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The concept of sustainability is currently a prominent topic of discussion within the field of packaging. In the context of numerous product manufacturing enterprises, the integration of sustainability principles into their operational framework is often perceptible to external observers solely through the packaging of the final product. In addition to the established criteria, fundamental concepts and principles, the predominant discourse surrounding the pursuit of sustainable packaging goals primarily revolves around the specific models and practices implemented by the industry. Growing environmental concerns from industry and consumers have driven the development of innovative bio-based materials as alternatives to fossil-derived plastic polymers for packaging applications. This thesis project is in collaboration with the Swedish-Finnish company, Stora Enso Oyj; a paper mill company specialized in packaging, biomaterials, wooden construction and paper solutions, made by renewable materials, such as wood and biomass. The study involves the execution of a comparative Life Cycle Assessment (LCA) to evaluate two distinct packaging solutions. The EcoFlowerBox (EFLB), a packaging solution developed by Stora Enso, is specifically designed to cater to the needs of the floral industry. It serves as an alternative to the conventional flower bucket, fulfilling the essential functions of flower storage and transportation. The materials that were compared in the study are corrugated board and plastic, respectively. The main objective of the study was to determine the most environmentally sustainable option based on the greenhouse gas emissions generated throughout their entire life cycle. In addition to the criterion related to material composition, the ecological durability of the products was assessed by considering their end-of-life treatment and potential.  The assessment methodology discussed in this study covers the complete life cycle of the products being examined. This includes the extraction of raw materials, the manufacturing and utilization processes, and the eventual end-of-life stage.  The research findings indicate that the EcoFlowerBox (EFLB), produced by Stora Enso, exhibits a carbon footprint that is 29% lower compared to the PP bucket in the baseline scenario. This suggests that, from a climate standpoint, the EFLB functions as a more environmentally advantageous packaging solution, in terms of greenhouse gas (GHG) emissions, in comparison to the PP bucket. Additionally, the study identified that the recycling rates and the weight of the PP bucket are crucial factors that exert a substantial impact on the study's outcomes. The results underscore the significance of addressing these areas of concern in order to improve the environmental performance of the EFLB when compared to other PP buckets, and to attain more sustainable packaging solutions for flowers.

The need to transition the global economy to net zero greenhouse gas emissions is clear. Commitments made by the majority of the world’s governments declare that a global net zero economy should be achieved by the middle of the century. What is less clear is how this transition should take place, or how these commitments impact companies. This paper explores senior management perspectives on a net zero transition, identifying drivers, enablers, and barriers to transitioning effectively. In line with Kurt Lewin’s work, we collectively refer to these as forces. Through 11 in-depth interviews and a quantitative survey to managers across several industries and geographies, we identify, rank, and visually conceptualise 26 forces that act on companies in the context of a net zero transition. The strongest driving force is ‘Societal awareness of climate change and net zero targets’, while the biggest current constraining force is ’Business priorities’. The force considered to have the single biggest potential impact is ‘government disincentives and bans’. Based on the collective analysis of our findings, there are possible policy implications that could be explored further in order to successfully manage a transition to net zero.

Länder som skrivit under parisavtalet har som mål att sänka sina utsläpp av växthusgaser, men också med hjälp av mänsklig aktivitet absorbera växthusgasutsläpp för att förhindra den globala uppvärmningen till max 1,5°C under 2000-talet. Satsningar som berör mänsklig aktivitet är bland annat Bio-energy with carbon capture and storage (BECCS) och tillämpning av biokol. Sektorer som måste genomgå förändringar angående hur de bedriver sina verksamheter är bland annat transport, energiproduktion, fastigheter och lantbruk med mera. Studiens syfte är att beskriva kolsänkemetoderna BECCS och biokol samt kartlägga växthusgasutsläppen på en fastighet med hänsyn till dess drift och påvisa hur stor andel lagrad koldioxid som behövs för att fastigheten skall bli klimatpositiv. Kartlägga studenters intressen med hänsyn till klimatpositiva produkter och tjänster. Studien utgår från att göra en bokförings-livscykelanalys (BLCA) och information hämtas direkt från bolaget som ansvarar för driften av fastigheten samt utgår från hållbarhetsrapporter från företag och organisationer som har emissionsfaktorer gällande el och fjärrvärme kopplat till fastigheten. En marknadsundersökning riktad mot studenter genomfördes även för att kartlägga studenters intresse angående klimatpositiva produkter och tjänster. Fastigheten släpper ut ca 15,4 kg CO2e / m2 per år med avseende på drift. Det framkommer även att ca 70 procent av de tillfrågade studenterna är intresserade av klimatpositiva produkter och tjänster. Några slutsatser ur studien är att de kan installeras en förnybar energikälla på fastigheten och teckna elavtal gällande grön el för att minska koldioxidutsläppen. / Countries that have signed the Paris agreement aims to decrease their greenhouse gas emissions, and with the help of human activity absorb greenhouse gases to prevent a global temperature increase of 1,5°C before the turn of the century. Investments that involve human activity include Bioenergy with carbon capture storage (BECCS) and usage of biochar. Among the sectors that need to make a change regarding their behavior towards greenhouse gas emission are transportation, energy production, real estate, and agriculture. The purpose of this study is to describe BECCS and biochar as a method for decrease greenhouse gas emissions. Calculate the greenhouse gas emissions for a premise regarding its operational activities and determine how much carbon dioxide the premise need to store in order to obtain climate positive status. Determine students interests regarding climate positive products and services. The study used the life-cycle analysis method to determine how much greenhouse gas emissions the premise emitted. The information was obtained by the company that is responsible for the operational activities and emission factors for the district heating and electricity was obtained from secondary sources connected to the premise. A survey aimed at students was conducted to determine the interest of the students regarding climate positive products and services. The premise emitted approximately 15,4Kg CO2e/m2 Atemp per year for its’ operational activities. Approximately 70 percent of the students that answered the survey are interested in climate positive products and services. Conclusion from the study involves installing a renewable energy source on the premise and signing an electricity agreement regarding green electricity in order to decrease carbon dioxide emissions.

A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs). Specifically, heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses are modeled. The models are developed based on the Virginia Tech Comprehensive Power-based Fuel consumption Modeling (VT-CPFM) framework. The results demonstrate that the model estimates are highly consistent with field observations as well as the estimates of the Comprehensive Modal Emissions Model (CMEM) and MOtor Vehicle Emissions Simulator (MOVES). It is also found that the optimum fuel economy cruise speed ranges between 32 and 52 km/h for the tested trucks and between 39 and 47 km/h for the tested buses on grades varying from 0% to 8%, which is significantly lower than LDVs (60-80 km/h). The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The dynamics model varies throttle and brake level with running speed rather than assuming constants as was done by previous studies, and the energy consumption model considers instantaneous energy regeneration. Both models can be easily calibrated using non-engine data and implemented in simulation systems and eco-transit applications. The results of the dynamics modeling demonstrate that the proposed model can adequately capture instantaneous acceleration/deceleration behavior and thus produce realistic train trajectories. The results of the energy consumption modeling demonstrate that the model produces the estimates consistent with the National Transit Database (NTD) results, and is applicable for project-level analysis given its ability in capturing the energy consumption differences associated with train, route and operational characteristics. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). Given that the car-following model within a micro-simulator controls longitudinal vehicle motion and thus determines the resulting vehicle trajectories, the research effort mainly focuses on the performance of the built-in car-following models from the energy and environmental perspective. The vehicle specific power (VSP) distributions resulting from each of the car-following models are compared to the field observations. The results demonstrate that the Rakha-Pasumarthy-Adjerid (RPA) model (implemented in the INTEGRATION software) outperforms the Gipps (AIMSUM), Fritzsche (PARAMICS) and Wiedemann (VISSIM) models in generating accurate VSP distributions and fuel consumption and emission estimates. This demonstrates the advantage of the INTEGRATION model over the other three simulation models for energy and environmental analysis. A new eco-routing model, comprehensively considering microscopic characteristics, is then developed, followed by a numerical experiment to test the benefit of the model. With the resulting eco-routing model, an on-road vehicle dynamic eco-routing system is constructed for in-vehicle navigation applications, and tested for different congestion levels. The results of the study demonstrate that the proposed eco-routing model is able to generate reasonable routing suggestions based on real-time information while at the same time differentiate eco-routes between vehicle models. It is also found that the proposed dynamic eco-routing system achieves lower network-wide energy consumption levels compared to the traditional eco-routing and travel time routing at all congestion levels. The results also demonstrate that the conventional fuel savings relative to the travel time routing decrease with the increasing congestion level; however, the electric power savings do not monotonically vary with congestion level. Furthermore, the energy savings relative to the traditional eco-routing are also not monotonically related to congestion level. In addition, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler's age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives. / Ph. D. / A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs) including heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses. The model estimates are demonstrated to provide a good fit to field data. The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The proposed dynamics model is able to produce realistic acceleration behavior, and the proposed energy consumption model can provide robust energy estimates that are consistent with field data. Both models can be calibrated without mechanical data and thus easily implemented in complex frameworks such as simulation systems and eco-transit applications. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). The results demonstrate that INTEGRATION outperforms the other three simulation models for energy and environmental analysis. Also, INTEGRATION is able to generate measures of effectiveness (MOEs) for electric vehicles, which makes it more competitive than the state-of-the-art counterpart. A dynamic eco-routing system is then developed in the INTEGRATION simulation environment. The built-in eco-routing model of the system comprehensively considers microscopic characteristics and is demonstrated to generate reasonable routing solutions based on real-time information while at the same time differentiate vehicle models. The system is able to provide routing suggestions for both conventional gasoline/diesel and electric vehicles. The testing results demonstrate that the proposed eco-routing system achieves network-wide energy savings compared to the traditional eco-routing and travel time routing at all tested congestion levels. Also, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler’s age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives.

In the electronics industry, several greenhouse gases (GHGs) are used as process gases in manufacturing processes. The organization RF360 as a Qualcomm Inc. subsidiary is using GHGs as input gases in the manufacturing processes dry etching, Chemical Vapor Deposition (CVD) and trimming in the fabrication plant in Munich.The estimation of GHG emissions from the use of process gases under Scope 1 requires a global and comprehensive approach to determine emission sources. This work provides the basis for the GHG emission estimation from process gas use under consideration of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Dry etching and CVD process GHG emissions arecalculated using the Tier 2c method with process specific default emission factors. The process GHG emissions from trimming are characterized under Tier 3a, by determination of site-specific process emission factors. These emission factors are obtained from FTIR measurements in the inline. The measurement results show the input gas NF3 is largely not converted or destroyed in the trimming process. The total GHG emissions resulting from process gas use in the considered processes are determined by emissions of NF3, CF4 and N2O. The implementation and improvement of the approach requires further measurements of site-specific emission factors in the processes and Destruction Removal Efficiencies of the abatement systems.

Climate change impacts hydrology, nutrient cycling, agricultural conservation practices, and greenhouse gas (GHG) emissions. The Chesapeake Bay and its watershed are subject to the largest and most expensive Total Maximum Daily Load (TMDL) ever developed. It is unclear if the TMDL can be met given climate change and variability (e.g., extreme weather events). The objective of this dissertation is to quantify the impact of climate change and climate on water resources, nutrient cycling and export in agroecosystems, and agricultural conservation practices in the Chesapeake Bay watershed. This is accomplished by developing and employing a suite of modelling tools. GHG emissions from agroecosystems, particularly nitrous oxide (N2O), are an increasing concern. To quantify N2O emissions a routine was developed for the Soil and Water Assessment Tool (SWAT) model. The new routine predicts N2O and di-nitrogen (N2) emissions by coupling the C and N cycles with soil moisture, temperature, and pH in SWAT. The model uses reduction functions to predict total denitrification (N2 + N2O production) and partitions N2 from N2O using a ratio method. The SWAT nitrification routine was modified to predict N2O emissions using reduction functions. The new model was tested using GRACEnet data at University Park, Pennsylvania, and West Lafayette, Indiana. Results showed strong correlations between plot measurements of N2O flux and the model predictions for both test sites and suggest that N2O emissions are particularly sensitive to soil pH and soil N, and moderately sensitive to soil temperature/moisture and total soil C levels. The new GHG model was then used to analyze the impact of climate change and extreme weather conditions on the denitrification rate, N2O emissions, and nutrient cycling/export in the 7.4 km2 WE38 watershed in Pennsylvania. Climate change impacts hydrology and nutrient cycling by changing soil moisture, stoichiometric nutrient ratios, and soil temperature, potentially complicating mitigation measures. To quantify the impact of climate change we forced the new GHG model with downscaled and bias-corrected regional climate model output and derived climate anomalies to assess their impact on hydrology, nitrate (NO3-), phosphorus (P), and sediment export, and on emissions of N2O and N2. Model-average (± standard deviation) results indicate that climate change, through an increase in precipitation, will result in moderate increases in winter/spring flow (2.7±10.6 %) and NO3- export (3.0±7.3 %), substantial increases in dissolved P (DP, 8.8±19.8 %), total P (TP, 4.5±11.7 %), and sediment (17.9±14.2 %) export, and greater N2O (63.3±50.8 %) and N2 (17.6±20.7 %) emissions. Conversely, decreases in summer flow (-12.4±26.7 %) and the export of P (-11.4±27.4 %), TP (-7.9±24.5 %), sediment (-4.1±21.4 %), and NO3- (-12.2±31.4 %) are driven by greater evapotranspiration from increasing summer temperatures. Increases in N2O (20.1±29.3 %) and decreases in N2 (-13.0±14.6 %) are also predicted in the summer and driven by increases in soil moisture and temperature. In an effort to assess the impact of climate change at a regional level, the model was then scaled-up to the entire Susquehanna River basin and was used to evaluate if agricultural best management practices (BMPs) can offset the impact of climate change. Agricultural BMPs are increasingly and widely employed to reduce diffuse nutrient pollution. Climate change can complicate the development, implementation, and efficiency of BMPs by altering hydrology, nutrient cycling, and erosion. We select and evaluate four common BMPs (buffer strips, strip crop, no-till, and tile drainage) to test their response to climate change. We force the calibrated model with six downscaled global climate models (GCMs) for a historic period (1990-2014) and two future scenario periods (2041-2065) and (2075-2099) and quantify the impact of climate change on hydrology, NO3-, total N (TN), DP, TP, and sediment export with and without BMPs. We also tested prioritizing BMP installation on the 30% of agricultural lands that generate the most runoff (e.g., critical source areas-CSAs). Compared against the historical baseline and excluding the impact of BMPs, the ensemble model mean (± standard deviation?) predictions indicate that climate change results in annual increases in flow (4.5±7.3%), surface runoff (3.5±6.1%), sediment export (28.5±18.2%) and TN (9.5±5.1%), but decreases in NO3- (12±12.8%), DP (14±11.5%), and TP (2.5±7.4%) export. When agricultural BMPs are simulated most do not appreciably change the overall water balance; however, tile drainage and strip crop decrease surface runoff generation and the export of sediment, DP, and TP, while buffer strips reduced N export substantially. Installing BMPs on critical source areas (CSAs) results in nearly the same level of performance for most practices and most pollutants. These results suggest that climate change will influence the performance of BMPs and that targeting BMPs to CSAs can provide nearly the same level of water quality impact as more widespread adoption. Finally, recognizing that all of these model applications have considerable uncertainty associated with their predictions, we develop and employ a Bayesian multi-model ensemble to evaluate structural model prediction uncertainty. The reliability of watershed models in a management context depends largely on associated uncertainties. Our Objective is to quantify structural uncertainty for predictions of flow, sediment, TN, and TP predictions using three models: the SWAT-Variable Source Area model (SWAT-VSA), the standard SWAT model (SWAT-ST), and the Chesapeake Bay watershed model (CBP-model). We initialize each of the models using weather, soil, and land use data and analyze outputs of flow, sediment, TN, and TP for the Susquehanna River basin at the Conowingo Dam in Conowingo, Maryland. Using these three models we fit Bayesian Generalized Non - Linear Multilevel Models (BGMM) for flow, sediment, TN, and TP and obtain estimated outputs with 95% confidence intervals. We compare the BGMM results against the individual model results and straight model averaging (SMA) results using a split time period analysis (training period and testing period) to assess the BGMM in a predictive fashion. The BGMM provided better predictions of flow, sediment, TN, and TP compared to individual models and the SMA during the training period. However, during the testing period the BGMM was not always the best predictor; in fact, there was no clear best model during the testing period. Perhaps more importantly, the BGMM provides estimates of prediction uncertainty, which can enhance decision making and improve watershed management by providing a risk-based assessment of outcomes. / Ph. D. / Climate change impacts hydrology, nutrient cycling, agricultural conservation practices, and greenhouse gas (GHG) emissions. The Chesapeake Bay and its watershed are subject to the largest and most expensive Total Maximum Daily Load (TMDL) ever developed. It is unclear if the TMDL can be met given climate change and variability. The objective of this dissertation is to quantify the impact of climate change and climate on water resources, nutrient cycling and export in agroecosystems, and agricultural conservation practices in the Chesapeake Bay watershed. This is accomplished by developing and employing different modeling tools. First, GHG emissions model was developed to quantify nitrous oxide (N₂O) emissions from agroecosystems, which are an increasing concern. The new model was then tested using observed N₂O emissions data at University Park, Pennsylvania, and West Lafayette, Indiana. Results showed strong correlations between plot measurements of N₂O flux and the model predictions for both test sites. Second, the new GHG model was then used to analyze the impact of climate change and extreme weather conditions on the N₂O emissions, and nutrient cycling/export in small and regional watershed scale. To quantify the impact of climate change we forced the new GHG model with downscaled and bias-corrected regional climate model date to assess their impact on hydrology, nitrate (NO₃-), phosphorus (P), and sediment export, and on emissions of N₂O and N₂. Finally, recognizing that all of these model applications have considerable uncertainty associated with their predictions, we developed and employed a Bayesian multi-model ensemble to evaluate structural model prediction uncertainty.

Global CO2 emissions produced by energy-related processes, mainly power plants, have increased rapidly in recent decades; and are widely accepted as the dominant contributor to the greenhouse gas (GHG) effect and consequent climate changes. Among countermeasures against the emissions, CO2 capture and storage (CCS) is receiving much attention. Capture of CO2 is the core step of CCS as it contributes around 75% of the overall cost, and may increase the production costs of electricity by over 50%. The reduction in capture costs is one of the most challenging issues in application of CCS to the energy industry. Using limestone in CaO-looping cycles is a promising capture technology to provide a cost-effective separation process to remove CO2 content from power plants operations. Limestone has the advantage of being relatively abundant and cheap, and that has already been widely used as a sorbent for sulphur capture. However, this technology suffers from a critical challenge caused by the decay in the sorbent capture capacity during cyclic carbonation/calcination, which results in the need for more sorbent make-up; hence a reduction in cost efficiency of the technology. The performance of sorbent influenced by several operating and reaction conditions. Therefore, much research involves investigation of influencing factors and different methods to reduce the sorbent deactivation. Cont/d.

A water distribution system (WDS) design deals with achieving the desired network performance. WDS design can involve new and / or existing network redesigns in order to keep up with the required service performance. Very often, WDS design is expensive, which encourages cost effectiveness in the required investments. Moreover, WDS design is associated with adverse environmental implications such as greenhouse gas (GHG) emissions due to energy consumption. GHGs are associated with global warming and climate change. Climate change is generally understood to cause reduction in water available at the sources and increase water demand. Urbanization that takes into account factors such as demographics (population ageing, household occupancy rates, etc.) and other activities are associated with water demand changes. In addition to the aforementioned issues, the challenge of meeting the required hydraulic performance of WDSs is worsened by the uncertainties that are associated with WDS parameters (e.g., future water demand). With all the factors mentioned here, mitigation and adaptive measures are considered essential to improve WDS performance in the long-term planning horizon. In this thesis, different formulations of a WDS design methodologies aimed at mitigating or adapting the systems to the effects of future changes such as those of climate change and urbanization are explored. Cost effective WDS designs that mitigate climate change by reducing GHG emissions have been investigated. Also, water demand management (DM) intervention measures, i.e., domestic rainwater harvesting (RWH) systems and water saving appliance schemes (WSASs) have been incorporated in the design of WDSs in an attempt to mitigate, adapt to or counteract the likely effects of future climate change and urbanization. Furthermore, flexibility has been introduced in the long-term WDS design under future uncertainty. The flexible methodology is adaptable to uncertain WDS parameters (i.e., future water demand in this thesis) thereby improving the WDS economic cost and hydraulic performance (resilience). The methodology is also complimented by strategically incorporating DM measures to further enhance the WDS performance under water demand uncertainty. The new methodologies presented in this thesis were successfully tested on case studies. Finally, conclusions and recommendations for possible further research work are made. There are potential benefits (e.g., cost savings, additional resilience, and lower GHG emissions) of incorporating an environmental objective and DM interventions in WDS design. Flexibility and DM interventions add value in the design of WDSs under uncertainty.

The iron and steel industry has become increasingly globalised. Market conditions are also changing and de-carbonisation of production is challenging. The objective of this thesis is to assess how energy efficiency and greenhouse gas emissions reductions can be promoted and effectively monitored in the steel industry. The thesis contributes with analyses based on the Malmquist Productivity Index for a top-down analysis of the energy efficiency of EU Member States’ iron and steel production, and Partial Least Squares regression for bottom-up assessments of different monitoring tools. The thesis also contributes with a scrap availability assessment module to enhance the energy system model ETSAP-TIAM. The first phase of the research showed that future production needs to shift towards innovative low-CO2 technologies even when all available recycled material is fully used. Techniques using carbon capture and storage (CCS) as well as hydrogen-based technologies can be expected to become economically viable under tightened climate policies. The second phase of the research showed that current indicators are insufficient. System boundaries of energy use and emissions data do not align with production statistics. Indicators based on energy use or emissions in relation to production in physical terms may be useful to track specific processes. However, current indicators fail to reflect the companies’ product mix. Enhanced energy and climate indicators that adjust for the product mix provide better estimates while failing to reflect the increasing globalisation. Effective monitoring of industrial transformation will be increasingly important as pressure from climate policy via global CO2-pricing is unlikely in the short term. Current or enhanced indicators do not fully capture industrial transformation and are not recommended. Future research should focus on defining indicators to estimate energy use and emissions along industrial value chains in climate policy contexts. / Järn- och stålindustrin har blivit alltmer globaliserad. Marknadsvillkoren förändras samtidigt som utfasningen av fossila bränslen är utmanande. Målet med den här avhandlingen är att bedöma hur energieffektivitet och växthusgasutsläppsminskningar kan främjas och effektivt utvärderas inom stålindustrin. Avhandlingen bidrar med analyser baserade Malmquists produktivitetsindex för att analysera energieffektivitet av EU:s medlemsstaters järn- och stålproduktion, och partiell minsta- kvadrat-regression för att bedöma olika utvärderingsmått. Avhandlingen bidrar även med en modul som bedömer skrottillgång för att förbättra energisystemmodellen ETSAP-TIAM. I en första fas visade forskningen att framtida produktion behöver ställas om mot innovativa teknologier med låga CO2-utsläpp även när allt tillgängligt återvunnet material används fullt ut. Tekniker som använder koldioxidinfångning och -lagring (CCS) samt vätebaserade teknologier kan förväntas bli ekonomiskt försvarbara under åtstramade klimatpolitiska styrmedel. I en andra fas visade forskningen att nuvarande indikatorer är otillräckliga. Systemgränser för energianvändnings- och växthusgasutsläppsdata stämmer inte överens med produktionsstatistik. Indikatorer utifrån energianvändning eller utsläpp i relation till fysisk produktion kan vara användbara för att följa upp specifika processer. Nuvarande indikatorer lyckas dock inte spegla företagens produktmix. Förbättrade energi- och klimatindikatorer som justerar för produktmixen ger bättre uppskattningar, men speglar inte branschens ökande globalisering. Effektiv utvärdering av industriell transformation blir alltmer viktig då påtryckning från klimatpolitiska styrmedel via global CO2-prissättning är kortsiktigt osannolik. Nuvarande eller förbättrade indikatorer fångar inte industriell transformation fullt ut och rekommenderas inte. Framtida forskning bör fokusera på att definiera indikatorer som uppskattar energianvändning och växthusgasutsläpp längs industriella värdekedjor. / <p>QC 20170428</p>