This page offers a summary of the process for conducting techno-economic assessments (TEAs) as outlined in the Techno-Economic Assessment & Life Cycle Assessment Guidelines for CO2 Utilization (Version 2.0) published by the Global CO2 Initiative. Please note that the guidance offered on this page is primarily tailored toward carbon capture and utilization (CCU) technologies although the general framework can be adapted to any technology.
If you are conducting your own techno-economic assessment, you may find value in reading through this page to gain a high-level understanding of the process and then using the guidelines document directly to complete the study. Our templates and instructional videos may also be useful when conducting the study.
TEA evaluates the technical and economic performance of technological processes. Performance is described by a variety of metrics, or indicators, identified before the study begins. Those conducting TEAs use data on the process’ raw material needs, energy requirements, needed capital equipment, labor requirements, and other relevant factors to inform the calculation of the indicators. The indicators are often analyzed in various ways to help decision-makers understand how they can improve technical or economic performance and accelerate commercialization of early-stage technologies.
The graphic below depicts the steps of TEA. The goal and scope must be completed first and serve as a foundation for the rest of the study.
Collection of data—known as the inventory phase—can begin once the goal and scope are described. Indicators can be calculated once the proper inventory data has been found. Interpretation is an iterative process that takes place all throughout the study depending on how the analysis progresses. For example, if those conducting the study cannot find inventory data needed to accomplish a specific goal, then the interpretation phase demands that the goal must be revised with this in mind. Finally, once the indicators are finalized and subsequent analysis (including scenario, uncertainty, and sensitivity analyses) is complete, the study can be described in a clear report for the study commissioners or the relevant decision-makers. The information in the report will guide further action taken with respect to the technology under consideration.
Identifying a Goal for the Assessment
The goal for the TEA is crucial as it guides all other steps of the assessment. Clear, transparent goals can also make the study easier and enable easier comparison with other studies upon completion. Often, the commissioner of the study—commonly a research agency or a business—will have specific goals in mind. Many goals are, at a high level, related to better understanding some aspect of the product or process in order to improve the technology’s chances at commercialization and impact.
In addition to the intended application and reasons for conducting the TEA, the goal should also include the time horizon and location used for the study, the scale of production, the target audience, the commissioners, and potential limitations in usability due to the structure of the assessment. All of this information provides useful context for the rest of the analysis, and it eases the processes of peer review and study replication.
Scenario analysis is another important aspect of TEA, and scenarios to be analyzed should be identified in the goal phase. Scenarios are potential, physically and economically plausible, future developments that could affect the ultimate indicators of the study and thus the commercialization of the technology.
Common TEA scenarios include the introduction of carbon prices and the use of energy from renewable sources. If the practitioner is conducting a life cycle assessment (LCA) in tandem, it is recommended to use the same scenarios across studies. The “base case” or default scenario used in the study often assumes status quo conditions continuing into the future, while other scenarios generally involve some change or something identified by the practitioner or a stakeholder as less likely but still plausible. Results of scenario analysis can help reveal political, economic, or scientific conditions or changes that could help make the product or process viable in the marketplace.
Setting the Scope
The scope phase builds on the previous goal phase by outlining how the technology will be assessed and compared to the benchmark technology. This information allows for inventory data collection as well, as it identifies the relevant factors in the production and use of the technology. If the person or group conducting the TEA is distinct from those who are directly developing the technology, they often must spend a significant amount of time ensuring they fully understand the technology, how it works, and its intended uses, all of which are covered in the scope phase.
The product application is the first thing that must be defined as part of the scope of the assessment. This is how the ultimate customer or user will use the product. Some products, like concrete, have many possible applications, and their application should generally be described in terms of the amount of the product rather than a specific use. Once the application is specified, the functional unit and reference flow must be described. The functional unit is a quantified description of the application. The reference flow is the quantified amount of the product necessary to achieve the performance described by the functional unit.
For example, the product application or function of a lighting system might be to provide light to the user. The functional unit could then be described in terms of lumens, which is a measure of brightness. The reference flow could be the number and type of light bulbs necessary to reach that level of brightness. A TEA comparing LED and fluorescent bulbs, for example, might consider the different numbers of each type of bulb necessary to reach the same functional unit of brightness. All of these metrics help clarify how much of the product is necessary to meet customer needs, which has direct implications for its economic performance. There are certain guidelines for setting functional units that are especially relevant for CCU technologies shown in the below decision tree.
In some cases, the CCU process might involve the manufacture of a product that is a molecularly identical substitute of an existing product. An example might be a commodity chemical that is currently made using a fossil feedstock but could be made using captured CO2 instead. In this case, it would make the most sense to compare these products on the basis of mass instead of product performance, as 1 kg of the CCU product is equivalent in nature to 1 kg of the conventional product.
A similar logic applies to fuels; gasoline from a CCU source should be compared to conventional gasoline on the basis of energy content, using a unit such a 1 megajoule (MJ) of fuel. In cases where the CCU product is molecularly different from the conventional product, the TEA may require comparison on the basis of some lifetime performance measure to capture subsequent differences in the product’s performance. For example, concrete that sequesters CO2 and involves a different mix than conventional concrete may last longer, in which case the basis of comparison could potentially be normalized to years of service rather than merely mass.
System elements are key activities within the product system, which could be viewed as stages of production. Energy and mass usually flow into and out of each system element. There may be multiple flows for any given system element. The graphic below demonstrates a blank example of what a product system looks like.
The system boundary, to be defined as part of the scope phase, is the set of all system elements that will be analyzed within the TEA. This boundary is derived from the goal of the assessment and must be held constant for the whole study as well as in the LCA, if applicable. The graphic below lists different potential system boundaries that are used in LCA and TEA studies. Gate-to-gate TEAs are very common as many TEAs are commissioned by businesses aiming to understand manufacturing costs and technical performance within their production facilities.
While system boundaries that are more limited in scope often allow for easier studies due to fewer data requirements, expanding the system boundary is often useful to gain a more holistic view of the technology’s performance. However, some carbon capture and utilization products, such as CCU methanol, are exact substitutes of existing products, meaning that analysis past the factory gate is generally unnecessary as there are no relevant differences past that point. Analysis of the use and end-of-life phases of the technology—which can be done in a TEA using life cycle costing (LCC)—is particularly important when analyzing a product that is similar in function but different in substance to the benchmark product.
Benchmark products are the products, processes, or technologies that are currently used to perform the specified function of the product. Examples might include conventional concrete, regular gasoline, or common consumer products. Often, TEAs will compare the manufacturing cost of producing a product that utilizes carbon dioxide to the manufacturing cost of a benchmark product to understand its potential performance in the marketplace. The market price of the benchmark product may also be used as a comparator, but market prices often include factors unrelated to the core manufacturing process that can complicate the analysis. The difference between manufacturing cost and market price can, however, serve as an indicator of economic viability; if the manufacturing cost of the CCU product does not even beat the market price of a benchmark product, then it will likely not be economically viable without substantial improvements or regulatory interventions.
The most common or best-in-class benchmark products should be selected for comparison to not make the technology under consideration seem more economically viable than it actually would be upon deployment. If a product is fundamentally new and has no easily discernible alternative product for comparative purposes, customer needs should be clearly identified along with how customers currently go about meeting those needs in order to describe the utility of the new product.
The final step of the scope phase is to identify the indicators that will be used as part of the indicator phase. This allows for relevant data collection during the inventory phase. TEA indicators must be derived from the goal of the study, and they often focus on both technical and economic aspects of the process (e.g., energy efficiency, raw materials usage per kilogram of output, net present value of a new plant, lifetime economic benefit to customers, etc.). Desired indicators may need to be revised if sufficient data to inform them cannot be found. The table below lists different potential criteria and indicators that a practitioner may want to calculate as part of a TEA.
Collecting Inventory Data
Note: this website contains a TEA databases page that links to various sources where inventory data for TEAs can be found.
Collecting inventory data may be the most intensive aspect of the TEA process. The graphic below depicts the linked phases of this step of TEA.
It is quite important to ensure that collected data is of sufficient quality to calculate the indicators and satisfy the goal of the study. Insufficient quality or the need to estimate certain parameters creates limitations in the usability and the conclusions of the study, and thus transparency surrounding assumptions and sources is useful to users of the assessment’s results.
The three different types of data that are used in TEA are process-specific, average, and generic. Process-specific data is measured from a known process, meaning that it is known with reasonable certainty for a given part of the product system. Average data reflects general industry assumptions, which are usually based on process-specific data measured as part of some other process. Generic data is not from a measured source but rather is estimated using something like stoichiometry or expert knowledge. For example, a given study might use process-specific data for the amount of a given raw material input, average data for the price of electricity used in the location of the production facility, and generic data for the scaling factors of the machines. Based on this example, one can imagine how all of these types of data are common in all TEAs. Using more process-specific data increases the certainty of the results, but there may have to be significant use of average and generic data for developing technologies or if the TEA practitioner cannot acquire the proper data. Those conducting TEAs should strive to increase data quality for parameters to which the final indicators are particularly sensitive, meaning that they may have to return to inventory data collection after conducting sensitivity and uncertainty analyses in the interpretation phase.
Both technical and economic data must be collected in order to conduct the TEA. Technical data often describes material and energy flows between the individual system elements, and economic data involves factors such as raw materials costs, energy costs, and capital expenditures. If the person or group conducting the TEA is not the same as those directly developing the technology under consideration, it is vital for them to work closely with the developer to fully understand all of the different technical features that go into producing the product. To ease the communication process here, extra work may be required on both sides to understand the technological aspects of the product as well as the purpose for techno-economic assessment generally.
The most common types of economic data to be collected relate to operating expenditures (OpEx), capital expenditures (CapEx), and general expenses. Operating expenditures are continuing expenses incurred during normal operation of the plant, such as costs for energy, labor, and raw materials. Capital expenditures generally relate to upfront expenses for the land, machinery, or other equipment that collectively enable production. General expenses are other costs that usually cannot be easily allocated to production, such as marketing, sales, and research costs. Many early-stage TEAs focused on finding economic hotspots and increasing the viability of a developing technology omit consideration of general expenses due to high uncertainty and low relevance. Inclusion of certain kinds of costs and even other technical considerations depends on the goal of the study and the availability of relevant data.
Another relevant economic data point that TEA practitioners often collect is the sales price of the competing, benchmark product, which can be found directly or calculated using a cost-plus approach that adds a constant amount of profit on top of the costs of producing the benchmark product. TEA practitioners may also choose to build an additional cost model to ascertain the manufacturing cost of the benchmark product—if this cost measure is not already available—to understand the extent to which customer willingness-to-pay and non-manufacturing costs influence the current market price of the product. This kind of analysis may be informative for pricing and go-to-market strategies for the new product as well as comparisons of primary cost drivers.
Many processes lead to the production of more than just one product. A given process may produce multiple products known as co-products, or it may involve the production of by-products. Processes may also utilize wastes from other processes as an input, and they may even recycle some of their own wastes back into the production system. When a system produces a primary product but also has these other functions, it is known as multifunctionality. Multifunctionality has implications for the allocation of production costs. How this allocation occurs depends on the goal of the study. While certain studies may only analyze total costs and total outputs, others may require some level of cost allocation for decision-making purposes. A common method is economic allocation that allocates the costs of production based on the expected selling prices of each product.
Multifunctionality also has implications for the allocation of environmental impacts, which are discussed in section C.4.3.3 of the LCA guidelines and the corresponding section on this site. If both an LCA and a TEA are being conducted, the approach for addressing multifunctionality should be consistent across the studies.
Common data points used in TEA studies for CCUS technologies include CO2 prices, CO2 purity levels, electricity prices, hydrogen prices, mineral prices, fossil-based organic compound prices, and catalyst prices. For each of these, regional context, price volatility, and potential pre-processing and transportation costs must be considered carefully, as different plant locations, production times, or product feature requirements respectively can affect the overall cost of production. The full TEA guidelines contain more guidance related to each of these inputs.
Calculating Informative Indicators
Indicators, which are chosen during the scope phase and refined as needed throughout the study, are the metrics that communicate meaningful results of the study. They can be either technical or economic in nature as well as techno-economic if they incorporate both technical and economic aspects of the production process. As there are CCUS technologies from many different fields, there are many different possible technical indicators.
Economic indicators generally relate to the operating expenditures, capital expenditures, or profitability of the project. The graphic below shows a breakdown of potential economic indicators. Regardless of the indicators chosen for a study, (a) methods that comply with the goal and scope of the study should be chosen; (b) their motivation and explanation should be provided; and (c) all assumptions, equations, and adjacent estimates should be clearly stated.
Operating expenditures are common to most production processes and capture cost categories for energy, labor, and raw materials. Certain operating expenditures will be fixed, meaning they do not change as production volume changes, while others will be variable, meaning they do change as production volume changes. For example, raw materials costs will increase as more units are produced, while property taxes on the factory are fixed and do not change with production levels (at least in the short run). The mass and energy flows throughout the product system along with general industry practices determine the majority of operating expenditures. Just as with all other aspects of the TEA, indicators should be calculated using more specific and detailed operating expenditures for more mature technologies.
Capital expenditures will likely be significant for many CCUS projects and are part of common profitability indicators. These expenses are generally for initial investments in machinery, land, the factory, contingencies, engineering, start-up expenses, and infrastructure necessary to connect the plant to utilities. Capital investments can be grouped into inside battery limits (ISBL) and outside battery limits (OSBL) categories, which respectively refer to core plant costs and infrastructure costs to connect the plant to utilities.
There are many different methods for calculating capital expenditures. These methods include scaling from base/reference machines using scaling factors or formulas, estimating expenses based on various equipment parameters, factor methods that multiply a predetermined factor by some cost to estimate a particular part of CapEx, unit cost line items that use high levels of detail for particular machines, or cost transformation that adjusts the capital costs for similar plants using some predetermined method. This report provides an overview of the many types of capital cost estimation techniques. More detailed and exact methods should be used for more developed technologies. Learning curve effects as well as economies (and potentially diseconomies) of scale should be taken into account if possible, as the capital costs per unit of output of nth-of-a-kind (NOAK) and larger plants will likely be lower due to learning effects and efficiencies at higher levels of production. This article discusses the application of technology learning curves to carbon capture and utilization technologies in particular.
Profitability indicators involve metrics such as capital and operating expenditures paired with metrics related to sales prices, sales volumes, discount rates, and more. A very common indicator of plant profitability is the payback period, which is the amount of time it takes a given operation to recover the initial investment in the project. Another common indicator is relative profit, which represents how much money is made on each unit sold after subtracting allocated operating and capital expenses. Absolute profit, which is the total sum that a plant makes over a specified time period given a certain production level, can also be calculated. More advanced profitability indicators can also be calculated, and these include return on investment (ROI), net present value (NPV), internal rate of return (IRR), earnings before interest and taxes (EBIT), and so on. Many of these indicators consider the time value of money and other investor preferences, but given their advanced nature, they may only be applicable to and useful for more developed technologies.
Finally, indicators can also combine technical, economic, and environmental aspects of the technology under consideration. This is where there is opportunity to integrate TEA results with those from corresponding LCAs. A useful metric that could allow for comparison across different technologies is the levelized cost of carbon abatement (LCCA) that divides the cost of implementing the new technology by the difference in emissions to demonstrate the cost or benefit per metric ton of CO2 reduced or removed. Please see the report at the link below from the Center on Global Energy Policy at Columbia University to learn more about this indicator.
While aspects of the interpretation phase occur throughout all phases of the TEA to check for consistency, data quality, and whether the inventory data can sufficiently meet the goal of the study, the phase also includes uncertainty and sensitivity analyses that are conducted after satisfactory calculation of the indicators. Scenario analysis using the scenarios identified in the goal phase should also be performed as part of the interpretation.
Uncertainty is associated with unknowns related to data measurement or quality, model complexity, modeling choices, or potential future conditions. While there are many different methodologies for conducting uncertainty analysis, many revolve around analyzing the extent to which the indicators change based on ranges of potential values for different inputs to the indicator calculations. For example, it may be the case that prices for electricity in a given location fluctuate significantly and are expected to continue fluctuating into the future. During uncertainty analysis, the practitioner may want to calculate the range of costs of the final product using the possible range of electricity prices. If the range is substantial or if part of the range makes it such that the product would be more expensive than the benchmark technology, then the report author must make note of this and emphasize the importance of low electricity prices to ensuring a profitable operation. Separately, many types of uncertainty analysis are probabilistic in nature and may make use of methods such as Monte Carlo analysis to understand how price and performance ranges for technologies might affect the indicators.
The Association for the Advancement of Cost Engineering (AACE) provides cost estimate classification systems that allow for a top-down approach to uncertainty analysis. Instead of varying individual input parameters and analyzing how this changes indicators, this approach involves directly applying accuracy range factors to final indicators based on how advanced the technology or model is. At the earliest stages, the expected accuracy range is -50% to +100%, meaning that if the technology were deployed the actual cost would likely fall in a range from either 50% lower or 100% higher than the calculated indicator. At the most advanced stages of modeling and development, this range drops to -10% to +15%. For more on this approach to uncertainty classification, please refer to AACE International Recommended Practice No. 18R-97.
Sensitivity analysis reveals how sensitive the TEA indicators are to different inputs to the study. Local sensitivity analysis varies one variable at a time to analyze how the indicators change while global sensitivity analysis varies multiple inputs and may analyze the interaction effects among different variables. For example, CO2 capture energy demand and CO2 purity for the process are two different possible parameters for a TEA model that generally affect one another, and global sensitivity analysis would attempt to capture and analyze this relationship. The graphic below outlines how the results of local sensitivity analysis might be presented using tornado diagrams and spider charts. The “Nominal value” is the default value for the parameter used in the default model.
The combination of uncertainty and sensitivity analyses can also allow for an iterative process with other parts of the study. If this combination reveals highly uncertain data to which the indicators are highly sensitive, then the practitioner will likely have to return to the inventory phase to find better data or even consider changing the goal of the study if such data cannot be found. The quadrants in the graphic below can help classify data priorities at this step in the study. Naturally, high quality data to which the indicators are not sensitive should not be a priority for improvement whereas highly uncertain and influential data should be.
Reporting of results can take many forms depending on audience and commissioner needs. Regardless of the format, it is vital to ensure transparency, accuracy, and consistency. Specifically, transparency surrounding assumptions and uncertainties is important to prevent readers from having unwarranted confidence in conditional and uncertain results. Authors of TEA reports must be intentional about their use of language to reduce the possibility of incorrect conclusions and generalizations based on the results of the study.
Also, in certain cases, offering enough data to enable reproducibility of one’s results by others is important and serves as an accountability mechanism. In other cases, certain aspects of the study may need to be kept confidential, which will influence what is included in the report.
The TEA Reporting Checklists can help guide authors as they are writing executive summaries and reports based on TEA studies.