Evaluating Preliminary Estimation Bias and Operational Delays in Indian Public Procurement: Evidence from Infrastructure Projects and Alignment with SDGs

This study examines the empirical nexus between early-stage budgeting bias, post-award contract execution timelines, and macroeconomic sustainability within India’s premier construction agency, the Central Public Works Department (CPWD). Moving away from subjective stakeholder surveys, this paper uses an unaggregated, project-specific baseline built from two large-scale historical datasets (n = 994 for planning; n = 861 for execution). A parametric paired t-test confirms a systemic pre-award overestimation trend (t (993) = 14.83, p < .001), with baseline preliminary estimates exceeding final expenditures by an average of ₹1,319.98 lakhs. This practice creates an artificial capital lock-up that directly undermines SDG 9. Conversely, multiple linear regression reveals that post-award schedule delays exhibit a minor but statistically significant negative partial correlation with cost overruns (β = -.09, p = .012), supporting the budgetary cushion hypothesis. Prolonged project standstills do not drive linear cost growth; instead, they cause execution halts that freeze expenditures while causing severe environmental degradation, raw material waste, and compromises on green design standards (SDG 12, SDG 13). Ultimately, this paper outlines data-driven validation filters, such as Reference Class Forecasting, to mitigate artificial resource tie-ups and optimize public funds for sustainable infrastructure networks.