A future for digital public goods for monitoring SDG indicators

The paper addresses how the global community can promote consensus and develop mechanisms to harness digital resources to achieve the SDGs. It situates the problem within the broader digital transformation that has reshaped communication, business, education, and access to services. The UN’s Technology Facilitation Mechanism, the High-level Panel on Digital Cooperation, and the Secretary-General’s Roadmap underscore the need for inclusive digital cooperation, including digital public goods. Although DPGs—encompassing datasets, software, models, and standards—are increasingly cited as enablers for SDGs, their definition and operationalization remain under debate, evolving from the economic notion of public and global public goods. The paper frames DPGs as essential to translate multi-source digital data into actionable information for policy and decision making within the SDG indicator framework. Recognizing data access, interoperability, and capacity gaps, especially across regions, the study aims to identify core principles and key actions to guide DPG development for scalable, comparable SDG monitoring.

The study synthesizes existing initiatives and concepts: the Digital Public Goods Alliance (DPGA) defines five categories (open AI models, content, data, software, standards) and nine indicators (SDG relevance, open licensing, clear ownership, platform independence, documentation, data extraction mechanisms, privacy/legal compliance, adherence to standards/best practices, safe/legal content), aligning with UNESCO’s open science principles. Critiques argue current DPG definitions may conflate free/open-source outputs with DPGs and emphasize production over adoption in local contexts. The SDG indicator framework provides global benchmarks but faces challenges in governance-level applicability and cross-jurisdiction comparability. Technological advances—big data, AI, Earth observation (EO), IoT—offer potential to monitor indicators across Earth systems (geosphere, biosphere, cryosphere, hydrosphere, atmosphere) and support policy decisions. UNEP promotes environmental data as DPGs. Persistent barriers include uneven access to qualified, timely datasets and methodologies, legal constraints on data sharing, and heterogeneity in data standards, motivating strategies for standardization, open access, and community-driven solutions.

CBAS designed and executed a consultative survey targeting over 300 experts identified from registrants of CBAS-organized conferences over the past five years. The survey received 51 responses from 13 countries/regions (36 from China, including 1 from Hong Kong; others from Israel, Ethiopia, Russia, Switzerland, Mexico, Morocco, the UK, Italy, Sudan, Hungary, Finland [2], and the USA [3]). The questionnaire consisted of seven open-ended questions to elicit views on: (a) viability of DPGs for SDGs; (b) core principles; (c) key actions; (d) existing resources; and (e) expected challenges and prospects for multi-stakeholder cooperation. Scope was bounded by agreed keywords (digital public goods, SDGs, science/technology/innovation, data/information, multi-stakeholder cooperation). Responses (max ~500 words per question) were collected by email, proofread for grammar/clarity, anonymized, and manually analyzed by CBAS. Ideas were collated and grouped, core principles and key actions synthesized, and a preliminary community-driven framework was drafted and refined through author deliberations, culminating in proposed governance and QA/QC processes. No custom code was used. Anonymized responses are available in supplementary files.

• Broad agreement that DPGs can facilitate achievement of SDGs, though not unconditionally: ~13% of respondents judged that DPGs might only conditionally advance SDGs, contingent on clear concepts, recognition of initiatives, cooperation, and political support.
• Key digital resources: ~35% explicitly cited Earth observation/remote sensing/Big Earth Data as core assets; mobile data and in-situ/model-generated data were also noted. Big data and AI models were widely viewed as important enablers.
• Openness and FAIRness: ~63% of responses emphasized open data/databases/models and open-source software; FAIR principles (Findable, Accessible, Interoperable, Reusable) were frequently endorsed.
• Infrastructure and interoperability: Data cubes, high-performance computing, and interoperable multi-source data practices were identified as necessary to produce high-quality DPGs.
• Capacity and funding gaps: ~23% highlighted education/training as essential; funding shortages, data sharing constraints, and limited access to modeling infrastructures slow progress, particularly in developing countries. Legislative, geopolitical, socio-economic, and cultural barriers, and duplication from isolated efforts, were noted.
• Core principles (synthesized from responses):
  1. Universality of science
  2. Scalability
  3. Inclusive and innovation-driven development process
  4. Availability and accessibility to all without restrictions
  5. Quality, acceptability, impartiality, and reproducibility
  6. Enhanced training and upskilling across sectors/nations
• Associated key actions include promoting open science/data/knowledge; multi-stakeholder engagement with bottom-up mechanisms; initiating community-supported open-source projects; setting SMART objectives; ensuring participation of developing countries and local communities; avoiding duplication via engaging related projects; interdisciplinary collaboration and resource pooling; building open distribution platforms with user-friendly interfaces and FAIR compliance; establishing QA/QC, standardization, and accreditation under expert oversight and UN custodian agency; case studies/pilots for usability; user support and documentation; and comprehensive education and inspirational outreach.

Findings support a move from fragmented efforts to a cooperative, community-driven open science framework for DPGs that directly addresses the research question of how to operationalize digital resources for SDG monitoring. The proposed governance structure includes community-supported development with designated product leaders aligned to DPG classes (models, content, data, software, standards), preliminary QA/QC by leads, second-level review by a Committee of Experts, and verification by National Statistical Offices (NSOs) for local compatibility, culminating in adoption through political forums. An open catalog and distribution platform can reduce duplication and enhance transparency. Funding mechanisms could leverage initiatives such as the World Bank’s Global Data Facility and UN-hosted funds, alongside platforms like Google Earth Engine, CASEarth Big Earth Data Platform, GEOSS, UN Global Pulse, and Invest in Open Infrastructure. To tackle data/model standardization, the paper suggests standardized public datasets and metrics for benchmarking methods, with NSOs validating performance at relevant scales, and a UN program to collect representative data samples across formats to inform interoperability solutions. Capacity building is emphasized, particularly for developing countries, paired with infrastructure development and user-oriented training for any DPGs advancing to implementation stages. Legislative and political barriers require engagement via platforms such as the STI Forum and policy-level oversight, while pilots and demonstrations by NSOs can build trust and evidence for adoption.

The study introduces six core principles and a set of practical key actions to guide the systematic development of digital public goods for SDG indicator generation and maintenance. It proposes a community-driven, open-source development process with multi-level QA/QC and policy oversight to ensure scalability, inclusivity, and quality, thereby transforming multi-source digital data into actionable, comparable SDG information products. The principles and framework are preliminary and intended to catalyze broad dialogue and consensus-building through global platforms, including the Summit of the Future and the Global Digital Compact. Future work includes refining principles, formalizing standards and processes, cataloging existing problems and solutions, coordinating multinational initiatives on DPGs and open science, and securing political commitment for implementation to fully harness DPGs’ potential in achieving the 2030 Agenda.

• The survey sample is limited (51 respondents) and geographically skewed (36 from China, including Hong Kong), which may affect generalizability.
• Responses were open-ended and analyzed manually, introducing potential subjectivity in synthesis.
• The proposed framework is preliminary and acknowledged by the authors as incomplete; principles are provisional and subject to refinement.
• Data availability constraints for several SDG indicators and legal/privacy restrictions on data sharing may limit immediate applicability of DPGs and hinder cross-country standardization and interoperability.