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Challenges in Sustainability
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Challenges in Sustainability (CiS)
ESM
ISSN (print): 3134-6022
ISSN (online): 2297-6477
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2026: Vol. 14
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Challenges in Sustainability (CiS) is a peer-reviewed open-access journal dedicated to advancing research on sustainability across environmental, social, and economic dimensions. The journal provides a scholarly platform for studies that investigate the drivers, impacts, and solutions related to global sustainability challenges in both developed and developing contexts. CiS encourages conceptual, empirical, and policy-focused contributions that address climate resilience, resource management, sustainable technologies, social equity, and responsible governance. The journal values interdisciplinary approaches that integrate scientific evidence with policy and practice to support sustainability transitions and long-term societal well-being. Committed to research integrity, rigorous peer-review, and timely knowledge dissemination, CiS is published bimonthly by Acadlore, releasing six issues per year in February, April, June, August, October, and December.

  • Professional Editorial Standards - Every submission undergoes a rigorous and well-structured peer-review and editorial process, ensuring integrity, fairness, and adherence to the highest publication standards.

  • Efficient Publication - Streamlined review, editing, and production workflows enable the timely publication of accepted articles while ensuring scientific quality and reliability.

  • Gold Open Access - All articles are freely and immediately accessible worldwide, maximizing visibility, dissemination, and research impact.

Editor(s)-in-chief（1）
katie kish
Shannon School of Business, Cape Breton University, Canada
kate_kish@cbu.ca; katiekish@gmail.com | website
Research interests: Ecological Footprint; Complexity Thinking; Ecological Economics

Aims & Scope

Aims

Challenges in Sustainability (CiS) is an international peer-reviewed open-access journal dedicated to advancing research on sustainability from environmental, social, and economic perspectives. The journal serves as a platform for high-quality studies that examine global sustainability challenges, resilience strategies, and pathways for driving a just and sustainable transition.

CiS aims to foster interdisciplinary scholarship that connects scientific analysis, sustainable technologies, governance frameworks, and behavioural transformation. The journal welcomes conceptual, empirical, and applied contributions addressing issues such as climate adaptation and mitigation, circular resource management, clean energy development, social inclusion, and sustainable policy-making in diverse geographical contexts.

Through its strong commitment to bridging academic insights with practical solutions, CiS promotes rigorous research that supports evidence-based decision-making and informs sustainable development practices. The journal particularly values contributions that provide actionable models, evaluation frameworks, sustainability assessment tools, and policy-relevant strategies to enhance societal well-being and long-term ecological integrity.

Key features of CiS include:

  • A strong emphasis on sustainability research that integrates environmental, social, and economic dimensions;

  • Support for interdisciplinary approaches linking scientific knowledge, technological innovation, and governance mechanisms;

  • Encouragement of contributions that evaluate sustainability performance and inform policy and practical decision-making;

  • Promotion of insights that advance resilience, resource efficiency, social inclusion, and long-term ecological integrity;

  • A commitment to rigorous peer-review standards, research ethics, and responsible dissemination of open-access knowledge.

Scope

The scope of CiS encompasses a broad range of subjects, providing an in-depth and comprehensive investigation into issues related to sustainability:

  • Climate Resilience and Adaptation: Advanced research on strategies to enhance the resilience of communities, ecosystems, and economies to climate variability and change.

  • Circular Economy and Waste Reduction: Studies focusing on the principles of circular economy, waste management practices, and strategies for reducing waste generation across different sectors.

  • Renewable Energy Technologies and Systems: Innovative research on the development, integration, and optimization of renewable energy sources, including solar, wind, hydro, and bioenergy.

  • Sustainable Agriculture and Food Systems: Investigations into sustainable farming practices, food systems planning, and the role of agriculture in maintaining biodiversity and ecosystem services.

  • Water Resources Management: Comprehensive research on sustainable water use, watershed management, and strategies to address water scarcity and quality issues.

  • Sustainable Transportation and Mobility: Exploration of sustainable transportation solutions, including electric and alternative fuel vehicles, public transportation systems, and urban mobility planning.

  • Green Infrastructure and Sustainable Urban Planning: Studies on the design and implementation of green infrastructure, sustainable building technologies, and urban planning approaches that contribute to sustainable urban development.

  • Social Sustainability and Equity: Research on social aspects of sustainability, including social equity, community engagement, and the intersection of social justice with environmental sustainability.

  • Corporate Sustainability and Responsibility: Analysis of corporate practices in sustainability, including sustainability reporting, corporate social responsibility initiatives, and sustainable business models.

  • Technology for Sustainability: Examination of the role of technology in promoting sustainability, including information and communication technologies (ICT), artificial intelligence (AI), and big data analytics in environmental monitoring and sustainability assessments.

  • Environmental Policy and Governance: Evaluation of policy frameworks, governance mechanisms, and international agreements that facilitate sustainable development goals.

  • Sustainability Education and Literacy: Studies on the integration of sustainability into education systems, development of sustainability curricula, and promotion of environmental literacy.

  • Biodiversity Conservation and Ecosystem Services: Research on the conservation of biodiversity, restoration of ecosystems, and valuation of ecosystem services.

  • Health and Well-being in the Context of Sustainability: Explorations of the connections between environmental sustainability and public health, including studies on pollution, environmental justice, and access to green spaces.

Articles
Recent Articles
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Open Access
Research article
Nonlinear Effects of Agricultural Land and Value Added on Freshwater Withdrawals in Azerbaijan: An XGBoost–SHAP Analysis
anar eminov ,
ramil i. hasanov ,
jeyhun mahmudov ,
rodolfo m. nayga jr. ,
abdulhuseyn zamanov
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Available online: 04-30-2026

Abstract

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Effective management of freshwater resources in agriculture is essential for ensuring sustainable economic development and environmental resilience, particularly in transitional economies such as Azerbaijan. Over the period 2000–2021, agricultural land area in Azerbaijan exhibited a steady increase, while the sector’s contribution to GDP declined, indicating structural transformation and potential inefficiencies in resource utilization. This study investigates the nonlinear effects of agricultural land use and agricultural value added on freshwater withdrawals using an interpretable machine learning framework. Specifically, Extreme Gradient Boosting (XGBoost) is employed to model complex relationships, while Shapley Additive Explanations (SHAP) quantify feature importance and elucidate threshold and asymmetric effects. The analysis draws on annual country-level data integrating national and international statistics to ensure temporal consistency and comparability. Results indicate that agricultural land area constitutes the dominant driver of freshwater withdrawals, contributing 57% of the model’s predictive gain, whereas agricultural value added accounts for 43%. SHAP dependence plots reveal pronounced nonlinearities: moderate land expansion exacerbates freshwater stress, whereas allocations beyond a critical threshold mitigate pressure, reflecting potential efficiency gains at scale. Agricultural value added exhibits a U-shaped relationship, wherein both low and high productivity levels are associated with increased freshwater use, while intermediate productivity generates the greatest negative impact. The XGBoost model achieves substantial predictive performance (Coefficient of Determination (R²) = 0.78, Root Mean Squared Error (RMSE) = 0.806, Mean Absolute Percentage Error (MAPE) = 0.86%), demonstrating its capacity to capture heterogeneous, nonlinear dynamics that linear models fail to detect. The robustness of the model was further assessed using Leave-One-Out Cross-Validation (LOOCV) to evaluate its out-of-sample predictive performance and mitigate potential overfitting arising from the limited sample size. These findings underscore the necessity of adaptive water management strategies that incorporate scale-dependent effects and productivity heterogeneity. Policies optimizing land allocation and promoting efficient agricultural practices can enhance water-use efficiency while sustaining sectoral output. The study highlights the value of interpretable machine learning in advancing empirical understanding of the water–agriculture nexus under conditions of structural economic change.
Open Access
Research article
Spatiotemporal Dynamics of Urban Heat Vulnerability in Kushtia, Bangladesh (2010–2024) Using Environmental Indices and Population Data
md. rahedul islam ,
mahmuda hossain mou ,
tamanna yesmin ,
rabeya sultana ,
md. anik hossain
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Available online: 04-27-2026

Abstract

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Rapid urbanization and land-use transformation have intensified thermal stress in mid-sized cities of Bangladesh; however, spatially explicit environmental screening of heat-related risk remains limited. This study investigates the spatiotemporal dynamics of urban heat risk in Kushtia District from 2010 to 2024 using an environmentally weighted, indicator-based geospatial framework integrating remote sensing and demographic data. Multi-temporal Landsat (Thematic Mapper (TM); Operational Land Imager (OLI); OLI/Thermal Infrared (TIRS)) and WorldPop datasets were employed to derive five environmental indices: Land Surface Temperature (LST), Albedo, Urban Thermal Field Variance Index (UTFVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), along with Population Density as a proxy indicator of human concentration. A composite Heat Vulnerability Index (HVI) was developed using Principal Component Analysis (PCA) to integrate these environmental and demographic variables into a spatial heat-risk surface. Results indicate a substantial rise in LST (>5 °C), particularly across urban centers such as Kushtia Sadar and Khoksa, alongside a consistent decline in NDVI and NDWI, signifying degradation of green and blue spaces. Correlation analysis revealed strong negative relationships between NDVI–LST and NDWI–LST, underscoring the mitigating role of vegetation and surface moisture. PCA results confirmed that vegetation–moisture interactions dominate environmental variability, while demographic concentration exerts a secondary yet persistent influence. High and very high heat-risk zones expanded from 211.89 km² in 2010 to 424.42 km² in 2024, reflecting intensifying spatial thermal stress. The findings represent an environmentally weighted spatial screening of heat risk rather than a comprehensive socio-ecological vulnerability assessment. The study highlights priority areas for nature-based adaptation strategies, including urban greening, waterbody restoration, and reflective surface planning, to reduce localized heat exposure in rapidly urbanizing regions of Bangladesh.

Abstract

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This paper aims to examine the association of the specific governance indicators, which are the regulatory quality, the rule of law, and government effectiveness, on sustainable development in 60 countries around the world in 2024. This is explained by the key role that the state institutions and the institutional framework may play in enhancing economic, social, and environmental results and in achieving the Sustainable Development Goals (SDGs). The study employed the quantitative approach that is based on 2024 cross-sectional data. The data were obtained from the 2024 SDG Index and the World Bank's Worldwide Governance Indicators (WGI). The Eviews software was used to compute an Ordinary Least Squares (OLS) multiple linear regression model to examine the relationship between the independent variables (regulatory quality, the rule of law and government effectiveness) and the dependent variable (the SDG Index). The results reflected that the rule of law and the efficacy of the government have a positive and substantial effect on sustainable development, but the regulatory quality did not show a direct significant impact. This shows that sustainable development is based on the unity of the institutional framework which consolidates legal, regulatory, and administrative potential to achieve quantitative results.
Open Access
Research article
Do ESG Practices Matter for Investors? Corporate Image as a Transmission Mechanism
huong thi thu tran ,
hung manh pham ,
quyen le do ,
tuan duong tran ,
uyen thu viet pham ,
thu anh bui
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Available online: 04-18-2026

Abstract

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This study investigates how investors’ perceptions of environmental, social, and governance (ESG) factors influence equity investment decisions in Vietnam, emphasising the mediating role of corporate image and the moderating effect of investment horizon. Using survey data from 549 individual investors and analysing the model through partial least squares structural equation modelling (PLS-SEM), the findings indicate that environmental and governance dimensions significantly and positively affect investment decisions, while the social dimension exhibits a weaker direct effect. Corporate image plays a key mediating role, transmitting the impact of ESG practices to investor behaviour. However, investment horizon does not significantly moderate the relationship between ESG factors and investment decisions. The study contributes empirical evidence from an emerging market and highlights the importance of integrating ESG performance with corporate image management to attract sustainable equity investment.
Open Access
Research article
Measuring Regional Resilience to Disasters Using a Composite Index: A Case Study of West Java Province
asti istiqomah ,
akhmad fauzi ,
sri mulatsih ,
pini wijayanti ,
nuva
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Available online: 04-16-2026

Abstract

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West Java Province is being exposed to a high risk of natural disasters, especially hydrometeorological disasters such as floods and landslides, hence hindering potential economic growth. The increasing frequency of disasters has shed light on the issue of regional resilience, an important concern for public authorities. Therefore, efforts to assess and strengthen regional resilience are crucial to reducing disaster risks and supporting the achievement of sustainable development. However, up till recently there has been no practical and applicable methodology for resilience assessment, which has become more complicated at the regional level, taking into account the economic, social, ecological, infrastructural, and institutional dimensions. The present paper proposed a composite indicator-based approach to evaluate the level of regional resilience to disasters in West Java Province. To describe the current conditions of resilience in each regency/city in the province, this study adopted 17 indicators that were adjusted for measurement in the actual context. The composite index combined by the macro-regional indicators in five main dimensions were calculated using arithmetic, geometric, harmonic, entropy, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The integrated Regional Disaster Resilience Composite Index (RDRCI) scores across the 27 regencies/cities ranged from 6.33 to 33.98, with 9 regions recording values above the provincial mean of 15.26. The results of this analysis could be employed by policy-makers to evaluate the resilience of a region to natural disasters. Furthermore, the findings highlight the necessity of incorporating all dimensions into policy formulation to strengthen regional resilience to disasters.
Open Access
Research article
Predictive Landslide Risk Assessment Using Synthetic Data and Machine Learning
fathey mohammed ,
narishah mohamed salleh ,
aw kay rong ,
chan jun cong ,
kenneth haw mun ban ,
kua er shiun ,
teo chuan wei ,
syafiq ashraf ahmad khalid
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Available online: 04-16-2026

Abstract

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Landslides remain one of the most critical natural hazards, posing significant threats to infrastructure, the environment, and human life. Traditional approaches to landslide risk prediction, such as rainfall threshold models and image-based classification, often face limitations including data imbalance, low generalizability, and poor performance in capturing medium- to high-risk scenarios. This study introduces a predictive framework that integrates synthetic data generation with a multiple logistic regression model to improve landslide risk assessment in the Malaysian context. The model was trained on balanced datasets and evaluated through confusion matrices, performance metrics, and validation using unseen data across three distinct scenarios. Results demonstrate that a multiple‑logistic‑regression model trained on this balanced data achieved an overall accuracy of 0.73, precision of 0.73, recall of 0.73, and a Receiver Operating Characteristic-Area Under Curve (ROC‑AUC) of 0.80. In three validation scenarios using unseen data from 2015–2024 (three months before, during, and three months after known landslide events), the model correctly identified medium and high‑risk periods when other machine‑learning models defaulted to low‑risk predictions. The study highlights the trade‑off between accuracy and generalization in machine‑learning‑based early warning systems and underscores the importance of class‑balancing and rigorous validation for real‑world applicability. Our findings, therefore, demonstrate that the logistic‑regression model, when paired with synthetic data augmentation, can serve as a cost‑effective, interpretable pre‑screening tool for regional landslide risk assessment in Malaysia.

Abstract

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To respond to global climate change, promote climate governance, and develop a master plan for sustainable development, carbon neutrality has become a common goal and vision of both developed and developing countries. In view of this objective, the interaction among energy transition, energy intensity, economic growth, and financial development is considered an important tool to harmonize economic development and environmental governance. This study aims to investigate the impacts of these four variables on Vietnam’s carbon neutrality objective during the period of 1995–2022. The Johansen cointegration analysis and a Vector Error Correction Model (VECM) were employed to disentangle short- and long-run relationships among the variables; the results of these analyses revealed asymmetric temporal effects. Energy transition and economic growth were found to increase CO₂ emissions in both the short and long run, hence suggesting that expansion of renewable energy could not effectively substitute fossil fuels and that economic growth remains energy intensive. In contrast, energy intensity and financial development reduced CO₂ emissions in the short run but contributed to rising emissions in the long run. This indicates the presence of rebound effects and scale-driven financial expansion without green investment target. It was concluded that Vietnam should plan and implement appropriate low carbon-intensive policies to achieve its carbon neutrality objective in the years ahead.
Open Access
Review article
Graph Theory Approach to Automated Environmental Content Analysis: A Systematic Review on the Topic of Marine Debris
ritzkal ,
mohammad aftaf muhajir ,
sutriawan ,
zumhur alamin ,
fitrah satrya fajar kusumah ,
haikal
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Available online: 04-01-2026

Abstract

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Marine debris is one of the major environmental concerns in the 21st century, owing to its impact on the ocean ecosystems, the biodiversity of marine inhabitants, and human well-being. Through the utilization of automated content analysis (ACA) and graph theory in the context of a systematic literature review (SLR), the purpose of this investigation is to comprehensively map and assess the global research landscape concerning marine trash. Leximancer was used in this study to extract semantic links among important ideas, which were then displayed as directed acyclic graphs (DAG). The research used 357 Scopus-indexed papers that were published between 2017 and 2024. Core conceptual clusters relating to microplastics, plastics, and soil were identified through the ACA method. These clusters each reflected a different aspect of marine pollution that was interrelated with the others. The utilization of graph theory enabled the identification of structural links and core nodes that were shared by several themes. These connection points might be quantified by adjacency matrices and normalized grouping was accomplished by k-means analysis. According to the findings, phrases such as “waste”, “plastics”, and “marine” were the most prominent notions, and they served as the foundation for study on marine debris on a worldwide scale. These findings not only contribute to the advancement of automated environmental informatics but also highlight how graph-based content analysis may be used to identify hidden patterns in scientific knowledge. Taking into account both theoretical and methodological considerations, this study have implications for academics who use computational bibliometric analysis in the field of environmental science.

Abstract

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Despite advances, spatial resilience planning remains constrained in its integration of complex system principles to address slow-variable disturbances. This study provided a methodological test of a novel multi-faceted and network-based hybrid resilience assessment that examined rural shrinkage in paired regions of Germany (Lüneburg) and Türkiye (Trakya). The method integrated Specified Resilience Assessment (SRA) and General Resilience Assessment (GRA) under Socio-Ecological Systems (SES) and Complex Adaptive Systems (CAS) lenses and operated through five steps. SRA employed (i) a multi-faceted survey to identify prioritized factors, solutions, and institutional roles/success and (ii) Relational Network Analysis (RNA) to assess complex factors and leverage points; GRA computed (iii) Spatial Network Analysis (SNA) to identify physical connectivity as hubs and sub-clusters; (iv) correlation analysis to determine significant variables among socio-demographic, land-use, facility, and network variables, and (v) k-means clustering to map shrinkage urgency levels. The synthesized outputs generated two operational strategies: strengthening sub-centers and connecting shrinking settlements to these hubs. While the strategies of Germany focused on the needs of the elderly and innovative digital solutions (wd ≈ 28), examples of Türkiye emphasized ecological concerns and the support of cooperatives as a leverage (wd = 54). GRA highlighted weighted degree (up to r = 0.79) and urban-industrial land cover (r ≈ 0.6) as critical drivers of stability; meanwhile, distance to the center (r ≈ -0.55) significantly correlated with shrinkage. Despite limitations of sample size and manual network construction, the study operationalized SES/CAS concepts for slow variables and integrated both qualitative and quantitative insights. It advances resilience research in sustainable spatial development by demonstrating a proof-of-concept and transferable decision-support workflow, while scaling and automation point to the directions for future research.

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