OCDE - Mind the SDG data gaps

MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

September 2025 Key messages Since the adoption of the 2030 Agenda, significant progress has been made in building the statistical capacity needed to monitor the Sustainable Development Goals (SDGs). At the global level, the work of the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) has supported the development and refinement of the Global Indicator Framework. In 2017, fewer than half of the SDG indicators were classified as “Tier I” (with established methodologies and regular data collection), whereas by 2024, this share has increased substantially. The number of SDG targets with available OECD data has grown from around 100 in 2017 to 146 today. This brief takes stock of the remaining gaps that bear possible implications for implementation of SDGs in OECD countries. This brief highlights the need to further address the following issues: • Timeliness of data: Outdated SDG indicators delay policy responses, particularly in critical areas like climate action and social well -being. Over 40% of SDG indicators in OECD countries rely on outdated data, hampering real-time responses in key areas such as climate action (SDG 13), social equity (SDGs 5 and 10), and biodiversity (SDG 15). • Definition of targets: Fragmented target definitions, especially under SDG 5, 10, and 11, weaken countries’ ability to track progress consistently and develop responsive policies. For example, some indicators under Gender Equality (SDG 5) do not adequately reflect the intersecting inequalities that women face – such as how gender interacts with income, education, ethnicity, or location – resulting in gaps in capturing both economic participation and aspects of social well-being. Similarly, urban-rural disparities in SDG reporting mask sub -national differences in progress towards SDGs. • Data granularity: Demographic disaggregation by sex, age, income, and geographic location remains insufficient, limiting the ability to track disparities effectively. Gender -disaggregated data is available for employment and education but is largely absent for environmental and

progress towards SDGs. • Data granularity: Demographic disaggregation by sex, age, income, and geographic location remains insufficient, limiting the ability to track disparities effectively. Gender -disaggregated data is available for employment and education but is largely absent for environmental and digital inclusion indicators. Limited intersectional data (e.g. gender and disability) restricts policies aimed at vulnerable populations. • Sectoral gaps: Despite global advances, measurement gaps persist. For example, over 30% of targets in the Planet dimension lack sufficient data for either distance-to-target or trend analysis. Environmental sustainability indicators have significant data gaps, making it difficult to track biodiversity loss and pollution levels. While health-related indicators under SDG 3 are relatively well-covered, for example, data on mental health are not systematically incorporated into SDG monitoring framework. • Data innovations and new initiatives are emerging (e.g. big geo-spatial and administrative data, AI and machine-learning approaches, real-time analytics, and other non-traditional sources of data) that have potential to support data advancements, however, their validity is being assessed to comply with statistical standards of reliability, accuracy, comparability and overall statistical soundness and relevance. Mind the SDG data gaps – Insights from the OECD SDGs Hub2  MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights SDG measurement at a crossroads: Progress has been made but gaps remain The Sustainable Development Goals (SDGs) provide a universal framework for driving global progress on sustainable development. Since the adoption of the 2030 Agenda in 2015, the international statistical community has worked to build the infrastructure needed for effective SDG monitoring. Through the efforts of the Inter -Agency and Expert Group on SDG Indicators (IAEG -SDGs), the leadership of National Statistical Offices (NSOs), and the contributions of international organisations, statistical capacity has improved, coordination has increased, and data availability has expanded. The OECD has supported this progress by tracking member countries’ performance through its Measuring

Statistical Offices (NSOs), and the contributions of international organisations, statistical capacity has improved, coordination has increased, and data availability has expanded. The OECD has supported this progress by tracking member countries’ performance through its Measuring Distance to SDG Targets reports (since 2016) and by launching the OECD Hub on Sustainable Development Goals (Box 1) to support countries assess performance, compare trends, and identify data gaps. The gaps undermine SDGs policy relevance and limit countries’ implementation of Goals accountability. The UN Quality Assurance Frameworks Manual (UNSD, 2019[1]) highlights “completeness” and “relevance” as critical dimensions of data quality. Nearly a decade into the 2030 Agenda, significant progress has been made in measurement capacity. In 2017, OECD countries could assess just 57% of all SDG targets on average with wide variation across Goals and countries, and below 40% coverage in many cas es. Today, data coverage reaches 85% or more of SDG targets in some areas. However, this progress remains uneven. Despite a structured global framework, critical blind spots persist. The UN National Quality Assurance Frameworks Manua l (UNSD, 2019[1]) and the OECD Quality Framework and Guidance for Statistical Data (OECD, 2011[2]) provide steer for assessing and improving the integrity, coherence, and usability of sustainable development data. Box 1. The OECD Hub on Sustainable Development Goals Aligned with the Global Indicator Framework for the SDGs, the Hub supports the Road Map on Statistics for SDGs by enhancing the availability of high -quality, timely, and comparable data disaggregated by income, gender, age, location, and other relevant cha racteristics. It offers a consistent inventory of SDG data and indicators across the OECD through the SDG Data Explorer accessible in the Hub. The OECD’s methodology for measuring the distance to SDG targets is known for its statistical rigor and the integration of multiple data sources, aligned with the UN’s Global Indicator Framework and complemented by OECD datasets for greater coverage and granularity. This approach assesses both current performance and t rends to estimate future progress. By examining specific targets rather than

the integration of multiple data sources, aligned with the UN’s Global Indicator Framework and complemented by OECD datasets for greater coverage and granularity. This approach assesses both current performance and t rends to estimate future progress. By examining specific targets rather than broader Goals, the methodology supports precise, evidence -based policymaking and effective prioritysetting. In the OECD Hub on Sustainable Development Goals, users can see how far OECD countries are from achieving SDG targets across multiple Goals (also shown in Figure 1). Each Goal is represented with a distribution of progress levels, highlighting disparities between countries. The visuali sation highlights both leading and lagging countries. Color-coded sections indicate proximity to SDG achievement, helping identify areas needing urgent action. In particular, t he data underlines systemic challenges in achieving Sustainable Development Goals. Several OECD countries have made significant progress in education (SDG 4) and health (SDG 3), nearing target achievement. Climate action (SDG 13) and responsible consumption (SDG 12) remain among the most challenging Goals, with many countries far from their targets. Nordic countries generally perform better across well -being-related SDGs, while some Eastern European nations lag behind. The distribution of progress shows stark contrasts between high-income OECD nations and those with weaker data infrastruc tures. Certain social indicators, such as gender equality (SDG 5), also display uneven progress. 3

MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights Figure 1. Distribution of distance to target and progress, by Goal

Note: Numbers from 1 to 17 stand for the Goals: 1 for No poverty, 2 for Zero hunger, 3 for Good health and well-being, 4 for Quality education, 5 for Gender equality, 6 for Clean water and sanitation, 7 for Affordable and clean energy, 8 for Decent work and economic growth, 9 for Industry, innovation and infrastructure, 10 for Reduced inequality, 11 for Sustainable cities and communities, 12 for Responsible consumption and

5 for Gender equality, 6 for Clean water and sanitation, 7 for Affordable and clean energy, 8 for Decent work and economic growth, 9 for Industry, innovation and infrastructure, 10 for Reduced inequality, 11 for Sustainable cities and communities, 12 for Responsible consumption and production, 13 for Climate action, 14 for Life below water, 15 for Life on land, 16 for Peace, justice and strong institutions and 17 for Partnerships for the Goals. These Goals are grouped under five broad themes (the “5Ps”): People, Planet, Prosperity, Peace and Partnership. The upper figure shows how OECD countries perform, on average, at a given point in time, in terms of their distance from the target lev el they have committed to meet by 2030. The lower figure shows how OECD countries perform, on average, based on recent developments for the different indicators; it shows the likelihood of meeting the different targets by 2030 based on recent trends. The OECD average is measured as the simple average across OECD countries with available data. Averages for each target are based on the simple average of the distances across each of the indicators pertaining to a given target. Percentages are computed based on the targets where data is available and assessable.

Source: All data are extracted and adapted from the UNSDG database (accessed May 17, 2024) and OECD databases.

People Planet Prosperity Peace Part. 1 2 3 4 5 6 12 13 14 15 7 8 9 10 11 16 17 0 25 50 75 100 Small distance Medium distance Large distance (%) Panel 1: Distance to target by Goal (OECD average, 2024 or latest) People Planet Prosperity Peace Part. 1 2 3 4 5 6 12 13 14 15 7 8 9 10 11 16 17 0 25 50 75 100 Target is achieved or on track to being achieved Progress has been made, but is insufficient to meet the target No progress or moving away from the SDG target (%)

0 25 50 75 100 Target is achieved or on track to being achieved Progress has been made, but is insufficient to meet the target No progress or moving away from the SDG target (%) Panel 2: Progress to target by Goal (OECD average, 2024 or latest)4  MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights What the numbers show: Undressing key challenges in measuring the SDGs

1. Completeness and relevance of data across targets The SDG data availability has expanded from covering about 100 targets in 2017 (OECD, 2018[3]) to 146 of 169 targets today across OECD countries. Yet, gaps remain. Over 20% of targets under key Goals such as gender equality (SDG 5), climate action (SDG 13), sustainable cities (SDG 11), and peace and justice

(SDG 16) still lack sufficient data. More than 30% of SDG indicators remain unmeasurable in several countries ( Figure 2). The Planet dimension stands out as particularly underdeveloped, with major gaps in climate action (SDG 13), life below water (SDG 14), and partnerships (SDG 17), which are the areas critical for long-term sustainability. The People dimension, for example, presents large cross -country variation: countries like Germany and Canada report broadly comprehensive coverage, while others (like Türkiye and Mexico) lag b ehind. Even in better-covered areas like health (SDG 3), the absence of mental health indicators limits insight into one of today’s most pressing challenges. Figure 2. Measurement gap in OECD countries

Note: Numbers from 1 to 17 represent the SDG targets, which are grouped under five broad themes. Distance to Goal can be measured if the data meet certain conditions, such as (1) normative direction, endpoint, and type of Goal can be defined, (2) reference year can be defined,

Note: Numbers from 1 to 17 represent the SDG targets, which are grouped under five broad themes. Distance to Goal can be measured if the data meet certain conditions, such as (1) normative direction, endpoint, and type of Goal can be defined, (2) reference year can be defined, (3) more than 15 countries are included, and so on. Progress over time can be gauged if the data series has 3 or more data points and can be defined as a dynamic indicator.

Source: All data are extracted and adapted from the UNSDG database (accessed May 17, 2024) and OECD databases.

Distance to Target can be measured Progress over time can be gauged 0 25 50 75 100 0 25 50 75 100 5 4 3 2 1 15 14 13 12 6 11 10 9 8 7 16 17 Average Max Min (%) 5

MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights

2. Cross-country comparability and coherence Uneven national reporting practices (when it comes to scope and quality of reporting) may compromise comparability and overall effectiveness. Countries like the UK, France, and Finland report on over 85% of SDG targets, whereas others (e.g. Israel, Chile, and Colombia) fall below 75%. Newly acceded OECD member countries often do not have data series related to Official Development Assistance. Additionally, some countries, such as Israel and Luxembourg, do not have full data coverage on specific issues (e.g. number of people directly affected by disasters). These disparities reduce international data comparability, as highlighted in the OECD’s Measuring the Distance to the SDG Targets report (OECD, 2022[4]), which also stresses the need for comparable and methodologically aligned data across countries.

3. Timeliness and currency Most SDG indicators show a data lag of several years, particularly in environmental and social dimensions.

as highlighted in the OECD’s Measuring the Distance to the SDG Targets report (OECD, 2022[4]), which also stresses the need for comparable and methodologically aligned data across countries.

3. Timeliness and currency Most SDG indicators show a data lag of several years, particularly in environmental and social dimensions.

This limits the actuality of insights derived and hinders timely intervention, evaluation, and responsive policy-making – a problem explicitly noted in the UN and OECD data quality frameworks (UNSD, 2019[1]; OECD, 2011[2]). In most OECD countries, over 40% of SDG data points are more than three years old, especially for environmental and agricultural Goals (Figure 3). This includes critical indicators like greenhouse gas emissions (SDG 13.2) and agricultural productivity (SDG 2.3), where tracking lags hinder progress monitoring. Economic indicators such as employment (SDG 8.5) and income inequality (SDG 10.1) are typically more current, possibly reflecting longstanding investment in economic statistics. But the result is an unintended bias in visibility, favouring what appears easiest to measure over what may also matter for understanding sustainable development. Social and environmental indicators, including agricultural productivity and incomes of small -scale food producers (SDG 2.3), greenhouse gas emissions (SDG 13.2), and ener gy transition metrics under Affordable and Clean Energy (SDG 7) exhibit larger reporting lags, making it difficult to assess progress in critical areas, such as, reducing carbon footprints. Figure 3. Timeliness gap: The share of latest available year in SDG data series

Note: Numbers from 1 to 17 represent the SDG targets, which are grouped under five broad themes. The figure shows the share of SDG data series according to the OECD data processing procedure (see Methodology Note for details) and is not weighted for target or indicator. The latest available year of the data series is the mode value of the latest available year among the countries. If there is more than one mode value, the most recent one is taken.

series according to the OECD data processing procedure (see Methodology Note for details) and is not weighted for target or indicator. The latest available year of the data series is the mode value of the latest available year among the countries. If there is more than one mode value, the most recent one is taken.

Source: All data are extracted and adapted from the UNSDG database (accessed May 17, 2024) and OECD databases.

People Planet Prosperity Peace Part. 1 2 3 4 5 6 12 13 14 15 7 8 9 10 11 16 17 0 25 50

75 100 2018 or before 2019 to 2021 2022 to 2024 (%)6  MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights

4. Granularity and disaggregated insights Despite the SDGs’ emphasis on “leaving no one behind” , many indicators fail to meet the granularity requirements necessary to show distributions by gender, age, income, or geography. Without highresolution data, critical inequities may remain hidden, and the accessibility of data for subnational analysis is constrained. The UN Fundamental Principles of National Official Statistics (UNSD, 2019[1]) and OECD’s Data Quality Framework (OECD, 2011 [2]) stress the necessity of disaggregated data for equity -based decision-making.

The availability of disaggregated data for measuring distance and progress toward the SDGs remains largely unchanged from 2022, with selective improvements (defined as cases where at least one data series for any country within the target has gained additional breakdowns) in specific targets. Some of the key a dvancements include increased disaggregation by sex for Target 16.6 (Accountable institutions), expanded age-based disaggregation for Target 8.10 (Access to financial institutions) and Target 16.6, and enhanced data granularity by degree of urbanisation across multiple targets: such as Target 4.4 (Skills for

expanded age-based disaggregation for Target 8.10 (Access to financial institutions) and Target 16.6, and enhanced data granularity by degree of urbanisation across multiple targets: such as Target 4.4 (Skills for employment), Target 8.10, Target 16.6, and Target 16.7 (Inclusive decision-making). Nevertheless, these incremental gains do not yet provide a comprehensive lens for identifying the differences among diverse population groups. Many SDG indicators still lack the demographic detail needed to detect disparities (Figure 4). Figure 4. Disaggregation: Available breakdown by sex, age and degree of urbanisation

Note: Numbers from 1 to 17 represent the SDG targets, which are grouped under five broad themes. Disaggregated information is considered to be available if any of the OECD countries has at least 1 breakdown by sex, age or degree of urbanisation for any data series under the target.

Source: All data are extracted and adapted from the UNSDG database (accessed May 17, 2024) and OECD databases.

People Planet Prosperity Peace Partnership 1 2 3 4 5 6 12 13 14 15 7 8 9 10 11 16 17 Breakdown is available Breakdown is not available or not suitable 7

MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights Limited demographic disaggregation in SDG data weakens in -depth assessments in specific areas of the Goals and may undermine the effectiveness of policies intended to support vulnerable populations . Disaggregation by sex, age, income, and location is largely limited or inconsistent across countries , in particular: • Gender breakdowns are more common for indicators related to health (SDG 3), quality education (SDG 4), and decent work and economic growth (SDG 8). The lack of intersectional data (e.g. gender combined with other dimensions like environment or disability) further constrains the ability to develop targeted social policies.

(SDG 4), and decent work and economic growth (SDG 8). The lack of intersectional data (e.g. gender combined with other dimensions like environment or disability) further constrains the ability to develop targeted social policies. • Older population groups are especially underrepresented in age -specific indicators, notably in health and well-being domains of SDGs. • The urban-rural divide remains a significant blind spot in SDG reporting, making it difficult to assess regional inequalities and disparities in access to resources.

5. Validity and directionality of target metrics Among the 146 targets, only 118 can on average across OECD countries be used for assessing distance to target, indicating validity issues. Furthermore, 28 of those lack a normative direction, impeding interpretability (i.e. it’s unclear whether trends reflect progress or regression). These include data series such as forest area as a proportion of total land area, for which no target level can be defined across countries. Consequently, these data series had to be excluded from the assessment of progress towards achieving the targets.

6. Data innovations and new initiatives Data innovations and new initiatives are changing the ways how countries and institutions compile, process and use data for different purposes, which have potential to support advances in SDGs assessments by informing or complementing traditional statistics with real-time, citizen -centric, and technology -enabled approaches. Estonia and Korea exemplify national innovations by leveraging administrative interoperability and urban Internet of Things type of data for SDG reporting. The potential of AI with machine learning approaches, and Big Earth Data, such as satellite imagery and machine learning, is being tested to fill environmental and socio-economic data gaps (e.g. Earth Observation Data to Support the SDGs ). The role of surveys and polls in capturing subjective indicators like trust and life satisfaction, offering timely and more disaggregated insights has been increasingly discussed and tested for selected official SDG indicators with complementary “pulse survey” data (e.g. UNCTAD SDG Pulse, (MacFeely, 2019[5])).

Global platforms such as the UN Data Commons, the World Bank SDG Atlas, and FAO’s DATA Explorer

more disaggregated insights has been increasingly discussed and tested for selected official SDG indicators with complementary “pulse survey” data (e.g. UNCTAD SDG Pulse, (MacFeely, 2019[5])). Global platforms such as the UN Data Commons, the World Bank SDG Atlas, and FAO’s DATA Explorer enhance access, harmonization, and usability of diverse data sources (Table 1). These advances, supported by the IAEG-SDGs and organisations like OECD, UNSD, and the World Bank, point to a future where inclusive, accurate, and responsive data ecosystems may contribute to filling in some of the gaps (e.g. in areas lacking timely and granular administrative coverage ). The validity of new approaches is currently subject to assessments to meet international standards of comparability, a ccountability and statistical soundness. In addition, new initiatives are being tested for selective sets of SDG indicators and their replicability and upward scalability across all Goals is subject to validation.8  MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights Table 1. SDG data innovations and new initiatives Area Data innovation or initiative Examples of applications Administrative data Interoperable national data systems for SDGs 4 and 8 E.g. Estonia’s e-government and SDGs (OECD, 2019[6]) Urban data integration IoT-enabled urban data hubs for SDG 11 indicators E.g. Korea’s urban SDGs data integration (OECD, 2023[7]), UNSD Data Commons for the SDGs (UNSDSN, 2014[8]) Big geo-spatial data Remote sensing to monitor SDGs 13-15 on climate, land & oceans E.g. Earth Observation Data to Support the SDGs AI & machine-learning Machine-learning for real-time poverty, education and food insecurity insights E.g. Stockholm School of Economics, UNSTATS Survey and poll data Subjective well-being, trust and inclusion relevant data from polls and household surveys E.g. UNCTAD SDG Pulse, (MacFeely, 2019[5])

food insecurity insights E.g. Stockholm School of Economics, UNSTATS Survey and poll data Subjective well-being, trust and inclusion relevant data from polls and household surveys E.g. UNCTAD SDG Pulse, (MacFeely, 2019[5]) Global data platforms SDG Atlas, UN Data Commons, FAO Data Explorer E.g. FAO SDGs, OECD SDG Data Explorer, UNSDG Data Portal, World Bank SDG Atlas Dissemination tools SDG data hubs, visual dashboards, open-access interfaces, citizen-focused metadata navigation E.g. OECD SDG Data Hub, World Bank SDG Tracker

7. Metadata standards and methodological transparency Efforts to standardise metadata (i.e. including reference years, measurement units, and definitions) have strengthened coherence of definitions and metadata management. Strong alignment of methodologies is essential to enable cross-national benchmarking and long-term tracking (UNSD, 2019[1]; OECD, 2011[2]).

The OECD Hub on Sustainable Development Goals is aimed to improve the data quality and harmonisation, including by integrating OECD data with the UN Global SDG Database. In total, 80 OECD data series were added to the 912 UN series, representing up to 33% of the data used for measuring SDG target distances depending on the Goal (Box 2). Box 2. The role of OECD in complementing the UN data on SDGs For the assessment on the OECD Hub on Sustainable Development Goals, the degree of harmonisation and quality of the data used in the assessment has been enhanced by complementing the data retrieved from the UN Global SDG Database with the data from relevant OECD sources. The selection of OECD data sources followed extensive consultations with the OECD experts and affiliated bodies to ensure their relevance, comparability and analytical soundness. Main reasons for integrating additional OECD data are: • Higher quality and standardi sation: OECD data undergo rigorous validation and standardi sation, facilitating cross-country comparisons. They provide accurate indicators; for example, while the UN database includes GDP per employed person as a productivity measure, the OECD data

• Higher quality and standardi sation: OECD data undergo rigorous validation and standardi sation, facilitating cross-country comparisons. They provide accurate indicators; for example, while the UN database includes GDP per employed person as a productivity measure, the OECD data additionally account for the hours worked, providing a more precise labour input assessment. • Country-specific relevance: OECD data reflect conditions accurately for its member countries ; for example, for the mortality rates, the OECD data are age-standardised to avoid distortions caused by demographic differences among countries. • Wider coverage: OECD data typically include a longer time-series coverage, providing more up-todate statistics.

In total, 80 additional OECD data series complement the 912 data series in the UNSDG database, covering 70 targets across all 17 SDGs. Figure 5 shows the weighted share of UN and additional OECD data used for the measurement of distance to SDG targets by OECD countries . After weighting for each target and indicator, these additional OECD data series represent between 4% and 33% of the total data, depending on the Goal (indicated by dark blue in Figure 5). 9

MIND THE SDG DATA GAPS – INSIGHTS FROM THE OECD SDGS HUB © OECD 2025

OECD WISE Centre Policy Insights Figure 5. Weighted share of data sources

Note: The share of data source is weighted for each target and indicator, reflecting the methodology used to average the results at Goal level.

Source: All data are extracted and adapted from the UNSDG database (accessed May 17, 2024) and OECD databases.

Key recommendations Strengthening the implementation of the Sustainable Development Goals (SDGs) across countries raises several critical issues related to data systems and monitoring frameworks, including: • Further integration of disaggregated data, for example, to improve demographic granularity along intersections of gender, income, age, location and disability, where gaps persist in critical SDG areas such as inclusion, education, environmental sustainability and trust. • Addressing methodological ambiguities, aligning metadata standards and target definitions to

• Further integration of disaggregated data, for example, to improve demographic granularity along intersections of gender, income, age, location and disability, where gaps persist in critical SDG areas such as inclusion, education, environmental sustainability and trust. • Addressing methodological ambiguities, aligning metadata standards and target definitions to improve cross-country benchmarking that undermine comparability and policy alignment across SDGs (e.g. under SDG 5 of Gender Equality, SDG 10 of Reduced Inequalities, and SDG 11 of Sustainable Cities. • Leveraging emerging data technologies with appropriate governance, as real -time data sources

(e.g. AI, IoT, administrative big data) remain underutilised due to privacy, interoperability, and capacity constraints that may require further investments in sec ure, scalable data innovations drawing on successful models (e.g. Estonia’s e-governance data system, Korea’s urban data hub). Further reading MacFeely, S. (2019), You Say You Want a [Data] Revolution: A Proposal to Use Unofficial Statistics for the SDG Global Indicator Framework, Statistical Journal of the IAOS, https://doi.org/10.3233/SJI-180486. [5] OECD (2023), Smart City Data Governance: Challenges and the Way Forward,