# Everything counts in large amounts: A systemic discourse analysis of official texts related to the UN's sustainable development goals

Albin Wagener

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Albin Wagener. Everything counts in large amounts: A systemic discourse analysis of official texts related to the UN's sustainable development goals. European Journal of Sustainable Development Research, 2026, 10 (2), pp.em0364. 10.29333/ejosdr/17635. hal-05447520

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# Everything counts in large amounts: A systemic discourse analysis of official texts related to the UN's sustainable development goals

Albin Wagener <fnref n="1" />*

**Citation:** Wagener, A. (2026). Everything counts in large amounts: A systemic discourse analysis of official texts related to the UN's sustainable development goals. *European Journal of Sustainable Development Research, 10*(2), em0364. https://doi.org/10.29333/ejosdr/17635

**ARTICLE INFO**

Received: 16 Jan. 2025

Accepted: 25 Jul. 2025

**ABSTRACT**

Since 2015 and its adoption by the United Nations (UN), the sustainable development goals (SDGs) program and its 17 SDGs have been a source of inspiration for numerous sectors, in order to reach the 2030 agenda for sustainable development. In sectors as diverse as industry, human rights, ecology, or education, several institutions, organizations, and stakeholders have used the opportunity offered by the SDGs to make sustainable choices or apply specific policies. Yet it is also true that the sheer application of the SDGs have triggered grounded criticism, insofar as it offered readymade templates to reproduce inequalities or foster wrong decisions. In this sense, the 2030 agenda has raised some concern regarding a new form of colonialism it seems to rely on, its permanent links to an economy of growth and non-decent work, or the pervasive impacts on education and social inequalities. Such critical points have motivated scholars to work on a reinterpreted application of the SDGs, underline the positive evolution of the SDGs in comparison to the millenium development goals, and call for an adaptation of the SDGs regarding climate change and planetary limits. As a linguist, my approach regarding the 17 SDGs is rooted in systemic discourse analysis-an applied discourse study that simultaneously draws on critical discourse analysis, corpus studies and lexicometric analysis, which I will define later in the present chapter. My goal is to analyze every official text published in order to present and encourage the application of each of the 17 SDGs, thus building a corpus by extracting texts from official sources published on the Internet website of the UN, dedicated to the 2030 agenda for sustainable development. In this sense, a link is made between discourse structures, social structures, and social representations.

**Keywords:** SDG, discourse analysis, lexicometrics, language, narratives, corpus

## SUSTAINABLE DEVELOPMENT GOALS: CRITICAL PERSPECTIVES

Since 2015 and its adoption by the United Nations (UN), the sustainable development goals (SDGs) program and its 17 SDGs have been a source of inspiration for numerous sectors, in order to reach the 2030 agenda for sustainable development. In sectors as diverse as industry (Wynn & Jones, 2020), human rights (Kaltenborn et al., 2020), ecology (Kish & Quilley, 2023), or education (Shulla et al., 2020), several institutions, organizations and stakeholders have used the opportunity offered by the SDGs to make sustainable choices or apply specific policies. Yet it is also true that the sheer application of the SDGs have triggered grounded criticism, insofar as it offered readymade templates to reproduce inequalities or foster wrong decisions. In this sense, the 2030 agenda has raised some concern regarding a new form of colonialism it seems to rely on (Ziai, 2016), its permanent links to an economy of growth and non-decent work (Frey, 2017), or the pervasive impacts on education and social inequalities

(Kopnina, 2020). Such critical points have motivated scholars to work on a reinterpreted application of the SDGs (Montesano Montessori & Lautensach, 2024), underline the positive evolution of the SDGs in comparison to the millenium development goals (MDGs) (Fukuda-Parr, 2016), and call for an adaptation of the SDGs regarding climate change and planetary limits (Kotzé et al., 2022).

The 2030 agenda for sustainable development and its 17 SDGs have also been thoroughly analyzed through critical discourse analysis, in order to unravel the social and political representations they actually rely on. Such scholarly works have notably studied the political and social implications regarding knowledge (Cummings et al., 2018), the lack of perspective regarding the evolution from MDGs to SDGs (Briant Carant, 2017), or the instrumental role of education (Mitter & Brissett, 2017) or health promotion (Spencer et al., 2019) in the process, even leading several scholars to state that a number of countries actually use SDGs to achieve other political gains than an actual implication of the 2030 agenda for sustainable development (Essa & Harvey, 2022, p. 9). In other words, as any official political agenda published by

<footnote n="1">¹Université Catholique de Lille, Lille, FRANCE</footnote>

<footnote>*Corresponding Author: albin.wagener@gmail.com</footnote>
international or national institutions, the SDGs have allowed a lot of scholars, across various disciplines, to investigate the framing and practical implications of the 2030 agenda for sustainable development, and to provide interesting perspectives and results in order to call for change and adaptation in the light of postcolonial studies, climate change and the global economic model of development.

As a linguist, my approach regarding the 17 SDGs is rooted in systemic discourse analysis-an applied discourse study that simultaneously draws on critical discourse analysis, corpus studies (Baker, 2006) and lexicometric analysis, which I will define later in the present chapter. My goal is to analyze every official text published in order to present and encourage the application of each of the 17 SDGs, thus building a corpus by extracting texts from official sources published on the internet website of the UN, dedicated to the 2030 agenda for sustainable development. In this sense, I do believe in a productive link between discourse structures (Biber et al., 2007) and social structures (Baker, 2010) and define discourse as follows (Wagener, 2019, p. 39):

'Discourse' is defined as any language phenomenon (therefore not exclusively textual) whose purpose is the construction, exchange, and transformation of meaning that is socially situated and structuring, potentially politically significant, and necessarily anchored in an essential intersubjectivity, whether intentional or not. In other words, it is produced and received by subjects who may express themselves individually or collectively about shareable objects. In this sense, discourse is not exclusively linguistic, communicational, or social: it is simultaneously at the core and on the margins of these dimensions, while uniting them.

This definition of discourse draws both on the tradition of critical discourse analysis (Fairclough, 2005; van Dijk, 1993), as well as on what is called the *analyse de discours à la française* or French discourse analysis (Charaudeau, 1995; Cislaru & Sitri, 2009), who all share the same epistemological roots regarding the French theory of the 1970's (Pêcheux, 1975).

In this perspective, the goal of this study is to explore the discursive construction of sustainable development within the official texts produced by the UN in support of the 2030 agenda and its 17 SDGs. While these texts are often considered neutral policy instruments, this research investigates the underlying ideological, political, and epistemological frameworks that shape their content and structure, through discourse analysis. In this sense, I wish to analyze to what extent do the official discourses surrounding the SDGs reflect, reinforce, or challenge dominant paradigms of development, sustainability, and global governance. By applying a systemic discourse analysis to the full corpus of official UN texts related to each of the 17 SDGs, the study seeks to identify patterns of lexical choices, semantic clusters, and representational hierarchies that may reveal implicit assumptions or contradictions.

The underlying hypothesis is that, despite their global and inclusive ambition, the SDG-related discourses reproduce dominant neoliberal and anthropocentric narratives that prioritize economic growth, managerial approaches, and

institutional legitimacy, while sidelining alternative paradigms such as degrowth, intersectionality, or ecocentrism. Through the combination of quantitative corpus analysis and qualitative critical interpretation, this research offers a critical linguistic perspective on the performative role of institutional discourse in shaping global policy agendas.

## SYSTEMIC DISCOURSE ANALYSIS: A METHODOLOGICAL PROPOSITION

The difference between critical discourse analysis and systemic discourse analysis mostly resides in a digital and statistical analysis of linguistic forms, before relying on critical discourse analysis *per se*. In this sense, it uses software which offer preliminary findings regarding textual occurrences and collocations, including a first analysis of the definition of words and their use in specific contexts. In this sense, it offers a quantitative approach which adds a relevant layer of analysis to qualitative discourse analysis by drawing on the corpus as a central point for discourse studies (Pollach, 2012), while comparing giving access to the representational spaces that actually structure the texts (Bendinelli, 2011, p. 110):

> Representational spaces provide an original key to interpreting the speech acts that underpin them, as they allow us to “see” the world the speaker constructs and the relationships they maintain with its components. Along the way, they offer the possibility of identifying part of the knowledge mobilized during interpretation. [...] The resulting figure reconstructs a prototype of the considered productions, subsuming their most representative elements, independently of thematic variations.

These spaces can be accessed through the following steps:

(1) a thorough construction of the corpus as a research object (Mayaffre, 2005),

(2) the use of softwares that will calculate the frequency of words and collocations in order to give access to the representational spaces (Marchand, 1998; Martinez, 2012), and

(3) specific algorithms that are actually able to cross-reference semantic definitions of words, actual textual collocations and contexts of use (Reinert, 1990).

In order to apply these operations, I have been choosing the software Iramuteq (Ratinaud, 2014) in order to study large corpora of texts, insofar as it provides satisfying results regarding the preparation of discourse analysis in a systemic and ecosystemic perspective (Jacques, 2016; Wagener, 2022).

More precisely, this methodological proposition suggests to take a look at the collocations, the relations between the clusters of collocation, and the narratives developed by these clusters-and the relations between theses narratives. These four steps are applied to the corpus in general, then to each SDG as a sub-corpus of official texts presenting its objectives and applicational targets. The goal is to understand how each SDG carries social and political presentations and specific
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Word cloud of the SDG corpus (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

viewpoints and offer a critical study of the relationships between these viewpoints within the whole corpus.

## 17 SDGS: A PRELIMINARY CORPUS ANALYSIS

The first operation of this systemic discourse analysis is to take a look at what is often used as a classic step to offer a first glance at a corpus-a word cloud. In terms of applied linguistics and corpus analysis, a word cloud does not offer a thorough and meaningful analysis of the corpus; it only shows which words are the most used. However, in the case of the whole SDG corpus, it offers some interesting insights that may provide relevant hypotheses for the next steps of this analysis (Figure 1).

The most used words are clearly related to the topic of the corpus, such as ‘development’ (489 occurrences), ‘sustainable’ (452 occurrences), ‘target’ (245 occurrences), ‘country’ (240 occurrences), ‘develop’ (195 occurrences), ‘agenda’ (179 occurrences), ‘include’ (158 occurrences), ‘proportion’ (116 occurrences), ‘international’ (116 occurrences), ‘national’ (110 occurrences), ‘economic’ (105 occurrences), and ‘global’ (102 occurrences)-other words appear below 100 occurrences, yet remain important, such as ‘water’, ‘support’, ‘energy’, ‘promote’, ‘poverty’ or ‘climate’. The twelve words above 100 occurrences really show that these official texts are about general guidelines that can inspire future action depending on the context of application, the country and the organization. Both the national and the international levels are mentioned, as well as the economic dimension of the 2030 agenda for sustainable development.

As displayed in Figure 2, it is far more interesting, however, to study the collocations build a whole network within the corpus and show how topics are linked together- which ultimately offers a first glance at the representations conveyed through the organization of discourse in the SDGs. In Figure 2, we can for instance see that the words ‘sustainable’ and ‘development’ are closely linked together and build networks of their own, yet it is even more interesting to see how they interact with other topics. First, ‘development”

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Lexical network of the SDG corpus (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

is linked with clusters related to economics, gender equality, the reduction of risks regarding disasters, the topic of social and political inclusion regarding stakeholders, and also the fact that development is seen as a national and strategic matter that could impact every sector of the countries of the world. On the other hand, the word ‘sustainable’ comes with a string of clusters such as green economy, climate change, equality regarding women and girls (while gender, as a more generic term, is linked to ‘development’), land and the management of environmental resources. In this sense, sustainability seems to be clearly defined as an environmental topic in the broad sense of the word, including climate issues. Somehow, ‘development’ and ‘sustainable’ are also linked together through the word ‘target, which builds a bridge between environmental management and development issues. More specifically, ‘target’ is also linked to the topics of age and child violence. A first analysis of this network seems to separate sustainability, as a topic linked to land, population and environmental issues, and development, as a strategic process impacting economy, inclusion, and the management of more general issues relying on national situations.

As shown in Figure 3, the SDG corpus is divided into 6 main narratives that seem to structure the representations conveyed in the selected texts. The first narrative class (24.4% of the corpus in terms of word collocations and frequency) is dedicated to environmental issues, which seems to show that a quarter of the SDG corpus actually deals with planetary sustainability. The second one (24.1%) represents human vulnerability (with a clear focus on children and women in particular). The third one is dedicated to the development of countries (13.4%), including technology, innovation, research and infrastructure, and is closely linked to the fourth one (12.4%), which describes the strategy and policies applied by the countries to reach their development goals. The smallest and fifth class (6.4%) highlights the institutional actions which led to the 2030 agenda, while the final one (17.3%) underlines
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Main narratives of the SDG corpus (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Map of correspondence between narratives of the SDG corpus (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

the international context which fosters the 17 SDGs. Both class 5 and class 6 depict the strong institutional context which tries to strengthen and promote the 2030 agenda for the whole world: they represent 23.7% of the corpus-almost a quarter of it.

The weight of the narrative classes represents one very interesting element of the analysis, yet it is even more interesting to see how the corpus is structured around these narratives. In this perspective, **Figure 4** show that the whole corpus actually revolves around the classes 3 (development as a target) and 4 (national strategies for development), thus showing that the main focus of the 17 SDGs seems to rely on development *per se*, while sustainability is not in the center of the corpus at all. In other words, development seems to matter much more than sustainability, which appears to represent a mere excuse to foster development. Both central classes are mainly linked to class 6 (international context) and 5 (institutional actions), which highlights the role of the UN in the process. It is interesting and also a little bit worrying to

<figure><img src="image_6.png" /><figcaption>**Figure 5.** Map of correspondence between SDGs and narratives in the SDG corpus (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

notice that class 1 (environmental issues) and 2 (human vulnerability) are not only peripheral in this context, but also disconnected from each other, while we know that they are heavily linked, especially in the context of climate crisis. The fact that both narratives are not central in **Figure 4**, while they are often depicted as essential to the very existence of those 17 SDGs, really underlines the fact that the 2030 agenda for sustainable development is more concerned with its institutional functioning and strategic impacts regarding development indicators than it could be with actual environmental and humanitarian issues, for instance. Yet this first interpretation needs to be confirmed; in fact, each SDG conveys its own sets of representations.

This new version of the map of correspondence shows how SDGs are represented in each narrative class, thus depicting a more precise visualization of the corpus in terms of lexicometric statistics (Figure 5). The list of correspondence is as follows:

- class 1 (environmental issues) encompasses SDG 2 (zero hunger), SDG 12 (responsible consumption and production), SDG 14 (life below water), and SDG 15 (life on land),

- class 2 (human vulnerability) is tied to SDG 1 (no poverty), SDG 3 (good health and well-being), SDG 5 (gender equality), and SDG 16 (peace, justice, and strong institutions),

- class 3 (development as a target) gathers SDG 7 (affordable and clean energy), SDG 9 (industry, innovation and infrastructure), and SDG 10 (reduced inequalities),

- class 4 (national strategies) is linked to SDG 8 (decent work and economic growth) and SDG 17 (partnerships for the goals),

- class 5 (institutional actions) draws on SDG 13 (climate action), and
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Lexical network for SDG 1 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

– class 6 (international context) is rooted in SDG 4 (quality education), SDG 6 (clean water and sanitation), and SDG 11 (sustainable cities and communities).

This last step of the corpus analysis as a whole speaks numbers about the way certain topics are framed within the official texts published by the UN. Indeed, it shows for instance that SDG 2 (zero hunger) is mainly treated as an environmental matter, while it obviously heavily impacts issues regarding human vulnerability. Moreover, certain issues seem to be regarded as environmental matters (such as SDGs 12, 14, and 15), while others are oddly disconnected from these matters; how is it, for instance, that SDG 6 (clean water and sanitation), SDG 7 (affordable and clean energy), and SDG 13 (climate action) are not related to environmental issues, while they could obviously be connected to them? Why are industrial innovation (SDG 9) and the reduction inequalities (SDG 10) both mainly treated as developmental targets, while innovation and industrial matters are not necessarily linked to the reduction of inequalities? Furthermore, why are certain topics mostly linked to international actions (quality education, clean water and sanitation, sustainable cities and communities), while others seem to remain at the national level (decent work and economic growth, partnerships for the goals)? These differences clearly imply that a targeted analysis of each of the 17 SDGs is necessary in order to understand the specific representations that are conveyed in the official texts.

# TARGETING THE 17 SDGS THROUGH SYSTEMIC DISCOURSE ANALYSIS

## SDG 1. No Poverty

It is not a surprise to notice that poverty is the central topic of SDG 1, yet it is interesting to understand its network of meaning. Poverty is indeed linked to specific targets that are

to be reached in terms of food production and agriculture, income, and vulnerable populations (including women and children). It is, however, intriguing to see these targets linked to genetic plants in terms of food production. Moreover, poverty is also tied to national strategies, institutional declarations (and the mention of millennium development goals, social and economic factors, and general politics of inclusion. The reduction in poverty should also, according to Figure 6, rely on cooperation in order to reach the targets. To sum it up, according to the official texts, SDG 1 should be reached by working on food production and agriculture, economic and social factors, and the inclusion of vulnerable populations in general. Yet economic and social factors, if not linked to a robust and precise national political strategy, may appear a bit vague in this context.

The reduction of poverty is linked to 6 narrative classes (Figure 7). Class 1 (15.9%) represents the agenda and its challenges in itself, which is linked to class 2 (18.2%), comprising international declarations regarding poverty. Class 6 (18.2%) implies the reduction of disasters and risks in order to protect vulnerable populations, and class 3 (18.2%) deals with social and demographic categories of populations. Finally, class 4 (13.6%) targets the development path of countries in terms of general political choices, while class 5 (15.9%) includes the access to food as a resource that needs to be produced through specific means. In the corpus, genetic diversity is quoted insofar as it is important to 'maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species', to foster 'the utilization of genetic resources and associated traditional knowledge' and to secure 'genetic resources for food and agriculture (...) in either medium or long term conservation facilities'. In this sense, genetics are addressed regarding the importance of agricultural biodiversity (Feliciano, 2019)-yet other economic or social issues are not treated with the same level of precision.

The reduction of disasters and risks to reduce poverty (class 6) is central in the corpus, and it appears to be closely linked to the international context of declarations regarding poverty (class 2). This interesting connection highlights the importance of international action regarding the reduction of risks and disasters, which can be seen as a positive trait. On the

<caption>**Figure 7.** Narratives of SDG 1 (Source: Author's own elaboration, using Iramuteq software)</caption>
<figure><img src="image_8.png" /><figcaption>**Figure 8.** Map of correspondence between narratives of SDG 1 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 10.** Narratives of SDG 2 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

left part of **Figure 8**, the agenda and its challenges (class 1) is present, but it seems disconnected from social and demographic categories (class 3), as well as the access to food and sustainable agriculture (class 5). Moreover, the countries' development agenda (class 4) connects agriculture and food to international challenges, which implies the fact that food policy cannot only be treated at a national level. However, the fact that the word 'implement' is to be seen at the center of **Figure 8** clearly shows that the reduction of poverty is about concrete policies and pragmatic actions that need to be applied on the field; yet the slight disconnection from social categories and agricultural matters seem to show that SDG 1 is centered around mere declarations, while means and actions are yet to be described-which may seem weird, since poverty is perceived as one of the main obstacles to reach the SDGs (Leal Filho et al., 2021).

<figure><img src="image_10.png" /><figcaption>**Figure 9.** Lexical network for SDG 2 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

## SDG 2. Zero Hunger

This lexical network clearly shows that SDG 2 understandably focuses on agriculture in order to solve the problem of hunger. Yet this topic is also linked to rural development in general, the sustainability of the agricultural model, the inclusion of farmers in the international market, health issues (such as malnutrition among children), access to land and the role of governments, including local ones. Food is seen as a resource that should be accessible to everyone, which means that hunger needs to be connected to strong policies. In fact, hunger is not only about access to food: it is clearly linked to the implementation of local and regional agricultural models that could help reduce hunger, which also means that the use of land is crucial in this respect (Figure 9).

In the official texts related to SDG 2, 6 narrative classes can be observed. Class 1 (18.6%) is about the victims of malnutrition and hunger, especially children. Class 2 (16.3%) deals with topics that are linked to hunger reduction, including the use of energy and water, the role of education or economic choices (including, again, the model of growth). Class 3 (18.6%) highlights the role of market economy and technologies in order to implement solutions regarding hunger, while class 4 (16.3%) underlines governmental strategies and agenda regarding this issue. Class 5 encompasses the management of resources, dealing again with genetic biodiversity (16.3%), and class 6 (13.9%) shows that hunger reduction is connected to the promotion of food security and nutrition. Indeed, a lot of narrative classes deal with management and willful policies, always linked to the mainstream economic model (classes 3, 5, 4, and 2), while it is this very economic model that is unfortunately causing structural episodes of hunger in a lot of countries (Albritton, 2009). The impact of food security on people (classes 6 and 1) only represents a third of the corpus (Figure 10).
<figure><img src="image_11.png" /><figcaption>**Figure 11.** Map of correspondence between narratives of SDG 2 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 12.** Lexical network for SDG 3 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

While food clearly appears at the center of the map of correspondence, food security (class 6), socio-political topics (class 2) and the management of resources (class 5) represent a strong cluster that show how concrete actions could help reduce hunger. This managerial stance seems to be prevalent in the corpus, and it remains strongly disconnected from economic and technological innovations (class 3), as well as from the victims of hunger themselves (class 1). In fact, these victims should be at the center of the corpus, but it is governmental policy (class 4) accompanies the topic of food. In this perspective, hunger reduction obviously needs strong managerial, economic and political choices; yet if these

<figure><img src="image_13.png" /><figcaption>**Figure 13.** Narratives of SDG 3 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

choices are made within the same model which permits hunger as a global and local phenomenon, the risk is to keep this SDG at a sheer strategic and managerial level. This scope is obviously caused by the institutional and organizational nature of the official texts (Figure 11).

### SDG 3. Good Health and Well-Being

Health is a central topic within the 2030 agenda, since it is also mentioned in SDG 1 and SDG 2 related to poverty and hunger, respectively. On this map, health is linked to specific targets regarding mortality and birth, death reduction, pollution and traffic injuries. Of course, health is also tied to the prevention and treatment of diseases, access to health, and strategic development agendas in general. It is interesting to notice that specific topics are indeed treated through health, while managerial and strategic agendas seem to be less present than in previous SDGs. The presence of clear targets is also noticeable, while there is no mention of the economic and social models that could indeed help to reduce health risks—including, for instance, access to healthcare in countries that need it the most (Olaniyi et al., 2022) (Figure 12).

Class narratives of SDG 3 shows what kind of representations are actually conveyed within the official texts regarding health. While class 1 (19.2%) focuses on country development, class 2 (15.4%) deals with health metrics, including mortality, birth, treatment of diseases and prevention. Class 3 (15.4%) encompasses medicine as a tool and policy regarding health, and class 4 (19.2%) comprises target populations, notably women and children, in order to develop the right policies. Class 5 (15.4%) is dedicated to the achievement of SDG 3, and class 6 (15.4%) is linked to the reduction of mortality. Many classes actually echo with each other, which shows that health seem to be conceived as an integrative and cohesive strategy that needs to be implemented for the good of human beings, with a focus on the most vulnerable ones (Figure 13).

The center of the map of correspondence seems to be a little weak, with several topics circulating around it. Health issues (class 2) and the reduction of mortality (class 6) can be seen of the right-hand side of the map, yet they seem to be disconnected from the achievement of SDG 3 (class 5), medical policies and instruments (class 3), and country development (class 1). The fact that country development seems to be inevitably linked to medical policies is noticeable-so is also the
<figure><img src="image_14.png" /><figcaption>**Figure 14.** Map of correspondence between narratives of SDG 3 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 15.** Lexical network for SDG 4 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

fact that the only narrative class to be found in the center of the corpus is linked to the targeted populations (class 4). This fact is interesting and probably a good opportunity to assess that it is important to keep beneficiaries of SDG policies at the center of efforts, preoccupations and agendas. In this perspective, official texts related to SDG 3 seem to fulfil this purpose (Figure 14).

## SDG 4. Quality Education

Within the corpus, education seems to be regarded as a key target related to sustainable development. It is merely linked to inclusion, sustainable development itself, targets in general, and targeted populations. Yet again, this sub-corpus

<figure><img src="image_16.png" /><figcaption>**Figure 16.** Narratives of SDG 4 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 17.** Map of correspondence between narratives of SDG 4 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

for SDG 4 seems very general, which appears odd when it comes to the gaps and stakes regarding education in a lot of countries throughout the world-including skills and knowledge, differences between boys and girls as well as rural and urban situations, without mentioning the fact that social and economic differences depending on the family remain a key issue regarding access to education (Webb et al., 2017) (Figure 15).

The fact that only 3 narrative classes are isolated in the sub-corpus when it comes to education says a lot about its generic treatment. Class 1 (37.5%) is dedicated to target populations and their access to primary and secondary education, class 2 (29.2%) describes the vocations and skills of educators and teachers, while class 3 (33.3%) is linked to the global aspect of SDG 4 in itself (Figure 16).

Even in the context of a small corpus, connections and disconnections can be obvious when it comes to lexicometric analysis (Figure 17). In this case, education is well-positioned at the center of the graph, yet educators (class 2), target populations (class 1) and global sustainable development
<figure><img src="image_18.png" /><figcaption>**Figure 18.** Lexical network for SDG 5 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

(class 3) appear to be revolving around SDG 4, with poor to little connections-it still needs to be highlighted that social aspects appear to link educators to learners. In this case, social variables such as disability (class 1) and gender (class 2) manage to be close enough to ensure a loose connection between those two narrative classes.

### SDG 5. Gender Equality

Writing that gender equality and gender policies are still important in the 21st century would be an understatement. In fact, with women's rights getting attacked almost everywhere in the beginning of this century, the struggle for gender equality is more important than ever (Htun & Weldon, 2018). In this sub-corpus, many issues are underlined, including laws regarding equality, gender, land ownership, access to health, economic and political power, violence towards women, sexual rights, discriminations, empowerment, recognition and the role of women in agriculture. In this sense, the corpus appears to be listing what is at stake regarding gender equality, yet it *de facto* excludes other gender minorities-especially LGBTQI+ citizens (Figure 18).

However, due to the size of the SDG 5 sub-corpus and its lack of complexity, it is impossible for Iramuteq to calculate statistics regarding narrative classes, which obviously represents a flaw in the present study. Further investigations would be needed regarding the addition of more texts, yet it remains easy to see that there are no words regarding concrete actions that could be implemented in specific countries, implying for instance political pressure, economic incentives and other initiatives. Furthermore, as written before, gender equality is not only about women; what about gay and trans rights, for instance? If the issue is linked to heteronormative patriarchal societies, then women's rights should definitely relate to them in an intersectional manner (Swiebel, 2009).

<figure><img src="image_19.png" /><figcaption>**Figure 19.** Lexical network for SDG 6 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

### SDG 6. Clean Water and Sanitation

The SDG 6 corpus suffers from the defect: Iramuteq is not able to compute its narrative classes. Despite this unfortunate shortcoming, the lexical network already offers several indicators regarding the treatment of access to clean water and sanitation, which is already becoming a huge issue because of the impacts of climate change (Mukheibir, 2010). In this sense, water is obviously treated regarding waste and reuse, the importance of international unity regarding this problem, sustainability, access to drinkable water and local management, impacts on ecosystems and the impact on people. In fact, it probably is one of the only SDGs to really make connections between the national and international level, even if it is only declarative, and to call for concrete actions in this perspective. Access to drinkable water and drought will, indeed, become even more important in the face of climate evolution-and a lot of regions throughout the world will be impacted (Figure 19).

### SDG 7. Affordable and Clean Energy

Affordable and clean energy is a key issue-not only because it represents one of the pathways to the reduction of carbon emissions, but also because a lot of industrial and technological actors are investing in the field. In fact, it is mostly perceived as an economic opportunity that could make the world a better place, while somehow erasing the social aspects of energy consumption and its role in our economic and political models (Hillerbrand, 2018). These specificities are underlined in the present network, with shows that energy is linked to renewable energy, various stakeholders, climate change (and the Paris agenda, rarely quoted in the SDGs), technology and innovation, access to energy and efficient networks (Figure 20).

It is, perhaps, easier for economic and industrial actors to imagine the financial benefits they could gain from SDG 7 than it could be for other SDGs: this particular hypothesis also
<figure><img src="image_20.png" /><figcaption>**Figure 20.** Lexical network for SDG 7 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Figure 21.** Lexical network for SDG 8 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

underlines the fact that SDGs actually differ from nature and proportion, and that their variations make it difficult to interconnect certain issues and create relevant incentives for countries and local actors alike. On another note, the lack of complexity of the SDG 7 sub-corpus makes it impossible, again, for Iramuteq to isolate the narrative classes.

## SDG 8. Decent Work and Economic Growth

Decent work and economic growth probably represent one of the most complex SDGs-as shown in **Figure 21**. The interconnection of various topics shows that there is no clear center in the network, since the definition of decent work (and

<figure><img src="image_22.png" /><figcaption>**Figure 22.** Narratives of SDG 8 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

decency in general) and the weight of growth as the main economic ideology become increasingly criticized in the face of climate change-and could even be renamed to 'sustainable work and economic growth' (Kreinin & Aigner, 2021). Here, the lexical nexus reveals 7 clusters: economic growth, finances, practical targets, sustainability in general (including tourism and the environment), development policies, the implementation of green economy, and international labor policies. This complex map shows the interconnection of several very complex topics that appear to be separated and linked at the same time; for instance, green economy seems to differ from financial issues, which raises questions regarding the economic model fostered by the 2030 agenda.

The complexity of the SDG 8 sub-corpus shows in the narrative classes. Class 1 (19.2%) oddly regroups a huge variety of stakeholders (including financial actors and workers depending on their social characteristics), class 2 (21.1%) is dedicated to growth, policy and the green economy, class 3 (19.2%) deals with international strategies regarding environmental and socio-economic issues, class 4 (17.3%) comprises local issues and actions, and class 5 (23.1%) regroups work, labor and employment in general. To sum it up, it seems that a lot of stakes are thrown together in order to hopefully elaborate an integrative discourse that actually regroups a huge variety of stakeholders and aims that can be completely unconnectable. For example, it is not easy to understand why class 1 seems to build on so many different topics, but the map of narratives may provide useful keys to understand the way this SDG is built in the official texts (Figure 22).

Growth, policy and green economy (class 2), international strategies (class 3) and work policies (class 5) are all regrouped together near the center of the sub-corpus, which shows a logical interconnection of these important topics: what is addressed here is the necessity to unite and foster international policies regarding economic growth, a greener approach to economic development and work policies in general. Yet this mixed bag of complex initiatives is heavily disconnected from local specificities (class 4) including tourism, jobs, culture, activity and environmental issues. Moreover, the link between financial matters and citizens
<figure><img src="image_23.png" /><figcaption>**Figure 23.** Map of correspondence between narratives of SDG 8 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Figure 24.** Lexical network for SDG 9 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

seems to be linked, indeed, to individual rights regarding access to banks and finance, and the role of the financial sector in social matters. The general confusion within this sub-corpus is probably linked to the fact that the development of employment and workers' rights is still treated within the scope of a classic economic growth model. This shows that it remains tricky to make the world a more sustainable place within the economic and financial framing of the current social and political system, which at the same time represents one of the main causes of the lack of sustainability (Bauhardt, 2014) (Figure 23).

<figure><img src="image_25.png" /><figcaption>**Figure 25.** Lexical network for SDG 10 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

## SDG 9. Industry, Innovation, and Infrastructure

The SDG 9 corpus appears relatively small and limited in terms of official texts, regarding the complexity of addressed topics. The concept of development is mainly linked to industry, economics, industry, innovation, infrastructure and resilience, yet other clusters appear. In fact, sustainability clearly appears in terms of actions, inclusion, employment and industrialization, and the notions of target and agenda are also highlighted. However, the development of countries strictly linked to technology, without a critical use of the term or any concrete applicable agenda in this matter, raises questions—particularly if one thinks about AI and its huge impact on sustainable development (Sætra, 2021). The inability of Iramuteq to generate narrative classes for this sub-corpus limits the analysis, yet the present network still underlines the clear lack of thorough thought regarding the actual possibility for sustainability policies to rely on industry, innovation and infrastructure as they are promoted in the actual economic system (Figure 24).

## SDG 10. Reduced Inequalities

Regarding inequalities, the African continent seems to occupy a central place. The situation of countries is obviously clearly at the center of the network (including financial and technical support, migration and assistance), but the fact that inequalities are mostly associated with African states and communities, despite the economic situation of the continent, offers the hypothesis of potential colonial stance regarding this SDG. In this perspective, sustainable development is highlighted as an interesting tool to reduce inequalities, and the place of education and health is also central. Targets in terms of policies and growth is also to be seen. Another possible hypothesis could be to link migration issues to the situation of the African continent. Moreover, the reduction of inequalities cannot be treated without addressing other SDGs, such as SDG 1, 2, and 13 for instance (Figure 25).
<figure><img src="image_26.png" /><figcaption>**Figure 26.** Narratives of SDG 10 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>**Figure 27.** Map of correspondence between narratives of SDG 10 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

The narrative classes of the corpus are particularly interesting when it comes to understanding possible policies regarding the reduction of inequalities. Class 1 (21.2%), a fifth of the sub-corpus, exclusively deals with Africa and the Sahara. Class 2 (12.1%) is a global topic regarding sustainability, while class 3 (12.1%) details specific regional and local situations. Class 4 (12.1%) is linked to population demographics, while class 5 (15.2%) offers information regarding migration policies and their international stakes. Finally, class 6 (15.2%) encompasses development programs for specific countries, and class 7 (12.1%) addresses poverty in itself, which shows evident links to SDG 1 (Figure 26).

There is no clear topical center within the SDG 10 sub-corpus, which is probably rooted in the fact that this SDG actually tackles many other SDGs, and that the reduction of inequalities is far more complex than textual indications, since it would remain difficult to achieve without fundamental questions regarding the actual international economic system

<figure><img src="image_28.png" /><figcaption>**Figure 28.** Lexical network for SDG 11 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

and their geopolitical implications-not to mention the fact that climate change impacts the situation (Campagnolo & Davide, 2019), since the chances that the Paris agreement is met are now close to zero. The situation of specific countries (class 6) appears sufficiently close to the center, yet three main topics remain disconnected from each other. First, Africa (class 1), poverty in Africa (class 7) and global sustainability regarding various topics (class 2) show the specific situation of a whole region of the world. Local and regional situations (class 3) and, more lightly, development programs (class 6) are treated independently from the African situation, while other places in the world also suffer from important inequalities. Finally, demographics (class 4) and migration policies (class 5) are also distant from the two other clusters. However, the fact that demographics is treated in relation to migration already represents a particular stance, but the situation of Africa does not seem to be treated by relying on migration issues. Moreover, the fact that most of the sub-corpus deal with Africa, could also show how the SDGs also encompass neocolonial reflexes that are still heavily present in the official texts (Langan, 2018) (Figure 27).

## SDG 11. Sustainable Cities and Communities

The sustainability of cities and communities is treated through the following clusters, namely: the reduction of disaster risk, the implementation of global development agendas, the management of human settlements, the organization or urban areas, city management (including natural and cultural heritage), public transport systems, sustainability in general, and the implementation of local targets. This network really seems to address the complexity of the situation regarding urban settlements and their particularities, especially in a world where more than half of the population actually lives in large cities (Ritchie et al., 2018-2024). Obviously, this reality is more than central if one wishes to address sustainable development at both international and local levels (Figure 28).
<figure><img src="image_29.png" /><figcaption>**Figure 29.** Narratives of SDG 11 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>**Figure 30.** Map of correspondence between narratives of SDG 11 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

6 narrative classes can be observed in the SDG 11 sub-corpus. Class 1 (15.6%) deals with local and domestic resilience, while class 2 (15.6%) is linked to public governmental systems. Class 3 (13.3%) encompasses the reality of urban areas, and class 4 (17.8%) tackles the reduction of disaster risk. Finally, class 5 (20%) addresses global sustainability topics, while class 6 (17.8%) offers a viewpoint on the management of human settlement. This overall managerial approach tries to tackle resilience and risk management with urban realities and a global awareness regarding the life conditions of urban populations (Figure 29).

Indeed, management (class 6) is central to the SDG 11 sub-corpus, which also shows the complex approach regarding the reality of urban areas and the sustainability issues regarding public transportation, the presence of biodiversity or water cycles, for instance. In this perspective, this managerial approach is obviously tied to urban areas (class 3) and local

<figure><img src="image_31.png" /><figcaption>**Figure 31.** Lexical network for SDG 12 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

resilience (class 1). However, the reduction of risks and disasters (class 4) seems to be slightly disconnected from the central cluster, which highlights representations regarding a managerial approach regarding urban areas, which demands a focus on daily issues and the general functioning of cities as human settlements. Finally, public systems (class 2) and global sustainability topics (class 5) show the weight of concrete policies and applied actions regarding life in larger cities. The public sphere, however, remains distinctively distant from the management of cities, which by contrast underlines the possible role of the private sector in relation to governmental decisions regarding urban areas. Yet with such a huge focus on cities and urban areas, what about local communities? (Figure 30).

## SDG 12. Responsible Consumption and Production

SDG 12 offers a complex view on responsible consumption and production. Several discourse clusters are interconnected: waste reduction and management already encompass 4 clusters, while sustainable development is linked to the environment, health, energy, education, and information programs. However, consumption and production themselves occupy two smaller clusters which seem to be tied to domestic matters, fuel, footprint and governmental strategies. Companies are also present, but since the systems of production really weigh a lot in terms of sustainability and climate issues, it may seem weird to see them underrepresented in this network (Figure 31).

The 5 narrative classes of the SDG 12 sub-corpus are really informative about the representations conveyed in the official texts, regarding consumption and production models. Class 1 (24.5%, a quarter of the corpus) is dedicated to waste management and the circulation of waste at an international level, while class 2 (18.6%) concerns the 10YFP, the 10-year framework of programs on sustainable consumption and production patterns, which proposes frames and actions to
<figure><img src="image_32.png" /><figcaption>**Figure 32.** Narratives of SDG 12 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>**Figure 33.** Map of correspondence between narratives of SDG 12 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

encourage the evolution of the global consumption and production model. It is interesting to see that consumption comes first in the program, while it is production that actually weighs the most regarding climate issues, environmental problems and carbon emissions, despite the concept of shared responsibility (Lenzen et al., 2007). Class 3 (17%) deal with environmental issues, class 4 (24.5%) is linked to countries' development patterns and the local implementation of specific programs, while class 5 (15.1%) describes the impact of chemicals on the environment and human health (Figure 32).

Unsurprisingly, the words 'recycle' and 'development' remain at the center of the map, which also underlines the role of recycling and reusing in order to build a new consumption and production model. However, the fact that the word 'government' is also central in the sub-corpus might highlight the role of public laws and governmental decisions in order to propose incentives for both consumers and producers alike.

<figure><img src="image_34.png" /><figcaption>**Figure 34.** Lexical network for SDG 13 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

Environmental issues (class 3) is the most central narrative of the sub-corpus and actually manages to build links with other narrative classes. The management of chemicals and their impact on human health and the environment (class 5), on the bottom-left side of the map, is also very present of the corpus, while waste management (class 1), which represents a quarter of the sub-corpus, occupies the top-left side of the corpus. This combination of issues linked to chemicals, waste management and environmental stakes really gets to the point when it comes to sustainable consumption and production. However, the disconnection with the 10YFP (class 2) and development patterns chosen by countries (class 4) seems to show how difficult it is to transform environmental issues into concrete policies. This difficulty may be linked to the fact that there is, indeed, no balanced shared responsibility between producers and consumers-notably because consumers are not a cohesive whole and because they do not always share the same social, economic or regional specificities (Figure 33).

## SDG 13. Climate Action

Obviously, climate action appears to be pivotal in order to foster the 2030 agenda for sustainable development. In fact, within SDG 13, climate change is linked to national strategies, local vulnerability, UN agencies, concrete actions, global temperatures, resilience, greenhouse gas emissions, disasters and impacts. In the top of the network, sustainable development is tied to institutional matters, agendas, partnerships, and the support of the UN department of economic and social affairs (DESA) in terms of ocean, land and atmosphere protection. In this perspective, it seems that the impacts of climate change are accurately described, and that the sustainable development policy is drawing on the role of the DESA, which is interesting: the economic and social situation needs to be considered when it comes to climate action (Figure 34).
<figure><img src="image_35.png" /><figcaption>**Figure 35.** Narratives of SDG 13 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

SDG 13's narrative classes are emblematic of the way climate change is treated through sustainable development issues, and probably also echoes the way the conference of the Parties functions regarding the treatment of climate issues (Wagener, 2024). Class 1 (13%) deals with environmental observations and measures, class 2 (14.8%) is dedicated to the impacts of climate change and particularly the increase of water levels on the planet, while class 3 (14.8%) is linked to greenhouse gas emissions. Class 4 (14.8%) deals with resilience and mitigation in the context of vulnerabilities caused by climate change, and class 5 (13%) exposes the UN' institutional implications. Finally, class 6 (11.1%) highlights the role of the DESA to support vulnerable countries, while class 7 (18.5%) is dedicated to the small islands developing states (SIDS), the small islands developing states, which are particularly threatened by climate change. In other words, both classes 6 and 7 show how economic instruments can help these countries in the face of the rise of sea levels throughout the globe (Figure 35).

The center of this map encompasses a cluster of several different narratives, including: the UN' commitment regarding climate change (class 5), the DESA's support (class 6), as well as the SDGs actions and resilience propositions (class 4). The role of both the UN and the DESA is notably linked to the economic support of the SIDS (class 7), yet the link with environmental observations and measures (class 1) seems to be a bit loose-probably because it is important to focus on actions regarding the consequences of climate change for specific regions of the world. Greenhouse gas emissions (class 3) and the impacts of climate change regarding the rise of sea levels (class 2) also seem to be a bit more disconnected from the center of the corpus. More surprisingly, a thorough look at Figure 36 actually shows that environmental observations (class 1), greenhouse gas emissions (class 3) and the impacts of climate change (class 2) all appear on opposite sectors of the map, which implies that the SDG 13 sub-corpus divides these topics, while they should really remain tied together in order to highlight a clear understanding of climate change-how we measure it, what causes it, and what implications are linked to it. These disconnections and the focus on institutional instruments and strategies reveals the treatment of climate issues according to the UN: dealing with the consequences of

<figure><img src="image_36.png" /><figcaption>**Figure 36.** Map of correspondence between narratives of SDG 13 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

climate change, not with the causes, which are linked to economic, financial and industrial factors. Of course, treating the causes is important-but without efficiently treating the reasons, the SDG will always be playing catchup with foreseeable catastrophes.

## SDG 14. Life Below Water

Life below water is linked to marine impacts of pollution, marine life as a resource that needs to be preserved, and ocean conservation. For once, the role of human activity and contribution to the preservation and evolution of marine ecosystems is clearly highlighted, which also means that every actor needs to mobilize in order to reach SDG 14. Sustainable development targets are also mentioned, particularly regarding the regulation of fish population and the recognition of small fishery, and the use of technology and research to preserve marine life. Yet again, the targets of SDG 14 mostly seem aspirational (Cormier & Elliott, 2017) and would need more details regarding concrete implementations-not to mention the definition of marine life as a resource that has to serve the human species, and not just as living beings that should be protected even if they do not serve any economic or social purpose (Adelman, 2018).

The SDG 14 sub-corpus offers 6 narrative classes. Class 1 (14.8%) mentions the management of human activity in marine environments, class 2 (18.5%) deals with countries with small fishery activities, especially islands, and class 3 (22.2%), the largest one, highlights the role of the UN regarding ocean conservation. Class 4 (14.8%) is dedicated to the environment, class 5 (14.8%) shows the services provided by the environment to human life and marine ecosystems in general, while class 6 (14.8%) defines marine life as resources needed by human societies. These narratives actually validate the hypothesis elaborated under Figure 37: marine life is first and foremost described as a resource that can provide services to human beings and that has to be sustainably managed. Of course, the lack of management would make smaller
<figure><img src="image_37.png" /><figcaption>**Figure 37.** Lexical network for SDG 14 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>**Figure 38.** Narratives of SDG 14 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

communities vulnerable, but the rapport constructed in the sub-corpus between human beings and marine species reveals the representation of planetary life (Descola, 2005) conveyed by the UN official texts (Figure 38).

The very fact that the SDG 14 corpus is centered around marine resources (class 6) is particularly impactful in terms of anthropocentric representations. On the bottom-left side of the map, these resources are linked with the environment (class 4), the services provided by this very environment (class 5), and the management of human activity (class 1). Yet disconnections exist with the role of the UN regarding ocean conservation (class 3), as well as with (class 2) small fishery activities and specific communities, such as smaller islands. It is important that to see that, such as in SDG 13, communities on islands as perceived as victims that need to be protected and integrated in SDG programs, yet the fact that three different clusters revolve around a definition of marine life as a mere resource speaks numbers about the way the UN, as an

<figure><img src="image_39.png" /><figcaption>**Figure 39.** Map of correspondence between narratives of SDG 14 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

international institution, defines non-human life in general-especially in a context of sustainability, which in return should question the way we define sustainable development (Figure 39).

## SDG 15. Life on Land

Interestingly, SDG 15 mentions biodiversity loss-while it wasn't the case for SDG 14, albeit marine life should also be concerned with the same issues. According to this network, life on land is linked to the Rio +20 UN Conference on sustainable development, land degradation and desertification, agriculture and human activity, ecosystem management, resource management, economic support of sustainability, forests, mountains and general targets regarding biodiversity. The difference of treatment between SDG 14 and SDG 15 is striking, insofar as the representations regarding life on land seem to be much more complex and elaborate, while marine life was tied to resource management. Resource management is still present in SDG 15, yet it is not the only treatment. Moreover, the presence of the Conference of the parties on climate offers another difference of treatment between life below water and life on land (Figure 40).

7 narrative classes are present in the SDG 15 sub-corpus (Figure 41). Class 1 (12.9%) deals with institutional agendas, class 2 (14.5%) is dedicated to land degradation, and class 3 (16.1%) shows concrete targets regarding biodiversity protection. Class 4 (14.5%) is linked to the Rio +20 UN conference on sustainable development, and class 5 (12.9%) underlines the role of climate change. Finally, class 6 (12.9%) underlines the importance of terrestrial ecosystems, while class 7 (16.1%) deals with resource management. Clearly, biodiversity, climate change, land degradation and terrestrial ecosystems are present in more than a half of the corpus-a proportion that is completely inexistant in SDG 14. The role of institutional conferences and agendas is also well-represented, and resource management seems to integrate a
<figure><img src="image_40.png" /><figcaption>**Figure 40.** Lexical network for SDG 15 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>**Figure 41.** Narratives of SDG 15 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

social and cultural component, which is central in this issue, since local knowledge needs to be used in order to preserve land, ecosystems and biodiversity (Bocco & Winklerprins, 2016).

Interestingly, the narrative classes seem to be really tied together; close interconnections usually show that the texts are well organized, with close relations concerning the different topics and narratives. In this sense, SDG 15 seems cohesive as a whole, offering a center which gathers resource management (class 7), climate change (class 5) and the Rio +20 UN conference on sustainable development (class 4). Land degradation also appears to be linked to this central cluster (class 2); terrestrial ecosystems (class 6) and targets regarding biodiversity protection (class 3) appear on the top-left side of the map, with a looser connection to the center, yet are still part of a relevant whole. Institutional agendas (class 1) seem a

<figure><img src="image_42.png" /><figcaption>**Figure 42.** Map of correspondence between narratives of SDG 15 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

bit less tied to the center of the sub-corpus, which seems to imply the fact that institutional strategies still remain far away from the concrete needs and actions that have to be implemented regarding SDG 15 (Figure 42).

## SDG 16. Peace, Justice, and Strong Institutions

Unsurprisingly, this very thick and complex SDG offers a sub-corpus that is packed with very different issues, inasmuch as the need for strong institutions covers a large array of topics. This reality leads to a nexus of interrelated topics, including violence, children rights, national and international targets regarding stronger institutions, social categories, decision level, sustainability policies, country development, and future outcomes regarding conferences. Indeed, many topics deal with institutional strategies and agendas, yet the future of stronger institutions seem to revolve around urgent topics like violence and children rights-a strong commitment that underline the need for more inclusive societies in order to protect vulnerable communities and encourage the emergence of accountable institutions across the globe (Hope Sr., 2020). SDG 16 also show that institutions can be key actors regarding the shift to a more sustainable world, particularly if they foster democratic change (Glass & Newig, 2019) (Figure 43).

Peace, justice and strong institutions encompass 6 narrative classes. Class 1 (14.8%) deals with political outcomes linked to institutional forums, conferences and agendas, class 2 (20.4%) mentions the protection of citizens and vulnerable people regarding all forms of abuse and violence, and class 3 (14.8%) underlines the need for inclusive and responsive institutions. Class 4 (14.8%) is linked to social variables regarding targeted populations, class 5 (14.8%) deals with studies regarding the progress of violence, while class 6 (20.4%) mentions official texts and decisions regarding sustainability and inclusion policies. The narrative classes seem to differ a bit from the general lexical network, insofar as
<figure><img src="image_43.png" /><figcaption>**Figure 43.** Lexical network for SDG 16 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>**Figure 44.** Narratives of SDG 16 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

the weight of institutional discourse seems to be much more important than expected (Figure 44).

There is no clear center regarding SDG 16, which objectively raises some concern. In fact, all 6 narrative classes seem to occupy 4 corners of the map, which indicates an important disconnection between the sub-corpus' topics. In this perspective, 4 clusters occupy the map: social characteristics (class 4) on the bottom-left corner, violence (class 2) and its progress (class 5) on the top-left corner, political and institutional outcomes (class 1) on the top-right corner, and finally the need for inclusive and responsive institutions (class 3) and official policies (class 6) on the bottom-right corner of the map. This poly-opposition is striking indeed, disconnection between violence, the social

<figure><img src="image_45.png" /><figcaption>**Figure 45.** Map of correspondence between narratives of SDG 16 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

characteristics of people targeted by violence, the need for more inclusive institutions and political outcomes shows the lack of cohesive discourse regarding the need for peace, justice and strong institutions. It is as if all the ingredients for an ambitious policy were present in the official texts, without being interconnected in order to build up a clear and pragmatic strategy (Figure 45).

## SDG 17. Partnerships For the Goals

This analysis of each SDG ends with the lexical supernova of SDG 17, with an explosion of separate clusters that need to be described in order to understand what is at stake regarding the needed partnerships for sustainable development. It seems as if the lexical clusters are thrown in in bulk within the text, thus gathering the implementation of the 2030 agenda for sustainable development, the level of participation of each actor, the mobilization of both the private and public sector, the capacity to build strong collaborations, national strategies, global situations, science and technology, and also the inclusion of social, economic, and environmental issues. Furthermore, the SDG 17 sub-corpus also comprises inclusion, business markets, domestic targets in different countries, financial needs, health, governmental programs, and general development-notably regarding smaller island states and countries from the global South. To sum it up, SDG 17 encompasses a galaxy of potential stakeholders, as well as a huge number of various topics that do not appear to be linked to each other (Figure 46).

SDG 17 proposes 6 narrative classes. Class 1 (14.1%) is first and foremost linked to financial needs and resources that could foster the achievement of SDGs, and class 2 (19%) is dedicated to the SDGs. Class 3 (15.7%) addresses technical coordination and enhancement, including science, finances and innovation, while class 4 (16.5%) comprises the necessary targets to achieve in order to foster the development of countries. Class 5 (19%) is dedicated to the 2030 agenda for
<figure><img src="image_46.png" /><figcaption>**Figure 46.** Lexical network for SDG 17 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>**Figure 48.** Map of correspondence between narratives of SDG 17 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>**Figure 47.** Narratives of SDG 17 (Source: Author's own elaboration, using Iramuteq software)</figcaption></figure>

sustainable development, and class 6 (15.7%) highlights the promotion of partnerships between the public and the private sector. The main stakes of SDG 17 appear to be technical, inasmuch as it seems to serve as a basis for the implementation of all other SDGs in order to fulfil the 2030 agenda. However, partnerships are mostly drawing on financial needs and support, with the addition of science, innovation and technology (Figure 47).

The promotion of partnerships between the public and private sector (class 6) occupy the center of the map, with tight links to innovation, technology and science (class 3), as well as the SDGs themselves (class 2). In this sense, the way these three narrative classes are interconnected represent a definition of the way the UN see the necessary partnerships that are needed in order to fulfil the SDGs: an alliance of public and private sectors, innovation, technology and science-while social approaches are completely absent. Two more narratives

revolve around the center, namely the 2030 agenda for sustainable development (class 5) on the top-left side, and the necessary means to reach the 2030 agenda's targets (class 4) on the bottom-left side. The fact that the partnerships need to be rooted in the 2030 agenda's targets is obviously interesting, yet the loose connection with financial needs and support (class 1), especially for the other SDGs (infrastructure, work, gender equality, resilience, economic growth, etc.), raises some concern. In fact, how can partnerships help reach the SDGs' targets? Is the relationship between the public and private sector mainly linked to financial reports, how and why? (Figure 48).

## DISCUSSION: THE LIMITATIONS OF THE SDGS' GENERIC FRAMING

First, it is important to walk through the main findings and analytical critics that have to be voiced regarding each SDG, in order to structure a systemic discourse study of the corpus.

SDG 1 (no poverty) seems to treat poverty mainly through the management of agriculture, while economic and social factors remain underdeveloped in the official texts. SDG 2 (zero hunger) is also centered around management, this time regarding resources, energy strategies, political choices and health issues that impact food policies and agricultural choices in general. SDG 3 (good health and well-being) are more centered around health and medical strategies than well-being, yet keep targeted populations at the center of discussions, which is a positive thing to notice. SDG 4 (quality education) seems to be poorly treated in terms of targets and measures, despite its pivotal role in the development of societies and countries and its importance for building a sustainable future for all. SDG 5 (gender equality) seems to
erase other minorities from the agenda (especially LGBTQI+
citizens), which seems to be a problem regarding the links
between progressive policies regarding women's rights and
LGBTQI+ rights. SDG 6 (clean water and sanitation), however,
appears to be one of the rare SDGs to actually encompass both
the national and international levels and the need for
coordinate actions regarding water issues, especially in the
face of climate change. SDG 7 (affordable and clean energy)
encompasses a lot of clear issues and applicable actions,
probably due to its technological, industrial and economic
potential which makes it easier for various stakeholders to act
accordingly. SDG 8 (decent work and economic growth) shows
how complex it is to promote sustainability and a greener
economy within a financial and economic system that heavily
draws on non-sustainable activities that represent a threat to
worker's rights.

SDG 9 (industry, innovation and infrastructure) may offer
a naïve view of the role of industries in terms of workers' rights
and also mentions technologies without explicitly stating that
modern innovations, such as AI, represent a threat to climate
change mitigation. SDG 10 (reduced inequalities) heavily
focuses on Africa, as if inequalities were not present in other
regions; moreover, it seems to treat migration as a side effect
of inequalities, without addressing the economic roots
implemented by our capitalist world-system. SDG 11
(sustainable cities and communities) offers a managerial
approach regarding cities and larger urban areas, with a take
on sustainability that slightly sets risk reduction aside and
does not really shed a light on sustainable communities that
would live outside urban centers. SDG 12 (responsible
consumption and production) is centered around waste
management, environmental and health issues as well as the
impact of chemicals, yet these issues seem to remain distant
from the 10YFP which is necessary to build a new production
and consumption model, where the shared responsibility of
actors would not mostly rely on consumers, thus ignoring the
economic and social differences between them.

SDG 13 (climate action) heavily focuses on resilience and mitigation, in order to address the dramatic consequences of climate change, particularly linked to SIDS–yet it appears that the causes are not mentioned in the official texts, and the treatment seems to be importantly tied to economic compensation alone, through the role of the UN DESA. SDG 14 (life below water) highlights the way the UN, through the SDGs, define non-human life as a mere resource that need to be preserved because they provide services to human societies-not just because they are to be recognized as life, without any purpose-oriented ideology. Surprisingly, SDG 15 (life on land) offers different representations: while resource management still lies at the center of the sub-corpus, the connections with climate change, land degradation, biodiversity and concrete targets are clearly highlighted-which clearly shows that representations regarding life below water and life on land seem to be completely different in official texts.

SDG 16 (peace, justice and strong institutions) shows its
incantatory nature, without being able to connect the stakes
and issues linked to violence on the one hand, and the
necessary concrete political actions that need to be
implemented on the other hand; this discursive and

argumentative situation is almost, in itself, a concentrated
form of what critics traditionally object when describing the
lack of political consistency of international institutions. SDG
17 (partnerships for the goals) offers an interesting overview
of the way partnerships are represented in the official texts, in
order to reach the 2030 agenda for sustainable development:
the risk here is also to drown social, ethical and environmental
issues in a multi-stakeholder approach where the need for
financial resources is so high that the private sector can see the
public sector as a business opportunity (Cruz, 2023), which
may erase some of the SDGs' aspects and hinder the necessary
and constructive criticism of the economic and political
systems that actually make the world unsustainable.

Clearly, the SDGs prioritize economic growth and
institutional frameworks, often sidelining core issues like
environmental and social equity; the SDGs lean heavily on
current economic and political systems, particularly
partnerships with private stakeholders, which risks
perpetuating inequalities and diluting transformative
ambitions. For instance, 'development' frequently dominates
the discourse, while sustainability is treated as secondary or
instrumental, which is frequently the case for sustainable
development, in order to soften the tensions linked to the
factors that cause our societies to be unsustainable (Krieg-
Planque, 2010). Furthermore, key interconnected issues (e.g.,
poverty and hunger, climate action and economic policies) are
addressed in isolation, leading to disjointed narratives. In fact,
this risk of general disconnection is rooted in a managerial
approach with vague or generic targets, which creates a gap
between declarative statements and actionable policies.

This disconnection is even more visible in the analysis of
SDG 13 (climate action), which separates environmental
observations, greenhouse gas emissions, and impacts-while
climate issues are one of the roots of the need for sustainable
development. Moreover, the SDGs often treat non-human life
(e.g., marine and terrestrial biodiversity) as resources for
human benefit, rather than recognizing their intrinsic value.
Regarding social aspects, gender equality (SDG 5) marginalizes
broader intersectional concerns, such as LGBTQI+ rights. On
another note, textual aspects regarding countries from the
global South, especially for the African continent, risk
reinforcing neocolonial narratives rather than addressing
global systemic inequities.

Since 2030 will probably see the emergence of new goals in
a world that is getting more and more unstable, it would be
interesting for the future of SDGs to reframe sustainability as
central rather than auxiliary, in order to escape the dominance
of economic narratives. In this perspective, alternative
development paradigms could be considered, such as
degrowth or solidarity economies, which could challenge the
prevailing growth-centric model-a model that is indeed
incompatible with SDGs. Moreover, the SDG multi-
stakeholder may indeed mask the fact that corporate influence
might override public interests, while public-led initiatives
also exist and prioritize equity and sustainability, in a world
where financial interests can threaten the fulfilment of the
2030 agenda. In fact, this multi-stakeholder approach is not
sufficient; it is important to link the SDGs together and
integrate them, which is probably the main flaw of the 2030
agenda. For example, it would be relevant to link SDG 13's
focus on climate resilience with SDG 12's calls for sustainable consumption and production, emphasizing shared systemic causes and solutions.

Furthermore, the 2030 agenda seems disconnected from current social and societal issues. In this sense, SDG 5 could benefit from a more intersectional approach of gender equality, in order to explore the broader implications of excluding marginalized groups. Contemporary concepts could also be needed to avoid the neocolonialism present in SDG 10: it is important to unravel the historical patterns of resource exploitation and global inequities perpetuated by international frameworks (Newell, 2005), which is also the case for the 2030 agenda, in a certain way (Gruber & Scherling, 2020). Moreover, SDGs 14 and 15 suffer from an anthropocentric logic and could benefit from an eco-centric perspective that values ecosystems independently of human utility (Davidson, 2013).

Of course, official texts regarding the SDGs, when published on the UN internet websites, are here to offer general guidelines for stakeholders, in order to share a general spirit for the 2030 agenda. In this sense, one could argue that it is not the purpose of such texts to advocate for a change of paradigm or for new social organizations. Yet there is no such thing as a harmless and neutral text, even in institutional discourse: the way narratives are framed in the SDGs obviously show that there is a general will to make the world a better place, yet it would be naive to believe that a change can be made by keeping the same paradigms, economic structures and social orders that led the world to what it has become. Addressing the consequences without addressing the causes, or turning a blind eye to the industrial, financial and technological issues that make the world a less sustainable place, without even questioning the culture and philosophical model that fuel the representations of non-human life perceived as a mere resource, is a mistake that will make the SDGs unattainable-or only partially. Discourse is never only a matter of words or semantics: it is a matter of viewpoints that are particularly important when it comes to texts that are to be shared globally, for every country, every stakeholder, and every human being that wants to play their part in the 2030 agenda.

## CONCLUSION: DISCOURSE MATTERS WHEN IT COMES TO SUSTAINABILITY

By examining the full corpus of texts related to the 17 SDGs through both lexicometric tools and critical discourse analysis, this research shows that despite the stated inclusivity and universality of the agenda, the discourse often privileges a managerial, economically driven, and institutionally centered worldview. The dominant narratives emphasize development over sustainability, and global institutional frameworks over local complexities. These discursive tendencies risk reinforcing existing geopolitical hierarchies and economic paradigms, rather than fostering the structural change the SDGs ostensibly call for.

Moreover, the analysis highlights the fragmentation of themes across different goals-environmental and social issues

are frequently decoupled, while fundamental intersections (e.g., between poverty and ecology, or gender and climate) are downplayed or left implicit. The use of technical vocabulary and policy-oriented formulations contributes to a depoliticization of pressing global challenges, reducing them to targets and indicators rather than framing them as part of larger systemic transformations.

By proposing a methodological framework that combines corpus-based linguistic tools with critical discourse theory, this study contributes to the growing field of critical sustainability studies. It calls for greater attention to the performative and ideological dimensions of global policy discourse. Future research could compare institutional discourses across languages or regions, analyzing the reception and reinterpretation of the SDGs in local contexts, or investigating the role of non-state actors in reshaping sustainability narratives.

Ultimately, discourse matters-not only as a reflection of institutional priorities but as a powerful instrument in defining what is thinkable, desirable, and actionable on a global scale. As we approach 2030, the challenge is not only to implement the SDGs, but to question the very language through which they are imagined, framed, and justified.

**Funding:** No funding source is reported for this study.

**Ethical statement:** The author stated that the study does not require any ethical approval.

**AI statement:** The author stated that no generative AI tool has been used to prepare, produce the study or write the present paper.

**Declaration of interest:** No conflict of interest is declared by the author.

**Data sharing statement:** Data supporting the findings and conclusions are available upon request from the author.

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