This session on “data” will disclose how literature based LCA data can be made or maintained accessible, inform about internet based collaborative database development, give recommendations for data quality indicators which improve interoperability and provide insights in new developments of electricity data sets in ecoinvent 3.2.

Key Discussion Points:

  1.  How can the accessibility of data based on literature publications can be improved? Is this a mainly a technical problem to be solved?
  2.  How to overcome the situation that everybody wants to access data e.g. via an internet platform, but when it comes to sharing the datasets and models generated as well – confidentiality issues restrict these attempts until now?
  3.  Will aligning data quality indicators be sufficient to ensure interoperability of databases and datasets?
  4.  Looking at the average of the updated electricity data of ecoinvent 3.2, leads the higher resolution in the datasets in average to lower or to higher environmental impacts?
  5. What are most important learnings and recommendation for the development of datasets and databases which the authors can provide?

GHG Mitigation Options Database (GMOD) and Analysis Tool

ABSTRACT. There is a growing consensus among scientists that the primary cause of climate change is anthropogenic greenhouse gas (GHG) emissions. Given the strengthening science behind the human influence on climate change, it will be necessary for the global community to use low-carbon technologies in both the energy and industrial sectors. As a result of the recent focus on GHG emissions, the U.S. Environmental Protection Agency (EPA) and state agencies are implementing policies and programs to quantify and regulate GHG emissions from key emitting sources in the United States. These policies and programs have generated a need for a reliable source of information regarding GHG mitigation options for both industry and regulators. In response to this need, EPA developed a comprehensive GHG mitigation options database (GMOD) that was compiled based on information from industry, government research agencies, and academia. The database is a repository of data on available GHG technologies in various stages of development for several high-emitting industry sectors. It is designed to address multi-sector GHG emissions from stationary sources and help the user determine the most attractive options from performance and cost perspectives. It can be used for analyses of various GHG control technology options and their implications on sector-specific output, economics, and environmental parameters. The current version of the GMOD contains three sectors including Power, Cement and Pulp & Paper. A refinery sector is under development and is scheduled to be available in GMOD database in 2017.


Adding Value to Your Valuable Data: What can the National Agricultural Library do for You?
SPEAKER: Ezra Kahn

ABSTRACT. Currently, to access LCA data associated with literature, practitioners must search for supporting information with journal publishers or contact the corresponding author directly. Even worse, data may become inaccessible when the original researchers retire, or the hard drive holding the data becomes damaged. The National Agricultural Library (NAL) is expanding its contribution to the LCA community with library and metadata curation services to assist in the discovery, re-use, archiving, and preservation of LCA research data and resources. The tools employed in library services contribute to a convergence of LCA data representation through controlled vocabulary and structured data models. Here we present the LCA Commons collection at the NAL’s Ag Data Commons, and the National Agricultural Library Thesaurus (NALT), a structured agricultural thesaurus recently expanded with sustainability and LCA terminology.

The Ag Data Commons is a web catalogue and repository of research data and resources from a wide range of fields related to agriculture, allowing visitors to easily find and access information using controlled keywords and search terms. Similar to the general purpose Dryad Data Repository (, Ag Data Commons also connects research data to the associated publications, provides descriptive metadata and data dictionaries, and facilitates tracking of re-use by assigning unique DOIs to the datasets themselves (as opposed to the research publication associated with the data). Through these services the LCA Commons collection at the Ag Data Commons will add value to the already valuable data source underpinning an LCA.

Controlled keywords and search terms are critical for effective access to archived resources. Since 2002, the National Agricultural Library has maintained and developed a rich, expertly-constructed thesaurus, NALT, which it uses to index its collection. NALT is being expanded with LCA terminology to provide preferred terms and often-used synonyms, as well as the vertical relationships between general and specific concepts. This functionality aids data discovery and interoperability by providing a consistent concept model translating between nomenclatures, documentation styles, and file formats. The NALT is a tool that can be used to implement a controlled vocabulary for LCA, to lay the foundation for semantic modeling, data management and data discovery applications.


Addressing data quality indicators to support interoperability: recommendations for further developments in Life Cycle Assessment data quality systems
SPEAKER: Ashley Edelen

ABSTRACT. As the benefits of using Life Cycle Assessment (LCA) becomes more apparent to global decision makers, countries are seeking to capitalize on these benefits by supporting the creation of national life cycle inventory (LCI) databases or networks. Such systems would allow users to integrate data from more than one LCI source, a major driving force behind the Global Network for LCA Databases to enable interoperability and data exchange on an international level (Joint Research Centere: European Platform on Life Cycle Assessment, 2014). Data quality is a major issue to be resolved when combining data from various sources. This study addresses the use of data quality indicators, focusing on differences in indicator systems and reproducibility of data quality indicators (DQIs). In order to show global trends in implementing data quality systems, we perform a methodological review of data quality standards and systems used in existing databases. Two major types of systems emerged from this study; the criteria-based system and the more widely used matrix-based systems. Using a sample process data set, we then compare the reproducibility of data quality indicators for the criteria-based and matrix-based data quality systems across seven data quality indicators. These indicator categories are: reliability, completeness, temporal, geographic, technological, uncertainty, and precision. We find current quality practices can be highly subjective for applications and reporting, which produce variability in data quality ratings. This lack of consistency and reproducibility in data quality ratings undermine the purpose and effectiveness of a data quality system. Evaluation of a sample data set using all systems confirms the findings of our review and shows no discernable pattern of reproducibility. No indicator reached a 100% agreement using either DQI system. 32% of indicators achieved a >80% agreement using the criteria-based system, while only 14% of indicators using the matrix-based system achieved this same level of agreement. This study shows the need for more structured methodologies in the field of LCA data quality to ensure reproducibility while maintaining the desired practitioner autonomy.

References Joint Research Centere: European Platform on Life Cycle Assessment. (2014). EPLCA. Retrieved from Life Cycle Data Network:


The electricity sector in the ecoinvent database: updates and extensions of inventory data for ecoinvent v3.2

ABSTRACT. Life cycle assessment (LCA) results often crucially depend on the life cycle inventories (LCI) of electricity generation and supply drawn from background databases. Therefore, their up-to-dateness and representativeness are essential factors [1, 2]. We provide an overview of the new LCI data for the electricity sector (power generation and power supply, i.e. electricity markets) implemented in ecoinvent v3.2 [3]. New LCI data are based on latest statistics and recently published literature. Inventory data for power generation technologies representative for single countries or smaller regions were harmonized throughout the database concerning parent-child relationship, use of parameters, variables and mathematical relations, water balance as well as technology levels. Global averages were coherently calculated. Six additional countries were included, allowing for complete coverage of the EU-28 member states. Additionally, China was split into 31 provinces on the generation technology level and into two electricity markets according to the two main grid operators in China. Unit processes for electricity production in 107 geographic regions covering 56 countries are now available in ecoinvent v3.2, representing 89% of global generation in 2012. LCI data for several new electricity (and heat) generation technologies were introduced: hard coal, lignite and oil plants with combined heat and power generation and solar thermal power plants. Region-specific power plant efficiencies as well as operational emissions were updated and latest annual yields for photovoltaics and wind turbines implemented. Additional updates include deep geothermal power, CO2 and CH4 emission factors from hydro reservoirs due to decomposition of biomass, geography-specific electricity losses in the power grid and technology-specific market shares. The updates and extensions of LCI data for the electricity sector in ecoinvent v3.2 once more stress the importance of high spatial resolution of these data due to substantial geography-specific variations. The observed changes in the LCI data compared to ecoinvent v3.0 [4, 5] highlight the importance of regular updates of the electricity sector. With the new electricity markets representative for year 2012, ecoinvent v3.2 provides the most up-to-date, complete and representative LCI data for electricity generation and power supply to users of generic background LCI data.

[1] Masanet, E. et al. (2013) Life-Cycle Assessment of Electric Power Systems. Annual Review of Environment and Resources 38:107-136. doi:10.1146/annurev-environ-010710-100408. [2] Turconi, R., Boldrin, A., Astrup, T. (2013) Life cycle assessment (LCA) of electricity generation technologies: Overview, comparability and limitations. Renewable and Sustainable Energy Reviews 28:555-565. doi: [3] The ecoinvent LCA database, v3.2 (2015) The ecoinvent center. [4] Treyer, K., Bauer, C. (2013) Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database – part I: electricity generation. Int J Life Cycle Assess:1-19, doi:10.1007/s11367-013-0665-2. [5] Treyer, K., Bauer, C. (2014) Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database – part II: electricity markets. Int J Life Cycle Assess:1-14. doi:10.1007/s11367-013-0694-x.