Korte promotievideo voor het doen van een afstudeeronderzoek of stage op de Klimaatproeftuin op BuildinG. Daarin vertellen studenten van Built Environment over hun onderzoeken die ze daar gedaan hebben.
YOUTUBE
The Northern Netherlands is like many delta’s prone to a wide range of climate change effects. Given the region its long history with floods and adaptation, there are numerous initiatives to be found that tried to battle these effects. As part of the Climate Adaptation Week Groningen, an inventory was made of these initiatives. The most inspiring ones were coined ‘best practices’, and analysed in order to learn lessons. A distinction was made between 4 regional landscape types. The first consists of the coastline itself, where the effects of the rising sea level begin to show. The second covers the farmlands near the coastlines, where challenges such as salinisation and the loss of biodiversity prevail. A third landscape covers the historically compact cities, which have to deal with rising temperatures and heavy rainfall in increasingly limited spaces. The fourth and final landscape comprises the wetlands surrounding the cities, where the natural capacity to retain and store rainwater is undermined by its agriculture usage. Most of these challenges form a risk for maintaining a liveable region. The best practices that were collected show a diverse set of innovations and experiments, both on small and large scales. Three main characteristics could be distinguished that illustrate trends in climate adaptation practices. First, many best practices were aimed at restoring and embracing the natural capacity of the different landscapes, giving more and more room for the building with nature concept as part of building resilience. Second, climate adaptation is seldomly focussed on as the sole function of a spatial intervention, and is almost always part of integrated plans in which biodiversity, agriculture, recreation or other themes are prolonged with it. A third and last characteristic shows that many projects embed a strong focus on the historical context of places as a source of inspiration and cultural identity. The best practices show how different ways of adapting are emerging and can inspire planners across the world.
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Effects of climate change in cities are evident and are expected to increase in the future, demanding adaptation. In order to share knowledge, raise awareness, and build capacity on climate adaptation, the first concept of a “ClimateCafé” has been utilized since 2012 in 25 events all over the world. In 8 years ClimateCafé grew into a field education concept involving different fields of science and practice for capacity building in climate change adaptation. This chapter describes the need, method, and results of ClimateCafés and provides tools for organizing a ClimateCafé in a context-specific case. Early ClimateCafés in the Philippines are compared with the ClimateCafé in Peru to elucidate the development of this movement, in which one of the participants of ClimateCafé Philippines 2016 became the co-organizer of ClimateCafé Peru in 2019. The described progress of ClimateCafés provides detailed information on the dynamic methodological aspects, holding different workshops. The workshops aim at generating context-specific data on climate adaptation by using tools and innovative data collection techniques addressing deep uncertainties that come with climate change adaptation. Results of the workshops show that context-specific, relevant, multidisciplinary data can be gathered in a short period of time with limited resources, which promotes the generation of ideas that can be used by local stakeholders in their local context. A ClimateCafé therefore stimulates accelerated climate action and support for adaptation solutions, from the international and the local, from the public and private sector, to ensure we learn from each other and work together for a climate resilient future. The methodology of ClimateCafé is still maturing and the evaluation of the ClimateCafés over time leads to improvements which are applied during upcoming ClimateCafés, giving a clear direction for further development of this methodology for knowledge exchange, capacity building, and bridging the gap between disciplines within climate adaptation.
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''Heritage buildings are often subjected to loading conditions that they were not exposed to in their earlier life span. Induced earthquakes in non-seismic regions caused by energy exploitation activities, or strains in the ground that are caused by the climate changes, are new phenomena that alter the usual loading situations for historical buildings.In this paper, monitoring results of a historical building in Groningen (Netherlands) in case of induced seismicity as well as climate change effects has been presented. Long-term monitoring results, detected cracks and relevance of the monitoring data are discussed. In the special case of Groningen, weak and agricultural soil properties dominate the structural response in the region. The gas extraction activities caused a soil subsidence in the giant Groningen Gas Field, resulting decameters of settlement in the entire area, thus an increase of the ground water level in respect to the ground surface. This is the reason why the heritage structures in the region are more vulnerable to soil-water-foundation interactions caused by climate change as compared to the time these heritage structures were constructed. The ground water monitoring as well as the interaction of soil movements with the structural response become important. The study presented here suggests ways on how to effectively monitor historical structures subjected to induced seismicity as well as harsh climate effects at the same time.It was shown here that the newly developed cracks on the structure were detected in a very narrow time window, coinciding with extreme drought and a small induced earthquake at the same time. One explanation provided here is that the soil parameters, such as shrinking of water-sensitive soil layers, in combination with small earthquakes, may cause settlements. The soil effects may superimpose with the earthquake effects eventually causing small cracks and damage. The effects of the climate change on historical buildings is rather serious, and structures on similar soil conditions around the world would need detailed monitoring of not only the structure itself but also the soil-foundation and ground water conditions.''
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There is a clear demand for a collaborative knowledge-sharing by online climate adaptation platforms that contribute to (inter)national knowledge exchange and raising awareness about climate change. Climate adaptation platforms (CAPs) can contain decision-support tools to facilitate the process of decision-making, and may include capacity building, networking, dissemination to assist planning and implementation of proven adaptation concepts such as Nature-based solutions (NBS) to mitigate floodings, drought, and heat stress. From 2014 over 6000 global climate adaptation projects have been mapped on an open source platform ClimateScan using citizen science. This chapter describes the potential of this climate adaption platform by illustrated case studies with mapped climate adaptation measures in Africa, Asia, and Europe. The case studies illustrate engagement and tangible results related to online platforms such as: the period of relevance of ClimateScan, inclusiveness and engagement of users in different stages and continents. Workshops in Indonesia illustrate the need for validation of needs from potential users before implementing CAPs. Analyzing projects in Africa showcase best management practices in water conservation and water demand management that are of interest in many other regions in the world facing drought. In Europe detailed analysis of over 3000 climate adaptation measures in relation to neighborhood typologies is inspiring urban planners and stormwater managers to design, plan, and implement climate resilient measures with more confidence. These three global examples illustrate that mapping, promoting, and sharing knowledge about implemented proven concepts is raising awareness, contribute to community-building, and accelerate climate action around the world.
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ClimateCafé is a field education concept involving dierent fields of science and practice for capacity building in climate change adaptation. This concept is applied on the eco-city of Augustenborg in Malmö, Sweden, where Nature-Based Solutions (NBS) were implemented in 1998.ClimateCafé Malmö evaluated these NBS with 20 young professionals from nine nationalities and seven disciplines with a variety of practical tools. In two days, 175 NBS were mapped and categorised in Malmö. Results show that the selected green infrastructure have a satisfactory infiltration capacity and low values of potential toxic element pollutants after 20 years in operation. The question “Is capacity building achieved by interdisciplinary field experience related to climate change adaptation?” was answered by interviews, collecting data of water quality, pollution, NBS and heat stress mapping, and measuring infiltration rates, followed by discussion. The interdisciplinary workshops with practical tools provide a tangible value to the participants and are needed to advance sustainabilityeorts. Long term lessons learnt from Augustenborg will help stormwater managers within planning of NBS. Lessons learned from this ClimateCafé will improve capacity building on climate change adaptation in the future. This paper oers a method and results to prove the German philosopher Friedrich Hegel wrong when he opined that “we learn from history that we do not learn from history”
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This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
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Virtual communities are online spaces with potential of integration of (member-generated) content and conversations [7,8]. In our research project we are interested in the adoption and building of virtual communities in organized sports, that is to say in the voluntary sports clubs (VSCs) in the Netherlands. Since these VSCs have massively transferred their communication with members from paper club magazines to online channels, these virtual communities arise from the use of a growing number of websites, e-mail and social network sites (SNSs). Although virtual communities are broadly investigated, such as social communities, brand communities, and public communities, there is little scholarly interest in virtual communities of member organizations that VSCs are an example of. The study that is to be presented at SECSI 2019 concerns the clubs’ use of SNSs (ClubSNSs), such as Facebook and Twitter, within the virtual communities. These SNSs are increasingly used by the VSCs to facilitate organizational communication and to obtain a good internal climate [9]. However, academic understanding of the impact of ClubSNSs’ content and conversations on the organizational performance of the VSC is in its infancy. In our study, we examined this impact of ClubSNSs use on the involvement among members and whether we can explain this by members’ identification with the club. Furthermore, we have tried to categorize ClubSNSs by content types, such as informative, conversational or sociable ClubSNSs, and their role in stimulating the use of ClubSNSs. In this way we attempted to gain insight into the effect of types of ClubSNSs’ content and conversations on membership involvement and the mediating role of identification with the club. This insight can help VSCs to develop effective ClubSNS channels that contribute to organizational goals such as supportive and loyal membership.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Climate change is now considered more than just an environmental issue, with far-reaching effects for society at large. While the exact implications of climate change for policing practice are still unknown, over the past two decades criminologists have anticipated that climate change will have a number of effects that will result in compromised safety and security. This article is informed by the outcome of a co-creation workshop with 16 practitioners and scholars of diverse backgrounds based in The Netherlands, who sought to conceptualize and systematize the existing knowledge on how climate change will most likely impact the professional practice of the Dutch (or any other) police. These challenges, with varying degrees of intensity, are observable at three main levels: the societal, organizational, and individual level. These levels cannot be separated neatly in practice but we use them as a structuring device, and to illustrate how dynamics on one level impact the others. This article aims to establish the precepts necessary to consider when exploring the intersection between climate change and policing. We conclude that much still needs to be done to ensure that the implications of climate change and the subject of policing are better aligned, and that climate change is recognized as an immediate challenge experienced on the ground and not treated as a distant, intangible phenomenon with possible future impacts. This starts with creating awareness about the possible ways in which it is already impacting the functioning of policing organizations, as well as their longer-term repercussions.
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