If a climate tech grant helped you begin research and carry out a demonstration, what comes next? I suggest looking at the questions to answer next rather than the names of the stages. Chapter 18 of Technology and Solutions for Demonstrating Carbon Reduction discusses the conditions for connecting research and demonstration with commercialization and growth capital. The evidence needed for climate tech scale-up can differ at each stage.
What the Book First Helps Us Distinguish
For support to continue, the next reviewer must be able to understand the evidence from the previous stage. We need to document under what conditions the technology worked and what still needs to be checked. Requirements and formats for the next stage should be confirmed with the responsible institution and its actual call for proposals. Common testing and data standards can help, but access conditions should also be considered so that a particular supplier is not unfairly advantaged. Support, investment, and customer purchases involve different decision-makers and purposes. Let’s prepare separately for the evidence each requires.
Basic transition support, bonuses for verified outcomes, and support that lowers measurement and consulting costs can be designed for distinct purposes. This is a policy structure proposed in the book, not a payment rule already applied to every program. Distinguish the evidence for additional cost support from the basis for reduction outcomes, and clarify who is responsible for sales, application support, quantification, and payment decisions. Actual participation and payment conditions must be checked in the rules of the relevant program.
Farm field data, research on technological effects, factors to be included in national statistics, and determinations needed for compensation are connected, but they are not the same process. Specify which institution receives the data and in what format, and who makes decisions using which criteria. The same data may serve multiple purposes, but the boundaries and recognition requirements of each system must be checked separately. Defining the conditions that connect data production with application under a system is a task for standardization.
Recognition of product efficacy and compensation for outcomes on actual farms answer different questions. While respecting existing recognition and support systems, additional field-effect data can help bring continuity of feeding and differences in conditions into compensation. The detailed criteria and operating costs for connecting the two stages should be examined in pilot programs.
A shared demonstration platform should be more than a place to install equipment. Design for farms with different seasons and husbandry conditions, comparable baseline measurements, and the costs of maintenance and independent review. Set farms’ workload and conditions for ending participation in advance, and propose sharing failure conditions and data quality within boundaries that protect trade secrets and personal information. Demonstrations that retain only days with good observations make it difficult to assess the costs of wider deployment.
If this support leads to additional reductions in actual greenhouse gas emissions, the livestock transition can contribute, alongside reductions in other sectors, to limiting warming and lowering long-term climate damage costs. The IPCC also assesses that rapid and sustained emissions reductions help lower future adaptation costs and loss and damage. Low-carbon subsidies are a policy option for society to share the costs farms must bear upfront in pursuit of these public benefits. However, an individual program’s contribution cannot be directly converted into a reduction in domestic disaster recovery costs, and reduction support must be pursued alongside immediate disaster preparedness.
How to Examine This in the Field
Imagine a program to procure test equipment and another to provide recurring services to farms. They require different uses of funds. After equipment development, it may be necessary to check maintenance, field training, data quality, and customer contracts. Results from support at an earlier stage do not eliminate all risks at the next stage.
In the early development stage, funding is needed to discover the conditions under which a technology works. This book proposes considering research and development support for measurement gaps with public significance and comparative trials, while defining research objectives and criteria for determining failure together. If repayment obligations are imposed first on a technology with no revenue yet, there may be an incentive to rush sales instead of conducting sufficient verification. But technical potential alone is not a reason to continue support indefinitely.
At the demonstration stage, allocate costs for representative farms, baseline measurements, data quality management, independent review, and operating staff. Do not hide all trial costs within equipment purchase expenses. Determine who will bear calibration, communications, and repair costs after support ends, as well as farms’ additional labor. The book proposes deciding on follow-on support based not only on whether targets were met, but also on whether the causes of failure can be addressed and whether further verification is worthwhile.
Questions to Ask Alongside the Investment Review
In investment materials, let’s look separately at grant funding and revenue from paying customers. It is also helpful to distinguish research results from the costs of recurring services. The book’s policy design proposal does not guarantee current program eligibility or the chance of being selected, so check actual calls for proposals and requirements separately.
Disclose results that are small or negative as well. Such results do not, by themselves, justify increasing the support rate. Compare changes to dosage or feeding methods, switching to another technology, changing support conditions, and ending the project. Consider expansion only when verified public benefits and cost-effectiveness justify additional support.
Field support staff check farms’ work and recordkeeping burdens, while researchers examine effects and uncertainty. Companies confirm maintenance and data handover conditions; financial institutions and support agencies check funding gaps and eligibility for support. Independent reviewers assess evidence within their assigned scope. The support systems discussed in Chapters 17 and 18 should also connect to efforts to resolve these open questions. What matters is which questions have been answered, not how many institutions participated.
Questions to Keep in Mind Before Closing the Book
As you prepare for growth, I hope you will write the evidence secured and the questions that remain on a single page. You should be able to explain which risks the next funding round is intended to reduce. Read this as a process of assembling the evidence needed for the next decision, rather than simply accelerating the schedule.
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.