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This article examines the role of context and underlying assumptions in the formulation of econometric models for economic policy analysis. While econometric models are widely employed to support policy decisions, their construction is often treated as a purely technical process, overlooking the contextual factors that shape variable selection, model specification, and interpretation of results. This study explores how institutional settings, policy objectives, and behavioral considerations influence the assumptions embedded in econometric modeling. By drawing on policy documents, academic literature, and illustrative cases from applied economic research, the article highlights how implicit assumptions may affect the validity and relevance of econometric outcomes. The analysis emphasizes that econometric models are not value-neutral tools but are shaped by theoretical choices and contextual judgments made during their formulation. Understanding these dimensions is crucial to avoid misinterpretation of empirical findings and to enhance the usefulness of econometric analysis in policymaking. The study contributes to the methodological discussion in applied econometrics by encouraging greater transparency and reflexivity in model construction, particularly in the context of economic policy evaluation.      

In 2022, Indonesia will host the G20 Summit with one of the goals agreed upon by G20 members is to reduce carbon emissions produced by each G20 country. Therefore, a model design was made to consider the most optimal policy to overcome this. The research uses a dynamic system model, with the process of identifying and defining problems, system conceptualization, model formulation, model simulation, verification and validation of policy analysis models, and policy implementation. The most optimum result is the imposition of a tax per carbon emission on producing sources with an effectiveness of 20%.