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Satellite remote sensing, machine learning, ecological monitoring, and household-level economic analysis increasingly provide complementary information on pastoral systems. Yet indicators in these domains are often interpreted as measuring the same underlying construct. Grassland gross primary productivity (GPP), vegetation condition, soil organic carbon (SOC), livestock production, value capture, and household welfare are related but distinct outcomes. This perspective develops the Productivity, Condition, Carbon, and Livelihood (PCCL) framework for Mongolian rangelands with explicit inference boundaries. The framework distinguishes six analytical components: ecological productivity, rangeland condition, soil carbon and related belowground processes, livestock production, value capture efficiency, and household welfare and resilience. It also separates measurement, prediction, statistical association, and causal inference. Recent studies in Mongolia that use remote sensing, machine learning, panel data, soil carbon information, and household outcomes illustrate different parts of this system rather than independently replicating a single relationship. Broader evidence shows that climatic variability can dominate livestock effects on primary productivity at some temporal scales, while grazing effects on SOC vary with intensity and environmental context. Accordingly, high remotely sensed productivity should not be interpreted automatically as healthy rangeland, increased SOC, or higher pastoral income. The paper introduces no new data or empirical estimates. Instead, the paper provides an organizing architecture for integrating ecological and economic evidence, preserving variable provenance, limiting interpretation to what research designs support, and generating testable propositions for future research across multiple scales.