Using machine learning to model technological heterogeneity in carbon emission efficiency evaluation : the case of China's cities
Ailun Wang, Shuo Hu, Jianglong Li
| Year of publication: |
2022
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|---|---|
| Authors: | Wang, Ailun ; Hu, Shuo ; Li, Jianglong |
| Published in: |
Energy economics. - Amsterdam : Elsevier, ISSN 0140-9883, ZDB-ID 795279-X. - Vol. 114.2022, p. 1-15
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| Subject: | Carbon emission efficiency | Directional distance function | Heterogeneity | Index decomposition analysis | Machine learning | Reduction potential | Treibhausgas-Emissionen | Greenhouse gas emissions | Künstliche Intelligenz | Artificial intelligence | China | Luftverschmutzung | Air pollution | Technische Effizienz | Technical efficiency | Technischer Fortschritt | Technological change | Data-Envelopment-Analyse | Data envelopment analysis | Umweltbelastung | Pollution | Effizienz | Efficiency |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.eneco.2022.106238 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013477406