Understanding green procurement dynamics: An assessment framework for public sector organizations.

Governments globally face increasing pressure from climate advocates and international agreements, such as the Paris Agreement, to enact policies addressing climate change. This paper addresses the imperative for sustainable practices outlined in such agreements, with a specific focus on assessing the drivers of Green Procurement Practices (GPP) within Public Sector Organizations (PSOs). A dearth of research exists in systematically analyzing and prioritizing these drivers, exploring their interdependencies, and elucidating their relative importance. GPP is pivotal in market transformation by promoting environmentally friendly products and endorsing low-carbon, energy-efficient alternatives. This, in turn, contributes significantly to mitigating climate change and fostering a shift towards a greener, more sustainable economy. Identification of the drivers has been performed by an extensive review of the literature combined with the author's viewpoint, while the analysis has been performed using the novel method of Dominance-based Rough Set Approach (DRSA) and Interpretive Structural Modelling (ISM) with Matriced' Impacts Croise's Multiplication Applique'e a UN Classement (MICMAC) analysis. The study's outcome reveals that the Demand for Eco-friendly products is the primary driver for the incorporation of GPP, followed by the drivers' Presence of guidelines support and Government Regulations. Findings of the research also demonstrate that suppliers' propensity to adopt green practices depends on several factors, including sustainable supplier cooperation, degree of commitment to embrace green initiatives, government interventions in the form of incentives and guidelines support, and the presence of a legal framework. The findings of this research will enrich the understanding of policymakers and managers to formulate strategies for advancing GPP structured and sustainable implementation in PSOs. The study's findings will also benefit green technology sector advancement through the widespread adoption of GPP.

Leveraging AI for advanced analytics to forecast altered tourism industry parameters: A COVID-19 motivated study.

COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research. This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.