Comparative analysis between AHP and ANP in prioritization of ecosystem services - A case study in a rice field area raised in the Guadalquivir marshes (Spain)

Assessing habitat selection parameters of Arabica coffee using BWM and BCM methods based on GIS

Arabica coffee, as one of the world's three native coffee species, requires rational planning for its growing areas to ensure ecological and sustainable agricultural development. This study aims to establish a decision-making framework using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM), with a focus on assessing the habitat suitability of Arabica coffee in Yunnan Province, China. The impacts of climate, topography, soil, and socio-economic factors were considered by selecting 13 criteria through correlation analysis. Indicator weights were determined using the Best-Worst Method (BWM), while weighted processing was conducted using the Base-Criterion Method (BCM). Sensitivity analysis was performed to verify the accuracy and stability of the model. Additionally, several decision models were evaluated to investigate regionalizing Arabica coffee habitats in Yunnan. The results highlighted that minimum temperature during the coldest month is crucial for evaluation purposes. The BWM-GIS model identified suitable areas comprising 13.55% of the total area as most suitable, 27.46% as suitable, and 59.00% as unsuitable, whereas corresponding values for the BCM-GIS model were 9.97%, 30.43%, and 59.59%. Despite employing different decision-making methods, both models yielded similar and consistent results. The suitable areas mainly encompass Dehong, Pu'er, Lincang, Xishuangbanna, Baoshan, southern Chuxiong, eastern Honghe, southern Yuxi, and parts of Wenshan. BWM-GIS achieved an area under curve (AUC) value of 0.891, while BCM-GIS obtained an AUC value of 0.890, indicating the stability and reliability of the models. Among them, the evaluation process of BCM-GIS was simpler and more realistic. Therefore, it has high feasibility and practical value in practical application. The findings from this study provide a significant scientific foundation for optimizing Yunnan Province.

Sustainability in maritime transport: Selecting ballast water treatment for a bulk carrier

The study provides a comprehensive assessment of ballast water treatment systems (BWTS) selection regarding crucial parameters such as energy efficiency, fuel consumption, and CO2 emissions. The focus of the study is investigating the environmental impacts of BWTS and how these impacts can be considered in decision-making processes. In this context, it comprehensively analyzes the importance of decision-making parameters and the environmental consequences of BWTS applications. The potential impacts of the system on the sustainability of the maritime industry are highlighted. In this context, seven different BWTSs for a bulk carrier of 83,000 deadweight tonnes are evaluated using Analytical Hierarchy Process (AHP) and Preference Ranking Technique by Similarity to Ideal Solution (TOPSIS). According to the analysis, Operational Expenditure (0.314508), Capital Expenditure (0.249515), and Capacity (0.159952) are the most critical factors. Among the seven systems analysed, product G (0.8561137) emerges as the most suitable option. The analysis represents 3% of total emissions (456.5 tonnes CO2) and highlights the impact of BWTS on CO2 emissions. The results underline the need for innovative approaches aimed at ensuring the long-term sustainability of the maritime industry.

Characterizing landscape fragmentation of Koitobos river sub-basin, Trans-Nzoia, Kenya

The changes in landscape structure and functioning due to unprecedented human interference are hastening across the globe and it is thus a compelling necessity to preserve and restore our ecosystems. This study aimed to characterize levels of landscape fragmentation, habitat structure, driving forces, and perceptions of the residents on the most preferred reconfiguration approaches. The land use/land cover [LULC] change was first determined by interpreting the 1973, 1986, 1995, 2002, 2014, and 2022 Landsat images using the QGIS 3.26 while the selected landscape fragmentation metrics were analyzed using FRAGSTATS 4.2. Forests, shrubs, and grasslands showed a declining trend, except for agriculture, water, and built-up areas, which depicted high increases for the study periods [1973 to 2022]. The landscape of the study area is characterized as progressively fragmenting as signified by high escalated values of patch number [374 %], edge density [7828 %] between 1986 and 2002, contagion [10.3%], and a declined value of Shannon Diversity Index [SHDI] [-17.42%], Shannon evenness index [SHEI] [-25.8 %] and connectedness [-43.3%]. Considering these results, high losses of forests and grasslands coupled with expansive farmlands and built-up areas have led to unprecedented landscape fragmentation From field surveys and oral interviews, this has not only left streams vulnerable to massive sediment loads but has also triggered annual floods which occur during wet months even though change in onset of rainfall seasons was also reported. The findings call for restoration and integrated and sustainable restoration efforts, especially for the forests, grasslands, and riparian corridors along with sustainable urban planning and community-based sensitization on watershed management.

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs' potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are "Internet of things (IoT)", "Cloud manufacturing", and "Artificial intelligence (AI)". The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

How geographical factors and decision-makers' perceptions influence the prioritization of ecosystem services: Analysis in the Spanish rice field areas in RAMSAR Mediterranean wetlands

Mediterranean wetlands provide many ecosystem services to humans and other organisms. However, these services are being increasingly damaged. The prioritization of ecosystem services is essential to start a decision-making process focused on environmental policies, highlighting the necessity of equilibrium between sustainability and human well-being. This study analyzes the similarities and differences among the ecosystem services provided by the Spanish RAMSAR Mediterranean wetlands, where rice production is the main economic activity. These areas are the Ebro Delta, the Albufera Natural Park and the Guadalquivir Marshes (Doñana Biosphere Reserve). Despite being different areas, environmental and agricultural policies sometimes treat their characteristics without distinction since they are conceptually englobed in the same category. This analysis aims to study whether geographical and sociocultural factors could influence the prioritization of ecosystem services. The prioritization of the three study areas was conducted using the Analytic Network Process (ANP), a multi-criteria decision-making method which allows decision-makers to manage the ecosystem's complexity. The results are helpful for future policies and in understanding the complex network of interconnections among ecosystem services. Additionally, results show that there are statistically significant differences in priorities among the three study areas due to geographical and cultural reasons. Moreover, results have also shown that decision-makers' judgements influenced the priorities depending on their background and personal or professional preferences. It emphasizes the necessity of implementing environmental policies from a theoretical and global scale to a participatory and local one, considering a broader range of stakeholders' perceptions to reflect the complexity of the ecosystem services network.