Foster Sustainable Development Research Articles (Page 1)

ABSTRACT Education is widely seen as both a means and an end to the Sustainable Development Goals (SDGs), yet its broader socio‐economic effects on growth, inequality, health and institutions remain contested across diverse Global South contexts. Prior reviews are largely Global North‐centric and rarely grade evidentiary strength, making it hard to prioritise and sequence policy reforms. This paper offers a structured narrative review of 134 peer‐reviewed studies (1984–2024) from 33 renowned journals focused on education, development and policy. Using a PRISMA‐inspired workflow, I code studies by region, level, SDG alignment, method, evidence type and effect direction/size. Four SDGs have sufficient coverage: SDG 8 (work and growth), SDG 10 (inequalities), SDG 3 (health) and SDG 16 (institutions). Findings support cautious optimism: education is associated with higher earnings and growth; reduced inequality when reaching disadvantaged groups; selected health gains, especially for women and later life; and lower violence with stronger civic competencies. Effects are larger where learning quality is high and skills can be absorbed by institutions and labour markets; early expansions can widen measured inequality, and credible nulls appear for some outcomes. The review contributes a Global South‐centred, cross‐SDG evidence map that distinguishes evidentiary strength and derives policy priorities: quality over quantity, governance capacity, and technology as a complement, not a substitute.

Applying Nudge Theory to Foster Sustainable Development Goals (SDGs) in Qatar

ABSTRACT This research investigates the impact of destination image, perceived value, and psychological well-being on domestic tourists’ behavioural intentions, integrating direct, mediated, and serially mediated effects into a unified framework. The study employed the Stimulus-Organism-Response (S-O-R) theory and adopted a quantitative research design using statistical methods for data analysis. Da Nang, a coastal tourism city in central Vietnam, served as the destination site. Data were collected through a structured questionnaire using a purposive sampling method, resulting in 409 valid responses from domestic tourists who had visited Da Nang. Partial least squares structural equation modelling (PLS-SEM) was used to test the hypotheses. Results revealed that psychological well-being plays a pivotal mediating role, amplifying the influence of destination image on behavioural intentions, both individually and as part of a serial mediation with perceived value. This study enhances the literature by shedding light on the critical role of psychological well-being in shaping tourists’ decisions and offers practical insights for tourism managers, providing a foundation for strategies to attract and retain tourists and foster sustainable destination development.

Emission reduction effect and transmission mechanism of carbon tax from the perspective of embodied carbon emissions.

Riverfront utilization structure shapes polycyclic aromatic hydrocarbon risks in the middle-lower Yangtze river.

The sustainable development of agrifood systems is a pressing global challenge, highlighting the need for frameworks that guide responsible investment and community engagement. The Principles for Responsible Investment in Agriculture and Food Systems (CSA-IRA), approved by the Food Security Council in 2014, provide such a framework. Recognizing this opportunity, the FAO selected the Gesplan Research Group of the Polytechnic University of Madrid in 2016 to promote these principles in Latin America, the Caribbean, and Spain, leveraging the expertise of PhD graduates in Projects and Planning for Sustainable Rural Development. The main objective of this research was to explore how teaching, research, and civil society engagement can be integrated to operationalize CSA-IRA principles and foster sustainable development. To achieve this, the study applied the “Working with People” model across multiple countries and contexts, using university–business collaborations to implement practical, socially responsible initiatives. Over nine years, the approach generated a network of 46 universities and 52 agrifood companies across 12 countries, demonstrating effective multi-stakeholder collaboration. The accumulated experience led to the proposal of the Metauniversity—a “university of universities”—as an innovative instrument to scale knowledge transfer, research, and community engagement. These findings highlight that structured, collaborative networks can translate CSA-IRA principles into tangible actions, offering a replicable model for sustainable agrifood development globally

The productivity of independent smallholder oil palm farmers remains significantly lower than that of corporate plantations, despite operating under similar agroecological conditions. This study aims to analyze the socioeconomic factors influencing technical efficiency among independent smallholders, focusing on the roles of education, farming experience, access to extension services, and household income. Using a quantitative explanatory design, the research applies the Cobb-Douglas Stochastic Frontier Analysis (SFA) model based on primary survey data from 125 smallholders in Dolok Masihul District, North Sumatra. Data were analyzed using Frontier 4.1 and STATA through a one-step procedure to assess the effects of socioeconomic characteristics on inefficiency. Results show an average technical efficiency of 0.78, suggesting a 22% potential output increase through better resource utilization. Land size and fertilizer use significantly enhance productivity, while labor and seed type exhibit moderate effects. Farming experience, education, and extension access significantly reduce inefficiency, whereas age and household size have no notable influence. The study’s novelty lies in integrating an econometric model with a sociological perspective, demonstrating that smallholder efficiency is shaped by social capacities rather than agronomic factors alone. It advances a socio-econometric framework for understanding smallholder productivity and recommends policy interventions emphasizing human capital, farmer education, and community-based extension systems to foster sustainable rural development.

Tunisia’s northwestern region offers a rich and diverse civilization heritage and cultural potential. However, it has been socially and economically marginalised since the 70s. This study explores the link between urban cultural heritage and the construction of collective identity, emphasising the potential of built heritage to foster sustainable community-based development. In addition to physical attributes, the literature highlights the importance of social interactions in shaping territorial identity. Identity, in this context, is not static but a dynamic territorial construction that integrates architectural, urban, and social dimensions. The cities of El Kef and Tabarka serve as case studies of spatially and culturally marginalised areas, facing significant challenges to both tangible and intangible heritage. With a negative population growth rate (−0.36% between 2004 and 2014) and the lowest economic development indicator nationally (0.3% in 2012), these cities reflect the urgent need for an alternative approach. Through spatial diagnosis, interviews, and stakeholder engagement, the research demonstrates that a renewed territorial model—grounded in heritage valorization and local identity—can support inclusive and adaptive development. Key findings reveal a generational gap in the perception and representation of heritage between younger and older residents. This indicates that cultural identity is not a static inheritance but a dynamic process requiring active community investment. Ultimately, the study concludes that urban identity assets critically influence the capacity of a community to build a shared vision for the enhancement of its territorial identity. This reconnection between territory, memory, and planning enables a collective reappropriation of space, proposing a long-term vision for heritage-integrated urban regeneration.

ABSTRACT Green innovation is increasingly recognized as a critical strategy for small‐ and medium‐sized enterprises (SMEs) to enhance sustainable performance. Drawing on the natural resource‐based view (NRBV), this study examines the impact of green innovation on sustainable performance and the mediating roles of green knowledge sharing and green dynamic capabilities. In addition, the moderating effect of artificial intelligence (AI) on these relationships was investigated. Data were collected from 230 SMEs in two phases, and the hypotheses were analyzed. The findings reveal that green innovation is positively related to sustainable performance, with green knowledge sharing and green dynamic capabilities mediating this relationship. Furthermore, AI significantly moderates the relationship between green knowledge sharing, green dynamic capabilities, and sustainable performance. These results suggest that integrating AI into SMEs can enhance the promotion and effectiveness of green innovation strategies. This study contributes to the literature by providing empirical evidence for NRBV and highlighting the importance of green knowledge sharing, green dynamic capabilities, and AI in the context of green innovation and sustainable performance. These findings offer valuable insights for SME managers and policymakers seeking to foster sustainable development through green innovation and AI adoption.

This work explores the relationship between landscape metrics and surface water in the Galicia-North Portugal Euroregion, employing 6,220,767 linear regression models through Python scripts to predict surface water quality. The Iberian Biological Monitoring Working Party (IBMWP) index, based on benthic macroinvertebrate communities from 40 sites across Portugal (PT) and Galicia (GL), served as the biological indicator. The models were initially selected based on linear regression assumptions (17 tests), and validated against real-world data, evaluating statistical performance through indicators such as R-squared, mean absolute percentage error (MAPE), and percentage bias (PBIAS). Results indicated that GL had a higher macroinvertebrate abundance, whereas Portugal showed greater diversity and family richness. Statistical analysis revealed that landscape significantly influenced water quality, with land use composition and configuration driving differences in ecological conditions between regions. The best-performing models demonstrated a high R-squared value of 0.7, a MAPE of 27% for calibration (PT) and 10% for the validation (GL), indicating a strong predictive relationship. The models provide valuable insights into the complex interactions between landscape patterns and water quality, highlighting how variations in landscape structure can directly affect ecological integrity. These findings reinforce the need for strategic land management to preserve water quality and emphasizing the importance of transboundary governance across the Euroregion to foster sustainable development.

National innovative industrial clusters serve as pivotal platforms for technological innovation and play a vital role in advancing urban green technological innovation (GTI). This study empirically examines the impact of the pilot innovative industrial cluster policy on urban GTI using panel data from 281 prefecture-level cities in China (2006–2021), employing a difference-in-differences (DID) model that treats the policy as a quasi-natural experiment. The findings reveal that the pilot policy has significantly catalyzed urban GTI, a conclusion that remains robust after a series of robustness tests. Heterogeneity analysis indicates that the promotional effect is more pronounced in eastern and western cities, as well as in cities with high environmental concern and strong intellectual property protection (IPP). Mechanism analysis demonstrates that the policy drives GTI primarily through three channels: enhancing entrepreneurial activity, agglomerating scientific and technological talent, and increasing financial and scientific investment. Furthermore, the policy significantly improves urban energy utilization efficiency, with GTI acting as a critical mediating mechanism. These findings provide valuable empirical evidence and strategic insights for policymakers to refine the cluster policy and foster sustainable urban development towards achieving the dual-carbon goals.

This study applies machine learning (ML) techniques to model and predict Assam’s agricultural Gross State Domestic Product (GSDP). Three predictive models—multiple linear regression, random forest regression, and gradient boosting—are evaluated. The random forest model achieved the best fit, exhibiting the highest R² and the lowest mean squared error (MSE) and Akaike information criterion (AIC), along with statistically significant coefficients. Ensemble methods (random forest and gradient boosting) markedly improve forecast accuracy of agricultural growth trends compared to traditional regression, yielding more reliable predictions of productivity and GSDP contributions. The findings underscore the vital role of agricultural productivity in driving economic growth, strengthening GSDP, and supporting food security and employment. Integrating advanced ML techniques with statistical analysis provides insights for policymakers to make data-driven decisions that foster sustainable agricultural development and economic prosperity in Assam. Objectives: Predict Assam’s agricultural sector performance using selected machine learning models. Evaluate and compare the effectiveness of these models in assessing the state’s agricultural economy. Methods: Data preprocessing involved handling outliers (using interquartile range and mean-max scaling) and feature selection via correlation heatmaps. Predictive models (multiple linear regression, random forest regression, and gradient boosting) were implemented in Python. Results: The gradient boosting model emerged as the most effective, achieving the highest accuracy and generalization (testing R² = 0.9867). Farm area, labour, maize yield, and autumn rice yield were the most significant positive contributors to GSDP. The random forest model performed similarly well (R² = 0.9867), while the multiple linear regression model was least accurate (R² = 0.9521), likely due to its inability to capture nonlinear relationships. Conclusions: Machine learning models offer transformative potential for Assam’s agricultural sector. Leveraging data-driven insights from these models can empower policymakers to design targeted interventions, promoting inclusive and sustainable economic growth in the region.

ABSTRACT Cities are increasingly vulnerable to the impacts of climate change, facing challenges such as increasing temperatures, intensified by the urban heat island effect, and extreme weather events. As hubs of socio-economic activity and critical infrastructure, urban areas require effective climate adaptation strategies to enhance resilience and foster sustainable development. Our study evaluates the climate adaptation action and strategic planning of 40 Romanian cities, all county capitals, to assess their compliance with international climate frameworks and preparedness for mitigating the impacts of climate change. The research employs a structured analytical framework to evaluate four key pillars: (i) political will, (ii) alignment with commitments, (iii) adaptation plans, and (iv) implementation ambition. They are assessed through ten specific indicators. The analysis reveals significant disparities: while some cities successfully integrate adaptation strategies into governance, others demonstrate deficiencies in comprehensive planning and the establishment of measurable actions. Key findings underscore the importance of political commitment and active participation in international climate networks as facilitators of adaptation progress. However, inconsistencies in reporting practices and implementation mechanisms remain significant challenges. By addressing existing planning, reporting, and implementation gaps, Romanian cities can align more effectively with national and European Union climate adaptation goals. It will enhance local climate resilience and contribute to broader sustainability and resilience objectives at the regional and international levels.

Lambadeuk Village in Aceh Province is known for Trigona bees, which produce honey and propolis, a valuable by-product often discarded due to limited knowledge and skills for its utilization. This study evaluates the impact of a soap-making workshop on community knowledge, behavior, and empowerment, employing Participatory Action Research (PAR). The program engaged 27 participants, primarily housewives and farmers, in theoretical and practical training on soap production using propolis. Pretest and posttest evaluations revealed significant improvements in participants’ knowledge of soap-making processes and ingredient roles, with knowledge levels shifting from low to high. However, confidence for independent production remained modest, indicating the need for continued mentorship. By transforming propolis into value-added products, the initiative aligned with circular economy principles and Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 8 (Decent Work and Economic Growth). This program demonstrates the potential of targeted empowerment efforts to enhance community skills, reduce waste, and foster sustainable development through innovative resource utilization.

The article explores the strategic development of renewable energy enterprises as a key driver in advancing global energy efficiency and promoting energy conservation. In the context of increasing environmental challenges, rising energy demand, and the urgency of transitioning to sustainable energy sources, renewable energy enterprises play a pivotal role in shaping the future of the global energy system. The purpose of the study is to examine strategic approaches that can accelerate the development of renewable energy enterprises and assess their potential impact on enhancing energy efficiency and reducing energy consumption at both national and international levels. The objectives of the research include analyzing global trends in the renewable energy sector, identifying key barriers and opportunities for strategic growth, and developing a framework that integrates innovation, policy support, and market mechanisms to foster sustainable development. The study also considers the role of renewable energy enterprises in achieving international climate goals, increasing energy independence, and supporting socio-economic development through job creation and technological advancement. The methodological foundation of the paper is based on a systems approach, incorporating both qualitative and quantitative methods, including comparative analysis, case studies of leading renewable energy companies, and data modeling of energy efficiency outcomes. The research reveals that strategic development, including investment in innovation, policy alignment, and international cooperation, significantly contributes to the scalability and competitiveness of renewable energy enterprises. It highlights that integrating smart technologies, digital monitoring systems, and decentralized energy production enhances operational efficiency and supports the broader goals of energy conservation. Furthermore, the study underscores the importance of public-private partnerships and long-term policy stability in enabling renewable energy enterprises to flourish. The findings of the research contribute to the theoretical and practical understanding of the strategic development of renewable energy enterprises and offer policy recommendations and strategic directions for governments, investors, and energy sector stakeholders. The proposed strategic framework emphasizes risk mitigation, capacity building, and regulatory incentives to unlock the full potential of renewable energy as a catalyst for global energy efficiency and sustainability.

ABSTRACT The link between uncertainty and innovation is well-documented. However, existing studies have focused on the impact of uncertainty on innovation, with limited evidence on the reverse, especially in developing regions. This paper, therefore, underscored the impact of innovation on uncertainty in Africa, a continent ridden with multiple uncertainties ranging from economic shocks to geopolitical tensions and climate change. The Negative Binomial (NB) regression model was employed to analyse relevant panel data from 33 African countries covering 2011–2022. The Bayesian NB regression was used to check the robustness of the estimates. The results unequivocally show that innovation is highly indispensable for tackling uncertainties in Africa. The finding is robust to alternative measures of uncertainty and estimators. Thus, African countries must intensify efforts towards mainstreaming innovation into all sectors of their economies to tackle uncertainty and foster sustainable development. Other practical insights to mitigate uncertainty and foster innovation are also provided.

Cross-border healthcare investments in emerging markets present a compelling opportunity to address unmet medical needs, improve healthcare access, and generate significant economic returns. However, these ventures are fraught with risks, including regulatory uncertainties, economic instability, and operational challenges. This paper introduces a Risk-Opportunity Model designed to guide investors in managing these complexities while capitalizing on growth opportunities in the healthcare sectors of emerging economies. The proposed model emphasizes a strategic approach to investment, integrating robust risk assessment frameworks with opportunity mapping. It identifies key risk factors such as inadequate regulatory infrastructure, currency volatility, and workforce shortages, providing targeted mitigation strategies for each. The model also highlights high-growth opportunities, including investments in digital health technologies, localized healthcare infrastructure, and public-private partnerships (PPPs), which align with the specific healthcare needs of emerging markets.Central to the model is a three-pronged strategy: (1) Risk Mitigation, involving scenario planning, stakeholder engagement, and regulatory compliance; (2) Opportunity Optimization, leveraging market insights, technology, and innovation to identify scalable solutions; and (3) Adaptive Implementation, focusing on culturally sensitive and sustainable deployment of healthcare initiatives. By integrating these elements, the model equips investors to achieve both financial returns and positive social impact. The paper further discusses the role of technological innovation—such as telemedicine, artificial intelligence, and mobile health applications—in enhancing the efficiency and accessibility of cross-border healthcare investments. Additionally, it underscores the importance of collaborative partnerships with local governments, non-governmental organizations, and community stakeholders to build trust and ensure alignment with local priorities. This Risk-Opportunity Model offers a comprehensive roadmap for navigating the complexities of cross-border healthcare investments, balancing potential risks with opportunities to foster sustainable healthcare development in emerging markets. The findings provide actionable insights for policymakers, investors, and healthcare practitioners aiming to drive transformative outcomes in global health.

The development of new energy vehicles, particularly electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), represents a strategic initiative to address climate change and foster sustainable development. Integrating PV with hydrogen production into hybrid electricity-hydrogen energy stations enhances land and energy efficiency but introduces scheduling challenges due to uncertainties. A multi-time scale scheduling framework, which includes day-ahead and intraday optimization, is established using fuzzy chance-constrained programming to minimize costs while considering the uncertainties of PV generation and charging/refueling demand. Correspondingly, trapezoidal membership function and triangular membership function are used for the fuzzy quantification of day-ahead and intraday predictions of photovoltaic power generation and load demands. The system achieves 29.37% lower carbon emissions and 17.73% reduced annualized costs compared to day-ahead-only scheduling. This is enabled by real-time tracking of PV/load fluctuations and optimized electrolyzer/fuel cell operations, maximizing renewable energy utilization. The proposed multi-time scale framework dynamically addresses short-term fluctuations in PV generation and load demand induced by weather variability and temporal dynamics. By characterizing PV/load uncertainties through fuzzy methods, it enables formulation of chance-constrained programming models for operational risk quantification. The confidence level – reflecting decision-makers’ reliability expectations – progressively increases with refined temporal resolution, balancing economic efficiency and operational reliability.

Food scarcity and social instability are twin social problems that are plaguing every rural community in Imo state in the recent times. This is regardless of various efforts made by both Federal and state governments to boost agricultural production in rural communities. The paper examines the impact of food scarcity, insecurity, poor road infrastructure on social instability in Imo state. The study utilized a survey research design in sample of 646 respondents across selected rural and semi-urban communities using a structured questionnaire. The findings reveal that there are high levels of food scarcity, insecurity, and poor road conditions which is strongly correlated with reported social instability in Imo state. The findings suggest that these factors form a vicious cycle that significantly undermines socio-economic stability. The study recommends integrated policy interventions focusing on enhancing security, agricultural investment, and critical road infrastructure to foster sustainable development and peace in the region

Rural tourism servicescape's influence on experiential value and citizenship behavior.