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Soil erosion induced by physical and anthropogenic activities needs serious concern to deal with. In recent days, watersheds have been facing acute soil loss because of deforestation, poor land use management and unscientific agricultural practices. The present paper highlights the goal to decipher soil erosion-susceptible zones in a rain-fed river basin of India having a complex topography comprising the extension of Chotonagpur plateau and parts of Bengal basin. Fuzzy logic algorithm-based analytical hierarchy process (FAHP) with remote sensing and geographical information system (GIS) were used to execute the objective. The soil erosion-susceptible zone (SESZ) was identified assimilating the 14 geo-environmental, hydro-meteorological and anthropogenic conditioning factors such as slope, drainage density, relative relief, stream power index, topographic wetness index, runoff, soil texture, land use and land cover, rainfall erosivity factor, distance from road, distance from settlement, distance from river, geomorphology and soil bareness index based on which sub-basin prioritization for sustainable soil conservation was made. The soil erosion-susceptible zone was classified into five classes, namely very low soil erosion zone (13.20%), low soil erosion zone (25.75%), moderate soil erosion zone (27.62%), high soil erosion zone (21.90%) and very high soil erosion zone (11.53%). Receiver operating characteristics (ROC) curve was generated where area under the curve (AUC) came as 0.82 and a correspondence analysis between the SESZ and the first three ranked factors was also made which together proves the accuracy of the model. [ABSTRACT FROM AUTHOR]

In this paper, various energy sources for sustainable development of the energy sector in India are evaluated from multiple sustainability factors. A fuzzy integrated analytical hierarchy process (AHP) -- weighted aggregated sum product assessment (WASPAS) multi-criteria decision making (MCDM) method has been developed for the evaluation and assessment of these sources. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy options are used as alternatives in the present study. Initially, the fuzzy AHP method is applied to determine the weights of decision criteria, and fuzzy WASPAS is applied to prioritize sustainable energy (SE) alternatives. Subsequently, the output of the proposed model is validated by a two-step validation process: (i) fuzzy AHP integrated MCDM approaches, and (ii) Best-Worst Method (BWM) integrated fuzzy WASPAS approach. The developed decision support model will help the decision- makers to acquire sufficient confidence in evaluating energy sources for sustainable development of the energy sector in India. [ABSTRACT FROM AUTHOR]

Developed countries have already implemented a vehicle scrap policy that prevents the on-road usage of old vehicles beyond a certain age to curtail pollution. Recently Indian government also announced its plans and policies for vehicle scraping. There is a multitude of barriers to the implementation of such a policy in India. This paper identifies and models all implementation barriers. Literature review and interactions with various stakeholders helped to identify the barriers. The identified barriers are processed with a hybrid methodology, which is a combination of the Fuzzy Analytic Hierarchy process and the Decision-Making Trial and Evaluation Laboratory. The data is modeled in a fuzzy context to account for issues of ambiguity and subjectivity. This study helps policymakers and other stakeholders by identifying the barriers to vehicle scrap policy implementation in India. Furthermore, this paper offers insights for future research in developing policies and practices for India's emerging vehicle scrap business. [ABSTRACT FROM AUTHOR]

This work presents an outline for adopting industry 4.0 enabling technologies, and appropriate strategies are prioritized to implement them. A hybrid fuzzy Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are applied to achieve the objectives. The enabling technologies and strategies were identified based on the literature review and expert’s opinions, a total of 26 enabling technologies and eight strategies were identified. Later fuzzy AHP technique is used to rank the enablers and TOPSIS is applied to order the implementation strategies. From 26 enablers, a total of ten enabling technologies were found to be the most effective. Artificial intelligence (AI), top management commitment and support, virtual reality, and enterprise resource planning (ERP) systems were the top-ranked enablers in the list, whereas edge computing was the least effective enabler. Among the strategies, lean manufacturing, green supply chain and logistics, and integrated and smart manufacturing systems were the top priorities in implementing industry 4.0, while recruiting and managing talents was the least important strategy in the study. The findings from this framework will provide a deep insight to the managers and practitioners of MSMEs to adopt the industry 4.0 technologies in their organizations. [ABSTRACT FROM AUTHOR]

This study intends to identify, analyse, and evaluate sustainability assessment (SA) methods, along with the critical factors of sustainable manufacturing (SM) practice. Based on the opinion of experts and a literature review, 10 SA methods and 20 critical factors of SM practice were identified and analysed in relation to their optimal selection in sustainable operations. This research uses a fuzzy MULTIMOORA (Multi-Objective Optimisation on the basis of a Ratio Analysis plus the full multiplicative form) to rank SA methods based on the weights of the critical factors of SM practice obtained using a fuzzy analytic hierarchy process (AHP). A case study of the leather processing industry in India describes the use of the technique in practice. The ranks obtained using the AHP-fuzzy MULTIMOORA is compared with those obtained using the best–worst method (BWM)-fuzzy MULTIMOORA. The results reveal that the Index of Social Progress and the Social Life Cycle Assessment are the top two reliable SA methods based on the identified critical factors of SM practice. Among the critical factors, the 'identification of alternative raw materials', 'methane mitigation', and 'opting for re-utilisation' are identified as the prominent critical factors of SM practices. In addition to the critical factors of SM, this study also evaluates SA methods as they help to monitor industrial progress towards sustainability. These findings provide useful insights to practitioners and can help them select optimal and robust SA methods. [ABSTRACT FROM AUTHOR]

Purpose: The present study aims to determine and benchmark the performance of socially responsible companies (SRCs) in India based on the financial, value-added and combined performance indicators by addressing the climate change problems at the grass-root level. Design/methodology/approach: The present study has used the traditional financial, value-added and combined performance indicators to evaluate and rank the performance of 14 SRCs under the Bombay stock exchange (BSE)-Greenex sustainability index. The technique for order performance by similarity to ideal solution (TOPSIS) and fuzzy analytic hierarchy process (FAHP) algorithms calculate performance scores and assign weights to the indicators from 2015 to 2019. Further, the Altman Z-score methodology has been applied to understand the SRCs propensity toward bankruptcy behavior. The parametric t-test is also performed on the outcomes of TOPSIS scores under different categories of indicators to check the statistical significance. Findings: The performance scores of the TOPSIS algorithm indicate that the financial indicators of SRCs govern the firm performance significantly over the value-added indicators (VAIs). Further, parametric t-test results validate the outcomes of the performance scores by exhibiting that there is no significant difference between the traditional financial and VAIs at a 5% significance level. However, a few SRCs overall performance rankings have improved significantly after including VAIs. Moreover, the Altman Z-score results also reveal that most of the SRCs evaluated in the study are stable, showcasing consistent performance and absent from bankruptcy behavior. Practical implications: The study has practical implications as follows: (1) to facilitate a clear understanding of investors and portfolio managers in selecting appropriate companies under socially responsible investing (SRI); (2) to provide portfolio diversification insights for domestic and international investors besides advocating the necessity of investing in better performing sustainable companies to safeguard their investments against the future uncertainty and (3) the study results would benefit the regulatory bodies to frame appropriate sustainability policy interventions at the organization level. Originality/value: In the context of ambiguous inferences on the performance of SRI, no prior study has been conducted to assess the performance of SRCs in the Indian version of sustainability index BSE-Greenex. [ABSTRACT FROM AUTHOR]

Purpose: This study prioritizes the supply chain risks (SCRs) and determines risk mitigation strategies (RMSs) for the Indian apparel industry to mitigate the shock of the COVID-19 pandemic disruption. Design/methodology/approach: Initially, 23 SCRs within the apparel industry are identified through an extant literature review. Further, a fuzzy analytical hierarchy process (FAHP) is utilized to prioritize the SCRs considering the epidemic situations to understand the criticality of SCRs and determine appropriate RMSs to mitigate the shock of SCRs during COVID-19. Findings: This study prioritized and ranked the SCRs within the Indian apparel industry based on their severity during the COVID-19 disruption. Results indicate that the demand uncertainty and pandemic disruption risks are the most critical. Based on the SCRs, the present work evaluated and suggested the flexibility and postponement mitigation strategies for the case under study. Research limitations/implications: This study has novel implications to the existing literature on supply chain risk management in the form of the FAHP framework. Supply chain practitioners from the other industrial sectors can extend the proposed FAHP framework to assess the SCRs and identify suitable mitigation strategies. The results aid the practitioners working in an apparel industry to benchmark and deploy the proposed RMSs in their firm. Originality/value: The present study is a unique and earlier attempt to develop a quantitative framework using FAHP to evaluate and determine the risk mitigation strategy for managing the SCRs during the coronavirus epidemic. [ABSTRACT FROM AUTHOR]

Purpose: The primary objective of the study is to discover the most prominent criteria and sub-criteria among environmental issues, social dimensions and corporate governance factors that may impact individual equity investors' investment decisions. Design/methodology/approach: The present study collected data from 438 individual equity investors from the North Indian region. To achieve the objectives of the study, a fuzzy analytic hierarchy process (Fuzzy AHP) was applied. The key considerations of the study were environmental, social and governance (ESG) factors. Findings: The governance criterion was discovered to be the most significant factor influencing individual equity investors' investment decisions among the three ESG factors, followed by environmental criteria, while social criteria were shown to be the least influential. Research limitations/implications: The present study solely looked at ESG issues as drivers of stock investors' investment decisions. In the current world, however, many other factors, including behavioral biases, accounting information, ownership structure and fundamental analysis, can have a substantial influence on investors' investment decisions. Practical implications: The study's findings widen the theoretical contribution in the field of responsible investment by asserting how ESG factors influence investors' investment decisions in the equity market. From a practical standpoint, this study applies to retail and institutional investors, portfolio managers, financial advisors, market regulators, corporations and society at large. Originality/value: To the best of authors knowledge, no attempt has been made to prioritize the ESG issues that impact the investment decisions of individual equity investors. Ergo, this study contributes to the existing literature on socially responsible investment. [ABSTRACT FROM AUTHOR]

The land is a vital source of agriculture. Agricultural land is utilized for the purpose of cropping. Delineation of potential agricultural land is pertinent when population pressure incrementally increases and a competitive trade-off between different land uses. The present study is conducted to evaluate the agrarian land suitability in the Ranchi district (Plateau region in India) using fuzzy analytical hierarchy process (FAHP). A land suitability evaluation technique is designed, combining nineteen thematic layers from five categories: climatic parameters, topographic features, soil characteristics, euclidean distance, and land elements. All thematic layers were combined into a single layer using the FAHP model to create agricultural land suitability map in a GIS environment. Our analysis demonstrates that 11.17% of the total area (509700 ha) is highly suitable, while about 20.73% and 13.81% of the study area is moderately suitable and marginally suitable for agricultural activities. The study also reveals that approximately 54.29% of the lands are not suitable for agriculture. Our findings provide additional information, and local governments can use the land suitability map to manage and develop agricultural resources. The study contributes to the studies on agricultural suitability by bringing in multiple variables centring on the FAHP method, which can be replicated in studies on plateau regions across the globe. [ABSTRACT FROM AUTHOR]

Growing economic crisis and strict government policies and regulations regarding the environment have forced the battery industries to practice the recycling process for both economic and environmental benefits. Moreover the consideration of sustainability issues makes the recycling process more significant, successful implementation of recycling activity in the battery industry requires several strategic decisions. Among these decisions, a complex decision faced by the management is to identify the suitable location for the establishment of a formal Battery Recycling Plant (BRP). In this research work, a three-stage hybrid methodology with the combination of ISM (Interpretive Structural Modeling), fuzzy AHP (Analytic Hierarchy Process), and fuzzy COPRAS (Complex Proportional Assessment of Alternatives) methods was proposed to select the best sustainable location from the available options for BRP in India. In this study, a total of eight experts were selected from both academia and industry. Initially in this paper, 4 main sustainable criteria and 20 sustainable sub-criteria were considered from the literature review and field visits along with experts' opinions for evaluation of alternatives. This work contains three stages. In the first stage, the ISM method was used to filter 18 important sustainable sub-criteria from the primary list of 20 sub-criteria, based on its higher driving power. Then, fuzzy AHP was applied in the second stage to compute the criteria weights. In the last stage, the fuzzy COPRAS was used to rank and choose the best sustainable location for BRP. The findings emphasize that the Environmental protection level, Distance from residential areas, Distance from water source, Capital investment, Government policies, Social acceptance, Availability of scrap batteries, and Availability of utilities were the most significant criteria in the sustainability dimension. Thus, the present work helps the management in the selection of an appropriate sustainable location for BRP. [Display omitted] • A hybrid methodology was proposed for selecting battery recycling plant location. • Sustainable criteria are considered for decision making. • Interpretive Structural Modeling was used to identify the noteworthy criteria. • Fuzzy Analytic Hierarchy Process was applied to obtain the criteria weights. • Fuzzy Complex Proportional Assessment of Alternatives was used to select location. [ABSTRACT FROM AUTHOR]

The Sonakhan greenstone belt in Central India is under-explored with respect to gold in spite of its similarity to auriferous greenstone belts in general, which prompted a prospectivity analysis. The workflow involved adoption of a conceptual mineral systems model, recognizing indicative spatial proxies, processing exploration datasets, generating evidence maps and integrating into GIS-based mineral prospectivity mapping. Available geological information such as key lithologic units and their contacts was combined with geochemical anomalies of selected pathfinder elements, geophysical data (aeromagnetic anomaly and K/Th ratio map) and satellite digital image data (ASTER and Landsat 7 ETM +), leading to generation of 17 evidential layers. The lack of a significant number of known mineral occurrences in the study area precludes the use of data-driven prospectivity modeling techniques. Therefore, knowledge-driven approaches such as binary and multiclass index overlay, fuzzy logic and fuzzy AHP (analytic hierarchy process) were adopted to integrate the evidential layers resulting in four prospectivity maps. The variation of cumulative prospectivity with respect to cumulative area in each model was used to determine threshold to produce binary prospectivity maps separating high and low prospectivity zones. An approach based on the unique conditions of the binary prospectivity maps was used to illustrate the combined results of different models. In order to quantify the intuitive uncertainty in exploration targeting that arose due to different model outputs, a modulated predictive model was generated taking the mean prospectivity values at each pixel. The pixels having mean values above 95th percentile were grouped and the area delineated as potential exploration targets for gold that comprises merely 5% of the study area. The estimated uncertainty and confidence values for each pixel were used in the risk analysis that returned 1.95% and 3.05% of the study area as low- and high-risk exploration targets, respectively. [ABSTRACT FROM AUTHOR]