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Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract The intervention on the existing building envelope thermal insulation is the main and effective solution in order to achieve a significant reduction of the building stock energy needs. The infrared technique is the methodology of the energy diagnosis aimed to identify qualitatively the principal causes of energy losses: the presence of thermal bridges. Those weak parts of the building envelope in terms of heat transfer result not easy to treat with an energy efficiency intervention, while they are gaining importance in the buildings total energy dispersion, as the level of insulation of opaque and transparent materials is continuously increasing. It is generally possible to evaluate the energy dispersions through these zones with a deep knowledge of the materials and the geometry using a numerical method. Besides, authors proposed in the past a methodology to assess the flux passing through thermal bridges with an infrared image correctly framed. The analysis of surface temperatures of the undisturbed wall and of the zone with thermal bridge, allows to define the Incidence Factor of the thermal Bridge (Itb). This parameter is strongly affected by the thermographic image accuracy, therefore, this paper deals with the development and validation of an innovative mathematical algorithm to enhance the image resolution and the consequent accuracy of the energy losses assessment. An experimental campaign in a controlled environment (hot box apparatus) has been conducted on three typologies of thermal bridge, firstly performing the thermographic survey and then applying the enhancement algorithm to the infrared images in order to compare the Itb and the linear thermal transmittance ψ values. Results showed that the proposed methodology could bring to an accuracy improvement up to 2% of the total buildings envelope energy losses evaluated by quantitative infrared thermography. Moreover, the proposed algorithm allows the implementation of a further process applicable to the images, in order to extract the physical boundaries of the hidden materials causing the thermal bridge, so revealing itself as a useful tool to identify exactly the suitable points of intervention for the thermal bridge correction. The application of the imaging process on the quantitative infrared thermography is an innovative approach that makes more accurate the evaluation of the actual heat loss of highly insulating buildings and reaching a higher detail on the detection and treating of thermal bridges.

With increasing air traffic, rising fuel costs and tighter environmental targets, efficient airport ground operations are one of the key aspects towards sustainable air transportation. This complex system includes elements such as ground movement, runway scheduling and ground services. Previously, these problems were treated in isolation since information, such as landing time, pushback time and aircraft ground position, are held by different stakeholders with sometimes conflicting interests and, normally, are not shared. However, as these problems are interconnected, solutions as a result of isolated optimisation may achieve the objective of one problem but fail in the objective of the other one, missing the global optimum eventually. Potentially more energy and economic costs are thus required. In order to apply a more systematic and holistic view, this paper introduces a multi-objective integrated optimisation problem incorporating the newly proposed Active Routing concept. Built with systematic perspectives, this new model combines several elements: scheduling and routing of aircraft, 4-Dimensional Trajectory (4DT) optimisation, runway scheduling and airport bus scheduling. A holistic economic optimisation framework is also included to support the decision maker to select the economically optimal solution from a Pareto front of technically optimal solutions. To solve this problem, a multi-objective genetic algorithm is adopted and tested on real data from an international hub airport. Preliminary results show that the proposed approach is able to provide a systematic framework so that airport efficiency, environmental assessment and economic analysis could all be explicitly optimised.

Abstract Nowadays, many countries adopt an active agenda to mitigate the impact of greenhouse gas emissions by moving towards less polluting energy generation technologies. The environmental costs, directly or indirectly generated to achieve such a challenging objective, remain however largely underexplored. Until now, research has focused either on pure economic approaches such as Computable General Equilibrium (CGE) and partial equilibrium (PE) models, or on (physical) energy supply scenarios. These latter could be used to evaluate the environmental impacts of various energy saving or cleaner technologies via Life Cycle Assessment (LCA) methodology. These modelling efforts have, however, been pursued in isolation, without exploring the possible complementarities and synergies. In this study, we have undertaken a practical combination of these approaches into a common framework: on the one hand, by coupling a CGE with a PE model, and, on the other hand, by linking the outcomes from the coupling with a hybrid input–output−process based life cycle inventory. The methodological framework aimed at assessing the environmental consequences of two energy policy scenarios in Luxembourg between 2010 and 2025. The study highlights the potential of coupling CGE and PE models but also the related methodological difficulties (e.g. small number of available technologies in Luxembourg, intrinsic limitations of the two approaches, etc.). The assessment shows both environmental synergies and trade-offs due to the implementation of energy policies. For example, despite the changes in technologies towards the reduction of greenhouse gas emissions, only marginal improvements are observed in the climate change mitigation scenario as compared to the Business-As-Usual. The energy related production and imports are indeed expected to increase over time and represent a large contribution to the country’s impacts. Interestingly, side effects on other impacts than climate change or fossil resource depletion (e.g. ionising radiation and water depletion) may also occur mainly due to the use of nuclear energy in neighbouring countries.

Abstract Within residences, normative messaging interventions have encouraged households to engage in various pro-environmental behaviors. In norm-based intervention campaigns, it is hypothesized that more personally relevant reference groups increase norm adherence, thus improving the effectiveness of normative messaging interventions. Advanced energy grid infrastructure, such as smart meters and cloud computing, enables the creation of highly personalized behavioral reference groups in a non-invasive manner by dynamically classifying households into highly similar user groups based on usage patterns. Unfortunately, it remains unclear how readily available data on household energy use and housing characteristics affect the classification performance of dynamic behavioral reference groups. Therefore, this research evaluates the classification performance of dynamic behavioral reference groups using readily available data. An energy-cyber-physical system for personalized normative messaging interventions is trained and tested using one-year of energy use data from 2248 households in Holland, Michigan. Dynamic behavioral reference group classification proved very accurate, 94.7–95.9% for weekly feedback and 89.9–93.1% for monthly feedback using only readily available data. In addition, using more historical energy use data contributes to enhancing classification accuracy. Lastly, high classification performance for each behavioral reference group is achieved at 97.6% of precision, recall and F1-score. With the proposed system, it is possible to dynamically assign highly personalized behavioral reference groups to households every billing cycle even if behavioral patterns are subject to change. Thus, interveners will be able to deploy personalized normative feedback messages on a large scale.

Abstract As our energy systems are transitioning towards low-carbon energy sources and their environmental and economic sustainability are assessed, their potential social impacts must also be determined. These social impacts may be disproportionate to a population, leading to energy justice concerns. The social life cycle assessment framework can be used to comprehensively address energy justice concerns by different stakeholder groups and at all life cycle stages associated with a low-carbon energy system. Indicators for a social life cycle assessment framework that addresses energy justice are introduced and discussed. These indicators are organized by four categories of stakeholders for electrical energy systems: workers, electricity consumers, local communities, and society as a whole. The social life cycle assessment framework allows for variations in justice and equity to be determined not only at the generation stage, but through multiple points in the life cycle of the same energy system, from raw material extraction, through manufacturing, transportation, distribution, electricity generation, and waste management. This framework can address potential energy justice issues along the life cycle of new energy systems and assist in their design and planning for optimizing their social sustainability without overlooking vulnerable populations.

Due to growing interest in wind energy harvesting offshore as well as in the urban environment, vertical axis wind turbines (VAWTs) have recently received renewed interest. Their omni-directional capability makes them a very interesting option for use with the frequently varying wind directions typically encountered in the built environment while their scalability and low installation costs make them highly suitable for offshore wind farms. However, they require further performance optimization to become competitive with horizontal axis wind turbines (HAWTs) as they currently have a lower power coefficient (CP). This can be attributed both to the complexity of the flow around VAWTs and the significantly smaller amount of research they have received. The pitch angle is a potential parameter to enhance the performance of VAWTs. The current study investigates the variations in loads and moments on the turbine as well as the experienced angle of attack, shed vorticity and boundary layer events (leading edge and trailing edge separation, laminar-to-turbulent transition) as a function of pitch angle using Computational Fluid Dynamics (CFD) calculations. Pitch angles of −7° to +3° are investigated using Unsteady Reynolds-Averaged Navier-Stokes (URANS) calculations while turbulence is modeled with the 4-equation transition SST model. The results show that a 6.6% increase in CP can be achieved using a pitch angle of −2° at a tip speed ratio of 4. Additionally, it is found that a change in pitch angle shifts instantaneous loads and moments between upwind and downwind halves of the turbine. The shift in instantaneous moment during the revolution for various pitch angles suggests that dynamic pitching might be a very promising approach for further performance optimization.