How Do Multidimensional Relational Networks Affect Large-Scale Grain Producers’ Adoption of Low-Carbon Fertilization Technology?

Fertilizer carbon emissions contribute the largest proportion to agricultural carbon emissions in China, while the extension of low-carbon fertilization technologies (LCFTs) is an effective measure to address this issue. Research suggests that the relational networks surrounding farmers significantly influence their carbon reduction behavior. This study conducted a field survey of 239 large-scale grain producers in August 2022 on China’s Poyang Lake Basin, which is the nation’s largest freshwater lake and a vital agricultural production area. Using cross-sectional data, probit and ordered probit models were employed to analyze the impacts of multidimensional relational networks (market, government, and social networks) on the adoption of LCFTs by large-scale grain producers. Additionally, a mediating-effect model was used to examine the pathways through which relational networks influence LCFT adoption. The findings indicated that relational networks not only increased the likelihood of large-scale grain producers adopting LCFTs but also enhanced the intensity of adoption. However, the effects of different relational networks on low-carbon behavior varied. The market network exerted the most prominent influence on LCFT adoption, followed by the social and government networks. A mediation analysis identified information sharing, demonstration effects, and resource guarantees as the mediating pathways between multidimensional relational networks and LCFT adoption by large-scale grain producers. Furthermore, a heterogeneity analysis revealed that the effects of multidimensional relational networks on LCFT adoption differed across generations and carbon intensity levels. The impact was greater among older grain producers than the younger generation, and those in the high-carbon-intensity group exhibited a stronger incentive compared to the medium- and low-carbon-intensity groups.