Green agriculture represents the future of agricultural transformation in developing countries, such as China. Identifying an effective resource combination path is vital for enhancing the green quality of agriculture in these nations. This study draws on the resource spatial mismatch theory from New Economic Geography, using a “multisource heterogeneous” approach that combines qualitative comparative analysis (QCA) with the EBM-GML index measurement model. Using panel data from 2005 to 2021, the study investigated the effects and mechanisms of spatial resource combinations on improving green agricultural quality. The key findings are as follows: (1) While improving spatial resource misallocation helps boost green agricultural quality, the diversity of resource combination patterns has diminished, decreasing from five modes in 2005 to four in 2021. (2) In terms of mechanisms, reducing externalities, such as pollutant emissions, while strengthening material and human capital offers a potential pathway for improvement. (3) Negative externalities, including emissions from fertilizers and petroleum, significantly hinder improvements in green agricultural quality. (4) The absence of sufficient pesticide and fertilizer resources is a critical factor influencing the outcome. These findings provide practical insights for developing countries seeking to enhance regional resource allocation efficiency and improve agricultural green quality. Additionally, they contribute theoretical support to the enrichment of theories on resource allocation and sustainable agricultural development.

Frequent shifts in economic policies not only inject uncertainty into the economic landscape but also pose significant challenges to corporate endeavors in green technological innovation. Drawing on a dataset of Chinese A-share listed companies spanning 2008 to 2020, this research delves into the repercussions of economic policy uncertainty on the green technological pursuits of manufacturing firms and elucidates the underlying dynamics at play. The empirical evidence underscores a marked reluctance among companies to champion green technological innovation in the face of economic policy ambiguity, a stance that holds water even after rigorous robustness checks. Delving into the mechanisms, the study pinpoints heightened financial constraints and a diminishing risk appetite within the managerial ranks as pivotal deterrents steering firms away from green innovation projects amidst such uncertainty. Intriguingly, the adverse interplay between economic policy uncertainty and green innovation is especially accentuated in firms marked by tenuous government–business affiliations, pronounced monopolistic inclinations, lax intellectual property safeguards, minimal pollution footprints, and a skewed labor-to-capital composition. This investigation augments the scholarly discourse on the nexus between economic policy volatility and corporate green innovation, shedding light on strategic imperatives for emerging economies as they chart out future environmental blueprints and cultivate a conducive milieu for green innovation.

The digital economy promotes economic development, industrial upgrading, and environmental protection. In this study, we calculated green total factor productivity (GTFP) based on the SBM-DDF model and used the entropy method and principal component analysis to calculate a digital economy index. We used panel data from 282 Chinese cities to measure the driving effect of the digital economy on green total factor productivity. The study results show that the digital economy significantly increases GTFP. We then assessed the heterogeneity of this impact. We also explored the mechanisms by which the digital economy promotes green development and found that the digital economy can indirectly increase industrial production efficiency by promoting innovation in green technologies.