Measuring Agricultural Modernization and Diagnosing Development Bottlenecks in China’s Provinces: Evidence from high-quality development and new quality productivity

Authors

  • Shuokai Wang 1 https://orcid.org/0009-0002-7439-1764
    • Formal Analysis
    • Investigation
    • Methodology
    • Visualization
    • Writing – Original Draft Preparation
    • Conceptualization
    • Data Curation
    • Project Administration
    • Resources
    • Software
    • Validation
    • Writing – Review & Editing
  • Bo Zeng 1,* https://orcid.org/0009-0007-6194-4645
    • Conceptualization
    • Project Administration
    • Validation
    • Writing – Review & Editing
    • Methodology
    • Formal Analysis
    • Resources
    • Supervision
1 School of Economics and Management, Beijing Forestry University, Beijing, China 100091
Corresponding author: zengbo@bjfu.edu.cn

DOI:

https://doi.org/10.63646/TYUX2614

Keywords:

high-quality development, new quality productivity, agricultural modernization, stacking ensemble learning, K-means clustering, obstacle-degree analysis

Abstract

This study presents a provincial framework for assessing agricultural modernization through high-quality development and new quality productivity. The reported 2020–2022 analysis combines indicator scoring, clustering, obstacle diagnosis, and a stacking-based prediction design. It describes spatial variation and potential province-specific constraints. Table 1 lists 28 indicators; the complete indicator-to-code mapping, X-category scores, and model-comparison outputs were unavailable for independent verification. The reported rankings, score thresholds, and claims of superior predictive performance therefore require verification against the underlying data and code. The framework and policy implications should be interpreted subject to this verification.

How to Cite

Wang, S., & Zeng, B. (2025). Measuring Agricultural Modernization and Diagnosing Development Bottlenecks in China’s Provinces: Evidence from high-quality development and new quality productivity. Journal of Business and Data Analytics, 3(4), 1-24. https://doi.org/10.63646/TYUX2614

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