## 4 Discussion
#### Practical Application--Monitoring Long-Term Behavior Changes in Peer-to-Peer Car Sharing  
After launching a peer-to-peer car-sharing system, it is essential to implement mechanisms for monitoring long-term behavior changes in users. This step ensures the sustainability and adaptability of the platform while aligning with the methodology outlined by [PERSON] et al. [77], which emphasizes the need to track and understand behavioral dynamics over time. Such an approach enables the platform to refine its service offerings, enhance user satisfaction, and foster a cultural shift toward sustainable mobility solutions. Understanding long-term behavior changes involves evaluating how users' preferences, motivations, and patterns of engagement with the system evolve. This can be achieved through a structured framework consisting of three stages: behavioral analysis,intervention design, and iterative monitoring. Together, these stages allow the platform to adapt its strategies dynamically, ensuring continued relevance and user retention. In the context of long-term behavioral change, it is essential to recognize its broader significance. Long-term behavioral change is crucial for addressing societal challenges such as fostering sustainable lifestyles and promoting healthier habits [78]. It also plays a role in overcoming personal challenges, such as adhering to medical treatments or diets. Achieving and sustaining these changes ethically requires balancing efficacy--producing desired effects--and effectiveness--ensuring real-world applicability [79]. Behavioral design bridges the gap between the creativity of design and the validation-driven approach of behavioral science, offering interventions that are both impactful and adaptable [80]. This interdisciplinary approach enables platforms to translate abstract behavior change techniques into real-world, context-specific interventions, ensuring their long-term effectiveness. The first stage, behavioral analysis, focuses on collecting data about user interactions with the platform. Key metrics might include the frequency of vehicle rentals, preferred vehicle types, trip durations, and geographic usage patterns. Additionally, surveys or interviews can uncover deeper insights into user motivations and barriers. For example, some users may prioritize affordability, while others might be more concerned about vehicle availability or trust in peer-owned cars. By segmenting users based on their behavior--such as frequent users, occasional users, and those at risk of dropping out--the platform can identify opportunities for targeted interventions. In the second stage, intervention design, the platform applies insights from the behavioral analysis to implement strategies that promote sustained engagement. These interventions could include loyalty programs rewarding users for frequent rentals or eco-conscious choices, such as selecting electric vehicles. Dynamic pricing models might incentivize off-peak rentals or offer discounts for repeat customers, while educational campaigns could focus on highlighting the cost and environmental benefits of car sharing, particularly for users hesitant to fully embrace the service. The third stage, iterative monitoring and refinement, involves the continuous tracking of user behavior to assess the effectiveness of implemented interventions. Advanced analytics, such as machine learning, can predict drop-off points and identify emerging trends, such as increased demand for specific vehicle types or declining engagement from certain user groups. Regular feedback loops, through in-app surveys or focus groups, provide qualitative insights to complement the data-driven analysis. For example, if electric vehicle (EV) adoption is slower than expected, targeted incentives, such as discounts for EV rentals, could be introduced to address barriers like charging infrastructure concerns. By systematically applying this framework, peer-to-peer car-sharing platforms can remain user-centric and responsive to evolving preferences.