APPLICATION OF STOCHASTIC PROCESSES TO REDUCE CO2 EMISSIONS IN TRANSPORTATION

This study develops and applies stochastic process models to design an optimized solar-powered battery swapping hub for electric tricycles (Kekes) in Benin City, Nigeria, with the aim of reducing urban CO2 emissions. Using first-order Markov chains to model solar irradiance variability and non-homogeneous Poisson processes to capture time-varying vehicle arrival patterns, the research addresses the inherent uncertainty in both energy supply and demand. Queueing theory analysis estimates service quality metrics, while Monte Carlo simulation-based optimization determines optimal battery inventory levels balancing capital investment against system reliability. Theproposedsystemcomprises228solarpanels(91.2kWcapacity)and60lithiumiron phosphate batteries (180 kWh total storage), designed to serve approximately 95 Kekes daily during 12-hour operations (6 AM to 6 PM). Comprehensive simulations validate system performance across 100 annual cycles, projecting 96.1% service reliability and 94.4% solar energy independence. The system achieves annual CO2 emission reductions of approximately252metrictonsthroughdisplacementoffossilfuelcombustion,representing a 97% per-vehicle reduction. Economic analysis indicates a 4.3-year payback period with 22.7% internal rate of return and net present value of N11.66 million over 20 years. A small-scale prototype operated continuously for 30 days validates the theoretical framework through empirical data collection, demonstrating close agreement between predicted and observed performance across all metrics (within 3% error). Sensitivity analyses confirm system robustness under parameter variations of ±20% in arrival rates and ±10% in solar irradiance, with solar resource availability identified as the dominant performance driver. 5 The methodology presented provides a replicable framework for designing renewable energy-powered transportation infrastructure under uncertainty, applicable to similar urban contexts across developing nations. The integration of multiple stochastic processes, validatedthroughbothsimulationandempiricaltesting,demonstratesthatmathematically rigorous approaches can effectively guide sustainable infrastructure investment decisions