# Urban Futures: Balancing Sustainability and Privacy in the Smart City Paradigm

Ana Tomičić, Marija Adela Gjorgjioska

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Ana Tomičić, Marija Adela Gjorgjioska. Urban Futures: Balancing Sustainability and Privacy in the Smart City Paradigm. Understanding of the Digital Era: Unveiling the Dark Sides of Technology, Istanbul University Press, pp.35-57, 2025, 978-605-07-1863-8. 10.26650/B/SSc5.2025.016.004. halshs-05443304

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# Urban Futures: Balancing Sustainability and Privacy in the Smart City Paradigm

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Ana Tomičić<fnref n="1" /> ⬒ & Marija Adela Gjorgjioska<fnref n="2" /> ⬒

## Abstract

Smart cities, leveraging technologies like IoT, AI, blockchain, and surveillance, aim to enhance efficiency and sustainability but also raise ethical concerns and privacy risks. This study critically evaluates the environmental, social, and ethical impacts of these technologies through the lens of the Sustainable Development Goals (SDGs). By applying the Digitainability Assessment Framework, we explore how surveillance technologies in Beijing, New York City, and Rio de Janeiro align or conflict with SDG 10 (Reduced Inequalities), SDG 11 (Sustainable Cities and Communities), and SDG 16 (Peace, Justice, and Strong Institutions). This comparative analysis highlights ideological influences on urban governance, offering insights into whether surveillance fosters sustainable and equitable urban development. The findings contribute to the debate on balancing innovation with inclusivity and privacy in smart city paradigms.

## Keywords

Smart Cities • Surveillance Technologies • Sustainability • Privacy • Sustainable Development Goals (Sdgs)

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“Citation: Tomičić, A. & Gjorgjioska, M. A. (2025). Urban Futures: Balancing Sustainability and Privacy in the Smart City Paradigm. in B. Önay Doğan & T. Yerlikaya (Eds.), *Understanding of the Digital Era: Unveiling the Dark Sides of Technology* (p. 35-57). Istanbul University Press. https://doi.org/10.26650/B/SSc5.2025.016.004

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© This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International License. ©

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© 2025. Tomičić, A. & Gjorgjioska, M. A.

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Corresponding author: Ana Tomičić ana@aretenet.org

<footnote n="1">¹ ARETE Institute for Sustainable Prosperity, Zagreb, Croatia</footnote>

<footnote n="2">² ARETE Institute for Sustainable Prosperity, Skopje, North Macedonia</footnote>
# Introduction: Smart Cities And The Surveillance Dilemma

Smart cities are heralded as the future of urban living, utilizing digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and surveillance systems to optimize infrastructure, enhance public services, and foster sustainability (Allam & Dhunny, 2019). Governments worldwide adopt these technologies, with implementations varying based on political, economic, and cultural contexts (Konninos, 2015). These differences raise questions about how ideologies shape the design and societal impacts of smart city technologies, particularly surveillance systems. Surveillance technologies, including facial recognition, geolocation tracking, and sensor networks, are pivotal to the smart city paradigm. Proponents argue these systems enhance safety, streamline urban management, and improve resource efficiency (Narain, 2024). However, critics highlight the ethical dilemmas, privacy risks, and disproportionate effects on marginalized groups (Sannon & Forte, 2022).

The Digitainability Assessment Framework (Gupta et al., 2023) serves as a methodological tool to evaluate the synergies and trade-offs between digitalization and sustainability. By incorporating dimensions such as social equity, environmental impacts, and economic feasibility, this framework provides a holistic approach to assessing whether there is enough evidence that digital technologies like surveillance align with or undermine Sustainable Development Goals (SDGs). To enhance clarity, we have updated the original design of the framework while maintaining the integrity of its conceptual foundation.

This chapter investigates how ideological frameworks shape the integration of surveillance technologies in urban governance, the trade-offs and synergies of these technologies in achieving SDG 10, SDG 11, and SDG 16, and their broader implications for privacy, equity, and sustainability. To address these objectives, it examines three cities: Beijing, New York City, and Rio de Janeiro. Each self-describes as and represents a distinct ideological model—state-centric socialism, capitalist liberal democracy, and a mixed economy—providing a comparative lens to evaluate the societal outcomes of surveillance technologies. It is important to clarify that this chapter does not seek to validate or critique these ideological claims or assess the veracity of their self-descriptions. Instead, the focus is on how these ideological frameworks influence the deployment and societal impacts of surveillance technologies. By applying the DAF, the analysis systematically explores how these technologies align with or diverge from the goals of sustainable urban development. This chapter moves beyond simplistic narratives of surveillance as inherently beneficial or harmful. Instead, it provides a nuanced exploration of the intersections between ideology, technology, and societal outcomes. By maintaining a focus on detailed case studies and rigorous analysis, it offers actionable insights for policymakers and urban planners striving to balance innovation with inclusivity, privacy, and sustainability within the smart city paradigm.

# Background and Literature Review: Surveillance and Sustainability in Smart Cities

The adoption of surveillance technologies in smart cities has become a global trend, driven by the promise of enhanced security, efficient urban management, and sustainable development (Kitchin, 2014; Gupta et al., 2023). However, the integration of these technologies into urban life varies significantly based on the political, economic, and cultural context in which they are deployed. Beijing, New York City, and Rio de Janeiro each represent different ideological models—state-centric socialism, capitalist liberal democracy, and a mixed economy, respectively. Understanding how surveillance aligns with these diverse governance frameworks is crucial for unpacking its complex social, environmental, and economic implications.

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## Surveillance Technologies: Tools of Control and Management

In smart cities, surveillance technologies such as facial recognition, geolocation tracking, and sensor networks are deployed for a variety of purposes. These include monitoring public spaces, managing traffic, optimizing resource use, and enhancing security. However, their implementation reflects the broader ideological goals of the governments using them. Surveillance can serve as a tool of social control, population management, and data extraction, with varying degrees of public acceptance and impact on urban life (Zuboff, 2019).

Beijing adopts surveillance as part of a state-centric approach to governance. The Chinese government implements technologies like facial recognition, social credit systems, and networked cameras to monitor behavior, manage social stability, and promote collective welfare. Research suggests that the public largely views these systems positively, as they align with cultural norms that prioritize social harmony and collective security over individual privacy (Qin & Qi, 2021). This perspective is reinforced by China's governance model, which emphasizes the state's role in safeguarding public order and the common good (Chui, Vasant & Liu, 2019). These interpretations of China's political model are subject to extensive debate, particularly within the European left, which engages with varying conceptualizations of socialism in the Chinese context (Gjorgjioska & Tomičić, forthcoming).

In contrast, New York City illustrates how surveillance technologies operate within a capitalist, liberal-democratic framework. Here, surveillance is driven not only by the state's interest in public safety but also by private corporations seeking to monetize data. The city employs a mix of public and private surveillance networks, including extensive CCTV coverage, facial recognition, and predictive policing. Critics argue that these technologies disproportionately target marginalized communities, reinforcing existing social hierarchies and serving economic interests (Brayne, 2020; Zuboff, 2019). This approach reflects the broader neoliberal emphasis on security, efficiency, and market-based solutions to social issues, often at the expense of privacy and social equity (Eubanks, 2018).

Rio de Janeiro, operating within a mixed-economy model, showcases a third approach to surveillance. The city's implementation of surveillance technologies is framed as a response to crime and public safety concerns, particularly in favelas and low-income areas. While the government justifies these systems as necessary for security and social welfare, research indicates that they often exacerbate social inequalities, disproportionately monitoring and controlling poor communities (Freeman, 2014; Pilo', 2018). This context highlights the contradictions inherent in deploying surveillance under neoliberal policies, where state intervention intersects with deep socio-economic disparities (Goldstein, 2010; Monahan, 2006).

## Surveillance and the SDGs

Technology in smart cities are often justified in terms of their alignment with the SDGs, particularly SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice, and Strong Institutions) (United Nations, 2015). However, the real-world impacts of surveillance technologies vary depending on the governing ideology and the socio-economic context of their deployment (Bibri & Krogstie, 2017).

In Beijing, surveillance is integrated into a broader strategy aimed at achieving social harmony, security, and efficient urban management. Studies suggest that these technologies have contributed to reduced crime rates and improved public order (Chui, Vasant & Liu, 2019). The state-centric model allows for a centralized approach to urban governance, where the government actively shapes surveillance policies to align with collective welfare objectives. As a result, surveillance in Beijing often supports SDG 11 by fostering a sense of security and social stability (Qin & Qi, 2021). However, the trade-offs include

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potential constraints on individual freedoms and privacy, highlighting the tension between collective security and personal autonomy.

For New York City, surveillance technologies intersect with capitalist imperatives. The deployment of these technologies is driven by both public safety concerns and the interests of private entities involved in data collection and analysis (Zuboff, 2019). Surveillance here serves to protect economic centers and manage public spaces, often leading to practices that reinforce social inequalities. For example, predictive policing technologies have been critiqued for their role in targeting minority communities, exacerbating racial and economic disparities (Brayne, 2020). This approach raises questions about the compatibility of such surveillance with SDG 10 (Reduced Inequalities) and SDG 16, as it appears to prioritize economic security over social equity and justice (Eubanks, 2018).

In Rio de Janeiro, surveillance is employed to manage urban spaces, particularly in areas marked by high poverty and crime. While these technologies are intended to promote public safety (SDG 11) and justice (SDG 16), they often serve to control and monitor marginalized populations, reinforcing existing power structures (Freeman, 2014; Pilo, 2018). The literature indicates that surveillance in Rio's favelas contributes to a form of spatial governance that maintains social divides and limits the autonomy of residents (Monahan, 2006; Goldstein, 2010). This dynamic reflects the challenges of implementing surveillance in a context of deep socio-economic inequality, where the intended benefits for public safety come into conflict with the goals of reducing inequality and promoting inclusive development.

## The DAF: A Tool for Contextual Analysis

Given the varied impacts of surveillance technologies in different urban contexts, the DAF provides a holistic method for evaluating their alignment with the SDGs (Gupta et al., 2023). The DAF considers both the direct and indirect effects of digital interventions, accounting for social, economic, environmental, and cultural factors. Unlike assessments focused solely on technological efficiency, the DAF allows us to examine how surveillance technologies interact with existing power structures, resource distribution, and public attitudes (Bibri & Krogstie, 2017).

Applying the DAF to the cases of Beijing, New York City, and Rio de Janeiro will enable a comparative analysis that highlights the synergies, trade-offs, ambivalent impacts, and uncertainties unique to each context (Gupta et al., 2023). This approach moves beyond simplistic narratives of surveillance as either wholly positive or negative, providing a nuanced understanding of how ideology, governance, and socio-economic conditions shape the outcomes of digital interventions.

## Case Studies as a Comparative Lens

Through an in-depth examination of Beijing, New York City, and Rio de Janeiro, we aim to explore how different governance models influence the deployment and societal impact of surveillance technologies. The case studies will reveal how surveillance is framed within state-centric, capitalist, and mixed-economy ideologies, offering insights into its varied implications for public security, social equity, and sustainability. This comparative lens sets the foundation for the methodology that follows, where the DAF will be applied to assess these impacts systematically and objectively.

## Methodology: Applying the DAF

The impact of surveillance technologies in smart cities varies significantly depending on the governing ideology and socio-economic context. To objectively assess how these differing approaches influence sustainable urban development, we employ the DAF (Gupta et al., 2023). The DAF offers a holistic means of evaluating digital interventions, accounting for their direct and indirect impacts on

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social, economic, and environmental dimensions. This chapter utilizes the DAF to systematically com-
pare the surveillance systems in Beijing, New York City, and Rio de Janeiro, examining their alignment
with the SDGs and the resulting implications for public welfare.

Overview of the DAF

The DAF is designed to assess digital interventions through a multi-dimensional lens, focusing on how technologies interact with social structures, power dynamics, and resource distribution (Bibri & Krogstie, 2017; Gupta et al., 2023). It considers both the positive and negative impacts of these interventions, capturing the complexities and contradictions that arise when digital technologies, such as surveillance, are integrated into urban governance. By examining synergies, trade-offs, ambivalent effects, and uncertainties, the DAF facilitates a nuanced analysis of how surveillance technologies contribute to—or conflict with—the SDGs.

In this chapter, the DAF is applied to evaluate how surveillance technologies in Beijing, New York City, and Rio de Janeiro impact SDGs related to inequality (SDG 10), sustainable cities (SDG 11), and justice (SDG 16). The analysis will consider how each city's ideological framework shapes the implementation of surveillance and its broader social, environmental, and economic effects.

Comparative Analysis of Case Studies

The three cities chosen for this study—Beijing, New York City, and Rio de Janeiro—represent different
governance models: state-centric socialism, capitalist liberal democracy, and a mixed economy. The
methodology involves a comparative assessment of these cities using the DAF to identify the ways in
which ideology, policy, and socio-economic context influence the outcomes of surveillance technolo-
gies (Gupta et al., 2023). The application of the DAF will allow us to contextualize each city's surveillance
practices within its broader ideological and socio-economic framework<fnref n="1" />:

*   **Beijing:** The assessment will focus on how the state-centric approach uses surveillance to promote social stability, public order, and collective welfare. The analysis will explore whether this model effectively balances security with individual rights and privacy (Qin & Qi, 2021; Chui, Vasant & Liu, 2019).

*   **New York City:** The DAF will highlight how surveillance aligns with neoliberal priorities, particularly in protecting economic interests and managing public spaces. We will examine how these technologies affect marginalized communities and whether they support or undermine the city's goals of inclusivity and justice (Brayne, 2020; Zuboff, 2019).

*   **Rio de Janeiro:** The evaluation will consider how surveillance intersects with the city's deep socio-economic inequalities, assessing whether it contributes to safety and social welfare or reinforces existing power structures (Freeman, 2014; Pilo', 2018; Goldstein, 2010).

Identifying the Digital Intervention (DI) and scope

The Digital Intervention (DI) examined in each case is the use of surveillance technologies, including
facial recognition, geolocation tracking, sensor networks, and data analytics. The scope of the analysis
encompasses both the intended outcomes (e.g., public safety, crime prevention, urban management)

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<footnote n="1">¹The in-text citations in this chapter reference articles that support the arguments made in the analysis. However, the specific sources used in constructing the DAF grid itself are not included in the reference list. These additional sources, including datasets and reports that informed the categorization of synergies, trade-offs, and uncertainties, can be accessed via the following Google Drive link: https://docs.google.com/spreadsheets/d/1uONHOPCDZjb-SYRG9ocYGvlHraM82NuojUvvh-1BAos/edit?usp=sharing. This distinction ensures transparency regarding the evidentiary basis for the DAF assessment while maintaining a clear separation between the conceptual discussion and the supporting empirical data.</footnote>
and the broader socio-environmental impacts, such as privacy concerns, social equity, and resource
use. The assessment will focus on the following SDGs:

* **SDG 10 (Reduced Inequalities):** Evaluating whether surveillance technologies reduce or exacerbate social and economic disparities.

* **SDG 11 (Sustainable Cities and Communities):** Assessing how surveillance contributes to or undermines inclusivity, safety, and urban sustainability.

* **SDG 16 (Peace, Justice, and Strong Institutions):** Investigating the implications of surveillance for civil liberties, public trust, and governance.

* **SDG 7 (Affordable and Clean Energy) and**

* **SDG 12 (Responsible Consumption and Production):** Considering the environmental costs of surveillance, including energy consumption and electronic waste (Bibri & Krogstie, 2017).

## Data Collection and Analysis

The assessment employs a mixed-methods approach, drawing on diverse data sources to provide a comprehensive evaluation of each city's surveillance system:

* **Literature Review:** We examine publicly available research, policy reports, and technical documents from 2014 to 2024 to understand the societal, economic, and environmental impacts of surveillance in each city. This includes studies on public perception, government policy, crime statistics, and environmental assessments (Qin & Qi, 2021; Brayne, 2020; Freeman, 2014; Pilo, 2018).

* **Case Studies:** In-depth exploration of Beijing, New York City, and Rio de Janeiro as real-world examples of how surveillance technologies are employed under different ideological frameworks. The case studies will help contextualize the deployment of surveillance, providing empirical evidence for the DAF assessment (Kitchin, 2014; Chui, Vasant & Liu, 2019).

* **Qualitative Insights:** Incorporating perspectives from residents, policymakers, and civil society organizations as documented in literature and reports. This step emphasizes how public attitudes and lived experiences shape, and are shaped by, the implementation of surveillance technologies (Goldstein, 2010; Monahan, 2006).

## DAF Implementation

Using the collected data, we apply the DAF to identify and map the impacts of surveillance technologies across the selected SDGs for each city. The DAF's structured process allows us to explore the complex ways in which surveillance aligns with—or conflicts with—the goals of sustainable urban development (Gupta et al., 2023). To enhance methodological clarity, Figure 1 illustrates how qualitative insights are systematically processed within the DAF to generate comparative metrics. This visual representation clarifies the translation of qualitative assessments into structured evaluations, addressing potential biases in source selection and data interpretation.

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<caption>**Figure 1**
A flow diagram illustrating the translation of qualitative insights into comparative metrics</caption>

Flow Diagram: Translating Qualitative Insights into Comparative Metrics

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For each city, surveillance technologies will be mapped against relevant SDG indicators. For instance, in Beijing, we will explore how surveillance supports public order (SDG 11) and social welfare, while also considering trade-offs related to privacy and individual freedoms (SDG 16). In New York City and Rio de Janeiro, we will assess how surveillance influences social equity (SDG 10) and whether it contributes to sustainable urban management or reinforces spatial and social segregation.

1. **Identifying impact types:** Each impact will be classified into one of the following categories:

* **Synergies:** Positive impacts that support multiple SDGs. For example, Beijing's use of surveillance in traffic management could foster a more sustainable urban environment (SDG 11) while also improving public safety (Chui, Vasant & Liu, 2019).

* **Trade-offs:** Negative impacts where the benefits of surveillance (e.g., crime reduction) may come at the cost of increased social inequality or erosion of privacy rights (Brayne, 2020).

* **Ambivalent Impacts:** Impacts that have both positive and negative aspects. For instance, surveillance in Rio may improve public safety but also contribute to the over-policing of marginalized communities, affecting social cohesion (Freeman, 2014; Pilo', 2018; Monahan, 2006).

* **Uncertainties:** Areas where the long-term effects of surveillance are unclear, especially concerning technological advancements and evolving public policies (Qin & Qi, 2021).

2. **Impact scoring and visualization:** Qualitative scores will be assigned to each SDG indicator to reflect the extent of the impact (e.g., low, medium, high) and its nature (positive, negative, ambivalent). These scores will be used to create visual matrices or heatmaps, illustrating the overall impact profiles of surveillance technologies in each city. This visual representation will facilitate a comparative analysis of the different approaches and outcomes (Gupta et al., 2023).

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## Limitations of the Methodology

While the framework provides a comprehensive tool for assessing digital interventions, its application is not without limitations. The reliance on qualitative data introduces potential subjective biases, especially in interpreting nuanced impacts related to social equity, governance models, and public perception (Gupta et al., 2023). Moreover, as surveillance technologies and their implementations evolve rapidly, this analysis represents only a snapshot within a dynamic and continuously changing landscape. The limitations also extend to the availability and selection of data sources, which could inadvertently reflect inherent biases or gaps in representation. This challenge underscores the importance of iterative reviews and updates to ensure that the methodology remains relevant and responsive to emerging trends. Despite these constraints, the framework offers valuable insights into the interplay between governance models and technological impacts, serving as a guiding tool for policymakers and urban planners aiming to foster equitable and sustainable smart cities.

## Case Studies' Results: Applying The DAF To Surveillance Technologies

In this section, we apply the DAF to the three selected cities—Beijing, New York City, and Rio de Janeiro. By examining how surveillance technologies align with or conflict with various SDGs, we aim to provide a nuanced understanding of the different impacts these technologies have under varying governance models (Gupta et al., 2023). The DAF assessment highlights the synergies, trade-offs, ambivalent impacts, and uncertainties in each context, revealing how ideology, policy, and socio-economic factors shape the use and outcomes of surveillance.

# Case study 1: Beijing – State-centric socialism and surveillance for collective welfare

## Background

Beijing represents a state-driven, socialist model where surveillance is primarily a tool for maintaining social stability, public safety, and collective welfare. The government employs extensive surveillance systems, including facial recognition, social credit mechanisms, and networked cameras, as part of a comprehensive strategy for urban governance (Qin & Qi, 2021; Creemers, 2018). Proponents argue that these technologies enhance public security, promote efficiency, and align with the government's broader goal of fostering a harmonious society Socha & Kogut, 2020). Public perception, shaped by cultural norms that emphasize collective security over individual privacy, generally supports this model of surveillance (Kao & Sapp, 2022). **DAF Assessment** (see Figure 1):

* **SDG 11 (Sustainable Cities and Communities):** Surveillance in Beijing supports urban management through intelligent traffic monitoring, crime prevention, and environmental monitoring. Studies indicate that these systems have enhanced public order and safety, contributing positively to urban livability (Chui, Vasant & Liu, 2019). The integration of surveillance with public services, such as smart traffic management, reflects a synergy between technological intervention and the city's sustainability goals, reducing congestion and promoting efficient use of urban spaces. Additionally, the development of smart city technologies, which leverage deep learning and data processing (Chen et al., 2019; Atha et al., 2020), is pivotal in promoting sustainable urbanization. However, while these innovations contribute to city efficiency, they can also raise questions of resource consumption (Jameel, Ali & Ali, 2019).

* **SDG 16 (Peace, Justice, and Strong Institutions):** Beijing's surveillance network is closely tied to the state's approach to governance, emphasizing public security and social stability. Research

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suggests that many residents view surveillance positively, perceiving it as a means to foster a
safe and harmonious society (Qin & Qi, 2021). However, the system's centralization also raises
concerns about privacy, individual freedoms, and the potential for state overreach (Pan, 2020;
Zhou, 1993). This presents an ambivalent impact: while the government's approach aligns with
the goal of strong institutions that maintain public order, it simultaneously limits certain civil
liberties, such as freedom of expression and privacy (Kim, 2021). To mitigate these concerns,
potential strategies include the implementation of legal frameworks that ensure oversight,
accountability, and citizen participation in discussions about digital governance (Almeida,
Shmarko, & Lomas, 2022).

*   **SDG 10 (Reduced Inequalities):** Surveillance in Beijing operates under the notion of promoting collective welfare and social harmony. Yet, the implementation of systems like the social credit system introduces complexities. While it aims to encourage socially responsible behavior, it can also lead to unequal treatment based on social credit scores, potentially affecting access to services and employment (Creemers, 2018; Lin & Milhaupt, 2023). Additionally, research highlights how such systems may disproportionately affect marginalized groups, exacerbating social inequality (Jiang, 2022). The impact on reducing inequalities is therefore uncertain; while the system's intent is to promote fairness, its application may reinforce social stratification. A mitigative approach could include regulatory reforms that prevent discrimination and ensure greater transparency in how surveillance data is used (Almeida, Shmarko, & Lomas, 2022).

*   **SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production):** The extensive surveillance infrastructure in Beijing, including numerous cameras and data processing centers, requires significant energy consumption. While efforts are made to integrate renewable energy sources into the city's grid, the environmental footprint of maintaining such a vast surveillance network is considerable (Gupta et al., 2023). Furthermore, initiatives are being undertaken to enhance energy efficiency within smart city systems, but significant challenges remain in balancing technological innovation with sustainability (Atha et al., 2020; Jameel, Ali & Ali, 2019). Addressing this trade-off requires investment in energy-efficient computing and policies that regulate the environmental impact of mass surveillance technologies.

**Outcome:** The DAF assessment of Beijing shows that the state's approach to surveillance has generated certain synergies with urban efficiency and public safety. However, the trade-offs and ambivalent impacts related to privacy, inequality, and environmental sustainability indicate that these technologies should be managed carefully to align with holistic sustainability goals. While proponents emphasize the benefits of surveillance for governance and security, ethical and regulatory considerations must be incorporated to ensure that these systems do not disproportionately disadvantage specific groups or compromise fundamental rights (McDonald & Forte, 2022). Addressing these challenges requires stronger legal frameworks that regulate data collection and usage, public oversight mechanisms to ensure accountability, and greater transparency in algorithmic decision-making processes. Additionally, integrating privacy-preserving technologies and fostering public discourse on digital governance could help balance the benefits of surveillance with the need for citizen protections.

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<caption>**Figure 2**</caption>

Illustrates the application of the Digitainability Assessment Framework (DAF) to Beijing's surveillance system, highlighting its synergies with urban efficiency and security, while also revealing trade-offs related to privacy, social stratification, and environmental sustainability

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# Case Study 2: New York City – Capitalist Liberal Democracy and Surveillance for Economic Interests

**Background:** In New York City, surveillance is implemented within a capitalist, liberal-democratic framework. The city's surveillance network includes extensive CCTV systems, facial recognition, and predictive policing, often deployed through public-private partnerships (Zuboff, 2019; Brayne, 2020). This approach reflects the dual interests of protecting economic assets and managing public spaces.

## DAF Assessment (see Figure 2)

* **SDG 11 (Sustainable Cities and Communities):** Surveillance technologies in New York City contribute to urban management and public safety, particularly in areas with high economic value, such as financial districts. While these technologies can support a sense of security and facilitate urban planning, they have been criticized for their uneven distribution, favoring affluent neighborhoods while intensifying policing in marginalized areas (Eubanks, 2018). Moreover, smart city initiatives like LinkNYC have raised concerns about surveillance and privacy, particularly in lower-income areas (Hornbeck, 2017; Shapiro, 2018). The impact here is ambivalent, as surveillance enhances urban efficiency for some while potentially increasing social tensions and distrust in others (Osakata, 2019).

* **SDG 16 (Peace, Justice, and Strong Institutions):** The use of surveillance in New York, particularly predictive policing, has raised significant concerns about justice and equity. Studies show that these technologies disproportionately target minority and low-income communities, reinforcing existing racial and economic disparities (Brayne, 2020; McClain, 2016). Predictive policing software, such as HunchLab (The Markup, 2023), exacerbates these biases by focusing police efforts on certain neighborhoods, further marginalizing already vulnerable populations (Shapiro, 2018). This creates a trade-off: while surveillance may reduce crime in certain areas, it can simultaneously perpetuate systemic biases and undermine public trust in law enforcement, conflicting with the ideals of peace, justice, and strong institutions (Stuart and Levine, 2017).

* **SDG 10 (Reduced Inequalities):** Surveillance in New York tends to align with capitalist priorities, focusing on protecting economic interests. The resulting data commodification and privacy infringements often affect marginalized groups more severely, reinforcing socio-economic inequalities (Zuboff, 2019). The New York City subway's failed smart surveillance project demonstrated how technology aimed at ensuring safety can have a disproportionately negative impact on minority and lower-income populations when technological limitations intersect with social biases (McClain, 2018). Furthermore, the involvement of private companies in smart city initiatives raises concerns about who benefits from these systems and who is subject to surveillance (Shah, Kothari & Doshi, 2019; Hornbeck, 2017). The impact on inequality is therefore predominantly negative, suggesting that the city's surveillance model exacerbates rather than reduces social and economic divides (Eubanks, 2018).

* **SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production):** New York's reliance on digital surveillance technologies contributes to significant energy consumption and electronic waste. While some initiatives aim to use greener technologies, the scale of data processing and storage remains an environmental concern (Gupta et al., 2023; Doran, Gokhale & Dagnino, 2013). Additionally, the deployment of IoT sensors and other smart city infrastructure has increased energy demand, raising concerns about the sustainability of such projects (Shah, Kothari & Doshi, 2019). This creates a trade-off between the perceived benefits of surveillance for urban management and its environmental impact (Monahan, 2016).

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**Outcome:** The DAF assessment of New York City reveals that while surveillance technologies enhance economic security and urban management, they also introduce significant trade-offs related to social equity, justice, and sustainability. The alignment of surveillance with capitalist priorities means that its benefits are not equitably distributed, often reinforcing economic and racial divides. Predictive policing, while designed to prevent crime, often results in heightened law enforcement in marginalized communities, leading to social tensions and privacy violations. The role of corporate actors in surveillance governance raises questions about who ultimately controls urban security and who benefits from smart city surveillance systems. To counteract these disparities, a policy framework that emphasizes transparency, accountability, and equitable access to urban security is necessary.

## Case Study 3: Rio de Janeiro – Mixed Economy and Surveillance Amidst Inequality

### Background

Rio de Janeiro operates within a mixed-economy model, employing surveillance to address crime and public safety, particularly in areas marked by poverty and social inequality. The city's surveillance initiatives, including CCTV networks and police monitoring, reflect a response to both public demand for security and state attempts to manage urban spaces. The Integrated Command and Control Center (CICC) plays a central role in coordinating surveillance efforts across Rio de Janeiro. Established in preparation for the 2014 FIFA World Cup and the 2016 Olympic Games, the CICC consolidates live feeds from cameras, police operations, and urban infrastructure monitoring, reinforcing a centralized security apparatus. While proponents argue that this system enhances crisis response and crime prevention, critics highlight that it disproportionately targets favelas, deepening spatial inequalities. Unlike New York, where surveillance is tied to economic security, or Beijing, where it is embedded in state governance and social order, Rio's surveillance is primarily reactive, reflecting a persistent struggle to manage violence and urban fragmentation. DAF Assessment (see Figure 3):

* **SDG 11 (Sustainable Cities and Communities):** Surveillance in Rio de Janeiro aims to improve public safety and urban management, particularly in favelas and high-crime areas. However, its implementation often results in the increased policing of marginalized communities, reinforcing spatial segregation and social exclusion (Freeman, 2014; Pilo, 2018). Additionally, Rio's smart city initiatives have been criticized for focusing primarily on wealthier areas and excluding the city's more vulnerable communities, deepening existing urban inequalities (Irazábal & Jirón, 2021). Surveillance technologies in Rio's favelas function less as public safety measures and more as tools of spatial governance, reinforcing socio-economic hierarchies. Studies indicate that high levels of CCTV and drone surveillance in these areas have led to increased police militarization rather than improved security for residents. The criminalization of poverty through predictive policing algorithms further exacerbates tensions, with reports showing that AI-driven surveillance disproportionately flags residents of marginalized communities as potential threats. While surveillance contributes to the city's safety and urban planning, it simultaneously exacerbates the marginalization of certain populations (Schreiner, 2016). A more equitable approach could include community-driven surveillance initiatives, where local residents participate in determining how surveillance is deployed to better serve collective safety concerns rather than reinforcing state control.

<figure><img src="image_21.png" /></figure>
<figure><img src="image_22.png" /><figcaption>**Figure 3**
Presents the DAF assessment for New York City, showcasing how surveillance technologies align with economic security and urban management, yet simultaneously reinforce social inequalities and raise concerns about justice, privacy, and environmental impact</figcaption></figure>

<figure><img src="image_23.png" /></figure>
* **SDG 10 (Reduced Inequalities):** Rio's use of surveillance technologies often aligns with a top-down approach to managing urban space, resulting in unequal treatment of residents. The monitoring and policing of favelas contribute to the stigmatization of these communities, limiting their access to public services and opportunities for social mobility (Goldstein, 2010). Smart city initiatives have tended to serve wealthier communities while excluding poorer areas, exacerbating social and spatial inequalities (Gaffney & Robertson, 2018; Molinaro et al., 2020). The impact on reducing inequalities is predominantly negative, as surveillance serves to reinforce existing social and spatial hierarchies (Pilo', 2021). A policy alternative could involve redistributing surveillance resources to community-led safety initiatives, reducing the top-down, punitive focus of Rio's security infrastructure.

* **SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production):** The city's use of surveillance technologies incurs energy costs and generates electronic waste. However, Rio's limited investment in renewable energy and waste management systems exacerbates these environmental impacts, indicating a clear trade-off between the benefits of surveillance and its ecological footprint (Gupta et al., 2023). While some smart city initiatives in Rio have attempted to integrate sustainability, the focus on technological solutions over social issues has often led to environmental inefficiencies (Mendes, 2022). A sustainable approach would involve investing in energy-efficient AI processing for surveillance, as well as implementing regulatory frameworks to curb the environmental impact of mass digital surveillance infrastructures.

**Outcome:** The DAF assessment of Rio de Janeiro highlights the complexities of implementing surveillance in a context marked by deep social inequalities. While the intention is to promote safety and urban management, the resulting impacts often reinforce socio-economic divides, spatial segregation, and environmental strain. The growing role of AI-driven surveillance and predictive policing raises ethical concerns, particularly regarding racialized policing and the exclusion of marginalized communities from decision-making processes. Although surveillance technologies contribute to public security, their benefits are not equitably distributed, with wealthier districts often having greater access to real-time monitoring and response systems while lower-income areas remain subject to punitive law enforcement practices. Additionally, the environmental costs of large-scale surveillance infrastructure present sustainability challenges, as Rio's investment in security technology has largely overlooked energy-efficient solutions. Addressing these challenges requires a more inclusive approach to urban security that prioritizes transparency, accountability, and equitable access to safety-enhancing technologies. Integrating civilian oversight mechanisms, ensuring algorithmic transparency in predictive policing, and redirecting resources toward community-driven security initiatives could help mitigate some of the negative externalities of Rio's surveillance model.

<figure><img src="image_24.png" /></figure>
<caption>**Figure 4**</caption>

Applies the DAF to Rio de Janeiro, demonstrating how surveillance technologies are employed for public safety yet disproportionately target marginalized communities, exacerbating socio-economic inequalities while generating mixed outcomes for urban sustainability and governance.

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>
## Comparative Analysis and Visual Representation

The DAF assessments of Beijing, New York City, and Rio de Janeiro demonstrate that the impacts of surveillance technologies vary significantly depending on the governing ideology and socio-economic context. A matrix visualization will be used to synthesize these findings, highlighting areas of synergy, trade-offs, ambivalence, and uncertainty across the three cities. This comparative analysis underscores that surveillance in smart cities is not a one-size-fits-all solution; its effectiveness and fairness are deeply influenced by the underlying political, economic, and cultural framework.

## Key Insights and Implications

The results indicate that while surveillance technologies can contribute to urban management and public safety, they often come with significant trade-offs related to privacy, inequality, and environmental sustainability. These outcomes suggest that the deployment of surveillance technologies in smart cities should be carefully managed to align with the goals of equity and holistic sustainability (Gupta et al., 2023). The findings also reveal that ideology plays a crucial role in shaping the implementation and public perception of surveillance, influencing its potential to either support or undermine sustainable urban development.

## Discussion: Ideology, Surveillance, and Sustainable Urban Futures

The application of the DAF to the cases of Beijing, New York City, and Rio de Janeiro reveals that surveillance technologies in smart cities have multifaceted impacts, shaped largely by the underlying political and economic ideologies (Gupta et al., 2023). This section discusses these findings, highlighting the key synergies, trade-offs, and ambivalent effects uncovered in each context. It underscores the importance of ideology in determining how surveillance is integrated into urban life, the distribution of its benefits, and the potential conflicts with SDGs.

## Ideological Influences on Surveillance Implementation

The three case studies illustrate how differing ideological frameworks—state-centric socialism, capitalist liberal democracy, and a mixed economy—shape the design, objectives, and societal outcomes of surveillance technologies. In Beijing, surveillance is a state-driven tool for promoting social order, collective welfare, and efficient urban management (Qin & Qi, 2021). The centralized governance model allows the state to position surveillance as part of its broader strategy for achieving social stability and public security. This approach aligns with a cultural preference for collective harmony over individual privacy, resulting in relatively higher public acceptance of surveillance.

Conversely, New York City operates under a capitalist, liberal-democratic model where surveillance technologies serve multiple, often conflicting interests. On one hand, they enhance public safety and urban management, particularly in economically vital areas. On the other, they reinforce social hierarchies by disproportionately targeting marginalized communities (Brayne, 2020). Here, the com-modification of data and the prioritization of economic security reflect the neoliberal emphasis on market-driven solutions, often at the expense of social equity and justice (Zuboff, 2019).

Rio de Janeiro offers a third perspective, where surveillance is implemented in a mixed-economy context marked by sharp socio-economic inequalities. The focus on deploying surveillance in low-income areas, such as favelas, reveals a top-down approach to urban management. This approach often results in the control and stigmatization of vulnerable populations, suggesting that surveillance under a mixed-economy model can serve to maintain social divides rather than foster inclusivity (Freeman, 2014; Pilo, 2018).

<figure><img src="image_27.png" /></figure>
## Synergies and Positive Impacts

Despite the ideological differences, there are areas where surveillance technologies have generated synergies with the goals of sustainable urban development, particularly in Beijing. The city's use of surveillance for traffic management, crime prevention, and public safety aligns with certain aspects of SDG 11 (Sustainable Cities and Communities). By integrating surveillance into public services, Beijing has managed to improve urban livability and promote a sense of social stability (Chui, Vasant & Liu, 2019). These outcomes suggest that, under a state-centric model, surveillance can be harnessed effectively to address collective welfare concerns, provided that it is implemented with a focus on public benefit.

In New York City, surveillance has supported urban efficiency and security, particularly in business districts and other economically important areas. This reflects a synergy with aspects of SDG 11 related to creating safe and resilient urban spaces. However, this synergy is confined to certain demographics, primarily serving those who already possess economic and social advantages (Eubanks, 2018).

## Trade-offs: Privacy, Inequality, and Environmental Costs

The trade-offs associated with surveillance technologies are stark across all three cities, indicating that the use of surveillance often comes with significant costs to privacy, social equity, and environmental sustainability. In Beijing, the state's extensive use of surveillance raises concerns about individual freedoms and the potential for state overreach, despite its contributions to public safety and social harmony (Qin & Qi, 2021). The social credit system, for example, introduces a mechanism of social control that, while intended to promote responsible behavior, can also result in social stratification (Creemers, 2018).

New York City's deployment of surveillance technologies, particularly predictive policing, reveals a direct trade-off between public safety and social equity. The disproportionate targeting of minority and low-income communities perpetuates existing inequalities (SDG 10) and undermines the goal of building just and inclusive institutions (SDG 16) (Brayne, 2020). This trade-off underscores the tension between surveillance as a tool for protecting economic interests and its impact on marginalized groups, reflecting the capitalist model's inherent contradictions.

In Rio de Janeiro, surveillance technologies are employed as a means to manage public spaces and address crime. However, this often results in the criminalization and over-policing of low-income areas, reinforcing spatial segregation and social exclusion (Freeman, 2014; Pilo', 2018). The trade-offs here highlight the challenges of implementing surveillance in a context marked by socio-economic disparities, where the intended benefits for public safety conflict with efforts to reduce inequalities (SDG 10).

Environmental concerns also present a significant trade-off in all three cities. The energy consumption and electronic waste generated by surveillance infrastructures conflict with SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production) (Gupta et al., 2023). This is particularly evident in Beijing and New York City, where the scale of data processing required for surveillance demands substantial energy resources, raising questions about the long-term sustainability of such practices.

## Ambivalent and Uncertain Impacts

The analysis reveals several ambivalent impacts of surveillance technologies, particularly concerning public perception and social cohesion. In Beijing, the use of surveillance is seen as both a means of promoting social harmony and a potential infringement on personal freedoms. This duality creates an uncertain impact, as the long-term effects of such comprehensive monitoring on individual autonomy

<figure><img src="image_28.png" /></figure>
and social trust remain unclear (Qin & Qi, 2021). The public's acceptance of surveillance in China appears linked to cultural norms that prioritize collective welfare, but future changes in public sentiment or policy could shift these dynamics.

In New York City, surveillance technologies such as facial recognition and data analytics are ambivalently received. While they contribute to public safety and urban management, they also raise concerns about data privacy, corporate control, and racial profiling. The bi-directional influence of these technologies on public perception and policy-making indicates that the impact of surveillance is context-dependent, shaped by ongoing debates over privacy rights and state authority (Zuboff, 2019; Eubanks, 2018).

Rio de Janeiro exemplifies ambivalence in its use of surveillance to both manage urban spaces and control marginalized populations. While surveillance can enhance security in high-crime areas, its implementation often reflects and reinforces socio-economic divides. The uncertain impact here lies in whether surveillance can be reoriented to serve the public good in a more inclusive and equitable manner, or if it will continue to entrench existing power structures (Freeman, 2014; Pilo', 2018).

## Policy implications and the path forward

The findings from the DAF assessments underscore the need for a careful, context-aware approach to deploying surveillance technologies in urban settings. The ideological differences between Beijing, New York City, and Rio de Janeiro shape not only the use of surveillance but also its societal impacts.

* In Beijing, surveillance can potentially align with collective welfare if managed transparently and with safeguards to protect individual rights. Policy-making should focus on ensuring that surveillance does not lead to excessive state control or social stratification (Creemers, 2018).

* In New York City, the emphasis should be on addressing the inequalities perpetuated by surveillance practices, particularly through reforms that limit discriminatory practices in policing and data collection. Public-private partnerships must be scrutinized to ensure that surveillance technologies do not merely serve economic interests at the expense of social justice (Brayne, 2020; Eubanks, 2018).

* In Rio de Janeiro, the challenge lies in integrating surveillance into a broader strategy for reducing inequalities. Policies should aim to shift the focus from control and policing to community engagement, social welfare, and the equitable distribution of public services (Freeman, 2014; Pilo', 2018; Goldstein, 2010).

The comparative analysis suggests that while surveillance technologies can support sustainable urban futures, their success depends on how they are integrated into governance frameworks that prioritize social equity, justice, and environmental responsibility (Gupta et al., 2023). Policymakers and urban planners should navigate the trade-offs, ambivalences, and uncertainties inherent in surveillance deployment to ensure it aligns with the ideals of sustainable and inclusive development.

## Conclusion: Ideological Contexts and the Future of Surveillance in Smart Cities

The comparative analysis of Beijing, New York City, and Rio de Janeiro through the DAF reveals that the impacts of surveillance technologies in smart cities are deeply intertwined with the political, economic, and cultural contexts in which they are deployed (Gupta et al., 2023). While surveillance can offer benefits such as improved public safety and urban management, its implications for privacy, social equity, and environmental sustainability vary significantly depending on the underlying ideology guiding its implementation.

<figure><img src="image_29.png" /></figure>
In Beijing, surveillance is embedded within a state-centric socialist model that prioritizes collective welfare and social stability. The DAF assessment shows that, under this framework, surveillance can generate synergies with certain SDGs, particularly those related to urban efficiency (SDG 11). However, this comes with trade-offs involving potential infringements on personal freedoms and risks of social stratification through mechanisms like the social credit system (Qin & Qi, 2021; Creemers, 2018). The Beijing case illustrates that while surveillance can be aligned with state objectives and cultural values emphasizing social harmony, it must be managed carefully to avoid reinforcing state overreach and inequality.

New York City, operating under a capitalist liberal-democratic model, demonstrates how surveil-lance technologies can serve multiple interests, from public safety to economic security. However, the trade-offs here are stark: the city's surveillance practices often exacerbate existing social inequal-ities (SDG 10) and disproportionately target marginalized communities, conflicting with the ideals of justice and strong institutions (SDG 16) (Brayne, 2020). This outcome reflects the contradictions within neoliberal governance, where technologies designed to protect and optimize urban spaces may simul-taneously reinforce social hierarchies and limit public freedoms (Zuboff, 2019).

In Rio de Janeiro, a mixed-economy model reveals the complexities of implementing surveillance in a context of deep socio-economic divides. While surveillance is intended to address public safety and urban management, it frequently results in the control and marginalization of poor communities, reinforcing spatial segregation (Freeman, 2014; Pilo', 2018). The city's experience underscores the chal-lenges of using surveillance to foster inclusive development, particularly when societal inequalities are deeply entrenched.

## Key Takeaways

The findings from the DAF assessments highlight several key insights regarding the role of surveil-lance technologies in sustainable urban futures:

1. **Ideological influence matters:** The outcomes of surveillance technologies are not solely a func-tion of technological efficiency but are significantly shaped by the ideological context in which they are deployed. State-centric models like Beijing's can leverage surveillance for collective welfare, but this requires a delicate balance to prevent state overreach. In capitalist models like New York City, surveillance tends to prioritize economic interests and security, often at the cost of social equity (Brayne, 2020; Zuboff, 2019). Mixed-economy settings, as seen in Rio, struggle to use surveillance inclusively in the face of pervasive socio-economic inequality (Freeman, 2014; Pilo', 2018).

2. **Trade-offs are inevitable:** Surveillance technologies introduce trade-offs related to privacy, social equity, and environmental sustainability. Addressing these trade-offs requires careful policy design that considers the socio-political context. Surveillance practices that focus on crime prevention and urban efficiency could be balanced against the need to protect individual rights, promote social inclusion, and minimize environmental impact (Gupta et al., 2023).

3. **Public perception and cultural context:** The public's acceptance of surveillance is closely linked to cultural values and trust in government. In Beijing, the relative acceptance of surveillance aligns with cultural norms that emphasize collective welfare (Qin & Qi, 2021). In contrast, public skepticism in cities like New York and Rio is often rooted in experiences of inequality and discrimination, reflecting broader societal dynamics that influence the perceived legitimacy of surveillance technologies (Eubanks, 2018).

<figure><img src="image_30.png" /></figure>
## Policy recommendations

Given the complexities revealed by the DAF assessments, several policy considerations emerge for the future of surveillance in smart cities:

* For state-centric models (e.g., Beijing): Policies should focus on transparency and public accountability to ensure that surveillance serves the collective good without infringing on individual rights. Mechanisms for public oversight and the protection of personal data are essential to mitigate the risks of social stratification and state overreach (Creemers, 2018).

* For capitalist models (e.g., New York City): Reforms are needed to address the unequal impacts of surveillance, particularly in predictive policing and data commodification. Policies must aim to limit discriminatory practices, protect privacy rights, and ensure that surveillance technologies do not disproportionately target vulnerable communities. Public-private partnerships in surveillance should be scrutinized to prioritize social welfare over profit-driven motives (Brayne, 2020; Zuboff, 2019; Eubanks, 2018).

* For mixed-economy models (e.g., Rio de Janeiro): Surveillance policies should be integrated into a broader strategy for reducing social inequalities. Rather than focusing solely on control and policing, the emphasis should be on community engagement, social welfare, and equitable access to public services. Investing in education, health, and social programs can help shift the use of surveillance from enforcement to empowerment, fostering a more inclusive urban environment (Freeman, 2014; Pilo', 2018; Goldstein, 2010).

## Conclusion

This chapter's comparative analysis reveals the dual nature of surveillance technologies in smart cities, serving as both a catalyst for progress and a source of potential harm. The ideological frameworks within which these technologies operate profoundly influence their deployment, public reception, and ultimate impact. While surveillance can contribute to sustainable urban development, its benefits are contingent upon governance structures that prioritize equity, justice, and environmental stewardship (Gupta et al., 2023). The findings underscore the critical need to contextualize surveillance technologies within their broader socio-political implications. Innovations such as facial recognition, geolocation tracking, and sensor networks hold promise for enhancing urban functionality, yet they carry the risk of entrenching power disparities, exacerbating social inequities, and eroding individual freedoms if implemented without transparency and accountability (Zuboff, 2019). Additionally, the increasing export of surveillance technologies raises concerns about their adaptability across different political and regulatory environments, particularly when deployed in contexts with weaker oversight mechanisms or authoritarian tendencies (Cuomo, 2024).

To chart a sustainable and inclusive course for urban futures, policymakers and urban planners must navigate the inherent trade-offs posed by surveillance technologies. This demands not only a technical mastery of these tools but also a steadfast commitment to ethical governance that upholds citizens' rights and foregrounds collective well-being. Ethical governance requires a participatory approach that involves diverse stakeholders in decision-making processes, ensuring that policies reflect the interests and values of all segments of society, especially those who have historically been excluded from decision-making power. It also demands robust accountability mechanisms to monitor the deployment and impact of surveillance technologies, safeguarding against misuse and promoting trust between governments and citizens. Furthermore, ethical governance must prioritize transparency, with clear frameworks for data collection, usage, and retention that empower citizens to understand and challenge practices that may infringe on their rights. The application of evaluative frameworks

<figure><img src="image_31.png" /></figure>
such as the DAF equips cities to critically examine the multifaceted impacts of surveillance and to
craft policies that align with principles of equity, justice, and sustainability. Ultimately, the future of
smart cities must rest on a foundational principle: the technologies we create and deploy should serve
collective interests, not perpetuate systems of inequality or prioritize profit over human well-being. By
reimagining urban governance as a shared endeavor grounded in solidarity and mutual responsibility,
surveillance technologies can evolve from tools of control into instruments of empowerment. Only
through such a transformative approach can cities achieve their potential to be not only intelligent but
also equitable, inclusive, and truly sustainable for all.

<figure><img src="image_32.png" /></figure>

Author Details Ana Tomičić (Dr.)

¹ ARETE Institute for Sustainable Prosperity, Zagreb, Croatia

<figure><img src="image_33.png" /></figure>

0000-0002-2504-4945

<figure><img src="image_34.png" /></figure>

ana@aretenet.org

Marija Adela Gjorgjioska (Dr.)

² ARETE Institute for Sustainable Prosperity, Skopje, North Macedonia

<figure><img src="image_35.png" /></figure>

0000-0002-9864-3412

<figure><img src="image_36.png" /></figure>

adela@aretenet.org

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