Introduction

Due to current linear economic structures and concepts, negative impacts such as resource insecurity have resulted in calls for alterations towards a circular economic model and subsequent economic, environmental and social shifts. (UNFCCC, 2015; United Nations, 2015). To assist in this process on a global scale, the 2015 United Nations Sustainable Development Goals (SDGs), or commonly referred to as the Global Goals were signed in September 2015, as a succession of the previous 2000–2015 Millennium Development Goals (MDGs). The SDGs are seventeen goals, with 169 related targets aimed at more sustainable practices universally, in a variety of areas and fields, including social, environmental and economic practices, and the transition to a more sustainable future via planetary protection, peace and prosperity (United Nations, 2015). The SDGs are also utilised as a tool to assist in the transition to a wider usage of evidence for policy makers via factual and numerical data through individual goal indicators.

As hubs of innovation towards numerous research and teaching areas, the role of Higher Education Institutions (HEI's) and their subsequent student led–organisations in expanding the development of the sustainability agenda is of paramount importance in solving many of the world's current and future challenges (Ellen MacArthur Foundation, 2013; Cleverdon et al., 2017). However, in alignment with the pressures from HEI's, student–led organisations, such as student unions, have a joint duty in pressuring and developing the sustainability agenda within their respective institutions to create change (Tilbury, 2004). The unique position of student unions, as being both an integral part of – but external from – universities allows for an exclusive opportunity and challenge in assisting with the enhancement and development of sustainability–literate graduates (Sammalisto et al., 2016; Dlouhá et al., 2017). Additionally, the nature of working in partnership with and independently allows for differing levels of dissemination to students of the attributes required to understand the complex nature of sustainability (Kerr and Hart–Steffes, 2012; Brooks et al., 2015a).

HEI's and student led–organisations have a duty of care and responsibility as the producers of the future (student) workforce as lifelong learners (European University Association, 2008), with the requirements of a future–proofed workforce allowing for an opportunity for all institutions and establishments to play a vital role in tackling the world's challenges. As students are the future change–makers and future leaders required to make these changes, a key driver of their future ambitions in relation to the SDGs are the student–led institutions which represent them.

Due to the overarching nature of Student led organisations, collaborative bodies are utilised to underpin organisations both on a local scale – such as via university–based student unions, as well as on a national and continental level. On a continental level, the European Union of Students (ESU) has been in operation since 1982, representing 46 National Student Unions from 39 countries, with general topics regarding students in their respective cultural, learning and societal environments (Hong, 2014). All members are "student–run, autonomous, representative and operate according to democratic principles." (ESU, 2018). In 2014, the ESU merged with the ESIB – The National Union of Students in Europe – to form its current adaptation, resulting in an umbrella organisation representing approximately 15 million students (ESU, 2018).

A current key policy aspect of the ESU is upholding a signatory commitment to the Bologna Accord of 1999, created from previous European discussions in creating a consistent European education system, such as the Sorbonne declaration of 1998 (EHEA, 1998). The Bologna process has ultimately created the European Higher Education Area. 29 Countries initially signed the Bologna Accord in 1999, with all but six countries in the European Cultural Convention of the Council of Europe being signatories to the agreement at present. All members of the EU are part of the Bologna process, with other external signatories such as UNESCO and the European Commission supporting and helping to implement this process. The importance of the Bologna Accord is far–reaching, particularly in relation to the overall education levels on a state by state basis – despite no formal link with EU legislation, resulting ultimately in a voluntary commitment. As such, the ESU has a key position in allowing for students to have a similar level of education, with it also being part of their vision statement.

As a result of the ever–changing nature of student demands, student led organisations (in conjunction with all organisations) have to be adaptable to respective changes and developments of given agendas and policy. A key example of a student–led organisation from a UK context is the National Union of Students (NUS). The NUS is a student organisation body founded in 1922, currently with approximately 600 student unions and 95 per cent of higher and further education unions in the UK, covering several strands of education providers, student ages and education levels (NUS, 2008). According to 2016–17 data, there were 2.32 million students studying within a UK Higher Education institution, with 162 higher education institutions in the UK in receipt of public funding (Universities UK, 2017). Additionally, there are a further 319 UK Further Education Colleges, educating and/or training 2.2 million people (Association of Colleges, 2018). The NUS also have devolved members in Scotland, Wales and Northern Ireland – with the latter being trilaterally ran with the Union of Students in Ireland (NUS–USI, Unknown).

Founded in 1922, the UK NUS has developed and altered over time (Brooks et al., 2015a), with current values of collectivism, democracy and equality. The results of these values cover a wide range of areas applicable to the SDGs, alterable in relation to both local, national and international government policy relating to students. Furthermore, the overall complexion of the students running the NUS changes, via yearly elections both on a national NUS and local organisational level, thus providing opportunities for further alterations on an internal scale as both the NUS and local unions alter and develop (NUS, 2013; Brooks et al., 2015a; Guan et al., 2015).

The NUS also undertake several forms of high–level research into student matters, many of which cover the SDGs, including both Higher and Further education and funding, as well as the Welfare, Equality, Diversity and Liberation of both home and international students. In terms of specific sustainability research elements, the NUS, Environmental Association for Universities and Colleges (EAUC), University and College Union (UCU), the Association of Colleges (AOC) and the College Development Network (CDU) have undertaken research entitled "Sustainability in Education" since 2015, aimed at assessing the levels of sustainability across UK colleges and universities. (EAUC, et al. 2016; 2017). Also, research into sustainability skills of students via a national online survey, funded by the Higher Education Academy has helped to track and analyse expectations of learning and teaching of sustainability and sustainable development concepts from 2010/11–2015/16 (NUS, 2015; 2016). In terms of direct SDG research, the NUS have also released research entitled "Student Opinion – Sustainable Development Goals" highlighting student attitudes and perspectives of the SDGs – with 76% of (c.1550) students surveyed agreeing with the statement that Universities should actively lead and support achievement of the SDGs (NUS, 2018).

One of the NUS Initiatives directly attributable to sustainability concepts is the Dissertations for Good (DfG) programme. This is defined as is a unique programme of interaction between students, the NUS and organisations, to allow for the creation of student academic pieces of work, whilst also benefiting the organisations participating in the programme in their future work. The collaboration between the student and organisation is ultimately aimed at creating academic work which contributes towards economic, environmental and social sustainability (Croasdale, 2015).

This paper is undertaken under the Dissertations for Good programme, with the NUS being the primary host organisation – in conjunction as the founders of the programme itself – and have been a key stakeholder throughout this project. Due to the linkages within the HEI sector, the NUS are currently looking to extend the reach and depth of their already successful sustainability initiatives into wider markets outside of the UK, including, but not limited to the Green Impact and Responsible Futures accreditations, and are a key stakeholder through their Dissertations for Good Scheme. The UK NUS Head of Sustainability, Jamie Agombar was utilised as a direct NUS contact as a stakeholder throughout the process of this research, providing further research dissemination and knowledge regarding the initial research aims and objectives.

Despite efforts to categorise many of the UN SDGs, via initiatives and narrower elements such as Education for Sustainable Development (ESD), past academic research is primarily limited into specific business sectors or regions, as opposed to a global perspective – despite statistical and index knowledge (Hsu and Zomer, 2016; Allen et al., 2017). On a global level, there is, at present no single defined, scientific ranking of each system on a state basis directly from the UN, potentially resulting in siloed approaches to multi–faceted issues and problems. Despite this, reference points and country profiles are being developed as data becomes available in relation to specific targets and continents. Rankings include both the overall level of success in adopting the SDGs, with several streams of reporting (UN, 2017a; UN 2017b), both on an incremental level, and in the overall scheme of the targets that make up the goals themselves. As the timeline of the SDGs continues voluntary reviews of data are becoming a key aspect of this step–change (UN, 2017b). Aside from the UN directly, organisations externally such as the Sustainable Development Solutions Network (SDSN) and Bertelsmann Stiftung have produced reports with an aim to assist in this process, such as the SDG Index and Dashboards Report of 2017 (SDSN and Bertelsmann Stiftung, 2017).

In terms of current index knowledge on a UK context, despite informed knowledge of reporting tools regarding sustainability, impact is currently limited, despite work to enhance knowledge transfer aspects of the goals (EAUC, 2016). Current governmental departments, such as the Office for National Statistics (ONS) has provided a UK–wide overview, however, this includes limited tailored analysis at present (ONS, 2016).

As such, this research will be linking and developing on an under–researched demographic in student unions, utilising a unique framework for potential future SDG related data inquiries, applicable to local, national and international outputs – to ultimately assist in the data–based level of the SDGs and the knowledge transfer of elements of work into the SDGs undertaken by student unions.

The aims and objectives of this project are as follows:

• To highlight the extent of current thinking on the SDG's and their target indicators within European Student Union (ESU) members after a period of implementation since their initial inception in 2015, in relation to the UK NUS.
  • Undertaken via searching on public web domain environments using pre–assigned, key terms, by utilising the UK NUS as a baseline, control knowledge source
• To highlight any areas of improvement required, primarily on a spatial aspect. Thus, allowing for stakeholders (NUS) to gain valuable information for future work.

Methods

The methodology of this project involves the creation of a unique, semi–automatic content analysis approach utilising ESU member nations and their respective web environments as a proxy for SDG levels of influence. The method was created from October 2017, with data collection over an eight–week period from December 2017–January 2018.

An initial list of 17 key terms were created as proxy indicators of progress towards the SDGs. These were both taken directly and adapted from information within the 2016 & 2017 progress towards the SDGs, in conjunction with the 2015 SDG targets and indicators (UN, 2015). These terms were subsequently spilt into Economic, Environmental, General and Social categories, defined by the researcher in question.

The overall research methodology was adapted from several previous works, including Jones and Lee's initial environment category in relation to global corporations' environmental management policies and procedures (Jones and Lee, 2007), which was later altered by Joseph and Taplin in 2012, in relation to work leading to the SDGs in assessing the influence of the previous Agenda 21 and Local Agenda plan formed at the Rio de Janeiro Earth Summit in 1992, in regard to Malaysian local government websites (Joseph and Taplin, 2012).

The key terms were selected to match as closely as possible with the SDGs, whilst also providing a tailored approach with minimal key term/term usage, to prevent potential overlap on non–SDG related issues.

Each key term has one or more specific SDG links, pre–selected by the researcher for results purposes against specific goals. However, there is an understanding that the SDGs (and as such, the key term/terms chosen) link to multiple goals in context – the key terms (where possible) have been selected as a proxy for one goal to aid in specific goal outputs, as shown by Table 1.

Table 1: Original and confirmed key term/terms prior to website data mining analysis, including overall section, specific wording and selected SDG application.

*Sustainability and Sustainable Development Goals/SDGs were perceived to have value across all goals

Prior to testing, 7 of the initial 46 ESU member unions were discarded from testing due to a lack of web environment. This resulted in 39 unions, representing 33 nations applicable for full testing, as shown by Tables 2 and 3. In terms of non–applicable testers, this was based entirely off ESU member information, found within the ESU directory.

Figure 1: Visual Representation of all countries participating in study, as taken from Microsoft Excel 3D Map Creator, with Map Credits to; HERE, DSAT for MSFT, Microsoft, Navteq, Wikipedia, GeoNames

Table 2: Full ESU member listing of countries and respective unions participating in study.

Table 3: ESU Union and country members who did not participate in study.

Firstly, a pilot test method in December 2017 was utilised to test the approach, with the NUS–UK, and three other, randomly selected national student unions – namely Sweden (SFS), Netherlands (ISO) and Netherlands (LSVB). This sample size was utilised as a 10% selection of all remaining ESU members, with the NUS–UK being used as a baseline control level of current knowledge, to compare unions against. This baseline was used to compare a standardised approach to multiple unions, despite potential website changes.

Websites were mined as defined in Figure 2, with the site link, presence/absence of key terms and subsequent frequency of key terms (where applicable) collected, both automatically utilising external Pearls Extension software, in conjunction with computer control prompts, assisted by manual human checking. The extent of the term usage in–text was formulated using a pre–defined depth of terms element, as defined below in Figure 2;

A key aspect of the method is the usage of the site levels, to allow an overall data collection of the entire site, despite differing site layouts. As described in Figure One, a maximum of five levels were used within this research, with the initial homepage becoming level one. This allowed for an in depth analysis of a given area of a website, without over–extending the research within multiple site directories. This was also used due to constraints of both time and resources, within the pre–defined limits of the research as noted below.

The following limits were also imposed for the pilot test:

• 190–page limit
• Must be based on direct, initial, visible site information only
  • E.g. No downloadable data
• No external sites to the union/organisation (e.g. advertising)
  • Subsidiary sites allowed linked directly to union
• A maximum of 5 level website depth elements
• Information limited from January 2014–Present day of testing (Dec 2017–Feb 2018), where dated on site.
• Data collection was to be undertaken utilising Google Translate where applicable, utilising the automatic website translations of the Google Chrome Browser

The pilot test provided a number of key alterations for the full testing element. Firstly, the introduction of the Google Chrome–Google Translate extension toolbar, as some webpages were not automatically translating through the Google Chrome browser. However, the translations utilising both techniques were confirmed to be identical in all sites used in the pilot tests in comparison to later testing. Furthermore, the pilot test also provided a prospective time frame to cover the remaining ESU member unions, as a result of the testing period required during the pilot itself

Figure 2: Dichotomous key showing chronological, methodological process of website data mining via initial processes and standardised processes, including depth of terms element.

For the full testing, page limits per union were lifted to 200 sites – with no further alterations to the pilot test method, providing a standardised confidence interval; when sites are determined as samples of a given website population – or the entire population, whereby all sites were covered prior to the 200–page limit.

Using Google's site operator tools, indexes for all ESU member sites were found, providing a maximum potential population of sites (c. 5,220 – NUS UK). Google Translate was utilised to assist this semi–automatic research, to assist in the translation of foreign languages, either automatically via the Google Chrome browser, or manually using a Google Chrome–Google Translate extension.

Results

In total, 39 unions from 33 countries were successfully analysed from an initial set of 46 unions (39 countries), as shown by Table Four, however, within this group, 24 unions failed to meet the 200–page upper limit, restricting the maximum webpage count from 7800 to 5142. Despite this, 20 unions, representing 17 countries had at least one key term (Figures 3 and 5), making up 52% percentage of all countries surveyed and 51% of all unions surveyed.

Of the 5142 key terms found, approximately 8% of pages contained at least one key term/term, with 935 total key terms found. Of these 935, as shown by Figure 4, 89% of all key term sites were located within the UK & Switzerland unions, creating 86% of all key terms found in totality. However, this trend alters with the number of specific key terms from the initial set – with the Swiss Union registering two key terms.

From the initial 17 key terms selected, 11 terms were found, as highlighted by Table 4 and Table 5, 75% of Economic key terms and 40% of social key terms/terms were not found in any site. Furthermore, there is a clear disparity between the types of terms used within unions who registered key terms. General category search terms were found by 16 of 20 unions, as opposed to 3 of 20, in the Economic and Environmental categories, and 4 of 20 in the Social Section. This is also shown by Figure 6, in which over 92% of individual key term occurrences counted were within the General category.

In terms of all key terms – regarding the overall depth of key terms/terms, the average is low at 1.56, residing between a singular word (1.0) and one sentence (2.0).

Table 4: Data variables regarding union page testing, key term frequency and depth of terms in relation to ESU Website environments.

Figure 3: Visual Representation of all countries and respective number of Key Terms from list found per country participating in study, as taken from Microsoft Excel 3D Map Creator, with Map Credits to; HERE, DSAT for MSFT, Microsoft, Navteq, Wikipedia, GeoNames

Figure 4: Visual Representation of all countries and respective number of Key Terms from list found per country participating in study, as taken from Microsoft Excel 3D Map Creator, with Map Credits to; HERE, DSAT for MSFT, Microsoft, Navteq, Wikipedia, GeoNames