基于CBW方法的美丽中国建设学习路径选择研究

坚持因地制宜、分区分类探索并打造各美其美的美丽中国建设样本是美丽中国建设实践的内涵。目前各地在美丽中国先行区等试点建设活动中已进行了大量评价,在筛选典范、分区分类等方面做了充分探索。虽然评价体系日趋成熟,但仍存在榜样的学习路径不够明确,表彰充分但分类指导力度不足等问题。为了解决此类问题,本文充分融合大数据技术与生态文明建设实践,提出基于大数据的CBW(Category-Better-Way)方法体系,依据标杆管理“分类、标杆、学习路径”三要素建立了具有通用性的美丽中国建设辅助绩效评价体系和学习路径选择机制,不仅可以帮助辅助评比的机构确定生态案例分类类别、树立分类标杆、提取标杆的学习路径,还可帮助各地区定位美丽中国建设新案例的学习标杆并找到学习路径。该方法具有通用性,适用于各地实践美丽中国建设。

Study on the Learning Path Selection of Beautiful China Construction Based on CBW Method

It is the connotation of the construction practice of beautiful China to adhere to local conditions, explore and create beautiful China construction samples by districts and classifications. At present, a large number of evaluations have been carried out in pilot construction activities such as the beautiful China pioneer area. In the screening model, zoning classification and other aspects have been fully explored. Although the evaluation system is becoming more and more mature, there are still some problems such as the lack of clear learning path of role models, sufficient recognition but insufficient classification guidance. In order to solve such problems, CBW (Category-Better-Way) method system based on big data is proposed, which fully integrates big data technology and ecological civilization construction practice. Based on the three elements of benchmarking management" classification, benchmarking and learning path", a universal auxiliary performance evaluation system and learning path selection mechanism for beautiful China construction is established, which can not only helps auxiliary evaluation institutions to determine ecological case classification categories, establish classification benchmarks and extract benchmarking learning paths, but also helps all places to locate the learning benchmark of the new case of beautiful China construction and find the learning path. The results are compared with the original classification and are basically consistent. The generality of this method is good, and can be applied to the construction of beautiful China.