Promotion of rural industrial revitalization through the development of the rural digital economy
Bibliographic record
Abstract
Introduction The rise of digital technologies has reshaped rural development strategies, offering new opportunities for industrial revitalization in agricultural regions. In China, the rural digital economy—spanning both infrastructure and digital service adoption—has emerged as a critical driver of localized innovation. This study explores the mechanisms through which digital transformation influences rural industrial upgrading. Using a structured survey in a major navel orange production area, the study examines how hardware and software elements of digitalization affect farmers’ innovation intentions, entrepreneurial behaviors, and outcome perceptions. By identifying heterogeneity across business models and farm scales, the study provides empirical insights into the role digital inclusion plays in revitalizing rural economies. Methods This study draws on 1,042 survey responses from a representative navel orange-producing region in China. Key variables reflect three dimensions of rural industrial revitalization: innovation intentions, entrepreneurial action, and perceived outcomes. The independent variables reflect the development of the digital economy through two dimensions: digital infrastructure and service usage. Ordered Probit and OLS models were applied to estimate relationships, with robustness checks performed using instrumental variables to address endogeneity. Instrument relevance and validity were confirmed through standard econometric tests. Heterogeneity was further examined by disaggregating impacts across production types and farm sizes. Results Findings demonstrate that both infrastructure (hardware) and service use (software) aspects of the rural digital economy significantly enhance farmers’ innovation intention, entrepreneurial engagement, and outcome perception. These effects remain statistically significant and become more pronounced after addressing endogeneity. While hardware shows limited effects across different business types, software-related digital adoption significantly benefits most producers. Additionally, the digital economy’s impact on entrepreneurial action and outcomes is more pronounced among medium- and large-scale farms than smaller producers. Three mechanisms—employment, income growth, and improved well-being—mediate this effect. Discussion The results highlight the transformative potential of rural digital economy development in advancing industrial revitalization. Tailored digital infrastructure, training, and inclusive service access are critical to unlocking innovation capacity at the household level. To enhance equitable digital transformation in agriculture, policies should prioritize narrowing digital divides in underdeveloped regions and facilitate the adoption of adaptable digital farming models, including smart production systems and agricultural traceability platforms. Beyond infrastructure, broader institutional, household, and community efforts—ranging from financial literacy to organizational participation—must complement digital investment. Future studies should expand the scope, adopt longitudinal designs, and explore institutional drivers to deepen the understanding of sustainable rural transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".