This study aims to identify the knowledge structure, thematic evolution, and future research directions at the intersection of Corporate Social Responsibility (CSR) and Tax Avoidance (TA) through a comprehensive bibliometric analysis. Data were collected from the Scopus database for the period 2003–2025, yielding 285 valid publications after data screening and cleaning. The analysis employed co-citation, keyword co-occurrence, and bibliographic coupling techniques using VOSviewer, complemented by a temporal keyword analysis to capture the evolution of research trends. The quantitative results reveal a steady increase in publications after 2015, reflecting the influence of global initiatives such as the OECD BEPS program and the ESG reporting movement. Four major clusters were identified through co-citation analysis: (1) CSR, ethics, and legitimacy; (2) Governance mechanisms, taxation, and incentives; (3) CSR and tax avoidance linkages; and (4) CSR, reputation, and market reactions. Bibliographic coupling further highlighted six thematic fronts, including CSR as a legitimacy tool in taxation, the CSR–tax avoidance paradox, and ESG disclosure and digital transparency. A comparison of thematic groups and keyword clusters indicates a conceptual shift from early ethical debates to more integrated approaches that link governance, sustainability, and digital accountability. Based on these findings, the study proposes three strategic directions for future research: (1) examining CSR as a strategic mechanism for responsible tax behavior, (2) exploring the moderating or mediating role of CSR between organizational factors and tax outcomes, and (3) integrating corporate governance, digital transformation, and methodological innovations into CSR–TA research. The results carry significant implications for policymakers, regulators, and corporate leaders in promoting tax transparency and aligning CSR practices with the Sustainable Development Goals (SDGs)—particularly SDG 8 (Decent Work and Economic Growth), SDG 12 (Responsible Consumption and Production), SDG 16 (Peace, Justice, and Strong Institutions), and SDG 17 (Partnerships for the Goals). This study represents the first comprehensive synthesis of CSR–TA research within a sustainability framework, thereby enriching the theoretical understanding of ethical corporate behavior and suggesting actionable paths for future interdisciplinary inquiry.

This paper presents a multi-objective optimization framework for Distributed Generation (DG) planning in smart Distribution Networks (DNs), with the aim of minimizing carbon dioxide emissions (CO₂) and Green Accounting Costs (GAC). A hybrid model is formulated to incorporate power flow constraints, lifecycle emission factors, and environmental cost components under a green accounting perspective. To solve the proposed problem, an enhanced Grey Wolf Optimizer (GWO), termed Eco-Aware Multi-Objective Grey Wolf Optimizer (EMOGWO), is developed by introducing environmentally guided leader selection, adaptive convergence control, and a fuzzy-based decision mechanism. The algorithm is tested on the IEEE 33-bus and IEEE 69-bus systems and benchmarked against Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Arithmetic Optimization Algorithm (AOA), and the standard GWO. Simulation results demonstrate that EMOGWO consistently achieves the lowest power loss, emissions, and environmental cost across all cases, while providing the highest Emission Reduction Ratio (ERR) and Green Cost Savings (GCS). The proposed approach offers a practical and effective tool for sustainable DG planning aligned with environmental and economic objectives.