International Journal of Environment and Sustainable Development (15 papers in press)

Special Issue on: OA Behavioural Economics of Climate Change An Interdisciplinary Approach

• Open Access

  Analysis of environmental product/service marketing performance of e-commerce live streaming platform based on artificial intelligence and big data management ( Free Full-text Access )

  by Yuanyuan Wei, Xingchen Pan 
  Abstract: With the continuous expansion of the e-commerce live streaming industry, environmentally friendly products and services have gradually become an important component in platform marketing competition, consumption guidance, and sustainable development practices. The study constructs an analysis framework consisting of user environmental preference analysis, environmental product/service attribute evaluation, and green market trend prediction, and optimises key parameters during model training. The results show that reasonable parameter configuration can improve the generalisation ability of the model and enhance the matching effect between users and environmentally friendly products and services. Artificial Intelligence (AI) and big data management can effectively support e-commerce live streaming platforms in improving the marketing performance of environmentally friendly products and services, and also provide a reference for platform operations under the guidance of green consumption.
  Keywords: artificial intelligence; big data management; BDM; environmentally friendly products/services; e-commerce live broadcast platform; green marketing performance.
  DOI: 10.1504/IJESD.2026.10079046
   
  

• Open Access

  Intelligent optimisation of financial decision-making for environmental initiatives and credit assessment framework under graph neural network ( Free Full-text Access )

  by Zhikun Huang 
  Abstract: This study proposes a credit risk assessment framework that integrates graph neural networks (GNN) to enhance prediction accuracy and interpretability by analysing associations between enterprises and the significance of multi-source features. A dual-layer attention mechanism is employed to weigh the importance of nodes and relationship types. Experiments utilise public financial credit datasets, constructing feature similarity edges and business association edges to simulate risk transmission networks. Results demonstrate that the models accuracy, F1-score, and area under the curve (AUC) on training, validation, and test sets significantly outperform benchmark GNN models, including GNN, graph convolutional network (GCN), and graph attention network (GAT). This study provides a high-performance and interpretable credit assessment method and establishes a foundation for subsequent integration of fairness constraints and decision optimisation, serving as a valuable reference for advancing responsible financial artificial intelligence.
  Keywords: graph neural network; GNN; credit assessment; financial decision-making; attention mechanism; financial risk management.
  DOI: 10.1504/IJESD.2026.10079466
   
  

• Open Access

  The impact of fiscal investment in environment science and technology on the development of cross-border logistics in digital economy ( Free Full-text Access )

  by Jiejie Cui, Zixuan Hu, Fanshu Wang 
  Abstract: The digital economy has become a core driver for the transformation and sustainable development of cross-border logistics. This study explores the impacts of fiscal science and technology investment on the operational performance of cross-border logistics from the perspective of industrial sustainability. Combining qualitative theoretical analysis and quantitative empirical methods, this research takes logistics efficiency, operational cost, delivery time and customer satisfaction as core evaluation indicators. A variety of datasets are collected and analysed via professional statistical tools. The results show that the digital economy significantly optimises the overall operation of cross-border logistics, with all key indicators improved by over 15%. This paper further elaborates the influencing mechanisms of digitalisation and public investment on the logistics industry. This research provides empirical evidence and decision references for governments and enterprises to formulate industrial development strategies in the digital era.
  Keywords: digital economy; fiscal science and technology investment; cross-border logistics; logistics performance; industrial sustainability; digital transformation.
  DOI: 10.1504/IJESD.2026.10080025
   
  

• Open Access

  Hotel interns’ career choice intentions from a high-versus-low climate changing region background ( Free Full-text Access )

  by Feifei Yu, Lu Liu, Zhongming Zuo 
  Abstract: The hotel industrys reliance on a stable labour force is threatened by low career choice intentions among graduates, exacerbated by occupational stigmatisation. This study situates the talent challenges of the hotel industry within Chinas high versus low climate changing region context, where occupational prestige biases linked to the high low climate change dichotomy compound preexisting stigma, particularly undermining the professional identity of graduates from high climate changing regions a critical talent pool. Analysis of data from 314 Chinese hotel interns using structural equation modelling revealed that perceived occupational stigma negatively affects both vocational identity and career choice intention. These findings contribute to understanding how future talent perceives and responds to occupational stigma, offering theoretical and practical insights for hospitality education and talent retention, particularly within evolving labour dynamics between high and low climate changing regions.
  Keywords: occupational stigma; vocational identity; vocational skill; career choice intention; climate changing regions.
  DOI: 10.1504/IJESD.2026.10080026
   
  

• Open Access

  Ecological protection for prediction of freight volume of Pinglu Canal downstream based on integration of deep learning and internet of things ( Free Full-text Access )

  by Tongzhi Lin 
  Abstract: Pinglu Canal, as the key project of the new land-sea passage in the west, the forecast of freight volume after its navigation is of great significance to regional economic development and ecological protection. Traditional forecasting models are often difficult to capture complex nonlinear time series characteristics and lack dynamic response to real-time environmental data. In this study, a multi-modal prediction framework integrating deep learning and internet of thing (IoT) perception is proposed. By deploying environment-aware IoT devices at key nodes of the canal, hydrological, meteorological and ship automatic identification system (AIS) data are collected in real time, and they are introduced into the prediction model as auxiliary variables. A hybrid deep learning model based on temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU)-attention is studied and constructed.
  Keywords: Pinglu Canal; freight volume forecast; deep learning; internet of things perception; ecological constraints; TCN-BiGRU-attention.
  DOI: 10.1504/IJESD.2026.10080087
   
  

Special Issue on: OA Green Supply Chain Management Innovations in Sustainable Business Environment and Digital Transformation

• Open Access

  Virtual preassembly of transmission tower based on 3D laser scanning technology ( Free Full-text Access )

  by Honglin Yan, Xuan Qi, Yinying Liu, Guangning Lv, Xiao Tu, Biying Pei 
  Abstract: This paper proposes a novel method for the virtual preassembly of transmission tower members. First, a laser point cloud model of the tower is acquired using both the base station scanning method and handheld scanners. Using data fusion technology, a comprehensive point cloud model of the entire tower, including its local features, is generated. This approach enables the rapid acquisition of the towers point cloud model while ensuring high measurement accuracy. This method provides a crucial digital solution for enhancing quality control in the manufacturing of transmission towers, effectively addressing the industrys challenges of low efficiency, high cost, and safety risks associated with traditional physical preassembly. Next, a point cloud data processing algorithm is developed, and a virtual preassembly algorithm based on point cloud data is proposed. The algorithm includes point cloud registration, geometric feature extraction, and assembly measurement calculation. It supports intelligent dimension inspection, automatic member assembly, and error calculation during assembly. Applied to a real-world project, the experimental results validate the methods effectiveness.
  Keywords: transmission tower member; three-position laser scanning technology; point cloud data algorithm; feature extraction; finite element analysis; virtual preassembly technology.
  DOI: 10.1504/IJESD.2026.10079045
   
  

Regular Issues

• Prediction of domestic waste removal volume using GM (1, N, r, ) model: a case study of Shanghai City  
  by Afi Laeticia Awaga, Yan Yan, Hongyao Zhang, Wei Xu, Lu Zhang 
  Abstract: This paper aims to address domestic waste generation issues in urban planning by enhancing the accuracy of domestic waste removal forecasts through the development of an optimised grey prediction model. Focusing on optimising the cumulative order and background coefficient value of the GM (1, N) model, the analysis of Shanghais domestic waste data reveals that the optimised model achieves a markedly reduced mean absolute percentage error (MAPE) of 8.1%, mean absolute error (MAE) of 59.89, and the root mean squared error (RMSE) of 78.00, conversely to the unoptimised model (10.09%, 73.01, and 93.29, respectively) and the partially optimised model (9.49%, 70.16, and 94.36, respectively). These results validate the efficiency of the parameter optimisation method, and the model predicts that Shanghais domestic waste removal volume will reach 11.666 million tons by 2030, offering essential insights for sustainable urban waste management.
  Keywords: domestic waste removal; waste forecasting; parameter optimisation; grey models; GM(1; N; r; ξ) model; urban waste management; Shanghai.
  DOI: 10.1504/IJESD.2026.10077100
   
  
• Sustainable solid waste management in smart cities: expanding services to indigenous communities in Brazil  
  by Hendrigo Venes, Rodrigo De Alvarenga Rosa, Renato Ribeiro Siman 
  Abstract: Rapid global urbanisation and population growth (projected to exceed nine billion by 2050) have intensified the challenges associated with solid waste management (SWM). In Brazil, as in many developing nations, municipalities assume the responsibility of effective SWM, often spending up to 70% of their budgets on collection and transport. While algorithmic optimisation can significantly cut costs and emissions in urban centres, it often overlooks marginalised groups. For instance, the municipal sanitation plan in Aracruz (Espirito Santo) currently excludes indigenous communities from recyclable waste collection. This research proposes a computational approach to design a reverse logistics framework for recyclable waste, using Aracruz as a case study. Using an algorithm based on simulated annealing (SA) metaheuristic for route planning, we demonstrate the feasibility of expanding collection coverage to the entire population, including these previously excluded communities, without increasing operational costs. The findings suggest that optimising SWM in smart cities can go beyond efficiency as it can drive social inclusion and support sustainable development goals. Future research should focus on refining these models to boost efficiency further, integrating new technologies, and evaluating the broader social impacts on urban, rural and indigenous.
  Keywords: municipal solid waste management; MSWM; smart cities; route planning; indigenous communities.
  DOI: 10.1504/IJESD.2026.10077988
   
  
• The impact of economic complexity and institutional quality on environmental performance: evidence from the Central and Eastern European countries (2006-2022)  
  by Abeer Adel Zaki Abd El Menaam 
  Abstract: Institutional quality acts as a cornerstone in shaping the relation between economic complexity and environmental performance; as it determines how a nation can translate its sophisticated products into environmental friendly outcomes. While existing research often presents conflicting views on this relationship, this study suggests that these mixed findings may stem from variations in institutional quality. To explore this, the research employed two models: first, a pooled OLS model, refined with GLS for 83 developed and developing countries, and second, dynamic fixed effects estimators using an autoregressive distributed lag (ARDL) model for 12 Central and Eastern European countries. This allowed for examination of economic complexity and environmental performance in both the short and long terms during the period (2006-2022). The findings indicate that economic complexity positively impacts environmental performance, particularly when coupled with robust institutions, confirming that influence of economic complexity on the environment is moderated by institutions.
  Keywords: economic complexity; environmental performance; institutions.
  DOI: 10.1504/IJESD.2026.10079664
   
  
• Forecasting Turkiyes progress towards the sustainable development goals (20232030): a time series analysis approach  
  by İsmet Söylemez, Mehmet Eren Nalici, Ramazan Ünlü 
  Abstract: This study forecasts Turkiyes progress toward the United Nations Sustainable Development Goals (SDGs) from 2023 to 2030. Employing multiple time series models - exponential smoothing, Holts linear trend, additive damped trend, ARIMA, and moving average - this research projects trajectories across all 17 SDG indices. The findings indicate a mixed outlook: significant advancements are anticipated in poverty reduction (Goal 1), health (Goal 3), and education (Goal 4). Conversely, progress is stagnating or regressing in zero hunger (Goal 2), gender equality (Goal 5), clean water (Goal 6), reduced inequalities (Goal 10), and sustainable cities (Goal 11). These results underscore the urgent need for targeted policy interventions and strategic adaptations to address deficient areas. Ultimately, utilising robust forecasting techniques provides a vital framework for continuous monitoring to ensure comprehensive sustainable development.
  Keywords: sustainable development goals; SDGs; sustainability; time series forecasting; policy interventions.
  DOI: 10.1504/IJESD.2026.10079842
   
  
• Carbon emission trading and green innovation: evidence from radical and incremental perspectives  
  by Mingqi Zhu, Qiong Yao, Mubin Chen 
  Abstract: This study examines how Chinas carbon emission trading pilot policy influences firms green innovation under the dual-carbon goals. Using panel data of listed firms from 2011 to 2016, with propensity score matching and a DID approach, it investigates: 1) the effects on radical and incremental green innovation; 2) the moderating roles of institutional and technological factors. Results show that the policy significantly promotes incremental but not radical green innovation. Environmental regulation intensity negatively moderates both relationships, government support strengthens the effect on incremental innovation, and technological complexity enhances the effect on radical innovation. By distinguishing types of green innovation and incorporating institutional and technological perspectives, this study enriches the Porter hypothesis in the Chinese context and provides policy implications for refining carbon trading schemes and encouraging firms active participation in sustainable innovation.
  Keywords: carbon emission trading; Porter hypothesis; incremental green innovation; radical green innovation.
  DOI: 10.1504/IJESD.2026.10079844
   
  

Special Issue on: Joint Implications of Circular Economy and Digital Transformation for Resilient Business Ecosystems

• Redefining EU competitiveness: unlocking the power of digitalisation, institutional quality, environmental factors and human capital in shaping a sustainable future  
  by Bernard Vaníček, Tomáš Fišera, Jan Stejskal 
  Abstract: In todays turbulent times, understanding sustainable competitiveness is crucial. This study fills important gaps in existing research by examining the integrated relationship between digitalisation, environmental factors, institutional quality and human capital in 24 EU countries, and using stepwise regression to identify significant factors influencing the Global Sustainable Competitiveness Index (GSCI). This study contributes by developing a holistic framework that integrates drivers of sustainable competitiveness, addressing the limitations of prior research that examined these factors in isolation. Practically, it offers policy recommendations tailored to different EU country groups based on their sustainability and economic performance. Findings reveal that government effectiveness negatively impacts low-GSCI-growth countries (Bulgaria, Croatia, Poland and Romania), while environmental factors, particularly circular economy material flows, drive high-GSCI-growth nations (France, Italy, Netherlands, Portugal and Spain). These insights emphasise the need for targeted policies, including digital infrastructure improvements, circular economy support, and institutional reforms, particularly for lower-performing countries, to enhance sustainable competitiveness.
  Keywords: sustainable competitiveness; digitalisation; institutional quality; environmental factors; human capital.
  DOI: 10.1504/IJESD.2025.10072648
   
  
• The impact of institutional quality on environmental sustainability: evidence from ASEAN countries  
  by Lien Do Thi Hoa, Phuong Hoang Vo Hang 
  Abstract: The article evaluates the influence of national institutional quality, as represented by the Worldwide Governance Indicators (WGI), on the load capacity factor (LCF), which serves as an indicator of environmental sustainability. Employing panel data regression methodologies, the study analyses data from nine developing ASEAN countries (Cambodia, Brunei Darussalam, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Thailand and Vietnam) spanning the years 2002 to 2021. The PCA method was utilised to synthesise multiple representative factors of institutional quality into a single institutional quality index. The research data was analysed using pooled OLS, FEM, REM and FGLS techniques. The findings demonstrate significant relationships among the variables under investigation. Notably, the variable representing institutional quality exerts a negative and statistically significant effect on environmental sustainability within the ASEAN region. Based on these results, the article articulates various policy implications aimed at enhancing institutional quality across multiple dimensions, thus striving to improve environmental outcomes in the context of economic growth and foreign direct investment within developing ASEAN countries.
  Keywords: institutional quality; environmental sustainability; ASEAN.
  DOI: 10.1504/IJESD.2026.10077678
   
  
• Philippines transition to carbon neutrality: a review of emission trends, nature-based climate solutions, low-carbon policies, and financing mechanisms  
  by Monaliza Joy Zaragoza-Magsayo, Hernando P. Bacosa 
  Abstract: This review paper integrates various themes to examine the Philippines pathway to a carbon-neutral future. Energy, agriculture, and transportation are the primary contributors to greenhouse gas (GHG) emissions due to continued reliance on fossil fuels. The countrys nature-based climate solutions (NbCS), must be scaled-up, particularly mangrove forests, that centres on community-based forest management (CBFM). Sustained investment, enhanced institutional support, and broader implementation of these ecosystem-based solutions help maximise their potential. The low-carbon technologies and policy frameworks face recurring issues, including fossil fuel dependence, high investment costs, fragmented policies, and reliance on international funding. To advance carbon financing mechanisms, coordinated policies, simplified bureaucratic procedures, multiple funding sources, and investments in advanced technologies are imperative. The findings are relevant to policy discussions on how the Philippines can advance sector-wide reduction emissions including the integration of NbCs into the countrys national action plan, strengthening policies, and financing mechanisms for decarbonisation and climate targets.
  Keywords: carbon neutral; emission; nature-based climate solutions; low-carbon; carbon financing mechanisms.
  DOI: 10.1504/IJESD.2026.10078267
   
  
• Sustainability in the family business in the context of industry, ecology and succession  
  by Mariana Sedliačiková, Jiří Strouhal, Martin Halász, Denisa Malá, Hussam Musa 
  Abstract: Family businesses play a crucial role in the global economy, serving as a cornerstone for economic stability and growth. Long-term orientation and adaptability highlight relevance in addressing global challenges. Recent EU and Slovak government initiatives underline the importance of supporting family businesses to enhance sustainability and competitiveness. The main goal of the research was to identify the sustainability of family business in Slovakia from the point of view of three aspects: sustainability in industry, ecological sustainability and succession. The methodological tool was a questionnaire survey. Established hypotheses were tested using the Test of the relative abundance and Interval estimation of the relative abundance. The findings indicate that Slovak family firms recognize the strategic imperative of sustainability, but implementation strategies remain nascent, restricted mainly to basic waste management rather than holistic practices. A significant proportion lack eco-certifications, highlighting potential for ecological advancements and effective succession solutions.
  Keywords: sustainability; family business; ecology; industry; succession.
  DOI: 10.1504/IJESD.2026.10080086