1,720,979 research outputs found
Understanding barriers to CO<sub>2</sub> abatement: The Y-factor applied
No full textBarriers to CO2 abatement impede their materialisation. In this research the Y-factor was used in expert interviews to gather information on the barriers to the materialisation of four abatement options in the Netherlands: Insulation, Carbon Capture Storage, Biofuels and Geothermal energy. The potential of these options for the Netherlands is relevant, although no acceleration of their realisation has taken place. The Y-factor proved to be a suitable research method to systematically research materialisation barriers from a system’s perspective as it included barriers related to costs and financing, multi-actor complexity, physical interdependencies and behaviour. The supporting stories of the barrier scores and links between barriers revealed the complexity of materialisation. Additional scoping of abatement options and clarification of barrier definitions can increase informative value of the supporting stories and increase the scoring quality. Researching abatement options when using a system’s perspective can improve the understanding of barriers to CO2 abatement.Complex Systems Engineering and Management (CoSEM
Revealing the complexity of reducing GHG emissions in Mexico: Constructing an emission abatement curve to improve comprehension on reducing GHG emissions using the Y-factor
No full textReducing GHG emissions has become a widely publicized topic to halt future effects of global warming. In an effort to accelerate the energy transition a group of policy-maker from McKinsey developed a tool named marginal abatement cost curve (MACC) capable of illustrating the relationship between the cost-effectiveness of different abatement options and the total amount of GHG abated (Bockel et al, 2011). Even though the MACC has become popular for government reports and environmental analysis of abatement options through their abatement cost it lacks to analyze the options beyond the financial perspective. In 2016, Chappin published the introduction of the Y-factor method with the aim of solving the why factors that were hampering the pursual of implementing the abatement options. The method relies on the use of grading each abatement option in a scale of 0 to 2 through 12 socio-technical factors that are divided into four categories: multi-actor complexities, physical embeddedness, behavior and the cost & financing. This new method is a more robust approach than the MACC and it helps in providing new insights across different categories. The Y-factor is a relatively new method that has been furtherly assessed by Arensman (2018), Cheung (2018), and Soana (2018). This master thesis follows in the method of the Y-factor and goes a step beyond in proving its reliability when applied to a case-study, in this case applying the Y-factor for Mexico. The main research question is What emission abatement curve can capture the complexity of reducing GHG emissions in Mexico? For the construction of the emission abatement curve this research focuses on 20 abatement options that are relevant for the country. These options were selected through a process to provide diversity in the sector and reflect the reality of the biggest GHG emissions contributors of the country (energy creation, transport sector). Through a preliminary scoring based on literature review including government reports, scientific and news articles a preliminary Y-curve was constructed. The validation of the emission abatement curve was provided by contacting different experts in the country. This validation relied on interviews made to provide insights of the current situation of Mexico further understand what is hampering the implementation of the abatement options. To remove subjectivity for the validation each of the abatement option was graded by 2 or 3 experts’ interviews reducing personal bias and increasing result accuracy. The validated Y-curve results had interesting insights when comparing to the initial MACC developed by the US government for the low emission development program in Mexico and presented by Rebolledo et al (2016). The energy sector had the highest scoring abatement options on average which included renewable options such as Geothermal, Wind-Energy, Small Hydroelectric while also including fossil-based options that have become a priority for the new government administration Coal CCS for new plants. From all the options the highest ranked option according to the Y-score was the Coal CCS retrofit with a score of 22 out of 24. This means that this is the least convenient 6 option to be pursued according to the grading across the four categories. It was also interesting to note how the transport sector with options such as modal shift freight transport, transport policy changes, hybrid and electric vehicles, among others conform a highly diverse group each with different goals and widely different factors that obstruct its pursual. A key area is how the transport sector is dependent on a lot of different actors for any structural change or policy implementation meaning that these options are some of the most difficult to follow specially in the cities. Results of the Forestry & Agriculture cluster can be misleading if only the McKinsey data is available given the peculiarities of how the plot areas of land are owned in the country making it a unique situation that is hard to conceive in different countries. A general link between Mexico’s current affairs and the abatement options selected are explained to provide valuable information on the country and possible pitfalls when dealing with similarities in other countries. At the end of the report a recapitulation of the process and the main steps of the thesis are provided, as well as, concluding remarks for each chapter are mentioned to highlight the most important aspects of them. Valuable suggestions given by the interviewees on how to improve the Y-factor method have been highlighted, as well as the limitations of the study and how it can be improved. The societal and academic relevance of the project, as well as the limitations of the study are addressed and given an opinion of the added value of using the Y-factor for future research purposes is given. Concluding, the Y-factor approach adds value to decision makers and serves its main purpose of understanding the factors that hamper the abatement option implementation while also helps in unravel the complexity associated with such abatement options to a better overall understanding.Engineering and Policy Analysi
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Edge Computing on the Rise: Towards a Business Model Tool for Analyzing the Potential of Edge Computing for IoT Applications
No full textEdge computing can deliver substantial value to the general idea of the Internet of Things (IoT). However, there is a myriad of potential IoT applications for edge computing. Stakeholders are left with uncertainty about how the business potential of edge computing for these IoT applications can be identified. This research contributes in solving this, by designing a business model tool that can be used to identify the business model potential of edge computing for distinct IoT application areas, based on business model viability and feasibility. Through the Design Science Research Methodology (DSRM), the tool has been designed, demonstrated, and evaluated. Based on the STOF ontology, and supplemented by the theoretical domains of business ecosystems and platform theory, nine generic variables have been identified to explain business model viability and feasibility. These generic variables have in turn been contextualized towards the edge computing domain, in terms of 45 contextual input variables. This is the first research that unfolds these business model variables for edge computing.Management of Technology (MoT
Uncertainty in Long-Term Grid Planning: Approaching Transmission Expansion Planning through the Framework of Decision Making under Deep Uncertainty
No full textMotivations for sustainability are initiating an energy transition that is changing the European energy domain. The transition effectuated the adaptation of large volumes of wind and solar based generation capacity. The intermittent power-output of these Variable Renewable Energy Sources challenges the balancing operation of the electricity network in particular. Despite the availability of different solutions like storage, smart applications and infrastructure substitution, large investments in transmission capacity are inevitable. While the need for additional transmission capacity is evident, the realization of transmission capacity has become increasingly complex due to the uncertainty surrounding the future landscape in which this expansion would take place. The many possible pathways towards a sustainable future make it increasingly difficult to predict the development of generation and load profiles and thereby complicate the identification of capacity requirements within the electricity network. This raises the need for new approaches that address the high degree of uncertainty present within the electricity domain. Literature describes the framework of Decision Making under Deep Uncertainty as an alternative approach to addressing the role of uncertainty in Transmission Expansion Planning. In contrast to traditional scenario planning approaches, this approach focuses on the computational evaluation of large numbers of scenarios that are sampled from a constrained uncertainty space. The idea is to inform decision making by exploring the uncertainty space and identifying conditions under which certain outcomes occur. Consequently, decision makers are aware of the conditions under which interventions might succeed or fail and are therefor able to design strategies that perform in different futures. The potential of the framework of Decision Making under Deep Uncertainty in the context of Transmission Expansion Planning is explored through a proof-of-concept approach that focuses on Transmission Expansion Planning in the context of The Netherlands. In this approach a simplified integrated market simulation and network model are used to explore the effects of different quantities of wind and solar based generation capacity on the required transmission capacity within the electricity network. Instead of using merely three traditional scenarios, this thesis has evaluated and analyzed 20,000 different scenarios. The results of these analyses have been reviewed by domain experts during two workshop sessions. These sessions established that approaches to Decision Making under Deep Uncertainty could provide useful insights in relation to model sensitivity, the reduction of dimensional complexity of the uncertainty space and the development of scenarios that describe areas within the uncertainty space. The sessions furthermore established that the application of Decision Making under Deep Uncertainty in relation to Transmission Expansion Planning requires further development in order to become a viable alternative to traditional scenario planning in a corporate environment. The application of Decision Making under Deep Uncertainty approaches within the context of Transmission Expansion Planning provides a unique opportunity to make the uncertainty space more visible for Transmission System Operators. The approach provides the building blocks to design adaptive investment strategies which in turn are geared towards facilitating the energy transition in a robust manner.Engineering and Policy Analysi
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Analysing the impact of cyber insurance on the cyber security ecosystem: Utilising agent-based modelling to explore the effects of insurance policies
No full textCyberattacks are a constant threat to organisations worldwide. The uncertainty and difficulty of properly conducting cyber risk management processes do not make it easier for organisations to cope with cyberattacks. Cyber insurance can be a partial solution to the dilemma that organisations face. However, it has not seen the expected growth which is likely because the actual effects of cyber insurance are still unclear. Furthermore, barely any literature is available on the insurance policies that insurers can utilise to positively influence the ecosystem. Additionally, the researches in current literature, whilst providing useful insights, still lack the chaos, interconnectedness and unpredictability (dynamicity) that characterises the cyber security ecosystem. In order to close this knowledge gap and tackle the issue of dynamicity, a modelling study was conducted utilising agent-based modelling. The agent-based model was built to simulate the cyber security ecosystem and to test the effects of various cyber insurance policies. The main findings from this research were that the various insurance policy options had positive but rather small influences. The combination of several policy options into a synergetic design provided results with more observable effects on ecosystem level. However, altogether there were still no large positive effects brought forth by the insurance policies on the cyber security ecosystem.Complex Systems Engineering and Management (CoSEM
Citizen and consumer preferences for non-market environmental impacts of wind and solar energy farms
No full textThis thesis assesses the empirical differences between citizen and consumer preferences for (non-market) environmental impacts of government financed renewable energy farms, by designing citizen-based and consumer-based discrete choice experiments. The results indicate that to some extent citizens and consumers make different trade-offs between the environmental impacts of wind and solar energy farms. Moreover, the results infer that these differences may lead to different policy recommendations in environmental valuation studies of similar renewable energy technology alternatives
The impact of adjusted thermostat practices in the residential sector
No full textThe residential sector is responsible for over 55% of the natural gas consumption in the Netherlands. In the climate accord of Paris, the Netherlands came to an agreement with the rest of the world leaders to limit the overall temperature rise by reducing the consumption of and by switching away from carbon-based fuels. The gas mining induced earthquakes in the northern part of the Netherlands increases the pressure on Dutch society to reduce natural gas consumption. The residential sector can play a role in reducing the consump- tion of natural gas in accordance with the Paris accord and to mitigate gas mining induced earthquakes. The amount of natural gas consumed per household is dependent on the behavioural aspects of residents, which are the biggest cause of uncertainty in estimating natural gas consumption. Current natural gas consumption based calculations are based upon dwelling characteristics and are not adjusted to individual behavioural as- pects. The inside temperature in a dwelling is seen as the primal indicator of residential heat consumption. The behavioural aspects of residents in the form of thermostat interaction are analysed in this thesis. The potential saving in the residential sector is addressed by including thermostat practices of residents in the estimation of potential savings.The goal of this thesis is to identify thermostat practice in dwellings by the use of disaggregated energy con- sumption data and estimate the impact of adjusting individual thermostat practices on the natural gas con- sumption in the residential sector. Disaggregated energy consumption data is seen as detailed individual household consumption data. To reach the goal the following research question is answered:What insights in thermostat practices that influence natural gas consumption of individual house- holds can be identified by a combined analysis of electricity and thermostat use?Practice theory is used to understand the underlying mechanisms at play in household interaction with their thermostat. Disaggregated consumption data is used to gain insights in thermostat practices of individual dwellings. The thermostat practices of households are used to group specific practices and indemnify poten- tial savings in the residential sector.The smart meter/ thermostat Toon is used to gather individual thermostat interactions and gas and electricity consumption data. Grouping of individual households with the use of clustering on the basis of thermostat settings is used to determine similar thermostat practices. Households with similar thermostat practices are grouped together, with the use of unsupervised classification in the form of hierarchal clustering. Similar thermostat practices groups are used to shape potential thermostat adjustments and assess the impact of these adjustments.Thermostat practices of households are evaluated with the use of occupancy detection to identify potentials savings. A connection between thermostat practices and household occupancy is made with the use of elec- tricity consumption data. Individual electricity consumption data of households is used to determine the occupancy in a dwelling, by detecting moments of relative high consumption. Residential occupancy is de- tected with model ensemble of a Hidden Markov Model and a Rolling Mean Model to determine an overall occupancy schedule. The combined analysis of occupancy and thermostat practices is used to determine potential savings and determine the extent of these savings.The generated insight in saving possibilities by a combination of thermostat practices and residential occu- pancy is used to develop and estimate the impact of 4 different thermostat practice adjustments. The esti- mations are based upon 3 different calculation methods: relative shift, heat demand and temperature and gas regression model, to gain a comprehensive understanding of the impact of each of the saving options. The potential savings are expressed in condition based lowering of the thermostat settings inside individ- ual dwellings. The relative shift method calculates the potential saving by relating gas consumption to the difference between in and outside temperature. Heat demand calculations are based upon the number of non-active heating hours for each of the saving options. In the regression method, a linear regression model for every individual household is build, to estimate the gas consumption on the basis of the difference be- tween the in and outside temperature.More than half of the households in the sample group heats their home during daytime while the occupancy of these dwellings is detected at around 50%. The other half of the population has a clear heating pattern ofiii morning and evening heating. For each of the detected thermostat practices, adjusting thermostat settings result in a potential gas saving. There is a factor 2.5 difference in potential saving between the different ther- mostat practices. Residents are able to save from 2% to 5% depending on their thermostat practice, resulting in an overall average saving of 34 euro per year. The largest saving potential of lowering the thermostat to 15 degrees overnight. Thermostat practices of households have a bigger impact on the gas consumption than currently used household characteristics. Natural gas consumption of households with similar thermostat practices have shown disparate consumption due to dwelling specific characteristics. Household specifics as thermostat practices and thermal inertia have shown to impact natural gas consumption. The detected ther- mal inertia of dwellings with the use of disaggregated consumption data is not in line with estimated values based upon physical characteristics of dwellings. The potential savings based upon physical characteristics of dwellings over estimates the potential saving in the residential sector. The impact of thermostat practices and thermal inertia of dwellings determined with the use of disaggregated consumption data is substantial. Cur- rent energy consumption studies and energy labelling are based upon physical characteristics of dwellings. The mismatch between estimated energy consumption and measured consumption indicates that estimat- ing residential gas consumption for individual households on the basis household characteristics alone is redundant. By including actual residential consumption data in energy labelling and shifting policy towards adapting household behaviour future policy measures can be improved.The overall potential savings in the residential sector by adjusting thermostat settings are relatively small but can help to reach the climate goals. The results are based upon occupancy detection in a dwelling by analysing dis-aggregated consumption data. By improving the occupancy detection, the confidence in the results and number of included households can be improved. Expanding the size and representativeness of the sample group improves the applicability and confidence in the results. The personal preferences of residents in both the adoption adjustments and the actual impact on their thermal comfort is unknown. Fu- ture research is needed in order to close the gap between actual savings and potential savings by thermostat adjustments and to determine the impact on thermal comfort
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