Evaluation of Carbon Footprints Associated with Cryptocurrency Mining using q-Rung Orthopair Fuzzy Hypersoft Sets

Abstract

The environmental impact of Bitcoin mining in Kazakhstan, which is currently the third-largest market in the world by hash rate, is coming under further scrutiny. Data on the production of renewable energy and related carbon footprints are essential for evaluating the situation. To create a thorough picture of how Bitcoin mining and environmental responsibility connect in Kazakhstan, this paper allows for the analysis and prediction of the interactions between carbon emissions, renewable energy use, and Bitcoin mining. Using a q-rung orthopair fuzzy hypersoft set (q-ROFHS)-based multi-criteria decision-making technique can improve research on the environmental effects of Bitcoin mining, the integration of renewable energy sources, and the corresponding carbon footprints. The analytic hierarchy process is used to identify the best pollution reduction strategies while taking feasibility and cost-effectiveness into account. The proposed approach will assist the business in achieving its environmental objectives, lessen its negative effects on the environment, and promote a greener future. This study guarantees a more precise and dependable evaluation of pollution control tactics, considering not only the effects on the environment but also practicality and affordability. The outcomes highlight the developed approach's effectiveness and stability in managing complicated information within the parameters of q-ROFHS.