• Energy Research

Abstract Millions of tonnes (teragrams) of plastic waste are traded around the world every year, which plays an important role in partially substituting virgin plastics as a source of raw materials in plastic product manufacturing. In this paper, global plastic waste trade networks (GPWTNs) from 1988 to 2017 are established using the UN-Comtrade database. The spatiotemporal evolution of the GPWTNs is analyzed. Attention is given to the country ranks, inter- and intra-continental trade flows, and geo-visual communities in the GPWTNs. We also evaluate the direct and indirect impacts of China’s plastic waste import ban on the GPWTNs. The results show that the GPWTNs have small-world and scale-free properties and a core-periphery structure. The geography of the plastic waste trade is structured by Asia as the dominant importer and North America and Europe as the largest sources of plastic waste. China is the unrivaled colossus in the global plastic waste trade. After China’s import ban, the plastic waste trade flows have been largely redirected to Southeast Asian countries. Compared with import countries, export countries are more important for the robustness of GPWTNs. Clearly, developed countries will not announce bans on plastic waste exports; these countries have strong motivation to continue to shift plastic waste to poorer countries. However, the import bans from developing countries will compel developed countries to build new disposal facilities and deal with their plastic waste domestically.

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 It is essential to reveal the optimal structure of global crude oil supply and demand, which has become one of the most important factors affecting every country’s energy strategy and economic development. However, the existing crude oil supply and demand structure does not function well. This paper proposes a distributed bipartite network to model crude oil trade. The optimal network structure, which has the minimal total trade cost is obtained by Simulated Annealing Algorithm. The presented optimization model encompasses the framework of complex network theory and crude oil trading issues, providing a good solution for the crude oil trade system. Comparing with the pre-optimized trading network, the proposed model can effectively reduce trade cost. The Robustness Indicator is proposed to reveal that the major oil-exporting countries choose their partners more wisely and the trade relations are steadier. In the optimal distributed network, both the major oil-exporting countries and major oil-importing countries play important roles in distributing crude oil among the trading countries. Overall, the proposed model offers fresh insights for structural reconstruction of crude oil supply and demand network centered on the efficient usage of crude oil and keeping the international oil trade running smoothly.

Abstract Copper raw materials (CRM) and copper waste and scrap (CWS) are the two main sources of copper manufactured products. Due to the uneven geographical distribution of copper production and consumption, international CRM and CWS trade developed. However, no study has explored the complicated interdependencies between CRM trade and CWS trade or investigated the characteristics of this multiplex trade system. This study uses trade records from 1988 to 2017 to construct multiplex trade networks: a global CRM trade network and a global CWS trade network. The evolution of copper trade from 1988 to 2017 is reviewed, and the intricate relationships in the multiplex trade network are identified. It is found that CWS trade has a highly positive correlation with CRM trade, but there are obvious differences between CWS trade and CRM trade in the multilateral trade structure. Multilateral trade structures driven by core exporting countries and core importing countries are prominent in CRM trade and CWS trade, respectively. In addition, the impacts of China's restrictive policies on the multiplex trade system are analyzed. The results provide policy implications for countries regarding copper resource security strategies and safeguarding the multiplex trading system.

PurposeWith rapid industrialization and urbanization, municipal solid waste (MSW) management has become a serious challenge worldwide, especially in developing countries. The Beijing Municipality is a representative example of many local governments in China that are facing MSW management issues. Although there have been studies in the area of MSW management in the literature, less attention has been devoted to developing a structured framework that identifies and interprets the barriers to MSW management in megacities, especially in Beijing. Therefore, this study focuses on identifying a comprehensive list of barriers affecting the successful implementation of MSW management in Beijing.Design/methodology/approachThrough an extensive review of related literature, 12 barriers are identified and classified into five categories: government, waste, knowledge dissemination, MSW management process and market. Using an integrated approach including the decision-making trial and evaluation laboratory (DEMATEL), maximum mean de-entropy algorithm (MMDE) and interpretive structural modeling (ISM), a conceptual structural model of MSW implementation barriers is constructed to provide insights for industrial decision-makers and policymakers.FindingsThe results show that a lack of economic support from the government, imperfect MSW-related laws and regulations, the low education of residents and the lack of publicity of waste recycling knowledge are the main barriers to MSW management in Beijing. Combined with expert opinions, the paper provides suggestions and guidance to municipal authorities and industry practitioners to guide the successful implementation of MSW management.Practical implicationsThe findings of this study can provide a reference for MSW management in other metropolises in China and other developing countries.Originality/valueThis study proposes a hybrid DEMATEL-MMDE-ISM approach to resolve the subjectivity issues of the traditional ISM approach and it analyzes the barriers that hinder MSW management practices in Beijing.

PurposeThe transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.Design/methodology/approachThis research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.FindingsThe prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R2). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.Research limitations/implicationsThe case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.Originality/valueIn prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.