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The purpose of this study is to compare the life cycle environmental impacts of the University of Cincinnati College of Engineering and Applied Sciences' current printed annual report to a version distributed via the internet.

Life cycle environmental impacts of both versions of the report are modeled using the online environmental input‐output life cycle assessment (EIO‐LCA) tool. Most monetary model inputs were obtained from the University of Cincinnati and the others were estimated. Results are presented for greenhouse gas (GHG) emissions, energy use, water use, and human and ecosystem health impacts. Alternative scenarios reflecting different reader behaviors were evaluated.

The electronic report reduces economic costs and all categories of environmental impacts so long as the recipients do not print the report at home. Impacts of the printed report were higher than the electronic report due to impacts associated with paper production and disposal and to a lesser extent differences in the impacts of mail versus electronic distribution. The environmental preferability of the options is heavily influenced by the number of users who choose to print the electronic report at home; if more than 10 percent print at home, it offsets the benefits of the e‐report.

Using the EIO‐LCA tool limited the accuracy of the results by using average US data for a specific supply chain. It was limited by assumptions about reader behavior with the e‐report.

This case study demonstrates how a screening level life cycle assessment (LCA) might be used by a university administrator to make decisions supported by quantitative environmental information.

The screening level LCA‐based approach can provide grounding for environmental decision making within a reasonable time period and cost while maintaining sufficient accuracy for guiding purchasing or product decisions.

The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.

Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO₂-equivalent, whereas the economic outputs were measured in million dollar economic activity.

The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO₂ intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”

This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development.

A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy.

The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements.

The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development.

Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.

The aim of this paper is to introduce uncertainty analysis within an environmentally extended input‐output technological model of life cycle costing. The application of this approach will be illustrated with reference to the ceramic floor tiles manufacturing process.

Input‐output analysis (IOA) provides a computational structure which is interesting for many applications within value chain analysis and business processes analysis. A technological model, which is built bottom‐upwards from the operations, warrants that production planning and corporate environmental accounting be closely related to cost accounting. Monte Carlo methods have been employed to assess how the uncertainty may affect the expected outcomes of the model.

It has been shown, when referring to a vertically‐integrated, multiproduct manufacturing process, how production and cost planning can be effectively and transparently integrated, also taking the product usage stage into account. The uncertainty of parameters has been explicitly addressed to reflect business reality, thus reducing risk while aiding management to take informed actions.

The model is subject to all the assumptions characterizing IOA. Advanced issues such as non linearity and dynamics have not been addressed. These limitations can be seen as reasonable as long as the model is mostly tailored to situations where specialized information systems and competences about complex methods may be lacking, such as in many small and medium enterprises.

Developing a formal structure which is common to environmental, or other physically‐driven, assessments and cost accounting helps to identify and to understand those drivers that are relevant to both of them, especially the effects different design solutions may have on both material flows and the associated life cycle costs.

This approach integrates physical and monetary measures, making the computational mechanisms transparent. Unlike other microeconomic IOA models, the environmental extensions have been introduced. Uncertainty has been addressed with a focus on the easiness of implementing the model.

This paper aims to provide an integrated framework of life cycle assessment (LCA) and life cycle cost analysis (LCCA) for assessing alternative technologies, processes, and/or activities, with focus on concrete pavements.

LCA and LCCA are used to evaluate environmental and economic impacts of substituting different percentages of fly ash and slag into continuously reinforced concrete pavement (CRCP) and jointed plane concrete pavement (JPCP). Impacts are determined over different life cycle phases.

An LCA of the extraction phase indicated that JPCP pavement had 33‐62 percent less emissions than CRCP pavements, when only steel consumption was considered. When cement was considered, JPCP pavement had almost 40 percent greater emissions then CRCP for all mix types. An LCCA showed that over the entire life cycle phases studied, CRCP pavements had about 46 percent more costs than JPCP. However, when only maintenance costs were considered, CRCP pavement cost 80 percent less to maintain than JPCP over the studied period of 35 years.

The study is a step towards using an integrated framework to evaluate the performance of different materials and technology. The same framework could be conducted for different kinds of asphalt pavements and concrete pavements, as well as other infrastructure that makes up the built environment, with the goal of making decisions that take into account design considerations, environmental impacts, and cost effectiveness.

Bioconcrete is widely believed to be environmentally beneficial over conventional concrete. However, the process of bioconcrete production involves several steps, such as waste recovery and treatment, that potentially present significant environmental impacts. Existing life-cycle assessments of bioconcrete are limited in the inventory and impact analysis; therefore, they do not consider all the steps involved in concrete production and the corresponding impacts. The purpose of this study is to extensively study the cradle-to-gate environmental impacts of all the production stages of two most common bioconcrete types (i.e. sludge-based bioconcrete and cement kiln dust-rice husk ash (CKD-RHA) bioconcrete) as opposed to conventional concrete.

A cradle-to-gate life-cycle assessment process model is implemented to systematically analyze and quantify the resources consumed and the environmental impacts caused by the production of bioconcrete as opposed to the production of conventional concrete. The impacts analyzed in this assessment include global warming potential, ozone depletion potential, eutrophication, acidification, ecotoxicity, smog, fossil fuel use, human toxicity, particulate air and water consumption.

The results indicated that sludge-based bioconcrete had higher levels of global warming potential, eutrophication, acidification, ecotoxicity, fossil fuel use, human toxicity and particulate air than both conventional concrete and CKD-RHA bioconcrete.

The contribution of this study to the state of knowledge is that it sheds light on the hidden impacts of bioconcrete. The contribution to the state of practice is that the results of this study inform the bioconcrete production designers about the production processes with the highest impact.

This study aims to share experiences of an easy to adapt service-learning approach in a graduate course on life cycle assessment (LCA). Specifically, it reports on how students helped the university’s cafeteria to assess meals by conducting an LCA for 25 meals and identifying environmental hotspots.

A descriptive case study of a graduate course at Ulm University is presented. The course included lectures and problem-based exercises, both theoretical and software assisted. A course evaluation was conducted during the course and one year after completion to poll improvement potentials, as well as its impacts on students’ everyday life.

It was found that although it was the first LCA for all students, the resulting LCA information of 25 different meals were homogeneous, comparable to the scientific literature and beneficial to the cafeteria’s sustainable development strategy. The concept of service-learning had a higher impact on students’ motivation than a good grade and active-learning is explicitly requested by students. The course design sensitized students to the real-life problems of LCA and made their consumption patterns more elaborate and ecological. Furthermore, this digitization of higher education could be carried out with only minor changes in the present COVID-19 pandemic situation.

As the subject of service-learning in natural sciences is still expandable, this study presents an easy to adapt case study on how to integrate such an approach into university curricula dominated by traditional learning. To the best of the authors’ knowledge, this case study presents the first published LCA university course explicitly describing and evaluating a service-learning approach. The topic touches the everyday lives of students, allows comparisons between different student groups, is easily scalable to different group sizes and credits, and supports learning both how to study in small groups and cooperation between groups to ensure comparability of LCA results.

A greenhouse gas (GHG) inventory was conducted for Yale University's procurement of goods and services over a one‐year period. The goal of the inventory was to identify the financial expenditures resulting in the greatest “indirect” GHG emissions. This project is part of an ongoing effort to quantify and reduce the university's environmental impacts.

The impacts of institutional purchases were analyzed using publicly available economic input‐output life cycle assessment software. This model allows users to estimate the indirect GHG emissions of procured goods and services using expenditure data for different categories of purchases. The results are based on national averages for the USA.

The findings of this inventory indicate that indirect GHG emissions from procured goods and services are the greatest source of the university's emissions. A total of 15 of the university's 142 financial expenditure categories accounted for 80 percent of the GHG emissions. Many of these categories were expected, including energy purchases, construction activities, and air travel. Others were more surprising, particularly architectural and engineering services, laboratory supplies, and software.

This study is expected to assist Yale University in its efforts to reduce GHG emissions by providing a quantitative basis for prioritizing green supply chain management decisions.

This study demonstrates that universities and other organizational entities can proficiently assess indirect GHG emissions from goods and services using publicly available software, and that these efforts are significant for understanding the environmental impacts of higher education.

Carbon footprint assessment requires a holistic approach, where all possible lifecycle stages of products from raw material extraction to the end of life are considered. The purpose of this paper is to develop an analytical sustainability assessment framework to assess the carbon footprint of US economic supply chains from two perspectives: supply chain layers (tiers) and carbon footprint sources.

The methodology consists of two phases. In the first phase, the data were collected from EORA input output and environmental impact assessment database. In the second phase, 48 input-output-based lifecycle assessment models were developed (seven CO₂ sources and total CO₂ impact, and six supply chain tiers). In the third phase, the results are analyzed by using data visualization, data analytics, and statistical approaches in order to identify the heavy carbon emitter industries and their percentage shares in the supply chains by each layer and the CO₂ source.

Vast majority of carbon footprint was found to be attributed to the power generation, petroleum refineries, used and secondhand goods, natural gas distribution, scrap, and truck transportation. These industries dominated the entire supply chain structure and found to be the top drivers in all six layers.

This study decomposes the sources of the total carbon footprint of US economic supply chains into six layers and assesses the percentage contribution of each sector in each layer. Thus, it paves the way for quantifying the carbon footprint of each layer in today’s complex supply chain structure and highlights the importance of handling CO₂ source in each layer separately while maintaining a holistic focus on the overall carbon footprint impacts in the big picture. In practice, one size fits all type of policy making may not be as effective as it could be expected.

This paper provides a two-dimensional viewpoint for tracing/analyzing carbon footprint across a national economy. In the first dimension, the national economic system is divided into six layers. In the second dimension, carbon footprint analysis is performed considering specific CO₂ sources, including energy production, solvent, cement and minerals, agricultural burning, natural decay, and waste. Thus, this paper contributes to the state-of-art sustainability assessment by providing a comprehensive overview of CO₂ sources in the US economic supply chains.

The aim of the research is to examine the role of property tax in land and building administration and to develop a dynamic model. The paper investigates the extent to which local governments take advantage of property tax in generating revenue and encouraging certain life cycle assessment-oriented land and building speculation patterns in Shashemene, Ethiopia.

The study was conducted using case study and survey research strategies. Shashemene's administrative area (i.e. specific to four peri-urban villages) was purposively selected as the case study area. A combination of different data collection instruments was employed: questionnaires and field observation. Moreover, an extensive survey of owners of undeveloped land and building, throughout the study area, was conducted. Multiple regression analysis was applied to the analyzed data as well as the use of dynamic modeling of land and building via qualitative and numerical analysis of property.

Results indicate that speculators will hold land and building for a marginal period only if the difference between present net rates of return exceeds the difference between discounted expected percent return.

This paper provides a simple model to recognize the optimum length of time to hold a parcel of land and building from the market by land speculators.

The introduction and potential implementation of dynamics modeling to the local government calls for controlling speculation that has resulted in local revenue enhancement.