Land consolidation (LC) is an important land use activity in China. Especially under the background of global climate change and national carbon neutrality strategy, it is particularly important to study the relationship between LC and carbon cycle. Existing studies lack the carbon effects analysis of the whole process and the exploration of low-carbon optimization strategies. Therefore, from the perspective of the whole life cycle, this study applied Life Cycle Assessment (LCA) method to construct a research framework and accounting system for carbon footprint assessment of LC, and then explored the decision-making optimization path of low-carbon LC construction based on the ISM model. Results showed that: (a) The carbon effect of the project area was characterized as carbon sink during the whole life cycle of LC, with the amount of 492tCE. (b) Carbon effect varied among different stages of LC. The Restoration Period (RP) and the Benefit Period (BP) were characterized as carbon sink, while all the other stages were manifested as carbon emission. Among them, as to the carbon emission, the Construction Period (CP) played a decisive role with the most proportion, followed by DP, and the carbon effect of PP was negligible. (c) Based on the calculation of ISM model, 17 low-carbon measures were divided into three levels. The analysis results show that measures such as improving the quality of cultivated land and protecting the Ecological Redline would play a decisive role in the low-carbon development of LC. This study contributes to providing certain theoretical guidance and method reference for the realization of Low-Carbon LC project planning.

As artificial intelligence (AI) becomes increasingly integrated into spatial planning and governance, a central challenge is translating AI-enabled technical capabilities into institutionally usable spatial decision-making capacity. Taking territorial spatial governance (TSG) in China as a context-specific governance setting, this study combines bibliometric analysis and structured review to examine its knowledge evolution, technical pathways, and spatial decision-making constraints. The results show that: (1) research has progressively shifted from digital support and spatial monitoring toward intelligent analysis and governance-oriented applications; (2) four recurrent pathways of AI embedding are synthesized from the literature—sensing and identification, analytical simulation, decision support, and platform coordination; (3) the translation of these technical capabilities into spatial decision-making capacity is constrained by heterogeneous data foundations, limited model interpretability and transferability, institutional misalignment, and uneven organizational implementation; and (4) spatial decision-making capacity emerges through the interaction of technical pathways and constraint mechanisms under the multi-scale, differentiated, and rule-bound spatial logic of TSG. These findings provide an analytical basis for understanding how AI-enabled technical capabilities are translated from technical feasibility into governance usability under institutionally and spatially specific conditions.

Land consolidation (LC) is increasingly expected to support food security, ecological restoration, rural development, and climate mitigation, yet evidence on its carbon effects remains fragmented across engineering, ecological, spatial, and governance research. This review combines bibliometric mapping with structured evidence synthesis of 355 records from WoS and CNKI to examine knowledge evolution, analytical paradigms, and their integration. The field has expanded from component-based assessments of construction emissions, soil carbon, and land-cover change toward life-cycle accounting, carbon fractions, ecosystem-service interactions, spatial optimization, and policy evaluation. WoS-indexed and Chinese-language literature show distinct but increasingly convergent orientations shaped by differences in intervention contexts, disciplinary traditions, analytical scales, and available evidence. Four complementary paradigms are identified: carbon accounting, biogeochemical processes, spatial land systems, and decision support and governance. Together, these paradigms reveal interconnected carbon pathways but remain constrained by inconsistent accounting boundaries, weak process–scale–time linkages, and limited integration with land-governance decisions. Future research should therefore advance standardized life-cycle and multi-scale accounting, mechanism-based assessment of long-term carbon and ecosystem-service dynamics, and digitally and institutionally enabled low-carbon governance.