This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

Ecological environment damage events will destroy or damage the balance between animal and plant habitats and ecosystems, and even pose a threat to China’s ecological security. However, at present, there are some problems in the identification and evaluation of forest ecosystem damage, such as imperfect evaluation system, insufficient quantitative evaluation methods, imperfect damage compensation management system, and lack of analysis of the overall damage of the interaction between human activities and forest ecosystem. Based on the damaged object, the system involves a total of four first-class indicators, including physical damage, mental damage, economic forest fruit loss, forest by-products loss, processing and manufacturing loss, forest tourism loss, scientific research literature and history loss, soil conservation loss, water conservation loss, wind prevention and sand fixation loss, carbon fixation and oxygen release loss, atmospheric purification loss. There are 14 secondary indicators of emergency treatment fee and investigation and evaluation fee, as well as 22 tertiary indicators, and the value quantification method of each indicator is clarified by using market value method, alternative cost method, shadow engineering method, recovery cost method and other methods. The article also discusses the management system of forest ecosystem damage from the two aspects of forestry technology department and judicial administration department. The purpose is to provide reference for the quantification and standardization of forest ecosystem damage assessment technology and the improvement of management system.

The CO₂  heat pump air conditioning system of new energy vehicle is designed, and the vehicle model of CO₂  heat pump module and heat management system is established based on KULI simulation. The effects of refrigerant charge, running time and compressor speed on the heat pump air conditioning system is studied, and the energy consumption is compared with the PTC heating system and the CO₂ heat pump air conditioning system without waste heat recovery. The results show that the optimal charge for full-service operation is 750 g; increasing the compressor speed can increase the cooling capacity, so that the refrigerant temperature in the passenger compartment and battery inlet can quickly reach the appropriate temperature, but the COP_h, COP_c are reduced by 2.5% and 1.8% respectively. By comparing it with PTC heating and CO₂ heat pump air conditioning systems without waste heat recovery, it is found that the energy consumption of this system is only for the PTC heating systems 42.5%, without waste heat recovery carbon dioxide heat pump air conditioning system of 86.6%. It greatly saves energy, but also increased the waste heat recovery function, so that the system supply air temperature increased by 26%, improve passenger cabin comfort. This  provides a reference for the future experimental research of CO₂ heat pump air conditioning and heat management system.