Digital Enablers of Sustainability: Insights from Sustainable Development Goals (SDGs) Research Mapping

Abstract

As the global emphasis on sustainable development intensifies, the integration of digital technologies (DTs) into efforts to address the Sustainable Development Goals (SDGs) has gained increasing attention. However, existing research on the link between the SDGs and DTs remains fragmented and lacks a comprehensive perspective on their interconnections. We aimed to address this gap by conducting a large-scale bibliometric analysis based on Elsevier’s SDG research mapping technique. Drawing on approximately 1.17 million publications related to both the 17 SDGs and 11 representative DTs, we explored research trends in the SDG–DT association, identified DTs that are most frequently tied to specific SDGs, and uncovered emerging areas of research within this interdisciplinary domain. Our results highlight the rapid expansion in the volume and variety of SDG–DT studies. Our findings shed light on the widespread relevance of artificial intelligence and robotics, the goal-specific applications of technologies such as 3D printing, cloud computing, drones, and extended reality, as well as the growing visibility of emerging technologies such as digital twins and blockchain. These findings offer valuable insights for researchers, policymakers, and industry leaders aiming to strategically harness DTs to support sustainable development and accelerate progress toward achieving the SDGs.

1. Introduction

The urgency to achieve the United Nations (UN) Sustainable Development Goals (SDGs) has never been greater, and global challenges such as climate change and widening inequality demand immediate attention. The 17 goals, which comprise 169 targets, set forth an ambitious agenda to eradicate poverty, protect the planet, and ensure prosperity by 2030 [1,2]. The SDGs encompass critical areas such as poverty alleviation, health, education, gender equality, clean water, and climate action, all underpinned by three core pillars: the environment, the economy, and social sustainability.

Table 1. UN SDGs: The 17 goals [1].

SDGs		Details
Goal 1 (G1)	No Poverty	End poverty in all its forms everywhere.
Goal 2 (G2)	Zero Hunger	End hunger, achieve food security and improved nutrition, and promote sustainable agriculture.
Goal 3 (G3)	Good Health and Well-Being	Ensure healthy lives and promote well-being for all at all ages.
Goal 4 (G4)	Quality Education	Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.
Goal 5 (G5)	Gender Equality	Achieve gender equality and empower all women and girls.
Goal 6 (G6)	Clean Water and Sanitation	Ensure availability and sustainable management of water and sanitation for all.
Goal 7 (G7)	Affordable and Clean Energy	Ensure access to affordable, reliable, sustainable, and modern energy for all.
Goal 8 (G8)	Decent Work and Economic Growth	Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all.
Goal 9 (G9)	Industry, Innovation, and Infrastructure	Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation.
Goal 10 G10)	Reduced Inequalities	Reduce inequality within and among countries.
Goal 11 (G11)	Sustainable Cities and Communities	Make cities and human settlements inclusive, safe, resilient, and sustainable.
Goal 12 (G12)	Responsible Consumption and Production	Ensure sustainable consumption and production patterns.
Goal 13 (G13)	Climate Action	Take urgent action to combat climate change and its impacts.
Goal 14 (G14)	Life Below Water	Conserve and sustainably use the oceans, seas, and marine resources for sustainable development.
Goal 15 (G15)	Life on Land	Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and biodiversity loss.
Goal 16 (G16)	Peace, Justice, and Strong Institutions	Promote peaceful and inclusive societies for sustainable development, provide access to justice for all, and build effective, accountable, and inclusive institutions at all levels.
Goal 17 (G17)	Partnerships for the Goals	Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development.

lists the 17 SDGs along with detailed descriptions of each one.

Achieving the SDGs requires broad, collaborative efforts across countries, industries, institutions, and civil society. In this context, the wise and ethical application of digital technologies (DTs) has emerged as a key enabler of sustainable development [3]. Technologies such as artificial intelligence (AI), big data, blockchain (BC), the Internet of Things (IoT), and extended reality (XR) offer transformative potential to address global challenges and accelerate progress toward achieving the SDGs [4,5,6,7].

However, these technologies have raised significant ethical, environmental, and governance concerns. While AI, big data, robotics, and the IoT have driven revolutionary advances in efficiency, productivity, and predictive analytics across various sectors, they simultaneously pose serious risks—ranging from surveillance and privacy violations to cybersecurity threats, biased or opaque decision-making due to algorithmic bias, and job displacement—which may exacerbate economic inequality. In addition, the energy-intensive operations of data centers and BC infrastructure contribute significantly to carbon emissions and environmental degradation [8,9,10].

These trade-offs underscore the paradox of DTs: if misused or insufficiently regulated, they may hinder—rather than support—sustainable development. These challenges highlight the importance of responsibly governing and designing DTs inclusively. Through ethical design principles, effective regulatory frameworks, and equitable access strategies, DTs can be harnessed to accelerate meaningful progress toward the SDGs.

To ensure the wise and responsible use of DTs, it is crucial to monitor and understand the evolving connections that link them to the SDGs. Despite growing interest, current research often remains fragmented, focusing on individual technologies or specific goals while lacking holistic, data-driven analyses that capture the complex interconnections between the SDGs and DTs. Moreover, the rapid evolution of DTs necessitates a timely reassessment of their role and integration in SDG-oriented research and practice.

We aimed to address this gap by systematically exploring the intersection of DTs and SDGs. Figure 1 provides an overview of this study, for which we conducted a large-scale, comprehensive bibliometric analysis based on the Elsevier Sustainable Development Goals Mapping (hereinafter referred to as SDG mapping) suggested by Bedard-Vallee et al. (2023) [11]. SDG mapping consists of search queries applicable to the Scopus platform, which associate an article with the relevant SDG(s) grounded in the basic metadata, such as titles, abstracts, and author keywords. Since it was introduced in 2018, SDG mapping has been supported and validated by numerous studies (e.g., [12,13]), recognized for its high precision, and adopted by the Times Higher Education (THE) for university impact rankings.

We performed a bibliometric analysis of the intersection between DTs and the SDGs by applying and refining the SDG mapping approach. Using the original set of SDG mapping queries, we initially identified more than 22.51 million publications related to the 17 SDGs. From this corpus, using an expanded version of the queries, we extracted approximately 1.17 million publications that simultaneously addressed both the SDGs and DTs (hereinafter referred to as SDG–DT research). We focused the analysis on 11 representative DTs: 3D printing (3DP), 5th-generation mobile networks (5G), AI, BC, cloud computing (CC), data analytics (DA), digital twinning (DTw), drones (DR), IoT, robotics (RB), and XR.

To provide a holistic view of SDG–DT research, we quantitatively analyzed global research trends over time within individual SDGs and across technologies. Specifically, we aimed to (1) assess the overall volume and growth trajectories of SDG–DT research; (2) identify SDG–DT pairs with strong associations; and (3) uncover emerging areas within this interdisciplinary field. We addressed the following research questions (RQs):

• RQ 1: How have research trends in the SDGs and their associations with DTs evolved in recent decades?
• RQ 2: How have the SDG–DT research pairs evolved? Which DTs are most frequently associated with each SDG? What differences exist between the DTs regarding their associations with specific goals?
• RQ 3: What are the emerging areas of research at the intersection of the SDGs and DTs?

By addressing these questions, we evaluated the overall thematic linkage between DTs and sustainable development, identified the most prominent technologies in SDG-related research, and explored evolving research trends. To the best of our knowledge, this is one of the most comprehensive studies to date on SDG–DT research. We utilized SDG mapping to systematically examine the broader relationship between the 17 SDGs and 11 representative DTs, going beyond fragmented studies that have focused on individual technologies or specific goals.

For the analysis, we relied solely on quantitative associations derived from automated keyword-based SDG mapping, which inevitably entailed accuracy limitations such as false positives and false negatives, distinguishing the present study from prior bibliometric investigations through its unprecedented scale. The structural mapping of our study offers novel macro-level insights that complement previous case-based or content-driven studies, emphasizing in-depth and contextual interpretations of SDG–DT linkages. Our findings provide meaningful insights into how DTs have been connected to sustainable development and may support researchers, policymakers, educators, and industry leaders in the effective and strategic adoption of DTs to advance the SDGs.

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature, Section 3 details the methodology, Section 4 presents key findings on the intersection of DTs and the SDGs, Section 5 discusses core insights and broader implications, and Section 6 concludes with a summary and recommendations for future research.

2. Relevant Studies

Global interest in sustainable development has continued to grow, leading to active research on the role of DTs in advancing the UN SDGs.

Table 2. Relevant studies on the linkage between the SDGs and DTs: black dot (●) indicates mention of the DT.

Study	Main Interest and Scope of the Study	Evidence	Representative DTs Mentioned in the Paper
			AI	XR	IoT	DA	BC	DTw	CC	RB	5G	3DP
Hoosain et al. (2020) [14]	DTs for the circular economy and SDGs	Case study	●	●	●		●		●	●		●
Chen et al. (2020) [15]	DTs for sustainability of manufacturing	Case study	●	●	●	●			●	●		●
Aparicio-Gómez et al. (2024) [16]	DTs for the SDGs	Case study	●	●	●	●	●
Dionisio et al. (2024) [17]	DTs for social innovation for SDGs	Case study	●	●	●	●	●	●	●	●		●
Yoo and Song. (2021) [18]	DTs in international cooperation for SDGs	Case study	●				●		●
Vinuesa et al. (2020) [19]	Artificial intelligence (AI) for SDGs	Case study	●
Khan et al. (2021) [20]	Industry 4.0 technologies for SDGs	81 articles			●	●		●	●
Alhammadi et al. (2023) [21]	Industry 4.0 technologies for SDGs	163 articles	●	●	●	●		●	●	●		●
Alhammadi et al. (2024) [22]	Industry 4.0 technology for SDGs in the United Arab Emirates	138 articles	●	●	●	●	●	●	●	●		●
Khan et al. (2023) [23]	Industry 4.0 innovations and their implications in sustainable development perspective	58 articles	●	●	●	●	●	●		●		●
Pizzi et al. (2020) [24]	Management research for SDGs	266 articles			●		●
Costa et al. (2022) [25]	Digital transformation for company sustainability	70 articles	●	●	●	●	●	●	●	●	●	●
Bachmann et al. (2022) [26]	Data-driven technologies for SDGs	193 articles	●			●
Feroz et al. (2021) [27]	Digital transformations for environmental sustainability	106 articles	●		●	●			●
Hariyani et al. (2025) [9]	DTs for SDGs	473 articles	●		●	●	●		●		●
Javaid et al. (2022) [5]	Industry 4.0 for sustainable manufacturing	218 articles	●		●	●
Han et al. (2023) [28]	DTs for circular economy	79 articles	●		●	●	●	●
Hariyani et al. (2024) [29]	DTs for circular economy and life cycle analysis	153 articles	●		●	●	●	●	●

Note: DA: data analytics, big data; CC: cloud computing; RB: robotics; and 3DP: 3D printing.

lists relevant studies that highlight the intersection between the SDGs and DTs. These studies can be grouped into two categories: research based on case studies, and research based on systematic or bibliometric literature reviews.

Numerous investigations have presented case studies that have demonstrated the significant contribution of DTs to achieving the SDGs. For example, Hoosain et al. (2020) [14] analyzed the role of Industry 4.0, such as AI, machine learning (ML), IoT, and big data, in advancing the circular economy and supporting the SDGs. Their research presents cases that show the impact of these technologies on the environmental and economic sectors. Likewise, Chen et al. (2020) [15] explored the influence of Industry 4.0, exemplifying how the technologies it encompasses enhance environmental sustainability within the manufacturing sector. Aparicio-Gómez et al. (2024) [16] emphasized the potential and significance of DTs in achieving the SDGs across diverse domains, underscoring the importance of technological innovation. Dionisio et al. (2024) [17] examined the role of digital social innovation in addressing social issues and attaining the SDGs by focusing on technologies such as BC, IoT, AI, and autonomous robots. They highlighted innovative use cases in healthcare, smart cities, agriculture, and poverty reduction. Yoo and Song (2021) [18] discussed the international community’s view of DTs as crucial enablers of SDG achievement, with many countries emphasizing their roles in development cooperation projects. Vinuesa et al. (2020) [19] provided a detailed analysis of how AI impacts each SDG by identifying both enabling and inhibiting effects.

Another stream of research has focused on bibliometric analyses and systematic literature reviews to assess how DTs have helped to fulfill the UN SDGs. Khan et al. (2021) [20] conducted a systematic review of 81 studies published between 2012 and 2020 sourced from five major databases. They mapped the intersection of Industry 4.0 and sustainability, revealing that most studies focus on conceptual analyses, with IoT being the most frequently cited technology. Alhammadi et al. (2023) [21] examined 163 papers from the Scopus database related to Industry 4.0 and the UN SDGs. They found that Goals 4 (quality education) and 3 (good health and well-being) were the most frequently represented SDGs, with IoT and big data being the most prominent. Alhammadi et al. (2024) [22] analyzed 138 articles to explore the relationship between Industry 4.0 technologies and SDGs, focusing on the United Arab Emirates (UAE). They highlighted the technologies and SDGs that have been the most and least commonly applied in the UAE context. Khan et al. (2023) [23] conducted a systematic literature review of 58 journal articles from three databases to explore the relationship between Industry 4.0, technologies, innovations, and their implications for sustainable development. They found that process, product, and business model innovations were the most discussed, with significant emphasis on their contributions to sustainability, the circular economy, and sustainable business models. However, these four investigations were limited by the relatively small number of studies analyzed.

Pizzi et al. (2020) [24] carried out a bibliometric study and systematic review of 266 articles published between 2012 and 2019 that focused on the intersection of management research and the UN SDGs. They identified four primary research themes: technological innovation, the contributions of firms in developing countries, non-financial reporting, and education on SDGs. They noted growing interest in these areas and highlighted the need for further research, particularly in understanding the complex interconnections between business practices and attaining the SDGs. Costa et al. (2022) [25] focused on papers related to digital transformation and sustainability from Scopus and other academic databases to explore how DTs enhance corporate sustainability. By analyzing 70 papers categorized by technology, this study provides insights into the contributions of various technologies (particularly AI, IoT, and 3D printing) to corporate sustainability. Bachmann et al. (2022) [26] conducted a review of 193 papers linking “Data-Driven Technologies” and the UN SDGs across multiple databases. They analyzed how technologies such as AI, the IoT, and machine learning (ML) contribute to each SDG, highlighting that data-driven analytics and tools improve information reliability and optimize resource allocation. Feroz et al. (2021) [27] reviewed 106 papers from the Scopus and ScienceDirect databases, focusing on technologies driving digital transformation and their relationships with environmental sustainability. These studies can be categorized under four key themes: pollution management, waste management, sustainable production, and urban sustainability. Javaid et al. (2022) [5] reviewed various applications and the adoption of Industry 4.0 technologies to enhance environmental sustainability. They analyzed 218 papers on Industry 4.0 and sustainability, highlighting 20 major applications of its role. Hariyani et al. (2025) [9] carried out a systematic literature review of 473 articles published until 2024 to provide a comprehensive overview of the contributions, challenges, barriers, and opportunities at the intersection of DTs and the SDGs.

Several studies have focused on other SDG-related topics such as circular economies. For instance, Han et al. (2023) [28] analyzed 79 papers from the Scopus database on the circular economy and DTs, examining how DTs support the implementation of the circular economy. They proposed a conceptual framework to demonstrate the role of DTs in a circular economy. Hariyani et al. (2024) [29] conducted a systematic study on leveraging DTs to advance circular economy practices and enhance life-cycle analyses. Their review of 153 studies from the Scopus database revealed the contributions of technologies such as the IoT, BC, and AI to resource optimization and waste reduction, although the relatively small number of studies limits the generalizability of their findings.

In summary, while numerous studies have analyzed and confirmed the significance of DTs in achieving the SDGs, these analyses have often centered on specific technologies, case studies, or a limited number of publications. Consequently, the existing literature frequently lacks a holistic perspective that captures the complex interconnections between multiple DTs and various SDGs. A comprehensive understanding of how DTs are linked to the SDGs remains underdeveloped.

A data-driven, large-scale analysis is essential to empirically confirm the growing volume of SDG–DT research, assess the relative strength of linkages between specific SDGs and DTs, identify strongly associated SDG–DT pairs, and forecast emerging trends in this domain. This approach is crucial for making strategic decisions regarding the adoption and development of DTs to support sustainable growth.

We sought to address these gaps by conducting a bibliometric analysis of a large-scale SDG–DT research dataset (over 1.17 million publications) and systematically identifying SDG–DT research linkages across all 17 SDGs and 11 representative DTs. Our paper provides empirical evidence of the quantitative growth and diffusion of SDG–DT linkages across the global research landscape. Furthermore, by providing a comprehensive overview of the SDG–DT ecosystem, this study complements existing research that focuses on capturing the content or contextual nuances of selected technologies or goals using case-based or narrow-scope approaches.

3. Data Collection

We explored the contributions of DTs to the SDGs through a bibliometric analysis. To gather SDG research papers, the Elsevier 2023 SDG mapping was used. Since 2018, Elsevier has developed and continuously refined SDG-specific search queries. These queries consist of carefully curated keywords for each SDG and have been enhanced using empirical evidence and ML algorithms, such as logistic regression, natural language processing (NLP), and term frequency–inverse document frequency (TF-IDF). SDG mapping queries are directly applicable to the Scopus platform and assist researchers and institutions in tracking and demonstrating research progress toward achieving SDGs. Figure 2 illustrates an example of SDG mapping of an article.

The validity of this mapping approach has been supported by several recent studies, including Rivest et al. (2021) [30], Raman et al. (2024) [12], and Kashnitsky et al. (2024) [13]. While reported performance metrics vary depending on the validation dataset, the overall precision, recall, and F1 scores consistently range between 70 and 80%. Although further refinement may improve performance, Elsevier’s SDG mapping remains one of the most reliable and advanced mapping tools currently available.

Given its direct applicability to the Scopus platform and relatively high accuracy, we adopted Elsevier’s SDG mapping queries without additional modification or revalidation. Enhancing the query model is recognized as an important task; however, it is beyond the scope of our study. For the analysis, we employed the 2023 version of the mapping framework (the most advanced and up-to-date implementation [13] at the time of writing this paper). For additional details on the methodology and original search queries, see Elsevier (2023) [31] and Bédard-Vallée et al. (2023) [11].

We focused on SDG–DT research at the intersection of the SDGs and DTs. To identify relevant publications, we applied an expanded version of the mapping queries with additional filtering to ensure that each article explicitly referred to specific DTs in its title, abstract, or author keywords. A clear operational definition of DTs was essential to ensure the consistency and reliability of the modified search queries.

DT selection followed a three-step process. First, we identified candidate technologies based on existing Industry 4.0 literature, including studies by Pereira and Romero (2017) [32], Rüßmann et al. (2015) [33], and Javaid et al. (2022) [5], as well as reports by Emeritus [34] and PwC [35]. Second, we selected key technologies based on their prevalence in prior research on the SDG–DT linkage (see Table 2), supplemented by Google Scholar search counts. Third, to improve the comprehensiveness of the bibliometric retrieval, we identified and incorporated synonyms as well as closely related terms for each technology into the search queries. To support this process, we used ChatGPT-4 to generate initial candidate terms, which all four authors independently reviewed. We included only those terms approved by at least three of us in the final query set.

Table 3. Eleven DTs and their synonyms and related terms chosen for this study.

Digital Technology		Synonyms and Related Terms Applied in This Study	Precision (%)
1	3D Printing (3DP)	3D-printing, Three-Dimensional Printing, Additive Manufacturing, Additive Layer Manufacturing, 3D Manufacturing	96%
2	5th-Generation Mobile Network (5G)	5G, Next-Generation Networks, 5G Communication Technology	84%
3	Artificial Intelligence (AI)	Machine Learning, Deep Learning, Neural Networks	94%
4	Blockchain (BC)	Block-chain, Digital Distributed Ledger, Block Technology	90%
5	Cloud Computing (CC)	Cloud-computing, Cloud Computing, Cloud Technology, Online Computing, Remote Servers, Digital Storage, Distributed Computer Systems	71%
6	Data Analytics (DA)	Data Science, Big Data Analytics, Data Mining, Predictive Analytics	88%
7	Digital Twin (DTw)	Digital Twins	94%
8	Drones (DR)	Drone, UAV, Unmanned Aerial Vehicles, Unmanned Aerial Systems, Autonomous Aircraft, Drone Technology	100%
9	Internet of Things (IoT)	Internet of thing, IoT, Smart Devices, Connected Devices, Machine-to-Machine (M2M) Communication	92%
10	Robotics (RB)	Robot, Robots, Robot Programming, Automation, Robot Technology, Robotic Engineering, Autonomous Systems	86%
11	Extended Reality (XR)	Mixed Reality, Virtual Reality, Augmented Reality, MR, VR, AR	71%

presents the 11 selected DTs along with their associated synonyms and related terms used in the search queries. It is important to acknowledge the inherent limitations of keyword-based bibliometric searches, particularly the risk of false positives (primarily due to broad or ambiguous terms) and false negatives (stemming from omitted or missing terms). Although we carefully constructed the keyword set through a systematic, consensus-driven process, it ultimately reflects our judgment and has practical limitations. A fully exhaustive list was not feasible, and the keyword set reflects how each DT was conceptually framed and operationalized within this particular study.

To assess its effectiveness, we evaluated the precision of the keyword set for each DT using a random sample of 50 articles retrieved per DT. Each article was manually reviewed to determine whether it was genuinely related to the corresponding DT. On average, the precision—defined as the proportion of true positives among all retrieved cases—was 87.82%.

Table 4. Example of the search query applied (the newly added part in this study is underlined).

Query for 3D Printing and Goal 1 (No Poverty)

(TITLE-ABS-KEY(“3D Printing”) OR TITLE-ABS-KEY(“3D-printing”) OR TITLE-ABS-KEY(“Three Dimensional Printing”) OR TITLE-ABS-KEY(“Additive Manufacturing”) OR TITLE-ABS-KEY(“Additive Layer Manufacturing”) OR TITLE-ABS-KEY(“3D Manufacturing”)) AND ((((TITLE-ABS(“unesco”) AND TITLE-ABS(“poverty”) AND TITLE-ABS(“program”)) OR(AUTHKEY(“unesco”) AND AUTHKEY(“poverty”) AND AUTHKEY(“program”)) OR(TITLE-ABS(“poverty*-reducing* polic*”) OR TITLE-ABS(“povertyreducing* polic*”)) OR(AUTHKEY(“poverty*-reducing* polic*”) OR…the rest omitted

presents an example of the SDG–DT search queries used in this study. It comprises two parts: a DT keyword search query and an original SDG mapping query. The example illustrates a combined search query using DT-specific terms (e.g., 3D printing) and the original SDG mapping query for Goal 1 (no poverty). Likewise, we generated search queries for all 187 pairs (17 SDGs × 11 DTs). The full DT query set is available in Appendix A (

Table A1. DT search query applied in this study.

DT	Query
3DP	(TITLE-ABS-KEY(“3D Printing”) OR TITLE-ABS-KEY(“3D-printing”) OR TITLE-ABS-KEY(“Three Dimensional Printing”) OR TITLE-ABS-KEY(“Additive Manufacturing”) OR TITLE-ABS-KEY(“Additive Layer Manufacturing”) OR TITLE-ABS-KEY(“3D Manufacturing”))
5G	(TITLE-ABS-KEY(“5G”) OR TITLE-ABS-KEY(“5th Generation Mobile Networks”) OR TITLE-ABS-KEY(“Next-Generation Networks”) OR TITLE-ABS-KEY(“5G Communication Technology”))
AI	(TITLE-ABS-KEY(“AI”) OR TITLE-ABS-KEY(“Artificial Intelligence”) OR TITLE-ABS-KEY(“Machine Learning”) OR TITLE-ABS-KEY(“Deep Learning”) OR TITLE-ABS-KEY(“Neural Networks”))
BC	(TITLE-ABS-KEY(“Blockchain”) OR TITLE-ABS-KEY(“Digital Distributed Ledger”) OR TITLE-ABS-KEY(“Block Technology”))
CC	(TITLE-ABS-KEY(“Cloud-computing”) OR TITLE-ABS-KEY(“Cloud Computing”) OR TITLE-ABS-KEY(“Cloud Technology”) OR TITLE-ABS-KEY(“Online Computing”) OR TITLE-ABS-KEY(“Remote Servers”) OR TITLE-ABS-KEY(“Digital Storage”) OR TITLE-ABS-KEY(“Distributed Computer Systems”))
DA	(TITLE-ABS-KEY(“Data Analytics”) OR TITLE-ABS-KEY(“Data Science”) OR TITLE-ABS-KEY(“Big Data Analytics”) OR TITLE-ABS-KEY(“Data Mining”) OR TITLE-ABS-KEY(“Predictive Analytics”))
DR	(TITLE-ABS-KEY(“Drones”) OR TITLE-ABS-KEY(“Drone”) OR TITLE-ABS-KEY(“UAV”) OR TITLE-ABS-KEY(“Unmanned Aerial Vehicles”) OR TITLE-ABS-KEY(“Unmanned Aerial Systems”) OR TITLE-ABS-KEY(“Autonomous Aircraft”) OR TITLE-ABS-KEY(“Drone Technology”))
DTw	(TITLE-ABS-KEY(“Digital Twin”) OR TITLE-ABS-KEY(“Digital Twins”))
IoT	(TITLE-ABS-KEY(“IOT”) OR TITLE-ABS-KEY(“Internet of things”) OR TITLE-ABS-KEY(“Internet of thing”) OR TITLE-ABS-KEY(“IoT”) OR TITLE-ABS-KEY(“Smart Devices”) OR TITLE-ABS-KEY(“Connected Devices”) OR TITLE-ABS-KEY(“Machine-to-Machine (M2M) Communication”))
RB	(TITLE-ABS-KEY(“Robotics”) OR TITLE-ABS-KEY(“Robot”) OR TITLE-ABS-KEY(“Robots”) OR TITLE-ABS-KEY(“Robot Programming”) OR TITLE-ABS-KEY(“Automation”) OR TITLE-ABS-KEY(“Robot Technology”) OR TITLE-ABS-KEY(“Robotic Engineering”) OR TITLE-ABS-KEY(“Autonomous Systems”))
XR	(TITLE-ABS-KEY(“XR(MR,VR,AR)”) OR TITLE-ABS-KEY(“XR”) OR TITLE-ABS-KEY(“Mixed Reality”) OR TITLE-ABS-KEY(“Virtual Reality”) OR TITLE-ABS-KEY(“Augmented Reality”) OR TITLE-ABS-KEY(“MR”) OR TITLE-ABS-KEY(“VR”) OR TITLE-ABS-KEY(“AR”))
All DTs	Combine all queries above in series with “OR”

), whereas the full SDG mapping queries are referenced in [11]. Given the precision of the individual SDG and DT queries (approximately 70–80% and 87.82%, respectively), and the stricter filtering effect of the conjunctive AND logic, the combined queries were assumed to yield comparable precision. Therefore, they were considered sufficiently reliable and used without additional manual validation.

Data collection was conducted on 5 September 2024, and was repeated for each pairing of an SDG and a DT. To enable annual trend analysis, we included only research articles published up to 2023; we excluded publications from 2024 because that year was incomplete at the time of data collection. Across all 187 pairs, we identified a total of 22,513,007 SDG publications and 1,170,340 SDG–DT publications. These figures include duplicates, as papers associated with multiple SDGs were counted under each corresponding goal. We present a detailed analysis of these studies in the following sections.

4. Results

This section presents the key results of the bibliometric analysis in the following sequence: (1) the volume of SDG research by goal and its changes over time; (2) the volume of SDG–DT research by goal and its share within the overall SDG literature; (3) SDG–DT pairs that have been the most strongly associated in the literature; and (4) emerging areas within SDG–DT research. Due to space limitations, the detailed raw data are provided in

Table A2. Volume of SDG and SDG–DT research and the ratio of DT-related publications.

SDG	Volume of SDG Research (|SDG|)			Volume of SDG–DT Research (|SDG–DT|)			Ratio of DT-Related Research (|SDG–DT|/|SDG|)
	Total in All Years	Past 5 Years		Total in All Years	Past 5 years		All Years	Before Last 5 Years	Last 5 Years
		Volume	CAGR		Volume	CAGR
G1	268,153	93,691	7.47%	4787	3341	22.43%	1.79%	0.83%	3.57%
G2	632,428	256,118	12.90%	33,129	24,634	27.55%	5.24%	2.26%	11.63%
G3	9,911,209	2,820,028	8.40%	326,275	186,787	25.86%	3.29%	1.97%	6.62%
G4	608,162	236,921	10.54%	42,690	25,608	22.97%	7.02%	4.60%	10.81%
G5	452,721	155,175	9.65%	5083	3435	30.32%	1.12%	0.55%	2.21%
G6	963,508	339,493	9.31%	41,565	24,831	21.00%	4.31%	2.68%	7.31%
G7	1,982,122	789,410	8.66%	152,090	92,708	19.50%	7.67%	4.98%	11.74%
G8	784,518	329,343	13.14%	32,393	24,028	30.99%	4.13%	1.84%	7.30%
G9	1,285,695	539,735	13.09%	266,021	159,580	18.20%	20.69%	14.27%	29.57%
G10	712,342	276,171	10.41%	14,021	9493	26.20%	1.97%	1.04%	3.44%
G11	1,081,855	443,156	8.44%	107,509	72,012	18.33%	9.94%	5.56%	16.25%
G12	669,161	284,773	14.33%	33,269	23,270	31.44%	4.97%	2.60%	8.17%
G13	706,537	338,469	17.88%	36,094	25,064	32.97%	5.11%	3.00%	7.41%
G14	490,088	170,740	8.09%	14,643	9093	24.40%	2.99%	1.74%	5.33%
G15	636,693	229,904	8.40%	18,505	12,649	28.34%	2.91%	1.44%	5.50%
G16	737,067	257,381	7.31%	19,773	12,060	20.65%	2.68%	1.61%	4.69%
G17	590,748	226,518	12.14%	22,493	15,505	33.76%	3.81%	1.92%	6.84%
Median	706,537	276,171	9.65%	33,129	24,028	25.86%	4.13%	1.92%	7.30%

and

Table A3. Research volume of each pair of SDG and DT.

	3DP	5G	AI	BC	CC	DA	DR	DTw	IoT	RB	XR	Total (|SDG–DT|)
G1	30	52	2186	361	417	583	87	18	365	1122	428	4787
G2	177	275	13,737	577	2106	1970	4661	121	3886	13,224	1573	33,129
G3	4920	3386	155,531	1841	7117	22,707	4478	531	10,480	64,637	85,226	326,275
G4	794	277	18,179	579	3207	5551	388	209	2249	7539	12,125	42,690
G5	29	23	2579	51	154	475	64	7	231	985	1013	5083
G6	622	524	18,438	225	4357	2234	1441	188	4111	12,235	3025	41,565
G7	3214	8302	60,669	2685	26,561	6581	6529	1644	20,795	29,581	11,572	152,090
G8	564	385	14,175	2112	3068	3220	1009	306	3470	9629	2484	32,393
G9	73,395	6925	56,722	7362	20,436	12,339	4417	5825	39,952	96,404	14,574	266,021
G10	121	102	7023	1071	907	1727	247	52	703	2353	1992	14,021
G11	707	3026	51,911	2813	10,588	10,562	6805	1458	20,632	17,954	8822	107,509
G12	1971	454	12,022	1494	4067	2628	1115	517	4381	8868	3152	33,269
G13	765	504	14,071	834	8851	2161	1354	428	2550	6579	3213	36,094
G14	174	241	6384	92	1303	904	1312	79	961	3460	1807	14,643
G15	156	136	8996	173	1743	1157	2282	76	655	4046	1551	18,505
G16	126	142	9240	1351	1877	2543	996	123	1436	2832	2691	19,773
G17	361	364	9609	1744	2639	2626	601	350	2428	5313	1722	22,493

Note: The column labeled |SDG–DT| represents the total volume of SDG–DT research for each goal. Notably, this value does not equal the sum of the figures in each row, as some publications are simultaneously linked to multiple technologies.

in Appendix B.

4.1. SDG Research: Overall Trends in the Volume of Research by Goal

To examine the overall trends in SDG research, we analyzed the total volume of research and its changes over time for each goal, as shown in Figure 3.

The stacked area chart in Figure 3a illustrates a continuous increase in SDG research volume. A noticeable expansion began in the 1990s, and growth accelerated sharply after 2000, particularly between 2017 and 2023. By 2023, the total number of publications reached nearly 1.8 million. This suggests rapid growth in recent decades, likely driven by greater interest in sustainability and sustainable development.

Figure 3b presents the total number of publications for each SDG over the entire study period. Of the 17 SDGs, Goal 3 (good health and well-being) accounted for the highest number of publications, comprising approximately 45% of all SDG research. Despite being overshadowed by the high number of studies on Goal 3, other SDGs such as Goal 7 (affordable and clean energy), Goal 9 (industry, innovation, and infrastructure), and Goal 11 (sustainable cities and communities) also represent a significant portion of the total research volume. They have consistently large volumes of research, indicating deepening attention to areas such as clean energy, infrastructure, and urban development for sustainable research.

However, Figure 3c indicates that the relative share of each goal within SDG research is evolving. While Goal 3 remains the most prominent, the proportion of other SDGs has gradually increased, suggesting a shift toward greater diversity in SDG research as multifaceted strategies for sustainable development emerge. Figure 3d further supports this trend toward diversification in SDG research. It displays the compound annual growth rate (CAGR) for the research volume of each SDG over the past five years (2019–2023). All goals exhibited positive growth rates, with a median of 9.65%. Among the 17 SDGs, Goal 13 (climate action) had the highest CAGR at 17.88%, followed by Goal 12 (responsible consumption and production) at 14.33%, Goal 8 (decent work and economic growth), Goal 9 (industry, innovation, and infrastructure), Goal 2 (zero hunger), and Goal 17 (partnerships for the goals) at approximately 13%.

In summary, SDG research has increased significantly, with a noticeable rise across various goals. Overall, Goal 3 remains the dominant research area; however, there is growing diversification in SDG research. Goals tied to climate action, responsible consumption, economic growth, infrastructure development, and security are experiencing rapid growth, indicating a shift in attention toward diverse sustainability challenges. Moderate but steady growth in other goals reflects a broadening focus on various aspects of sustainable development, demonstrating a move toward a more holistic approach to addressing global issues.

4.2. SDG–DT Research: Overall Trends in Volume and Its Share Within Total SDG Research

To examine the associations between the SDGs and DTs, we analyzed the total volume of SDG–DT research (i.e., SDG research associated with any of the 11 DTs), as displayed in Figure 4.

The stacked area chart in Figure 4a illustrates the rising trend in SDG–DT research, indicating cumulative growth across various goals. The association between DTs and SDG research has increased prominently over time, with a marked escalation beginning in the 2000s. The most significant surge was observed from 2017 to 2023, indicating accelerated growth in recent years, likely driven by growing recognition of the impact of DTs in advancing sustainability. In 2023, the annual publication volume of SDG–DT research reaches approximately 0.2 million.

Figure 4b shows the total volume of SDG–DT research. Similar to overall SDG research, Goal 3 (good health and well-being) remains the most dominant in terms of total publications, with approximately 28% of all SDG–DT research focusing on this goal. However, a notable difference from SDG research is the prominence of Goals 9 (industry, innovation, and infrastructure), 7 (affordable and clean energy), and 11 (sustainable cities and communities). These goals involve larger volumes of research within the SDG–DT domain, underscoring the need to strengthen linkages with DTs in these areas.

Another difference is displayed in Figure 4c. The share of each goal in SDG–DT research evolved significantly over time. Goals 3 and 9 account for the majority of research publications, suggesting that the initial linkages between SDGs and DTs focused on these goals. However, this trend has expanded rapidly across the research community. The rising volume of SDG–DT research related to Goals 7 and 11 is noteworthy, as their expanding shares have contributed to greater diversification within the SDG–DT research landscape.

The CAGR graph in Figure 4d reinforces the observed diversification trend. Over the past five years (2019–2023), all SDGs have experienced positive growth in research associated with DTs, highlighting the rapid expansion of DT-related studies across the entire SDG spectrum. Notably, for every goal, the growth rate of SDG–DT research surpasses that of overall SDG research. Figure 5 compares the CAGR of the SDG and SDG–DT research for each goal. All data points lie above the 1:1 proportional growth line (dashed line), implying that SDG–DT research has grown faster than general SDG research across all goals over the past five years.

On average, the median CAGR of SDG–DT research is 25.86%, significantly exceeding the median CAGR of 9.65% of overall SDG research. We noted a moderate positive correlation (Pearson’s r = 0.581, 95% CI = [0.139, 0.830]) between the two, suggesting that goals with rapidly growing SDG research also tend to experience faster growth in SDG–DT research. However, the correlation is not particularly strong, implying that the pace of digital integration varies across goals. Some SDGs have seen a relatively accelerated adoption of DTs, while others are progressing more slowly, reflecting differentiated patterns of digital engagement.

This more rapid expansion of SDG–DT research has led to an increasing share of DT-related research in the broader SDG literature. Figure 6 analyzes the proportion of SDG–DT research in the total body of SDG research for each goal. We calculated this as the number of SDG–DT publications (|SDG–DT|) divided by the total number of publications for a given SDG (|SDG|). This metric provides insights into the relative degree of DT involvement across different goals. The results revealed that DTs are most prominently linked to Goal 9 (industry, innovation, and infrastructure), with 20.69% of the corresponding SDG research involving DTs. We also observed high involvement in Goals 11 (sustainable cities and communities), 7 (affordable and clean energy), 4 (quality education), and 2 (zero hunger). In contrast, although Goal 3 (good health and well-being) accounts for the largest volume of SDG–DT research, its share within the SDG literature is moderate.

As expected, the involvement of DTs in SDG research has become more pronounced in recent years. Although the overall trend in the proportions of SDGs has remained consistent, the absolute share of DT-related studies has grown considerably over the past five years. For example, the share of SDG–DT research in Goal 1 (no poverty) has risen from 0.83% to 3.57% in the past five years, representing a 4.3-fold increase. Likewise, the share of Goal 2 (zero hunger) increased from 2.26% to 9.62%. Overall, the median share of DT-related studies was 1.97% before 2019, rising to 7.30% within the past five years.

In summary, the association between SDG research and DTs has significantly strengthened in recent years. Although the degree of association varies across the SDGs, there is a consistent trend of deepening DT involvement across all SDGs. This suggests heightened attention to digital transformation throughout the SDG spectrum and highlights the growing importance of DTs in achieving the SDGs.

4.3. Most Associated Linkages Between the SDGs and DTs

To investigate the associations between each SDG and DT, we further analyzed the volume of research corresponding to each SDG–DT pair. (Detailed data for the individual pairs are provided in Table A3 of Appendix B).

We performed the analysis from two complementary perspectives: (1) the share of each DT within SDG–DT research for a specific SDG, revealing the most associated DTs across SDGs; and (2) the share of each SDG within the research related to a specific DT, showing the most associated SDGs across DTs. We discuss these in detail in Section 4.3.1 and Section 4.3.2, respectively.

4.3.1. Most Associated DTs for Each SDG

Figure 7 presents the share of each DT within SDG–DT research for each goal. This share was calculated by dividing the number of articles associated with a specific SDG–DT pair by the total number of SDG–DT publications for that goal (i.e., |SDG–DT|/|SDG|). For example, among the 4787 publications addressing Goal 1 in association with any of the 11 DTs, 0.63% involved 3DP, 1.09% involved 5G, and 45.67% involved AI. The color-coding scheme depicts these shares. Greener cells indicate higher shares within each row, facilitating intuitive comparisons across DTs and revealing common patterns across the SDGs.

AI consistently ranked as the most associated DT for all goals except Goal 9. AI occupied a significant share across most SDGs, with a median share of 43.76%, indicating that nearly half of all SDG–DT studies involved AI. RB also demonstrates a strong association, with a median share of 21.86%. This suggests that both AI and RB are relevant to various aspects of sustainable development. In contrast, other technologies (such as XR, CC, DA, and IoT) have exhibited moderate levels of association, whereas the remaining DTs show limited connections. These overall trends are reinforced by Figure 8, which depicts a boxplot of the DT shares across the SDGs.

Although the boxplot illustrates common patterns across goals, it also highlights outliers, implying that certain SDGs exhibit uniquely strong associations with specific DTs. To further explore these distinctive patterns,

Table 5. Top five most frequently associated DTs for each SDG (DTs with <5% share excluded).

SDG	Top 1	Top 2	Top 3	Top 4	Top 5
G1	AI	RB	DA	XR	CC
G2	AI	RB	DR	IoT	CC
G3	AI	XR	RB	DA	-
G4	AI	XR	RB	DA	CC
G5	AI	XR	RB	DA	IoT
G6	AI	RB	CC	IoT	XR
G7	AI	RB	CC	IoT	XR
G8	AI	RB	IoT	DA	CC
G9	RB	3DP	AI	IoT	CC
G10	AI	RB	XR	DA	BC
G11	AI	IoT	RB	CC	DA
G12	AI	RB	IoT	CC	XR
G13	AI	CC	RB	XR	IoT
G14	AI	RB	XR	DR	CC
G15	AI	RB	DR	CC	XR
G16	AI	RB	XR	DA	CC
G17	AI	RB	CC	DA	IoT

ranks the top five most frequently associated DTs for each SDG based on their proportional share. DTs with less than a 5% share were excluded to focus on stronger, more meaningful linkages.

As expected, AI and RB dominate most SDGs. However, other DTs—such as XR, IoT, CC, DA, DR, and 3DP—display greater rank variability across goals, reflecting more specialized relevance to particular sustainability challenges. For example, 3DP, though marginal overall (with a median share of 1.5%), is predominantly linked to Goal 9, accounting for 27.59% of G9–DT research. This highlights its role in industry and infrastructure development. XR shows strong connections with Goals 3 (Good Health and Well-Being), 4 (Quality Education), and 5 (Gender Equality). The IoT is notably associated with Goal 11 (Sustainable Cities and Communities), comprising 19.19% of G11–DT research. CC is closely tied to Goal 13, contributing to 24.52% of G13–DT research, signifying its application in climate monitoring and mitigation strategies.

In general, these differentiated linkages suggest that while certain technologies (particularly AI and RB) have been broadly applied across multiple goals, others have displayed more targeted relevance to specific domains. This pattern indicates the emergence of context-specific digital strategies tailored to advance specific SDGs.

4.3.2. Most Associated SDGs for Each DT

To further explore the specialized association of each DT with the SDGs, Figure 9 shows the share of each SDG within SDG–DT research involving a specific DT. For instance, among the 88,126 publications on 3DP-related SDG research, 0.03% are related to Goal 1, 0.2% to Goal 2, and so on. Greener cells indicate larger shares within each column, enabling intuitive comparisons across the SDGs. For example, over 83% of 3DP-related research is linked to Goal 9, whereas 5G-related research is mostly associated with Goals 7, 9, 11, and 3. Figure 10 presents a boxplot of these SDG shares to emphasize both the general patterns and distinctive associations across the DTs.

Figure 9 and Figure 10 help us understand how each DT is linked with SDGs, suggesting which SDGs have gained interest in each DT research community. In general, the DTs exhibit relatively strong linkages with Goals 9, 7, 3, and 11, constituting a greater research volume for these goals, as identified in Section 4.1. However, as indicated by the outliers in the boxplot, some DTs have stronger linkages with certain goals than other DTs, implying a unique combination of SDG associations for each DT.

Table 6. Top five most relevant SDGs for each digital technology (SDGs with < 5% share excluded).

DT	Top 1	Top 2	Top 3	Top 4	Top 5
3DP	G9	G3	-	-	-
5G	G7	G9	G3	G11	-
AI	G3	G7	G9	G11	-
BC	G9	G11	G7	G8	G3
CC	G7	G9	G11	G13	G3
DA	G3	G9	G11	G7	G4
DR	G11	G7	G2	G3	G9
DTw	G9	G7	G11	G3	-
IoT	G9	G7	G11	G3	-
RB	G9	G3	G11	G2	-
XR	G3	G9	G4	G7	G11

ranks the top five SDGs associated with each DT based on their share in each DT’s SDG-related research. While associations with Goals 9, 7, 3, and 11 are generally dominant, variations in the detailed rankings reveal unique linkages. Notable examples include BC and its association with Goal 8 (decent work and economic growth), cloud computing (CC) with Goal 13 (climate action), data analytics (DA) with Goal 4 (quality education), drones (DR) with Goal 2 (zero hunger), robotics (RB) with Goal 2, and XR with Goal 4. These distinct pairings reflect how specific technologies align with sustainability agendas.

4.4. Emerging Areas in SDG–DT Research

To identify emerging areas in SDG–DT research, we analyzed the proportion of articles published in the past five years (2019–2023) for each SDG–DT pair. A high proportion of recent publications suggests that a given pair may represent an emerging topic in SDG–DT research.

Figure 11 shows a color-coded overview of these results, where green indicates a high degree of emergence (greater than 75%) and yellow signifies a moderate degree of emergence (between 50% and 75%). We excluded pairs with fewer than 30 publications to ensure minimal statistical relevance. For example, 70% of the research associated with the G1–3DP pair was published in the past five years, whereas 92.24% of the G1–BC research was published during the same period. This implies that G1–BC research is somewhat newer than G1–3DP, representing a higher level of novelty in SDG research.

To supplement this analysis, Figure 12 presents a boxplot illustrating the distribution of recent publication shares for each DT across all SDGs, allowing for comparisons of general patterns and variability by goal.

Among all DTs, DTw and BC have emerged as the most prominent technologies across all SDGs, with the most related research being conducted in the past five years. DTw has the highest median share in recent publications (98.68%), followed by BC (94.44%). These values indicate that these technologies have only recently begun to be meaningfully integrated into SDG-related research, positioning them as rapidly emerging frontiers in the SDG–DT landscape.

In addition to these broadly emerging technologies, several SDG-specific linkages have been identified. Notably, 3DP in Goals 6 (clean water and sanitation) and 13 (climate action), 5G in Goal 4 (quality education), and IoT in Goal 2 (zero hunger) and Goal 15 (life on land) show relatively high proportions of recent research, indicating growing interest in these specific applications.

Conversely, XR has the lowest average proportion of recent research (46.75%), followed by CC (55.95%). However, the lower averages do not necessarily imply a decline in interest. Instead, they likely reflect that the integration of these technologies with sustainable development is a well-established, ongoing issue that has been researched extensively over a long period, resulting in a reduced sense of newness compared with rapidly emerging technologies such as DTw and BC.

5. Discussion

This study provides key insights into the relationship between the UN SDGs and DTs through a comprehensive bibliometric analysis. This section summarizes the key findings and addresses the RQs presented in Section 1. In this discussion, we further explore the study’s academic and practical implications.

5.1. Key Findings and Implications of RQ 1: Trends in the Volume of SDG–DT Research

The first research question explored the evolution of SDG research trends and their association with digital technology. This study reveals significant shifts over the past few decades, reflecting growing emphasis on sustainable development and an expanding linkage with various DTs. We have outlined the key aspects of this evolution below:

• Overall growth and diversification in SDG research: Research on SDGs has experienced substantial growth, beginning to rise notably in the 1990s and accelerating sharply after 2000. A significant surge occurred between 2017 and 2023, likely fueled by the heightened global awareness of sustainability challenges and the UN’s formal adoption of the SDGs. In 2023, the annual number of publications reached nearly 1.8 million, and this upward trend is expected to continue as sustainability remains a key research priority. Although Goal 3 continues to dominate, the research landscape is rapidly diversifying. Many other goals are now experiencing accelerated growth, suggesting a broader and more balanced focus across the full spectrum of SDGs.
• Rapid expansion of SDG–DT research: Although the level of digital integration varies across SDGs, there is an overall trend of consistent growth across all goals. Notably, SDG–DT research has expanded at a much faster rate than general SDG research, with a median CAGR of 25.86% over the past five years, significantly outpacing the average 9.65% CAGR of general SDG research. Consequently, the proportion of SDG–DT research within the total body of SDG publications has increased.

Figure 13 provides a visual summary of the current landscape and recent dynamics in SDG–DT research. It plots each SDG according to two key variables: the total number of SDG–DT publications (x-axis) and the CAGR of SDG–DT research over the past five years (y-axis). In addition, the size of each bubble represents the proportion of DT-related publications within the overall SDG literature for each goal, indicating the relative level of digital integration.

The figure shows several noteworthy patterns. Goal 3 exhibits the largest research volume; however, its DT involvement ratio is relatively low. Notwithstanding, its SDG–DT research volume seems to have continuously dominated given a medium growth rate. Goals 9, 7, and 11 exhibit the largest research volumes and highest ratios of DT integration, reinforcing their alignment with technology-intensive themes such as smart infrastructure, urban innovation, energy systems, and industrial transformation. However, their growth rates in DT-related research are comparatively low, suggesting that these areas are more mature and well established in digital integration.

Despite the small research volume, other goals are expected to grow rapidly. Goals 17 (partnerships for the goals), 13 (climate action), 12 (responsible consumption and production), 8 (decent work and economic growth), and 5 (gender equality) demonstrate relatively high CAGRs, indicating an emerging interest and rapidly growing engagement with DTs in these domains.

These findings empirically support the growing significance of DTs in SDG research across a wide range of goals. The simultaneous expansion and diversification of SDG–DT research highlights the transformative potential of DTs and underscores the need for continued exploration and strategic integration.

5.2. Key Findings and Implications of RQ 2: Associations Between the SDGs and DTs

The second research question underscores the most frequent associations between the SDGs and DTs in the literature. The analysis revealed distinct patterns of association, demonstrating that certain DTs are prominently aligned with particular goals and vice versa. These links offer insights into how digital tools can be used to address diverse sustainability challenges.

Figure 14 visualizes the findings through two complementary Sankey diagrams. Figure 14a emphasizes the share of each DT within SDG–DT research for a specific SDG, thus identifying the most associated DTs per goal. By contrast, Figure 14b illustrates the share of each SDG within the research involving a specific DT, showing the most associated SDGs across DTs. Together, these diagrams reveal overarching patterns and distinctive associations in the SDG–DT linkages, as outlined below.

• DTs most associated with each SDG: AI and RB (robotics) show dominant associations across nearly all goals, indicating their widespread applicability. Goal-specific associations are also observed, such as 3DP for Goal 9, XR for Goals 3 to 5, IoT for Goal 11, and CC for Goal 13. These results suggest that such technologies are particularly relevant for addressing specific sustainability issues.
• SDGs most associated with each DT: From the perspective of each DT, the most frequently targeted goals are Goals 9, 3, 7, and 11. These goals serve as focal points in digital applications. Additionally, some DTs have revealed unique priorities, such as BC for Goal 8, CC for Goal 13, DA and XR for Goal 4, and DR and RB for Goal 2. The results imply that each DT has focused primarily on addressing these goals.

One interesting insight from this analysis is that the relevance between SDGs and DTs differs depending on the perspective of analysis. To further explore this asymmetric attention, Figure 15 presents a positioning matrix that visualizes the relative significance of each SDG–DT pair from both perspectives.

For a given SDG–DT pair, the x-axis represents the proportion of DT within all research related to the given SDG (importance from the SDG’s perspective). The y-axis represents the share of a given SDG among all SDG studies involving the DT (importance from the DT perspective).

Each quadrant of the matrix provides unique insights into the nature of the association between the SDGs and DTs, as follows:

• Quadrant I (strong mutual relevance): These SDG–DT pairs demonstrate fairly high shares from both perspectives. The DT is widely adopted within the SDG domain and represents a key application area for the DT. This reflects strategically aligned and mature research intersections, offering high potential for integrated innovation and policy development.
• Quadrant II (SDG-side adoption opportunity): These pairs are prominent within the DT research landscape but remain underutilized within corresponding SDG research. This asymmetry suggests an opportunity to promote technology adoption on the side of the SDGs. The SDG community may benefit from greater awareness and integration of these digital tools.
• Quadrant III (peripheral or emerging linkages): These pairs show low engagement on both sides, indicating limited current research activity. While some may reflect weak synergies, others may represent nascent or underexplored frontiers in SDG–DT integration, offering promising avenues for interdisciplinary exploration and innovation.
• Quadrant IV (DT-side expansion opportunity): The DT is heavily referenced in a particular SDG, but that SDG accounts for only a small fraction of the DT’s broader research applications. Broadening the impact horizons for SDGs is recommended for the DT research community.

These findings emphasize that an SDG–DT pair exhibits a distinct pattern of alignment. Recognizing these patterns can help researchers, practitioners, and policymakers establish goal-specific digital strategies. By identifying relatively well-established or underexplored associations, they can adopt more targeted and strategic approaches to leverage DTs to support sustainable growth.

5.3. Key Findings and Implications of RQ 3: Emerging SDG–DT Research

The third research question aims to identify emerging research areas at the intersection of the SDGs and DTs. These areas are characterized by a high proportion of recent publications (within the past five years), indicating a deepening focus on leveraging advanced technologies for sustainable growth. The analysis highlights specific technologies that are experiencing rapid growth in their application to the SDGs, signaling their increasing importance in addressing sustainability challenges.

• Emergence of DTw and BC across broad SDGs: The analysis revealed that DTw and BC have emerged as the fastest-growing technologies in SDG research. DTw leads, with 98.68% of related publications occurring in the past five years, followed by BC with 94.44%. Their rapid growth underscores their emerging application for various SDGs, with DTw focusing on simulation and predictive modeling, and BC enhancing secure data sharing and transparency.
• Emerging linkages in a specific goal: Several SDG-specific emerging linkages are also identified, showcasing the dynamic integration of the SDGs and DTs. 3D printing is prominent in Goals 6 and 13, with over 88% of recent publications focusing on these areas. 5G technology has emerged in Goal 4, with more than 87% of its publications appearing in the past five years. The IoT is also emerging in various SDG studies, especially in Goals 2 and 15.

5.4. Methodological and Practical Implications

This study contributes to the growing discourse on the intersection of DTs and SDGs by providing a comprehensive, data-driven analysis based on SDG mapping and bibliometric methods. Although the significance of DTs in sustainable development is increasingly acknowledged, past research often lacks a systematic and macroscopic perspective on how DTs are integrated across the full spectrum of SDGs. By employing structured SDG mapping techniques, this study offers a scalable and replicable methodology for uncovering well-established, underexplored, and emerging linkages between SDGs and DTs.

• Methodological implications and limitations: This study introduces a novel methodological framework that combines automated SDG mapping with pairwise SDG–DT analysis. This approach enables the systematic identification of trends, concentrations, and gaps in SDG–DT research. Such a structural mapping-based bibliometric analysis provides valuable insights into how digital transformation evolves across sustainability domains. Furthermore, this method is easily transferable to other interdisciplinary contexts linking SDGs with technologies, industry sectors, or academic disciplines, such as SDG–biotechnology, SDG–liberal arts, and SDG–education.
  However, the limitations of this approach must be acknowledged. The analysis relies on keyword-based bibliometric data, which are inherently subject to false positives and false negatives, despite being informed by carefully curated queries. The selection of DT categories and associated keywords reflects subjective decisions that may not fully or precisely capture the nuances of digital integration. In addition, SDG-mapping tools primarily identify quantitative associations and do not assess the depth, context, or quality of SDG–DT relevance within articles.
  The strength of this method lies in its ability to reveal macrolevel patterns. With future improvements and complementary research (such as the incorporation of NLP, topic modeling, and expert validation), SDG mapping–based bibliometric methods can serve as powerful tools for diagnosing research priorities and guiding cross-sector collaboration.
• Practical implications for stakeholders: The findings have meaningful implications for various stakeholders, including researchers, policymakers, businesses, and educators.
  Researchers can leverage these results to identify underexplored opportunities. In particular, the SDG–DT pairs situated in Quadrants II and III of the positioning matrix represent areas where novel academic contributions are both needed and feasible.
  Policymakers can use these findings to facilitate more balanced digital integration across the SDGs. In cases where particular SDGs show weak DT engagement (Quadrant II), efforts may be directed toward encouraging greater adoption of the corresponding DTs. Conversely, when DTs have not yet deeply engaged with specific SDGs (Quadrant IV), strategies may be developed to promote DT-side involvement. For pairs in Quadrant III, that remain underexplored on both sides, policymakers can initiate discourse and funding to explore their potential as emerging frontiers.
  Businesses and technology developers can identify digital solutions that align with sustainability goals. Well-established SDG–DT linkages can inform strategic roadmaps for deploying technologies to address clearly defined sustainability challenges. At the same time, SDG–DT pairs with emerging momentum highlight areas with high potential for innovation and market development.
  Educators and academic institutions can align their curricula with the technologies most relevant to the SDG agenda. Emphasizing DTs with strong linkages to SDGs—such as AI, RB, IoT, or XR—within sustainability-focused programs will better prepare students for leadership roles in addressing complex global challenges. In addition, strengthening education for sustainable development (ESD) within DT-related disciplines can help equip the next generation of professionals with both technical expertise and sustainability awareness.

6. Conclusions

The accelerated integration of DTs into sustainable development efforts underscores the need for a comprehensive understanding of their roles across the 17 UN SDGs. Despite growing recognition, existing research has rarely provided macroscopic or data-driven accounts of how DTs collectively contribute to SDG-related research. This study addresses such a gap through a large-scale bibliometric analysis using Elsevier’s SDG research mapping technique to investigate the associations between 11 representative DTs and all of the SDGs.

These findings demonstrate how DTs form close thematic links. The relationship between SDGs and DTs reveals patterns of broad applicability and specialized focus. AI and RB have emerged as the most versatile and widely applicable technologies across a range of SDGs, whereas others (such as XR, IoT, CC, DA, DR, and 3DP) display more goal-specific associations, reflecting their targeted applications in sustainability areas. Notably, DTw and BC have experienced rapid growth in research over the past five years, highlighting their increasing potential to address sustainability challenges and foster innovation. By identifying well-established and emerging SDG–DT linkages, this study underscores the different ways in which DTs are being integrated to address sustainability challenges.

Our findings have strategic value for multiple stakeholders, including researchers, policymakers, industry leaders, and educators. The mapping of the SDG–DT connections provides a clearer picture of where digital solutions are already significant and where additional engagement may be most beneficial. Importantly, we identified underexplored SDG–DT pairs, suggesting potential frontier areas that merit further academic inquiry and investment.

Several areas for future research have emerged from this study. First, methodological refinement is necessary to enhance the accuracy and scope of SDG mapping. Expanding the taxonomy of DTs, refining keyword sets, and improving SDG mapping algorithms will help to mitigate the limitations of keyword-based analyses and reduce the risk of misclassification or omission.

Second, although we focused on publication volume and trend patterns, we did not account for the qualitative content of SDG–DT relationships. We do not distinguish between the positive and negative impacts of DTs, nor do we consider the content and context of linkages. Future studies should move beyond co-occurrence analysis to examine the impact of DTs in the context of sustainable development. Combining bibliometric approaches with content analysis (e.g., topic modeling) and network analysis could reveal hidden themes, co-evolving technologies, and synergistic relationships across the SDGs, thereby offering a more nuanced understanding of how digital solutions interact with complex sustainability issues.

Finally, the current analysis was limited to academic publications in English. Future studies should consider broader linguistic ranges to ensure global inclusivity. Additionally, we drew solely upon academic bibliometric data and did not capture practical dimensions such as real-world applicability, feasibility, or the measurable impact of DTs across sectors. Incorporating practitioner perspectives and empirical evidence from implementation contexts (e.g., policy documents, implementation case studies, and stakeholder interviews) would strengthen the practical relevance of future research.