An exploration of materials taxonomies to support streamlined life cycle assessment

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 130-134).As life cycle assessment (LCA) gains prominence as a reliable method of environmental evaluation, concerns about data availability and quality have become more important. LCA is a resource intensive methodology, and thus data gaps pose a frequent challenge, motivating the need for robust streamlining approaches. Existing methods for filling data gaps often ignore the effects of the uncertainty inherent in estimated data. Under-specification, or using structured data to provide less information in product characterization, is one option to incorporate uncertainty, and has been shown to be a viable method both for streamlining and decision-making under uncertainty. However, previous work did not emphasize developing robust data structures intended to balance trade-offs between effectiveness and efficiency in streamlining methods. Furthermore, there was little consideration given to analyzing the environmental profile (multiple impacts) of a process, rather than a single impact. This thesis explores how data mining techniques can be used to quantitatively develop data structures to enable streamlined assessment. The use of clustering and principal component analysis is explored in an effort to identify potential material classifications, and other statistical methods further assess the classifications. These insights are used to create hierarchical taxonomies that are evaluated in terms of effectiveness and efficiency. The method is applied to life cycle inventory process datasets for three material types (metals, polymers, and precious metals). Four environmental midpoints from the TRACI 2.0 impact assessment method are used to illustrate the uncertainty reduction enabled by classification. It was found that the most useful classification method for both metals and polymers was based on price, and for precious metals, material type and recycled content. In general, the method was able to select efficient groupings that accounted for a large percentage of the overall variation in the data. With one additional level in the taxonomy, the overall median percent error rates were approximately 30- 40% for all impacts except non carcinogenicity, which was 65-80%. This is compared to initial error rates that were on average twice as high for the metals and precious metals datasets. Case studies demonstrated how the analysis and structure provided by this methodology can be useful in comparative decision-making, to reduce the number of elements prioritized for detailed data collection in triage methods, and for developing models to predict materials' impacts. This work serves as a framework for structuring data to enable streamlined LCA as well as provides guidance for predictive model development. By showing the feasibility of developing effective and efficient taxonomies, the work demonstrates a method to reduce the amount of information required to characterize a product while achieving relatively low uncertainty in the final product impact.by Lynn Reis.S.M. in Technology and Polic

The Long-Term Performance of Initial Public Offerings (IPOs): Venture Capitalists, Reputation of Investment Bankers, and Corporate Structure

The Initial Public Offerings (IPOs) literature has uncovered the underpricing, hot issue markets, and long-term underperformance anomalies. The long-term underperformance of IPO firms has gained the focus of recent academic attention. Recent studies document that venture capitalists, and the reputation of investment bankers are associated with the long-term performance of firms going public. The lack of venture capitalists has been shown to relate with the long-term underperformance of IPO firms. On the other hand, IPO firms underwritten by less reputable underwriters have been found to experience more negative long-term market adjusted returns. Unlike previous studies, this study examines the interactive effects of venture capitalists, and the reputation of investment bankers on the long-term performance of IPOs using alternative performance measures. Moreover, we examine the possible interactive effects of institutional ownership with venture capitalists and the reputation of investment bankers. It is argued that the investigation of the joint effects of venture capitalists, reputation of investment bankers, and institutional investors on the long-term performance of IPO firms is more likely to throw additional light on the long-term underperformance of IPO firms than examining the role of these factors independently. In addition, this study investigates whether the corporate structure of the firm is associated with the long-term performance of IPOs. This investigation relies on 456 IPO transactions over the period of 1989–1994. Results based on raw and adjusted buy- and-hold returns show that the reputation of investment bankers on the long-term performance of IPO firms is negligible, if any. These results are inconsistent with the findings of Carter, Dark, and Singh (1998). However, venture backed IPOs with considerable institutional ownership experience superior long-term performance. Consistent with Brav and Gampers (1997), our evidence shows that long-term performance of IPO firms is not significantly different from counterpart IPO firms. Size/book-to-market/industry adjustment not only decreases underperformance of non-venture backed IPO firms, but also eliminates the superior performance of venture-backed IPO firms relative to both, market and non-venture backed IPO firms. Finally, the analysis provides little evidence in support of the corporate diversification hypothesis which states that diversified IPO firms have lower long-term performance in comparison to focused IPO firms

Modeling the oriented strandboard manufacturing process and the oriented strandboard continuous rotary drying system

Oriented Strand Board (OSB) is the leading structural panel product used in residential building construction. This dissertation describes three models and a statistical process control technique all designed to aid manufacturers to cost effectively manufacture OSB. The first model is an OSB Mill Process Flow Model that defines the processing steps and the desired outcomes. The second model is an OSB Mill Model, an ExcelRTM based computer program, designed to answer operational what if and trade-off questions. The model is a spreadsheet representation of the OSB production process. The third model is an OSB Dryer Model that predicts the dryer outlet moisture content derived using a multivariate data analysis technique called projection to latent structures by means of Partial Least Squares (PLS). PLS was instrumental in identifying outlet temperature and heat source temperatures as the most influential dryer system variables in predicting dryer outlet moisture content. The SPC technique is Multivariate Statistical Process Control (MSPC) that uses multivariate scores or Hotelling T2 to determine the state of the drying process; and if the drying process is out of control, what process variables influenced the process shift

Application of Arterial Spin Labelling as perfusion imaging in acute and chronic ischaemic stroke patients

The assessment of cerebral blood flow (CBF) perfusion is an important measure in clinical practice for evaluating the clinical and imaging outcomes in ischaemic stroke patients. Various imaging methods been applied to measure CBF, including applications of nuclear medicine, computed tomography perfusion (CTP) and contrast-enhanced Magnetic Resonance Imaging (MRI). However, each of these modalities has some disadvantages, such as excess radiation or contraindications to contrast agents, and limited repeatability. The thesis aimed to explore the clinical application of arterial spin labelling (ASL) as perfusion imaging among acute and chronic ischaemic stroke patients. Clinical and imaging data in this thesis were obtained from two prospective ischaemic stroke databases, namely WHISPER and XILOFIST. Both studies used ASL as one of the perfusion imaging methods. Methods and results. Before further studies, I conducted various detailed image post-processing steps to acquire the quantitative value of CBF from ASL raw data. The image post-processing steps include structural T1-image processing, creation of grey matter, white matter and lesion masks, distortion correction, and image coregistrations. These post-processing steps were performed using Statistical Parametric Mapping (SPM12) and Bayesian Inference for Arterial Spin Labelling (BASIL) software for studies in Chapter 3 until Chapter 6. In Chapter 3, two commonly used ASL sequences which were PCASL with multi-post labelling delays (multi-PLDs) and PASL (single-PLD), were compared to investigate their agreement. 35 subjects from WHISPER study underwent ASL scanning for both sequences. Grey matter and white matter CBF for these subjects were compared. Although there was a significant correlation between PCASL and PASL in measuring grey matter (r=0.997, p<0.001) and white matter (r=0.991, p<0.001) cerebral perfusion, the Bland-Altman analysis demonstrated large agreement between these ASL sequences suggesting the several systematic biases. These findings suggested that multi-PLDs PCASL sequence is recommended in patients with delayed blood flow, especially in ischaemic stroke patients. Further to this work, the assessment of reperfusion status among 63 WHISPER subjects was measured using PCASL sequence. Reperfusion index (RI) was established as a quantitative indicator by calculating the difference between the reperfusion among recanalised and non recanalised subjects. RI £ 0 – 0.4 indicated mild reperfusion, RI 0.41 to 0.70 indicated moderate reperfusion and RI 0.71 to 1.0 indicated high reperfusion. Correlation and predictions between ASL reperfusion index and clinical and imaging outcomes were statistically analysed. Reperfusion index was significantly correlated with infarct growth (r = 0.421, p<0.001) and positively correlated with penumbra salvage (r = 0.297, p=0.021). Regression analyses showed reperfusion index was a significant independent predictor for early neurological improvement (OR 1.370, 95% CI 0.572 to 16.721; p<0.036) and 90-day good functional outcome (OR 49.817; 95% CI 3.097 – 801.435; p=0.006). In Chapter 6, perfusion assessment of white matter hyperintensities (WMH) among 159 chronic ischaemic stroke patients was investigated in XILOFIST study. Baseline WMH volume and perfusion were calculated. Correlation and predictions between ASL volume and perfusion with associated WMH risk factors and WMH progression were analysed. The result of this study showed WMH perfusion was significantly associated with age and WMH volume. Furthermore, lower WMH perfusion was significantly associated with increased WMH burden as scored using Fazekas score. Conclusions. The studies presented in this thesis demonstrated the clinical application of ASL in quantifying cerebral blood flow among patients with acute and chronic ischaemic stroke. It is concluded that ASL is a suitable imaging technique for continuous cerebral perfusion assessments as it has no radiation risks and is noninvasive. In addition, the reperfusion index measured by ASL can serve as potential imaging biomarkers in predicting imaging and clinical outcomes

Subjective Beliefs and Asset Prices

Asset prices are forward looking. Therefore, expectations play a central role in shaping asset prices. In this dissertation, I challenge the rational expectation assumption that has been influential in the field of asset pricing over the past few decades. Different from previous approaches, which typically build on behavioral theories originated from psychology literature, my approach takes data on subjective beliefs seriously and proposes empirically grounded models of subjective beliefs to evaluate the merits of the rational expectation assumption. Specifically, this dissertation research: 1). collects and analyzes data on investors' actual subjective return expectations; 2). builds models of subjective expectation formation; 3). derives and tests the models' implications for asset prices. I document the results of the research in two chapters. In summary, the dissertation shows that investors do not hold full-information rational expectations. On the other hand, their subjective expectations are not necessarily irrational. Rather, they are bounded by the information environment investors face and reflect investors' personal experiences and preferences. The deviation from fully-rational expectations can explain asset pricing anomalies such as cross-sectional anomalies in the U.S. stock market. In the first chapter, I provide a framework to rationalize the evidence of extrapolative return expectations, which is often interpreted as investors being irrational. I first document that subjective return expectations of Wall Street (sell-side, buy-side) analysts are contrarian and counter-cyclical. I then highlight the identification problem investors face when theyform return expectations using imperfect predictors through Kalman Filters. Investors differ in how they impose subjective priors, the same way rational agents differ in different macro-finance models. Estimating the priors using surveys, I find Wall Street and Main Street (CFOs, pension funds) both believe persistent cash flows drive asset prices but disagree on how fundamental news relates to future returns. These results support models featuring heterogeneous agents with persistent subjective growth expectations. In the second chapter, I propose and test a unifying hypothesis to explain both cross-sectional return anomalies and subjective return expectation errors: some investors falsely ignore the dynamics of discount rates when forming return expectations. Consistent with the hypothesis: 1) stocks' expected cash flow growth and idiosyncratic volatility explain significant cross-sectional variation of analysts' return forecast errors; 2). a measure of mispricing at the firm level strongly predicts stock returns, even among stocks in the S&P500 and at long horizon; 3). a tradable mispricing factor explains the CAPM alphas of 12 leading anomalies including investment, profitability, beta, idiosyncratic volatility and cash flow duration