Vollständiger Abstract
Worum geht es in dieser Arbeit?
Digital technologies are increasingly integrated into agricultural production, rural services, finance, and information infrastructure, creating opportunities to improve low-carbon agricultural performance. This study develops an applied evaluation framework linking rural digital technology systems with agricultural carbon emission efficiency. Using panel data from 30 Chinese provinces during 2011–2023, we construct a digital village technology index covering digital infrastructure, digital financial infrastructure, digital service platforms, and digital living scenarios. Agricultural carbon emission efficiency is measured using a super-efficiency slacks-based measure (SBM) model incorporating inputs, desirable output, and undesirable carbon emissions. Two-way fixed-effects models, instrumental-variable estimation, mediation tests, moderation analysis, and heterogeneity tests are then employed. The results show that rural digital technology systems are positively associated with agricultural carbon emission efficiency. Agricultural technological innovation and agricultural socialized services are examined as potential channels, and fiscal support for agriculture is considered as an institutional condition that may shape the DVC–ACEE relationship. The subgroup results suggest that this association is more evident in flat-terrain and large-/medium-urban-scale regions. This study provides an applied framework for evaluating how digital infrastructure and service systems are linked to low-carbon agricultural transformation.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Yuming Li, Liudi Li, Xiuguang Bai, Bingbing Wei, Jing Zhang
- Quelle
- Applied Sciences
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2076-3417
- Zitationen
- 0 laut Crossref
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Zitierfähiger Nachweis
Yuming Li, Liudi Li, Xiuguang Bai, Bingbing Wei, Jing Zhang (2026). Digital Village Technologies and Low-Carbon Agricultural Performance in China: An Applied Evaluation Using Super-Efficiency SBM and Provincial Panel Data. Applied Sciences. https://doi.org/10.3390/app16178752
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