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Catalyst with Shayle Kann
Climate tech startups need strong techno-economic analysis (TEA)
Oct 5, 2023
The podcast discusses the importance of techno-economic analysis (TEA) for evaluating climate tech startups. It highlights common mistakes in TEA models, such as unrealistic inputs and focusing on individual components instead of the entire system. The podcast also emphasizes the significance of accurate comparisons, competitiveness, and considering distribution and transport costs in energy technologies. Overall, techno-economic analysis plays a crucial role in determining the success factors for climate tech startups.
49:39
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Quick takeaways
- When conducting techno-economic analysis (TEA) for novel climate technologies, it is important to avoid unreasonable assumptions and focus on the system as a whole.
- Techno-economic analysis (TEA) plays a crucial role in understanding the economics and feasibility of novel climate technologies, guiding decision-making and resource allocation.
Deep dives
Common Pitfalls in Techno-Economic Analysis
When conducting techno-economic analysis (TEA) for novel climate technologies, it is important to avoid some common pitfalls. One such pitfall is making unreasonable assumptions, such as assuming a 100% capacity factor for renewable energy sources or underestimating the cost of organic molecules. Another pitfall is focusing only on the core component of a system and neglecting other factors that contribute to the overall cost, like balance of system costs in battery or solar installations. Comparing levelized costs to market selling prices is also problematic, as they are not equivalent and do not account for profit margins. Similarly, comparing today's costs to future prices without considering potential technology advancements can lead to misleading conclusions. Additionally, it is crucial to prioritize the right metrics in TEA. Efficiency or performance metrics might not always have a significant impact on overall economics, and it's essential to identify the drivers that truly matter. Lastly, false precision should be avoided in TEA models, as it is better to focus on the major sensitivities and critical path instead of trying to achieve unrealistic levels of precision.
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