Calls for transformative change and participatory modes of knowledge production demand researchers to assume new roles. This paper synthesizes the literature on knowledge co-production and researcher roles to explore challenges for researchers involved in transdisciplinary environmental management projects. Our research methods combine a scoping review and reflections on personal experiences with three transdisciplinary projects. To conceptualize researcher roles in transdisciplinary knowledge co-production, we distinguish between three spaces: knowledge, formal policy, and stakeholder. Knowledge co-production requires collaboration between actors from different spaces and integration of diverse knowledge sources and types. Depending on whether researchers adopt knowledge-oriented, change-oriented or intermediating roles, they will experience different challenges. When researchers combine knowledge development with change-oriented and/or intermediating roles, they encounter new challenges, such as, maintaining independence or objectivity. To assist researchers in transdisciplinary projects, we conclude with a checklist of four elements to reflect upon: orientation, norms and values, expectations and resources.
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This paper examines a co-production arrangement between private actors, households, and community actors occurring within the framework of scheme of commercialised spring water in peri-urban Bandung, Indonesia. We argue that the provision of spring water in Ujungberung District is a form of co-production, characterised by: (1) any one, or the elements, of the service production process being shared; (2) the presence of a fundamental shift in the balance of power between the primary producers and users/communities, and (3) the existence of mutual support and relationship networks, rather than a clearly defined delineation between providers and clients. Actor contributions defined as inputs along the value chain of spring water production were examined. We describe interactions between local private actors and community members in planning, service delivery, and conflict management with respect to disruption of water supplies, free-riding behaviour, and the geographical distribution of services. This paper identifies several institutional innovations that may yield a safer and more affordable water supply and nurture equity in the sense of: (1) improved access to water for the previously unserved people by piped water and boreholes; (2) the opportunity to negotiate from below; and (3) transparency and accountability.
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ClimateCafé is a multi-, trans-, interdisciplinary and international event of several days in which young professionals, stakeholders and scientist come together to collect data and design (potential) solutions for climate change adaptation in rural or urban areas. ClimateCafé mainly aims to enhance resilience and reduce vulnerability of communities by sharing knowledge, raising awareness and building capacity. ClimateCafé addresses global issues, such as climate change and sustainable development, on a local scale. In a ClimateCafé, context specific challenges, related to climate change and sustainable development, are proposed by local stakeholders and often relate to a specific problem area. Over the past decade, more than 28 ClimateCafés have been organised around the globe, including the Netherlands-Rotterdam, Sweden-Malmö, the Philippines-Manila, and Peru-Pirua. Since the first edition in Thailand (2012), ClimateCafé evolved in content and adopted a ‘learning by doing’ paradigm. Our results indicate ClimateCafé fosters integrated thinking across disciplines, cultures and knowledge sectors while reducing uncertainties affiliated with climate change adaptation. This is demonstrated by participants of previous ClimateCafés having various backgrounds including: law, civil engineering, water management, art, urban planning and environmental engineering. Furthermore, co-production is composed through workshops facilitated by stakeholders of the, so called, quadruple helix including academia, government, civic society and industry. Workshops make use of scientifically embedded methods, always related to the contextual challenge. For example, urban heat stress is measured by sensors on a bike and collecting urban green with online platform ClimateScan, community perceptions are collected through interviews, water quality is measured with the use of drones and perceptions and responsibilities of institutional actors are identified by interviews and field visits. Additionally, data is processed and design workshops facilitate integrated design of potential solutions which is disseminated through participants presenting their findings at conferences. Although ClimateCafé is resource intensive, requires active participation of stakeholders and currently mainly attracts students of affiliated universities, we argue this multi-, inter-, transdisciplinary and international knowledge exchange methodology fosters the innovation that is dearly needed to address global sustainability challenges and climate change adaptation.
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This open access book is a valuable resource for students in health and other professions and practicing professionals interested in supporting effective change in self-management behaviors in chronic disease, such as medication taking, physical activity and healthy eating. Developed under the auspices of the Train4Health project, funded by the Erasmus+ program of the European Union, the book contains six chapters written by international contributors from different disciplines. This chapter presents open-access educational products that supplement this book: case studies and a web application to simulate behaviour change support in persons with chronic disease. The former is of particular interest for academic educators, while the latter may interest students independently pursuing training outside the classroom. These products can also be useful for professionals aiming to enhance behaviour change competencies in practice. First, it addresses key aspects of product development, including hallmarks such as the incorporation of behaviour change science and transnational co-production with users. Then, the main features of case studies and the web application with 2D virtual humans are described.
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Eco-innovations that reduce carbon emissions help advance sustainability transitions in tourism. This article examines the analytical potential of actor-network theory (ANT) to study eco-innovation. ANT assumes that reality consists of actor-networks made of human and non-human elements that perform actors as network effects. We argue that, in a time when climate change is the simultaneous product and producer of human actions, eco-innovation is better understood when research gives the human and non-human elements that perform eco-innovations equal analytical treatment. We therefore develop an ANT-inspired framework, which we apply in a case study to investigate the development of a specific eco-innovation: CARMACAL, a web-based carbon management application in the Dutch travel industry. We find that technological novelty alone is insufficient to instigate transition. CARMACAL affords multiple new practices with opposite implications for socio-economic and environmental sustainability. The practices triggering most industry support are least effective in addressing tourism's climate impacts and vice versa. Examining eco-innovation through ANT helps us put eco-innovation in a different light. Seemingly contradictory practices may be mutually supportive: their individual strengths and weaknesses may help prevent the failure of eco-innovations. This new possibility opens the way for concerted policies strengthening the contribution of eco-innovations to sustainability transitions.
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Within this paper, biomass supply chains, with different shares of biomass co-combustion in coal fired power plants, are analysed on energy efficiency, energy consumption, renewable energy production, and greenhouse gas (GHG) emissions and compared with the performance of a 100% coal supply chain scenario, for a Dutch situation. The 60% biomass co-combustion supply chain scenarios show possibilities to reduce emissions up to 48%. The low co-combustion levels are effective to reduce GHG emissions, but the margins are small. Currently co-combustion of pellets is the norm. Co-combustion of combined torrefaction and pelleting (TOP) shows the best results, but is also the most speculative. The indicators from the renewable energy directive cannot be aligned. When biomass is regarded as scarce, co-combustion of small shares or no co-combustion is the best option from an energy perspective. When biomass is regarded as abundant, co-combustion of large shares is the best option from a GHG reduction perspective.
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Purpose: This case study is presented to inform the reader of potential speech, language, cognitive, and emotional characteristics in preadolescent cluttering. Method: This case study describes a 10-year-old boy who started to clutter during preadolescence. The case illustrates that, in some adolescents, cluttering can co-occur with temporary stuttering-like behavior. In this case, signs of disturbances in speech-language production associated with behavioral impulsiveness as a young child were noted. Speech, language, cognitive, and emotional results of the case are reported in detail. Results: The changes in fluency development are reported and discussed within the context of changes in the adolescent brain as well as adolescent cognitive and emotional development. While being unaware of their speech condition before adolescence, during preadolescence, the changes in brain organization lead to an increase in rate and a decrease in speech control. Given that the client had limited understanding of what was occurring, they were at risk of developing negative communication attitudes. Speech-language therapists are strongly advised to monitor children with cluttering signals in the early years of their adolescence.
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Biogas plays an important role in many future renewable energy scenarios as a source of storable and easily extracted form of renewable energy. However, there remains uncertainty as to which sources of biomass can provide a net energy gain while being harvested in a sustainable, ecologically friendly manner. This study will focus on the utilization of common, naturally occurring grass species which are cut during landscape management and typically treated as a waste stream. This waste grass can be valorized through co-digestion with cow manure in a biogas production process. Through the construction of a biogas production model based on the methodology proposed by (Pierie, Moll, van Gemert, & Benders, 2012), a life cycle analysis (LCA) has been performed which determines the impacts and viability of using common grass in a digester to produce biogas. This model performs a material and energy flow analysis (MEFA) on the biogas production process and tracks several system indicators (or impact factors), including the process energy return on energy investment ((P)EROI), the ecological impact (measured in Eco Points), and the global warming potential (GWP, measured in terms of kg of CO2 equivalent). A case study was performed for the village of Hoogkerk in the north-east Netherlands, to determine the viability of producing a portion of the village’s energy requirements by biogas production using biomass waste streams (i.e. common grass and cow manure in a co-digestion process). This study concludes that biogas production from common grass can be an effective and sustainable source of energy, while reducing greenhouse gas emissions and negative environmental impacts when compared to alternate methods of energy production, such as biogas produced from maize and natural gas production.
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Glycerol is an attractive bio-based platform chemical that can be converted to a variety of bio-based chemicals. We here report a catalytic co-conversion strategy where glycerol in combination with a second (bio-)feed (fatty acids, alcohols, alkanes) is used for the production of bio-based aromatics (BTX). Experiments were performed in a fixed bed reactor (10 g catalyst loading and WHSV of (co-)feed of 1 h-1) at 550 °C using a technical H-ZSM-5/Al2O3 catalyst. Synergistic effects of the co-feeding on the peak BTX carbon yield, product selectivity, total BTX productivity, catalyst life-time, and catalyst regenerability were observed and quantified. Best results were obtained for the co-conversion of glycerol and oleic acid (45/55 wt%), showing a peak BTX carbon yield of 26.7 C%. The distribution of C and H of the individual co-feeds in the BTX product was investigated using an integrated fast pyrolysis-GC-Orbitrap MS unit, showing that the aromatics are formed from both glycerol and the co-feed. The results of this study may be used to develop optimized co-feeding strategies for BTX formation. This journal is
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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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