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This study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).

Using the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.

The study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.

The novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.

In Bangladesh, the chemical industry is one of the expanding industries based on current statistical data analysis. Green supply chain management (GSCM) is pivotal in order to compete with the global competition. This paper main aim is to discuss a systematic approach to build a structural outline. The purpose of the proposed structural outline is to predict the constructive implementation of GSCM especially on chemical industry in Bangladesh.

This proposed structural framework evaluates the suitable interrelationship next to the barriers of GSCM in the Bangladesh's chemical industry. Here, on the basis of literature review and survey from expert opinions by the use of the Delphi methodology in total eight barriers were concluded. Here additionally, MICMAC analyses were applied to determine the driving and dependence power. Furthermore, the frameworks outline for the barriers were included by means of total interpretive structural modeling (TISM) method.

Based on the analysis, the most significant barriers were found lack of supporting laws and guidance from the government and cost of disposal of hazardous products.

The TISM technique only has implemented to develop the framework, whereas other tools or structural equation modeling (SEM) technique can be used to develop and validate the frameworks for barriers.

In this research, Delphi method questionnaire generated based on the GSCM in the Bangladesh chemical sector. This study will assist the industrial managers to assess and evaluate the crucial sectors, whereas they should give priority to apply the GSCM in the Bangladesh chemical industry.

The recent outbreak of the coronavirus disease 2019 (COVID-19) pandemic has severely disrupted the operations of global supply chains (SCs) providing an opportunity for decision-makers to rethink and tune their existing strategies. To tackle the impacts caused by such a pandemic, this study aims to examine the contextual relations among the factors influencing supply chain viability (SCV) for achieving long-term Sustainable Development Goals (SDGs).

A decision-aid approach by integrating Pareto analysis, grey theory and total interpretive structural modeling (TISM) has been propounded. The proposed approach examines contextual relations among the factors for SCV for small and medium-sized enterprises (SMEs) in an emerging economy. This study contributes to theory, methodology and practice by exploring a new research problem in the context of the SCV and COVID-19 pandemic and by developing a new decision-aid approach.

The findings reveal that the creation of SC digital twin and transformation of SCs to supply networks would help the policymakers to deal with the “new normal.” Also, SC crowdfunding and policy development for health protocols are critical driving factors influencing SCV.

This research work is perhaps one of few initial attempts to advance the theoretical and practical understanding of SCV and to achieve SDGs in industries following a pandemic risk.

Due to extensive industrial activities, the manufacturing sector is deteriorating the environment through resource depletion and rising pollution levels which led to a significant transition toward green supply chain practices (GSCP). Therefore, internal and external GSCP and green training (GT) gaining momentous attention. This study aims to explore the interconnections among the internal and external GSCP, GT, green innovation (GI), pro-environmental behavior (PEB), competitive advantage (CA), green knowledge sharing (GKS), green self-efficacy (GSE), environmental and financial performance (EP) and (FP).

To check the hypothesized model, researchers used cross-sectional data based on survey questionnaires which were gathered from Pakistani manufacturing firms. The theoretical framework was validated through the utilization of partial least square structural equation modeling.

The findings suggest that internal and external GSCP and GT are correlated with GI, PEB, CA, EP and FP. Additionally, this study discovers that PEB and GI act as intermediaries among internal and external GSCP, GT and CA. GKS positively moderates the connection among internal and external GSCP, and GT, GI, and PEB. Similarly, GSE also serves as a moderator among between PEB and GI.

This study is a significant contribution to the literature by studying potential mediators and moderators that improve the association among outlined constructs. Moreover, findings suggest that firms should adopt an integrated and holistic green approach to combat environmental deterioration, maintain environmental integrity and attain sustainable development.

There is a scarcity of studies concerning the holistic framework of interrelated constructs studied in this research and it is the pioneer research to offer insights with an innovative model and empirical evidence.

Drawing on the resource-based view, ecological modernization theory and upper-echelon theory, this study aims to explore how green manufacturing practices (GMGP) affect the sustainable performance of small and medium enterprises (SMEs).

It also examines the mediating role of green innovation (GIN) and the moderating role of managerial discretion (MD). To test the hypothesized model, the data was collected from 394 manufacturing SMEs though survey and analyzed using SPSS and AMOS (SEM).

The results provide evidence for the positive relationship between GMGP and corporate sustainable performance (CSPR), mediated by GIN. Furthermore, the presence of MD enhances the positive effect of GMGP on CSPR through GIN. Thus, this study enhances the understanding of the relationship between GMGP and CSPR, including its underlying mechanism and conditional effects.

This study collected sample from SMEs located in Punjab province of Pakistan which represents majority of the SMEs; however, future research can take data from other province and with large sample size.

The findings highlight the significance of GMGP and GIN in the manufacturing sector for attaining CSPR goals. GMGP guides business leaders to be more inclined toward the energy and resource consumption as well as waste generation within their organizations which lead to improved CSPR outcomes.

The findings of this study make significant contributions to the existing literature, shedding light on the dynamics between GMGP and CSPR. Moreover, the study offers managerial implications for organizations aiming to enhance their sustainable performance by implementing effective GMGP.

Big data analytics capabilities are the driving force and deemed as an operational excellence approach to improving the green supply chain performance in the post COVID-19 situation. Motivated by the COVID-19 epidemic and the problems it poses to the supply chain's long-term viability, this study used dynamic capabilities theory as a foundation to assess the imperative role of big data analytics capabilities (management, talent and technological) toward green supply chain performance.

This study was quantitative and cross-sectional. Data were collected from 374 executives through a survey questionnaire method by applying an appropriate random sampling technique. The authors employed PLS-SEM to analyze the data.

The findings revealed that big data analytics capabilities play a significant role in boosting up sustainable supply chain performance. It was found that big data analytics capabilities significantly contributed to supply chain risk management and innovative green product development that ultimately enhanced innovation and learning performance. Moreover, innovation and green learning performance has a significant and positive relationship with sustainable supply chain performance. In the post COVID-19 situation, organizations can enhance their sustainable supply chain performance by giving extra attention to big data analytics capabilities and supply chain risk and innovativeness.

The paper specifically emphasizes on the factors that result in the sustainability in supply chain integrated with the big data analytics. Additionally, it offers the boundary condition for gaining the sustainable supply chain management.

This study examines the relationships between collaboration capability (CC) and supply chain risk management (SCRM) and the mediating role of supply chain alignment (SCA) between CC and SCRM. It also investigates the moderating role of data-driven culture (DDC) on the path joining CC and SCA.

A survey was conducted via a structured questionnaire among the 297 managers of Business-to-business (B2B) firms. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).

The result found a positive and significant relationship between CC and SCRM, CC and SCA, and SCA and SCRM. The research found a significant mediating role of SCA between CC and SCRM. The study also found a significant moderating role of DBC between CC and SCA.

The outcomes demonstrate the management and societal relevance of the study from the viewpoint of businesses in developing nations. To cope with dynamic shifts, managers and decision makers need to take initiative for collaboration among the supplier, to align with their supply chain operations and enhance preparedness of DDC to overcome supply chain-related risks in the future.

The results of this empirical study have the significant potential to provide valuable guidance and insights about the B2B firms’ CC to develop SCA to enhance SCRM as risk management for the supply chain can aid in loss prevention and provide an edge over competitors. To the best of the authors’ knowledge, these relationships based on the dynamic capability view (DCV) add to existing studies on B2B firms’ supply chains in a novel way.

The recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers.

To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers.

The findings reveal that the major barriers are in a four-level hierarchical relationship where “Insufficient SC strategic plans to ensure agility during crisis” acts as the most critical barrier, followed by “Poor information structure among SC contributors,” and “Inadequate risk management policy under pandemic.” Finally, the MICMAC analysis validates the findings from the ISM approach.

This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.

Solid waste (SW) is the result of rapid urbanization and industrialization, and is increasing day by day by the increasing number of population. This thesis paper emphasizes on the prediction of SW generation in the city of Dhaka and finding sustainable pathways for minimizing the gaps in the existing system.

In this paper, the survey of different questionnaires of the Dhaka South City Corporation (DSCC) was conducted. The data of SW generation, for few years of each month, in the city of Dhaka were collected to develop a model named Artificial Neural Network (ANN). The ANN model was used for the accurate prediction of SW generation.

At first, by using the ANN model with the one hidden layer and changing the number of neurons of the layer different models were created and tested. Finally, according to R values (training, test, all) the structure with six neurons in the hidden layer was selected as the suitable model. Finally, six gaps were found in the existing system of solid waste management (SWM) in the DSCC. These gaps are the main barrier for the better SWM.

The authors propose that the best model for prediction is 12-6-3, and its training and testing results are given as 0.9972 and 0.80380, respectively. So the resulting prediction is so much close in comparison with actual data. In this paper, the opportunities of those gaps are provided for working properly and the DSCC will find the better result in the aspect of SW problem.

This study provides an integrative model that facilitates green product innovation (GPI) by adopting dynamic capabilities theory and the motivation-opportunity-ability (MOA) framework. Drawing on dynamic capabilities theory, this study emphasizes green supply chain integration (GSCI), consisting of internal and external integration, as a dynamic capability that drives GPI. Moreover, this study analyzes the environmental conditions that benefit the development of dynamic capabilities using the MOA framework and focuses specifically on government support (GS) and market greenness (MG) as precursors to GSCI.

Survey data were collected from 300 Chinese manufacturing firms. The proposed hypotheses were tested using hierarchical multiple regression analysis.

The regression analysis reveals that (1) GS and MG positively affect both internal and external integration and (2) internal and external integration positively affect GPI.

In explicating a model of GPI, this study extends the theoretical lens of dynamic capabilities beyond the intraorganizational level to the supply chain level. Moreover, this study enhances the understanding of dynamic capability development by considering the environmental conditions that represent motivation- and opportunity-based drivers of GSCI.