Coastal ecological restoration is a key approach to enhancing ecosystem resilience; however, the stage-wise evolution of restoration outcomes and the underlying driving mechanisms remain insufficiently quantified. Using Qingdao City as the study area, this research integrates remote sensing inversion, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and time-series data from 2010 to 2020 to develop a comprehensive evaluation system for ecological restoration effectiveness, comprising 17 indicators across five dimensions: vegetation, biology, hydrology, economy, and climate. Based on this system, the entropy-weighted method is applied to conduct a dynamic assessment of restoration outcomes. The results indicate that (i) the composite evaluation score in the study area decreased from 0.36 in 2010 to 0.19 in 2015 and then increased to 0.74 in 2020, forming a “V-shaped” nonlinear trajectory with 2015 as a turning point, which is temporally consistent with a delayed response of ecological restoration outcomes following the implementation of major anthropogenic interventions. (ii) Dimension-specific analysis indicates that the decline in the composite score during 2010–2015 was mainly associated with the hydrological dimension, within which chemical oxygen demand (COD) and ammonia nitrogen emissions showed marked increases and were among the highest-weighted indicators. After 2015, following the intensive implementation of regional and system-oriented restoration projects such as the Blue Bay Initiative, pollutant emissions were observed to be effectively controlled, and Bare land area showed a continuous decline. These changes coincided with the rapid rebound of the composite score, within which Bare land area, as the highest-weighted indicator, played a prominent regulatory role. Marked differences were observed among dimensional responses: the biological and vegetation dimensions showed sustained improvement throughout the study period, whereas the hydrological dimension exhibited greater variability over time and stronger temporal alignment with policy-related phases. (iii) Robustness tests indicate that, after completely excluding climate-related variables, the composite score still increased from 0.36 and 0.24 to 0.77, with the “V-shaped” recovery pattern remaining unchanged. This result suggests that the observed improvement in restoration effectiveness in 2020 was more closely associated with systematic human interventions, rather than with short-term climatic fluctuations. This study provides a quantitative and transferable methodological framework for the dynamic evaluation and stage-oriented analysis of coastal ecological restoration effectiveness.

Improving natural resource total factor productivity (NTFP) is sential for easing resource and environmental constraints, advancing high-quality development, and supporting China’s dual-carbon goals. This study aims to evaluate the static efficiency, intertemporal productivity change, regional heterogeneity, and hierarchical frontier differences in environmentally adjusted natural resource total factor productivity in China’s aggregated agriculture, forestry, animal husbandry, and fishery sector from 2008 to 2020. To achieve this aim, we apply a non-radial, non-oriented undesirable-output SBM-DEA model under constant returns to scale, national, regional, and province-specific frontier comparisons. A Global Malmquist–Luenberger index is further used to decompose productivity change into efficiency change and technical change. The analysis shows an overall upward trajectory in national mean NTFP, a persistent eastern advantage, and co-movement among provincial, regional, and national frontier indices, while the dynamic productivity change indicates that productivity improvement was driven mainly by technical change rather than efficiency change. Sensitivity analyses using alternative undesirable-output specifications and cluster-based groupings support the robustness of the main conclusions and inform more targeted discussion and policy implications.