Sustainability and Transportation at WPI

This project was developed for Worcester Polytechnic Institute in Worcester, Massachusetts. As part of a campus-wide sustainability effort, this project looks at how the WPI community can reduce their carbon footprint by driving less. A general survey to gather data about the community\u27s driving habits was used to estimate the annual carbon dioxide emission caused by transportation. The results make a convincing appeal that carpooling is one of the easiest and best ways the University can reduce its carbon footprint

Building a Carbon Footprint of Clemson University\u27s Main Campus

Greenhouse gas (GHG) inventories have become a popular means for colleges and universities to better understand their environmental impact and quantify sustainability efforts. Clemson University is one of the many institutions that signed the American College & University Presidents Climate Commitment, which explicitly calls for a comprehensive inventory of GHG emissions to be created. In the past, Clemson University has contracted an external consulting firm to quantify Clemson\u27s GHG emissions, however, a transparent method of calculating emissions is needed. Carbon footprinting is an effective method to measure GHG emissions, and carbon footprinting of higher education institutions is currently an underdeveloped research area. As a contribution to efforts on the subject, this research presents the carbon footprint for Clemson University\u27s main campus. This footprint was built using a consumption-based, hybrid life cycle assessment approach and included scope 1 (direct), 2 (indirect from electricity), and 3 (other indirect) GHG emissions. The scope 1 emissions include steam generation, refrigerant usage, univeristy owned vehicles, univeristy owned aircraft, fertilizer application, and wastewater treatment. Scope 2 is electricity generation. Then, scope 3 includes electricity life cycle, transmission and distibution losses, commuting, univeristy related travel, paper usage, waste and recycling transportation, wastewater treatment chemicals, and water treatment. The total carbon footprint of Clemson University\u27s main campus in 2014 was calculated to be 95,000 metric tons CO2-e, sources of uncertainty include data quality and the streamlined life cycle assessment approach. This research found that 49% of GHG emissions were from electricity related activities, while fossil fuel dependent activities such as automotive commuting (18%), steam generation (16%), and university related travel (13%) added significantly to the footprint. Overall, creating a reproducible baseline carbon footprint can be used to compare Clemson against other higher education institutions, while helping develop goals, strategies, and policies to reduce emissions. The high emissions related to electricity could be decreased through increased renewable energy sourcing. Therefore, as a further component of this research, LiDAR data was utilized in GIS to demonstrate campus rooftop photovoltaic potential

Social indicators for use with multi-regional input-output analysis

Accounting for social impacts in supply chain analysis is of increasing importance. Global trade has increased significantly since 1970, as has inequality. As global supply chains have become more prevalent, the need to understand and analyse these supply chains has also grown. Excellent work on quantitative analysis of environmental impacts in supply chains has taken place in the past two decades. However, relatively few methodologies have been applied to quantitative analysis of social impacts in supply chains. This thesis considers how social indicators for supply chain analysis can be developed through the use of socially extended multi-regional input-output analysis. Chapter 1 provides an introduction and context. Chapter 2 considers the history of social accounting. Chapter 3 looks at quantitative accounting for social-economic indicators and the development of national accounts, particularly in reference to standardised collection of data for social-economic indicators and socially-extended input- output analysis. Chapter 4 presents a case study of coltan mining and methodological analysis using deaths in the Democratic Republic of Congo as a social indicator for the electronics supply chain. Chapter 5 analyses the results of the same case study and considers how enumerating social impacts in upstream supply chains can influence environmental and social justice actions in downstream supply chains. Chapter 6 provides a review of input-output analysis used as a tool for analysing consumption since 2010. Chapter 7 proposes the use of a suite of quantitative social indicators for analysis in the form of a social footprint. Chapter 8 provides a conclusion. This thesis tracks the author’s interest in understanding social impacts in global supply chains and proposes a social footprint for supply chain analysis using the multi-regional input output methodology

Tracing Pathways of Resource Use in the World Economy: An Analysis of National and Sectoral Influence across the Global Water-Energy-Land System

A research and policy agenda has emerged in recent years to understand the interconnected risks natural resource systems face and their exploitation drives. The so-called Water-Energy-Food (WEF) nexus has served as a focal point for the conceptual, theoretical and empirical development of this agenda. However, boundaries for WEF nexus assessment are usually established without a foundational understanding of major interactions and risks across the water-energy-land (WEL) system. Consequently, priorities drawn from nexus studies might simply be an artefact of the partial scope of nexus assessment rather than a reflection of major risks to the WEL system and the activities which it supports. This thesis demonstrates how macro-economic methods of resource accounting can be used to broaden nexus assessment, sectorally and spatially, to identify and compare different sources of water, energy and land use, in individual countries and globally. A study of water and land use embodied in international soybean trade (Chapter 3) reveals that while single commodities can be analysed in this way, data and time constraints involved in using Material Flow Analysis (MFA) data make global assessment of water, energy and land use pathways across different production and consumption systems challenging. However, Multi-Regional Input-Output Analysis (MRIOA) is found to offer a practical approach to this end. By combining economic and environmental accounts from the Eora MRIO database, resource risk indices, and techniques for production source decomposition, this thesis examines thewater, energy and land footprints of 189 countries. Chapter 4 evaluates the scale of national water, energy and land use embodied in domestic production and international trade; Chapter 5 compares the contribution of food and non-food related sectors within this context; and, Chapter 6 reveals how these impacts are distributed across supply networks. Linking national consumption to resource origins reveals that countries are often highly exposed to over-exploited, insecure, and degraded water, energy, and land resources. These risks are found to originate from multiple sectors, including food, textiles and construction, and are primarily indirect, stemming from international trade and production up-stream national supply networks. These findings highlight the partiality of studying the WEL system within a single sector, across a limited supply chain scope, and at a sub-global scale. Policy interventions within this context need to reflect how resource pressures are transmitted through consumption and production systems between local, national, and global scales. However, further research is also needed to expose the links between inequality, ideology, overconsumption and environmental exploitation which drive decisions in relation to water, energy and land resources.Cambridge Commonwealth Trust: Vice Chancellor's Awar

Urban carbon footprints across scale: Important considerations for choosing system boundaries

Cities dominate global anthropogenic carbon emissions. Here, we develop an approach to interpret carbon footprints of cities by focusing on their system boundaries, double counting recognition, spatial paths and policy sensitivities. Using four megacities in China as a case study, we quantify and map urban carbon footprints from various accounting perspectives: territorial carbon emissions, community-wide infrastructure carbon footprint, consumption-based carbon footprint, wider production carbon footprint, and full-scope carbon footprint. We find that the megacities’ infrastructure carbon footprints are dominated by electricity-related emissions, whereas their consumption-based carbon footprints are significantly impacted by imports of both electricity and other products and services. Over 55% of the full-scope carbon footprints (sums of all three scopes) of Beijing and Shanghai can be attributed to upstream emissions, while in Chongqing and Tianjin territorial emissions are more important. Key urban infrastructure contributes over 70% to the total carbon emissions in import supply chains, determining the spatial paths and the carbon intensities of imports for these megacities. The main destinations of outsourced carbon emissions across the country from the megacities are found to be similar due to market domination of bulk suppliers of infrastructure-related and other carbon-intensive products. In addition, double counting of certain footprint indicators is considered small in this case, but could be amplified with increasing number of cities being assessed

Modeling spatio-temporal enhancer expression in Drosophila segmentation

Thermodynamic models are a key tool to investigate transcription control in the segmentation of Drosophila. By modeling the binding of transcription factors to DNA sequences and their effect on transcription initiation, thermodynamic models predict expression patterns directly from the enhancer sequence, given the binding motifs and concentrations of all relevant transcription factors (TFs). However, many parameters of the model are impossible to measure, e.g. the interaction strength between the TFs and the core promoter. Hence, it is necessary to estimate these parameters by training the thermodynamic model on known data, i.e. to fit the model predictions to already measured expression patterns of known enhancers. The quality of the parameter training result, evaluated on independent test data, indicates how well the model recapitulates the biological measurements, which can help us to improve our understanding of the underlaying mechanisms of transcription control. Therefore, proper parameter training is a crucial step for the construction of thermodynamic models. In this thesis, I develop a thorough parameter training setup that uses the limited amount of available training data efficiently and reduces parameter overfitting significantly. This optimized training setup applies a global parameter training algorithm, a method to artificially increase the amount of training data, called data augmentation, and parameter penalties, which is a technique to limit overfitting. I apply the novel training setup to expand the scope of thermodynamic models of Drosophila segmentation considerably by incorporating additional TFs into the model, and to investigate many aspects of transcription control in greater detail than it was possible before. Among these topics are the specificity of TF binding motifs, the nature of TF cooperativity and DNA accessibility. With the help of the here developed impact score, I assess the influence of all relevant TFs in silico, delineate the cooperativity range of the key TF bcd, and determine the importance of weak binding sites. Finally, I develop and discuss two alternative models of transcription control that lack the prediction quality of thermodynamic models, but, nevertheless, give valuable insights into the architectural principles of enhancers. This project is part of a larger effort to advance our current understanding of transcription regulation by reconstructing the segmentation network of Drosophila in silico. The results of this thesis facilitate future modeling efforts by optimally leveraging the available data as well as by improving our understanding of thermodynamic models

Enterprise Audit Modeling of Large-Scale Agencies' Energy and Carbon Dioxide Accounting

Calculating and accounting of embodied and operational energy and carbon emissions within buildings is still not standardized. No regulations exist for standard equations, databases, or best practice methods to evaluate energy and carbon. The inaccuracies and incompatibilities found among common process, hybrid databases, and evaluation methods leave wide margins for error. This thesis proposes a standardized method, a Large-Scale Agency Analysis (LSAA), to evaluate carbon and energy emissions and proposes a new dynamic modeling method for large-scale agencies. The Comprehensive Dynamic Carbon Analysis (CDCA) method utilizes computer technology to evaluate nonlinear carbon emissions systems that can be applied to both individual buildings and large-scale agencies