To address the disconnection between carbon emission accounting and building information modeling (BIM), inconsistent accounting boundaries, the lack of systematic carbon optimization methods, and the absence of construction-stage tracking in hydraulic engineering projects, a BIM-based life-cycle carbon emission accounting and optimization method is proposed. A BIM carbon information model is established based on the IFC standard, with a three-level “component-material-emission factor” mapping that enables automatic extraction of material quantities and automatic updating upon design changes. An emission factor database covering four stages (material production, construction, operation and maintenance, demolition and recycling) is constructed and a life-cycle carbon accounting model with itemized, traceable calculation sheets is developed. Three-dimensional heat maps are used to visualize component-level carbon distribution. Taking life-cycle carbon emission and total project cost as objectives, the NSGA-II algorithm is employed to derive the carbon-cost Pareto front under structural safety, schedule and cost constraints. IoT-based real-time data enables dynamic carbon tracking during construction. A case study of a concrete gravity dam shows that the total life-cycle emission is 0.3945 MtCO2, with the material production stage accounting for 84.1% and cement production accounting for 72.4% of the total, making it the largest emission source. Substituting slag or fly-ash cement reduces cement-related emissions by 24.7%-28.8% (equivalent to 21.3%-24.8% of the material production stage). Rail-road combined transport and waterway-road combined transport cut aggregate transport emissions by 61.9% and 57.1%, respectively. NSGA-II yields 15 Pareto-optimal solutions; within the near-zero-cost interval, emissions are reduced by 19.8% at only 0.2% additional cost (unit abatement cost about 26 CNY/t), while the deep-decarbonization scenario reduces emissions by 33.5% with the marginal abatement cost rising to 559 CNY/t, revealing an increasing marginal abatement cost pattern. Dynamic tracking corrects the overrun deviation from +8.2% to +3.5%. The proposed method provides systematic technical support for low-carbon construction of hydraulic engineering projects.

This study presents a comparative scientometric analysis of 526 publications from the Web of Science Core Collection and China National Knowledge Infrastructure (CNKI) database to decode the global research trajectory on urban green space (UGS) carbon sinks and to delineate China’s distinctive role. Analysis reveals a clear evolution in research focus and output. Early work (before 2011) established foundational concepts. The subsequent period (2011–2015) witnessed significant methodological diversification, driven by advances in remote sensing and quantitative carbon flux assessment. Following the Paris Agreement in 2015, research emphasis shifted markedly towards policy-integrated applications and the exploration of synergies between carbon sequestration and sustainable urban development. A key finding is the rapid ascendancy of Chinese research output post-2020, a surge that has established China as a leading contributor to the field and is closely aligned with the national “dual carbon” goals. The synthesis identifies a critical methodological shortcoming: an “ecological analogy trap,” in which an over-reliance on forest-derived biomass equations systematically overlooks urban-specific anthropogenic factors. The methodological frontier is increasingly defined by the integration of multi-source remote sensing (e.g., LiDAR-optical fusion) and machine learning to overcome urban heterogeneity, though challenges in model transferability persist. The study concludes that while international scholarship increasingly emphasizes the coupling of carbon sink analysis with socio-ecological equity, China’s policy-driven research has prioritized the optimization of technological pathways for carbon accounting. To advance the field, future research must develop urban-specific protocols, leverage advanced computational techniques, and foster interdisciplinary, multi-scale frameworks to provide robust decision-support for achieving urban carbon neutrality.