1,355 research outputs found

    Integrating expert-based objectivist and nonexpert-based subjectivist paradigms in landscape assessment

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    This thesis explores the integration of objective and subjective measures of landscape aesthetics, particularly focusing on crowdsourced geo-information. It addresses the increasing importance of considering public perceptions in national landscape governance, in line with the European Landscape Convention's emphasis on public involvement. Despite this, national landscape assessments often remain expert-centric and top-down, facing challenges in resource constraints and limited public engagement. The thesis leverages Web 2.0 technologies and crowdsourced geographic information, examining correlations between expert-based metrics of landscape quality and public perceptions. The Scenic-Or-Not initiative for Great Britain, GIS-based Wildness spatial layers, and LANDMAP dataset for Wales serve as key datasets for analysis. The research investigates the relationships between objective measures of landscape wildness quality and subjective measures of aesthetics. Multiscale geographically weighted regression (MGWR) reveals significant correlations, with different wildness components exhibiting varying degrees of association. The study suggests the feasibility of incorporating wildness and scenicness measures into formal landscape aesthetic assessments. Comparing expert and public perceptions, the research identifies preferences for water-related landforms and variations in upland and lowland typologies. The study emphasizes the agreement between experts and non-experts on extreme scenic perceptions but notes discrepancies in mid-spectrum landscapes. To overcome limitations in systematic landscape evaluations, an integrative approach is proposed. Utilizing XGBoost models, the research predicts spatial patterns of landscape aesthetics across Great Britain, based on the Scenic-Or-Not initiatives, Wildness spatial layers, and LANDMAP data. The models achieve comparable accuracy to traditional statistical models, offering insights for Landscape Character Assessment practices and policy decisions. While acknowledging data limitations and biases in crowdsourcing, the thesis discusses the necessity of an aggregation strategy to manage computational challenges. Methodological considerations include addressing the modifiable areal unit problem (MAUP) associated with aggregating point-based observations. The thesis comprises three studies published or submitted for publication, each contributing to the understanding of the relationship between objective and subjective measures of landscape aesthetics. The concluding chapter discusses the limitations of data and methods, providing a comprehensive overview of the research

    Essays on Corporate Disclosure of Value Creation

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    Information on a firm’s business model helps investors understand an entity’s resource requirements, priorities for action, and prospects (FASB, 2001, pp. 14-15; IASB, 2010, p. 12). Disclosures of strategy and business model (SBM) are therefore considered a central element of effective annual report commentary (Guillaume, 2018; IIRC, 2011). By applying natural language processing techniques, I explore what SBM disclosures look like when management are pressed to say something, analyse determinants of cross-sectional variation in SBM reporting properties, and assess whether and how managers respond to regulatory interventions seeking to promote SBM annual report commentary. This dissertation contains three main chapters. Chapter 2 presents a systematic review of the academic literature on non-financial reporting and the emerging literature on SBM reporting. Here, I also introduce my institutional setting. Chapter 3 and Chapter 4 form the empirical sections of this thesis. In Chapter 3, I construct the first large sample corpus of SBM annual report commentary and provide the first systematic analysis of the properties of such disclosures. My topic modelling analysis rejects the hypothesis that such disclosure is merely padding; instead finding themes align with popular strategy frameworks and management tailor the mix of SBM topics to reflect their unique approach to value creation. However, SBM commentary is less specific, less precise about time horizon (short- and long-term), and less balanced (more positive) in tone relative to general management commentary. My findings suggest symbolic compliance and legitimisation characterize the typical annual report discussion of SBM. Further analysis identifies proprietary cost considerations and obfuscation incentives as key determinants of symbolic reporting. In Chapter 4, I seek evidence on how managers respond to regulatory mandates by adapting the properties of disclosure and investigate whether the form of the mandate matters. Using a differences-in-differences research design, my results suggest a modest incremental response by treatment firms to the introduction of a comply or explain provision to provide disclosure on strategy and business model. In contrast, I find a substantial response to enacting the same requirements in law. My analysis provides clear and consistent evidence that treatment firms incrementally increase the volume of SBM disclosure, improve coverage across a broad range of topics as well as providing commentary with greater focus on the long term. My results point to substantial changes in SBM reporting properties following regulatory mandates, but the form of the mandate does matter. Overall, this dissertation contributes to the accounting literature by examining how firms discuss a central topic to economic decision making in annual reports and how firms respond to different forms of disclosure mandate. Furthermore, the results of my analysis are likely to be of value for regulators and policymakers currently reviewing or considering mandating disclosure requirements. By examining how companies adapt their reporting to different types of regulations, this study provides an empirical basis for recalibrating SBM disclosure mandates, thereby enhancing the information set of capital market participants and promoting stakeholder engagement in a landscape increasingly shaped by non-financial information

    Revisiting the capitalization of public transport accessibility into residential land value: an empirical analysis drawing on Open Science

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    Background: The delivery and effective operation of public transport is fundamental for a for a transition to low-carbon emission transport systems’. However, many cities face budgetary challenges in providing and operating this type of infrastructure. Land value capture (LVC) instruments, aimed at recovering all or part of the land value uplifts triggered by actions other than the landowner, can alleviate some of this pressure. A key element of LVC lies in the increment in land value associated with a particular public action. Urban economic theory supports this idea and considers accessibility to be a core element for determining residential land value. Although the empirical literature assessing the relationship between land value increments and public transport infrastructure is vast, it often assumes homogeneous benefits and, therefore, overlooks relevant elements of accessibility. Advancements in the accessibility concept in the context of Open Science can ease the relaxation of such assumptions. Methods: This thesis draws on the case of Greater Mexico City between 2009 and 2019. It focuses on the effects of the main public transport network (MPTN) which is organised in seven temporal stages according to its expansion phases. The analysis incorporates location based accessibility measures to employment opportunities in order to assess the benefits of public transport infrastructure. It does so by making extensive use of the open-source software OpenTripPlanner for public transport route modelling (≈ 2.1 billion origin-destination routes). Potential capitalizations are assessed according to the hedonic framework. The property value data includes individual administrative mortgage records collected by the Federal Mortgage Society (≈ 800,000). The hedonic function is estimated using a variety of approaches, i.e. linear models, nonlinear models, multilevel models, and spatial multilevel models. These are estimated by the maximum likelihood and Bayesian methods. The study also examines possible spatial aggregation bias using alternative spatial aggregation schemes according to the modifiable areal unit problem (MAUP) literature. Results: The accessibility models across the various temporal stages evidence the spatial heterogeneity shaped by the MPTN in combination with land use and the individual perception of residents. This highlights the need to transition from measures that focus on the characteristics of transport infrastructure to comprehensive accessibility measures which reflect such heterogeneity. The estimated hedonic function suggests a robust, positive, and significant relationship between MPTN accessibility and residential land value in all the modelling frameworks in the presence of a variety of controls. The residential land value increases between 3.6% and 5.7% for one additional standard deviation in MPTN accessibility to employment in the final set of models. The total willingness to pay (TWTP) is considerable, ranging from 0.7 to 1.5 times the equivalent of the capital costs of the bus rapid transit Line-7 of the Metrobús system. A sensitivity analysis shows that the hedonic model estimation is sensitive to the MAUP. In addition, the use of a post code zoning scheme produces the closest results compared to the smallest spatial analytical scheme (0.5 km hexagonal grid). Conclusion: The present thesis advances the discussion on the capitalization of public transport on residential land value by adopting recent contributions from the Open Science framework. Empirically, it fills a knowledge gap given the lack of literature around this topic in this area of study. In terms of policy, the findings support LVC as a mechanism of considerable potential. Regarding fee-based LVC instruments, there are fairness issues in relation to the distribution of charges or exactions to households that could be addressed using location based measures. Furthermore, the approach developed for this analysis serves as valuable guidance for identifying sites with large potential for the implementation of development based instruments, for instance land readjustments or the sale/lease of additional development rights

    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

    Learning, future cost and role of offshore renewable energy technologies in the North Sea energy system

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    The pace of cost decline of offshore renewable energy technologies significantly impacts their role in the North Sea energy transition. However, a good understanding of their remains a critical knowledge gap in the literature. Therefore, this thesis aims to quantify the future role of offshore renewables in the North Sea energy transition and assess the impact of cost development on their optimal deployments. The following findings were observed in this thesis, 1) Fixed-bottom offshore wind is well established in the North Sea region and is already competitive with onshore renewables 2) Floating wind is emerging and their current costs are high, but it can reach about 40 EUR/MWh by early 2040 and would require 44 billion EUR of learning investment.3) Grid connection costs will become a major factor as wind farm moves further away. Policy actions and innovation is needed in this space to avoid increasing integration costs. 4) Offshore wind (fixed-bottom and floating) can play a significant role in the North Sea energy system, comprising 498 GW of deployments in 2050 (222 GW of fixed-bottom and 276 GW of floating wind) and contributing up to a maximum of 51% of total power generation in the North Sea power system. 5) The role of the investigated low-TRL offshore renewables, including the tidal stream, wave technology, and bioethanol, was limited in all scenarios considered, as they remain expensive compared to other mature technologies in the system

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Machine learning techniques for sensor-based household activity recognition and forecasting

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    Thanks to the recent development of cheap and unobtrusive smart-home sensors, ambient assisted living tools promise to offer innovative solutions to support the users in carrying out their everyday activities in a smoother and more sustainable way. To be effective, these solutions need to constantly monitor and forecast the activities of daily living carried out by the inhabitants. The Machine Learning field has seen significant advancements in the development of new techniques, especially regarding deep learning algorithms. Such techniques can be successfully applied to household activity signal data to benefit the user in several applications. This thesis therefore aims to produce a contribution that artificial intelligence can make in the field of activity recognition and energy consumption. The effective recognition of common actions or the use of high-consumption appliances would lead to user profiling, thus enabling the optimisation of energy consumption in favour of the user himself or the energy community in general. Avoiding wasting electricity and optimising its consumption is one of the main objectives of the community. This work is therefore intended as a forerunner for future studies that will allow, through the results in this thesis, the creation of increasingly intelligent systems capable of making the best use of the user's resources for everyday life actions. Namely, this thesis focuses on signals from sensors installed in a house: data from position sensors, door sensors, smartphones or smart meters, and investigates the use of advanced machine learning algorithms to recognize and forecast inhabitant activities, including the use of appliances and the power consumption. The thesis is structured into four main chapters, each of which represents a contribution regarding Machine Learning or Deep Learning techniques for addressing challenges related to the aforementioned data from different sources. The first contribution highlights the importance of exploiting dimensionality reduction techniques that can simplify a Machine Learning model and increase its efficiency by identifying and retaining only the most informative and predictive features for activity recognition. In more detail, it is presented an extensive experimental study involving several feature selection algorithms and multiple Human Activity Recognition benchmarks containing mobile sensor data. In the second contribution, we propose a machine learning approach to forecast future energy consumption considering not only past consumption data, but also context data such as inhabitants’ actions and activities, use of household appliances, interaction with furniture and doors, and environmental data. We performed an experimental evaluation with real-world data acquired in an instrumented environment from a large user group. Finally, the last two contributions address the Non-Intrusive-Load-Monitoring problem. In one case, the aim is to identify the operating state (on/off) and the precise energy consumption of individual electrical loads, considering only the aggregate consumption of these loads as input. We use a Deep Learning method to disaggregate the low-frequency energy signal generated directly by the new generation smart meters being deployed in Italy, without the need for additional specific hardware. In the other case, driven by the need to build intelligent non-intrusive algorithms for disaggregating electrical signals, the work aims to recognize which appliance is activated by analyzing energy measurements and classifying appliances through Machine Learning techniques. Namely, we present a new way of approaching the problem by unifying Single Label (single active appliance recognition) and Multi Label (multiple active appliance recognition) learning paradigms. This combined approach, supplemented with an event detector, which suggests the instants of activation, would allow the development of an end-to-end NILM approach

    The Role of Spatial Scale in Electricity System Optimisation Models

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    To investigate possible pathways to reduce greenhouse gas emissions in the electricity sector, researchers build optimisation models that typically minimise the total system costs such that all technical and physical constraints are met. For systems based on renewable energy, whose greatest expansion potentials are found for wind and solar generation, the chief challenge is dealing with their variability. To tackle this challenge, the optimisation models typically include large transmission networks to smooth renewable feed-in in space or storage technologies to smooth the variability in time. However, all aspects of the energy system at all levels of detail cannot currently be contained in a single model because of computational constraints. Instead, one must make simplifications and compromises that affect the optimality of the result from the point of view of the complete system. While reductions on the temporal scale and linearisation approaches of the model formulation have been previously analysed, in this thesis we focus on the quantification of the impact of the spatial scale. This is important because it is scientific practice to simplify models spatially while only little is known on the error made by the aggregation. The contents of this dissertations spatial scale analysis are three-fold and build upon one another: (i) A novel clustering methodology enables us to disentangle and quantify the error that is made by spatially aggregating generation sites where renewable electricity can be sourced versus the error made by aggregating transmission lines and, thus, electricity interactions between spatially distributed substations. By clustering the network on both features in tandem, we can verify the results and learn which of these two effects dominates the optimisation. (ii) Insights from (i) are used to improve existing spatial aggregation methods and to develop novel similarity measures to be applied for clustering electricity system models such that the spatially simplified model can better approximate the original, highly-resolved model with respect to renewable generation sites and the transmission grid. (iii) The prevailing best clustering method is applied on optimisation models with high shares of renewable generation to investigate if the spatially clustered low-resolved solutions are feasible with regard to the full, spatially highly-resolved model. To this end we propose novel inverse methods to spatially disaggregate the coarse optimisation solution in terms of the resulting, aggregated variables across the highly dimensioned model

    A high-resolution geospatial and socio-technical methodology for assessing the impact of electrified heat and transport on distribution network infrastructure

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    There is an increasing need to decarbonise both heating and transport sectors in the UK, and the uptake of low carbon technologies (LCTs) will be central to this. The impact of LCTs on electricity network infrastructure varies both spatially and temporally, and is driven by the diversity in technology type, consumer behaviour, variable weather patterns, variation of the building stock and the incumbent network assets. In recognition of this diversity and household energy variability, LCT adoption and utilisation will be influenced by the distribution of socio-economic factors within a local area. This has the potential to impact network decision-making across different regions. As such, there is a requirement to consider socio-technical and socio-spatial dimensions when modelling LCT impact on network infrastructure. This research, presented within a UK context, demonstrates a novel high-resolution methodology that enables assessment of electrified heat and transport impact on transformer headroom using socio-economic indicators to inform the application of LCT consumption. This includes mapping of spatially linked datasets to identify relationships between consumption and social deprivation. These relationships are used as inputs to a heat pump modelling methodology that converts gas demand to equivalent electrical heat demand. This approach is compared with a generalised trial data approach to ascertain the impact of incorporating socio-economic elements. Electric vehicles are then introduced, where charging is based on socially disaggregated behaviour in the form of travel diaries showing the combined impact of different LCTs. Findings are considered from the perspective of the distribution network operator and other key stakeholders
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