The Impact of Human Capital, Natural Resources, and Renewable Energy on Achieving Sustainable Cities and Communities in European Union Countries

Abstract

This study investigates the influence of human capital and natural resource productivity on achieving sustainable cities and society (SDG-11) within the European Union (EU) while also considering the contribution of renewable energy (RE). This research analyzes data from the European Union between 2011 and 2020 by deploying the first-difference generalized method of moments (FM-GMM) model to distinguish between two different effects of the human capital variable—a low effect (negative influence) and a high effect (positive influence). The analysis has identified an optimal threshold value of 1.867 for the human capital index (HCI) score in the context of European Union countries. This threshold value represents a critical point at which the effect of human capital on achieving SDG-11, which aims to make cities and human settlements inclusive, safe, resilient, and sustainable, undergoes a significant shift. The impact of renewable energy consumption on SDG-11 exhibits a non-linear pattern. There is a negative relationship at lower levels of renewable energy adoption (below a certain threshold), with renewable energy negatively impacting SDG-11 progress at a 1% significance level. However, the relationship becomes significantly positive once renewable energy consumption surpasses this threshold. This non-linearity suggests that achieving mass renewable energy adoption is crucial to unlocking its full potential in promoting the sustainable urban development goals captured by SDG-11. The results also demonstrate a positive effect on natural resource productivity both before and after exceeding a specific threshold, although the magnitude of this effect varies. This robust evidence underscores the necessity for targeted policies in the European Union to enhance human capital, increase renewable energy adoption, and boost natural resource productivity, thereby securing sustainable funding mechanisms for SDG-11.

1. Introduction

Sustainable development is a broad and complex notion that covers multiple facets of life on Earth, but it can be summarized as achieving the well-being of people, achieving socio-economic development, and protecting the environment. Proposed in 2015, the 2030 Agenda for Sustainable Development includes 17 main goals covering different areas. The socio-economic structure of society will face severe challenges in achieving sustainable development, and the human capital index (HCI) seems to be the most critical factor for reaching sustainable development [1]. The theory of the HCI is based on investing in education and its key role in fostering economic growth, which has been developed since the 1960s [2]. Many developed economies started to invest in education through active state support. The HCI can benefit from investing in education, health care, social protection, and vocational training [3]. The HCI shows how education, health, social protection, and training can contribute to the labor productivity of the next generations. Without developing their HCI, countries (no matter the development stage) cannot reach sustainable and inclusive economic growth and cannot compete globally. In the current race to achieve economic and environmental targets as per the SDGs, the HCI may improve both the environment and quality of life through direct and moderating effects.

Previous studies have demonstrated that the direct impact of the HCI on emissions is positive. However, when it is included with value added in industry and gross domestic product (GDP) per capita, its impact on emissions becomes negative [4]. Advanced technologies, clean energy, and a skilled labor force can support economic growth and preserve the environment through increasing labor productivity, education, and knowledge in operating advanced technologies and reducing waste in the production process [5,6]. Companies with a better educated HCI will embrace environmental regulations more easily in their activity, but this relation depends on the level of economic development of the specific country and the strong or weak adopted environmental regulations [7]. Based on these previous studies, we can summarize that increasing the HCI can support sustainable cities and society (SDGs), but at the same time, it can increase the use of natural resources, which leads to resource depletion, waste, and pollution. In the context of pervasive economic growth and a growing population, the issue of natural resource usage and increasing productivity has gained much interest in the latest research [8]. There is a solid and essential relation between the quality of life in cities and the natural resources needed for that. Urbanization and large urban agglomerations have put significant pressure on the ecosystem. Cities are vulnerable to natural disasters, resource depletion, and environmental change because of the growing number of inhabitants and the growing infrastructure, housing, and economic activities [9]. Increasing natural resource productivity will avoid natural resource depletion and make cities and human settlements inclusive, safe, resilient, and sustainable (SDG-11). Previous research has proved that each SDG is related to at least one resource category [10,11], but the management of natural resources is closely related to national and local policies [12]. The SDGs require all countries to estimate progress toward efficient natural resource usage in economic activities, households, or public areas [12].

Sustainable development generally, including resilient and sustainable cities and human settlements, is closely linked to renewable energy use. Economic growth, socio-economic development, urbanization, and population growth necessitate increased energy consumption while simultaneously requiring the preservation of ecosystems and the environment. Consequently, renewable energy has received significant research attention in the last decade, particularly following the launch of Agenda 2030 and the European Union’s (EU) 2050 carbon neutrality goal [13]. Access to clean and affordable energy, climate action, and carbon neutrality are among the SDGs, and all sustainable development goals are interconnected. Renewable sources are important for improving citizens’ quality of life and achieving sustainable development [14]. Renewables present many benefits that can support governments, households, and companies in attaining SDGs [15]. Their use can reduce greenhouse gas emissions and contribute to carbon neutrality and environmental protection [16], provide food security and eradicate hunger and poverty, ensure more equitable access to sustainable energy for the population, and create jobs which support the employment and decent work conditions for people and, thus, overall economic development [17]. Sustainable cities strive to reduce energy use, promote renewable energy sources, and decrease their carbon footprint [18]. SDG-11 aims to sustainably develop cities and other human settlements, providing opportunities for all inhabitants with equitable access to essential services, energy, housing, and transportation while reducing resource consumption, depletion, and negative environmental impacts. Projections indicate that over half of the population will have been living in cities starting in 2007, and this share will reach 60% by 2030. In Europe, this share is even higher (75%) and is expected to increase to 80% by 2030. Cities are responsible for 60–80% of global energy use, producing 85% of the global GDP and around 70% of human-induced greenhouse gas emissions [19]. Consequently, the SDGs cannot be attained unless there is a significant focus on the sustainable development of cities and SDG-11. This focus will foster resilient communities and sustainable economies. Integrating SDG-11 into local authorities’ agenda is crucial to enhance public involvement in urban life, protect cultural assets, address urban resilience and environmental challenges, improve pollution and waste management, secure public spaces, and improve urban regulations [20].

While the role of sustainable communities is undeniable for local authorities, a gap persists between sustainable urban development and the measures implemented by these authorities. This gap can be attributed, in part, to the substantial costs that local authorities must bear, which poses a significant limitation, particularly for developing countries. There are still important limitations (not only in terms of costs) in creating a sustainable urban ecosystem; overcoming those will require new local management arrangements [21]. Among over 200 investigated countries, only one country has achieved its SDG-11 targets, and 143 countries (65% in Europe, North America, Australia, and New Zealand) face essential challenges in implementing the SDG-11 goals. In Europe, significant progress was noted up to 2023 in achieving the SDG-11 targets, but EU member states are concerned about reaching the 2030 targets [22]. A solid management framework at the local level is necessary to connect green infrastructure, environmental protection, and human well-being as key factors in sustainable local strategies [22]. In Northern European countries (Ireland, the Netherlands, Sweden, Belgium, and Denmark), a deterioration in housing conditions has been observed because of the growing number of migrants, increased price of dwellings, and deterioration of living standards [23], although these are developed economies. Eastern European countries (Bulgaria, Romania, Poland, and Slovenia) are experiencing a growing housing deprivation rate because of the extensive depopulation of these countries [22]. Noise pollution levels in households have generally improved in the EU, with a few exemptions (Malta, Sweden, and Luxemburg) [24]. Joint efforts in the EU to reduce road traffic accidents and premature deaths because of exposure to high pollution are showing positive effects for the entire EU area.

Regarding the recycling rate of municipal waste, though there are significant differences among the EU countries (Germany displays the highest rate of 70%), an ascending trend of this ratio was noticed for all EU states [22]. EU countries represent a heterogeneous group in terms of renewable energy use or natural resource productivity, as well as SDG-11 score (according to data collected from Eurostat, World Development Indicators (WDI), Environmental Investigation Agency (EIA), and the Sustainable Development Goals (SDG) Report 2023,

Table 1. Variable description.

Data	Abbreviation	Measures	Source
SDG-11	SCS	Score 1 to 100	SDG Report 2023 [25]
Economic growth	GDP	Growth (%)	WDI (2024)
Population	POP	Million	WDI (2024)
Natural resources productivity	RSPR	EUR per kilogram	Eurostat
Environmental tax	ENT	% (GDP)	OCED
Renewable energy	RE	RE consumption as % of total energy	EIA (2024)
Human capital index	HCI	Index	WDI (2024)

Source: Authors’ elaboration.

). Concerning SDG-11, countries such as Estonia, France, Germany, Luxemburg, Austria, the Netherlands, and Spain exhibit high scores.

Conversely, Bulgaria and Italy exhibit the lowest scores among the EU countries. Sweden, Estonia, and Finland have the highest shares of renewable energy (RE) use. At the same time, Belgium, the Netherlands, Cyprus, and Ireland display the lowest values for renewable energy use as a percentage of their total energy consumption. Bulgaria, Estonia, Finland, Poland, Romania, Sweden, and Lithuania present low productivity levels for natural resources. Belgium, France, and Italy display high productivity ratios for natural resources.

Regarding the HDI, except for Croatia, which has a low HCI, the rest of the EU countries do not demonstrate substantial differences. Given the importance of achieving SDG-11 amidst growing populations and urbanization, particularly in the EU, our study investigates National Reporting System (NRS) productivity (as a proxy for natural capital, with natural resources being part of natural capital), HCI, RE use, economic growth, and the effect of environmental taxes on achieving sustainable cities and communities. The main objective of this paper is to investigate the impact of human capital, natural resources, and renewable energy on sustainable cities and community scores within the EU member states from 2011 to 2020. Only a few studies have dealt with SDG-11 within the EU. A novelty of this study is the empirical investigation of the achievement of SDG-11 in the EU member states, focusing on natural and human capital factors, renewable energy, population, environmental taxation, and economic growth rate. Its contribution lies in the identification of the main factors that are supporting the achievement of this goal in the EU and in designing some policy recommendations based on the results. Establishing clear and measurable indicators to track progress in achieving SDG-11 targets is necessary. This enables data-driven policy adjustments and resource allocation to support the triggers that boost the achievement of SDG-11. To analyze this relation between factors and the SDG-11 goal, we checked for multicollinearity using the variance inflation factor (VIF) test, cross-sectional dependence (CSD), and homogeneity. We then applied the first-difference generalized method of moments (FD-GMM) and quantile regression to check for the robustness of the FD-GMM results. The study is organized as follows: Section 2 presents a literature review with previous studies related to this topic, Section 3 presents the methodology, Section 4 details the empirical results and discussion of the findings, and the last section is dedicated to conclusions and policy recommendations.

2. Literature Review

By 2050, the global population is projected to reach 6.9 billion, with 70% residing in cities. Cities are central to the climate crisis, both as significant sources of greenhouse gasses and as areas highly susceptible to the consequences of climate change [26]. This concentration amplifies the importance of sustainable urban planning. Cities are hubs of intellect, social interaction, and resource consumption [27].

SDG-11 of the United Nations (UN) 2030 Agenda calls for creating inclusive, safe, resilient, and sustainable cities and human settlements, as [28] pointed out. Realizing this goal requires a comprehensive approach that addresses four key pillars: social development, economic growth, environmental protection, and institutional capacity [29]. Firstly, building strong social networks, promoting equity and inclusion, and ensuring access to necessities for all residents are crucial for a thriving urban environment. Secondly, fostering a dynamic and sustainable local economy that provides job opportunities and improves living standards is essential [30]. Thirdly, implementing practices that minimize pollution, conserve resources, and mitigate the effects of climate change are critical for a healthy urban ecosystem [31]. Fourthly, establishing effective governance frameworks, promoting citizen participation, and ensuring robust legal and regulatory structures are vital for long-term urban sustainability [32].

2.1. Nexus Between HCI and SDG-11

Creating sustainable cities and societies requires a multifaceted approach that considers environmental well-being, economic prosperity, and social equity. This study investigates the impact of six key factors on achieving supply chain sustainability (SCS): economic growth, HCI, urban population, renewable energy, natural resources productivity, and environmental taxes.

Economic prosperity enables investment in sustainable infrastructure, research and development in clean technologies, and social programs that promote environmental responsibility. A strong GDP can provide the resources needed to implement sustainable solutions. The authors of [33] demonstrated that economic growth determined by innovation could put pressure on natural resources, and it is necessary to promote sustainable cities by considering environmental protection. Furthermore, fostering economic growth and fewer regional disparities are key factors for ensuring sustainable cities in Central and Eastern Europe [34].

Investing in people’s education and skills empowers them to contribute to a sustainable future. The authors of [35] argued that fostering a more conscious and aware population, where individuals possess a deeper understanding of themselves and their environmental impact, will be the most effective approach to securing a sustainable future. An educated and skilled population fosters innovation, drives technological advancements in clean energy and sustainable practices, and promotes environmental awareness [36]. A high HCI can lead to better resource management and problem-solving for a sustainable future. It is important to highlight the growing importance of skilled human resources for developing modern cities. The authors of [37] described a strategy based on HCI to enforce sustainable cities by exploring the four greens: green opportunities, green savings, green talent, and green places. It is necessary to improve HCI management systems, specifically in the context of fostering innovation within a city. The authors of [38] developed a model with four key areas that influence a city’s overall HCI quality: individual skills (education and training of the workforce), standard of living (housing, health care, and income levels), social services (quality and accessibility of social programs), and environmental quality (clean and healthy environment). The authors of [39] showed a clear link between a city’s knowledge-based economy and innovative and sustainable urban environment development. Furthermore, sustainable social services and local transportation emerged as the most crucial components for achieving smart sustainable cities (SSCs). The authors of [40] examined the effect of HCI on the quality of sustainable development goals (SDGs) in the Association of Southeast Asian Nations (ASEAN) countries from 1986 to 2018 using a cross-sectionally augmented autoregressive distributed lag model (CS-ARDL), confirming the positive impact of HCI. Similarly, [41] highlighted that despite economic progress, the Next Eleven (N-11) countries have yet to meet their environmental goals. The study assessed the relationship of HCI with natural resources and eco-friendly technologies using data from 1980 to 2019 and the Method of Moment Quantile Regression (MMQR) statistical model. The authors indicated that sustainable energy and HCI positively impact environmental quality, while economic globalization harms it. The authors of [42] emphasized the importance of natural capital for economic growth and sustainable development policies. The study analyzed data from 53 developing countries (2011–2022) to assess the impacts of human, natural, and produced capital on sustainable development. From the regression analysis results, it was found that HCI, innovation, green energy, political stability, and lack of violence, among other variables, are positively correlated with sustainable development [42]. Natural and produced capital have negative impacts, emphasizing the need for more investments in HCI, innovation, green energy, and the modernization of produced capital to achieve sustainability. Based on the results we have discussed in this section, we can state the following hypothesis:

Q1. 

HCI is positively linked to sustainable cities and communities.

2.2. Nexus Between Natural Resource Efficiency and SDG-11

Efficient use of natural resources is crucial for sustainability. High natural resource productivity signifies responsible management of water, land, and minerals, minimizing environmental degradation and ensuring sufficient resources for future generations.

Policies that create synergies between smart specialization and sustainable development approaches by supporting eco-innovation, ecosystem services, and resource efficiency can contribute to sustainable cities in Central and Eastern Europe [34]. The authors of [43] identified green patent output, particularly utility patents, as an effective mechanism for amplifying the positive effects of policy on resource dependence. Cities that prioritize green innovation have shown a stronger reduction in reliance on natural resources. Moreover, [43] showed that 283 pilot Chinese cities implementing the sustainable development policy in 2009–2019 experienced a significant (30.81%) decrease in their dependence on natural resources. This effect was particularly pronounced in cities with resource-based solid or industrial economies. The authors of [44] examined the ecological impacts of natural resources (NTRs) in the Global South, considering government stability (GNST) across ten emerging nations from 1989 to 2020. Using the load capacity factor (LCF) as a measure of environmental quality and applying robust empirical methods, the results showed that NTRs decrease the LCF, representing a deterioration of ecological quality.

Based on the findings of previous studies, we state the following hypothesis:

Q2. 

Natural resource efficiency is positively linked to sustainable cities and communities.

2.3. Nexus Between Environmental Taxes and SDG-11

Implementing environmental taxes discourages unsustainable practices and encourages investment in clean technologies. Environmental taxes (ENT) can incentivize businesses and individuals to adopt environmentally friendly behaviors, contributing to a more sustainable future. Increased environmental taxes can contribute to sustainable cities by controlling the pollution. China’s 2018 Environmental Protection Tax Act (EPTA) represents a significant step towards sustainable development through pollution reduction. The authors of [45] investigated the impact and underlying mechanisms of the EPTA on pollution reduction in 287 Chinese cities between 2010 and 2019. They identified three fundamental mechanisms through which the EPTA has achieved pollution reduction: internalizing environmental costs (the tax incentivizes businesses to control pollution by converting external environmental costs into internal production costs), promoting green innovation (regulatory bodies can utilize the EPTA to track and limit pollutant emissions, encouraging companies to invest in green technologies), and boosting environmental infrastructure (the tax revenue fosters the development of environmental protection facilities, further enhancing pollution control capabilities).

Rapidly growing urban populations can strain resources and infrastructure. However, well-managed urban centers can be hubs of innovation and efficiency. Focusing on sustainable urban planning, green spaces, and efficient transportation systems can create a more livable and sustainable environment for a growing population. In [43], it was suggested that cities within multi-regional urban agglomerations benefit more significantly from policies designed to break the resource dependence cycle. Collaboration within these urban clusters appears to enhance the application of sustainable principles in cities.

Q3. 

Environmental tax is positively impacting SDG-11.

2.4. Nexus Between Renewable Energy and SDG-11

Promoting energy efficiency and transitioning to renewable sources like solar and wind power is crucial for sustainable cities [46]. Transitioning to renewable energy sources, such as solar, wind, and geothermal power, reduces reliance on fossil fuels, mitigating climate change and air pollution. Increased RE use fosters a cleaner and healthier environment for a sustainable society. It is essential to examine how integrating RE sources is crucial for smart cities to achieve sustainable development. For example, ref. [47] analyzed Philadelphia’s Greenworks plan, which focused on its goal of increasing renewable energy use. The research proposed a city-based ecological footprint analysis rather than a per capita approach to assess the plan’s impact on the city’s overall energy footprint. It argued that utilizing internal renewable resources, even if land use increases within the city limits, can still reduce a city’s reliance on external resources and contribute to achieving broader urban sustainability goals. However, unforeseen challenges may arise that could not be known from the very beginning. The authors of [48] confirmed that renewable energy sources (RESs) are essential for sustainable development in smart cities. Still, they highlighted potential challenges in fully relying on them due to unforeseen circumstances (implementation of RES technologies may encounter atypical issues). The findings aim to inform researchers, urban planners, and economists designing future sustainable cities powered by renewable energy.

Sustainable cities could also be achieved using renewable energy in domestic and commercial buildings. Biogas or bio-synthesis gas, generated from the organic waste collected in urban centers—houses, transportation hubs, hospitality facilities, and retail spaces—offers a promising solution [49]. These buildings can reduce their environmental impact by harnessing decentralized renewable energy sources. Rooftop solar photovoltaic (PV) systems can meet electricity needs for lighting and cooling, while solar thermal collectors can provide heating. For larger-scale cities’ energy demands, a combination of centralized renewable energy systems such as solar PV, wind, and bioenergy can be implemented. This process creates a comprehensive approach that leverages various renewable resources.

The authors of [50] developed a methodology for developing smart energy cities (SECs) that integrate seamlessly with a national goal of 100% renewable energy. The study reinforced the need for cities and municipalities to prioritize local energy needs even as they integrate broader national and global considerations into resource allocation, industrial land use, and transportation planning. We demonstrated the developed methodology in a case study of the transition to a 100% renewable smart energy system in the Danish municipality of Aalborg. Cases of SECs can pave the way for national goals followed by global concerns, such as those of Denmark and the EU regarding RE goals. The framework behind the methodology is designed to be adaptable and applicable to other cities worldwide, making it a valuable tool for transitioning to a sustainable energy future. The authors of [51] demonstrated the effect of renewable energy on environmental sustainability in Pakistan, aligning with SDG-11 and SDG-13 using data from 2004 to 2021 and employing the ARDL model. The findings indicated that financial inclusion and digital finance positively contribute to environmental sustainability, highlighting the importance of renewable energy in reducing carbon emissions.

Q4. 

Renewable energy use is positively linked to sustainable cities and communities.

2.5. Research Gap

The following perspectives can be drawn from the current literature and highlight the research gap: (1) Conspicuously, the narrative on how HCI, RE, and natural resources are addressed is key to SDG-4, -7, -12, and -13. Despite the existing narrative, limited efforts have been made to highlight how the interplay between HCI and resource productivity affects sustainable cities and societies (SDG-11). (2) Moreover, using natural resources is crucial for economic development, but it also leads to the depletion of natural wealth in the long term. Despite its importance, limited evidence demonstrates how natural resource productivity and HCI contribute to achieving SDG-11 in the EU. This study aims to fill this gap by examining the role of these factors in promoting sustainable urban development within the European Union. (3) Although extensive studies have utilized econometric techniques to examine the effect of natural resources and RE on environmental quality, such as CS-ARDL, GMM, pooled mean group (PMG), and various panel data methods, there remains a methodological gap. By employing the FD-GMM model, this research aims to fill this gap and provide robust and reliable results on the role of natural resource productivity and HCI in promoting sustainable urban development within the European Union.

3. Data and Methodology

3.1. Data

This study examines the effect of the human capital index (HCI), natural resource productivity (NRP), renewable energy (RE), environmental taxes (ENTs), economic growth (GDP), and urban population (POP) on sustainable cities and society (SCS) using annual data from 2011 to 2020 from 24 European Union economies. To measure sustainable cities and societies (SDG-11), a set of indicators from the SDG Report 2023 was utilized [25]. Key indicators include the proportion of the urban population living in slums, annual mean concentration of PM2.5, access to improved water sources, satisfaction with public transport, population with rent overburden, and access to points of interest within a 15-minute walk. These indicators capture various dimensions of urban life, such as housing quality, air quality, water access, transportation efficiency, housing affordability, and accessibility to services.

A skilled and educated urban population is crucial for innovation, economic productivity, and city social development. A higher HCI generally correlates with better urban planning, infrastructure, and service delivery. It also relates to citizen engagement and participation in urban life. While cities are often seen as consumers of resources, the productivity of the surrounding natural resources is vital. This includes resources like water, land for agriculture (impacting food security), and materials used in construction. Efficient resource use contributes to sustainable urban development. It can also relate to green spaces and urban ecosystems. Cities are major energy consumers. Adopting renewable energy sources in urban areas reduces reliance on fossil fuels, mitigates air pollution (directly impacting the PM2.5 measure), and contributes to climate change mitigation, a critical aspect of urban sustainability. Environmental taxes are a policy tool to incentivize environmentally friendly behavior within cities. They can discourage pollution, promote sustainable transportation, and fund green initiatives, all contributing to sustainable cities. Economic growth is often associated with urban development, creating jobs and improving living standards. However, it is crucial to consider how growth occurs. Sustainable cities require economic growth that does not come at the expense of the environment or social equity. This variable helps to understand the relationship between economic prosperity and other sustainability indicators. Furthermore, the size and density of the urban population directly impact urban planning, resource management, and service delivery. Understanding population dynamics is essential for addressing challenges like housing, transportation, and infrastructure sustainably. It also links directly to the “slums” indicator.

The period from 2011 to 2020 was chosen for this study due to significant policy developments, data availability, and notable trends in renewable energy adoption and economic recovery from the sovereign debt crisis. This decade includes key sustainability initiatives such as the European Green Deal and the Paris Agreement, and their targets were largely adopted within the EU area, offering a comprehensive timeframe to analyze the impact of the HCI, natural resource productivity, renewable energy, environmental taxes, economic growth, and urban population on sustainable cities and society. We chose the variables based on the existing literature [52,53,54,55,56], which can be written as follows:

$${SCS}_{it}={\beta }_0+{\beta }_1{HCI}_{it}+ {\beta }_2{NRS}_{it}+{\beta }_3{RE}_{it}+{\beta }_4{ENT}_{it}+{\beta }_5{GDP}_{it}+{\beta }_6{POP}_{it}+\kappa \left({GDP}_{it}- \gamma \right)I\left(q_{it}>\gamma \right)+{\alpha }_i+{\epsilon }_{it}$$

(1)

This methodology helps reduce abrupt variations within the regression function, thus promoting a unified and coherent analysis. The term ${\mu }_i$ denotes an unobserved individual fixed effect, while ${\epsilon }_{it}$ refers to a distinctive random disturbance with a mean of zero. The coefficients $\beta$₁ to $\beta$₅ are integral to the estimation.

Drawing on the existing literature, we would then expect the signs of the coefficients as specified in Equation (1), with α₁, α₂, and α₃ expected to be positive and α₄ and α₅ expected to be negative. Table 1 details the primary variables, including their abbreviations and summary statistics. The research aims to pinpoint a precise metric that differentiates between two disparate impacts of the variable mediating HCI interaction: a minimal effect (adverse influence) and an augmented effect (affirmative influence). The dynamic panel threshold model (DPTR) is the model used in [57], in which we specialized directly in the kink framework to allow for a smooth system transition from the reduced regime to the expanded one at the limit. It covers the regression function as much as possible, avoiding jarring shifts in the regression function. Furthermore, it represents an inscrutable individual fixed effect while standing for a zero-mean idiosyncratic random disturbance that differently impacts the unobserved heterogeneity encompassed by the fixed component and the outcome variable. $\beta$₁ to $\beta$₆ are the coefficients.

The research aims to ascertain a distinct threshold value that differentiates the lesser (adversely influencing) and greater (beneficially influencing) impacts of the HCI variable. To achieve this objective, we employed the DPTR model recommended by [58], incorporating a kink model to facilitate a smooth transition between the lower and higher regimes at the identified threshold.

3.2. Methodology

The dynamic panel threshold model (DPTR) represents a significant advancement over the traditional PTR model by offering greater flexibility and adaptability. Unlike the model in [59], which mandates the exogeneity of the threshold variable, the DPTR model accommodates the endogeneity of this variable, enhancing its capability to analyze the interactions between the threshold variable and other factors. This feature is particularly vital in economic research, where interactions are often intricate. Moreover, [57] elucidated that the DTR model relinquishes the homogeneity assumption prevalent in conventional models, thereby aptly capturing the asymmetric effects of the threshold variable. This aspect aligns well with prior studies [31,60] that often predicted divergent outcomes based on whether variables exceeded certain thresholds:

$$y_{it}=u_i+{\beta }_1^{\prime }{x}_{it}1\left(q_{it}\le \gamma \right)+{\beta }_2^{\prime }{x}_{it}1\left(q_{it}>\gamma \right)+{\epsilon }_{it}$$

(2)

where $\left\{\left.y_{it},q_{it},x_{it},1\le i\le N,1\le t\le T\right\}\right.$, with the subscript i representing the individual and the subscript t representing time; the dependent variable $y_{it}$ is a scalar; the threshold variable $q_{it}$ is also a scalar; and the regressor $x_{it}$ is a vector with k components. The slope parameters associated with different regimes are represented by ${\beta }_1^{\prime }$ and ${\beta }_2^{\prime }$.

Building upon the previous framework,

$$y_{it}=\left(1,x_{it}^{\prime }\right){\Phi }_{1}1\left(q_{it}\le \gamma \right)+\left(1,x_{it}^{\prime }\right){\Phi }_{2}1\left(q_{it}>\gamma \right)+{\mu }_i+{\epsilon }_{it} i=1,\dots ,N; t=1,\dots ,T$$

(3)

Thus, $x_{it}^{\prime }=\left(y_{i,t-1}, x_{it}^{\prime }\right)$ are the independent variables, and the lagged dependent variable represents the sequential progression of economic catalysts. In contrast, the threshold variable designates the tipping point that shifts the framework from one state to another. This individual fixed effect remains undiscovered, and a random disturbance of zero means introduces unpredictability. Based on the equation of

$$∆y_{it}={\beta }^{\prime }∆x_{it}+{\delta }^{\prime }{X}_{it}^{\prime }{1}_{it}\left(\gamma \right)+{∆\epsilon }_{it}$$

(4)

where ∆ is the first differences and $\underset{k1\times 1}{\mathsf{\beta }}={({\Phi }_{12},{\Phi }_{1,k1+1})}^{\prime }$, as $\underset{(k1+1)\times 1}{\mathsf{\delta }}={\Phi }_2-{\Phi }_1$, we also have

$$\underset{2\times (1+k1)}{\mathrm{X}}=\left(\begin{array}{c}(1,x_{it}^{\prime })\\ (1,x_{i,t-1}^{\prime })\end{array}\right)\mathrm{and} \underset{2\times 1}{1_{\mathit{it}}\left(\gamma \right)}=\left(\begin{array}{c}I\left(q_{it}\le \gamma \right)\\ -I\left(q_{it}>\gamma \right)\end{array}\right)$$

(5)

Here, we utilized the GMM estimation approach [61]. This necessitated finding a suitable set of instrumental variables. Unlike standard regression focusing on the mean, this method considers all features, including non-linearity, heteroskedasticity, and outliers [62,63,64].

Before making the estimations to explain the impact of human capital, natural resources, and renewable energy on sustainable cities and communities’ scores, preliminary tests were applied: the cross-sectional dependence test and slope heterogeneity test.

3.3. CSD Test

Before employing the FD-GMM, this research examined the presence of CSD (cross-sectional dependence) using the test proposed in [65]. The CSD test statistic can be represented as follows in Equation (6):

$$\mathrm{C}\mathrm{D}=\sqrt{{\displaystyle \frac{2T}{N(N-1)}}}\left({\sum }_{i=1}^{N-1} {\sum }_{j=i+1}^N {\widehat{\rho }}_{ij}\right)\to N(\mathrm{0,1})$$

(6)

3.4. Slope Heterogeneity Test

According to [66], slope homogeneity (SH) is the degree to which the coefficient of slope in a regression model is constant or comparable across various subsamples or groups. Researchers can determine whether data heterogeneity exists by using SH analysis. One can use SH analysis to assess whether the slopes of several subgroups differ significantly and determine whether two variables interact under various circumstances. The existence of SH indicates that, to capture the differences between distinct entities adequately, the investigation must use more sophisticated models. Thus, carrying out this test aids in directing the choice of model (Equation (7)).

$${\tilde{\Delta }}_{ASH}=(N{)}^{{\displaystyle \frac{1}{2}}}{\left({\displaystyle \frac{2k(T-k-1)}{T+1}}\right)}^{-{\displaystyle \frac{1}{2}}}\left({\displaystyle \frac{1}{N}}\tilde{S}-2k\right)$$

(7)

4. Results

4.1. Descriptive Statistics

Before examining the model estimation results, performing a statistical analysis of the variables specified in the table was crucial. The mean values provide insights into the average SCS, NRP, RE, HCI, GDP, ENT, and POP levels. For instance, the means of all variables are positive. The standard deviation measures the dispersion or spread of the data around the mean, which indicates high deviation for population and RE. Skewness measures the symmetry of the distribution of the data. All skewness is positive, except for the SCS variable. Kurtosis measures the “tailedness” of the distribution of the data. A higher kurtosis value indicates heavier tails than a normal distribution, and all values are above or close to three. For example, the kurtosis of GDP is 11.381, indicating that the GDP distribution has heavier tails than a normal distribution. The Jarque–Bera test is a test for normality. A low p-value (e.g., 0.000 for SCS, RE, HCI, GDP, and POP) indicates that the distribution significantly deviates from a normal distribution (

Table 2. Statistical characteristics of variables.

	SCS	RE	HCI	GDP	NRP	ENT	POP
Mean	87.794	22.138	1.772	1.476	1.722	7.247	17.842
Std. Dev.	5.262	11.868	0.496	3.522	1.164	2.009	22.601
Skewness	−0.852	0.740	0.424	0.234	0.387	0.253	1.626
Kurtosis	4.093	2.788	4.307	11.381	3.312	2.424	4.356
Jarque–Bera	40.989	22.328	24.263	704.611	7.824	5.876	124.183
Probability	0.000	0.000	0.000	0.000	0.026	0.053	0.000

).

The correlation coefficients presented in

Table 3. Correlation matrix.

Correlation	SCS	RE	HCI	GDP	NRP	ENT	POP
SCS	1
RE	0.058	1
HCI	0.236	−0.095	1
GDP	0.061	−0.069	−0.254	1
NRP	0.285	−0.473	0.329	−0.061	1
ENT	−0.461	−0.113	−0.427	0.063	−0.286	1
POP	−0.025	−0.325	0.266	−0.152	0.336	−0.306	1

indicate a statistical correlation between the explanatory variables RE, HCI, ENT, POP, and GDP and the dependent variable SCS. SCS exhibits weak positive correlations with GDP and RE, with values of 0.09 and 0.06, respectively. The correlation value of 0.25 for HCI indicates that countries with higher human capital index scores tend to have more sustainable urban development. However, SCS shows weak negative correlations with ENT and POP, with values of −0.46 and −0.03, respectively.

The variance inflation factor (VIF) test results, presented in

Table 4. Variance inflation factor (VIF) test results.

Variable	VIF	1/VIF
RE	1.69	0.590104
NRP	1.65	0.607546
ENT	1.47	0.682008
HCI	1.43	0.698836
POP	1.37	0.727703
GDP	1.13	0.882994
Mean		1.46

, indicate the absence of multicollinearity among the independent variables. The VIF coefficients for all variables are below in Table 4, averaging at 1.46, which underscores the robustness of the model concerning multicollinearity issues.

4.2. CSD Test Results

The CSD findings are shown in

Table 5. Results of the cross-sectional dependence test.

Test	Test Statistics	p-Value
BP test	346.0234	0.000
Pesaran LM	4.135393	0.000
Pesaran CD	4.221575	0.000

Note: 0.000 indicates statistical significance at the 1% level.

using the methodology from [65,67] (2006), and they indicate strong evidence of CSD in the data, with highly significant test statistics and p-values of 0.000. Re-estimating the CSD using Pesaran’s CD test yielded consistent results, ensuring the robustness of the test [65,67]. These findings support the CSD issue concept within the residuals, suggesting the presence of CSD across the EU members. This finding aligns with the following econometric step, including the DPTR model.

4.3. Homogeneity Tests

Table 6. Results of the slope homogeneity test.

	Statistics	p-Value
Δ	3.056	0.000

presents the results of the slope homogeneity test, which demonstrate statistical significance at the 1% level. These results provide strong support for rejecting the null hypothesis of slope uniformity. Embracing and accounting for the heterogeneity in slopes enables researchers to devise statistical models and techniques that more effectively capture the varied correlations and dynamics among variables across different groups. This approach enhances the accuracy and reliability of the analysis, allowing for a more profound understanding of the underlying patterns and implications within the panel data.

4.4. DPTR Model for Short Panel Estimation

The estimation results of the DPTR model are presented in

Table 7. FD-GMM results.

SDG	Coefficient	Std. Err	z	p-Value	[95% Conf. Interval]
Lag_y_b	0.567	0.607	0.930	0.350	−0.622	1.756
RE_b	−2.504	0.621	−3.990	0.000	−3.518	−1.202
NRP_b	0.637	0.069	9.21	0.000	0.502	0.773
ENT_b	−4.394	1.898	−2.230	0.026	−7.934	−0.514
GDP_b	0.011	0.161	0.430	0.664	−0.257	0.404
POP_b	7.367	4.345	1.920	0.054	−0.151	15.976
cons_d	−76.933	72.892	−1.060	0.291	−219.800	65.933
Lag_y_d	−0.555	0.838	−0.660	0.508	−2.198	1.088
RE_d	2.51	0.654	3.720	0.000	0.994	3.214
NRP_d	0.3809	0.159	2.311	0.021	0.687	0.054
ENT_d	7.186	2.940	2.970	0.003	2.334	11.410
GDP_d	−0.873	0.794	−1.710	0.087	−0.921	0.062
POP_d	2.031	0.901	2.250	0.024	0.264	3.798
R (HCI)	1.867	0.461	13.050	0.000	1.333	1.804

. The results identify an optimal threshold value of 1.867 for the human capital index (HCI) score in the context of European Union countries, representing the critical point at which the effect of human capital on achieving Sustainable Development Goal 11 (SDG-11) undergoes a significant change. When the HCI score exceeds 1.867, it suggests that countries with higher levels of human capital development can more effectively contribute to attaining SDG-11 targets related to sustainable cities and communities.

Natural resource productivity positively impacts SDG-11 attainment in Europe by promoting the efficient use of resources, reducing environmental impact, and fostering sustainable urban development. RE negatively impacts SGD-11 at a 1% significance level at levels below this threshold, known as the low regime. The impact of environmental taxes on attaining SDG-11 targets exhibits a positive effect at upper regimes of environmental taxation. Furthermore, it has a more significant positive effect when surpassing a certain threshold.

In

Table 8. FD-GMM results without RE as a moderating factor.

SDG	Coefficient	Std. Err	z	P > |z|	[95% Conf. Interval]
Lag_y_b	0.010	0.358	0.03	0.977	−0.691	0.712
GDP_b	0.0349	0.0157	2.22	0.026	0.0041	0.0657
NRP_b	0.944	1.275	0.74	0.459	−1.556	3.444
ENT_b	−1.925	0.391	−4.92	0.000	−2.693	−1.158
POP_b	3.67	18.376	0.20	0.842	−32.346	39.687
cons_d	−25.052	24.765	−1.01	0.312	−73.59	23.486
Lag_y_d	1.666	3.528	0.47	0.637	−5.249	8.581
GDP_d	−0.037	0.0148	−2.49	0.013	−0.066	−0.0079
NRP_d	0.085	1.387	0.06	0.951	−2.633	2.804
ENT_d	0.0424	0.385	0.11	0.912	−0.7127	0.797
POP_d	1.0189	0.011	1.72	0.085	−0.002	0.040
R (HCI)	2.313	0.285	8.11	0.000	1.752	2.870

, we exclude the role of RE to highlight its importance as a moderator for HCI and NRP. For GDP, below the threshold, the coefficient is 0.034, indicating a positive impact on SDG-11. However, after surpassing the threshold, GDP’s coefficient becomes statistically significant and negative, around −0.037. Regarding HCI, the threshold is set at 2.31. Regarding NRP, the coefficient below the threshold is 0.94 but is insignificant, suggesting that natural resource productivity does not have a statistically significant impact on SDG-11 in the lower regime without the moderating role of RE. After surpassing the threshold, the coefficient is 0.085, but it remains insignificant. For POP, the coefficient below the threshold is 3.67, but it is insignificant, indicating that population growth does not significantly impact SDG-11 in the lower regime without RE. However, after surpassing the threshold, the coefficient is 1.018 and is significant. ENT shows a negative impact on SDG-11 with a coefficient of −1.92 below the threshold, and this effect is significant. This suggests that environmental taxes might impose burdens at lower levels that could hinder sustainable urban development efforts. However, above the threshold, the coefficient is 0.042 but is insignificant, indicating that at higher levels of environmental taxation, the impact on SDG-11 is not statistically meaningful without the moderating role of RE. When comparing the results with and without the impact of RE on SDG-11, distinct differences emerge. This highlights the importance of renewable energy as a moderator, amplifying the positive effects of human capital and natural resource productivity through economies of scale, technological advancements, policy support, and positive externalities such as improved air quality and public health. Without RE, the impacts of GDP, HCI, and NRP are less pronounced and insignificant, underscoring the crucial role of renewable energy in achieving the sustainable urban development goals encapsulated in SDG-11.

4.5. Robustness Test

Table 9. Quantile regression results.

	Quantile Estimate	Coeff.	Standard Error	t-Value	p-Value
HCI	0.100	6.124003	3.096653	1.977620	0.0492
	0.200	5.336822	2.449981	2.178312	0.0304
	0.300	6.496431	2.475528	2.624261	0.0093
	0.400	7.326058	2.308411	3.173636	0.0017
	0.500	7.649194	2.258132	3.387399	0.0008
	0.600	8.863830	2.172637	4.079757	0.0001
	0.700	9.916306	2.178697	4.551485	0.0000
	0.800	12.23351	2.059364	5.940432	0.0000
	0.900	10.13100	2.203920	4.596809	0.0000
HCI2	0.100	−0.941457	0.517218	−1.820235	0.0700
	0.200	−0.998881	0.447297	−2.233148	0.0265
	0.300	−1.291498	0.471620	−2.738426	0.0067
	0.400	−1.456814	0.478226	−3.046287	0.0026
	0.500	−1.594225	0.483510	−3.297191	0.0011
	0.600	−1.831830	0.467552	−3.917915	0.0001
	0.700	−2.126605	0.468011	−4.543923	0.0000
	0.800	−2.744524	0.404425	−6.786246	0.0000
	0.900	−2.460166	0.410021	−6.000104	0.0000
	0.100	0.393485	1.491348	0.263845	0.7922
NRP	0.200	0.212698	0.659568	0.322480	0.7474
	0.300	1.624592	0.630671	2.575975	0.0107
	0.400	1.822639	0.602384	3.025708	0.0028
	0.500	2.168185	0.420074	5.161430	0.0000
	0.600	2.175262	0.381739	5.698293	0.0000
	0.700	1.950926	0.392594	4.969326	0.0000
	0.800	1.953530	0.398472	4.902551	0.0000
	0.900	1.618906	0.650125	2.490147	0.0136
GDP	0.100	0.006144	0.169532	0.036239	0.9711
	0.200	0.266580	0.102499	2.600803	0.0099
	0.300	0.252848	0.106933	2.364546	0.0189
	0.400	0.166030	0.078715	2.109238	0.0360
	0.500	0.122332	0.060528	2.021090	0.0444
	0.600	0.179313	0.064291	2.789074	0.0057
	0.700	0.204173	0.080983	2.521181	0.0124
	0.800	0.240772	0.106838	2.253618	0.0252
	0.900	0.262305	0.131784	1.990407	0.0477
RE	0.100	0.074344	0.042402	1.753313	0.0809
	0.200	0.044787	0.035753	1.252657	0.2116
	0.300	0.014314	0.036859	0.388339	0.6981
	0.400	0.003153	0.033015	0.095490	0.9240
	0.500	−0.020786	0.030224	−0.687742	0.4923
	0.600	−0.014669	0.030819	−0.475981	0.6345
	0.700	−0.029169	0.040104	−0.727338	0.4677
	0.800	−0.085965	0.031509	−2.728229	0.0069
	0.900	−0.093475	0.025239	−3.703524	0.0003
POP	0.100	−0.166117	0.044061	−3.770194	0.0002
	0.200	−0.060620	0.057249	−1.058883	0.2907
	0.300	−0.013864	0.036459	−0.380275	0.7041
	0.400	0.006586	0.018789	0.350515	0.7263
	0.500	0.002997	0.017658	0.169709	0.8654
	0.600	−0.001104	0.018067	−0.061102	0.9513
	0.700	−0.013458	0.023746	−0.566733	0.5714
	0.800	−0.050875	0.013665	−3.722888	0.0002
	0.900	−0.054080	0.011180	−4.837144	0.0000
ENT	0.100	−1.249672	0.390028	−3.204057	0.0015
	0.200	−1.218672	0.316688	−3.848180	0.0002
	0.300	−1.205942	0.312619	−3.857542	0.0001
	0.400	−0.906601	0.209360	−4.330353	0.0000
	0.500	−0.831398	0.196268	−4.236043	0.0000
	0.600	−0.793506	0.196310	−4.042116	0.0001
	0.700	−0.948491	0.266462	−3.559581	0.0005
	0.800	−1.201478	0.182058	−6.599443	0.0000
	0.900	−1.090199	0.177378	−6.146194	0.0000
C	0.100	83.98095	6.906667	12.15940	0.0000
	0.200	86.88680	5.357603	16.21747	0.0000
	0.300	86.96105	4.980322	17.46093	0.0000
	0.400	85.25640	3.486513	24.45320	0.0000
	0.500	86.37703	2.877380	30.01934	0.0000
	0.600	85.29848	2.663810	32.02123	0.0000
	0.700	87.01649	3.006257	28.94513	0.0000
	0.800	90.18727	2.733772	32.99005	0.0000
	0.900	93.58156	2.763776	33.86004	0.0000

shows the robustness findings of the FD-GMM using quantile regression. This confirms the findings presented earlier. An examination of the impacts of several parameters on SDG-11 was conducted across nine quantiles (10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, and 90th). The findings confirm the non-linearity of the effect of the HCI on SDG-11 across all quantiles, which indicates a negative effect, with the squared HCI also exhibiting a negative effect. Similarly, NRP and RE continue to be beneficial, particularly in enhancing SDG-11 at the medium and upper quantiles.

In contrast, the negative effect of ENT on SCS is diminished at the medium quantile. The influence of POP becomes somewhat significant and positive in the medium quantiles and negative in the lower and upper quantiles. Simultaneously, there is an adverse influence of GDP on the SDG across quantiles.

5. Discussion

The main results are synthetized in

Table 10. Main results.

Hypothesis	Result Before Threshold	Result After Threshold
Q1: HCI is positively linked to sustainable cities and communities.	Supported	Not supported
Q2: Natural resource efficiency is positively linked to sustainable cities and communities.	Supported	Supported
Q3: Environmental tax is positively impacting SDG-11.	Supported	Supported
Q4: Renewable energy use is positively linked to sustainable cities and communities.	Not supported	Supported

. Hypotheses Q2 and Q3 are validated before and after a certain threshold, while a non-linear relationship is observed between HCI and SCCs and between RE and SCCs.

This positive impact of human capital on sustainable cities and communities could be attributed to several factors, including a skilled workforce capable of driving innovation and adopting sustainable practices; enhanced institutional capacity and technical expertise for effective, sustainable urban planning; increased public awareness and advocacy for sustainability issues; and the availability of resources and infrastructure investments, facilitated by the economic development associated with higher human capital [46]. For EU members with an HCI below 1.867, increasing human capital investments can be highly beneficial, particularly for those whose mean in the descriptive statistics is lower than 1.86. Hypothesis Q1, stating a positive association between HCI and SDG-11, is validated for countries with an HCI below the threshold. After surpassing that critical point, the HCI’s impact on sustainable cities and communities becomes negative. The relationship between HCI and SDG-11 is not linear in any case.

The findings revealed a noteworthy pattern regarding the relationship between GDP and SDG-11. Before reaching a certain threshold, the coefficient for GDP on SDG-11 is positive, with a value of 0.01. However, after surpassing this threshold, the coefficient becomes negative, with a value of −0.873. These values are statistically significant, indicating a meaningful change in the impact of GDP on SDG-11 after the threshold is reached [68]. Specifically, the coefficient is statistically significant after the threshold but insignificant below it, highlighting the importance of considering this threshold’s effect in the analysis. Increasing GDP is detrimental to sustainable cities and communities beyond a certain point. At high development levels, the demand for comfort among urban populations increases a lot, which can become a burden for SDG-11 goals (for example, rising needs for air conditioners, which are high in pollutants, and increased energy consumption of households). A high GDP growth rate also implies high digitization of the economy and easy access to smart technologies and AI tools, which also require large electricity consumption; as long as this electricity production mainly relies on fossil fuels and not renewables, it will increase pollution.

This information suggests that policymakers and stakeholders should consider this threshold effect when planning and implementing strategies related to sustainable development, particularly in urban areas in the EU. This outcome aligns with the previous study [68]. The findings also revealed a noteworthy pattern in the relationship between NRP and SDG-11. Initially, before reaching a certain threshold, the coefficient for NRP on SDG-11 is positive, with a value of 0.63. After surpassing this threshold, the coefficient remains positive, albeit with a reduced value of 0.38. Both values are statistically significant, indicating a meaningful change in the impact of natural resource productivity on SDG-11. Hypothesis Q2 is thus validated both before and after the reaching threshold.

Natural resource productivity enhances the sustainable development of cities and communities. Efficient resource use leads to achieving more economic output with less input, which helps reduce waste and lower the ecological footprint, contributing to cleaner and healthier cities. By minimizing the extraction and consumption of raw materials, Europe can protect natural ecosystems, decrease pollution, and conserve biodiversity, all of which are essential for resilient urban areas. The economic benefits of higher productivity can be reinvested in sustainable infrastructure, such as public transportation, green buildings, and renewable energy, enhancing urban sustainability [33]. Europe’s leadership in technological innovation and resource efficiency, supported by strong policies and governance frameworks, further boosts natural resource productivity. Cities like Amsterdam, Copenhagen, and Berlin exemplify the positive effects of RSPR through initiatives in circular economy, green infrastructure, and smart city technologies. Overall, natural resource productivity significantly contributes to making European cities inclusive, safe, resilient, and sustainable, aligning with the objectives of SDG-11. This finding corroborates with prior studies [33,43].

Higher environmental tax levels create incentives for sustainable practices, generate revenue for urban initiatives, drive behavioral changes, and boost technological innovation, all of which contribute to more sustainable cities and communities. This result aligns with the study in [45], which found that environmental policy reduced pollution in 287 Chinese cities between 2010 and 2019. Hypothesis Q3 is thus validated, showing a positive impact of environmental tax on sustainable cities and communities.

Renewable energy negatively affects sustainable cities and communities in the early stages. This could be due to high initial costs, technological barriers, and potential trade-offs between RE expansion and urban sustainability efforts in the early stages. On the other hand, when RE use exceeds a certain level, its influence on SDG-11 changes to a significant and positive effect, which is also statistically significant at the 1% level, as evidenced by the coefficients of 2.5%. While renewable energy is generally considered beneficial for sustainable cities and communities, certain types can have drawbacks if not implemented carefully. Large-scale hydropower projects can flood vast areas, displacing communities, destroying habitats, and altering river ecosystems. Additionally, unsustainable biomass harvesting for energy can lead to deforestation, soil erosion, and biodiversity loss.

The impact of RE consumption on SDG-11 exhibits a non-linear pattern. Higher RE levels likely benefit from economies of scale, technological advancements, policy support, and positive externalities like improved air quality and public health. This non-linearity suggests that achieving mass RE adoption is crucial to unlocking its full potential in promoting sustainable the urban development goals captured by SDG-11. This result aligns with prior studies [52]. Hypothesis Q4, stating a positive association between RE and SDG-11, is thus validated in the long run after surpassing a certain point. However, the relationship between RE and SDG-11 is not linear.

6. Conclusions and Policy Implications

This study revealed significant insights into the influence of HCI and natural resource productivity on SDG-11 within the EU from 2011 to 2020 using the FD-GMM, focusing on the role of RE. The analysis identified an optimal HCI threshold of 1.867, beyond which the positive impact of the HCI on achieving SDG-11 becomes substantially stronger. The finding demonstrated that improvements in NRP positively affect SDG-11 both before and after surpassing a specific threshold. The impact of RE consumption on SDG-11 exhibits a non-linear pattern. At lower levels of renewable energy adoption (below a certain threshold), there is a negative relationship between RE consumption and the dependent variable. However, once clean energy consumption surpasses this threshold, the relationship becomes significantly positive. This non-linearity suggests that achieving mass RE adoption is crucial to unlocking its full potential in promoting the sustainable urban development goals captured by SDG-11. The robustness analysis validated our results.

The analysis identified an optimal HCI threshold of 1.867, beyond which the positive impact of the HCI on achieving SDG-11 becomes substantially stronger. The study provides valuable insights for corporate and practice professionals in the industry and technology sectors, which must focus on developing human resources to work towards natural resource-efficient production systems, necessitating research and development investments through public–private partnerships. Additionally, procuring machinery and equipment that use cleaner energy can reduce dependence on fossil fuels, contributing to lower carbon emissions.

Governments should improve education quality to meet industrial needs, creating a conducive environment for research and development and producing highly skilled professionals. Financial resources should support vocational training programs for a skilled workforce. They should also support the health system, as education and health are important for increasing the HCI. Urbanization, a focal point for social and economic activity, can significantly impact the environment if not managed sustainably. However, large, densely populated cities offer opportunities for effective environmental action. This finding has significant policy implications and practices for the EU.

Furthermore, as one of the SDGs, health complements the HCI in sustainable development. Accelerating the digital transformation of health systems can further support sustainable development, as healthy, well-educated individuals foster overall sustainability. The EU’s progress in reducing the environmental impacts of urban areas is monitored by indicators such as municipal waste management, wastewater treatment, and artificial land cover. Achieving SDG-13 (climate action) is intertwined with addressing SDG-7 (affordable and clean energy) and SDG-9 (industry, innovation, and infrastructure), all heavily reliant on HCI development, ultimately contributing to SDG-8 (decent work and economic growth) and SDG-11.

The study presents a few limitations. The analysis was conducted until 2020 because of the lack of available data. Moreover, it is limited to the country level. Future research should explore these dynamics across different EU cities, capital, and sectors; conduct longitudinal studies to assess long-term impacts; and evaluate the effectiveness of various policy interventions to better inform and refine strategies aimed at achieving SDG-11.