Evaluating data quality of the sustainable development goals in national innovation demonstration zones, China

• Data quality assessment plays a crucial role in the Sustainable Development Goals (SDGs). • A holistic data quality assessment framework consisting of nine dimensions is developed. • The data quality of the SDGs in 11 National Sustainable Development Agenda Innovation Demonstration Zones was assessed. • IPA are performed to analyze relationships between data importance and data user satisfaction. Data can better measure progress towards achieving the Sustainable Development Goals (SDGs) in a way that is both inclusive and fair. However, several challenges remain, such as insufficient data and poor data quality. As a pivotal part of ensuring high-quality and trustworthy data for evidence-based decision-making and efficient measurement, data quality assessment plays a crucial role. This paper proposes a multidimensional data quality evaluation framework for SDGs data on the basis of fuzzy comprehensive evaluation theory. Nine quality dimensions are defined. Finally, we distribute questionnaires in 11 National Innovation Demonstration Zones for 2030 Sustainable Development Agenda in China to collect data for importance‒performance analysis (IPA). The results reveal that Huzhou has the highest SDG data quality score of 3.912, followed by Guilin with 3.663. Despite its high score, Huzhou’s data quality still has room for improvement. In contrast, Zaozhuang records the lowest SDG data quality score of 2.039, showing that it is particularly struggling in the ecological dimension. Notably, SDG9 exhibits the least variation (the smallest score disparity) among the 11 municipalities, followed closely by SDG8 and SDG10. IPA reveals the following four quadrants: the advantageous area, maintenance area, opportunity area, and focused improvement area. Xuzhou has the most indices in the advantageous area, whereas Ordos has the least in the opportunity area. The focused improvement area highlights the need for improvement in the quality dimensions of credibility and granularity. The study is valuable for data-based decision-making and continuous evaluation on SDG implementation at local, national, and even global scale.