With growing urbanization, there are increasing demands on waste management systems that can be performed in an environmentally friendly way as well as efficiently. Current approaches to managing waste often have issues with efficiency, transparency, and engaging with the public. Blockchain technology has been identified as one potential solution to these problems because it offers several benefits including decentralization, security, and transparency. The selection of the best blockchain-based waste management (BBWM) system is very difficult due to the many different evaluation criteria that may conflict with each other. Therefore this research uses a multi-criteria decision making (MCDM) approach using CIMAS (Criteria Importance Assessment), for determining weights based upon subjective input, and LOPCOW (Logarithmic Percentage Change-Driven Objective Weighing), for determining weights based upon objective data within the MCDM framework. To rank alternatives effectively, an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) technique is applied, ensuring a precise evaluation process. The use of T-Spherical Fuzzy Sets (T-SFS) captures all three (membership, non-membership, hesitation degree) and is used to address the variability that exists when making an expert judgment. Some of the key factors include; Technological Feasibility, Operational Costs, Scalability, Data Security, Regulatory Compliance, Environmental Impact. Based on the evaluation criteria, it appears that the Blockchain Enabled Waste Tracking System is the most appropriate alternative due to its high potential for Transparency, Regulatory Compliance and Fraud Prevention. In addition, this research will provide Policymakers, Urban Planners and Investors with a methodical way of making Data Driven Decisions on BBWM Investments.

Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation (CRITIC) method with the Criteria Importance through Means and Standard Deviation (CIMAS) technique. The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC, incorporate decision-makers' preferences through CIMAS, and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets. A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework. A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results. Furthermore, a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.