Assessing the Impact of <scp> PM ₁₀ </scp> and <scp> CO ₂ </scp> Greenhouse Gases From Road Transportation on Global Climate Change: A Mathematical Modeling Approach

ABSTRACT This study develops a mixed‐integer nonlinear optimisation model to jointly minimise PM 10 and CO 2 emissions from urban road transport in nine of Türkiye's most densely populated provinces, aligning with the targets of the Paris Agreement. The model is novel in its integration of environmental baselines (city‐level PM 10 and CO 2 levels), economic feasibility (GDP), and urban form variables (population density and land‐use ratios), creating a realistic and context‐aware decision framework. It applies three tiers of constraints: (i) 2023 regulatory emission ceilings, (ii) GDP‐based economic capacity thresholds, and (iii) nonlinear constraints reflecting how urban morphology influences policy adoption. A composite climate impact index—capturing post‐policy PM 10 , CO 2 , and their interaction—serves as the objective function to yield granular, city‐specific mitigation scenarios. Results indicate that cities like Ankara, Bursa, and Antalya can virtually eliminate PM 10 emissions, while Istanbul achieves the highest CO 2 reduction (87%), collectively delivering nearly 20 Tg CO 2 ‐equivalent reduction. Sensitivity analysis confirms that moderate increases in GDP can enhance mitigation outcomes by expanding feasible policy space. As the first model in the Turkish context to embed both urban planning and economic structure into dual‐pollutant optimisation, this framework offers not only predictive capacity but also prescriptive guidance for policymakers. It enables comparative assessment of emission‐reduction strategies—such as fleet electrification, low‐emission zones, and transit infrastructure investment—offering a robust and transferable tool for other rapidly urbanising regions aiming to simultaneously advance air quality, public health, and climate resilience.