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Producción Científica, Cities consume over 75% of natural resources, produce over 50% of global waste, and emit 60–80% of greenhouse gases. The scenario that by 2050 two thirds of the world population will live in cities, highlights how cities are still responsible for the growing consumption characterized by linear economic processes, with the production of various types of waste. In this unsustainable framework, the Circular Economy offers the opportunity to shape the urban system by means of rethinking the possibility to produce and use goods and services, exploring new ways to ensure long-term prosperity. The Circular City paradigm contains in fact all the principles of the Circular Economy: recovery, recycling, and sharing. In particular, Circular City also introduces actions related to the development of renewable energy communities, use of green materials, CO2 absorption approaches, and Proximity Cities. This work aims to develop a methodology to build a composite index (Circular City Index) capable of measuring the degree of implementation of urban policies that may enable an ecological transition of public assets. Circular City Index was applied to the military cluster of the city of Cagliari (Sardinia, Italy), a significant case study to guide circular policies in public properties for civil and military uses., Autonomous Region of Sardinia (POR Sardegna FESR 2014/2020- PRIORITY AXIS I-"SCIENTIFIC RESEARCH, TECHNOLOGICAL DEVELOPMENT AND INNOVATION", UK Centre for Research into Energy Demand Solutions, grant reference number EP/R035288/1

Several governments are opting to move from flat electricity tariffs to Time of Use pricing in an effort to curb peak electricity demand. However, despite the fact that numerous trial studies looking to quantify the effectiveness of pricing schemes have been carried out, these have produced mixed results, and in most cases, the observed temporal shifts in demand in response to the introduction of time-varying pricing are marginal at best. If changes in the timing of energy demand should be of substantial magnitude, marginal behavioural shifts in electricity consumption will not be sufficient. This prompts conceptual efforts to research what constitutes an (in)effective price-driven demand management intervention, and what their implementation may mean for energy demand.\ud \ud Most failures in price-driven interventions have been interpreted by energy economists in terms of setting the wrong price, which for time-varying tariffs translates into inadequate price ratios between peak and off-peak periods. This view is rooted in principles of economic rational choice theories that assume people will always respond to changes in the price of goods or services in a rational, predictable manner. However, this fails to consider the fact that price is only one of many factors at play when it comes to making decisions that will result in demand for energy.\ud \ud This paper suggests that more radical reductions in demand for energy and associated carbon emissions can be achieved through approaches that aim to find a better alignment of time-varying pricing and the (value of) social practices which constitute everyday life. The paper concludes by suggesting ways to integrate concepts explaining ToU pricing effects and the timing of electricity demand with methodologies which disaggregate price elasticity, and offer alternatives to the estimation of market and non-market values of practices and time.

In order to have the best possible chance of achieving ‘decent work’ and ‘climate action’\ud as laid forth in the UN Sustainable Development Goals, government and policy makers must pay\ud close attention to current time-use patterns, as well as the way these might change in the near future.\ud Here we contribute to the existing literature on time-use behaviour through a systematic exploration\ud of the relationship between working patterns and energy consumption from the perspective of timeuse. Our starting point is the premise that different work arrangements impact the timing of energy\ud demand not only in workplaces, but also at home. Using the data from the 2014–2015 UK time-use\ud survey, we were able to capture patterns of time-use behaviours and to assess their relationship\ud with daily energy consumption. We propose a systematic time-use-based approach for estimating\ud residential energy consumption with regards to activity timing, activity location, activity coordination, and appliance type. We use this method to discover patterns in residential activities and energy\ud consumption, as well as the causal relationship between residential energy consumption and work\ud patterns. In this study, we unpack the heterogeneity in the work–energy relationship, particularly\ud when comparing full-time and part-time workers. Our results suggest that full-time employees have\ud a higher potential to reduce their energy use compared to part-time employees. We also discover a\ud non-linear change in total energy consumption for respondents with varying levels of work time.\ud Energy consumption reductions associated with differences in work schedules are greatest during\ud the first few hours of the workday, but then level off. Our findings suggests that time-use data can

Whilst flexible electricity tariffs, such as Time-of-Use (ToU) and real-time, play an important role in motivating the shift of electricity demand away from the peak period and progressing towards the Net Zero, their widespread implementation for residential customers may have an adverse effect on some groups of consumers. The objective of research work within the DEePRED (Distributional Effects of dynamic Pricing for Responsive Electricity Demand) project\ud is to evaluate the distributional impact of flexible tariffs and identify groups of consumers who might be advantaged or disadvantaged based on by their socio-demographic parameters. As the project comes to its conclusion, this paper presents key findings, appraises the impact of ToU on smart meter data and explores the application of such findings in the context of transition to Net Zero. The key findings are as follows: (i) bottom-up(clustering) impact analysis method clearly identifies the most affected household groups; (ii) there is no clear set of socio-demographic parameters that can describe these groups; (iii)\ud grouping by household composition demonstrates that the presence of children increases the probability and intensity of energy-related activities at peak-time and hence increases the likelihood of adverse impact of ToU; and (iv) the impact on consumer groups who do not change their behaviour in response to ToU is defined by the peak to off-peak price ratio, which is confirmed by analysis of ToU impact on smart meter profiles.

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of long-term dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supply-side fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.

Introduction of Time-of-Use (ToU) tariffs have the potential to motivate consumers to flex their energy use and, by utilising their flexibility, support the reduction in peak electricity demand.\ud In return, lower peak demand could also reduce the system costs due to the reduced need for peaking generation and network reinforcement. \ud By their nature, ToU tariffs would penalise consumers with high consumption during peak periods and who are not able to exercise flexibility.\ud Therefore to ensure the affordability of energy bills it is important to understand the relationship between the timing of activities in the household and socio-demographic properties of the consumers.\ud \ud This paper uses UK Time Use survey data to cluster households by their energy-related activities during the peak electricity demand periods, model the corresponding electricity demand and analyse the impact of ToU tariffs across several socio-demographic parameters.\ud Results show that similar patterns of energy related activities exist for the clusters with different socio-demographic parameters (e.g. family structure or income). \ud Findings also show that there is no single dominant socio-demographic parameter that defines the winners or losers from the introduction of ToU tariff.

The British and German residential sectors account for similar fractions of national energy demand and carbon emissions. They also exhibit underlying differences in the building stock, fuel split, tenure and household load profiles. The temporal habits in British and German households are also quite different, which is challenging to measure due to the paucity of German smart meter data. This contribution takes this background as a starting point to explore some of the temporal and socioeconomic\ud characteristics of residential energy demand in Britain and Germany. The Centre for Renewable Energy Systems Technology (CREST) residential load profile generator is updated for the UK and extended to the German context and validated with standard load profiles, providing high levels of accuracy according standard normalized root-mean-squared error (NRMSE) measures. The paper then analyzes the energy-related activities of different socioeconomic household groups based on with National Time\ud Use Survey data from both countries. The analysis showed some clear differences between groups and countries, which are a reminder of the importance of non-energy policy (e.g. school hours) in determining peaks. As well as encountering useful insights into international differences in energy related behaviour, the results showed some key differences within specific socioeconomic groups, such as single persons, families with children, and pensioners. Further work will focus on extending the\ud German CREST model to include a German appliance stock, as well as allocating these appliances according to households’ socioeconomic characteristics. The definition of the groups themselves needs to be refined, perhaps to include multiple variables and based on clustering or similar techniques, and validation with smart meter data.