To achieve the Paris Agreement’s temperature goal, greenhouse gas emissions should be reduced as soon as, and by as much, as possible. By mid-century, CO₂ emissions would need to be cut to zero, and total greenhouse gases would need to be net zero just after mid-century. Achieving carbon neutrality is impossible without carbon dioxide removal from the atmosphere through afforestation/reforestation. It is necessary to ensure carbon storage for a period of 100 years or more. The study focuses on the theoretical feasibility of an integrated climate project involving carbon storage, emissions reduction and sequestration through the systemic implementation of plantation forestry of fast-growing eucalyptus species in Brazil, the production of long-life wood building materials and their deposition. The project defines two performance indicators: a) emission reduction units; and b) financial costs. We identified the baseline scenarios for each stage of the potential climate project and developed different trajectory options for the project scenario. Possible negative environmental and reputational effects as well as leakages outside of the project design were considered. Over 7 years of the plantation life cycle, the total CO₂ sequestration is expected to reach 403 tCO₂∙ha⁻¹. As a part of the project, we proposed to recycle or deposit for a long term the most part of the unused wood residues that account for 30% of total phytomass. The full project cycle can ensure that up to 95% of the carbon emissions from the grown wood will be sustainably avoided.

Afforestation is a main tool for preventing desertification and soil erosion in arid and semiarid regions of Iran. Large-scale afforestation, however, has poorly understood consequences for the future ecosystems in the term of ecosystems protection. The objective of the present study is to identify changes in soil properties following different intervals of planting of Ailanthus altissima (tree of heaven) in semiarid afforestation of Iran (Chitgar Forest Park, Tehran). For this purpose, sand, silt and clay ratios, bulk density, soil moisture, pH, electrical conductivity, phosphorus, potassium, magnesium, calcium, sodium, total soil N, and total carbon was measured. Our study highlighted the potential of the invasive trees by A. altissima, to alter soil properties along chronosequence. Almost all soil quality attributes showed a declining trend with stand age. A continuous decline in soil quality indicated that the present land management may not be sustainable. Therefore, an improved management practice is imperative to sustain soil quality and maintain long-term productivity of plantation forests. Thinning activity will be required to reduce the number of trees competing for the same nutrients especially in a older stand to protect forest soils.

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.

The structure and diversity of tree species in a temperate forest in northwestern Mexico was characterized. Nine sampling sites of 50 × 50 m (2,500 m²) were established, and a census of all tree species was carried out. Each individual was measured for total height and diameter at breast height. The importance value index (IVI) was obtained, calculated from the variable abundance, dominance and frequency. The diversity and richness indices were also calculated. A total of 12 species, four genera and four families were recorded. The forest has a density of 575.11 individuals and a basal area of 23.54/m². The species of Pinus cooperi had the highest IVI (79.05%), and the Shannon index of 1.74.

Simple mathematical expressions are given for the betweenness centrality of nodes in trees, forests and cycles. As application, a centrality test is given for when a network might be a forest.