Sustainable Development (SD) was first defined as “the development that meets the needs of the present without compromising the ability of future generations to meet their own needs,” in the document “Our Common Future” by the United Nations Commission on Environment and Development (Brundtland Commission). SD aims to address humanity’s aspirations of a better life within the limitations imposed by nature [1
Subsequently, in 1997, the United Nations Agenda for Development building on the Brundtland SD definition and the Elkington [2
] triple bottom line approach (people, planet, profit) approach, stated that: “Development is a multidimensional undertaking to achieve a higher quality of life for all people. Economic development, social development, and environmental protection are interdependent and mutually reinforcing components of sustainable development” [3
]. Each of these factors has played a major role in recent years in terms of efforts for innovation, financing and global development. In terms of social development, besides the eradication of poverty and well-being of the population, quality education is another significant factor nowadays, that is bringing also innovation in ways of teaching, especially in terms of digital teaching, but also increased mobility of pupils and students, notably since the integration in the European Union and the Bologna process started [4
]. In the economic field entrepreneurial entries, innovation, knowledge economy development and digitalization, such as the introduction of robotic automation processes for the business have become some of the main variables for enhancing competitiveness and further market and business development [5
]. Another main focus point today is the environmental protection and sustainable development in the form of renewable energy, such as wind, solar and other forms of green energy, for which also a sustainable development has to be ensured through diverse support policies, community project inclusion and financing programs [7
]. Moreover, research has shown that, at country level, there is high correlation (and a possible relationship) between social sustainability, innovation and competitiveness [8
In 2015, the General Assembly of the United Nations (UN) formally adopted ‘‘The 2030 Agenda for Sustainable Development,” which provides a framework for ‘‘peace and prosperity for people and the planet, now and into the future” [9
]. As part of this agreement, all United Nations Member States, after a participated process involving multiple stakeholders, agreed upon the Sustainable Development Goals (SDGs), which can be used to provide an indication and measure of progress towards the main objective of sustainable development [10
]. The SDGs represent a shared expression of stakeholder needs at a global level balancing economic, social, and environmental development [11
]. The 17 SDGs, presented in Table 1
, comprehend themes such as ending world poverty to undertaking urgent action to combat climate change and its impacts by 2030, and are outlined in the UN’s document “Transforming our World: The 2030 Agenda for Sustainable Development” [9
] and in the United Nations sustainable development goals platform [12
The SDGs aim to inspire the operationalization and integration of Sustainability into organizations worldwide, addressing current and future stakeholder needs, and contributing to the achievement of sustainable development for society at large. However, although this global initiative is an authoritative source of inspiration, the different interpretation of the SDGs calls for further efforts by policymaking to improve the understanding and scientific resonance of future SDG-like initiatives, and there are still open issues regarding SDG performance measurements, operationalization, and interlinkages [13
]. Assessment of the 17 SDGs has considerably focused on formulating appropriate targets and indicators for each goal [14
]. Moreover, as outlined by Sachs [15
] (p. 2206), the SDGs ‘‘aim for a combination of economic development, environmental sustainability, and social inclusion”, and thus, by definition, must embrace a wide range of targets and indicators. The interlinkages and integrated nature of the SDGs are critical to attaining sustainable development [16
]. It is, therefore, relevant to research the possible relationships (trade-offs and complementarities) in achieving the various SDGs. After the introduction, a literature review of the SDGs relationships and the Sustainable Development Goal Index (SDG-I) is presented, followed by the methodology section. The paper ends with the results presentation and the discussion of the relevant findings and its implications and limitations.
This research aims to map the relationships between the SDGs, supported on a correlation analysis of the results of 17 SDGs for all the 193 UN member states, selected as the source of data for the subsequent correlation analysis.
Due to its conceptual complexity, it is challenging to translate some of the SDGs into measurable indicators. Moreover, the data is not always available, and some countries have difficulty reporting these indicators with reliability, making it difficult for cross-country comparability, or agreed-upon methodologies for measurement.
To overcome these limitations, and considering its international legitimacy and acceptance, the Sustainable Development Goal Index (SDG-I) [29
] was chosen as data source for this analysis. The SDG-I aggregates indicators relate to each of the 17 SDGs and ‘average’ the results into a single metric. To check data normality, Kolmogorov-Smirnov Test was applied. The results of the K-S normality test highlighted that most SDGs do not follow a normal distribution (Table 3
), so correlation coefficient Spearman’s Rho (that does not require normally distributed data and provides more robust results) was adopted.
In order to clarify the Kolmogorov-Smirnov test results, the Kernel density (a variation of the histogram) of each SDG variable was plotted. The Kernel density plots are visually depicted as smoothed curves estimating the probability density function of a continuous variable from a set of scores (likely comprising some error) (Figure 1
and Figure 2
The correlation coefficient measures the intensity of the relationship between ordinal variables and varies between -1 e 1. As near the values are from these extremes, the stronger is the linear association between the variables. The sign indicates the direction of the association between X (the independent variable) and Y (the dependent variable). If Y tends to increase when X increases, the correlation coefficient is positive. If Y tends to decrease when X increases, the correlation coefficient is negative. If the value is zero, this means there is no linear relationship between the variables. Statistical analysis was carried with SPSS Statistics Version 26, and the overall results, showing the existence of several significant relationships, are presented in Table 4
The literature review and the assessment of the SDGs’ relationships confirm that there are indeed relevant interactions between the SDGs. However, the existence of blind spots recommends the need for further research on those interactions. While positive, the interactions between the SDGs are more numerous than the negative ones, considering such a complex system of relationships, synergies, and trade-offs represent a challenge for planners and decision-makers. In support of this view, the IPPC simulations show that there is no simulation where all the SDGs are reached [32
]. Nevertheless, the relationships identified in these investigations represent an opportunity for policy and decision-makers, by suggesting the frequently linear development paths of economic growth ahead of social equity and environmental protection might be challenged by other systemic approaches, that offer multiple solutions and drivers for different contexts, as suggested by Biggeri et al. [19
Barbier and Burgess [10
] recommend prioritizing SDGs associated with the highest monetary returns and contributions to social welfare, e.g., childhood health, that generates significant returns due to long-term gains. Another possible approach is to prioritize the conservation of supporting ecosystems to avoid irreversible effects (e.g., actions to address climate change and global warming), and then optimize socio-economic goals taking into consideration the environmental constraints. Breuer et al. [33
] identified several models and approaches that can support policy-makers to prioritize the SDGs. The World in 2050 model [34
] conceptualizes the SDGs as delineated by the planetary boundaries, with global partnerships for sustainable development (SDG17) and governance (SDG16) providing the framework for the other SDGs, clustered into five main categories of SDGs: social and economic development (SDGs 8, 9, 11), universal values (SDGs 4, 5, 10), basic human needs (SDGs 1, 2, 3), and sustainable resource use (SDGs 6, 7, 12). However, the priorities can change within different development contexts, e.g., basic conditions of life in more developing countries, or sustainable resource use in more developed ones. Other simulation models like the World Economic Forecasting Model (WEFM), the iSDG model, developed by the Millennium Institute, can also support decision-makers and civil society stakeholders to visualize the long-term trajectory of their country’s current development path and help them to devise coherent alternative policies that are better suited to achieving the SDGs [33
At the micro level, organizations emphasize the need to adopt more flexible and innovative approaches with a more substantial open systems perspective (influence of the environment, dynamic environment, need for survival), e.g., within those that adopt ISO International Standard Management Systems [35
]. Moreover, authors such as Domingues et al. [36
], Poltronieri et al. [37
], and Rebelo et al. [38
], stress the need for a systemic approach while reporting the efforts carried out to operationalize this integration process among the organizations, taking into account the needs and expectations of the stakeholders. The adoption of systemic and integrated approaches is, therefore, recommended at both the macro and the micro level to contribute to the SDGs.
Specifically concerning the correlation study, the results support Pradhan et al.’s [21
] conclusions that Poverty elimination (SDG1) and health and well-being (SDG3) have a synergetic relationship with most of the other goals. There is also confirmation that SDG12 (Responsible consumption and production) is the goal most associated with trade-offs.
Accordingly, with the literature, SDG7 (Affordable and clean energy) has a significant relationship with other SDGs (e.g., SDG1 (No poverty), SDG2 (Zero hunger), SDG3 (Good health and well-being), SDG8 (Decent work and economic growth) and SDG13 (Climate action)), requiring coordinated policy interventions to protect the vulnerable, ensure equity and manage competing demands over natural resources to support sustainable development [26
]. The correlation study confirmed the existence of strong positive correlations between SDG7 and SDG2 (Zero hunger), SDG3 (Good health and well-being), SDG4 (Quality education) and SDG9 (Industry, innovation, and infrastructure), highlighting the importance of the access to affordable and clean energy for economic, environmental and social performance. However, there is a moderate negative correlation with SDG12 (Responsible consumption and production), which emphasizes the need to improve energy efficiency, increase the share of clean and renewable energies and improve sustainable consumption patterns worldwide.
While there is also consistency between the correlation analysis and the UN study [28
], e.g., relating to the relationships addressing synergies between SDG1 (Zero hunger), SDG01 (No poverty) and SDG3 (Good health and well-being), no significant relationships between SDG13, SDG14, SDG15 and SDG17 with other SDGs was found. Particularly in the case of SDG13 (Climate action), it is surprising no significant correlation with other SDGs was found. This is in line with Stafford-Smith et al. [13
] who argue that there are still open issues regarding SDG performance measurements, operationalization, and interlinkages.
Relating to the existence of negative relationships (trade-off), the correlation result supports Pradhan et al. [21
], since SDG12 (Responsible consumption and production) is the goal most associated with trade-offs. However, the trade-offs identified in the UN study [28
] are not confirmed by the correlation results.
An overall conclusion is that effective action for the advancement of the SDGs and, ultimately, sustainable development for all, demands that the relationships between the SDGs must be identified and tackled, e.g., the connections between No poverty and Zero hunger, and Good health and well-being, or between climate change and human health. This should lead to the increased relevance of SDG17 (Partnerships for the goals) and more intense and effective cooperation between governments, institutions, agencies, private sector and public organizations, and society at large, across different industries, locations, and levels.
A common support of that relevance is developing a sustainable intellectual capital [39
], based on knowledge dynamics [40
], at the organizations’ and communities’ levels.
To sum up, this research suggests that change towards achieving the Sustainable Development Goals offers many opportunities for reinforcing rather than inhibiting itself. Moreover, as The World Health Organization (WHO) declared a public health emergency of international concern over the global outbreak of COVID-19 (30th January 2020) and escalated it to a global pandemic on 11th March 2020 [41
], we are once more reminded that we do live in one global and interconnected world. Hence the relevance of the SDGs’ framework. The limitation of the correlation analysis, and the potential problems related to the use of an index based on the arithmetic mean (which assumes that different targets and indicators are perfect substitutes for each other, without accounting for positive synergies or negative externalities, as stated by Biggeri et al. [19
]) should be acknowledged. These limitations recommend the replication of this investigation with more powerful statistical techniques and a longitudinal perspective.