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Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Based on the extended resource-based view (ERBV), this paper aims to investigate the relationship between sustainable supply chain management (SSCM), dynamic capabilities (DCs) and enterprise economic performance (EEP). Both the direct effects of SSCM on economic performance and the mediation effect of DCs are investigated. This empirical study also examines the moderating role of firm size.

This study applies hierarchical regression analyses to test our hypotheses, and then the mediation test was performed using the macro PROCESS. Data were collected from 178 Chinese manufacturing firms.

The findings show that SSCM practices significantly and positively influence both economic performance and DCs. The results indicate that DCs partially mediate the relationship between SSCM practices and EEP. Moreover, firm size has a moderating effect on external SSCM practices that influence EEP, but the moderating effect was not found to be significant for the effects of internal SSCM practices on economic performance and SSCM practices on DCs.

This study reveals insights into the potential benefits for large enterprises and SMEs related to the utilization of SSCM practices in China and puts forward differentiated suggestions for SSCM practices in large enterprises and SMEs.

Drawing on the ERBV, this study provides a deeper perspective on the relationship between SSCM and EEP by regarding DCs as a mediating variable and firm size as a moderating variable.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

This study reveals the green building development path and analyzes the optimal government subsidy equilibrium through evolutionary game theory and numerical simulation. This was done to explore the feasible measures and optimal incentives to achieve higher levels of green building in China.

First, the practice of green building in China was analyzed, and the specific influencing factors and incentive measures for green building development were extracted. Second, China-specific evolutionary game models were constructed between developers and homebuyers under the market regulation and government incentive mechanism scenarios, and the evolutionary paths were analyzed. Finally, real-case numerical simulations were conducted, subsidy impacts were mainly analyzed and optimal subsidy equilibriums were solved.

(1) Simultaneously subsidizing developers and homebuyers proved to be the most effective measure to promote the sustainability of green buildings. (2) The sensitivity of developers and homebuyers to subsidies varied across scenarios, and the optimal subsidy level diminished marginally as building greenness and public awareness increased. (3) The optimal subsidy level for developers was intricately tied to the building greenness benchmark. A higher benchmark intensified the developer’s responsiveness to losses, at which point increasing subsidies were justified. Conversely, a reduction in subsidy might have been appropriate when the benchmark was set at a lower level.

The expeditious advancement of green buildings holds paramount importance for the high-quality development of the construction industry. Nevertheless, the pace of green building expansion in China has experienced a recent deceleration. Drawing insights from the practices of green building in China, the exploration of viable strategies and the determination of optimal government subsidies stand as imperative initiatives. These endeavors aim to propel the acceleration of green building proliferation and materialize high-quality development at the earliest juncture possible.

The model is grounded in China’s green building practices, which makes the conclusions drawn more specific. Furthermore, research results provide practical references for governments to formulate green building incentive policies.

The purpose of this study is to explore how particle shape influences the screening, including screening efficiency per unit time, and the relationship between vibration parameters and screening efficiency per unit time in discrete element method (DEM) numerical simulations.

In this paper, a three-dimensional discrete element model of vibrating screen with composite vibration form of swing and translation was proposed to simulate the screening process. In total, 11 kinds of non-spherical particles whose shapes changed in a continuous regularity gradual process were established using a multi-sphere method. In the DEM simulations, vibration parameters, including vibration frequency, vibration amplitude and stroke angle, and swing parameters, including swing frequency and swing angle, were changed to perform parametric studies.

It shows that the effect of particle shape on screening efficiency is quantitative actually. However, the trends of different shape particles’ screening efficiency per unit time are mainly consistent.

Some simple particle shapes can be expected to be explored to do screening simulation studies reasonably with modification of the simulation data in DEM numerical simulations. That may improve the computational efficiency of numerical simulations and provide guidance to the study of the screening process.

This study examines the impact of climate legislation on green agricultural production and tests the heterogeneous impact of different types of climate legislation on agricultural green production.

In this study, the super-slacks-based measure (super-SBM) model is used to calculate agricultural green total factor productivity (AGTFP). The impact of climate legislation (including legislative acts and executive orders) on AGTFP is examined through regression analysis. The transmission mechanism of climate legislation affecting agricultural green production is further investigated.

This study shows that climate legislation has a positive long-term effect on AGTFP. It stimulates innovation in agricultural green technology but has a negative impact on resource allocation efficiency. Executive orders have a more significant effect on AGTFP than climate legislative acts. The effectiveness of climate legislation is more significant in countries with stronger legislation. Moreover, climate legislation reduces AGTFP in low-income countries while enhancing AGTFP in high-income countries. This effect is most prominent in upper-middle-income countries.

This study examines the different effects of various types of climate legislation, considering the level of economic development and the strength of the legal system on AGTFP. The findings can offer a global perspective and insights for China’s policymaking.

Graphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search.

After reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters.

The experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied.

This research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.

The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies.

Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships.

Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns.

The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor.

Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking.

While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

Abnormal behaviors such as rework, backlog, changes and claims generated by project organizations are unavoidable in complex projects. When abnormal behaviors emerge, the previously normal state of interactions between organizations will be altered to some extent. However, previous studies have ignored the associations and interactions between organizations in the context of abnormal organizational behaviors (AOBs), making this challenging to cope with AOBs. As a result, the objective of this paper is to explore how to reduce AOBs in complex projects at the organizational level from a network perspective.

To overcome the inherent limitations of a single case study, this research integrated two data collection methods: questionnaire survey and expert scoring method. The questionnaire survey captured the universal data on the influence possibility of AOBs between complex project organizations and the expert scoring method got the influence probability scores of AOBs between organizations in the case. Using these data, four organizational influence network models of AOBs based on a case were developed to demonstrate how to destroy AOBs networks in complex projects using network attack theory (NAT).

First, the findings show that controlling AOBs generated by key organizations preferentially and improving the ability of key organizations can weaken AOBs network, enabling more effective coping strategies. Second, the owners, government, material suppliers and designers are identified as key organizations across all four influence networks of AOBs. Third, change and claim behaviors are more manageable from the organizational level.

Project managers can target specific organizations for intervention, weaken the AOBs network by applying NAT and achieve better project outcomes through coping strategies. Additionally, by taking a network perspective, this research provides a novel approach to comprehending the associations and interactions between organizations in the context of complex projects.

This paper proposes a new approach to investigating AOBs in complex projects by simultaneously examining rework, backlog, change and claim. Leveraging NAT as a novel tool for managing the harmful effects of influence networks, this study extends the knowledge body in the field of organizational behavior (OB) management and complex project management.