The disparities and development trajectories of nations in achieving the sustainable development goals

Domain: earth_science
Published: 2025-01-28
Editorial outcome: minor_revision

Abstract
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.

Introduction
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 SDGs, 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 analyses3–6 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 performance6–8, explored development dimensions (e.g., socioeconomic development, environment, and equality)9–11, and mapped the interactions (synergies and trade-offs) among the SDGs12–19. 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-spaces25–29, as well as addressing broader issues like inequality, resource efficiency, and regional sustainability30–32.

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).

Results
Underlying structures of the SDG space of nations
We adopt the concept of ‘product space’20,23 to construct an SDG space (Fig. 1 and Supplementary Fig. 1), representing the sustainability specialization of nations. The global SDG space of nations shows a ‘dumbbell’ structure with two main tight clusters (Fig. 1a, b and Supplementary Fig. 2), highlighted herein blue (high-GSI indicators) and red (low-GSI indicators). The blue cluster of SDG indicators (more commonly specialized by countries with high SDG scores, see Supplementary Table 1) is located on the right side of the SDG space. It includes indicators mainly related to SDGs 1 (No Poverty), 3–5 (Good Health and Well-Being, Quality Education, Gender Equality), 9–11 (Industry, Innovation and Infrastructure, Reduced Inequalities, Sustainable Cities and Communities), and 16 (Peace, Justice and Strong Institutions). The red cluster of SDG indicators (mainly specialized by countries with low SDG scores, see Supplementary Table 1) is on the left side of the SDG space. It includes indicators mainly related to SDGs 12 (Responsible Consumption and Production) and 13 (Climate Action). The indicators in these two clusters have relatively higher node degrees, indicating that they are connected to a larger number of edges (Fig. 1a).

Notably, countries at different stages can have different specialisation patterns for specific SDGs. Countries with high SDG scores tend to have relative advantages in indicators related to poverty reduction (1.A), hunger reduction (2.E, 2.G), good health and well-being (3.B, 3.D, 3.F, 3.G, 3.I, 3.K, 3.M), wastewater and air pollution treatment (3.H, 6.D, 14.B), access to clean energy and water (6.B, 6.E, 7.A), industry, innovation and infrastructure (Goal 9), as well as government administration (16.B, 16.C, 16.I, 17.B, 17.C). In contrast, countries with low SDG scores show relative advantages in achieving SDG indicators related to overnutrition (2.B, 2.F), embodied social and environmental impacts in international trade (6.C, 8.C, 8.D, 12.D, 12.F, 13.C, 14.A, 15.A, 16.A), resource use, waste and emissions (6.A, 7.B, 12.A, 12.C, 12.E, 12.G, 13.B), and issues related to corporate tax havens (17.A).

The ‘dumbbell’ structure of the SDG space reaffirms the unevenness in global sustainable development that has been previously observed3; however, from a dynamic evolutionary perspective, it is encouraging to notice that the trend of unevenness is weakening, as shown in Supplementary Figs. 3–4. In addition to the global SDG space, we constructed a ‘country space’ based on countries’ performance on SDG indicators (see Methods on how to build the country space and Supplementary Fig. 5), which provides more detailed insights into the world’s unevenness in sustainable development.

Figure 2 presents strong regional specialization patterns within the SDG space. The SDG indicators with RCA > 0 for each region are color-coded, indicating that the region has a relative advantage in these indicators. Africa occupies the red cluster on the left side of the SDG space (Fig. 2a), primarily associated with overnutrition, embodied social and environmental impacts in international trade, and waste and emissions. In contrast, Europe occupies the blue cluster on the right side (Fig. 2d), which includes indicators related to poverty and hunger reduction, good health and well-being, education, innovation, and government administration. The Americas, Asia, and Oceania regions exhibit relatively similar patterns (Fig. 2b, c, e), with a more even distribution and smaller RCA values for their specialized indicators. However, Oceania has more advantages in high-GSI indicators within the blue cluster. The SDG space of all 166 nations ranked by SDG scores in 2000, 2015, and 2022, are shown in Supplementary Figs. 12–177. It is notable that the findings and insights derived from this method may not be universally applicable to all countries in each region, given the local variations in socio-economic conditions, culture, governance, and policy environments.

Sustainable development paths of nations
Through a moving-window technique18,35 (see Methods), Fig. 3 reveals a notable pattern: as countries progress in their development, measured in their absolute and aggregate SDG scores, their trajectories tend to manifest an X-shaped pattern over time (Fig. 3a–c, and detailed results in Supplementary Tables 2–13). With improvement in countries’ aggregate SDG scores, indicators positioned at the top and bottom of the y-axis start to diverge, moving in opposite directions and sequentially crossing at around the 80th and 40th window groups of countries.

The dynamics of RCA values of SDG indicators is mainly characterized by three trends. The first trend is characterized by a continuous decrease in specialization in low-GSI indicators represented in red in Fig. 3a–c. These indicators mainly represent issues related to the overnutrition, embodied social and environmental impacts in trade, and waste and emissions. Specifically, Fig. 4 referring to the year 2022, associates this trend with indicators 2.B, 2.C, and 2.F in Goal 2 (No Hunger), 12.A-G in Goal 12 (Responsible Consumption and Production), 13.A-C in Goal 13 (Climate Action), 14.A and 14.D-F in Goal 14 (Life Below Water), 15.A in Goal 15 (Life on Land), 16.K in Goal 16 (Peace, Justice and Strong Institutions), and 17.A in Goal 17 (Partnerships for the Goals). These indicators, predominantly found in the left cluster (red cluster) of the SDG space (Fig. 1), are identified as ‘orphaned’ indicators, indicating areas where there is significant need for enhanced focus and policy intervention.

The second trend is associated with the early increase in specialization of indicators with a high GSI, represented by light blue in Fig. 3a–c. This trend primarily focuses on the areas of poverty eradication, basic health, basic education, and livelihood security. Specifically, in the year 2022, as illustrated in Fig. 4, this trend includes indicators 1.A and 1.B in Goal 1 (No Poverty), 2.A and 2.E in Goal 2 (No Hunger), 3.B, 3.G, 3.I, 3.K, and 3.M in Goal 3 (Good Health and Well-Being), 4.A and 4.D in Goal 4 (Quality Education), 6.B and 6.E in Goal 6 (Clean Water and Sanitation), 7.A and 7.C in Goal 7 (Affordable and Clean Energy), and 8.A in Goal 8 (Decent Work and Economic Growth). This upward trend in specialization on high-GSI indicators reflects a prioritization of foundational aspects of sustainable development.

The third trend pertains to the follow-up increasing specialization in objectives that have been identified as ‘deep blue indicators’ distinguished by their higher GSI values as depicted in Fig. 3a–c. They are mainly related to wastewater treatment, labor rights, and research & development, such as indicators 6.D in Goal 6 (Clean Water and Sanitation), 8.E in Goal 8 (Decent Work and Economic Growth), and 9.A and 9.E-F in Goal 9 (Industry, Innovation and Infrastructure) in 2022 (Fig. 4). These indicators predominantly fall within the right cluster (blue cluster with high GSI values) of the SDG space (Fig. 1). From a dynamic analysis spanning the years 2000 to 2022, it is observed that while the first two trends have remained relatively stable, the third trend-particularly the emphasis on goals related to innovation-has advanced, appearing in earlier window groups.

In exploring the evolutionary patterns underpinning sustainable development trajectories, the product space and economic complexity approach allow to divide SDG indicators into nations’ orphaned indicators (red ones with low GSI values) and specialized indicators (blue ones with high GSI values). To identify more refined trends, we categorize the SDG indicators into six groups based on their GSI values (Fig. 5): three groups of environment-related indicators (Table 1) and three groups of non-environment-related indicators (Table 2). Our findings reveal that environment-related indicators with high GSI values primarily involve wastewater treatment, air pollution control, and access to clean energy, which tend to be specialized by countries with high SDG scores. On the other hand, indicators with low GSI values, such as trade-related pollution and emissions, as well as waste and emissions from production and living, are more commonly associated with countries with low SDG scores.

Similarly, non-environment-related indicators with high GSI values predominantly include no poverty, zero hunger, good health and well-being, quality education, industry, innovation and infrastructure, and effective government administration. In contrast, those with low GSI values mainly involve overnutrition and the social impacts embodied in international trade.

For both environment-related and non-environment-related SDG indicators, the RCA values for higher GSI indicators increase with the growth of the national SDG index, while the RCA values for lower GSI indicators tend to decrease as the national SDG index rises.

However, when analyzing absolute SDG scores outside the comparative lens, we observe an ambiguous convergence between two clusters of indicators (Fig. 3d–f and Supplementary Fig. 6). This ambiguous convergence presents a challenge in discerning the relative advantages and disadvantages of countries at various stages of sustainable development, particularly when the scores of distinct indicators are only narrowly separated. For example, in 2022, indicator 16.A (Timeliness of administrative proceedings) and indicator 12.C (Municipal solid waste) in the 1st window group of countries both registered an average SDG score of 65 (Supplementary Table 10). Despite their identical scores, a comparative analysis reveals a stark contrast in their global standings, with their average RCA rankings positioned at 10 and 90, respectively, out of 96 SDG indicators. This disparity underscores the value of comparative analysis in unveiling the relative strengths and weaknesses of nations on their path of designing future sustainability strategies.

‘Orphaned’ indicators in the SDGs
To examine ‘orphaned’ indicators that have received insufficient attention among nations across different development stages, we grouped countries into quartiles based on their SDG score rankings (Supplementary Table 1): Stage 1 (Primary stage), Stage 2 (Low medium stage), Stage 3 (High medium stage), and Stage 4 (Advanced stage). Figure 6a–d show that when countries progress from Stage 1 to Stage 4, they shift their relative disadvantages (weaknesses, measured in low RCA with red color) from the right cluster (indicators with low-GSI values), mainly poverty reduction, education, and innovation goals, to the left cluster (indicators with high-GSI values), mainly environmental and climate goals. The move of relative advantages (measured in high RCA with blue color) is the opposite.

The ‘orphaned’ indicators in the SDGs are concentrated in the left side of Fig. 6e, where the values of GSI for indicators are low. Specifically, when a country develops from Stage 1 to Stage 2, it loses its comparative advantage in the areas of overnutrition (e.g., 2.B, 2.F) and environmental impacts (e.g., 6.C, 12.A, 13.C). Countries which progress from Stage 2 to Stage 3 probably place a lesser emphasis on the issues of overnutrition (2.F) and fish caught related indicators (14.D, 14.E, 14.F). When countries develop from Stage 3 to Stage 4, they lose further specialization in overnutrition (2.F) and waste and emissions (12.A, 13.C). These easily overlooked indicators across stage changing, are important forewarning indicators for the formulation of sustainable development policies. On the other side, the high-GSI-value indicators on the right side of Fig. 6e, are what countries specialize in when they progress to higher sustainable development stage. These indicators are mainly related to poverty eradication (1.A) and livelihood security (7.A) for Stage 1, scientific research (9.E, 9.G) for Stage 2, and environmental protection (6.D) and expenditure on research and development (9.E) for Stage 3.

The GSI indicator, derived from the product space and economic complexity approach, categorizes SDG indicators that countries tend to over- or under-specialize in during their development process. This categorization can help inform the formulation of sustainable development policies. The details of changes in RCA between stages in 2000, 2015 and 2022 are presented in Supplementary Tables 14–22.

Can countries avoid losing specialization and creating orphan SDG indicators when they progress to higher stages? For the most part no. This is because of both, the way in which SDGs are defined (with goals that are sometimes opposites, such as over- and undernutrition)18,36, and because measures of specialization, such as RCA are relative measures, meaning that improvements in the score of one indicator must decrease the specialization in other indicators. Figure 7 illustrates this strong trade-off. Still, there are a few exceptions. For example, Serbia specializes in indicators of 2.D (Sustainable nitrogen management) and 12.E (Production-based nitrogen emissions), Japan and Korea perform very well in 2.B (Prevalence of obesity), and Moldova has relative advantages in 12.A (Electronic waste) and 13.B (CO2 emissions from fossil fuel combustion and cement production). These anomalies can be attributed to distinctive industrial structures (i.e., the composition and organization of industries within a country’s economy) or national cultures. For instance, Serbia’s nitrogen management and Moldova’s CO2 emissions from fossil fuel combustion and cement production are outcomes of their specific industrial frameworks. Similarly, Japan’s traditional dietary practices and culinary habits (i.e., its food culture) influence its unique sustainable development indicators.

Methods
To reveal SDG development patterns, we provide a lens by constructing the relatedness networks of SDGs, and by employing the measures and techniques of revealed comparative advantage (RCA), goal (country) sustainability index (GSI or CSI), and the SDG space. The relatedness networks of SDGs, derived from data collected between 2000 and 2022, measure the overall affinity between a specific SDG indicator and a country to uncover the SDG development patterns, such as how a country gains or loses a comparative advantage in an SDG indicator during its evolution.

Revealed comparative advantage (RCA)
We use the revealed comparative advantage (RCA) in SDGs to determine a country’s specializations, evidenced by scores of 17 Sustainable Development Goals and their related 96 indicators. The RCA is based on the Ricardian comparative advantage concept, which commonly refers to the Balassa index33.

The RCA of country c in SDG indicator i is defined as Eq. (1):

RCA c,i = RCA0 c,i (Score(c,i) / Score(c,i))^-1 = (Score(c,i) / Score(c,i)) / (Score(c,i) / Score(c,i)) (1)

Where Score(c,i) is the score on SDG indicator i that country c has, Score(c,i) is country c‘s overall SDG indicator score, Score(c,i) is the total score on SDG indicator i across all countries, and Score(c,i) is the total score across all countries and indicators.

RCA0 c,i, which is the main part of RCA, is the ratio of two shares. The numerator is the share of a country’s score on a given indicator in its overall score across the 96 indicators, and the denominator is the share of the world score on the same indicator in total scores across all countries and indicators.

If country c has a higher share of SDG indicator i in terms of SDG scores than the world average, then RCA c,i > 0, and that country is considered to be specialized in that SDG indicator. For example, if the share of a country’s score on 2.G (Prevalence of undernourishment) in its overall score across the 96 indicators is higher than the world average share of 2.G. In that case China is regarded as having relative advantage in 2.G.

Country sustainability index (CSI) and goal sustainability index (GSI)
Inspired by the economic complexity index (ECI) and product complexity index (PCI) set out by Hidalgo and Hausmann23, we propose a method, which is equivalent to a spectral clustering algorithm, to calculate the country sustainability index (CSI) and goal sustainability index (GSI) based on countries’ RCA in SDG indicators. The CSI (GSI), similar to the ECI (PCI), is defined through an iterative, self-referential method of reflections algorithm. After setting initial values for the CSI and GSI (e.g., the number of indicators a country specializes in, and the number of countries that specialize in each indicator), we calculate a country’s CSI by taking the average GSI values of the SDG indicators in which the country has a relative advantage. In the same way, we calculate an SDG indicator’ GSI by the average CSI values of countries with a relative advantage. And then, we recursively use the obtained GSI and CSI values to correct each other until the values are stable. The GSI and CSI can help gain significant insights into SDG development patterns.

The SDG space and country space
We construct the SDG space from the SDG proximity matrix obtained from countries’ RCA in SDG indicators, where nodes represent the SDG indicators and edges represent the similarity between them. The proximity between SDG indicators in the nation-indicator bipartite network is defined by the conditional probability that two indicators are co-specialized within a nation. Similarly, we construct the country space from the country proximity matrix obtained from countries’ RCA in SDG indicators, where nodes represent countries and edges represent the similarity between them.

To identify the structure of the SDG space, we use a Kamada-Kawai algorithm67 to expose two main clusters. Subsequently, we define the membership of each SDG indicator in the two central communities in the network using the Girvan–Newman method68. The Girvan–Newman algorithm identifies communities by progressively eliminating graph edges. At each stage, it traditionally eliminates the ‘most useful’ edge with the highest betweenness centrality. As the graph fragments, the tight-knit community structure is revealed. The networks connecting SDG indicators tend to have a dumbbell structure.

The proximity between SDG indicator i and j is defined as the product of the pairwise conditional probabilities of a country having an advantage (RCA c,i > 0) on one indicator given an advantage in the other (RCA c,j > 0), as shown in Eq. (2):

S i,j = P(RCA c,i > 0 | RCA c,j > 0) * P(RCA c,j > 0 | RCA c,i > 0) (2)

S is a 96x96 matrix that captures the proximity between pairs of SDG indicators, which is equal to the cosine similarity (S i,j). The cosine similarity (S i,j) can be calculated by Eqs. (3) and (4):

S i,j = (sum(RCA0 c,i * RCA0 c,j)) / (sqrt(sum(RCA0 c,i^2)) * sqrt(sum(RCA0 c,j^2))) (3)

where

RCA0 c,i = 1, if RCA c,i > 0; 0, if RCA c,i <= 0 (4)

RCA c,i > 0 means country c has a comparative advantage in the indicator i, while RCA c,i <= 0 means country c doesn’t have a comparative advantage in the indicator i.

The resulting similarity S i,j ranges from 0 to 1, where 1 means the probability that each country simultaneously has comparative advantage over the two indicators is 100%, and 0 means when a country has a comparative advantage in one indicator, it always has a relative disadvantage in the other. The in-between values indicate the synergy degree of the two indicators. We call the similarity S i,j as the synergy similarity. By contrast, the trade-off similarity is defined as T:S: i,j = 1 - S i,j, which also ranges from 0 to 1, but the meanings are opposite to S i,j, where 1 means when a country has a comparative advantage in one indicator, it always has a relative disadvantage in the other. The in-between values indicate the trade-off degree of the two indicators.

To provide a visualization that includes all 96 SDG indicators, we reached all nodes by calculating the maximum spanning tree, which consists of the 95 edges that maximize the added proximity of the tree. To show the strong edges, we overlay all edges with a proximity greater than a certain threshold of 0.7. We perform a sensitivity analysis of the threshold settings (Supplementary Fig. 9) and find that the determination of the proximity threshold does not affect the SDG space’s structure. We only consider the proximity threshold to be larger than 0.5, which indicates the probability of two indicators having similarity is greater than 50%.

The relatedness networks of SDGs
Using the RCA, CSI, GSI, and SDG space, we recognize the relatedness networks of SDGs, which illustrate the affinity between a specific SDG indicator and a country. On a global level, by displaying the GSI values of SDG indicators in the SDG space, we identify which groups of countries have revealed comparative advantage in which groups of SDG indicators since the CSI values are related to the GSI values. At the national scale, the SDG space with each country’s RCA in various SDG indicators shows each country’s SDG performance. By analyzing the structures of the relatedness networks of SDGs and the patterns of how countries evolve in SDGs, we provide maps for the world and each country to know where they are and where they will go in SDG development.

The moving-window approach
We examine the impact of countries’ rising SDG ranks on the RCA values of SDG indicators and use a moving-window method18,35 to smooth out the fluctuation and highlight the RCA trends. In all, we sort 166 countries along the SDG rank gradient from the lowest to the highest SDG rank. We set the moving-window size at 50, resulting in a total of 117 windows or country groups (that is, the 1st country group where countries rank 1–50, the 2nd country group where countries rank 2–51, ..., the 117th country group where countries rank 117–166).

To test the sensitivity of moving-window size on results, we use different sizes (from 20 to 70) to compare the trends and the turning points (i.e., intersections). Similar development paths are found under all moving-window sizes (Supplementary Fig. 7). The smaller the size is, the more volatile the RCA lines are and the flatter the X shape is. We set a middle size of 50 to prevent the X-shaped trend from being too volatile or flat.

Data
We collected the data of SDG scores from the Sustainable Development Report (SDR) 20236 via the following website34. Although SDG Index rankings and scores from one edition cannot be compared with the results from previous editions, the latest report provides time series for the SDG Index, calculated retroactively using the same indicators and methods. The scores on individual SDG goals and indicators indicate percentages of optimal performance. The difference between any score and the maximum value of 100 is therefore the distance in percentage points that a country must overcome to reach optimum SDG performance. This ensures that all indicators are comparable. To ensure data continuity and quality, we extracted data for 96 indicators from 98 indicators for 166 countries from 2000 to 2022, while 2 indicators which cannot cover most of countries are excluded. Although the SDG agenda begins in 2015, similar to the SDRs6,69, the starting year for our analysis is 2000, in order to provide a more comprehensive review of the historical data. The selected SDG indicator list is shown in Tables 1 and 2.

The SDR includes data from both official and unofficial sources. Most of the data come from international organizations which have extensive and rigorous data validation processes. The other data sources (around a third) come from household surveys, civil society organizations and networks, and peer-reviewed journals. The details of the data sources can be found in the Sustainable Development Report6. In addition, the related SDG Index methodology and datasets have undergone multiple peer reviews and have been used to substantiate previous notable studies in this field6,69–71. Naturally, the dataset has its advantages and disadvantages. The advantages include its broad coverage and consistency over time, which allow for meaningful comparisons across countries and time periods. The disadvantages include a lack of granularity needed to capture local (e.g., urban level) variations and specific economic activities (e.g., industries) within countries. Additionally, some indicators are not fully applicable to all countries (e.g., 14.C. Mean area that is protected in marine sites important to biodiversity).

Robustness check
To ensure the robustness of our findings, we evaluated the results from the following perspectives: (1) whether a consistent SDG space structure can be reproduced using data from different years; (2) whether the SDG space structure remains similar (i.e., stable) after excluding overlapping indicators; (3) whether the structure is consistent across different data sources; (4) whether the bipolar world results hold under varying similarity thresholds; and (5) whether consistent evolution patterns of SDG indicators emerge with different moving window sizes.

Supplementary Fig. 3 demonstrates that a similar SDG space structure is observed across the years of 2000, 2015, and 2022. This consistency indicates that our finding of a bipolar world within the SDG space is robust and not influenced by the choice of years.

Notably, some SDG indicators may overlap, such as 2.B (Prevalence of obesity) and 2.F (Human Trophic Level) in this study. To ensure the robustness of our results, we removed the overlapping indicators, retaining only one unique indicator, and re-examined the patterns (Supplementary Fig. 10). The findings remained robust, confirming the consistency of our analysis.

To validate the robustness of our results using different data sources, we collected additional data from the SDG database of United Nations Statistics Division72. For consistency in comparison, we included only the data on targets that matched the 96 indicators used in this study (see Tables 1 and 2 for the matching process). Data source: https://unstats.un.org/sdgs/dataportal/database. As shown in Fig. 1 (results of SDR 2023 database) and Supplementary Fig. 11 (results of UNSD SDG database), the SDG spaces exhibit a similar structure.

In this study, we generated the SDG spaces by setting the similarity threshold between SDG indicators at 0.7. We tested the robustness of our results by varying the similarity values. As seen in Supplementary Fig. 9, the indicator clusters remain consistent, supporting the SDG space structure.

We also examined whether varying the moving-window sizes would yield similar evolution patterns of SDG indicators. Supplementary Fig. 7 shows that the evolution patterns of SDG indicators and the ranking of countries’ SDG scores in 2022 remain consistent across moving-window sizes of 20, 30, 40, 50, 60, and 70.

Overall, our findings demonstrate robustness across various tests and conditions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper applies the “product space” technique developed by Hidalgo et al. (2007) to construct an SDG space for countries. Analyzing data for 166 countries between 2000 and 2022, the results suggest that there exists a bipolar world, with distinct groups of countries specializing in specific targets. The results also point to under-developed SDG targets and three country development trajectories: i) under-specialization in targets relating to environmental quality and overnutrition; ii) specialization in targets relating to poverty eradication and education; and iii) a follow-up emphasis on targets pertaining to labor rights, and research and development.\nWhile the research questions are relevant and the results appear reasonable (but note some of them are not exactly new findings), the analysis leaves room for improvement. I have some comments below that are in no particular order that may help improve the paper.\nMajor comments\n1. The paper can make new, important contributions on two fronts: cross-country analysis and country-specific analysis. While the paper demonstrates to some degree that it could analyze cross-country trends over time, it generally falls short of offering the level of detailed country-specific analysis that can provide country-specific relevant advice for actionable policies. So both types of analysis will need more improvement. I will go into more detailed feedback on each below.\n2. For cross-country analysis, the main results in the paper are that developed countries have a relative advantage related to zero hunger, living security, economic development, labor rights, national statistical system performance, and protection of life on land. In contrast, developing countries have a relative advantage in SDG targets related to overnutrition, social and environmental impacts of imports, resource use and carbon emissions, and corporate tax havens. I am not sure I am convinced by this somewhat simple dichotomy of findings. Two populous developing countries, China and India, account for roughly more than 40% of the population in the developing world. These two countries have long been known to struggle with environmental degradation issues, including air and water pollution. Some emerging countries with a large population such as Bangladesh and Vietnam also face with similar environmental challenges recently, both of these countries frequently top the list of the globally most polluting cities. In fact, on page 17, the authors acknowledge themselves that China has a problem with CO2 emission from fossil fuel and cement production. These contradictory results need careful explanation.\n3. Regarding country-specific analysis, I similarly find the brief discussion on China (page 17) inadequate. The paper would add practically useful advice for countries if it can zoom in on a couple of countries as case studies (perhaps with different development trajectories), where it applies the proposed framework of analysis and subsequently clearly identifies the weaknesses and strengths for each country. For now, the current analysis appears to be too vague and high-level to be useful for any particular country.\n4. I would also like to see more engagement and comparison of the paper findings with those in the existing literature. For example, (despite the shortcomings discussed in point 2 above), how does the dichotomy of findings for developed and developing countries discussed above relate to previous studies? Are they completely new? Are they backed up by at least some previous qualitative analysis? I have similar questions with the country development trajectories discussed in Figure 3 and Figure 6.\n5. The paper seems to take for granted that the SDG targets (and indicators) are non-overlapping. This is not true, as discussed in many previous studies. In fact, a useful robustness check is to better understand whether these non-overlapping (similar) targets would end up having similar score/ color/ node size in the map?\n6. Certain SDG goals/ targets may involve tradeoffs for countries, particularly poorer countries. For example, faster poverty reduction may often come with faster environmental degradation. The paper does not discuss this issue at all. One implication is that should these tradeoff targets end up, by definition, in the red and blue clusters of the proposed framework?\n7. In general, are there any test statistics (or math quantities) to show that the bipolar patterns in the figures are not artificially caused by certain parameters? In other words, could the authors show different robustness checks to confirm these bipolar patterns? In addition, besides a bipolar pattern, could there be instead a spectrum/ range of values where countries may form several smaller clusters on this range instead of the bipolar pattern?\n8. To better anchor the paper findings with the current literature, it would be useful to assess how the country score rankings using the proposed framework compares with other overall SDG indexes such as that employed by the Sustainable Development Reports. In particular, it would be useful to pick out some countries that change ranks using different methods for more discussion.\nOther minor comments\n9. Not all readers are familiar with the SDGs, so it could be useful to add a table listing each goal and target to the end of the Method section for easy reference.\n10. Since the authors obtain their data from the Sustainable Development Report 2023, how do their findings compare with those in this report? To what extent these data affect the paper findings (compared with other data sources, such as from or the UN or the World Bank)?", "reviewer_2": "The manuscript provides a valuable contribution to the analysis of countries’ pathways towards greater sustainability as envisioned in the SDG framework through the lens of development economics. The study compares the trajectories of 166 countries (from 2000-22), and explores whether countries’ performance follows distinct patterns by considering 96 global indicators from the SDR. The approach chosen is systematic and sophisticated, employing methodologies innovatively in the context of sustainable development. Overall, the manuscript contributes to advancing knowledge in the field of sustainable development, and can inform more effective strategies for achieving the SDGs across different countries. Notably, I sincerely appreciate that the authors want to make their results on SDG spaces publicly available and easily accessible through the dedicated web-interface.\nHowever, there are some points I would like to raise which I hope can support the authors in improving the manuscript:\n1. The authors’ choice of referring to the assessed indicators with capital letters (such as 1A, 1B…) can be misleading, as some the official SDG targets referring to the means of implementation under the respective goals are named alike, although with lower case letters (1a, 1b, …). If the authors want to stick to this format, they should be explicit about the distinction.\n2. Relatedly, the authors could mention earlier in the manuscript that the study uses (proxy) indicators for assessing performance on SDG targets from the SDR (not SDG targets), and that these not always correspond to the official SDG indicators. In this respect, they could further explain why they decided to do so (I suppose due to issues of data availability in the official data set). In Supplementary table 1, consider adding a column indicating whether the indicator is an official SDG indicator (“UNSD Match” in column S, tab “Codebook” in SDR2023_data excel file).\n3. While being aware that the terms “developed” vs. “developing” countries is commonly used, I would encourage the authors to refrain from this terminology, particularly since they do not specify where this categorization stems from and is based on. In my opinion, it would be better to distinguish between countries by income levels (from low- to high-income countries) and rename the groups accordingly.\n4. In this respect, I strongly encourage the authors to revise the regional groupings in Fig. 2. If the authors want to display regional differences, they should stick to official regional groupings (e.g., (aggregates of) those indicated in SDR, or the M49 standard). Distinguishing between Africa/East & South Asia/Latin America and the Caribbean vs. Industrialized Countries is an unfortunate choice.\n5. The findings and insights derived from the method may not be universally applicable to all countries or regions, as they are based on aggregated data and global trends. The authors might want to acknowledge more explicitly that local variations in socio-economic conditions, culture, governance, policy environments, and institutional capacities may not be adequately captured, but might be necessary to consider for providing a more comprehensive understanding of SDG development patterns.\n6. The authors also might want to acknowledge that some indicators are not applicable to all countries in their entirety (e.g., indicator 14.C on protected areas in marine sites not relevant for landlocked countries).\n7. L129-31: I think this statement should be rephrased and needs further (potential) explanation. First, some of the indicators for which “developing” countries have an RCA could indeed be a preference (e.g. environmental impacts of imports). Second, your data allows you for stating which advantages/specializations are likely to be decreasing when a country “develops”. But does this qualify to speak about (un)intentional specialization? Maybe it would be useful to further reflect on the RCA construction and its limitations/implications in this respect as well.\n8. The meaning and interpretation of the network links (rather called edges, and I would recommend to use this term consistently throughout the manuscript) between SDGs and the degree values are barely being addressed in the text.\n9. L286-90: I would appreciate if the authors could elaborate on how they interpret these findings.\n10. To better highlight and acknowledge the differences shown in Fig.6, the discussion section would benefit from an additional example besides China.\n11. The interpretation of proximity values as indicators of synergy and trade-off should be cautiously considered, recognizing the complexity of sustainable development dynamics.\n12. Finally but importantly, some of the research aims set out in the beginning in the manuscript (whether countries taking different paths have achieved different degrees of success / whether and under which circumstances countries make progress towards their SDG commitment) have not been sufficiently answered by the authors. While I highly appreciate the extensive data and visualization presented (also in the supplementary material), the authors could have used more space for interpreting and reflecting on their results (sometimes less can be more).\nMinor comments:\n13. L52: Please correct “constitute” to “constitutes”\n14. L55: Are we not already beyond the halfway point to meet the SDGs?\n15. L62: Please be consistent in the use of italics for goal descriptions\n16. L65: Key development dimensions such as?\n17. L68: Variables or entities such as?\n18. L94-96: Briefly describe here what “specialization” in the RCA means in your study\n19. L98: Explain here briefly what the CSI and GSI cover\n20. L102: I would change “correlation” to “relationship to” to avoid misunderstandings (or correspondence, as used in Supplementary Table 1)\n21. L184 vs L194: Parenthesis “(Fig 3a-c)” inconsistent\n22. L210-11: Please elaborate on what you mean with “moved ahead” in a few words\n23. L241-42: Please add here that countries were sorted from lowest to highest SDG rank\n24. Fig 5: Add headings (Stage 1-4) similar to other figures in manuscript\n25. L246: “The conditions of 17 respective goals” could be reformulated to be more precise\n26. L322: When speaking of SDGs related to “economic development and basic livelihood security (e.g., SDGs 1,2,3,4,7)”, you should clearly include SDG 8 and might want to consider also SDG 11\n27. L324-27: You could also refer here more strongly to SDG-related research, indicating a general trend in prioritization of social and economic SDGs over environmental ones (e.g. Chapter 6: “Planetary integrity” (Kotzé et al., 2022) in “The Political Impact of the Sustainable Development Goals Transforming Governance Through Global Goals?”, pp. 140 – 171; DOI: https://doi.org/10.1017/9781009082945.007\n28. L369: “and the other targets are not shown” – consider rephrasing this\n29. L370: “countries” needs to be written with a capital letter\n30. L377-79: Consider is you can justify the sentence “these are not their active choices” based on your analysis (see my comment above)\n31. L398-402: These limitations should be addressed more explicitly earlier in the manuscript (see my comment above)\n32. L434-455: If I understand correctly, GSI (similar to PCI) aims at reflecting the complexity in achieving more sustainable outcomes. I would suggest including this explanation in this subsection, and maybe also already earlier in the manuscript\n33. L438: Please correct method “is” instead of “are”\n34. L441: You refer to SDG score in the text, but to SDG index value in Supplementary Fig. 8 – please check your figures, text and tables for consistency\n35. L450: Further details on the criteria for setting initial values would be helpful\n36. L463: Can you provide any reference to the kamada_kawai layout algorithm\n37. L463: Consider rephrasing “And then” to “Subsequently”\n38. L494: Please correct space with small “s”\n39. L524-5: Please indicate that you refer to 98 “global” indicators (not targets) available, since the dataset contains additional (e.g. regional) data as well. Furthermore, you could reflect a bit more on the quality and potential limitation of the SDR data. Despite efforts to include data from a range of sources, there may still be gaps in coverage, particularly for certain regions or SDG targets. Uneven availability of data across countries and sectors could result in skewed representations of SDG performance, limiting the generalizability of the findings.\n40. Supplementary material, S87, Fig 9: “c” for Figure 9c. should be written in bold", "reviewer_3": "The paper is well written and the authors invested a lot of efforts in explaining the methodology and the potential value of its output by providing valuable orientation for countries to find out to what extent they are on track in regard to the UN SDGs to be achieved by 2030.\nIt is argued that so far country trajectories in the pursuit of the UN Sustainable Development Goals have been poorly explored because the methodology to do so was largely missing. The authors propose to change this by making use of the methodology developed to capture a country's degree economic complexity(EC). Its product space method employs network analysis to reveal the relatedness, or affinity, between economies and applies it to cluster the measured targets of the different UN SDGs highlighting also trajectories of specialization patterns of regions and nations.\nThe resulting ‘SDG space’ reveals the sustainable development trajectories of 166 nations for the period 2000 to 2022. The SDG space then allows to identify ‘orphaned’ areas with insufficient progress while highlighting future challenges in realizing SDGs.\nIn their mapping and analysis of country-based UN SDG performance and evolution, the authors largely rely on data collected by numerous international institutions to assess if countries are on track in regard to the numerous targets for each of the 17 UN SDGs. In this context, they heavily rely on the 2023 Sustainable Development Report that indexed and evaluated the evolving country performance in regard to the UN SDGs and its targets. Based on secondary macro data analysis, they have identified and visualized 3,818 SDG spaces for 166 nations spanning the years 2000 to 2022 (http://www.spacelab.team/#/SDGSpace) and found underlying structures that largely mirror the fact the low income countries and high income countries face different challenges in meeting the different UN SDGs.\nI wonder however to what extent it makes sense to talk of 'comparative advantage' of developing countries (based on RCA Assessment) in meeting certain SDG Goals and Targets that largely mirror the fact that they are low income countries with a lower environment footprint and lower health risk exposure related to problems that largely affect affluent countries (e.g. obesity). None of these countries probably have an interest in remaining 'competitive' with regard to these goals and targets because they largely reflect the lack of ability to cover the essential material needs of the people. This is also reflected in the obvious fact that they perform poorly with regard to UN SDG 1,2,4, 8 and 9 that improve with economic development. So talking of a comparative advantage may be misleading. Another weakness is the relatively uncritical embrace of the secondary data gathered on the macro-level by international institutions. They tend to start from the misleading baseline assumption (reflected in many UN Reports on the SDGs) that state bureaucracies are in a position to enable a country to meet the UN SDGs and its targets. The state may provide well-designed policies to incentivize innovation and entrepreneurship moving into a publicly desirable direction; but, ultimately, inclusive and sustainable change is not enabled by regulation and protest but investments in the real economy. UN SDG 8 and 9 emphasize this point, but none of the indicators used to measure its development-related targets actually care about entrepreneurship on the ground. This is a major weakness of gathering data only on the macro-level because they do not allow for any differentiation within a country (emergence of vibrant and innovative economic ecosystems in unexpected places). There is a lack of references on these controversial aspects behind the official UN SDGs rhetoric.\nNevertheless, applying the EC methodology to do a mapping that shows the trajectories of countries in their pursuit of the UN SGS and its targets may help provide valuable orientation for policy makers by making it transparent that the main enemy of sustainability in high income countries is affluence (leading to overuse of natural resources, large greenhouse gas emissions and material waste) whereas it is poverty in low income countries (lack of ability to provide the right to food, health, education, employment, etc as well as depletion of natural resources and political instability due to population growth and lack of economic opportunities). This should also be made more explicit in the paper. The methodology is surely sound but the authors need to drop the 'comparative advantage' language and they will have to highlight the limits of the approach in view of the exclusive reliance on macro-level data. Finally, the paper does not contain any concluding remarks that describe major findings as well as weaknesses that may have to be addressed in future research."}, "author_response": "Response to Referees for Nature Communications manuscript NCOMMS-24-21670\nResponse to Referees for Nature Communications manuscript NCOMMS-\n24-21670, The Sustainable Development Trajectories of Nations\nAcknowledgements\nWe would like to thank the anonymous reviewers, for providing insightful comments and\nconstructive suggestions for improving and balancing the manuscript, adding important\nexplanations, discussions and examples. We have responded to each in the respective sections\nbelow.\nGeneral remarks\nWe enthusiastically submit our revised manuscript, in light of reviewers' helpful remarks and\noverall endorsement (reviewer 1, “…the paper can make new, important contributions… the\npaper would add practically useful advice for countries…”; reviewer 2, “…the manuscript\nprovides a valuable contribution to the analysis of countries’ pathways towards greater\nsustainability… the approach chosen is systematic and sophisticated, employing\nmethodologies innovatively in the context of sustainable development… the manuscript\ncontributes to advancing knowledge in the field of sustainable development… I sincerely\nappreciate that the authors want to make their results on SDG spaces publicly available and\neasily accessible through the dedicated web-interface”; reviewer 3, “…the paper is well written\nand the authors invested… efforts in explaining the methodology and the potential value of its\noutput by providing valuable orientation for countries to find out to what extent they are on\ntrack in regard to the UN SDGs to be achieved by 2030… the resulting 'SDG space' allows to\nidentify ‘orphaned’ areas with insufficient progress while highlighting future challenges in\nrealizing SDGs…”).\nWe confirm that this manuscript is 5,262 words in length (excluding Abstract, Methods, and\nReferences, and Figure captions), slightly above the 5,000 words limit – to allow major\nrevisions, to acknowledge limitations, and to satisfy the review process as a whole.\nWe note that the majority of reviewers' comments do not relate to issues of article aims and\nscope, structure, analytical approach, or contribution to knowledge with respect to our original\nsubmission. Rather, reviewers' comments mainly focus on requests for additional explanations\n(e.g., methods; RCA values, CSI and GSI values), discussions and elaborations on findings\n(e.g., comparisons to the UN dataset, and previous selected studies), and examples to\nemphasize the value of the Product Space approach and associated techniques (e.g., country-\nspecific analysis and comparisons), along with acknowledging several limitations (e.g., overlap\nof indicators withing the original SDG framework).\nWhile we were able to address all reviewers’ comments to – what we hope is – a satisfactory\nextent, we note that the word limits of the Article format confine the extent to which we were\nable to provide further analyses warranted by a number of comments (e.g., we extend our\ncountry-specific analysis, but cannot discuss all countries; at the same time, our online webpage\nallows further, open-access, analyses and comparisons). Nonetheless, the revised manuscript\npays attention to all these important matters (e.g., country-specific analysis, elaboration of the\nresults, discussing the comparative advantage approach).\nWe hope that our revisions have given suitable recognition of these matters, while keeping the\ncontent of our manuscript in line with our aim of providing a single, succinct piece that\nhighlights the potential of the Product Space approach and techniques to reveal useful patterns\nunderpinning the sustainable development trajectories of nations, as well as providing\nsomewhat of a \"road map\" for future policies and studies in this consequential space.\nWe wish to thank the reviewers, for the opportunity to elaborate, and precise our arguments,\nand improve the manuscript as a whole.\nReviewer #1\nGeneral Remarks of Reviewer #1\nThis paper applies the “product space” technique developed by Hidalgo et al. (2007) to\nconstruct an SDG space for countries. Analyzing data for 166 countries between 2000 and 2022,\nthe results suggest that there exists a bipolar world, with distinct groups of countries\nspecializing in specific targets. The results also point to under-developed SDG targets and three\ncountry development trajectories: i) under-specialization in targets relating to environmental\nquality and overnutrition; ii) specialization in targets relating to poverty eradication and\neducation; and iii) a follow-up emphasis on targets pertaining to labor rights, and research and\ndevelopment. While the research questions are relevant and the results appear reasonable (but\nnote some of them are not exactly new findings), the analysis leaves room for improvement. I\nhave some comments below that are in no particular order that may help improve the paper.\nResponse to 1\nThank you for your summary of our manuscript and your overall support regarding the research\nquestions and results, as well as your recommendations for improvement. We greatly appreciate\nyour feedback, and observations concerning the need for further clarification and elaboration\non the cross-country and country-specific analyses. These insights enhance the rigor of our\nstudy.\nIn response, we have undertaken substantial revisions to address these comments, ensuring that\nthe manuscript aligns with the journal’s guidelines while also enriching the analysis to better\nserve the objectives of our research.\nComment 1.1\n1. The paper can make new, important contributions on two fronts: cross-country analysis and\ncountry-specific analysis. While the paper demonstrates to some degree that it could analyze\ncross-country trends over time, it generally falls short of offering the level of detailed country-\nspecific analysis that can provide country-specific relevant advice for actionable policies. So\nboth types of analysis will need more improvement. I will go into more detailed feedback on\neach below.\nResponse to 1.1\nWe fully agree with this comment. The original manuscript could have provided a more detailed\nexploration of country-specific and cross-country developments over time. In this revised\nversion, we have included such analyses for China (see Fig. 8).\nTo further strengthen the manuscript, we have incorporated additional case studies (or use cases)\nto better illustrate the advantages of our approach. Specifically, for the country-specific\nanalysis, we have added more case studies and designed new analytical charts that highlight\npotential future policy directions for the countries studied.\nFor the cross-country analysis, we have enhanced our approach by categorizing environment-\nrelated SDG indicators separately from other SDG indicators, allowing for a more nuanced\nanalysis.\nDetailed responses to each of your specific comments, along with the corresponding revisions,\nare presented in the point-by-point response that follows.\nComment 1.2\n2. For cross-country analysis, the main results in the paper are that developed countries have a\nrelative advantage related to zero hunger, living security, economic development, labor rights,\nnational statistical system performance, and protection of life on land. In contrast, developing\ncountries have a relative advantage in SDG targets related to overnutrition, social and\nenvironmental impacts of imports, resource use and carbon emissions, and corporate tax havens.\nI am not sure I am convinced by this somewhat simple dichotomy of findings. Two populous\ndeveloping countries, China and India, account for roughly more than 40% of the population\nin the developing world. These two countries have long been known to struggle with\nenvironmental degradation issues, including air and water pollution. Some emerging countries\nwith a large population such as Bangladesh and Vietnam also face with similar environmental\nchallenges recently, both of these countries frequently top the list of the globally most polluting\ncities. In fact, on page 17, the authors acknowledge themselves that China has a problem with\nCO2 emission from fossil fuel and cement production. These contradictory results need careful\nexplanation.\nResponse to 1.2\nThank you for this comment. We agree that the previous manuscript did not sufficiently\nsummarize the findings from the cross-country analysis. To address this, we have revised the\nsection titled \"Underlying Structures of the SDG Space of Nations\" in the manuscript.\nSpecifically, we have rephrased the description of the SDG space as follows:\nLines 134-142:\nCountries at an advanced stage tend to have relative advantages in indicators related to poverty\nreduction (1.A), hunger reduction (2.E, 2.G), good health and well-being (3.B, 3.D, 3.F, 3.G,\n3.I, 3.K, 3.M), wastewater and air pollution treatment (3.H, 6.D, 14.B), access to clean energy\nand water (6.B, 6.E, 7.A), industry, innovation and infrastructure (Goal 9), as well as\ngovernment administration (16.B, 16.C, 16.I, 17.B, 17.C) . In contrast, countries at a primary\nstage show relative advantages in achieving SDG indicators related to overnutrition (2.B, 2.F),\nembodied social and environmental impacts in international trade (6.C, 8.C, 8.D, 12.D, 12.F,\n13.C, 14.A, 15.A, 16.A), resource use, waste and emissions (6.A, 7.B, 12.A, 12.C, 12.E, 12.G,\n13.B), and issues related to corporate tax havens (17.A).\nFurthermore, in the section \"Sustainable Development Paths of Nations,\" we categorized the\nenvironment-related and non-environment-related SDG indicators into six distinct groups\nusing the Goal Sustainability Index (GSI) (please refer to the figure below). Our analysis\nrevealed that for both environment-related and non-environment-related SDG indicators, the\nRCA values for higher GSI indicators tend to increase as the national SDG index grows.\nConversely, the RCA values for lower GSI indicators decrease as the national SDG index grows.\nThis finding provides a solid foundation for summarizing our findings.\nWe have modified the corresponding text as follows:\nLines 259-275:\nTo identify more refined trends, we categorize the SDG indicators into six groups based on\ntheir GSI values (Fig. 5): three groups of environment-related indicators (Table 1 in Methods)\nand three groups of non-environment-related indicators (Table 2 in Methods). Our findings\nreveal that environment-related indicators with high GSI values primarily involve wastewater\ntreatment, air pollution control, and access to clean energy, which tend to be specialized by\ncountries at advanced stages of development. On the other hand, indicators with low GSI values,\nsuch as trade-related pollution and emissions, as well as waste and emissions from production\nand living, are more commonly associated with countries in the early stages of development.\nSimilarly, non-environment-related indicators with high GSI values predominantly include no\npoverty, zero hunger, good health and well-being, quality education, industry, innovation and\ninfrastructure, and effective government administration. In contrast, those with low GSI values\nmainly involve overnutrition and the social impacts embodied in international trade.\nFor both environment-related and non-environment-related SDG indicators, the RCA values\nfor higher GSI indicators increase with the growth of the national SDG index, while the RCA\nvalues for lower GSI indicators tend to decrease as the national SDG index rises.\nFig. 5 | The change in RCA for six groups of SDG indicators across window groups ranked\nby SDG scores in 2022. Panels a, b, and c, show the condition for three groups of environment-\nrelated indicators, categorized by the goal sustainability index (GSI). a, Group 1, GSI > 0.5; b,\nGroup 2, -1.0 ≤GSI ≤ 0.5; c, Group 3, GSI < -1.0. Panels d, e, and f, display the conditions for\nthree groups of non-environment-related indicators. d, Group 4, GSI > 0.5; e, Group 5, -1.0\n≤GSI ≤ 0.5; f, Group 6, GSI < -1.0. Note: the window size was determined by grouping 50\ncountries at a time, resulting in 117 window groups (i.e., countries 1-50, 2-51, …, 117-166).\nFinally, we have carefully reviewed and addressed any contradictory results to ensure\nconsistency throughout the manuscript. In particular, we have added more case studies and\nrevised the analysis for China, which is categorized as Stage 3 (High-medium stage), in the\nDiscussion Section. Please refer to our response to your Comment 1.3 for detailed information\non these revisions.\nComment 1.3\n3. Regarding country-specific analysis, I similarly find the brief discussion on China (page 17)\ninadequate. The paper would add practically useful advice for countries if it can zoom in on a\ncouple of countries as case studies (perhaps with different development trajectories), where it\napplies the proposed framework of analysis and subsequently clearly identifies the weaknesses\nand strengths for each country. For now, the current analysis appears to be too vague and high-\nlevel to be useful for any particular country.\nResponse to 1.3\nThank you for your valuable and constructive suggestion. In line with your recommendation,\nwe have incorporated additional case studies that focus on country-specific analyses.\nFurthermore, we have developed scatter diagrams to illustrate potential future policy directions\nfor different countries.\nArea I: Countries with high RCA and SDG scores for SDG indicators have reached a relatively\nideal situation.\nArea II: Countries with high RCA for SDG indicators but low SDG scores find it relatively\ndifficult to improve these indicators, likely because the global average for the indicator is low.\nThis suggests it could be challenging for other countries to enhance these indicators and\nprovide successful examples.\nArea III: Countries with low RCA and SDG scores for SDG indicators demonstrate poor\nrelative and absolute performance on these indicators, indicating these indicators should be\nprioritized for improvement.\nArea IV: Countries with low RCA for SDG indicators but relatively high SDG scores show\npoor relative performance but strong absolute performance. Improvement in these indicators\nmay be more feasible, as the high global average for these indicators suggests that other\ncountries are performing better and could serve as a reference for success.\nBy categorizing countries' performance on these indicators, we can offer more tailored and\nprecise policy recommendations.\nThe detailed revisions are as follows:\nLines 418-432:\nIn terms of policy guidance, the comparative insights and identified trends in sustainable\ndevelopment trajectories can provide targeted recommendations (Fig. 8). From a static\nperspective, countries’ performance on the indicators can be categorized into four areas (Fig.\n8b, d, f, h): Area I (high RCA and SDG Scores) represents an ideal situation. Area II (high RCA\nbut low SDG scores) where indicators are relatively difficult for a specific country to improve,\nas other countries are also likely to struggle in making progress and providing successful\nexamples. Area III (low RCA and SDG Scores) represents the worst situation, where both\nrelative and absolute performance on these indicators is poor. Area IV (low RCA but relatively\nhigh SDG scores) where indicators may be easier to improve, as other countries might be\nperforming better and could offer models for success.\nTaking the USA as an example (Fig. 8h), beyond the indicators in Area III — such as\novernutrition (2.B, 2.F) and waste and emissions (12.A, 12.C, 12.E, 13.B) — focus should also\nbe placed on addressing social and environmental impacts linked to imports (8.D, 12.F, 14.A),\ninequality (10.A), and Corporate Tax Haven (17.A) in Area IV, despite these indicators having\nabsolute scores above the global average.\nAdditionally, in our original manuscript, we categorized countries into four development stages:\nStage 1 (Primary stage), Stage 2 (Low medium stage), Stage 3 (High medium stage), and Stage\n4 (Advanced stage). From a dynamic viewpoint, the revealed patterns of trajectories can serve\nas early warnings of potential challenges that countries may face as they progress from a lower\nto a higher stage of sustainable development.\nLastly, we offered a detailed presentation of SDG spaces for 166 countries from 2000 to 2022\nin our Supplementary Materials and on a dedicated website\n(http://www.spacelab.team/#/SDGSpace). This allows further analyses.\nFig. 8 | The SDG space and indicator performance for four representative countries in\n2022. Panels a-b, Ethiopia (Stage 1). Panels c-d, India (Stage 2). Panels e-f, China (Stage 3).\nPanels g-h, United States (Stage 4). In panels a-h, blue nodes represent the top 20 SDG\nindicators by RCA values, while red nodes represent the bottom 20. The remaining indicators\nare not highlighted. The size of each node corresponds to the absolute RCA value. Countries\nare evenly divided into four stages according to their absolute score rankings: Stage 1 (Primary\nstage), Stage 2 (Low medium stage), Stage 3 (High medium stage), and Stage 4 (Advanced\nstage). Stage 4 is further divided into two halves to identify the top 20 increasing and decreasing\nindicators in RCA for the USA (in the first half of Stage 4) between its current stage and the\nnext stage (see Supplementary Table 1). In panels b, d, f, h, scatter diagrams are divided into\nfour areas by the lines at RCA = 0 and the global average SDG score of 63. Area I: Countries\nwith high RCA and SDG Scores for these indicators have reached a relatively ideal situation.\nArea II: Countries with high RCA but low SDG scores for these indicators face relative\ndifficulties in improving them, likely due to low global averages for these indicators. Area III:\nCountries with low RCA and SDG Scores for these indicators show both relative and absolute\nunderperformance, indicating that these indicators should be prioritized for improvement. Area\nIV: Countries with low RCA but relatively high SDG scores for these indicators demonstrate\npoor relative performance but strong absolute performance.\nComment 1.4\n4. I would also like to see more engagement and comparison of the paper findings with those\nin the existing literature. For example, (despite the shortcomings discussed in point 2 above),\nhow does the dichotomy of findings for developed and developing countries discussed above\nrelate to previous studies? Are they completely new? Are they backed up by at least some\nprevious qualitative analysis? I have similar questions with the country development\ntrajectories discussed in Figure 3 and Figure 6.\nResponse to 1.4\nThank you for your helpful suggestion. Based on this comment, we have restructured the\ndiscussion to provide a more thorough comparison between previous relevant studies and our\ncurrent research.\nRegarding your question: “Are they backed up by at least some previous qualitative analysis?”,\nwe have addressed this in the Discussion Section:\nLines 374-382:\nThis bipolar world revealed by the SDG space is supported by previous studies37–39 that have\nidentified global disparities in SDGs, and it can be partly explained by the trade-offs between\nSDGs12,18,40–43. For instance, the SDGs related to economic development and basic livelihood\nsecurity (e.g., SDGs 1, 2, 3, 4, 7, 8, 11), are often associated with higher environmental and\nresource footprints44–47 and increased greenhouse gas emissions48, which can hinder progress\non SDGs 12 and 1349. These trends align with the concept of the environmental Kuznets\ncurve50,51, which suggests that in the early stages of a nation’s economic development, the\npursuit of economic growth tends to take precedence over environmental preservation concerns.\nRegarding the comparison with previous studies, we have addressed this as follows:\nLines 390-417:\nDistinct from earlier notable studies that assessed countries’ SDG performance3–8, such as those\nranking countries and evaluating their progress towards achieving the SDGs6 or exploring\ndevelopment patterns12–19, our study introduces several key features that set it apart.\nFirst, we employed a comparative framework to identify each country’s relative strengths and\nweaknesses, as well as to pinpoint ‘orphaned’ SDG indicators in specific development areas.\nThis comparative analysis, which contextualizes performance by benchmarking different\nvariables or entities against each other, offers a clearer understanding of each country’s relative\nstanding. In contrast, absolute scoring (Figs. 3d-f) can obscure these insights by focusing solely\non an overarching SDG score without the nuanced understanding that comes from comparison\nwith other entities and SDGs.\nSecond, we utilized the ‘product space’ method and the broader economic complexity approach\nas effective tools for pattern recognition. These methods help identify national sustainable\ndevelopment trends and reveal whether countries specialize in certain goals. For instance, the\nderived GSI indicator divides SDG indicators into low-GSI indicators that countries tend to\noverlook and high-GSI indicators that countries tend to specialize in during their development\nprocess (Figs. 3, 5, 6). Additionally, we delineated the development trajectories of nations\nbased on the historical performance of all countries, differing from the Sustainable\nDevelopment Report6, which evaluates whether a country is on track based on its individual\nperformance.\nThird, we introduced the concept of the ‘SDG space’ as a high-resolution tool to monitor\ncountries’ performance across 96 SDG indicators. This tool visualizes countries’ relative\nperformance and assists in guiding the formulation of development strategies (Supplementary\nFigs. 12-177). Moreover, we developed a dedicated website to present 3,818 SDG spaces for\n166 nations spanning the years 2000 to 2022: http://www.spacelab.team/#/SDGSpace. Unlike\nthe traditional product space20, which typically ranks countries and guides them towards\nadvanced products (e.g., motor vehicles and electronics), the SDG space is designed to assist\ncountries monitor their progress within the SDG framework, identifying relative strengths and\nweaknesses, rather than ranking countries.\nComment 1.5\n5. The paper seems to take for granted that the SDG targets (and indicators) are non-\noverlapping. This is not true, as discussed in many previous studies. In fact, a useful robustness\ncheck is to better understand whether these non-overlapping (similar) targets would end up\nhaving similar score/ color/ node size in the map?\nResponse to 1.5\nThank you for your comment. The authors do not take for granted that the SDG targets (and\nindicators) are non-overlapping. To reflect this recognition, a reference source\n(https://www.nature.com/articles/s41598-021-01801-6) was added to the Methods section,\nacknowledging the potential overlap between several SDG indicators.\nMoreover, in response to your comment, we screened six groups of similar indicators: (1.A &\n1.B), (2.B & 2.F), (2.E & 2.H), (3.G & 3.L), (10.A & 10.B), and (14.D, 14.E & 14.F). To\nconduct a robustness check, we retained only the non-overlapping indicators within these\ngroups and redrew the SDG space map (Supplementary Fig. 10). The resulting map exhibits\npatterns similar to those observed in Fig. 1 of the main text, confirming the consistency of our\nfindings.\nThe revisions are as follows:\nLines 709-739 in Robustness check of Methods (please also see our response to 1.7):\nTo ensure the robustness of our findings, we evaluated the results from the following\nperspectives: (1) whether a consistent SDG space structure can be reproduced using data from\ndifferent years; (2) whether the SDG space structure remains similar (i.e., stable) after\nexcluding overlapping indicators; (3) whether the structure is consistent across different data\nsources; (4) whether the bipolar world results hold under varying similarity thresholds; and (5)\nwhether consistent evolution patterns of SDG indicators emerge with different moving window\nsizes.\n... ...\n(2) Notably, some SDG indicators may overlap, such as 2.B (Prevalence of obesity) and 2.F\n(Human Trophic Level) in this study. To ensure the robustness of our results, we removed the\noverlapping indicators, retaining only one unique indicator, and re-examined the patterns\n(Supplementary Fig. 10). The findings remained robust, confirming the consistency of our\nanalysis.\nSupplementary Fig. 10 in the Supplementary Materials is as follows:\nSupplementary Figure 10 | The SDG space in 2022 with non-overlapping indicators. We\nscreened six groups of similar indicators: (1.A and 1.B), (2.B and 2.F), (2.E and 2.H), (3.G and\n3.L), (10.A and 10.B), and (14.D, 14.E, and 14.F). For the robustness check, we retained only\nthe non-overlapping indicators within these groups. The node colour represents Goal\nSustainability Index (GSI), and the node size corresponds to the node degree, indicating the\nnumber of edges connected to the node.\nComment 1.6\n6. Certain SDG goals/ targets may involve tradeoffs for countries, particularly poorer countries.\nFor example, faster poverty reduction may often come with faster environmental degradation.\nThe paper does not discuss this issue at all. One implication is that should these tradeoff targets\nend up, by definition, in the red and blue clusters of the proposed framework?\nResponse to 1.6\nThank you for this comment. Indeed, certain SDG goals and targets may involve tradeoffs for\ncountries. Although critiquing the SDG framework is not the purpose of our study, we agree\nwith your observation. Following your suggestion, we have highlighted this issue in the\nDiscussion Section.\nMoreover, we have added several reference sources to support this claim, including:\nFader, M., Cranmer, C., Lawford, R., & Engel-Cox, J. (2018). Toward an understanding of\nsynergies and trade-offs between water, energy, and food SDG targets. Frontiers in\nEnvironmental Science, 6, 112.\nSingh, G. G., Cisneros-Montemayor, A. M., Swartz, W., Cheung, W., Guy, J. A., Kenny, T. A., ...\n& Ota, Y. (2018). A rapid assessment of co-benefits and trade-offs among Sustainable\nDevelopment Goals. Marine Policy, 93, 223-231.\nScherer, L., Behrens, P., De Koning, A., Heijungs, R., Sprecher, B., & Tukker, A. (2018). Trade-\noffs between social and environmental Sustainable Development Goals. Environmental Science\n& Policy, 90, 65-72.\nBarbier, E. B., & Burgess, J. C. (2019). Sustainable development goal indicators: Analyzing\ntrade-offs and complementarities. World Development, 122, 295-305.\nWe have modified the text as follows:\nLines 374-382:\nThis bipolar world revealed by the SDG space is supported by previous studies37–39 that have\nidentified global disparities in SDGs, and it can be partly explained by the trade-offs between\nSDGs12,18,40–43. For instance, the SDGs related to economic development and basic livelihood\nsecurity (e.g., SDGs 1, 2, 3, 4, 7, 8, 11), are often associated with higher environmental and\nresource footprints44–47 and increased greenhouse gas emissions48, which can hinder progress\non SDGs 12 and 1349. These trends align with the concept of the environmental Kuznets\ncurve50,51, which suggests that in the early stages of a nation’s economic development, the\npursuit of economic growth tends to take precedence over environmental preservation concerns.\nComment 1.7\n7. In general, are there any test statistics (or math quantities) to show that the bipolar patterns\nin the figures are not artificially caused by certain parameters? In other words, could the authors\nshow different robustness checks to confirm these bipolar patterns? In addition, besides a\nbipolar pattern, could there be instead a spectrum/ range of values where countries may form\nseveral smaller clusters on this range instead of the bipolar pattern?\nResponse to 1.7\nThank you for your comment and suggestion.\nIn the previous manuscript, we conducted statistical tests for the bipolar patterns and performed\na sensitivity analysis on the threshold settings (Supplementary Fig. 9). Our analysis revealed\nthat the determination of the proximity threshold does not affect the structure of the SDG Space.\nWe apologize for not making this clearer in the original text.\nIn this revised manuscript, we have added the robustness check part in the Methods section.\nIn addition, following your suggestions, we examined the robustness of our results from the\nfollowing perspectives: (1) whether a consistent SDG space structure can be reproduced with\ndata from different years; (2) whether the SDG space structure remains similar after excluding\noverlapping indicators; (3) whether the structure is consistent across different data sources;\n(4) whether the bipolar world results hold under varying similarity thresholds; and (5)\nwhether consistent evolution patterns of SDG indicators emerge with different moving window\nsizes.\nThe revisions are as follows.\nLines 709-739, Robustness check Section of the Methods:\nTo ensure the robustness of our findings, we evaluated the results from the following\nperspectives: (1) whether a consistent SDG space structure can be reproduced using data from\ndifferent years; (2) whether the SDG space structure remains similar (i.e., stable) after\nexcluding overlapping indicators; (3) whether the structure is consistent across different data\nsources; (4) whether the bipolar world results hold under varying similarity thresholds; and (5)\nwhether consistent evolution patterns of SDG indicators emerge with different moving window\nsizes.\n(1) Supplementary Fig. 3 demonstrates that a similar SDG space structure is observed across\nthe years of 2000, 2015, and 2022. This consistency indicates that our finding of a bipolar world\nwithin the SDG space is robust and not influenced by the choice of years.\n(2) Notably, some SDG indicators may overlap, such as 2.B (Prevalence of obesity) and 2.F\n(Human Trophic Level) in this study. To ensure the robustness of our results, we removed the\noverlapping indicators, retaining only one unique indicator, and re-examined the patterns\n(Supplementary Fig. 10). The findings remained robust, confirming the consistency of our\nanalysis.\n(3) To validate the robustness of our results using different data sources, we collected additional\ndata from the UNSD SDG database. For consistency in comparison, we included only the data\non targets that matched the 96 indicators used in this study (see Tables 1 and 2 in Methods for\nthe matching process). Data source: https://unstats.un.org/sdgs/dataportal/database. As shown\nin Fig. 1 (SDR 2023 database) and Supplementary Fig. 11 (UNSD SDG database), the SDG\nspaces exhibit a similar structure.\n(4) In this study, we generated the SDG spaces by setting the similarity threshold between SDG\nindicators at 0.7. We tested the robustness of our results by varying the similarity values. As\nseen in Supplementary Fig. 9, the indicator clusters remain consistent, supporting the SDG\nspace structure.\n(5) We also examined whether varying the moving-window sizes would yield similar evolution\npatterns of SDG indicators. Supplementary Fig. 7 shows that the evolution patterns of SDG\nindicators and the ranking of countries’ SDG scores in 2022 remain consistent across moving-\nwindow sizes of 20, 30, 40, 50, 60, and 70.\nOverall, our findings demonstrate robustness across various tests and conditions.\nSupplementary Figs. 3, 7, 9-11 in the Supplementary Materials are as follows:\nSupplementary Figure 3 | The evolution of SDG space. Panels a, c, and e show the global\nSDG space in 2000, 2015, and 2022, respectively. Panels b, d, and f display the similarity of\nSDG indicators in 2000, 2015, and 2022, respectively.\nSupplementary Figure 7 | The evolution of SDG indicators alongside the ranking of\ncountries’ SDG scores in 2022 with varying moving-window sizes. Panels a, b, c, d, e, and\nf display the results using moving-window sizes of 20, 30, 40, 50, 60, and 70, respectively.\nSupplementary Figure 9 | Sensitivity analysis of SDG indicators’ similarity in 2022. Panel\na, SDG indicators with similarity ≥ 0.6. Panel b, SDG indicators with similarity ≥ 0.7. Panel c,\nSDG indicators with similarity ≥ 0.8.\nSupplementary Figure 10 | The SDG space in 2022 with non-overlapping indicators. We\nscreened six groups of similar indicators: (1.A and 1.B), (2.B and 2.F), (2.E and 2.H), (3.G and\n3.L), (10.A and 10.B), and (14.D, 14.E, and 14.F). For the robustness check, we retained only\nthe non-overlapping indicators within these groups. The node colour represents Goal\nSustainability Index (GSI), and the node size corresponds to the node degree, indicating the\nnumber of edges connected to the node.\nSupplementary Figure 11 | The SDG space in 2022 using UN SDG data. The node colour\nrepresents Goal Sustainability Index (GSI), and the node size indicates the node degree,\nrepresenting the number of edges connected to the node. This SDG space was produced using\ndata from the UNSD SDG targets framework, which were matched with the 96 SDG indicators\n(see Tables 1-2 in Methods). Data source: https://unstats.un.org/sdgs/dataportal/database.\nComment 1.8\n8. To better anchor the paper findings with the current literature, it would be useful to assess\nhow the country score rankings using the proposed framework compares with other overall\nSDG indexes such as that employed by the Sustainable Development Reports. In particular, it\nwould be useful to pick out some countries that change ranks using different methods for more\ndiscussion.\nResponse to 1.8\nThank you for this comment. In the original manuscript, we compared the Country\nSustainability Index (CSI) with the SDG Index scores.\nWe found that the CSI values are positively correlated with the SDG Index scores of countries\n(please see Lines 509-510 in Methods and Supplementary Fig. 8 in the Supplementary\nMaterials). Please see this comparison below.\nIt is important to note that our focus is not on the rankings of countries in SDG performance.\nInstead, we concentrate on the patterns underlying the sustainable development trajectories of\nnations across indicators and over time, and whether countries have so-called ‘orphaned’ (i.e.,\noverlooked) SDG indicators in certain development areas.\nTherefore, we did not add discussions on countries’ rankings in this revised manuscript, as it is\noutside the scope and aims of our study.\nLines 590-591:\nIn general, the CSI values show a positive correlation with the SDG scores of countries\n(Supplementary Fig. 8).\nSupplementary Figure 8 | The relationship between the country sustainability index (CSI)\nand the SDG index of countries in 2022. Panel a shows the CSI value versus SDG index\nvalue. Panel b indicates the CSI rank versus the SDG index rank.\nComment 1.9\nOther minor comments\n9. Not all readers are familiar with the SDGs, so it could be useful to add a table listing each\ngoal and target to the end of the Method section for easy reference.\nResponse to 1.9\nThank you for your suggestion\nBased on your recommendation, we have added two tables listing each indicator to the end of\nthe Data Section of Methods.\nThe detailed revision is as follows (Lines 699-707):\nTable 1 | Environment-related SDG indicators ranked by the Goal Sustainability Index\n(GSI) and their alignment with UN SDG taxonomies. The environment-related indicators\nare divided into three groups based on their GSI values. Group 1: GSI > 0.5. Group 2: -1.0 ≤\nGSI ≤ 0.5. Group 3: GSI < -1.0.\nIndicator\nGSI\ncode in Indicator code UNSD\nGroup in Indicator definitions UNSD match\nthis in SDR 2023 target\n2022\nstudy\n1.79 6.D sdg6_wastewat Anthropogenic wastewater that receives treatment 6.3.1 Match\nPopulation with access to clean fuels and technology\n1.08 7.A sdg7_cleanfuel 7.1.2 Closely aligned\nfor cooking\n0.83 3.H sdg3_pollmort 3.9.1 Match\nair pollution and ambient air pollution\n0.79 14.B sdg14_cleanwat Ocean Health Index: Clean Waters score 14.1.1 Closely aligned\nMean area that is protected in marine sites important\n0.27 14.C sdg14_cpma 14.5.1 Closely aligned\nto biodiversity\n0.27 2.C sdg2_pestexp Exports of hazardous pesticides 3.9 Closely aligned\n0.19 15.D sdg15_forchg Permanent deforestation 15.2 Closely aligned\nRenewable energy share in total final energy\n0.05 7.D sdg7_renewcon 7.2.1 Match\nconsumption\nAnnual mean concentration of particulate matter of\n-0.03 11.B sdg11_pm25 11.6.2 Match\nless than 2.5 microns in diameter (PM2.5)\n-0.21 14.E sdg14_fishstocks Fish caught from overexploited or collapsed stocks 14.4.1 Closely aligned\nMean area that is protected in terrestrial sites\n-0.27 15.C sdg15_cpta 15.1.2 Match\n2 important to biodiversity\nMean area that is protected in freshwater sites\n-0.30 15.B sdg15_cpfa 15.1.2 Match\nimportant to biodiversity\n-0.34 14.D sdg14_discard Fish caught that are then discarded 14.4 Closely aligned\n-0.34 14.F sdg14_trawl Fish caught by trawling or dredging 14.4 Closely aligned\n-0.57 15.E sdg15_redlist Red List Index of species survival 15.5.1 Match\n-0.65 2.D sdg2_snmi Sustainable Nitrogen Management Index 2.4 Closely aligned\n-0.72 13.A sdg13_co2export CO2 emissions embodied in fossil fuel exports 13.2 Closely aligned\n-0.82 6.A sdg6_freshwat Freshwater withdrawal 6.4.2 Match\nCO2 emissions from fuel combustion per total\n-0.95 7.B sdg7_co2twh 7.2 Closely aligned\nelectricity output\n-1.26 12.B sdg12_explastic Exports of plastic waste 12.4 Closely aligned\n-1.40 14.A sdg14_biomar Marine biodiversity threats embodied in imports 14.4 Closely aligned\n-1.54 12.E sdg12_nprod Production-based nitrogen emissions 9.4 Closely aligned\n-1.59 12.D sdg12_nimport Nitrogen emissions embodied in imports 9.4 Closely aligned\n-1.59 13.C sdg13_co2import CO2 emissions embodied in imports 13.2 Closely aligned\n-1.62 12.A sdg12_ewaste Electronic waste 12.4.2 Match\nTerrestrial and freshwater biodiversity threats\n3 -1.64 15.A sdg15_biofrwter 15.5 Closely aligned\nembodied in imports\n-1.66 12.F sdg12_so2import SO2 emissions embodied in imports 9.4 Closely aligned\n-1.67 6.C sdg6_scarcew Scarce water consumption embodied in imports 6.4 Closely aligned\n-1.67 12.C sdg12_msw Municipal solid waste 12.5 Closely aligned\nCO2 emissions from fossil fuel combustion and\n-1.76 13.B sdg13_co2gcp 13.2.2 Closely aligned\ncement production\n-1.84 12.G sdg12_so2prod Production-based SO2 emissions 9.4 Closely aligned\nTable 2 | Non-environment-related SDG indicators ranked by the Goal Sustainability\nIndex (GSI) and their alignment with UN SDG taxonomies. The non-environment-related\nindicators are divided into three groups based on their GSI values. Group 4: GSI > 0.5. Group\n5: -1.0 ≤ GSI ≤ 0.5. Group 6: GSI < -1.0.\nIndicator\nGSI\ncode in Indicator code in\nGroup in Indicator definitions UNSD target UNSD match\nthis SDR 2023\n2022\nstudy\nExpenditure on research and\n1.98 9.E sdg9_rdex 9.5.1 Match\ndevelopment\n1.87 9.A sdg9_articles Articles published in academic journals 9.5 Closely aligned\n1.30 3.G sdg3_neonat Neonatal mortality rate 3.2.2 Match\nUniversal health coverage (UHC) index\n1.29 3.M sdg3_uhc 3.8.1 Match\nof service coverage\nAdults with an account at a bank or\n1.23 8.A sdg8_accounts other financial institution or with a 8.10.2 Match\nmobile-money-service provider\nProportion of urban population living in\n1.17 11.C sdg11_slums 11.1.1 Match\nslums\nThe Times Higher Education\n1.08 9.G sdg9_uni Universities Ranking: Average score of - Not in UNSTATS\ntop 3 universities\n1.08 3.I sdg3_swb Subjective well-being 3.4 Closely aligned\nLogistics Performance Index: Quality of\n1.08 9.C sdg9_lpi 9.1 Closely aligned\ntrade and transport-related infrastructure\n1.07 16.C sdg16_cpi Corruption Perceptions Index 16.5.1, 16.5.2 Closely aligned\n1.06 3.K sdg3_traffic Traffic deaths 3.6.1 Match\n1.05 1.A sdg1_lmicpov Poverty headcount ratio at $3.65/day 1.1.1 Match\n1.04 4.D sdg4_second Lower secondary completion rate 4.1.2 Match\n1.03 3.D sdg3_lifee Life expectancy at birth 3.1:3.9 Closely aligned\n1.02 9.B sdg9_intuse Population using the internet 17.8.1 Match\n4 1.01 9.F sdg9_roads Rural population with access to all- 9.1.1 Match\nseason roads\nParticipation rate in pre-primary\n1.01 4.A sdg4_earlyedu 4.2.2 Closely aligned\norganized learning\n0.98 3.B sdg3_fertility Adolescent fertility rate 3.7.2 Match\nPopulation using at least basic sanitation\n0.97 6.B sdg6_sanita 6.2.1 Closely aligned\nservices\nPrevalence of stunting in children under\n0.95 2.E sdg2_stunting 2.2.1 Match\n5 years of age\n0.94 9.D sdg9_mobuse Mobile broadband subscriptions 9.c.1, 17.6.1 Closely aligned\n0.91 2.G sdg2_undernsh Prevalence of undernourishment 2.1.1 Match\n0.84 17.C sdg17_statperf Statistical Performance Index 17.18.1: 17.19.2 Closely aligned\nExpropriations are lawful and\n0.81 16.E sdg16_exprop 16.6 Closely aligned\nadequately compensated\nAge-standardized death rate due to\ncardiovascular disease, cancer, diabetes,\n0.75 3.F sdg3_ncds 3.4.1 Match\nor chronic respiratory disease in adults\naged 30–70 years\n0.66 2.A sdg2_crlyld Cereal yield 2.3, 2.4 Closely aligned\nGovernment spending on health and\n0.66 17.B sdg17_govex 1.a.1 Closely aligned\neducation\n0.58 16.B sdg16_clabor Children involved in child labor 8.7.1 Closely aligned\nFundamental labor rights are effectively\n0.52 8.E sdg8_rights 8.8.2 Match\nguaranteed\nPopulation who feel safe walking alone\n0.51 16.I sdg16_safe at night in the city or area where they 16.1.4 Match\nlive\n0.47 10.B sdg10_palma Palma ratio 10.1 Closely aligned\n5\n0.47 16.D sdg16_detain Unsentenced detainees 16.3.2 Match\nIndicator\nGSI\ncode in Indicator code in\nGroup in Indicator definitions UNSD target UNSD match\nthis SDR 2023\n2022\nstudy\n0.42 1.B sdg1_wpc Poverty headcount ratio at $2.15/day 1.1.1 Match\nDemand for family planning satisfied by\n0.42 5.B sdg5_familypl 3.7.1 Match\nmodern methods\n0.39 16.H sdg16_rsf Press Freedom Index 16.1 Closely aligned\n0.37 16.F sdg16_homicides Homicides 16.1.1 Match\n0.36 16.G sdg16_justice Access to and affordability of justice 16.3.1, 16.3.3 Closely aligned\nPopulation using at least basic drinking\n0.30 6.E sdg6_water 6.1.1 Closely aligned\nwater services\nPrevalence of wasting in children under\n0.28 2.H sdg2_wasting 2.2.2 Match\n5 years of age\nTimeliness of administrative\n0.26 16.A sdg16_admin 16.6 Closely aligned\nproceedings\n0.05 11.A sdg11_pipedwat Access to improved water source, piped 11.1 Closely aligned\nSurviving infants who received 2 WHO-\n0.05 3.N sdg3_vac 3.b.1 Closely aligned\nrecommended vaccines\n0.00 8.F sdg8_slavery Victims of modern slavery 8.7 Closely aligned\n-0.01 16.J sdg16_u5reg Birth registrations with civil authority 16.9.1 Match\n-0.07 3.L sdg3_u5mort Mortality rate, under-5 3.2.1 Match\n-0.13 8.B sdg8_adjgrowth Adjusted GDP growth 8.1.1 Closely aligned\n-0.15 7.C sdg7_elecac Population with access to electricity 7.1.1 Match\nRatio of female-to-male mean years of\n-0.15 5.A sdg5_edat 4.5.1 Closely aligned\neducation received\n-0.15 10.A sdg10_gini Gini coefficient 10.1 Closely aligned\nSeats held by women in national\n-0.15 5.D sdg5_parl 5.5.1 Match\nparliament\n-0.19 11.D sdg11_transport Satisfaction with public transport 11.2.1 Closely aligned\n-0.19 4.B sdg4_literacy Literacy rate 4.6.1 Match\n-0.27 3.C sdg3_hiv New HIV infections 3.3.1 Match\nBirths attended by skilled health\n-0.37 3.A sdg3_births 3.1.2 Match\npersonnel\n-0.39 3.E sdg3_matmort Maternal mortality rate 3.1.1 Match\n-0.51 3.J sdg3_tb Incidence of tuberculosis 3.3.2 Match\n-0.57 4.C sdg4_primary Net primary enrollment rate 4.1.2 Closely aligned\nRatio of female-to-male labor force\n-0.82 5.C sdg5_lfpr 5.5 Closely aligned\nparticipation rate\n-1.06 8.G sdg8_unemp Unemployment rate 8.5.2 Match\n-1.45 17.A sdg17_cohaven Corporate Tax Haven Score - Not in UNSTATS\nVictims of modern slavery embodied in\n-1.60 8.D sdg8_impslav 8.7 Closely aligned\nimports\n6 -1.67 8.C sdg8_impacc Fatal work-related accidents embodied 8.8.1 Closely aligned\nin imports\n-1.68 2.F sdg2_trophic Human Trophic Level - Not in UNSTATS\n-1.69 16.K sdg16_weaponsexp Exports of major conventional weapons 16.1 Closely aligned\n-1.81 2.B sdg2_obesity Prevalence of obesity, BMI ≥ 30 2.2 Closely aligned\nComment 1.10\n10. Since the authors obtain their data from the Sustainable Development Report 2023, how do\ntheir findings compare with those in this report? To what extent these data affect the paper\nfindings (compared with other data sources, such as from or the UN or the World Bank)?\nResponse to 1.10\nIndeed, we have obtained the SDG data from the Sustainable Development Report (SDR) 2023.\nThe SDR 2023 provides the primary data used in our analysis, as detailed in the Data Section\nof the Methods in our original submission. It is a reputable and widely used source that has\nundergone multiple peer reviews and has been utilized in numerous previous analyses.\nTo enhance the context in our manuscript, we have supplemented the introduction with\nadditional information about the SDR dataset, particularly highlighting its advantages and\ndisadvantages.\nLines 690-698:\nIn addition, the related SDG Index methodology and datasets have undergone multiple peer\nreviews and have been used to substantiate previous notable studies in this field6,66–68. Naturally,\nthe dataset has its advantages and disadvantages. The advantages include its broad coverage\nand consistency over time, which allow for meaningful comparisons across countries and time\nperiods. The disadvantages include a lack of granularity needed to capture local (e.g., urban\nlevel) variations and specific economic activities (e.g., industries) within countries.\nAdditionally, some indicators are not fully applicable to all countries (e.g., 14.C. Mean area\nthat is protected in marine sites important to biodiversity).\nRegarding the comparison with the findings from the Sustainable Development Report 2023,\nwhich ranked countries and evaluating their progress towards achieving the SDGs, this study\nhas the following key features as added in the Discussion Section:\nLines 390-417:\nDistinct from earlier notable studies that assessed countries’ SDG performance3–8, such as those\nranking countries and evaluating their progress towards achieving the SDGs6 or exploring\ndevelopment patterns12–19, our study introduces several key features that set it apart.\nFirst, we employed a comparative framework to identify each country’s relative strengths and\nweaknesses, as well as to pinpoint ‘orphaned’ SDG indicators in specific development areas.\nThis comparative analysis, which contextualizes performance by benchmarking different\nvariables or entities against each other, offers a clearer understanding of each country’s relative\nstanding. In contrast, absolute scoring (Figs. 3d-f) can obscure these insights by focusing solely\non an overarching SDG score without the nuanced understanding that comes from comparison\nwith other entities and SDGs.\nSecond, we utilized the ‘product space’ method and the broader economic complexity approach\nas effective tools for pattern recognition. These methods help identify national sustainable\ndevelopment trends and reveal whether countries specialize in certain goals. For instance, the\nderived GSI indicator divides SDG indicators into low-GSI indicators that countries tend to\noverlook and high-GSI indicators that countries tend to specialize in during their development\nprocess (Figs. 3, 5, 6). Additionally, we delineated the development trajectories of nations\nbased on the historical performance of all countries, differing from the Sustainable\nDevelopment Report6, which evaluates whether a country is on track based on its individual\nperformance.\nThird, we introduced the concept of the ‘SDG space’ as a high-resolution tool to monitor\ncountries’ performance across 96 SDG indicators. This tool visualizes countries’ relative\nperformance and assists in guiding the formulation of development strategies (Supplementary\nFigs. 12-177). Moreover, we developed a dedicated website to present 3,818 SDG spaces for\n166 nations spanning the years 2000 to 2022: http://www.spacelab.team/#/SDGSpace. Unlike\nthe traditional product space20, which typically ranks countries and guides them towards\nadvanced products (e.g., motor vehicles and electronics), the SDG space is designed to assist\ncountries monitor their progress within the SDG framework, identifying relative strengths and\nweaknesses, rather than ranking countries.\nWe would like to mention that, although this was referred to as a 'minor comment', addressing\nit alone required four full weeks of several rounds of data analysis and multiple discussions\namong the authorship team. This was due to the relatively poor quality of the UN SDG data,\nwhich necessitated extensive cleaning and standardization by our team—a time-consuming\nprocess. Following this, we had to construct a new SDG Space using the UN data for\ncomparison and perform robustness checks.\nAs reviewers can now see in Supplementary Fig. 11, we identified a similar bipolar structure,\nwhich confirms the robustness of our original results. We have supplemented this information\nin the Robustness Check section of the Methods (please also refer to our response to Comment\n1.7).\nLines 724-730:\n(3) To validate the robustness of our results using different data sources, we collected additional\ndata from the UNSD SDG database. For consistency in comparison, we included only the data\non targets that matched the 96 indicators used in this study (see Tables 1 and 2 in Methods for\nthe matching process). Data source: https://unstats.un.org/sdgs/dataportal/database. As shown\nin Fig. 1 (SDR 2023 database) and Supplementary Fig. 11 (UNSD SDG database), the SDG\nspaces exhibit a similar structure.\nSupplementary Figure 11 | The SDG space in 2022 using UN SDG data. The node colour\nrepresents Goal Sustainability Index (GSI), and the node size indicates the node degree,\nrepresenting the number of edges connected to the node. This SDG space was produced using\ndata from the UNSD SDG targets framework, which were matched with the 96 SDG indicators\n(see Tables 1-2 in Methods). Data source: https://unstats.un.org/sdgs/dataportal/database.\nConcluding remark\nThank you once again for all your invaluable suggestions, which have significantly enhanced\nthe quality of our paper.\nReviewer #2\nGeneral Remarks of Reviewer #2\nThe manuscript provides a valuable contribution to the analysis of countries’ pathways towards\ngreater sustainability as envisioned in the SDG framework through the lens of development\neconomics. The study compares the trajectories of 166 countries (from 2000-22), and explores\nwhether countries’ performance follows distinct patterns by considering 96 global indicators\nfrom the SDR. The approach chosen is systematic and sophisticated, employing methodologies\ninnovatively in the context of sustainable development. Overall, the manuscript contributes to\nadvancing knowledge in the field of sustainable development, and can inform more effective\nstrategies for achieving the SDGs across different countries. Notably, I sincerely appreciate\nthat the authors want to make their results on SDG spaces publicly available and easily\naccessible through the dedicated web-interface. However, there are some points I would like\nto raise which I hope can support the authors in improving the manuscript:\nResponse to 2\nWe acknowledge this summary of the content of our manuscript, and endorsement of our aims,\nanalytical approach, and aspired contribution to literature and practice, including via a\ndedicated open-access web-interface. Thank you.\nWe further thank you for your comments and suggestions. Please see our response to your\ncomments in the following.\nComment 2.1\n1. The authors’ choice of referring to the assessed indicators with capital letters (such as 1A,\n1B…) can be misleading, as some the official SDG targets referring to the means of\nimplementation under the respective goals are named alike, although with lower case letters\n(1a, 1b, …). If the authors want to stick to this format, they should be explicit about the\ndistinction.\nResponse to 2.1\nThank you for this remark. In the previous version, we explained the difference in indexing\nSDG targets and indicators by the UN and by the 2023 Sustainable Development Report (upon\nwhich this study relies).\nThis explanation was included in the SI (Supplementary Table 1), not in the main text.\nFollowing this comment, and Comment 1.9 above, we have provided an explanation of the\nindexing system in the main text and transferred Supplementary Table 1 into the Methods\nsection.\nWe have modified the text as follows (Lines 699-707):\nTable 1 | Environment-related SDG indicators ranked by the Goal Sustainability Index (GSI) and\ntheir alignment with UN SDG taxonomies. The environment-related indicators are divided into three\ngroups based on their GSI values. Group 1: GSI > 0.5. Group 2: -1.0 ≤ GSI ≤ 0.5. Group 3: GSI <\n-1.0.\nIndicator\nGSI\ncode in Indicator code UNSD\nGroup in Indicator definitions UNSD match\nthis in SDR 2023 target\n2022\nstudy\n1.79 6.D sdg6_wastewat Anthropogenic wastewater that receives treatment 6.3.1 Match\nPopulation with access to clean fuels and technology\n1.08 7.A sdg7_cleanfuel 7.1.2 Closely aligned\nfor cooking\n0.83 3.H sdg3_pollmort 3.9.1 Match\nair pollution and ambient air pollution\n0.79 14.B sdg14_cleanwat Ocean Health Index: Clean Waters score 14.1.1 Closely aligned\nMean area that is protected in marine sites important\n0.27 14.C sdg14_cpma 14.5.1 Closely aligned\nto biodiversity\n0.27 2.C sdg2_pestexp Exports of hazardous pesticides 3.9 Closely aligned\n0.19 15.D sdg15_forchg Permanent deforestation 15.2 Closely aligned\nRenewable energy share in total final energy\n0.05 7.D sdg7_renewcon 7.2.1 Match\nconsumption\nAnnual mean concentration of particulate matter of\n-0.03 11.B sdg11_pm25 11.6.2 Match\nless than 2.5 microns in diameter (PM2.5)\n-0.21 14.E sdg14_fishstocks Fish caught from overexploited or collapsed stocks 14.4.1 Closely aligned\nMean area that is protected in terrestrial sites\n-0.27 15.C sdg15_cpta 15.1.2 Match\n2 important to biodiversity\nMean area that is protected in freshwater sites\n-0.30 15.B sdg15_cpfa 15.1.2 Match\nimportant to biodiversity\n-0.34 14.D sdg14_discard Fish caught that are then discarded 14.4 Closely aligned\n-0.34 14.F sdg14_trawl Fish caught by trawling or dredging 14.4 Closely aligned\n-0.57 15.E sdg15_redlist Red List Index of species survival 15.5.1 Match\n-0.65 2.D sdg2_snmi Sustainable Nitrogen Management Index 2.4 Closely aligned\n-0.72 13.A sdg13_co2export CO2 emissions embodied in fossil fuel exports 13.2 Closely aligned\n-0.82 6.A sdg6_freshwat Freshwater withdrawal 6.4.2 Match\nCO2 emissions from fuel combustion per total\n-0.95 7.B sdg7_co2twh 7.2 Closely aligned\nelectricity output\n-1.26 12.B sdg12_explastic Exports of plastic waste 12.4 Closely aligned\n-1.40 14.A sdg14_biomar Marine biodiversity threats embodied in imports 14.4 Closely aligned\n-1.54 12.E sdg12_nprod Production-based nitrogen emissions 9.4 Closely aligned\n-1.59 12.D sdg12_nimport Nitrogen emissions embodied in imports 9.4 Closely aligned\n-1.59 13.C sdg13_co2import CO2 emissions embodied in imports 13.2 Closely aligned\n-1.62 12.A sdg12_ewaste Electronic waste 12.4.2 Match\nTerrestrial and freshwater biodiversity threats\n3 -1.64 15.A sdg15_biofrwter 15.5 Closely aligned\nembodied in imports\n-1.66 12.F sdg12_so2import SO2 emissions embodied in imports 9.4 Closely aligned\n-1.67 6.C sdg6_scarcew Scarce water consumption embodied in imports 6.4 Closely aligned\n-1.67 12.C sdg12_msw Municipal solid waste 12.5 Closely aligned\nCO2 emissions from fossil fuel combustion and\n-1.76 13.B sdg13_co2gcp 13.2.2 Closely aligned\ncement production\n-1.84 12.G sdg12_so2prod Production-based SO2 emissions 9.4 Closely aligned\nTable 2 | Non-environment-related SDG indicators ranked by the Goal Sustainability Index (GSI)\nand their alignment with UN SDG taxonomies. The non-environment-related indicators are divided\ninto three groups based on their GSI values. Group 4: GSI > 0.5. Group 5: -1.0 ≤ GSI ≤ 0.5. Group\n6: GSI < -1.0.\nIndicator\nGSI\ncode in Indicator code in\nGroup in Indicator definitions UNSD target UNSD match\nthis SDR 2023\n2022\nstudy\nExpenditure on research and\n1.98 9.E sdg9_rdex 9.5.1 Match\ndevelopment\n1.87 9.A sdg9_articles Articles published in academic journals 9.5 Closely aligned\n1.30 3.G sdg3_neonat Neonatal mortality rate 3.2.2 Match\nUniversal health coverage (UHC) index\n1.29 3.M sdg3_uhc 3.8.1 Match\nof service coverage\nAdults with an account at a bank or\n1.23 8.A sdg8_accounts other financial institution or with a 8.10.2 Match\nmobile-money-service provider\nProportion of urban population living in\n1.17 11.C sdg11_slums 11.1.1 Match\nslums\nThe Times Higher Education\n1.08 9.G sdg9_uni Universities Ranking: Average score of - Not in UNSTATS\ntop 3 universities\n1.08 3.I sdg3_swb Subjective well-being 3.4 Closely aligned\nLogistics Performance Index: Quality of\n1.08 9.C sdg9_lpi 9.1 Closely aligned\ntrade and transport-related infrastructure\n1.07 16.C sdg16_cpi Corruption Perceptions Index 16.5.1, 16.5.2 Closely aligned\n1.06 3.K sdg3_traffic Traffic deaths 3.6.1 Match\n1.05 1.A sdg1_lmicpov Poverty headcount ratio at $3.65/day 1.1.1 Match\n1.04 4.D sdg4_second Lower secondary completion rate 4.1.2 Match\n1.03 3.D sdg3_lifee Life expectancy at birth 3.1:3.9 Closely aligned\n1.02 9.B sdg9_intuse Population using the internet 17.8.1 Match\n4 1.01 9.F sdg9_roads Rural population with access to all- 9.1.1 Match\nseason roads\nParticipation rate in pre-primary\n1.01 4.A sdg4_earlyedu 4.2.2 Closely aligned\norganized learning\n0.98 3.B sdg3_fertility Adolescent fertility rate 3.7.2 Match\nPopulation using at least basic sanitation\n0.97 6.B sdg6_sanita 6.2.1 Closely aligned\nservices\nPrevalence of stunting in children under\n0.95 2.E sdg2_stunting 2.2.1 Match\n5 years of age\n0.94 9.D sdg9_mobuse Mobile broadband subscriptions 9.c.1, 17.6.1 Closely aligned\n0.91 2.G sdg2_undernsh Prevalence of undernourishment 2.1.1 Match\n0.84 17.C sdg17_statperf Statistical Performance Index 17.18.1: 17.19.2 Closely aligned\nExpropriations are lawful and\n0.81 16.E sdg16_exprop 16.6 Closely aligned\nadequately compensated\nAge-standardized death rate due to\ncardiovascular disease, cancer, diabetes,\n0.75 3.F sdg3_ncds 3.4.1 Match\nor chronic respiratory disease in adults\naged 30–70 years\n0.66 2.A sdg2_crlyld Cereal yield 2.3, 2.4 Closely aligned\nGovernment spending on health and\n0.66 17.B sdg17_govex 1.a.1 Closely aligned\neducation\n0.58 16.B sdg16_clabor Children involved in child labor 8.7.1 Closely aligned\nFundamental labor rights are effectively\n0.52 8.E sdg8_rights 8.8.2 Match\nguaranteed\nPopulation who feel safe walking alone\n0.51 16.I sdg16_safe at night in the city or area where they 16.1.4 Match\nlive\n0.47 10.B sdg10_palma Palma ratio 10.1 Closely aligned\n5\n0.47 16.D sdg16_detain Unsentenced detainees 16.3.2 Match\nIndicator\nGSI\ncode in Indicator code in\nGroup in Indicator definitions UNSD target UNSD match\nthis SDR 2023\n2022\nstudy\n0.42 1.B sdg1_wpc Poverty headcount ratio at $2.15/day 1.1.1 Match\nDemand for family planning satisfied by\n0.42 5.B sdg5_familypl 3.7.1 Match\nmodern methods\n0.39 16.H sdg16_rsf Press Freedom Index 16.1 Closely aligned\n0.37 16.F sdg16_homicides Homicides 16.1.1 Match\n0.36 16.G sdg16_justice Access to and affordability of justice 16.3.1, 16.3.3 Closely aligned\nPopulation using at least basic drinking\n0.30 6.E sdg6_water 6.1.1 Closely aligned\nwater services\nPrevalence of wasting in children under\n0.28 2.H sdg2_wasting 2.2.2 Match\n5 years of age\nTimeliness of administrative\n0.26 16.A sdg16_admin 16.6 Closely aligned\nproceedings\n0.05 11.A sdg11_pipedwat Access to improved water source, piped 11.1 Closely aligned\nSurviving infants who received 2 WHO-\n0.05 3.N sdg3_vac 3.b.1 Closely aligned\nrecommended vaccines\n0.00 8.F sdg8_slavery Victims of modern slavery 8.7 Closely aligned\n-0.01 16.J sdg16_u5reg Birth registrations with civil authority 16.9.1 Match\n-0.07 3.L sdg3_u5mort Mortality rate, under-5 3.2.1 Match\n-0.13 8.B sdg8_adjgrowth Adjusted GDP growth 8.1.1 Closely aligned\n-0.15 7.C sdg7_elecac Population with access to electricity 7.1.1 Match\nRatio of female-to-male mean years of\n-0.15 5.A sdg5_edat 4.5.1 Closely aligned\neducation received\n-0.15 10.A sdg10_gini Gini coefficient 10.1 Closely aligned\nSeats held by women in national\n-0.15 5.D sdg5_parl 5.5.1 Match\nparliament\n-0.19 11.D sdg11_transport Satisfaction with public transport 11.2.1 Closely aligned\n-0.19 4.B sdg4_literacy Literacy rate 4.6.1 Match\n-0.27 3.C sdg3_hiv New HIV infections 3.3.1 Match\nBirths attended by skilled health\n-0.37 3.A sdg3_births 3.1.2 Match\npersonnel\n-0.39 3.E sdg3_matmort Maternal mortality rate 3.1.1 Match\n-0.51 3.J sdg3_tb Incidence of tuberculosis 3.3.2 Match\n-0.57 4.C sdg4_primary Net primary enrollment rate 4.1.2 Closely aligned\nRatio of female-to-male labor force\n-0.82 5.C sdg5_lfpr 5.5 Closely aligned\nparticipation rate\n-1.06 8.G sdg8_unemp Unemployment rate 8.5.2 Match\n-1.45 17.A sdg17_cohaven Corporate Tax Haven Score - Not in UNSTATS\nVictims of modern slavery embodied in\n-1.60 8.D sdg8_impslav 8.7 Closely aligned\nimports\n6 -1.67 8.C sdg8_impacc Fatal work-related accidents embodied 8.8.1 Closely aligned\nin imports\n-1.68 2.F sdg2_trophic Human Trophic Level - Not in UNSTATS\n-1.69 16.K sdg16_weaponsexp Exports of major conventional weapons 16.1 Closely aligned\n-1.81 2.B sdg2_obesity Prevalence of obesity, BMI ≥ 30 2.2 Closely aligned\nComment 2.2\n2. Relatedly, the authors could mention earlier in the manuscript that the study uses (proxy)\nindicators for assessing performance on SDG targets from the SDR (not SDG targets), and that\nthese not always correspond to the official SDG indicators. In this respect, they could further\nexplain why they decided to do so (I suppose due to issues of data availability in the official\ndata set). In Supplementary table 1, consider adding a column indicating whether the indicator\nis an official SDG indicator (“UNSD Match” in column S, tab “Codebook” in SDR2023_data\nexcel file).\nResponse to 2.2\nThank you for this suggestion. In response to this comment and Comment 2.1 above, we have\nadded the “UNSD Match” in new tables, Tables 1-2. Please refer to the tables provided in our\nresponse to Comment 2.1. Additionally, we have changed the term “targets” to “indicators,”\nwhich are obtained from the SDR.\nFollowing your suggestion, we have also introduced the selection of these SDG indicators at\nthe beginning of the main text.\nLines 109-113:\nThe indicators covered in this study are carefully indexed and evaluated in the Sustainable\nDevelopment Report (SDR) 20236, with details provided in Tables 1-2 of Methods, including\ntheir relationship to the official UNSD SDG targets. Please note that, for clarity in the figures,\nthe SDG indicator abbreviations used in this study (e.g., 1.A) are simplified versions of the\nindicators provided in the SDR report and do not correspond directly to the UNSD SDG targets.\nComment 2.3\n3. While being aware that the terms “developed” vs. “developing” countries is commonly used,\nI would encourage the authors to refrain from this terminology, particularly since they do not\nspecify where this categorization stems from and is based on. In my opinion, it would be better\nto distinguish between countries by income levels (from low- to high-income countries) and\nrename the groups accordingly.\nResponse to 2.3\nThank you for this suggestion. Following your comment, we have replaced the terms\n\"developed\" and \"developing\" countries with \"countries at a primary stage of development\"\nand \"countries at an advanced stage of development\", respectively, to align with our\ncategorization of countries into quartiles based on their SDG index score rankings — Stage 1\n(Primary stage), Stage 2 (Low-medium stage), Stage 3 (High-medium stage), and Stage 4\n(Advanced stage).\nComment 2.4\n4. In this respect, I strongly encourage the authors to revise the regional groupings in Fig. 2. If\nthe authors want to display regional differences, they should stick to official regional groupings\n(e.g., (aggregates of) those indicated in SDR, or the M49 standard). Distinguishing between\nAfrica/East & South Asia/Latin America and the Caribbean vs. Industrialized Countries is an\nunfortunate choice.\nResponse to 2.4\nThank you for this suggestion. Following your comment, we have revised the regional\ngroupings according to M49 standard.\nWe have modified the text as follows:\nLines 170-181:\nFig. 2 presents strong regional specialization patterns within the SDG space. The SDG\nindicators with RCA > 0 for each region are colour-coded, indicating that the region has a\nrelative advantage in these indicators. Africa occupies the red cluster on the left side of the\nSDG space (Fig. 2a), primarily associated with overnutrition, embodied social and\nenvironmental impacts in international trade, and waste and emissions. In contrast, Europe\noccupies the blue cluster on the right side (Fig. 2d), which includes indicators related to poverty\nand hunger reduction, good health and well-being, education, innovation, and government\nadministration. The Americas, Asia, and Oceania regions exhibit relatively similar patterns\n(Figs. 2b, 2c, 2e), with a more even distribution and smaller RCA values for their specialized\nindicators. However, Oceania has more advantages in high-GSI indicators within the blue\ncluster. The SDG space of all 166 nations ranked by SDG scores in 2000, 2015, and 2022, are\nshown in Supplementary Figs. 12-177.\nFig. 2 | The SDG space of different regions of the world in 2022. Panel a, Africa. Panel b,\nAmericas. Panel c, Asia. Panel d, Europe. Panel e, Oceania. The node colour represents the\nGoal Sustainability Index (GSI). The coloured SDG indicators are those with RCA>0,\nindicating that the region has a revealed comparative advantage (or specialization) in these\nindicators. The node size corresponds to the RCA value for each indicator.\nComment 2.5\n5. The findings and insights derived from the method may not be universally applicable to all\ncountries or regions, as they are based on aggregated data and global trends. The authors might\nwant to acknowledge more explicitly that local variations in socio-economic conditions, culture,\ngovernance, policy environments, and institutional capacities may not be adequately captured,\nbut might be necessary to consider for providing a more comprehensive understanding of SDG\ndevelopment patterns.\nResponse to 2.5\nWe agree that local variations in socio-economic conditions, culture, governance, policy\nenvironments, and institutional capacities are crucial factors that may not be fully captured by\nour aggregated data and global trends.\nIn response, we have explicitly acknowledged this limitation in the revised manuscript and\nemphasized the importance of considering these local variations when using SDG development\npatterns to guide countries’ policies.\nAdditionally, we have introduced three more representative countries to demonstrate how our\nSDG evaluation framework can be applied in different contexts. Please also refer to our\ncomplementary response (and discussion in response) to Comment 2.10, below.\nThe detailed revisions are as follows:\nLines 181-183:\nIt is notable that the findings and insights derived from this method may not be universally\napplicable to all countries in each region, given the local variations in socio-economic\nconditions, culture, governance, and policy environments.\nIn view of this Comment, and Comments 2.7 and 3.2, we have added a subsection entitled\n\"Limitations and clarifications on the Product Space terminology\", to acknowledge the data\nlimitations, clarify the use of the Product Space techniques and terminology, and discuss their\nimplications.\nLines 504-556:\nLimitations and clarifications on the Product Space terminology\nIn this study, we constructed ‘SDG spaces’ for 166 nations from 2000 to 2022, revealing a\nbipolar world while identifying ‘orphaned’ development indicators by different nations. These\npatterns, along with the SDG space, provide a high-resolution tool for evaluating progress and\noffering targeted guidance for countries in achieving the SDGs. In doing so, we have applied\nthe Product Space and Economic Complexity framework, analytical techniques, and\nterminology, including ‘revealed comparative advantage’ (RCA) and ‘specializations’.\nWe acknowledge that some of the sustainable development indicators for which certain\ncountries exhibit an RCA (a specialization) might reflect an intentional, premeditated national\npreference over time, such as the environmental impacts of imports, as part of a larger\nimportation policy. While our data allow us to identify which advantages — or, as they are also\nreferred to in this study (and in Economic Complexity) ‘specializations’ — are likely to\ndiminish as a country develops, they do not enable us to differentiate between intentional (i.e.,\npreferred, premeditated) and unintentional specializations. Indeed, ‘unintentional\nspecializations’ may arise due to a country’s geography or geology, such as being landlocked\nor possessing mineral resource reserves60. Additionally, other detrimental factors, such as\ncolonial legacy, may predetermine a country’s development trajectory60.\nTo clarify, the use of RCA to discuss developing countries’ performance on certain SDG\nindicators is done within the context of the Product Space approach. However, it does not imply\nthat these countries have intentionally pursued this RCA, nor does it suggest that the historical\nreasons and circumstances underpinning it are inherently ‘advantageous’. Using the terms\n‘advantage’ or ‘specialization’ outside the context of the Product Space approach – when\nreferring to a country meeting certain SDG targets – may be misleading, especially when these\n‘advantages’ may reflect low-income status rather than deliberate policies or capabilities. For\nexample, several indicators, such as lower environmental footprints and reduced health risks\nlike obesity, may result from limited resources. At the same time, this does not inherently render\nthem disadvantages — a low ecological footprint can be considered a positive attribute,\nregardless of its underlying causes. This invites further discussion and underscores the need for\nfuture research.\nMoreover, our intention is not to suggest that so-called developing countries (or any country,\nfor that matter) should maintain their current status to preserve these ‘advantages’ (RCAs).\nInstead, the aim of this study is to use the RCA framework to identify national sustainable\ndevelopment trajectories as well as areas where targeted interventions are needed to enhance\ndevelopment outcomes.\nIn the same vein, the degree of agency we can attribute to national governments in such\ncircumstances (e.g., ‘being landlocked’, ‘being resource-endowed’, and ‘being post-colonial’)\nwhen developing a relative specialization remains a matter of debate. Addressing the\ndistinction between intentional and unintentional specializations, as well as the role of national\ngovernments’ agency, requires further study and may involve subjective judgments. While this\nstudy opens new avenues for such discussions, the interpretation of RCAs as normative\npreferences lies outside its scope.\nOn a related note, it should be emphasized that local variations at subnational levels and units\nof analysis (regional, provincial, urban, peri-urban, rural) in socio-economic conditions, culture,\ngovernance, policy environments, and institutional capacities are important factors that may\nnot be fully captured by our aggregated data and global trends. We acknowledge that even\nwithin the same country, SDG performance and strategies may differ between regions and\nbetween urban and rural areas61. Due to data limitations, this study focuses on global and\nnational sustainable development through 96 SDG indicators. Future research could extend this\nanalysis to regional, state, and city levels.\nLastly, disparities in data availability across countries and economic sectors could lead to\nbiased representations of SDG performance, limiting the generalizability of the findings. With\nincreasing calls to improve the SDG database62,63, future studies should critically and\ncomparatively investigate SDG indicators and the pathways toward their realization.\nComment 2.6\n6. The authors also might want to acknowledge that some indicators are not applicable to all\ncountries in their entirety (e.g., indicator 14.C on protected areas in marine sites not relevant\nfor landlocked countries).\nResponse to 2.6\nThank you for noticing this. We acknowledge that some indicators, such as indicator 14.C, are\nnot applicable to all countries. Following this comment, we have added a note to address this\nlimitation in the Data section of the Methods.\nThe detailed revision is as follows:\nData section of Methods (Lines 692-698):\nNaturally, the dataset has its advantages and disadvantages. The advantages include its broad\ncoverage and consistency over time, which allow for meaningful comparisons across countries\nand time periods. The disadvantages include a lack of granularity needed to capture local (e.g.,\nurban level) variations and specific economic activities (e.g., industries) within countries.\nAdditionally, some indicators are not fully applicable to all countries (e.g., 14.C. Mean area\nthat is protected in marine sites important to biodiversity).\nComment 2.7\n7. L129-31: I think this statement should be rephrased and needs further (potential) explanation.\nFirst, some of the indicators for which “developing” countries have an RCA could indeed be a\npreference (e.g. environmental impacts of imports). Second, your data allows you for stating\nwhich advantages/specializations are likely to be decreasing when a country “develops”. But\ndoes this qualify to speak about (un)intentional specialization? Maybe it would be useful to\nfurther reflect on the RCA construction and its limitations/implications in this respect as well.\nResponse to 2.7\nThank you for this comment.\nWe acknowledge that some of the sustainable development indicators for which “developing”\ncountries exhibit an RCA might reflect a national preference over time, such as the\nenvironmental impacts of imports, as part of a larger importation policy. While our data allows\nus to identify which advantages, or as they are referred to in this study, ‘specializations’,\nare likely to diminish as a country develops, it does not enable us to differentiate between\nintentional (preferred) and unintentional specializations. In this regard, unintentional\nspecializations may arise due to a country’s geography or geology, such as being landlocked or\npossessing mineral resource reserves (Sachs, 2006). Additionally, other detrimental factors,\nsuch as colonial legacy, may predetermine a country's development trajectory (Sachs, 2006).\nThe extent of agency we attribute to national governments in such circumstances to develop a\nrelative specialization remains a matter of debate. Addressing this distinction between\nintentional and unintentional specializations requires further study and might involve\nsubjective judgments. This study opens new vistas to such discussions, yet the interpretation\nof RCAs as preferences lies outside its scope.\nIn view of this Comment, and Comments 2.5 and 3.2, we have added a subsection entitled\n\"Limitations and clarifications on the Product Space terminology\", to acknowledge the data\nlimitations, clarify the use of the Product Space techniques and terminology, and discuss their\nimplications.\nPlease refer to our response to Comment 2.5 above, and to lines 503-555 in the revised\nmanuscript.\nReference: Sachs, J. D. (2006). The end of poverty: Economic possibilities for our time.\nPenguin.\nComment 2.8\n8. The meaning and interpretation of the network links (rather called edges, and I would\nrecommend to use this term consistently throughout the manuscript) between SDGs and the\ndegree values are barely being addressed in the text.\nResponse to 2.8\nThank you for your suggestion. Following this comment we have modified the text by changing\n‘links’ into ‘edges’, and added a sentence as follows (Lines 131-132):\nThe indicators in these two clusters have relatively higher node degrees, indicating that they\nare connected to a larger number of edges (Fig. 1a).\nSince the network edges and node degrees are not the focus of this study, we did not delve into\ntheir interpretation.\nComment 2.9\n9. L286-90: I would appreciate if the authors could elaborate on how they interpret these\nfindings.\nResponse to 2.9\nIn view of this comment, we have added the interpretation of these findings as follows:\nLines 337-343:\nThese anomalies can be attributed to distinctive industrial structures (i.e., the composition and\norganization of industries within a country’s economy) or national cultures. For instance,\nSerbia’s nitrogen management and Moldova’s CO emissions from fossil fuel combustion and\n2\ncement production are outcomes of their specific industrial frameworks. Similarly, Japan’s\ntraditional dietary practices and culinary habits (i.e., its food culture) influence its unique\nsustainable development indicators.\nComment 2.10\n10. To better highlight and acknowledge the differences shown in Fig.6, the discussion section\nwould benefit from an additional example besides China.\nResponse to 2.10\nThank you for your suggestion. In response, we have added three additional countries as case\nstudies in the Discussion Section. We have modified the text as follows:\nLines 418-432:\nIn terms of policy guidance, the comparative insights and identified trends in sustainable\ndevelopment trajectories can provide targeted recommendations (Fig. 8). From a static\nperspective, countries’ performance on the indicators can be categorized into four areas (Fig.\n8b, d, f, h): Area I (high RCA and SDG Scores) represents an ideal situation. Area II (high RCA\nbut low SDG scores) where indicators are relatively difficult for a specific country to improve,\nas other countries are also likely to struggle in making progress and providing successful\nexamples. Area III (low RCA and SDG Scores) represents the worst situation, where both\nrelative and absolute performance on these indicators is poor. Area IV (low RCA but relatively\nhigh SDG scores) where indicators may be easier to improve, as other countries might be\nperforming better and could offer models for success.\nTaking the USA as an example (Fig. 8h), beyond the indicators in Area III — such as\novernutrition (2.B, 2.F) and waste and emissions (12.A, 12.C, 12.E, 13.B) — focus should also\nbe placed on addressing social and environmental impacts linked to imports (8.D, 12.F, 14.A),\ninequality (10.A), and Corporate Tax Haven (17.A) in Area IV, despite these indicators having\nabsolute scores above the global average.\nLines 452-469:\nFig. 8 | The SDG space and indicator performance for four representative countries in\n2022. Panels a-b, Ethiopia (Stage 1). Panels c-d, India (Stage 2). Panels e-f, China (Stage 3).\nPanels g-h, United States (Stage 4). In panels a-h, blue nodes represent the top 20 SDG\nindicators by RCA values, while red nodes represent the bottom 20. The remaining indicators\nare not highlighted. The size of each node corresponds to the absolute RCA value. Countries\nare evenly divided into four stages according to their absolute score rankings: Stage 1 (Primary\nstage), Stage 2 (Low medium stage), Stage 3 (High medium stage), and Stage 4 (Advanced\nstage). Stage 4 is further divided into two halves to identify the top 20 increasing and decreasing\nindicators in RCA for the USA (in the first half of Stage 4) between its current stage and the\nnext stage (see Supplementary Table 1). In panels b, d, f, h, scatter diagrams are divided into\nfour areas by the lines at RCA = 0 and the global average SDG score of 63. Area I: Countries\nwith high RCA and SDG Scores for these indicators have reached a relatively ideal situation.\nArea II: Countries with high RCA but low SDG scores for these indicators face relative\ndifficulties in improving them, likely due to low global averages for these indicators. Area III:\nCountries with low RCA and SDG Scores for these indicators show both relative and absolute\nunderperformance, indicating that these indicators should be prioritized for improvement. Area\nIV: Countries with low RCA but relatively high SDG scores for these indicators demonstrate\npoor relative performance but strong absolute performance.\nComment 2.11\n11. The interpretation of proximity values as indicators of synergy and trade-off should be\ncautiously considered, recognizing the complexity of sustainable development dynamics.\nResponse to 2.11\nWe recognize the complexity of sustainable development dynamics and agree that interpreting\nproximity values as indicators of synergy and trade-off should be approached with caution.\nFollowing this comment, we have added a clarification in the Methods section as follows:\nLines 610-611:\nThe proximity between SDG indicators in the nation-indicator bipartite network is defined by\nthe conditional probability that two indicators are co-specialized within a nation.\nComment 2.12\n12. Finally but importantly, some of the research aims set out in the beginning in the manuscript\n(whether countries taking different paths have achieved different degrees of success / whether\nand under which circumstances countries make progress towards their SDG commitment) have\nnot been sufficiently answered by the authors.\nWhile I highly appreciate the extensive data and visualization presented (also in the\nsupplementary material), the authors could have used more space for interpreting and reflecting\non their results (sometimes less can be more).\nResponse to 2.12\nBased on the findings and contributions of this study, we have rephrased the research aims as\nfollows:\nLines 55-59:\nThe year of 2023 marked the halfway point in the implementation of the SDGs2, prompting us\nto question whether universal patterns underpinned nations’ sustainable development\ntrajectories, and whether some SDG indicators were neglected or overlooked (referred to herein\nas ‘orphaned’) in specific areas, across nations, and over time.\nLines 74-76:\nSpecifically, we examined whether countries have so-called ‘orphaned’ SDG indicators in\ncertain development areas and whether there are underlying patterns or rules governing these\n‘orphaned’ areas.\nRegarding your comment on interpreting and reflecting on the results, we have added more in-\ndepth interpretation of our findings (e.g., please see our response to Comment 2.9),\nsupplemented additional cases (please see our response to Comment 2.10), and compared our\nresults with previous studies (please see our response to Comment 1.4).\nComment 2.13\n13. L52: Please correct “constitute” to “constitutes”\nResponse to 2.13\nThank you for noticing this. We have corrected the text.\nComment 2.14\n14. L55: Are we not already beyond the halfway point to meet the SDGs?\nResponse to 2.14\nThank you for noticing this. Please see our answer to comment 2.12 above.\nComment 2.15\n15. L62: Please be consistent in the use of italics for goal descriptions\nResponse to 2.15\nThank you for noticing this. We have corrected the text accordingly.\nComment 2.16\n16. L65: Key development dimensions such as?\nResponse to 2.16\nIn light of this comment, we have modified the text as follows:\nLines 68-69:\n…explored key development dimensions (e.g., socioeconomic development, environment, and\nequality)\nComment 2.17\n17. L68: Variables or entities such as?\nResponse to 2.17\nIn light of this comment, we have changed ‘variables or entities’ into ‘goals or indicators’.\nLines 71-73:\n…that is, comparing different goals or indicators against each other to understand the relative\nperformance or characteristics of each entity in relation to the others.\nComment 2.18\n18. L94-96: Briefly describe here what “specialization” in the RCA means in your study\nResponse to 2.18\nIn light of this comment, we have modified the text as follows.\nLines 99-101:\nFor example, if a country’s score for a specific SDG indicator constitutes a higher proportion\nof its total score across all indicators compared to the world average, that country is considered\nto be specialized in that SDG indicator.\nComment 2.19\n19. L98: Explain here briefly what the CSI and GSI cover\nResponse to 2.19\nIn light of this comment, we have modified the text as follows:\nLines 104-108:\nThe rationale behind the calculation of GSI and CSI indicates that high-CSI countries (most of\nwhich are at an advanced stage of development), are more likely to dominate high-GSI\nindicators, whereas low-CSI countries (most of which are at a primary stage of development)\ntend to dominate low-GSI indicators overall (see Methods).\nComment 2.20\n20. L102: I would change “correlation” to “relationship to” to avoid misunderstandings (or\ncorrespondence, as used in Supplementary Table 1)\nResponse to 2.20\nThank you for this suggestion. We have modified this following your suggestion.\nComment 2.21\n21. L184 vs L194: Parenthesis “(Fig 3a-c)” inconsistent\nResponse to 2.21\nThanks for noticing this. We have modified the text as follows.\nLines 199-200:\nThe first trend is characterised by a continuous decrease in specialization in low-GSI indicators\nrepresented in red in Figs. 3a-c.\nComment 2.22\n22. L210-11: Please elaborate on what you mean with “moved ahead” in a few words\nResponse to 2.22\nIn light of this comment, we have modified the text as follows.\nLines 224-227:\nFrom a dynamic analysis spanning the years 2000 to 2022, it is observed that while the first\ntwo trends have remained relatively stable, the third trend - particularly the emphasis on goals\nrelated to innovation - has advanced, appearing in earlier window groups.\nComment 2.23\n23. L241-42: Please add here that countries were sorted from lowest to highest SDG rank\nResponse to 2.23\nThank you for this suggestion. We have revised the text as follows.\nLines 242-244:\nTo address this, we determined the window size by grouping 50 countries at a time, resulting\nin 117 window groups (i.e., countries 1-50, 2-51, …, 117-166, with countries sorted from\nlowest to highest SDG rank).\nComment 2.24\n24. Fig 5: Add headings (Stage 1-4) similar to other figures in manuscript\nResponse to 2.24\nThank you for noticing this. We have revised this accordingly.\nComment 2.25\n25. L246: “The conditions of 17 respective goals” could be reformulated to be more precise\nResponse to 2.25\nIn light of this comment, we have modified the text as follows.\nLines 247-248:\nThe change of RCA of SDG indicators across 17 goals for each window group, aligned with\nthe rank of SDG scores in 2022.\nComment 2.26\n26. L322: When speaking of SDGs related to “economic development and basic livelihood\nsecurity (e.g., SDGs 1,2,3,4,7)”, you should clearly include SDG 8 and might want to consider\nalso SDG 11\nResponse to 2.26\nIn light of this comment, we have modified the text as follows.\nLines 377-378:\nFor instance, the SDGs related to economic development and basic livelihood security (e.g.,\nSDGs 1, 2, 3, 4, 7, 8, 11).\nComment 2.27\n27. L324-27: You could also refer here more strongly to SDG-related research, indicating a\ngeneral trend in prioritization of social and economic SDGs over environmental ones (e.g.\nChapter 6: “Planetary integrity” (Kotzé et al., 2022) in “The Political Impact of the Sustainable\nDevelopment Goals Transforming Governance Through Global Goals?”, pp. 140 – 171; DOI:\nhttps://doi.org/10.1017/9781009082945.007\nResponse to 2.27\nThank you for this suggestion. We have added this reference accordingly.\nComment 2.28\n28. L369: “and the other targets are not shown” – consider rephrasing this\nResponse to 2.28\nIn light of this comment, we have modified the text as follows.\nLine 456:\n…the remaining indicators are not highlighted in colour.\nComment 2.29\n29. L370: “countries” needs to be written with a capital letter\nResponse to 2.29\nThank you for noticing this. We have modified this mistake.\nComment 2.30\n30. L377-79: Consider is you can justify the sentence “these are not their active choices” based\non your analysis (see my comment above)\nResponse to 2.30\nThank you for your comment. We have revised this sentence as follows:\nLines 473-475:\nHowever, they may lose these relative advantages as they progress in their pursuit of greater\nwealth (i.e., income) and well-being.\nThis change is related to our response to comment 2.7 above.\nComment 2.31\n31. L398-402: These limitations should be addressed more explicitly earlier in the manuscript\n(see my comment above)\nResponse to 2.31\nIn view of this comment, we have addressed this limitation in the Results Section, and stated it\nagain in the Discussion Section. Please also see our response to Comments 2.5, 2.7 and 3.2.\nLines 181-183:\nIt is notable that the findings and insights derived from this method may not be universally\napplicable to all countries in each region, given the local variations in socio-economic\nconditions, culture, governance, and policy environments.\nLines 545-552:\nOn a related note, it should be emphasized that local variations at subnational levels and units\nof analysis (regional, provincial, urban, peri-urban, rural) in socio-economic conditions, culture,\ngovernance, policy environments, and institutional capacities are important factors that may\nnot be fully captured by our aggregated data and global trends. We acknowledge that even\nwithin the same country, SDG performance and strategies may differ between regions and\nbetween urban and rural areas61. Due to data limitations, this study focuses on global and\nnational sustainable development through 96 SDG indicators. Future research could extend this\nanalysis to regional, state, and city levels.\nComment 2.32\n32. L434-455: If I understand correctly, GSI (similar to PCI) aims at reflecting the complexity\nin achieving more sustainable outcomes. I would suggest including this explanation in this\nsubsection, and maybe also already earlier in the manuscript\nResponse to 2.32\nIn this study, the country sustainability index (CSI) and goal sustainability index (GSI) are\nprimarily used within a spectral clustering algorithm.\nWhile it is true that CSI values are positively correlated with the SDG scores of countries and\nmay indicate the complexity of achieving more sustainable outcomes to some extent, this is not\nthe primary reason for using these indexes.\nTherefore, we did not state in the main text that CSI and GSI reflect the complexity you refer\nto.\nWe explain these two indexes (CSI and GSI) as follows:\nLines 101-108:\nInspired by the economic complexity index (ECI) and product complexity index (PCI) set out\nby Hidalgo and Hausmann23, we use a method equivalent to a clustering algorithm21,22 to\ncalculate the country sustainability index (CSI) and goal sustainability index (GSI) based on\ncountries’ RCA in SDG indicators. The rationale behind the calculation of GSI and CSI\nindicates that high-CSI countries (most of which are at an advanced stage of development), are\nmore likely to dominate high-GSI indicators, whereas low-CSI countries (most of which are at\na primary stage of development) tend to dominate low-GSI indicators overall (see Methods).\nLines 154-160 (in the caption of Fig. 1):\nGSI and CSI are twin clustering indicators, which are calculated based on RCA of indicators\nusing the method developed by Hidalgo and Hausmann23 (see Methods). The algorithm is\nequivalent to finding the eigenvalues of a matrix and is related to a spectral clustering algorithm\nthat divides SDG indicators (or countries) into two groups: those with higher and lower GSI\nvalues (or countries with higher and lower CSI values). As a result of this reflections algorithm,\ncountries with high CSI values tend to dominate the high-GSI indicators, and vice versa.\nLines 323-326:\nThe GSI indicator, derived from the product space and economic complexity approach,\ncategorizes SDG indicators that countries tend to over- or under-specialize in during their\ndevelopment process. This categorization can help inform the formulation of sustainable\ndevelopment policies.\nLines 402-405:\nFor instance, the derived GSI indicator divides SDG indicators into low-GSI indicators that\ncountries tend to overlook and high-GSI indicators that countries tend to specialize in during\ntheir development process (Figs. 3, 5, 6).\nComment 2.33\n33. L438: Please correct method “is” instead of “are”\nResponse to 2.33\nThank you for noticing this. We have corrected the text accordingly.\nComment 2.34\n34. L441: You refer to SDG score in the text, but to SDG index value in Supplementary Fig. 8\n– please check your figures, text and tables for consistency\nResponse to 2.34\nThank you for noticing this. We have corrected the text, following your suggestion.\nComment 2.35\n35. L450: Further details on the criteria for setting initial values would be helpful\nResponse to 2.35\nIn view of this comment, we have modified the text as follows.\nLines 600-603:\nAfter setting initial values for the CSI and GSI (e.g., the number of indicators a country\nspecializes in, and the number of countries that specialize in each indicator), we calculate a\ncountry’s CSI by taking the average GSI values of the SDG indicators in which the country has\na relative advantage.\nComment 2.36\n36. L463: Can you provide any reference to the kamada_kawai layout algorithm\nResponse to 2.36\nYes, of course. In view of this comment, we have added this reference.\nReference no.61: Pospisil, L., Hasal, M., Nowakova, J. & Platos, J. Computation of Kamada-\nKawai Algorithm Using Barzilai-Borwein Method. in 327–333 (IEEE, 2015).\nComment 2.37\n37. L463: Consider rephrasing “And then” to “Subsequently”\nResponse to 2.37\nThank you for noticing this. We have corrected the text accordingly.\nComment 2.38\n38. L494: Please correct space with small “s”\nResponse to 2.38\nThank you for noticing this. We have corrected the text accordingly.\nComment 2.39\n39. L524-5: Please indicate that you refer to 98 “global” indicators (not targets) available, since\nthe dataset contains additional (e.g. regional) data as well. Furthermore, you could reflect a bit\nmore on the quality and potential limitation of the SDR data. Despite efforts to include data\nfrom a range of sources, there may still be gaps in coverage, particularly for certain regions or\nSDG targets. Uneven availability of data across countries and sectors could result in skewed\nrepresentations of SDG performance, limiting the generalizability of the findings.\nResponse to 2.39\nThank you for this suggestion. Following this comment we have modified the text as follows.\nLines 553-556:\nLastly, disparities in data availability across countries and economic sectors could lead to\nbiased representations of SDG performance, limiting the generalizability of the findings. With\nincreasing calls to improve the SDG database62,63, future studies should critically and\ncomparatively investigate SDG indicators and the pathways toward their realization.\nWe also introduced the advantages and disadvantages of the SDR database.\nLines 690-698:\nIn addition, the related SDG Index methodology and datasets have undergone multiple peer\nreviews and have been used to substantiate previous notable studies in this field6,66–68. Naturally,\nthe dataset has its advantages and disadvantages. The advantages include its broad coverage\nand consistency over time, which allow for meaningful comparisons across countries and time\nperiods. The disadvantages include a lack of granularity needed to capture local (e.g., urban\nlevel) variations and specific economic activities (e.g., industries) within countries.\nAdditionally, some indicators are not fully applicable to all countries (e.g., 14.C. Mean area\nthat is protected in marine sites important to biodiversity).\nComment 2.40\n40. Supplementary material, S87, Fig 9: “c” for Figure 9c. should be written in bold\nResponse to 2.40\nThank you for noticing this. We have corrected the text accordingly.\nConcluding remark\nThank you once again for all your invaluable suggestions, which have significantly enhanced\nthe quality of our paper.\nReviewer #3\nGeneral Remarks of Reviewer #3\nThe paper is well written and the authors invested a lot of efforts in explaining the methodology\nand the potential value of its output by providing valuable orientation for countries to find out\nto what extent they are on track in regard to the UN SDGs to be achieved by 2030.\nIt is argued that so far country trajectories in the pursuit of the UN Sustainable Development\nGoals have been poorly explored because the methodology to do so was largely missing. The\nauthors propose to change this by making use of the methodology developed to capture a\ncountry's degree economic complexity (EC). Its product space method employs network\nanalysis to reveal the relatedness, or affinity, between economies and applies it to cluster the\nmeasured targets of the different UN SDGs highlighting also trajectories of specialization\npatterns of regions and nations. The resulting ‘SDG space’ reveals the sustainable development\ntrajectories of 166 nations for the period 2000 to 2022. The SDG space then allows to identify\n‘orphaned’ areas with insufficient progress while highlighting future challenges in realizing\nSDGs.\nResponse to 3\nWe acknowledge this summary of the content of our manuscript, and overall support of our\naims, and aspired contribution to literature and practice.\nWe further thank you for your comments and suggestions. Please see our response to your\ncomments in the following.\nComment 3.1\nIn their mapping and analysis of country-based UN SDG performance and evolution, the\nauthors largely rely on data collected by numerous international institutions to assess if\ncountries are on track in regard to the numerous targets for each of the 17 UN SDGs. In this\ncontext, they heavily rely on the 2023 Sustainable Development Report that indexed and\nevaluated the evolving country performance in regard to the UN SDGs and its targets. Based\non secondary macro data analysis, they have identified and visualized 3,818 SDG spaces for\n166 nations spanning the years 2000 to 2022 (http://www.spacelab.team/#/SDGSpace) and\nfound underlying structures that largely mirror the fact the low income countries and high\nincome countries face different challenges in meeting the different UN SDGs.\nResponse to 3.1\nWe acknowledge this summary of the analytical approach, and findings of our study, as well\nnoticing the dedicated open-access web-interface.\nComment 3.2\nI wonder however to what extent it makes sense to talk of 'comparative advantage' of\ndeveloping countries (based on RCA Assessment) in meeting certain SDG Goals and Targets\nthat largely mirror the fact that they are low income countries with a lower environment\nfootprint and lower health risk exposure related to problems that largely affect affluent\ncountries (e.g. obesity). None of these countries probably have an interest in remaining\n'competitive' with regard to these goals and targets because they largely reflect the lack of\nability to cover the essential material needs of the people. This is also reflected in the obvious\nfact that they perform poorly with regard to UN SDG 1,2,4, 8 and 9 that improve with economic\ndevelopment. So talking of a comparative advantage may be misleading.\nResponse to 3.2\nThank you for your comment. We acknowledge this issue regarding the use of Revealed\nComparative Advantage (RCA) to discuss developing countries' performance on certain SDG\nGoals and Targets.\nThis performance is referred to in the Product Space (our analytical approach) as an RCA.\nIt does not, however, imply that countries have intentionally, deliberately, pursued this RCA,\nnor that the historical reasons and circumstances underpinning it are so-called ‘advantageous’.\nIn this regard, please see our replies to Comments 2.5 and 2.7, above, and corresponding\nrevisions in the main text.\nMoreover, while it is true, based on historical experience, that social and environmental\nindicators may improve with economic development, the latter may also cause significant\nenvironmental harm (e.g., pollution), or widen inequality.\nTo restate our reply to Comments 2.5 and 2.7 above, the exploration of the underpinnings, or\ndrivers, of these RCAs (or specializations, as they are referred to in Economic Complexity and\nthe Product Space), lie outside the scope of the current study, and can be debated at length.\nRegarding the use of RCA, there are several benefits, so to speak, to use a relative (comparative)\nanalytical approach to measure development, rather than using absolute numbers. First, it\nallows for the identification of unique strengths and weaknesses specific to each country, which\nmay facilitate more tailored and effective policy interventions in the future. Second, it\nhighlights disparities and inequities between countries, drawing attention to areas needing\nurgent policy and/or international support. Third, it enables tracking progress over time relative\nto other countries, which can be more meaningful for policy evaluation and improvement.\nFinally, this approach helps in understanding the contextual differences that absolute measures\nmight overlook, providing a more nuanced perspective on development.\nAll this adds to our ability to identify ‘orphan’ indicators — goals that may be overlooked\nduring different stages of a country's development process — in order to address development\nshortcomings.\nHaving said that, we acknowledge that talking about an ‘advantage’ in meeting certain SDG\nGoals and Indicators can be misleading, especially when these advantages may reflect low-\nincome status rather than intentional policies or capabilities. Indeed, many of these indicators,\nsuch as lower environmental footprints and reduced health risks like obesity, may be a\nconsequence of limited resources. Still, this does not render them apriori, in and of themselves,\ndisadvantages (low ecological footprints is a so-called good attribute, it’s underpinnings\nnotwithstanding). Indeed, this is a complex territory.\nReflecting on this reply, this is exactly the sort of discussions we so hope to stir and support\nwith our study.\nTo be clear, our intention is not to suggest that developing countries (or any country for this\nmatter) should maintain their current status to keep these 'advantages'. Instead, our goal is to\nuse the RCA framework to uncover areas where targeted interventions can help improve\ndevelopment outcomes without compromising social and environmental well-being.\nIn view of this Comment, and Comments 2.5 and 2.7, we have added a subsection entitled\n\"Limitations and clarifications on the Product Space terminology\", to acknowledge the data\nlimitations, clarify the use of the Product Space techniques and terminology, and discuss their\nimplications. Please refer to our response to Comment 2.5 above, and to lines 503-555 in the\nrevised manuscript.\nThank you for your feedback, which has helped us clarify and refine our approach.\nIn addition to these revisions, we have added three additional countries as case studies in the\nDiscussion Section to further explore these issues. Please see the newly designed Fig. 8 below.\nWe remain bounded by the Article format (and wordcount) limitations, yet hope to inspire and\ninform such future discussions.\nLines 418-450:\nIn terms of policy guidance, the comparative insights and identified trends in sustainable\ndevelopment trajectories can provide targeted recommendations (Fig. 8). From a static\nperspective, countries’ performance on the indicators can be categorized into four areas (Fig.\n8b, d, f, h): Area I (high RCA and SDG Scores) represents an ideal situation. Area II (high RCA\nbut low SDG scores) where indicators are relatively difficult for a specific country to improve,\nas other countries are also likely to struggle in making progress and providing successful\nexamples. Area III (low RCA and SDG Scores) represents the worst situation, where both\nrelative and absolute performance on these indicators is poor. Area IV (low RCA but relatively\nhigh SDG scores) where indicators may be easier to improve, as other countries might be\nperforming better and could offer models for success.\nTaking the USA as an example (Fig. 8h), beyond the indicators in Area III — such as\novernutrition (2.B, 2.F) and waste and emissions (12.A, 12.C, 12.E, 13.B) — focus should also\nbe placed on addressing social and environmental impacts linked to imports (8.D, 12.F, 14.A),\ninequality (10.A), and Corporate Tax Haven (17.A) in Area IV, despite these indicators having\nabsolute scores above the global average.\nFrom a dynamic viewpoint, the revealed patterns of trajectories can help forewarn potential\nchallenges for countries developing from low to high sustainable development stage (Fig. 8a,\nc, e, g, Fig. 5e and Supplementary Tables 18, 20, 22). Taking China as an example, as shown\nin red ▽ in Fig.8, China would face challenges in realizing indicators 13.B (CO emissions\n2\nfrom fossil fuel combustion and cement production), 14.F (Fish caught by trawling or dredging),\nand 17.A (Corporate Tax Haven), in which the country has already shown comparative\nweakness. Attention should also be given to indicators regarding overnutrition (2.B, 2.F),\nin which China has relative strength but may lose it in the future (as forewarned by Fig. 5e). A\nrecent study provided evidence that the prevalence of obesity of adults in China is more than\ndoubled between 2004 (3.1%) and 2018 (8.1%)53. It is notable that indicators related to\novernutrition (2.B, 2.F) are some of the main ‘orphaned’ indicators in the SDG framework, not\nonly for high-income countries, but also for low-income ones54.\nThese cases illustrate how the structured analysis of nations’ sustainable development\ntrajectories can provide insights for shaping development policies. However, it is imperative to\nview these patterns not as deterministic ‘destinies’ (or fates) but rather as early warnings that\ncan guide preventative strategies. This recognition underscores that countries, even at similar\nstages of development, may exhibit distinct approaches to specific SDG indicators, particularly\nthose that are prone to being overlooked (Fig.7).\nFig. 8 | The SDG space and indicator performance for four representative countries in\n2022. Panels a-b, Ethiopia (Stage 1). Panels c-d, India (Stage 2). Panels e-f, China (Stage 3).\nPanels g-h, United States (Stage 4). In panels a-h, blue nodes represent the top 20 SDG\nindicators by RCA values, while red nodes represent the bottom 20. The remaining indicators\nare not highlighted. The size of each node corresponds to the absolute RCA value. Countries\nare evenly divided into four stages according to their absolute score rankings: Stage 1 (Primary\nstage), Stage 2 (Low medium stage), Stage 3 (High medium stage), and Stage 4 (Advanced\nstage). Stage 4 is further divided into two halves to identify the top 20 increasing and decreasing\nindicators in RCA for the USA (in the first half of Stage 4) between its current stage and the\nnext stage (see Supplementary Table 1). In panels b, d, f, h, scatter diagrams are divided into\nfour areas by the lines at RCA = 0 and the global average SDG score of 63. Area I: Countries\nwith high RCA and SDG Scores for these indicators have reached a relatively ideal situation.\nArea II: Countries with high RCA but low SDG scores for these indicators face relative\ndifficulties in improving them, likely due to low global averages for these indicators. Area III:\nCountries with low RCA and SDG Scores for these indicators show both relative and absolute\nunderperformance, indicating that these indicators should be prioritized for improvement. Area\nIV: Countries with low RCA but relatively high SDG scores for these indicators demonstrate\npoor relative performance but strong absolute performance.\nComment 3.3\nAnother weakness is the relatively uncritical embrace of the secondary data gathered on the\nmacro-level by international institutions. They tend to start from the misleading baseline\nassumption (reflected in many UN Reports on the SDGs) that state bureaucracies are in a\nposition to enable a country to meet the UN SDGs and its targets. The state may provide well-\ndesigned policies to incentivize innovation and entrepreneurship moving into a publicly\ndesirable direction; but, ultimately, inclusive and sustainable change is not enabled by\nregulation and protest but investments in the real economy. UN SDG 8 and 9 emphasize this\npoint, but none of the indicators used to measure its development-related targets actually care\nabout entrepreneurship on the ground. This is a major weakness of gathering data only on the\nmacro-level because they do not allow for any differentiation within a country (emergence of\nvibrant and innovative economic ecosystems in unexpected places). There is a lack of\nreferences on these controversial aspects behind the official UN SDGs rhetoric.\nResponse to 3.3\nThank you for this comment.\nWe acknowledge the importance of investments in the “real economy” in achieving the SDGs,\nalong with well-designed, evidence-based policies, among other areas, in incentivizing\ninnovation.\nInvestments in the “real economy” refers to financial resources directed towards tangible\neconomic activities and infrastructure that directly contribute to production, employment, and\neconomic growth. This includes investments in industries such as manufacturing, agriculture,\nservices, and infrastructure projects like transportation, energy, and telecommunications. These\ninvestments are crucial for driving sustainable development, fostering innovation, and creating\ninclusive economic opportunities.\nBased on your comment, we have added the important role of investments in achieving the\nSDGs in the Discussion Section as follows:\nLines 494-503:\nIn addition, local investments in tangible economic activities and infrastructure — that is the\n‘real economy’ — that directly contribute to production, employment, and economic growth\nare essential for promoting sustainable development. These include the allocation of financial\nresources to industries such as manufacturing, agriculture, services, and infrastructure projects\nlike transportation, renewable energy, and advanced telecommunications. Such local\ninvestments are crucial for fostering innovation and creating inclusive economic opportunities.\nHowever, the relevant SDG targets and indicators in these areas, particularly those under UN\nSDGs 8 and 9, are ill-defined. Notably, none of the indicators used to measure these\ndevelopment-related targets adequately address these issues, which represents a significant\nweakness in the SDG framework. This issue warrants further attention.\nRegarding the use of secondary data, we have chosen the 2023 Sustainable Development\nReport (SDR) as our data source because it provides the most comprehensive and comparable\ndatasets available. The SDR offers time series data for the SDG Index, calculated retroactively\nusing consistent indicators and methods, and covers more than 160 countries worldwide.\nTo the best of our knowledge, this database is one of the few that provides long-term,\ncomparable SDG indicators, which is crucial for constructing the SDG space and performing\nrobust trend analysis.\nMoreover, the SDR dataset has been widely used, and repeatedly scrutinized and reviewed, in\nnumerous studies published in high-impact journals, including Nature64, Nature Geoscience65,\nDublin University Press6, and Cambridge University Press63.\nAt the same time, we recognize that the dataset has its own advantages and disadvantages. The\nadvantages include its broad coverage and consistency over time, which allow for meaningful\ncomparisons across countries and time periods. The disadvantages include the lack of\ngranularity needed to capture local variations and emerging economic ecosystems within\ncountries.\nIn the revised manuscript, we have acknowledged these limitations as follows:\nLines 181-184:\nIt is notable that the findings and insights derived from this method may not be universally\napplicable to all countries in each region, given the local variations in socio-economic\nconditions, culture, governance, and policy environments.\nIn view of this Comment, and Comments 2.5, 2.7 and 3.2, we have added a subsection entitled\n\"Limitations and clarifications on the Product Space terminology\", to acknowledge the data\nlimitations, clarify the use of the Product Space techniques and terminology, and discuss their\nimplications.\nLines 504-556:\nLimitations and clarifications on the Product Space terminology\nIn this study, we constructed ‘SDG spaces’ for 166 nations from 2000 to 2022, revealing a\nbipolar world while identifying ‘orphaned’ development indicators by different nations. These\npatterns, along with the SDG space, provide a high-resolution tool for evaluating progress and\noffering targeted guidance for countries in achieving the SDGs. In doing so, we have applied\nthe Product Space and Economic Complexity framework, analytical techniques, and\nterminology, including ‘revealed comparative advantage’ (RCA) and ‘specializations’.\nWe acknowledge that some of the sustainable development indicators for which certain\ncountries exhibit an RCA (a specialization) might reflect an intentional, premeditated national\npreference over time, such as the environmental impacts of imports, as part of a larger\nimportation policy. While our data allow us to identify which advantages — or, as they are also\nreferred to in this study (and in Economic Complexity) ‘specializations’ — are likely to\ndiminish as a country develops, they do not enable us to differentiate between intentional (i.e.,\npreferred, premeditated) and unintentional specializations. Indeed, ‘unintentional\nspecializations’ may arise due to a country’s geography or geology, such as being landlocked\nor possessing mineral resource reserves60. Additionally, other detrimental factors, such as\ncolonial legacy, may predetermine a country’s development trajectory60.\nTo clarify, the use of RCA to discuss developing countries’ performance on certain SDG\nindicators is done within the context of the Product Space approach. However, it does not imply\nthat these countries have intentionally pursued this RCA, nor does it suggest that the historical\nreasons and circumstances underpinning it are inherently ‘advantageous’. Using the terms\n‘advantage’ or ‘specialization’ outside the context of the Product Space approach – when\nreferring to a country meeting certain SDG targets – may be misleading, especially when these\n‘advantages’ may reflect low-income status rather than deliberate policies or capabilities. For\nexample, several indicators, such as lower environmental footprints and reduced health risks\nlike obesity, may result from limited resources. At the same time, this does not inherently render\nthem disadvantages — a low ecological footprint can be considered a positive attribute,\nregardless of its underlying causes. This invites further discussion and underscores the need for\nfuture research.\nMoreover, our intention is not to suggest that so-called developing countries (or any country,\nfor that matter) should maintain their current status to preserve these ‘advantages’ (RCAs).\nInstead, the aim of this study is to use the RCA framework to identify national sustainable\ndevelopment trajectories as well as areas where targeted interventions are needed to enhance\ndevelopment outcomes.\nIn the same vein, the degree of agency we can attribute to national governments in such\ncircumstances (e.g., ‘being landlocked’, ‘being resource-endowed’, and ‘being post-colonial’)\nwhen developing a relative specialization remains a matter of debate. Addressing the\ndistinction between intentional and unintentional specializations, as well as the role of national\ngovernments’ agency, requires further study and may involve subjective judgments. While this\nstudy opens new avenues for such discussions, the interpretation of RCAs as normative\npreferences lies outside its scope.\nOn a related note, it should be emphasized that local variations at subnational levels and units\nof analysis (regional, provincial, urban, peri-urban, rural) in socio-economic conditions, culture,\ngovernance, policy environments, and institutional capacities are important factors that may\nnot be fully captured by our aggregated data and global trends. We acknowledge that even\nwithin the same country, SDG performance and strategies may differ between regions and\nbetween urban and rural areas61. Due to data limitations, this study focuses on global and\nnational sustainable development through 96 SDG indicators. Future research could extend this\nanalysis to regional, state, and city levels.\nLastly, disparities in data availability across countries and economic sectors could lead to\nbiased representations of SDG performance, limiting the generalizability of the findings. With\nincreasing calls to improve the SDG database62,63, future studies should critically and\ncomparatively investigate SDG indicators and the pathways toward their realization.\nIn addition, to validate our results, we have added a sub-section of Robustness check, for\nexample, we compared our results with the newly calculated results using the UN-SDG dataset.\nPlease also see our reply to Comment 1.10 above.\nLines 708-739, Robustness check Section of the Methods:\nTo ensure the robustness of our findings, we evaluated the results from the following\nperspectives: (1) whether a consistent SDG space structure can be reproduced using data from\ndifferent years; (2) whether the SDG space structure remains similar (i.e., stable) after\nexcluding overlapping indicators; (3) whether the structure is consistent across different data\nsources; (4) whether the bipolar world results hold under varying similarity thresholds; and (5)\nwhether consistent evolution patterns of SDG indicators emerge with different moving window\nsizes.\n(1) Supplementary Fig. 3 demonstrates that a similar SDG space structure is observed across\nthe years of 2000, 2015, and 2022. This consistency indicates that our finding of a bipolar world\nwithin the SDG space is robust and not influenced by the choice of years.\n(2) Notably, some SDG indicators may overlap, such as 2.B (Prevalence of obesity) and 2.F\n(Human Trophic Level) in this study. To ensure the robustness of our results, we removed the\noverlapping indicators, retaining only one unique indicator, and re-examined the patterns\n(Supplementary Fig. 10). The findings remained robust, confirming the consistency of our\nanalysis.\n(3) To validate the robustness of our results using different data sources, we collected additional\ndata from the UNSD SDG database. For consistency in comparison, we included only the data\non targets that matched the 96 indicators used in this study (see Tables 1 and 2 in Methods for\nthe matching process). Data source: https://unstats.un.org/sdgs/dataportal/database. As shown in\nFig. 1 (results of SDR 2023 database) and Supplementary Fig. 11 (results of UNSD SDG\ndatabase), the SDG spaces exhibit a similar structure.\n(4) In this study, we generated the SDG spaces by setting the similarity threshold between SDG\nindicators at 0.7. We tested the robustness of our results by varying the similarity values. As\nseen in Supplementary Fig. 9, the indicator clusters remain consistent, supporting the SDG\nspace structure.\n(5) We also examined whether varying the moving-window sizes would yield similar evolution\npatterns of SDG indicators. Supplementary Fig. 7 shows that the evolution patterns of SDG\nindicators and the ranking of countries’ SDG scores in 2022 remain consistent across moving-\nwindow sizes of 20, 30, 40, 50, 60, and 70.\nOverall, our findings demonstrate robustness across various tests and conditions.\nSupplementary Figs. 3, 7, 9-11 in the Supplementary Materials are as follows:\nSupplementary Figure 3 | The evolution of SDG space. Panels a, c, and e show the global\nSDG space in 2000, 2015, and 2022, respectively. Panels b, d, and f display the similarity of\nSDG indicators in 2000, 2015, and 2022, respectively.\nSupplementary Figure 7 | The evolution of SDG indicators alongside the ranking of\ncountries’ SDG scores in 2022 with varying moving-window sizes. Panels a, b, c, d, e, and\nf display the results using moving-window sizes of 20, 30, 40, 50, 60, and 70, respectively.\nSupplementary Figure 9 | Sensitivity analysis of SDG indicators’ similarity in 2022. Panel\na, SDG indicators with similarity ≥ 0.6. Panel b, SDG indicators with similarity ≥ 0.7. Panel c,\nSDG indicators with similarity ≥ 0.8.\nSupplementary Figure 10 | The SDG space in 2022 with non-overlapping indicators. We\nscreened six groups of similar indicators: (1.A and 1.B), (2.B and 2.F), (2.E and 2.H), (3.G and\n3.L), (10.A and 10.B), and (14.D, 14.E, and 14.F). For the robustness check, we retained only\nthe non-overlapping indicators within these groups. The node colour represents Goal\nSustainability Index (GSI), and the node size corresponds to the node degree, indicating the\nnumber of edges connected to the node.\nSupplementary Figure 11 | The SDG space in 2022 using UN SDG data. The node colour\nrepresents Goal Sustainability Index (GSI), and the node size indicates the node degree,\nrepresenting the number of edges connected to the node. This SDG space was produced using\ndata from the UNSD SDG targets framework, which were matched with the 96 SDG indicators\n(see Tables 1-2 in Methods). Data source: https://unstats.un.org/sdgs/dataportal/database.\nComment 3.4\nNevertheless, applying the EC methodology to do a mapping that shows the trajectories of\ncountries in their pursuit of the UN SDGs and its targets may help provide valuable orientation\nfor policy makers by making it transparent that the main enemy of sustainability in high income\ncountries is affluence (leading to overuse of natural resources, large greenhouse gas emissions\nand material waste) whereas it is poverty in low income countries (lack of ability to provide\nthe right to food, health, education, employment, etc as well as depletion of natural resources\nand political instability due to population growth and lack of economic opportunities). This\nshould also be made more explicit in the paper.\nResponse to 3.4\nThank you for your helpful suggestion and for acknowledging our contribution in using the\neconomic complexity and product space method to map the trajectories of countries in their\npursuit of the SDGs, providing valuable orientation for policymakers.\nFollowing this comment, we have added a relevant discussion on the explanations of SDG\ndevelopment patterns in the Discussion Section, as follows:\nLines 383-389:\nAdditionally, scientists have warned that global increases in affluence have consistently driven\nresource use and pollutant emissions to rise more rapidly than technological improvements\nhave been able to mitigate. These impacts are primarily driven by affluent citizens49. Therefore,\nit is imperative to achieve absolute decoupling of economic growth from resource consumption\nand pollutant emissions through various approaches, such as technological advancements,\nshifts in consumption patterns, and the adoption of more effective policies50.\nComment 3.5\nThe methodology is surely sound but the authors need to drop the 'comparative advantage'\nlanguage and they will have to highlight the limits of the approach in view of the exclusive\nreliance on macro-level data. Finally, the paper does not contain any concluding remarks that\ndescribe major findings as well as weaknesses that may have to be addressed in future research.\nResponse to 3.5\nThank you for the support of the analytical approach.\nTo underscore the limitations of the data used, we have modified the text as follows:\nLines 690-698:\nIn addition, the related SDG Index methodology and datasets have undergone multiple peer\nreviews and have been used to substantiate previous notable studies in this field6,66–68. Naturally,\nthe dataset has its advantages and disadvantages. The advantages include its broad coverage\nand consistency over time, which allow for meaningful comparisons across countries and time\nperiods. The disadvantages include a lack of granularity needed to capture local (e.g., urban\nlevel) variations and specific economic activities (e.g., industries) within countries.\nAdditionally, some indicators are not fully applicable to all countries (e.g., 14.C. Mean area\nthat is protected in marine sites important to biodiversity).\nTo underscore the key findings along with weaknesses, we have added a subsection of\n‘Limitations and clarifications on the Product Space terminology’ as follows:\nLines 504-556:\nLimitations and clarifications on the Product Space terminology\nIn this study, we constructed ‘SDG spaces’ for 166 nations from 2000 to 2022, revealing a\nbipolar world while identifying ‘orphaned’ development indicators by different nations. These\npatterns, along with the SDG space, provide a high-resolution tool for evaluating progress and\noffering targeted guidance for countries in achieving the SDGs. In doing so, we have applied\nthe Product Space and Economic Complexity framework, analytical techniques, and\nterminology, including ‘revealed comparative advantage’ (RCA) and ‘specializations’.\nWe acknowledge that some of the sustainable development indicators for which certain\ncountries exhibit an RCA (a specialization) might reflect an intentional, premeditated national\npreference over time, such as the environmental impacts of imports, as part of a larger\nimportation policy. While our data allow us to identify which advantages — or, as they are also\nreferred to in this study (and in Economic Complexity) ‘specializations’ — are likely to\ndiminish as a country develops, they do not enable us to differentiate between intentional (i.e.,\npreferred, premeditated) and unintentional specializations. Indeed, ‘unintentional\nspecializations’ may arise due to a country’s geography or geology, such as being landlocked\nor possessing mineral resource reserves60. Additionally, other detrimental factors, such as\ncolonial legacy, may predetermine a country’s development trajectory60.\nTo clarify, the use of RCA to discuss developing countries’ performance on certain SDG\nindicators is done within the context of the Product Space approach. However, it does not imply\nthat these countries have intentionally pursued this RCA, nor does it suggest that the historical\nreasons and circumstances underpinning it are inherently ‘advantageous’. Using the terms\n‘advantage’ or ‘specialization’ outside the context of the Product Space approach – when\nreferring to a country meeting certain SDG targets – may be misleading, especially when these\n‘advantages’ may reflect low-income status rather than deliberate policies or capabilities. For\nexample, several indicators, such as lower environmental footprints and reduced health risks\nlike obesity, may result from limited resources. At the same time, this does not inherently render\nthem disadvantages — a low ecological footprint can be considered a positive attribute,\nregardless of its underlying causes. This invites further discussion and underscores the need for\nfuture research.\nMoreover, our intention is not to suggest that so-called developing countries (or any country,\nfor that matter) should maintain their current status to preserve these ‘advantages’ (RCAs).\nInstead, the aim of this study is to use the RCA framework to identify national sustainable\ndevelopment trajectories as well as areas where targeted interventions are needed to enhance\ndevelopment outcomes.\nIn the same vein, the degree of agency we can attribute to national governments in such\ncircumstances (e.g., ‘being landlocked’, ‘being resource-endowed’, and ‘being post-colonial’)\nwhen developing a relative specialization remains a matter of debate. Addressing the\ndistinction between intentional and unintentional specializations, as well as the role of national\ngovernments’ agency, requires further study and may involve subjective judgments. While this\nstudy opens new avenues for such discussions, the interpretation of RCAs as normative\npreferences lies outside its scope.\nOn a related note, it should be emphasized that local variations at subnational levels and units\nof analysis (regional, provincial, urban, peri-urban, rural) in socio-economic conditions, culture,\ngovernance, policy environments, and institutional capacities are important factors that may\nnot be fully captured by our aggregated data and global trends. We acknowledge that even\nwithin the same country, SDG performance and strategies may differ between regions and\nbetween urban and rural areas61. Due to data limitations, this study focuses on global and\nnational sustainable development through 96 SDG indicators. Future research could extend this\nanalysis to regional, state, and city levels.\nLastly, disparities in data availability across countries and economic sectors could lead to\nbiased representations of SDG performance, limiting the generalizability of the findings. With\nincreasing calls to improve the SDG database62,63, future studies should critically and\ncomparatively investigate SDG indicators and the pathways toward their realization.\nThe relative comparative advantage is integral to the analytical approach and methodology (one\nof the main innovations of this study). Please, kindly refer to our replies to comments 1.3,\n2.5, 2.7, 3.2, above.\nConcluding remark\nThank you once again for all your invaluable suggestions, which have significantly enhanced\nthe quality of our paper.\n69"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily responded to my most of my questions and suggestions. I think the paper can make useful contributions to the literature. I would recommend it for publication conditional on some final, minor comments below.\n1. For completeness, the authors should provide concrete policy suggestions for all the four countries shown in Figure, using four groups they construct and show in Figure 8. These include Ethiopia, China, India and the US. Currently, they only discuss the US and China.\n2. The 4 countries in Figure 8 have their indicators mostly falling into two groups, I and III. Does this result similarly hold for other countries, given the dominant bipolar pattern revealed in this study? If so, it is useful to note. Otherwise, it would be more illustrative to show other country examples with their indicators falling more into groups II and IV for richer analysis.\n3. Finally, while the authors have provided clearer discussion on the main data source that they analyze and some other data sources, it can be useful to provide a more nuanced note on data quality issues. Since countries are at different statistical capacity levels, the quality of the data they produce (and provide to international organizations) can vary. Furthermore, there is also an aspect of political economy with data generation and comparability. See, for example, Dargent et al (2018) and Dang et al (2023). These can be discussed as caveats or limitations that should be explored in further research.\nReferences\nDargent, E., Lotta, G., Mejía-Guerra, J. A., & Moncada, G. (2018). The Political Economy of Statistical Capacity in Latin America: Who Wants to Know?. Inter-American Development Bank.\nDang, H. A. H., Pullinger, J., Serajuddin, U., & Stacy, B. (2023). Statistical performance indicators and index—a new tool to measure country statistical capacity. Scientific Data, 10(1), 146.", "reviewer_2": "I thank the authors for their thoughtful consideration of my comments and suggestions. I am pleased to see that my concerns have been addressed thoroughly and satisfactorily.", "reviewer_3": "The response of the authors is very detailed and I appreciate that the concerns raised have been taken into account to the greatest extent possible in the revised manuscript. The publication will be of great significance because it highlights the potential of the Product Space approach and techniques to reveal useful patterns underpinning the sustainable development trajectories of nations."}, "author_response": "Response to Referees for Nature Communications manuscript NCOMMS-24-21670A\nResponse to Referees for Nature Communications manuscript NCOMMS-\n24-21670A, The Sustainable Development Trajectories of Nations (original\narticle title, now revised)\nAcknowledgements\nWe are grateful to the anonymous reviewers and editors for their recognition of this study’s\ncontributions and for their invaluable feedback. In particular, we would like to thank Reviewer\n#1 for their specific and constructive comments, which have helped improve key aspects of the\narticle. We also appreciate Reviewers #2 and #3 for their satisfaction with our previous\nresponses and their continued acknowledgement of the work. Below, we provide detailed\nresponses to Reviewer #1's comments and have made corresponding revisions in the\nmanuscript.\nReviewer #1\nGeneral Remarks of Reviewer #1\nThe authors have satisfactorily responded to my most of my questions and suggestions. I think\nthe paper can make useful contributions to the literature. I would recommend it for publication\nconditional on some final, minor comments below.\nResponse to 1\nWe are pleased to note your satisfaction with our previous response and deeply appreciate your\nthoughtful and detailed comments. These specific suggestions will further strengthen the rigor\nof our analysis and enhance the policy relevance of our study's findings.\nComment 1.1\n1. For completeness, the authors should provide concrete policy suggestions for all the four\ncountries shown in Figure 8, using four groups they construct and show in Figure 8. These\ninclude Ethiopia, China, India and the US. Currently, they only discuss the US and China.\nResponse to 1.1\nWe fully agree with your insightful comment. Including concrete policy suggestions for\nEthiopia and India will indeed enrich our discussion and provide a more comprehensive\nperspective. In line with your recommendation, we have incorporated the relevant discussion\nas follows:\nLines XX:\nFor Ethiopia, a representative country at Stage 1 (Primary stage), priority should be given to\naddressing indicators with the lowest SDG scores and Revealed Comparative Advantage (RCA)\nin Area III (Fig. 8b), such as 6.B (Population using at least basic sanitation services) and 6.D\n(Anthropogenic wastewater that receives treatment). For India, in addition to the indicators in\nArea III (Fig. 8d), attention should be directed towards biodiversity-related indicators, such as\n15.C (Mean area that is protected in terrestrial sites important to biodiversity) and 15.E (Red\nList Index of species survival). India exhibits comparative weaknesses in these two indicators,\nand as it transitions to the next stage, it is likely to face greater challenges in these areas (see\nthe red ▽ in Fig. 8c).\nComment 1.2\n2. The 4 countries in Figure 8 have their indicators mostly falling into two groups, I and III.\nDoes this result similarly hold for other countries, given the dominant bipolar pattern revealed\nin this study? If so, it is useful to note. Otherwise, it would be more illustrative to show other\ncountry examples with their indicators falling more into groups II and IV for richer analysis.\nResponse to 1.2\nThank you for this valuable comment. We apologize for not explaining this more clearly in the\noriginal submission. The reason most indicators for these four countries fall into two groups is\nthat, for clarity and to provide more focused recommendations, we only displayed the top and\nbottom 20 SDG indicators based on RCA values in Fig. 8. To clarify this point, we have added\nthe following note to the caption of Fig. 8.\nLines XX:\nNote: Fig. 8 displays only the top and bottom 20 SDG indicators based on RCA values; the\nremaining indicators are not highlighted.\nComment 1.3\n3. Finally, while the authors have provided clearer discussion on the main data source that they\nanalyze and some other data sources, it can be useful to provide a more nuanced note on data\nquality issues. Since countries are at different statistical capacity levels, the quality of the data\nthey produce (and provide to international organizations) can vary. Furthermore, there is also\nan aspect of political economy with data generation and comparability. See, for example,\nDargent et al (2018) and Dang et al (2023). These can be discussed as caveats or limitations\nthat should be explored in further research.\nReferences\nDargent, E., Lotta, G., Mejía-Guerra, J. A., & Moncada, G. (2018). The Political Economy of\nStatistical Capacity in Latin America: Who Wants to Know?. Inter-American Development\nBank.\nDang, H. A. H., Pullinger, J., Serajuddin, U., & Stacy, B. (2023). Statistical performance\nindicators and index—a new tool to measure country statistical capacity. Scientific Data, 10(1),\n146.\nResponse to 1.3\nThank you for your constructive suggestion. In line with your recommendation, we have\nexpanded the discussion on data quality issues and highlighted that data generation and\ncomparability can be influenced by various political and economic factors.\nThe detailed revisions are as follows:\nLines XX:\nLastly, due to the difference in political and economic conditions62,63, disparities in data\navailability and quality across countries may result in biased representations of SDG\nperformance, limiting the generalizability and comparability of our findings. With growing\ncalls to enhance the SDG database64,65, future studies should critically and comparatively\nexamine SDG indicators and the pathways toward their realization.\nReviewer #2\nGeneral Remarks of Reviewer #2\nI thank the authors for their thoughtful consideration of my comments and suggestions. I am\npleased to see that my concerns have been addressed thoroughly and satisfactorily.\nResponse to 2\nWe are pleased to hear that you are satisfied with our responses and revisions. We greatly\nappreciate your constructive and detailed comments, which have significantly contributed to\nelevating the quality of the manuscript.\nReviewer #3\nGeneral Remarks of Reviewer #3u\nThe response of the authors is very detailed and I appreciate that the concerns raised have been\ntaken into account to the greatest extent possible in the revised manuscript. The publication\nwill be of great significance because it highlights the potential of the Product Space approach\nand techniques to reveal useful patterns underpinning the sustainable development trajectories\nof nations.\nResponse to 3\nWe are pleased to learn that you are satisfied with our responses and revisions. We sincerely\nappreciate your insightful comments and your recognition of the key contributions made by\nthis study.\n4"}]

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