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The existing housing stock needs substantial adaptation to meet national and international carbon reduction targets. The largest proportion of housing is owner‐occupied, and will require improvement works which go beyond those measures provided through the Green Deal and similar programmes. Therefore, the motivation of owner‐occupiers to perform more substantial energy efficiency refurbishments is essential to facilitate greater action. This paper aims to address these issues.

A synthesis of the extant literature from a range of disciplines reveals the role of motivation and the factors influencing motivation and pro‐environmental action in the context of the home. Based on this synthesis of the literature, a new motivation model for energy efficiency refurbishment in the owner‐occupied housing stock is then described.

The study has found that multiple factors affect motivation to refurbish in the owner‐occupied housing stock. Key motivations for energy efficient refurbishment can be categorized into the broad themes of economic, social, and environmental motivations. These motivations will be affected by a wide number of interrelated internal and external factors and mediated by the emotions of the individual. The model presented demonstrates the relationship between the multiple factors that affect energy efficiency refurbishment in relation to specific contexts.

The study represents a potential addition to motivational theory and concepts for use within the field of energy efficient refurbishment of the owner‐occupied housing stock. Implications for future government policy and towards raising the motivation of owner‐occupiers are identified: it can be used to shape national and local policy and information campaigns to motivate energy efficiency refurbishment in the owner‐occupied housing stock. To be successful, this should take differing internal factors and contexts into consideration and the dynamic nature of owner‐occupier motivation. The model can also be used by industry professionals to better understand the owner‐occupier customer motivations for energy efficiency refurbishment and therein provide a better service.

The purpose of this paper is to consider whether consumers can recover from a service failure by utilizing internal and external energy resources that are available to them at the time of an online complaint. Based on the Conservation of Resources (COR) theory, this research conceptualizes the complainers' act of complaining through internal and external energy resources. By investing (direct utilization of resources) and mobilizing (utilizing resources to change the trajectory of a loss) these resources, this study aims to understand which resources (internal or external) and what strategies (investment or mobilization) are effective in the face of a resource loss.

Study 1 aimed to test the impact of energy resources (motivation and affordance) on consumers' negative emotions and satisfaction with their complaints through an online panel survey. Study 2 was a between-subjects design experiment aimed to overcome the diversity of the circumstances around a service failure, complaint motivation and complaints that were captured in Study 1.

This study provides evidence of the negative and positive effects of internal and external energy resources, respectively, in altering the consumer's emotions and behavioral intentions. The findings of this study underline the role of affordances of features, specifically perceived conversationality of digital features, in improving consumers' relationship with the defaulting firm.

Based on the findings related to the perceived conversationality of digital features, managers are urged to explore the affordances of online features that consumers use for communications, in general, or for complaints, in particular.

To our understanding, this paper is the first study to employ COR theory as a conceptual background, and in turn, the first to conceptualize complaint motivations and online complaint features as internal and external resources, respectively. As such, this study is the first of its kind to examine complaint media systematically.

This study is concerned with dynamic effects when we use explicit FEM for sheet metal forming analysis. Since sheet metal forming is a quasi‐static process, time scaling and mass scaling techniques have been widely used in order to save computation time. However, to define suitable scale factor still requires several trials. In this study, an internal energy error estimate which indicates accumulated incremental errors due to dynamic effects is suggested. This estimate is applied to each subdomain to consider the locality of deformation. Draw bending problem is analyzed to show the effectiveness of the suggested method. An analytic solution is derived for simplified draw bending to compare with the computed results. The cause of stress error in this problem is investigated and the method to reduce the stress error is suggested. Finally, the suggested method is applied to S‐rail benchmark test.

Clean energy stocks are exhibiting signs of increasing volatility reflecting the varied and conflicting strategies employed by nations to pursue energy security objectives. In this regard, this paper aims to examine the response of NASDAQ clean energy stock returns volatility to the influences of external energy security elements including oil price, natural gas price, coal price, carbon price and green information technology stock price.

The paper uses symmetric and asymmetric generalised autoregressive conditional heteroskedasticity models (GARCH and TGARCH, respectively), which incorporate external energy security elements as exogenous variables, to estimate volatility models for clean energy stock returns.

Although, prices of oil, coal and natural gas are negatively associated with NASDAQ clean energy returns volatility, only the effect of natural gas price is significant. While carbon price affects NASDAQ clean energy returns volatility positively, green information technology price affects the volatility negatively. These results are robust to exponential GARCH and lead-and-lag robust ordinary least-squares as alternative estimation methods.

The study lumps the effects of all other external and internal factors, including internal energy security elements, in the autoregressive conditional heteroscedasticity (ARCH) term to predict NASDAQ clean energy returns conditional variance. GARCH method does not disentangle individual roles of the factors captured in the ARCH term in predicting volatility.

Results documented imply that natural gas appears a closer substitute for renewable energy sources than crude oil and coal, such that its price rise is perceived as good news in the NASDAQ clean energy financial market, while a fall is considered bad news. Furthermore, both an increase in carbon price and a decrease in green information technology stock performance are perceived as negative shocks.

In assessing risks associated with clean energy stocks, investors and fund managers should carefully consider the effects of external energy security elements.

To the best of the author’s knowledge, the paper is the first to identify external energy security elements and examine their effects on clean energy stock volatility.

This paper discusses logistics service providers' (LSPs’) energy efficiency initiatives for sustainable development, both from an evolutionary perspective and based on a framework consisting of actions, processes (i.e. at the operations interface) and services (i.e. at the customer interface).

Following a qualitative research design, semi-structured interviews were conducted with sustainability managers at LSPs and the data were analysed via inductive coding. Based on the results and the literature, the authors developed a maturity model for LSPs' transitions to environmental sustainability.

LSPs' sustainable development occurs via operational processes, services at the customer interface, and actions that support those processes and services. Energy efficiency efforts are characterised by process depth that helps LSPs to align with their customers' energy efficiency improvement processes. While services related to energy efficiency connect LSPs and their customers, actions in support vary depending on the logistics activities in which LSPs participate.

Further research is needed to test and verify the maturity model and to clarify the interdependency of its three dimensions.

By categorising energy efficiency initiatives and proposing a maturity model for LSPs' sustainable development via energy efficiency, the authors have developed a tool for logistics actors to assess their progress towards improved sustainability.

The paper contributes to the literature by providing a three-pillar framework to understand the sustainability transitions of LSPs through energy efficiency. Developing a maturity model using this framework also contributes to the literature with an approach to assess sustainability advancement in the logistics industry.

This investigation aims to reframe the sizeable literature on barriers and drivers for energy efficiency measures (EEMs) and the phenomenon of the energy efficiency gap. The authors identify a gap between academic methods and industrial needs, as well as a neglect of the cultural dimension, despite its considerable impact. On the basis of this insight, the purpose of this paper is to integrate all of the various influences on industrial energy behavior previously identified in the literature in a refined energy cultures framework.

This paper includes a systematic literature review of research in the field of energy management, energy efficiency and cultural aspects within barriers and drivers of energy behavior. The authors select and refine an existing energy cultures framework for the industrial context. To meet industrial needs, the authors applied an ontology mapping of its core elements onto an international standard common for industrial energy management practice.

First, the authors present a refined framework for industrial energy cultures incorporating past barriers and drivers as factors. The framework enables an evaluation of attitude and behavioral aspects, underlying technologies, organizational culture and actions related to energy as a system of interdependencies. Second, the factors are ranked on the basis of the number of appearances and empirical metadata. Economic aspects such as “purchase, installment and hidden costs”, “general investment and risk behavior” and “regulatory conditions” are the highest ranked factors, but “existing knowledge about EEM”, “hierarchy approach: top down” and “environmental concerns” follow closely and represent cultural aspects, which are still underrated. Third, while illustrating a successful mapping onto a standardized process of continuous improvement, the authors also argue for heightened academia–practice efforts.

Reframing the energy efficiency gap as a problem of what aspirations play a role, what technology is chosen and how technologies are used should increase the level of implementation of EEMs in the real business world. Introducing the refined energy cultures framework serves as a starting point for future transdisciplinary collaboration between academia and practice.

Targeting the energy efficiency gap is an essential part of the sustainable development goals. The refined energy cultures framework allows for a better understanding of the industrial energy behaviors that are responsible for a significant share of a company’s success. The introduction of energy cultures serves as a starting point for future scholarly research within sustainability management accounting.

The investigation combines existing research streams, their concepts and their results about cultural aspects related to energy efficiency for both academics and practitioners. This review is the first to capture all of the various factors analyzed in academic literature using the energy cultures framework as a basis. The authors add to the theoretical development of that framework with its application to the industrial context. This is identified as a gap. Its refinement helps to holistically understand barriers and drivers of industrial EEMs to support its practical implementation.

The purpose of this paper is to calculate the Hugoniot relations of polyurea; also to investigate the atomic-scale energy change, the related chain conformation evolution and the hydrogen bond dissociation of polyurea under high-speed shock.

The atomic-scale simulations are achieved by molecular dynamics (MD). Both non-equilibrium MD and multi-scale shock technique are used to simulate the high-speed shock. The energy dissipation is theoretically derived by the thermodynamic and the Hugoniot relations. The distributions of bond length, angle and dihedral angle are used to characterize the chain conformation evolution. The hydrogen bonds are determined by a geometrical criterion.

The Hugoniot relations calculated are in good agreement with the experimental data. It is found that under the same impact pressure, polyurea with lower hard segment content has higher energy dissipation during the shock-release process. The primary energy dissipation way is the heat dissipation caused by the increase of kinetic energy. Unlike tensile simulation, the molecular potential increment is mainly divided into the increments of the bond energy, angle energy and dihedral angle energy under shock loading and is mostly stored in the soft segments. The hydrogen bond potential increment only accounts for about 1% of the internal energy increment under high-speed shock.

The simulation results are meaningful for understanding and evaluating the energy dissipation mechanism of polyurea under shock loading, and could provide a reference for material design.

This paper aims to develop a selective energy optimisation of the photovoltaic–thermal (PV/T) system performance. The PV cell inside the PV/T system could be periodically manipulated to produce domestic hot water without applying an external power supply.

A numerical simulation model of the proposed PV/T model was developed in MATLAB/Simulink to analyse the selective energy optimisation of the model. The extrinsic cell resistance (R_se) is adjusted to control the ratio of thermal to the electrical energy, generated from the PV cell inside the PV/T system. Therefore, the internal heat of the PV cell inside the PV/T system is periodically used as a thermal element to produce electrical power and hot water.

The optimisation of PV/T energy shows that the electrical power efficiency can increase by 11.6% when R_se was 0 Ω, and the 200 L water tank temperature increased by 22ºC when R_se was 50 Ω.

This study showed that the use of the PV cell could be extended to domestic hot water and space heating, and not only for electricity.

This study seeks to develop a systematic means of identifying regression models using a complex regression model with a statistical method.

As a widely adopted statistical scheme for analyzing multifactor data, regression analysis provides a conceptually simple algorithm for examining functional relationships among variables. This investigation assesses the proposed relationship using a sample of data in regression analysis and then estimates the fit using statistics. Furthermore, several algorithms and added variable plots are presented to obtain an appropriate regression model and the relationship between response variables y, p and explanatory variables x₀,x₁,x₂, … ,x_p.

The proposed statistical scheme is demonstrated by the analysis of experimental data on internal waves, in which the results can well illustrate what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.

In previous studies, field observations of internal waves were carried out. Owing to the limit of stationary measurement in situ, sufficient experiment data were not easily obtained. On the other hand, data collected by laboratory experiments express more information on wave mechanism, such as energy dissipation, mixing efficiency, and stratification thickness in a stratified layer fluid system. In present study, the authors supply valuable experiment data and analytic method, which will be a great contribution to the geophysical fluid dynamics. The results illustrate well what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.

More recently, it has been proposed that internal wave mixing may contribute significantly to internal mixing in the ocean and hence has an important influence on world climatic changes. Based on the statistical algorithm and regression model, the reduction in internal wave energy can be predicted on a sloping bottom due to frictional effect. Since, interaction between internal waves and uniform slopes has occurred in an estuary, a lake or in the ocean, the results available in this paper would benefit future study on internal wave hydrodynamics.

The purpose of this study is to present the theoretical framework of the “data envelopment analysis (DEA) Energy Management System (DEMS)” proposed to assist individual departments occupying the same buildings on university campus in assessing the energy efficiencies of their facilities, as well as to demonstrate the implementation results of the DEMS applied in the case of the Department of Architecture of NTUST in Taiwan.

The proposed DEMS considers each “space” within a department in a given “time” (such as a month) as a decision-making unit (DMU). Then, regression analysis is performed on data of “existing environment”, “occupancy” factors and “actual energy consumption EUI (energy usage intensity)” related variables. The regression equation derived is then used to calculate the “predicted EUI” for all DMUs. The “actual EUI” is further considered as the input data and the “predicted EUI” as the output data of the DEMS, on which data envelopment analysis is conducted to produce three types of energy-efficiency scores (overall efficiency, scale efficiency, pure technical efficiency) to indicate the energy efficiencies of all DMUs.

The DEMS was developed and further implemented in the Department of Architecture of NTUST in Taiwan to illustrate how it can be used to assist individual departments within universities in assessing the energy management effectiveness of their spaces.

The accuracy of the energy-efficiency scores depends greatly on the accuracy of the predicted EUIs of spaces, and, therefore, it is critical to identify a better regression model with higher predictability (R2). The relatively low actual EUIs of certain student spaces during winter and summer breaks may greatly affect the resulting energy-efficiency scores.

The DEMS allows facility managers to assess and compare the energy-efficiency scores “among different spaces”, to further review the energy efficiency of a space “over time” and to recognize the benchmark cases and pursue actions for energy improvement.

This study explores the research concepts of “space type” and “internal benchmark” with an analytical method “data envelopment analysis” to assess the energy efficiency of an individual department which may only occupy certain floors of a building.