Statistics Explained

SDG cross-cutting issues - interlinkages

The interlinked nature of the SDGs


Data extracted in May 2021.

Planned article update: 23 May 2022.

Highlights


This article is a part of a set of statistical articles, which are based on the Eurostat publication ’Sustainable development in the European Union — Monitoring report on progress towards the SDGS in an EU context — 2021 edition’. This report is the fifth edition of Eurostat’s series of monitoring reports on sustainable development, which provide a quantitative assessment of progress of the EU towards the SDGs in an EU context.

Full article

The interlinked nature of the SDGs

The 2030 Agenda for Sustainable Development represents a complex holistic challenge. Understanding the scope of interlinkages among SDGs is key to unlocking their full potential as well as ensuring that progress in one area is not made at the expense of another one. Hence, investigating trade-offs and synergies emerging from relationships between the goals is crucial for achieving long-lasting sustainable development outcomes. This report uses a quantitative approach, based on Spearman’s rank correlation analysis, to identify interlinkages between the SDGs.

Measuring the interlinkages between the SDGs: existing approaches

Interlinkages can be identified as positive (synergies) or negative (trade-offs). Trade-offs are negative interactions between different SDGs and targets when improvements in one dimension can constrain progress in another dimension. If achieving economic growth requires higher resource and energy consumption, it can create a trade-off between SDG 8 and SDGs 12 and 7. In contrast, synergies are positive interactions between goals and targets, when achieving one target, such as a 20 % share of renewable energy in the EU, can also help achieving another target, such as reducing greenhouse gas (GHG) emissions.

Several attempts have been made by international organisations and academics to assess interlinkages, synergies and trade-offs. A study by the European Commission’s Joint Research Centre (JRC) [1] found five main approaches to identify interlinkages between the SDGs: linguistic, literature review, expert judgement, quantitative analysis, and modelling complex system interactions. The International Council for Science published ‘A Guide to SDG interactions’, which, based on expert judgment, explored the nature of interlinkages between the SDGs and found more synergies than trade-offs between the goals [2]. The Interlinkages Working Group of the IAEG-SDGs also conducted a study that identified positive interlinkages between the goals and targets in order to help countries focus on those targets with the greatest potential for positive externalities [3]. The Italian National Institute of Statistics (Istat) based its analysis of interlinkages on the aforementioned work of the IAEG-SDGs and compared the identified interlinkages with the statistical information contained in the Istat-SDGs information system [4]. The National Institute of Statistics and Economic Studies in France applied principal component analysis (PCA) to the EU SDG indicators to identify correlations between the SDGs [5]. A study by E. Barbier and J. Burgess identified trade-offs among the SDGs using an economic model [6]. Some academic studies also used integrated assessment models to identify interactions, synergies and trade-offs between the SDGs [7].

In general, all these studies agree there are many more synergies between the SDGs than trade-offs, and that it is important to identify the positive and negative interlinkages in order to design the most efficient policy actions for delivering on the SDGs. However, the interlinkages strongly depend on the method and data used and on the geographical scope of the report (meaning whether the interlinkages are analysed on country, region or world level). This 2021 edition of the EU SDG monitoring report attempts to identify interlinkages between the SDGs in an EU context by applying Spearman’s rank-order correlation analysis to the EU SDG indicator set.

Methodology for assessing interlinkages in the EU context

Applying quantitative statistical methods for identifying correlations between the SDGs appears to be the most appropriate approach for a statistical office such as Eurostat. Such methods were also used by the JRC [8] and in several academic articles [9][10][11][12]. In line with these studies, Spearman’s rank correlation was chosen over Pearson’s correlation due to its suitability for monotonic non-linear relationships and little sensitivity to outliers [13].

To avoid false associations, prior to the correlation analysis a positive sign was assigned to indicators with values that would need to increase to achieve the SDGs (for example employment rate) and a negative sign to indicators with values that would need to decrease (for example greenhouse gas emissions). The correlation analysis was carried out across all indicator pairs with more than three common data pairs in the time series, using annual data from 2009 to 2020 from all Member States. However, depending on the data availability for a specific indicator and country, many time series were actually shorter. Multipurpose indicators were only included once in all calculations, to avoid double-counting.

A correlation between an indicator pair is considered significant (and sufficiently strong) if its p-value is below 0.1 and if its correlation coefficient is above or below the threshold of ± 0.5. If the correlation coefficient is above 0.5, it is considered a positive interlinkage (synergy), while coefficients below – 0.5 are considered a negative interlinkage (trade-off). Indicator pairs with a correlation coefficient between – 0.5 and 0.5 or with a p-value above 0.1 are labelled as non-correlations.

It is important to keep in mind that correlation does not necessarily imply causality. For example, it is obvious that the correlation between the sales of ice-cream and the sales of sun glasses does not reflect a causal relationship between the two variables. Instead, both variables are likely to be driven by an independent third variable, namely weather. Nevertheless, even though a significant correlation between two indicators does not imply that the indicators are causally linked, correlation analysis is still helpful in quantitatively assessing whether improvements in one SDG coincide with improvements in other SDGs [14]. Moreover, if the correlation analysis is applied to many countries and a specific synergy or trade-off is found repeatedly, it is likely that it does not appear by chance.

It must also be noted that because of data issues not all interlinkages can be captured by this method. Some indicators only show three or fewer data points and thus were excluded from the analysis, while many other, mostly environmental, indicators lack country-level data. Consequently, out of 5 050 possible combinations of indicators with country-level data, the actual number of indicator pairs included in the analysis varied from 4 613 for Belgium to 3 588 for Malta.

Results of the analysis of interlinkages between the SDGs

In line with other studies using correlation analysis [15][16][17][18], the results for the EU show there are more positive (25 %) than negative (14 %) interlinkages. However, almost two-thirds of indicator pairs (62 %) are not significantly correlated with each other, which signals that the indicators in the EU SDG set to a large extent monitor distinct phenomena that are not necessarily directly related to each other.

Figure 1 shows the positive correlations between the SDGs at EU level, with the thickness of the line corresponding to the share of positive correlations between the two SDGs in question. The shares of positive interlinkages between any two goals (among all possible interlinkages between these two goals) varied from 12 % to 54 %. The figure, however, does not show connections between goals that have less than 30 % of positive interlinkages. This means that even though some goals, such as SDG 15, do have positive correlations with other goals, this is not reflected in the figure.

Not surprisingly, the network of Figure 1 reveals that the way we live, produce and consume is strongly interconnected with many other areas, both acting as a driving force for, as well as being impacted by, other developments. Consumption and production patterns (SDG 12) have a large impact on resource [19] and energy efficiency [20] and thus directly impact on a number of energy-related aspects (SDG 7) [21]. In turn, reliable and sustainable energy systems relate to the transition towards more sustainable transport patterns and a resilient low-carbon society, thus having considerable influence on climate (SDG 13) and infrastructure (SDG 9). It is also known that climate change (SDG 13) has a synergetic relationship with human health (SDG 3) [22], while urban areas (SDG 11) affect the EU’s climate (SDG 13) since they act as a focal point of environmental change due to land take (soil sealing), transport, housing and mobility issues, food supply and waste generation.

Some goals, such as life on land (SDG 15), zero hunger (SDG 2) or reduced inequalities (SDG 10) show only very few connections to other SDGs, based on the correlation analysis applied to the EU SDG indicator set. For SDG 15, this is in part due to the lack of Member States’ data for some indicators and only a few data points for other indicators that are not collected annually, which increases the likelihood that the correlation results are not significant. However, there is a wide agreement that these goals are cross-cutting topics that are crucial for meeting the 2030 Agenda as a whole [23][24][25]. Biodiversity and ecosystem services (SDG 15) provide a basis for human life on earth and human well-being, while sustainable agriculture practices (SDG 2) help to maintain biodiversity and end hunger. Reducing inequalities (SDG 10) in society helps to maintain peace and security (SDG 16) and to increase access to common goods and services, which in turn has a positive influence on economic growth (SDG 8), education (SDG 4) and health (SDG 3).

When looking at synergetic relationships that occur in the majority of EU Member States (that is, in more than three-quarters of EU countries), the connection between social and economic indicators becomes clear. In most Member States, poverty indicators are strongly correlated with each other, as well as with labour market indicators. This is not surprising given that the income generated from employment helps workers to obtain goods and services to meet their basic needs. Labour market indicators are associated with each other and with real GDP per capita, indicating that economic growth usually goes hand in hand with improvements in the employment situation. In most Member States the number of young people not in employment, education or training (NEET) shows a synergetic relationship with GDP per capita, meaning that countries with higher GDP show lower NEET rates, a finding that has also been confirmed by previous research [26].

Some indicators referring to the environmental pillar of sustainability also show strong synergetic relationships with each other in the majority of the EU Member States. Organic farming, for example, is associated with higher energy productivity. This is also confirmed by a review of 50 studies that found that organic farming systems are more energy efficient than their conventional counterparts [27]. Energy productivity is also showing a synergetic relationship with the share of renewable energies as well as with CO2 emissions from cars. Greenhouse gas emissions are correlated with primary energy consumption, meaning that improvements in one area are associated with improvements in another — a connection that can also be found in previous research [28].

Negative interlinkages between the SDGs, i.e. when a positive development in one SDG goes hand in hand with negative development in another SDG, present less variation compared with positive interlinkages, with the shares of negative correlations varying from 7 % to 22 %. Figure 2 shows SDG pairs with more than 18 % negative correlations. Decreasing poverty in the EU (SDG 1) seems to be associated with negative (unsustainable) trends in consumption and production (SDG 12), climate change (SDG 13), energy (SDG 7) and gender equality (SDG 5). This means that progress on social goals such as SDG 1 and SDG 10 can lead to increased material consumption (SDG 12) and energy consumption (SDG 7), carbon footprint and other environmental impacts (SDG 13) [29][30][31]. Material consumption is, in turn, one of the most significant drivers of environmental pressures [32][33]. Therefore, ensuring the well-being of citizens while protecting and enhancing the EU’s natural capital is a key policy challenge.

Goals for gender equality (SDG 5), reduced inequalities (SDG 10) and partnerships for the goals (SDG 17) seem to have the biggest shares of negative correlations with other goals at the EU level. This might be related to the fact that many EU Member States have shown negative trends towards these SDGs over the past years, especially in terms of growing gender gaps for many indicators. It is important to keep in mind that the assessment of progress towards SDG 5 in an EU context focuses on equal chances for both for men and women. In particular, in the area of education, men are falling behind women in many Member States, which leads to a negative assessment of those indicators.

In contrast to positive interlinkages, very few SDG indicator pairs show a negative correlation in more than half of the EU Member States, indicating that no major trade-offs can be identified as universal at the EU level. Domestic material consumption, for example, shows a negative correlation with employment rate and NEET rate in 14 EU Member States, meaning that improvements in the labour market situation in these countries have coincided with increased resource consumption.

Although the correlation analysis of the SDG interlinkages on the EU level does not cover the whole complexity of the connections between the goals, it is able to demonstrate that the SDGs are deeply interconnected and that achieving one goal is not possible in isolation from the others. Policy measures can contribute to delivering on different SDGs at the same time. In addition, the calculation results demonstrate that interlinkages are context dependent and can differ greatly between countries. Nevertheless, the analysis of interlinkages indicates that for a transition towards a more sustainable and resilient society, citizens and all stakeholders in the different policy areas, sectors and levels of decision-making have important roles to play and are sharing the same responsibility.

Figure 1: Visualisation of SDG interlinkages based on shares of positive correlations between the goals


Figure 2: Visualisation of SDG interlinkages based on shares of negative correlations between the goals

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More detailed information on EU SDG indicators for monitoring of progress towards the UN Sustainable Development Goals (SDGs), such as indicator relevance, definitions, methodological notes, background and potential linkages, can be found in the introduction of the publication ’Sustainable development in the European Union — Monitoring report on progress towards the SDGS in an EU context — 2021 edition’.

Notes

  1. ↑ Miola A, Borchardt S, Neher F, Buscaglia D (2019), Interlinkages and policy coherence for the Sustainable Development Goals implementation: An operational method to identify trade-offs and co-benefits in a systemic way, Publications Office of the European Union, Luxembourg.
  2. ↑ International Council for Science (2017), A Guide to SDG Interactions: from Science to Implementation [D.J. Griggs, M. Nilsson, A. Stevance, D. McCollum (eds)], International Council for Science, Paris.
  3. ↑ IAEG-SDGs (2019), Interlinkages of the 2030 Agenda for Sustainable Development, Background document.
  4. ↑ Istat (2019), 2019 SDGs report: Statistical Information for 2030 Agenda in Italy.
  5. ↑ INSEE (2019), The Differences between EU Countries for Sustainable Development Indicators: It is (mainly) the Economy!
  6. ↑ Barbier, Edward B. and Burgess, Joanne C. (2017), The Sustainable Development Goals and the systems approach to sustainability, Economics 11 (2017-28): 1–22.
  7. ↑ See e.g. Moyer, Jonathan D. and Bohl, David K. (2019), Alternative pathways to human development: Assessing trade-offs and synergies in achieving the Sustainable Development Goals, Futures (105), 199–210; and Van Soest, Heleen L; van Vuuren, Detlef P.; Hilaire, Jérôme; Minx, Jan C.; Harmsen, Mathijs J.H.M.; Krey, Volker; Popp, Alexander; Riahi, Keywan; Luderer, Gunnar (2019), Analysing interactions among Sustainable Development Goals with Integrated Assessment Models, Global Transitions (1), 210–225.
  8. ↑ Miola A, Borchardt S, Neher F, Buscaglia D (2019), Interlinkages and policy coherence for the Sustainable Development Goals implementation: An operational method to identify trade-offs and co-benefits in a systemic way, Publications Office of the European Union, Luxembourg.
  9. ↑ Pradhan, P., Costa, L., Rybski, D., Lucht, W., & Kropp, J. P. (2017), A Systematic Study of Sustainable Development Goal (SDG) Interactions, Earth's Future, 5(11), 1169–1179.
  10. ↑ Kroll, C., Warchold, A., & Pradhan, P. (2019), Sustainable Development Goals (SDGs): Are we successful in turning trade-offs into synergies? Palgrave Communications, 5(1), 140.
  11. ↑ Ronzon, T., & Sanjuan, A. (2019), Friends or foes? A compatibility assessment of bioeconomy-related Sustainable Development Goals for European policy coherence, Journal of Cleaner Production, 254, 119832.
  12. ↑ Warchold, A., Pradhan, P., & Kropp, J. P. (2020), Variations in sustainable development goal interactions: Population, regional, and income disaggregation, Sustainable Development.
  13. ↑ Hauke, J., & Kossowski, T. (2011), Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data, Quaestiones Geographicae, 30(2), 87–93.
  14. ↑ Pradhan, P., Costa, L., Rybski, D., Lucht, W., & Kropp, J. P. (2017), A Systematic Study of Sustainable Development Goal (SDG) Interactions, Earth's Future, 5(11), 1169–1179.
  15. ↑ Pradhan, P., Costa, L., Rybski, D., Lucht, W., & Kropp, J. P. (2017), A Systematic Study of Sustainable Development Goal (SDG) Interactions, Earth's Future, 5(11), 1169–1179.
  16. ↑ Kroll, C., Warchold, A., & Pradhan, P. (2019), Sustainable Development Goals (SDGs): Are we successful in turning trade-offs into synergies? Palgrave Communications, 5(1), 140.
  17. ↑ Ronzon, T., & Sanjuan, A. (2019), Friends or foes? A compatibility assessment of bioeconomy-related Sustainable Development Goals for European policy coherence, Journal of Cleaner Production, 254, 119832.
  18. ↑ Warchold, A., Pradhan, P., & Kropp, J. P. (2020), Variations in sustainable development goal interactions: Population, regional, and income disaggregation, Sustainable Development.
  19. ↑ O’Neill, Daniel W., et al. (2018), A good life for all within planetary boundaries, Nature Sustainability 1.2: 88.
  20. ↑ von Stechow, Christoph, et al. (2016), 2° C and SDGs: united they stand, divided they fall?, Environmental Research Letters 11.3: 034022.
  21. ↑ Weitz, N., Carlsen, H., Skånberg, K., Dzebo, A. and Viaud, V. (2019), SDGs and the Environment in the EU: A Systems View to Improve Coherence. Project Report, Stockholm Environment Institute.
  22. ↑ Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Belesova, K., Boykoff et al. (2019), The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate, The Lancet.
  23. ↑ Scherer, L., Behrens, P., de Koning, A., Heijungs, R., Sprecher, B., Tukker, A. (2018), Trade-offs between social and environmental Sustainable Development Goals, Environmental Science & Policy, Volume 90, pp. 65–72.
  24. ↑ Ronzon, T., & Sanjuan, A. (2019), Friends or foes? A compatibility assessment of bioeconomy-related Sustainable Development Goals for European policy coherence, Journal of Cleaner Production, 254, 119832.
  25. ↑ International Council for Science (2017), A Guide to SDG Interactions: from Science to Implementation [D.J. Griggs, M. Nilsson, A. Stevance, D. McCollum (eds)], International Council for Science, Paris.
  26. ↑ Caroleo, F.E., Rocca, A., Mazzocchi, P. et al. (2020), Being NEET in Europe Before and After the Economic Crisis: An Analysis of the Micro and Macro Determinants, Social Indicators Research 149, 991–1024.
  27. ↑ Smith, L., Williams, A., & Pearce, B. (2015), The energy efficiency of organic agriculture: A review, Renewable Agriculture and Food Systems, 30(3), 280–301.
  28. ↑ Lapinskienė, G., Peleckis, K., & Slavinskaitė, N. (2017), Energy consumption, economic growth and greenhouse gas emissions in the European Union countries, Journal of Business Economics and Management, 18(6), 1082–1097.
  29. ↑ Scherer, L., Behrens, P., de Koning, A., Heijungs, R., Sprecher, B., Tukker, A. (2018), Trade-offs between social and environmental Sustainable Development Goals, Environmental Science & Policy, Volume 90, pp. 65–72.
  30. ↑ Malerba, D. (2020), The Trade-off Between Poverty Reduction and Carbon Emissions, and the Role of Economic Growth and Inequality: An Empirical Cross-Country Analysis Using a Novel Indicator, Social Indicators Research 150, 587–615.
  31. ↑ Chakravarty, S., Tavoni, M. (2013), Energy poverty alleviation and climate change mitigation: Is there a trade off?, Energy Economics, 40:1, S67–S73.
  32. ↑ Bradshaw, C.J.A., Giam, X., Sodhi, N.S. (2010), Evaluating the Relative Environmental Impact of Countries, PLoS ONE 5(5).
  33. ↑ Wiedmann, T., Lenzen, M., Keyßer, L.T., Steinberger J.K. (2020), Scientists’ warning on affluence, Nature Communications 11, 3107.
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