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| By-Production Technology DEA× | Nätverksanalys av datainhämning (Network DEA)× | |
|---|---|---|
| Ämnesområde≠ | Beslutsfattande | Effektivitetsanalys |
| Familj≠ | MCDM | Regression model |
| Ursprungsår≠ | 2005 | 2000 |
| Upphovsperson≠ | Färe, Grosskopf, Noh et al. | Färe & Grosskopf |
| Typ≠ | Non-parametric efficiency with undesirable outputs and by-products | Multi-stage nonparametric efficiency model |
| Ursprungskälla≠ | Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400-410. DOI ↗ | Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. DOI ↗ |
| Alias≠ | By-Production DEA, Joint Production DEA | Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi |
| Närliggande | 2 | 2 |
| Sammanfattning≠ | By-Production Technology DEA is a variant of Data Envelopment Analysis designed for production systems that generate both desirable outputs and undesirable by-products or emissions. Rather than ignoring or arbitrarily penalizing undesirable outputs, this method explicitly models them as joint products of the production process. It evaluates efficiency while accounting for the trade-off between desired production and environmental impact. | Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model. |
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