Land use change frequency influences the achievement of land degradation neutrality: Evidence from the Yunnan-Guangxi-Guizhou key rocky desertification areas in China.

This study examines the role of Green Zakat in poverty alleviation and economic sustainability in Indonesia through a qualitative comparative approach based on secondary data from 2025. Green Zakat represents an innovative integration of Islamic social finance with principles of the green economy and sustainable development. By analyzing patterns of zakat distribution, sustainable financing initiatives, and emerging policy frameworks, the study explores how this model expands the traditional function of zakat beyond short-term charitable relief toward long-term socio-economic and environmental impact. The findings reveal that Green Zakat contributes to poverty reduction by promoting productive economic empowerment, supporting environmentally responsible livelihoods, and enhancing financial inclusion among vulnerable communities. Furthermore, the framework strengthens the linkage between Islamic philanthropy and sustainable finance, encouraging resource allocation that aligns with climate resilience and social equity goals. Institutional collaboration between zakat agencies, Islamic financial institutions, and development organizations also appears to be a key factor in maximizing impact. Overall, Green Zakat demonstrates strong potential as a strategic instrument for inclusive and environmentally conscious economic development, offering valuable insights for researchers, practitioners, and policymakers seeking to optimize the role of Islamic social finance in achieving sustainable development objectives.

Achieving sustainable economic growth in resource-rich economies remains a major challenge in the context of the global energy transition. While fossil fuel dependence has historically supported economic expansion, it has also generated structural vulnerabilities associated with the resource curse. This study examines whether changes in energy structure can help move beyond the resource curse in Kazakhstan, a fossil-fuel-dependent transition economy. Using annual data for the period 1993–2023, the analysis applies the Autoregressive Distributed Lag (ARDL) bounds testing approach to investigate the short- and long-term relationships between per capita income, fossil fuel consumption, renewable energy production, carbon emissions, and capital formation. The results provide evidence of a long-term association, supported mainly by the error-correction mechanism. Fossil fuel consumption exerts a statistically significant negative effect on per capita income, supporting the resource curse hypothesis. In contrast, renewable energy production does not show a statistically significant impact on economic growth, suggesting that its contribution to economic growth remains limited within the sample period. Overall, the findings highlight the importance of energy diversification and structural transformation of the energy sector for aligning energy transition strategies with sustainable development objectives in resource-rich economies.

Multi-criteria decision-making (MCDM) methods for evaluating complex systems often face challenges in effectively addressing uncertainty, indeterminacy and interdependencies among factors, particularly in decision-makers (DMs) based assessments. To overcome these limitations, this study proposes a hybrid framework that integrates the Neutrosophic Set (NS) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with the Maclaurin Symmetric Mean (MSM) aggregation operator. The MSM operator accounts for the symmetric interrelationships among expert evaluations, enabling more robust and consistent aggregation compared to traditional techniques. The proposed method is applied to a case study on food security (FdS) which is a study characterized by multidimensional and interrelated factors. By combining the strengths of NS and MSM aggregation within the DEMATEL framework, the model yields clearer causal structures and more reliable prioritisation of influencing factors. This facilitates improved strategic planning and policy development in complex and uncertain environments. The integration of MSM into NS-DEMATEL represents a methodological enhancement in MCDM under uncertainty, offering practical value for enhancing FdS strategies and supporting broader sustainable development objectives.

Abstract - The growing complexity of sustainable development challenges requires policy systems capable of integrating diverse data sources, anticipating long-term impacts, and supporting evidence-based decision-making across sectors. Artificial Intelligence based Decision Support Systems offer a transformative approach to sustainable development policy and planning by combining predictive analytics, optimization modeling, and real-time data integration within adaptive governance frameworks. This paper conceptualizes the architecture and functional dimensions of AI-enabled decision support systems tailored to sustainable development objectives, examining their capacity to enhance policy design, resource allocation, risk forecasting, and cross-sectoral coordination. Drawing on interdisciplinary scholarship in artificial intelligence, public policy and sustainability studies, the study proposes an integrated framework that aligns AI capabilities with Sustainable Development Goal monitoring and implementation processes. It further evaluates governance, ethical and institutional considerations that shape responsible deployment, including transparency, fairness, data privacy, and digital capacity constraints. The analysis argues that AI-based decision support systems function not merely as technical tools but as enabling infrastructures that strengthen institutional intelligence and policy responsiveness. When implemented within accountable and inclusive governance structures, such systems hold significant potential to improve the effectiveness, efficiency, and equity of sustainable development planning. Key Words: Artificial Intelligence; Decision Support Systems, Sustainable Development, Policy Planning, SDGs, Digital Governance.

Purpose This study aims to investigate the impact of digital economy on fostering entrepreneurship and innovative systems. Through comprehensive analysis, the study identifies potential contributors shaping the digital economy’s involvement to entrepreneurship and innovation systems. Design/methodology/approach The study uses a modified total interpretive structural modeling approach which delves into the complex interrelations between digital economy, entrepreneurial activities, innovation ecosystems and sustainable development objectives. In addition, the authors explain the hierarchical structure and mutual influences among various contributors, highlighting critical pathways and feedback loops. Findings The findings from the study illustrate the contributors of digital economy on entrepreneurship and innovative systems such as “supporting regulatory framework”, “information democratization” and “outreach and awareness” at the bottom level. Furthermore, the study assesses the implications of these findings, including economic growth, job creation, enhanced competitiveness and societal well-being. Moreover, it explores the potential for addressing environmental challenges and fostering inclusive development through digital innovation. Originality/value By providing a nuanced understanding of the dynamics between digital economy, entrepreneurship, innovation and sustainable development, this study offers actionable insights for policymakers, businesses and stakeholders seeking to leverage digital technologies for long-term socioeconomic progress.

Coal continues to play a significant role in Poland’s electricity generation system, making the sustainable management of environmental impacts from hard coal mining a critical challenge during the ongoing energy transition. In line with the European Green Deal and circular economy principles, reducing and managing mining-related waste emissions is an important component of sustainable development in regions undergoing a gradual phase-out of fossil fuel extraction. This study analyzes rock mass and dust emissions associated with underground hard coal mining in Poland over the period 2017–2025 using the most recent statistical data, including estimates for 2025 based on the first three quarters of the year. The scale, structure, and trends of emissions are examined to assess their implications for environmental sustainability, resource efficiency, and long-term land use. Particular attention is paid to the relationship between declining coal production and the relatively slower reduction in waste rock emissions, which indicates increasing contamination of extracted material and poses challenges for sustainable mining practices. The results show that while total coal output has decreased substantially, reductions in rock mass emissions have been less dynamic, highlighting the need for improved waste management strategies from a sustainability perspective. The study demonstrates that increasing the utilization of mining waste, through underground use and circular economy applications, can reduce environmental pressure, support compliance with sustainability policies, and mitigate long-term impacts on post-mining regions. Although the analysis focuses on Poland, the findings provide transferable insights for other countries seeking to balance energy security, mining sector restructuring, and sustainable development objectives during the transition away from fossil fuels.

Against the backdrop of China’s vigorous promotion of green and low-carbon development, this study empirically examines the impact of ESG greenwashing on corporate financial sustainable development performance, using a sample of Chinese A-share-listed companies from 2018 to 2023. Empirical results indicate that ESG greenwashing significantly undermines corporate financial sustainable development performance. Furthermore, accounting conservatism mediates the relationship between ESG greenwashing and corporate financial sustainable development performance, whereas negative external media coverage moderates it. This research provides robust theoretical and empirical support for standardizing corporate ESG practices and advancing the achievement of green sustainable development objectives.

Machine learning (ML) in the real estate industry has transformed property assessment, administration, and promotion, tackling significant issues including market instability and pricing precision. Notwithstanding considerable progress in predictive, descriptive, prescriptive analytics, and automation, current research mostly emphasises technological and operational efficiencies, overlooking the integration of environmental, social, economic, and governance (ESEG) sustainability dimensions. This monitoring constrains the advancement of comprehensive and accountable real estate solutions corresponding to sustainable development objectives. This study aims to address these gaps by systematically analyzing publication trends, key contributors, and thematic clusters, incorporating sustainability principles via a combination of bibliometric and content analysis approaches. The study uncovers publication trends, key research themes, and their alignment with ESEG criteria. The results highlight significant research clusters in predictive and descriptive analytics while revealing a notable deficiency in sustainability-focused studies. Implications of this study underscore the necessity for incorporating ESEG dimensions into ML-driven real estate practices, promoting resilient, equitable, and environmentally responsible industry advancements. This study provides actionable insights for stakeholders to enhance sustainable ML adoption, fostering long-term viability and societal well-being in the real estate sector.

Abstract to be supplied.

Ensuring access to water and sanitation for all on the one hand, and ensuring sustainable and equitable management of water resources on the other hand, is one of the objectives for sustainable development. In the department of Bongouanou, most of the population's drinking water is supplied by groundwater contained in granite and schist basement aquifers This study delineates potential groundwater recharge zones in the Bongouanou department through an integrated geospatial and multi-criteria decision-making (MCDM) approach. The methodology combines remote sensing and GIS-based multi-criteria analysis with hydrochemical validation. Satellite imagery, base maps, and hydrochemical data were used to generate thematic layers representing the main factors controlling groundwater recharge, including slope, drainage density, lithology, fracture density, soil type, rainfall, and land use/land cover. These layers were weighted and overlaid to produce a groundwater recharge potential map. The results show that high recharge potential zones cover approximately 45% of the study area, while medium and low-to-moderate potential zones account for 33% and 21%, respectively. The reliability of the generated recharge potential map was validated using chloride concentration data. The development of a large-scale hydrogeological map of potential recharge zones for the fractured aquifers of Bongouanou, based on the integration of multiple datasets and methods, highlights the value of combining diverse sources of information.

This study examines the role of digital finance and financial inclusion in enhancing community income in the Pulau Ketam Forest Reserve Area, Kuala Perlis, Malaysia. Communities living in forest reserve and conservation-based areas often face limited access to formal financial services, which constrains income growth and economic resilience. Using a quantitative approach, this study collected primary data from 135 respondents comprising households and micro-entrepreneurs through structured questionnaires. Digital finance and financial inclusion were analyzed as independent variables, while community income served as the dependent variable. Data were analyzed using descriptive statistics, reliability tests, and multiple linear regression analysis. The findings reveal that digital finance has a positive and significant effect on community income, indicating that the use of mobile banking, digital payment systems, and fintech services contributes to income growth and stability. Financial inclusion is also found to have a positive and significant impact on community income, highlighting the importance of access to formal financial services such as savings accounts and credit facilities. The regression results show that digital finance and financial inclusion jointly explain a substantial proportion of the variation in community income. These findings suggest that inclusive digital financial systems can support livelihood enhancement while aligning with sustainable development objectives in forest reserve areas. The study provides important policy implications for promoting digital financial inclusion as a strategy for community empowerment and sustainable economic development in environmentally sensitive regions.

Building maintainability (BM) is often overlooked within the design process (DP), leading to higher costs and reduced building performance. In Egypt, this challenge is intensified by the limited implementation of Digital Twin (DT) technology, which provides real-time data integration and Insightful forecasts that can enhance BM. However, implementation faces technical, financial, cultural, and regulatory barriers. This study aims to develop a framework for overcoming the challenges of integrating DT technology into DP in Architectural Design Firms (ADFs) in Egypt, to enhance BM, promote sustainability, and drive digital transformation in construction. A mixed-methods research approach was employed, comprising a comprehensive literature review, three case studies evaluating the impact of DT on BM, and an online survey targeting Egyptian ADFs to capture their perceptions and identify key challenges. Findings revealed key BM challenges, including abstract maintainability concepts, short-term design focus, and weak enforcement of building codes, alongside DT challenges, including lack of awareness, limited knowledge sharing, and skill shortages. The proposed framework addresses these barriers, promoting proactive BM, cost efficiency, and improved building performance. This research fills a gap in construction literature by offering a context-specific model for early DT adoption in design, supporting Egypt’s sustainable development and digital transformation objectives.

Water scarcity in arid/semi-arid regions restricts agricultural sustainability systems and hinders the achievement of regional sustainable development goals, especially in northwest China’s extremely arid areas, where acute water supply–demand conflicts and inefficient traditional practices intensify competition for water between agricultural and ecological sectors. This study aims to verify the effectiveness of an intelligent automatic irrigation system in mitigating water scarcity pressures and enhancing agricultural sustainability in the Shule River Basin of northwestern China, a region where traditional irrigation methods not only yield suboptimal crop outputs but also undermine long-term water resource sustainability. A smart irrigation module, integrating “sensing–decision–execution” processes, was embedded within a digital twin platform to enable precise, resource-efficient water management that aligns with sustainable development principles. Sunflower (Helianthus annuus L.), the most popular cash crop in the area, was used as the test crop, with three soil moisture-based irrigation levels compared against traditional farmer practices. Key indicators including leaf area index (LAI), dry biomass, grain yield, and irrigation water use efficiency (IWUE) were systematically evaluated. The results showed that (1) LAI increased from the seedling to flowering stage, with smart irrigation treatments significantly outperforming farmer practices in both crop growth and water-saving effects, laying a foundation for sustainable yield improvement; (2) total dry biomass at maturity was positively correlated with irrigation amount but smart irrigation optimized the allocation of water resources to avoid waste, balancing productivity and sustainability; (3) grain yield peaked within 70–89% field capacity (fc), with further increases leading to diminishing returns and unnecessary water consumption that impairs sustainable water use; (4) IWUE followed a parabolic trend, reaching its maximum under the same optimal irrigation range, indicating that smart irrigation can maximize water productivity while preserving water resources for ecological and future agricultural needs. The digital twin-driven smart irrigation system enhances both crop yield and water productivity in arid regions, providing a scalable model for precision water management in water-stressed agricultural zones. The results provide a key empirical basis and technical approach for sustainably using irrigation water, optimizing water–energy–food–ecology synergy, and advancing sustainable agriculture in arid regions of Northwest China, which is crucial for achieving regional sustainable development objectives amid worsening water scarcity.

In recent years, Vietnam has invested in modern transport systems, such as metro lines, to reduce pollution and traffic congestion. Urban transport systems in Vietnam have a major impact on achieving sustainable development objectives by shaping accessibility, affordability, and environmental performance. This paper measures the sustainability of five urban mobility solutions, including public bus, metro/urban rail, ride-hailing, electric taxi, and motorcycle taxi, through the multi-criteria framework combined with the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) compromise ranking approach. It considers 12 indicators across the economic, social, and environmental dimensions, including fare affordability, operational cost efficiency, reliability, waiting time, comfort, CO2 emissions, energy efficiency, noise pollution, safety, accessibility, policy support, and user satisfaction. The normalization of expert judgment and secondary data is done to develop a VIKOR decision matrix, using which the values of S (group utility), R (individual regret), and Q (compromise index) are obtained and visualized using radar and scatter plots. Findings reveal that metro/urban rail and public buses are the most sustainable options, with good performance in safety, accessibility, and environmental indicators, whereas motorcycle taxis are the least sustainable, with high emissions, noise, and safety risks. Intermediate roles are enjoyed by ride-hailing and electric taxis, where trade-offs are a factor in service quality, cost, and environmental impact. The suggested framework provides policymakers with a clear instrument for prioritizing investments and policies toward sustainable urban transit in terms of mobility.

Abstract Xanthan gum, a high molecular weight exopolysaccharide synthesized by Xanthomonas campestris , is employed extensively in the food, pharmaceutical, cosmetic, and petroleum industries due to its exceptional rheological properties, thermal and pH stability, and biodegradability. Despite its versatility, large‐scale industrial production remains constrained by the high costs associated with conventional carbon sources such as glucose and sucrose. This review provides a comprehensive analysis of cost‐effective and sustainable alternatives, emphasizing the valorization of agroindustrial and food‐processing residues, including molasses, whey, melon waste, jackfruit seed powder, rice bran, and olive mill effluents as viable substrates for xanthan gum fermentation. Numerous studies have demonstrated that these unconventional feedstocks, when subjected to suitable pretreatment protocols and nutrient optimization, can yield xanthan gum at levels comparable to or exceeding those obtained from traditional sources. Incorporating circular economy principles into the production chain not only enables significant cost reductions but also mitigates the environmental burden associated with organic waste disposal. Advances in artificial intelligence (AI), particularly through machine learning (ML) techniques such as artificial neural networks, support vector machines, and evolutionary algorithms, have further expanded the potential for xanthan gum production by accurately modeling and optimizing the complex parameters of bioprocesses. These data‐driven methodologies outperform traditional statistical tools in predicting sugar release, enhancing fermentation efficiency, and reducing process variability. By facilitating the efficient utilization of low‐cost substrates and improving process control in both hydrolysis and fermentation stages, AI‐based optimization contributes to the development of scalable, resilient, and environmentally sustainable bioprocesses. Ultimately, the synergistic integration of waste‐derived feedstocks, bioprocess engineering, and intelligent modeling aligns xanthan gum manufacturing with the broader objectives of sustainable development and the global bioeconomy.

During the 13th National Party Congress (2021 - 2025), the Vietnamese economy demonstrated strong resilience in its recovery and achieved remarkable development amidst a complex, unpredictable, and unprecedented global situation, with many factors beyond forecast, especially the severe impact of the Covid-19 pandemic. This article provides a systematic analysis of Vietnam’s major socio-economic achievements during the term of the 13th National Party Congress (2021 - 2025), set against a global backdrop marked by complex fluctuations and unprecedented shocks, particularly the profound impacts of the Covid-19 pandemic and the growing trends of fragmentation and protectionism in global trade. By clarifying key outcomes in economic growth, macroeconomic stability, economic scale, and endogenous capacity, the article identifies the critical factors underpinning these achievements, while also recognizing existing barriers and challenges affecting the quality and sustainability of growth. On that basis, the article examines the emergence of new growth drivers for the 2026 - 2030 period and elucidates the development vision and prospects outlined in the documents submitted to the 14th National Party Congress. The article affirms the logical continuity and inheritance between the achievements of the 13th Congress and the objectives of rapid, sustainable development and stronger advancement during the term of the 14th National Party Congress (2026 - 2030).

Purpose The purpose of this study is to discuss how knowledge management (KM) practices can help in sustainable infrastructure growth in various regions with specific reference to energy consumption and economic development. Design/methodology/approach The study employs the panel data of 65 Belt and Road Initiative (BRI) countries across the period between 1981 and 2016 to analyze using the econometric modeling techniques. Energy consumption, financial development, utilization of renewable energy and technological advancement are key variables. A theoretical lens of KM is incorporated in comprehending the data-driven decision-making and knowledge-based policy implementation. To ensure robust results, advanced econometric techniques are applied, including cross-sectional dependence tests (LM Breusch–Pagan and CD test of Pesaran), unit root tests (ADF Fisher Chi-square and CIPS CADF) to capture long-run relationships. Findings The findings indicate that energy consumption and economic expansion have significant implications to the quality of the environment whereas use of renewable energy and economic development have positive environmental consequences within the chosen regions. The research reveals that the regional differences influence the manner in which the KM mediates the interrelation of development and sustainability. Research limitations/implications The research depends on secondary sources of KM inferences and is restricted to the chosen BRI countries. Practical implications The study also gives policymakers, planners of infrastructure and development institutions recommendations of how to incorporate KM in infrastructure policy in line with the sustainability objectives. Social implications International organizations and development partners should fund capacity-building programs and knowledge-sharing platforms to enable BRI nations to execute sustainable development objectives. Originality/value The research examines how KM is linked to sustainable development in 65 BRI countries, taking into account differences among areas and difficulties in using cross-sectional information.

Purpose This study aims to examine the impact of intellectual capital (IC) on Islamic bank performance (IBP), using four performance indicators: asset turnover, return on assets, return on equity and non-performing loans. It further investigates how corporate governance (CG) mechanisms – specifically board size, board independence and Shariah board involvement – mediate the relationship between IC and IBP. The study supports the broader objectives of socioeconomic justice and sustainable development in Islamic finance. Design/methodology/approach The study uses data from 136 Islamic banks across 29 Organization of Islamic Cooperation (OIC) countries from 2012 to 2023. Analytical methods include structural equation modeling with partial least squares and path analysis to evaluate direct and mediated effects. Findings The findings indicate that IC significantly enhances IBP. All three IC components – human capital efficiency, structural capital efficiency and relational capital efficiency – positively affect performance. Moreover, board size and Shariah board involvement serve as effective mediators. However, Board Independence exhibits a significant adverse effect, suggesting that excessive independence may undermine the efficient use of IC. Research limitations/implications The study recommends that Islamic banks strategically optimize IC and reinforce governance practices, particularly those related to board structure and Shariah compliance. These efforts are critical for enhancing performance and ensuring financial resilience. Further research is suggested to expand CG variables and assess their effects on financial inclusion and ethical banking outcomes. Originality/value This study offers a novel contribution by specifically analyzing Islamic Bank Performance (IBP) across 29 OIC countries – an object of study that has received limited attention in the literature. To the best of the authors’ knowledge, it is among the first to empirically test the mediating role of CG in the IC – IBP relationship within the context of Islamic finance. The findings offer actionable insights into optimizing IC and governance design to foster competitive advantage and support Islamic finance’s ethical and development-oriented mission.

ABSTRACT The circular economy is gaining traction as a transformative approach to address climate change and the shortcomings of the linear economic model, which is defined by unsustainable production and consumption patterns. This study explores the implementation of circularity principles in the Spanish automotive industry, with particular attention to the sector's ongoing transition toward sustainability in alignment with the United Nations Sustainable Development Goals and broader sustainable development objectives. The evolution of electric vehicles exemplifies this shift, underscoring the need to redesign production systems and manage resources more efficiently. Using a survey‐based methodology complemented by expert panel input, and applying Principal Component Analysis, the research identifies critical challenges such as resource consumption, eco‐design, technology development, and waste generation. The findings reveal a pressing need for a comprehensive green transition, highlighting specific deficiencies that require targeted action. To address these gaps, the study proposes a strategic action plan rooted in the principles of circularity, offering guidance for industry stakeholders and policymakers to accelerate the adoption of sustainable practices and foster long‐term resilience in the automotive value chain.