Search results

The purpose of this paper is to identify the carbon intensive building elements or “carbon hotspots” of office buildings in order to maximise the carbon reduction potential during design stages.

Embodied carbon (EC) estimates of 28 office buildings in the UK were obtained and carbon hotspots of the sample (in accordance with the new rules of measurement (NRM) element classification) were identified using the 80:20 Pareto principle.

Frame, substructure, external walls, services and upper floors were identified as carbon hotspots of the selected sample. However, findings do not support the 80:20 ratio in this case but propose a ratio of 80:36. Stairs, internal walls and partitions, internal doors, wall finishes, ceiling finishes and fittings and furnishings were identified as carbon insignificant elements that have a lower EC reduction potential compared to the rest.

The findings are applicable to office buildings in the UK but the methodology is adaptable to different types of buildings in other countries.

Findings unveil carbon intensive and carbon insignificant building elements of typical office buildings in the UK. This informs designers of the elements that could yield the highest potential EC savings via effective design choices. In addition, a logical design timeline is proposed for building elements based on their element hotspot category and design sequence to assist design decision making.

The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

From the quantum game perspective, this paper aims to study a green product optimal pricing problem of the dual-channel supply chain under the cooperation of the retailer and manufacturer to reduce carbon emissions.

The decentralized and centralized decision-making optimal prices and profits are obtained by establishing the classical and quantum game models. Then the classical game and quantum game are compared.

When the quantum entanglement is greater than 0, the selling prices of the quantum model are higher than the classical model. Through theoretical research and numerical analysis results, centralized decision-making is more economical and efficient than decentralized decision-making. Publicity and education on carbon emission reduction for consumers will help consumers accept carbon emission reduction products with slightly higher prices. When the emission reduction increases too fast, the cost of emission reduction will form a significant burden and affect the profits of manufacturers and supply chain systems.

From the perspective of the quantum game, the author explores the optimal prices of green product and compares them with the classical game.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

Based on the significance of ectomycorrhizae (ECM) and increased publication activity on this subject, it was decided to carry out a bibliometric analysis of scientific outputs in this area. The purpose of this study is to reveal the research trends of scientific outputs on ECM for the past 30 years and provide a potential guide for future research.

A method of bibliometric analysis was performed, based on the online version of the Science Citation Index Expanded, Web of Science, from 1986 to 2017. The authors evaluated the publication types, languages, source countries, journals, the patterns of publication outputs, most-cited articles, most-productive authors, institutional distributions, subject categories, high-frequency keywords and keywords plus and high-frequency terms in the title and abstract of ectomycorrhizal research. Keywords, keywords plus and high-frequency terms in the title and abstract were analyzed via VOSviewer to illustrate the extent of co-occurrence. This study further describes the recent research priority or hotspots and reveals the research trends.

From 1986 to 2017, the publication output on ECM showed a rising trend; the number of articles has rapidly increased after 2003. Based on co-occurrence analysis for keywords, keywords plus and terms in the title and abstract, “ectomycorrhizal fungi” is the most popular keyword and keywords plus; “concentration” is the most high-frequency terms in the title and abstracts. Plant biology, mycology and ecology are the hotspots in the ectomycorrhizal research. Ectomycorrhizal taxonomy, the molecular mechanisms of ectomycorrhizal symbioses and the common mycorrhizal networks are the future direction.

A bibliometric analysis has been carried out to analyze the trends of ECM research with 30 years. This study provides a potential guide for future research related to ectomycorrhizae.

This paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.

A systematic review is conducted to summarize the selected 109 studies published during 2012–2021 related to quantifying building energy consumption and its major estimation methodologies, tools and key influence parameters of three energy sources.

Results show that the method limitations and the variety of potential parameters lead to significant energy estimation errors. An in-depth qualitative discussion is conducted to identify research knowledge gaps and future directions.

With societies and economies developing rapidly across the world, a large amount of energy is consumed at an alarming rate. Unfortunately, its huge environmental impacts have forced many countries to take energy issues as urgent social problems to be solved. Even though the construction industry, as the one of most important carbon contributors, has been constantly and academically active, researchers still have not arrived at a clear consensus for system boundaries of life cycle energy. Besides, there is a significant difference between the actual and estimated values in countless current and advanced energy estimation approaches in the literature.

AFTER BRIEFLY considering the surface areas of aircraft for which lightning strikes are a major factor in the selection of constructional materials, a review is made of published information concerning the effects of lightning on these various materials. The materials are considered under three headings: metallic, non‐conducting (e.g. glass‐fibre‐reinforced‐plastics) and semi‐conducting (e.g. carbon‐fibre‐reinforced plastics). Simulated lightning current tests are the main source of information and the review is primarily concerned with the results of such tests. To aid assessment of the relevance of the test currents that have been used, an outline of the current characteristics of lightning discharges is also given.