1,721,114 research outputs found
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Characterizing Uncertainties in Life Cycle Assessment
Full text linkLife cycle assessment (LCA) aims to support corporate and public policy decisions by quantifying the environmental performance of a product. Understanding uncertainties in LCA results is therefore important for making informed decisions. Monte Carlo simulation (MCS), which uses random samples of the parameters from pre-determined probability distribution, has been widely utilized to characterize uncertainties in LCA. However, as the size of an LCA database grows, running a full MCS is becoming increasingly challenging. Furthermore, uncertainty literature in LCA has focused on life cycle inventory (LCI), while the uncertainties from the remaining steps—including characterization, normalization, and weighting—have not been addressed, despite their perceived relevance in overall uncertainty characterization in LCA.The objectives of my dissertation are: (1) to develop a new method to improve the computational efficiency of large-scale MCS in LCA, (2) to empirically test the reproducibility of comparative decisions obtained using the method, and (3) to develop and test an analytical method to decompose the overall uncertainty in LCA into its constituents. The new method for uncertainty characterization in LCA involves pre-calculating and storing the distribution profiles of the most widely used LCA database, ecoinvent. Using parallel computing, I have generated the distribution functions for 22 million life cycle inventory (LCI) items of the database. I then tested 20,000 randomly selected comparative LCI cases, and showed that pre-calculated uncertainty values can be used as a proxy for understanding the uncertainty and variability in a comparative LCA study without compromising the ability to reproduce the comparative results. Another key barrier to conducting uncertainty analysis in LCA occurs in life cycle impact assessment (LCIA), an important step of LCA calculation flowered LCI phase, because characterization models for LCIA do not typically provide uncertainty information for the input parameters, and lack detailed information about the relationships between those inputs. A Pedigree matrix for characterization factor in LCIA was developed to fill in the gap in the uncertainty characterization in LCA. Expert opinions of the use of Pedigree method in estimating uncertainty in LCIA and the Pedigree scores for both LCI and LCIA were collected through an online survey. Finally, I demonstrated a new method to decompose the overall uncertainties of an LCA result over the contributing factors including those from LCI, characterization, normalization, and weighting, which are the steps involved in LCA calculation. To do so, I adopted the logarithmic mean Divisia index (LMDI) decomposition method into MCS parse out the overall uncertainty into its constituents. I believe that my research helps improve the efficiency and analytical power of uncertainty analysis in LCA. The findings can be applied to other problems outside of LCA that utilize MCS
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Improving the Usefulness of Environmental Information for Decision Making in Organizations
Full text linkWith the growing attention to environmental issues, knowledge in the environmental and sustainability sciences is increasingly needed to inform decision making in policy, industry and at the consumer level. At the same time, the tools and communication strategies produced by this scientific community have been criticized for their lack of practicability and usefulness. Many questions remain about the mechanisms that create these knowledge transfer barriers and the avenues that should be explored to find solutions. The following work analyzes three different case studies where environmental information has been criticized for its limited usefulness. The theoretical background and research methodologies used throughout the chapters draw from several disciplines across both the natural and social sciences: industrial ecology, life cycle assessment, organizational science, management science, communication studies, and behavioral psychology. Chapter one considers the usefulness of life cycle assessment (LCA) results in aiding product developers and businesses in drawing comparisons and identifying hotspots of environmental impacts. LCA has been broadly criticized for both the degree of uncertainty introduced through data gaps and the use of input parameters of variable quality. This work evaluates the tension between improving the accuracy of LCA results by filling in data gaps and decreasing the precision by incorporating less certain inputs. Through a real-world case study, the uncertainty implications of the hybrid LCA approach is analyzed in this context of accuracy versus precision. For firms to manage the environmental impacts of their value chains, they first must be able to quantify those impacts. This study presents an iterative hybrid approach to LCA that allows industry LCA practitioners to efficiently identify which parameters are most critical to understand the impacts, facilitating an efficient data collection process and an improvement to both the accuracy and precision of the quantified impacts used to support decision-making. Chapter two focuses on an organization’s internal knowledge about chemical risks in consumer products and the mechanisms that prevent this information from being used to educate the public. Companies are taking proactive approaches to mitigating the risks of chemicals in their products, but new chemical risk identifications and removals are done quietly and rarely promoted to the public. Through an experimental survey design this study analyzes how consumer behavior is affected by a company’s voluntary disclosure of these proactive actions, and special attention is payed to the influence of media on the consumer response in these scenarios. This work examines the mechanisms that drive consumer response and the incentive that consumer behavior creates for companies to stay silent. Consumer trust in the information source helps to explain why the same information can be interpreted and acted upon very differently depending on where the information is coming from. Understanding the negative implications of voluntarily disclosing these positive actions offers insights into how creative solutions, such as disclosure through information intermediaries or third-party certifications, might be necessary for firms to retain consumers’ trust. The last chapter focuses on the challenge of integrating knowledge generated within an organization’s sustainability function throughout an entire organization. Sustainability is an increasingly adopted practice within present-day organizations; however, very little research has empirically studied the micro-level process through which it is implemented and adopted by employees. Research on the decoupling of policy from practice in management programs – often brought on by external pressures – sheds light on the initial adoption of sustainability within organizations. This work examines individual employee behavior and the different motivations that may drive ceremonial adoption of sustainability versus actual integration of knowledge and practices. A survey completed by 886 employees within a Fortune 500 consumer goods manufacturing company based in the United States was used to measure employee characteristics, attitudes and both formal and technical sustainability-related behaviors. Employees’ perceptions of the business value of sustainability and their own personal sustainability interests may act as drivers of sustainability adoption throughout the organization. This study examines the relationship between these potential drivers and the prevalence of sustainability communication and information seeking behavior. This work offers insights into how management strategies can be employed to increase the technical adoption of sustainability, not just the expansion of a formal rhetoric
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Machine Learning for Addressing Data Deficiencies in Life Cycle Assessment
Full text linkLife Cycle Assessment (LCA) is a tool that can be used to assess the impacts of chemicals over the entire life cycle. As the large number of new chemicals being invented every day, the costs and time needed to collect necessary data for LCA studies pose a challenge to LCA practitioners, as the speed of LCA studies cannot keep up with the speed of new chemical development. In practice, therefore, LCAs are conducted in the presence of data gaps and proxy values, limiting the relevance and quality of the results. As the techniques of machine learning evolves, a new opportunity to improve on data deficiencies and on the quality of LCA emerged. This dissertation is an attempt to harness the power of machine learning techniques to address the data deficiencies in LCA. It consists of four chapters: (1) Introduction. (2) Rapid life-cycle impact screening for decision-support using artificial neural networks. (3) Species Sensitivity Distributions Derived for a Large Number of Chemicals Using Artificial Neural Networks. IV. (4) Reducing the Uncertainty of the Characterization Factors in USEtox by Machine Learning – A Case Study for Aquatic Ecotoxicity. Each chapter is elaborated briefly below.The first chapter is the general introduction. The second chapter aims to demonstrate the method of estimating the characterized results using Artificial Neural Networks (ANNs). Due to the lack of necessary data, very limited amount of characterized results for organic chemicals exist. In this chapter, I developed ANNs to estimate the characterized results of chemicals. Using molecular structure information as an input, I trained multilayer ANNs for the characterized results of chemicals on six impact categories: (1) global warming. (2) acidification. (3) cumulative energy demand. (4) human health. (5) ecosystem quality. (6) eco-indicator 99. The application domain (AD) of the model was estimated for each impact category within which the model exhibits higher reliability. As a result, the ANN models for acidification, human health, and eco-indicator 99 showed relatively higher performances with R2 values of 0.73, 0.71, and 0.87, respectively. This chapter indicates that ANN models can serve as an initial screening tool for estimating life-cycle impacts of chemicals for certain impact categories in the absence of more reliable information. The second chapter aims to estimate the ecotoxicological impact of chemicals using machine learning models. In chemical impact assessment, the overall ecotoxicological impact of a chemical to ecosystem, also known as the Effect Factor (EFs), is derived from the toxicity to multiple species through Species Sensitivity Distribution (SSDs). In the third chapter, I turned to estimate the chemical toxicities to several aquatic species with machine learning models, and then use them to build SSD, and to estimate the EF of organic chemicals. Over 2,000 experimental toxicity data were collected for 8 aquatic species from 20 sources, and an ANN model for each of the species was trained to estimate the Lethal Concentration (LC50) based on molecular structure. The 8 ANN models showed R2 scores of 0.54 to 0.75 (average 0.67, medium 0.69) on testing data. The toxicity values predicted by the ANN models were then used to fit SSDs using bootstrapping method. At the end, the models were applied to generate SSDs for 8,424 chemicals in the ToX21 database. The last chapter of this dissertation aims to reduce the uncertainty of an existing chemical fate model using machine learning techniques. Fate Factor (FF), which accounts the persistence of chemicals in environmental compartments, is an intermediate input in to calculate the characterized results of life cycle impact assessment. The most widely used tool to calculate chemical FFs: USEtox, requires several chemical properties as inputs, including: octanol-water partitioning coefficient (Kow) and vapor pressure at 25 ℃ (Pvap25). When those chemical properties are missing, USEtox provides proxy methods to estimate them. In the fourth chapter, I seek to answer the question that whether replacing the current proxy methods with machine learning models are always improving the accuracy of FFs. The contribution of each chemical property to the FFs was evaluated. And ANN-based predictive models were developed to predict these chemical properties. The uncertainty of the current proxy methods in the USEtox’s FF model and the newly developed ANN models were compared. New FFs for the chemicals in the ToX21 database were calculated using the best predictive model when experimental properties were unknown. The EFs generated by the models in the second chapter were estimated. Lastly, more than 300 new CFs with good prediction confidence for the organic chemicals in the ToX21 database were calculated. These CFs are new to the field of LCA and can be used to reduce the uncertainty of LCA studies when the measured data isn’t available
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Evaluation of the environmental impacts of agricultural systems using life cycle thinking, focusing on marginal changes, technological advances, and regional characteristics
Full text linkDriven by rapid adoption and sustained improvements of genetic technologies and agronomic management practices, agricultural productivity has experienced a substantial growth worldwide since the start of the green revolution in 1960s. This growth has enabled the humanity to escape from the well-known Malthusian Trap. With the success in agricultural productivity, however, comes what is also known as “the other inconvenient truth” (Foley 2009). That is, modern agriculture has become one of the major drivers of global environmental change and is pushing the earth system beyond its safe operating boundaries (Rockström et al. 2009). Further, even more challenges lie ahead, given the growing number of population and increasing diversion of foods to fuels. In this dissertation, three topics related to US agricultural systems are explored. In the first chapter, the environmental properties of US corn and cotton production and implications of land cover change from cotton to corn are evaluated using state-specific data and life cycle impact assessment. The results show that U.S. cotton and corn productions per hectare on average generate roughly similar impacts for most impact categories such as eutrophication and smog formation. Life cycle water use and freshwater ecotoxicity impacts of corn per hectare on average are smaller than those of cotton. When marginal impact is analyzed, however, the results show that the shifts from cotton to corn in cotton-growing states aggravates most of the regional environmental impacts while relieving freshwater ecotoxicity impact. The differences in the two estimates are due mainly to underlying regional disparities in crop suitability that affects input structure and environmental emissions. In the second chapter, the carbon payback time (CPT) of corn ethanol expansion is re-examined considering three aspects: (1) yields of newly converted lands (i.e., marginal yield), (2) technology improvements over time within the corn ethanol system, and (3) temporal sensitivity of climate change impacts. The results show that without technological advances, the CPT estimates for corn ethanol from newly converted Conservation Reserve Program (CRP) land exceed 100 years for all Marginal to Average (MtA) yield ratios tested except for the case where MtA yield ratio is 100 %. When the productivity improvements within corn ethanol systems since previous CPT estimates and their future projections are considered, the CPT estimates fall into the range of 15 years (100 % MtA yield ratio) to 56 years (50 % MtA yield ratio), assuming land conversion takes place in early 2000s. Incorporating diminishing sensitivity of GHG emissions to future emissions year by year, however, increases the CPT estimates to17 to 88 years. For 60 MtA yield ratio, the CPT is estimated to be 43 years, which is relatively close to previous CPT estimates (i.e., 40 to 48 years) but with very different underlying reasons. In the third chapter, the trends and underlying drivers of changes in non-global environmental impacts of major crops in the U.S. are investigated. The results show that the impact per hectare corn and cotton generated on the ecological health of freshwater systems decreased by about 50% in the last decade. This change is associated with the use of genetically modified (GM) crops, which has reduced the application of insecticides and relatively toxic herbicides such as atrazine. However, the freshwater water ecotoxicity impact per hectare soybean produced increased by 3-fold, mainly because the spread of invasive species, soybean aphid, has resulted in an increasing use of insecticides. In comparison, other impact categories remained relatively stable. By evaluating the relative ecotoxicity potential of a large number of pesticides, our analysis offers new insight into the benefits associated with genetically modified (GM) crops. The finding that different impact categories show different degrees of changes suggests that agricultural inventory data can be updated selectively in LCA to maximize cost-effectiveness
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Spatial Heterogeneity in Modeling Environmental and Human Health Impacts of Chemicals
Full text linkABSTRACTSpatial Heterogeneity in Modeling Environmental and Human Health Impacts of ChemicalsbyMengya TaoThe number of chemicals that humanity is using for producing goods and services is rapidly increasing, while our understanding of their environmental and human health impacts improves slowly. Life cycle assessment (LCA) is one of the tools that evaluate environmental and human health impacts of chemicals. Traditional LCAs often rely on the models that represent broad spatial boundaries at regional, national, or global scales. However, the use, release, fate, and transport of chemicals, which are collectively referred to as biophysical processes, may vary substantially within those boundaries. This misalignment in spatial attributes between LCA models and the biophysical processes that determine environmental and human health impacts is one of the major sources of uncertainties in LCA. This dissertation is an attempt to close the gap between the spatial resolutions of the models used in LCA and the biophysical processes relevant for understanding the environmental and human health impacts of chemicals.This dissertation covers three topics that collectively addresses the aforementioned objectives: (1) measuring spatial variabilities in LCI, (2) modeling the fate of chemicals in the environment at the spatial resolution that matches with the underlying biophysical processes, (3) building a systematic release framework to estimate chemical releases that supports the fate modeling.First, I demonstrated that spatial disparities in state-specific LCI for four major crops in the USA can lead to two to fourfold differences in characterized results for most impact categories. The differences, however, increase to over an order of magnitude for freshwater ecotoxicity and human health non-cancer. Among the crops analyzed, winter wheat shows higher variability partly due to a larger difference in yield. As a result, the use of national average data derived from top corn and soybean producing states significantly underestimates the characterized impacts of corn and soybean in the states where land conversion from cotton to corn or soybean actually took place. Secondly, I developed a spatially explicit and time-dependent multimedia fate modeling framework, ChemFate, that can be incorporated into regional LCIA. ChemFate consists of four multimedia fate models: (1) organoFate, a model for non-ionizable organic chemicals, (2) ionOFate, a model for ionizable organic chemicals, (3) metalFate, a model for metals, and (4) nanoFate, a model for nanomaterials. ChemFate is able to not only provide predictions for four different classes of chemicals, but also incorporate dynamic emissions and dynamic environmental conditions. The dynamic capability of ChemFate supports the model to simulate with real regional climatic data and produce better model performance. Thirdly, I built a comprehensive release framework, OrganoRelease, to estimate the release of organic chemicals from the use and post-use of consumer products with limited information. OrganoRelease connects 19 unique functional uses and 14 product categories across 4 data sources and provides multiple pathways for chemical release estimation. The results can be used as input for methods estimating environmental fate and exposure
Technology choice model for consequential life cycle assessment
No full textConsequential Life Cycle Assessment (CLCA) aims at capturing the environmental consequences of decisions such as the introduction of a new technology, the implementation of a policy, or the purchase of a product. CLCA combines technical and economic modeling approaches to track the consequences of decisions throughout the economy, considering both technical relationships within industrial production systems and market-mediated effects. However, although CLCA is well defined at a conceptual level, a commonly accepted modeling framework for CLCA is still missing, leading to wide differences in CLCA practice. To promote the systematization of the CLCA approach, this thesis proposes a comprehensive modeling framework for CLCA: the Technology Choice Model (TCM). Compared to existing approaches, TCM captures market-mediated effects in multiple markets at a substantially higher level of technical detail, while systematically considering constraints in factor availability, uncertainty, and suboptimal decisions. Due to its higher level of technical detail, TCM can model changes in technology mixes through both capacity adaptions and substitution effects among competing technologies. These changes in technology mixes are shown to substantially affect the CLCA results in two illustrative case studies on the introduction of new technologies and climate policy. Furthermore, the consideration of uncertainties and suboptimal decisions provides the basis for a first comprehensive uncertainty assessment in CLCA. The practical application of TCM is demonstrated in a large-scale industrial case study on novel Carbon Capture and Utilization (CCU) technologies in the chemical industry. These technologies use carbon dioxide from industrial point sources or ambient air as alternative carbon feedstock for chemical production. The case study shows that CCU in the chemical industry can reduce up to 3.5 Gt CO2-eq greenhouse gas emissions per year by 2030 and highlights potential barriers for CCU implementation. The results provide a strong scientific basis for the integration of CCU into international policy frameworks and research agendas. The application of TCM in this case study demonstrates the ability of CLCA to provide sound environmental decision support
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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