Measuring Green Efficiency and Predicting Carbon Emissions in Agriculture After Fertilizer and Pesticide Reduction Actions: A Study from Shandong Province, China

Abstract

Reducing agricultural carbon emissions while maintaining farm productivity is central to sustainable agricultural development in China. Focusing on Shandong Province, this study estimates agricultural carbon emissions from 2000 to 2021 using the Intergovernmental Panel on Climate Change approach and measures agricultural green total factor productivity with the epsilon-based measure–global Malmquist–Luenberger (EBM–GML) index. An extended STIRPAT model and scenario analysis are then used to examine emission drivers and project the implications of alternative fertilizer-reduction and pesticide-reduction pathways. To complement the aggregate analysis, we use survey data from 174 wheat farms and a slack-based measure of ecological efficiency to assess whether lower chemical input use is compatible with farm performance. Provincial agricultural carbon emissions followed an inverted V-shaped trajectory, rising initially and declining after 2008. Under the simulated reduction scenarios, fertilizer and pesticide use in 2035 are projected to fall by 0.85–1.13 million tons and 0.04–0.05 million tons, respectively, relative to 2020, while agricultural carbon emissions decline by 14.09–20.18 million tons. At the farm level, fertilizer and pesticide use are negatively associated with ecological efficiency. Nonlinear estimates further indicate that fertilizer application beyond approximately 121 jin per mu for yield and 103 jin per mu for net returns is associated with diminishing farm performance. Together, the macro-level and micro-level evidence suggests that more efficient chemical input use can support emission reduction without necessarily compromising productivity. Policies should therefore combine input optimization with green technological innovation and low-carbon farming practices.  

Received: 1 December 2025 | Revised: 29 July 2026 | Accepted: 12 August 2026

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data are available from the corresponding author upon reasonable request.

Author Contribution Statement

Chenyang Liu: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Wei Guo: Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization.