Urban Development and Sustainable Energy in EU Countries

Abstract

Cities are considered one of the most important elements in achieving the SDGs (Sustainable Development Goals) and are specifically addressed by SDG11, which identifies actions to ensure safe, resilient and sustainable urban living for residents. Sustainable energy is of key importance to the challenges facing the modern world, hence cities should be designed and built to use as little energy as possible, which translates into the implementation of SDG7 (Affordable and Clean Energy). The sustainable goals are interrelated, so it is crucial to study their interaction. This study formulated the following research objectives: to assess changes in the degree of SDG achievement in EU countries and to investigate interactions between SDGs, in particular between SDG11 and SDG7. Using the TOPSIS method for the years 2015 and 2021, it was found that there are more frequent synergies between the SDGs and SDG11. The top rankings in terms of SDG11 implementation were Sweden, Denmark, Finland and Austria, while the last positions were held by Romania, Bulgaria and Poland. In 2021, the relationship between the positions of countries in terms of SDG11 and SDG7 implementation rates, compared to 2015, increased significantly, indicating that action is being taken to implement green energy solutions.

1. Introduction

Nowadays there is an increase in the importance of cities on the global geopolitical stage. They are becoming not only the arena of social and cultural life but also their influence on the development of the economy is discernible. Progressive urbanization is increasingly causing interference and overexploitation of the natural environment. It is estimated that in 2050 about 70% of the population will live in urban agglomerations [1].

The form of modern cities is seen as a source of environmental and social problems. Cities consume about 70% of the world’s resources and are, therefore, major consumers of energy resources and significantly contribute to greenhouse gas emissions due to their population density and intensity of economic and social activities [2].

The transformation of cities over the years needs to move towards sustainable and balanced development, according to which problems such as environmental degradation, resulting from the expansion of urban space, which often causes negative impacts on air, climate, soil, fauna and flora and the overexploitation of natural resources, need to be addressed.

Achieving sustainability on a global scale requires different types of actions than on an urban scale. According to Tola and Murtagh [3], there is no single definition of urban sustainability. However, there is a set of characteristics of urban sustainability. These include, for example, intragenerational equality, protection of the natural environment, limiting the use of non-renewable resources and high quality of life of citizens.

Goals for “sustainable cities and communities” were formulated as part of SDG11. The links between sustainable development and urbanization lead to the question: is urbanization beneficial to sustainable development? According to Bettencourt and West [4], many issues related to urban development and global sustainability tend to be treated as independent issues, often leading to ineffective policies and unfortunate, and sometimes disastrous, unintended consequences.

The Sustainable Development Goals (SDGs) adopted in 2015 concern areas of life such as hunger, gender equality and sustainable energy and indicate common actions for the member states of the United Nations until 2030 [5]. The adopted 17 Sustainable Development Goals influence each other [6], so work on one goal may have a positive or negative impact on other goals. The links between the SDGs can be divided into two categories. The first informs about the simultaneous progress of the SDGs under study (synergy), while the second concerns their implementation in opposite directions (compromise). Achieving the tasks included in the 2030 Agenda depends on the ability to maximize synergies and resolve existing trade-offs [7]. Hence, the added value of this paper is to capture the relationship between the dimensional goals, which include 169 measures and over 200 indicators. In this article, we primarily analyze the link between SDG11’s nationwide indicators and other Sustainable Development Goals.

The study formulated two research objectives. The first is to assess changes in the degree of SDG implementation in EU countries. The second is to examine the interaction of SDG11 (Sustainable Cities and Communities) and other Sustainable Development Goals and to assess the potential of EU countries to implement SDG11. The study aims to fill a gap in research on assessing the implementation of SDG11. A unique aspect of the study is the analysis in both unidimensional and multidimensional terms of the indicators that characterize SDG11. The study uses information from the Eurostat database and the Sustainable Development Report on EU Countries in the years for an in-depth assessment of SDG11 implementation: 2015 and 2021.

The structure of our article is as follows: Section 1 defines the main objectives of the work and indicates the motivations for taking up the topic. Section 2 contains a literature review for the evaluation of SDG interactions with a special focus on SDG11. Section 3 of the paper is dedicated to the research method. Section 4 presents the results of the study. Finally, the paper ends with a discussion and conclusions.

2. Literature Review

2.1. Smart Cities

The city of the future is increasingly spoken of today in the smart category. However, the terminology is richer and one can also find such terms for modern cities as smart, innovative, cognitive, or creative [8]. Researchers are devoting a lot of attention to the analysis of the Smart City (SC) concept and the problems associated with it. In fact, there is still no general consensus on the meaning of the term SC or what its descriptive attributes are. However, there is a general consensus that SCs are characterized by the ubiquitous use of information and communication technologies (ICT), which in various urban areas help cities make better use of their resources [9,10].

ICT-based solutions, however, can be considered just one of the various input resources for urban planning and livability projects and approaches that aim to improve the city’s economic, social and environmental sustainability. This means that those cities that are better equipped with ICT systems are not necessarily better, and the number of “smart” initiatives undertaken by the authorities is not an indicator of city performance, but may instead result in an intermediate product that reflects efforts to improve the quality of the life of citizens. Many authors emphasize the greater importance of human capital or social and environmental networks as factors in urban development and revitalization [11,12]. Other studies emphasize the two closely related dimensions of technology and social capital [13]. According to Koviazina and Kucheriavaia [14], approaches to defining a Smart City vary widely and depend on what the smart component is about—the technological or social aspect.

The 21st century has seen a shift from sustainability assessments to smart city goals. A study by Ahvenniemi et al. [15] shows that smart city frameworks place a much greater emphasis on modern technology and “smartness” compared to urban sustainability frameworks. In addition, cities in the sustainable development framework operate with a large number of indicators measuring environmental sustainability, while smart cities lack environmental indicators; instead, social and economic aspects are emphasized. The overall goal of smart cities is to improve sustainability through technology. According to the United Nations, a smart city is described as smart technologies for a sustainable community, enabling the achievement of the 17 Sustainable Development Goals and Agenda 2030 [16].

Cities are centers of innovation that drive the world’s economic development [17]. They play a key role in the fight against climate change, and the implementation of new smart technologies is seen as a key factor in reducing greenhouse gas emissions and improving urban energy efficiency. These technologies must be smart, integrated and resource-efficient, and should not only affect environmental sustainability goals but also the well-being of citizens and financial stability [5]. At the same time, urban development is associated with various problems that tend to threaten the environmental, economic and social sustainability of cities [18,19].

2.2. Interactions between Sustainable Development Goals

SDG goals are multidimensional and integrated with each other; progress toward one goal is also linked through complex feedback to other goals. A quantitative analysis of the interactions between different sustainability goals (which is one of the most pressing research priorities related to the SDGs) can reveal their nature and provide decision-makers with more detailed information [20,21].

Studies of the interaction between the Sustainable Development Goals are being conducted by a number of authors at the global, regional and local levels. For the former, one can cite, for example, a study by Yuan et al. [22] of 163 countries around the world that reported progress toward each SDG and a study by Kroll et al. [7] that analyzed the 17 goals and the interactions between them for 193 countries around the world. Research at the local level has been conducted by Wang et al. [23], among others, evaluating interactions between the Sustainable Development Goals for nutrient water pollution in China, and Egbende et al. [24], who examined interactions between the Sustainable Development Goals and health in the Democratic Republic of Congo. Also worth citing is a study by Warchold et al. [25], which found that SDG interactions vary by regional and national income, as well as gender, age and population location. In the remainder of this article, abbreviations for individual SDGs are used;

Table A1. The 17 Sustainable Development Goals.

SDG	Short Title	Description
1	No poverty	End poverty in all its forms everywhere
2	Zero hunger	End hunger, achieve food security and better nutrition and promote sustainable agriculture
3	Good health and well-being	Ensure healthy lives and promote well-being for all people of all ages
4	Quality education	Ensure high-quality education for all and promote lifelong learning
5	Gender equality	Achieve gender equality and empower women and girls
6	Clean water and sanitation	Ensure access to water and sanitation for all people through sustainable management of water resources
7	Affordable and clean energy	Provide everyone with access to sources of stable, sustainable and modern energy at an affordable price
8	Decent work and economic growth	Promote stable, sustainable and inclusive economic growth, full and productive employment and decent work for all people
9	Industry, innovation, and infrastructure	Build stable infrastructure, promote sustainable industrialization and support innovation
10	Reduced inequalities	Reduce inequalities within and between countries
11	Sustainable cities and communities	Make cities and human settlements safe, stable, sustainable and socially inclusive
12	Responsible consumption and production	Ensure sustainable consumption and production patterns
13	Climate action	Take urgent action to combat climate change and its effects
14	Life below water	Protect and use oceans, seas and marine resources sustainably
15	Life on land	Protect, restore and promote the sustainable use of terrestrial ecosystems, sustainable forest management, combat desertification, stop and reverse land degradation and halt biodiversity loss
16	Peace, justice and strong institutions	Promote peaceful and inclusive societies, ensure access to justice for all people, and build effective, accountable and inclusive institutions at all levels
17	Partnerships for the goals	Strengthen implementation measures and revitalize the global partnership for sustainable development

(in Appendix A) presents their full name and characteristics.

To analyze SDG interactions, synergies and trade-offs must be identified. A significant positive correlation between a pair of SDG indicators is classified as a synergy, while a significant negative correlation is classified as a trade-off. Evaluating synergies and trade-offs between pairs of SDGs globally and nationally helps identify the most common SDG interactions.

The first full quantitative analysis of synergies and trade-offs within and between the Sustainable Development Goals was conducted by Pradhan et al. [26]. According to these authors, for most countries, between pairs of indicators for a specific Sustainable Development Goal, positive correlations outweigh negative ones. As an example, the authors cite SDG1, which is positively correlated with most of the Sustainable Development Goals, such as SDG3, SDG4, SDG5, SDG6 and SDG10, while SDG3 is highly consistent with progress on SDG1, SDG4, SDG5, SDG6 and SDG10. In contrast, SDG12 shows compromises with SDG10, SDG1, SDG6, SDG3, SDG4, SDG5 and SDG2. Other authors have conducted similar studies. Moyer and Bohl [27] presented three alternative development paths (technology, lifestyle change and decentralized governance) to achieve sustainability goals related to social development. In their view, these alternative development paths lead to synergies that enhance goal achievement, while others lead to trade-offs. The authors of a further study [28] studied trade-offs and synergies between SDG7 and other goals.

The results of Fonseca et al. [29] confirm that SDG1 and SDG3 have synergistic links with most other goals. SDG7 has significant links with, for example, SDG1, SDG2, SDG3, SDG8 and SDG13. However, there is a moderate negative correlation with SDG12, which requires improving energy efficiency, increasing the share of clean and renewable energy and improving sustainable consumption patterns worldwide. It was also confirmed that SDG12 is a goal strongly associated with many compromises.

Yang et al. [30] examined how experts from various countries assess the implementation of the SDGs. In their opinion, the number of priority synergies between the SDGs and the ES far exceeded the trade-offs. The greatest synergies were observed for SDG1, mainly with SDG2, SDG3, SDG5 and SDG8.

A study by Bie et al. [31] referring to Arctic countries indicates that the SDG1, SDG3, SDG9, SDG10 and SDG11 sustainability goals had a relatively higher percentage of synergies, while a higher percentage of trade-offs were observed for SDG pairs 8-9, 8-11, 3-12 and 10-12.

2.3. Sustainable Cities and Sustainable Energy

Modern, smart cities require a lot of energy. In urban areas, there is an increase in its consumption, especially from fossil fuels, which are mostly used in transport, residential and commercial buildings and industry. Energy systems powered by fossil fuels have a negative impact on air pollution. Hence the move towards sustainable energy that does not affect nature once it is consumed. There are many sources of such energy, including solar, wind, hydro and biomass, but these have their own limitations, such as energy density and rapid production [32].

In any sustainable city, energy infrastructure plays a major role in the production and distribution system. The challenge of building infrastructure using modern methods that consume little energy and have a low environmental impact is a challenge that smart cities have to face. Tackling climate change and other environmental problems requires the development of ‘smart buildings’ and a more efficient transport system [33]. In this regard, the role of clean energy is extremely important to ensure the comfort and well-being of citizens. In addition, ensuring high safety and quality of urban services in many sectors, such as housing, telecommunications, mobility, transport, telecommunications, lighting and leisure, are also important factors for residents [34]. Thornbush and Golubchikov [35] conducted a study on the importance of the city–energy–sustainability nexus based on smart energy cities. Through a combination of the development of ICT-based smart cities and sustainable energy, the concept of an energy-smart city has moved closer to the low-carbon city variant. According to Wyrwicka et al. [36], the transformation of energy systems in smart cities is a multifaceted process that requires careful consideration of sustainability, policy and technology and their interconnection with the natural and social environment. The authors observed that there are strong links between research on smart cities and sustainability, energy efficiency, climate change, etc. Regardless of the research center, it is possible to indicate a scheme for conducting research. Researchers recognize smart city indicators in their regional environment and then conduct increasingly detailed research on prosumer attitudes, smart grids, smart grids and artificial intelligence in different development contexts.

Achieving SDG11 will not only make cities and societies sustainable but will also contribute to other goals. This is because the 2030 Agenda is considered a system of interacting elements that is more than a set of goals, targets and indicators. According to the United Nations Association Poland [37] report, cities today are at the center of sustainable development policy, especially in the spheres of education (SDG4), healthcare (SDG3), industry and infrastructure (SDG9), or combating climate change (SDG13). SDG11, which revolves around sustainable cities and communities, brings all of the above elements together into one whole.

Sustainable urban water management directly contributes to SDG11 and SDG6. This interaction between SDG11 and SDG6 was highlighted in a recent study on sub-Saharan cities, which found that decentralized water management would be part of sustainable cities in terms of providing clean water [38]. Similarly, energy-efficient buildings, which are essential for cities to be sustainable, directly contribute to SDG7 and SDG13 [39].

Brelsford et al. [40] showed that larger cities have better economic productivity (SDG8), but also pose environmental challenges to achieving, for example, SDG13, SDG14, SDG15 and SDG10. Rocha et al. [41], conversely, noted that healthier lifestyles and the availability of medical assistance are disproportionately higher in larger cities (SDG3), and deaths from non-communicable diseases are relatively less frequent, despite the fact that there is a higher prevalence of these diseases. According to Sakketa [42], achieving SDG11 plays an important role in achieving other sustainable development goals, such as SDG1, SDG3, SDG5 and SDG8. Moreover, after the first decade of the 21st century, sustainability has become a major issue in urban planning, as well as the integration of cities into rural development.

The progress of each region (including cities) in many dimensions (economic, environmental and social), including urban areas, is undoubtedly influenced by the achievement of the SDG7 goals. Energy is inextricably linked to many key aspects of human life, and shortages of energy supply have always been an obstacle to human and economic development. Without it, it will not be possible to reduce poverty, end hunger, boost education, improve health, increase water supply and industrialization and fight climate change [43].

There are links (positive and negative) between the use of renewable energy sources that can have a significant impact on the development of communities. However, there is a certain risk associated with energy prices, leading some households to give up access to the energy grid from renewable energy sources [44].

In order to reduce pollution and carbon intensity and to protect natural resources, green spaces and environmentally friendly practices are being incorporated into the urban environment. Ullah et al. [45], conducting research in six of Australia’s most populous cities, identified factors that affect the economic and environmental viability of smart cities. In their opinion, the most important of these are environmental management, land use and modernization.

Smart cities need to address the issue of building their infrastructure using cutting-edge technologies that use energy efficiently and have minimal impact on the environment. Creating “smart buildings” and a more efficient transportation system is critical in the fight against climate change and other environmental issues [46]. Urbanization and population density have made it more challenging for cities to control energy consumption and environmental impact. Future urban environments are projected to be dominated by carbon emissions from transport systems, and energy demand and consumption are expected to change dramatically. It is important to minimize the consumption of energy from non-renewable sources and reduce energy demand through energy-efficient and environmentally friendly technologies.

It is, therefore, important to study the interaction between sustainable cities (SDG11) and sustainable clean energy (SDG7). This kind of research takes place in different regions of the world. A total of 35 experts assessed 240 interactions between the 17 SDGs in the Democratic Republic of Congo. Experts saw SDG11 as a weak constraint to progress towards the environment-related SDGs. This weak constraint may reflect a trade-off between interest in economic development and industrialization and environmental protection and combating climate change [24]. Pakkan et al. [47] studied the interactions between the 17 SDGs in India and found that there is a significant positive correlation between SDG7 and 11.

Fader et al. [48], analyzing synergies and trade-offs between the Sustainable Development Goals on water, energy and food, noted that SDG7 (strengthening international cooperation to facilitate access to clean energy) does not conflict with other goals and is characterized by varying levels of synergy with most goals.

A review of the literature on smart cities and the Sustainable Development Goals by Blasi and Ganzaroli [49] shows that the concept of a smart, sustainable city is still in its early stages. Smart cities are still understood as the field of smart technology applications to improve the efficiency of managing major urban processes. The Sustainable Development Goals, conversely, are still seen as a set of largely independent goals, rather than an overall system to be achieved through urban development.

3. Methods

In the decision-making process, an important issue is to identify the optimal solution or ordering of a set of objects with regard to the values of one or more criteria. In the case of multicriteria decision-making (MCDM), the problem becomes difficult to solve. One of the many methods that allow for a reasonable solution of a decision-making problem with a finite number of decision variants and a finite number of criteria is the TOPSIS method. It has been developed as a tool to support the decision-making process in complex situations where many different criteria need to be taken into account. The main advantages of this method are simplicity, comprehensibility and easy interpretation of the results.

In this paper, the TOPSIS method was applied to the linear ordering of EU countries, taking into account many criteria. This method is a modification of the distance calibration method proposed and described by Hwang and Yoon [50] in 1981. It differs from the classic distance method by introducing an anti-pattern called the anti-ideal reference solution, while the standard is called the ideal reference solution. Between each decision variant and the ideal and anti-ideal solution, distances are counted, on the basis of which the value of the measure is determined [51,52,53,54]. The best object is considered to be the one that has the smallest distance from the ideal solution and at the same time the largest distance from the anti-ideal solution. The TOPSIS method is successfully used in decision-making issues to determine the best variant and to determine the ranking of variants. The procedure for calculating a synthetic variable using the TOPSIS method is as follows:

Stage 1. Selection of diagnostic features in accordance with substantive and statistical premises, divided into stimulants and destimulants.

Stage 2. Data Matrix Design:

$$X=\left[x_{ij}\right]$$

(1)

where:

• i—object number (i = 1, 2, …, n);
• j—diagnostic feature number (j = 1, 2, …, m);
• $x_{ij}$—the value of the j-th feature for the i-th object.

Stage 3. Normalization of diagnostic features:

$$z_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{\sum _{i=1}^n{x}_{ij}^2}}}$$

(2)

where:

• $z_{ij}$—the value of the j-th standardized diagnostic feature for the i-th object.

Stage 4. Determining weighting factors for indicators:

$$V=\left[v_{ij}\right]=\left[{w_{j}z}_{ij}\right]$$

(3)

for:

$$\sum _{j=1}^m{w}_j=1$$

(4)

where:

• $v_{ij}$—coefficient of variation of the j-th feature for the i-th object;
• $w_j$—weight of the j-th diagnostic feature.

Stage 5. Setting coordinates of Positive Ideal Solution and Negative Ideal Solution coordinates, respectively:

$$v_j^{+}=\left\{\begin{array}{c}\begin{array}{cc}\underset{i}{\max }{v}_{ij}& \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\end{array}\\ \begin{array}{cc}\underset{i}{\min }{v}_{ij}& \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{d}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\end{array}\end{array}\right.$$

(5)

$$v_j^{-}=\left\{\begin{array}{c}\begin{array}{cc}\underset{i}{\min }{v}_{ij}& \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\end{array}\\ \begin{array}{cc}\underset{i}{\max }{v}_{ij}& \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{d}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\end{array}\end{array}\right.$$

(6)

where:

• $v_j^{+}$—j-th coordinate of Positive Ideal Solution;
• $v_j^{-}$—j-th coordinate of Negative Ideal Solution.

Stage 6. Calculation of the Euclidean distances from the positive and negative ideal values:

$$d_i^{+}=\sqrt{\sum _{j=1}^m{\left(v_{ij}-v_j^{+}\right)}^2}$$

(7)

$$d_i^{-}=\sqrt{\sum _{j=1}^m{\left(v_{ij}-v_j^{-}\right)}^2}$$

(8)

where:

• $d_i^{+}$—Euclidean distance of the i-th object from Positive Ideal Solution;
• $d_i^{-}—$Euclidean distance of the i-th object from Negative Ideal Solution.

Stage 7. Determining the value of the aggregate variable, meaning the relative proximity of the i-th object to the Positive Ideal Solution:

$$R_i={\displaystyle \frac{d_i^{-}}{d_i^{-}+d_i^{+}}}$$

(9)

where: $0\le R_i\le 1$.

The preferred object has the shortest distance from the positive ideal value and, at the same time, the most significant distance from the negative ideal value, i.e., it has the highest value of the coefficient R_i.

Stage 8. Linear ordering of objects performed due to the aggregate variable’s non-increasing value (9).

The TOPSIS method ensures that the survey is multidimensional. It makes it possible not only to rank individual countries but also to determine the distance of each country from the abstract country with the most favorable values of individual indicators. Of course, one should always be aware of the limitations of the method used, especially taking into account the initial set of diagnostic characteristics and the way that they are normalized.

After obtaining the linear ordering of objects, it is possible to determine the relationship between the positions occupied by individual objects due to the examined criteria. The assessment of the relationship is made both in terms of strength and direction. Measures such as Sperman’s rank correlation coefficient or Kendall’s $\tau$ rank correlation coefficient can be used for this purpose. The main difference between these coefficients is that Spearman’s rank correlation coefficient indicates the correlation between two sets of ranks and Kendall’s $\tau$ rank correlation coefficient is based on the number of rank inversions [55]. In addition, Kendall’s $\tau$ coefficient is more robust to extreme values, i.e., it is not as sensitive to outlier observations. In the present study, Kendall’s $\tau$ correlation coefficient was used to assess the relationship between the obtained country rankings due to the realization of SGDs. The choice of coefficient was due to the large number of inversions observed in the rankings.

4. Results

The 97 indicators that make up each SDG were used to build rankings of EU countries in 2015 and 2021 in terms of the degree of SDG achievement. SDG14 was not included in the study due to the lack of data for EU countries. A taxonomic measure was determined for each EU country and each SDG, based on which country rankings were made. Sweden was ranked first, leading the way in 2015 in terms of SDG3, SDG5, SDG10 and SDG13, while in 2021, it led in terms of SDG4, SDG6, SDG8 and SDG13. Countries such as Denmark, Finland and Austria generally ranked in the top positions. By contrast, Bulgaria, Romania and Hungary ranked last in every year studied. EU countries vary in their degree of achievement of the SDGs. Achieving some SDGs results in a higher ranking and others in a lower ranking, e.g., in 2015, France ranked second in terms of achieving SDG12 and third in terms of achieving SDG13 but ranked 17th in terms of achieving SDG9 and 18th considering the achievement of SDG15. In contrast, in 2021, Poland ranked 7th in terms of SDG1 implementation, 16th in terms of SDG3 implementation, 27th in terms of SDG9 implementation and 22nd in terms of SDG2 implementation.

Some countries have significantly improved their rankings. Such countries include, for example, Poland (SDG1—from 20th to 7th place), Portugal (SDG4—from 21st to 9th), Ireland (SDG7—from 12th to 3rd), Greece (SDG10—from 20th to 4th) and Slovenia (SDG15—from 24th to 7th). In addition to the improvement in the position of some countries in the implementation of the SDGs, there is also a large deterioration in the implementation of the SDGs reflected in the decline in country rankings. Examples include France (SDG1—from 4 to 17), Cyprus (SDG2—from 6 to 17), the Czech Republic (SDG4—from 10 to 21), Bulgaria (SDG5—from 9 to 19) and Latvia (SDG17—from 7 to 23). The largest number of countries (15) improved their position in terms of implementation of SDG5 and SDG13—13 countries). In the case of SDG3, SDG7, SDG11, SDG12 and SDG15, at least 17 countries did not worsen their position in the rankings.

Table 1. Positions of countries in terms of SDG implementation in 2015.

Countries	SDG
	1	2	3	4	5	6	7	8	9	10	11	12	13	15	16	17
Austria	1	8	17	7	14	6	5	10	12	2	11	5	13	10	6	17
Belgium	13	24	5	18	2	10	23	5	8	9	9	14	15	25	10	5
Bulgaria	25	12	20	27	9	24	25	18	24	25	26	26	12	8	24	25
Croatia	14	13	16	11	11	12	4	24	21	17	22	12	7	6	14	24
Cyprus	10	25	6	9	19	26	26	19	26	7	14	17	20	16	22	21
Czechia	3	2	10	10	22	15	17	11	15	12	18	9	23	17	17	23
Denmark	12	9	3	1	4	13	1	1	2	4	4	4	14	23	5	4
Estonia	24	1	25	16	24	20	6	14	25	22	2	27	19	3	26	18
Finland	9	11	8	3	3	5	20	4	5	10	1	24	4	1	7	12
France	4	20	13	5	5	8	10	9	17	14	6	2	3	18	12	14
Germany	15	16	4	15	10	11	14	8	6	6	13	7	2	24	4	13
Greece	27	4	23	19	21	14	22	27	20	20	23	21	17	9	21	10
Hungary	21	18	18	22	18	18	18	13	16	18	25	19	18	21	19	19
Ireland	16	22	9	13	12	1	12	7	11	11	3	15	27	20	3	22
Italy	22	6	21	25	26	16	16	25	23	23	21	1	9	19	20	11
Latvia	23	7	27	20	17	25	7	22	9	21	20	8	11	4	27	7
Lithuania	19	14	24	12	7	23	24	15	14	19	19	16	5	12	25	16
Luxembourg	7	23	11	6	16	2	27	6	1	5	7	25	26	22	1	2
Malta	5	27	7	24	27	21	21	16	13	3	16	20	22	27	15	26
Netherlands	11	26	2	4	8	9	19	3	4	8	8	3	25	26	8	1
Poland	20	19	19	14	23	19	11	21	27	16	24	10	21	15	9	27
Portugal	17	15	22	21	13	22	15	20	22	24	15	13	10	11	16	8
Romania	26	17	26	26	25	27	3	23	19	27	27	22	6	14	23	15
Slovakia	8	3	15	17	15	7	13	17	18	15	10	18	16	7	18	20
Slovenia	2	21	12	8	6	4	8	12	7	13	17	11	24	5	11	9
Spain	18	10	14	23	20	17	9	26	10	26	12	6	8	13	13	6
Sweden	6	5	1	2	1	3	2	2	3	1	5	23	1	2	2	3

and

Table 2. Country positions in terms of SDG achievement in 2021.

Countries	SDG
	1	2	3	4	5	6	7	8	9	10	11	12	13	15	16	17
Austria	3	15	11	10	13	7	6	10	9	3	7	8	9	11	4	16
Belgium	13	24	5	13	1	13	23	8	7	6	10	16	16	25	7	2
Bulgaria	25	10	21	27	19	25	25	24	24	26	26	27	11	7	23	27
Croatia	6	12	18	11	10	10	8	18	19	17	22	12	6	5	18	25
Cyprus	2	26	17	20	22	27	24	21	25	1	12	17	18	15	24	18
Czechia	1	1	10	21	17	19	19	9	13	13	16	9	25	17	13	20
Denmark	20	4	4	5	11	8	1	2	1	14	5	4	20	23	5	7
Estonia	24	2	25	8	20	21	4	15	26	21	3	26	27	3	26	26
Finland	14	9	12	2	6	5	22	4	4	11	1	25	17	1	9	13
France	17	19	15	15	9	9	10	13	10	12	9	3	3	18	20	9
Germany	15	18	3	26	18	6	11	7	6	8	8	7	2	24	2	5
Greece	27	5	23	14	23	20	21	26	20	4	23	11	14	8	21	8
Hungary	10	17	14	25	26	14	20	12	12	22	24	19	15	20	17	17
Ireland	12	21	7	6	15	4	3	5	8	7	4	5	26	21	3	14
Italy	21	7	19	23	27	15	12	27	21	25	20	2	13	19	10	11
Latvia	22	8	27	17	21	24	5	22	17	23	21	13	23	4	27	23
Lithuania	18	16	24	12	5	23	26	19	16	20	17	20	5	12	25	19
Luxembourg	8	23	6	7	8	2	27	6	3	2	6	24	19	22	1	3
Malta	4	27	8	16	25	22	17	14	14	15	19	18	21	27	15	15
Netherlands	9	25	1	3	7	12	15	3	5	5	11	1	24	26	6	1
Poland	7	22	16	18	14	17	14	16	27	16	25	21	22	16	12	24
Portugal	19	11	22	9	4	18	13	20	23	19	15	14	10	10	11	12
Romania	26	13	26	24	24	26	9	23	22	27	27	22	4	14	22	21
Slovakia	11	3	13	22	12	11	18	17	18	18	18	15	12	9	16	22
Slovenia	16	20	20	4	3	3	7	11	11	10	13	10	7	6	8	4
Spain	23	14	9	19	16	16	16	25	15	24	14	6	8	13	19	10
Sweden	5	6	2	1	2	1	2	1	2	9	2	23	1	2	14	6

present the positions of countries in terms of SDG implementation in 2015 and 2021.

Analyzing the positions of countries, it can be seen that France, Germany, the Netherlands, Sweden and Austria achieve high positions (above thirteenth place) in the rankings in terms of the realization of the SDGs, while Romania, Poland, Hungary and Bulgaria only ranked higher than thirteenth for at most four SDGs.

The next stage of the study was to assess the correlations between the degree of SDG implementation by individual EU countries. Based on the established rankings of EU countries for each pair of SDGs, the values of Kendall’s rank correlation coefficients were determined (

Table A2. Kendall’s coefficient values between SDGs in 2015.

SDG	Kendall’s Coefficient Values between SDGs in 2015
	1	2	3	4	5	6	7	8	9	10	11	12	13	15	16	17
1	1.000	−0.157	0.436	0.476	0.271	0.447	0.020	0.390	0.328	0.556	0.345	0.140	−0.100	−0.094	0.396	0.060
2	−0.157	1.000	−0.288	−0.031	−0.100	−0.048	0.242	−0.197	−0.111	−0.236	−0.003	−0.003	0.179	0.470	−0.202	0.009
3	0.436	−0.288	1.000	0.402	0.356	0.419	−0.077	0.544	0.447	0.652	0.419	0.134	−0.083	−0.316	0.573	0.168
4	0.476	−0.031	0.402	1.000	0.453	0.425	0.123	0.538	0.373	0.544	0.493	0.151	−0.077	0.043	0.453	0.208
5	0.271	−0.100	0.356	0.453	1.000	0.368	0.031	0.493	0.385	0.293	0.333	0.094	0.242	0.066	0.316	0.288
6	0.447	−0.048	0.419	0.425	0.368	1.000	0.105	0.430	0.425	0.425	0.453	0.077	−0.071	−0.02	0.595	0.225
7	0.020	0.242	−0.077	0.123	0.031	0.105	1.000	0.003	0.077	−0.071	0.094	0.231	0.208	0.271	0.134	0.060
8	0.390	−0.197	0.544	0.538	0.493	0.43	0.003	1.000	0.550	0.573	0.521	0.009	−0.094	−0.214	0.470	0.248
9	0.328	−0.111	0.447	0.373	0.385	0.425	0.077	0.550	1.000	0.442	0.390	0.105	0.037	−0.14	0.510	0.481
10	0.556	−0.236	0.652	0.544	0.293	0.425	−0.071	0.573	0.442	1.000	0.390	0.060	−0.145	−0.242	0.533	0.083
11	0.345	−0.003	0.419	0.493	0.333	0.453	0.094	0.521	0.390	0.390	1.000	0.043	−0.048	−0.009	0.425	0.282
12	0.140	−0.003	0.134	0.151	0.094	0.077	0.231	0.009	0.105	0.060	0.043	1.000	0.054	−0.236	0.162	0.123
13	−0.100	0.179	−0.083	−0.077	0.242	−0.071	0.208	−0.094	0.037	−0.145	−0.048	0.054	1.000	0.219	−0.054	0.134
15	−0.094	0.470	−0.316	0.043	0.066	−0.020	0.271	−0.214	−0.140	−0.242	−0.009	−0.236	0.219	1.000	−0.219	−0.054
16	0.396	−0.202	0.573	0.453	0.316	0.595	0.134	0.470	0.510	0.533	0.425	0.162	−0.054	−0.219	1.000	0.219
17	0.060	0.009	0.168	0.208	0.288	0.225	0.060	0.248	0.481	0.083	0.282	0.123	0.134	−0.054	0.219	1.000

and

Table A3. Kendall’s coefficient values between SDGs in 2021.

SDG	Kendall’s Coefficient Values between SDGs in 2021
	1	2	3	4	5	6	7	8	9	10	11	12	13	15	16	17
1	1.000	−0.299	0.350	0.105	0.134	0.242	−0.066	0.356	0.208	0.442	0.157	0.003	−0.100	−0.168	0.236	0.009
2	−0.299	1.000	−0.219	−0.009	−0.105	−0.077	0.197	−0.145	−0.145	−0.299	−0.003	−0.054	−0.020	0.447	−0.197	−0.242
3	0.35	−0.219	1.000	0.208	0.225	0.470	0.026	0.584	0.561	0.407	0.396	0.219	−0.088	−0.442	0.544	0.464
4	0.105	−0.009	0.208	1.000	0.493	0.385	0.225	0.453	0.362	0.345	0.516	0.043	−0.117	0.157	0.219	0.311
5	0.134	−0.105	0.225	0.493	1.000	0.390	0.026	0.356	0.311	0.271	0.339	0.026	0.174	0.105	0.214	0.282
6	0.242	−0.077	0.470	0.385	0.390	1.000	0.248	0.521	0.578	0.390	0.493	0.202	0.134	−0.071	0.573	0.413
7	−0.066	0.197	0.026	0.225	0.026	0.248	1.000	0.157	0.100	−0.077	0.208	0.271	0.054	0.134	0.128	0.003
8	0.356	−0.145	0.584	0.453	0.356	0.521	0.157	1.000	0.658	0.459	0.527	0.066	−0.117	−0.208	0.470	0.333
9	0.208	−0.145	0.561	0.362	0.311	0.578	0.100	0.658	1.000	0.379	0.527	0.225	0.043	−0.254	0.504	0.527
10	0.442	−0.299	0.407	0.345	0.271	0.390	−0.077	0.459	0.379	1.000	0.385	0.140	−0.077	−0.168	0.419	0.407
11	0.157	−0.003	0.396	0.516	0.339	0.493	0.208	0.527	0.527	0.385	1.000	0.117	−0.066	−0.020	0.362	0.362
12	0.003	−0.054	0.219	0.043	0.026	0.202	0.271	0.066	0.225	0.140	0.117	1.000	0.020	−0.299	0.242	0.311
13	−0.100	−0.020	−0.088	−0.117	0.174	0.134	0.054	−0.117	0.043	−0.077	−0.066	0.020	1.000	0.168	−0.077	0.117
15	−0.168	0.447	−0.442	0.157	0.105	−0.071	0.134	−0.208	−0.254	−0.168	−0.02	−0.299	0.168	1.000	−0.339	−0.339
16	0.236	−0.197	0.544	0.219	0.214	0.573	0.128	0.470	0.504	0.419	0.362	0.242	−0.077	−0.339	1.000	0.453
17	0.009	−0.242	0.464	0.311	0.282	0.413	0.003	0.333	0.527	0.407	0.362	0.311	0.117	−0.339	0.453	1.000

). Positive values indicated synergies and negative values indicated trade-offs. Among the 128 values of Kendall correlation coefficients, 53 coefficient values (41.4%) indicated significant interactions in 2015. Among the significant values of Kendall coefficients, as many as 49 are positive linkages (synergies) and only 4 are negative linkages (trade-offs). In 2021, the number of significant linkages was also 53, of which 47 were positive linkages. The higher number of positive linkages between SDGs compared to the number of negative linkages is highlighted in the literature as a result of many studies.

The most significant linkages in 2015 were among countries in terms of meeting such targets as SDG1, SDG3, SDG4, SDG5, SDG8, SDG9 and SDG10. In 2021, conversely, it is noted that there are many significant correlations between the positions of EU countries also in terms of meeting other targets, namely, SDG3, SDG6, SDG8, SDG9, SDG10 and SDG11. In 2021, the number of linkages of countries’ positions in terms of achieving SDG1, SDG4 and SDG5 with other SDGs had decreased. There were significant linkages in terms of the implementation of SDG6 and SDG11 with other SDGs. In 2015, the positions of EU countries in terms of achieving SDG11 were significantly correlated with the positions of countries in terms of achieving targets such as SDG1 (compromise), SDG3 (synergy), SDG4 (synergy), SDG6 (synergy), SDG8 (synergy), SDG9 (synergy), SDG10, SDG16 (synergy) and SDG17 (synergy). In contrast, in 2021, there was no significant linkage of EU countries’ positions due to the implementation of SDG11 and SDG1.

Linking SDG11 to SDG1 is important for urban residents, as sustainable cities provide them with better living conditions resulting in reduced poverty and social inequality. SDG11 activities also contribute to the achievement of SDG3 by providing space for physical activity, which translates into the achievement of SDG7 by lowering urban temperatures [56]. The relationship between different types of economic activities is also an important issue, which stems from the connection between SDG8 and other SDGs including SDG11. Most economic activities show a positive association with SDG11. The literature [57] indicates that, for example, appropriate materials and technologies used in housing construction and real estate activities provide access to adequate, safe and affordable housing.

In the next step of the analysis, special attention was paid to the ordering of EU countries for 2015 and 2021 in terms of SDG11 achievement. The ordering of countries using the TOPSIS method was determined on the basis of the values of sub-indicators characterizing SDG11 from the Eurostat database.

The leading positions in the rankings of EU countries in terms of SDG11 implementation were taken in 2015 by Finland, Estonia and Ireland, and in 2021 by Finland, Sweden and Estonia (Table 1). The last places were taken in 2015 by Romania, Bulgaria and Hungary, and in 2021 by Romania, Bulgaria and Poland, the latter of which in 2015 was also in one of the last places—24.

In line with the SDG11 objectives of the 2030 Agenda, cities and human settlements should be made safe, resilient, sustainable and inclusive (17 Goals Campaign, 2023). The implementation of these objectives is particularly important in relation to SDG7 because energy is at the heart of the SDG implementation and residents should be provided with affordable, reliable, sustainable and modern energy.

Accordingly, in our research, we pay special attention to the implementation of SDG11 in relation to SDG7. The value of the Kendall rank correlation coefficient indicates the existence of a very weak relationship between the degree of SDG7 and SDG11 implementation in EU countries in 2015. However, in the next year of the analysis—2021—the value of the Kendall rank correlation coefficient increased significantly, which proves the increased importance of clean and sustainable energy in urban development. Analyzing the positions of individual EU countries in terms of achieving SDG7 and SDG11, it can be seen that in 2015 they were very diverse. Only Denmark and Sweden ranked high in both SDG7 and SDG11. Some countries (e.g., Bulgaria, Greece and the Czech Republic) are in the lowest places for both rankings. However, most countries achieve the SDG7 and SDG11 goals to a very different extent, e.g., Finland is in a distant twentieth position in the SDG11 ranking, but in the SDG7 ranking, in the first position. The situation indicating the growing importance of clean and sustainable energy in urban development has changed, which can be seen in the next year of the survey—2021. Several EU countries, such as Austria, Estonia, Ireland and Slovenia, stand out due to the improvement in their positions in the implementation of SDG7 and SDG11. Especially in the case of Ireland, a large improvement in the implementation of the SDGs can be noticed, from position 12 in 2015 to 3rd in 2021 in the ranking relating to the degree of implementation of SDG7. However, in the case of the ranking showing the implementation of SDG11, Ireland ranks high at 4th. Sweden and Denmark continue to rank high in 2021 in both analyzed country cleanups. Sweden reached second place in both 2015 and 2021 in both rankings.

5. Discussion

The Sustainable Development Goals (SDGs) are conceived as an “indivisible whole”. However, as research has shown, so far, the implementation of individual SDGs can be based on synergy (progress on one goal promotes progress on another) or trade-off (progress on one goal hinders progress on another). Assessing potential synergies and trade-offs between the SDGs is considered to be one of the most urgent research priorities for the SDGs.

A lot of space in the scientific literature is devoted to regional research, e.g., concerning a group of countries pursuing a common socio-economic policy and living in a specific territory. This kind of research has also been carried out in this study. The implementation of the SDGs varies greatly from country to country due to a variety of factors, including social, economic and environmental and political and institutional. However, it is important to stress that this is largely the result of actions taken by governments that promote the achievement of two or more SDGs at the same time. That is why it is so important to conduct research to understand how individual EU countries are implementing the SDGs and how these goals are linked.

The linear ordering of objects carried out in the article using the TOPSIS method showed that France, Germany, the Netherlands, Sweden and Austria achieved high positions (above the thirteenth position) in the rankings in terms of the implementation of the SDGs, while Romania, Poland, Hungary and Bulgaria ranked higher than that only in the case of at most four SDGs. The results of our research are consistent with those of other authors [58,59,60]. It should also be noted that most member states improved their position in 2021 compared to 2015. Similar conclusions can be drawn from the study [61] on the Sustainable Development Goals 2010–2020, which suggest that all 27 member states have made positive progress, although not to the same extent, and differences between countries seem to be narrowing over the years.

The research carried out in this article also shows that the achievement of the objectives in most EU countries manifested synergies between them. The higher number of positive links between the SDGs compared to the number of negative links is also highlighted in the literature as a result of many studies on the study of SDGs in EU countries [5,62,63]. A similar pattern was observed in the Arctic countries, where synergies also prevailed over compromises.

The Sustainable Development Goals (SDGs) are an “indivisible whole”, which means that they are interconnected and should be implemented in an integrated manner. Recognizing and understanding the interactions between the different goals is key to successfully implementing sustainability strategies [64]. In the works indicated in the literature review and many others, it is indicated that positive links (synergies) prevail between the objectives pursued in given countries. With regard to SDG7, in [25,26], synergies but also trade-offs that relate to SDGs 8, 9, 12 and 15 have been observed. In this work, it was obtained that SDG7 shows trade-offs with SDG1, SDG3 and SDG10. Conversely, in [63], it was found that there are no significant dependencies between SDG7 and the other SDGs.

In the assessment of sustainable development of countries or selected aspects of it, the linear ordering of objects is often used and the relationships between the positions of countries in terms of the implementation of the SDGs are determined using different correlation measures [26,34,63]. In this study, the Kendall rank correlation coefficient was used due to the existence of many inversions between the positions occupied by countries, while in [29], Spearman’s rank correlation coefficient was used to assess the associations and the authors found that there is a predominance of positive over negative interactions between the SDGs.

While there is much research in the area of interaction between SDGs, little attention has been paid to the links between actions taken to achieve SDG11 and their relationship to other goals. Two studies [40,42] highlight that achieving SDG11 plays an important role in achieving the other Sustainable Development Goals (SDG1, SDG3, SDG5). Our research also confirms the existence of a correlation between SDG11 and other SDGs, for example, with SDG4, SDG6 and SDG8, and at the same time, we emphasize the existence of connections between SDG11 and SDG7. Access to clean energy is an important element of economic and social development and contributes to improving the quality of life of people. The use of renewable energy is a key element in the pursuit of sustainable development, which has a direct impact on several SDGs in particular SDG7.

Modern cities play a key role in sustainable development, which is clearly reflected in SGD11 of the 2030 Agenda. Therefore, in the next step of the analysis, special attention was paid to the ordering of EU countries for the years 2015 and 2021 in terms of the implementation of SDG11, whose purpose is to make cities more sustainable, resilient, inclusive and safe. This can be achieved by continuously increasing the achievement of the SDGs in terms of reducing material consumption, reducing energy consumption, mitigating pollution and minimizing waste, as well as improving equity, social inclusion, quality of life and well-being [65]. Therefore, there is a conscious effort to make sustainable cities smarter and thus, more sustainable, through the particular use of new technologies and their novel applications in the hope of achieving an optimal level of sustainability. In this context, sustainable cities around the world have adopted ambitious, smart goals that reach far into the future. According to BerishaF et al. [66], not all SDG11 targets can be easily implemented. Although they are linked to one or more monitoring indicators, achieving them is not simple in some cases.

Cities play a special role in the global drive for sustainable development due to the use of energy. By 2030, the number of urban residents is projected to increase from around 3.5 billion to 5 billion, with a significant increase in energy demand to support greater economic activity, infrastructure expansion and a growing demand for utilities. That is why energy efficiency is such an important issue, as it provides less energy needed for the functioning of cities. In addition, energy efficiency reduces greenhouse gas emissions and mitigates climate change. For these reasons, SDG7 calls for a doubling of the energy efficiency rate globally by 2030 (target 7.3).

Many authors emphasize the connections between SDG7 and other SDGs in their works [26,63], but the research conducted so far does not exhaust the topic of the relationship between the degree of SDG7 and SDG11 implementation. Our study focuses on such an analysis because the role of clean and sustainable energy in cities is important. Municipalities should develop appropriate policy frameworks, including energy efficiency as a priority objective. Energy efficiency requires investment and the involvement of a range of actors, from policymakers and investors to service providers and consumers. To improve energy efficiency in cities, it is crucial to raise awareness among residents of the opportunities offered by energy efficiency. Therefore, cities are essential to achieving SDG7 and energy efficiency is an integral part of SDG11—the goal of making cities inclusive, safe, resilient and sustainable.

Our study shows that the link between SDG7 and SDG11 is becoming more important and increasing. The presented analysis is a proposal to assess the links between the distinguished SDGs and should be continued in the following years, using other quantitative methods and an interdisciplinary approach.

6. Conclusions

The conducted research, using the selected method of multidimensional comparative analysis, complements the discussion on the operationalization of synergies and trade-offs between the degree of implementation of the SDGs. Cities play a special role in achieving the SDGs due to the occurrence of many adverse factors such as noise, air pollution, or smog. The study shows the degree of implementation of SDG11 in EU countries from the perspective of indicators included in the Eurostat database and the Sustainable Development Report. Based on Eurostat data, the highest level of implementation of SDG11 was recorded in 2021 in countries such as Finland, Estonia and Sweden, while the lowest level of implementation of SDG11 took place in Romania, Bulgaria and Poland. Taking into account the data included in the Sustainable Development Report, the leading positions in terms of the degree of implementation of SDG11 were taken by the Czech Republic, Estonia and Luxembourg. The last places in the ranking were taken by such countries as Bulgaria, Cyprus and Croatia. It should be emphasized that the indicators in the two documents are different.

The survey provides policymakers with information on achievements and gaps in the implementation of SDG11 and other SDGs, which is particularly important for low-ranking countries. Policymakers are facing major challenges to show progress towards the SDGs. Studies using multivariate benchmarking can help to identify the monitoring and sequencing of the SDGs.

We attach particular importance to three research conclusions:

• There are more synergies than trade-offs between the SDGs.
• In 2015, the following SDGs showed the most synergies with other SDGs: SDG1, SDG3, SDG4, SDG5, SDG8, SDG9 and SDG10; and in 2021: SDG3, SDG6, SDG8, SDG9, SDG10 and SDG11.
• SDG11 has interactions with many goals, mainly synergies, with the exception of the compromise with SDG1 in 2015 and no interaction with SDG1 in 2021.
• In 2015, a weak relationship was observed between the degree of SDG7 and SDG11 implementation in EU countries. However, in the next year of the analysis—2021—the value of the Kendall rank correlation coefficient increased significantly, which proves the increased importance of clean and sustainable energy in urban development.

SDG11 and SDG7, which are part of the 2030 Agenda adopted by the United Nations, have their own specific challenges and limitations with regard to their assessment and implementation. The limitations of the research are related to the acquisition of data from different sectors and regions and countries and the lack of uniform standards to measure the progress of the SDGs in different cities and countries. Studies of the degree of SDG implementation are therefore limited by the availability of data, which is a major challenge in the process of monitoring the achievement of the SDGs. The existence of data gaps and available indicators for different countries makes it difficult to carry out studies on, for example, the assessment of interactions between the SDGs. In addition, certain limitations are related to the methods used, which do not indicate a cause-and-effect relationship, and therefore comprehensive studies over time using different quantitative methods should be carried out [25,26]. Future research on SDGs, including SDG11 and SDG7, should take into account a variety of challenges and new technologies and approaches. They should combine technologies, policies, urban planning and social engagement. New technologies, innovative financing models and the integration of diverse data and systems will be key to achieving the SDGs [67].