Although the global financial crisis has adversely affected finances, governments continue to prepare substantial budgets annually to promote major economic development projects. However, the projects that are actually implemented by the current system often encounter delays, low public usage after completion, or failure to achieve the original objectives or expected benefits. This issue demands that the government immediately establish the countermeasures needed for effective improvement. The objective of this study is to provide a mechanism for promptly terminating ineffective projects and for minimizing the risks of ineffective project execution and unused budgets. To achieve the goal of the efficient use of financial resources, this study applied a qualitative research method and the process of reengineering concepts to provide government agencies with standard operating procedures and to review checklists for project assessment. The analysis results can be applied to public infrastructure projects before or during execution and provide an exemplary urban policy evaluation model for a three-tier administration system. The contributions of this study aim to reduce the misuse and waste of national resources and to maximize the efficiency of resource use by developing a standard review procedure, effective assessment procedures, a checklist, and a review checklist that can serve as a reference for government agencies.

We provide a computational piecewise linear regression (CPLR) method with endogenous segmentation to construct accurate mathematical representations of data. The method models the unemployment–inflation relationship through a deterministic segmentation algorithm, rather than imposing a predetermined functional form to fit all data for one time. The CPLR method procedure sequentially fits local linear regressions on observations ordered by the explanatory variable and detects structural breakpoints according to a specific rule of breakpoint detection property in the coefficient of determination. A minimum segment size is imposed to ensure numerical stability constraint, and the optimal minimum sample size is determined by minimizing the global root mean squared error (RMSE) across candidate segmentations. We establish a theoretical property of the CPLR method algorithm, based on the fact that the breakpoint detection rule follows directly from the minimum least-squares error principle rather than from heuristic segmentation. The example results, using annual data from 1991 to 2022 as an illustration, reveal clear structural breaks and regime-dependent slope heterogeneity across the G7 economies. The number of segments varies substantially across countries, ranging from a stable two-segment structure in the United States to a six-segment structure in France. Positive slopes appear in specific unemployment ranges for several countries, except in the case of the United States. Comparisons with the results of conventional linear and nonlinear regressions show that in several countries, including Japan, Germany, Italy, and France, the CPLR representation provides a closer local fit to the observed data than conventional global linear or nonlinear specifications. The CPLR method, therefore, provides a deterministic and data-driven algorithm for detecting endogenous structural changes and constructing accurate piecewise linear representations without imposing prior theoretical assumptions or relying on statistical inference.