Climate change and food security are inextricably linked—a relationship particularly evident in the dairy sector. With livestock supply chains contributing 14.5% of global greenhouse gas (GHG) emissions and cattle—including both beef and dairy production—accounting for around two-thirds of livestock emissions, the urgency of sustainable solutions has never been greater. Yet the challenge is complex: How can we reduce emissions from dairy farming without compromising food security or farm livelihoods?
This question becomes even more pressing when we consider that global demand for dairy products is expected to rise dramatically in the coming decades, driven by population growth and increasing incomes, particularly in developing countries. The dairy sector must therefore navigate a delicate balance between meeting growing demand and addressing climate commitments.
Existing research has identified several mitigation measures with significant emission reduction potential in the dairy sector, from feed production and dairy farming to dairy processing and consumption. However, the economic feasibility of these measures remains unclear. Cost-effectiveness analysis (CEA) is commonly used to identify economically efficient options for achieving specific objectives or to assess associated costs. In the context of climate change, CEA is employed to evaluate and rank the performance of various mitigation measures. While CEA has been widely applied across sectors such as electricity, building infrastructure, agricultural practices, and transportation, its application in the dairy sector remains limited. Recent studies that analyze the cost-effectiveness of GHG mitigation measures in dairy production often rely on national or regional statistics, or on average farm data collected through field surveys, to construct a standardized reference farm. This approach overlooks the significant variability that exists among individual dairy farms.
Building on existing literature, we developed a comprehensive framework that analyzes cost-effectiveness using real data from diverse dairy operations. Instead of assuming all farms are the same, our approach captures the variety in farm characteristics and economic conditions that determine whether mitigation strategies will work in practice. In a recent paper in the Journal of Environmental Management, we applied this framework to 97 dairy farms across three major milk-producing provinces in China—Inner Mongolia, Yunnan, and Heilongjiang, representing 33.7% of the country’s total milk output—ranking the most cost-effective approaches to climate mitigation.
The framework
Evaluating the variability among dairy operations is important because what works for a large commercial dairy farm in one region may be completely unsuitable for a smallholder farm in another. Factors like farm size, available capital, local feed sources, and management expertise all influence which emission reduction strategies will actually succeed.
Our six-step framework (Figure 1) provides policymakers and practitioners with a systematic approach to identify and implement the most cost-effective emission reduction strategies.
Figure 1
The framework highlights the importance of using micro-level data and actual adoption behavior to analyze the cost-effectiveness of such measures. By accounting for the heterogeneous characteristics of individual implementers and the diversity of production activities under real market conditions, this framework enables a more accurate estimation of the cost-effectiveness of mitigation measures.
Our analysis examined three categories of mitigation measures: Feed management, energy management, and manure management.
Feed management: The win-win solution
The most promising results came from feed management improvements. Two strategies stood out as particularly cost-effective:
Improving forage quality: Increasing the proportion of corn silage in forage from less than 50% to more than 70% reduced emissions by 0.289 tons of CO₂-equivalent per ton of FPCM (1 metric ton of fat- and protein-corrected milk) while lowering feed costs—resulting in a saving of $540 per ton of CO₂-equivalent reduced.
Optimizing concentrate inclusion: Adjusting the proportion of concentrate feed from less than 35% to 35%-50% of the diet reduced emissions by 0.179 tons of CO₂-equivalent per ton of FPCM while yielding a saving of $325 per ton of CO₂-equivalent reduced.
These feed improvements essentially pay for themselves through increased productivity, making them immediately attractive to farmers without requiring subsidies or external incentives. Notably, our study found that adding feed supplements like inhibitors or oilseeds did not show significant effects on emission reduction, possibly due to the quantity-dependent nature of their effectiveness.
Energy management: Efficiency pays off
Energy efficiency measures also proved cost-effective. Farms with low energy intensity achieved emission reductions of 0.600 tons of CO₂-equivalent per ton of FPCM while saving $143 in energy costs per ton of FPCM, yielding emission reduction benefits of $238 per ton of CO₂-equivalent. Similarly, farms with medium energy intensity achieved benefits of $256 per ton of CO₂-equivalent reduced.
Regarding energy structure, farms with medium levels of diesel oil use emitted 0.192 tons more CO₂-equivalent per ton of FPCM compared to those with low diesel use. Reducing diesel oil usage from medium to low levels can yield emission reduction benefits of $149 per ton of CO₂-equivalent.
Manure management: Environmental benefits with costs
Compared to recycling manure to make cow bedding, some manure management measures were effective at reducing emissions but came with varying cost implications (Figure 2).
Figure 2
Direct application to farmland: Reduced emissions by 0.733 tons of CO₂-equivalent per ton of FPCM while yielding savings of $11 per ton of CO₂-equivalent reduced.
Composting only: Achieved the largest emission reduction (0.838 tons of CO₂-equivalent per ton of FPCM) at just $1 per ton of CO₂-equivalent reduced
Composting after/and storage: Storing manure for a period before composting, or storing part of the manure while composting and fermenting the remainder, reduced emissions by 0.700 tons of CO₂-equivalent per ton of FPCM, with a unit reduction cost of $1.
However, some measures like covered storage, while environmentally effective (reducing emissions by 0.688 tons of CO₂-equivalent per ton of FPCM), proved more expensive at $63 per ton of CO₂-equivalent reduced.
Our findings reveal highly cost-effective opportunities for GHG mitigation in dairy production. The results highlight potential measures to reduce GHG emissions across feed, manure, and energy management, with many effective mitigation measures offering both environmental and economic benefits.
While our case study focused on farm-level operations, the framework can be expanded to encompass the entire dairy value chain, from feed production to retail. This broader application would provide an even more comprehensive understanding of where the most cost-effective mitigation opportunities exist.
The framework also offers potential for adaptation to other agricultural sectors and developing country contexts, providing a valuable tool for evidence-based climate policy development.
We are currently applying this same approach to methane reduction in rice production systems in China, demonstrating its broader applicability across agricultural sectors and developing country contexts.
Kevin Z. Chen is Qiushi Chair Professor at the China Academy for Rural Development, School of Public Affairs, Zhejiang University and a Senior Research Fellow with IFPRI’s Development Strategies and Governance Unit. Lingling Hou is a Research Fellow at the China Center for Agricultural Policy, Peking University. Opinions are the authors’.
The study was supported by the CGIAR Research Initiative on Low-Emission Food Systems (Mitigate+), now part of CGIAR Climate Action Science Program, the National Nature Science Foundation of China, and the Key Social Science Project for Social Science and Humanity Base of the Chinese Ministry of Education.
The use of ChatGPT-4o is acknowledged for refining language. The AI-generated content was reviewed and edited to ensure accuracy, relevance, and alignment with the report’s insights and conclusions. Full responsibility is taken for the content presented in this report.
Reference:
Li, Saiwei; Zhang, Mingxue; Hou, Lingling; Gong, Binlei; and Chen, Kevin. 2024. A framework for cost-effectiveness analysis of greenhouse gas mitigation measures in dairy industry with an application to dairy farms in China. Journal of Environmental Management 370 (November 2024): 122521. https://doi.org/10.1016/j.jenvman.2024.122521
