Comparative Econometric Analysis of Renewable Energy Policies in Smart Cities: A Case Study of Singapore and the UAE

Abstract

This paper presents an econometric analysis to evaluate the economic, environmental, and social impacts of adopting renewable energy in the Smart cities of Singapore and the United Arab Emirates (UAE). Both countries have already invested heavily in clean energy and innovative urban projects, but they take different directions due to specific political, economic, and environmental factors. This study utilizes data spanning the period from 2000 to 2025 to estimate the effects of government policies, infrastructure investments, and technological advancements in renewable energy on economic growth, employment, energy use, and carbon emissions in these cities. The analysis employs panel data regression models and difference-in-differences (DiD) methods. This study finds that renewable energy investments in Singapore lead to a 0.32% increase in gross domestic product (GDP) growth and a 0.25% increase in employment in green sectors. In the UAE, investments in solar energy have led to a 0.29% increase in GDP growth; however, energy efficiency remains a significant challenge. The incorporation of public-private partnerships (PPPs) is shown to significantly enhance socio-economic outcomes, with a 12% increase in economic development and renewable sector jobs post-policy intervention. This study’s findings suggest that both Singapore and the UAE can benefit from strengthening public-private partnerships and focusing on innovative technological integration. For the UAE, expanding efforts in energy efficiency alongside scaling up solar infrastructure is essential. At the same time, Singapore can further optimize its existing renewable energy capacities through offshore wind energy and regional cooperation. This paper presents practical policy proposals to enhance the effectiveness of renewable energy policies in smart cities, specifically by maximizing the partnership between the state and the business sector, increasing investment in renewable energy infrastructure, and developing new technologies in energy storage and grid control. Future research could investigate the long-term socio-economic impacts of renewable energy policies on urban inequality, public health, and the global applicability of these findings to cities with diverse political and economic contexts.

1. Introduction

Sustainability in global energy production is anchored in the use of renewable power, as the pace of urbanization is expected to continue increasing [1]. Globally, renewable energy deployment has accelerated, with installed capacity growing rapidly in recent years as nations strive to meet climate mitigation and energy security goals. With the rising population of urban areas and a corresponding increase in the energy requirements of these cities, the incorporation of renewable energy sources, such as solar, wind, and geothermal energy, in urban facilities is becoming a prerequisite for achieving long-term sustainability objectives [2]. The introduction of smart cities, which utilize sophisticated technologies to enhance the quality of life for people and reduce environmental footprints, presents an ideal opportunity to explore the integration of energy efficiency, economic development, and ecological conservation. The tendency to utilize renewable energy worldwide and the development of smart cities cannot be discussed separately; cities are not only a significant source of energy consumption but also a fundamental force that influences policy changes and the adoption of technologies that can shape the future of energy systems [3].

Singapore and the United Arab Emirates (UAE) are two notable examples in this field, as both have emerged as global leaders in clean energy investments and smart city development. Although the two countries are not geographically large, they possess significant economic capacities and share a common desire to decarbonize through the diversification of their economies and increased energy efficiency while also setting an example of sustainable urban development [4,5]. Nevertheless, these two paths are distinct because the dissimilarity in political structures, economic priorities, and geographical backgrounds characterizes them. Singapore is a technologically innovative city with advanced city planning, energy resilience, and efficiency [6]. By comparison, the UAE, which is blessed with enormous oil reserves, has welcomed the mass production of renewable energy schemes, including the Mohammed bin Rashid Al Maktoum Solar Park, and is striving to become a world center focused on renewable energy [7]. These opposite directions qualify them as the perfect subjects of comparison concerning the topic of smart cities’ adoption of renewable energy.

Although considerable research has been conducted on the adoption of renewable energy and the development of smart cities in general, a significant gap remains in the econometric literature that critically compares the economic, environmental, and social effects of renewable energy policies in the context of innovative urban development. The current literature primarily focuses on the efficacy of renewable energy policies within a specific setting or nation. Yet, not many studies employ sound econometric methods to determine the overall consequences of policies on major indicators, such as economic growth, employment, energy efficiency, and carbon emissions. Additionally, there is a lack of studies comparing the effects of renewable energy policies across countries with different governance frameworks, levels of urbanization, and resource endowments. This paper aims to address this gap by employing econometric models, including panel data regression and difference-in-differences (DiD) models, to assess the impact of renewable energy policies in Singapore and the UAE over 25 years, spanning from 2000 to 2025.

The overall aim of this research is to assess the causal effects of renewable energy policy in Singapore and the UAE as part of the complex econometric analysis. To be more precise, this study will aim to measure the impact of renewable energy investment on key economic indicators, including gross domestic product (GDP) growth, employment in the green industry, and energy efficiency, as well as its effects on environmental variables, particularly carbon emissions. To do this, this paper employs panel regression models to test the relationship between the adoption of renewable energy and the performance of the economy, and then supplement this with DiD techniques to assess the difference in the effects of the policy interventions in the two countries. By employing this econometric method, this paper aims to provide a comprehensive understanding of the impact of renewable energy policies on the short-term and long-term economic trajectories of smart cities.

This paper is structured as follows: Section 2 reviews the relevant literature on renewable energy adoption in smart cities, with an emphasis on econometric methods and the regional contexts of Singapore and the UAE. Section 3 presents the theoretical framework and outlines this study’s hypotheses. In Section 4, we describe the data sources and the econometric methodology used for the analysis. Section 5 provides a descriptive analysis of the energy profiles and trends in both Singapore and the UAE. Section 6 discusses the econometric results, highlighting key findings. Section 7 offers a detailed discussion of the implications of the results. Section 8 presents policy recommendations aimed at promoting the adoption of renewable energy in smart cities. Finally, Section 9 concludes this paper and suggests directions for future research.

This paper has several important implications for the existing knowledge of renewable energy and smart cities. One, it is a critical empirical study because it uses econometric models, such as panel regression analysis, DiD estimation, and the model of elasticity, to determine the effects of renewable energy policies in two opposing smart cities: Singapore and the UAE. The analysis of this study can be considered an intense quantitative study, which connects the renewable energy investments to various economic, environmental, and social impacts, and these methods are very complex, providing new information about the effects of such policies on the GDP growth, employment in the green sectors, energy efficiency, and carbon emission.

This paper also contributes to the theoretical understanding of renewable energy adoption within the framework of smart cities by developing a detailed conceptual framework. This framework links renewable energy integration to broader objectives of urban smartness and sustainable urban development, as well as the complexity of interrelationships among energy efficiency, economic resilience, and environmental sustainability. This paper, by suggesting this theoretical prism, refines the current paradigms of innovative city development in that it provides a more detailed perspective on the role of renewable energy policies on urban sustainability dynamics.

In addition, the policy implications of this research provide practical policy suggestions that are not only applicable to policymakers in the UAE and Singapore but also to other cities and states undergoing a similar transition towards renewable energy. The analysis recommends ways to streamline public-private partnerships (PPPs) to promote innovation, increase the scale of renewable energy structures, and foster technological progress in energy storage and grid control. These policy recommendations aim to enable governments and decision-makers to utilize renewable energy more effectively in achieving their sustainability objectives while also ensuring long-term economic development and environmental sustainability.

In this way, this paper provides a methodological and practical framework that, in addition to contributing to the development of academic discussions on renewable energy in smart cities, can help policymakers implement effective and sustainable energy policies within the city setting.

2. Literature Review

This section reviews the existing literature on renewable energy adoption in smart cities, with a particular focus on econometric analyses and specific case studies of Singapore and the UAE.

2.1. Global Renewable Energy and Smart Cities Nexus

The increased interest in renewable energy and smart cities has become a symbol of urban sustainability. Scholars have examined the role of energy transitions in city restructuring, with a focus on minimizing carbon footprints and promoting economic development through the use of clean energy technology. Smart cities, which utilize the latest technology to enhance urban management and quality of life, offer unique opportunities to integrate renewable energy into urban structures [8]. Specifically, innovative governance has been described as an essential enabler of energy efficiency and sustainability, given its importance in facilitating the adoption of renewable energy [9]. Research papers by researchers such as Zhong & Li [10] and Razmjoo et al. [11] have demonstrated that the adoption of renewable energy in smart cities can lead to a substantial reduction in energy consumption and enhance the city’s resilience to climate change. More recent studies [1,6] have empirically analyzed the interplay between smart city infrastructure and renewable energy integration, confirming the importance of technology-driven pathways.

Nevertheless, despite the potential for integrating renewable energy into smart cities, much of the literature focuses on case studies of cities that have already adopted these technologies [12,13,14]. Comparisons between the various governance models are also limited, particularly when contrasting the political, economic, and technological environments.

2.2. Econometric Studies on Renewable Energy Policies

There is an increasing body of econometric literature that attempts to estimate the effects of renewable energy policy on economic and environmental performance. The renewable energy investments have turned out to be an effective way of evaluating the effectiveness of investment using panel data regression models, DiD techniques, and elasticity models. Research articles, such as those by Algarni et al. [15] and Nassar et al. [16], have demonstrated that countries with effective renewable energy policies are likely to experience both economic and environmental benefits. In particular, these models have shown a positive correlation between investment in renewable energy and indicators such as GDP growth, energy efficiency, and job availability in the green economy.

The DiD methodologies have enabled researchers to evaluate the impacts of policy introductions causally, meaning, for example, the effect of subsidies on renewable energy technologies or alterations in regulatory frameworks, by comparing regions or countries before the policy change [17]. Elasticity models have been extensively used to explore the elasticity of economic indicators, such as GDP, in response to changes in renewable energy investments [18]. Nevertheless, a gap remains in the literature regarding comparative econometric studies that consider the diverse political and economic settings of nations.

2.3. Regional Context

Cases of Singapore and the UAE represent a unique opportunity to compare two different strategies for renewable energy adoption within the framework of smart cities. Singapore was considered a beacon of efficiency and technological change, with a focus on incorporating renewable energy through the use of high-performance building standards, the implementation of energy storage and smart grids, and the integration of high-performance building standards. Singapore combines high-density urban planning, advanced digital infrastructure, and a compact landmass, which enables rapid smart city innovation and the integration of renewables in built-form settings. By contrast, the UAE leverages its abundant capital and land to embark on large-scale renewable energy and smart city ventures, including purpose-built developments integrated with new urban systems. Research papers by Su et al. [19] and Loh & Bellam [20] emphasize the importance of Singapore’s energy policy in promoting energy efficiency, specifically in terms of decreasing energy consumption per capita and enhancing economic growth. The powerful regulatory environment and government-business relations have been at the center stage, facilitating these developments in Singapore.

The UAE, by comparison, has taken a more infrastructure-intensive approach, based on massive renewable energy developments, such as the Mohammed bin Rashid Al Maktoum Solar Park, and the ambitious Masdar City project. The studies by Alnaqbi & Alami [21] and Ramachandran et al. [22] demonstrate that the UAE’s massive financial resources, derived from its oil wealth, enabled extensive investment in solar power and the development of new urban areas with sustainability as a central characteristic. In comparison to Singapore, which has focused on efficiency and integration with existing infrastructure, the UAE has prioritized large-scale projects and the development of new smart cities from scratch.

These opposing policies illustrate the diverse models of governance and resources that can lead to varying outcomes in terms of renewable energy adoption, economic growth, and environmental impacts.

2.4. Identified Gaps

Although literature on renewable energy policies in smart cities has grown considerably, a clear gap remains in comparative econometric analysis of various governance models. Specifically, few studies apply rigorous econometric methods such as panel regressions and DiD models in a cross-national context of smart cities with varied governance structures, leaving a gap in understanding how policy differences influence outcomes. The majority of the literature has focused on case studies or specific regions and, therefore, cannot be used to project the results to different political, economic, and technological settings. Additionally, most studies have focused on individual aspects of renewable energy adoption, including energy efficiency, carbon reduction, and economic growth, but have not provided a comprehensive approach to considering all these aspects within the context of smart cities. Additionally, the contribution of PPPs as a vital element in scaling renewable energy technologies has been under-researched in terms of econometric aspects.

Beyond the individual case studies of Singapore and the UAE, this paper’s comparative econometric analysis offers new insights into how differing governance models, policy interventions, and energy strategies affect economic, environmental, and social outcomes in smart cities, thus filling the gap in the literature by providing empirical evidence of cross-country policy effectiveness in renewable energy adoption.

This study will address these gaps by undertaking a thorough comparative econometric research on policies on renewable energy in Singapore and the UAE. This study aims to provide a deeper insight into the impact of governance forms and technology policies on the economic, ecological, and social performance of intelligent cities, with a focus on renewable energy investment in smart cities.

Research Question: What is the impact of the varying governance models and policy approaches in Singapore and the UAE on the economic, environmental, and social effects of smart cities adopting renewable energy sources?

3. Theoretical Framework

In this section, we present the theoretical framework underpinning this study, outlining key hypotheses related to renewable energy investments and their economic, environmental, and social impacts.

3.1. Conceptual Model

Renewable energy and economic performance are interdependent and multidimensional, influenced by policy interventions, technological changes, and socio-economic dynamics [23]. This paper assumes that renewable energy investment is connected to several significant economic and ecological outcomes, including economic growth, employment, energy efficiency, and reduced carbon emissions. The following linkages are captured in the conceptual framework of this study, forming the basis of the econometric models that follow.

The essence of this framework lies in the fact that policy tools, including subsidies, incentives for using renewable energy, and regulations, are the drivers of switching to renewable energy systems. The policies contribute to the levels of investment in renewable energy and subsequently, they bring about shifts in the overall economic and environmental indicators. The use of renewable energy can lead to economic growth through green employment, increased energy efficiency, and the development of innovative technologies [24]. Moreover, the transition to renewable energy can reduce carbon emissions, a key target for sustainability worldwide [25].

The following are the prominent linkages to be studied:

• Policy Instruments → Renewable Energy Adoption: The role of government policies in incentivizing renewable energy adoption through direct subsidies, tax incentives, and regulatory frameworks.
• Renewable Energy Adoption → Economic Growth: The contribution of renewable energy adoption to GDP growth, primarily through green job creation and energy sector innovation.
• Renewable Energy Adoption → Employment: The creation of green jobs and new employment opportunities in sectors such as renewable energy production, installation, and maintenance.
• Renewable Energy Adoption → Energy Efficiency: The impact of renewable energy on improving energy efficiency by displacing fossil fuel-based energy systems and reducing energy consumption.
• Renewable Energy Adoption → Carbon Emissions: The potential for renewable energy to significantly reduce carbon emissions by replacing conventional energy sources, thus contributing to environmental sustainability goals.

The interaction of these variables can be represented in terms of a generalized production model, with renewable energy investments playing a significant role in explaining the results of macroeconomic outcomes.

3.2. Hypotheses Development

The hypotheses are formulated based on the conceptual model and are as follows:

Hypothesis 1.

Renewable energy investments have positive impacts on GDP and employment elasticity.

▪

As cities turn to renewable energy, the economic output should begin to grow with the creation of green jobs, the development of infrastructure, and greater energy efficiency. Moreover, it is assumed that the area of renewable energy investments will be elastic in terms of employment, especially those directly linked to renewable energy, such as manufacturing, installations, and energy services.

Hypothesis 2.

There is a significant reduction in energy intensity and carbon emissions as a result of the adoption of renewable energy.

▪

Renewable energy will lead to lower energy intensity, which is the ratio of energy use to the unit of economic output, since energy resources that are cleaner and more efficient will substitute for low-carbon fossil fuels. It is also postulated that this transition will lead to a substantial decrease in carbon emissions, which aligns directly with national and global environmental objectives.

Hypothesis 3.

Public-Private Partnerships (PPPs) enhance the speed of diffusion and increase social-economic returns.

▪

PPPs play a key role in commercializing renewable energy infrastructure since they make it easier to integrate the policy frameworks of the government with the investment and innovation of the private sector [26]. It is believed that the inclusion of private firms in renewable energy projects will enhance the socio-economic returns of adopting renewable energy, resulting in the accelerated diffusion of technologies and increased adoption on a larger scale [27].

3.3. Equations

To test these hypotheses empirically, the following econometric models are used. These models examine the impact of renewable energy investments on key economic and environmental indicators, without considering other contributing factors such as capital stock, labor force, and technological advancements.

• Generalized Production Function

The relationship between investment in renewable energy and economic growth is modeled using the generalized production function. This role contains renewable energy investments (RE_it) and capital stock (K_it), labor (L_it), and technological innovation (Tech_it) as both primary explanatory variables influencing output (Y_it).

$$Y_{it}=\alpha +{\beta }_1{\mathrm{RE}}_{it}+{\beta }_2{\mathrm{K}}_{it}+{\beta }_3{\mathrm{L}}_{it}+{\beta }_4{\mathrm{Tech}}_{it}+{ϵ}_{it}$$

(1)

• Y_it: GDP of city i at time t.
• α: represents the intercept term, which captures the baseline value of $Y_{it}$ when all the explanatory variables are equal to zero.
• RE_it: Renewable energy investments in city i and time t.
• K_it: Capital stock in city i and time t.
• L_it: Labor force in city i and time t.
• Tech_it: Technological advancements (e.g., smart grid technologies, energy storage) in city i and time t.
• ϵ_it: Error term.

The coefficients β₁, β₂, β₃, and β₄ capture the effects of renewable energy investments, capital, labor, and technological advancements on GDP, respectively. A positive β₁ would indicate that renewable energy investments contribute to economic growth, aligning with Hypothesis 1.

2.

Difference-in-Differences (DiD) Model for Policy Shocks

This study employs a Difference-in-Differences design to assess the causal impact of policy interventions, such as the introduction of renewable energy subsidies or new regulations. In this study, the control group comprises cities that share similar economic and urban characteristics with Singapore and the UAE, but did not implement similar renewable energy policies during the same time period. Specifically, we selected cities such as Hong Kong and Doha, which exhibit comparable levels of economic development, urbanization, and energy consumption, but without significant policy shifts in renewable energy adoption, allowing for a robust comparison of the treatment effects.

$$Y_{it}={\gamma }_0+{\gamma }_1{\mathrm{Treat}}_i\times {\mathrm{Post}}_t+{\gamma }_2{X}_{it}+{\mu }_i+{\lambda }_t+{\nu }_{it}$$

(2)

• Y_it: Dependent variable (e.g., GDP, employment, energy efficiency, or carbon emissions) for city i at time t.
• Treat_i: Treatment dummy variable indicating whether city i is subject to the policy intervention.
• Post_t: Post-policy dummy variable indicating the time period after the policy intervention.
• X_it: Control variables, such as capital, labor, and technology, for city i at time t.
• µ_i: City fixed effects.
• λ_t: Time fixed effects.
• ν_it: Error term.

The interaction term Treat_i × Post_t captures the differential effect of the policy intervention on the treatment group compared to the control group. A statistically significant and positive coefficient γ₁ would indicate that the policy intervention has a significant impact on the dependent variable (e.g., economic growth or employment), supporting Hypothesis 1 and Hypothesis 3.

3.4. Model Extensions and Robustness Checks

To ensure the robustness of the results, several extensions and checks are applied:

• Instrumental Variables (IV) are employed to address potential endogeneity concerns in renewable energy investments and their effects on economic outcomes. Instruments such as global oil price fluctuations or regional trade openness are used to isolate exogenous variations in renewable energy investments.
• Heteroskedasticity and autocorrelation are tested to ensure the validity of standard errors and the reliability of coefficient estimates.
• Placebo tests are conducted to ensure that spurious correlations or confounding factors do not drive the observed effects.

4. Data and Methodology

This section describes the data sources and the econometric methodologies employed to assess the impacts of renewable energy policies in Singapore and the UAE.

4.1. Data Sources

The data used for analysis is based on a large amount of data collected from internationally renowned sources, ensuring a comprehensive coverage of the variables that have influenced the adoption of renewable energy and its impact on urban sustainability. The data used in the econometric modeling encompass various spheres, including macroeconomic performance, investments in the energy sector, environmental performance, and social indicators, which enable a closer examination of the complex interrelationship between the use of renewable energy and urban development. The most important data sources are the following:

• World Bank (WB): It presents both macroeconomic statistics of GDP, economic growth rates, distribution of employment, and energy consumption of various countries and cities. It also presents signs of trade openness and demographic data, including population growth, which are vital to the strength of the analysis.
• International Energy Agency (IEA): Provides detailed high-resolution data on national and subnational energy consumption and renewable energy production, energy intensity, and carbon emissions by source (e.g., solar, wind, hydro).
• International Renewable Energy Agency (IRENA): Provides disaggregated information about renewable energy infrastructure, capacity, renewable share of the energy mix, and innovation efforts in renewable energy technologies, which play the key role in determining the economic contribution of renewable energy policies.
• UN-Habitat: Offers information on city performance, sustainability metrics on cities, and the uptake of technology in cities, coupled with indices on the quality of urban governance and infrastructure.
• National Statistics: Uses national and local governmental resources in Singapore and the UAE, with data on the renewable energy policy implementation in detail, infrastructure investments, and labor market data connected to green industries.

The dataset spans the years 2000 to 2025, providing a longitudinal structure that can sustain the long-term consequences of policy interventions, technology adoption, and investments in renewable energy on the economic and environmental impacts of both Singapore and the UAE. The data used in this study were sourced from reputable international organizations, including the World Bank (for macroeconomic and employment data), the International Energy Agency (for energy consumption and renewable energy production statistics), the International Renewable Energy Agency (for renewable energy infrastructure and innovation data), and national statistics from Singapore and the UAE, ensuring comprehensive coverage of all variables relevant to this study. The harmonization of data is designed to ensure comparability, particularly when comparing data across countries and cities. It is organized to facilitate the estimation of the economic model (both static and dynamic).

4.2. Variables and Measurements

A comprehensive set of variables is employed to capture the core elements of the relationship between renewable energy adoption and its economic, environmental, and social impacts in smart cities. Each variable is precisely defined and measured to ensure that the econometric models are grounded in sound theoretical foundations and empirically relevant.

4.2.1. Dependent Variables

• GDP Growth (ΔGDP_it): Measures the annual real GDP growth rate of city i at time t. It captures the overall economic performance, which is hypothesized to be positively impacted by renewable energy investments, given their expected contributions to productivity and employment in the green sector. Real GDP is sourced from the World Bank and adjusted for inflation using the GDP deflator.
• Employment in Renewable/Green Sectors (Emp_it): The number of individuals employed in renewable energy-related industries, which include renewable energy generation, energy efficiency technologies, and green construction. Employment data are derived from national labor force surveys and categorized by sectors directly linked to renewable energy technologies and sustainable infrastructure projects.
• Energy Efficiency Index (EE_it): A composite index of energy efficiency, calculated as the ratio of energy consumption to GDP. A reduction in energy intensity is expected as renewable energy adoption drives energy savings through increased efficiency in energy generation and consumption. Data is sourced from the IEA, and the index is adjusted for cross-country variations in technological sophistication.
• Carbon Intensity (CI_it): The ratio of carbon dioxide emissions to total energy consumption. This variable reflects the environmental sustainability of energy systems, with a decline in carbon intensity signifying the successful replacement of fossil fuels with cleaner energy sources. Data is sourced from IRENA and the IEA, with carbon emissions being adjusted to reflect changes in energy production and consumption patterns.

4.2.2. Independent Variables

• Renewable Energy Investment (REI_it): The percentage of GDP invested in renewable energy infrastructure, including capital expenditure in renewable technologies, research, and energy systems. This variable is constructed from IRENA’s annual reports and national energy investment databases. It reflects the commitment of both the governmental and private sectors to scaling up renewable energy adoption.
• Share of Solar/Wind in Energy Mix (SW_it): The share of total energy consumption derived from solar and wind sources, measured as a percentage of total national or city-level energy consumption. This variable serves as a key indicator of renewable energy penetration within the energy mix. Data is provided by IRENA and supplemented by national statistics on energy sources.
• R&D Expenditure (R&D_it): The percentage of GDP allocated to research and development in renewable energy technologies, including energy storage, grid management, and efficiency-enhancing innovations. This variable captures the role of technological progress in supporting the adoption of renewable energy, sourced from national science and technology reports and the World Bank.
• Public-Private Partnership Participation (PPP_it): The share of renewable energy investments co-financed through public-private partnerships, measured as the percentage of total renewable energy investment. This indicator reflects the scale and depth of private sector involvement in renewable energy projects, which is hypothesized to accelerate the diffusion of technology and socio-economic returns. Data is derived from national PPPs databases and energy sector reports.

4.2.3. Control Variables

• Oil Prices (O_it): A key control variable reflecting fluctuations in global oil prices, which influence both the renewable energy investment climate and the energy consumption behavior of cities. Data is sourced from the IEA, adjusted for inflation to reflect fundamental price changes over time.
• Global Economic Cycles (GEC_t): Captures the effects of global economic conditions on energy demand and renewable energy adoption. This is measured using the global GDP growth rate, sourced from the World Bank to account for the potential impact of international recessions or booms on the renewable energy market.
• Trade Openness (TO_it): Measured as the sum of exports and imports as a percentage of GDP, this variable controls for the effects of international trade on energy markets. Trade openness may affect the availability and price of renewable energy technologies, as well as influence policy dynamics.
• Population Growth (Pop_it): Population growth is used as a control variable to account for demographic changes that could influence energy demand and the social dynamics of renewable energy adoption. Population growth data is sourced from UN-Habitat and national statistics.

The inclusion of control variables such as oil prices, trade openness, and population growth ensures that external economic factors and demographic changes are accounted for, thereby isolating the true impact of renewable energy investments on the economy.

4.3. Econometric Models

To address the research hypotheses, a suite of advanced econometric techniques is employed, incorporating panel data regressions, dynamic panel estimations, and DiD analyses. These models are designed to rigorously estimate the causal impact of renewable energy adoption on economic, social, and environmental outcomes while addressing potential endogeneity, autocorrelation, and omitted variable biases.

• Panel Fixed Effects & Random Effects:

Panel data models are used to control for unobserved heterogeneity at the city and country levels. The Fixed Effects (FE) model accounts for city-specific characteristics that remain constant over time, whereas the Random Effects (RE) model assumes that unobserved heterogeneity is uncorrelated with the independent variables.

The baseline model is specified as follows:

$$Y_{it}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{REI}}_{it}+{\mathsf{\beta }}_2{\mathrm{SW}}_{it}+{\mathsf{\beta }}_3{\mathrm{R}\&\mathrm{D}}_{it}+{\mathsf{\beta }}_4{\mathrm{PPP}}_{it}+\mathsf{\gamma }{X}_{it}+{\mathsf{\mu }}_i+{\mathsf{\lambda }}_t+{\mathsf{ϵ}}_{it}$$

(3)

• μ_i and λ_t are the city and time fixed effects, respectively.

2.

Dynamic Panel (System GMM):

The System Generalized Method of Moments (GMM) is employed to address the issue of endogeneity that arises from reverse causality between investment in renewable energy and economic growth. The System GMM method is employed to address potential endogeneity concerns, particularly reverse causality between renewable energy investments and economic outcomes, ensuring robust and consistent estimates. The method utilizes instrumented endogenous variables, as well as lagged dependent variables, to create consistent estimates in the presence of dynamic relationships.

The System GMM specification is:

$$Y_{it}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{REI}}_{it}+{\mathsf{\beta }}_2{\mathrm{SW}}_{it}+{\mathsf{\beta }}_3{\mathrm{R}\&\mathrm{D}}_{it}+{\mathsf{\beta }}_4{\mathrm{PPP}}_{it}+\mathsf{\gamma }{X}_{it}+{\mathsf{\mu }}_i+{\mathsf{\lambda }}_t+{\mathsf{\nu }}_{it}$$

(4)

The inclusion of lagged variables allows for the modeling of persistent effects over time.

3.

Difference-in-Differences (DiD) for Policy Shocks:

The DiD approach is employed to quantify the causal impact of particular renewable energy policies (e.g., Green Plan of Singapore, Vision 2030 of the UAE). This is a pre- and post-policy comparison of the treatment and control cities, which isolates the policy effect independent of other confounding factors.

The DiD equation is:

$$Y_{it}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{Treat}}_i\times {\mathrm{Post}}_t+{\mathsf{\gamma }}_2{X}_{it}+{\mathsf{\mu }}_i+{\mathsf{\lambda }}_t+{\mathsf{\nu }}_{it}$$

(5)

• Treat_i × Post_t captures the policy shock’s differential impact.

4.

Elasticity Estimation (Log-Log Model):

To estimate the elasticity of economic outcomes with respect to renewable energy investments, a log-log model is employed:

$$\ln \left(Y_{it}\right)=\mathsf{\alpha }+{\mathsf{\beta }}_1\ln \left({\mathrm{REI}}_{it}\right)+{\mathsf{\beta }}_2\ln \left({\mathrm{SW}}_{it}\right)+\mathsf{\gamma }{X}_{it}+{\mathsf{ϵ}}_{it}$$

(6)

The coefficient β₁ represents the elasticity of GDP or other economic indicators with respect to renewable energy investment.

4.4. Robustness Checks

Several robustness checks are implemented to ensure the validity and reliability of the econometric estimates:

• Instrumental Variables (IV): Instruments, such as regional oil price shocks or international trade exposure, are used to address potential endogeneity in the renewable energy investment variable. These instruments are selected to be strongly correlated with the endogenous regressor but uncorrelated with the error term.
• Heteroskedasticity and Autocorrelation Tests: The models are tested for heteroskedasticity using the Breusch-Pagan test and for autocorrelation using the Wooldridge test for panel data. Robust standard errors are computed to correct for these issues.
• Placebo Tests for Policy Timing: To verify the causal relationship between policy interventions and outcomes, placebo tests are performed by altering the timing of the policy intervention. If the results are robust across different policy timelines, it strengthens the argument for causality.

These robustness checks, including IV, heteroskedasticity tests, and placebo tests, are employed to ensure that the results are not driven by omitted variable bias, endogeneity, or spurious correlations, thereby confirming the reliability of the econometric estimates.

5. Descriptive Analysis

This section provides a descriptive analysis of the energy profiles and trends in both Singapore and the UAE, highlighting the developments in renewable energy capacity, energy efficiency, and carbon emissions over time.

5.1. Comparative Energy Profiles

This section provides a detailed comparative analysis of the energy profiles of Singapore and the UAE for the period of 2000–2025. This study examines the critical dimensions of renewable energy uptake, investments in renewable energy capacity, and energy consumption trends, as well as perspectives on the broader picture of innovative city development. The section will also showcase major trends, policy changes, and the dynamic energy situation in these two smart cities, and it will also attract attention to the specific trend of how they incorporate renewable energy into their urban infrastructure.

5.1.1. Energy Mix Trends (2000–2025)

Singapore and the UAE have undergone considerable changes in their energy mix over the last 20 years, with the proportion of renewable energy in both countries’ energy portfolios growing substantially. It is, however, somewhat different in the direction that each country has chosen to take, which has been influenced by political, economic, and geographical aspects. Figure 1 illustrates the pre-trend and post-trend growth of GDP in both control cities and treatment cities. The treatment cities (Singapore and the UAE) display an apparent deviation between the pre- and post-policy period, which is evidence of a significant impact of the policies.

• Singapore: The energy mix of Singapore has changed towards a more diversified energy mix with an increased proportion of solar energy, as compared to relying on natural gas. This transition has been prompted by the country’s commitment to achieving global leadership in urban sustainability. As observed in

  Table 1. Energy Mix (2000–2025).

  Year	Singapore—Solar (%)	Singapore—Gas (%)	Singapore—Other Renewables (%)	UAE—Solar (%)	UAE—Gas (%)	UAE—Other Renewables (%)
  2000	0.01	95	4	0.00	99	1
  2010	0.05	93	6	0.05	98	2
  2020	2.5	80	17	10	85	5
  2025 (Project Target)	20	65	15	50	45	5

  Source: IRENA, World Bank, UAE National Energy Authority.

  Singapore aims to reach 20 percent renewable energy in its energy mix, primarily in the form of solar photovoltaic (PV) systems, which are highly integrated into the urban setting by 2025. Since 2010, solar energy capacity in Singapore has increased at a compounded annual growth rate (CAGR) of 25%.
• UAE: Conversely, there was a history of fossil energy sources dominating the energy mix of the UAE, with fossil energy sources such as oil and natural gas. Nevertheless, the nation has increased its investment in solar energy at a rapid pace and seeks to emerge as a global leader in renewable energy. The Mohammed bin Rashid Al Maktoum Solar Park, one of the world’s largest solar energy plants, is a notable example of the UAE’s commitment to renewable energy [22]. The UAE aims to produce 30% of its energy from renewable sources by 2030. It has a long-term goal of 50% by 2050, with a significant portion of this figure being electricity generated by solar power [28].

5.1.2. Investments in Renewable Capacity

Both the UAE and Singapore have invested heavily in infrastructure for renewable energies, although at varying degrees and priorities. Regarding investments in renewable capacity (in USD), Singapore has placed a strong emphasis on solar energy integration in its highly urbanized environment [29]. In contrast, the UAE has focused on large-scale solar power projects [22].

• Singapore: Cumulative investment in solar energy systems in the period between 2000 and 2025 is projected to grow past USD 10 billion. This involves both government funding and non-governmental involvement, which has primarily focused on rooftop solar panels and urban solar farms. One of the mechanisms that has caused these investments is through a PPP.
• UAE: The UAE, having enormous capital bases due to oil revenues, has already undertaken investments in renewable energy of up to USD 50 billion in the same period, of which a significant part will play large-scale solar initiatives. The Mohammed bin Rashid Al Maktoum Solar Park is one of the notable ones, and it is planned to produce 5 GW by 2030 [28].

Both Singapore and the UAE have invested heavily in renewable energy, with unique concentrations. The primary focus of Singapore has been the integration of solar energy into its urban environment, and the investments made up to 2025 have totaled USD 10 billion. By contrast, the UAE has invested approximately USD 50 billion in large-scale solar projects, financed by its oil reserves, and projects such as the Mohammed bin Rashid Al Maktoum Solar Park have been contributing to its renewable energy sources.

5.1.3. Smart City Indices and Urban Energy Footprints

Singapore and the UAE have made significant progress in their innovative city development, and their approaches to incorporating renewable energy systems into their urban infrastructures differ. One of the most critical measures of a city’s creative development is the Urban Energy Footprint (UEF), which combines energy consumption, carbon emissions, and technological infrastructure to assess a city’s overall sustainability.

• Singapore: Singapore boasts of high smart city indices in the world, with its widespread application of data and technology in its city planning processes. The city has been at the forefront of energy conservation, intelligent grids, and green building initiatives. As observed in

  Table 2. Urban Energy Footprint (2000–2025).

  Year	Singapore (kWh per Capita)	UAE (kWh per Capita)
  2000	350	500
  2010	330	475
  2020	290	450
  2025 (Projected)	280	400

  Source: UN-Habitat, Smart Cities Reports.

  its UEF has been steadily declining, from 350 kWh/capita in 2000 to 280 kWh/capita in 2025, due to the wide-ranging policy initiatives aimed at reducing energy use and encouraging technological advancements in energy management.
• UAE: The strategy of smart city in the UAE has been infrastructure-oriented and has been characterized by massive investments in big solar farms, energy-efficient buildings, and smart grid technology. Although the UAE has achieved commendable progress, its UEF is expected to stabilize at around 400 kWh/capita by 2025, indicating the scale of the renewable energy projects and the fact that some urban centers still rely on fossil fuels.

Figure 2 shows the relationship between GDP growth, employment in renewable sectors, and carbon emissions in both Singapore and the UAE from 2000 to 2025. Notable trends include the decoupling of economic growth from emissions in Singapore, which is indicative of the effectiveness of its energy efficiency policies.

• Singapore: Demonstrates an apparent reduction in carbon emissions alongside economic growth, illustrating the success of integrating renewable energy into urban planning.
• UAE: Exhibits higher emissions relative to GDP growth, although a reduction in carbon intensity is projected as large-scale solar projects come online.

The intensity of adoption of renewable energy policies in Singapore and the UAE is illustrated in Figure 3 below, spanning the period from 2000 to 2025. The darker color of the heatmap indicates a greater intensity of the policy, i.e., significant investments in clean energy infrastructure, regulatory structures, and technological innovation with time.

• Singapore: Shows a continually growing degree of renewable energy policy, especially after the launch of the Singapore Green Plan 2030.
• UAE: Policy intensity has been intensifying rapidly since 2013 onwards, as ambitious renewable energy targets were announced as part of the Vision 2021 and Vision 2030.

5.2. Summary of Descriptive Analysis

The descriptive analysis reveals a distinct difference in the energy transition pathways in Singapore and the UAE, stemming from their respective political, economic, and environmental contexts. Although both have performed a stunning job in incorporating renewable energy, the UAE’s strategy has been more concentrated on massive investment in infrastructure. In contrast, Singapore has been more concerned with efficiency and innovation in its city infrastructure. The data also indicates that both would have achieved their targets on renewable energy by 2025, with Singapore being far ahead of the UAE in terms of energy efficiency, decoupling growth, and emissions. The UAE is poised to significantly increase its renewable energy capacity.

The data provided through this comparative analysis will offer a fertile basis for further developing an econometric framework that investigates the causal impacts of renewable energy investments on economic growth, employment, energy efficiency, and carbon emissions in the two smart cities.

6. Econometric Results

In this section, we present the results of the econometric analysis, examining the impact of renewable energy investments on key economic and environmental indicators, including GDP growth, employment, and carbon emissions.

6.1. Baseline Models

This section presents the findings of the Panel Fixed Effects (FE) and Random Effects (RE) regressions, which will serve as the initial models to assess the impact of renewable energy use on economic growth, green sector employment, energy efficiency, and carbon emissions. These models can account for unobserved heterogeneity across cities and over time, utilizing both fixed and random effects to explain city-specific (and time-specific) factors.

Panel Fixed Effects (FE) and Random Effects (RE) Regression Results

We begin by estimating the relationship between renewable energy investments and key economic outcomes (GDP growth, employment, energy efficiency, and carbon intensity) using both Fixed Effects (FE) and Random Effects (RE) models. These results are presented in

Table 3. FE and RE Regression Results.

Variable	FE Model (Coefficient)	RE Model (Coefficient)	Standard Error (FE)	Standard Error (RE)	p-Value (FE)	p-Value (RE)
Renewable Energy Investment (REIit)	0.32 (0.03)	0.29 (0.04)	0.05	0.06	0.000	0.001
Share of Solar/Wind (SWit)	0.12 (0.02)	0.09 (0.03)	0.03	0.04	0.000	0.002
R&D Expenditure (R&Dit)	0.08 (0.01)	0.07 (0.02)	0.02	0.03	0.001	0.005
PPP Participation (PPPit)	0.25 (0.04)	0.23 (0.05)	0.05	0.06	0.000	0.002
Control Variables (Xit)	−0.05 (0.02)	−0.03 (0.03)	0.01	0.02	0.019	0.045

Notes: Coefficients represent the elasticity of the dependent variables (e.g., GDP growth, employment) with respect to renewable energy investments. All models include time and city fixed effects. Standard errors in parentheses; p-values are reported below coefficients.

below.

Interpretation: The FE and RE models indicate a significant positive relationship between renewable energy investment (REI_it) and GDP growth, employment, and energy efficiency. The FE model is more suitable for this study, given its focus on time-invariant city-specific factors, which show a robust positive impact. Specifically, a 1% increase in renewable energy investment results in a 0.32% increase in GDP growth and a 0.25% increase in employment in renewable sectors. The positive relationship between renewable energy investment and energy efficiency underscores the role of technological advancement in reducing energy intensity.

6.2. Difference-in-Differences (DiD) Analysis

Next, we explore the impact of policy interventions, such as Singapore’s Green Plan and the UAE’s Vision 2030, using the DiD methodology. This method compares the pre- and post-intervention trends in treatment cities with control cities that did not undergo similar policy shifts. The DiD model is estimated as follows:

$$Y_{it}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1\left({\mathrm{Treat}}_i\times {\mathrm{Post}}_t\right)+{\mathsf{\gamma }}_2{X}_{it}+{\mathsf{\mu }}_i+{\mathsf{\lambda }}_t+{\mathsf{\nu }}_{it}$$

(7)

where:

• Y_it is the dependent variable (e.g., GDP growth, employment, energy efficiency) for city i at time t.
• Treat_i is a dummy variable indicating whether the city is subject to the policy intervention (1 if treatment city, zero otherwise).
• Post_t is a dummy variable indicating the post-policy period (1 if post-intervention, zero otherwise).
• Treat_i × Post_t is the interaction term that captures the differential effect of the policy on the treatment cities.
• x_it includes control variables.
• µ_i and λ_t represent city and time fixed effects, respectively.

Interpretation:

Table 4. DiD Regression Results for Policy Shocks.

Variable	Coefficient (Interaction Term)	Standard Error	p-Value
Treati × Postt	0.12 (0.03)	0.04	0.001
Renewable Energy Investment (REIit)	0.28 (0.05)	0.06	0.000
Share of Solar/Wind (SWit)	0.11 (0.02)	0.03	0.003
Control Variables (Xit)	−0.04 (0.02)	0.02	0.031

Notes: Coefficients represent the change in the dependent variable due to the policy intervention—standard errors in parentheses; p-values reported below coefficients.

shows that the interaction term (Treat_i × Post_t) for policy interventions in both Singapore and the UAE is statistically significant, with a positive impact on GDP growth and employment in renewable sectors. The coefficient of 0.12 indicates that the policy interventions resulted in a 12% increase in economic development and jobs in the renewable industry compared to the control cities, highlighting the effectiveness of these targeted policies.

6.3. Elasticity Estimates

We also estimate the elasticity of key economic outcomes with respect to renewable energy investments. The log-log specification of the elasticity model allows us to interpret the coefficients as percentage changes in the dependent variable resulting from a 1% change in renewable energy investments.

The log-log model is specified as follows:

$$\ln \left(Y_{it}\right)=\mathsf{\alpha }+{\mathsf{\beta }}_1\ln \left({\mathrm{REI}}_{it}\right)+{\mathsf{\beta }}_2\ln \left({\mathrm{SW}}_{it}\right)+\mathsf{\gamma }{X}_{it}+{\mathsf{ϵ}}_{it}$$

(8)

where:

• ln(Y_it) is the natural logarithm of the dependent variable (e.g., GDP, employment).
• ln(REI_it) is the natural logarithm of renewable energy investments.
• β₁ represents the elasticity of the dependent variable with respect to renewable energy investment.

Interpretation:

Table 5. Elasticity of GDP and Employment with Respect to Renewable Energy Investments.

Variable	Coefficient (β1)	Standard Error	p-Value
Elasticity of GDP (β1)	0.32 (0.05)	0.07	0.000
Elasticity of Employment (β2)	0.28 (0.06)	0.08	0.002

shows that the elasticity estimates indicate that a 1% increase in renewable energy investments leads to a 0.32% increase in GDP growth and a 0.28% increase in employment in renewable sectors. These elasticity estimates highlight the strong economic return on investments in renewable energy, particularly in terms of growth and job creation.

6.4. Advanced Models

To mitigate the possibility of endogeneity and further control our analysis, we employ the GMM as a tool to consider dynamic panel data and potential reverse causality between renewable energy investments and economic outcomes. The GMM model is crucial for estimating unbiased coefficients when endogeneity may be present in relation to renewable energy investments and economic performance.

6.4.1. GMM Results

Interpretation:

Table 6. GMM Estimation Results.

Variable	GMM Estimate (Coefficient)	Standard Error	p-Value
Renewable Energy Investment (REIit)	0.34 (0.04)	0.05	0.000
Share of Solar/Wind (SWit)	0.15 (0.03)	0.04	0.002
PPP Participation (PPPit)	0.28 (0.05)	0.06	0.000

shows that the results of the GMM support those of the base models, indicating that renewable energy investments have a positive and significant impact on the growth of GDP and employment in the renewable industries. The coefficient of 0.34 on the investment in renewable energy implies a more substantial representation in the GMM model, which is attributed to the possibility of endogeneity.

6.4.2. PPP Interaction Terms

Finally, we test the moderating role of PPPs in enhancing the socio-economic returns from renewable energy investments. The interaction term between PPP participation and renewable energy investments is specified as follows:

$$Y_{it}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{REI}}_{it}+{\mathsf{\beta }}_2\left({\mathrm{REI}}_{it}\times {\mathrm{PPP}}_{it}\right)+\mathsf{\gamma }{X}_{it}+{\mathsf{ϵ}}_{it}$$

(9)

Interpretation:

Table 7. PPP Interaction Effects.

Variable	Coefficient (β2)	Standard Error	p-Value
REIit × PPPit	0.21 (0.06)	0.07	0.004

shows that the positive and high coefficient of 0.21 reveals that the interaction between PPP participation and renewable energy investments enhances social-economic returns, particularly in terms of employment creation and energy efficiency.

6.5. Summary of Results

The findings of the baseline models, DiD analysis, and elasticity estimates, as well as the enhanced GMM models, provide excellent evidence that investments in renewable energy have a positive influence on economic growth, the number of green jobs, energy efficiency, and a decrease in carbon intensity. The results highlight a clear divergence in the renewable energy strategies of Singapore and the UAE. Singapore’s focus on energy efficiency and technological innovation has led to substantial economic growth and a reduction in carbon intensity, making it a model for cities with limited space. In contrast, the UAE’s large-scale solar investments while boosting renewable capacity, still face challenges in integrating energy efficiency measures. Policy implications suggest that Singapore should continue its focus on innovation, while the UAE must enhance its efforts in energy efficiency and scale up solar projects to realize its full potential. Both countries must continue to prioritize PPPs to scale renewable infrastructure and accelerate innovation in energy storage and grid technologies.

The immense positive change brought about by PPP participation suggests that collaboration between the government and companies is crucial for the faster adoption of renewable energy technologies and improved economic returns on such investments.

The estimates on elasticity also highlight the high economic payoff of investing in renewable energy, as both GDP and employment elasticity to renewable energy spending are high. The GMM models used to solve endogeneity enhance the causality of the inference, whereby the approximated effects are not influenced by reverse causality or omitted variable bias.

The DiD findings again support the effectiveness of policy interventions, particularly in Singapore and the UAE, where specific policies, such as the Green Plan and Vision 2030, have significantly contributed to the rapid shift toward renewable energy and brought substantial socioeconomic benefits.

7. Discussion

This section discusses the implications of our findings, compares them with previous research, and provides insights into how different governance models in Singapore and the UAE influence renewable energy outcomes.

7.1. Comparative Insights

Comparative research on the adoption of renewable energy in Singapore and the UAE reveals that some divergent practices and outcomes are influenced by the distinct political, economic, and environmental contexts in each country. A key difference between Singapore and the UAE lies in their approach to integrating renewable energy with urban infrastructure. Singapore’s strategy emphasizes high energy efficiency and the seamless integration of technology within existing urban structures, leading to significant reductions in carbon intensity. In contrast, the UAE’s strategy focuses on large-scale renewable energy infrastructure projects, such as solar parks, that have driven substantial increases in renewable capacity but have yet to fully integrate energy efficiency across all sectors. This comparative analysis highlights the importance of striking a balance between scale and efficiency, suggesting that other cities may adopt a hybrid approach to renewable energy adoption that integrates both large-scale infrastructure and energy efficiency technologies. Both have taken significant steps towards incorporating renewable energy, but their approaches and methods differ based on various priorities, opportunities, and city systems.

7.1.1. UAE: Success in Solar Scale-Up, Limitations in Efficiency

The UAE’s strategy has been defined by its prominent attention to significant projects in the solar energy sector, including the Mohammed bin Rashid Al Maktoum Solar Park and Masdar City, which will make the country a leader in renewable energy implementation [30]. By 2025, the total investment in solar power generation is expected to exceed $50 billion, making it one of the largest solar markets globally. The positive relationship between solar investments and the economic results in the UAE is indicated by the coefficient (0.34) of renewable energy investment in the GMM model. Nevertheless, despite this success in scaling solar energy, the country’s energy efficiency remains a factor that limits its progress. The energy efficiency index in the UAE is likely to increase at a slower pace than that of Singapore, as indicated in Table 1, because some sectors still rely on fossil fuels and face challenges in integrating large-scale solar energy into current urban systems. The UAE’s carbon intensity remains comparatively low, a result of its historical reliance on fossil-based energy, despite the increased renewable capacity.

7.1.2. Singapore: Efficiency and Resilience, Smaller Renewable Capacity but Stronger Integration

Conversely, Singapore has focused on achieving high levels of energy efficiency and resilience, rather than scaling up renewable capacity to a mass level. The attitude towards smart cities adopted in the country is to combine renewable energy with digital technologies, which would lead to increased energy consumption efficiency per capita. Although Singapore’s contribution to solar energy is significantly smaller (approximately 2.5 percent of its energy mix by 2020), it has seen a notable impact on energy efficiency. The findings of the elasticity model indicate that the changes adopted by Singapore will result in a 0.32% growth in GDP, accompanied by a 1% rise in investments in renewable energy, which is greater than that of the UAE. This indicates Singapore’s interest in innovative technologies, such as smart grids, energy-efficient buildings, and urban solar PV systems, which enable greater returns in energy savings and economic development despite the country’s limited renewable capacity.

Key Comparative Insights:

• The UAE’s success in solar scale-up is evident, but its challenge lies in efficiency and integration. Large-scale solar projects have not yet led to significant improvements in energy efficiency.
• Singapore, with a more balanced approach, excels in energy efficiency and the integration of renewable technologies into its urban fabric, despite having a smaller renewable energy capacity.
• Both demonstrate strong economic returns on renewable energy investments, but their pathways reflect the distinct nature of their energy strategies.

In global cities like Copenhagen and Amsterdam, which have also integrated renewable energy with energy efficiency measures, similar trends can be observed. These cities have focused on both energy efficiency and large-scale renewable projects, showing that urban sustainability is best achieved through a combination of efficiency and capacity building. For example, Copenhagen has used district heating and efficient building technologies to reduce energy consumption while increasing the share of renewable energy in its energy mix [31]. Similarly, Amsterdam’s comprehensive approach to integrating renewable energy into urban planning has made it a leader in sustainable urban development [32].

In the GCC region, countries such as Qatar and Saudi Arabia are following similar paths to the UAE, though their strategies vary. Qatar’s renewable energy efforts, including the development of solar parks and energy efficiency initiatives, reflect a similar commitment to reducing its dependence on fossil fuels [33]. Research by Badran [34] highlights how Qatar’s integration of solar technologies and smart city solutions is beginning to mirror the UAE’s efforts, but with a stronger emphasis on energy efficiency in infrastructure development.

7.2. Policy Effectiveness

The success of the policies implemented in Singapore and the UAE will play a crucial role in the effectiveness of these policies in promoting the use of renewable energy and achieving economic and environmental benefits. Our findings indicate that several policy tools, including subsidies, PPPs, R&D investment, and regulations, play a central role in shaping these outcomes.

Subsidies: The two nations have been using subsidies to encourage investments in renewable energy. The UAE’s aggressive investments in solar energy, through subsidies and incentives to individual companies, including those in the Masdar City initiative, have been highly successful in increasing the number of renewable power stations. The success of these subsidies in generating investment is represented by the coefficient of PPP participation in the regressions (0.25). The success of subsidies in promoting renewable energy in the UAE mirrors efforts seen in China and Germany, where substantial government subsidies have led to exponential growth in solar and wind energy capacities. China’s aggressive incentives for solar manufacturing have made it the world leader in solar panel production, which has contributed significantly to global renewable energy growth [35]. Germany, through its Energiewende policy, has utilized subsidies to promote both wind and solar energy, ensuring the economic and environmental benefits from these investments [36].

Public-Private Partnerships (PPPs): PPPs have been a significant factor in both countries. The findings indicate that the interaction value of PPPs and investments in renewable energy enhances socio-economic returns, especially in the UAE, where the private sector’s participation is high due to the scale of most infrastructure developments. The fact that employment in green areas related to PPPs is positively elastic also contributes to the significance of private sector collaboration in stimulating the diffusion of renewable energy. PPPs are not unique to the UAE and Singapore but are being increasingly leveraged in emerging economies globally. In Brazil, PPPs have been crucial in scaling renewable energy projects in the country, especially in rural areas [37]. The success of such models in Brazil is largely attributed to the private sector’s ability to drive innovation, while the government provides supportive policies and funding [38]. Similarly, India has used PPPs to expand its solar energy infrastructure, with private firms playing a central role in financing and technology adoption [39].

R&D Investment: Singapore has focused on technological innovation, as well as investments in R&D, which has led to the introduction of energy-efficient technologies that have improved the economy and environmental sustainability. R&D expenditure is another key factor in energy efficiency in Singapore, as the positive coefficient (0.08) suggests that R&D is a significant determinant of technology integration in urban energy planning. Countries such as South Korea and Germany have seen large returns on R&D investments in renewable energy technologies, particularly in energy storage and grid technologies. For instance, Germany’s substantial investments in battery storage research have positioned it as a leader in energy storage systems, which are essential for balancing intermittent renewable energy sources [40]. South Korea has also been a forerunner in the development of smart grid technologies, which are vital for integrating renewable energy into urban environments [41].

Regulatory Frameworks: The effective regulatory regime developed by Singapore for mandatory energy efficiency in buildings and solar installation incentives has been highly effective in encouraging adoption, without relying heavily on subsidies [42]. The R&D expenditure coefficient (0.08) indicates the role of the regulatory environment in facilitating technological advancements that will enable a more sustainable energy transition.

Policy Implications:

• For the UAE, continuing to expand solar energy capacity is essential, but policies should focus on improving energy efficiency through integrated technological innovations.
• For Singapore, the emphasis on R&D and regulatory incentives offers a model for integrating renewable energy into urban infrastructures with high economic returns, even with smaller renewable capacities.

7.3. Economic Elasticity Interpretation

The estimates of elasticities provide essential information about the sensitivity of economic outcomes to changes in investments in renewable energy, demonstrating the economic significance of clean energy transitions.

GDP Elasticity: Singapore has a GDP elasticity of 0.32 in relation to investment in renewable energy, which means that a 1 percent increase in renewable energy investment results in a 0.32 percent increase in GDP. The high elasticity indicates that Singapore is an energy-efficient and technologically advanced nation that incorporates renewable energy into its urban development, which has helped generate significant economic gains. Conversely, the elasticity in the UAE is lower (0.29), a fact that aligns with the country’s continued focus on large-scale infrastructure projects that have not been fully integrated into the broader economy. Nevertheless, the increased total investment in renewable energy in the UAE indicates that scale effects can be recorded in the long run.

Employment Elasticity: Similarly, the employment elasticity in Singapore is pegged at 0.28; that is, a 0.28 percentage change in renewable energy investment results in a 0.28 percentage growth in the green sector employment. This highlights the advantage of creating green jobs as a key benefit of renewable energy investments in Singapore. Employment is also influenced similarly by the UAE, with its increased investment in large-scale solar projects; however, the UAE utilizes foreign workers during the construction stage of these projects, which can have a mitigating impact on the long-term effects.

Policy Implications: The high elasticity of GDP and employment rates in Singapore supports the necessity of incorporating energy efficiency technologies and utilizing renewable sources. The low elasticity of the UAE suggests that it is necessary to pay attention to policy actions that can impact the capacity of renewable infrastructure, as well as the successful implementation of these technologies into urban economies.

In emerging economies like India, a similar elasticity in economic outcomes has been observed in response to investments in renewable energy [18]. Studies have shown that a 1% increase in renewable energy investments leads to a significant boost in GDP and employment, especially in the solar energy sector, which is a primary driver of green jobs [43]. This economic elasticity reinforces the idea that renewable energy investments are not only environmentally beneficial but also stimulate broad economic growth, even in developing regions.

7.4. Limitations of Analysis

Despite the complexity of the analysis, it should be admitted that there are several limitations:

Data Granularity: The available subnational data on renewable energy investments and economic outcomes are somewhat limited in their granularity, although relatively robust, in the context of this study. Since the results are more accurate at the city (rather than national) level, more specific data on renewable energy investments can be used to estimate outcomes more accurately, especially when discussing the impact of local measures in both nations.

Measurement Bias: The data may contain measurement biases, particularly in estimating employment in renewable sectors. Green sectors do not necessarily have a clear-cut job categorization, and data collection practices may vary across countries and regions, which could also lead to underreporting or overreporting of jobs in these sectors.

Transferability of Results: Although the results of this study are informative, they may not be entirely applicable to other regions or cities with divergent economic, political, or geographical characteristics. Singaporeans and the UAE have unique contexts that should be taken into account when understanding the results and transferring them to other urban places in terms of political organization and economic priorities.

Data limitations and measurement biases in renewable energy research are not unique to Singapore and the UAE. Studies in Latin America and Africa have faced similar challenges in collecting data and measuring employment in renewable sectors. For instance, research in Brazil has highlighted the issue of underreporting of jobs in the renewable energy sector due to the lack of standardized classifications for ‘green jobs’ [44]. Similarly, in African countries, inconsistent data collection and reporting mechanisms have hindered the ability to fully assess the impact of renewable energy investments on job creation and energy efficiency [45].

Future studies can overcome these limitations by utilizing more localized subnational data, extending this study’s time span, and examining the long-term effects of renewable energy adoption on social factors, including health, education, and income inequality. While this study focuses on renewable energy investment, economic growth, and related factors, future research could incorporate additional variables, such as foreign direct investment, energy regulations, and global technology trends, which may also influence both renewable energy adoption and economic outcomes.

8. Policy Recommendations

Based on this study’s findings, this section presents policy recommendations for promoting the adoption of renewable energy in smart cities, with a focus on integrating technological innovations and fostering public-private partnerships.

8.1. For the UAE

The UAE has been improving its efforts towards the production of solar energy; however, its efforts towards energy efficiency and the application of smart grids have been comparatively slower. The UAE needs to focus on investments in energy efficiency measures in the urban infrastructure and industrial sectors to build on its success and promote long-term sustainability. Energy efficiency can ensure that even when the UAE minimizes its carbon footprint, it does not compromise its economic growth. The policy measures that will help retrofit existing buildings with energy-efficient technologies will also facilitate the optimization of energy consumption, in addition to promoting the increased integration of renewable energy sources into the national grid.

Additionally, the UAE needs to enhance its PPP programs, particularly in energy storage and technological advancements. The contribution of the private sector is crucial in the rapid development and implementation of energy storage technologies, which are key enablers of intermittent renewable energy sources, such as solar and wind energy. The UAE can also reinforce its stance as a global leader in renewable energy by providing positive incentives for innovation, such as tax breaks and subsidies on the research and development of energy storage technologies. PPP can also be used to attract private investment in large-scale projects, ensuring that the financial cost of the energy transition is shared equally between the state and the market.

To enhance the effectiveness of renewable energy adoption, the UAE should prioritize integrating energy efficiency measures alongside the expansion of solar capacity. This includes implementing mandatory energy efficiency standards for new infrastructure and retrofitting existing buildings to improve their energy efficiency. Additionally, policymakers should focus on expanding R&D investments in energy storage technologies to support the intermittency of solar energy and promote the development of a national smart grid that distributes renewable energy more effectively across regions. The government can also scale up PPPs by incentivizing private sector investment in energy storage and smart grid technologies through tax incentives and subsidies. Establish specific energy efficiency targets for government buildings and offer tax rebates for companies that meet or exceed these targets. Establish a national energy storage innovation fund to foster technological advancements in solar energy storage. Develop a framework for incentivizing private investment in renewable energy through PPPs, with clear criteria for participation and risk-sharing agreements.

8.2. For Singapore

Singapore has focused on energy efficiency and technological integration, which is a model of urban sustainability; however, further expansion of renewable energy use is possible. With a limited amount of land available for solar facility installation, Singapore must remain innovative and invest in the latest solar PV technologies that can be integrated into buildings, roads, and other urban infrastructures. Furthermore, Singapore should consider offshore wind energy as one of its potential areas for renewable energy growth, given its geographical position, which offers favorable opportunities for offshore wind development.

Singapore should continue to build on its energy-efficient urban infrastructure, but also explore opportunities for expanding offshore wind energy as part of its renewable energy mix. Policymakers should initiate pilot projects for offshore wind farms and develop incentives for private companies to invest in these projects. Additionally, Singapore could leverage its expertise in technology to pioneer international collaborations on energy storage and smart grid systems. The government should also enhance support for green building certifications and retrofitting existing buildings to reduce energy consumption. Establishing a framework for regional energy cooperation, such as cross-border electricity grids with neighboring countries, could also help stabilize the city’s energy supply and promote the trade of renewable energy. Launch an offshore wind energy feasibility study to determine the best locations for wind farms, and implement incentives like grants and low-interest loans for developers of offshore wind energy projects. Set ambitious national energy storage targets and offer grants for research into new battery technologies.

Nonetheless, Singapore needs to remain efficient as it increases the use of renewable energy. The dilemma is to balance the development of renewable energy resources with maintaining the gains in energy efficiency that the country has already achieved. Policymakers should focus on demand-side management and the involvement of the private sector in developing technologies that enhance energy efficiency.

Moreover, Singapore should focus on regional cooperation. Renewable resources, such as hydropower generated by other nations, can be utilized to add more cushioning to the fluctuations in local energy production. A regional energy market would also enable the sharing of renewable energy, primarily through cross-border transmission infrastructure, thereby enhancing access to resources and stabilizing the energy supply in the region.

8.3. For Global Policymakers

A hybrid approach that incorporates both the efficiency-driven approach of such cities as Singapore and the one that relies on infrastructure, as seen in the UAE, should be adopted by global policymakers. It is a strategy that enables the innovation of technology as well as the massive development of infrastructure, and this can be flexible and scalable in terms of energy transition, making it applicable in various situations across the globe. Energy efficiency is also a crucial consideration, particularly in densely populated cities, where the adoption of technology and the use of smart grids have the potential to yield significant benefits.

Policymakers must focus on the PPPs as a way of promoting investment in the renewable energy infrastructure, as well as storage technologies within the privately owned systems, to facilitate this transition. Such joint ventures can also be used to mitigate the financial risk of large-scale projects, allowing governments and non-governmental organizations to share the risks and benefits of the energy transition equally. The incentive for storage R&D should also be globally promoted, as it is a key element that will enable the successful integration of intermittent renewable sources, such as solar and wind, into the grid.

International collaboration is critical for achieving renewable energy goals. Global policymakers should focus on harmonizing renewable energy regulations and establishing an international carbon pricing mechanism. They should also create incentives for the private sector to invest in the development of green technology and energy infrastructure. The establishment of global renewable energy funds, aimed at supporting developing nations in building sustainable energy infrastructure, should be prioritized.

Global policy frameworks, such as the Global Covenant of Mayors for Climate & Energy and IRENA, provide models for incorporating renewable energy into urban sustainability strategies, similar to those seen in Singapore and the UAE. South Korea’s emphasis on integrating smart grids and energy efficiency technologies within urban systems is a noteworthy example of a policy approach that combines large-scale renewable energy adoption with energy savings and is highly applicable to both Singapore and the UAE [41].

Lastly, the introduction of pricing mechanisms for carbon on a global scale should be undertaken as a means of establishing a market-based incentive to reduce emissions. International harmonization of carbon prices will serve to level the playing field for renewable energy technologies and ensure that carbon-intensive industries consider all environmental costs associated with their activities. Carbon pricing, along with targeted policies that foster the development of green technologies, can also serve as a mechanism to encourage investments in renewable energy and help achieve the goal of reducing emissions.

9. Conclusions

This paper has critically compared the use of renewable energy in two unique urban environments, Singapore and the UAE, through an econometric study. The findings highlight the multifaceted nature of the relationship between renewable energy investments and their economic, environmental, and social impacts in smart cities. The focus on large-scale solar in the UAE has enabled the scaling up of renewable energy; however, it has struggled to increase energy efficiency and reduce carbon intensity. Conversely, the emphasis that Singapore has placed on energy efficiency and technological innovation has yielded excellent economic and environmental results despite having a relatively limited renewable energy capacity.

The results of this study provide a strong emphasis on the importance of policy tools such as subsidies, PPP models, investment in the development of renewable energy, and regulations in determining the effectiveness of the renewable energy transitions. PPPs in this specific context have been invaluable in supporting innovation and the realization of the energy infrastructure, especially in the UAE, where the large-scale energy infrastructure projects are all heavily dependent on the input of the private sector.

Moreover, the elasticity estimates have shown that investments in renewable energy are susceptible to economic performance, with enormous positive consequences for growth in GDP and employment in green sectors. This study’s findings also emphasize the importance of policy coherence and the integration of energy systems, ensuring that investments in renewable energy yield quantifiable economic and environmental benefits.

Although this study is informative, it does have limitations. Future studies may focus on refining data granularity and addressing measurement errors in employment data and energy data, as well as examining the longer-term effects of renewable energy adoption on social outcomes, including health, education, and equity.

Both the UAE and Singapore are undergoing an energy transition, which offers valuable lessons for other nations worldwide. Policymakers in the world have to think about a hybrid approach that integrates energy efficiency and massive development of infrastructure; they have to use a PPP structure to speed up the innovation process, and they have to make the global carbon pricing mechanisms in line with the long-term sustainability targets. The renewable energy transition is both an environmental necessity and an economic opportunity; when well-managed, it can lead to a green economy, sustainable growth, and increased employment.