## 5 Conclusions  
The LUEmax values were quantified using data from 23 global flux stations, and the change patterns in LUEmax across various vegetation types and climate zones were analyzed. The extent of the significant increases or decreases in LUEmax during different phenological stages of vegetation growth was evaluated using trend analysis methods. The contribution rates of environmental factors were determined using the Geodetector method. The key findings and conclusions are as follows: (1) LUEmax of the same vegetation type varies across different climate types. The annual average LUEmax values were 0.084 \(\pm\) 0.025 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)m1 PPFD for ENF, 0.078 \(\pm\) 0.05 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)m1 PPFD for DBF, 0.08 \(\pm\) 0.05 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)m1 PPFD for GRA, 0.06 \(\pm\) 0.04 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)m1 PPFD for WET, and 0.05 \(\pm\) 0.03 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)m1 PPFD for CORN. (2) The LUEmax varies significantly across different growth stages and under various growth conditions. During the early growth stages of plants, the LUEmax tends to be relatively low, as young plants have not yet fully developed their photosynthetic systems. As the plant grows, the LUEmax gradually increases, reaching its peak during the growth cycle. More variable climates (e.g., polar and alpine climates) are associated with greater fluctuations and peaks in LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show lower LUEmax values. (3) The mechanisms influencing the LUEmax of different vegetation types vary significantly across diverse environmental conditions. The LUEmax of forests (ENF and DBF) are largely influenced by temperature and radiation, while grasslands and wetlands are more responsive to vegetation indices and hydrological factors. CORN exhibits a unique sensitivity to EVI and temperature conditions. The results show that various influencing factors have different combined effects on the LUEmax of different vegetation types. Radiative and TA combinations predominantly influence ENF and DBF, while CORN and WET show a stronger dependency on SWC and EVI factors. GRA demonstrates a unique sensitivity to EVI and TA. The findings of this study play an important role in advancing the theoretical development of gross primary productivity (GPP) models and enhancing the accuracy of carbon sequestration simulations in terrestrial ecosystems. Conceptualization, D.H. and H.C.; methodology, Y.H., D.H. and H.C.; software, Y.H. ; validation, Y.H., Y.S. and D.H.; formal analysis, H.C. and D.H.; investigation, Y.S. ; resources, H.C.; data curation, Y.S. ; writing--original draft preparation, Y.H., Y.S., S.Z. and D.H.; writing--review and editing, D.H., H.C. and Y.S. ; visualization, D.H. and Y.H. ; supervision, H.C., D.H. and Y.S. ; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Jiangxi Provincial Key Research Base of Philosophy and Social Sciences Project (Remote sensing assessment of vegetation carbon sinks in Poyang Lake wetland ecosystem and strategies for carbon sequestration and enhancement, 24 ZXSKJD30); Jiangxi Provincial Natural Science Foundation (20224 BAB213038); Jiangxi Provincial Department of Education Science and Technology Project (GJ)2200740); East China University of Technology Ph.D. Project (DHBK2019179); Open Fund of the State Key Laboratory of Remote Sensing Science (OFSLRSS202308); Jiangxi Provincial Water Conservancy Science and Technology Project, China (202325 ZDKT12); and Science and Technology Innovation Program from Water Resources of Guangdong Province (2024-02). The data supporting this study are available upon request from the author. The authors sincerely appreciate the editors and anonymous reviewers for their valuable feedback on this manuscript. The authors declare no conflicts of interest. A list of abbreviations used in this paper are shown below.