Sustainable Development Goals

• Sustainable Development Goals are a set of 17 interlinked objectives that promote sustainable development by addressing social, economic, and environmental challenges.
• The framework includes 169 targets that guide interdisciplinary research and drive innovative applications in ICT, AI, and data science for real-world impact.
• Empirical studies using network science and policy analysis reveal complex interdependencies among SDGs, emphasizing tailored strategies for diverse economic settings.

The Sustainable Development Goals (SDGs) are a set of 17 interconnected global objectives, formally adopted by the United Nations General Assembly in 2015 as part of the 2030 Development Agenda (“Transforming our world: the 2030 Agenda for Sustainable Development”). Encompassing 169 associated targets, the SDGs constitute a multifaceted framework for promoting economic growth, social inclusion, and environmental sustainability within planetary boundaries. Although originally defined in a policy context, the SDGs have catalyzed a substantial body of research across fields including network science, information and communications technology (ICT), AI, economics, earth observation, and bibliometrics, all focused on operationalizing, monitoring, and accelerating global progress toward these goals (1802.09345, 1804.09095, 2004.09318, 2111.04724, 2112.11367, 2202.07424, 2209.07285, 2303.11931, 2304.11703, 2307.07983, 2308.02622, 2311.06716, 2407.02711, 2411.09708, 2412.03620, 2501.05314, 2503.17373, 2506.15208).

1. Structure and Scope of the SDG Framework

The SDG framework is formally defined as 17 goals and 169 targets, representing a comprehensive agenda across three main pillars:

• Social (e.g., poverty eradication, health, education, gender equality)
• Economic (e.g., decent work and economic growth, industry and innovation, reduced inequalities)
• Environmental (e.g., climate action, clean water and sanitation, life below water and on land)

While the SDGs do not explicitly mandate the use of technology, the 2030 Agenda implies that technological innovations—particularly in ICT, data science, and AI—can be key enablers for bridging divides, fostering innovation, and accelerating progress across all goals (1802.09345).

2. Interactions and Network Dynamics Among SDGs

A central theme in recent scholarship is the complex and often non-linear interdependence among SDGs. Network science approaches, such as the sustainome model, conceptualize the SDGs and their targets as a connected graph where nodes represent goals or targets and edges encode empirical correlations or conditional dependencies (1804.09095, 2004.09318, 2501.05314, 2503.17373). For example:

• Signed, weighted graphs: Inter-SDG relationships are quantified using empirical data (e.g., World Bank indicators) to create adjacency matrices (A₍ᵢⱼ₎), with signed weights representing positive or negative associations.
• Stability analysis: The Laplacian of the SDG network, $L$, is derived as:

$$L_{ij} = \begin{cases} \sum_k A_{ik} & \text{if } i = j \ -A_{ij} & \text{otherwise} \end{cases}$$

Stability (all eigenvalues ≤ 0) indicates harmonious SDG interactions; instability (positive eigenvalues) reveals antagonistic subgroups (1804.09095).

Key findings reveal that network topology and the centrality of SDGs differ markedly by country income level: low-income countries often display harmonious structures centered around poverty alleviation, while high-income countries exhibit antagonistic clusters, particularly between climate action and other development objectives (1804.09095, 2503.17373).

Nonlinear conditional dependence and eigenvector centrality have further shown that goals such as clean water and sanitation (SDG 6) and quality education (SDG 4) consistently occupy central positions in the global SDG interaction network, whereas traditional economic growth metrics (SDG 8) may be less central than expected (2004.09318).

3. Measurement, Monitoring, and Data Science Approaches

The global ambition of the SDGs has led to a proliferation of methods for mapping, monitoring, and benchmarking progress:

• Standardized Benchmark Suites: SustainBench consists of 15 machine learning tasks covering economic development, agriculture, health, education, water, and environmental sustainability (2111.04724). Data sources span satellite and street-level images, time series, and multi-country surveys.
• Earth Observation and Remote Sensing: Deep learning applied to earth observation data enables automated mapping of agricultural output, urban infrastructure, informal settlements, land cover, and real-time disaster response, enhancing SDG-aligned decision-making (2112.11367).
• Text Classification and Knowledge Extraction: Classifying scientific literature, policy documents, and organizational outputs by SDG relevance is achieved through pipelines such as OSDG (ontology and machine learning-based) (2005.14569), hybrid Boolean/ML approaches (Elsevier, Aurora, Auckland, SIRIS) (2209.07285), and LLM adaptation (2506.15208). These methods underpin global impact rankings and institutional benchmarking.
• Rule-Based Data Discovery: Systematic mapping studies combine manual coding and automated rule extraction to identify data sources and entities relevant to specific SDGs, as demonstrated for SDG 7 (affordable and clean energy) (2307.07983).

These approaches are critical for tracking the SDGs’ multi-level, regionally heterogeneous progress and for targeting resources in a data-driven manner.

4. Enabling Technologies: ICT, AI, and Beyond

ICT and AI are deeply implicated, both as cross-cutting enablers and as objects of SDG-aligned research:

• ICT Contributions: ICTs are used for digital inclusion (e-learning, telemedicine), smart agriculture (sensor networks, optimization), real-time urban management (smart cities, network analytics), and environmental monitoring (IoT, remote sensing) (1802.09345).
• AI for Health, Urban Sustainability, and Climate: AI advances optimize health system resource allocation, enhance disease surveillance, automate diagnosis, and support "One Health" frameworks for pandemic monitoring (SDG 3). In cities (SDG 11), AI models analyze spatial patterns of pollution, infrastructure quality, and plan for climate resilience. For climate action (SDG 13), AI supports energy system optimization, early warning, and predictive analytics, though energy consumption and fairness remain areas for active research (2202.07424, 2407.02711, 2304.11703).
• Soft Robotics and Sustainable Industry: The development of biodegradable, energy-autonomous, and environmentally responsible soft robots supports multiple SDGs by enabling precision agriculture, disaster response, healthcare, and environmental remediation (2303.11931).
• Broadband as Infrastructure: Universal broadband access—via multi-technology strategies that balance coverage, cost, and carbon emissions—is a recognized driver of SDG 9 (infrastructure), SDG 10 (reduced inequalities), and SDG 13 (climate action), with sustainability challenges for next-generation networks (2411.09708).

A recurring theme is the call for interdisciplinary integration, stakeholder collaboration, and the design of ICT/AI solutions that are ethically aligned and sensitive to local context (1802.09345, 2407.02711, 2304.11703).

5. Policy Integration, Prioritization, and Governance

The multi-layered nature of SDG implementation necessitates strategic prioritization and governance reform:

• Income-Level Sensitive Prioritization: Empirical evidence suggests policies focused on poverty alleviation (SDG 1) in low-income countries and reducing inequalities (SDG 10) in high-income countries provide compounded, systemic benefits (network centrality and “multiplier” effects) (1804.09095, 2503.17373).
• Nationally Determined Contributions (NDCs) and Climate Synergies: AI-driven semantic analyses of NDCs reveal that explicit and implicit linkages between climate action (SDG 13) and other SDGs are income-dependent and can lock in global inequalities if maladapted. The paper shows that high-income nations focus on health, education, and equity, while lower-income countries prioritize water-energy-food and infrastructure for climate adaptation (2503.17373).
• Complexity and Capability Assessment: The SDGs-GENEPY framework applies economic complexity analytics to SDG progress across Indian states, identifying nuanced centrality and “difficulty” of goals and enabling data-driven, differentiated policy interventions (2501.05314).
• Finance and Governance: Integrated SDG frameworks inform sustainable investing, automated impact scoring, and central bank digital currency (CBDC) policy, with direct implications for SDG 8 (economic growth) and secondary effects on a broad set of SDGs via economic feedbacks and resource flows (2308.02622, 2311.06716).

The growing use of advanced quantitative models, AI-aided policy analysis, and fine-grained complexity measures supports contextualized and adaptive governance for SDG implementation.

6. Challenges, Gaps, and Emerging Directions

Research identifies persistent challenges and directions for future SDG scholarship and practice:

• Lack of Holistic Social Good Perspectives: Technical research, especially within ICT and AI, remains siloed, often failing to integrate ethical, cultural, and institutional dimensions (1802.09345, 2407.02711).
• Evaluation Metrics and Modeling: Metrics often privilege technical accuracy over long-term social and environmental impact. New frameworks and multi-objective optimization are needed to reflect genuine sustainability (2412.03620).
• Scalability, Equity, and Representation: Broadband connectivity studies highlight underrepresentation of LIC contexts, and AI-based frameworks must avoid replicating global inequities (2411.09708, 2304.11703).
• Explainability, Fairness, and Trust: Recommender systems, impact scoring, and LLM-based classifiers must offer transparent, auditable recommendations and address fairness across demographics and regions (2412.03620, 2308.02622, 2506.15208).

Emerging research directions include integrating sustainability-specific metrics in AI and decision support, coupling recommendations with simulation models for policy forecasting, and advancing context-rich, explainable AI deployed with explicit resource and fairness constraints (2412.03620, 2303.11931, 2407.02711, 2506.15208).

7. Conclusion

The Sustainable Development Goals constitute a globally recognized, interconnected, and technically rich framework for promoting planetary prosperity within ecological limits. The academic literature reveals that realizing the SDGs requires integrated network analysis, context-aware prioritization, and the conscientious deployment of advanced technologies—including AI, ICT, and earth observation—while addressing persistent gaps in data, metrics, social inclusion, and governance. The continued evolution of measurement strategies, computational models, and collaborative frameworks is central to fast-tracking SDG progress, especially as the 2030 timeline approaches. The SDGs ultimately serve as both a barometer and catalyst for holistic, evidence-driven development at global, national, and local scales.