强化数据赋能，推动非道路移动机械治理

Core Viewpoint - Non-road mobile machinery is crucial in various sectors such as construction, agriculture, and logistics, and plays a significant role in air pollution prevention. However, traditional regulatory models face challenges due to the widespread and mobile nature of these machines, necessitating a data-driven approach to establish an intelligent regulatory system [1]. Challenges in Non-Road Mobile Machinery Governance - The governance of non-road mobile machinery faces three main challenges: 1. Lack of clarity on the number of machines and data barriers across regions create regulatory blind spots. The frequent cross-regional operation of rented machinery leads to data discrepancies and makes it difficult for regulatory bodies to grasp the actual ownership and movement of these machines [2]. 2. Ambiguity in responsibility between rental parties exacerbates enforcement difficulties. Without clear delineation of ecological and environmental responsibilities, parties often evade accountability when violations occur, resulting in unresolved issues and low penalties for infractions [2]. 3. Ineffective governance mechanisms lead to a cycle of temporary measures without sustainable solutions. While some regions implement short-term corrective actions, the absence of dynamic tracking throughout the machinery's lifecycle results in recurring enforcement challenges [2]. Proposed Solutions for Effective Governance - To address these challenges, a modern governance system should be established focusing on data management: 1. Create a cross-regional foundational database to eliminate information silos. A data-sharing platform should be established in key areas like Beijing-Tianjin-Hebei and the Yangtze River Delta, standardizing core data such as machine identity codes and emission standards to ensure comprehensive tracking of each machine's history and status [3]. 2. Develop a closed-loop regulatory data system to clarify responsibilities. A tripartite mechanism involving enterprises, rental parties, and regulatory bodies should be established to record key information such as rental agreements and environmental inspection reports, ensuring accountability in the usage and maintenance of machinery [3]. 3. Leverage intelligent data capabilities to enhance regulatory precision. Utilizing big data algorithms to analyze operational and emission data can help identify high-risk machinery and areas, allowing for proactive rather than reactive enforcement, especially during high pollution events [4].