A national greenhouse gas inventory is not built by simply adding up individual farms’ emissions-reduction reports. Improving livestock emission factors requires connecting representative data, conditions of application, quality management, and review requirements. Chapter 17 of Technology and Solutions for Demonstrating Carbon Reduction treats the connections among data production, review, and application within a program as a policy-design challenge.
Define the concept and scope of application first
Connections between institutions require clearly defined submissions and decision-making authority. It is necessary to specify who reviews data produced by research, against which criteria, and how they may later be used in different systems. Creating a new organization alone does not solve the problem of connecting data. National factors, recognition of a particular technology’s effects, and eligibility for compensation based on emissions reductions each require separate assessment. Even when the same data can be reused, different criteria and boundaries cannot be collapsed into one. Standardization should make the necessary conditions for connection explicit, rather than erase these differences.
An emission-factor-based baseline is also an option. IPCC Tier 1 uses default factors, while Tier 2 for enteric fermentation reflects more detailed characteristics, including husbandry, gross energy intake, and the methane conversion rate. Average factors may not capture the effects of a technology on an individual farm, but using factors is not inherently unsuitable for compensation. The criteria are whether the applicable methodology permits the factors and activity data used, and whether conditions of application, treatment of uncertainty, and verification are adequate.
Farm field data, research on technology effects, factors to be included in national statistics, and decisions needed for compensation are connected, but they are not the same process. It is necessary to define what data are submitted to which institution and in what format, and who makes decisions under which criteria. Even if the same data serve multiple purposes, the boundaries and recognition requirements of each system need to be checked separately. Standardization requires specifying the conditions that connect data production with application under a program.
National statistics, research, and support programs have different purposes and review systems. To improve their connections, relevant institutions should start with existing guidance and registered results, then clarify together the additional data and review procedures needed for new emissions-reduction technologies.
Compare existing data and review requirements to design samples that address gaps. A controlled efficacy trial with a small number of animals can explain effectiveness, but may not represent the country’s livestock species, growth stages, and feeding systems. Design new research to avoid duplicating data already obtained and to focus on reducing uncertainty.
Fifth, make intercomparison of equipment and methods a regular practice. Assess bias, precision, and representativeness through recovery tests using the same gas and animal trials under comparable conditions. Simply applying a chamber and GreenFeed at the same time or creating a single conversion factor will not resolve differences across all conditions. Validate the scope of comparison equations using external data.
At the first point of connection, distinguish default emission factors from the effects of mitigation technologies. It is not accurate to diagnose the situation as lacking an existing approval system. The area for improvement is to clarify how technology-specific effects, activity data, and uncertainty are reviewed within that system and connected to national statistics.
Conditions and limits for field application
Suppose an effect is confirmed in a feed trial at a particular farm. To reflect that result at the national level, we need to examine which livestock species and husbandry conditions it represents and how activity data are obtained. Recognition of an individual trial’s results and acceptance of a national factor are different decisions; requirements for corporate accounting or credits must also be checked separately.
Before research begins, compare data already available, conditions that lack representativeness, and items required for review. An academic efficacy trial and research for nationwide application have different purposes, but they do not conflict. Using findings from the former to design samples for the latter can reduce unnecessary retesting.
Annual review of factors and program criteria. Relevant institutions should jointly review newly registered or updated factors, their application in the national inventory, and their effects on support programs, and publish whether they are reflected and why. Publication of a document does not guarantee implementation, so the responsible institution, deadlines, and unfinished tasks should also be managed.
Feedback from program data into emission factors. Activity data such as purchases, feeding, target animals, and days raised can contribute to improving factors when combined with suitable direct measurements from representative samples. Whether data are collected from every participant and which fields are included should be checked for each program; data volume and data quality should not be treated as equivalent.
Whether program settlement data are also suitable for improving factors needs a separate review. Data collected for settlement alone may lack necessary variables such as feed intake, representativeness, and measurement quality. First examine the actual data collected in current programs and how they are transferred, then identify and address gaps; do not make a blanket claim that such data are never used.
Evidence needed for investment review
Institutions and investors should distinguish which systems are intended to use program results. The existence of research findings does not automatically determine policy compensation or inclusion in national statistics. Specify the data deliverables, responsible institutions, and any additional reviews needed to make the path to actual use visible.
The results will be made public. Agreements and unresolved issues will both be disclosed. Disclosing unresolved issues is especially important. When people can see what is blocked and why, political momentum can develop to address it.
If each institution repeats the same trial from the beginning, the burden on farms and companies increases. Yet automatically converting one institution’s assessment report into an approval for another purpose would go beyond the scope of verification. The approach proposed in this book is to preserve both data reuse and independent decision-making. Manage a shared evidence package containing the location of source data, calibration records, sampling and missing-data treatment, uncertainty, costs, and usage rights; receiving institutions then check additional requirements suited to their own purposes.
Criteria to keep in view when making a decision
Standardizing national MRV does not end with collecting data in the same format. Sampling and methods, review authority, and links to activity data must also be designed. The possibility of reusing common data should be maintained alongside each system’s independent decision requirements.
About the author
Bongjun Lee graduated from the Graduate School of Management of Technology (MOT) at Sogang University. He spent seven years at Infitron Co., Ltd. and 20 years at Honeywell, working for a total of 27 years in gas measurement in service engineering, technical sales, and channel management. He is now the founder of AI Safety Korea Co., Ltd., where he is researching livestock methane monitoring and measurement, reporting, and verification (MRV) systems using sensors, AI, and data analytics.