How Policy Mix and Digital Literacy Promote Adoption of Low-Carbon Fertilization Technology: Experimental Evidence from Shandong Farmers

Introduction

Global climate change, soil degradation, and chemical pollution are threatening sustainable agricultural development and food security. Agriculture is one of the major sources of greenhouse gases, with nitrous oxide emissions from fertilizer application being particularly prominent. As the world's largest agricultural country and fertilizer consumer, China's fertilizer-related carbon emissions have been declining since 2015, but further promotion of low-carbon fertilization technologies (LCFTs), such as soil testing and formulated fertilization, organic fertilizer substitution, and slow/controlled-release fertilizers, is still needed. However, these technologies are difficult to significantly improve farmers' short-term economic benefits and rely on policy incentives. Therefore, accurately identifying farmers' preferences for different policy instruments is crucial for designing efficient policy mixes.

Research Methods and Key Findings

In June 2026, a research team from Shandong Agricultural University and Shandong University of Technology published a paper in *Frontiers in Sustainable Food Systems*. Based on a choice experiment with 732 farmers in Shandong Province, they used a mixed logit model to analyze farmers' preferences for four policy attributes—subsidies, insurance coverage, technical support, and multi-channel promotion—and their combinations. Key findings include:

• Policy preference ranking: Farmers have significant positive preferences for subsidies, insurance, technical support, and promotion, but with clear heterogeneity. Preferences are strongest for subsidies and insurance, followed by technical support, and weakest for promotion.
• Complementarity and substitution: Subsidies and insurance, as well as subsidies and technical support, show strong complementarity; while insurance and promotion exhibit a substitution tendency. This means that a policy combination providing both subsidies and insurance is more effective than using either alone, while insurance and promotion may have functional overlap.
• Moderating role of digital literacy: Farmers with higher digital literacy have stronger preferences for various policy incentives, and improving digital literacy helps enhance policy effectiveness.
• Marginal preference trade-offs: Farmers are willing to give up relatively higher subsidies in exchange for insurance coverage, followed by giving up subsidies for technical support, and the least for promotion. This suggests that insurance may be the most attractive non-subsidy policy tool.

Industry Impact

This study provides empirical references for designing low-carbon agricultural policies under smallholder contexts in developing countries:

• Agricultural production efficiency: Precise policy mixes can incentivize farmers to adopt efficient fertilization technologies, reduce excessive fertilizer use, lower agricultural carbon footprints, while maintaining or increasing yields.
• Farm operation models: Policy support directions influence farmers' input decisions; the combination of insurance and subsidies can reduce the risk of technology transition and promote sustainable production behaviors.
• Agricultural investment direction: Policymakers should prioritize synergistic schemes such as "subsidies + insurance" or "subsidies + technical support" to avoid resource waste from redundant policies.
• Digital agriculture and information dissemination: The positive effect of digital literacy indicates that promoting digital agricultural information platforms and training can increase policy acceptance and accelerate the diffusion of low-carbon technologies.

Future OutlookLooking ahead 3-5 years, as China's "dual carbon" strategy deepens and digital rural construction progresses, the low-carbon transformation of agriculture will exhibit the following trends:

• Refined Policy Packages: Local governments will design tiered policy packages based on the heterogeneity of local farmers. For example, farmers with low digital literacy may receive more technical training, while those with high literacy may be offered insurance and subsidies.
• Integration of Digital Tools: Agricultural AI and precision agriculture platforms can embed policy incentive recommendation systems to deliver personalized optimal policy packages to farmers.
• Carbon Finance Innovation: Insurance products linked to carbon emission reduction targets may emerge as a new direction, such as "low-carbon fertilization insurance," which reduces the risk for farmers adopting new fertilizers.
• Global Applicability: The experimental methodology of this study is applicable to other smallholder farming economies in Asia and Africa, providing a paradigm for global agricultural carbon reduction policies.