4 LCA as a business case
The following section illustrates the authors' position, addressing concrete implications of LCA in daily business. These examples do not claim to be exhaustive and are open for comment.
Success factors for LCA in industry
If we understand LCA as a business imperative, we must be able to clearly present its success factors. In most companies and associations where it is applied today, LCA is no longer a purely “voluntary” or “freestyle” activity; rather, it is seen as a fundamental activity of the organization. It is recognized as the best available methodology to investigate environmental sustainability performance in a reliable and transparent way. It can be used for communication, both along the value chain and throughout one's own organization. It supports and helps to cross-check both research and development and strategic decisions. LCA in practice must be time-efficient and investment costs and resource availability must be accounted for. Therefore, it is essential that data generation/acquisition and execution of LCA is efficient. This includes the ability to acquire industry-average data from multiple sites/companies and effective opportunities to close data gaps in an optimal tradeoff of precision and effort. Several approaches and methods to close data gaps are discussed in theory; however, practical engineering expertise and a meaningful exchange of information between industry, consulting, and science are fundamental.
In industry, it is imperative that LCA results and the underlying data be converted into a technical conclusion, the nature of which is determined by the recipient of the result (e.g., product engineers, marketing, suppliers, or consumers). LCA is applied for quantitative environmental management and should reflect the industrial reality adequately. It is important that hot spots for product optimization can be identified along the entire value chain, based on a common understanding of the chain links. Standardization of procedures is the key to ensure a common interpretation of results within the chain links. LCA is considered an internationally accepted method and a firm basis for a dialogue with internal and external stakeholders.
It is common practice in industry to benchmark one's own processes and products against the competition—commonly on a cost basis. It must be understood that following presentation of an LCA study, non-LCA experts will ask for an evaluation of the results within the competitive landscape. Although a specific result may not be simply “compared” with a database value—it is common practice for internal use in industry.
Aside from the use of LCA as an internal planning tool within a single organization, another potential lies in connecting partners along the value chain. By collaborating on an LCA, suppliers and customers strengthen their relationship, glean valuable insights in markets and their success factors, and enhance an overall exchange of experiences. This fosters innovation.
Single scores and footprinting approaches seem simple to understand for a wider audience, can deem benefits for communicating results, and may demand somewhat less data. Nevertheless, one of the key advantages of using LCA is being able to communicate credibly and comprehensively. If at all, most LCA practitioners only consider using (single) score approaches when they have been able to see the range of impacts from the standard LCA approach. If adequate LCI foreground and background data are available—as is the case for many companies and organizations—the potential time saved by using a single impact approach can be insignificant. Single impact methods can be produced from any LCA simply by “hiding” other impacts. When the market is asking for indicators in specific impact categories, it is much better to provide information through a sound LCA than to wait for someone from outside the LCA community to provide whatever result based on non-LCA methods.
It is mainly method developers and scientists who think weighting is so crucial for application. The real-world users who apply LCA for decision making usually appreciate being able to understand trade-offs and the associated learning's involved rather than looking for black and white solutions. Professional decision makers are used to make decisions in uncertain, multidimensional situations. They do not trust single score values. Many decision makers who use LCA repeatedly on their products realize that they do not need weighting, many do not even think or discuss on impact assessment level. Having done LCIA a few times, they know which inventory flows or rather process data determine the performance for a particular product. In some cases, like communication of environmental information of products to consumers, aggregation of LCA results into a single score may be relevant, provided it has reached a level of consensus in a category of products, and that it is possible to trace back the results in a transparent manner.
Financially speaking, today LCA even influences the economics—e.g., the shareholder value—of organizations directly as structured and continuous LCA use are increasingly acknowledged as a positive aspect in company ratings.
Unit processes and aggregated processes
For some LCA stakeholders, there still seems to be a debate on “unit processes versus aggregated processes”: In practice, there is no right or wrong, but simply a more suitable or less suitable approach depending upon what questions are being asked.
Depending on the goal, both unit processes and aggregated processes are needed in applied LCA. It is rather a question of what needs to be achieved, in which time frame and with which representation. One should pose the questions: “Which foreground system processes are needed to make my model specific to my technological situation and which are needed in the background system to represent the rest of the world adequately?”
Unit processes are often company or technology specific. Therefore, the sole existence of one unit process for a certain material, fuel, or chemical may not guarantee appropriateness or representativeness without adequate technological meta-information. The foreground system often calls for company- or supplier-specific information to make it relevant for the actual case.
Company- or supplier-specific information is often unsuitable for public use or publication. Competition, patent, and anti-trust regulations often call for a protection of such data to avoid public or competitive alignment. Aggregated processes therefore facilitate the use of up-to-date unit process data in LCA data provision, which would otherwise not be available to LCA users.
Using aggregated processes enables LCA practitioners to produce results relatively quickly. Moreover, aggregating data makes it possible to represent suitable technology mix data, where several different supply routes exist for one product on the market. Aggregated processes are needed in background systems to bring meaningful information along the value chain. They are also used to reflect the interconnected nature of many supply chains, industrial networks, and integrated sites properly. Processes are often connected in specific ways, and recombining them can cause over or underestimation or just be incorrect.
To be transparent, it is not necessary to disclose sensitive company or industry-specific data or to infringe confidentiality regulations. Suitable documentation of the modeled processes and technology routes and the relation of technology upstream routes to specific production technologies are of importance and serve the legitimate call for transparency well. Random connection of a process chain can lead to completely unrealistic figures if certain precursor technologies simply do not fit to the reality of downstream production technologies. As such, unit process data should be “industry-borne” or “industry-validated” and must be interconnected to the correct choice of up and downstream unit processes. These unit processes must not necessarily be public domain.
Thus the “unit process versus aggregated process” debate is rather a myth than of practical relevance and aggregated data are without alternative in applied LCA, side by side with company or technology-specific unit process data. If representative and actual industrial situations are of importance, then the “premier league” of versatile LCA data for the general public is industry-borne information that has been compiled by suitable organizations with access to company or industry data and is validated by neutral third parties. Dynamic developments in industrial value chains means that using information from literature sources or other public data that have no suitable validation can pose a high risk of inappropriate results.
Frequency of updating database information and industrial processes
The issue of updating of data is fueled by two critical factors: relevancy of changes in technology and frequency of changes. The right timing of data updates is essential. Some processes hardly change at all over decades, others change annually. The user should not be forced to spend time and money on irrelevant updates.
On account of costs, efficiency, and stability of relevant results, updates of LCI technology data should only be performed if relevant changes or improvements have taken place. It is important to be able to explain the changes in LCI data based on facts and tangible changes in technology/supply chain and not solely point towards changes in methodology. Changes in methodology should—if at all—rather lead to additional datasets (which can be implemented or neglected by the user depending on the goal and scope).
An increasing challenge in industry is the demand for regular reporting along the value chain, e.g., to customers. Updates of databases can cause changes in LCA results without any change in the actual process data, because raw materials and energy systems upstream have been updated. These changes must be documented properly so that organizations can explain their own achievements and the changes caused by the upstream and background systems.
Conversely, necessary updates must not come too late. If processes or process chains change, this information should be conveyed to the LCA community as soon as possible and older processes should be removed or updated. The fading-out or withdrawing of outdated data reduces the risk of inappropriate LCA results.
Verification of LCI datasets and the critical review of LCA data in studies are both essential for checking, validating, and reviewing data, results, and conclusions. A very effective review of datasets occurs when they are used and benchmarked in practice by practitioners that know the process or supply chain and can judge the results with technological expertise.
The future will likely bring more governance concerning the format, type, and documentation of data, certain practitioners might become accredited and data will only be accepted for certain use if validated, verified, or reviewed. This may result in updates becoming more frequent, at least concerning the exchange of data where changes are significant.
Source and quality of information
It is important to know where information is from and how it is technically validated. Unfortunately, just because information is cited from literature sources or publications does not guarantee a quality seal in terms of its technical content, even if the scientific background and framework is acceptable. A technical proof is assumed when professional review procedures are installed in journal publications—such as here in the
International Journal of LCA—and the editors in charge can involve technically knowledgeable reviewers with practical background experience. However if a user aims to produce results and conclusions of practical relevance, taking published information from scientific journals does not release them from their personal responsibility to ensure that the published data sits on adequate primary sources that is relevant to their specific goal.
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Transparency of the information source is critical. The data sources must be transparently outlined, e.g., from which companies, associations /member companies, or engineering consultants or engineering institutes it comes from. This is important to facilitate contact to the organizations in case further supportive information is required and to judge the data adequately. Further, it should be clear whether primary data is acquired from running operations with a significant production volume or if it is based on technical reference, design, or planning data.
Another important aspect is the transparency of data representativeness. Is the data covering a representative technology or technology (market or production) mix, a representative production volume, time frame, and region? Supply chain and technology domain expertise is needed to qualify (unit process) data and supply chains to be adequate, realistic, and correct.
Not any upstream or precursor unit process step fits to each downstream production step. Country, technology, process improvement, product quality, market, or use situation often define distinct value chains or production mixes of commodities. Where appropriate validation of the supply chain is lacking, unintentional misuse and/or misinterpretation of results is possible, even likely. Verification by industry or certification-organizations with technology domain expertise and respective documentation reduces this danger.
The responsibility for data as well as the ability to reproduce, disclose, and explain data from the data provider (or data compiler) is crucial in professional data supply. The consistent communication of data to an internal or external audience is equally important. If key decisions are made on the basis of applied LCA, e.g., process or product design decisions, it is important for the user to know who is responsible for the data and whom to contact in order to provide additional back-up when important decisions are to be made on the basis of the LCA results.
As for dataset validation, a lot will depend on the effective collaboration between qualified dataset developers (conducting and documenting software-supported plausibility checks during dataset generation) and qualified reviewers validating such development notes and documentation to ascertain whether data quality requirements (temporal, geographical, technological representativeness, completeness, precision, and methodological consistency) have been fulfilled.
These questions are important additions to the structured “goal and scope”-thinking in the ISO standards. In practice, you often need an approximate answer very soon, and a detailed fully validated one within a certain period of time, i.e., a funnel-like escalation of detail. This requires flexible not rigid procedures—also in quality assurance. The answers to the questions above determine what approach, information, data, and reporting is needed to provide timely information, with the appropriate quality and within a defined budget. In summary, the intended application, which is per definition beyond the ISO standard, is an important key factor when LCA is to be brought into practice. An LCA practitioner strives to provide answers that are not too complicated by nature, acknowledging relevancy (or non-relevancy) of specific aspects, focusing on the core parameters, systems and issues and have as much precision as needed to get good answers while avoiding irrelevant complexity. In practice, LCA results produced in-time that point >80 % in the right direction are improving applications whereas a “100 %” solution, which can't stick to any deadline does not have any impact in the real world.
Any goal and scope in practice needs its case-specific and responsibly chosen approach and framework. Assessments must be done at the right level, knowing the relevance of the whole life cycle. For example, arguing on the basis of material choice and manufacturing phase scenarios or on other single phases like use phase or end-of-life may be appropriate, but only if one has a good understanding of the environmental performance and sensitivity of all life cycle phases.
Equalization in LCA is often discussed as some stakeholders find ISO standards “imprecise” and call for more rules and a “cookery book” approach. Using LCAs automatically implies accountability and a lack of user responsibility cannot be compensated with rigid rules. Restricting the possibilities reduces useful applications, information, and result exchange, learning curves, and communication of LCA and its (positive) impact overall.
LCAs are often as individual as a diagnosis from a physician. Would you like to see a doctor applying a “medical encyclopedia” in your consultation rather than to check your individual body and come to an individual diagnosis and cure for you? Similarly, health professionals exchange individual results and information while keeping confidentiality and patients profit because the health professionals can decide which approach fits best.
Harmonization and standardization are useful to help to identify those in the business whose intent is to produce and communicate misleading results. LCA is not here to nominate winners, but rather to understand relevant differences, to inform possible trade-offs and to ensure unexpected consequences are minimized. LCA does not aim to announce “absolute” best performers, but rather to indicate individual potentials and relevancy of options. LCA is not a “religion” to tell what is good or bad, it is a tool for finding out what makes sense and what does not. Standardization is highly appreciated in practice. The ISO 14040 series is a core principle and important to prevent LCA “abuse”. Further validation or review procedures with meaningful background reports are essential to document the systems, data and results and their transferability or non-transferability towards comparable or other situations.
LCA is a very important “tool in a toolbox” next to others tools like risk assessment and management systems, which is used in companies or organizations to examine a broader range of sustainability impacts of products on society and the natural environment.