Step 1: Identify Significant Issues 

Significant “issues” are the data elements that contribute most to the results of both the LCI and LCIA for each product, process, or service. Examples include

• inventory parameters (e.g. energy use, emissions, waste)
• impact category indicators (e.g. resource use, emissions, waste)
• essential contributions for life cycle stages to LCI or LCIA results such as individual unit processes or groups of processes (e.g. transportation, energy production)

When these issues have been identified, the results are used to evaluate the completeness, sensitivity, and consistency of the LCA study (step 2). The identification of significant issues guides the evaluation step.

Before determining which parts of the LCI and LCIA have the greatest influence on the results for each alternative, the previous phases of the LCA (e.g. study goals, ground rules, impact category weights, results, and external involvement, etc.) should be reviewed in a comprehensive manner.

A review of the information collected and the presentations of results developed indicates that if the goal and scope of the LCA study have been met. If they have, the significance of the results can be determined. Several analytical approaches are possible.

• Contribution analysis – The contribution of the life cycle stages or groups of processes are compared to the total result and examined for relevance.
• Dominance analysis – Statistical tools or other techniques, such as quantitative or qualitative ranking (e.g. ABC Analysis), are used to identify significant contributions to be examined for relevance.
• Anomaly assessment – Based on previous experience, unusual or surprising deviations from expected or normal results are observed and examined for relevance.

Step 2: Evaluate the Completeness, Sensitivity, and Consistency of the Data 

The evaluation step of the interpretation phase establishes the confidence in and reliability of the results of the LCA. This is accomplished by performing completeness, sensitivity, and consistency checks to ensure that products/processes are fairly compared.

Completeness check – The completeness check ensures that all relevant information and data needed for the interpretation are available and complete. A checklist should be developed to indicate each significant area represented in the results. Using the established checklist, it is possible to verify that the data comprising each area of the results are consistent with the system boundaries (e.g. all life cycle stages are included) and that the data is representative of the specified area (e.g. accounting for 90% of all raw materials and environmental releases).

The result of this effort will be a checklist indicating that the results for each product/process are complete and reflective of the stated goals and scope of the LCA study. If deficiencies are noted, an attempt must be made to remedy them. If this is not possible because data are not available, areas inadequately characterized because of insufficient data must be highlighted in the final results and their impact on the comparison estimated either quantitatively (percent uncertainty) or qualitatively (alternative A’s reported result may be higher because “X” is not included in its assessment).

Sensitivity check – The objective of the sensitivity check is to evaluate the reliability of the results by determining whether the uncertainty in the significant issues identified in step 1 affect the decision‐maker’s ability to confidently draw comparative conclusions. Three common techniques for data quality analysis can be used in performing sensitivity checks.

1. Gravity analysis – Identifies the data that has the greatest contribution on the impact indicator results.
2. Uncertainty analysis – Describes the variability of the LCIA data to determine the significance of the impact indicator results.
3. Sensitivity analysis – Measures the extent that changes in the LCI results and characterization models affect the impact indicator results.

Additional guidance on how to conduct a gravity, uncertainty, or sensitivity analysis can be found in the EPA document entitled “Guidelines for Assessing the Quality of Life Cycle Inventory Analysis” (USEPA 1995). If one of these analyses has been conducted as part of the LCI and LCIA phases, these results can be used. Then the sensitivity check will serve to verify that the goals for data quality and accuracy defined early in have been met. If deficiencies exist, additional efforts are required to improve the accuracy of the LCI data collected and/or impact models used in the LCIA. If better data or impact models cannot be obtained, the deficiencies for each relevant significant issue must be reported and its impact on the comparison estimated either quantitatively or qualitatively, as with the completeness check.

Consistency check – The consistency check determines whether the assumptions, methods and data used throughout the LCA process are consistent with the goal and scope of the study, and for each product/process evaluated. Verifying and documenting that the study was completed as intended at the conclusion increases confidence in the final results. A formal checklist should be developed to communicate the results of the consistency check. Table 6.2 lists seven categories and provides examples of inconsistencies that can creep into the data. The goal and scope of the LCA determines which categories should be used.

If, after completion of steps 1 and 2, it is determined that the results of the impact assessment and the underlying inventory data are complete, comparable, and acceptable as bases for drawing conclusions and making recommendations then stop! If any inconsistency is detected, document the role it played in the overall consistency evaluation. Although some inconsistency may be acceptable, depending upon the goal and scope of the LCA, the presence of inconsistencies usually means that it is necessary to repeat steps 1 and 2 until the results are able to support the original goals for performing the LCA.

Table 6.2 Examples of checklist categories and potential inconsistencies.

Category	Example of inconsistency
Data source	Alternative A is based on literature and Alternative B is based on measured data.
Data accuracy	For Alternative A, a detailed process flow diagram is used to develop the LCI data. For Alternative B, limited process information was available and the LCI data developed was for a process that was not described or analyzed in detail.
Data age	Alternative A uses 1980s era raw materials manufacturing data. Alternative B used a one‐year‐old study.
Technological representation	Alternative A is bench scale laboratory model. Alternative B is a full‐scale production plant operation.
Temporal representation	Data for Alternative A describe a recently developed technology. Alternate B describes a technology mix, including recently built and old plants.
Geographical representation	Data for Alternative A were data from technology employed under European environmental standards. Alternative B uses the data from technology employed under US environmental standards.
System boundaries, assumptions, and models	Alternative A uses a Global Warming Potential model based on 500‐year potential. Alternative B uses a Global Warming Potential model based on 100‐year potential.

Step 3: Draw Conclusions and Recommendations 

The objective of this step is to interpret the results of the LCIA (not the LCI) to determine which product/process has the overall least impact on human health and the environment, and/or on one or more specific areas of concern as defined by the goal and scope of the study. Depending upon the scope of the LCA, the results of the impact assessment will return either a list of unnormalized and unweighted impact indicators for each impact category for the alternatives or a single grouped, normalized, and weighted score for each alternative. In the latter case, the recommendation may simply be to accept the product/process with the lowest score. The assumptions underlying the analysis should be borne in minds, however. If an LCIA stops at the characterization stage, the LCIA interpretation is less clear‐cut. The conclusions and recommendations rest on balancing the potential human health and environmental impacts in light of study goals and stakeholder concerns.

It is essential to understand and communicate the uncertainties and limitations in the procedures that have produced the final recommendations. Perhaps no one product or process is better than another because of underlying uncertainties and limitations in the methods used to conduct the LCA. Perhaps insufficient good data were available, or restrictions on time or resources prevented analysts from thoroughly exploring certain aspects of the problem. Even so, the results of the LCA can be used to help inform decision‐makers about the human health and environmental pros and cons and understand the significant impacts of each. Such LCA results will reveal whether effects are occurring locally, regionally, or globally, and will provide at least a rough estimate of the magnitude of each type of impact in comparison to the proposed alternatives being investigated.