Interaction Effects of Green Finance and Digital Platforms on China’s Economic Growth

Abstract

This study examines the interaction effects of green finance and digital platforms on China’s economic growth, employing panel data from 30 provinces spanning the period 2013–2023. Green finance is measured using green bonds and green credit, while digital platforms are proxied by electronic payment and e-commerce penetration. Previous empirical findings indicate that both green finance and digital platforms significantly contribute to economic growth. A 1% increase in the interaction term between green finance and digital platforms, based on fixed effects models with robust standard errors, results in a 0.0204% increase in GDP, supporting the hypothesis of a positive interaction. Control variables including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure, are included to isolate the net effects of green finance and digital platforms on GDP growth, reinforcing the study’s econometric robustness. This study contributes novel evidence on how the integration of green finance and digital infrastructure fosters sustainable and inclusive economic development.

1. Introduction

In recent years, China has increasingly prioritized economic growth through the integration of green finance and digital platforms. This strategic direction was reaffirmed at the Forum on Sustainable Development and Digital Technology Application Demonstrations, held in Beijing on 6 September 2023. During the event, the Initiative on Digital Technology for Achieving the Sustainable Development Goals was introduced, highlighting the critical role of digital technology in promoting economic development and enhancing resilience. The initiative emphasized the synergy between green finance and digital platforms, advocating for the expansion of China’s green digital finance market and encouraging green foreign direct investment. Such initiatives are designed to strengthen economic resilience and promote sustainable recovery [1].

Existing research highlights the dynamic and interdependent relationship among digital finance, green finance, and economic performance across China’s regional economies. Empirical evidence indicates that the integration of digital finance and green finance can significantly enhance economic outcomes and stimulate growth, particularly when advanced digital technologies are embedded in financial services [2,3,4]. However, the coordinated development of green finance and the digital economy in China remains at an early stage, marked by regional disparities and substantial untapped potential [5]. These observations underscore the need for further empirical investigation into the interaction between green finance and digital platforms, with the goal of promoting sustainable and high-quality economic growth across diverse regions [6,7,8].

China’s remarkable economic growth over the past few decades—driven by industrialization, technological advancement, and global trade integration—has elevated the country to the position of the world’s second-largest economy. However, this rapid expansion has incurred significant environmental costs, including resource depletion and rising carbon emissions [9,10]. In response, China has begun shifting from an export- and investment-led growth model to one focused on sustainability and innovation. The COVID-19 pandemic, which disrupted global economic activity, further underscored the urgency of adopting resilient and sustainable development strategies, such as promoting green finance and accelerating digital transformation [11]. Consequently, the integration of economic development with environmental objectives has become a central pillar of China’s long-term growth strategy.

Digital platforms have become central to the modern economy, fundamentally transforming business operations and financial transactions. They contribute to economic growth and productivity by fostering innovation, expanding market access, and addressing critical challenges such as environmental sustainability [12]. During periods of economic disruption, such as the COVID-19 pandemic, digital platforms have exhibited notable resilience and adaptability, reinforcing their role as essential enablers of economic recovery and sustainable development [13].

While there is a growing body of literature addressing green finance and digital platforms individually [14,15,16,17], empirical studies examining their combined impact on sustainable economic growth, particularly through panel data frameworks within the Chinese context are relatively scarce and insufficiently examined [18,19,20,21]. Although green bonds and green credit are conceptually aligned with the Sustainable Development Goals (SDGs), their empirical contribution to economic growth remains inconclusive and necessitates further investigation [22,23]. Similarly, digital platforms, including electronic payment systems and e-commerce, offer innovative channels for economic activity; however, their interactive effects with green finance on key economic indicators such as GDP remain underexamined.

This study seeks to investigate the contribution of integrating green finance and digital platforms to economic growth in China by addressing the underexplored empirical relationship between these domains. Accordingly, the study addresses the following research question:

Does the interaction between green finance and digital platforms significantly enhance economic growth in China?

By addressing this research gap, the study offers empirical insights into the interactive role of green finance and digital platforms in fostering economic growth. The anticipated findings are expected to advance theoretical discourse in green finance and the digital economy, while providing practical implications for policymakers seeking to design balanced and resilient economic strategies aligned with China’s dynamic development landscape.

2. Literature Review

This study draws upon financial development theory, which offers a conceptual framework for analyzing how financial systems contribute to economic growth through five key functions: (1) mobilizing savings, (2) facilitating risk management, (3) allocating capital efficiently, (4) monitoring investments, and (5) facilitating the exchange of goods and services [24]. The theory posits that well-functioning financial systems lower information and transaction costs, and enhance the efficiency of capital allocation, thereby promoting economic growth.

Building on King and Levine’s (1993) seminal work demonstrating that financial development predicts economic growth, this study extends the framework to explore how contemporary financial innovations, including green finance and digital platforms, that augment traditional financial functions [25]. Green finance constitutes a progression in financial development by integrating environmental criteria into capital allocation, while digital platforms improve the efficiency of financial interactions through technological advancements.

Green finance, encompassing instruments such as green bonds and green credit, aims to direct financial resources toward environmentally sustainable projects, thereby aligning capital flows with global sustainability objectives [26]. Existing research suggests that green finance supports economic growth by funding initiatives that promote environmental protection and stimulate innovation that factors essential to long-term economic resilience and stability [27]. Concurrently, digital platforms (measured by electronic payment systems and e-commerce) that play a critical role in advancing economic growth by lowering transaction costs, improving access to financial services, and enhancing operational efficiency [28]. This impact is particularly evident in China, where digitalization has helped reduce regional economic disparities and expand services to underserved populations, thereby fostering more inclusive and equitable growth.

The convergence of green finance and digital platforms offers transformative opportunities for economic growth by enhancing financial inclusivity, reducing transaction costs, and expanding access to capital across diverse regions. Digital platforms play a critical role in streamlining the allocation, mobilization, and oversight of green financial resources, thereby promoting greater transparency and accountability that both essential for building trust and ensuring the effectiveness of green financial instruments [29,30]. Furthermore, the digitization of financial services contributes to economic expansion by lowering transactional barriers, enabling broader participation, and improving the accessibility of financial products and services [31].

The interaction between green finance and digital platforms holds particular importance for China’s economic growth. Digital financial mechanisms, such as e-commerce and electronic payment systems that have improved financial efficiency, reduced operational costs, and expanded financial inclusion, thereby accelerating the development of green industries [32]. This integration supports the transition toward a low-carbon economy, as digital finance fosters innovations that align economic objectives with environmental sustainability, promoting both economic development and ecological preservation [33].

This interaction also contributes to reducing regional disparities by extending access to green finance in less economically developed areas, thereby promoting more equitable growth across regions [34]. By facilitating investments in green technologies, digital platforms stimulate innovation that not only complies with environmental regulations but also enhances productivity. This dual impact supports GDP growth while strengthening China’s economic resilience, thereby paving the way for a more balanced and sustainable development trajectory.

Drawing on theoretical frameworks and empirical evidence, this study hypothesizes that the interaction between green finance and digital platforms generates a synergistic effect that significantly enhances economic growth. This hypothesis aligns with the expanding body of literature on digital–green convergence, which posits that the combined influence of these domains amplifies their individual contributions, thereby supporting resilient and sustainable economic development [35].

Despite advancements in both theory and empirical research, a notable gap remains in comprehensive studies that examine the combined effects of green finance and digital platforms on economic growth, particularly within China’s unique regulatory and economic context. While green finance instruments, such as green bonds and green credit, have demonstrated effectiveness in supporting sustainable initiatives, their broader impact on economic growth, especially when integrated with digital platforms which has yet to be thoroughly explored [36,37]. Moreover, limited scholarly attention has been directed toward the role of digital platforms in addressing critical challenges associated with green finance, including greenwashing and transparency that factors essential to ensuring that these initiatives meaningfully contribute to environmental sustainability [30].

Most existing studies either focus on single-factor impacts or adopt descriptive approaches. Few have applied panel data regression frameworks to evaluate the interaction effects within a provincial Chinese context. This study addresses this gap by empirically analyzing the interaction between green finance and digital platforms in the context of China’s economic growth. Drawing on financial development theory, the research to examine the interactive between these two factors and their impact on key economic outcomes. In doing so, it provides critical insights for policymakers and stakeholders seeking to foster sustainable and inclusive growth.

The theoretical framework is constructed in alignment with the research hypothesis and is informed by a review of relevant theories and empirical studies, as illustrated in Figure 1.

Figure 1 presents the theoretical framework grounded in financial development theory, which serves as the primary conceptual foundation for this study. Within this framework, the interaction between green finance and digital platforms is defined as the independent variable, while economic growth (measured by GDP) serves as the dependent variable. To ensure the robustness of the analysis, the model incorporates several control variables, including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure. These variables provide a comprehensive basis for examining the relationships among green finance, digital platforms, and economic growth. The study posits the following hypothesis: the interaction between green finance and digital platforms has a significant positive impact on economic growth in China. ‘H’ is the abbreviation of hypothesis.

This study’s conceptual framework integrates green finance and digital platforms within the broader perspective of financial development theory. The theory underscores the role of financial innovations in fostering inclusive and sustainable growth. Green finance channels resources into environmentally beneficial sectors, while digital platforms improve the reach and efficiency of financial services [38]. Their interaction contributes to better capital mobilization, risk mitigation, and innovation diffusion, which are key channels through which economic growth is promoted.

3. Research Design

3.1. Empirical Model

This study employs a panel data model to examine the impact of green finance and digital platforms on economic growth, with GDP as the dependent variable. The analysis is conducted using data from 30 Chinese provinces spanning the period from 2013 to 2023. STATA 17.0 software is utilized for statistical analysis.

To assess the economic impact of green finance and digital platforms in China, this study adopts a robust panel data regression framework. This methodology enables a comprehensive analysis of data characterized by dual heterogeneity across both cross-sectional and temporal dimensions [39]. Panel data—also referred to as longitudinal data—is structured along two dimensions: a spatial dimension (1 < $\mathrm{i}$ < N), representing distinct entities such as provinces, and a temporal dimension (1 < $\mathrm{t}$ < T), representing time periods. Each observation is indexed by both dimensions ($\mathrm{i}$, $\mathrm{t}$), thereby allowing for the tracking of specific units over time [40].

For this analysis, panel data from 30 Chinese provinces spanning the years 2013 to 2023 are employed to test the study’s hypotheses. The panel data regression model offers a systematic framework for examining the interaction effects of green finance and digital platforms on economic growth. The general specification of the panel data regression model is expressed as follows:

$$Y_{it}={\beta }_0+{\beta }_1{X}_{it}+{\beta }_2{X}_{it}+\dots +{\beta }_n{X}_{it}+{\epsilon }_{it}$$

(1)

${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}$ denotes the dependent variable, while ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ represents the independent variables across the pooled cross-sectional and time-series data. The parameter ${\mathsf{\beta }}_{\mathrm{k}}$ indicates the intercept, and ${\mathsf{ϵ}}_{\mathrm{i}\mathrm{t}}$ captures the error term. The indices $\mathrm{i}$ and $\mathrm{t}$ refer to the cross-sectional units (provinces) and the temporal dimension of the study (2013–2023), respectively.

This study employs a panel data regression framework to investigate how the interaction between green finance and digital platforms affects economic growth across 30 Chinese provinces over the period 2013–2023. The analysis incorporates pooled ordinary least squares (POLS), fixed effects (FE), and random effects (RE) models to effectively account for unobserved heterogeneity among provinces. The Hausman test is applied to determine the most appropriate model specification, while robust standard errors are used to address potential heteroskedasticity.

By leveraging panel data from 30 provinces, this study provides a detailed assessment of regional variations in the adoption of green finance and digital platforms and their respective impacts on economic growth. To ensure the robustness of the results, diagnostic tests—including the Breusch-Pagan test for heteroskedasticity and the Durbin-Watson test for serial correlation—are conducted. The application of robust standard errors further enhances the reliability of the estimates, thereby ensuring the precision and validity of the conclusions.

This study investigates the role of green finance (measured by green bonds and green credit) and digital platforms (measured by electronic payments and e-commerce) in promoting economic growth by enhancing capital accessibility and market reach. The model incorporates an interaction term between green finance and digital platforms to evaluate their combined effect on economic performance. Key control variables, including money supply (MS), fiscal expenditure (FE), inflation rate (IR), fixed asset investment (FAI), and industrial structure (IS), are included to account for other macroeconomic influences. The core regression equation is specified as follows:

$$ln{GF}_{it}={{lnGB}_{it}+lnGC}_{it}$$

(2)

${\mathrm{G}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ denotes green finance in province $\mathrm{i}$ at time $\mathrm{t}$, serving as the primary variable of interest and encompassing green bonds (GB) and green credit (GC). ${\mathrm{G}\mathrm{B}}_{\mathrm{i}\mathrm{t}}$ represents the total value of green bond transactions in province $\mathrm{i}$ at time $\mathrm{t}$, while ${\mathrm{G}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$ reflects the total value of green credit transactions for the same province and period. The natural logarithm $\mathrm{l}\mathrm{n}$ is applied to both GB and GC to transform the data, a standard approach for linearizing relationships between variables and mitigating skewness, thereby enhancing the robustness of the statistical analysis.

$${lnDPs}_{it}={ln{EP}_{it}+lnEC}_{it}$$

(3)

${\mathrm{D}\mathrm{P}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ represents digital platforms in province $\mathrm{i}$ at time $\mathrm{t}$, serving as a key variable of interest that integrates both electronic payment (EP) and electronic commerce (EC). ${\mathrm{E}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ captures the total value of electronic payment transactions in province $\mathrm{i}$ at time $\mathrm{t}$, while ${\mathrm{E}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$ measures the total value of electronic commerce transactions for the same province and period. To enhance the analytical robustness, the natural logarithm $\mathrm{l}\mathrm{n}$ is applied to both ${\mathrm{E}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ and ${\mathrm{E}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$, facilitating the linearization of relationships and reducing data skewness.

Interaction between digital platforms and technological innovation:

$$\begin{array}{c}{lnGDP}_{it}={\beta }_0+{\beta }_1\left({lnGF}_{it}\right)+{\beta }_2\left(ln{DPs}_{it}\right)+{\beta }_3(ln{GF}_{it}\times ln{DPs}_{it})+{\beta }_4\left({lnMS}_{it}\right)+{\beta }_5{(lnFE}_{it})\\ +{\beta }_6\left({lnIR}_{it}\right)+{\beta }_7\left({lnFAI}_{it}\right)+{\beta }_8\left({lnIS}_{it}\right)+{\mu }_i+{\lambda }_t+{\epsilon }_{it}\end{array}$$

(4)

$\mathrm{l}\mathrm{n}{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ represents the natural logarithm of GDP, serving as the dependent variable for measuring economic growth in province $\mathrm{i}$ at time $\mathrm{t}$. $\mathrm{l}\mathrm{n}{\mathrm{M}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$ denotes the natural logarithm of the money supply for the same province and period, while $\mathrm{l}\mathrm{n} {\mathrm{F}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$ captures the natural logarithm of fiscal expenditure. Similarly, $\mathrm{l}\mathrm{n}{\mathrm{I}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$, $\mathrm{l}\mathrm{n}{\mathrm{F}\mathrm{A}\mathrm{I}}_{\mathrm{i}\mathrm{t}}$, and $\mathrm{l}\mathrm{n}{\mathrm{I}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$ represent the natural logarithms of the inflation rate, fixed asset investment, and industrial structure, respectively, all within province $\mathrm{i}$ at time $\mathrm{t}$. ${\mathsf{\mu }}_{\mathrm{i}}$ captures province-specific fixed effects, addressing unobserved characteristics unique to each province that remain constant over time. ${\mathsf{\lambda }}_{\mathrm{t}}$ accounts for time-specific fixed effects, reflecting common influences such as macroeconomic shocks or national policies that simultaneously affect all provinces. ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$ represents the error term, encompassing unobserved factors influencing GDP growth in province $\mathrm{i}$ at time $\mathrm{t}$. $\mathrm{l}\mathrm{n}{\mathrm{G}\mathrm{F}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{l}n\mathrm{D}\mathrm{P}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ represents the natural logarithm of the interaction term between green finance (GF) and digital platforms (DPs) for province $\mathrm{i}$ at time $\mathrm{t}$. This term captures the joint influence of green finance and digital platforms on economic growth. The model evaluates the extent to which their interaction affects GDP growth while controlling for key macroeconomic variables such as money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure. The application of the natural logarithmic transformation facilitates coefficient interpretation in percentage terms, providing a clearer understanding of their proportional impacts on GDP growth.

Panel data regression analysis commonly employs three key models: pooled ordinary least squares (POLS), fixed effects (FE), and random effects (RE), each designed to capture both within-entity and between-entity variations [41,42,43,44]. POLS uses ordinary least squares (OLS) estimation to minimize the sum of squared residuals, yielding coefficients that reflect the average effect of independent variables on the dependent variable across all entities [45]. When the assumption of parameter homogeneity holds, POLS produces efficient and unbiased estimates [46].

To address within-group variations, the fixed effects (FE) model is employed, effectively controlling for time-invariant factors that differ across entities and may otherwise introduce bias [47]. The FE model is particularly valued in econometric research for its ability to account for unobserved heterogeneity by isolating entity-specific characteristics that remain constant over time [48,49].

In contrast, the random effects (RE) model is appropriate when unobserved heterogeneity is assumed to be uncorrelated with the explanatory variables. By incorporating both within-entity and between-entity variations, the RE model provides an efficient and unbiased estimation approach, making it a cornerstone of panel data analysis in econometric research [50].

Detecting heteroskedasticity is a critical step, as its presence can result in inefficient parameter estimates and compromised statistical inferences. The Breusch–Pagan test is commonly employed to assess whether the variance of residuals in the POLS model remains constant [51]. Ensuring homoscedastic residuals is essential for improving the accuracy and reliability of POLS estimates, particularly when analyzing the effects of green finance (measured by green bonds and green credit) and digital platforms (measured by electronic payments and e-commerce) on economic growth, as measured by GDP.

The validity of POLS results relies on the assumption of homoscedastic residuals. Applying the Breusch–Pagan test helps verify this assumption, thereby enhancing the credibility and robustness of the study’s conclusions [52,53]. To address potential confounding factors and endogeneity, the analysis incorporates control variables and employs fixed effects models, enabling the identification of causal relationships while reducing the risk of spurious correlations [54].

The Hausman test, developed by Hausman, is a widely used statistical tool for model specification in panel data analysis. Its primary function is to determine whether the fixed effects (FE) or random effects (RE) model is more appropriate for a given dataset [55]. The test evaluates the correlation between individual-specific effects and the explanatory variables, a critical consideration when selecting the appropriate model [56]. A statistically significant Hausman test result leads to the rejection of the null hypothesis, thereby indicating that the fixed effects model is more suitable [57].

All models include control variables, including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure which are grounded in the theoretical foundations of financial development theory [58,59]. In addition, spatial econometric models are employed to account for spatial dependencies and regional heterogeneity, acknowledging the uneven distribution of economic growth across provinces [41]. This multifaceted methodological approach enhances the rigor of the analysis and ensures that it reflects the complexity and diversity of regional economic dynamics.

3.2. Data and Sample

This study utilizes a panel dataset comprising 30 Chinese provinces from 2013 to 2023, with data sourced from the China Statistical Yearbook, China Banking and Insurance Regulatory Commission and National Bureau of Statistics. Economic growth, measured by GDP, serves as the dependent variable. The independent variables capture the interaction between green finance and digital platforms. To enhance the robustness of the analysis, the model incorporates control variables, including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure. The data source as show in

Table A1. Data source.

Variables	Indicators	Measurement	Data Source
Economic Growth (EG)	Gross Domestic Product (GDP)		China Statistical Yearbook
Green Finance (GF)	Green Bonds (GBs)	Issuance volume	China Banking and Insurance Regulatory Commission
	Green Credit (GC)	Total balance of green loans
Digital Platforms (DPs)	Electronic Payment (EP)	Total transaction value	China Banking and Insurance Regulatory Commission
	Electronic Commerce (EC)	Online retail sales
Money Supply (MS)			China Statistical Yearbook
Fisal Expenditure (FE)			National Bureau of Statistics
Inflation Rate (IR)			China Statistical Yearbook
Fixed Asset Investment (FAI)			National Bureau of Statistics
Industrial Structure (IS)			National Bureau of Statistics

.

To maintain scientific rigor, regions with incomplete data, including Hong Kong, Macau, Tibet, and Taiwan that are excluded due to their distinct financial reporting systems [60]. In conclusion, the study employs robust panel data econometric techniques to examine the interaction between green finance and digital platforms and their combined impact on China’s economic growth, with particular attention to both short-term and long-term effects.

4. Empirical Results and Analysis

4.1. Descriptive Statistics

The descriptive statistics offer a detailed summary of the key variables, including the interaction between green finance and digital platforms, economic growth (measured by GDP), and the selected control variables [61].

Table 1. Descriptive statistics.

Variable	Mean	Std. Dev.	Min	Max	Observations
GDP	2.299568	0.0890134	2.036031	2.469624	330
Green Finance	2.700385	0.044583	2.561816	2.802886	330
Digital Platforms	2.733101	0.163718	2.184168	3.063224	330
Green Finance ×Digital Platforms	7.386637	0.5472032	5.654221	8.585865	330
Money Supply	2.359542	0.088133	2.111903	2.550851	330
Fiscal Expenditure	2.269543	0.086544	2.049869	2.410506	330
Inflation Rate	0.473482	0.656685	−2.30259	1.360977	330
Fixed Asset Investment	1.657923	0.036115	1.576605	1.731033	330
Industrial Structure	1.505491	0.012828	1.462365	1.527158	330

presents the descriptive statistics for the main variables used in this study, which examines the interaction between green finance and digital platforms as drivers of China’s economic growth. The dependent variable, economic growth (measured by GDP), has a mean of 2.299568 and a standard deviation of 0.0890134, indicating moderate variation across observations. The interaction variable between green finance and digital platforms has a mean of 7.386637 and a higher standard deviation of 0.5472032. The relatively high variation in the interaction term suggests diverse regional integration of green finance and digital platforms. The control variables, including money supply (MS), fiscal expenditure (FE), inflation rate (IR), fixed asset investment (FAI), and industrial structure (IS) that also exhibit varying degrees of dispersion. Among them, the inflation rate (IR) demonstrates the highest variability, with a standard deviation of 0.656685, which indicates significant fluctuations across the sample period. This descriptive overview provides a foundational understanding of the dataset, facilitating the subsequent analysis of how the interaction between green finance and digital platforms influences economic growth in China.

4.2. Empirical Findings

The Breusch–Pagan test is employed to assess the appropriateness of the pooled ordinary least squares (POLS) model relative to alternative panel data models [62]. In contrast, the choice between the fixed effects (FE) and random effects (RE) models is determined using the Hausman test, which ensures that the selected model aligns with the stochastic properties of the dataset [63].

Table 2. Breusch and Pagan Lagrangian multiplier test for random effects.

Statistic	GDP	e	u
Var	0.7435062	0.0125553	0.0170141
SD = sqrt (Var)	0.8622681	0.1120503	0.130438
Test: Var(u) = 0
chibar2 (01)	338.68
Prob > chibar2	0.0000

presents the results of the Breusch and Pagan Lagrangian Multiplier test for random effects, which evaluates the suitability of a random effects model for analyzing the interaction between green finance and digital platforms in the context of China’s economic growth. The variance of the dependent variable, GDP, is estimated at 0.7435062, with variance components for the idiosyncratic error term (e) and the random intercept (u) estimated at 0.0125553 and 0.0170141, respectively. The corresponding standard deviations are 0.8622681 for GDP, 0.1120503 for the error term, and 0.1304383 for the random intercept, indicating moderate variability with the highest dispersion observed in GDP. That the test statistic chibar² (01) = 338.68, with a p-value of 0.0000, confirms that the variance of u is significantly different from zero. This result supports the appropriateness of the random effects model, indicating substantial unobserved heterogeneity across provinces and enhancing our understanding of how green finance and digital platforms contribute to regional differences in economic growth.

Table 3. Random versus fixed effects model: Hausman test.

Variables	(b)	(B)	(b-B)	sqrt(diag(V_b-V_B))
	Fixed	Random	Difference	Std. Err.
Green Finance	0.5213279	0.7944762	−0.2731483	0.030652
Digital Platforms	0.0777863	0.1365664	−0.0587801	0.0111197
Green Finance × Digital Platforms	0.0352328	0.0441126	−0.0088797	0.0020449
Money Supply	0.0991447	0.2165683	−0.1174236	0.0026525
Fiscal Expenditure	0.2333382	0.3926496	−0.1593114	0.0174439
Inflation Rate	−0.0000416	−0.0008785	−0.0009201	0.0013496
Fixed Asset Investment	0.1007129	−0.075717	0.17643	0.0140519
Industrial Structure	1.245468	0.8195222	0.4259459	0.123088
chi (6)	76.92
Prob > chi2	0.0000

reports the results of the Hausman test, which yields a p-value of 0.0000 (p < 0.05), leading to the rejection of the null hypothesis that the random effects model is appropriate. Consequently, the fixed effects model is preferred for analyzing the impact of green finance, digital platforms, and their interaction on economic growth in China. This result underscores the importance of controlling for unobserved heterogeneity across entities, aligning with the study’s objective of capturing the nuanced regional effects of green finance and digital platforms on economic performance. The FE model, supported by the Hausman test, confirms that regional heterogeneity plays a key role in the green-digital growth nexus.

Table 4. Diagnostic checks and serial correlation.

Diagnostic Checks					Serial Correlation
Multicollinearity			Heteroskedasticity
Variable	VIF	1/VIF	Test	Value	Test	Value
Green Finance	2.21	0.45263	chi2 (30)	2035.13	F (1, 29)	1.592
Digital Platforms	5.92	0.168914	Prob > chi2	0.0000	Prob > F	0.2171
Green Finance × Digital Platforms	7.68	0.130258
Money Supply	6.50	0.153744
Fiscal Expenditure	2.24	0.445813
Inflation Rate	1.59	0.627431
Fixed Asset Investment	1.53	0.6519
Industrial Structure	1.17	0.85305
Mean VIF	3.61

presents the results of diagnostic tests for multicollinearity, heteroskedasticity, and serial correlation. These tests are critical to ensuring the robustness and reliability of the econometric model that examines the interaction between green finance and digital platforms, as well as other explanatory factors influencing economic growth. The mean Variance Inflation Factor (mean VIF) of 3.61 is below the commonly accepted threshold of 10, indicating no evidence of multicollinearity. However, the p-value for the heteroskedasticity test is 0.0000, which is less than 0.05, suggesting the presence of heteroskedasticity. In contrast, the p-value for the serial correlation test is 0.2171, exceeding 0.05, indicating no significant serial correlation [64,65]. While multicollinearity and serial correlation are not concerns in this model, heteroskedasticity is present and should be corrected using robust standard errors to ensure the accuracy and validity of the estimation results [63].

4.3. Estimation Results and Robustness Analysis

To enhance the reliability of the findings, a series of diagnostic tests was conducted to assess multicollinearity, heteroskedasticity, and serial correlation [66]. The Variance Inflation Factor (VIF) analysis revealed no significant multicollinearity, as all VIF values were below the commonly accepted threshold of 10 [67]. Additionally, the Breusch–Pagan test confirmed the presence of heteroskedasticity, while the Durbin–Watson test indicated no evidence of serial correlation [68]. Although multicollinearity and serial correlation do not pose concerns in the estimated models, the presence of heteroskedasticity necessitates the use of robust standard errors to ensure the validity and accuracy of the results.

Table 5. Use robust standard error estimation.

Variables	(1)	(2)	(3)	(4)
	Pooled OLS	RE Model	FE Model	FE Robust Model
Green Finance	0.00256	0.0380 ***	0.0254 **	0.0254
	(0.188)	(0.133)	(0.127)	(0.224)
Digital Platforms	0.00833	0.0181 *	0.0211 **	0.0211
	(0.142)	(0.0994)	(0.0935)	(0.0133)
Green Finance × Digital Platforms	0.00604	0.0184 **	0.0204 ***	0.0204 *
	(0.0121)	(0.00840)	(0.00785)	(0.0118)
Money Supply	0.224 ***	0.0901 ***	0.0397	0.0397 **
	(0.0315)	(0.0267)	(0.0268)	(0.0191)
Fiscal Expenditure	0.430 ***	0.699 ***	0.545 ***	0.545 ***
	(0.0580)	(0.0728)	(0.0845)	(0.145)
Inflation Rate	0.00166	−0.00220	−0.00676	−0.00676
	(0.0132)	(0.00911)	(0.00863)	(0.00473)
Fixed Asset Investment	0.372 ***	0.243 ***	0.175 ***	0.175 ***
	(0.0236)	(0.0336)	(0.0364)	(0.0528)
Industrial Structure	0.750 ***	1.647 ***	1.964 ***	1.964 ***
	(0.232)	(0.358)	(0.457)	(0.671)
Constant	−1.045	−2.789	−3.063	−3.063
	(2.454)	(2.242)	(2.500)	(3.655)
Observations	330	330	330	330
R-squared	0.956		0.825	0.825
Number of Province		30	30	30

Note: *, **, and *** denote the 10%, 5%, and 1% levels of significance, respectively. Standard errors in parentheses: this indicates that the values in parentheses below the estimated coefficients represent the standard errors.

presents the results of four econometric models, including pooled OLS, random effects (RE), fixed effects (FE), and fixed effects with robust standard errors (FE Robust Model), that used to examine the interaction effect between green finance and digital platforms on China’s economic growth. The results show that a 1% increase in the interaction term (green finance × digital platforms) is associated with a 0.0204% increase in GDP. Although modest in magnitude (0.0204%), the interaction effect indicates that digital platforms enhance the marginal contribution of green finance to growth, a result with significant policy relevance over time. The interaction effect is statistically significant and positive, supporting the study’s objective of evaluating how the integration of green finance and digital platforms promotes economic growth. Additionally, fiscal expenditure, money supply, and industrial structure are found to be significant contributors, while the inflation rate does not exhibit a statistically significant effect. Among the models, the FE Robust Model provides the most reliable estimates due to its correction for heteroskedasticity and is thus selected as the preferred model for interpretation.

5. Discussion

This study provides robust empirical evidence of the significantly positive interaction effect of green finance and digital platforms on China’s economic growth, as evidenced by GDP growth. A 1% increase in the interaction term is associated with a statistically significant increase in GDP, supporting the hypothesis of a positive interaction. This result is consistent with Razzaq and Yang (2022), who found that the interaction of digital finance and green innovation significantly boosted green GDP in other Asian economies [29]. China’s experience in green digital finance integration positions the country to lead global efforts in sustainable financial development. By enabling efficient capital allocation and expanding access to financial services, the interplay between green finance and digital platforms promotes economic activity across China’s regions.

Green finance, facilitated by instruments such as green bonds and green credit, exhibits a strengthened impact on economic growth when integrated with digital platforms, including electronic payment systems and e-commerce. This combination enhances financial accessibility, transparency, and inclusivity while simultaneously reducing transaction costs. Digital platforms serve as catalysts, accelerating the adoption of green finance and broadening economic participation, thereby reinforcing their positive effect on GDP. This finding is consistent with recent studies in emerging markets, suggesting that digitalization enhances the developmental impact of green finance [29].

The analysis of control variables, including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure that reveals varying effects on economic growth. Among them, fiscal expenditure emerges as a primary driver, directing resources toward infrastructure and public services that facilitate the integration of green finance and digital platforms. The significant contributions of money supply and industrial structure underscore the importance of maintaining a stable macroeconomic environment to fully realize the benefits of these interactions. These findings suggest that favorable macroeconomic conditions are essential for enhancing the positive impact of green finance and digital platforms on economic development [69].

Regional disparities in the adoption and economic impact of green finance and digital platforms underscore the need for context-specific policy interventions. Economically advanced regions with well-developed digital infrastructure exhibit stronger responses to green finance initiatives, as their digital ecosystems more effectively support financial transactions and innovation. In contrast, less-developed regions face challenges in realizing similar benefits, highlighting the need to prioritize digital infrastructure development in these areas to promote inclusive growth. Targeted policies aimed at expanding digital financial services and infrastructure in underdeveloped provinces could help mitigate these disparities and contribute to more balanced economic development across China [70].

This study makes a significant contribution to the academic literature by empirically analyzing the interaction between green finance and digital platforms in the context of economic growth. The findings highlight the transformative potential of integrating these two domains to accelerate growth while fostering resilience, innovation, and inclusivity. For policymakers, the results offer actionable insights for leveraging digital and financial policy instruments to support sustainable development, aligning with China’s long-term economic and environmental objectives. Embedding these interactions within policy frameworks could further advance China’s transition toward a low-carbon, innovation-driven economy, thereby enhancing both economic performance and environmental sustainability [71].

6. Conclusions and Policy Implications

This study provides empirical evidence on the interconnected roles of green finance and digital platforms as key drivers of economic growth in China. By analyzing the interaction between green finance (measured by green bonds and green credit) and digital platforms (measured by electronic payments and e-commerce), the research demonstrates their joint positive impact on economic growth, as evidenced by China’s GDP growth. The findings confirm that the integration of green finance and digital platforms, grounded in financial development theory, offers significant potential for promoting economic growth. The evidence-based policy recommendations provide a roadmap for leveraging these synergies while addressing regional disparities and institutional capacity constraints.

A notable observation is that regions with advanced digital infrastructure derive greater benefits from green finance initiatives, underscoring the catalytic role of digital platforms in enhancing the effectiveness of green finance. These platforms reduce transaction costs, improve transparency, and broaden access to financial services, thereby fostering inclusivity and regional economic balance. Digitalization thus emerges as a cornerstone for optimizing the economic impact of green finance across China.

The analysis also highlights the significance of a stable macroeconomic environment, as demonstrated by the influence of control variables, including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure. In particular, fiscal expenditure and money supply play essential roles in providing the infrastructure and stability necessary for integrating green finance with digital platforms. These findings emphasize the importance of coherent and coordinated macroeconomic policies to unlock the full potential of these synergistic initiatives.

While the fixed effects model accounts for time-invariant provincial heterogeneity, it does not address potential reverse causality or simultaneity bias. Provinces with higher economic output may also be more capable of investing in digital infrastructure and green finance. Although more advanced identification strategies, such as system GMM, could enhance causal inference, data limitations preclude its application in the current study. Future research could explore quasi-experimental designs or dynamic panel models to derive more robust causal insights.

In conclusion, this research advances the literature on finance and the digital economy by empirically validating the combined effects of green finance and digital platforms on China’s economic growth. It offers actionable insights for policymakers, including:

Expanding digital infrastructure in underdeveloped provinces to amplify green finance impacts.

Promoting targeted subsidies that integrate e-payment with green financing tools.

Developing integrated green–digital finance platforms to support SMEs.