asia results in carbon leakages, transport emissions and export-related industrial waste, which adversely affect GG across Asian economies. In contrast, the findings of the study do not support Zhang and Choi (2025), who empirically evidenced positive renewable energy practices in reducing environmental deterioration in developing nations. Meanwhile, in the case of B&L, the debt burden and the trade-off between economic growth and environmental considerations deteriorate the GG of Asian economies.

We further show that FDI is not significant in Models 1 and 4. These findings suggest that FDI does not play a role in promoting GG within Asian economies, thus rejecting the first hypothesis of the study. According to Khalid and Abdul (2025), one possible reason is that Asian economies, including Bangladesh, have weak regulatory mechanisms and poor environmental governance, preventing funding from being invested in cleaner practices.

The negative coefficient of the control variable NRR, significant at the 1% level in Models 1–4, is aligned with empirical support (Safdar et al., 2022). Economically, these findings support the resource curse view, indicating that excessive dependence on natural resource rents results in technological avoidance and ecological degradation, which adversely affect GG. The control variable FA is insignificant across all models.

4.3. Robustness of Findings

4.3.1. Dynamic Panel Data Analysis

In the previous section, pooled OLS is used as the baseline model, whereas in this section, robustness is established using the system GMM estimation technique. System GMM is preferred over fixed-effects, random-effects and panel-corrected standard-error methods, as these methods cannot handle the endogeneity problem and the dynamic nature of the GG variable.

Table 4 reports system GMM results, which are aligned with the initial findings of OLS across all four models (Models 1–4). Lagged levels of FT and FDI are taken as internal instruments. The Hansen test confirms the validity of the instruments. The findings indicate that financial flows in the form of FT adversely affect GG. FT has a statistically significant negative coefficient at the 5% significance level in Models 3 and 4. The financial flows in the form of B&L also negatively affect GG at the 1% significance level. The control variable NRR also has a negative coefficient at the 1% significance level, while FA is insignificant, which is aligned with the OLS results. Thus, the findings are robust to an alternative estimation method.

Table 4.

Dynamic Panel Data Analysis Using System Generalized Method of Moments (SGMM) Estimations.

	Model (1) GG	Model (2) GG	Model (3) GG	Model (4) GG
GG _t ₋ ₁	0.512*** (0.037)	0.511*** (0.036)	0.524*** (0.038)	0.487*** (0.056)
FDI	−0.010 (0.011)			−0.008 (0.010)
B & L		−0.007** (0.003)		−0.013** (0.005)
FT			−0.266*** (0.084)	−0.322*** (0.102)
NRR	−0.467*** (0.038)	−0.475*** (0.000)	−0.456*** (0.040)	−0.476*** (0.047)
FA	−0.021 (0.061)	−0.020 (0.025)	−0.019 (0.044)	0.052 (0.070)
Constant	0.492*** (0.189)	0.473*** (0.063)	1.537*** (0.356)	1.782*** (0.390)
Observations	729	729	729	729
AR (1)(p value)	−2.540 (0.011)	−2.530 (0.012)	−2.660 (0.008)	−2.600 (0.009)
AR (2)(p value)	−0.780 (0.433)	−0.820 (0.411)	−0.800 (0.425)	−0.870 (0.382)
Hansen(p value)	16.960 (1.000)	21.370 (1.000)	19.990 (1.000)	19.390 (1.000)
Wald(p value)	178,306.49 (0.000)	242,786.47 (0.000)	307,158.27 (0.000)	391,027.58 (0.000)

Notes: This table reports the impact of financial flows on green growth. Financial flows include foreign direct investment (FDI), borrowing and lending (B & L) and foreign trade (FT). Models 1–3 show individual effects, while Model 4 reports the joint effect. Natural resource rents (NRR) and forest area (FA) are control variables. Parentheses report robust standard errors. *** and * indicate significance at the 1%, 5% and 10% levels, respectively.

4.3.2. Alternative Measurement

The measurement errors in GG can cause spurious correlations between variables of financial flows and GG. Following earlier studies, we use the Human Development Index minus carbon emissions (HDI-CO₂) as an alternative proxy for GG. Earlier studies suggest that HDI-CO₂ is a measure of sustainable and eco-friendly economic growth (Chen et al., 2024; Zhang et al., 2024). Table 5 reports the results, which remain robust and consistent when using an alternative proxy for GG.

Table 5.

Robustness Testing using Alternate Proxy.

	Model (1) GG	Model (2) GG	Model (3) GG	Model (4) GG
GG _t ₋₁	0.354*** (0.033)	0.460*** (0.078)	0.532*** (0.0607)	0.534*** (0.047)
FDI	0.013 (0.012)			−0.009 (0.007)
B & L		−0.020*** (0.009)		−0.023*** (0.008)
FT			−0.054*** (0.020)	−0.048** (0.019)
NRR	−0.626*** (0.035)	−0.490*** (0.093)	−0.472*** (0.064)	−0.480*** (0.051)
FA	−0.025 (0.016)	0.068 (0.109)	−0.037 (0.028)	−0.023 (0.026)
Constant	0.927*** (0.141)	0.766** (0.306)	0.993*** (0.187)	1.116*** (0.173)
Observations	729	729	729	729
AR (1) (p value)	−2.120 (0.034)	−2.160 (0.031)	−2.320 (0.020)	−2.320 (0.020)
AR (2) (p value)	−0.200 (0.840)	−0.360 (0.721)	−0.210 (0.835)	−0.310 (0.757)
Hansen (p value)	23.390 (1.000)	13.550 (0.094)	11.450 (0.075)	13.650 (0.091)
Wald (p value)	41,351.42 (0.000)	12,282.17 (0.000)	67,930.11 (0.000)	243,218.46 (0.000)

Notes: This table reports the impact of financial flows on green growth. Financial flows include foreign direct investment (FDI), borrowing and lending (B & L) and foreign trade (FT). Models 1–3 show individual effects, while Model 4 reports the joint effect. Natural resource rents (NRR) and forest area (FA) are control variables. Parentheses report robust standard errors. *** and * indicate significance at the 1%, 5% and 10% levels, respectively.

4.4. Moderation Analysis

4.4.1. Moderation Effects of Energy Consumption

EC fuels economic growth but also affects environmental quality (Khan et al., 2020). In this section, we examine the potential moderating role of EC in the relationship between financial flows and GG. As per Wang et al. (2024), we take fossil fuel usage in exajoules as a proxy for EC. In Table 6a, we report moderation by interaction terms between EC and financial flows. Model 1 reports the interaction term of EC with FDI, Model 2 presents the interaction term of EC and net B&L, and Model 3 reports the interaction term of EC with FT. All interaction terms have a statistically significant negative relationship with GG at the 1% significance level.

Table 6a.

Moderation Effects of Energy Consumption.

	Model (1) GG	Model (2) GG	Model (3) GG
FDI	0.009 (0.011)
FDI*EC	−0.002*** (0.000)
B & L		−0.010* (0.006)
B & L*EC		−0.001*** (0.000)
FT			−0.150*** (0.030)
FT*EC			−0.002*** (0.000)
NRR	−0.918*** (0.010)	−0.933*** (0.011)	−0.920*** (0.009)
FAs	0.013 (0.014)	0.016 (0.279)	0.009 (0.013)
Constant	0.850*** (0.074)	0.919*** (0.050)	1.544*** (0.138)
Observations	729	729	729
R ²	0.9422	0.939	0.944
F-statistics(p value)	2,948.50 (0.000)	2,823.53 (0.000)	3,084.80 (0.000)

Notes: This table reports the moderating role of energy consumption in the relationship between financial flows and green growth, controlling for FA and NRR. Columns 1–3 show moderation effects for FDI, B & L and FT, respectively. All variables are winsorized at the 1% and 99% levels. Parentheses report robust standard errors. *** and * indicate significance at the 1%, 5% and 10% levels, respectively.

These findings support our hypotheses 4a, 4b and 4c, proposing the moderating role of EC in the relationship of FDI, net B&L and FT and GG. The negative interaction term suggests that, as EC rises, the adverse effect of financial flows on GG further intensifies.

It is important to note that in earlier models presented in Tables 3 –5, FDI remained insignificant. However, the moderating role of EC in the relationship between FDI and GG is significant. The insignificant main effect is an indicator that FDI does not independently contribute to a sustainable environment. It depends on EC levels that moderate the relationship between FDI and GG. The negatively significant coefficients of the interaction terms in the context of FDI and FT highlight a non-constant relationship between financial flows (FDI, FT) and GG. The relationship between financial flows and GG varies with the level of energy use. The findings of the study show that the coefficients of the interaction term are negatively significant, which supports the view that excessive reliance on EC practices weakens the positive contribution of FDI and FT to the green economy.

The moderating outcome of the study is in contrast with Si et al. (2024), who focus on the positive contribution of FDI to environmental sustainability using energy-efficient production practices in host countries. However, the results of the study are aligned with Tanveer et al. (2021) and Zhuo and Qamruzzaman (2022), who support the view that host countries invest in these energy-intensive industries to meet FT demand, which potentially leads to positive economic growth.

However, these economic activities require high EC, which results in environmental degradation. Consequently, the reliance on fossil fuels overshadows the potential benefits of capital investment and trade, supporting the Pollution Haven Hypothesis in Asian economies. The insufficient and vulnerable administrative systems in Asian economies further heighten the risks associated with adopting non-renewable technologies from developed countries, ultimately impeding GG (Islam, Ali, et al., 2022). Therefore, the government should design policies aligned with environmentally responsible investment that not only promote renewable energy practices to reduce environmental degradation but also maintain energy-efficient standards in the administrative system to protect the economy from energy-intensive investments that deteriorate the environment. Moreover, empirical evidence supports the negative moderating role of EC in the relationship between B&L and GG by showing that when developing countries take on sovereign debt, they intensify EC. While this may promote economic growth, it has a detrimental effect on the environment (Raouf, 2022; Xu et al., 2022).

4.4.2. Moderation Effects of Energy Transition

Concerns about climate change have spotlighted the importance of ET, which triggers economic and ecological effects on economies (Genc & Kosempel, 2023; Yang et al., 2024). In this section, we examine the potential moderating role of ET in the relationship between financial flows and GG. Similar to Islam, Sohag, et al. (2022), we take fossil fuel usage as a ratio of renewable EC as a proxy for ET. In Table 6b, we report moderation by interaction terms between ET and financial flows. Model 1 reports the interaction term of ET with FDI; Model 2 presents the interaction term of ET and net B&L; and Model 3 reports the interaction term of ET with FT. All interaction terms have a statistically insignificant negative relationship with GG. These findings reject our hypotheses 5a, 5b and 5c, proposing the moderating role of ET in the relationship between FDI, net B&L and FT and GG.

Table 6b.

Moderation Effects of Energy Transition.

	Model (1) GG	Model (2) GG	Model (3) GG
FDI	−0.011 (0.011)
FDI*ET	−0.021 (0.022)
B & L		−0.018*** (0.006)
B & L*ET		0.002 (0.016)
FT			−0.189*** (0.031)
FT*ET			−0.005 (0.017)
NRR	−0.931*** (0.010)	−0.940*** (0.011)	−0.932*** (0.010)
FA	−0.008 (0.014)	0.003 (0.014)	−0.011 (0.014)
Constant	0.937*** (0.076)	0.948*** (0.050)	1.687*** (0.141)
Observations	729	729	729
R ²	0.938	0.938	0.941
F-statistics(p value)	2,739.19 (0.000)	2,767.61 (0.000)	2,880.82 (0.000)

Notes: This table reports the moderating role of energy transition in the relationship between financial flows and green growth, controlling for FA and NRR. Columns 1–3 show moderation effects for FDI, B & L and FT, respectively. All variables are winsorized at the 1% and 99% levels. Parentheses report robust standard errors. *** and * indicate significance at the 1%, 5% and 10% levels, respectively.

The insignificant interaction terms of the three financial flows indicate that countries heavily dependent on capital collected from various sources might face challenges in pursuing GG. Similarly, Razzaq et al. (2023) employed the COP26 framework to demonstrate that the ET index fails to encourage GG, particularly in developing economies with weak environmental governance.

The insignificant role of ET as a moderator is contradictory to prior studies (Adejumo & Asongu, 2020; Dong et al., 2023) that support the positive influence of ET in strengthening the relationship between financial flows and GG through investing funds in clean technologies for sustainability. The findings of the study do not support the Pollution Halo Hypothesis in Asian countries, which facilitates sustainable energy initiatives with climate-resilient growth.

Barua (2022) argues that financial flows having insignificant interaction terms indicate that there is no certainty that financial flows in any form promote GG through ET. However, Aghion et al. (2024) claim that Asian economies, no doubt, heavily rely on external debt for economic expansion, facing difficulties in aligning productive expansion with environmental resilience.

5. Conclusion

The research aims to explore the influence of financial flows on GG, with the moderating roles of EC and ET. Financial flows are measured using FT, FDI and net B&L. The findings suggest a negative effect of FT on GG across Asian economies. FT adversely affects GG due to the heavy reliance on an export-centred approach, which results in excessive carbon emissions and resource depletion.

Net B&L also negatively affects GG, mainly due to the financial limitations of developing economies. Excessive reliance on borrowing to meet the basic needs of healthcare, education and administrative needs has left Asian economies with limited financial resources for strategic green initiatives and infrastructure, which has hampered GG. FDI did not explain GG due to weak environmental regulations and a lack of technology-transfer mechanisms. These findings also supplement the earlier arguments that financial flows are not inherently green; their impact depends on how smartly they are directed towards green technologies, infrastructure and environmentally regulated sectors of the economy.

EC negatively moderates the relationship of FDI, FT and net B&L with GG. In many Asian economies, FDI is linked to the exploitative use of natural resources and primarily arrives in energy-intensive sectors. Although these energy-intensive sectors might promote economic growth, they do so at the cost of environmental degradation.

Likewise, excessive reliance on traditional energy sources to boost exports results in economic gains; however, the boost is achieved at the cost of environmental degradation. Sovereign borrowing is frequently used to support conventional energy-intensive economic activities in Asian economies, providing a temporary boost to economic growth at the expense of environmental health, thereby negatively impacting GG.

ET does not moderate the relationship between financial flows and GG. The reasons include dependence on fossil fuels, slow ET, regulatory gaps, underdeveloped green finance markets and technological barriers in Asian economies. Thus, the study contributes to the existing debate by showing that financial flows cannot drive GG until the ET in Asian economies is stronger and institutionally backed. Future research may be carried out by looking into patterns of sector-specific financial flows in affecting GG. In this context, the moderating role of institutional quality can be examined as well.

5.1. Limitations of Study

The study covers 26 Asian economies, primarily based on the availability of data, and therefore, many small Asian economies are excluded. This restricts the wider generalization of results across all Asian economies. Similarly, another limitation is the data constraint in the World Development Indicators database of the World Bank. Data on natural resource depletion are not available beyond 2021, which is required to calculate GG.

Similarly, data on the control variable, natural resource rents, are also not available beyond 2021; therefore, the time period of the study is restricted to 2021. Moreover, financial flows such as FDI are measured at the aggregate level. Disaggregating FDI into different sectors could have provided detailed insights, but this was not possible due to the non-availability of sector-specific FDI data.

5.2. Policy Implications

As FT adversely affects GG due to high emissions and resource depletion, Asian economies need a paradigm shift in their trade policies by focusing on environmental compliance, cleaner manufacturing standards and low-carbon value chains. In this regard, emissions-linked certifications, customs incentives for eco-friendly trade and a stringent regional monitoring framework can help to promote economic growth.

Likewise, net B&L also negatively influences GG due to the prioritization of projects by Asian economies that, although they result in economic growth, damage the environment. Therefore, there should be national-level policies to deploy borrowing into eco-friendly, renewable energy and energy-efficient projects. As Asian economies tend to borrow due to fiscal constraints, green financial instruments, including green bonds and green development grants, can help to bridge the fiscal space and achieve economic growth that is environmentally friendly.

EC is a negative moderating factor in the relationship between financial flows and GG. Therefore, government policies related to financial flows and EC should be closely aligned so that financial flows drive eco-friendly economic growth. Governments can offer tax incentives, subsidies and public-private partnerships to ensure that financial flows are directed towards projects that use smart and renewable energy sources.

Footnotes

Acknowledgements

The authors would like to acknowledge the opportunity to conduct this research and submit it for consideration. Appreciation is extended to the National University of Modern Languages for providing a conducive research environment and facilities.

Authors’ Contributions

Sadia Saeed: Conceptualization, methodology and writing the article.

Sabtain Fida: Data collection, analysis, interpretation and writing the original article.

Abdul Ghafoor: Review, editing and supervision.

Declaration of Conflicting Interests

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

Ethical Declaration

The authors abide by all the ethics involved in this academic work and have not submitted it to any other journal.

Funding

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

ORCID iDs

Sadia Saeed

Sabtain Fida

Abdul Ghafoor

Appendix A

Appendix B

Appendix C

Appendix D

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