大数据时代，由于数据治理体现出明显的跨区域特征，地方政府大数据治理的政策安排深刻影响着我国政府大数据治理的最终成效，对其协同程度进行研究是确有必要的。当前对政府大数据治理政策协同研究主要集中于某一特定领域，如政府数据开放、政府数据保护等，难以反映政府大数据治理政策体系全貌；聚焦于顶层设计，主要分析中央层面的政策协同情况，地方层面的政策协同度无从得知。

基于可操作性的现实考虑，本文选取省级层面2015—2019年间的政府大数据治理政策为研究对象。从力度、目标、工具三个方面构建分析框架，并通过多主体参与、多轮赋值的方式制定和完善量化标准，依据效力测算公式以及协同度测算公式，探讨我国省级政府大数据治理的整体情况和协同程度。当前，省级政府大数据治理政策安排主要呈现出“方向一致”和“步调有别”的特点。从总效力和平均效力的分布来看，省级政府依照“迅速反应，集中发力”的政策安排模式，效力短期内迅速增长而后逐渐下降。通过对政策目标以及政策工具协同度的分析可知，在政府大数据治理过程中，大多数省份呈现出一致的协同组合偏好，但在协同度大小上存在较大差异。此外，各省份使用不同目标组合、工具组合的均衡程度差异较大。从协同发展趋势来看，不同目标间、工具间的协同度差异正在逐步减少，反映出省级层面在推动政府大数据治理不断深入的过程中，对政策目标、政策工具形成更为全面的认识，强调不同组合间的协同配合。在此基础上，本文提出优化完善省级政府大数据治理政策的几点建议：一是重发展，要根据实际情况形完善政策体系，提高政策质量；二是补短板，关注政策协同中的协同弱项，加大需求型政策工具的使用，强调数据安全保障的重要性；三是找差距，注重横向比较，学习先进经验，同时不断探索各地政府大数据治理的新模式、新方法。

In the era of big data, due to the obvious cross-regional characteristics of data governance, the policy arrangements of local governments’ big data governance have a profound impact on the overall final effectiveness, and it is indeed necessary to study the degree of synergy. The current research on policy synergy of government big data governance is mainly focused on specific fields, such as government data opening and government data protection, etc. It is difficult to reflect the overall picture of the government big data governance policy system; focusing on the top-level design, mainly analyzing the policy synergy at the central level, the degree of policy synergy at the local level is unknown.

Based on the practical considerations of operability, this paper selects the provincial governments’ big data policies from 2015 to 2019 as the research objects. and builds an analytical framework from three aspects: strength, goals, and tools, formulates and improves quantitative standards through multi-agent participation and multiple rounds of valuation. Then we discuss the overall situation and synergetic degree of the big data governance in china’s provincial governments using the effectiveness calculation formula and the synergetic degree calculation formula. At present, the provincial governments' big data governance policy arrangements mainly show the characteristics of "same direction" and "different steps". From the distribution of total effectiveness and average effectiveness, the provincial government followed the policy arrangement model of “response rapidly, act together”, and the effectiveness increased rapidly in the short term and then gradually declined. Through the analysis of the synergy of policy objectives and policy tools, it can be seen that most provinces present a consistent preference in synergistic combination in the process of big data governance, but there is a big difference in the degree of synergy. In addition, the degree of equilibrium used by different provinces in target combinations and tool combinations varies greatly. From the perspective of synergetic development, the difference is gradually decreasing in the synergetic degree between different targets and tools, it shows that provincial governments have more comprehensive formation of policy goals and policy tools in the process of big data governance, they emphasize the synergy of different combinations. On this basis, this paper From the perspective of the difference in the synergy degree, the difference in the degree of synergy between different targets and tools is gradually decreasing, reflecting the more comprehensive formation of policy goals and policy tools at the provincial level in the process of promoting the deepening of government big data governance Understanding, emphasizing the enhancement of the synergy of its different combinations. Based on the research conclusions, this article puts forward several suggestions for optimizing and improving the provincial governments' big data governance policies: First, focus on development, provincial governments should complete the policy system and improve policy quality according to the actual situation; Second, make up for the shortcomings, provincial governments must pay attention to the synergetic weaknesses, for example, governments should increase the use of demand-based policy tools, emphasize the importance of data security; the third is to find the gaps between different provincial governments, focus on horizontal comparison, learn advanced experience, and continue to explore new models and methods of governments’ big data governance.