Lebanon’s economic crisis has disrupted the country’s energy and water sectors, highlighting their interdependence. The methodologyinvolves surveying 150 municipalities across all Lebanese governorates, ensuring a comprehensive coverage of public and private waterresources. Data on water and energy were collected before and during the crisis to explore this nexus during periods of economic turmoil.The findings reveal a decline in water provision during the crisis, with the average weekly water supply plummeting from 49 h in 2019 to 22 hin 2023. Concurrently, the use of water tankers has surged from 26 to 44%, indicating a concerning shift in water acquisition methods.Despite the crisis, conventional water sources remain predominant, while unconventional sources account for less than 1% of the totalsupply. In response to the energy shortage, renewable energy sources have gained traction in residential, commercial, and industrial sectors.The scarcity and rising cost of electricity have driven the adoption of solar photovoltaics in the water sector, reaching 4.8% for extraction fromunderground reservoirs and 2.8% for distribution. Similarly, the use of solar water heaters has increased from 7.9 to 15.4% in 2023. Thesefindings underscore the interplay between energy and water security during periods of economic instability.
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Urban communities are particularly vulnerable to the future demand for food, energy and water, and this vulnerability is further exacerbated by the onset of climate change at local. Solutions need to be found in urban spaces. This article based around urban design practice sees urban agriculture as a key facilitator of nexus thinking, needing water and energy to be productive. Working directly with Urban Living Labs, the project team will co-design new food futures through the moveable nexus, a participatory design support platform to mobilize natural and social resources by integrating multi-disciplinary knowledge and technology. The moveable nexus is co-developed incrementally through a series of design workshops moving around living labs with the engagement of stakeholders. The methodology and the platform will be shared outside the teams so that the knowledge can be mobilized locally and globally.
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The importance of water and energy accessibility and use has become more important as new insight into their role for sustainable development goals has become mainstream. The inclusion of water and energy in strategic decision-making is thus key. Supply chain network design (SCND) in the food industry is an interesting case study for the incorporation of water and energy utilization during the design process of global production systems. In the current green SCND research, frequently, single indicators are used such as carbon emissions to measure environmental impact. This paper presents a case study applied to an orange juice supply chain, formulated as a multi-objective optimization model. A single environmental impact indicator optimization approach is paired against one that includes water and energy use explicitly in the objective function set. Mixed conclusions are shown from the results pairing the two strategies side by side.
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This essay is a contribution to the research project ‘From Prevention to Resilience’ funded by ZonMw. Motivated by the Covid-19 pandemic, this research project explored how public space and forms of civic engagement can contribute to working towards more resilient urban neighborhoods. The project engaged a community of practice (CoP) to inform the research and to disseminate and critically discuss research outcomes. This essay, and the bundle it is part of, is the outcome of one of these engagements. The authors of this specific essay were asked to offer their disciplinary perspective on a first version of the Human / Non-Human Public Spaces design perspective, at that time still titled Nexus Framework on Neighborhood Resilience (click here and a PDF of this version will be downloaded). The authors were asked to do so based on their field of expertise, being climate-resilient cities. The authors have written this essay in coordination with the research team. To grasp the content of this essay and to take lessons from it, we encourage readers to first get familiar with the first version of the design perspective.
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We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.
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The design of cities has long ignored the flows that shape the city. Water has been the most visible one, but energy and materials were invisible and/or taken for granted. A little over 50 years ago, Abel Wolman was the first to illuminate the role of water flows in the urban fabric. It has long been a search for quantitative data while the flows were mostly seen as separated entities. The fact they invisibly formed the way the city appears has been neglected for many years. In this thematic issue the “city of flows” is seen as a design task. It aims to bring to the fore the role flows can play to be consciously used to make spatial decisions in how and where certain uses and infrastructure is located. Efficient and sustainable
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Het lectoraat Juridische en Economische Vraagstukken binnen de Energietransitie draagt bij aan de overgang naar een duurzame samenleving gebaseerd op het gebruik van schone energiebronnen. Dat vraagt niet alleen om nieuwe technologieën, maar ook om juridische kaders en nieuwe verdienmodellen voor duurzame investeringen in de energiemarkt. Het lectoraat maakt deel uit van ENTRANCE – Centre of Expertise Energy van de Hanze. ENTRANCE – Centre of Expertise Energy draagt als lerende, praktijkgerichte kennisgemeenschap bij aan een robuuste, veerkrachtige en duurzame energievoorziening. Door middel van hoogstaand toegepast onderzoek en onderwijs stimuleren we duurzame innovaties in samenwerking met burgers, bedrijven, studenten, maatschappelijke organisaties en overheden
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This research contributes to understanding and shaping systems for OFMSW separation at urban Small and Medium Enterprises (SMEs, such as offices, shops and service providers). Separating SMEs’ organic fraction of municipal solid waste (OFMSW) is both an opportunity and a serious challenge for the transition towards circular cities. It is an opportunity because OFMSW represents approximately 40% of the total waste mass generated by these companies. It is challenging because post-collection separation is not feasible for OFMSW. Therefore, SMEs disposing of waste should separate their solid waste so that processing the organic fraction for reuse and recycling is practical and attainable. However, these companies do not experience direct advantages from the extra efforts in separating waste, and much of the OFMSW ends up in landfills, often resulting in unnecessary GHG emissions. Therefore, governments and waste processors are looking for ways to improve the OFMSW separation degree by urban companies disposing of waste through policies for behaviour change.There are multiple types of personnel at companies disposing of waste. These co-workers act according to their values, beliefs and norms. They adapt their behaviour continuously, influenced by the physical environment, events over time and self-evaluation of their actions. Therefore, waste separation at companies can be regarded as a Socio-Technical Complex Adaptive System (STCAS). Agent-based modelling and simulation are powerful methods to help understand STCAS. Consequently, we have created an agent-based model representing the evolution of behaviour regarding waste separation at companies in the urban environment. The model aims to show public and private stakeholders involved in solid waste collection, transport and processing to what extent behaviour change policies can shape the system towards desired waste separation degrees.We have co-created the model with participants utilising literature and empirical data from a case study on the transition of the waste collection system of a business park located at a former harbour area in Amsterdam, The Netherlands. First, a conceptual model of the system and the environment was set up through participatory workshops, surveys and interviews with stakeholders, domain experts and relevant actors. Together with our case participants, five policies that affect waste separation behaviour were included in the model. To model the behaviour of each company worker’s values, beliefs and norms during the separation and disposal of OFMSW, we have used the Value-Belief-Norm (VBN) Theory by Stern et al. (1999). We have collected data on waste collection behaviour and separation rates through interviews, workshops and a literature study to operationalise and validate the model.Simulation results show how combinations of behaviour profiles affect waste separation rates. Furthermore, findings show that single waste separation policies are often limitedly capable of changing the behaviour in the system. Rather, a combination of information and communication policies is needed to improve the separation of OFMSW, i.e., dissemination of a newsletter, providing personal feedback to the co-workers disposing of waste, and sharing information on the (improvement of) recycling rates.This study contributes to a better understanding of how policies can support co-workers’ pro-environmental behaviour for organic waste separation rates at SMEs. Thus, it shows policymakers how to stimulate the circular transition by actively engaging co-workers’ waste separation behaviour at SMEs. Future work will extend the model’s purpose by including households and policies supporting separating multiple waste types aimed at various R-strategies proposed by Potting et al. (2016).
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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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Measurement methodologies are increasingly being deployed to monitor energy poverty or energy access, and to provide insights for policy development, both in the South and more recently also in the North. However, care should be taken with interpretation and use of the data, particularly if a gender perspective is lacking. This paper argues that taking a gender perspective is vital to understanding energy access and outcomes related to interventions, through consideration of gendered user differences in energy needs, access to energy services and gendered outcome pathways. We show that the standard practice of focusing on numbers of energy connections, availability and quality of supply, is insufficient to provide insights relevant to realising gender equal access and benefits. It is a political decision about what is measured and who decides on what is measured. Based on the literature, we discuss key elements of the use of gender approaches in the assessment of energy access and energy poverty. We show that by including gender approaches in the design and execution of qualitative and quantitative data collection and analysis, there is the potential to contribute to more equitable outcomes from improved energy access.
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