Abstract The increasing global industrialization and over-exploitation of fossil fuels has induced the release of greenhouse gases, leading to an increase in global temperature and causing environmental issues. There is therefore an urgent necessity to reach net-zero carbon emissions. Only 4.5% of countries have achieved carbon neutrality, and most countries are still planning to do so by 2050–2070. Moreover, synergies between different countries have hampered synergies between adaptation and mitigation policies, as well as their co-benefits. Here, we present a strategy to reach a carbon neutral economy by examining the outcome goals of the 26th summit of the United Nations Climate Change Conference of the Parties (COP 26). Methods have been designed for mapping carbon emissions, such as input–output models, spatial systems, geographic information system maps, light detection and ranging techniques, and logarithmic mean divisia. We present decarbonization technologies and initiatives, and negative emissions technologies, and we discuss carbon trading and carbon tax. We propose plans for carbon neutrality such as shifting away from fossil fuels toward renewable energy, and the development of low-carbon technologies, low-carbon agriculture, changing dietary habits and increasing the value of food and agricultural waste. Developing resilient buildings and cities, introducing decentralized energy systems, and the electrification of the transportation sector is also necessary. We also review the life cycle analysis of carbon neutral systems. 

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Strategies to achieve a carbon neutral society: a review

The iron and steel industry (ISI) is energy-intensive and is responsible for approximately 25% of the global direct greenhouse gas (GHG) emissions from industrial sectors. As the largest steel producer and consumer, China bears the primary responsibility for saving energy and reducing GHG emissions; accordingly, they have developed many strategies for GHG abatement. However, owing to the high investment costs and long equipment service lives, the ISI must carefully weigh the cost and emission reduction potential of these approaches. This review discusses research findings aimed at technological improvements and ultra-low carbon technologies relevant to the ISI, emphasizing their cost-effectiveness and development prospects. Based on the life cycle analysis method, this review establishes a comprehensive analytical framework to integrate the results from different studies to consider more factors in the design of GHG emission reduction strategies. The results indicate that the full application of mainstream technological improvements can reduce CO2 emissions by approximately 43%. Furthermore, combining these strategies with ultra-low carbon technologies can achieve a reduction of 80%–95%. The marginal cost reduction associated with implementing such technological improvements is in the range of −5 to 0.5 USD/kgCO2. Applying carbon capture, utilization, and storage strategies or hydrogen-based technologies in China's ISI for deep decarbonization scenarios is expected to lead to cost reductions between 12 and 35 billion USD by 2050. We propose that China's ISI requires technological improvements in the short term and should prioritize ultra-low carbon technology development for the long term.

A review of CO2 emissions reduction technologies and low-carbon development in the iron and steel industry focusing on China

With the rapid industrialization, increasing of fossil fuel consumption and the environmental impact, it is an inevitable trend to develop clean energy and renewable energy. Hydrogen, for its renewable and pollution-free characteristics, has become an important potential energy carrier. Hydrogen is regarded as a promising alternative fuel for fossil fuels in the future. Therefore, it is very necessary to summarize the technological progress in the development of hydrogen energy and research the status and future challenges. Hydrogen production and storage technology are the key problems for hydrogen application. This study applied bibliometric analysis to review the research features and trends of hydrogen production and storage study. Results showed that in the 2004-2018 period, China, USA and Japan leading in these research fields, the research and development in the world have grown rapidly. However, the development of hydrogen energy still faces the challenge of high production cost and high storage requirements. Photocatalytic decomposition of water to hydrogen has attracted more and more research in hydrogen production research, and the development of new hydrogen storage materials has become a key theme in hydrogen storage research. This study provides a comprehensive review of hydrogen production and storage and identifies research progress on future research trend in these fields. It would be helpful for policy-making and technology development and provide suggestions on the development of a hydrogen economy.

Trends and future challenges in hydrogen production and storage research

Experimental study on heat pipe thermoelectric generator for industrial high temperature waste heat recovery

Improving carbon emissions performance in Chinese cities is a crucial way to promote China’s sustainable development. Employing the super-efficiency SBM model, we first estimate the carbon emissions efficiency (CEE) of 262 Chinese cities from 2003 to 2016. Then we study and explain the club convergence of CEE combining Markov and spatial Markov models and Moran’s I test method. The results show that CEE has improved, especially for the western and northeastern cities. The efficiency of the northwest cities is low, while those of the central and coastal cities are relatively high. Club convergence exists in China’s urban CEE. Cities with high- and low-level efficiency have much higher convergence levels. There are significant spatial agglomeration and spillover effects in China’s urban CEE, contributing to the club convergence. Our analysis suggests that “cross-border” cooperation and communication between cities in different clubs should be highly promoted. Cities in high-level efficiency clubs are encouraged to play its role in radiating the lower-level cities. And the Chinese government is encouraged to strengthen carbon emissions mitigation in low-level areas through combining the green “Belt and Road” construction with the establishment of a national carbon market.

Carbon emissions performance trend across Chinese cities: evidence from efficiency and convergence evaluation

Assessment of the carbon emissions reduction potential of China's iron and steel industry based on a simulation analysis

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0. 

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A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Mining consensus sequence in multi-criteria group decision making with incomparability of alternatives and conflicts of experts

The ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French, meaning organization, storage and synthesis of relevant data in English) method is an effective multi-criteria decision-making method which takes into account the importance of both criteria and alternatives. Nevertheless, how to objectively determine the thresholds used in the ORESTE method is still an unsolved issue. In this regard, this study proposes a model that combines the threshold learning method based on historical data and the traditional MCDM (multi-criteria decision-making) method, to learn the thresholds used in the ORESTE method from the preference information provided by stakeholders. We implement a simulation analysis to verify the feasibility of this model and reflect the relation between the weights balancing the importance of alternatives and criteria and the thresholds, and the relation among different thresholds. Finally, an illustration concerning the selection of lung cancer screening methods is given to show the applicability of the threshold learning model. Sensitivity analysis, comparative analysis, and managerial insights are also provided. 

Learning the thresholds in the ORESTE method from historical preference information

Preference change, also known as preference drift, is one of the factors that online retailers need to consider to accurately collect consumer preferences and make personalised recommendations. Online reviews have been widely used to analyse the preference drift of consumers. However, previous studies on online reviews ignored the psychological perceptions of consumers in terms of satisfaction. This paper aims to develop a method for dynamic preference elicitation from online reviews based on exploring the theory of consumer satisfaction formation. Based on the framework of expectation confirmation theory, we develop formulas for expressing the relations among expectation, perceived performance, confirmation, and satisfaction. We then use the proposed dynamic preference elicitation model to predict the change of consumer overall preference after each review and rank products for consumers’ next purchase. We test the proposed approach with a case study based on a data set from Amazon.com. It is founded that the satisfaction changes in each purchase, and this change will affect the prediction of the next product ranking. The case study is based on one product group, and further research is needed to see if the operation of the proposed method can be extended to other kinds of products. 

Keyu Lu

Papers

Assessment of the carbon emissions reduction potential of China's iron and steel industry based on a simulation analysis

A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Mining consensus sequence in multi-criteria group decision making with incomparability of alternatives and conflicts of experts

Learning the thresholds in the ORESTE method from historical preference information

Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory