{"id":17229,"date":"2026-02-16T11:30:50","date_gmt":"2026-02-16T10:30:50","guid":{"rendered":"https:\/\/it-u.at\/?post_type=research-project&p=17229"},"modified":"2026-02-17T12:41:23","modified_gmt":"2026-02-17T11:41:23","slug":"techno-economic-assessment-tea-simple-calculations-deep-insight","status":"publish","type":"research-project","link":"https:\/\/it-u.at\/en\/research\/research-groups\/energy-transition-and-climate-futures\/projects\/techno-economic-assessment-tea-simple-calculations-deep-insight\/","title":{"rendered":"Techno-economic assessment (TEA): simple calculations, deep insight"},"content":{"rendered":"\n

Techno-economic assessment (TEA) is a widely used\u2014and comparatively straightforward\u2014method for comparing the costs of providing an energy or material service across alternative technologies, processes, locations, or supply chains.<\/p>\n\n\n\n

In our work, we use TEA not only for direct cost comparisons, but also as a tool to uncover deeper, system-level insights. The two examples below illustrate what we mean.<\/p>\n\n\n\n

Example 1: Overlapping MACCs reveal where e-fuels make sense\u2014and where they don\u2019t<\/h5>\n\n\n\n

Marginal abatement cost curves (MACCs)<\/strong> are an established concept. In our Nature Climate Change<\/em> paper on e-fuels, we advanced this approach by overlapping multiple MACCs<\/strong> across different mitigation options. Comparing MACCs for direct electrification and e-fuels<\/strong> across sectors helps identify where e-fuels are cost-competitive and where they are not. We refer to this as a \u201cmerit order of e-fuel applications.\u201d<\/strong><\/p>\n\n\n\n

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Publication:<\/strong> Ueckerdt et al. (2021) Potential and risks of hydrogen-based e-fuels in climate change mitigation. <\/em>Nature Climate Change. https:\/\/www.nature.com\/articles\/s41558-021-01032-7<\/a><\/p>\n\n\n\n

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  1. Applications where direct electrification is clearly cheaper<\/strong> (green area)<\/strong>
    Direct electrification is substantially less costly\u2014for example, battery-electric mobility for cars and light-duty vehicles can reach cost levels comparable to fossil mobility (petrol and diesel). In these applications, e-fuels are unlikely to become competitive.<\/li>\n\n\n\n
  2. Applications where electrification may be complemented by hydrogen\u2014and niche e-fuels<\/strong> (orange area)<\/strong>
    In some cases, direct electrification could be supplemented by hydrogen and, potentially, e-fuels in niche roles, such as long-haul heavy-duty trucking and selected very high-temperature industrial processes.<\/li>\n\n\n\n
  3. Large \u201cno-regret\u201d markets where electrification faces hard limits (blue area)<\/strong>
    In sectors where direct electrification is constrained\u2014most notably aviation, shipping, and chemical feedstocks for basic chemicals\u2014e-fuels can play a major role.<\/li>\n<\/ol>\n\n\n\n

    Example 2: Comparing value-chain configurations to quantify the \u201crenewables pull\u201d<\/h4>\n\n\n\n

    Meeting climate targets requires a fundamental transformation of basic materials industries (e.g., steel and chemicals). Low-cost renewable electricity<\/strong> and hydrogen-based processes<\/strong> can make green materials competitive. However, renewable resource quality varies sharply across locations. Regions with abundant, low-cost renewables can therefore gain a structural energy-cost advantage\u2014often described as \u201crenewables pull.\u201d<\/strong> As a result, the energy transition could reshape the global geography of materials production and trade<\/strong>.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n\n

    Using a relatively simple, generic TEA, we compare production costs for green steel<\/strong>, green ethylene<\/strong> (for plastics), and green ammonia and urea<\/strong> (for fertilisers) across alternative value-chain configurations. We contrast:<\/p>\n\n\n\n