One of the contributing factors to climate change is the release of gases, particularly carbon dioxide (CO2), which is amplified by the expanding E-commerce industry. E-commerce enterprises heavily depend on recommender systems as a means to incentivize consumers towards making product purchases. This master's thesis investigates the positive impacts of presenting information regarding carbon dioxide (CO2) emissions values on user behavior and recommendation accuracy within sustainable recommender systems. Through the creation of a new dataset, CarEmissions, this study explores whether displaying emission values influences sustainable choices in recommendations. Findings demonstrate that recommendation models trained without CO2 values consistently outperform those with CO2 values, enhancing both accuracy and greenness. This suggests that the inclusion of CO2 values introduces variability to user ratings, thereby influencing recommendation outcomes. Furthermore, this research examines correlations between user demographics, knowledge, and ratings, revealing insights into the lack of significant links. Additionally, it assesses the differences in recommendation quality between datasets with and without CO2 values, highlighting the advantages of omitting CO2 values in enhancing recommendation performance. While considering the limitations inherent to domain-specific data and convenience sampling, the thesis outlines avenues for refining data collection and exploring automated strategies for balancing recommendation accuracy and greenness. By advancing the understanding of user behavior and ethical considerations in sustainable recommender systems, this study contributes to the evolving landscape of technology-driven sustainable consumption.