Derivation and assessment of regional electricity generation emission factors in the USA.

Purpose: Electricity production is one of the largest sources of environmental emissions-especially greenhouse gases (GHGs)-in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs). Methods: To address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs. Results and discussion: We explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated. Conclusion: This work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers-considering the detailed sources of the information and the breadth of emissions covered by it.

Analyzing Economy-Scale Solid Waste Generation Using the United States Environmentally-Extended Input-Output Model.

The United States Environmentally-Extended Input-Output (USEEIO) model includes commercial enterprises from 386 industrial sectors of the economy. The purpose of this work is to model the commercial generation of three streams of solid waste from USEEIO sectors: hazardous waste, non-hazardous waste excluding construction, and non-hazardous waste from construction. The waste accounts cover 536 waste materials, with commercial non-hazardous waste presently limited to municipal solid waste and construction and demolition debris. Total combined generation for all streams based on 2015 economic activity is approximately 775 million metric tons, with concrete from construction activities accounting for 44% of this mass. The chemical and plastics industries generate the most commercial hazardous waste per dollar of economic output. In most cases, waste materials such as paper, plastic, and metals are generated in greater quantities per dollar of industry output when compared to commercial construction materials and hazardous waste. When considering direct waste generation within an industry, USEEIO model rankings identified the highway and street construction and chemical manufacturing industries as potential areas to continue to pursue new innovations in material use. The rankings change when considering final consumption of goods and services, with various construction industries and state and local governments becoming more prominent. The full detailed waste models are publicly available and will be incorporated into future USEEIO releases. Quantification of waste material generation across the economy is an essential part of decision making because it will highlight areas where intervention may be beneficial.

Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States.

The accuracy of direct and indirect resource use and emissions of products as quantified in life cycle models depends in part upon the geographical and technological representativeness of the production models. Production conditions vary not just between nations, but also within national boundaries. Understanding the level of geographic resolution within large industrial nations needed to reach acceptable accuracy has not been well-tested across the broad spectrum of goods and services consumed. Using an aggregate 15-industryenvironmentally-extended input-output model of the US along with detailed interstate commodity flow data, we test the accuracy of regionalizing the national model into two-regions (state - rest of US) versus 51 regions (all US states + DC). Our findings show the two-region form predicts life cycle emissions and resources used within 10-20% of the more detailed 51-region form for most of the environmental flows studied. The two-region form is less accurate when higher variability exists in production conditions for a product.

The creation, management, and use of data quality information for life cycle assessment.

PURPOSE: Despite growing access to data, questions of "best fit" data and the appropriate use of results in supporting decision making still plague the life cycle assessment (LCA) community. This discussion paper addresses revisions to assessing data quality captured in a new US Environmental Protection Agency guidance document as well as additional recommendations on data quality creation, management, and use in LCA databases and studies. APPROACH: Existing data quality systems and approaches in LCA were reviewed and tested. The evaluations resulted in a revision to a commonly used pedigree matrix, for which flow and process level data quality indicators are described, more clarity for scoring criteria, and further guidance on interpretation are given. DISCUSSION: Increased training for practitioners on data quality application and its limits are recommended. A multi-faceted approach to data quality assessment utilizing the pedigree method alongside uncertainty analysis in result interpretation is recommended. A method of data quality score aggregation is proposed and recommendations for usage of data quality scores in existing data are made to enable improved use of data quality scores in LCA results interpretation. Roles for data generators, data repositories, and data users are described in LCA data quality management. Guidance is provided on using data with data quality scores from other systems alongside data with scores from the new system. The new pedigree matrix and recommended data quality aggregation procedure can now be implemented in openLCA software. FUTURE WORK: Additional ways in which data quality assessment might be improved and expanded are described. Interoperability efforts in LCA data should focus on descriptors to enable user scoring of data quality rather than translation of existing scores. Developing and using data quality indicators for additional dimensions of LCA data, and automation of data quality scoring through metadata extraction and comparison to goal and scope are needed.

Bridge Processes: A Solution for LCI Datasets Independent of Background Databases.

INTRODUCTION: New platforms are emerging that enable more data providers to publish life cycle inventory data. BACKGROUND: Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software this requires modifying the original process. RESULTS: The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution. DISCUSSION: Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size. CONCLUSION: Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution, but provide a solution that works within the existing LCA data model.

LCA Capability Roadmap: Product System Model Description and Revision.

Life cycle assessment (LCA) practitioners face many challenges in their efforts to describe, share, review, and revise their product system models; and to reproduce the models and results of others. Current Life cycle inventory modeling techniques have weaknesses in the areas of describing model structure; documenting the use of proxy or non-ideal data; specifying allocation; and including modeler's observations and assumptions -- all affecting how the study is interpreted and limiting the reuse of models. Moreover, LCA software systems manage modeling information in different and sometimes non-compatible ways. Practitioners must also deal with licensing, privacy / confidentiality of data, and other issues around data access which impact how a model can be shared. The aim of this SETAC North America working group is to define a roadmap of the technical advances needed to achieve easier LCA model sharing and improve replicability of LCA results among different users in a way that is independent of the LCA software used to compute the results and does not infringe on any licensing restrictions or confidentiality requirements.

Critical review of elementary flows in LCA data.

PURPOSE: Elementary flows are essential components of data used for life cycle assessment. A standard list is not used across all sources, as data providers now manage these flows independently. Elementary flows must be consistent across a life cycle inventory for accurate inventory analysis and must correspond with impact methods for impact assessment. With the goal of achieving a global network of LCA databases, a critical review of elementary flow usage and management in LCA data sources was performed. METHODS: Flows were collected in a standard template from various life cycle inventory, impact method, and software sources. A typology of elementary flows was created to identify flows by types such as chemicals, minerals, land flows, etc. to facilitate differential analysis. Twelve criteria were defined to evaluate flows against principles of clarity, consistency, extensibility, translatability, and uniqueness. RESULTS AND DISCUSSION: Over 134,000 elementary flows from five LCI databases, three LCIA methods, and four LCA software tools were collected and evaluated from European, North American, and Asian Pacific LCA sources. The vast majority were typed as "Element or Compound" or "Group of Chemicals" with less than 10% coming from the other seven types Many lack important identifying information including context information (environmental compartments), directionality (LCIA methods generally do not provide this information), additional clarifiers such as CAS numbers and synonyms, unique identifiers (like UUIDs), and supporting metadata. Extensibility of flows is poor because patterns in flow naming are generally complex and inconsistent because user defined nomenclature is used. CONCLUSIONS: The current shortcomings in flow clarity, consistency, and extensibility are likely to make it more challenging for users to properly select and use elementary flows when creating LCA data and make translation/conversion between different reference lists challenging and loss of information will likely occur. RECOMMENDATIONS: We recommend the application of a typology to flow lists, use of unique identifiers and inclusion of clarifiers based on external references, setting an exclusive or inclusive nomenclature for flow context information that includes directionality and environmental compartment information, separating flowable names from context and unit information, linking inclusive taxonomies to create limited patterns for flowable names, and using an encoding schema that will prevent technical translation errors.

USEEIO: a New and Transparent United States Environmentally-Extended Input-Output Model.

National-scope environmental life cycle models of goods and services may be used for many purposes, not limited to quantifying impacts of production and consumption of nations, assessing organization-wide impacts, identifying purchasing hotspots, analyzing environmental impacts of policies, and performing streamlined life cycle assessment. USEEIO is a new environmentally-extended input-output model of the United States fit for such purposes and other sustainable materials management applications. USEEIO melds data on economic transactions between 389 industry sectors with environmental data for these sectors covering land, water, energy and mineral usage and emissions of greenhouse gases, criteria air pollutants, nutrients and toxics, to build a life cycle model of 385 US goods and services. In comparison with existing US models, USEEIO is more current with most data representing year 2013, more extensive in its coverage of resources and emissions, more deliberate and detailed in its interpretation and combination of data sources, and includes formal data quality evaluation and description. USEEIO is assembled with a new Python module called the IO Model Builder capable of assembling and calculating results of user-defined input-output models and exporting the models into LCA software. The model and data quality evaluation capabilities are demonstrated with an analysis of the environmental performance of an average hospital in the US. All USEEIO files are publicly available bringing a new level of transparency for environmentally-extended input-output models.

Life cycle assessment of a commercial rainwater harvesting system compared with a municipal water supply system.

Building upon previously published life cycle assessment (LCA) methodologies, we conducted an LCA of a commercial rainwater harvesting (RWH) system and compared it to a municipal water supply (MWS) system adapted to Washington, D.C. Eleven life cycle impact assessment (LCIA) indicators were assessed, with a functional unit of 1 m3 of rainwater and municipal water delivery system for toilets and urinals in a four-story commercial building with 1000 employees. Our assessment shows that the benchmark commercial RWH system outperforms the MWS system in all categories except Ozone Depletion. Sensitivity and performance analyses revealed pump and pumping energy to be key components for most categories, which further guides LCIA tradeoff analysis with respect to energy intensities. Tradeoff analysis revealed that commercial RWH performed better than MWS in Ozone Depletion if RWH's energy intensity was less than that of MWS by at least 0.86 kWh/m3 (249% of the benchmark MWS energy usage at 0.35 kWh/m3). RWH also outperformed MWS in Metal Depletion and Freshwater Withdrawal, regardless of energy intensities, up to 5.51 kWh/m3. An auxiliary commercial RWH system with 50% MWS reduced Ozone Depletion by 19% but showed an increase in all other impacts, which were still lower than benchmark MWS system impacts. Current models are transferrable to commercial RWH installations at other locations.

Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing.

Demands for quick and accurate life cycle assessments create a need for methods to rapidly generate reliable life cycle inventories (LCI). Data mining is a suitable tool for this purpose, especially given the large amount of available governmental data. These data are typically applied to LCIs on a case-by-case basis. As linked open data becomes more prevalent, it may be possible to automate LCI using data mining by establishing a reproducible approach for identifying, extracting, and processing the data. This work proposes a method for standardizing and eventually automating the discovery and use of publicly available data at the United States Environmental Protection Agency for chemical-manufacturing LCI. The method is developed using a case study of acetic acid. The data quality and gap analyses for the generated inventory found that the selected data sources can provide information with equal or better reliability and representativeness on air, water, hazardous waste, on-site energy usage, and production volumes but with key data gaps including material inputs, water usage, purchased electricity, and transportation requirements. A comparison of the generated LCI with existing data revealed that the data mining inventory is in reasonable agreement with existing data and may provide a more-comprehensive inventory of air emissions and water discharges. The case study highlighted challenges for current data management practices that must be overcome to successfully automate the method using semantic technology. Benefits of the method are that the openly available data can be compiled in a standardized and transparent approach that supports potential automation with flexibility to incorporate new data sources as needed.

Identifying/Quantifying Environmental Trade-offs Inherent in GHG Reduction Strategies for Coal-Fired Power.

Improvements to coal power plant technology and the cofired combustion of biomass promise direct greenhouse gas (GHG) reductions for existing coal-fired power plants. Questions remain as to what the reduction potentials are from a life cycle perspective and if it will result in unintended increases in impacts to air and water quality and human health. This study provides a unique analysis of the potential environmental impact reductions from upgrading existing subcritical pulverized coal power plants to increase their efficiency, improving environmental controls, cofiring biomass, and exporting steam for industrial use. The climate impacts are examined in both a traditional-100 year GWP-method and a time series analysis that accounts for emission and uptake timing over the life of the power plant. Compared to fleet average pulverized bed boilers (33% efficiency), we find that circulating fluidized bed boilers (39% efficiency) may provide GHG reductions of about 13% when using 100% coal and reductions of about 20-37% when cofiring with 30% biomass. Additional greenhouse gas reductions from combined heat and power are minimal if the steam coproduct displaces steam from an efficient natural gas boiler. These upgrades and cofiring biomass can also reduce other life cycle impacts, although there may be increased impacts to water quality (eutrophication) when using biomass from an intensely cultivated source. Climate change impacts are sensitive to the timing of emissions and carbon sequestration as well as the time horizon over which impacts are considered, particularly for long growth woody biomass.

Life cycle assessment of domestic and agricultural rainwater harvesting systems.

To further understanding of the environmental implications of rainwater harvesting and its water savings potential relative to conventional U.S. water delivery infrastructure, we present a method to perform life cycle assessment of domestic rainwater harvesting (DRWH) and agricultural rainwater harvesting (ARWH) systems. We also summarize the design aspects of DRWH and ARWH systems adapted to the Back Creek watershed, Virginia. The baseline design reveals that the pump and pumping electricity are the main components of DRWH and ARWH impacts. For nonpotable uses, the minimal design of DRWH (with shortened distribution distance and no pump) outperforms municipal drinking water in all environmental impact categories except ecotoxicity. The minimal design of ARWH outperforms well water in all impact categories. In terms of watershed sustainability, the two minimal designs reduced environmental impacts, from 58% to 78% energy use and 67% to 88% human health criteria pollutants, as well as avoiding up to 20% blue water (surface/groundwater) losses, compared to municipal drinking water and well water. We address potential environmental and human health impacts of urban and rural RWH systems in the region. The Building for Environmental and Economic Sustainability (BEES) model-based life cycle inventory data were used for this study.

Dataset of 2012-2020 U.S. National- and State-Level Greenhouse Gas Emissions by Sector.

The dataset contains ∼1.1 million records of total greenhouse gases directly emitted annually by economic sectors and households in the US from 2012-2020. Data are given for 16 unique greenhouse gases by 118 aggregate sectors for each state, and as totals by these aggregate sectors as well as by 540 detailed sectors at the national level. The dataset is a product of updated sector attribution models that improve upon the National Greenhouse Gas Industry Attribution Model. This paper provides documentation of the methods used to produce these datasets and proof of validation of the dataset, along with relevant supporting tables, figures, and source code.

USEEIO v2.0, The US Environmentally-Extended Input-Output Model v2.0.

USEEIO v2.0 is an environmental-economic model of US goods and services that can be used for life cycle assessment, footprinting, national prioritization, and related applications. This paper describes the development of the model and accompanies the release of a full model dataset as well as various supporting datasets of national environmental totals by US industry. Novel methodological elements since USEEIO v1 models include waste sector disaggregation, final demand vectors for US consumption and production, a domestic form of the model that can be used to separate domestic and foreign impacts, and price adjustment matrices for converting outputs to purchaser price and in various US dollar years. Improvements in modeling national totals of industry and environmental flows are described. The model is validated through reproduction of national totals from input data sources and through analysis of changes from the most recent complete USEEIO model that can be explained based on data updates or method changes. The model datasets can all be reproduced with open source software packages.

useeior: An Open-Source R Package for Building and Using US Environmentally-Extended Input-Output Models.

useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input-output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model.

Life Cycle Data Interoperability Improvements through Implementation of the Federal LCA Commons Elementary Flow List.

As a fundamental component of data for life cycle assessment models, elementary flows have been demonstrated to be a key requirement of life cycle assessment data interoperability. However, existing elementary flow lists have been found to lack sufficient structure to enable improved interoperability between life cycle data sources. The Federal Life Cycle Assessment Commons Elementary Flow List provides a novel framework and structure for elementary flows, but the actual improvement this list provides to the interoperability of life cycle data has not been tested. The interoperability of ten elementary flow lists, two life cycle assessment databases, three life cycle impact assessment methods, and five life cycle assessment software sources is assessed with and without use of the Federal Life Cycle Assessment Commons Elementary Flow List as an intermediary in flow mapping. This analysis showed that only 25% of comparisons between these sources resulted in greater than 50% of flows being capable of automatic name-to-name matching between lists. This indicates that there is a low level of interoperability when using sources with their original elementary flow nomenclature, and elementary flow mapping is required to use these sources in combination. The mapping capabilities of the Federal Life Cycle Assessment Commons Elementary Flow List to sources were reviewed and revealed a notable increase in name-to-name matches. Overall, this novel framework is found to increase life cycle data source interoperability.

Incorporating New Technologies in EEIO Models.

We propose a methodology to add new technologies into Environmentally Extended Input-Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially substitute for a functionally-similar existing commodity of the baseline economy. The level of substitution is controlled by a percentage (%) as a variable of the model. In the Use table, a percentage of the current use of the existing commodity is transferred to the new commodity. The Supply or Make table is modified assuming that the new industries are the only ones producing the new commodity. We illustrate the method for the USEEIO model, for the addition of second generation biofuels, including naphtha, jet fuel and diesel fuel. The new industries' inputs, outputs and value-added components needed to produce the new commodity are drawn from process-based life cycle inventories (LCIs). Process-based LCI inputs and outputs per physical functional unit are transformed to prices and assigned to commodities and environmental flow categories for the EEIO model. This methodology is designed to evaluate the environmental impacts of substituting products in the current US economy with bio-versions, produced by new technologies, that are intended to reduce negative environmental impacts. However, it can be applied for any new commodity for which the substitution assumption is reasonable.

FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries.

Quantifying industry consumption or production of resources, wastes, emissions, and losses-collectively called flows-is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges surrounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA's US Environmentally Extended Input-Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA's capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA's Data Commons.

StatelO - Open Source Economic Input-Output Models for the 50 States of the United States of America.

Subnational input-output (IO) tables capture industry- and region-specific production, consumption, and trade of commodities and serve as a common basis for regional and multi-regional economic impact analysis. However, subnational IO tables are not made available by national statistical offices, especially in the United States (US), nor have they been estimated with transparent methods for reproducibility or updated regularly for public availability. In this article, we describe a robust StateIO modeling framework to develop state and two-region IO models for all 50 states in the US using national IO tables and state industry and trade data from reliable public sources such as the US Bureau of Economic Analysis. We develop 2012-2017 state IO models and two-region IO models at the BEA summary level. The two regions are state of interest and rest of the US. All models are validated by a series of rigorous checks to ensure the results are balanced at state and national levels. We then use these models to calculate a 2012-2017 time series of macro economic indicators and highlight results for I I states that have distinct economies with respect to size, geography, and industry structure. We also compare selected indicators to state IO models created by popular licensed and open-source software. Our StateIO modeling framework is consolidated in an open-source R package, stateior, to ensure transparency and reproducibility. Our StateIO models are US-focused, which may not be transferrable to international accounts, and form the economic base of state versions of the US environmentally-extended IO models.

A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data.

The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons.