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Coronavirus disease 2019 (COVID-19) has resulted in traveling restrictions and decreased the demand among travelers since the end of 2019. Policies and people’s behaviors are gradually changing with the increase in the number of new diagnoses of COVID-19. Tourism increases tourists’ risk of contracting COVID-19 and will undoubtedly be affected by this disease. Based on this assumption and social cognitive theory, this paper aims to explore the impact of COVID-19 on tourism in terms of people’s willingness to travel.

Based on social cognitive theory, this study has designed the model and variables. The data of the paper came from a survey conducted in 29 provinces of China and a total of 618 responses were used for data analysis after deleting invalid questionnaires. Drawing on the structural equation modeling method, this paper processes the data collected from questionnaires to test the hypotheses and come to the results.

The results indicate that the negative impact of COVID-19 will affect the severity perceptions, personal negative effects and positive effects of tourism during the outbreak, which will ultimately influence people’s willingness to travel. During the outbreak of COVID-19, impacted by environmental factors and personal factors, Chinese people’s willingness to travel has been significantly reduced.

This study mainly focuses on people’s willingness to travel and the demand side of tourism to analyze the effects of COVID-19 on tourism, which neglects the supply side of tourism for analysis. Subsequent research should take account of the supply side of tourism and expanding the sample size worldwide to determine the impact of COVID-19 on the tourism industry from a broader perspective.

This study quantitatively analyzes how COVID-19 influenced the tourism industry and precisely determines the possible mechanism of the effect of COVID-19 on tourism by using social cognitive theory to build a model. The insights from the work help to understand how COVID-19 affects people’s willingness to travel during the pandemic and how to address this issue.

新型冠状病毒（COVID-19）自2019年底以来开始被发现, 随后逐渐蔓延至全球, 给人们的生活带来不小的影响。该病毒的发展使得人们的出行受到了较大的限制, 因而减少了人们对于旅游的需求。随着新冠疫情确诊人数的增加, 政府政策和人们的行为也处在变化之中。基于这一假设和社会认知理论, 本文旨在从人们的旅游意愿角度探究新冠疫情对旅游业的影响。

本文所设计的变量和模型是在社会认知理论的基础之上提出的。本研究的数据来源于面向中国29个省市自治区的问卷调查。在剔除无效问卷后, 总共搜集618份有效问卷进行数据分析。本研究采用了结构方程模型, 对假设进行检验、并得出最终结论。

研究结果表明, 新冠疫情带来的负面影响会增加人们对疫情严重性的认知程度、疫情期间旅游给人带来的负面影响增大、正面影响减少, 从而最终影响人们的旅游意愿。在新冠疫情之下, 受到环境、个人因素的影响, 人们的旅游意愿显著降低。

本研究的数据均来源于中国, 所以将本文的结论应用于其他地区时, 由于不同地区的自然和社会环境差异, 结论可能会产生差异。本研究主要聚焦于人们旅游意愿的变化, 即旅游业的需求端, 来分析新冠疫情对旅游业的影响, 忽视了旅游业的供给端在新冠疫情期间对旅游业的影响分析。后续的研究可以考虑从旅游业供给端的变化进行分析、以及将样本数量扩大到全球范围, 从而可以从更广阔的视角来探究新冠疫情对旅游业的影响。

本研究定量分析了新冠疫情如何影响旅游业, 并利用社会认知理论建立模型来精确判断这一影响的可能机理。本文的发现有助于了解新冠疫情如何影响人们在疫情行期间旅行的意愿, 以及制定相关的应对措施。

El coronavirus (COVID-19) se descubrió desde finales de 2019 y luego se extendió gradualmente a todo el mundo, lo que tuvo un gran impacto en la vida de las personas. La propaganda del coronavirus ha provocado muchas restricciones en los viajes de las personas, por lo tanto, reducía la demanda de viaje de las personas. Con el aumento en el número de casos confirmados de COVID-19, las políticas gubernamentales y el comportamiento de las personas también se están cambiando. Basado en esta hipótesis y teoría cognitiva social, este artículo tiene como objetivo explorar el impacto del COVID-19 en el turismo desde la perspectiva de la disposición de las personas a viajar.

Las variables y modelos diseñados en este artículo se proponen sobre la base de la teoría cognitiva social. Los datos para este estudio provienen de las encuestas por cuestionario de 29 provincias, municipios y regiones autónomas de China. Después de excluir los cuestionarios no válidos, se recopilaron un total de 618 cuestionarios válidos para el análisis de datos. En este estudio, se utiliza el modelo de ecuación estructural y la prueba de la hipótesis, al final llegar a la conclusión.

El resultado de la investigación muestra que el impacto negativo del COVID-19 aumenta la conciencia de las personas sobre la gravedad de la epidemia, y el impacto negativo del turismo durante la epidemia se aumenta y el impacto positivo se disminuye, lo que al fianl afecta la disposición de las personas a viajar. Bajo el COVID-19, afectada por factores ambientales y personales, la disposición de las personas a viajar se ha reducido significativamente.

Los datos de este estudio son todos de China, por lo que cuando las conclusiones de este artículo se aplican a otras regiones, las conclusiones pueden diferir debido a las diferencias en los entornos naturales y sociales de diferentes regiones. Esta investigación se centra principalmente en el cambio del deseo de viajar de las personas, es decir, el lado de la demanda del turismo, para analizar el impacto del COVID-19, por eso ignora el análisis del impacto de la oferta turística en el turismo durante el COVID-19. Los estudios posteriores se pueden considerar desde el aspecto de analizar los cambios en el lado de la oferta del turismo y expandir el número de muestras a una escala global, por lo tanto, se puede explorar el impacto del COVID-19 desde una perspectiva más amplia.

El encuentro de este artículo ayuda a comprender cómo el COVID-19 afecta la disposición de las personas a viajar durante la epidemia y a formular medidas de respuesta relevantes.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The development of modern building planning has become inseparable from the concept of green energy saving, which has gradually become an important method for energy-saving design of building planning and has become the research direction of planners. Based on this, the status quo of research on green building at home and abroad was investigated and the principle of energy saving for green building planning was put forward; then the building shape coefficient, eco-strategy design of green building energy, building energy-saving layout and other aspects were analyzed in detail; finally, the Amsterdam large CBD complex Valley green building was analyzed. The results show that the building has fully considered the energy-saving design in the process of planning and design, which not only ensures the commercial demand, but also reflects the residential use and finally achieves the goal of green energy saving.

There are particularly high fresh agricultural product (FAP) loss rates in actual supply chain operation and the development of FAPs e-commerce is hindered to some extent. The purpose of this paper is to achieve the coordination of three-layer FAP supply chain and maximize profit through the contracts among the supply chain members.

A three-layer FAP supply chain that consists of a fresh produce e-commerce enterprise, third-party logistics service provider (TPLSP) and community convenience store under e-commerce environment is considered. New game models are developed and optimal decisions in centralized and decentralized channel are characterized. Different contract coordination mechanisms are designed to improve the supply chain performance. Finally, computational studies are conducted.

The decentralized supply chain cannot be coordinated by a freshness-keeping cost-sharing contract, and it can be coordinated by a freshness-keeping cost-sharing and revenue-sharing contract. The optimal unit online selling price, unit logistics distribution price, fresh-keeping effort and unit self-collection service price can all be achieved.

The paper provides a practical guideline to managers in fresh produce industry in terms of how to cooperate with other supply chain members so as to maximize total profit and achieve Pareto improvement while also supply the freshest and safest produce to the target market under e-commerce environment.

Few studies have explored the coordination of three-layer FAP supply chain under e-commerce environment with TPLSP and community convenience store’s participation in decisions, especially considering that the market demand for FAPs is affected by freshness and unit online selling price. In this paper, all these scenarios are taken into account and corresponding mathematical models are developed. In particular, different contract coordination mechanisms are designed and examined simultaneously.

The purpose of this paper is to measure the high-quality development level of China's marine economy and analyze corresponding spatial and temporal distribution characteristic.

Design and optimize the index system of high-quality development level of marine economy and use entropy and TOPSIS method for comprehensive evaluation.

The research finds that from 2017 to 2019, the high-quality development tendency of China's marine economy is on the rise, but the overall level is still low. The level of each subsystem has different distribution characteristics in different provinces and cities. Guangdong, Shandong and Shanghai have a high comprehensive level. According to the comprehensive level of high-quality development of marine economy, 11 coastal provinces are divided into three types: leading, general and backward.

This paper clarifies the temporal and spatial distribution law of high-quality development level of China's marine economy, providing basis for promoting comprehensive and coordinated improvement of coastal provinces and cities.

An indicator system for the high-quality development level of the marine economy has been established, including social development guarantee, marine economic foundation, marine science and technology drive and green marine sustainability.

This study aims to the service life of TA15 alloy by solving the problem of the binding force between the matrix and AlTiSiN coating. The effect of a plasma nitriding (PN) interlayer on the magnetron-sputtered AlTiSiN coating was also investigated in detail.

The double-glow plasma alloying (DGPA) and magnetron sputtering (MS) techniques were combined as a new approach to realize a bilayer on TA15 consisting of an AlTiSiN layer with a PN interlayer. A TiN interlayer was formed via co-diffusion during the PN conducted at 1050°C for 3 h.

The PN interlayer can effectively improve the adhesion between coating and matrix; the PN/AlTiSiN coating presented excellent adhesion (80.1 N) and anti-wear property with a nano-hardness of 18.62 GPa. The resulting three-dimensional wear-track morphology exhibited a shallow depth and a narrow width.

The novel combination of the DGPA and MS technologies, using an infiltration layer rather than a coating one as the intermediate layer, can effectively enhance the adhesion between AlTiSiN coating and TA15 matrix. Meanwhile, the gradient layer can effectively improve both surface bearing and wear resistance.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals.

This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors.

Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite.

This paper adopts the three-stage SBM–DEA model of undesired output and takes CO₂ as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.

This paper aims to provide decision support for precast concrete contractors about both precast concrete supply chain strategies and construction configurations.

This paper proposes a simulation-based optimisation for supply chain and construction (SOSC) during the planning phase of PC building projects. The discrete event simulation is used to capture the characteristics of supply chain and construction processes, and calculate construction objectives under different plans. Particle swarm optimisation is combined with simulation to find optimal supply chain strategies and construction configurations.

The efficiency of SOSC is compared with the parametric simulation approach. Over 70 per cent of time and effort used to simulate and compare alternative plans is saved owing to SOSC.

Building simulation model costs a lot of time and effort. The data requirement of the proposed method is high.

The proposed SOSC approach can provide decision support for PC contractors by optimising supply chain strategies and construction configurations.

This paper has two contributions: one is in providing a decision support tool SOSC to optimise both supply chain strategies and construction configurations, while the other is in building a prototype of SOSC and testing it in a case study.