Agricultural New Quality Productive Forces and Green High-Quality Development of China’s Apple Industry: Evidence from Double Machine Learning

Green high-quality development (GHD) is essential for transforming China’s apple industry from extensive to sustainable production. As a core embodiment of green productivity, agricultural new quality productive forces (ANQPFs)—characterized by high technology, efficiency, and quality—offer a new pathway for overcoming resource and environmental constraints in the sector. Using a balanced panel of 31 Chinese provinces from 2011 to 2023, this study estimates the effect of ANQPFs on the GHD of the apple industry with a partially linear double/debiased machine learning (DML) model that combines cross fitting with random forest learners. ANQPFs are measured by the entropy-weighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method across three dimensions: new agricultural laborers, new means of labor, and new objects of labor. The results show that ANQPFs significantly promote GHD in the apple industry, and this finding is robust to winsorization tests, alternative base learners, and an instrumental variable specification (DDML-IV). Mechanism analysis identifies two indirect channels—agricultural green innovation and agricultural socialized services. Heterogeneity analyses show that this empowering effect is concentrated in eastern regions and high-urbanization provinces. Policy implications include locally tailored ANQPF cultivation, stronger green technology R&D, and expanded socialized service provision in lagging apple-producing regions.