The Sustainable Development Goals (SDGs) provide a comprehensive framework for societal progress and planetary health. However, it remains unclear whether universal patterns exist in how nations pursue these goals and whether key development areas are being overlooked. Here, we apply the product space methodology, widely used in development economics, to construct an ‘SDG space of nations’. The SDG space models the relative performance and specialization patterns of 166 countries across 96 SDG indicators from 2000 to 2022. Our SDG space reveals a polarized global landscape, characterized by distinct groups of nations, each specializing in specific development indicators. Furthermore, we find that as countries improve their overall SDG scores, they tend to modify their sustainable development trajectories, pursuing different development objectives. Additionally, we identify orphaned SDG indicators — areas where certain country groups remain under-specialized. These patterns, and the SDG space more broadly, provide a high-resolution tool to understand and evaluate the progress and disparities of countries towards achieving the SDGs.
Adopted by United Nations member states in 2015, the 2030 Agenda for Sustainable Development constitutes a comprehensive framework of 17 Sustainable Development Goals (SDGs) and 169 targets to inspire and guide policies for eradicating poverty, protecting planetary ecosystems, and promoting peace and prosperity for humankind1. The year of 2023 marked the halfway point in the implementation of the SDGs2, prompting us to question whether universal patterns underpinned nations’ sustainable development trajectories, and whether some SDG indicators were neglected or overlooked (referred to herein as ‘orphaned’) in specific areas, across nations, and over time.
Indeed, previous analyses3,4,5,6 have revealed that different countries are performing differently in targets and pursuing alternative sustainable development paths. Rwanda, for instance, has nearly realized SDG 13 (Climate action) while faltering on SDGs 1 and 4 (No poverty and Quality education), while Russia had pursued the opposite sustainable development trajectory, meeting SDGs 1 and 4 while making less progress in SDG 13. China has performed well in SDG 2 (No hunger) yet had lower performance in SDG 14 (Life below water), while Chile has performed poorer on the former than China and better on the latter.
Using historical data, recent studies have also ranked and evaluated countries by their sustainability performance6,7,8, explored key development dimensions (e.g., socioeconomic development, environment, and equality)9,10,11, and mapped the interactions (synergies and trade-offs) among the SDGs12,13,14,15,16,17,18,19. However, the trajectories of countries pursuing SDGs have been poorly explored from a comparative lens, that is, comparing different goals or targets against each other to understand the relative performance or characteristics of each entity in relation to the others. Moreover, it remains unclear whether there are universal patterns underlying the sustainable development trajectories of nations across targets and over time. Specifically, we examined whether countries have so-called ‘orphaned’ SDG indicators in certain development areas and whether there are underlying patterns or rules governing these ‘orphaned’ areas.
Uncovering such structural patterns and properties — should they exist — can assist in identifying ‘orphaned’ targets or areas where progress has been insufficient (not necessarily intentionally or purposefully); forewarning future challenges in realizing SDGs; and, inspiring future development policies at multiple scales (international, regional, and national). Such a nuanced understanding of sustainable development trajectories offers a basis for precise efforts to realize the SDGs, ensuring no area, and consequently no community, is left behind.
In this work, we use the ‘product space’ method to inform these questions20. This method employs network analysis to reveal the relatedness, or affinity, between economies and activities21,22, and can be used to investigate the clustering and evolution of specialization patterns of regions and nations. For instance, it can help reveal how countries transition from light-manufacturing (e.g., garments) to electronics and which countries tend to specialize in which products. The product space approach, along with the economic complexity index, has proven effective in explaining and anticipating variations in performance across various dimensions and domains23,24, including industry-, occupation-, research- and technology-spaces25,26,27,28,29, as well as addressing broader issues like inequality, resource efficiency, and regional sustainability30,31,32.
Applying the product space and economic complexity framework, we are able to investigate whether countries’ performance follows distinct patterns, and whether countries under-specialize in certain SDG areas. To do so, we devise an ‘SDG space’ to reveal the sustainable development trajectories of 166 nations for the period 2000–2022. In other words, we consider performance across 96 SDG indicators as types of ‘products’, and use measures of ‘specialization’ to quantify their sustainable development trajectories. The revealed comparative advantage (RCA)33 is used to measure which country specializes in which area. For example, if a country’s score for a specific SDG indicator constitutes a higher proportion of its total score across all indicators compared to the world average, that country is considered to be specialized in that SDG indicator. Inspired by the economic complexity index (ECI) and product complexity index (PCI) set out by Hidalgo and Hausmann23, we use a method equivalent to a clustering algorithm21,22 to calculate the country sustainability index (CSI) and goal sustainability index (GSI) based on countries’ RCA in SDG indicators. The rationale behind the calculation of GSI and CSI indicates that high-CSI countries, are more likely to dominate high-GSI indicators, whereas low-CSI countries tend to dominate low-GSI indicators overall (see Methods).
The indicators covered in this study are carefully indexed and evaluated in the Sustainable Development Report (SDR) 20236, with details provided in Tables 1 and 2, including their relationship to the official United Nations Statistics Division (UNSD) SDG targets. For clarity in the figures, the SDG indicator abbreviations used in this study (e.g., 1.A, Poverty headcount ratio at 3.65 US Dollars ($)/day) are simplified versions of the indicators provided in the SDR report. They do not correspond directly to the UNSD SDG targets. National ‘SDG spaces’ are further quantified for each country, including data on SDG indicator clusters and RCAs (see Supplementary Information). Moreover, we have made available the visualization of 3818 SDG spaces for 166 nations spanning the years 2000–2022 on a dedicated website