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To cope with the severe situation of the global climate, China proposed the “30 60” dual-carbon strategic goal. Based on this background, the purpose of this paper is to investigate scientifically and reasonably the interprovincial pattern of China’s power carbon emission intensity and further explore the causes of differences on this basis.

Considering the principle of “shared but differentiated responsibilities,” this study measures the carbon emissions within the power industry from 1997 to 2019 scientifically, via the panel data of 30 provinces in China. The power carbon emission intensity is chosen as the indicator. Using the Dagum Gini coefficient to explore regional differences and their causes.

The results of this paper show that, first, China’s carbon emission intensity from the power industry overall is significantly different. From the perspective of geospatial distribution, the three regions have unbalanced characteristics. Second, according to the decomposition results of the Gini coefficient, the overall difference in power carbon emission intensity is generally expanding. The geospatial and economic development levels are examined separately. The gaps between the eastern and economically developed regions are the smallest, and the regional differences are the source of the overall disparity.

Further exploring the causes of differences on this basis is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies. This study provides direction for analyzing the green and low carbon development of China’s power industry.

As an economic indicator of green and low-carbon development, CO₂ intensity of power industry can directly reflect the dependence of economic growth on the high emission of electricity and energy. and further exploring the causes of differences on this basis is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies.

For a long time, with the rapid economic development, resulting in the unresolved contradiction between low energy efficiency and high carbon emissions. To this end, scientifically and reasonably investigating the interprovincial pattern of China’s power carbon emission intensity, and further exploring the causes of differences on this basis, is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies.

Third, considering the influence of spatial factors on the convergence of power carbon emission intensity, a variety of different spatial weight matrices are selected. Based on the β-convergence theory from both absolute and conditional perspectives, we dig deeper into the spatial convergence of electricity carbon emission intensity across the country and the three regions.

More and more work zone projects come with the needs of new construction and regular maintenance-related investments in transportation. Work zone projects can have many significant impacts socially, economically and environmentally. Minimizing the total impacts of work zone projects by optimizing relevant schedules is extremely important. This study aims to analyze the impacts of scheduling long-term work zone activities.

Optimal scheduling of the starting dates of each work zone project is determined by developing and solving using a bi-level genetic algorithm (GA)–based optimization model. The upper level sub-model is to minimize the total travel delay caused by work zone projects over the entire planning horizon, whereas the lower level sub-model is a traffic assignment problem under user equilibrium condition with elastic demand.

Sioux Falls network is used to develop and test the proposed GA-based model. The average and minimum total travel delays (TTDs) over generations of the proposed GA algorithm decrease very rapidly during the first 20 generations of the GA algorithm; after the 20th generations, the solutions gradually level off with a certain level of variations in the average TTD, showing the capability of the proposed method of solving the multiple work zone starting date optimization problem.

The proposed model can effectively identify the near-optimal solution to the long-term work zone scheduling problem with elastic demand. Sensitivity analysis of the impact of the elastic demand parameter is also conducted to show the importance of considering the impact of elastic demand parameter.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

With the yearly increase of mileage and passenger volume in China's high-speed railway, the problems of traditional paper railway tickets have become increasingly prominent, including complexity of business handling process, low efficiency of ticket inspection and high cost of usage and management. This paper aims to make extensive references to successful experiences of electronic ticket applications both domestically and internationally. The research on key technologies and system implementation of railway electronic ticket with Chinese characteristics has been carried out.

Research in key technologies is conducted including synchronization technique in distributed heterogeneous database system, the grid-oriented passenger service record (PSR) data storage model, efficient access to massive PSR data under high concurrency condition, the linkage between face recognition service platforms and various terminals in large scenarios, and two-factor authentication of the e-ticket identification code based on the key and the user identity information. Focusing on the key technologies and architecture the of existing ticketing system, multiple service resources are expanded and developed such as electronic ticket clusters, PSR clusters, face recognition clusters and electronic ticket identification code clusters.

The proportion of paper ticket printed has dropped to 20%, saving more than 2 billion tickets annually since the launch of the application of E-ticketing nationwide. The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. Meanwhile, problems of paper ticket counterfeiting, reselling and loss have been generally eliminated.

E-ticketing has laid a technical foundation for the further development of railway passenger transport services in the direction of digitalization and intelligence.

China's economic transition is essentially the process of China's institutional changes. During the changes, the appearance of institutional innovation is not regular; instead, it is intermittent and random. The purpose of this paper is to show that the fitful appearance of institutional innovation is the root of China's economic growth and fluctuations.

This paper constructs a real business cycle (RBC) model introducing the institutional factor expressed in the quantitative form under the dynamic stochastic general equilibrium (DSGE) framework by measuring China's institutional changes quantitatively.

By comparing the characteristics of the actual economic data with those of the simulated economic data, we find that this RBC model can explain 94.44%, 66.07%, 23.46%, 21.03% and 15.45% of the cyclical fluctuations in output, investment, labor, consumption and capital, respectively.

The impulse response analysis finds that the institutional shocks have a relatively long duration, lasting about 30 years, and decline slowly over time, while technological shocks decline relatively fast, lasting approximately ten years.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Metaverse is a virtual application spawned by digital technology that is becoming increasingly relevant to our lives. However, for the opportunities created and challenges posed by the metaverse, its important elements and future evolution trend remain largely unknown. Thus, this paper aims to understand the current status of metaverse research and its future research directions.

Based on the analysis of the literature data on the metaverse both in English and Chinese using Latent Dirichlet allocation (LDA) topic modeling and bibliometrics, this study discussed the related research and development trend of the metaverse. The authors first defined the concept of the metaverse and analyzed 1,378 English articles from seven publishers and 590 Chinese articles from the CNKI database. Following that, the authors summarized three important themes from the current studies: virtual world, metaverse technologies and metaverse applications. Finally, a framework of future directions on metaverse research was proposed.

The review found that during the rapid development of the metaverse, opportunities and challenges coexisted. In the virtual world, metaverse technologies drive the implementation of application scenarios, and in turn, applications promote the improvement of technologies. The interrelationship between technology and application lays the foundation for the development of the metaverse. Future metaverse research will generate different research directions.

This review provides a valuable, systematic perspective for individuals who want to understand the metaverse. The conceptual framework on metaverse research proposed in this paper offers a comparison of literature analysis from domestic and international perspectives and brings new insights into the development of the metaverse.

This study aims to investigate the antecedents and consequences of green packaging behavior (GPB). A conceptual model has been developed wherein green packaging awareness (GPA) and green packaging initiatives (GPI) are precursors of GPB, and environmental concern and availability of various green packaging alternatives are moderators. The outcomes of GPB in terms of environmental and personal benefits are examined.

Unlike most papers focusing on green packaging from a marketing perspective, this study explores the behavior of rural households from 47 villages in southern India. A carefully crafted survey instrument was developed, and data were collected from 395 respondents. After checking the instrument’s psychometric properties, the results were analyzed using Hayes’s PROCESS macros.

The results indicate that GPA and GPI are positively associated with GPB, GPA predicts GPI, and GPI mediates the relationship between GPA and GPB. Furthermore, findings suggest that environmental concern moderates the relationship between GPI and GPB, and the three-way interaction between the availability of green packaging (second moderator), environmental concern (first moderator), and GPI influences the GPB. Moreover, the outcomes of GPB in terms of environmental and personal benefits are established.

This research has several theoretical implications. It documents that individual awareness of green packaging is a precursor to GPB. This study focused on the rural population from a developing country (India) and hence may suffer from a lack of generalizability across developed nations. However, the results could be generalizable across other developing nations worldwide.

This study motivates individuals to engage in proenvironmental behavior. Moreover, it highlights the importance of GPB in deriving environmental and personal benefits. It is also equally crucial for individuals to engage in proper waste management practices so that the environment is not polluted.

The findings from this research are helpful to society as it focuses on the proenvironmental behavior of individuals. Particularly concerning packaging, this study points out that buying products with green packaging and reusing and recycling such packages is essential to protect the environment.

This study fills the gaps in the literature by focusing on the GPB of the rural population. To the best of the authors’ knowledge, the moderated-mediation model developed and tested in this research is the first of its kind and thus makes a significant contribution to the literature on green packaging and waste management.

Scholarly research on e-procurement has been limited and, like e-government, e-procurement has been researched primarily from the perspective of adoption/non-adoption. This paper aims to focus on public administration employees’ perceptions of the quality of národní elektronický nástroj (NEN) – the Czech national e-procurement tool they are required to use.

The paper is based primarily on statistical analysis of data obtained through two questionnaire surveys addressed to contacts from of all Czech central state administration bodies using NEN; 175 completed questionnaires were gathered in 2020 and 128 in 2022 and subjected to statistical analysis in SPSS.

NEN was launched as fully operational in August 2015. The research indicates that in 2022 there were still important gaps in the quality of NEN as perceived by public employees.

The paper has important practical implications for e-procurement policymakers. It shows that making the e-procurement system compulsory is not sufficient. The government needs to guarantee that it would be competitive with tools that would otherwise be preferred. Otherwise, the application of the digital-by-default principle may lead to institutionalisation of services that are not user-friendly. This has important implications for e-government/e-procurement management and change management.

Little is known about public employees’ perceptions of the quality of e-government and e-procurement. Although e-procurement is an area where the digital-by-default principle was implemented rather early, the quality of e-procurement has still received limited attention in research.

To examine whether and how the different levels of regional economic incentives would have an effect on underwriters' market share in general.

Drawing on Chinese IPO firms during the period 2006-2016, this study examines the impact of different levels of regional economic incentives on underwriters' market share.

The authors find that regional economic incentives have a positive impact on underwriters' market share and that local economic incentives have a significantly stronger impact than central economic incentives. Furthermore, the authors find that IPO firms with underwriters driven by regional economic incentives experience worse post-IPO performance than firms with underwriters driven by central economic incentives, which do not experience a significant decline in post-IPO performance.

Taken together, the authors’ findings are consistent with the notion that performance assessment motivates officials at various levels of government to bring companies in their jurisdiction to the IPO market prematurely. In addition, the results indicate that central economic incentives play a significant role in driving China's macroeconomic development and market-oriented system reforms. As such, they are one of the major driving forces behind China's market-oriented system reforms.