A quantitative method to assess the ecological indicator system's effectiveness: a case study of the Ecological Province Construction Indicators of China

•A quantitative method is developed to assess the effectiveness of an ecological indicator system (EIS).•Linkage, coverage and representation degrees were applied to compare a practical EIS with the ideal one.•Case study on the Ecological Province Construction Indicators (EPCI) proved that our conceptual method is useful to assess the effectiveness of a practical EIS.•The EPCI indicator system has a high degree of linkage and coverage but biased representation in Fujian province. The ecological indicator system (EIS) is widely used. It is vital to develop a scientific and rational EIS for practical application. Assessment of an EIS's effectiveness is important to understand the gaps between practical indicators and indicators that are considered theoretically ideal. In this paper we develop a conceptual method to quantitatively assess the effectiveness of a practical EIS by comparing it with the ideal EIS that is considered perfect by using three assessment indices: linkage, coverage and representation degrees. Linkage degree reveals the relevance of individual indicators to the ideal system. Coverage degree shows the integrity of the practical indicator system compared with the ideal system. Representation degree is the ultimate goal of the quantitative assessment and can detect the gap and overlap between the practical indicator system and the ideal one. Case study on the Ecological Province Construction Indicators (EPCI) proved that our conceptual method is useful to assess the effectiveness of a practical EIS. Compared with an ideal EIS, the EPCI has a high linkage and coverage degree but biased representation. Although the practical EIS covers most of the objectives detailed in the Fujian eco-province construction documents, many social development indicators are overlooked, including ecological culture and green consumption. Using the quantitative method will improve the practical application of an EIS by detecting its redundancy and weakness.