Urban Planning Policies and Architectural Design for Sustainable Food Security: A Case Study of Smart Cities in Indonesia

Abstract

The urgent need for sustainable food systems in Indonesia is hindered by urban planning policies that are disconnected from food security priorities. Smart city planning policies in Indonesia have been subject to numerous misconceptions compared to successful implementations in developed countries. This study examines the relationship between urban planning policies and architectural design in fostering sustainable food systems, employing a mixed-methods approach that combines multiple linear regression analysis with a sample of 75 smart cities, correlation analysis, and case studies from six representative cities that demonstrate best practices. Key findings reveal that food security is significantly undermined by the Gross Regional Domestic Product (GRDP), indicating distributional inequalities, high food expenditure, and a lack of clean water, while access to electricity improves resilience. Case study analysis showed that Semarang is the city with the highest readiness level (97%), followed by Makassar (91%), which employs a Holistic Benchmark approach, Jakarta (91%), which follows a Technological—fragmented approach, Samarinda (86%) and Medan (79%), which are in a Developing Transition phase, and Surabaya (66%), which utilizes a Community and Local Initiatives approach. Each city adopted a different approach, which means the national strategy for developing Smart Cities will also differ; however, they must prioritize equitable infrastructure and architectural innovation, such as urban farming integration and a water–energy–food nexus system. Smart cities extend beyond technological innovations, encompassing integrated urban planning policies and architectural practices that foster sustainable food systems through infrastructure management and environmental sustainability.

1. Introduction

Food security has become a priority in international discourse following the 2008 food price crisis [1]. Both global and local institutions have undertaken various efforts to achieve the Sustainable Development Goals, particularly the goal of eradicating hunger by 2030 [2]. This stems from food security’s intrinsic link to economic stability, livelihoods, and national security, prompting many nations to prioritize this issue within their political agendas [3]. In developing countries, urbanization and demographic changes are creating complex, unanticipated challenges in terms of food insecurity and malnutrition [4]. In urban areas where population growth drives higher food demand, food insecurity is particularly acute, disproportionately exposing lower-income communities to heightened vulnerability [5]. Maintaining food security remains Indonesia’s paramount priority as it confronts growing complexities in the post-pandemic period, compounded by a fragile economic recovery and persistent food price inflation [6,7].

The pace of urbanization is expected to increase, and by 2050, two-thirds of the world’s population are projected to reside in metropolitan centers [8]. Indonesia is home to several densely populated cities, yet urban areas are increasingly used for infrastructure and housing, a trend spiraling out of control due to urban sprawl [9]. Furthermore, Indonesia continues to face low food security, as evidenced by food demand consistently outpacing that for non-food goods [10].

While urbanization was implied in the previous Millennium Development Goals (MDGs), the current Sustainable Development Goals (SDGs) explicitly recognize the direct link between urbanization and natural resource depletion, food insecurity, poverty, and sustainable development [4]. The phenomenon of urban sprawl, accompanied by environmental pressures, has created complex transformations in urban food systems, where challenges related to access, distribution, and sustainability have become increasingly multidimensional [11,12]. In an urban context, achieving a sustainable food system requires a multidisciplinary approach that integrates economics, policy, and architecture. The smart city framework offers innovative solutions by leveraging digital technologies and data-driven governance to enhance the efficiency of food systems. Recent empirical evidence indicates that implementing smart city characteristics can substantially contribute to strengthening urban food systems.

In many Indonesian municipalities, policy approaches are shaped by the misconception that adopting advanced technologies and models from developed countries is sufficient, rather than prioritizing solutions suited to local contexts [13]. A critical component of realizing a smart city is establishing a smart environment [14], which hinges upon robust government policies and urban spatial planning frameworks [15]. The architectural design quality of smart cities directly influences urban development trajectories and residents’ quality of life [16], mitigates conflict [17], and optimizes water distribution systems.

Consequently, efforts to establish resilient and sustainable food systems will be pivotal in ensuring food availability and underpinning socio-economic stability within urban communities. However, current municipal strategies predominantly emphasize digitalization and technological innovation, often overlooking the significant impact of urban planning policies and architectural design on food sustainability and environmental stewardship [13,14,15]. Addressing food security within smart cities necessitates a focused commitment to developing efficient, eco-friendly food systems through integrated urban planning and architectural design policies.

Previous research has examined governance models and the readiness of smart cities [18,19,20,21,22,23]. In the existing literature, researchers have employed diverse approaches to measure and evaluate the complex realities of smart cities [24,25,26,27,28]. Several studies have explored the interrelations among urban planning, architectural design, and food security, yet most address these dimensions in isolation rather than integrating them into a comprehensive framework. Reference [29] presents a case study from Jakarta examining government policies that fail to support sustainable urban agriculture and food security. A study by Panjaitan et al. [15] examines the Indonesian government’s approach to addressing the knowledge gap in transforming management processes and standardizing smart city development, focusing on smart city projects that have been funded but have remained incomplete since 2017.

Pratama [30] examined smart city policies in four Indonesian cities—Yogyakarta, Surabaya, Magelang, and Madiun—by comparing each city’s medium-term development planning documents using a policy narrative analysis approach. The study analyzes the factors influencing the search for and dissemination of information on smart city digital platforms, drawing on a survey of eight Indonesian smart cities: Bandung, Banjarmasin, Denpasar, Jakarta, Makassar, Medan, Semarang, and Surabaya [31]. A bibliometric analysis by [32,33] highlighted a transition from an initial emphasis on technology and sustainability to broader aspects of sustainable and sociological development. These findings underscore the need for an interdisciplinary approach and the enhancement of regional and international partnerships to address complex urban challenges.

Mahesa et al. [20] systematically examined how specific smart city indicators influence urban food security outcomes. In this context, urban food systems encompass the interconnected network of activities, actors, infrastructure, and policies that govern the production, processing, distribution, consumption, and waste management of food within urban areas, as well as their linkages to peri-urban and rural food sources [34,35,36]. Through a comprehensive evaluation framework, we quantitatively assess the impact of each smart city dimension—governance, infrastructure, and technology application—on food resilience metrics in urban areas. This study makes an original contribution by developing an integrated framework that links urban planning policies, architectural design, and sustainable food systems within the context of smart cities, an approach that has not been previously explored in the literature.

In contrast to previous studies that have examined policy and design dimensions in isolation, this research analyzes the spatially dynamic interplay between these two elements in fostering urban food security across six case study cities. By focusing on cities at various stages of smart city transformation, the study captures the variations in how policy and design factors interact under diverse governance systems, technological adoption levels, and urban planning approaches. Although the analysis does not employ longitudinal time series data, the spatial heterogeneity among the selected cities provides a basis for assessing how the relationships among variables shift across contexts. This approach enables the study to reveal evolving interaction patterns that reflect differences in institutional priorities, infrastructure readiness, and socio-economic conditions, which shape urban food security outcomes. The findings not only identify key determinants of food security, but also offer context-specific recommendations, tailored to varying local circumstances. The findings provide a critical foundation for developing a smart city model grounded in food sustainability and offer global insights into integrating SDG 2 (Zero Hunger) and SDG 11 (Sustainable Cities and Communities) within urban development strategies.

1.1. Literature Review

Smartness refers to the extent to which urban food systems integrate advanced technologies, data-driven decision-making, and innovative governance to enhance the availability, affordability, and sustainability of food within smart cities. Its operationalization entails the convergence of Industry 4.0 and Agriculture 4.0 practices via mechanical, electronic, and communication subsystems in controlled environments, enabling real-time monitoring through sensors and automated responses that optimize resource utilization, minimize manual intervention, and increase crop yields [37]. This intelligence encompasses the adoption of blockchain and the Internet of Things (IoT) in the food supply chain to ensure traceability, preserve provenance, and maintain transparency in digital food logistics, thereby bolstering consumer trust and mitigating inefficiencies [38].

Smart logistics networks, underpinned by the Internet of Everything (IoE), further enhance efficiency through real-time tracking, demand forecasting, and secure data exchange, which are critical for ensuring timely food distribution in urban contexts [39]. Smartness also integrates climate-smart and nature-based approaches that embed biodiversity conservation, reduce dependency on chemical inputs, and employ portable digital decision-support systems to reinforce the resilience of food production [40]. From a technological infrastructure perspective, IoT-enabled agricultural frameworks provide the continuous monitoring of soil, water, plant health, and nutrient requirements, leveraging big data analytics to enable adaptive management and improve both yield and quality [41].

The 100 Smart Cities initiative in Indonesia involves two stages of implementation, based on the selection of cities and districts. The first stage includes cities such as Semarang, Singkawang, Makassar, Bogor, Tomohon, Jambi, Bandung, Cirebon, Bekasi, Sukabumi, Samarinda, Tangerang, and South Tangerang, as well as districts like Sleman, Badung, Siak, Mimika, Gresik, Sidoarjo, Purwakarta, Kutai Kartanegara, Banyuasin, Pelalawan, Bojonegoro, and Banyuwangi. The second stage is broader in scope, consisting of 50 regions, including major cities like Surabaya, Medan, Denpasar, Yogyakarta, Surakarta, Depok, Banjarmasin, Palembang, Pontianak, Manado, Padang, Pekanbaru, and Mataram, along with their respective districts, such as Sukoharjo, Boyolali, Banyumas, Magelang, Bantul, Bogor, Kulon Progo, Sumenep, Langkat, and others. The selection of these cities and districts is based on their potential and infrastructure readiness to support digital transformation and the development of sustainable smart cities nationwide [20].

This study builds upon the work of Mahesa et al. [20], which shows that Semarang ranks as the city with the highest readiness (97%) for implementing smart city initiatives among Indonesian cities. The city’s infrastructure and superstructure achieved maximum scores, with all smart city pillars reaching their highest levels. Makassar recorded a smart city readiness score of 91%, with 65/74 for the main elements and 66/70 for smart city pillars. Jakarta demonstrates a high level of smart city readiness (91%), with scores of 63/74 for the main elements and 68/70 for smart city pillars. This score reflects Jakarta’s advanced digital and physical infrastructure, as well as its technology-based public service systems. Samarinda holds a high smart city readiness score of 86%, with 61/74 for the main elements and 63/70 for smart city pillars. Medan has a smart city readiness score of 79%, with 60/74 for the main elements and 54/70 for smart city pillars. In contrast, Surabaya records the lowest smart city readiness score (66%), with 61/74 for the main elements but only 35/70 for smart city pillars.

1.2. Research Questions

1.

How are public policies interlinked with urban spatial design in shaping food security within cities, and how do these interlinkages contribute to the transformation of smart cities?;

2.

What are the key determinants of food security across cities with varying levels of smart city readiness, and how can policy frameworks and urban design synergistically support it?;

3.

In what ways can an integrated model of policy and urban design enhance urban food security?

2. Materials and Methods

2.1. Study Area

This study was conducted in Indonesia, focusing on cities designated as smart cities. As of 2024, Indonesia’s population stood at 281.6 million, distributed across 514 administrative regions, comprising 416 districts and 98 cities [42]. According to the Ministry of Communication and Information Technology of the Republic of Indonesia, 75 cities are currently in the process of smart city development [43]. These cities exhibit diverse characteristics that can be analyzed in terms of policy and design. As such, this research will explore the relationship between urban planning policies and architectural design in promoting food security and environmental management.

The analysis of these 75 cities was followed by the selection of six case study cities, namely Semarang, Makassar, Jakarta, Surabaya, Samarinda, and Medan, as detailed in

Table 1. The profile of cities selected to represent key islands. Source: [20,44,45,46,47,48,49].

City Name	Location	Population (People)	Annual Urban Growth Rate	Area (/sq.km)	Percentage of Indonesia	Smart City Website
Semarang	Java Island	1694.74	0.90%	34,337.49	1.81%	https://smartcity.semarangkota.go.id (accessed on 28 March 2025)
Makassar	Sulawesi Island	1474.393	−0.29%	45,330.55	2.40%	https://makassarkota.go.id/ (accessed on 28 March 2025)
Jakarta	Java Island	10,684.946	0.31%	660.98	0.03%	https://smartcity.Jakarta.go.id (accessed on 28 March 2025)
Samarinda	Kalimantan Island	861,878	1.43%	126,981.28	6.71%	https://samarindakota.go.id/smart-city/smart-society (accessed on 28 March 2025)
Medan	Sumatra Island	2474.166	1.45%	72,460.74	3.83%	https://smartcity.pemkomedan.go.id (accessed on 28 March 2025)
Surabaya	Java Island	3,009,286	0.42%	48,036.84	2.54%	https://surabaya.go.id/ (accessed on 28 March 2025)

Note: Population figures are presented in terms of the number of people as of 2023. The annual urban growth rate is expressed as a percentage per year (percentage per year). Area is measured in square kilometers (sq.km). “Percentage of Indonesia” refers to the proportion of each city’s area relative to the total land area of Indonesia.

. These cities were chosen based on their high levels of preparedness and exemplary practices in integrating policies and designs that promote sustainable food systems and effective environmental management. The selection also reflects representation across key islands in Indonesia and considers criteria such as the availability of regional logistics facilities and supporting infrastructure.

2.2. Research Framework and Design

This study employs a mixed-methods approach, integrating quantitative and qualitative analyses within a framework that combines policy and urban planning design. The selection of a mixed-methods approach is highly appropriate for conducting socio-technical studies on smart cities [50], constructing theoretical models of behavior and sustainable usage [51], and effectively identifying causal relationships among determinants and the perceived success of cross-sectoral collaboration within smart city initiatives [52]. The research framework encompasses urban planning policies related to food, environment, energy, and urban spatial planning. Additionally, it encompasses architectural design practices that support public space management, the development of urban farming initiatives, and the integration of design elements that contribute to food security and environmental sustainability.

To determine potential drivers of food security, the quantitative analysis involves four independent socio-economic variables that were not used in the development of food security in smart city areas. The selection of variables for this purpose was guided by their successful use in previous studies to analyze country-level phenomena related to development issues, including food security [53,54,55]. These independent variables include GDP per capita, infrastructure development, water and sanitation provision, and energy. The per capita GDP is a measure of a country’s economic growth and standard of living and has been shown to have a significant impact on food security [7,10]. Most food security indicators refer to the access and utilization of food [11], as in the Global Hunger Index (GHI) or Food Consumption Score (FCS) [56,57]. However, several studies employ alternative measures such as food expenditure [20].

The qualitative analysis employed document analysis of municipal policy documents, strategic plans, and regulatory frameworks (

Table 2. Summary of primary datasets and their applications in this study.

Dataset	Data Source	Application in Study
Food Security Index	Food Security and Vulnerability Atlas (FSVA) [58]	Measuring food security in smart cities in Indonesia to link it with other variables.
Smart City Readiness Data	Data in Brief, Mahesa et al. [20]	Assessing the readiness of 6 major cities in Indonesia to become smart cities based on smart city elements and pillars.
GRDP	Central Bureau of Statistics (Indonesia)	Measuring the economic capacity of a city to support food security policies and related infrastructure.
Food Expenditure	Food Security and Vulnerability Atlas (FSVA) [58]	Measuring the contribution of food expenditure to food security in each city.
Data Infrastructure & Social	Data in Brief, Mahesa et al. [20]	Using data on physical and social infrastructure to analyze its impact on food security.
Government Policy Data	Policy documents from the local government/the government website	Assessing policies that support food security and sustainability through smart city policies.

). This process involved reviewing policies related to food security, urban agriculture, infrastructure, and smart city development to assess their alignment with sustainable urban food systems. The qualitative component complemented the quantitative findings by contextualizing the statistical results, identifying policy gaps, and revealing the governance mechanisms that influence the achievement of food security in smart cities.

2.3. Data Used

This study employs a comprehensive, literature-based data collection approach by systematically gathering secondary data from multiple authoritative sources to ensure a robust analysis. The dataset comprises six key indicators influencing urban food security and smart city development in Indonesia. Specifically, the data includes information from 75 cities, alongside detailed data from 6 cities identified as having the highest levels of smart city preparedness.

2.4. Data Collection

This study employs a documentary analysis methodology, drawing on authoritative data sources and publicly accessible databases related to food security and urban readiness for smart city transformation. Data collection is undertaken through a systematic literature review of publications issued by governmental agencies, statistical institutions, and verified academic sources. Quantitative data are obtained secondarily from the Food Security and Vulnerability Atlas (FSVA) to measure both the degree of food security and the proportion of household expenditure allocated to food across six case study cities. Indicators of smart city readiness encompassing core components and supporting pillars are derived from the assessment framework developed by Mahesa et al. (Data in Brief) [20], which provides standardized evaluations of the urban transformation capacity. The economic capacity is quantified using Gross Regional Domestic Product (GRDP) data from the Badan Pusat Statistik (BPS). Data on physical and social infrastructure are also obtained from BPS, enabling correlation analyses with the identified food security indicators. Qualitative data on policy assessments are curated from strategic planning documents, regional regulations, and municipal master plans retrieved via official local government portals and regional policy circulars. All datasets are subsequently systematized in Table 2, which maps data sources to their respective applications within the research framework.

2.5. Data Analyzed

This study employs a tripartite analytical framework to examine the determinants of urban food security. The primary analysis applies Multiple Linear Regression (MLR) using IBM SPSS Statistics (Version 27) to quantitatively assess the relationship between food security and four key predictors: (1) Regional Gross Domestic Product (GDP); (2) food expenditure; (3) access to electricity; and (4) clean water access. These variables were selected based on their established theoretical relevance in urban planning policy formulation and sustainable infrastructure development [20]. The econometric model specification follows the Ordinary Least Squares (OLS) estimation method, expressed as follows:

FSI = β₀ + β₁GDP + β₂FExp + β₃Elec + β₄Water + ε

where FSI represents the Food Security Index, β₀ is the intercept term, β₁–β₄ are regression coefficients for respective predictors, and ε denotes the error term. This modeling approach offers robust empirical insights into the socio-economic and infrastructural factors that shape sustainable food resilience in smart city contexts, while controlling for potential confounding variables through the inclusion of relevant covariates in subsequent model specifications.

To ensure the validity of our regression analysis, we conducted comprehensive diagnostic tests addressing four critical assumptions: the (1) linearity of relationships, (2) independence of observations, (3) homoscedasticity of residuals, and (4) normal distribution of error terms. We employed the Variance Inflation Factor (VIF) with a threshold of <5 to detect potential multicollinearity among predictors. These methodological safeguards help to mitigate biases and strengthen the reliability of our findings.

Correlation analysis and case studies are employed to assess the specific relationships between factors influencing food security in the six selected cities, which are based on their preparedness for becoming smart cities and their success in integrating sustainable food systems with environmental management. These two analyses measure the extent to which smart city variables interact, encompassing core elements and smart city pillars, as depicted in Figure 1.

3. Results

Urban planning policies aimed at supporting sustainable food systems and environmental management are not solely based on food availability, but are also influenced by accessibility and utilization, which are closely tied to the socio-economic conditions and infrastructure of the city. Indonesia has seventy-five cities with planning to become smart cities, as depicted in Figure 2.

Urban planning policies that support sustainable food systems and environmental management in Indonesia exhibit considerable regional variation. As illustrated in Figure 2, seventy-five cities across the country have been designated for development into smart cities, each with varying degrees of readiness and infrastructural capacity. This distribution reflects both regional development priorities and the socio-economic as well as geographical disparities between areas.

The concentration of economic activity and infrastructure development is significantly higher in Java and Sumatra, thereby enabling these regions to demonstrate greater preparedness for implementing smart city concepts that integrate food security strategies. Conversely, cities in Eastern Indonesia that are part of the national smart city program face more substantial challenges in terms of accessibility, connectivity, and socio-economic capacity. These factors can significantly influence a city’s ability to effectively integrate sustainable food systems.

Urban spatial planning policies must consider access to clean water and stable electricity as critical components of a food-sensitive planning approach to developing food systems and urban design [59]. Therefore, spatial interventions and environmental design, implemented through policies and urban agriculture programs, can also serve as solutions to addressing food access disparities [60]. Moreover, each city’s Gross Regional Domestic Product (GRDP) needs to be analyzed for its impact on food security, as a high economic output does not guarantee high food security if its distribution is not spatially inclusive. Together with food security and vulnerability indices, this analysis provides a basis for analyzing urban spatial policies, enabling the identification of areas requiring attention in terms of basic infrastructure. Figure 3 illustrates the food security status of 514 districts/cities in Indonesia based on data from the National FSVA 2024 [58]. The map employs a color-coded system to indicate levels of food vulnerability, with categories divided into six priority scales. Red represents highly vulnerable regions (Priority 1), while green suggests highly resilient areas (Priority 6), with some regions showing more moderate levels of vulnerability.

Based on the analysis presented in Figure 3, this study assessed the impact of the GRDP, food expenditures, access to electricity, and lack of clean water on food security in the seventy-five cities designated for smart city development in Indonesia. Before conducting the regression analysis, this study assessed multicollinearity among the predictor variables using the Variance Inflation Factor (VIF). A VIF value below 5 indicates an acceptable level of multicollinearity. As presented in

Table 3. The relationship between infrastructure variables and the basic structure of food security.

Variable	UCβ	SE	SCβ	T	Sig.	R	R Sq	Adj R	Sig. F	TOL	VIF
GRDP	−0.048	0.000	−0.505	−4.450	0.000	0.717	0.514	0.486	0.000	0.540	1.853
Food Expenditure	−0.171	0.049	−0.333	−3.508	0.001					0.770	1.299
Access to Electricity	−1.634	0.781	−0.224	−2.093	0.040					0.604	1.656
Lack of Clean Water	−0.284	0.046	−0.566	−6.111	0.000					0.811	1.233
Dependent Variable: Food Security Index

Note: UCβ = unstandardized coefficients; SCβ = standardized coefficients; SE: std. error; TOL: tolerance; R Sq = R square; and Adj R = adjusted R square; VIF: variance inflation factor.

, the VIF values range from 1.233 to 1.853, suggesting that multicollinearity is minimal (no indication of multicollinearity). The second diagnostic involved testing the normality assumption, which was evaluated through residual analysis. The results, illustrated in Figure 4, demonstrate that the residuals generally conform to a normal distribution. The normal distribution of residuals is crucial for ensuring that the parameter estimates remain unbiased. The third assumption examined was homoscedasticity, which is essential for the accuracy of standard errors and the reliability of hypothesis testing. Figure 5 presents a diagnostic plot that indicates consistent residual variance, confirming that the homoscedasticity assumption is satisfied. This analysis aims to determine whether these variables influence food security, thereby providing informed suggestions for policy implications in regions with food vulnerabilities.

Innovative architectural design can serve as a nexus between socio-economic dimensions such as life expectancy, living standards, and food production systems. One architectural innovation involves integrating Controlled Environment Agriculture (CEA) into building design. This approach addresses food security and enhances energy efficiency [61]. The potential of such innovative architectural solutions is supported by research conducted by [41,62], which proposed a building design in Indonesia measuring 44 m in length and 40 m in width. The structure features a rice cultivation chamber spanning 1344 square meters, capable of producing 24 tons of rice annually. Architectural interventions of this nature can bolster the sustainability of urban food systems in densely populated cities while contributing to improvements in Indonesia’s urban population.

The regression coefficient (ß = −0.505, p < 0.01) indicates that an increase in the GRDP reduces food security, contrary to the conventional hypothesis. This finding contradicts the assumption that a robust economy always supports food security. This reinforces the notion that the smart city planning program in the 75 cities in Indonesia has yet to achieve equitable economic development that ensures adequate food access for all socio-economic groups. Cities with a higher GRDP exhibit greater distributional inequalities, restricting food access for lower-income populations. [63,64,65]. Smart city programs that prioritize urban efficiency—such as transportation or digitization—often overlook local food systems, resulting in economic growth that is not distributed inclusively [65]. Cities in the Global South demonstrate that industrialization without redistributive policies exacerbates food inequality, as observed in Indonesia. Furthermore, the increase in the GRDP driven by the industrial and service sectors does not directly impact the food sector [66,67,68].

One innovative architectural design strategy to address disparities in food access involves tailoring designs to meet community needs. Low-income populations can be supported through local markets backed by robust policies that both incentivize corporate participation in community-building partnerships and mandate the use of modern distribution channels [69]. Given their significance in cultural and socio-economic dimensions, local markets play a pivotal role in poverty alleviation and strengthening food security [70]. Moreover, this approach can mitigate food access inequalities by optimally integrating smart city initiatives with environmental and social considerations. A significant negative impact is also observed between food security and expenditures (ß = −0.333, p > 0.001). This result indicates that as the proportion of household spending on food increases, food security decreases. This underscores the high level of academic vulnerability, as a substantial share of household income is allocated to basic needs [71,72]. Therefore, in smart city planning, focusing on reducing food costs through strengthening local distribution systems and promoting urban farming should be a priority.

Appropriate architectural design can reduce food costs by utilizing urban open spaces for subsistence agriculture, as it enhances access to fresh produce, lowers food expenditures, and fosters community engagement in sustainable farming practices [73]. Such architectural interventions are critical, as evidenced by research in [74] conducted in a major Indonesian city, which demonstrated that diverse urban agricultural production can meet the food needs of community members, with some becoming self-sufficient farmers. Urban agriculture also provides an income source for city residents and promotes community sustainability. However, to develop innovative architectural designs, it is essential to address three key areas requiring further attention to ensure a transformative impact: a stronger conceptualization of urban contexts, a more precise definition and articulation of governance and policy frameworks, and a deeper focus on issues of power and inequality [75].

Access to electricity has a significant impact on food security (ß = −0.224, p < 0.040). This finding underscores the critical role of electrical energy infrastructure within urban food systems. Energy availability enhances the efficiency of food distribution chains, as urban food security requires the coordinated integration of architectural design with essential infrastructure. This observation aligns with Bhattacharyya et al. [76], who highlighted the substantial influence of electrification on agricultural production and food distribution. Moreover, Kemmler [77] corroborates the nexus between infrastructure development and improved food access, mediated by electricity availability. Access to electricity plays a pivotal role in food storage and distribution, while its absence significantly impedes these processes [78].

Access to clean water emerges as the strongest predictor, significantly negatively affecting food security (ß = −0.566, p < 0.000). This result suggests an inverse correlation between the proportion of the population lacking access to clean water and the level of food security. Access to clean water significantly affects food security through its dual role as both a vital natural resource for agricultural production and an essential ecosystem service that sustains life [79,80]. This result is consistent with Falkenmark & Rockstrom (2004) [81], who elucidate the detrimental impact of water scarcity on food production and supply stability. Empirical evidence from Mekonnen & Hoekstra (2016) [82] further substantiates the critical effects of limited water access on food security. Consequently, municipal policy failures to ensure equitable access to clean water precipitate multifaceted vulnerabilities among urban populations, including heightened food insecurity.

Innovative architectural design approaches for sustainable water management in urban environments can incorporate water metabolism frameworks [83]. Additionally, the Urban Water Use (UWU) model serves as a strategic planning instrument for mitigating the infrastructural and sanitation challenges of urbanization, as presented by Richter et al. [84], offering a viable concept for advancing urban architectural design. For more complex implementations, System Dynamics Models (SDMs) can guide the development of innovative architectural designs that integrate urban agriculture, such as the East End Market Urban Farm, with energy generation via energy centers and wastewater reclamation via reclamation facilities. They can also integrate rainwater harvesting, solid waste management, and biogas production from landfills [85]. This design paradigm supports urban food production through a stable water supply and alleviates pressure on natural resources, thus laying a foundational framework for sustainable smart city development. In this broader context, sustainable regional development requires careful planning that aligns policy relevance with architectural design. The analysis results have illustrated how basic infrastructure variables impact food security. Accordingly, the recommendations for policy relevance and architectural design in

Table 4. Recommendations for policy relevance and architectural design of 75 cities.

Variable	Sig.	Policy Relevance	Relevance of Architectural Design
GRDP	0.000	Redistributing economic growth to ensure fair access to food.	Design of inclusive local markets to address distribution inequality and support social and cultural community interactions.
Food Expenditure	0.001	Enhancing local food distribution systems and lowering food-related expenses.	Optimization of urban open spaces for urban farming and enhancing community engagement in food production.
Access to Electricity	0.040	Development of energy infrastructure integrated with the food system.	Design of self-sufficient, off-grid buildings that support food distribution and storage chains.
Lack of Clean Water	0.000	Ensuring equitable access to clean water and implementing sustainable sanitation systems.	Application of urban water metabolism and reuse systems, and HR integration for water, energy, and agriculture.

Source: created by authors, 2025.

are based on an analysis of the interrelationships between variables.

4. Discussion

4.1. Case Study Discussion: Representation of Cities from Key Islands

The smart city readiness of the six cities selected as representations of central islands in Indonesia can serve as a reference for other cities based on similarities in their profiles and approaches. The readiness level is measured based on the main elements of smart cities and the smart city pillars outlined in

Table 5. Regional readiness.

City	Smart City Main Elements			Smart City Pillars						Regional Readiness
	Stc [29]	Ifs [28]	SSt [17]	SGv [12]	SBr [16]	SEc [8]	SLv [6]	SSc [10]	SEv [18]
Semarang	86.21	100	100	100	100	100	100	100	100	97%
Makassar	86.21	100	42.86	100	87.5	75	100	100	100	91%
Jakarta	86.21	100	35.71	100	100	75	100	100	100	91%
Samarinda	89.66	82.14	42.86	100	75	100	100	100	83.33	86%
Medan	86.21	89.29	35.71	100	62.5	50	100	100	66.67	79%
Surabaya	93.10	100	21.43	50	50	50	50	50	50.00	66%

(Source: Mahesa et al. [20]).

. The readiness analysis is also accompanied by an analysis of the policies of each city, derived from official government websites and policy documents.

These results indicate that Semarang possesses a robust policy framework, infrastructure, and institutional capacity to support smart city development. Semarang’s city profile also demonstrates significant readiness, notably when correlated with the analysis of access to clean water, electricity, and food expenditure as factors influencing food security. The local government in Semarang has prioritized equitable access to clean water and electricity, with annual increases in both, thereby enhancing the welfare of the population [86]. The maximum scores in digital, public, and social infrastructure demonstrate the city’s capability to support technology-based distribution systems and social networks. Semarang also actively promotes urban farming and gardening practices as part of urban space design through community land programs, household agriculture training, and integration with thematic village programs [87]. In contrast to Semarang, which demonstrates a high capacity in both infrastructure and local food initiatives, Makassar’s strengths are focused on its digital and social infrastructure.

Makassar achieved maximum scores in digital and social infrastructure, indicating that equitable access to the Internet, educational facilities, and social services supports community mobilization. Additionally, water and energy management levels are high, aligning with the findings from the analysis of food security relationships. Developing waste and waste management and implementing more efficient energy utilization form the foundation of Makassar’s smart environment [88]. However, urban farming is still not prioritized in official policies, and there is no integration between local food data and the city’s dashboard, resulting in a reliance on food distribution from external regions. In contrast, Jakarta possesses substantial institutional capacity, yet faces pressing challenges such as population pressures, land scarcity, and inequalities that must be addressed [89,90]. The dependency on external food sources and high food expenditures present significant challenges for the city due to the weak regulatory framework regarding urban farming and edible design.

Samarinda demonstrates significant progress in the connectivity of basic infrastructure, strengthening social and institutional capacities, and revitalizing economic centers to improve service quality [91]. Although public green space provision is in place, urban farming still does not receive priority in official policies, resulting in a reliance on external food supplies. This dependency has led to fluctuating food prices and high household expenditure burdens.

Turning to Medan, the city is developing yet has a strong foundation, but requires strengthening in institutional, regulatory, and system integration. Medan faces challenges due to a gap between local production and consumer demand, as gardening is not structurally available. However, the city is currently developing a policy to achieve the vision of a well-organized, comfortable, modern, and competitive Medan [92].

In comparison, Surabaya ranks the lowest among the six cities, as cross-sector policy integration and the implementation of smart city programs remain relatively weak despite the city having adequate basic and digital infrastructure. Surabaya also faces land scarcity for food production and relies on food supplies from other areas in Java [93]. Nevertheless, the city has demonstrated a remarkable ability to address these challenges through a community-based approach, with programs such as vegetable gardens, waste bank initiatives, and the revitalization of small land plots into productive green spaces [94].

4.2. Correlation Analysis Between Smart City Readiness Indicators and Food Security

A smart city transcends mere technological implementation; it encompasses public policy, social infrastructure, bureaucracy, and institutional capacity in a complex interplay. Urban governments must adeptly devise methodologies for conducting comprehensive analyses of energy consumption behaviors through the lens of smart city frameworks, thereby enabling practical guidance and management that facilitate transformative enhancements of urban systems [95]. Prior studies have conducted correlational analyses on discrete facets of smart city domains, such as electrical power system load profiles [95], air quality, and traffic dynamics [96,97,98]. However, within the Indonesian smart city context, there is a critical imperative to prioritize human capital development in conjunction with bureaucratic or regional policy frameworks and institutional readiness to bolster food security resilience.

The correlational analysis indicates that smart city preparedness is not solely contingent on physical or digital infrastructure, but also on the synergy of social, policy, institutional, and economic dimensions that collectively impact food security resilience. The factors that interact to support the success of sustainable food systems in cities with the highest readiness levels can provide valuable insights for identifying strengths and challenges. The correlation heatmap in Figure 4 illustrates the degree of the relationship between public policy factors, social infrastructure, and financial capacity in strengthening or undermining food security in smart cities. This correlation analysis aims to evaluate how integrating urban planning policies and architectural design can support food security and environmental management in cities with high readiness levels.

The correlation analysis in Figure 6 reveals a complex interplay among various facets of the readiness indicators for the six smart cities exhibiting the highest preparedness levels. The aspects examined encompass urban planning policies and institutional readiness, collectively influencing food security resilience. The Pearson correlation coefficients in Figure 6 were interpreted following conventional thresholds: |r| ≥ 0.70 as strong, 0.40 ≤ |r| < 0.70 as moderate, and |r| < 0.40 as weak correlations. Positive values indicate a direct relationship between variables, whereas negative values indicate an inverse relationship. To assess statistical significance, two-tailed significance tests were applied for each correlation coefficient, with p-values < 0.05 considered statistically significant. Only correlations meeting this criterion were deemed meaningful for the interpretation of the results.

The correlation results indicate that human capital (HC) exhibits a highly significant negative correlation (−1) with several service delivery indicators, public policy measures, and bureaucratic efficiency metrics, including public services (PSVs), Efficient Bureaucracy Management (EBM), Effective Public Policy (EPP), and the Harmonization of Regional Layout (HRL). This inverse relationship suggests that regions with higher human capital quality experience less efficient or less synchronized policy and bureaucratic systems relative to community needs. These findings align with [99], which posits that bureaucratic inertia and hierarchical structures in several Indonesian cities are excessively rigid, often undermining required competencies and reducing effectiveness. Urban planning plays a pivotal role by fostering policies that systematically enhance Human Resource (HR) capacity while ensuring an adaptive bureaucratic framework that facilitates the design of environments conducive to HR development. Participatory processes involving community engagement contribute substantially to urban planning and governance, while governments can leverage information technology as a strategic tool for optimizing public service management [100]. Furthermore, innovations to improve public services, harmonization, and efficient administration, such as utilizing IoT data to optimize city operations and inform policy decision-making, are critical advancements [101].

The correlation between regional policies (RPs) and human capital (HC) is −0.91, indicating a significant misalignment between implemented policies and the developmental needs of human capital. This finding warrants serious attention to ensure that policies are more strategically directed toward strengthening human capital. This issue primarily arises from a mismatch between educational attainment and employment, particularly among individuals engaged in precarious or part-time work with low competency levels [102]. Zhao et al. [103] also highlight that fiscal subsidies may exacerbate the human capital mismatch. Therefore, these policies require comprehensive evaluation to ensure that they are responsibly aligned with the tangible developmental needs.

Regional Financing Capabilities (RFCs) and physical infrastructure (PI) exhibit a notably strong negative correlation (−0.83). This result may stem from regional financing priorities focusing more on non-physical aspects. Research by [104] highlights significant regulatory harmonization challenges regarding municipal bond regulations in Indonesia, particularly inconsistencies related to the issuance of regional bonds for strategic projects. Their study recommends adopting a more effective revenue bond model to finance strategic infrastructure projects, emphasizing that sustainable development necessitates the use of targeted financial instruments, especially for physical infrastructure undertakings.

Regional policies (RPs) demonstrate a strong positive correlation with Efficient Bureaucracy Management (EBM), Effective Public Policy (EPP), public services (PSVs), and the Harmonization of Regional Layout (HRL), with a coefficient of 0.91, as well as with Regional Institutional Readiness (RIR) at 0.86. These outcomes indicate that robust regional policies bolster bureaucratic efficiency, public service delivery, and spatial planning alignment. This result aligns with the findings of [105] that bureaucratic autonomy significantly influences the completion rates of public sector projects, highlighting the critical role of bureaucratic quality in the overall effectiveness of policy implementation. Furthermore, Ding et al. (2021) [106] reveal that bureaucracy aligned with socio-cultural contexts enhances the nexus between policy and outcomes. This is particularly important in urban spatial planning and architectural design, as it facilitates the development of infrastructure that is consonant with smart city principles. Effective urban spatial planning transcends the physical structuring of buildings; it necessitates the establishment of settlement networks with classified service centers to hierarchically and integratively support community needs [107].

Regional Institutional Readiness (RIR) also plays a crucial role, exhibiting a strong positive correlation of 0.86 with regional policies (RPs) and a moderate positive correlation of 0.59 with public services (PSVs), Efficient Bureaucracy Management (EBM), Effective Public Policy (EPP), and the Harmonization of Regional Layout (HRL). These findings reinforce the premise that entities with robust institutional readiness significantly facilitate the implementation of policies and the delivery of effective public services. This finding is corroborated by [74,108], who emphasize that a strong institutional capacity, including service provision, evaluation, and operational capabilities, is a critical determinant of successful public service delivery. However, the negative correlations between RIR and the Food Security Index (IKP) at −0.93 and Regional Gross Domestic Product (GRDP) at −0.87 reveal fundamental challenges, where institutional readiness does not necessarily translate into sustainable economic outcomes or improved welfare. Research by Huseynov (2020) [109] identifies additional influencing factors, such as access to agricultural technology, education, and infrastructure, which also play pivotal roles, indicating that RIR alone cannot guarantee enhanced food security. Therefore, urban architectural design must be aligned to support food and economic productivity through initiatives like urban farming and integrated environmental management systems, harmonizing with the urban spatial framework.

The Food Security Index (IKP) demonstrates a notably strong negative correlation with regional policies (RPs) at −0.81, Regional Institutional Readiness (RIR) at −0.93, and public services (PSVs) at −0.62. These results suggest that although regional policies, institutional readiness, and public services are critical factors, improvements in these areas are paradoxically associated with a decline in the Food Security Index. This result indicates that despite well-established policies and institutions, the absence of a strong synergy between policy frameworks, spatial planning, and tangible environmental design undermines the effective support of food security. The necessity for a systemic approach to urban food governance, encompassing multiple dimensions such as spatial planning and policy formulation processes, is thus underscored [75]. Significant negative correlations are also observed with Efficient Bureaucracy Management (EBM), Effective Public Policy (EPP), Creating Competitive Industrial Ecosystems (CCIE), and the Harmonization of Regional Layout (HRL), implying that optimization in these domains has yet to effectively support food security, and warrants further evaluation. Conversely, variables such as regional community organization (RCO) and the Gross Regional Domestic Product (GRDP) exhibit positive correlations with the IKP at 0.16, 0.51, and 0.63, respectively. These findings align with Béné et al. (2021) [110], who emphasize urban agricultural skills training as vital for enhancing food productivity. Therefore, strengthening regional community organizations and fostering a more robust regional economic environment are essential for strengthening food security.

4.3. Smart City Approach to Food Security

Urban agriculture in Indonesia is recognized as a multidimensional approach to addressing urban challenges, offering several potential social, economic, and environmental benefits. By expanding urban farming, Indonesia can draw from global best practices to tackle local challenges while creating healthier, more sustainable urban living spaces (Saputra et al., 2024) [93].

Table 6. National strategy recommendations.

Type of Approach	City Name	Characteristics	National Strategy
Holistic Benchmark	Semarang and Makassar	Strong infrastructure, cross-sector policies, and productive public spaces.	Replication of best practices; make the city a reference for training and incubation.
Technological—fragmented	Jakarta	High technology, but the food system is not yet integrated.	Spatial interventions and local food regulations.
Emerging Transition	Medan and Samarinda	The basic infrastructure is sufficient, but it requires the design of an integrated food system and the implementation of effective policies.	Strengthening regulations and institutions, fiscal incentives.
Local Communities and Initiatives	Surabaya	Strong social innovation and space design.	Institutionalization of citizen innovations into official city policies.

Source: created by the authors, 2025.

presents national strategy recommendations to refine the smart city framework, serving as a benchmark for other cities based on their approach types and the characteristics of cities with high readiness levels.

Semarang and Makassar can serve as national models for smart city planning through ecological and food-based approaches. These two cities excel due to their balanced integration of strategic policies, infrastructure, and community participation, positioning them as exemplary cities with high readiness for becoming smart cities and for developing sustainable local food systems. The success of Semarang should also involve expanding urban farming in urban areas by integrating local food data into the smart city dashboard system. Additionally, strengthening partnerships with the private sector and agritech startups is crucial. Meanwhile, Makassar holds the potential to be a pioneer of a smart city based on local wisdom and community empowerment. Jakarta, although technologically advanced, faces significant spatial and social pressures. Therefore, policy recommendations for the city include innovations based on micro-design, cross-sectoral policies, and community participation, which are key in achieving food security. A city-based food system is also appropriate for Jakarta, with incentives for local production, such as using rooftops for vertical farming, creating edible landscapes, and developing productive open spaces. These initiatives are vital due to the city’s challenges with land distribution, food access equity, and dependency on external food sources.

As a major city with significant regional influence, Medan faces challenges in food systems and basic infrastructure. However, with the right policy interventions and design strategies, Medan could become a model for smart city development focused on local food security and community empowerment. A strategic push for urban farming, integrating sustainable design approaches (such as edible infrastructure) to provide access to water and waste management, could significantly benefit the city. Similarly, Samarinda, adopting a similar approach to Medan, has the potential to become a laboratory for local food systems by facilitating integration between the food, water, and energy sectors to create a systematic food security model. Surabaya could serve as an example of a bottom-up smart city, demonstrating that a smart city does not necessarily require high levels of digitization. Instead, it can be built through social innovation, intelligent space design, and active citizen participation. Surabaya is particularly relevant as a role model for cities with limited resources but high social innovation. Recommendations for cities of this type include integrating urban farming into the spatial planning and control planning processes, establishing a simple data-based food monitoring system at various community levels, and formalizing these practices as part of the smart city policy framework.

5. Conclusions

Urban planning policies and architectural design are pivotal in creating sustainable food systems in Indonesia’s smart cities. Food security is determined not only by the availability of food, but also by its accessibility, utilization, and the socio-economic contexts of each city. The experiences of Semarang and Makassar show that integrating integration infrastructure, policy frameworks, and community participation is central to building sustainable food systems. Jakarta, despite its advanced technological capabilities, faces spatial and social challenges that limit equitable access to food. Medan and Samarinda show potential for transition through stronger institutional frameworks and integrated food policies. At the same time, Surabaya illustrates how social innovation and community-driven initiatives can offset the limitations of technological resources. However, a high GRDP alone does not guarantee equitable food access across all social groups, highlighting the importance of economic distribution strategies that are spatially inclusive.

5.1. Policy Implications

Best practices from cities like Semarang and Makassar, both demonstrating advanced smart city readiness, serve as national models by combining urban farming initiatives with supportive public infrastructure in a multidimensional approach. Cities facing spatial challenges, such as Jakarta, illustrate the need for micro-scale design interventions and targeted regulations to reduce food access disparities. Surabaya exemplifies a bottom-up smart city model, showing that successful smart cities do not always rely on extensive digitization. Instead, Surabaya demonstrates that social innovation and community-driven initiatives can compensate for limited technological resources. These findings contribute to theoretical understanding by illustrating that a food-sensitive integration of urban planning and architectural design can effectively foster inclusive and sustainable urban development. They also provide evidence-based guidance for policymakers and practitioners aiming to align smart city strategies with the objectives of food security and sustainability.

5.2. Limitations and Future Research

This study employs a multiple linear regression (MLR) model, which does not account for temporal dynamics. Future research should employ dynamic models or time series data to better understand how the relationships between smart city readiness and food security evolve over time. Furthermore, qualitative case studies can provide deeper insights into the socio-cultural dimensions that influence food system resilience.