Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

Purpose: To reveal the impact mechanism of rural museum intervention on the construction of local identity of rural community residents, and provide practical reference for the protection and utilization of rural cultural identity. Methods: This study takes the Weijiapo Rural Museum in Luoyang, China as the research object, uses participatory observation and in-depth interview methods, and explains the specific characteristics of rural community resident identity construction through identity process theory (IPT). Results: (1) The impact of the intervention of rural museums on rural areas is reflected in four aspects: local spatial reconstruction, transformation of livelihood methods, reconstruction of social relationships, and evolution of cultural customs; (2) under the influence of rural museum construction, the representation of community residents’ identity has shown complex characteristics, with both positive and negative impacts coexisting; (3) the local identity of community residents affects their perception and attitude towards the construction of rural museums.

The safeguarding of agricultural land is rooted in national land surveys and remote sensing data, which are enhanced by contemporary information technology. This framework facilitates the monitoring and regulation of unauthorized alterations in cultivated land usage. This paper aims to analyze land policies at the national, provincial, and local levels, investigate the cultivated land protection strategies implemented within the research region, where the policies have gained societal acceptance, and propose recommendations and countermeasures to enhance the development and utilization of land resources. The central issue of this study is to identify the challenges in achieving a balance between human activities and natural ecosystems. To address this issue, the research employs a combination of literature review, semi-structured interviews, text analysis, and content analysis, emphasizing the integration of empirical fieldwork and theoretical frameworks. Key areas of focus include: (a) the current state of the farmland protection system, (b) the legal foundations for local enforcement, (c) the systematic mechanisms for implementing arable land protection, and (d) the coordinated oversight system involving both the Party and government. Notably, the practice of cultivated land protection faces several challenges, primarily stemming from two factors. Firstly, there exists a disconnect between the economic interests of certain illegal land users and the objectives of land management, which hinders effective enforcement. Secondly, environmental repercussions arise from misinterpretations of land policy or non-compliant land development practices aimed at profit, which contradict the goals of ecological sustainability. The study examines two approaches to address the issue: the distribution and effective use of land resources, and the capacity for monitoring and early warning systems. Findings indicate that Dongtai City in Jiangsu Province has rigorously implemented all national land management policies, while also preserving the adaptability of local townships in practical applications, thereby ensuring the consistency of both the quality and quantity of arable land.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.