Simulating and projecting agricultural non-CO2 greenhouse gas emissions in China based on a bottom-up model

Agriculture is one of the world’s major anthropogenic sources of methane (CH₄) and nitrous oxide (N₂O), and reducing agricultural non-CO₂ greenhouse gas (GHG) emissions is essential for achieving the 1.5 °C climate target. As the world’s largest agricultural non-CO₂ emitter, China must deeply transform its agricultural sector to reach carbon neutrality by 2060, which requires a systematic and region-specific assessment framework for emissions and mitigation potential.

A research team from Renmin University of China and the International Institute for Applied Systems Analysis (IIASA) has developed the Agricultural non-CO₂ Greenhouse gAs InveNtory (AGAIN) model, addressing the current lack of long-term, province-level emission projections and mitigation assessments for China’s agricultural sector.

The study was published on September 19, 2025, in Energy and Climate Management.

“Our study aimed to identify priority regions and subsectors for mitigation and to quantify the gap between current mitigation efforts and technical potential,” said Minpeng Chen, corresponding author and professor at the School of Agricultural Economics and Rural Development, Renmin University of China.

The research first highlights the importance of developing subnational emission inventories and their advantages over national or gridded inventories. Subnational inventories, the authors note, incorporate administrative boundaries—allowing direct use for regional mitigation target-setting and burden-sharing—and balance spatial resolution with operational feasibility.

Professor Chen pointed out that although several subnational agricultural non-CO₂ inventories already exist in China, they have two major limitations: lack of long-term provincial emission projections, and existing projections rarely account for the effects of current mitigation policies; incomplete coverage of key emission sources, particularly freshwater aquaculture.

To fill these gaps, the team designed four scenarios representing different levels of policy implementation and technology adoption: a Business-as-Usual (BAU) scenario with no mitigation policies; a Current Policy (CP) scenario reflecting existing efforts; a Conventional Technical Potential (CTP) scenario; and a Maximum Technical Potential (MTP) scenario. The scenario framework incorporates eight quantitative mitigation targets and seventeen mitigation technologies. Notably, the AGAIN model explicitly includes freshwater aquaculture, providing a more comprehensive assessment of China’s agricultural non-CO₂ emission trajectories.

According to Professor Chen: “Under the BAU scenario, China’s agricultural non-CO₂ emissions will continue to increase, reaching 1,124 MtCO₂eq by 2060. In contrast, the CP scenario achieves an earlier peak by 2050 and a 12% reduction by 2060; however, this only realizes 26–45% of the total technical mitigation potential. At the provincial level, 16 provinces are projected to miss their 2030 emission peaks under the CP scenario, while only the MTP scenario ensures all provinces peak before 2030. This indicates that although current agricultural mitigation policies have curbed emissions and advanced the timing of the peak, their strength remains insufficient to fully unlock the available technical potential. Moreover, excluding freshwater aquaculture from the accounting framework would underestimate CH₄ emissions by 15% and total non-CO₂ emissions by 10%. Importantly, the consistency of priority regions and subsectors across scenarios reinforces the feasibility of developing region-specific mitigation policies.”

The research team hopes the study will support developers of agricultural non-CO₂ emission inventories and mitigation scenarios. Professor Chen added: “Future improvements could incorporate provincial policy targets, evolving emission factors, and regional food self-sufficiency levels to enhance the model’s accuracy and predictive power.”

Other authors of the paper include Eshi Hua and Siqi Li from Renmin University of China, and Nicklas Forsell from IIASA. The study was funded by the National Key R&D Program of China (2023YFE0113000) and Energy Foundation China (G-2304-34531).