Transportation and land use systems have environmental impacts on human settlements. Transportation emits pollutants through fuel combustion, while land use leaves ecological footprints. Addressing the exacerbation of climate change consequences requires diligent attention to policies aimed at reducing emissions. Meanwhile, the interplay between land use and transportation systems necessitates a coherent framework to guide future development and retrofit existing infrastructure. Furthermore, emerging autonomous vehicles (AVs) offer an opportunity to mitigate transportation-related environmental impacts that affect land use patterns. However, AV adoption rates (ARs) vary, and analyzing how AV usage together with land use allocation optimization could contribute to the CO2 footprint reduction urges investigation. This matter, which has analytical and prescriptive implications, requires the development of optimization models that combine land use and transportation systems concerning the carbon footprint objectives. Taking the interconnection and hierarchy between land use allocation and traffic assignment decisions, this chapter presents a bi-level co-evolution model. The upper level models the emission of the whole system (land use together with transportation), and the lower level formulates mixed traffic assignment. Then, a solution framework, including a genetic algorithm and gradient projection method, is introduced and applied to analyze several scenarios. This study contributes to the literature by providing a bi-level co-evolution model for land use and AV adoption optimization and presenting ideas for solving the formulated problem. It also offers insights into mitigating carbon footprint when communities go toward adopting AVs and environmental-based optimal land use and transportation patterns. The obtained results show that utilizing AVs and optimized land use allocation could be effective in decreasing the CO2 footprint.