• Energy Research

Abstract The rise in industrialization and urbanization has elevated consumer lifestyles and has dramatically increased copper consumption in the past three decades. Due to the growing scarcity of copper ore and lower manufacturing costs associated with using recycled copper, copper recycling has gained increasing attention. Because of the uneven geographical distribution of scrap copper, the international scrap copper trade has formed and is experiencing rapid growth. This study applies complex network theory to map the structural evolution of the global scrap copper trade from 1988 to 2017 according to the UN-Comtrade database. The results show that the global scrap copper trade has been reshaped from the pre-existing geopolitical realities through the interaction of geopolitical relations and geo-economics. The America-Asia community is the largest trading community and is driven by the USA and China. China is the largest recipient of scrap copper due to a lack of self-sufficiency with respect to copper and scrap copper resources. India is considered another long-term potential market for exporters, which can result in reducing dependence on a single market such as China. These findings, together with policy-related discussions, help authorities understand the complex international scrap copper trade relationships and propose effective import-export policies to reduce the risk of trade disruption.

Although the risk management of agricultural firms is important, studies on the extreme risk dependence among agribusiness firms are minimal due to the diversity and complexity of the risks faced by agricultural firms. In this study, we developed a hybrid approach to analyse and uncover the potential risk dependence among agricultural firms. We examined thirty-two agricultural companies to study their dependence structure for risk losses. Three primary findings emerged. First, risk dependence is strong with an average value of 0.96. Second, the dependence structure is hierarchical and includes two network communities. Third, some key agricultural firms were identified in the dependence structure. These key firms are critical for the transmission of negative impacts across agricultural firms. Correspondingly, we suggest measures and strategies (such as improving the level of technological innovation and joint risk resistance capability) to reduce the impact of risk dependence.

Abstract The China's chemical industry has been endeavouring to promote sustainable development through practicing green supply chain management (GSCM). This paper proposes a multi-criteria decision framework with twenty practices to guide companies in the industry to enact GSCM effectively. The exploratory factor analysis (EFA) has been used to cluster the proposed practices. We found five aspects, including economic initiatives, environmental management, eco-design, resource recycling, and stakeholder and employee, constitute the underlying structure of GSCM. A mixed decision tool combining the entropy weight method (EWM) and the analytic hierarchy process (AHP) has been developed and applied to identify key factors. Official website information has been collected and used to analyse the website contents of five benchmarking companies in the China's chemical industry. The results reveal that the aspects of environmental management, eco-design and resource recycling are the most important GSCM themes. Moreover, the top five practices are top management support, performing life cycle assessment, managing environmental risks, advancing recycling technologies and integrating reverse logistics. Conceptual and practical implications are discussed.

To reduce the excessive consumption of metal minerals and boost the development of the circular economy, scrap metals are increasingly recycled across the world. Due to the geographically uneven distribution of scrap metals, most countries are actively participating in the international scrap metal trade. This study collects international trade records on scrap metals from 1988 to 2017 and constructs the annual global scrap metals trade network (GSMTN) to analyze the characteristics and dynamic evolution of the scrap metal trade. The results reveal a globalization trend of the scrap metal trade, the scale-free characteristics of the trade network, and the increasing monopolization of the export market. The international scrap metal trade has experienced a dynamic evolution in the past 30 years and has developed into a complex system with a hierarchical structure that is led by a few core countries. Three relatively stable groups are the main players in the international scrap metal trade: East Asia-America-Oceania, Europe, and South Asia-Middle East. A review of the split and merger process of these communities clearly shows that geopolitics and economic turbulence are important elements in the fragmentation and integration of trade communities. The findings will enable governments to understand the complex trade relationships involved in scrap metals, which can help policy-makers propose effective import-export policies and ensure national resource security.

Abstract Coal consumption and energy production (CCEP) has received increasing attention since coal-fired power plants play a dominant role in the power sector worldwide. In China, coal is expected to retain its primary energy position over the next few decades. However, a large share of CO2 emissions and other environmental hazards, such as SO2 and NOx, are attributed to coal consumption. Therefore, understanding the environmental implications of the life cycle of coal from its production in coal mines to its consumption at coal-fired power plants is an essential task. Evaluation of such environmental burdens can be conducted using the life cycle assessment (LCA) tool. The main issues with the traditional LCA results are the lack of a numerical magnitude associated with the performance level of the obtained environmental burden values and the inherent uncertainty associated with the output results. This issue was addressed in this research by integrating the traditional LCA methodology with a weighted fuzzy inference system model, which is applied to a Chinese coal-to-energy supply chain system to demonstrate its applicability and effectiveness. Regarding the coal-to-energy supply chain under investigation, the CCEP environmental performance has been determined as “medium performance”, with an indicator score of 39.15%. Accordingly, the decision makers suggested additional scenarios (redesign, equipment replacement, etc.) to improve the performance. A scenario-based analysis was designed to identify alternative paths to mitigate the environmental impact of the coal-to-energy supply chain. Finally, limitations and possible future work are discussed, and the conclusions are presented.

Abstract This study aimed to improve the grid power quality by demand response control algorithm; by controlling the aggregate population of heating, ventilation, and air conditioning (HVAC) loads with lazy state switching mode to mitigate fluctuations of overall power consumption. It is known that the power quality is crucial to the efficiency of industrial activities. However, monitoring of power quality is not a trivial task. Increasing use of intermittent renewable power brings new problems to the reliability of the grid. New control technologies are required to maintain instantaneous balance between power generation and consumption. Like distributed generation, distributed storage, demand response is one important kind of Distributed Energy Resources (DER) in smart grid. In order to regulate power consumption of the aggregated population of loads, the units' thermostat set-point is adjusted according to the amount broadcast from the control center. In addition, the power fluctuations and high frequency of loads on/off state switching are always the cost of many control algorithms. This study provides a new concept of lazy state switching combined with the state-space model. When there are needs to change set-point, the aggregated HVAC units will change their thermostat set-point gradually. Some of the HVAC units keep their working state until the switch conditions are satisfied. Accurate estimation of the dynamics of large number of loads is really valuable for the power grid to integrate renewable sources and to provide load-shifting and other valuable services. The proposed mode is validated through realistic simulations of thousands of HVAC units using GridLAB-D. Simulation results indicate that the proposed mode can effectively reduce the fluctuations when adjusting the aggregated HVACs thermostat set-point with less on/off switching operations, without sacrificing end-use performance.

Employee training is not only important for the continuous growth of human resources but also guarantees sustainable human resource management in enterprises. It is very important to understand corporate behaviour related to employee training not only from the perspective of a single enterprise but also from that of multiple enterprises. The purpose of this study is to explore multiple enterprises’ employee training behaviours by conducting a content analysis of corporate social responsibility (sustainability) reports and a social network analysis. This study also seeks to find a way to achieve sustainable employee training by analysing the similarities in the different types of corporate training behaviours. Our analysis shows that, in 2017, 108 types of training activities were implemented by 53 enterprises; the key employee trainings (e.g., security training and skills training) and enterprises (e.g., bank of communication) are identified. The training behaviours of some of the enterprises are similar to some extent, and eight groups of firms that are very similar are identified. The results of this study show that social network analysis performs well for studying corporate employee training behaviours. Some suggestions to minimize the investment costs of training and to improve the sustainability of human resource management from the employee training perspective are provided.