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To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.

First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.

The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.

This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.

The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.

The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.

The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.

The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.

Prefabricated construction is an innovative technique for decreasing carbon emissions in the construction industry. However, as the investors of housing projects, a majority of developers are unwilling to adopt prefabricated housing in practice. To promote prefabricated housing, this study aims to develop an integrated framework of fuzzy-decision making trial and evaluation laboratory (fuzzy-DEMATEL) and system dynamics (SD) to understand the underlying influencing mechanism of developers' willingness.

Through literature review, a total of 17 influencing factors were identified. Then, the interrelationships among the factors were evaluated by 10 experienced professionals, and the impacts given and received by each factor were further analyzed through fuzzy-DEMATEL. Based on the technology acceptance model (TAM), a SD model was developed to explore the influencing mechanism.

The major cause factors were identified, including mandatory implementation policies, economic incentive policies, environmental protection policies, component standardization and developers' economic strength. This group of factors was expected to be given priority attention in the case of limited resources. On the other hand, the results indicated that economic incentive policies and mandatory implementation policies could affect the developers' willingness via perceived usefulness, while the others mainly influenced perceived ease of use.

Little research has focused on the interrelationships among the influencing factors of developers' willingness to adopt prefabricated housing. This study contributed to understanding the mechanism of developers' willingness from a systematic view and providing the priority of influencing factors. Several strategies were proposed to improve the practical implementation of prefabricated housing development.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

As the current Coronavirus 2019 pandemic eases, international tourism, which was greatly affected by the outbreak, is gradually recovering. The attraction of countries to overseas tourists is related to their overall performance in the pandemic. This research integrates the data of vaccination of different countries, border control policy and holidays to explore their differential impacts on the overseas tourists’ intention during the pandemic. This is crucial for destinations to built their tourism resilience. It will also help countries and industry organizations to promote their own destinations to foreign tourism enterprises.

This study proposes an analysis based on panel data for ten countries over 1,388 days. The coefficient of variation is used to measure monthly differences of Chinese tourists’ intention to visit overseas country destinations.

Results show that, for tourist intention of going abroad: border control of the destination country has a significant negative impact; daily new cases in the destination country have a significant negative impact; domestic daily new cases have a significant positive impact; holidays have significant negative impact; daily vaccination of the destination countries has significant positive impact; and domestic daily vaccination have negative significant impact.

First, there is a large uncertainty in studying consumers’ willingness to travel abroad in this particular period because of unnecessary travel abroad caused by the control of the epidemic. Second, there are limitations in studying only Chinese tourists, and future research should be geared toward a broader range of research pairs.

First, from the government perspective, a humane response can earn the respect and trust of tourists. Second, for tourism industry, to encourage the public take vaccine would be beneficial for both the tourism destination and foreign tourism companies. The same effect can be achieved by helping tourists who are troubled by border control.

First, this research provides suggestions for the government and the tourism industry to deal with such a crisis in the future. Second, this study found that vaccination has a direct impact on tourism. This provides a basis for improving people’s willingness to vaccinate. Thirdly, this study proves suggestion for the destinations to build tourism resilience.

This study analyzes the unique control measures and vaccination in different countries during the pandemic, then provides suggestions for the tourism industry to prepare for the upcoming postpandemic tourism recovery. This study is valuable for improving the economic resilience of tourism destinations. Additionally, it helps to analyze the advantages and disadvantages of different restrain policies around the world.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

Considering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced mandatory policies requiring the use of BIM. However, little is known about the impact of mandatory policies on BIM-based project performance. Therefore, the purpose of this paper is to provide a systematical understanding on the impact of policy interventions on the implementation practice of innovative technologies.

This paper utilizes the propensity score matching and difference in differences (PSM-DID) method to investigate the impact of policy interventions on BIM-based project performance. Using the panel data collected from 2015 to 2021 in the Hong Kong construction industry, this paper explores the impact of the first mandatory BIM policy on the BIM-based project performance of three key stakeholders.

The subjective BIM performance and BIM return on investment (ROI) have significantly improved after implementing the mandatory BIM policy. The promotion effect of mandatory BIM policy on BIM-based project performance gradually increases over time. Moreover, the promotion effect of mandatory BIM policy on BIM performance shows significant heterogeneity for different stakeholders and organizations of different sizes.

This study examined the impact of policy interventions on BIM-based project performance. The research findings can provide a holistic understanding of the potential implications of innovative mandatory policy in performance improvement and offer some constructive suggestions to policymakers and industry practitioners to promote the penetration of BIM in the construction industry.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.