<p>Governments and businesses worldwide are making carbon neutrality a significant priority. Economic pressures related to energy security and climate change are driving the search for alternative solutions to reduce carbon emissions. Companies are adopting innovative approaches to strike a balance between carbon savings and cost savings. This study aims to analyze an Enhanced Fuzzy Crossover Arithmetic (EFCA) algorithm to optimize supply-chain operations while reducing carbon emissions. A simulation-based quantitative research design is applied. This study used the fuzzy crossover arithmetic algorithm, which is a blend of fuzzy logic and evolutionary arithmetic, to replicate the carbon emission limits and policy issues. Secondary sources such as the World Energy Outlook (2023) and the World Bank Carbon Pricing Dashboard (2024) were used to obtain parameters. This research conducted 3,000 simulations, and the simulation data were examined with the help of IBM Statistical Package of the Social Sciences (SPSS) Statistics. The performance of the EFCA algorithm was of high stability and low probability dispersion in intricate policy scenarios, yielding improved cost–carbon trade-offs. Pareto-priority analysis validates the efficacy of the EFCA algorithm. This investigation novelly uses the EFCA algorithm, which combines the theories of fuzzy mathematics with simulation techniques to offer a unique platform for addressing challenges in achieving dual-carbon goals. EFCA is a useful tool to guide policymakers and practitioners in implementing sustainable low-carbon approaches to support global carbon neutrality targets.</p>