{ "background": "The evaluation of industrial machinery fleet adoption in developing economies is critical for infrastructure development and economic growth. Existing assessment methods often lack rigorous counterfactual analysis, leading to unreliable estimates of causal impact and adoption rates.", "purpose and objectives": "This article presents a robust methodological framework for quantifying the causal effect of policy interventions and market factors on the adoption rates of industrial machinery fleets. The objective is to provide engineers and planners with a formal, quasi-experimental tool for programme evaluation.", "methodology": "We detail a difference-in-differences (DiD) modelling approach, specifying a two-way fixed effects regression: $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the machinery adoption metric for region $i$ in period $t$. The key parameter $\\beta$ is identified under the parallel trends assumption. Inference is based on cluster-robust standard errors at the regional level.", "findings": "As a methodology article, this paper presents no empirical results. The framework's application is illustrated with a hypothetical scenario, demonstrating that a simulated tax incentive policy is estimated to increase fleet adoption by approximately 15 percentage points, with a 95% confidence interval of [11, 19].", "conclusion": "The proposed DiD framework provides a statistically rigorous and transportable methodology for evaluating adoption dynamics in the capital-intensive machinery sector. It formally addresses confounding variables that bias simpler before-after comparisons.", "recommendations": "We recommend that engineering firms and government agencies implement this DiD framework in pilot studies to evaluate procurement programmes. Future methodological work should extend the model to incorporate spatial autocorrelation.", "key words": "difference-in-differences, quasi-experimental design, programme evaluation, machinery fleets, adoption modelling, causal inference", "contribution statement": "This paper provides the first formal difference-in-differences econometric framework tailored to the