Developing Novel Sustainable-Based Model to Assess Cities’ Performance Using Enviro-Socio-Economic Impact Indicators: A Case Study in Egypt

Abstract

Essential research is required to assist several developing countries in transforming urban areas into sustainable cities by providing an assessment tool that covers the environmental, economic, and social pillars of sustainability. Hence, this study introduces a novel sustainable-based model by assigning scores to the sustainable development goals (SDGs) for maintaining inclusive, safe, and resilient cities in developing nations. Quantitative and qualitative data were collected to describe 50 sustainable indicators of 27 cities, representing Egypt’s diverse urban environments from 2012 to 2022. These indicators and SDG scores were used to classify the cities into “Low”, “Medium”, and “High” based on their progress toward achieving sustainability performance. Most coastal and inland cities depicted a “High” scoring performance regarding the advanced maritime infrastructure, farming market size, and cultural tourism context. Built-up area, population growth, green urban area, and economy were considered the main indicators influencing the SDG#11 “Sustainable cities” annual variation. The SDG-based model was employed to create different scenarios to improve SDG#11 fulfillment, showing the importance of investing in the agribusiness sector to raise the gross domestic product. The study outputs were beneficial in guiding most resource-constrained countries to establish the “Sustainable city” concept under the SDGs umbrella.

1. Introduction

The concept of sustainable cities has recently gained global attention because modern urban projects strive to balance population growth with environmental protection and social equity [1]. These urban projects can be evaluated using some targets and indicators [2], offering a comprehensive framework that covers the sustainability pillars: social, economic, and environmental (see Supplementary Table S1). These sustainability dimensions assess the city’s progress in overcoming the challenges of urbanization accompanied by inadequate public transportation systems, insufficient affordable housing, and air quality deterioration [3]. Furthermore, the use of urban-based indicators provides opportunities for decision-makers to classify the cities into “Low”, “Medium”, and “High” according to their sustainability levels (see Supplementary Table S2) [4]. In this classification procedure, a sustainable development goals (SDGs) framework assigns scores to each city, and then the cities are ranked according to the achievement of effective measurements, such as green urban space expansion, educational facility establishment, and infrastructure installation [5]. The SDG’s performance scores serve as a roadmap for cities, guiding urban planners to identify areas that need water quality improvement, poverty rate reduction, and clean energy utilization [6]. Hence, more research should create SDG-related strategies that can identify and classify the sustainability status in cities using a set of indicators (e.g., water, air, energy, poverty, education workforce, and housing), further achieving the highest sustainability performance scores. This strategy can also enable governments, policymakers, and public sectors to define the essential actions required to strengthen the resident’s quality of life [7].

Creating an SDG scoring model can offer multiple advantages to the cities of different developing countries by assessing diverse socio-economic contexts, such as gender equality [8], financial stability, and population growth [9]. This model can also evaluate a wide range of sustainability challenges, including climate-related events (i.e., Target 1.5: improve flood defenses and emergency shelters), agriculture, children’s learning (i.e., Target 16.6: develop reliable institutions), and food security compared with the conventional and localized sustainable assessment tools [10]. This SDG-based model can be established by defining a set of indicators that describe the three pillars of sustainable development to maintain a better quality of life in cities. These pillars include poverty reduction, “Target 1.2”, biodiversity conservation, “Target 15.1”, greenhouse gas (GHG) emission minimization, “Target 7.a”, and green infrastructure (e.g., shipping, logistics, and port services) establishment, “Target 11.2”. Further, quantitative data on these indicators are gathered from the cities under investigation, getting access to the available governmental reports and online surveys of published research [11]. These steps estimate the scores of SDGs assigned to each city, further defining the cities that require specific actions to develop their national and sub-national planning processes within the SDGs framework.

While recent studies have evaluated the sustainability performance of various cities worldwide (see Supplementary Table S3), their work should be upgraded to establish a reliable SDG-based assessment model designed at the local city level in developing countries. The importance of this model arises from the need for the urban transformation of traditional cities toward the sustainability strategy, overcoming the limitation of rapid unplanned urbanization, limited data accessibility, and natural resource constraints. Egypt was selected as a case study because it represents one of the essential rapidly urbanizing countries implementing national initiatives to promote sustainable urban living [12], particularly under the umbrella of Egypt Vision 2030 and SDG-aligned development plans [13]. While recent studies have defined the “Eco, Green, and Livable cities” concept in Egypt (see Supplementary Table S4), their research outputs should be connected to the SDGs achieved that help decision-makers protect and preserve the country’s cultural and natural heritage and share and compare ideas worldwide. To address this research gap, the current study introduces a comprehensive framework that assigns SDG-based sustainability scores to different cities based on their ability to achieve a global urban development agenda. These scores highly support urban planners in identifying context-specific development scenarios that aim at making human settlements more inclusive, safe, resilient, and sustainable (see the core research questions covered by the study in Supplementary Table S5).

The detailed procedures of the proposed framework are fivefold: (1) define about 50 enviro-socio-economic indicators to illustrate how the cities can contribute to sustainable development, ensuring that the proposed model is generalizable to other countries worldwide, (2) define the essential indicators and targets of each SDG, and estimate the contribution of each goal toward creating sustainable societies, (3) assign SDG scores to each city followed by categorizing these cities according to the scores fulfilled, (4) determine the synergetic interaction between the scoring of SDG#11 and other UN goals, and (5) verify the proposed model using a case study of Alexandria, Egypt, and suggest different scenarios to enhance the sustainability performance. This framework is flexible because it can be continuously updated by modifying the qualitative and quantitative indicators or adding new indicators to ensure that the scoring results remain relevant and effective in various urban scales globally.

2. Literature Review

2.1. Sustainability Assessment Tools for Urban Development

Extensive efforts have recently been exerted by scientific societies to quantify urban growth changes using several sustainability assessment tools (see Supplementary Table S6). For instance, many regions have developed their assessment tools, such as leadership in energy and environmental design (LEED; [14]) in the USA, building research establishment environmental assessment method (BREEAM; [15]) in the U.K., Canada Green Building Council [16], and Green Star Australia environmental rating system [17]. The LEED tool is used to evaluate the building’s environmental impact and energy performance, further enhancing the green building rating systems. Although the LEED certification framework has been recently applied to assess healthy, cost-efficient, and energy-saving green buildings, it can combine with socio-economic assessment tools that consider workers’ salaries, the number of children going to school, and the percentage of females in total labor force [17]. While the BREEAM method describes the construction and operation of buildings from the earliest design stages, it can be upgraded to address the environmental challenges associated with water scarcity and quality, low-carbon energy sources, and domestic waste recycling [1]. These urban assessment tools should also be combined with multiple indicators that support quality of life in smart cities (see Supplementary Table S7 for a literature survey evaluating Egyptian cities’ performance toward sustainability). Other sustainability assessment tools should consider the cities in developing countries that suffer from water limitations and electricity supply crises [18], further improving their housing, sanitation and hygiene, transportation, migration, and health conditions (see Supplementary Figure S1).

2.2. Role of SDG#11 in Urban Sustainability Assessment

Among the 17 SDGs reported by the United Nations, SDG#11 is particularly important for urban contexts [11] because it offers direct pathways to recycle residential wastes, improve outdoor air quality, and generate synergies with various SDG targets. For instance, the progress of Target 11.7, “access green and public spaces”, has a positive correlation with Target 3.9, “air quality management”, because the availability of landscapes can capture suspended pollutants from the dust-laden air, reducing the risks of respiratory system diseases [19]. Moreover, implementing integrated public transportation facilities and services, “Target 11.2”, and developing national libraries in underserved and low-income neighborhoods, “Target 11.4”, are essential in ensuring access to basic literacy levels, “Target 4.5” [20]. Avoiding the dumping of municipal and other wastes (e.g., garbage and plastics) into the streets and residential places, “Target 11.6”, contributes to the fulfillment of “Target 12.4” for the management of resources and wastes throughout their life cycle [21]. Policymakers can control the in-migration streams to the metropolitan areas, “Target 10.7”, by providing housing units, “Target 11.3: sustainable urbanization and human settlement planning”, to accommodate mobility and boost population growth [22]. Promoting adequate multilateral trading systems (e.g., the maritime and logistics sector) is essential to advance Target 17.10, “equitable international trade”, contributing to a strong and sustainable economic framework (i.e., Target 11.a: support positive economic performance) [12]. Hence, the synergetic interaction between SDG#11 and other UN goals should be explored to evaluate the city’s performance and identify the societal and governmental strategies required to adopt a culture of sustainability among residents and businesses.

2.3. Global Benchmarking vs. Localized Assessment in Sustainable Cities

Compared to global benchmarking models, the city’s SDG index provides a framework that scores urban areas based on publicly available SDG-aligned data, focusing on poverty reduction, education access, and environmental quality [23]. Likewise, the Arcadis Sustainable Cities Index assesses cities through three dimensions, e.g., people, planet, and profit, maintaining equitable community, social-ecological resilience, and financial strength [24]. While both models offer a high-level overview for developmental evaluation, they should be integrated into the world’s shared plan to end extreme poverty and reduce inequality. Alternatively, broader frameworks, such as the Urban Monitoring Framework (UMF) by UN-Habitat [25] and the World Bank Urban Sustainability Framework (USF), integrate governance, infrastructure, and inclusivity but face limitations related to institutional capacity and data scarcity in informal urban areas [26]. The OECD Green Growth Indicators and Mercer Quality of Living Index emphasize economic and quality-of-life metrics but remain less adaptable to contexts with African informal economies and trends in income inequality [27]. This comparison highlights the need for context-specific and disaggregated approaches that address both environmental and socio-economic dimensions of sustainability at the local level, giving sustainable solutions suitable for other countries worldwide (see Supplementary Table S8).

3. Theoretical Background and Hypothesis Development

3.1. Theoretical Frameworks for Transition to Sustainable and Smart Urban Paradigms

The sustainable development of cities has evolved through several theoretical paradigms in urban and regional planning. Early models, such as Christaller’s Central Place Theory and Perroux’s Growth Poles theory, emphasized the spatial organization of economic activities and the role of the city’s unique socio-spatial structure in driving regional development [28]. As urbanization intensified, other models, e.g., the Concentric Zone Theory (Burgess), Sector Theory (Hoyt), and Multiple Nuclei Model (Harris and Ullman), offered frameworks to understand urban formation, functional zoning, and landscape and sprawl conceptualization relevant to the current sustainability assessment models [29]. In recent decades, the paradigm has shifted to viewing cities as complex socio-ecological systems that must balance community empowerment, financial viability, environmental preservation, and health equity [30]. This systems-oriented view is captured by SDG#11, which calls for inclusive, safe, resilient, and sustainable urbanization (see Supplementary Figure S1). It also signifies an essential shift away from growth-centric development to sustainable-related models, emphasizing livability, ecological integration, smart mobility, green infrastructure, and participatory governance. The current study obtains benefits from this theoretical transition toward SDG#11 that can renew and plan cities and ensure access to main services, utilities, energy, housing, transport, and green public areas.

3.2. Conceptual Framework for SDG-Based Urban Sustainability Assessment

Figure 1 shows a schematic diagram of the methodological framework used to estimate the SDG scores assigned to each city. Certain indicators (e.g., housing, urban pollution, and access to services and amenities) were included in this framework, addressing the challenges related to sustainable urban development within the city boundaries. These indicators were chosen because they represented the key to achieving sustainable cities and are directly related to citizens’ quality of life in most developing countries [2]. The framework involves five key steps: (1) integrated collection of quantitative and qualitative data across environmental, social, and economic pillars; (2) indicator definition and assignment of SDG-related scores, including normalization and statistical evaluation using the Pearson Correlation Matrix to identify interdependencies among indicators; (3) selection of essential SDG targets corresponding to the three sustainability pillars; (4) creation of a classification model using normalized SDG scores and city ranking (low, medium, and high); and (5) model validation through statistical and sensitivity analysis. A case study of Alexandria is used to demonstrate the model’s application and inform scenario planning for enhancing SDG#11 performance. These steps are illustrated in the Section 4.

4. Materials and Methods

4.1. Data Source and Collection Strategy

Different sets of data were generated to fulfill the study objectives regarding the assessment of the cities’ performances in achieving the three pillars of sustainable development (see Supplementary Figure S2). Big and/or open urban data covered the 2012–2022 time span. The environmental-based data, such as the air and water quality parameters, were collected from reports and documents provided by the Egyptian Environmental Affairs Authority (EEAA), which collaborates closely with various governmental and non-governmental facilities and international bodies [31]. This authority is responsible for enforcing environmental laws and regulations in Egypt, improving the country’s environmental management capabilities. The United Nations Environment Program (UNEP) [32,33], with its Global Environment Outlook (GEO) for cities [34], was also used to retrieve data related to environmental quality assessment. The data related to remote sensing was collected from different sources, including OpenStreetMap (OSM) [35] and the geographical portal for planning management [36]. This data collection method complies with previous environmental monitoring studies exploring water quality [37] and air pollution [13] in Egypt. Socio-financial data about the population and economic sectors in Egypt was obtained from the Central Agency for Public Mobilization and Statistics (CAPMAS) year reports [38] and cultural statistics for Egypt [39]. The community economic data, covering employment rates, transportation, logistics, tourism, and hospitality, were determined from the Statista portal [40]. Data related to education (e.g., curriculum quality, finance, teachers, and capacity building) and culture (employment and activities) were obtained from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute of Statistics [41]. Data related to gender equality and poverty were collected from the United Nations Development Programme (UNDP) reports [42]. Data used to describe job opportunities, financial resources, sanitation services, and access to electricity were retrieved from the World Bank Group Annual Report [43]. Observations used to demonstrate the health, education, nutrition, and connection to the water system for every child in Egypt were gathered from the United Nations International Children’s Emergency Fund (UNICEF) reports [44]. The information associated with the economic pillar of sustainable development, including gross domestic product (GDP), labor force, currency exchange rate, and tourism, was gathered from the International Monetary Fund (IMF) [45], Central Bank of Egypt (CBE) [46], and UN Trade and Development (UNCTAD) [47] databases.

4.2. Selection Criteria of Representative Egyptian Cities

Among the 294 Egyptian cities, this study focused on assessing the sustainable performance of 27 cities that mainly represented the Egyptian governorates (see Supplementary Figure S2) [48]. These cities were chosen due to their administrative management, availability of urban infrastructure, and role in regional supply chain hubs, ensuring a representative and manageable scope for most developing countries. For instance, these cities covered the coastal and industrial hubs (e.g., Alexandria and Suez), agricultural centers (e.g., Kafr El-Sheikh and Damanhur), architectural historic areas, and tourism-driven activities (e.g., Luxor and Hurghada), providing a broad geographical coverage [49]. A brief description of the selected cities, regarding the geographic locations, dominant economic activities, and type, is summarized in Supplementary Table S7. Alexandria was selected to validate the proposed SDG-based model because it integrates coastal, industrial, and tourism activities with agricultural functions, illustrating its role as a multifunctional urban hub [7]. Satellite imagery of the main Alexandria’s urban planning areas was analyzed in 1985, 2013, 2017, and 2024 to gain insights into the city’s evolutionary patterns of urbanization and natural landscapes (see Supplementary Figure S3).

4.3. Indicator Selection, Categorization, and Linking to Sustainable Performance

Table 1. Categorization of the fifty sustainability indicators by the environmental, social, and economic pillars of sustainability, including indicator types and corresponding sustainable development goal (SDG) targets.

No.	Indicators	Indicator Type	Suggested SDGs’ Targets Interlinkages		Data Source
			Direct	Indirect
Environmental pillar indicators
01	Water consumption	Quantitative	6.1, 6.4	11.5, 12.2, 13.1	[38]
02	Percent of households connected to the public network sanitation	Quantitative	6.2, 6.3	3.9, 11.1, 12.4
03	Number of sanitation stations	Quantitative	6.2, 6.3	3.9, 9.1, 11.1
04	Amount of petroleum extracted	Quantitative	7.3, 12.2	9.4, 12.6, 13.2	[46]
05	Amount of gas extracted	Quantitative	7.1, 12.2	7.3, 9.4, 13.2
06	Percentage of households connected to the public network electricity	Quantitative	7.1, 11.1	1.4, 10.2
07	Gas generation by productive region	Quantitative	9.4, 12.4	7.2, 12.6, 13.2	[50]
08	Planted areas with strategic crops	Quantitative	2.4, 15.3	12.2, 13.2, 15.2
09	Productive areas with strategic crops	Quantitative	2.3, 2.4	8.2, 12.2, 15.2
10	Average concentration of sulfur dioxide (SO2)	Quantitative	3.9, 11.6	12.4, 13.2, 13.3	[42]
11	Average concentration of total suspended particles (TSP)	Quantitative	3.9, 11.6	12.4, 13.2, 13.3
12	Elevation above sea level	Quantitative	11.5, 13.1	14.2, 15.1
13	Measure of water quality (chemical oxygen demand; COD)	Quantitative	6.3, 14.1	12.4, 15.1, 15.2	[51]
14	Measure of water quality (biochemical oxygen demand; BOD)	Quantitative	6.3, 6.6	12.4, 14.1, 15.1
15	Number of maritime associations	Quantitative	14.7, 14.a	8.9, 17.17
16	Number of encroachments on agricultural lands	Quantitative	11.3, 15.1	2.4, 13.1	[50]
17	Protected areas in Egypt (wetlands, deserts, special geological formation)	Quantitative	15.1, 15.4	6.6, 14.5
18	Ecological richness and vegetation abundance	QualitativeScale (1–5)	15.2, 15.5	11.4, 13.1, 13.3
Social pillar indicators
19	Percentage of children who have never been to school	Quantitative	4.1, 4.2	1.4, 5.1, 10.2	[41]
20	Distribution of Egyptian population according to students enrolled and dropout	Quantitative	4.1, 4.3	8.6, 10.2
21	Illiteracy rate among Egyptian population	Quantitative	4.6, 4.7	1.4, 5.5, 10.2
22	Number of women recruited and employed	Quantitative	5.5, 8.5	4.5, 10.3	[32]
23	Number of marriage contracts	Quantitative	5.6, 16.9	3.7, 10.2
24	Number of divorces	Quantitative	5.6, 16.1	3.4, 10.3
25	Number of distributions of in-migration streams	Quantitative	10.7, 11.1	8.8, 1.4	[31]
26	Number of distributions of out-migration streams	Quantitative	10.7, 17.18	8.5, 11.3
27	Population growth	Quantitative	11.3, 3.7	13.1, 4.5	[38]
28	Build-up area	Quantitative	11.3, 9.1	6.6, 15.3, 13.1
29	Green areas	Quantitative	11.7, 15.1	3.9, 13.3, 6.6
30	Public safety and crime incidence	Qualitative	16.1, 16.3	11.7, 5.2, 10.3	[43]
31	Degree of digital governance and service accessibility	QualitativeScale (1–5)	16.6, 9.c	17.6, 10.2
32	Number of employees in cultural associations	Quantitative	11.4, 4.7	5.c, 8.3
Economic pillar indicators
33	Estimates of employed persons	Quantitative	8.5, 8.3	10.2, 5.5	[38]
34	Average employees’ salary	Quantitative	8.5, 10.1	1.2, 5.1, 10.4
35	Number of households	Quantitative	11.1, 1.4	5.6, 6.2
36	Growth rate of the cereal yield	Quantitative	2.3, 2.4	12.2, 13.2	[42]
37	Number of imported and local slaughtered livestock	Quantitative	2.1, 12.2	2.2, 3.4
38	Quantities of red meat production	Quantitative	2.1, 2.3	12.2, 3.9
39	Number of patients in public and central hospitals	Quantitative	3.8, 3.2	1.4, 10.2	[44]
40	Number of births	Quantitative	3.7, 5.6	4.2, 16.9
41	Number of deaths	Quantitative	3.1, 3.2	10.2, 16.1
42	Estimates of labor force	Quantitative	8.5, 8.6	4.4, 10.3	[32]
43	Gross domestic product	Quantitative	8.1, 8.2	9.2, 10.1
44	Capital investments in tourism sector	Quantitative	8.9, 12.b	11.4, 14.7
45	Total number of tunnels	Quantitative	9.1, 11.2	13.1, 11.3	[47]
46	Coverage and integration of public transport systems	QualitativeScale (1–5)	11.2, 9.1	13.2, 3.6
47	Accessibility and distribution of postal services	QualitativeScale (1–5)	9.c, 16.6	10.2, 17.8
48	Revenue generated from taxes charged on ships	Quantitative	17.1, 8.1	14.7, 9.3	[45]
49	Fund from developed countries	Quantitative	17.2, 17.3	10.b, 13.a
50	Foreign direct investments	Quantitative	17.5, 10.b	8.2, 9.3

summarizes a comprehensive set of 50 indicators selected to address the diverse sectors and sustainability pillars [9], covering the 17 SDGs. These indicators were chosen and arranged to describe the main features that could meet the standards of urban development and design, as previously demonstrated [20]. For instance, enhancing the air quality in cities is obtained by the achievement of SDG#13 on climate change, and hence, the indicators of I#10: sulfur dioxide concentration, I#11: total suspended particles, and I#12: elevation above sea level were chosen in this goal. Moreover, the use of clean and affordable energy in cities is highly linked to the fulfillment of SDG#7 on modern energy, and thus, I#4: amount of petroleum extracted, I#5: amount of gas extracted, and I#6: percentage of households connected to the public network electricity were selected as the three indicators of this goal. The quantitative indicators represented measurable numerical values (e.g., GDP per capita, population density, and CO₂; emissions), whereas the qualitative indicators captured subjective or policy-related aspects of urban sustainability (e.g., governance effectiveness, environmental policies, and social inclusion) [12].

4.4. Data Pre-Processing

Qualitative indicators were transformed into numerical values through a standardized scoring rubric, enabling their incorporation into the SDG modeling process. Binary (no or yes) indicator variables were assigned a value of 0 for the absence or failure of a policy or initiative, whereas a value of 1 reflected the presence or active enforcement of this indicator. An ordinal Likert scale (1–5) was considered for indicators that induced greater differentiation, such as those evaluating policy intensity or institutional engagement. These two scoring methods were applied to capture variation in implementation quality, ranging from minimal to fully comprehensive efforts, as previously reported [52]. The indicator values were then normalized by Equation (1), ensuring that all criteria were expressed on a comparable scale while preserving their relative proportions across cities:

$${\mathrm{N}}_{\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}={\displaystyle \frac{\mathrm{a}}{\sqrt{{\mathrm{a}}^2+{\mathrm{b}}^2+{\mathrm{c}}^2}}}$$

(1)

where N_score is the normalized score and a, b, and c are the raw data of the cities.

This data normalization procedure was arranged to create an indicator–city (I − C) matrix [4,53], giving a standardized form of [I − C]_50×27 (see Supplementary Table S9). Pearson’s correlation coefficient (r) was used to refine the dataset by removing redundant or statistically insignificant indicators, ensuring that only the most informative variables were retained for further assessment. This “r” coefficient ranged from −1 for a strong negative correlation to +1 for a strong positive correlation, where values near 0 signify no correlation.

4.5. Selection of Essential Sustainable Development Goal (SDG) Targets for the Three Pillars of Sustainable Development

The 169 SDG targets were screened to give the main targets that showed either direct or indirect correlations with the sustainable indicators in cities. This screening procedure was performed by describing each goal by one to four targets (see Supplementary Table S10), and then a whole point “1” was assigned to each accomplished target. The score of each indicator was calculated by Equation (2), using the correlation between the number of targets achieved and the total number outlined by each goal [4]:

$$I_{Score}=100\times ({\displaystyle \frac{{\mathrm{t}}_i}{{\mathrm{T}}_i}})$$

(2)

where I_score is the indicator score, t_i is the number of achieved targets of the indicator, and T_i is the total number of targets for the specific SDG. For example, SDG#15 was selected to describe the fulfillment of I#16, “number of encroachments on agricultural lands”, because the expansion of urban areas into the agricultural zones tends to reduce available farmland, disrupt terrestrial ecosystems, “Target 15.3”, and deteriorate biodiversity and natural habitats, “Targets 15.5” [19]. These two targets were selected out of the twelve targets of SDG#15, assigning a score of 2/12 × 100 = 16% to I#16. Further, the estimated scores were multiplied by (−1) if the indicator reduces sustainability achievements [53] (e.g., increasing I#19 for a number of children who did not attend school negatively impacts the fulfillment of Target 4.5 related to equal access to different educational levels). This step was repeated for all 50 indicators using expert judgments and interviews (see questionnaire sheet in Supplementary Table S11) [54]. The results of this step are summarized in the [SDG − I]_17×50 matrix (see Supplementary Table S10).

4.6. SDG-Based Model Scoring and Creation and City Ranking

There was a synergetic interaction between the 50 indicators, the 27 governorate capitals in Egypt, and SDGs targets, defining the three pillars of sustainable development (Figure 2). The previously estimated two matrices [SDG − I]_17×50 and [I − C]_50×27 were cross-multiplied to estimate the [SDG − C]_17×27 matrix (Equation (3)), representing the score of SDG assigned to each city [55]:

$${[SDG-C]}_{(17\times 27)}={[SDG-I]}_{(17\times 50)}\times {[I-C]}_{(50\times 27)}$$

(3)

The scores of the estimated [SDG − C]_17×27 matrix were analyzed by the Jenks Natural Breaks method [56], grouping the cities into three levels of sustainability performance. The cities were labeled as “Low”, “Medium”, and “High” according to the percentage of SDG achieved as <40%, 40–60%, and >60%, respectively (see Supplementary Table S2), following the ranking of cities from lowest to highest based on the smart city index [6].

5. Results

5.1. Assessment of City’s Progress Toward SDG Achievement

Initially, the selected number of indicators was screened to avoid redundancy of observations, improve the statistical validity of the SDG-based model, and enhance the interpretability of urban sustainability performance (see Supplementary Figure S4). This step also facilitated the classification of the 27 cities into the “Low”, “Medium”, and “High” categories based on the cities’ performance in fulfilling SDGs (Figure 3; see also the categorization of these cities in Supplementary Table S7).

The first category represented the “High” ranking cities, including Qalyubeya, Alexandria, Cairo, and Damanhur, owing to their ability to maintain several key indicators crucial for sustainable progress. Alexandria, as a coastal city, showed higher scores on I#15, “number of maritime associations” (74%), probably because it hosts around fifty such maritime companies and industries [57]. These organizations focus on coral reef restoration to reduce sea pollution (Target 14.2: protect marine and coastal ecosystems) and act as natural barriers against sea-level rise, “Target 13.2: climate change policies” [58]. These achievements are supported by developing active community engagement programs, where over 10,000 volunteers participate annually in beach litter clean-ups and marine conservation activities, “Target 14.a develop research capacity to contribute to marine biodiversity” [59].

Cairo showed higher scores on I#2, “households connected to public sanitation” (93%), and I#46, “infrastructure”, mainly related to Target 9.1, “sustainable and resilient infrastructure”. Cairo contains an integrated and highly connected transportation system, including extensive Metro lines, bus networks, and major highways, “Target 11.2 sustainable transport systems”. The Cairo Metro, for example, serves over 3 million passengers daily, making it one of the busiest and most active means of transportation in Africa and the Middle East [60]. Regarding human services, about 90% of households in Cairo are connected to the public sanitation system, which considerably reduces health risks “Target 3.8: health coverage” and promotes a cleaner urban environment, “Target 6.2: sanitation and hygiene services” [61]. This highly connected infrastructure enhances the ratio of green and public spaces to the number of people, facilitating more efficient and safer public areas (Target 11.7). Despite these advantages, some parts of Cairo face significant challenges related to air pollution. The city showed lower scores on I#10, “sulfur dioxide (SO₂)” (20%), and I#11, “total suspended particles” (44%), delaying the achievement of Target 3.9, “number of deaths and illnesses from air pollution”. The anthropogenic sources of air pollution in Cairo include traffic emissions, representing >50% of the city’s total air pollutants [62], “Target 11.6: air quality in cities”. Additionally, the city could fulfill Target 16.6, “accountable and transparent institutions”, by launching new digital platforms (I#31), such as mobile-based tools, to report urban issues (e.g., traffic problems and infrastructure maintenance requirements).

The second category represents the Mansoura, Zaqazeeq, Damietta, Giza, Fayoum Kafr El-Sheikh, Menofia, and Ismailia cities, with “Medium” performance toward sustainability. Some of these cities entail insufficient values for essential key indicators that negatively impact their overall performance to fulfill more SDGs. For example, Mansoura represented a higher percentage of I#16 (59%) for urban encroachments on agricultural land, representing a significant environmental concern that undermines the city’s progress toward achieving Target 15.3: restore degraded land and soil. This agricultural land loss also impacts flora abundance (I#17: 73%), delaying the city’s capacity to restore ecosystems (Target 15.5: protect biodiversity and natural habitats). Safeguarding agricultural land from desertification and drought is essential in ensuring secure and equal access to crop productivity in Mansoura (Target 2.3). This step is also influenced by grain water productivity (measured as tons of harvested product per unit of irrigation water consumed or applied) and irrigation methods, showing a direct correlation with Target 6.4, “address water scarcity” [19].

Menofia has a low scoring value on (I#5: 15%) regarding the quantity of gas extracted, where the city should increase the share of the population consuming greener electricity mixed with low-carbon emissions. The city has a plan to expand national grid infrastructure to ensure access to electricity services (Target 1.4: access to basic services and facilities) and apply carbon prices to the energy sector (Target 7.a: clean energy technology). For instance, enhancing the technical performance of the main cooking practices (the fuel and the cook stove) in the households’ daily life of Menofia would support modern energy facilities (Target 7.1: access to affordable energy).

The third group is defined by the “Low” category, comprising 14 cities that heavily depend on a single development sector. The tourism sector in Hurghada contributes to more than 10% of the country’s GDP [21], representing 92% achievement of I#44, “capital investments of tourism”. The city’s financial progress is established by attracting both international and domestic tourists to its renowned Red Sea beaches and diving sites [63], highlighting tourism as a dominant contributor to shaping Hurghada’s development and prosperity (Target 8.9: promote sustainable tourism). The city announced a strategic plan to develop and implement financial tools for monitoring sustainable tourism, creating jobs, and promoting local culture (Target 12.b).

Suez has a high scoring value of 85% on I#48, “revenue from taxes charged on ships”, where the maritime and logistics sector in the city shares approximately 22% of Egypt’s GDP profile (Target 17.1: improve tax administration) [64]. The city’s economy depends particularly on the Suez Canal (a strategic passageway for navies), connecting the Mediterranean Sea to the Red Sea. This waterway is considered a vital artery for international trade, “Target 17.10: participate in the multilateral trading system”. To sustain this growth pattern, the city intends to enhance the infrastructure quality and operational capacity of the Suez Canal, supporting a range of associated firms related to shipping, logistics, and port services, “Target 9.4: upgrade infrastructure and retrofit industries”. Furthermore, the city is implementing measures to improve the health and safety of migrant workers by carefully monitoring I#25 and I#26, “distribution of planned and well-managed migration streams”, enforcing local labor laws, “Target 10.7: safe migration and mobility”.

5.2. Identifying Key Indicators Influencing the Establishment of Sustainable Cities

Based on the scoring performance of Egyptian cities (see Figure 3), SDG#11 was assigned as the main criterion in evaluating urban sustainability and planning among cities. As such, this goal shows strong synergetic interactions with other SDGs, aligning with the global urbanization trends [11]. Four indicators out of fifty exhibited the greatest impact on the cities’ ability to meet SDG#11 performance (see efficient indicator selection in Supplementary Figure S5). These four indicators (I#27; I#28; I#29; I#43) showed a direct relevance to the key urban targets (i.e., the findings are compared with the results of other scoring-based evaluation methods in Supplementary Table S12), which could be described as follows.

5.2.1. Effect of Cities’ Population Growth on Sustainable City Development

Figure 4 shows the scoring values of the four main indicators analyzed to examine their impacts on SDG#11 achievement. The findings revealed that the cities experiencing higher population growth rates could demonstrate a stronger impact on SDG#11 performance compared with other cities (Figure 4a). For instance, Cairo implemented smart accommodation services and comprehensive urban master plans to maintain mixed-use compact, and densely populated areas. Promoting compact urban growth intends to maximize land-use efficiency by integrating residential and commercial spaces into walkable neighborhoods, meeting Target 11.3, “enhance inclusive urbanization”. The city’s population growth also has a direct correlation with Target 11.2, “sustainable transport systems”, by providing convenient and affordable access to bicycles and encouraging residents to use cheap vehicles for short trips [3]. Additionally, cities have made advances in achieving Target 11.1, “affordable housing and upgrading slums”, by accessing reasonable accommodation to support this growing urban population without serious urban sprawl [11].

5.2.2. Effect of Cities’ Built-Up Area on Sustainable City Development

As illustrated in Figure 4b, mega-cities with expansive built-up areas, typically exceeding 1000 km², often demonstrate a stronger impact on SDG#11 performance compared to cities with smaller built-up areas. This phenomenon could be explained by several interconnected factors, such as urban density, building orientation, and infrastructure development [22]. For instance, Cairo, with a built-up area of ≈2019 km², could partially fulfill Target 11.6, “reduce adverse environmental impacts”, by adjusting the orientation of high-rise buildings to take advantage of the prevailing wind patterns. This approach is suitable to enhance natural ventilation airflow, “Target 3.9: reduce illness from air pollution”, minimizing the need for air conditioners in dense built-up areas in Cairo. Incorporating renewable energy sources, such as solar panels and wind turbines, into these high-rise buildings reduces the energy consumption patterns, “Target 12.1: sustainable consumption” and GHG by approximately 25%, “Target 11.b: adaptation to climate change” [65]. Increasing the built-up area also has a positive correlation with SDG#11 fulfillment by expanding urban public transport services toward sustainable mobility (Target 11.2). Limited housing availability in small-sized built-up areas could resist the growing demand for suitable accommodation and fundamental necessities, “Target 11.1: affordable housing and basic services”.

5.2.3. Effect of Cities’ Green Urban Areas (GUAs) on Sustainable City Development

As clarified in Figure 4c, cities with a high percentage of GUAs have a considerable influence on SDG#11 performance compared to those with limited green spaces. This phenomenon occurs due to enhancing the natural ventilation airflow facilities, including vertical gardens and green rooftops, provided by these GUAs, “Target 11.7: access to public spaces”. The cities with better GUAs could also accomplish Target 11.6, “reduce environmental impacts”, by mitigating the urban air pollution levels caused by the occurrence and movement of dust storms. The GUA’s health can also be promoted by incorporating vertical greenery systems into residential buildings, creating a living green façade that improves air quality and provides natural insulation [1].

5.2.4. Effect of Cities’ Gross Domestic Product (GDP) on Sustainable City Development

As demonstrated in Figure 4d, mega-scale projects have greater impacts on SDG#11 scoring compared to small-scale projects. Some ancient Egyptian cities (e.g., Cairo, Luxor, and Alexandria) with high economic strengths could meet Target 11.4, “protect cultural and natural heritage” by restoring and conserving several historic buildings. Deploying the multi-faceted approach, such as the implementation of urban drainage systems in the Catacombs of Kom El Shoqafa [66] to manage rainwater runoff and reduce flood risks from the weathering of stone monuments, complies with Target 11.5, “decrease economic losses”. The involvement of environmentally friendly materials in masonry repairs, such as lime mortar in Karnak Temple [67], complies with Target 11.c, “utilize local materials in buildings”. This pattern helps in maintaining the structural integrity and historical aesthetics of the buildings [17], further increasing the monetary value of tourism services in Luxor. The city’s GDP could also increase by incorporating advanced glazing techniques (e.g., double or triple-glazed windows) to reduce the heat loss of the historic structures (e.g., Citadel of Salah El-Din) while preserving their original features [68].

5.3. Employing the SDG-Based Score Model for Case Study Evaluation and Proposing Improvement Scenarios

5.3.1. Evaluating Performance of Alexandria Using SDG#11 Scoring Approach

Three indicators, namely population growth, built-up area, and GDP of financial projects, exhibited an increasing trend over the 1985–2024 years (see Supplementary Table S13). However, there has been a decreasing pattern in the indicators related to GUAs, probably due to the lack of urban planning and design systems, negatively impacting Target 11.a, “ensure balanced territorial development”. The decline in the overall SDG#11 scoring performance by almost 10% encourages the policymakers to create strategic and conceptual frameworks to develop the selected indicators in Alexandria (see the city’s location in Figure 5a). For instance, a sharp decline in GUAs by about 86% from 1985 to 2024 severely impacted the city’s ability to meet Target 11.7, “accessible and environmental-friendly zones”. It was suggested that the urban planners would optimize the GUA configuration within the city by implementing additional parks and enlarging their tree canopy cover, as previously demonstrated in Vancouver, Canada [69].

5.3.2. Proposed Scenarios for Improving SDG#11 Achievement

The observations of the proposed SDG-based model could develop multiple strategies to address the challenges that delay SDG#11 fulfillment:

• Re-allocation of population density:

Because population growth was an essential factor in determining the SDG#11 score (Figure 5b), this scenario attempts to guarantee a homogeneous distribution of population density. The expected future population would rise to approximately 10.3 million by the year 2054 with a growth rate of almost 2% per year (Equation (4)):

$$Future population=Present population\times {\left.\left(1+growth rate\right.\right)}^{years}$$

(4)

The SDG#11 score of Alexandria is delayed due to dense population and household concentration in the northeastern districts (e.g., Montaza, East, and Central), along with the persistent underutilization of southwestern zones (e.g., Amreya and Borg El-Arab). Hence, the proposed urban strategy emphasizes a spatially inclusive and balanced development approach by allocating medium-density dwellings in the western areas. This option could optimize and reduce the cost of key urban services, such as water supply and disposal facilities, having significant potential to Target 11.1, “affordable accommodation and essential services” improvement. Attracting more citizens to move to Alexandria’s urban centers (i.e., the planned western areas) is also suitable for enhancing Target 11.3, “sustainable urbanization”, by developing new residential and economic hubs. Moreover, Target 11.2 achievement can be promoted by improving road safety, connecting infrastructure facilities to the major highways, and expanding public transportation networks. Due to improving Targets 11.1, 11.2, and 11.3, the SDG#11 fulfillment would rise from 37% in 2024 to 44% in 2054. As such, this scenario supports SDG#11 achievement by relieving urban traffic congestion around the core districts while encouraging the expansion and intensification of the city’s urban footprint through well-integrated and strategically guided population distribution.

• Increasing the built-up area:

The land area required for the extension and infill development of Alexandria was calculated according to the expected population number and the selected density of 12,000 person/km². In these medium-density urban zones, the built-up area could share 50–70% of the entire city’s land [70]. The additional urban parts are designed to maintain sufficient agricultural lands, making Alexandria suitable for investment in agribusiness, food processing, and smart irrigation systems. This approach complies with Target 11.a, “support development planning”, by uniformly re-distributing the city’s residents and mitigating the issues associated with the sprawling urban patterns (e.g., loss of agricultural lands and population agglomeration). This proposed scenario also has a positive impact on Target 11.3 fulfillment by ensuring compatible land uses and functions (Figure 5c), such as residential (30%), commercial (40%), community services (20%), infrastructure (10%), and others (10%). Developing these areas would necessitate the presence of urban nodes, such as parks, promenades, and other leisure spaces, complying with Target 11.7, “access to safe and public spaces”. These facilities would not only provide pleasurable experiences to the residents and tourists but also promote investment expansion and enhance the city’s function as a popular tourist destination, “Target 11.5: reduce economic losses”. As such, the balance between built-up areas, urbanized places, and open spaces prevented urban sprawl expansion, further improving the city’s SDG#11 score by 45%.

• Green urban areas (GUAs):

Figure 5d reveals that expanding GUAs by more than 50% due to the presence of Lake Mariout would serve as a “blue backbone” for the extension of green corridors and ecological buffers along the water body, “Target 11.7: green and public zones”. Establishing waterfront greenways and spaces near the Alexandria coastal zone could assist in adopting and implementing national disaster risk mitigation scenarios, effectively reducing the negative impacts of anthropogenic activities on human health, “Target 11.b: integrated plans for climate change adaptation”. Moreover, constructing this proposed green belt would conserve agricultural lands and maintain soil rhizosphere food webs, enhancing crop marketing to the farming community in response to population dynamics, “Target 11.a support positive economic”. It is suggested that the available groundwater and desalinated water in the study area could contribute to the cultivation of the proposed green buffer zone.

• Economic potential

Alexandria’s GDP is expected to rise by >70% in the year 2054, with a growth rate of nearly 6% (Equation (5)):

$${\mathrm{G}\mathrm{D}\mathrm{P}}_{\left(Futur\right)}={\left[\mathrm{G}\mathrm{D}\mathrm{P}\right]}_{\left(Current\right)}\times {\left.\left(1+{\displaystyle \frac{Growth Rate}{100}}\right.\right)}^{years}$$

(5)

The sensitivity analysis results demonstrated that developing profitable projects for the urban residents would exhibit the greatest impact on advancing the SDG#11 performance (see Supplementary Figure S6). This high correlation could be because the production of goods in Alexandria allows the city to establish transit-oriented development (TOD) to optimize transportation that guarantees flexible movement of people and heavy-duty trucks (Target 11.2). Moreover, increasing the amount of money that the Alexandria government spends on services or activities can attract small-scale chemical and plastic industries, workshops, and smart factories (see Figure 5e), promoting economic clustering and connectivity with the urban core (Target 11.a: positive economic planning). Also, increasing Alexandria’s exports minus its imports would create special economic zones between the urban core and peripheral regions, further expanding the number of schools, hospitals, roads, and bridges, “Target 11.4 cultural and natural heritage” [65].

6. Discussion

6.1. Classification of Cities According to Their Performances in Fulfilling Sustainable Development Goals (SDGs)

The urban transformation of traditional cities toward sustainability is an essential topic worldwide, requiring quantitative and qualitative investigations and collaboration between the public sector, individuals, and non-governmental organizations. For instance, several developing countries aim to maintain the socio-financial knowledge of inhabitants and reduce environmental impacts (e.g., low-carbon cities) in residential areas. This study provides much-needed and relevant information on the ranking of various cities in Egypt, which is one of the main developing countries recognized as the third largest economy in Africa, by assigning scores to the 17 SDGs achieved. This step was justified by defining 50 indicators highly correlated with the three pillars of sustainable development, giving the [SDG − I]_17×50 matrix. Quantitative and qualitative observations of these 50 indicators were collected to describe the urban sustainability of 27 cities in Egypt, denoting the [I − C]_50×27 matrix. The synergetic interaction of these two matrices was used to calculate [SDG − C]_17×27, describing the SDG scores assigned to each city [53]. Further, the cities were arranged into three ranking categories of “High”, “Medium”, and “Low” according to the SDGs achieved (see Supplementary Table S2). This arrangement was used to define the unmanaged sustainability goals of each city, where decision-makers would utilize the proposed SDG model to overcome the urban sustainability challenges. For example, Qalyubeya maintained higher scores on I#33, “number of employed persons”, and I#34, “average employee salaries”, by establishing competitive value-added and labor-intensive manufactories. This city contains several industries and firms (e.g., textile and cement), increasing the achievement of Target 8.2, “maintain higher levels of productivity of economies”. For instance, the textile industrial sector in this city is particularly prominent, with around 150 factories that employ about 50,000 workers [71]. This industry not only enhances financial productivity but also raises societal awareness by promoting female participation in the labor force (I#22: number of women employed), further encouraging gender equality “Target 5.5” achievement [8]. In addition, increasing employment rates above 70% and average wages above USD 10,000 per capita, such as the condition of some cities in this “High” category, would substantially fulfill Target 8.1, “sustain per capita economic growth” [72].

Damanhur depicted higher scores on I#36, “growth rate of cereal yield”, because the city aims at implementing resilient agricultural activities and plant gene banks for doubling farmers’ income “Target 2.5: crop genetic diversity”. In a comparable developing country, the Kilimanjaro region in Tanzania has implemented sustainable agriculture practices to safeguard food security and promote the livelihoods of about 70% of Tanzanian farmers [73], aligning with Target 2.3, “ensure cereal yield progress”. The city attempts to enhance international cooperation by employing diversified cropping systems and vegetation banks, “Target 2.a: focus on agricultural research from the practitioners’ and scientists’ perspective”. However, some parts of the city face social challenges regarding the progress in the educational sector (I#19), probably due to the high percentage of children not attending schools. This indicator negatively impacts the fulfillment of Target 4.1, “ensuring equitable education”, and hence, the city has a plan to develop a better quality of early childhood education [43]. Moreover, the city’s lower parental literacy (I#21) affects the development of an educated workforce essential for technical expertise and innovation in infrastructure projects, “Target 4.6: achieve literacy”.

6.2. Defining the Main Indicators Influencing the Fulfillment of SDG#11

It was found that the SDG#11 scores on an integrated assessment of urban sustainability were highly influenced by four indicators describing population growth, built-up area, green spaces, and GDP per capita.

Port Said is responsible for trading a substantial volume of the country’s imports and exports, linking I#43, “GDP of financial projects”, with the fulfillment of Target 11.5, “decrease the direct economic losses”. For instance, the city seeks to invest in repairing and strengthening structural parts that can improve the drainage system’s performance against extreme weather events. This objective further reduces the risk of economic disruptions and disasters to achieve Target 1.5, “reduce exposure to climate-related disasters”, by upgrading housing in areas vulnerable to floods. This target showed a synergetic interaction with I#35, “number of households” due to reducing the number of flood-affected households by 25%, further improving urban connectivity and the resilience of about 50,000 low-income residents.

Mansoura could fulfill Target 11.2 by expanding public transport and services to effectively manage the growing number of drivers and passengers, where this target is interconnected to I#27, “population growth”. This sustainable urban mobility plan is essentially linked to Target 3.6 by implementing dedicated bus lanes that can significantly reduce road traffic injuries and accidents [11]. For example, the city’s policymakers have noticed a reduction in road traffic injuries by 15% and traffic accidents by 20% after decreasing the number of private vehicles on the roads by 10% [74].

Target 11.3 on “inclusive and sustainable urbanization” is influenced by I#28, “built-up area”, where some cities such as Alexandria can attract more employees and tourists by promoting better green environments (plantation coverage rate). This objective positively matches Target 11.6, attempting to reduce the adverse per capita environmental impact with a particular focus on improving air quality (e.g., particulate matter PM10 and PM2.5). The city could also achieve Target 11.6 by implementing effective waste recycling strategies to manage the additional garbage, rubbish, and package and construction materials generated from the urban sprawl.

A similar pattern was noticed in Damietta that had a significant potential to enhance Target 11.7 by expanding the public and green spaces as a proportion of the total city space “I#29: green urban area”. This aim is considerably linked to Target 13.1, which strengthens the resilience to climate change (e.g., the plant biomass assimilates large amounts of CO₂ during photosynthesis), reducing the city’s overall carbon footprint. As such, Damietta intends to transform underutilized urban areas into parks, community gardens, and green corridors, acting as natural carbon sinks (Target 12.2: manage natural resources).

6.3. Validation of the Proposed Model Using Alexandria as a Coastal City That Faced Several Environmental Challenges over Recent Years

The proposed SDG-based model is beneficial for decision-makers in Alexandria to define the strategic plans, overcoming the limitations of urban sprawl characterized by dispersed and ribbon-like developments (i.e., the developed model was also applicable to other benchmark case studies in Supplementary Tables S14–S16). For instance, Alexandria’s rapid population growth negatively affects Target 11.3, “inclusive urbanization”, due to the presence of fragmented built environments, with sprawling low-density and disconnected high-density residential districts [7]. Furthermore, expanding the built-up area with improper planning adversely impacts Target 11.2, “sustainable transport systems”, because of creating isolated neighborhoods, further reducing accessibility to essential services (e.g., public transportation). Moreover, the lack of green spaces over the years has negatively impacted Target 11.7, “accessible green areas”, due to the absence of recreational zones responsible for managing the spatial separation and public activities of several city parts [75]. Investing in Alexandria’s infrastructure construction (e.g., smart waste management) attempts to improve the integrity and connectivity across various urban zones, contributing to Target 11.a, “financial interaction between urban areas”.

7. Conclusions, Implications, and Research Limitations

7.1. Theoretical and Practical Implications

The study broadens the understanding of sustainable cities, especially those that can attract both foreign and domestic investments, by investigating the connection between the SDG achieved and a set of enviro-socio-economic indicators. The estimate [SDG − C]_17×27 matrix (see Equation (3)), representing the score of SDG assigned to each city, serves as a theoretical approach for classifying the cities into “Low”, “Medium”, and “High” according to their sustainability levels. The conceptual framework (see Figure 1) can be used by decision-makers in different sectors, such as public health, education, and governance, to prioritize the environmental challenges accompanied by water scarcity and quality, low-carbon energy sources, and domestic waste recycling. The study’s theoretical contributions have implications for both practitioners and academics involved in the transition to sustainable and smart urban paradigms.

The study also exhibits considerable practical implications for decision-makers, policymakers, and stakeholders, particularly in identifying and classifying the sustainability status in cities using a set of indicators (e.g., water, air, energy, poverty, education workforce, and housing). The selected indicators in Table 1 are practical because they can be continuously updated by modifying the collected qualitative and quantitative data or introducing additional criteria to ensure that the scoring findings remain relevant and effective to other urban scales globally. The SDGs scoring in Figure 3 can guide the decision-makers to create more effective strategies for sustainable urbanization in most developing countries that experience rapid industrialization, population growth, waste generation, and energy demand. Especially, meeting the SDG#11 performance can enable governments, policymakers, and public sectors to define the essential scenarios needed to strengthen the residents’ quality of life and human settlement planning.

7.2. Overcoming Study Limitations

Like any academic and professional study in the context of urban development, this work has certain limitations.

Table 2. Limitations and suggested improvements to create resilient cities under the umbrella of the three pillars of SDGs (e.g., environmental, financial, and social).

Limitations	Suggestions
Limited data availability obstructs comprehensive city sustainability assessments, leading to missing observations and challenges in record justification	Collaborating with national and local authorities to enhance data collection and reporting mechanisms
Data used for the scoring analysis of cities covers a period from April 2012 to February 2022	Incorporating data from earlier stages, including historical records, would provide a more comprehensive and influential output
Expert judgment is needed to score qualitative indicators	Engage local stakeholders in assigning weights and validating qualitative indicators to reduce subjectivity
Equal weights were assigned to all SDG targets achieved, assuming uniform importance across indicators	Apply differentiated weights to SDG targets using methods such as the analytic hierarchy process (AHP) or expert-based scoring
Qualitative indicators are scored with fixed binary values or Likert scale regardless of context	Calibrate qualitative scoring using contextual expert input or adaptive scoring frameworks based on city-specific data
Uniform classification thresholds (low, medium, high) using the Jenks Natural Breaks method	Explore alternative classification schemes (e.g., clustering or fuzzy logic) to account for nonlinear patterns in city performance
Among the 50 indicators collected for analysis, SDG#11 exhibits a strong relationship with only four specific indicators	Expand the number of indicators considered to gain a more comprehensive understanding of urban planning and its impact on achieving SDG#11
The number of targets to which indicators have been assigned is limited to approximately three targets per goal	Enhancing the synergetic interaction between the targets and each indicator by expanding the number of sustainability dimensions (e.g., political, technical, and satisfaction)
Limited geographical scope	The proposed SDG-based model could be validated in diverse urban contexts, including cities from different regions, income levels, and development stages
Spatial resolution of satellite imagery may cause an underestimation of small-sized open spaces in urban areas	Use machine learning and deep learning techniques to improve the detection and classification of small open spaces within urban areas

highlights key challenges encountered during the sustainability assessment process and suggests corresponding strategies to address them. These limitations include issues related to data availability, indicator selection, and geographical scope, further affecting the depth and accuracy of sustainability evaluations. To address these challenges, recommendations are made to enhance data collection processes, expand indicator sets, and apply advanced techniques (e.g., machine learning) to improve analysis outcomes.

7.3. Final Remarks

This study successfully developed an adequate modeling tool that could classify the cities based on the achievement of most SDGs (i.e., low, medium, and high scoring), especially SDG#11 for sustainable urban development. This tool was appropriately employed to assess Egyptian sustainability status, where this country aims to overcome the lack of food, electricity, and water services and supplies. The SDG-based model was created by collecting multisource big data from different activities/sectors (e.g., industrial, agricultural, tourism, and fishing), identifying 50 techno-enviro-economic indicators. The proposed SDG-based model was also useful in creating four different scenarios involved in enhancing the achievement of SDG#11/targets by 2054 in Alexandria as a coastal city. As such, the study outputs are beneficial to urban planners and policymakers for establishing alternative strategies that could strengthen national and regional development urban planning in most developing countries worldwide. Future studies should focus on refining the model and expanding its applicability across diverse urban contexts, thereby offering a roadmap to understand urban sustainability and SDG progress on a global scale.