To survive in the long term, business needs to profit, controlling environmental impacts with social responsibility. Sustainability programs involve the integration of social and environmental issues in business models and organizational processes. The assessment of sustainability programs is a problem of multiple criteria decision analysis (MCDA). This work presents applications of MCDA for the assessment of sustainability programs in the textile industry. Applied methods for MCDA are analytic hierarchy process (AHP) and the technique for the order of preference by similarity to ideal solution (TOPSIS). The reasons to apply AHP and TOPSIS include providing an assessment index, ranging from 0 to 1, and that the MCDA model is expected to have more criteria than alternatives. Therefore, an application of other methods, such as data envelopment analysis, could be prejudiced. Concepts from the triple bottom line, economic, social as well as environmental criteria were inserted in the proposed model. Sustainability programs of six leading companies from the Brazilian textile industry were evaluated. The main finding of the research is that AHP and TOPSIS resulted in similar evaluations for sustainability programs. Both methods resulted in the same rank of alternatives. However, with TOPSIS, companies' sustainability indices were more disperse, varying from 0.10 to 0.92 against a range from 0.23 to 0.69 with AHP.
There is an increasing pressure by the community and customers forcing companies to insert environmental concerns in their practices. To help companies initiatives, the green bonds market was created. Our research question is “How to select bonds in a growing billion-dollar market?” This paper presents a multi-criteria decision analysis (MCDA) model to enable investors identify opportunities based not only in opinions, but grounded on objective facts. Analytic hierarchy process (AHP), complex proportional assessment (COPRAS), full consistency method (FUCOM), step-wise Weights Assessment Ratio Analysis (SWARA), and technique of order preference similarity to the ideal solution (TOPSIS) are MCDA methods applied in this paper. Top-fifteen green bonds ranked by specialized media were assessed with the proposed MCDA model. Criteria included the Environmental Performance Index (EPI) proposed by Yale University, and common financial indicators as assets, risks (β), and dividends. The new ranks from MCDA are compared each other and compared with the rank published by specialized media.
There is an increasing pressure by community and customers forcing companies to insert environmental concerns in their practices. To help companies initiatives, the green bonds market was incepted. Our research question is how to select bonds in a growing billion-dollar market. This paper presents a multi-criteria decision analysis (MCDA) model to enable investors identify opportunities based not only in opinions, but grounded on objective facts. Analytic Hierarchy Process (AHP) and Complex Proportional Assessment (COPRAS) are two MCDA methods applied in this paper. Top-fifteen green bonds ranked by specialized media were assessed with the proposed MCDA model. Criteria included the Environmental Performance Index (EPI) proposed by Yale University, and common financial indicators as assets, risks (β), and dividends. The new AHP–COPRAS rank is compared with another published by specialized media.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.