Reducing fuel consumption and greenhouse gas emissions is becoming increasingly important in the United States, and new legislation can be expected in the near future that will affect trucks either directly or indirectly. This work is a qualitative examination of operational strategies for reducing fuel consumption from freight trucking, and also compares them with vehicular strategies. A focus is placed on who implements, benefits from, and pays for each strategy, and what type of trucking each strategy is applicable to. / text

This study proposes a data-driven model for prediction of the energy consumption of fleet vehicles in various missions, by characterization as the linear combination of a small set of exemplar travel segments. The model was constructed with reference to a heterogenous study group of 29 light municipal fleet vehicles, each performing a single mission, and each equipped with a commercial OBD2/GPS logger. The logger data was cleaned and segmented into 3-minute periods, each with 10 derived kinetic features and a power feature. These segments were used to define three essential model components as follows: The segments were clustered into six exemplar travel types (called "eigentrips" for brevity) Each vehicle was defined by a vector of its average power in each eigentrip Each mission was defined by a vector of annual seconds spent in each eigentrip 10% of the eigentrip-labelled segments were selected into a training corpus (representing historical observations), with the remainder held back for testing (representing future operations to be predicted). A Light Gradient Boost Machine (LGBM) classifier was trained to predict the eigentrip labels with sole reference to the kinetic features, i.e., excluding the power observation. The classifier was applied to the held-back test data, and the vehicle's characteristic power values applied, resulting in an energy consumption prediction for each test segment. The predictions were then summed for each whole-study mission profile, and compared to the logger-derived estimate of actual energy consumption, exhibiting a mean absolute error of 9.4%. To show the technique's predictive value, this was compared to prediction with published L/100km figures, which had an error of 22%. To show the level of avoidable error, it was compared with an LGBM direct regression model (distinct from the LGBM classifier) which reduced prediction error to 3.7%. / Graduate

Research shows that the warming of the climate over the last century is extremely likely due to human activities. Furthermore, there is a need for an understanding how business activities counteract or contribute to climate change. In particular, the digitalization industry is often introduced as an important player in climate challenge. However, research also concludes that the digitalization industry’s impact on the climate is ambiguous, since it in some cases contributes to climate change and in other cases counteracts it. In order to understand the interplay between greenhouse gas emissions and digital solutions, it is necessary to outline and quantify the emissions from particular digitalization projects and furthermore the industry itself. The thesis takes off in a single case study at a Swedish digitalization consultancy company in order to investigate how both internal greenhouse gas emissions and greenhouse gas emissions from customer projects can be quantified as accurate and as often as possible. The findings disclose that greenhouse gas (GHG) emissions can be tracked with an extremely short time step, practically continuously, especially if the tracking is integrated with the company’s ERP1 . Furthermore, the findings show that greenhouse gas emissions from customer projects can be quantified if interpreting and implementing the GHG Protocol with a soft system methodology (SSM) approach. The thesis contributes with (1) a general interpretation of the Corporate Standard (part of the GHG Protocol) in the context of digitalization; (2) a specific example of that interpretation and implementation; (3) a practical interpretation and implementation of the Project Protocol in the context of digitalization and its avoided or caused greenhouse gas emissions; and (4) a general and an in-depth analysis on the topic of quantifying a Swedish digitalization company’s greenhouse gas emissions and feasible approaches to assumption making. / Forskning slår fast att uppvärmningen av klimatet under det senaste århundradet med extremt hög sannolikhet är orsakad av människan. Det finns ett behov att förstå hur affärsverksamheter motverkar eller bidrar till klimatförändringarna. En del av denna affärsverksamheten sker inom digitaliseringsindustrin, vilken ofta presenteras som en central spelare i klimatfrågan. Å andra sidan visar forskning även på en osäkerhet gällande digitaliseringsindustrins påverkan på klimatet eftersom den i vissa fall bidrar till klimatförändringar medan den i andra fall motverkar dem. För att förstå samspelet mellan växthusgasutsläpp och digitala lösningar är det nödvändigt att överskåda och kvantifiera utsläpp från specifika digitaliseringsprojekt och, vidare, från själva industrin. Uppsatsen grundar sig i en fallstudie på ett svenskt digitaliseringskonsultbolag för att undersöka hur både interna utsläpp och utsläpp från kundprojekt kan kvantifieras så precist och så frekvent som möjligt. Resultaten pekar på att växthusgasutsläppen kan spåras och följas med extremt kort tidssteg, i stort sett kontinuerligt, i synnerhet om spårningen kan integreras med företagets affärssystem. Dessutom visar resultaten på att växthusgasutsläpp från kundprojekt kan kvantifieras om GHG Protocol tolkas och implementeras med hjälp av en “soft systems” metod (SSM). Uppsatsen bidrar med (1) en generell tolkning av Corporate Standard (en del av GHG Protocol) i en digitaliseringskontext; (2) ett specifikt exempel på en sådan tolkning och implementering; (3) en praktisk tolkning och implementering av Project Prototcol (en del av GHG Protocol) in en kontext av digitaliseringsbranschen och dess undvikta eller orsakade utsläpp; och (4) en generell och djupgående analys angående kvantifiering av ett svenskt digitaliseringsbolags växthusgasutsläpp och gångbara inställningar till att göra antaganden.

With its multiple dining halls, close proximity to restaurants, and diesel vehicle fleet, the University of Texas at Austin (UT) has both the supply of raw materials to implement a waste vegetable oil to biodiesel recycling program and the capacity to use it. At face value, implementing a large-scale recycling program provides a source of cheap, low emissions fuel. However, the feasibility of such a program is contingent on its economic cost and environmental impact relative to alternative fuel sources. Thus, this research estimated the greenhouse gas (GHG) inventories and the unit cost associated with 1 megajoule worth of recycled biodiesel derived from three production processes –Alkali Catalyzed, Acid Catalyzed, and Supercritical Methanol–using environmental life cycle assessment and life cycle costing. These GHG inventories and unit costs were then compared to the conventional diesel and oilseed biodiesel sources that make up UT’s current fuel portfolio. This analysis suggested that implementing a recycling program using a Supercritical Methanol biodiesel conversion process would have the lowest combined GHG impact and unit cost, although as an emerging technology, it poses a high investment risk. In general, these findings are encouraging to the success and impact of a large-scale recycling program. / text

Climate Change has been related to GHG emissions, of both natural and anthropogenic origin. Agricultural management practices like reduced tillage and intensive cropping systems have a significant impact on the flow of C among its sources and sinks. These management practices involve complex biophysical interactions resulting in a range of impacts on farm income and GHG abatement. The focus of this study was on the impact of alternative annual crop tillage systems on GHG emissions and income to better inform climate change mitigation policy in agriculture. Besides tillage intensity, cropping intensity and crop mix and the interaction of these characteristics with the biological and physical attributes, the emission and income effects are a function of factor inputs, factor costs and commodity prices. Therefpre, the analysis was multi-disciplinary in nature and the tool of choice that depicts impacts on individual indicators is Trade-off Analysis (TOA). A component of risk analysis was also included. The analysis focused on short and long-term performance, the uncertainty of soil N2O emission coefficients as well as changes in weather patterns. As the adoption of reduced till has been a relatively recent development and as such, there is not a lot of long-term biophysical and economic data, which limits the effectiveness of econometric analysis. The different scenarios of uncertainty and long-term impacts were analysed by use of a simulation model. The model was parameterised with Intergovernmental Panel on Climate Change (IPCC) 1996 coefficients, a farmer survey, and cost data from Saskatchewan Agriculture Agri-Food and Rural Revitalization (SAFRR) for 2004. Results indicated that net GHG emissions were relatively lower for reduced tillage management while conventional tillage may be relatively more attractive from an economic perspective. However, results indicated that such economic factors as risk and economies of size may have a significant influence on this latter result. The study also highlighted the need to evaluate the GHG abatement potential of reduced tillage while simultaneously considering the abatement capability of the farm.

Climate Change has been related to GHG emissions, of both natural and anthropogenic origin. Agricultural management practices like reduced tillage and intensive cropping systems have a significant impact on the flow of C among its sources and sinks. These management practices involve complex biophysical interactions resulting in a range of impacts on farm income and GHG abatement. The focus of this study was on the impact of alternative annual crop tillage systems on GHG emissions and income to better inform climate change mitigation policy in agriculture. Besides tillage intensity, cropping intensity and crop mix and the interaction of these characteristics with the biological and physical attributes, the emission and income effects are a function of factor inputs, factor costs and commodity prices. Therefpre, the analysis was multi-disciplinary in nature and the tool of choice that depicts impacts on individual indicators is Trade-off Analysis (TOA). A component of risk analysis was also included. The analysis focused on short and long-term performance, the uncertainty of soil N2O emission coefficients as well as changes in weather patterns. As the adoption of reduced till has been a relatively recent development and as such, there is not a lot of long-term biophysical and economic data, which limits the effectiveness of econometric analysis. The different scenarios of uncertainty and long-term impacts were analysed by use of a simulation model. The model was parameterised with Intergovernmental Panel on Climate Change (IPCC) 1996 coefficients, a farmer survey, and cost data from Saskatchewan Agriculture Agri-Food and Rural Revitalization (SAFRR) for 2004. Results indicated that net GHG emissions were relatively lower for reduced tillage management while conventional tillage may be relatively more attractive from an economic perspective. However, results indicated that such economic factors as risk and economies of size may have a significant influence on this latter result. The study also highlighted the need to evaluate the GHG abatement potential of reduced tillage while simultaneously considering the abatement capability of the farm.

Climate change is a topic of social and political commentary and controversy, and is a topic that will continue to be addressed by future scientists and laypersons alike. This report contains information and laboratory exercises for use in a greenhouse gas (GHG) and global warming potential (GWP) learning module, to be employed in secondary or entry level university engineering and environmental science curricula. Exercises include a hands-on experience with the greenhouse effect and calculations of GWP for 20-year and 100-year timeframes. / text

This study reviews the literature and updates qualitative and quantitative impacts based on new research and applies a partial greenhouse gas (GHG) emissions cost benefit analysis to the Three Gorges Dam Project (TGDP) in China. The results of CBA suggested a 22.305 billion dollars net present value (using Nordhaus’s 2007 optimal carbon price trajectory with assumed average social discount rate (SDR) of 4% assumptions) and a 440.324 billion dollars net present value (based on Nordhaus’s Model using Stern’s assumption with 1% SDR). This sensitivity analysis indicates that social discount rates highly affect the final results. This study extends the GHG emissions impact component by updating carbon prices and calculation methods, thereby updating the GHG component of Morimoto and Hope’s 2004 study. Although the CBA is limited to the GHG component, a review of recent literature and preliminary impact analysis provides the groundwork for a more comprehensive analysis for future study.

This thesis will touch upon how investment decisions relate to different scopes of GreenHouse Gas (GHG) emissions reported by listed companies in the Nordic market. Two different time frames were examined, 10 years (2011-2020) and 5 years (2016-2020) respectively. The emission scopes consist of three levels that include GHG emissions originating from different stages throughout the whole value chain of the companies. Higher levels of GHG emissions might be considered as a risk that in turn requires a premium. By choosing total return as the dependent variable and the different scope levels as independent variables, the study was conducted through a firm fixed effects model with year fixed effects. The significant findings explain that investors in the Nordic market accept relatively high levels of GHG emissions when receiving higher total returns, a sort of risk premium for Scope 1 intensity level of GHG emissions. While the significant findings for the Scope 2 intensity GHG emissions shows that higher GHG emissions originating from purchased energy generates lower total returns. The findings also suggest stronger economic effects for the 5-year period, in comparison to the 10-year period. Furthermore, the findings in this thesis contribute to the debate on whether the transition to climate-adapted sustainable societies can be affected by reporting standards and interpretation regarding GHG emissions.

Die Arbeit handelt vom Greenhouse Gas Protocol und der Anwendung auf Hochschulen. Die Untersuchung zeigt, dass das Greenhouse Gas Protocol sehr universell verfasst wurde und sich somit auch auf Hochschulen anwenden lässt. Speziell für den Hochschulkontext werden keine Leitfäden gegeben. Des Weiteren hat sich herausgestellt, dass Energieverbrauch und Pendelverkehr der Studierenden den Großteil der Emissionen ausmachen. Im Fallbeispiel der HTWK Leipzig konnten Emissionen von rund 263 tCO2e im Jahr 2022 identifiziert werden. Die untersuchten Jahre 2020 bis 2022 unterlagen allerdings der Pandemie, weshalb die reellen Emissionen deutlich höher sein könnten.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis Abkürzungsverzeichnis 1 Einleitung 2 Theoretische Grundlagen der Treibhausgasbilanzierung 2.1 Carbon Footprint 2.2.1 Product Carbon Footprint 2.2.2 Corporate Carbon Footprint 2.2 Nutzen und Relevanz 3 Greenhouse Gas Protocol 3.1 Grundsätze 3.2 Grenzen der Bilanzierung 3.3 Die drei Scopes 3.4 Berechnung der Treibhausgasemissionen 3.4.1 Treibhauspotential und Emissionsfaktor 3.4.2 Formeln zur Berechnung 4. Die Anwendbarkeit des GHG Protocol auf Hochschulen 4.1 Nachhaltigkeit an Hochschulen 4.2 Praktikabilität 4.3 Anwendungsfälle 5. CO₂-Bilanz der Fakultät Wirtschaftswissenschaft & Wirtschaftsingenieurwesen 5.1 Die Fakultät Wirtschaftswissenschaft & Wirtschaftsingenieurwesen. 5.2 Methoden 5.2.1Systemgrenzen 5.2.2 Organisationsbedingte Grenzen 5.2.3 Geschäftsbedingte Grenzen 5.2.4 Datenerhebung und Berechnung 5.3 Identifizierung der Treibhausgasemissionsquellen 5.3.1 Gebäude 5.3.2 Mobilität 5.3.3 Papier 5.4 CO₂-Bilanz 5.5 Schlussfolgerungen 6 Zusammenfassung Anhang Literaturverzeichnis Eidesstattliche Erklärung