Mobility services and accessibility services could contribute to reduced car-dependency and a more sustainable transport system. However, uncertainty remains regarding what the effects will be and further research is needed. In this paper we examine potential effects on passenger car-travel in an urban context. To do so, we actuate the Avoid-Shift-Improve (ASI) framework using a System Dynamics approach and develop thematic Causal Loop Diagrams. We draw on the findings from a literature study and workshops engaging actors involved in creating visions and planning for the future of mobility and accessibility services in Stockholm, Sweden. The effects discovered are categorized as direct, enabling and structural/systemic, using a retrofitted version of the Three-Levels Model. Contributions include the mapping of mechanisms through which the services can have positive and negative effects in relation to ASI, demonstrating a high degree of interconnectedness. This includes potential synergetic and competitive relations between the services. In addition, the approach gives insight to potential cumulative impact of the services, relatable to Mobility as a Service, including ‘user near’ effects regarding, e.g., commuting and leisure travel, as well as systemic and structural level effects. A discussion is conducted on the implications for actors and policy-makers.

Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

Fast fluid dynamics (FFD) could provide informative and efficient airflow and concentration simulation. The commonly used turbulence model in FFD was Re-Normalization Group (RNG) k-epsilon turbulence model which solved two transport equations to obtain eddy viscosity. To reduce this part of time and further improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating several outdoor cases of varying complexity and comparing with experiment and CFD, this study assessed the accuracy and computing efficiency of FFD with four turbulence models. Compared with CFD, FFD greatly improved the computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased the computing speed while ensuring the same accuracy as RNG k-epsilon turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. Computation errors arose mainly from inaccurate prediction of turbulence dispersion. The computing cost was associated with the number of transport equations and calculation method of model coefficient. This investigation recommended the use of FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.

Despite the extensive literature on learning in urban transitions, we still have a limited understanding on how higher-order learning takes place in transition management and is spread within the transition arena. In this paper we analyze the efforts of transferring such embedded knowledge and its interrelation with learning through the examples of three Swedish municipalities engaged in urban transition management. To do so, we developed a framework of learning ripples that conceptualizes learning across social boundaries as an active and two-way process that goes beyond transferring and receiving knowledge, but also requires higher order learning that includes knowledge integration in the form of defining and formulating one's role and contributions to transitions. We found that higher order learning is largely influenced by the quality and frequency of interactions between the transferer and receivers. The further a stakeholder was located from the center of the transition arena in terms of direct interactions, the less chance occurred for higher order learning that resulted in tensions and conflicts in the collaboration. Our results show the problem as a lack of knowledge integration or a lack of conditions which allow stakeholders to articulate their views or develop an idea about their own role in the whole process.

Urban water and energy systems are crucial for sustainably meeting basic service demands in cities. This paper proposes and applies a technology-independent “reference resource-to-service system” framework for concurrent evaluation of urban water and energy system interventions and their ‘nexus’ or ‘interlinkages’. In a concrete application, data that approximate New York City conditions are used to evaluate a limited set of interventions in the residential sector, spanning from low-flow toilet shifts to extensive green roof installations. Results indicate that interventions motivated primarily by water management goals can considerably reduce energy use and contribute to mitigation of greenhouse gas emissions. Similarly, energy efficiency interventions can considerably reduce water use in addition to lowering emissions. However, interventions yielding the greatest reductions in energy use and emissions are not necessarily the most water conserving ones, and vice versa. Useful further research, expanding the present analysis should consider a broader set of resource interactions, towards a full climate, land, energy and water (CLEW) nexus approach. Overall, assessing the impacts, trade-offs and co-benefits from interventions in one urban resource system on others also holds promise as support for increased resource efficiency through integrated decision making.

The authors regret two instances of misinterpretation of input data and one formatting error in the previously published paper as titled above. First, the numerical estimates for water use in NYC electricity and natural gas supply were found to be incorrect due to a conversion error in a data file. This error has now been corrected and the estimates have been changed to correctly correspond to the references on which they are based on. These changes have led to a recalculation of indirect water use reduction potentials in the interventions studied in the paper. Second, two errors due to primary data misinterpretation related to the studied green roof intervention have been found and corrected. The first led to an overestimation of the green roofs’ energy use reduction potential in the previously published paper. The second led to an underestimation of their installation cost. These errors have also been corrected and all numerical results for the green roof intervention have been recalculated. In the updated sections 3 and 4 of the original publication (below), Table 2, Table 3, Fig. 2 and Fig. 3 are updated with the new results related to both indirect water use reductions and green roof performance and costs. The text in the below sections have been given minor adjustments to clarify this update. These changes make green roofs a less economically favourable intervention in comparison to the previously published results. It also makes indirect water use reductions relatively smaller compared to direct water use reductions. All other results as well as the conclusions of this paper are still valid and unchanged. Lastly, a typo in writing of Eq. (7) in the manuscript text has been corrected. There was no error in the equation used in the analysis; hence, no numerical results have been effected by this correction. The authors would like to apologise for any inconvenience caused. Corrected writing of Eq. (7), section 2.3.1: [Formula presented] Updated sections of the original publication.

Electromobility has gained momentum in the last years following the efforts to reduce transportation-related emissions. In this context, efficient charging infrastructure is vital to foster the expansion of electric vehicles. This paper presents a standardized framework for micro-scale analysis of potential charging locations for electric buses aiming at easing the analysis process and promoting the expansion of electric buses. The framework is tailor-made for the Municipality of Stockholm and tested in two city-centre public transport hubs, namely Odenplan and Slussen. However, the framework can be used in other locations by implementing minimum changes. Connecting charging stations to the power grid is identified as the main drawback in city-centre locations due to their high impact on the grid. Lack of available capacity at nearby connection points results in long distance connections, reaching up to 1 km in some cases. Such connections impact the overall cost of electrification directly, as they may account for up to 63% of the total cost. Although other issues regarding space availability and operational efficiency also need to be addressed, such as the lack of enough dwell time to charge the batteries, the framework proves the suitability of these inner-city locations as charging points.

The sustainable and continuous development of public transport systems is crucial to ensuring robust and resilient transport and economic activity whilst improving the urban environment. Through technological improvement, cities can increase the competitiveness of public transport, promote equality and pursue a multimodal shift to greener solutions. The introduction of vehicle automation technology into existing public transport systems has potential impacts on mobility behaviours and may replace conventional bus service in the future. This study examines travellers? preferences for automated buses versus conventional buses, using a contextdependent stated choice experiment. This experiment measured the effects of context variables (such as trip purpose, travel distance, time of day, weather conditions and travel companion) on the choice of automated buses versus conventional buses. The results were analysed using mixed logit models, and the findings indicate that, in general, choice behaviours do not diverge much between the choice of automated bus and conventional bus. However, individuals? choices are more elastic towards the changes in automated bus service levels compared to conventional bus service. The results show that poor weather conditions may lower the quality and reliability of public transport service, and the probability of choosing an automated bus over a conventional bus is reduced due to such disruptions. In addition, passengers travelling for work purposes, covering long distances, or travelling with companions are more likely to choose conventional buses than automated buses.

Current district heating networks are undergoing a sustainable transition towards the 4th and 5th generation of district heating networks, characterized by the integration of different types of renewable energy sources (RES) and low operational temperatures, i.e., 55 ◦C or lower. Due to the lower temperature difference between supply and return, it is necessary to develop novel methods to understand the loads accurately and provide operation scenarios to anticipate demand peaks and increase flexibility in the energy network, both for long- and short- term horizons. In this study, a hybrid machine-learning (ML) method is developed, combining a clustering pre-processing step with a multi-input artificial neural network (ANN) model to predict heat loads in buildings cluster-wise. Specifically, the impact of time-series data clustering, as a pre-processing step, on the performance of ML models was investigated. It was found that data clustering contributes effectively to the reduction of data training costs by limiting the training processes to representative clusters only instead of all datasets. Additionally, low-quality data, including outliers and large measurement gaps, are excluded from the training to enhance the overall prediction performance of the models.

As cities worldwide implement strategies to accelerate the transition toward a circular economy (CE), there is an increasing need for tools to monitor progress. However, a standardised metric for CE monitoring in urban areas is lacking. This study examines the potential of the EU Circular Economy Monitoring Framework (CEMF), an established indicator-based framework for measuring national- and EU-level circularity performance, as a monitoring tool for urban areas. For this purpose, available data sources that can support the framework's application at the urban level are mapped, and data quality is assessed following the pedigree matrix approach. Next, the CEMF indicators are computed for the urban area of Umeå, Sweden. The mapping showed limited availability of urban-level data, necessitating the downscaling of national-level data using proxy factors. Most available urban-level data are of high quality, while the quality of national-level data is reduced when used to compute indicators at the urban level. The application of the CEMF in Umeå indicates that there are areas where the municipality performs well, though further improvements are needed. We conclude that the CEMF has potential as a monitoring tool for urban areas. However, improvements in CEMF...s scope and data availability are recommended.

The influence of different ventilation levels on indoor air quality (IAQ) and energy savings were studied experimentally and analytically in a single-family house occupied by two adults and one infant, situated in Borlänge, Sweden. The building studied had an exhaust ventilation system with a range of air flow rate settings. In order to find appropriate ventilation rates regarding CO2, relative humidity (RH) and temperature as indicators of IAQ, four ventilation levels were considered, as follows: (I) A very low ventilation rate of 0.10 L s−1 m−2; (II) A low ventilation rate of 0.20 L s−1 m−2; (III) A normal ventilation rate of 0.35 L s−1 m−2; (IV) A high ventilation rate of 0.70 L s−1 m−2. In all cases, the sensor was positioned in the exhaust duct exiting from habitable spaces. Measurements showed that, for case I, the CO2 concentration reached over 1300 ppm, which was higher than the commonly referenced threshold for ventilation control, i.e. 1000 ppm, showing unacceptable IAQ. In case II, the CO2 level was always below 950 ppm, indicating that 0.20 L s−1 m−2 is a sufficient ventilation rate for the reference building. The case III showed that the ventilation rate of 0.35 L s−1 m−2 caused a maximum CO2 level of 725 ppm; showing the level recommended by Swedish regulations was high with respect to CO2 level. In addition, measurements showed that the RH and temperature were within acceptable ranges in all cases. An energy savings calculation showed that, in case II, the comparative savings of the combined energy requirement for ventilation fan and ventilation heating were 43% compared with case III.

This study gives emphasis to the techno-economic analysis of renovating the energy supply system of a grid-connected large office building through a Hybrid Renewable Energy System (HRES). The study is focused on how to minimize electricity consumption from the grid by producing as much as possible renewable energy, and in addition to that it also observes the economic impacts of integrating green vehicles, such as hydrogen cars, electric cars etc. which are indispensable elements of a sustainable city, in the proposed system. The work initiated collecting the sites monthly electrical load data, climate data and associated monetary data with the aim of investigating a renewable energy supply system feasibility study. Three alternative scenarios are developed according to the project needs and these scenarios are modelled by a hybrid renewable energy system design tool. The study concludes with a direct comparison of the economic feasibility, renewable energy fraction, and emissions among all systems, looking for the more appropriate and sustainable solution. It is found that integrating solar photovoltaic (PV) curtails more than 43% electricity consumption of the office building from the utility grid. The result also shows that per unit cost of electricity of PV/Grid system to satisfy the load demand is around 10% lower compared to the utility grid tariff and furthermore, it minimizes over 90% emission compared to the total emission in the study site. This study will provide helpful insights to the relevant stakeholders and policy makers in the development of grid connected HRES system.

Electrical power generation across the world is facing dramatic changes for a variety of reasons related to reliability, economics and environmental concerns. Over recent years a significant increase has been observed in installed capacity of photovoltaic systems. Due to their typical seasonal and diurnal energy conversion patterns their integration into power systems creates new opportunities as well as threats. This paper intends to show how photovoltaics can contribute to reducing peak load in office buildings and thereby minimise expenditure on electricity during time- and peak-load-dependent energy prices/tariffs. An additional benefit is also provided to the national power system by reducing the need for peaking power stations. The calculations are performed for energy tariffs commonly used for commercial buildings in Poland. The simulation relies on climatic and price data for 2016. The results show significant potential for photovoltaics to reduce the peak load (from almost 60 kW to slightly over 44 kW) whilst simultaneously minimising energy costs to the building (from 1.2% up to 5.8% depending on the selected tariff). This study demonstrates the economic benefits of using PV system for reducing peak loads. A sensitivity analysis with regard to photovoltaics investment costs is carried out showing that the increasing investment costs have different impact on total energy cost depending on the considered energy tariff.

The large-scale penetration of electric vehicles (EV) in road transport brings a challenging task to ensure the balance between supply and demand from urban districts. EVs, being shiftable loads can provide system flexibility. This work investigates the potential role of smart charging of EVs in mitigating the impact of the integration of a mix of residential and public EV charging infrastructure on power networks. Furthermore, the impact of integrating solar photo-voltaic (PV) and battery energy storage systems (BESS) has been explored where BESS improves PV self-consumption and helps in peak shaving during peak load hours. Annual losses, transformer congestion, and cost of electricity import assessment are detailed by considering the power network of Stockholm as a case study. Smart charging with loss-optimal and cost-optimal charging strategies are compared to uncoordinated charging. The cost-optimal charging strategy is more favorable as compared to the loss-optimal charging strategy as it provides more incentives to the DSOs. The loss-optimal charging strategy reduces 35.5 % of losses in the network can be reduced while the cost-optimal solution provides a 4.3 % reduction in the electricity cost. The combined implementation of smart charging, PV, and BESS considerably improves energy and economic performance and is more effective than EV smart charging alone.

Under the warming climate, providing thermal comfort to large urban population in city open spaces has become an important research topic. However, because of its dynamic and complex nature, the outdoor thermal comfort is difficult to predict. Skin temperature of human body may contain useful information of outdoor thermal comfort. In this paper, a Support Vector Machine (SVM) model was developed to predict the cool discomfort, comfort, and warm discomfort in outdoor environments using local skin temperatures and thermal load as inputs. In this study, the performances of models using different inputs were compared with each other. The results revealed that when using single local skin temperature as input, the skin temperature of exposed body parts exhibited the highest prediction accuracy (66 %–70 %), while that of abdomen or thorax was the lowest (42 %–58 %). The prediction accuracy increased by 1 %–5 % when the thermal load was added as an extra input feature, while that could be improved by 4%–7% when using skin temperature of two body parts as inputs. This study demonstrated that human outdoor thermal state can be captured with reasonable accuracy by monitoring skin temperatures from two local body parts.

Urban policy increasingly positions smart urban development as a transformative approach to deliver sustain- ability. In this paper, we question the transformative credentials of smartness and argue that it is better un- derstood as a partial fix for the economic, environmental and social challenges faced by cities. Drawing on the urban sustainability and smart city literatures, we develop the concept of the urban smart-sustainability fix. This concept focuses on how smart-sustainable city initiatives selectively integrate digital and environmental agendas via entrepreneurial forms of urban governance. We develop this concept by examining how the urban smart- sustainability fix is constructed in the European Commission’s flagship smart cities and communities lighthouse projects, focusing on the Triangulum initiative. Our research reveals three elements of the urban smart-sus- tainability fix: (1) the spatial development of smart-sustainable districts; (2) the digitisation of urban infra- structure to reveal hidden processes; and, (3) collaborative experimentation with low-carbon and digital tech- nologies. We argue that this has produced urban districts that are attempting to reduce their carbon emissions while promoting green economic growth. The main aim of the urban smart-sustainability fix is to make the urban realm more manageable resulting in amplification, rather than transformation, of the dominant ecological modernisation agenda of sustainable development.

This article provides a state-of-the-art review on emerging applications of smart tools such as data analytics and smart technologies such as internet-of-things in case of design, management and control of energy storage systems. In particular, we have established a classification of the types and targets of various predictive analytics for estimation of load, energy prices, renewable energy inputs, state of the charge, fault diagnosis, etc. In addition, the applications of information technologies, and in particular, use of cloud, internet-of-things, building management systems and building information modeling and their contributions to management of energy storage systems will be reviewed in details. The paper concludes by highlighting the emerging issues in smart energy storage systems and providing directions for future research.

This study introduces an agent-specific assessment method of traffic noise exposure in agent mobility simulations. The assessment is achieved through a combination of an energy-based noise exposure impact assessment using noise exposure cost, and the state-of-the-art traffic noise prediction tool NoiseModelling coupled with the activity-based agent mobility simulation software MATSim. The agent-specific noise exposure cost is a measure to evaluate how the noise emissions from the transport of agents relate to the noise-related impact on other agents performing stationary activities. By introducing an agent-specific level, each agent’s individual responsibility for the noise exposure may be estimated. The potential of the agent-specific noise exposure cost concept, combined with the MATSim-NoiseModelling framework, is illustrated through a case study, applying activity-based agent mobility simulations across Nantes, France. The results of the case study highlight, among other considerations, the insights that an agent-specific, activity-based noise exposure cost approach provides by visualizing the noise exposure ”footprint” resulting from an agent’s transportation activities.

The transition towards a circular economy (CE) is increasingly recognized as a promising pathway to tackle pressing sustainability challenges at the city-level. Indicator-based frameworks, that is, integrated systems of indicators, are considered as useful tools for monitoring this transition. Yet, studies that map and assess such frameworks are scanty. This article addresses this gap by assessing 15 indicator-based frameworks applicable to measure circularity at the city-level. The identified frameworks were assessed using eight criteria (transparency, stakeholder engagement, effective communication, ability to track temporal changes, applicability, alignment with CE principles, validity and relevance to sustainable development). Additionally, 12 validity requirements were defined to assess to what extent the indicators in the frameworks reflect CE aspects. The assessment reveals a wide variation regarding the extent to which the frameworks match the criteria with none of them satisfying all. In addition, in terms of validity criterion, none includes indicators that fulfill all the validity requirements. Furthermore, most frameworks consist mainly of environmental indicators and only three include indicators reflecting aspects related to the four pillars of sustainable development (environmental, social, economic and governance). Further research could develop a standardized framework for measuring circularity at the city-level and improving existing frameworks.

Sweden stands out among the other European countries by the degree of restrictive measures taken towards handling the 2019 coronavirus outbreak, associated with the CoViD-19 pandemic. While several governments have imposed a nationwide total or partial lockdown in order to slow down the spread of the virus, the Swedish government has opted for a recommendation-based approach together with a few imposed restrictions. In the present contribution, the impact of this strategy will be observed through the monitored variation of the city noise levels during the associated period. The data used are recorded during a campaign of over a full year of noise level measurements at a building facade situated in a busy urban intersection in central Stockholm, Sweden. The noise level reductions, observed during the period of restrictions, are shown to be comparable to those found for the two most popular public holidays in Sweden with a peak reduction occurring during the first half of April 2020. Contrary to what has been recently discussed in public media, the spread of the virus, the recommendations, and the restrictions imposed during the ongoing pandemic clearly have had a significant effect on the transport and other human-related activities in Stockholm. In this unique investigation, the use of distributed acoustic sensors has thus shown to be a viable solution not only to enforce regulations but also to monitor the effectiveness of their implementation.

This paper reflects the regional/urban planning, design, and building problems. It highlights local differences as important factors in the development process. As demands for new homes and towns increase the proportionality of urban development methods to the local situation must be respected. This in turn requires an increase in the professional regional recognition and practical experiences. This study reviews briefly the past, present and future of the underdevelopment cities. It applies a combination of semi structured interviews, a problem oriented method, POM, a benchmarking method, BM, a linear programming, and techniques of converting qualitative values to quantitative scores to assess the degree of sustainable urban development, SUD. The assessment has been done by introducing of indicators of sustainability, IS. Finally, this paper suggests a systematic program to develop a case study city. The program is useful in the similar cities everywhere.

Buildings’ energy consumption and greenhouse gas emissions are a major global concern. Healthcare buildings, being crucial to society, pose particular challenges due to their round-the-clock operation and stringent hygiene standards. This paper comprehensively reviews existing literature to promote energy-efficient and comfortable healthcare buildings. The research explores both passive measures, such as orientation, materials, and daylight, and active measures, including Heating, Ventilation, and Air Conditioning systems, energy management, and renewable resources. The paper emphasizes the critical role of user behavior in conserving energy and outlines how factors like building size, operation hours, and climate can impact resource consumption. It highlights the importance of solar power as a prominent renewable energy source. It offers design and retrofitting options to enhance healthcare buildings and addresses the lack of research on small-scale healthcare buildings. The paper emphasizes maintaining a balance between user comfort and energy reduction, involving diverse stakeholders, and exploring benchmarks, automated shading, geothermal sources, local materials, and their impact on carbon emissions. This review aims to contribute to environmentally responsible and socially resilient healthcare infrastructure and provide insights for future challenges in creating energy-efficient healthcare buildings.

Worldwide, cities are implementing circular economy (CE) strategies to reduce the resources they consume and their environmental impact. However, the evidence of the intended and unintended social consequences of the transition to “circular cities” is scattered. The lack of a coherent overview of the evidence on the subject can hinder effective decision-making in policy and practice. This study examines the extent to which the current literature addresses the social impacts that a transition to a CE produces in cities. We used a methodological approach related to systematic mapping to collate the evidence published over the past decade globally. The study finds that social impacts have rarely been considered in studies of circular cities, and where they have been discussed, the scope has been quite limited, only covering employment (mostly of informal sector workers) and governance practices. This scoping review highlights the need to further analyse and integrate social impact considerations into decision-making connected to transitions towards circular cities.

Swedish residential buildings are typically retrofitted on a case-by-case basis. Large numbers of building consultants are involved in the decision-making, and stakeholders find it difficult to quantify the sustainable profits from retrofits and to make an efficient selection of the optimal alternative. The present paper presents an approach to design and assess energy-demand retrofitting scenarios. This aims to contribute to retrofitting decision-making regarding the main archetypes of existing Swedish residential buildings and to the evaluation of their long-term cost effectiveness. The approach combines energy-demand modeling and retrofit option rankings with life-cycle cost analysis (LCCA). Four types of typical Swedish residential buildings are used to demonstrate the model. Retrofits in the archetypes are defined, analyzed and ranked to indicate the long-term energy savings and economic profits. The model indicates that the energy saving potential of retrofitting is 36-54% in the archetypes. However, retrofits with the largest energy-saving potential are not always the most cost effective. The long-term profits of retrofitting are largely dominated by the building types. The finding can contribute to the standardization of future retrofitting designs on municipality scale in Sweden. 

Energy and environmental issues are increasingly important in existing building service and energy systems around the world. Despite great efforts to implement retrofit techniques in Sweden, no stringent evaluation of the benefits of these techniques or their systematic design has been completed. Traditional evaluations have not taken into account the embodied energy and greenhouse gases emissions of different retrofit options. This omission leads to underestimation of the potential environmental benefits of modern retrofit techniques. In this study a novel hybrid modeling approach to quantify the sustainability of retrofit options is developed to fill these knowledge gaps. The compatibility of environmental and energy saving modeling of various energy-saving techniques for future transition of Swedish residential building stock is analyzed. Consolis Retro and the life cycle assessment (LCA) techniques are employed and further coupled to simulate retrofit options. The model integrates both energy demand (net operational energy), primary energy (operational energy from energy mix to buildings) into evaluation criteria. Embodied energy (energy required to produce materials of retrofitting options) and embodied greenhouse gas emissions (upstream CO<inf>2</inf> equivalent) are introduced as new measures in the evaluation criteria. The results showed that low-temperature heating retrofitting was the most effective option from both a primary and embodied energy perspective in the studied building types. Combining circulation pump renovations could further contribute to the efficiency of low-temperature heating for energy-demand savings. High operational energy-saving measures may not always lead to larger reduction in both embodied energy and greenhouse gas emissions, particularly for building envelope retrofitting. Neglecting the embodied energy of retrofit options will increase the risk of overrepresenting their energy-saving contributions. The sustainability improvements of retrofitting, particularly large-scale measures, should take into account the embodied energy and greenhouse gas emissions from the material productions.

Energy efficient control of energy systems in buildings is a widely recognized challenge due to the use of low temperature heating, renewable electricity sources, and the incorporation of thermal storage. Reinforcement Learning (RL) has been shown to be effective at minimizing the energy usage in buildings with maintained thermal comfort despite the high system complexity. However, RL has certain disadvantages that make it challenging to apply in engineering practices. In this review, we take a computer science approach to identifying three main categories of challenges of using RL for control of Building Energy Systems (BES). The three categories are the following: RL in single buildings, RL in building clusters, and multi-agent aspects. For each topic, we analyse the main challenges, and the state-of-the-art approaches to alleviate them. We also identify several future research directions on subjects such as sample efficiency, transfer learning, and the theoretical properties of RL in building energy systems. In conclusion, our review shows that the work on RL for BES control is still in its initial stages. Although significant progress has been made, more research is needed to realize the goal of RL-based control of BES at scale.

Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-theart deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs).