Who performs better? The heterogeneity of grain production eco-efficiency

Evidence from unsupervised machine learning

Datos Bibliográficos

ID11368308
AutoresHanjie Wang (0000-0001-9400-814X), Wang Hanjie (Southwest University), Jiali Han (0000-0001-5478-8663, Southwest University of Political Science & Law, autor de correspondencia), Xiaohua Yu (0000-0003-4257-8081, University of Göttingen, autor de correspondencia)
Año2024
Volumen106
Páginas107530
Fecha de publicación2024-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Impact Assessment Review (JOURNAL)
Identificadores de la revistaISSN: 0195-9255 • E-ISSN: 1873-6432
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.eiar.2024.107530
OpenAlexW4396764074
IdiomaEN
Citas recibidas5
Referencias citadas51

This study contributes to the existing literature by providing evidence for the microheterogeneity of agricultural eco-efficiency with machine learning techniques. Using the comprehensive dataset from the “China Rural Revitalization Survey” (CRRS), we employ unsupervised machine learning via the K-means clustering algorithm to dissect the heterogeneity of grain production eco-efficiency from the perspective of farmers. Our findings reveal the classification of grain producers into three distinctive groups: large-scale farmers, conventional self-sufficiency farmers, and novel smallholders. Notably, while large-scale farmers exhibit high grain production volumes, they concurrently generate substantial carbon emissions, reflecting the lowest level of eco-efficiency. Conversely, the novel smallholders emerge as a promising policy inclination due to their superior eco-efficiency, while conventional self-sufficiency farmers exhibit relatively lower eco-efficiency levels. Consequently, we argue that improving grain production eco-efficiency should fully consider the heterogeneity of millions of producers. Overall, this study provides a new perspective that enriches our understanding of the heterogeneity of grain production eco-efficiency, which is crucial for enhancing the effectiveness of policy interventions

Economics · Machine learning · Microeconomics · Production (economics · Computer Science · Efficiency Analysis Using DEA · Energy, Environment, Economic Growth · Environmental Impact and Sustainability · Artificial Intelligence

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