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Energy consumption and correlated greenhouse gas emissions are a big global problem. It affects all parts of society, and each industrial sector must work toward reducing itscarbon footprint. This thesis details the research of different methods to model the energyconsumption of video streaming, and works towards creating a final model. The videostreaming process is broken down into a core process consisting of head-end, distribution,transmission, and terminals. The process that contributes the most to energy consumptionat the head-end is found to be video encoding. This thesis explores the energy consumption of video encoding in depth and how it is affected by parameters such as hardware,codec choice, codec preset selection, and video details such as resolution, framerate, andduration, but these parameters are found to be insufficient to accurately model the energyconsumption of video encoding. In distribution and transmission, the highest contributor is found to be content delivery networks. The energy consumption of content deliverynetworks is investigated however no appropriate model is found. For terminals, the mostimportant factor is the kind of terminal used. The energy consumption of televisions, desktop computers, laptops, and mobile terminals is investigated, and models are presented foreach. The thesis also discusses the different models, their advantages, and their shortcomings. Additionally, an application to visualize features of the model is presented.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

This study generalizes the applications of learning curve theory. It extends the simple power learning model in two ways: (1) by extending the model to include other sift variables, the extensive learning model; (2) by generalizing the functional relationship to give greater flexibility in modelling the learning curve, the translog learning model. Through empirical analyses of gross domestic product, total energy consumption, petroleum consumption, and petroleum products consumption, different learning curve models are explored and compared.

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The vision of the future Internet of Things is posing new challenges due to gigabytes of data being generated everyday by millions of sensors, actuators, RFID tags, and other devices. As the volume of data is growing dramatically, so is the demand for performance enhancement. When it comes to this big data problem, much attention has been given to cloud computing and virtualization for their almost unlimited resource capacity, flexible resource allocation and management, and distributed processing ability that promise high scalability and availability. On the other hand, the variety of types and nature of data is continuously increasing. Almost without exception, data centers supporting cloud based services are monitored for performance and security and the resulting monitoring data needs to be stored somewhere. Similarly, billions of sensors that are scattered throughout the world are pumping out huge amount of data, which is handled by a database. Typically, the monitoring data consists time series, that is numbers indexed by time. To handle this type of time series data a distributed time series database is needed.   Nowadays, many database systems are available but it is difficult to use them for storing and managing large volumes of time series data. Monitoring large amounts of periodic data would be better done using a database optimized for storing time series data. The traditional and dominant relational database systems have been questioned whether they can still be the best choice for current systems with all the new requirements. Choosing an appropriate database for storing huge amounts of time series data is not trivial as one must take into account different aspects such as manageability, scalability and extensibility. During the last years NoSQL databases have been developed to address the needs of tremendous performance, reliability and horizontal scalability. NoSQL time series databases (TSDBs) have risen to combine valuable NoSQL properties with characteristics of time series data from a variety of use-cases.   In the same way that performance has been central to systems evaluation, energy-efficiency is quickly growing in importance for minimizing IT costs. In this thesis, we compared the performance of two NoSQL distributed time series databases, OpenTSDB and InfluxDB, based on the energy consumed by them in different scenarios, using the same set of machines and the same data. We evaluated the amount of energy consumed by each database on single host and multiple hosts, as the databases compared are distributed time series databases. Individual analysis and comparative analysis is done between the databases. In this report we present the results of this study and the performance of these databases based on energy consumption.

A major transformation within the traditional market exchange has taken place during the last decade. Sustainability concerns and individuals’ attempt to protest against the crazy consumption have giving birth to a new movement, namely collaborative consumption. A new trend is identified where the consumers desire more sustainable types of consumption and a preference of access over ownership. Collaborative consumption is aiming to maximize the utility by a peer-to-peer solution, providing access to a wide range of products and generating environmental, social and economic benefits. Whereas sharing is considered as a universal form of economic human behavior, its implementation within a business context is relatively new, creating a scarcity of existing research. Previous research revealed that consumers possess a positive attitude towards sharing activities, however the adoption of using the services and products available on the market has been slow. Hence, the authors deemed it interesting to investigate the inconsistency regarding attitude and behavior within the concept of collaborative consumption.   The preliminary purpose of the study was to contribute with knowledge of how a collaborative consumption firm can increase the attitude-behavior consistency. The authors conducted the study through the perspective of non-users familiar with and positive to the concept in regard to the objective of the research. The study has throughout the process been guided by the research question; “How can a collaborative consumption firm increase the consumers’ attitude-behavior consistency?”. In consideration of existing research mainly been conducted by a usage of quantitative methods, the authors found it relevant to apply qualitative methods in combination with an inductive approach. Hence, the authors hoped to discover additional insights that have been overlooked by previous studies. Additionally, the authors investigated the phenomenon by taking the epistemological position of interpretivism together with the ontological position of constructivism. The authors conducted the study in two partsapplying different methods to examine whether a group setting would impact the individuals’ responses. The study on individual level was based on four unstructured interviews with two representatives from each age group; 20-30 and 30-60. These findings served the purpose of creating a framework for the following study on group level, conducted by two semi-structured focus groups.     Whilst the findings from the study revealed that positive attitudes were possessed, stronger conflicting attitudes also existed. The findings further provided evidence that individuals’ attitude and behavior consistency is affected by intrinsic motives or social pressure. Hence, the authors recommend two strategies that could be applied in order to increase the consistency among non-users within collaborative consumption. The firms should enhance the intrinsic motives by emphasizing the desired attributes provided; convenience and the ability to match preferences with purpose and by educating the non-users in order to inspire a reflective thinking. A social pressure could be achieved by highlighting the attributes that are in line with today’s social norms and by encouraging current users to display their participation. These two strategies could be applied to minimize the attitude-behavior gap and transform a non-user into a user.

Restauranteurs' use of Styrofoam take-out containers was evaluated using an intervention package containing informational components, a written commitment, and an incentive system. A decrease in the use of Styrofoam take-out materials was observed with 4 of 6 restaurants. Across all participants, we observed a 15.7% increase in alternative containers and a 15.7% decrease in Styrofoam containers. Overall, a decreased use of Styrofoam take-out containers was observed with restaurateurs who were able to identify suitable alternative products.