Mingzhen Song, Lingcheng Kong and Jiaping Xie
Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of…
Abstract
Purpose
Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.
Design/methodology/approach
Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.
Findings
(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.
Originality/value
This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.
Highlights
Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.
Investment strategy of energy storage power stations on the supply side of wind power generators.
Impact of pricing method on the investment decisions of energy storage power stations.
Impact of pricing method, energy storage investment and incentive policies on carbon emissions.
A two-stage wind power supply chain including energy storage power stations.
Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.
Investment strategy of energy storage power stations on the supply side of wind power generators.
Impact of pricing method on the investment decisions of energy storage power stations.
Impact of pricing method, energy storage investment and incentive policies on carbon emissions.
A two-stage wind power supply chain including energy storage power stations.
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Yudan Dou, Xiliang Sun, Ankang Ji, Yuna Wang and Xiaolong Xue
Owing to multiple superiorities to traditional counterparts, prefabricated construction (PC) has gained increasing attention worldwide. The development of PC projects reflects the…
Abstract
Purpose
Owing to multiple superiorities to traditional counterparts, prefabricated construction (PC) has gained increasing attention worldwide. The development of PC projects reflects the effects of both policy supervision and PC practice, which aids the government in reasonably identifying the key issues of PC's promotion and rationally improving the policy deployment. However, existing studies fail to address this aspect, especially lacking quantitative exploration. This study explores the micro mechanism of PC's promotion, from the perspective of developing PC projects.
Design/methodology/approach
A tripartite evolutionary game model based on prospect theory of the government, developers and contractors is constructed. After rigorous theoretical deduction, this study adopts Changchun in China as a case city and collects the data using the Delphi technique, policy documents and literature analysis.
Findings
Results indicate that contractors are generally willing to implement PC projects and the government chooses to actively supervise PC's promotion. The negative investment behavior of developers is the main obstacle to promote PC in Changchun currently.
Practical implications
The conclusions are applicable to other comparable regions. This study is of value to promote PC with high efficiency and effect.
Originality/value
The tripartite evolutionary game model based on prospect theory proposed in this study is conducive to reveal the essence of PC's promotion. This is an important breakthrough in extant studies, with a broad applicability in the PC domain beyond China.
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Zhenshuang Wang, Wanchen Xie and Jingkuang Liu
The growth of the Chinese economy has resulted in a significant increase in construction and demolition waste (CDW), and regional differences in CDW generation are gradually…
Abstract
Purpose
The growth of the Chinese economy has resulted in a significant increase in construction and demolition waste (CDW), and regional differences in CDW generation are gradually increasing. The purpose of this study is to investigate the regional differences in CDW generation and the driving factors that influence CDW generation in different areas of China. To provide a systematic advisement for local governments to select the appropriate policy, reduce CDW generation.
Design/methodology/approach
The generation of CDW was calculated by region, based on the area estimation method, from 2005 to 2018. The relationship between CDW generation and economic development, and the driving factors of CDW generation in different regions of China, was investigated using the environmental Kuznets curve (EKC) model and the STIRPAT theoretical model.
Findings
CDW generation of China increased at the average annual growth rate of 10.86% from 2005 to 2018. The main areas of CDW generation were concentrated in the eastern and central regions, while the proportion of CDW generation in the northeast region decreased gradually, and the changes varied significantly across different regions. The EKC between CDW generation and economic development was established for the whole country, North China, Northeast China, East China, Central South China, Southwest China and Northwest China. Three main factors based on the STIRPAT theoretical model were identified and explained into a framework to reduce CDW generation. The results provided a useful theoretical basis and data support guide for devising effective policies and regulations for the Chinese context.
Practical implications
The findings from this study can ultimately support policymakers and waste managers in formulating effective policies for waste management strategies and CDW-specific legislation. Additionally, it can help the coordinated reduction of CDW generation across regions in China and can support construction enterprises (in their development strategies), similar developing economies and foreign firms planning to operate in China.
Originality/value
This study contributes to the field through the STIRPAT model on driving factors of CDW generation in the Chinese context, in different regions.
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Qinyang Bai, Xaioqin Yin, Ming K. Lim and Chenchen Dong
This paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic…
Abstract
Purpose
This paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost.
Design/methodology/approach
This study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set.
Findings
The result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax.
Research limitations/implications
There are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.
Originality/value
Existing research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.
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Saeed Loghman and Azita Zahiriharsini
Research focusing on psychological capital (PsyCap) has been mainly conducted at the individual level. However, recent research has expanded investigations to the collective level…
Abstract
Research focusing on psychological capital (PsyCap) has been mainly conducted at the individual level. However, recent research has expanded investigations to the collective level with a greater focus on team-level PsyCap. Although, as demonstrated by recent systematic reviews and meta-analyses, the relationships between individual-level PsyCap and the desirable/undesirable outcomes are fairly established in the literature, less is known about such relationships for team-level PsyCap. One of these important, yet least investigated, research areas is the research stream that focuses on the relationship between team-level PsyCap and the outcomes of health, Well-Being, and safety. This chapter aims to highlight the role of individual-level PsyCap as an important predictor of employees’ health, Well-Being, and safety outcomes, but also to go beyond that to provide insights into the potential role of team-level PsyCap in predicting such outcomes at both individual and team levels. To do so, the chapter first draws upon relevant theories to discuss the empirical research findings focusing on the relationship between individual-level PsyCap and the outcomes of health, Well-Being, and safety. It then focuses on team-level PsyCap from theoretical, conceptualization, and operationalization perspectives and provides insights into how team-level PsyCap might be related to health, Well-Being, and safety outcomes at both individual and team levels. Thus, this chapter proposes new research directions in an area of PsyCap that has been left unexplored.
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This work examines a repairable machining system’s reliability by considering multiple failure scenarios, including individual component failures, hardware and software…
Abstract
Purpose
This work examines a repairable machining system’s reliability by considering multiple failure scenarios, including individual component failures, hardware and software malfunctions, failures resulting from shared causes and failures caused by human error. When a system is susceptible to several modes of failure, the primary goal is to forecast availability and other reliability metrics as well as to calculate the expected profit of the repairable machining system.
Design/methodology/approach
The process of recovering after a system failure involves inspecting the system and fixing any malfunctions that may have occurred. The repair procedures for all kinds of faults are taken to follow a general distribution to represent real-time circumstances. We develop a non-Markovian stochastic model representing different system states that reveal working, failed, degraded, repair and delayed repair states. Laplace transformation and the supplementary variable technique are used to assess the transient states of the system.
Findings
Analytical expressions for system performance indices such as availability, reliability and cost-benefit analysis are derived. The transient probabilities when the system experiences in different states such as failed, degraded and delayed states are computed. The results obtained are validated using Mathematica software by performing a numerical illustration on setting default values of unknown parameters. This ensures the accuracy and reliability indices of the analytical predictions.
Originality/value
By methodically examining the system in its several states, we will be able to spot possible problems and offer efficient fixes for recovery. The system administrators would check to see if a minor or major repair is needed, or if a replacement is occasionally taken into consideration to prevent recurring repairs.
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Yiwei Zhao, Yindong Sun, Qianqian Zhou, Caiyun Cui and Yong Liu
The aim of this paper is to research the acceptance mechanism of building information modeling (BIM) technology and to explore the differences among…
Abstract
Purpose
The aim of this paper is to research the acceptance mechanism of building information modeling (BIM) technology and to explore the differences among Architecture/Engineering/Construction (A/E/C) professionals with different individual characteristics. The proposed acceptance mechanism of BIM technology is intended to be used by industry stakeholders to propose decisions and measures, and improve the degree of BIM adoption.
Design/methodology/approach
Traditional hypothesis testing is adopted by the current study to empirically research the specific mechanism of A/E/C professionals accepting BIM technologies. In the one phase, a conceptual model based on technology acceptance model (TAM) and technology organization environment (TOE) theory was established and a large-scale questionnaire survey was conducted. In the other phase, structural equation modeling (SEM) was used to analyze acquired sample data, so as to empirically test the validity of the proposed linkage.
Findings
The results show first that perceived ease of use has no significant influence on perceived usefulness, and perceived usefulness has no significant effect on behavior intention as well. Second, BIM technical features and government BIM policies have positive effects on perceived usefulness, BIM technical features and organization supports have positive effects on perceived ease of use. Third, the BIM acceptance mechanism of A/E/C professionals is diverse from respondents with different characteristics, e.g. regions and working time.
Originality/value
The authors highlight the large sample size, as well as the nationwide context, of the questionnaire survey. Meanwhile, acceptance differences among A/E/C professionals with different demographic characteristics have been clarified using profound data and t-test. The findings of this study enrich the research on the acceptance mechanism of BIM technology, and contribute to relevant stakeholders taking targeted measures to promote the effective application of BIM technology nationwide.
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Jinjie Xue, Hongping Yuan and Zizhen Geng
This study aims to investigate impacts of classic transaction cost-related factors (i.e. partner selection cost, specific asset investment and extorting rent cost) on joint…
Abstract
Purpose
This study aims to investigate impacts of classic transaction cost-related factors (i.e. partner selection cost, specific asset investment and extorting rent cost) on joint venture (JV) partner’s cooperative and opportunistic behaviour, from the perspective of transaction cost economics.
Design/methodology/approach
Item measurements, based on which the questionnaire was developed, were derived according to a thorough search and review of related literature. In all, 226 valid responses from manufacturing enterprises in China were collected. A structural equation modelling approach was used to analyse the data and examine the fitness of the proposed model.
Findings
This study shows that partner selection cost, specific asset investment and extorting rent cost are positively related to a JV partner’s cooperative behaviour. Specific asset investment exerts the most significant influence on partner’s cooperative behaviour. The results also reveal that partner’s opportunistic behaviour is not significantly affected by specific asset investment but is negatively influenced by extorting rent cost. Both partner selection cost and extorting rent cost show positive impacts on specific asset investment.
Research limitations/implications
The investigation focused on only manufacturing enterprises in one country. Future research could be directed to investigating other countries to increase the generalizability of the findings.
Practical implications
The findings suggest that increasing the extorting rent cost to promote the probability of specific asset investment is a core element to enhance JV partner cooperation.
Originality/value
The study not only empirically investigates the relative importance of classic transaction cost-related factors on JV partner opportunism and cooperation, but also enables a deeper understanding of the interrelationship among the classic transaction cost-related factors and their influences on partner cooperation and opportunism.
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Jane L.J. Hao, Vivian W.Y. Tam, H.P. Yuan, J.Y. Wang and J.R. Li
The aim of this paper is to develop a model based on system dynamics (SD) approach, which integrates three subsystems for simulating construction and demolition (C&D) waste…
Abstract
Purpose
The aim of this paper is to develop a model based on system dynamics (SD) approach, which integrates three subsystems for simulating construction and demolition (C&D) waste management in Shenzhen, Mainland China.
Design/methodology/approach
SD approach was first used to construct the model for C&D waste management in Shenzhen. The model was then converted for running on computer through the software package “iThink”, which was specifically designed for SD modeling. The data required for model simulation was derived through various ways, including literature review, examination of official reports and yearbooks, and questionnaires. After all the parameters in the model were determined, simulation was carried out.
Findings
The model proposed in this research can provide an experimental simulation platform to investigate the complexity and interdependencies of factors in managing C&D waste in Shenzhen, Mainland China. The simulation results show that the pressing situation of C&D waste management in Shenzhen would aggravate if no effective measures were taken to address it during the simulation period. Participants' active participation and cost consideration are the two major factors affecting C&D waste reduction. Furthermore, new landfills should be planned to properly handle the C&D waste accumulated in Shenzhen over the past few years.
Originality/value
Although some studies have been conducted under the topic of C&D waste management during the past few years in Shenzhen – how to effectively and efficiently handle the waste is still unsolved. Factors affecting the effectiveness of C&D waste management have separately been examined in the previous studies, without considering their interrelations. The SD‐based model is developed in this research to investigate the complex and interdependent relationships among these factors. The model can deepen participants' understandings about C&D waste management and help explore the major factors affecting the effectiveness of management activities. The measures, which are proposed based on the simulation results, can serve as a valuable reference for planning C&D waste management in Shenzhen.
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Precast construction has become increasingly popular in the construction industry. Nonetheless, the logistics of construction materials has been a neglected topic, and this…
Abstract
Purpose
Precast construction has become increasingly popular in the construction industry. Nonetheless, the logistics of construction materials has been a neglected topic, and this neglect has resulted in delays and cost overruns. Careful planning that considers all of the factors affecting construction logistics can ensure project success. The purpose of this paper is to examine the potential for using genetic algorithms (GAs) to derive logistics plans for materials production, supply and consumption.
Design/methodology/approach
The proposed GA model is based on the logistics of precast components from the supplier’s production yard, to the intermediate warehouse and then to the construction site. Using an activity-based costing (ABC) approach, the model not only considers the project schedule, but also takes into account the production and delivery schedule and storage of materials.
Findings
The results show that GAs are suitable for solving time-cost trade-off problems. The optimization process helps to identify the activity start time during construction and the delivery frequency that will result in the minimal cost. What-if scenarios can be introduced to examine the effects of changes in construction logistics on project outcomes.
Originality/value
This paper presents a method for using GAs and an ABC approach to support construction logistics planning decisions. It will help construction planners and materials suppliers to establish material consumption and delivery schedules, rather than relying on subjective judgment.