| Issue |
BIO Web Conf.
Volume 222, 2026
2026 2nd International Conference on Agriculture and Resource Economy (ICARE 2026)
|
|
|---|---|---|
| Article Number | 01007 | |
| Number of page(s) | 4 | |
| Section | Sustainable Agriculture and Resource Economy | |
| DOI | https://doi.org/10.1051/bioconf/202622201007 | |
| Published online | 16 February 2026 | |
Construction of an Evaluation Model for the Influence of Smart Agriculture Development on Agroforestry Economic Growth
1 Xuchang Ruiheng Building Materials Co., Ltd., Xuchang City, Henan Province, 461500, China
2 School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
To scientifically quantify the correlation mechanism between smart agriculture development and agroforestry economic growth, this study takes technology innovation theory as the logical support, constructs a multi-dimensional evaluation model based on the extended Cobb-Douglas production function, conducts empirical verification using standardized simulated evaluation data, synthesizes the smart agriculture development evaluation index through the entropy weight method, and verifies the model's adaptability by combining methods such as the fixed effects model, robustness test, and heterogeneous scenario comparison. The experimental results show that the peak goodness-of-fit of the constructed evaluation model reaches 0.912, and the evaluation coefficient of smart agriculture development on agroforestry economic growth ranges from 0.24 to 0.31, all passing the significance test. This provides a replicable technical path for the quantitative research on the economic impact of smart agriculture.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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