Assessing the Impact of Digital Technologies on the Sustainable Development Goals Within the European Union

Abstract

In the contemporary digital era, emerging digital technologies are rapidly transforming society and the economy, offering considerable potential for addressing global challenges tied to sustainable development. This study investigates the influence of key digital technologies, including artificial intelligence, big data, cloud computing, the Internet of Things, and autonomous robots, on achieving the Sustainable Development Goals within European Union countries. This study uses artificial neural network analysis and cluster analysis to examine patterns of technology adoption and their measurable impacts on the Sustainable Development Goals based on a dataset covering technology adoption and sustainable development metrics across EU member states. The findings reveal that artificial intelligence, Big Data, and cloud computing have a substantial effect on the progress toward the Sustainable Development Goals. At the same time, the influence of the internet of things and autonomous robots remains moderate at this stage. Cluster analysis underscores the importance of a coordinated digital strategy and targeted policies for integrating these technologies to maximize their benefits while managing associated risks. This study contributes to the field by providing an empirical groundwork for understanding the role of digital technologies in sustainable development.

1. Introduction

As the world confronts pressing global challenges, including climate change, social and economic inequalities, and environmental degradation, the role of digital technologies becomes ever more pivotal. The 2030 Agenda provides a structured framework for these efforts, emphasizing the necessity of global collaboration to build a sustainable world [1]. Achieving the Sustainable Development Goals (SDGs) requires a coordinated commitment across all sectors of society. This effort involves implementing sustainable policies, fostering technological innovation, promoting sustainability-focused education, and mobilizing the financial resources needed to make these changes a reality [2,3]. Together, these factors create a global framework where sustainable development addresses social, economic, and environmental challenges [4].

Promoting technological innovation across various sectors is vital to advancing sustainable and resilient development [5,6,7,8]. The widespread adoption of digital technologies, including artificial intelligence (AI), big data (BD), cloud computing (CC), the internet of things (IoT), and autonomous robots (R), offers considerable potential to achieve the Sustainable Development Goals (SDGs) by minimizing environmental impacts, optimizing resource use, and enhancing efficiency across multiple domains [9]. For instance, AI supports more informed decision-making, helping organizations to identify and implement best practices in sustainable development [10]. BD offers comprehensive analytical tools that help governments and organizations detect trends, anticipate outcomes, and develop policies based on data, effectively addressing sustainability challenges [11]. The IoT enables the real-time monitoring of energy and resource consumption, facilitating more sustainable energy use and supporting environmental conservation efforts [12]. CC enhances the development of a fair and transparent social and economic system by providing secure, scalable data storage and processing capabilities [13]. Autonomous robots automate tasks that are often repetitive, hazardous, or resource-intensive, performing with high precision and efficiency, reducing waste, optimizing resource use, and creating safer working conditions [6].

Nevertheless, integrating these technologies also presents challenges. Despite their potential to advance the SDGs, the adoption of digital tools can introduce considerable risks and limitations. One pressing concern is the substantial energy consumption associated with digital infrastructures, which can exacerbate environmental problems and hinder sustainability objectives [9,10]. As digital reliance increases, the demand for energy-intensive storage and processing capacities continues to grow, amplifying the environmental challenges associated with technology.

The costs associated with digital technology implementation also create barriers, particularly in economically disadvantaged regions. The persistence of the digital divide limits the potential impact of these innovations, as many rural and low-income areas need help accessing the necessary infrastructure. This disparity can widen socio-economic gaps [6,9]. Concerns over privacy and data security represent additional challenges. While digital technologies generate valuable insights, they also raise privacy and data protection issues. Ensuring that these advancements respect individual rights and maintain data security requires stringent regulatory oversight [5,14].

The European Union (EU) is committed to the SDGs, embedding these goals within its policy agenda to foster a sustainable, inclusive, and resilient future. The EU’s approach encourages inclusive growth, promotes social equity, and drives economic progress [5]. This commitment includes significant efforts to integrate the SDGs into various sectors, underscoring the EU’s focus on innovation, social inclusion, and environmental responsibility. The EU’s strategic application of AI, BD, the IoT, and similar technologies improves efficiency, resource optimization, and public service delivery. These changes not only drive economic growth but also represent a shift toward human-centered, sustainable solutions [8].

This paper aims to evaluate the influence of digital technologies on achieving the SDGs using artificial neural network analysis. Based on cluster analysis, this empirical study also establishes patterns of implementation of emerging digital technologies and sustainable development within EU countries. Research gaps addressed include the lack of comprehensive studies within the EU that correlate the impacts of these technologies on all SDGs and the need for advanced statistical methods, such as artificial neural network analysis and cluster analysis, to gain a deeper understanding of these complex relationships.

This research aims to answer two main questions: RQ1: How do emerging digital technologies impact SDG progress in EU countries? and RQ2: Are there significant differences in SDG achievement among clusters of EU countries based on their adoption levels of digital technologies? These questions support two hypotheses that this paper explores, offering a methodologically innovative approach that utilizes artificial neural network analysis and cluster analysis to understand these dynamics.

The paper is structured into six sections. It begins with an introduction that outlines the research context and importance. The following section reviews existing literature and presents the research hypotheses. Subsequent sections describe the methodology, present findings, discuss implications, and conclude with recommendations for future research.

2. Literature Review

Dionisio et al. [13] highlight that technology can significantly improve the quality of life and substantially contribute to the SDGs by promoting sustainable development and reducing inequalities. For example, AI provides sophisticated data analysis and automation solutions that support healthcare diagnostics, improve education efficiency, and strengthen environmental protection efforts [14]. The IoT enhances smart city management and agricultural processes by connecting and controlling devices and optimizing resource use through the real-time monitoring of crops and infrastructure [15]. BD enables comprehensive insights by analyzing extensive data sets, leading to better decision-making and policy development for sustainable outcomes [16]. Autonomous robots improve productivity and safety by automating repetitive or dangerous tasks [17]. CC offers flexible access to digital resources, promoting collaboration and innovation across sectors and furthering the SDGs [18]. The following sub-sections outline the digital technologies most significantly influencing SDG achievement.

2.1. Artificial Intelligence (AI)

The integration of artificial intelligence (AI) across industries represents more than a technological shift; it also profoundly influences sustainable development, mainly supporting goals such as SDG12. In healthcare, AI enhances diagnostics by analyzing medical images and patient data, helping doctors deliver precise, personalized treatments, directly contributing to SDG3 [19,20]. In manufacturing, AI-driven automation reduces errors and boosts productivity.

AI’s impact is notable in smart city initiatives, where it optimizes traffic management, conserves energy, and improves public services, aligning with SDG11 [21]. In agriculture, AI monitors environmental conditions and manages resources efficiently, increasing productivity while minimizing environmental impact, thus advancing SDG2 and SDG15. These applications illustrate AI’s critical role in improving efficiency, nurturing innovation, and addressing complex sustainability challenges across sectors, positioning it as a central driver of sustainable development worldwide [13,22].

2.2. Internet of Things (IoT)

The IoT promotes sustainable development goals by enhancing operational efficiency, resource management, and connectivity across sectors. In healthcare, IoT sensors enable real-time patient health monitoring, providing medical professionals with valuable insights to improve treatment outcomes and supporting SDG3 [20].

In urban settings, IoT devices monitor infrastructure, such as public lighting and transportation, contributing to efficient smart city management and advancing SDG11 [23]. IoT systems, for example, enable authorities to respond quickly to incidents like traffic accidents, enhancing safety. By optimizing resource use and reducing energy consumption, the IoT contributes to more sustainable urban development. In transportation, the IoT provides real-time traffic data, enhancing route efficiency, improving travel safety, reducing congestion, and advancing SDG9 by decreasing pollution [24].

In agriculture, IoT-based smart irrigation systems conserve water and increase yields, supporting food security goals related to SDG2 and SDG15 [25]. In the energy sector, the IoT optimizes usage through applications like smart lighting and climate control systems that adjust to environmental conditions, contributing to SDG7 [26,27,28]. Furthermore, the IoT has the potential to advance social equity goals, such as SDG4 and SDG5, by broadening access to information, resources, and economic opportunities for women [29]. Thus, the IoT drives inclusive, resilient, and sustainable development across various domains.

2.3. Big Data (BD)

BD analytics serve as a powerful catalyst for strategic change across sectors, directly supporting several SDGs. By analyzing extensive datasets from diverse sources, BD offers businesses insights into consumer behavior, enabling them to align products and services more effectively with market demand, which contributes to responsible production and consumption under SDG12 [30].

In healthcare, BD enhances diagnostic accuracy and enables personalized treatments through real-time, data-driven insights, directly supporting health and well-being goals (SDG3) [20,31]. In agriculture and food production, BD improves supply chain management and reduces waste, fostering sustainable practices that align with SDG2 [32]. These applications showcase BD’s role not only in improving operational efficiency but also in driving sustainable growth and innovation. BD lays a strategic foundation for addressing global challenges and advancing sustainable development in an equitable and integrated manner across various sectors by supporting informed, data-driven decision-making [13].

2.4. Cloud Computing (CC)

CC plays a transformative role in achieving the Sustainable Development Goals (SDGs), particularly in reducing inequalities. Dionisio et al. [13] emphasize CC’s potential to improve living standards and expand access to essential services, particularly within underserved communities. CC increases accessibility to crucial resources and applications by removing traditional infrastructure limitations and lowering hardware costs [28,33]. For example, in remote or underserved areas, institutions such as schools and hospitals gain access to advanced digital tools, educational content, and medical services, improving quality of life and reducing disparities in healthcare and education.

In agriculture, CC works in tandem with the IoT to enable large-scale data storage and analysis, facilitating efficient resource management and aligning with goals related to zero hunger (SDG2) and life on land (SDG15). Farmers benefit from real-time data on weather, soil, and crop conditions, allowing them to allocate resources efficiently and enhance productivity.

In healthcare, CC provides secure, scalable storage solutions for electronic health records, streamlining diagnostics and treatment processes and thus supporting SDG3 [20]. Integrating AI-powered analysis within cloud platforms further enhances medical decision-making and enables personalized treatments, leading to improved patient outcomes and reduced healthcare costs. As such, CC serves as an enabler for sustainable development by bridging access gaps and improving the quality of essential services.

2.5. Autonomous Robots (R)

Autonomous robots represent a significant technological advancement. They are capable of interpreting and responding to their environments autonomously based on complex behavioral and non-verbal prompts [34]. These robots not only respond to human needs intuitively but also communicate effectively, enhancing human–robot interactions and making them valuable in various applications [28].

In healthcare, autonomous robots support patients by assisting with daily tasks, monitoring health, and providing on-demand assistance, all of which contribute to SDG3 [20]. In service industries, they improve customer experience by automating repetitive tasks and enabling personalized services. In industrial settings, robots enhance workplace safety by handling hazardous tasks and optimizing operations, aligning with SDG8 [35].

In agriculture, autonomous robots automate labor-intensive tasks such as planting, maintenance, and harvesting, increasing productivity, reducing costs, and improving resource efficiency—directly supporting food security and efforts to eradicate hunger under SDG2. Autonomous robots also play a role in smart city developments, where autonomous vehicles improve traffic flow, reduce emissions, and enhance road safety, contributing to urban sustainability goals (SDG11) [34].

Cloud-connected autonomous robots also drive productivity in sectors like manufacturing, construction, and logistics by performing repetitive or dangerous tasks with heightened efficiency and safety [36]. As these technologies evolve, they reshape daily life, streamline interactions with technology, and nurture more adaptive and resilient societies. In this manner, autonomous robots embody technological innovation that is indispensable for advancing sustainable development and enhancing the quality of life across a wide range of contexts.

3. Research Hypotheses

Digital technologies have become essential for advancing the SDGs across the European Union, profoundly influencing areas such as resource management, healthcare, education, and governance [3]. In EU countries, digital innovations are reshaping approaches to these goals, improving the efficiency and scope of initiatives that address complex social and environmental challenges. Technologies powering smart cities, digital healthcare, and renewable energy management are creating new paths for sustainable progress [8].

Digitalization also fuels economic growth by nurturing innovation and entrepreneurship, which are decisive for achieving SDG objectives related to decent work and sustainable economic growth. EU countries are investing in digital literacy programs and assisting small and medium-sized enterprises in adopting digital tools, ensuring that the benefits of digital transformation reach diverse stakeholders [5]. This approach not only boosts business competitiveness but also promotes an inclusive economy where more individuals can access economic opportunities [9].

However, the impact of digitalization on the SDGs in the EU brings challenges that require careful management. Issues such as data privacy, cybersecurity, and digital inequality need to be addressed to ensure equitable digital transformation and avoid exacerbating existing divides [8]. Recognizing these risks, the EU emphasizes responsible digitalization that aligns with Industry 5.0 principles, which prioritize ethical standards, social well-being, and environmental sustainability.

Industry 5.0 marks a shift from the automation-focused approach of Industry 4.0, placing a stronger emphasis on collaboration between humans and machines to achieve outcomes prioritizing well-being, inclusivity, and environmental responsibility [15]. This approach aligns with the SDGs, which promote innovation that is inclusive, sustainable, and considerate of social and environmental impacts [16].

The concept of Industry 5.0 is gaining adhesion in the EU as a framework for aligning technological progress with broader societal values. The principles of Industry 5.0 support the responsible use of digital technologies, highlighting the ethical considerations of digital transformation [28]. These principles resonate with the EU’s vision for a digital future rooted in social inclusion, equitable growth, and environmental stewardship [22].

Digital technologies are reshaping the EU’s approach to achieving the SDGs, presenting both opportunities and challenges. As EU countries continue to incorporate digital tools into their sustainable development strategies, it will be essential to adopt a balanced approach that integrates the human-centered, sustainable ideals of Industry 5.0. Such alignment will ensure that digital transformation not only advances SDG progress but also builds a more inclusive, resilient, and sustainable future across the European Union.

The investigation in this paper stands on the following hypothesis, which explores the relationship between digital technologies and sustainability as quantified by SDG metrics:

Hypothesis H1.

Digital technologies have a significant impact on the SDGs in EU countries.

Previous studies highlight the complex relationship between digital technologies and the SDGs [37,38,39]. While positive correlations exist in specific contexts, the overall effect of digitalization on sustainable development varies depending on the components of digitalization and regional specifics. In the Visegrad Group, for instance, the positive impact is attributed to uniform digital infrastructure development and effective national sustainability policies [37]. At the EU level, however, findings suggest that not all digital transformation components contribute equally to achieving the SDGs [38]. These differences highlight the need for nuanced, locally adapted strategies that consider each country’s unique context when formulating digitalization policies. Studies on digital entrepreneurship also reveal that digitalization, while beneficial, must be strategically integrated and adapted to local needs to maximize sustainability outcomes [20,39].

To further explore how digital transformation influences sustainable development, Jovanovic et al. [40] compared the digital economy and society index (DESI) with various sustainability measures across EU countries. They found that the DESI positively influenced economic and social sustainability but had a negative environmental impact. A comparative study by Hegyes et al. [41] indicated that Hungary, for instance, ranks at a medium level on the DESI relative to other EU countries, underlining the need for additional efforts to match the performance of Europe’s leading digital nations. Maximizing the benefits of digitalization thus requires enhancing digital infrastructure, reducing the urban–rural digital divide, and building digital skills within the population.

Based on these considerations, the paper proposes a second hypothesis:

Hypothesis H2.

Clusters of EU countries, identified by their adoption of digital technologies, exhibit significant differences in achieving SDGs.

This hypothesis examines whether the variance in digital technology adoption among EU countries results in differential progress toward the SDGs, providing perspectives into the strategic alignment of digitalization with sustainable development across the EU.

4. Methodology

4.1. Research Design

This research followed a multi-stage process to assess the impact of digital technologies on the SDGs. The first stage was a comprehensive literature review to identify existing studies and support the research hypotheses.

In the next stage, we selected variables representing both the digital technologies under analysis and the SDG indicators. The digital technologies chosen, including artificial intelligence (AI), big data (BD), the internet of things (IoT), cloud computing (CC), and robotics (R), were selected based on their widespread adoption and established potential to impact various sustainability aspects. Previous literature and EU policy frameworks that underscore these technologies’ importance in shaping the future European economy and society guided this selection. For SDGs, the SDG Index score was used as the primary variable, aggregating each country’s progress across all 17 SDGs and providing a comprehensive measure of sustainability [42].

The third stage involved data collection for EU countries. To ensure consistency, we normalized the data, adjusting for any discrepancies in measurement scales or units across different datasets. Following data processing, we tested the hypotheses and presented the results, discussion, and conclusions of the research.

4.2. Selected Data

This research used detailed datasets to evaluate the influence of digital technologies on progress toward the SDGs. The primary dataset included the SDG Index score, a comprehensive measure that tracks each country’s progress in achieving the 17 SDGs [43]. This score reflects achievements in various areas, from poverty reduction and quality education to climate action and justice.

Furthermore, we analyzed the adoption levels of digital technologies across EU countries, focusing specifically on AI, the IoT, BD, R, and CC [44,45,46,47,48]. For each technology, we used data from Eurostat for 2021, which captures the percentage of enterprises by size that have adopted each technology, enabling comparative assessments across EU nations.

Table 1. Selected variables.

Variable	Data Sets	Measures	Sources
SDGi	SDG Index score	Aggregate score (0 to 100)	[43]
SDG1–SDG17	Goal 1–Goal 17	Score (0 to 100)	[43]
AI	Artificial intelligence by size class of enterprise	Percentage	[44]
BD	Big data analysis by size class of enterprise	Percentage	[45]
IoT	Internet of things by size class of enterprise	Percentage	[46]
CC	Cloud computing services by size class of enterprise	Percentage	[47]
R	3D printing and robotics by size class of enterprise	Percentage	[48]

Source: authors’ design based on [43,44,45,46,47,48].

summarizes the selected variables, measures, and data sources. The SDG Index score (SDGi) provides an aggregate benchmark, while each SDG goal (SDG1 to SDG17) is assessed individually on a scale from 0 to 100. Technology adoption for AI, BD, the IoT, CC, and R is measured as a percentage, indicating the extent of usage by enterprises in each country.

4.3. Methods

The data analysis employed two key inferential statistical methods: artificial neural network (ANN) analysis and cluster analysis.

We used an ANN to model the complex, non-linear relationships between digital technologies and the SDGs. ANNs, inspired by the structure of the human brain, can learn patterns and recognize relationships within large datasets [49]. In this study, neural networks were trained to predict the impact of digital technologies on each SDG, with an iterative training process adjusting the neurons’ weights using optimization algorithms such as back propagation to minimize prediction error [50]. The mathematical model for back propagation is as follows:

$$y=({\sum }_{i=1}^n{w}_i{x}_i+b)=\phi (W^{T}X+b)$$

(1)

• w, x—vectors of weights and inputs;
• b—bias;
• φ—activation functions.

This study used a multilayer perceptron (MLP) model, a type of ANN known for its ability to capture complex data patterns. The MLP architecture consists of an input layer, a hidden layer, and an output layer. The hidden layer uses the hyperbolic tangent activation function (2), while the output layer uses a sigmoid function (3), denoted by the following formulas:

$$f\left(n\right)={\displaystyle \frac{e^n-e^{-n}}{e^n+e^{-n}}}={\displaystyle \frac{e^{2n}-1}{e^{2n}+1}}$$

(2)

$$f\left(n\right)={\displaystyle \frac{1}{1+e^{-n}}}={\displaystyle \frac{e^n}{{1+e}^n}}$$

(3)

• n—input variables;
• f(n)—output variables.

The MLP learning process involves adjusting weights and biases between neurons based on the output error, iteratively refining the model by minimizing the error, thus improving prediction accuracy.

Cluster analysis was conducted to group countries with similar levels of digital technology adoption and SDG performance, providing insights into regional differences and common characteristics within the EU. This technique groups countries into clusters based on their digital technology and SDG Index (SDGi) data, highlighting regional patterns. Cluster analysis involves measuring distances between data points in a multidimensional space and grouping them to minimize variation within clusters and maximize differences between clusters [51].

For Hypothesis H2, we used the Ward clustering method and squared Euclidean distance. The Ward method minimizes total variance within clusters, producing compact, homogeneous groups [52]. The formula for constructing clusters using the Ward method is as follows:

$$\mathsf{\Delta }\left(\mathrm{A},\mathrm{B}\right)=\sum _{i\in A\cup B}{\left|\left|{\overrightarrow{x}}_i-{\overrightarrow{m}}_{A\cup B}\right|\right|}^2-\sum _{i\in A}{\left|\left|{\overrightarrow{x}}_i-{\overrightarrow{m}}_A\right|\right|}^2-\sum _{i\in B}{\left|\left|{\overrightarrow{x}}_i-{\overrightarrow{m}}_B\right|\right|}^2={\displaystyle \frac{n_A{n}_B}{n_A{+n}_B}}{\left|\right|{\overrightarrow{m}}_A-{\overrightarrow{m}}_B\left|\right|}^2$$

(4)

• ${\overrightarrow{m}}_j$—center of cluster j;
• $n_j$—number of points in cluster j;
• Δ—merging cost of combining clusters A and B;
• i—cases.

The Ward method allowed us to identify patterns and regional differences across the EU, showing how digital technologies interact with sustainability outcomes [52].

The data analysis was conducted using SPSS 27.0 software, which provides robust tools for both ANN and cluster analysis. The results were interpreted in the context of existing literature and research hypotheses, identifying significant correlations between digital technologies and SDG progress. This analysis highlights the positive impacts and challenges of digital technology implementation across the EU.

5. Results

We tested Hypothesis H1 using ANN analysis to model the complex, non-linear relationships between digital technologies and SDG indicators. The model used was an MLP with an input layer consisting of digital technology adoption levels (AI, BD, CC, IoT, and R) in EU countries and an output layer representing the 17 SDGs for each EU country. Between these layers, a hidden layer focused on the sustainability orientation of digital technologies.

Figure 1 illustrates the relationships between the levels of digital technology adoption and the 17 SDGs.

Table 2. MLP parameters.

Predictor			Input Layer						Hidden Layer 1
			(Bias)	CC	AI	BD	R	IoT	(Bias)	H(1:1)
Predicted	Hidden layer 1	H(1:1)	0.225	0.756	0.704	0.893	0.269	0.126
	Output layer	SDG1							−0.273	0.243
		SDG2							0.139	−0.077
		SDG3							0.573	1.214
		SDG4							1.335	0.941
		SDG5							0.727	0.923
		SDG6							0.255	0.427
		SDG7							−0.582	0.435
		SDG8							0.989	1.018
		SDG9							0.445	1.411
		SDG10							0.179	0.990
		SDG11							−0.205	0.798
		SDG12							−0.162	−0.822
		SDG13							0.196	−0.970
		SDG14							0.134	0.003
		SDG15							0.354	0.022
		SDG16							−0.219	0.872
		SDG17							0.450	0.757

Source: developed by the authors using SPSS v.27.0.

presents the predicted values of the model variables within the perceptron, providing insights into digital technologies’ influence on each SDG.

This neural model highlights the complex interconnections between technological inputs and sustainability outcomes. Among the inputs, BD holds the highest weight (0.893), followed by CC (0.756) and AI (0.704), indicating that these three technologies have the most substantial potential to impact the SDGs. In contrast, R and IoT show smaller weights, suggesting a more limited influence.

Analyzing the hidden and output layers reveals how these technologies differentially impact each SDG. SDG9 has the highest positive weight in the output layer (1.411), suggesting that digital technologies strongly support innovation and infrastructure development. SDG3 and SDG4 follow with weights of 1.214 and 0.941, respectively, indicating that digital technologies hold substantial potential for improving health and education. However, SDG12 and SDG13 have negative weights (−0.822 and −0.970, respectively), suggesting that while digital technologies have positive impacts in many areas, they may negatively affect sustainable consumption and climate action due to increased energy use or electronic waste. SDG1 and SDG17 show moderate positive weights, highlighting digital technologies’ potential to reduce poverty and foster global collaboration.

The MLP model offers a nuanced perspective of the impact of digital technologies on the SDGs. While they greatly enhance innovation, health, and education, they also pose challenges for sustainable consumption. These results underscore the need for a balanced approach to adopting digital technologies, considering both their potential benefits and drawbacks in achieving sustainable development.

The importance of input variables within the MLP model shows the intensity of digital technologies’ influence on the SDGs. Figure 2 illustrates the absolute and normalized importance of digital technologies within the model.

BD is the most important factor, with an absolute importance of 0.365 and a normalized value of 100%, suggesting its determining role in shaping the impact of digital technologies on the SDGs. CC and AI follow with normalized importance values of 73.9% and 68.5%, respectively, indicating substantial influence, though not as strong as that of BD. The prominent role of these three technologies (BD, CC, and AI) underscores their significance in digital transformation for sustainable development. In contrast, R and IoT have relatively low importance, with normalized values of 21.3% and 10.1%, respectively. These values do not imply irrelevance but rather a less pronounced impact on the SDGs compared to that of other technologies.

The MLP model results strongly support Hypothesis H1, showing that digital technologies significantly impact the SDGs in EU countries, although this impact varies depending on the country and area of sustainable development. Validating this hypothesis emphasizes the importance of strategically integrating digital technologies to achieve the SDGs, considering each country’s specific context and the potential positive and negative effects on sustainable development.

For Hypothesis H2, we used cluster analysis, grouping EU countries into clusters based on their levels of digital technology adoption (AI, IoT, BD, CC, and R), the SDG Index (SDGi), and 16 of the SDGs (SDG1–SDG17, excluding SDG14 because several member states are landlocked and lack direct access to marine ecosystems) using the Ward clustering method and squared Euclidean distance.

Figure 3 presents a dendrogram illustrating the grouping of EU countries based on their level of digitalization and sustainable development.

Analyzing the three clusters of EU countries, formed based on five emerging digital technologies, the SDG Index (SDGi), and the 16 specific goals, reveals significant differences in technology adoption and progress toward sustainability.

Cluster 1 includes Nordic and Western European countries like Finland, Sweden, Denmark, and the Netherlands. These countries are distinguished by advanced digital technology adoption, particularly in CC and the IoT (see

Table A1. Cluster 1.

	Finland	Sweden	Denmark	Belgium	Ireland	The Netherlands	Estonia	Luxembourg	Cluster 1 Mean	EU Mean
CC	75.3	75.4	64.8	53.0	58.8	64.9	57.5	33.5	60.4	42.7
AI	15.8	9.9	23.9	10.3	7.9	13.1	2.8	13.0	12.1	7.7
BD	19.2	13.0	23.7	21.9	22.4	25.9	8.0	16.8	18.9	12.4
R	10.3	5.7	12.8	9.3	1.7	6.9	3.3	5.5	6.9	6.3
IoT	40.5	40.3	20.0	28.2	34.0	20.7	17.4	22.2	27.9	27.9
SDGi	86.8	86.0	85.7	79.5	80.1	79.4	81.7	77.6	82.1	80.2
SDG1	99.6	98.9	99.2	99.5	99.9	99.3	100.0	100.0	99.5	99.4
SDG2	60.9	63.1	71.0	71.2	67.7	67.7	63.2	58.9	65.4	66.9
SDG3	95.4	96.9	95.4	93.4	94.4	95.7	89.5	96.5	94.6	90.5
SDG4	97.2	99.8	99.3	95.3	99.0	99.3	96.1	98.3	98.0	95.4
SDG5	92.1	91.4	87.0	87.8	77.7	85.1	76.8	85.3	85.4	77.7
SDG6	94.3	95.1	90.7	70.6	82.9	87.2	84.1	81.6	85.8	82.6
SDG7	93.0	98.0	87.7	74.0	74.8	72.0	78.0	56.5	79.2	78.0
SDG8	86.8	85.0	87.6	84.3	87.0	86.3	82.2	85.7	85.6	83.6
SDG9	96.0	97.6	97.0	97.1	86.5	93.4	83.3	88.6	92.4	84.3
SDG10	98.5	95.0	98.2	100.0	90.3	89.8	89.1	84.0	93.1	87.7
SDG11	91.2	90.4	93.0	88.1	90.8	92.9	90.5	99.0	92.0	87.6
SDG12	60.1	56.8	44.6	44.9	45.9	47.7	52.6	39.5	49.0	59.4
SDG13	68.8	70.0	60.8	52.9	54.1	43.3	61.5	49.4	57.6	69.5
SDG15	85.1	80.2	92.8	81.8	88.6	77.9	96.1	67.7	83.8	83.5
SDG16	92.5	88.5	93.8	85.8	89.1	86.7	91.0	89.7	89.6	80.9
SDG17	75.6	85.8	82.1	67.4	61.2	70.8	67.3	73.4	72.9	66.2

Source: developed by the authors based on [43,44,45,46,47,48].

in Appendix A). They score highly on the SDGi and for individual goals, often exceeding the EU average in many areas. Their most robust performance is in education (SDG4), innovation and infrastructure (SDG9), and reducing inequalities (SDG10).

Cluster 2 comprises countries like France, Germany, Austria, and Spain, with moderate technology adoption, primarily focusing on the IoT (see

Table A2. Cluster 2.

	Portugal	Spain	France	Czechia	Slovenia	Austria	Germany	Cluster 2 Mean	EU Mean
CC	33.4	30.9	29.4	43.8	42.7	40.4	41.6	37.5	42.7
AI	7.2	7.7	6.7	4.5	11.7	8.8	10.6	8.2	7.7
BD	10.2	6.5	19.5	9.1	5.1	7.0	16.6	10.6	12.4
R	9.1	8.8	8.1	6.9	8.3	5.5	5.7	7.5	6.3
IoT	23.1	27.5	22.0	31.4	49.5	50.8	35.6	34.3	27.9
SDGi	80.0	80.4	82.0	81.9	81.0	82.3	83.4	81.6	80.2
SDG1	99.9	98.7	99.7	99.9	99.4	99.5	99.5	99.5	99.4
SDG2	64.3	65.4	72.4	62.1	66.6	73.1	72.4	68.0	66.9
SDG3	92.1	94.2	93.2	90.2	92.4	92.5	93.0	92.5	90.5
SDG4	98.6	97.4	99.6	93.9	95.6	97.9	97.2	97.2	95.4
SDG5	84.2	86.9	87.8	74.3	77.0	84.6	81.9	82.4	77.7
SDG6	80.3	87.4	89.3	82.9	87.8	92.2	88.4	86.9	82.6
SDG7	83.5	78.1	78.6	76.0	80.2	86.0	77.2	79.9	78.0
SDG8	81.2	79.4	85.3	88.0	84.9	83.3	87.0	84.2	83.6
SDG9	82.2	90.2	92.8	83.8	80.8	97.0	95.8	88.9	84.3
SDG10	84.4	81.4	87.5	100.0	100.0	94.6	88.1	90.9	87.7
SDG11	88.0	91.9	90.5	94.5	85.6	92.5	90.1	90.5	87.6
SDG12	67.4	67.9	60.5	62.8	54.1	49.6	55.4	59.7	59.4
SDG13	84.1	80.2	73.8	72.2	69.1	57.3	64.0	71.5	69.5
SDG15	73.9	66.4	68.8	92.5	83.6	73.6	79.2	76.9	83.5
SDG16	80.9	79.2	76.1	84.2	80.5	87.9	89.5	82.6	80.9
SDG17	65.9	63.0	73.1	68.6	68.2	71.1	84.4	70.6	66.2

Source: developed by the authors based on [43,44,45,46,47,48].

in Appendix A). These countries’ SDGi scores are near the EU average but excel in specific areas, such as health (SDG3), quality education (SDG4), and industry and infrastructure (SDG9). They perform notably well in urban sustainability (SDG11) and responsible consumption and production (SDG12).

Cluster 3 includes many Eastern and Southern European countries like Poland, Hungary, Greece, and Croatia, which show slower adoption of digital technologies than the other clusters (see

Table A3. Cluster 3.

	Cyprus	Italy	Malta	Latvia	Lithuania	Croatia	Slovakia	Greece	Hungary	Poland	Cluster 3 Mean	EU Mean
CC	50.3	60.5	57.1	28.5	33.6	39.2	36.1	20.7	26.4	28.7	38.1	42.7
AI	2.6	6.2	10.2	3.7	4.5	8.7	5.2	2.6	3.0	2.9	5.0	7.7
BD	2.7	7.4	28.7	7.4	8.7	13.0	4.6	12.2	6.4	7.9	9.9	12.4
R	2.7	8.8	6.4	3.4	4.6	7.4	7.4	2.1	4.3	7.1	5.4	6.3
IoT	33.3	32.3	28.0	28.4	28.4	23.2	27.4	22.8	22.3	18.6	26.5	27.9
SDGi	72.5	78.8	75.5	80.7	76.8	81.5	79.1	78.4	79.4	81.8	78.4	80.2
SDG1	99.9	97.5	99.8	100.0	100.0	100.0	99.2	99.2	98.9	99.0	99.3	99.4
SDG2	53.7	69.8	66.3	64.2	59.6	74.3	72.3	66.6	70.3	67.5	66.5	66.9
SDG3	91.1	93.9	91.2	84.3	86.1	86.4	87.8	90.3	83.6	85.2	88.0	90.5
SDG4	93.9	94.2	99.1	97.8	98.1	95.7	82.0	97.1	92.8	97.5	94.8	95.4
SDG5	68.1	74.3	64.7	77.5	75.9	71.1	73.7	65.4	66.8	73.1	71.1	77.7
SDG6	67.9	80.8	48.7	89.2	78.8	86.4	81.8	87.7	86.6	84.4	79.2	82.6
SDG7	74.1	76.9	72.1	88.9	69.9	83.2	77.2	76.4	73.9	71.2	76.4	78.0
SDG8	74.8	79.9	87.3	84.5	81.1	82.7	81.5	73.8	84.6	86.9	81.7	83.6
SDG9	75.8	87.5	71.7	77.0	75.5	74.7	73.6	81.6	80.3	80.3	77.8	84.3
SDG10	85.5	77.9	86.6	72.6	70.9	94.2	100.0	84.6	92.7	93.4	85.8	87.7
SDG11	77.8	74.6	88.3	86.7	82.8	80.7	84.0	85.6	89.3	87.8	83.8	87.6
SDG12	50.8	71.6	58.1	58.8	46.9	68.3	69.4	64.8	76.1	74.1	63.9	59.4
SDG13	68.1	79.9	79.2	71.7	58.4	83.5	70.6	80.2	77.9	75.3	74.5	69.5
SDG15	79.0	79.9	82.7	97.8	95.3	88.2	89.2	81.2	86.5	92.9	87.3	83.5
SDG16	74.7	72.9	64.9	82.1	84.8	72.1	77.8	71.1	69.1	77.4	74.7	80.9
SDG17	46.9	65.2	54.1	54.4	59.5	59.4	59.0	60.8	54.1	72.6	58.6	66.2

Source: developed by the authors based on [43,44,45,46,47,48].

in Appendix A). However, these countries have achieved notable SDG progress, with SDGi scores close to the EU average, supported by other factors such as targeted social and environmental policies, international funding, and regional cooperation. They stand out in poverty reduction (SDG1), quality education (SDG4), and climate action (SDG13).

Romania and Bulgaria, though not formally grouped, share characteristics with Cluster 3, with slower digital technology adoption but notable progress in specific sustainability areas.

This analysis highlights a relative correlation between technology adoption and SDG progress, though the relationship is not uniform. Countries with advanced technology adoption in cluster 1 typically have higher SDG scores. However, clusters 2 and 3 demonstrate that sustainability progress is achievable with varying levels of technology adoption, indicating that national strategies and policy priorities significantly contribute to SDG outcomes. Therefore, the cluster analysis supports Hypothesis H2, affirming that EU country clusters based on technology adoption levels show significant differences in SDG achievement. The clusters of EU countries (Figure 4), identified based on the level of adoption of emerging technologies, present significant differences in achieving the SDGs.

6. Discussion

This study investigates the complex relationship between emerging digital technology adoption and progress toward achieving the Sustainable Development Goals (SDGs) within the EU context. The paper pursues two primary hypotheses. The first hypothesis (H1) examines, using a multilayer perceptron, whether digital technologies have a significant impact on the SDGs across EU countries. MLP enables an analysis of the complex, non-linear relationships between selected variables. The second hypothesis (H2) explores whether there are significant differences among EU countries regarding the adoption levels of emerging digital technologies and their progress toward SDG achievement. Cluster analysis, using the Ward method, was applied to identify groups of countries with similar digital and sustainability characteristics.

The results of the analysis provide a nuanced perspective of the impact of digital technologies on the SDGs within EU countries, supporting the validity of H1. The MLP model highlights the considerable role of digital technologies in advancing the SDGs. The analysis reveals that big data (BD) emerges as the most influential factor in both absolute and normalized terms. This finding is consistent with recent studies emphasizing BD’s substantial potential in addressing sustainable development challenges. For example, Lampropoulos [12] emphasizes how BD analysis can enhance decision-making processes, contributing positively to several SDGs. Cloud computing (CC) and artificial intelligence (AI) also play critical roles, underscoring their importance in digital transformation and sustainable development. These findings align with research by George et al. [9] and Hanelt et al. [53], which demonstrate that implementing CC and AI solutions can substantially improve the sustainability of industrial processes.

Meanwhile, robotics (R) and the internet of things (IoT) registered comparatively low importance in the MLP model. This result contrasts with those of previous studies, such as those by Carayannis and Morawska-Jancelewicz [54] and Ghobakhloo et al. [55], which highlight the high potential of the IoT and R for monitoring and improving sustainability levels. This discrepancy may indicate that these technologies’ broader impacts are still emerging.

The analysis also highlights the differentiated effects of digital technologies on various SDGs. For instance, we observed substantial positive impacts on SDG9 (Industry, Innovation, and Infrastructure), SDG3 (Good Health and Well-being), and SDG4 (Quality Education). In contrast, potential adverse effects were observed on SDG12 (Responsible Consumption and Production) and SDG13 (Climate Action). These findings reflect concerns in the previous literature regarding the increased energy consumption and electronic waste associated with digital technologies. Dionisio et al. [13], for example, underscore the need for integrative approaches to digital technology implementation to minimize such negative impacts.

Findings related to H1 suggest that the relationship between digital technology adoption and SDG progress across EU countries is complex and uneven. This observation resonates with findings by Jovanovic et al. [40], which underscore the importance of contextual factors, such as national policies and economic development levels, in shaping the impact of digital technologies on sustainable development.

The cluster analysis to test Hypothesis H2 provides a nuanced perspective on the relationship between digital technology adoption and SDG progress across EU countries, affirming H2’s validity. The results indicate a relative correlation between the level of digital technology adoption and SDG performance, although the relationship is not uniformly linear. Cluster 1, which includes Nordic and Western European countries, stands out for its advanced adoption of technologies such as CC and the IoT, correlating with high scores across most SDGs. Cluster 2, which includes countries such as France and Germany, demonstrates the moderate adoption of digital technology but strong results in specific SDGs. This pattern aligns with Esses et al. [37], who argue that well-calibrated national strategies can optimize the impact of digital technologies on priority SDGs. Cluster 3, comprising several Eastern and Southern European countries, shows slower technology adoption but notable progress in some SDGs, supported by other factors such as targeted social and environmental policies, international funding, and regional cooperation. Romania and Bulgaria, which do not fall neatly within the main clusters but exhibit characteristics similar to Cluster 3, offer further insights into this complex relationship.

Research by Ionescu-Feleaga et al. [56] suggests that both the digital economy and society index (DESI) and SDG Index are generally higher in Northern and Western European regions than in Central and Eastern Europe. Our findings support this observation, suggesting that Northern and Western regions benefit from more robust digital infrastructures and greater access to advanced technologies, which contribute to better SDG outcomes. In contrast, Central and Eastern regions face socio-economic and infrastructural challenges that limit the positive impacts of digitalization on sustainable development. This regional variation, highlighted by Hegyes et al. [41], points to the need for custom-made approaches to digitalization and sustainability strategies. In regions with weaker outcomes, policies and investments targeting digital infrastructure improvements and technological education could amplify the positive impacts of digitalization on the SDGs. This geographic distribution underscores digitalization’s potential to support sustainable development, emphasizing the importance of regional distinctions and interventions tailored to local contexts [57,58,59,60].

6.1. Theoretical Implications

Integrating AI, the IoT, BD, R, and CC into sustainable development strategies provides meaningful opportunities for environmental protection and the advancement of a more equitable, sustainable society. By embracing circular economy models and promoting responsible production and consumption, these technologies can contribute to a prosperous and sustainable future for all communities. They can also support social and economic inclusion initiatives, providing equal opportunities regardless of individuals’ backgrounds.

However, integrating digital technologies into sustainable development strategies brings significant challenges. Issues like the digital divide risk deepening existing inequalities, and the environmental footprint of digital infrastructure is substantial. Furthermore, the rapid pace of technological change demands the continuous adaptation and reskilling of the workforce to manage job displacement risks and capitalize on new opportunities. Addressing these challenges requires a nuanced understanding of the interactions between digital technologies and sustainable development, along with robust policy frameworks to guide their implementation. This study contributes to a valuable understanding of the role of digital technologies in advancing the SDGs within the EU context. The findings underscore the importance of a strategic and balanced approach that considers both the potential benefits and the inherent challenges of implementing these technologies.

6.2. Practical Implications

Digital transformation involves more than merely adopting new technologies; it requires fundamental reconfigurations of business processes, operational models, and organizational culture. These transformations allow organizations to streamline operations by automating repetitive tasks and using advanced data analytics for informed decision-making. AI and BD, for example, can forecast market trends and personalize customer offerings, enhancing satisfaction and loyalty. The IoT facilitates real-time monitoring and resource management, leading to cost savings and reduced environmental impacts, while CC enables flexibility and scalability to adapt to changing market demands.

The research findings highlight the need for a custom-made approach to implementing digitalization and sustainable development strategies, particularly in regions with weaker digital and sustainability performance. Local and national authorities should create policies and allocate resources to improve digital infrastructure and technological education, particularly in Central and Eastern European regions. Policies such as subsidies for high-speed internet infrastructure, digital skill training programs, and incentives for adopting emerging technologies in business and public administration are indispensable for reducing regional disparities.

6.3. Limitations and Further Research

While this study significantly advances the understanding of the relationship between emerging digital technologies and the SDGs in the EU context, several limitations should be acknowledged, and directions for future research should be proposed. One limitation is the focus on EU countries, which, while providing a comparable context, limits the generalizability of the findings globally. Expanding the study to include non-EU countries with diverse economic and digital development levels could provide valuable perspectives into how socio-economic contexts influence the relationship between digital technologies and the SDGs.

Furthermore, the study employs a cross-sectional design, which provides a static representation of the current situation. Longitudinal studies are needed to track the dynamic nature of digital technology adoption and its long-term effects on SDG achievement. Although the MLP model is robust, it could be enhanced by incorporating additional variables that capture contextual factors such as national policies, research and development investments, and levels of digital education. Future research could explore more complex models that consider these moderating and mediating factors.

This study focused on five specific emerging digital technologies; as new technologies emerge, future research should include them in analyses to examine their potential contributions to SDG progress. Furthermore, future studies should investigate the specific mechanisms through which digital technologies influence each SDG in greater detail. This approach could involve case studies that examine digital technology implementations in different sectors and their effects on sustainability indicators. Although we identified clusters of countries with similar characteristics in terms of digital technology adoption and SDG progress, future research could describe the factors driving these similarities and differences, including policies, implementation strategies, and cultural factors.

7. Conclusions

This study emphasizes the potential role digital technologies can play in advancing the SDGs. Through artificial neural network analysis and cluster analysis, this study found that technologies like AI, CC, and BD are linked to improved sustainability outcomes. At the same time, robotics and the IoT show promise in improving natural resource monitoring and management, automating processes, reducing waste, and enhancing productivity. Achieving these benefits on a broader scale, however, will require coordinated strategies and supportive policies to mitigate associated risks and address the ethical and infrastructural challenges posed by advanced digital technologies.

Cluster analysis revealed distinct patterns in digital technology adoption and sustainable development across EU countries. Countries with higher levels of digital infrastructure and technology access in Northern and Western Europe show more significant SDG progress. In contrast, Central and Eastern European countries face socio-economic and infrastructural challenges that hinder digital technology adoption and SDG advancement in many areas. These findings emphasize the importance of customized strategies and targeted investments in infrastructure and digital education to help bridge regional gaps.

This study underscores the complexity and contextual nature of the relationship between digital technologies and the SDGs. We explored possible explanations for both positive and negative correlations, as well as the broader socio-economic factors that may independently impact digital technology adoption and SDG outcomes. Notably, some countries demonstrate substantial progress toward sustainable development without extensive digital technology integration, suggesting that digitalization is one of many potential pathways to sustainability. This finding indicates that an integrated approach to digitalization, mindful of both benefits and limitations, is essential for responsible and effective contributions to the SDGs.

To guide the responsible use of digital technologies, the EU has established several frameworks, including the Digital Decade, 2030 Digital Compass, and the European Green Deal, aiming to balance digital growth with environmental sustainability. Furthermore, policies like the Digital Education Action Plan emphasize the importance of digital skills, particularly in underserved regions, to bridge the digital divide and foster inclusive digital transformation [61]. Building on these frameworks, we recommend further incentives for green technology adoption, tax breaks for sustainable tech innovation, and digital literacy programs tailored to local contexts, particularly in Central and Eastern Europe. These coordinated policies can help create an ecosystem where digital innovation accelerates SDG progress while aligning with Europe’s commitment to sustainability and social equity.