• Energy Research
• 11. Sustainability close
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Urbanisation poses new and complex sustainability challenges. Socio-economic activities drive material and energy flows in cities that influence the health of ecosystems inside and outside the urban system. Recent studies suggest that these flows, under the urban metabolism (UM) metaphor, can be extended to encompass the assessment of urban ecosystem services (UES). Advancing UM approaches to assess UES may be a valuable solution to these arising sustainability challenges, which can support urban planning decisions. This paper critically reviews UM literature related to the UES concept and identifies approaches that may allow or improve the assessment of UES within UM frameworks. We selected from the UM literature 42 studies that encompass UES aspects, and analysed them on the following key investigation themes: temporal information, spatial information, system boundary aspects and cross-scale indicators. The analysis showed that UES are rarely acknowledged in UM literature, and that existing UM approaches have limited capacity to capture the complexity of spatio-temporal and multi-scale information underpinning UES, which has hampered the implementation of operational decision support systems so far. We use these results to identify and illustrate pathways towards a UM-UES modelling approach. Our review suggests that cause–effect dynamics should be integrated with the UM framework, based on spatially-specific social, economic and ecological data. System dynamics can inform on the causal relationships underpinning UES in cities and, therefore, can help moving towards a knowledge base tool to support urban planners in addressing urban challenges.

Urbanisation poses new and complex sustainability challenges. Socio-economic activities drive material and energy flows in cities that influence the health of ecosystems inside and outside the urban system. Recent studies suggest that these flows, under the urban metabolism (UM) metaphor, can be extended to encompass the assessment of urban ecosystem services (UES). Advancing UM approaches to assess UES may be a valuable solution to these arising sustainability challenges, which can support urban planning decisions. This paper critically reviews UM literature related to the UES concept and identifies approaches that may allow or improve the assessment of UES within UM frameworks. We selected from the UM literature 42 studies that encompass UES aspects, and analysed them on the following key investigation themes: temporal information, spatial information, system boundary aspects and cross-scale indicators. The analysis showed that UES are rarely acknowledged in UM literature, and that existing UM approaches have limited capacity to capture the complexity of spatio-temporal and multi-scale information underpinning UES, which has hampered the implementation of operational decision support systems so far. We use these results to identify and illustrate pathways towards a UM-UES modelling approach. Our review suggests that cause–effect dynamics should be integrated with the UM framework, based on spatially-specific social, economic and ecological data. System dynamics can inform on the causal relationships underpinning UES in cities and, therefore, can help moving towards a knowledge base tool to support urban planners in addressing urban challenges.

This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).

This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).

Cities across the globe recognise their role in climate mitigation and are acting to reduce carbon emissions. Knowing whether cities set ambitious climate and energy targets is critical for determining their contribution towards the global 1.5 °C target, partly because it helps to identify areas where further action is necessary. This paper presents a comparative analysis of the mitigation targets of 327 European cities, as declared in their local climate plans. The sample encompasses over 25% of the EU population and includes cities of all sizes across all Member States, plus the UK. The study analyses whether the type of plan, city size, membership of climate networks, and its regional location are associated with different levels of mitigation ambition. Results reveal that 78% of the cities have a GHG emissions reduction target. However, with an average target of 47%, European cities are not on track to reach the Paris Agreement: they need to roughly double their ambitions and efforts. Some cities are ambitious, e.g. 25% of our sample (81) aim to reach carbon neutrality, with the earliest target date being 2020.90% of these cities are members of the Climate Alliance and 75% of the Covenant of Mayors. City size is the strongest predictor for carbon neutrality, whilst climate network(s) membership, combining adaptation and mitigation into a single strategy, and local motivation also play a role. The methods, data, results and analysis of this study can serve as a reference and baseline for tracking climate mitigation ambitions across European and global cities.