เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Structural Decomposition Analysis× | การแยกองค์ประกอบแบบ LMDI× | |
|---|---|---|
| สาขาวิชา≠ | เศรษฐศาสตร์ | ความยั่งยืน |
| ตระกูล≠ | Process / pipeline | Regression model |
| ปีกำเนิด≠ | 1998 | 2005 |
| ผู้ริเริ่ม≠ | Rose & Casler; Dietzenbacher & Los (decomposition formalization) | B. W. Ang |
| ประเภท≠ | Comparative-static decomposition of input-output change into structural determinants | Index-based factor decomposition |
| แหล่งต้นตำรับ≠ | Dietzenbacher, E., & Los, B. (1998). Structural decomposition techniques: sense and sensitivity. Economic Systems Research, 10(4), 307–324. DOI ↗ | Ang, B. W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 33(7), 867–871. DOI ↗ |
| ชื่อเรียกอื่น | SDA, Input-Output Structural Decomposition, IO Structural Decomposition Analysis, Additive Structural Decomposition | Logarithmic Mean Divisia Index, LMDI-I Additive Decomposition, LMDI-II Multiplicative Decomposition, Logaritmik Ortalama Divisia İndeksi |
| ที่เกี่ยวข้อง≠ | 4 | 2 |
| สรุป≠ | Structural decomposition analysis (SDA) explains how an input-output quantity — total output, value added, energy use, or emissions — changed between two periods by attributing the change to its underlying structural determinants, chiefly shifts in production technology (the Leontief inverse) versus shifts in the level and composition of final demand. Built on comparative statics over two or more comparable tables, SDA expresses the difference as a sum of effects and resolves the indeterminacy of multiplicative terms by averaging the two polar decomposition forms, the convention standardized by Dietzenbacher and Los. | Log-Mean Divisia Index (LMDI) Decomposition is a quantitative technique for attributing changes in an aggregate indicator — most commonly energy consumption or CO₂ emissions — to its underlying driving factors, such as activity level, structural mix, and intensity. Introduced in its definitive practical form by B. W. Ang in 2005, LMDI builds on Divisia index theory and uses the logarithmic mean as a weighting function to achieve a mathematically perfect, residual-free decomposition. |
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