关键词: 公共数据开发利用, PMC指数模型, 政策评价

Abstract:

[Purpose/Significance] As a national foundational strategic resource, public data resources play an important driving role in advancing national governance modernization and accelerating socioeconomic development through unleashing the potential of data elements. Systematically evaluating provincial-level public data resource development and utilization policies in China helps reveal the strengths and weaknesses of policy design and provides a scientific basis for policy makers to optimize policy frameworks. Existing research mainly focuses on horizontal comparisons or vertical evaluations of policy texts across different levels of government or conducts policy analysis within specific regions. Given the disparities in digital infrastructure, economic development, data resource endowments, and governance capabilities in China, these differences may lead to regional heterogeneity in the effectiveness of public data resource utilization policies. This study focuses on provincial policies across China's seven major geographic administrative regions to carry out policy quality assessment and regional comparison analysis. [Method/Process] This study used ROST CM6.0 to perform content mining on 66 policy documents. By analyzing high-frequency keywords and key content, it constructs an evaluation framework for provincial public data resource utilization policies. Drawing on variables from prior studies and characteristics of policy texts, the study established nine primary variables and 52 secondary variables, forming a policy management index (PMC) model for provincial-level policies. Based on China's seven major geographical administrative divisions, 14 representative policy samples were selected for empirical analysis. A PMC surface plot was generated based on PMC scores, visually illustrating the overall performance of each policy as well as the scores of primary and secondary variables. The PMC index model evaluates the consistency and completeness of policies through a multi-dimensional indicator system. ROST CM6.0 is used to extract high-frequency keywords from policy texts, providing an empirical basis for variable setting. Combining ROST CM 6.0 text mining with the PMC index model is a research approach that has been widely validated in the field of policy quantitative evaluation. Its applicability has been well confirmed in areas such as digital economy policies and digital health policies. [Results/Conclusions] The research results are summarized as follows. 1) Overall, the level of policy evaluation is relatively high. Three policies were rated as excellent, five as good, and six as qualified. No policy received a perfect or unsatisfactory evaluation. Based on the scores and ratings, the policies can be divided into three types: demonstration-leading, optimization-development, and potential-growth. 2) From a regional perspective, East China ranks the highest overall, while the scores of North China and South China are relatively similar. The differences between Central China and Northwest China are larger, but the policy evaluation levels of both are higher than the average level. The evaluation levels of Northeast China and Southwest China are relatively low. The study shows that there is an imbalance between policy formulation and practical implementation at the provincial level in China. There is no absolute correlation between government data openness and policy development. A gap exists between forward-looking policy design and top-level planning, and actual practices such as platform construction, data supply, and application ecosystem development. The regional development pattern of public data resource utilization in China exhibits clear gradient characteristics. The coastal eastern regions (East China and South China), lead in open practices due to their strong economic foundations and early-mover advantages. These regions' institutional frameworks are gradually shifting toward refinement. North China and Central China are rapidly catching up, actively implementing relevant policies and promoting data applications to narrow the gap. Although constrained by resources and industrial structures, Southwest, Northwest, and Northeast regions continue to strengthen infrastructure, explore distinctive application scenarios, and advance policy implementation. The main issues currently existing in provincial policies include: weak policy forecasting capabilities; incomplete coverage of policy content, particularly lacking provisions on data asset management, application scenarios, data collection, and resource registration; insufficient attention and guidance toward universities, research institutions, and relevant talent; and a narrow range of incentive and support measures that tend to focus on conventional subsidies while lacking market-oriented and socialized tools. Improvement measures can be approached in four aspects: supplementing forecasting elements, improving key policy content, encouraging multi-party participation, and actively exploring market-based incentives and support measures. This study proposes phased policy optimization suggestions for these four aspects and designs differentiated optimization paths for three types of policies and seven major regions. Future research could consider expanding the scope to include public data resource development policies from other countries or regions, and carry out comparative studies to offer a broader perspective. Moreover, for model building and variable settings, it might be worth adding policy text mining methods and adjusting the variable content settings.

Key words: data exploration, PMC index model, policy evaluation