Soil organic carbon (SOC) sequestration projects require unbiased, precise and cost-effective Monitoring, Reporting, and Verification (MRV) systems that balance sampling costs against uncertainty deductions imposed by regulatory frameworks. Design-based estimators guarantee unbiasedness but cannot exploit auxiliary data. Model-based approaches (VCS Methodology VT0014 v1.0 (2025)) can improve precision but require independent validation for each project. Model-assisted estimation offers a robust compromise, combining model predictions with probability sampling to retain design-based guarantees while improving precision. We evaluate the scientific integrity and efficiency of the simple regression estimator (SRE), a well-known model-assisted estimator, via an extensive simulation study. Our simulations span diverse SOC stock variances, sample sizes, and model performances. We assess three core properties: empirical bias, empirical confidence interval coverage, and precision gain relative to the design-based Horvitz-Thompson estimator (HTE). Results show negligible bias and valid coverage probabilities for n > 40, regardless of SOC stock variance. Below this threshold, variance approximations and normality assumptions yield unreliable uncertainty estimates. With correlated ancillary variables (r^2 = 0.3), SRE achieves 30% precision gains over HTE. With uncorrelated variables, no gains are observed, but performance converges to HTE for n >= 40. Model-assisted estimation can enhance project economics without compromising scientific rigor. Regulators should permit such estimators while mandating minimum sample size thresholds. Project proponents should routinely employ such estimators when correlated ancillary variables exist. The industry should prioritize the retrieval of high-quality, project-specific covariates to maximize precision gains and thereby the project economics.

翻译：土壤有机碳（SOC）封存项目需要无偏、精确且成本效益高的监测、报告与核查（MRV）系统，以平衡抽样成本与监管框架所要求的不确定性削减。基于设计的估计量能保证无偏性，但无法利用辅助数据。基于模型的方法（如VCS方法论VT0014 v1.0（2025））可提高精度，但需针对每个项目进行独立验证。模型辅助估计提供了一种稳健的折中方案，它将模型预测与概率抽样相结合，在保持基于设计之保证的同时提升精度。我们通过广泛的模拟研究，评估了简单回归估计量（SRE）——一种知名的模型辅助估计量——的科学严谨性与效率。我们的模拟涵盖了多样化的SOC储量方差、样本量及模型性能。我们评估了三个核心特性：经验偏差、经验置信区间覆盖率，以及相对于基于设计的霍维茨-汤普森估计量（HTE）的精度增益。结果表明，当n > 40时，无论SOC储量方差如何，偏差均可忽略不计，且覆盖率有效。低于此阈值时，方差近似与正态性假设会导致不可靠的不确定性估计。当存在相关性辅助变量（r^2 = 0.3）时，SRE相比HTE可实现30%的精度增益。对于不相关变量，虽未观察到增益，但当n >= 40时，其性能收敛于HTE。模型辅助估计可在不损害科学严谨性的前提下提升项目经济性。监管机构应允许使用此类估计量，同时规定最小样本量阈值。项目方在存在相关性辅助变量时应常规采用此类估计量。业界应优先获取高质量、项目特定的协变量，以最大化精度增益，从而提升项目经济性。