The-Sustainable-Development-Goals-Report-2026 (1)

The Sustainable Development Goals Report 2026Contents Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 SDG monitoring in the AI era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Goal 1 No poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Goal 2 Zero hunger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Goal 3 Good health and well-being . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Goal 4 Quality education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Goal 5 Gender equality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Goal 6 Clean water and sanitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Goal 7 Affordable and clean energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Goal 8 Decent work and economic growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Goal 9 Industry, innovation and infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Goal 10 Reduced inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Goal 11 Sustainable cities and communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Goal 12 Responsible consumption and production . . . . . . . . . . . . . . . . . . . . . . . . . 32 Goal 13 Climate action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Goal 14 Life below water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Goal 15 Life on land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Goal 16 Peace, justice and strong institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Goal 17 Partnerships for the Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 SDG progress by target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Note to the reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46The Sustainable Development Goals Report 2026The Sustainable Development Goals Report 2026 2 Foreword A little over a decade ago, the world embarked on a bold effort to fulfil the 2030 Agenda for Sustainable Development, our shared plan of action for people, planet and prosperity. With less than five years to achieve the Sustainable Development Goals (SDGs), this report offers a clear-eyed measure of our collective progress – and the urgent work ahead. The gains achieved are real and span the Goals. Social protection now reaches far more people. Maternal and child mortality continue to fall, progress against neglected tropical diseases has accelerated and harmful practices like child marriage are declining faster. Access to safe drinking water, sanitation and electricity has expanded at scale. Renewable energy is growing, disaster mortality is down and digital connectivity now reaches most of the world. Behind these results is an important enabler: data. We now measure more and better, with far greater precision than ever before. As we move into the last mile to 2030, this stronger evidence base can help countries to better target their development efforts. These are not small achievements. They are proof that progress is possible when political will, investment, innovation and international cooperation come together. But the report also shows that progress remains uneven and insufficient in the face of serious headwinds – from the collapse in development assistance, to growing debt burdens in developing countries, rising conflicts, slowing global economic growth, climate chaos and other shocks. And war in the Middle East has disrupted maritime traffic, choking off energy, fertilizer and food corridors and driving up inflation. These pressures are hitting vulnerable countries the hardest, widening the gap to achieving the SDGs and threatening to undo decades of hard-won progress. As this report demonstrates, just 36 per cent of the 139 assessable SDG targets are on track or making moderate progress, and 15 per cent have gone into reverse. The report calls for decisive action to correct this trajectory. By heeding the Sevilla Commitment’s pledge to close the annual $4 trillion SDG financing gap through scaled-up financing for developing countries, strengthened domestic resource mobilization, tripling the lending capacity of multilateral development banks and developing new tools for effectively managing sovereign debt. By reforming the international financial architecture to better support – and represent – developing countries. By harnessing frontier technologies, including artificial intelligence, to spur development – especially among those most likely to be left behind as technology races forward. By accelerating the transition to renewable energy to create decent jobs and economic opportunities, while safeguarding our planet’s future. By continuing to work towards gender equality and the empowerment of women and girls – a critical driver of progress across all the Goals. And by putting peace first, by reining in out-of-control military spending and investing in the instruments of peace and development. Guided by the data in this report, our vision of the 2030 Agenda remains within reach. Together, let us make a decisive final push to achieve the Sustainable Development Goals and build a healthy, prosperous future for all. António Guterres Secretary-General of the United Nations3 Introduction Introduction With just four years until the SDGs target date, the gap between ambition and achievement has never been more stark – or more consequential. This report candidly assesses our progress and the ground we have left to cover. The core promise of the 2030 Agenda for Sustainable Development to leave no one behind remains within reach but only if we act decisively and immediately. Progress through an era of crises The past decade proves that purposeful global action transforms lives at scale. Since 2015, hundreds of millions of people have gained access to safe drinking water and sanitation. Electricity now reaches 92 per cent of the world’s population, with a third powered by renewable sources. Internet access has surged from 40 to 74 per cent, connecting billions to new education and economic opportunities. Health outcomes have similarly improved: new HIV infections and AIDSrelated deaths have each fallen by roughly a third, 59 countries have eliminated at least one neglected tropical disease and 134 countries have met the target for reducing child mortality. For the first time in history, social protection now covers over half of the global population, and disaster-related deaths have dropped by 65 per cent compared with the previous decade. Beyond the statistics are families escaping poverty, children attending schools and communities building resilience. These gains affirm the central premise of the 2030 Agenda: that progress is achievable when sound policy meets political will and adequate investment. One lesser-known achievement deserves mention: our ability to track this progress has transformed. A decade ago, we lacked data for half the SDG indicators; today, a global database of 3 million data points covers nearly every indicator, allowing us to see clearly, target precisely and hold ourselves accountable. The work that remains Despite these gains, the broader picture is sobering. Of the 139 targets for which we can compute reliable historical trends, only 36 per cent are on track or making moderate progress. Nearly half (49 per cent) are moving too slowly, and 15 per cent have slipped below their 2015 baselines. The world is far from eradicating its most pressing challenges. One in 10 people still lives in extreme poverty. Food insecurity remains well above 2015 levels, affecting 2.3 billion people. About 2.1 billion people (roughly one in four people) lack access to safely managed drinking water services. More than 150 million children are stunted. Maternal mortality remains nearly three times the global target, and everyday discrimination – still felt by one in five people worldwide – continues to limit human potential. These shortfalls are compounded by intersecting planetary and humanmade crises. In 2025, global temperatures reached 1.43°C above preindustrial levels, and atmospheric carbon dioxide (CO2) reached its highest concentration in 2 million years. Violent conflict surged to its highest levels in decades, reversing years of development in mere months, forcibly displacing 118 million people. Meanwhile, official development assistance collapsed by a record 23 per cent in 2025, eroding the financial foundations that the poorest countries rely upon. As the annual SDG financing gap in developing countries hovers around $4 trillion, global military spending climbs to record highs. Because these crises are linked, they demand integrated solutions. The way forward The path forward is not obscure or beyond reach. The SDGs remain our most effective blueprint for peace, prosperity and sustainability. We must urgently return them to the centre of global decision-making. Closing the financing gap is paramount. This requires reforming the international financial architecture, enacting meaningful debt relief and expanding access to affordable finance. We must rapidly implement the Sevilla Commitment for financing and the Medellín Framework for data systems to ensure that support reaches the most vulnerable communities first. Furthermore, because no single nation can solve climate change, prevent pandemics or dismantle inequality alone, we must defend multilateralism and refuse to let geopolitical tensions derail cooperation. A shared vision for the final four years The 2030 Agenda recognizes that sustainable development is a shared endeavour, not a zero-sum game. Recent milestones, from legally protecting the high seas or the surge in renewable electricity-generating capacity in developing countries, prove that ambitious, coordinated action still delivers. The next four years will test our momentum. The choices we make now regarding financing, global cooperation and collective crisis management will echo for generations. We owe real change to those the world has often overlooked: the first girl in her family to finish school, the household escaping poverty for good and the community built to withstand the next storm. The evidence in this report proves the goals we set in 2015 were never beyond us. We must now summon the resolve to finish what we began. Li Junhua Under-Secretary-General for Economic and Social AffairsThe Sustainable Development Goals Report 2026 4 The past 10 years have demonstrated the critical role official statistics play in addressing the world’s biggest challenges. From guiding responses to disease outbreaks and monitoring climate impacts to measuring poverty reduction and strengthening transparent governance, quality data and statistical systems have been essential infrastructure in a decade of unprecedented change. Monitoring the SDGs has been central to this effort. Through a shared global language for development, it as expanded data availability, strengthened national statistical systems, exposed inequalities and built public trust – enabling governments and societies to make more informed decisions and develop better-targeted policies and resource allocations. As the world confronts interconnected crises – from climate change to conflict – the demand for timely, disaggregated data has never been greater. Yet at precisely the moment when it is most needed, financial support for national statistical systems is shrinking. At the same time, artificial intelligence (AI) is rapidly transforming how information is generated, accessed, analysed and consumed – often by systems built and controlled far from the communities and countries the data are meant to serve. While AI has the potential to support official statistics, it also risks generating biased or inaccurate information that erodes trust and undermines the public interest that official statistics are designed to protect. With four years left to deliver on the 2030 Agenda, and discussions for a post-2030 framework beginning, challenges facing the global statistical community have evolved. While how to measure development continues to be a significant task along a range of domains, governing the systems that shape what is measured, known and trusted has now emerged as a preeminent challenge. In this changing landscape, where AI is increasingly the default source for answers, the value of official statistics extends far beyond merely the number of data points. Instead, its ultimate purpose is to safeguard the public interest through evidence that is people-centred, trusted and nationally owned. SDG monitoring in the AI era: renewing official statistics for people, trust and sovereignty I. A decade of SDG monitoring: stronger evidence, unfulfilled promises Ten years of monitoring has generated an unprecedented evidence base for tracking sustainable development. The picture it reveals, however, is sobering. Of the 139 targets with sufficient trend data, only 15 per cent are on track, and 21 per cent show moderate progress. Nearly half are advancing only marginally or not at all, and 15 per cent have slipped below their 2015 baseline. These results underscore the scale of the challenges ahead, but they also make the case for official statistics. Without the data infrastructure built over the past decade, it would be impossible to know where progress is accelerating, where inequalities persist or which policies are working. Two different guide layers for two- and three-line titles. Guides for edge of right-aligned text and bars are uniform (and account for space for other languages). Swatches are of full, 75%, 50% and 25% color. All bars should be 0p6 wide with 0 spacing within country categories and 0p2 spacing between country categories. If World, LDC, LDS or SIDS categories are spaced apart, 0p6 spacing from main regions. Number labels should be inside color bars (ideally) and 0p3 from edge. If outside, left-aligned and 0p3 away. Number line 0p2 below bottom bar. Stroke .25pt. Tick marks 0p3 long. Text 0p1 below tick mark. Legend categories right-aligned, 1p0 apart, 0p3 between color bubble and text. Legend color bubble 0p6. Legend 0p6 below number line labels. Any note 0p6 below legend. Note uses hanging indent. Overall progress assessment across 139 targets with trend data, 2025 or the latest data On track or target met Moderate progress Marginal progress Stagnation Regression 15% 21% 32% 17% 15% Progress assessment for the 17 Goals based on assessed targets, by Goal (percentage) On track or target met Moderate progress Marginal progress Stagnation Regression 0 10 20 30 40 50 60 70 80 90 100 Goal 17 Goal 16 Goal 15 Goal 14 Goal 13 Goal 12 Goal 11 Goal 10 Goal 9 Goal 8 Goal 7 Goal 6 Goal 5 Goal 4 Goal 3 Goal 2 Goal 15 SDG monitoring in the AI era SDG monitoring marks one of the most significant advances by the global statistical community. Findings from the Task Team on Lessons Learned of the Inter-Agency and Expert Group on SDG Indicators showed how an ambitious measurement framework has evolved into a shared data infrastructure for sustainable development, with a common language, an effective process to translate shared aspirations into statistical measurement, a set of globally agreed methodologies and an expanding evidence base that supports policymaking, accountability and international cooperation. There has been considerable progress in the availability of internationally comparable data, as the number of indicators in the SDG database has expanded from 115 in 2016 to 233 in 2026, with data records rising from around 330,000 to 3.2 million. Significant strides have also been made in methodological development. When the SDG framework was first established, nearly 40 per cent of indicators lacked internationally agreed methodologies. Today, every indicator has an agreed statistical methodology. Perhaps the most enduring legacy of the SDGs is not the indicator framework itself but the strengthening of national statistical systems. Many countries have established SDG coordination mechanisms, developed national reporting platforms, improved data-sharing arrangements and expanded the use of administrative records, geospatial information, citizen data and other innovative sources. SDG monitoring has also driven a global capacity-development agenda, helping countries adopt new methodologies and data standards. As a result, the share of indicators with good country coverage has more than doubled, increasing from roughly one third to almost 70 per cent. At the same time, the experience demonstrates that ambitious global monitoring requires more than political commitment. Monitoring 17 Goals, 169 targets and more than 230 indicators has placed unprecedented demands on national and international statistical systems. Many indicators required years of methodological development. Others remain difficult to measure consistently, either because the concepts are complex or the data sources remain limited. The experience highlights the importance of balancing political ambition with statistical feasibility and ensuring that implementation costs and national capacities are considered from the outset. Persistent data gaps provide another important lesson. Although data availability has improved substantially, particularly across health, water, energy and partnerships – with some areas having trend data exceeding 80 per cent – data gaps remain a persistent challenge. Progress on measuring gender equality, sustainable cities, climate action, and peace and justice has been much slower, as fewer than one third of the SDG indicators have sufficient trend data. Many countries also struggle to disaggregate statistics by income, education, disability, migration status or other characteristics, leaving gaps in understanding who is being left behind. The central lesson is clear: sustainable development monitoring depends not only on ambitious global indicator frameworks but also on sustained investment in methodologies, institutions, partnerships and national statistical systems. The data infrastructure built through the SDGs is a global public good whose value extends well beyond 2030. Preserving, strengthening and building upon this foundation will be essential for the next generation of sustainable development monitoring and for navigating an increasingly complex data and AI landscape. II. Historic expansion of the data infrastructure: achievements and lessons Proportion of countries or areas with trend data (at least two data points since 2015), comparing 2019 and 2026 databases, by Goal (percentage) 2019 2026 0 20 40 60 80 100 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 III. Financing shocks and AI disruption: a changing landscape for official statistics Financing: a foundation under strain Sustainable SDG monitoring depends on long-term investment, but financing has not kept pace, leaving many countries to meet expanding responsibilities with limited, fragmented or declining resources. A historic reduction in official development assistance (ODA) has made this challenge more urgent, exposing the vulnerability of data systems that rely heavily on short-term external support or project-based funding. In 2025, ODA declined by 23.1 per cent from 2024, the largest annual drop ever recorded and one that pushed ODA back to levels seen at the start of the 2030 Agenda. Further cuts are expected in 2026. Recent surveys of national statistical offices in low- and middle-income countries, carried out by the Inter-Secretariat Working Group on Household Surveys, show that funding reductions are already affecting core statistical programmes of these countries, including SDG monitoring, household surveys, administrative data systems and dissemination platforms. This financing shock arrives at a moment when the world faces increasingly complex and interconnected challenges, from climate change and conflict to inequality and demographic shifts. Meeting these challenges demands reliable, timely and inclusive statistics. Proportion of national statistical offices reporting funding reductions since January 2025, by statistical programme (percentage) 0 10 20 30 40 50 60 70 Citizen data Agricultural census Civil registration and vital statistics Population census Economic census Administrative data Surveys Total statistical budget Experienced any budget reduction Experienced a >25% budget reductionThe Sustainable Development Goals Report 2026 6 AI: opportunity, responsibility and risk AI has the potential to support almost every stage of the statistical process. It can improve questionnaire design, assist interviewers, classify images and text, detect inconsistencies, automate quality checks and make complex statistics more accessible through natural-language interfaces. A recent meta-analysis of around 1,800 articles confirmed AI is already being applied across the full survey life cycle. But AI also introduces new risks. Large language models answer datarelated queries, often relying on outdated or unverified information rather than official statistics. When reliable official data are unavailable, AI systems fill gaps through extrapolation, interpolation or synthetic generation, producing figures that may appear authoritative while masking significant uncertainty. Without trusted official statistics, misinformation can scale faster than ever before. AI also raises governance questions: whose data are used, whose realities are represented and how trusted are those who control the systems themselves. The opportunity is not simply to adopt AI but to shape it in ways that reinforce the values that underpin official statistics: professional independence, transparency, methodological rigour, accountability and protection of confidentiality. But seizing that opportunity requires deliberate actions. Going forward, AI and future innovations for official statistics must be intentionally designed and governed and held to the same standards of public trust that statistical systems have been established over decades. Proportion of AI use in survey research tasks, 2019–2025 (percentage) 0 20 40 60 80 100 Post-data collection Data collection Pre-data collection Source: Recreated based on chart in Buskirk and others (2026), More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research. IV. Reinvigorating official statistics People: remaining central to the mission Official statistics must serve people, not technologies. A people-centred system begins with inclusion – ensuring that data production captures population diversity, allows meaningful disaggregation and gives communities opportunities to shape what is measured, how data are collected and how findings are used. Yet many population groups are still underrepresented or absent from official data systems because they are difficult to reach, insufficiently covered by traditional sources or not prioritized in the measurement. For example, only 29 per cent of responding countries reported statistical programmes for internally displaced persons, compared with more than 70 per cent for persons with disabilities and youth. Even where participation mechanisms exist, they do not always translate into action. In 2024, high levels of community participation in rural drinking-water policy were reported in only 30 per cent of cases, despite the availability of broader engagement processes. Trust: sustaining public confidence Data have little value if people do not trust them. For decades, official statistics has earned that trust by maintaining professional independence, transparent methodologies, high-quality and accessible data, and protecting confidentiality. When official statistics are outdated, incomplete or difficult to access, misinformation can fill the void. Though statistical performance indicators (SPI) have demonstrated improvement, with scores rising from 59 to 70 between 2016 and 2023, they also reveal persistent gaps. Low- and lowermiddle-income countries lag far behind high-income countries. While demand for trusted, timely and disaggregated data is growing, the countries with the greatest needs may face the steepest constraints as funding support declines. AI introduces a new layer of challenges to this already uneven environment. First, AI systems learn from available data and where data are missing, they may fill gaps with numbers that rest on weak empirical foundations. In populations that have historically been undercounted or completely left out, AI risks deepening those absences and masking the underlying invisibility rather than resolving it. AI has also changed the calculus on confidentiality – a core commitment of official statistics. A 2023 review found that 93 per cent of the 167 national statistical laws included a confidentiality clause. That near-universal legal commitment was built in an era when anonymization techniques were robust against available re-identification methods. Such is no longer the case. AI has made it possible to re-identify individuals, even from aggregate statistics, by combining open official data sets with the vast commercial data ecosystem. Without first strengthening the technical and governance frameworks that protect confidentiality, feeding official data into AI models risks eroding the trust that was built over past decades. Technology already has the potential to reach those that traditional data sources miss through satellite imagery, mobile phone data and data integration. AI can help realize such potential. AI tools can analyse community feedback, complaints and social discourse at scale. Assisted by AI, dissemination tools can make statistics more accessible to nonspecialists. And enhanced quality assurance can redirect capacity towards community engagement and other necessary areas of work. Making people the guiding principle builds a foundation for AI governance that technical frameworks alone cannot provide. The question of whom statistics are meant to serve must remain at the heart of every decision about AI adoption. Algorithmic shortcuts cannot replace engagement, and efficiency gains must not come at the cost of participation. National statistical offices (NSOs) that are committed to retaining these principles will be better placed to avoid the governance failures AI adoption can bring – not because they have a new rule book but because they have stayed true to what made official statistics trustworthy in the first place.7 SDG monitoring in the AI era Statistical performance indicators, 2016–2023, by income status 40 45 50 55 60 65 70 75 80 85 2016 2017 2018 2019 2020 2021 2022 2023 Low income Lower-middle income Upper-middle income High income World Sovereignty: enabling countries to shape their data future As AI use surges, so too does the risk that countries will lose sovereignty over their own data ecosystems. Sovereignty has three reinforcing dimensions: control over statistical priorities and data; sustainable national financing; and strong, independent institutions able to coordinate the data ecosystem and govern AI use. In the AI era, all three dimensions become more consequential. Without control over data, financing or institutions, countries will struggle to govern AI adoption in the public interest. In many countries, sovereignty over data is already limited. An analysis carried out in 2023 found that only around one third of low- and lowermiddle-income countries manage their own microdata repositories. Financial independence is similarly constrained. In 2025, 135 countries had national statistical plans under implementation, but only 59 per cent were fully funded. In sub-Saharan Africa, just 15 per cent of countries reported full funding. Countries most dependent on external support for statistical production are also the least positioned to finance the governance infrastructure that responsible AI adoption requires. These figures point to a structural vulnerability: countries are expected to produce trusted, timely and increasingly AI-ready statistics, but many do not yet have full control over the data infrastructure or the sustained financing needed to do so. Institutional sovereignty presents another challenge. Most countries legally mandate NSOs to coordinate national statistical systems, but this does not translate into the authority to govern the wider data ecosystem. This gap is particularly consequential in the AI era, when NSOs need to set standards, assess risks and shape how official statistics are accessed by others. In the 2021 survey on implementation of the Cape Town Global Action Plan for Sustainable Development Data, only 17 per cent of responding countries reported being satisfied with their capacity to coordinate across the data ecosystem. In low- and lower-middle-income countries, the figure was only 8 per cent, suggesting institutional sovereignty remains weakest precisely where stronger coordination, governance and public-interest safeguards are most needed. Sovereignty is the practical foundation on which countries can engage with AI on their own terms. Yet without control over their own data, sustained domestic financing and institutional authority, NSOs cannot audit AI systems, enforce safeguards or hold technology partners accountable. The result is a widening gap: countries with strong sovereignty will shape AI to serve the public interest, while those without it risk becoming consumers of systems built for someone else’s priorities. Perception of the capacity of NSO to coordinate with the entire data ecosystem, by income group, 2021 (percentage) 0 10 20 30 40 50 60 70 80 90 100 Total High-income countries Upper-middle-income countries Low- and lower-middle income countries It was already satisfactory and remained so It has improved, and is now satisficatory It has improved, but could be better It was and remains unsatisfactory V. The way forward: anchoring official statistics in people, trust and sovereignty The first decade of SDG monitoring built one of the most significant achievements of the global statistical system: a shared global public good that belongs to every nation. The next phase must ensure this foundation remains both sustainable and governed in the public interest. Doing so will require a renewed vision for official statistics in the AI era, one that is peoplecentred, trusted and sovereign. Realizing that vision requires action on three fronts simultaneously. It starts with investing in data systems that reflect population diversity, are meaningfully disaggregated to track inequalities and keep communities – not algorithms – at the centre of decisions. Next, it is imperative to hold AI adoption to the transparency and accountability standards that official statistics have long upheld, ensuring that innovation strengthens, not erodes, the credibility NSOs have spent decades building. Finally, NSOs must have an institutional say in governing the wider data ecosystem and the sustained domestic funding to act on it, so that countries can engage with AI as architects of their own data future. None of this departs from the fundamental principles of official statistics. Impartiality, transparency, professional independence, methodological accountability and public access are not relics of a pre-digital era. If anything, they are more relevant in the AI era, not less. What is needed is not a new normative framework but the capacity, financing and political will to apply the existing one. Renewing official statistics beyond 2030 means applying these values not only to how data are produced but to how AI systems and other innovations are adopted, how data ecosystems are coordinated and how the benefits of innovation are shared. AI also adds something genuinely new here: the public no longer reliably knows who is behind a statistic, and the institution responsible for an error may not be the one that actually produced it. NSOs must therefore assert their role as the verifiable source of record – documenting methods, quantifying uncertainty and labelling observed data as distinct from modelled output wherever statistics are used, not only where they are produced. Strengthening confidentiality safeguards should be a condition of AI-readiness, not an afterthought. The standard in both cases is the same: AI adoption should be held to the transparency and accountability the statistical system has spent decades earning, even when the system delivering the answer is no longer the NSO itself.The Sustainable Development Goals Report 2026 8 • Progress towards eradicating extreme poverty has stalled. The global extreme poverty rate stands at 10 per cent in 2026, only three percentage points below 2015 averages. At the current trajectory, about 9 per cent of the world’s population will still be living in extreme poverty in 2030, four in five of them in sub-Saharan Africa or in fragile and conflict-affected countries. • Social protection coverage has reached a major milestone but remains deeply uneven. For the first time, more than half the global population was covered by at least one social protection benefit in 2023. Yet 3.8 billion people remain unprotected, and coverage varies starkly by group: from 79.6 per cent for older persons to just 16.7 per cent for the unemployed. • Development aid focused on reducing poverty fell sharply, erasing a decade of progress. Official development assistance (ODA) grants targeting poverty reduction fell 8.7 per cent between 2023 and 2024, returning to 2015 levels. • Progress towards ending poverty will require targeted investment towards those living in persistent poverty. Both individual countries and the global community must commit to expanding inclusive economic opportunities, strengthening social protection systems and tackling the forces that continue to drive people into poverty in the first place, including inequality, climate shocks, conflict and rising living costs. No poverty One in 10 people still lives in extreme poverty, and 2030 is unlikely to look different In 2026, an estimated 10 per cent of the world’s population – some 826 million people – still live in extreme poverty, surviving on less than $3.00 a day in 2021 purchasing power parity (PPP). Since 2015, this share has fallen by only three percentage points. The lasting imprint of the coronavirus disease (COVID-19) pandemic is now unmistakable. The poorest regions were hit hardest and have recovered most slowly: sub-Saharan Africa saw extreme poverty rise by two percentage points during the pandemic and returned to pre-pandemic levels only in 2025. Oceania (excluding Australia and New Zealand) followed the same trajectory. The global outlook offers little reassurance going forward. Even setting aside the impact of the recent crisis in the Middle East, about 9 per cent of the world’s population is projected to still be in extreme poverty in 2030. That figure, however, hides considerable regional variations. While most regions will be close to eradicating extreme poverty by 2030, sub-Saharan Africa, the Middle East and North Africa, and Oceania remain critical exceptions. With 71 per cent of the world’s extreme poor now concentrated in sub-Saharan Africa, the prospects of ending extreme poverty globally depend largely on what happens there. Proportion of the population living below $3.00/day in 2021 PPP, with projections in dotted lines, 2015–2030 (percentage) 0 10 20 30 40 50 2015 2020 2025 2030 Central and South Asia East and South-East Asia Latin America and the Caribbean Middle East and North Africa Oceania* Sub-Saharan Africa World * Excluding Australia and New Zealand. Note: Regional grouping follows the World Bank grouping. Public health and education spending remains skewed away from the poorest 20 per cent “Pro-poor public social spending” measures the share of government spending in education, health and direct transfers (cash and near-cash) that directly benefits the poorest 20 per cent of the income distribution. Comparable data are available for 132 countries or areas in at least one sector, of which 54 cover all three. Across these sectors combined, the share of spending reaching the bottom 20 per cent ranges from 10 to 39 per cent, with an average of 26 per cent. The picture varies sharply by sector. Direct cash and near-cash transfers are by far the most effective at reaching the poorest quintile: on average, 32 per cent flows to the bottom fifth, climbing as high as 93 per cent in the bestperforming countries. Education spending is less targeted with an average of 21 per cent reaching the poorest 20 per cent and values ranging from 3 to 35 per cent across countries. Health spending shows the widest variation across countries – from 9 to 59 per cent – yet on average only 18 per cent of spending reaches the poorest 20 per cent. Young siblings sit outside their earthquake-destroyed home in Nurgal District, Afghanistan, where natural disasters compound the hardships of poverty.