The Impact of E-Commerce on Sustainable Development Goals and Economic Growth: A Multidimensional Approach in EU Countries

Abstract

In the digital age, e-commerce has become a critical part of modern economies, shaping global economic growth and the pursuit of the Sustainable Development Goals (SDGs). This study uses robust statistical methods to explore the complex relationships between traditional trade, e-commerce, and key economic and sustainability indicators. The General Linear Model (GLM), factor analysis, and linear regression reveal that conventional trade remains vital for GDP growth, even though e-commerce clearly influences SDG performance. The study emphasizes the catalytic role of e-commerce in advancing sustainability by showing how treating it as a dependent variable speeds up SDG progress through Brown, Holt, and ARIMA forecasting models. Additionally, cluster analysis uncovers a strong link between higher SDG scores and increased e-commerce activity, with countries scoring better on sustainability often having more companies in the digital economy and earning more online. This research provides a comprehensive understanding of how e-commerce can support global sustainability goals, along with integrated policy recommendations that promote digital transformation and long-term environmental and social resilience.

1. Introduction

E-commerce has seen rapid growth in recent decades, revolutionizing how individuals, businesses, and economies function globally. In 2023, worldwide e-commerce sales exceeded $5.8 trillion, accounting for roughly 19% of total retail trade. This growth continues, driven by digital platforms [1]. This shift reflects not only technological progress but also fundamental changes in trade, logistics, and consumption patterns. The proliferation of digital technologies, extensive internet access, and smart devices has reshaped traditional commercial frameworks. E-commerce has evolved from a supplementary sales channel into a distinct economic sector with its players, principles, and effects [2,3,4].

Similarly, the global sustainability agenda, outlined by the 17 Sustainable Development Goals (SDGs) adopted by the United Nations, encourages governments, businesses, and civil society to pursue more inclusive, equitable, and environmentally responsible development approaches. As e-commerce increasingly influences consumption and supply chains, its role in achieving goals like Responsible Consumption and Production (SDG 12) and Climate Action (SDG 13) has become a vital area of study.

E-commerce stands at the crossroads of technological progress and sustainability, presenting both advantages and challenges [5,6]. On the positive side, it enhances energy efficiency, reduces intermediaries, promotes digital service adoption, and broadens access to goods and services, particularly in remote and underserved regions. OECD reports highlight that digital trade grants SMEs access to global markets, supporting inclusive economic growth and sustainable development, especially in emerging economies [7]. Other research underscores digitalization’s potential to lower emissions via logistics improvements and to foster greater inclusion by easing market access [6,7,8]. Moreover, e-commerce has enabled SMEs to internationalize, implement advanced management systems, and maintain business continuity during crises like the COVID-19 pandemic [9,10,11].

However, the broad adoption of e-commerce brings significant challenges. Its environmental footprint, caused by increased deliveries, packaging waste, and decentralized urban logistics, sparks worries about long-term sustainability, particularly in the absence of regulation and corporate accountability [12,13,14]. Sometimes, attempts to address classic problems like urban congestion or supply chain inefficiencies may inadvertently lead to new forms of inequality, exploitation, or environmental harm.

The economic impact of e-commerce varies significantly. It can enhance productivity and competitiveness in certain cases, yet it may also increase market polarization, marginalize small businesses, and strain local labor markets and resources elsewhere [15,16]. Furthermore, a large portion of the economic value generated by digital commerce often bypasses local economies altogether, channeling through global platforms based in tax-advantaged regions [1].

This dual nature of e-commerce, serving as both an enabler and a disruptor, has prompted scholars to advocate for a more systemic and critical approach. The recent literature emphasizes viewing digital platforms as central nodes within commercial ecosystems, capable of influencing other actors through standards and governance models, and playing a key role in either speeding up or impeding sustainability transitions [5,17].

This study examines how e-commerce influences economic performance, using metrics like GDP and sustainable development indicators. It assesses whether digital commerce supports or obstructs progress toward the SDGs, particularly those focused on innovation, inclusion, responsible consumption, and climate action. The analysis highlights both the advantages and structural challenges of e-commerce within the sustainability framework.

Although academic interest is increasing, few studies explore how e-commerce relates to various aspects of sustainable development. Most research tends to focus on technological trends or consumer behavior, often neglecting the intricate links between e-commerce activity, economic performance, and sustainability results. There is a noticeable lack of empirical studies that examine the combined effect of e-commerce on SDG and GDP metrics, especially those utilizing cross-country data and quantitative methods.

This research addresses these gaps by integrating macroeconomic data with correlation and interpretive analysis. Its distinctive feature is a multidimensional framework that connects digital commerce indicators to broader structural shifts in society, the economy, and the environment. This approach provides valuable insights for scholars and policymakers seeking to understand how digitalization influences sustainability and economic growth over time.

The paper is organized as follows: the introduction describes the study’s context and goals; the literature review covers major theoretical and empirical works; the methodology section details the research design and data sources; results are analyzed in relation to previous findings; and the conclusion emphasizes the study’s contributions and future research opportunities.

2. Review of the Related Literature

2.1. The Comparative Impact of E-Commerce and Traditional Trade on SDGs and GDP

Over the last twenty years, there has been a growing academic focus on the relationship between e-commerce and sustainability, particularly in relation to climate change and global economic integration [18]. E-commerce has become central to the digital economy, frequently examined as a driver of economic growth that also poses intricate challenges to the Sustainable Development Goals (SDGs) [19,20].

While traditional trade continues to be vital, e-commerce has developed as a separate commercial model that is influenced by particular logistics, technology systems, and regulations [1]. It reduces transaction costs [3], enhances market access for SMEs [21,22], and also brings environmental issues such as high return rates and packaging waste [11,23].

The resource-based view (RBV) considers e-commerce as a strategic resource that boosts a company’s performance and competitiveness, especially for SMEs [24,25,26,27]. It enables entry into global value chains, enhances agility and connectivity, and broadens international reach [8,28]. Additionally, e-commerce supports the SDGs by lowering emissions and fostering social inclusion, gender equality, and education access, which are central to SDGs 4, 5, and 17 [4,7].

However, these advantages come with certain trade-offs. E-commerce can cause negative externalities like increased transport emissions and non-recyclable packaging waste [13,14,29,30]. Its key benefits—speed, accessibility, and variety—may conflict with sustainability goals unless they are supported by effective regulation and innovation [2,6]. As a result, research increasingly focuses on sustainable packaging, responsible logistics, and enhanced corporate social responsibility (CSR) efforts [31,32]. Moreover, changing consumer preferences play a role, with an increasing demand for eco-friendly products and brands [33,34,35].

Another emerging theme is the role of e-commerce in promoting sustainable practices among SMEs. It broadens market access and facilitates circular business models, strengthening local economies [36,37]. Additionally, the COVID-19 pandemic highlighted the significance of digital trade in supporting SMEs’ financial stability and environmental goals [11,38].

Concerning GDP, existing research offers a more detailed perspective. E-commerce can increase revenue and employment in specific sectors, but its overall effect on GDP in developed nations—where conventional retail dominates—remains modest [15,39]. Online transactions often contribute limited value due to slim profit margins or the import of finished goods [17]. Although e-commerce’s contribution is frequently undervalued in GDP calculations, it plays a crucial role in driving innovation, enhancing productivity, and fostering economic inclusion [16,40]. Additionally, it introduces hybrid business models that challenge traditional economic categories, leading to discrepancies between actual value generated and official figures.

Although many studies examine the microeconomic and environmental impacts of e-commerce, few directly compare how e-commerce and traditional trade jointly affect SDGs and GDP. This research fills that gap by integrating these effects into a single model [41,42,43].

2.2. E-Commerce and Sustainable Development Dynamics

The rapid growth of e-commerce is directly linked to improvements in ICT infrastructure and shifts in consumer habits, particularly after the COVID-19 pandemic [2]. By lowering costs and promoting logistics innovation, e-commerce facilitates the internationalization of SMEs [10,21] and encourages more sustainable consumption [3,6,36]. These developments are further supported by environmentally conscious consumers, leading companies to integrate sustainability into their branding and CSR strategies [33,34]. This integration ultimately boosts customer loyalty and competitiveness [35,44].

E-commerce contributes to the SDGs in multiple ways. Digital platforms enhance education, gender equality, and partnerships, supporting SDGs 4, 5, and 17 [4,7]. However, challenges such as excessive packaging and emissions from speedy deliveries remain [12,23]. Researchers recommend circular logistics models that emphasize reuse and recycling to tackle these issues [13,30].

However, substantial gaps still exist. Although current research emphasizes sustainability risks, few studies examine how e-commerce platforms serve as institutional actors that influence supply chain standards and facilitate structural change [5,42]. Some evidence indicates that companies participating in e-commerce experience enhanced financial and environmental outcomes [11,40,45]. When these improvements are reinvested in green initiatives, they can create a virtuous cycle that is aligned with sustainability objectives. Additionally, e-commerce promotes inclusion by connecting rural and marginalized communities to global markets, supporting SDG 10 (reduced inequalities) [5]. This inclusion highlights its potential as a transformative driver for sustainable development.

From an RBV perspective, e-commerce is a vital resource that supports sustained competitiveness through adaptability and innovation [24,25,26,27]. However, digital divides still pose challenges [17,46], underlining the need for public policies and digital education to promote equitable access to the advantages of e-commerce [47,48].

This study adds to the literature by empirically evaluating how e-commerce adoption enhances SDG performance, especially compared to traditional development paths without digital integration.

2.3. National-Level Digitalization and SDG Performance

E-commerce, as a key component of the digital economy, is transforming traditional economic models and opening new avenues for achieving SDGs [5,17,49]. Data indicates a positive link between increased e-commerce adoption and better SDG performance, implying systemic impacts beyond just individual companies [41].

Digitalization provides efficiency benefits such as lower operational costs and reduced reliance on physical infrastructure, supporting SDGs focused on sustainable consumption and infrastructure [2,6,10,50]. Businesses that leverage digital platforms can expand their geographic scope and often adhere to higher environmental and social standards [8,28]. SMEs adopting digital tools tend to embed sustainability into their operations, aligning with SDGs 5 and 17 [37]. Moreover, digital platforms act as knowledge exchange hubs, promoting environmental and social standards throughout supply chains [4,5]. Leading e-commerce companies increasingly integrate green criteria into supplier selection and logistics, motivated by consumer demand for sustainable products [17,32].

Moreover, investments at the national level in ICT, digital payment systems, and e-commerce regulation foster the growth of e-commerce and indirectly advance SDG progress [4,7,51,52]. Despite persistent challenges like waste production and inefficient delivery networks, companies continue to innovate with improved reverse logistics and environmentally friendly packaging options [53].

RBV-based theories affirm that e-commerce is a strategic resource for achieving a sustainable competitive advantage [24,26,54]. Incorporating sustainability into digital strategies helps firms enhance both their economic performance and societal contributions [36].

However, a significant research gap still exists: only a few studies systematically link national digitalization indicators (such as online sales and e-commerce participation) with SDG outcomes across different countries. This research aims to fill that gap within the European context.

3. Hypothesis Development

Although a growing body of research explores e-commerce’s links to sustainability and economic growth, significant gaps remain in understanding how e-commerce compares to traditional trade in affecting the Sustainable Development Goals (SDGs) and Gross Domestic Product (GDP). Many studies focus on how e-commerce supports specific sustainability aspects [18,19,20], but few examine its combined and possibly conflicting impacts alongside traditional trade models [29,30]. Additionally, while e-commerce is widely seen as a tool for operational agility and inclusion [24,25,26,27,28], these ideas are rarely tested empirically at the macroeconomic level, especially within the European Union.

This study addresses these gaps with a comparative, data-driven analysis of how e-commerce and traditional trade influence SDG and GDP outcomes across EU member states. While traditional trade remains a significant contributor to GDP [15,40], e-commerce is increasingly recognized as a strategic catalyst for sustainability and digital inclusion [4,7,36,41]. Nonetheless, current research lacks clear conclusions about the relative importance or nature of each trade model’s impact on these areas. It also does not explore whether SDG progress accelerates when e-commerce is actively incorporated into national economies, especially in light of the post-pandemic digital shift [2,11,38].

While the resource-based view (RBV) and digital transformation theories emphasize the strategic importance of e-commerce [24,26,54], their broader impact on sustainability at the macro level is still mostly unexamined within European policy contexts. This is especially significant when considering how rising business engagement in e-commerce relates to national SDG achievement, a link that has been acknowledged but seldom empirically tested [37,41,49].

Building on these identified gaps, this study introduces and aims to empirically evaluate the following hypotheses:

Hypothesis H1.

E-commerce impacts SDGs more significantly than traditional trade, although its effect on GDP is comparatively smaller.

This hypothesis comes from comparing the sustainability-focused flexibility of e-commerce platforms with the more direct, yet often less adaptable economic systems underlying traditional trade [41,42,43]. It highlights the dual role of e-commerce as both a promoter of sustainable development and a disruptor of conventional economic accounting methods [16,40].

Hypothesis H2.

The historical growth rate of SDGs is slower than when e-commerce is used as a dependent variable, highlighting the important role of e-commerce in advancing sustainability.

This proposition suggests that digitalization can speed up the achievement of sustainability targets beyond historical expectations by lowering transaction costs and increasing access for more people [5,11,45].

Hypothesis H3.

EU countries with higher business participation in e-commerce and larger online sales turnover tend to have higher Sustainable Development Goal (SDG) index scores, showing a strong link between digitalization and sustainability.

This third hypothesis expands the RBV framework by connecting e-commerce adoption and sales volumes to overall national sustainability performance. It highlights the systemic and structural impact of digital platforms and business networks on sustainable development [5,17,37,54].

By testing these hypotheses, the study adds both conceptual and empirical value to current debates on the digital economy, providing new perspectives on how e-commerce relates to global sustainability initiatives and economic performance indicators.

4. Materials and Methods

4.1. Research Design

This study uses a quantitative research approach with advanced statistical techniques to examine the links between e-commerce, traditional trade, and key sustainability and economic indicators. Its framework aims to achieve three main goals: assessing how e-commerce affects SDGs and economic growth (GDP), monitoring SDG progress amid digitalization, and grouping countries based on their sustainability and e-commerce performance.

The analysis relies on secondary data collected over a significant period, focusing solely on EU countries. This approach guarantees data consistency and comparability, since Eurostat and SDG index scores provide dependable longitudinal indicators for member states.

Figure 1 illustrates the conceptual model created by the authors, clearly depicting the main relationships examined in this study.

The model clarifies how variables interact and helps identify strategies that promote sustainable development.

4.2. Selected Variables

Five key variables were selected to align with the research goals, each capturing a different facet of the economy and sustainability.

The SDG score (SDG) assesses a country’s progress towards achieving the SDGs, based on data from the UN Sustainable Development Solutions Network (SDSN).

Total trade (TCOMM), indexed to 2010, indicates the volume of international trade and a country’s integration into global value chains. Sourced from Eurostat, this variable provides insights into the importance of traditional trade within the broader economy.

Two e-commerce variables—e-commerce turnover as a percentage of total enterprise turnover (ECOMM_PT) and the percentage of enterprises involved in online sales (ECOMM_PE)—indicate the level of digitalization in the economy. Also from Eurostat, these indicators show how much businesses utilize digital technologies to improve their commercial operations.

Real GDP per capita (RGDPpc), indexed to 2010, acts as an indicator of economic growth and prosperity. Sourced from Eurostat, this variable captures both the economic development level and the population’s well-being.

These indicators offer a thorough foundation for examining the relationship between digitalization, trade, and sustainability.

Table 1. Research variables.

Variable	Dataset	Measures	References
SDG	Sustainable Development Goals index score	Index	[55]
TCOMM	Total Trade of the EU	Index 2010 = 100	[56]
ECOMM_PT	Enterprises’ total turnover from e-commerce sales	Percentage of turnover	[57]
ECOMM_PE	Enterprises with e-commerce sales	Percentage of enterprises	[58]
RGDPpc	Real GDP per capita	Index 2010 = 100	[59]

Source: developed by the author based on Eurostat [56,57,58,59] and SDSN [55].

presents a summary of the chosen variables.

4.3. Methods

The analysis employs advanced statistical techniques that are customized for the research objectives. Factor analysis uncovers underlying structures in the data, reducing complexity by grouping variables into significant factors [60]. This approach decomposes the correlation matrix using Formula (1):

$$X=LF+\in$$

(1)

$X$—observed variables;

$L$—matrix of factor loadings;

$F$—latent factors;

$\in$—errors.

Factor analysis enhances the comprehension of how e-commerce and traditional trade influence sustainability and economic metrics by revealing shared underlying patterns that explain data fluctuations.

To assess how these two types of trade influence the Sustainable Development Goals (SDGs) and Gross Domestic Product (GDP), the study employs linear regression and multivariate analysis using the General Linear Model (GLM). The linear regression model is described by Equation (2):

$$Y={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\dots +{\beta }_n{X}_n+\in$$

(2)

$Y$—dependent variable;

$X_1,X_2, \dots ,X_n$—independent variables;

${\beta }_0$—intercept;

${\beta }_1,{\beta }_2, \dots ,{\beta }_n$—regression coefficients;

$\in$—error.

The multivariate GLM expands this framework by incorporating multiple dependent variables and modeling complex interactions among them [61]. This method allows the study to identify both direct effects and detailed interdependencies, emphasizing how e-commerce and traditional trade differently influence sustainability and economic outcomes.

To evaluate the progression of SDG indicators, the analysis uses forecasting models like Brown, Holt, and ARIMA [60]. The Brown model, also called simple exponential smoothing, is defined by Equations (3)–(5):

$$S_t=\alpha y_t+(1-\alpha )(S_{t-1}+b_{t-1})$$

(3)

$$b_t={\beta (S}_t-S_{t-1})+(1-\beta )b_{t-1}$$

(4)

$$F_{t+m}=S_t+m{b}_t$$

(5)

$y_t$—the observed value at time t;

$S_t$—the smoothed value for the level at time t;

$b_t$—the estimated trend at time t;

α—the smoothing parameter for the level;

β—the smoothing parameter for the trend;

$F_{t+m}$—forecasted value for m steps ahead of time t.

The Holt model enhances this structure by incorporating a more flexible trend component. Its forecasting and smoothing equations are (6)–(8):

$${\widehat{y}}_{t+\left.h\right|t}=l_t+h{b}_t$$

(6)

$$l_t={\alpha y}_t+(1-\alpha )(l_{t-1}+b_{t-1})$$

(7)

$$b_t={\beta (l}_t-l_{t-1})+(1-\mathsf{\beta })b_{t-1}$$

(8)

$l_t$—an estimate of the level of the series at time t;

$b_t$—an estimate of the trend (slope) of the series at time t;

α—the smoothing parameter for the level;

β—the smoothing parameter for the trend.

Lastly, the ARIMA (Autoregressive Integrated Moving Average) model captures more complex temporal dependencies using Equation (9):

$$\left(1-\sum _{i=1}^p{\phi }_i{L}^i\right){(1-L)}^d{X}_t=\left(1+{\sum }_{i=1}^q{\theta }_i{L}^i\right){\epsilon }_t$$

(9)

$X_t$—data series;

$L$—lag operator;

${\phi }_i$—parameters of the autoregressive part of the model;

${\theta }_i$—parameters of the moving average part;

${\epsilon }_t$—error.

These models not only help analyze past trends but also assist in projecting future developments, highlighting e-commerce’s role as a driver of sustainability.

The study employs cluster analysis to categorize countries based on their SDG progress and e-commerce activity. Techniques like k-means aim to reduce distances within clusters and increase those between different clusters [62]. The most successful method was found to be average linkage clustering, which is computed as shown in Equation (10):

$$d_{ij}={\displaystyle \frac{1}{kl}}\sum _{i=1}^k\sum _{j=1}^{l}d({\mathit{X}}_i,{\mathit{Y}}_j)$$

(10)

${\mathit{X}}_{1,}{\mathit{X}}_{2,},\dots ,{\mathit{X}}_{\mathit{k}}$—observations from cluster 1;

${\mathit{Y}}_{1,}{\mathit{Y}}_{2,},\dots ,{\mathit{Y}}_{\mathit{l}}$—observations from cluster 2;

d(X,Y)—the distance between a subject with observation vector x and a subject with an observation vector;

k, l—cases.

This clustering method provides a geographical and economic view of how digitalization and sustainability intersect, identifying groups of countries with similar traits and emphasizing areas where progress can be made.

By combining these methods, the study offers a thorough and reliable analysis of how e-commerce and traditional trade impact the economy and the environment. Each approach adds a unique perspective, and together they facilitate a better understanding of these intricate relationships, laying solid groundwork for crafting effective and sustainable policies.

5. Results

To test Hypothesis H1—claiming that e-commerce significantly impacts SDG more than traditional trade, but has a lesser effect on GDP—we used three statistical techniques: factor analysis, linear regression, and a multivariate GLM (General Linear Model). These methods enabled us to analyze complex variable relationships and evaluate the hypothesis. Factor analysis was particularly helpful in uncovering the underlying data structure and in reducing complexity by categorizing variables into meaningful factors.

The factor analysis of variables—namely, the Sustainable Development Goals index score (SDG), real GDP per capita (RGDPpc), Total EU trade (TCOMM), enterprises’ total turnover from e-commerce sales (ECOMM_PT), and enterprises with e-commerce sales (ECOMM_PE)—indicated a strong correlation pattern. This suggests the presence of a common underlying factor that significantly impacts these metrics. The correlation matrix showed robust relationships among all variables, with correlation coefficients ranging from 0.719 to 0.955 (

Table 2. Correlation matrix, KMO and Bartlett’s test.

		SDG	RGDPpc	TCOMM	ECOMM_PT	ECOMM_PE
Correlation	SDG	1.000	0.938	0.892	0.930	0.940
	RGDPpc	0.938	1.000	0.955	0.823	0.897
	TCOMM	0.892	0.955	1.000	0.719	0.902
	ECOMM_PT	0.930	0.823	0.719	1.000	0.858
	ECOMM_PE	0.940	0.897	0.902	0.858	1.000
Sig. (1-tailed)	SDG		0.000	0.000	0.000	0.000
	RGDPpc	0.000		0.000	0.000	0.000
	TCOMM	0.000	0.000		0.001	0.000
	ECOMM_PT	0.000	0.000	0.001		0.000
	ECOMM_PE	0.000	0.000	0.000	0.000

Source: author’s design with SPSS v.27.0. Kaiser–Meyer–Olkin measure of sampling adequacy = 0.772; Approx. Chi-Square = 111.836; df = 10; Sig.= 0.000_.

).

The strongest correlations were found between the SDG scores and RGDPpc (0.938), and between SDG scores and ECOMM_PE (0.940), suggesting a close connection between sustainable development, real GDP per capita, and digitalization levels, primarily through firms’ online sales. TCOMM also showed high correlations with RGDPpc (r = 0.955) and ECOMM_PE (r = 0.902), indicating that trade volumes are impacted by economic growth and e-commerce adoption. The lower correlation between TCOMM and ECOMM_PT (0.719) implies that, although total trade and e-commerce are related, e-commerce is not the main factor influencing the share of online sales within total firm turnover.

The Kaiser–Meyer–Olkin (KMO) measure (0.772) indicated that the data were appropriate for factor analysis, while Bartlett’s test (p < 0.001) confirmed significant correlations among variables.

Communalities show how effectively the extracted factor explains each variable. SDG scores had the highest communality at 0.991, suggesting they were nearly fully represented by the factor structure (see

Table 3. Communalities and factor matrix.

	Initial	Extraction	Factor 1
SDG	0.971	0.991	0.996
RGDPpc	0.958	0.931	0.965
TCOMM	0.961	0.841	0.917
ECOMM_PT	0.935	0.757	0.870
ECOMM_PE	0.921	0.919	0.959

Source: author’s design with SPSS v.27.0.

).

RGDPpc (0.931) and ECOMM_PE (0.919) were well explained, whereas TCOMM and ECOMM_PT showed lower shared variance (0.841 and 0.757), suggesting the presence of residual variability not accounted for by the factor. The factor matrix demonstrated consistently high loadings on Factor 1, with SDG scores (0.996), RGDPpc (0.965), and ECOMM_PE (0.959) as the strongest contributors. The lower loading of ECOMM_PT (0.870) implies that, although e-commerce is important, its revenue share is less influential than the number of firms participating in it.

Total variance analysis showed that Factor 1 explained 88.78% of the variability, a very high proportion, indicating that the five variables are closely related and can be summarized into a single underlying dimension (

Table 4. Total variance explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.545	90.907	90.907	4.439	88.784	88.784
2	0.314	6.286	97.193
3	0.099	1.974	99.168
4	0.024	0.476	99.644
5	0.018	0.356	100.000

Source: author’s design using SPSS v.27.0.

). This was the only factor with an eigenvalue over 1 (4.545), confirming its selection.

The factor analysis emphasizes the connection between economic development, sustainability, and digital trade, creating a combined indicator of economic digitalization and sustainability. Countries with a high GDP per capita, active trade, and a strong e-commerce sector tend to have higher SDG scores. Further investigation of causal links or other factors could improve these insights using regression or multivariate methods.

Linear regression was used as the main approach to measure how e-commerce, traditional trade, and the Sustainable Development Goal (SDG) scores are related. When SDG score was set as the dependent variable and TCOMM and ECOMM_PT were set as the independent variables, the model showed a strong link among these factors, supported by robust statistical data.

The Model Summary results show a correlation coefficient (R) of 0.984, indicating a strong relationship between the predictors and SDG scores. The R-Square value of 0.968 reveals that 96.8% of the variation in the SDG scores is explained by the two variables in the model (see

Table 5. Linear regression model.

Model Summary
Model		R		R-Square			Adjusted R-Square		Std. Error of the Estimate
1		0.984 a		0.968			0.963		0.4300
ANOVA
Model			Sum of Squares		df	Mean Square	F		Sig.
1	Regression		67.617		2	33.809	182.834		0.000 b
	Residual		2.219		12	0.185
	Total		69.836		14
Coefficients
Model			Unstandardized Coefficients			Standardized Coefficients		t		Sig.
			B		Std. Error	Beta
1	(Constant)		56.209		0.771			72.903		0.000
	TCOMM		0.036		0.006	0.461		6.224		0.000
	ECOMM_PT		0.538		0.067	0.599		8.085		0.000

Source: author’s design with SPSS v.27.0. a. Dependent Variable: SDG; b. Predictors: (Constant), ECOMM_PT, TCOMM.

). Additionally, the Adjusted R-Square (0.963) supports the model’s robustness and explanatory ability, accounting for the number of predictors. The standard error of the estimate (0.430) is quite low, suggesting that the predictions are highly accurate.

The ANOVA test confirms the model’s overall significance, with an F-statistic of 182.834 and a p-value below 0.001, indicating that at least one independent variable significantly impacts the SDG score outcome.

The regression coefficients show the specific effect of each independent variable on the SDG index. The constant (56.209) estimates the SDG score when both predictors are zero. The coefficient for the Total Trade of the EU (TCOMM), at 0.036, indicates that a one-unit rise in total trade leads to a 0.036 increase in the SDG score, assuming that ECOMM_PT stays constant. In contrast, the ECOMM_PT coefficient (0.538) indicates a more substantial influence.

Both predictors show high t-values, 6.224 for TCOMM and 8.085 for ECOMM_PT, with significance levels well below 0.001. This indicates that both variables significantly contribute to the model. Notably, the standardized beta coefficient for enterprises’ total turnover from e-commerce sales (0.599) surpasses the total trade in the EU (0.461), implying that e-commerce has a markedly greater influence on sustainable development compared to traditional trade volume.

The linear regression model highlights a strong link between sustainable development and economic activity, especially concerning e-commerce. The increasing portion of revenue from digital commerce appears to be a more significant factor in advancing SDGs than total EU trade volume, suggesting that digitalization and e-commerce could be crucial in promoting sustainable progress.

The General Linear Model (GLM) multivariate analysis allowed for the simultaneous analysis of multiple dependent variables, specifically the SDG index and real GDP per capita (RGDPpc), including their interactions.

This multivariate analysis showed that both the Total Trade of the EU (TCOMM) and an enterprises’ total turnover from e-commerce sales (ECOMM_PT) significantly impact the two dependent variables. The multivariate tests, such as Pillai’s Trace and Wilks’ Lambda, strongly indicate that both factors have a statistically meaningful effect, supported by very low p-values that confirm their consistent and notable influence (

Table 6. Multivariate tests.

Effect		Value	F	Hypothesis df	Error df	Sig.	Partial Eta Squared	Noncent. Parameter	Observed Power c
Intercept a	Pillai’s Trace	0.998	2585.532 b	2.000	11.000	0.000	0.998	5171.063	1.000
	Wilks’ Lambda	0.002	2585.532 b	2.000	11.000	0.000	0.998	5171.063	1.000
	Hotelling’s Trace	470.097	2585.532 b	2.000	11.000	0.000	0.998	5171.063	1.000
	Roy’s Largest Root	470.097	2585.532 b	2.000	11.000	0.000	0.998	5171.063	1.000
TCOMM a	Pillai’s Trace	0.886	42.760 b	2.000	11.000	0.000	0.886	85.521	1.000
	Wilks’ Lambda	0.114	42.760 b	2.000	11.000	0.000	0.886	85.521	1.000
	Hotelling’s Trace	7.775	42.760 b	2.000	11.000	0.000	0.886	85.521	1.000
	Roy’s Largest Root	7.775	42.760 b	2.000	11.000	0.000	0.886	85.521	1.000
ECOMM_PT a	Pillai’s Trace	0.853	31.873 b	2.000	11.000	0.000	0.853	63.745	1.000
	Wilks’ Lambda	0.147	31.873 b	2.000	11.000	0.000	0.853	63.745	1.000
	Hotelling’s Trace	5.795	31.873 b	2.000	11.000	0.000	0.853	63.745	1.000
	Roy’s Largest Root	5.795	31.873 b	2.000	11.000	0.000	0.853	63.745	1.000

Source: author’s design with SPSS v.27.0. a. Design: Intercept + TCOMM + ECOMM_PT. b. Exact statistic. c. Computed using alpha = 0.05.

).

Specifically, TCOMM significantly influenced both the SDG index and RGDPpc, accounting for much of their variation. Although ECOMM_PT had a greater impact on the SDG index than TCOMM, its effect on GDP was statistically significant but less pronounced than that of traditional trade.

In the analysis of between-subject effects, TCOMM was identified as having a significant influence on SDG scores and real GDP per capita. ECOMM_PT also showed a statistically significant impact on SDG scores, although its effect on RGDPpc was less noticeable (

Table 7. Tests of between-subject effects.

Source	Dependent Variable	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared	Noncent. Parameter	Observed Power c
Corrected Model	SDG	67.617 a	2	33.809	182.834	0.000	0.968	365.668	1.000
	RGDPpc	526.445 b	2	263.223	114.161	0.000	0.950	228.322	1.000
Intercept	SDG	982.800	1	982.800	5314.915	0.000	0.998	5314.915	1.000
	RGDPpc	1678.164	1	1678.164	727.828	0.000	0.984	727.828	1.000
TCOMM	SDG	7.162	1	7.162	38.733	0.000	0.763	38.733	1.000
	RGDPpc	151.371	1	151.371	65.650	0.000	0.845	65.650	1.000
ECOMM_PT	SDG	12.086	1	12.086	65.362	0.000	0.845	65.362	1.000
	RGDPpc	21.203	1	21.203	9.196	0.010	0.434	9.196	0.795
Error	SDG	2.219	12	0.185
	RGDPpc	27.669	12	2.306
Total	SDG	73,653.860	15
	RGDPpc	175,432.043	15
Corrected Total	SDG	69.836	14
	RGDPpc	554.114	14

Source: author’s design with SPSS v.27.0. a. R-Squared = 0.968 (Adjusted R-Squared = 0.963). b. R-Squared = 0.950 (Adjusted R-Squared = 0.942). c. Computed using alpha = 0.05.

).

When estimating parameters, the coefficients for TCOMM and ECOMM_PT revealed direct links with the SDG scores and RGDP per capita. Notably, ECOMM_PT had a significantly larger coefficient for SDG scores than TCOMM, signifying that e-commerce is more influential in promoting sustainable development goals, as shown in

Table 8. Parameter estimates.

Dependent Variable	Parameter	B	Std. Error	t	Sig.	95% Confidence Interval		Partial Eta Squared	Noncent. Parameter	Observed Power a
						Lower Bound	Upper Bound
SDG	Intercept	56.209	0.771	72.903	0.000	54.529	57.889	0.998	72.903	1.000
	TCOMM	0.036	0.006	6.224	0.000	0.023	0.048	0.763	6.224	1.000
	ECOMM_PT	0.538	0.067	8.085	0.000	0.393	0.683	0.845	8.085	1.000
RGDPpc	Intercept	73.450	2.723	26.978	0.000	67.518	79.382	0.984	26.978	1.000
	TCOMM	0.164	0.020	8.102	0.000	0.120	0.208	0.845	8.102	1.000
	ECOMM_PT	0.712	0.235	3.032	0.010	0.200	1.224	0.434	3.032	0.795

Source: author’s design with SPSS v.27.0. a. Computed using alpha = 0.05.

.

This analysis shows that total trade and e-commerce play essential roles in economic and sustainable growth. However, e-commerce has a much more substantial impact on sustainability indicators, while total trade more heavily influences real GDP per capita. These results imply that while traditional trade remains a key driver of economic growth, digitalization and the growth of electronic commerce are increasingly vital for meeting sustainability goals.

The multivariate GLM analysis confirmed the results of the factor analysis and linear regression. It emphasized that e-commerce plays a more important role in advancing the SDGs compared to its impact on GDP. Meanwhile, traditional trade has a more substantial effect on GDP than on sustainability objectives. The interaction effects between e-commerce and traditional trade were not statistically significant, highlighting the distinct contributions each makes to development.

Using these three complementary statistical methods, the analysis validated Hypothesis H1. The findings indicate that e-commerce more significantly contributes to achieving SDGs, whereas traditional trade has a more substantial influence on GDP growth. These insights offer a strong basis for crafting policies focused on economics and sustainability, underlining the importance of encouraging e-commerce as a key strategy for sustainable development while recognizing the continuing role of traditional trade in supporting overall economic growth.

We used three forecasting models—Brown, Holt, and ARIMA—to test Hypothesis H2. This hypothesis suggests that the SDG growth rate derived from past data is slower than the rate predicted when e-commerce is included as a dependent variable. These models helped us compare the historical trend in the SDG index with future projections that consider e-commerce’s impact, assessing its role in advancing sustainable outcomes.

We applied the Brown exponential smoothing model to forecast the SDG index trend, using only historical data, with time as the independent variable and the SDG score as the dependent variable.

Table 9. Brown model fit.

Fit Statistic	Mean	Min	Max	Percentile
				5	10	25	50	75	90	95
Stationary R-squared	0.378	0.378	0.378	0.378	0.378	0.378	0.378	0.378	0.378	1.378
R-squared	0.986	0.986	0.986	0.986	0.986	0.986	0.986	0.986	0.986	00.986
RMSE	0.263	0.263	0.263	0.263	0.263	0.263	0.263	0.263	0.263	0.263
MAPE	0.290	0.290	0.290	0.290	0.290	0.290	0.290	0.290	0.290	0.290
MaxAPE	1.031	1.031	1.031	1.031	1.031	1.031	1.031	1.031	1.031	1.031
MAE	0.202	0.202	0.202	0.202	0.202	0.202	0.202	0.202	0.202	0.202
MaxAE	0.696	0.696	0.696	0.696	0.696	0.696	0.696	0.696	0.696	0.696
Normalized BIC	−2.491	−2.491	−2.491	−2.491	−2.491	−2.491	−2.491	−2.491	−2.491	−2.491

Source: author’s design with SPSS v.27.

shows the fit statistics for this model.

The Brown model showed outstanding performance, explaining 98.6% of the variability in SDG data with a high R-squared of 0.986. Errors were minimal, indicated by an RMSE of 0.263 and an MAPE of only 0.29%. An alpha of 0.733 points to a quick adaptation to both level and trend changes. Trends from periods 16 to 26 revealed a moderate rise in the SDG score from 73.0 to 74.6, with narrow confidence intervals confirming the high reliability of the model’s forecasts.

Figure 2 and

Table A1. Historical and future trends.

Year	SDG	ECOMM_PT	Trend of SDG Using the Brown Model Depending on Time	Trend of ECOMM_PT Using the Holt Model Depending on Time	Trend of SDG Using the ARIMA Model Depending on ECOMM_PT
2010	66.7	13.46	-	-	-
2011	66.7	13.15	-	-	-
2012	67.5	13.76	-	-	-
2013	67.8	13.12	-	-	-
2014	68.4	13.81	-	-	-
2015	69.0	16.43	-	-	-
2016	69.6	15.75	-	-	-
2017	70.4	18.40	-	-	-
2018	70.8	17.17	-	-	-
2019	71.5	18.06	-	-	-
2020	72.0	19.83	-	-	-
2021	72.2	19.77	-	-	-
2022	72.5	17.62	-	-	-
2023	72.7	18.24	-	-	-
2024	72.8	19.12	-	-	-
2025	-	-	73.0	20.38	73.3
2026	-	-	73.1	20.88	73.7
2027	-	-	73.3	21.37	74.1
2028	-	-	73.5	21.87	74.5
2029	-	-	73.6	22.36	74.9
2030	-	-	73.8	22.86	75.3
2031	-	-	74.0	23.35	75.8
2032	-	-	74.1	23.85	76.2
2033	-	-	74.3	24.35	76.6
2034	-	-	74.5	24.84	77.0
2035	-	-	74.6	25.34	77.4

Source: author’s design with SPSS v.27.0.

in Appendix A provide detailed projections for the SDG index.

This method helped identify a gradual but consistent upward trend in SDG score performance, regardless of external factors like e-commerce.

We applied the Holt double exponential smoothing model to forecast the e-commerce share (ECOMM_PT) in national economies. In this approach, the dependent variable was the e-commerce share, incorporating both the level and trend components of the data.

Table 10. Holt model fit.

Fit Statistic	Mean	Min	Max	Percentile
				5	10	25	50	75	90	95
Stationary R-squared	0.708	0.708	0.708	0.708	0.708	0.708	0.708	0.708	0.708	0.708
R-squared	0.773	0.773	0.773	0.773	0.773	0.773	0.773	0.773	0.773	0.773
RMSE	1.230	1.230	1.230	1.230	1.230	1.230	1.230	1.230	1.230	1.230
MAPE	5.555	5.555	5.555	5.555	5.555	5.555	5.555	5.555	5.555	5.555
MaxAPE	11.946	11.946	11.946	11.946	11.946	11.946	11.946	11.946	11.946	11.946
MAE	0.947	0.947	0.947	0.947	0.947	0.947	0.947	0.947	0.947	0.947
MaxAE	2.198	2.198	2.198	2.198	2.198	2.198	2.198	2.198	2.198	2.198
Normalized BIC	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775

Source: author’s design with SPSS v.27.

presents the fit statistics for the Holt model.

Compared to the Brown model, the Holt model showed a more moderate performance. It accounted for 77.3% of the data variation, with a higher RMSE of 1.230 and a MAPE of 5.555%. Projections for periods 16 through 26 suggested an increase in the share of e-commerce from 20.38% to 25.34%. However, the wider confidence intervals indicate less certainty in the predictive results.

Figure 3 and Table A1 in Appendix A offer detailed projections for ECOMM_PT.

This modeling approach revealed a quick growth in enterprise involvement in electronic commerce, underscoring a wider move toward digitalization.

To examine the relationship between SDG scores and e-commerce participation, we used the ARIMA model, a more advanced analytical method. In this case, the SDG score was the dependent variable, while the share of e-commerce was the independent variable.

Table 11. ARIMA model fit.

Fit Statistic	Mean	Min	Max	Percentile
				5	10	25	50	75	90	95
Stationary R-squared	0.866	0.866	0.866	0.866	0.866	0.866	0.866	0.866	0.866	0.866
R-squared	0.866	0.866	0.866	0.866	0.866	0.866	0.866	0.866	0.866	0.866
RMSE	0.849	0.849	0.849	0.849	0.849	0.849	0.849	0.849	0.849	0.849
MAPE	0.971	0.971	0.971	0.971	0.971	0.971	0.971	0.971	0.971	0.971
MaxAPE	2.117	2.117	2.117	2.117	2.117	2.117	2.117	2.117	2.117	2.117
MAE	0.683	0.683	0.683	0.683	0.683	0.683	0.683	0.683	0.683	0.683
MaxAE	1.535	1.535	1.535	1.535	1.535	1.535	1.535	1.535	1.535	1.535
Normalized BIC	0.035	0.035	0.035	0.035	0.035	0.035	0.035	0.035	0.035	0.035

Source: author’s design with SPSS v.27.

shows the fit statistics for the ARIMA model.

The ARIMA(0, 0, 0) model demonstrated high accuracy, with an R-squared value of 0.866 and an RMSE of 0.849. Its parameters included a constant of 56.245 and a coefficient of 0.835 for ECOMM_PT, highlighting a significant influence of e-commerce on sustainable development performance. The SDG index trends from periods 16 to 26 showed an increase from 73.3 to 77.4. The tight confidence intervals suggest that these trends are highly reliable.

Figure 4 and Table A1 in Appendix A illustrate these detailed SDG projections.

These indicators offer a comprehensive foundation for examining the relationship between digitalization, trade, and sustainability. Table 1 summarizes the chosen variables.

The analysis, using an ARIMA model, shows that e-commerce greatly enhances the accuracy and explanatory power of forecasting sustainability trends. This underscores the vital role of digital commerce in promoting sustainable development.

The multi-method approach validated the research hypotheses and uncovered the changing interactions between traditional and digital trade models. These findings enhance the understanding of how trade structures impact development outcomes and provide practical advice for policymakers seeking to align economic growth with sustainability goals in a digital economy.

To test Hypothesis 3, the study employs cluster analysis to see if countries with higher SDG scores also show increased engagement in e-commerce and higher online turnover. Countries were grouped according to their SDG performance, e-commerce participation, and online sales revenue. The analysis utilized the Between-Groups Linkage method with Squared Euclidean Distance. Figure 5 presents the resulting dendrogram, while

Table A2. Cluster data.

	SDG	TCOMM	ECOMM_PT	ECOMM_PE	RGDPpc
Germany	74.93	167.81	17.71	20.91	111.37
Slovenia	73.72	301.50	17.15	19.95	125.79
Estonia	71.64	248.59	15.59	22.47	137.98
Malta	69.28	114.67	12.98	23.60	157.75
France	73.83	149.26	11.49	19.66	109.50
Latvia	70.47	319.24	10.22	18.01	158.61
Subcluster A1 means	72.31	216.84	14.19	20.77	133.50
Luxembourg	67.41	116.40	24.27	16.67	99.73
Slovakia	70.57	207.15	21.55	16.94	132.01
Italy	72.18	185.58	17.34	15.17	106.78
Poland	73.43	284.60	17.25	16.62	156.88
Portugal	70.48	210.38	18.99	12.56	113.18
Subcluster A2 means	70.81	200.82	19.88	15.59	121.71
Cyprus	62.45	406.19	12.58	13.73	123.99
Romania	64.01	245.93	11.55	12.85	161.88
Bulgaria	62.77	305.30	6.32	10.01	155.54
Greece	66.59	263.51	7.07	13.88	96.81
Subcluster A3 means	63.95	305.23	9.38	12.62	134.55
Cluster A means	69.03	240.97	14.48	16.33	129.92
Eu mean	71.47	222.70	18.26	23.37	129.74
Denmark	79.50	172.13	28.64	31.41	119.68
Finland	81.09	156.15	24.93	33.40	103.95
Ireland	71.51	229.45	28.39	39.52	195.24
Sweden	79.27	157.06	25.56	41.73	112.35
Subcluster B1 means	77.85	178.70	26.88	36.52	132.80
Netherlands	71.85	211.82	18.99	29.32	114.88
Spain	71.11	205.77	19.35	30.93	110.76
Croatia	72.35	272.70	15.28	31.50	140.30
Lithuania	67.74	283.13	15.54	30.53	171.66
Austria	77.28	175.01	14.20	27.26	107.65
Subcluster B2 means	72.07	229.69	16.67	29.91	129.05
Belgium	71.93	193.71	26.56	31.58	111.93
Czechia	73.66	229.53	30.49	26.31	121.84
Hungary	68.72	200.24	23.06	24.56	144.94
Subcluster B3 means	71.43	207.83	26.70	27.48	126.24
Cluster B means	73.78	205.40	23.42	31.30	129.36
Eu means	71.47	222.70	18.26	23.37	129.74

Source: author’s design with SPSS v.27.0.

(Appendix A) provides the details of the identified clusters.

Cluster A consists of three subclusters (A1, A2, A3). These include countries with SDG scores that are near the European average but with notable differences in total trade and digital economy involvement. Subcluster A1, which features countries like Germany, Slovenia, and Estonia, is distinguished by high total trade and GDP per capita, showcasing a diversified economy that is deeply integrated into global value chains. Conversely, subcluster A3, including Cyprus, Romania, and Bulgaria, has lower SDG scores and less participation in e-commerce, highlighting potential areas for growth in digital technology adoption and sustainability efforts.

Cluster B is divided into three subclusters (B1, B2, B3), representing countries excelling in sustainability and the digital economy. Subcluster B1, which includes Denmark, Finland, and Ireland, is notable for its very high SDG scores and large shares of e-commerce in total business revenue. This situation reflects their advanced integration of digital technologies with sustainable development principles. Subcluster B2 includes countries such as the Netherlands and Spain, which demonstrate strong digitalization efforts but have slightly lower SDG scores than B1. This fact indicates room for improvement in balancing economic growth and sustainability.

Compared to Cluster A, Cluster B demonstrates higher performance in sustainability and digital integration. Meanwhile, Cluster A shows internal diversity, suggesting opportunities for targeted policy efforts and knowledge sharing between countries.

Cluster analysis offers important insights into how European countries are tackling the dual challenges of sustainability and digitalization. While Cluster B represents best practices, Cluster A underscores the importance of developing strategies that are customized to each country’s context. This distinction improves the understanding of regional differences and helps inform coordinated European efforts to meet the SDGs and foster an inclusive digital transformation.

The findings confirm that countries with higher SDG scores generally report higher online turnover and more active business participation in e-commerce, supporting Hypothesis 3. Countries in Cluster B demonstrate the connection between digitalization and sustainability. Meanwhile, subcluster A3 nations could benefit from policies that encourage digital growth and sustainable development. Therefore, the cluster analysis not only backs Hypothesis 3 but also offers a basis for creating targeted policies to bridge the digital and sustainability gaps across Europe.

6. Discussion

Examining the link between e-commerce, sustainability, and economic growth provides a valuable framework for understanding how digitalization is transforming these areas. The study started with three main hypotheses, viewing e-commerce not just as a transactional tool but as a possible catalyst for sustainable development. These hypotheses were tested through the analysis of existing theories and empirical data.

The initial hypothesis suggested that e-commerce has a more significant impact on sustainable development than traditional trade, but a lesser effect on GDP. The results support this idea and align with the assessments of researchers like Ahi et al. [3], who emphasize e-commerce’s inclusive and adaptable role in furthering the SDGs. Recent studies increasingly highlight digital platforms’ contribution not only to boosting economic efficiency but also to expanding access to education, fostering global partnerships, encouraging innovation, and building inclusive supply chains [5,7,63,64].

This study confirms that digitalization plays a more significant role in advancing SDGs than traditional trade, aligning with the findings of Štofejová et al. [17], who noted the increase in sustainable consumption behaviors in digital settings. Additionally, comparisons with the works of Mangiaracina et al. [6], Chen & Zhang [15], and Cordes & Marinova [52] indicate that although e-commerce might have a smaller effect on GDP, this does not mean it is inefficient. Instead, it highlights the limitations of traditional macroeconomic models in fully capturing the value of digital economic activity.

The findings are consistent with those of Hao et al. [39], who highlight the gap between the real value produced by digital processes and the way this value is reflected in GDP figures. Conventional trade, backed by existing infrastructure and embedded in current accounting methods, still influences how economic growth is viewed. Therefore, the support for Hypothesis H1 confirms emerging theories that connect sustainability with digitalization, while economic growth continues to rely on traditional frameworks.

The second hypothesis examined whether digital commerce advances SDG progress more effectively than historical trends alone. To evaluate this, three forecasting models were used: Brown, Holt, and ARIMA.

The Brown model, which relies only on historical SDG data, forecasted a modest rise in the SDG index from 73.0 to 74.6, reflecting steady progress based on past trends, independent from digital developments. The Holt model, emphasizing e-commerce turnover (ECOMM_PT), predicted digital participation growth from 20.38% to 25.34%. Although it exhibited larger forecast errors, this model better captured the acceleration of digitalization in economies. The ARIMA model, combining SDG scores and e-commerce participation, anticipated a more significant increase in the SDG index from 73.3 to 77.4 when e-commerce was factored in. The coefficient for ECOMM_PT (0.835) was statistically significant, confirming its influence.

All three models show a positive trend in SDG performance, but the growth rate is noticeably stronger when e-commerce dynamics are included (ARIMA) compared to projections based solely on historical trends (Brown). The ARIMA model’s higher accuracy and predictive power support Hypothesis H2, indicating that e-commerce boosts progress toward sustainability.

This finding supports recent research [42,65,66,67,68,69,70] showing that integrating digital technologies helps businesses respond faster to environmental and social issues. Although scaling these practices in traditional commerce is challenging, the digital environment allows for easier replication and growth, speeding up sustainability initiatives. Overall, these comparisons demonstrate that digital commerce is vital in driving sustainable development and should be included in policies focused on sustainability.

Hypothesis H3 added a third analytical dimension: the link between a country’s sustainability performance and its level of digital economic integration. Aligning with Bouncken and Barwinski [8], who state that technologically advanced economies connect to global networks more effectively and adopt sustainable practices more easily, the cluster analysis showed distinct patterns among European nations. Additionally, Hajdukiewicz and Pera [43] support the idea that countries with strong digital infrastructure and sustainability-focused public policies generally achieve higher scores on the SDG index.

This study reinforces this view by showing that countries with strong SDG performance also have active business participation in digital commerce. Consistent with the statements of Ahi et al. [4] and the OECD [1], who highlight digitalization as a path toward social and ecological goals, the findings emphasize the connection between digital integration and sustainable development.

This study offers empirical evidence supporting emerging theories that consider e-commerce a core part of sustainability strategies. Once seen mainly as a means to improve efficiency and grow markets, e-commerce now increasingly functions as a key element of sustainable development. The confirmation of all three hypotheses demonstrates that economic and social policies should no longer treat digitalization and sustainability as separate areas. Instead, integrated approaches are essential to promote a more resilient, inclusive, and sustainable future.

6.1. Theoretical Implications

This study’s findings greatly enrich the theoretical discussion of how digitalization, e-commerce, and sustainability interconnect. It questions the traditional perspective that sees e-commerce simply as a trade-off between economic gains and environmental impacts by incorporating the SDGs as a key framework for assessment. Consequently, the research expands on existing models by shifting from straightforward, linear explanations to systems-based approaches, positioning digital commerce as a dynamic component within the broader global economic and ecological systems.

Furthermore, the developed analytical model connects e-commerce indicators with SDG and GDP data, providing a conceptual basis for exploring the non-linear relationship between technological innovation and sustainable development. This approach encourages a deeper theoretical understanding of how digital platforms influence socioeconomic changes, including concerns about economic power concentration and externalities. Sustainability is therefore seen not just as an end goal, but as a perspective to evaluate digital transformation.

6.2. Practical Implications

In addition to its theoretical significance, the study offers important insights for policymakers, businesses, and NGOs working towards a more sustainable economy. A crucial takeaway is the need for well-crafted digitalization and e-commerce policies that balance economic gains with environmental and social goals. E-commerce promotion should extend beyond efficiency improvements and be based on principles of fairness, inclusiveness, and environmental sustainability.

The research also emphasizes the importance of strong monitoring and evaluation tools that include SDG indicators in digital development strategies. E-commerce can only be a meaningful partner to sustainability if it follows transparent reporting standards and regulatory oversight. From a corporate point of view, the findings call for a rethinking of CSR strategies to align with changing societal expectations and more informed consumers.

6.3. Limitations and Future Research Directions

Although the study provides valuable insights, it has some methodological limitations. Relying on aggregated macro-level data might hide significant differences within sectors, regions, or demographic groups. Additionally, incorporating qualitative data is crucial to gain a deeper understanding of stakeholders’ experiences and perspectives on the complex effects of e-commerce.

Another challenge is the inconsistent availability of SDG-related data that are specific to e-commerce, especially in developing countries. This gap can distort results and emphasizes the urgent need for more cohesive and reliable international databases.

Future studies should investigate the indirect impacts of e-commerce on sustainability, including cultural consumption, psychological effects of digitalization, and how consumer behavior interacts with regulations. Case studies and regional comparisons can offer valuable insights into how infrastructure, policies, and cultural factors shape the relationship between e-commerce and sustainability. This research would support the development of more precise strategies to leverage digital technology as a driver of sustainable progress.

7. Conclusions

This research highlights the intricate relationship between the growth of e-commerce and worldwide sustainability objectives. It presents a comprehensive perspective on how digital advancements can both promote and obstruct sustainable development. By connecting e-commerce metrics with GDP and SDG trends, the study offers detailed insights into the conflicts between market-driven forces and social-environmental priorities.

The findings highlight the dual nature of e-commerce: it drives economic growth and enhances access to goods and services, yet it also presents sustainability issues like increased waste, greater transportation needs, and disruptions in the labor market. Addressing these effects requires a balanced strategy that considers negative externalities while also valuing the innovation and inclusion benefits offered by digitalization.

A key conclusion is that e-commerce growth alone does not automatically advance the SDGs. Without coordinated policies and strong regulation, digital expansion could increase inequality and harm long-term sustainability.

The study also emphasizes the importance of stronger collaboration among public institutions, academia, and the private sector to create frameworks that are suited to the realities of the digital age. Rethinking how we measure economic success—shifting beyond GDP toward indicators that reflect sustainable, inclusive development—is essential. In these conditions, e-commerce can become a valuable partner in sustainability, as long as ethical and socially responsible principles guide its growth.

Ultimately, this research establishes a foundation for the ongoing exploration of how digitalization relates to sustainability. It advocates for a comprehensive, cross-disciplinary approach to fully comprehend and guide digital transformation, aiming to foster a more balanced, equitable, and resilient future.