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

Estimating the carbon footprint of Mexican food consumption based on a high-resolution environmentally extended input-output model

Increased global attention is being paid to the food-health-climate trilemma. In this study, we evaluate the climate impacts of Mexico's food consumption patterns by creating a high-resolution (262 sectors) Environmentally Extended Input-Output (EEIO) model called MXEEIO. We focus on the differences between food away from home (FAFH) and food at home (FAH) and compare Mexico's results with those of the USA. The results show that the main components of food spending in Mexico were meat, baked products, and beverages, raising concerns about their potential negative health effects if consumed excessively. Mexico's total greenhouse gas (GHG) emissions from food consumption were estimated at 149 million metric tons (MMT) in 2013, as opposed to 797 MMT for the USA. Meat and dairy products were the main contributors to Mexico's food-related GHG emissions, accounting for 57% of total emissions. Mexico spent a much smaller proportion of food-related income on FAFH than the USA (13% vs. 52%), suggesting great potential for growth as Mexico's per capita GDP continues to rise. Detailed contribution analysis shows that reducing Mexico's food-related GHG emissions would benefit most from a transition to low-carbon cattle farming, but mitigation efforts in other sectors such as crop cultivation and electricity generation are also important. Overall, our study underscores the significance of food-related GHG emissions in Mexico, especially those from meat and dairy products, and the mitigation challenges these sectors face.

What a Waste! The Impact of Unused Surgical Supplies in Hand Surgery and How We Can Improve

BACKGROUND

The US health care system is the second largest contributor of trash. Approximately 20% to 70% of waste is produced by operating rooms, and very few of this waste is recycled. The purpose of this study is to quantify the opened but unused disposable supplies and generate strategies to reduce disposable waste.

METHODS

A single-center prospective study to evaluate the cost of opened but unused single-use operating room supplies was completed by counting the number of wasted disposable products at the end of hand surgery cases. We used χ2 test, t test, Wilcoxon rank-sum test, and simple linear regression to assess the associations between patient and case variables and the total cost of wasted items. Environmentally Extended Input Output Life Cycle Assessment methods were used to convert the dollar spent to kilograms of carbon dioxide equivalent (CO2-e), a measure of greenhouse gas emissions.

RESULTS

Surgical and dressing items that were disposed of and not used during each case were recorded. We included 85 consecutive cases in the analysis from a single surgeon's practice. Higher cost from wasted items was associated with shorter operative time (P = .010). On average, 11.5 items were wasted per case (SD: 3.6 items), with a total of 981 items wasted over the 85 cases in the study period. Surgical sponges and blades were 2 of the most unused items. Wasted items amounted to a total of $2193.5 and 441 kg of CO2-e during the study period.

CONCLUSIONS

This study highlights the excessive waste of unused disposable products during hand surgery cases and identifies ways of improvement.

Extension and update of multiscale monthly household carbon footprint in Japan from 2011 to 2022

Household consumption significantly contributes to greenhouse gas emissions as it is the largest component of final demand in the national accounting system. Nevertheless, there is an apparent lack of comprehensive and consistent datasets detailing emissions from household consumption. Here, we expand and update Japan's multiscale monthly household carbon footprint from January 2011 to September 2022, combining data from government statistics and surveys. We constructed a dataset comprising 37,692 direct and 4,852,845 indirect emission records, covering households at the national, regional, and prefectural city levels. The dataset provides critical spatiotemporal information that allows for revealing carbon emission patterns, pinpointing primary sources of emissions, and discerning regional variances. Moreover, the inclusion of micro-scale carbon footprint data enables the identification of specific consumption habits, thereby regulating individual consumption behavior to achieve a low-carbon society.

Japanese urban household carbon footprints during early-stage COVID-19 pandemic were consistent with those over the past decade

As urbanization accelerates worldwide, substantial energy and services are required to meet the demand from cities, making cities major contributors to adverse environmental consequences. To bridge the knowledge gap in the absence of fine-grained city-level climate protection measures due to data availability and accuracy, this study provides a detailed carbon emission inventory for analyzing the monthly fluctuations based on citizens' daily consumption behaviors. Here, carbon emissions embodied in approximately 500 household consumption items were calculated in 47 prefectural-level cities in Japan from 2011 to June 2021. We analyzed the results considering the regional, seasonal, demand, and emission way-specific aspects, and compared the emission before and during the COVID-19 pandemic. Notably, the carbon footprints during the pandemic were consistent with the previous level despite downtrends in specific categories. This study provides an example of utilizing city-level emission data to improve household green consumption behavior as references for enriching city-level decarbonization paths.

Modeling a localized metropolitan food system in the Midwest USA: Life cycle impacts of scenarios for Des Moines, Iowa

Food systems are energy-intensive, causing ≈25 % of anthropogenic global warming potential (GWP) and contributing to challenges across the food-energy-water nexus. The state of Iowa, USA, is of particular interest as a rainfed agricultural region of the upper Midwest; despite its highly productive landscape, a large proportion of food consumed by Iowa residents is imported. This study focused on the Des Moines Metropolitan Statistical Area (DM-MSA), a six-county area in central Iowa with a 2020 population of ≈700,000. A life cycle assessment approach was used to quantify environmental impacts (GWP, fossil energy and water consumption, land use); scenarios modeled provision and consumption of 50 % of nutritional requirements for the current DM-MSA population by food group (e.g., grains, proteins, vegetables). The four DM-MSA food system scenarios were: 1) current conditions (baseline), 2) local production for 50 % of food, 3) consumption changed to follow USA dietary guidelines, and 4) combined changes to production and consumption. Localizing food production reduced all environmental impacts more than following USA dietary guidelines. Compared to the baseline, 50 % local production scenarios reduced GWP and energy consumption (18-24 %) and water use (35-41 %) annually. Decreases by food group were least for protein (-10 % GWP) and greatest for fruits and vegetables (-58-62 % GWP). Local scenario alternatives could further reduce some environmental impacts if paired with a nutritionally- and environmentally-optimized diet (EAT-Lancet) providing the greatest change (-30-38 % for GWP and energy use) compared to the local scenario. A 50 % local production scenario for the DM-MSA could decrease GWP by 102 million CO_2eq yr^-1 and water use by 44 billion L yr^-1. However, this would require dietary changes based on seasonal food availability. Further development and co-simulation with other metropolitan-scale biophysical and social models will enhance understanding of food system drivers and support effective decision-making for urban food system improvements in the Midwest.

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.

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.

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.

China's investments in renewable energy through the belt and road initiative stimulated local economy and employment: A case study of Pakistan

Since China's announcement of the Belt and Road Initiative (BRI) in 2015, much focus has been drawn on the environmental impacts of China's energy investments in the countries along the BRI. The economic and social impacts of these investments, which are also important for the wellbeing for local people, left largely uninvestigated. In this paper, we used China's renewable energy investments in Pakistan as a case study to investigate the contributions of these investments on local economy and employment. Through IO table analysis, we found that the 28 renewable power plant projects invested by China till now potentially provided 8905 jobs and generated around USD 39.8 million production values in related sectors in Pakistan, including USD 30.7 million from wind power plants development and 9.1 million from solar. When Chinese companies act as engineers and constructors, the increase of production value in relevant sectors in Pakistan (USD 6.05 million per 100 MW) are higher than wind power plant projects with other magnitude of engagement (3.82 million as a fully sponsor, 4.19 million as only finance supporter and 2.29 as equipment provider). Wind power plants will create more jobs and increase more production values than solar power plants. This study identifies the economic and social benefits of BRI renewable energy investments from China and the driving mechanism, thus providing basis for promoting renewable energy investments in countries like Pakistan so that they can gain new drive for social and economic growth from the global trend of low carbon transition.

Using Deep Learning to Fill Data Gaps in Environmental Footprint Accounting

Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future IO tables but suffers from doubts for unreliable estimations. Here we develop a machine learning-augmented method to improve the accuracy of the prediction of IO tables using the US summary-level tables as a demonstration. The model is constructed by combining the RAS method with a deep neural network (DNN) model in which the RAS method provides a baseline prediction and the DNN model makes further improvements on the areas where RAS tended to have poor performance. Our results show that the DNN model can significantly improve the performance on those areas in IO tables for short-term prediction (one year) where RAS alone has poor performance, R² improved from 0.6412 to 0.8726, and median APE decreased from 37.49% to 11.35%. For long-term prediction (5 years), the improvements are even more significant where the R² is improved from 0.5271 to 0.7893 and median average percentage error is decreased from 51.12% to 18.26%. Our case study on evaluating the US carbon footprint accounts based on the estimated IO table also demonstrates the applicability of the model. Our method can help generate timely IO tables to provide fundamental data for a variety of environmental footprint analyses.

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.

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.

Biodiversity effects of food system sustainability actions from farm to fork

The food system’s negative impact on biodiversity is increasing over time. Conserving biodiversity requires immediate and widespread action to reduce the biodiversity footprint of food consumption, but biodiversity has historically been neglected in sustainability assessments. We combine high-resolution estimates of the biodiversity footprint with food system scenario modeling to predict the consequences of two key food system sustainability actions in the United States: diet shifts and food waste reduction. Taking these actions may benefit biodiversity in some places and harm it in others. The results of this study can help decision makers understand the trade-offs we must navigate to balance human health, economics, and environmental sustainability and help consumers understand how their diets and food waste behaviors influence global biodiversity.

Diet shifts and food waste reduction have the potential to reduce the land and biodiversity footprint of the food system. In this study, we estimated the amount of land used to produce food consumed in the United States and the number of species threatened with extinction as a result of that land use. We predicted potential changes to the biodiversity threat under scenarios of food waste reduction and shifts to recommended healthy and sustainable diets. Domestically produced beef and dairy, which require vast land areas, and imported fruit, which has an intense impact on biodiversity per unit land, have especially high biodiversity footprints. Adopting the Planetary Health diet or the US Department of Agriculture (USDA)–recommended vegetarian diet nationwide would reduce the biodiversity footprint of food consumption. However, increases in the consumption of foods grown in global biodiversity hotspots both inside and outside the United States, especially fruits and vegetables, would partially offset the reduction. In contrast, the USDA-recommended US-style and Mediterranean-style diets would increase the biodiversity threat due to increased consumption of dairy and farmed fish. Simply halving food waste would benefit global biodiversity more than half as much as all Americans simultaneously shifting to a sustainable diet. Combining food waste reduction with the adoption of a sustainable diet could reduce the biodiversity footprint of US food consumption by roughly half. Species facing extinction because of unsustainable food consumption practices could be rescued by reducing agriculture's footprint; diet shifts and food waste reduction can help us get there.

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.

Life cycle assessment of behind-the-meter Bitcoin mining at US power plant

PURPOSE

Due to its highly energy-intensive process, Bitcoin has attracted the global attention of climate research and media. At the time of this submission, behind-the-meter Bitcoin mining has gained significant traction; however, not a single environmental impact assessment has been conducted on this type of operation. This study seeks to fill the gap, applying the established Life Cycle Assessment methodology to estimate the environmental footprint of a single case study.

METHODS

A life cycle assessment methodology of a natural gas power plant mining Bitcoin behind-the-meter in the state of New York following the ISO 14040 guidelines was applied. The functional unit (FU) is defined as the attributed generation capacity of 14 MW over the course of a regular full-calendar year in the attributional model. The FU is scaled to 22 MW and 104 MW in the predictive models to represent planned expansion. The TRACI 2.1 method was applied to characterize the environmental impact. The environmental impact categories considered in this study included global warming, acidification, smog formation, and particulate emissions.

RESULTS AND DISCUSSION

Located in New York State, Greenidge LLC, a natural gas power plant produces an estimated 88,440 metric tons of CO₂-eq per year to mine Bitcoin behind-the-meter. Annual emissions would total 656,983 metric tons of CO₂-eq if the plant devotes 100% of its generation to Bitcoin mining. The primary driver of greenhouse gas emissions is the generation of electricity itself, accounting for ~ 79% of the total emissions. At full capacity, annual emissions are comparable to the annual emissions of 140,000 passenger vehicles or the emissions resulting from the burning of 600 million lb of coal. Further, additional planned cases could produce an estimated 1.9 million tons tCO₂-eq per annum.

CONCLUSIONS

Behind-the-meter Bitcoin mining makes the power plant a significant contributor to global warming at a time when New York State is attempting to radically reduce its greenhouse gas emissions by 85% by 2050 and to have 100% carbon-free electricity by 2040. The environmental impact of this business model is not limited to individual sites but is spread out over upstream impacts as well. In combination, we see that behind-the-meter Bitcoin mining not only goes against local climate initiatives but also poses a significant danger to national initiatives due to feasible scalability, caused by an availability of existing infrastructure and favorable financials.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11367-022-02025-0.

High-Resolution Environmentally Extended Input-Output Model to Assess the Greenhouse Gas Impact of Electronics in South Korea

South Korea is a global leader in electronics, but little is known about their climate change impact. Here, we estimate the direct and indirect greenhouse gas (GHG) emissions of Korean electronics by developing a new and high-resolution (∼380 sectors) environmentally extended input-output model, named KREEIO. We find that final demand for Korean electronics led to nearly 8% of national GHG emissions in 2017, mostly because of indirect emissions embodied in the electronics supply chain. Notably, the semiconductor and display sectors contributed 3.2% and 2.4% to national emissions, with capital investment accounting for 17% of the two sectors' total emissions or nearly 1% of national emissions. For other electronic products, scope 1, scope 2, and upstream scope 3 emissions on average accounted for 3%, 10%, and 87% of a sector's GHG intensity, respectively. Detailed contribution analysis suggests that reducing Korean electronics GHG emissions would benefit most from the transition to a low-carbon electricity grid, but mitigation efforts in many other sectors such as metals and chemicals are also important. Overall, our study underscores the significance of electronics GHG emissions in South Korea, especially those from semiconductors and displays, and the mitigation challenges these sectors face as demand continues to grow globally.

Recycling of Plastics in the United States: Plastic Material Flows and Polyethylene Terephthalate (PET) Recycling Processes

As efforts are made toward establishing a circular economy that engages in activities that maintain resources at their highest values for as long as possible, an important aspect is understanding the systems which allow recycling to occur. In this article a common plastic, polyethylene terephthalate, i.e., PET or plastic #1, has been studied because it is recycled at relatively high rates in the U.S. as compared to other plastics. A material flow analysis is described for PET resin showing materials collected, reclaimed for flake, and converted into items with recycled content. Imports/exports, reclaimer residue, and disposal with mismanaged waste are all shown for U.S. flows of PET. Barriers to recycling PET exist in the collecting, sorting, reclaiming, and converting steps, and this article describes them, offers some solutions, and suggests some research that chemists and engineers could focus on to improve the systems. This effort also models sorting at material recovery facilities (MRF) and reclaimers, with detailed descriptions of the material streams involved, to characterize the resource use and emissions from these operations that are key processes in the recycling system. Example results include greenhouse gas intensities of 8.58 kg CO₂ equiv per ton of MRF feed and 103.7 kg CO₂ equiv per ton of reclaimer PET bale feed. The results can be used in system analyses for various scenarios and as inputs in economic input-output and life cycle assessments.

The Impacts on Greenhouse Gases Emission during the COVID-19 lockdown in the US: An Economic Input-Output Life Cycle Assessment

The SARS-CoV-2 virus pandemic (COVID-19) is causing disruptions to energy, finance, tourism, and trade industries all around the world. These disruptions are the result of quarantining and lockdowns that cause reductions in production and consumptions. This change in production and consumption rates has environmental consequences. This study investigates the environmental effects of COVID-19 lockdown in the United States by Input-Output Life Cycle Assessment (IO-LCA) approach. The analysis is based on extraction of economic data in the US. The simulated results are based on different durations and strategies of lockdown measures. Among all industrial categories, utilities, which include power generation and supply, water supply, and natural gas supply sectors, saw the most significant reductions by approximately 110 kt CO₂-eq in the first quarter and 265 kt CO₂-eq in the second quarter of 2020. The assessed reductions were the results of both direct emission reductions caused by the shutdown of certain industries and also indirect emission reductions from upstream industries. The proposed methodology provides an effective guideline to predict the greenhouse gases emissions, which can be used as a prediction method for different regions in the world.

A Novel Hybrid Life Cycle Assessment Approach to Air Emissions and Human Health Impacts of Liquefied Natural Gas Supply Chain

Global interest in LNG products and supply chains is growing, and demand continues to rise. As a clean energy source, LNG can nevertheless emit air pollutants, albeit at a lower level than transitional energy sources. An LNG plant capable of producing up to 126 MMTA was successfully developed and simulated in this study. A hybrid life cycle assessment model was developed to examine the social and human health impacts of the LNG supply chain’s environmental air emission formation. The Multiregional Input–Output (MRIO) database, the Aspen HYSYS model, and the LNG Maritime Transportation Emission Quantification Tool are the key sources of information for this extensive novel study. We began our research by grouping environmental emissions sources according to the participation of each stage in the supply chain. The MDEA Sweetening plant, LNG loading (export terminal), and LNG transportation stages were discovered to have the maximum air emissions. The midpoint air emissions data estimated each stage’s CO2-eq, NOx-eq, and PM2.5-eq emissions per unit LNG generated. According to the midpoint analysis results, the LNG loading terminal has the most considerable normalized CO2-eq and NOx-eq emission contribution across all LNG supply chain stages. Furthermore, the most incredible intensity value for normalized PM2.5-eq was recorded in the SRU and TGTU units. Following the midpoint results, the social human health impact findings were calculated using ReCiPe 2016 characterization factors to quantify the daily loss of life associated with the LNG process chain. SRU and TGTU units have the most significant social human health impact, followed by LNG loading (export terminal) with about 7409.0 and 1203.9 (DALY/million Ton LNG produced annually), respectively. Natural gas extraction and NGL recovery and fractionation units are the lowest for social human health consequences.

Iowa Urban FEWS: Integrating Social and Biophysical Models for Exploration of Urban Food, Energy, and Water Systems

Most people in the world live in urban areas, and their high population densities, heavy reliance on external sources of food, energy, and water, and disproportionately large waste production result in severe and cumulative negative environmental effects. Integrated study of urban areas requires a system-of-systems analytical framework that includes modeling with social and biophysical data. We describe preliminary work toward an integrated urban food-energy-water systems (FEWS) analysis using co-simulation for assessment of current and future conditions, with an emphasis on local (urban and urban-adjacent) food production. We create a framework to enable simultaneous analyses of climate dynamics, changes in land cover, built forms, energy use, and environmental outcomes associated with a set of drivers of system change related to policy, crop management, technology, social interaction, and market forces affecting food production. The ultimate goal of our research program is to enhance understanding of the urban FEWS nexus so as to improve system function and management, increase resilience, and enhance sustainability. Our approach involves data-driven co-simulation to enable coupling of disparate food, energy and water simulation models across a range of spatial and temporal scales. When complete, these models will quantify energy use and water quality outcomes for current systems, and determine if undesirable environmental effects are decreased and local food supply is increased with different configurations of socioeconomic and biophysical factors in urban and urban-adjacent areas. The effort emphasizes use of open-source simulation models and expert knowledge to guide modeling for individual and combined systems in the urban FEWS nexus.

Potential Socioeconomic and Environmental Effects of an Expanding U.S. Bioeconomy: An Assessment of Near-Commercial Cellulosic Biofuel Pathways

This paper showcases the suitability of an environmentally extended input-output framework to provide macroeconomic analyses of an expanding bioeconomy to allow for adequate evaluation of its benefits and trade-offs. It also exemplifies the framework's applicability to provide early design stage evaluations of emerging technologies expected to contribute to a future bioeconomy. Here, it is used to compare the current United States (U.S.) bioeconomy to a hypothetical future containing additional cellulosic ethanol produced from two near-commercial pathways. We find that the substitution of gasoline with cellulosic ethanol is expected to yield socioeconomic net benefits, including job growth and value added, and a net reduction in global warming potential and nonrenewable energy use. The substitution fares comparable to or worse than that for other environmental impact categories including human toxicity and eutrophication potentials. We recommend that further technology advancement and commercialization efforts focus on reducing these unintended consequences through improved system design and innovation. The framework is seen as complementary to process-based technoeconomic and life cycle assessments as it utilizes related data to describe specific supply chains while providing analyses of individual products and portfolios thereof at an industrial scale and in the context of the U.S. economy.

The Environmental Impact of Interventional Radiology: An Evaluation of Greenhouse Gas Emissions from an Academic Interventional Radiology Practice

PURPOSE

To calculate the volume of greenhouse gases (GHGs) generated by a hospital-based interventional radiology (IR) department.

MATERIALS AND METHODS

Life cycle assessment (LCA) was used to calculate GHGs emitted by an IR department at a tertiary care academic medical center. The volume of waste generated, amount of disposable supplies and linens used, and the operating times of electrical equipment were recorded for procedures performed between 7:00 AM and 7:00 PM on 5 consecutive weekdays. LCA was then performed using purchasing data, plug loads for electrical hardware, data from temperature control units, and estimates of emissions related to travel in the area surrounding the medical center.

RESULTS

Ninety-eight procedures were performed on 97 patients. The most commonly performed procedures were drainages (30), placement and removal of venous access (21), and computed tomography-guided biopsies (13). Approximately 23,500 kg CO₂e were emitted during the study. Sources of CO₂ emissions in descending order were related to indoor climate control (11,600 kg CO₂e), production and transportation of disposable surgical items (9,640 kg CO₂e), electricity plug load for equipment and lighting (1,060 kg CO₂e), staff transportation (524 kg CO₂e), waste disposal (426 kg CO₂e), production, laundering, and disposal of linens (279 kg CO₂e), and gas anesthetics (19.3 kg CO₂e).

CONCLUSIONS

The practice of IR generates substantial GHG volumes, a majority of which come from energy used to maintain climate control, followed by emissions related to single-use surgical supplies. Efforts to reduce the environmental impact of IR may be focused accordingly.

Prototypes for automating product system model assembly

INTRODUCTION

The flexibility of life cycle inventory (LCI) background data selection is increasing with the increasing availability of data, but this comes along with the challenge of using the background data with primary life cycle inventory data. To relieve the burden on the practitioner to create the linkages and reduce bias, this study aimed at applying automation to create foreground LCI from primary data and link it to background data to construct product system models (PSM).

METHODS

Three experienced LCA software developers were commissioned to independently develop software prototypes to address the problem of how to generate an operable PSM from a complex product specification. The participants were given a confidential product specification in the form of a Bill of Materials (BOM) and were asked to develop and test prototype software under a limited time period that converted the BOM into a foreground model and linked it with one or more a background datasets, along with a list of other functional requirements. The resulting prototypes were compared and tested with additional product specifications.

RESULTS

Each developer took a distinct approach to the problem. One approach used semantic similarity relations to identify best-fit background datasets. Another approach focused on producing a flexible description of the model structure that removed redundancy and permitted aggregation. Another approach provided an interactive web application for matching product components to standardized product classification systems to facilitate characterization and linking.

DISCUSSION

Four distinct steps were identified in the broader problem of automating PSM construction: creating a foreground model from product data, determining the quantitative properties of foreground model flows, linking flows to background datasets, and expressing the linked model in a format that could be used by existing LCA software. The three prototypes are complementary in that they address different steps and demonstrate alternative approaches. Manual work was still required in each case, especially in the descriptions of the product flows that must be provided by background datasets.

CONCLUSION

The study demonstrates the utility of a distributed, comparative software development, as applied to the problem of LCA software. The results demonstrate that the problem of automated PSM construction is tractable. The prototypes created advance the state of the art for LCA software.

Health Care Pollution And Public Health Damage In The United States: An Update

An up-to-date assessment of environmental emissions in the US health care sector is essential to help policy makers hold the health care industry accountable to protect public health. We update national-level US health-sector emissions. We also estimate state-level emissions for the first time and examine associations with state-level energy systems and health care quality and access metrics. Economywide modeling showed that US health care greenhouse gas emissions rose 6 percent from 2010 to 2018, reaching 1,692 kg per capita in 2018-the highest rate among industrialized nations. In 2018 greenhouse gas and toxic air pollutant emissions resulted in the loss of 388,000 disability-adjusted life-years. There was considerable variation in state-level greenhouse gas emissions per capita, which were not highly correlated with health system quality. These results suggest that the health care sector's outsize environmental footprint can be reduced without compromising quality. To reduce harmful emissions, the health care sector should decrease unnecessary consumption of resources, decarbonize power generation, and invest in preventive care. This will likely require mandatory reporting, benchmarking, and regulated accountability of health care organizations.

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.

A Decision Analysis Approach to Electronics Standard Development Informed by Life Cycle Assessment Using Influence Diagrams

Life cycle assessment (LCA) provides holistic information on systems including the trade-offs between environmental impacts and the drivers of such impacts. Coupling life cycle assessment with a decision analysis (DA) method can help ensure that a life cycle assessment is focused on pertinent decision performance measures. In this paper, a framework integrating life cycle assessment with a decision analysis method to enhance the application of life cycle assessment is presented with a real-world case study of developing a material inclusion criterion for sustainable electronics standards. The proposed DA-LCA framework is a five-step process that tracks the flow of information between the steps of decision analysis and life cycle assessment. The case study considered the level of post-consumer-recycled or biobased content in laptop enclosures. Elicitation with a mock stakeholder panel was used to structure a means-ends network and create a utility-based influence diagram to link changes in material inclusion to environmental objectives using life cycle impact scores. Unlike typical life cycle assessment, the decision analysis approach allows for explicit incorporation of non-environmental factors and better constrains product options. Using this approach, the optimum decision for a possible range of 0-30% material content is 5% or 10%, depending on weighting. The DA-LCA framework can provide a blueprint for placing life cycle assessment results in context for decision-makers.

Assessing the environmental impacts of halving food loss and waste along the food supply chain

Reducing food loss and waste (FLW) is widely recognized as an important lever for lowering the environmental impacts of food systems. The United Nations Sustainable Development Agenda includes a goal to reduce FLW by 50% by 2030. Given differences in resource inputs along the food supply chain (FSC), the environmental benefits of FLW reduction will vary by stage of the FSC. Here, we identify the points along the supply chain where a 50% FLW reduction could yield the largest potential environmental benefits, assuming that decreases in consumption propagate back up the supply chain to reduce production. We use an environmentally extended input-output (EEIO) model combined with data on rates of FLW to calculate the scale of the total environmental impacts of the U.S. food system resulting from lost or wasted food. We evaluate the maximum potential environmental benefit resulting from 50% FLW reduction at all possible combinations of six supply chain stages (agricultural production, food processing, distribution/retail, restaurant foodservice, institutional foodservice, and households). We find that FLW reduction efforts should target the foodservice (restaurant) sector, food processing sector, and household consumption. Halving FLW in the foodservice sector has the highest potential to reduce greenhouse gas output and energy use. Halving FLW in the food processing sector could reduce the most land use and eutrophication potential, and reducing household consumption waste could avert the most water consumption. In contrast, FLW reduction at the retail, institutional foodservice, and farm level averts less environmental impact. Our findings may help determine optimal investment in FLW reduction strategies.

Impact of Locational Choices and Consumer Behaviors on Personal Land Footprints: An Exploration Across the Urban-Rural Continuum in the United States

Land is a scarce resource. We develop consumption-based land footprints (CBLF) for urban and rural U.S. residents to evaluate new levers for reducing land-demand by combining (1) direct land-use for human settlements including housing, (2) indirect land-use associated with personal consumption, for example, food and clothing. Results show that an average urban resident's indirect land-use (199 176 ft²/capita) is ∼23 times the direct land-use (8519 ft²/capita), for a total urban CBLF of 207 695 ft²/capita. Rural residents have a slightly higher (∼6%) indirect land-use and ∼10 times larger direct land-use compared to urban. Because in both cases, indirect land-use is much larger than direct, a strategic mix of individual actions including halving food waste (-4.7%), one-day weekly plant-based diet (-3.3%), reducing clothing consumption (-2.8%), and others, can together reduce CBLF by -12.8%. Meanwhile, housing and locational choices across the urban-rural continuum evaluated for the median-density Minneapolis-St. Paul Metropolitan Statistical Area (MSP MSA) yield CBLF reductions from -1.9% (from single- to multifamily housing) to -10.6% (from rural to the urban core). The analysis demonstrates that consumer behavior changes could rival housing/locational choices in order to reduce personal CBLF. Our method of combining input-output analysis with parcel data could be applied in different regions to provide customized information on CBLF mitigation strategies.

Effects of Spatial Scale on Life Cycle Inventory Results

Efforts to compile life cycle inventory (LCI) data at more geographically refined scales or resolutions are growing. However, it remains poorly understood as to how the choice of spatial scale may affect LCI results. Here, we examine this question using U.S. corn as a case study. We compile corn production data at two spatial scales, state and county, and compare how their LCI results may differ for state and national level analyses. For greenhouse gas (GHG) emissions, estimates at the two scales are similar (<20% of difference) for most state-level analyses and are basically the same (<5%) for national level analysis. For blue water consumption, estimates at the two scales differ more. Our results suggest that state-level analyses may be an adequate spatial scale for national level GHG analysis and for most state-level GHG analyses of U.S. corn, but may fall short for water consumption, because of its large spatial variability. On the other hand, although county-based LCIs may be considered more accurate, they require substantially more effort to compile. Overall, our study suggests that the goal of a study, data requirements, and spatial variability are important factors to consider when deciding the appropriate spatial scale or pursuing more refined scales.

A systems approach to assessing environmental and economic effects of food loss and waste interventions in the United States

Reducing food loss and waste (FLW) is critical for achieving healthy diets from sustainable food systems. Within the United States, 30% to 50% of food produced is lost or wasted. These losses occur throughout multiple stages of the food supply chain from production to consumption. Reducing FLW prevents the waste of land, water, energy, and other resources embedded in food and is therefore essential to improving the sustainability of food systems. Despite the increasing number of studies identifying FLW reduction as a societal imperative, we lack the information needed to assess fully the effectiveness of interventions along the supply chain. In this paper, we synthesize the available literature, data, and methods for estimating the volume of FLW and assessing the full environmental and economic effects of interventions to prevent or reduce FLW in the United States. We describe potential FLW interventions in detail, including policy changes, technological solutions, and changes in practices and behaviors at all stages of the food system from farms to consumers and approaches to conducting economic analyses of the effects of interventions. In summary, this paper comprehensively reviews available information on the causes and consequences of FLW in the United States and lays the groundwork for prioritizing FLW interventions to benefit the environment and stakeholders in the food system.

Greenhouse gas emissions, total food spending and diet quality by share of household food spending on red meat: results from a nationally representative sample of US households

Objective

To determine if US household food purchases with lower levels of red meat spending generate lower life-cycle greenhouse gas emissions (GHGE), greater nutritional quality and improved alignment with the Dietary Guidelines for Americans. Affordability of purchasing patterns by red meat spending levels was also assessed.

Design

Household food purchase and acquisition data were linked to an environmentally extended input–output life-cycle assessment model to calculate food GHGE. Households (n 4706) were assigned to quintiles by the share of weekly food spending on red meat. Average weekly kilojoule-adjusted GHGE, total food spending, nutrients purchased and 2010 Healthy Eating Index (HEI-2010) were evaluated using ANOVA and linear regression.

Setting

USA.

Participants

Households participating in the 2012–2013 National Household Food Acquisition and Purchase Survey.

Results

There was substantial variation in the share of the household food budget spent on red meat and total spending on red meat. The association between red meat spending share and total food spending was mixed. Lower red meat spending share was mostly advantageous from a nutritional perspective. Average GHGE were significantly lower and HEI-2010 scores were significantly higher for households spending the least on red meat as a share of total food spending.

Conclusions

Only very low levels of red meat spending as a share of total food spending had advantages for food affordability, lower GHGE, nutrients purchased and diet quality. Further studies assessing changes in GHGE and other environmental burdens, using more sophisticated analytical techniques and accounting for substitution towards non-red meat animal proteins, are needed.

Toward Sustainable Chemical Engineering: The Role of Process Systems Engineering

Products from chemical engineering are essential for human well-being, but they also contribute to the degradation of ecosystem goods and services that are essential for sustaining all human activities. To contribute to sustainability, chemical engineering needs to address this paradox by developing chemical products and processes that meet the needs of present and future generations. Unintended harm of chemical engineering has usually appeared outside the discipline's traditional system boundary due to shifting of impacts across space, time, flows, or disciplines, and exceeding nature's capacity to supply goods and services. Being a subdiscipline of chemical engineering, process systems engineering (PSE) is best suited for ensuring that chemical engineering makes net positive contributions to sustainable development. This article reviews the role of PSE in the quest toward a sustainable chemical engineering. It focuses on advances in metrics, process design, product design, and process dynamics and control toward sustainability. Efforts toward contributing to this quest have already expanded the boundary of PSE to consider economic, environmental, and societal aspects of processes, products, and their life cycles. Future efforts need to account for the role of ecosystems in supporting industrial activities, and the effects of human behavior and markets on the environmental impacts of chemical products. Close interaction is needed between the reductionism of chemical engineering science and the holism of process systems engineering, along with a shift in the engineering paradigm from wanting to dominate nature to learning from it and respecting its limits.

Modeling and Simulation of Energy Systems: A Review

Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.

A Process Systems Framework for Rapid Generation of Life Cycle Inventories for Pollution Control and Sustainability Evaluation

Life Cycle Assessment (LCA) is a tool that aids in sustainable decision-making among product and process alternatives. When implementing LCA, the efficient and accurate modeling of chemical processes for Life Cycle Inventory (LCI) generation is still challenging. Challenges include a lack of systematic design and simulation tools and approaches to develop chemical process models for obtaining and analyzing more realistic LCI results. In this contribution, a novel process systems framework is proposed for estimating LCI results when implementing pollution control technologies. This framework involves the development and incorporation of pollution control unit (PCU) modules into process simulation and generation of LCI data associated with the PCUs for use in a sustainability evaluation. Different pollution control modules are designed for rapid LCI estimation and applied to obtain emissions, utility consumption, material, and land footprint results related to waste streams of a process simulation. Then, the LCI results are analyzed with the objectives of minimizing the environmental impact and utility consumption. The proposed framework is illustrated via a biomass/coal gasification process for syngas production with the end goal of acetic acid manufacturing. Results associated with this case study show that the developed framework can provide guidelines for sustainable decision-making based on generated LCI results.

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.

Greenhouse gas emissions and energy use associated with production of individual self-selected US diets

Human food systems are a key contributor to climate change and other environmental concerns. While the environmental impacts of diets have been evaluated at the aggregate level, few studies, and none for the US, have focused on individual self-selected diets. Such work is essential for estimating a distribution of impacts, which, in turn, is key to recommending policies for driving consumer demand towards lower environmental impacts. To estimate the impact of US dietary choices on greenhouse gas emissions (GHGE) and energy demand, we built a food impacts database from an exhaustive review of food life cycle assessment (LCA) studies and linked it to over 6000 as-consumed foods and dishes from 1 day dietary recall data on adults (N = 16 800) in the nationally representative 2005-2010 National Health and Nutrition Examination Survey. Food production impacts of US self-selected diets averaged 4.7 kg CO₂ eq. person^-1 day^-1 (95% CI: 4.6-4.8) and 25.2 MJ non-renewable energy demand person^-1 day^-1 (95% CI: 24.6-25.8). As has been observed previously, meats and dairy contribute the most to GHGE and energy demand of US diets; however, beverages also emerge in this study as a notable contributor. Although linking impacts to diets required the use of many substitutions for foods with no available LCA studies, such proxy substitutions accounted for only 3% of diet-level GHGE. Variability across LCA studies introduced a ±19% range on the mean diet GHGE, but much of this variability is expected to be due to differences in food production locations and practices that can not currently be traced to individual dietary choices. When ranked by GHGE, diets from the top quintile accounted for 7.9 times the GHGE as those from the bottom quintile of diets. Our analyses highlight the importance of utilizing individual dietary behaviors rather than just population means when considering diet shift scenarios.

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.