In Côte d’Ivoire, the government and its development partners have implemented a national strategy to promote agroforestry and reforestation systems as a means to combat deforestation, primarily driven by agricultural expansion, and to increase national forest cover to 20% by 2045. However, the assessment of these systems through traditional field-based methods remains labor-intensive and time-consuming, particularly for the measurement of dendrometric parameters such as tree height. This study introduces a remote sensing approach combining drone-based Airborne Laser Scanning (ALS) with ground-based measurements to enhance the efficiency and accuracy of tree height estimation in agroforestry and reforestation contexts. The methodology involved two main stages: first, the collection of floristic and dendrometric data, including tree height measured with a laser rangefinder, across eight (8) agroforestry and reforestation plots; second, the acquisition of ALS data using Mavic 3E and Matrice 300 drones equipped with LiDAR sensors to generate digital canopy models for tree height estimation and associated error analysis. Floristic analysis identified 506 individual trees belonging to 27 genera and 18 families. Tree height measurements indicated that reforestation plots hosted the tallest trees (ranging from 8 to 16 m on average), while cocoa-based agroforestry plots featured shorter trees, with average heights between 4 and 7 m. A comparative analysis between ground-based and LiDAR-derived tree heights showed a strong correlation (R² = 0.71; r = 0.84; RMSE = 2.24 m; MAE = 1.67 m; RMSE = 2.2430 m and MAE = 1.6722 m). However, a stratified analysis revealed substantial variation in estimation accuracy, with higher performance observed in agroforestry plots (R² = 0.82; RMSE = 2.21 m and MAE = 1.43 m). These findings underscore the potential of Airborne Laser Scanning as an effective tool for the rapid and reliable estimation of tree height in heterogeneous agroforestry and reforestation systems.

The exploitation of timber has had a profound impact on tropical forest areas and their structures. This study assessed the effect of selective logging on natural regeneration and soil characteristics in post-loading bay sites at the Pra-Anum forest reserve in Ghana, West Africa. The results showed no difference in the number of species enumerated in the loading bays and the undisturbed area. More trees were observed in the RAT and RNT plots than in the undisturbed area. Relative to the RAT plot, species on the RNT and the undisturbed area were less diverse and less evenly distributed. Mean tree height, diameter, and basal area were higher in the RAT and RNT plots than in the undisturbed plots. Soil bulk density was lower in the RAT and undisturbed plot than in the RAT plot and increased with increased depth. Soil organic matter was 44% and 27% more in the undisturbed and RAT plots, respectively, than in the RNT plot and accounted for 84.75%, 83.97% and 45.33% of variations in soil bulk density, pH, and CEC. The study provides insight into the need to rehabilitate highly disturbed areas in forests, particularly the addition of topsoil on loading bays, skid trails, roads, and gaps after logging to improve the productivity of the forest soils.

One significant importance of street vending in South Africa is its role in providing livelihoods and economic opportunities, especially for marginalized and vulnerable populations. However, Street vendors, particularly those selling agricultural commodities, face numerous challenges. Street vending in Moletjie Mmotong is a vital source of income and employment, offering affordable goods and services, including food, clothing, and household items. One potential solution is online selling, but there is limited knowledge about it in the informal sector. This study aims to analyze the factors affecting street vendors’ willingness to sell fruits and vegetables online in Moletjie Mmotong under Polokwane Municipality. Data was collected from 60 street vendors using a questionnaire and simple random sampling. Descriptive statistics identified and described the socio-economic characteristics of the vendors, while a binary logistic regression model analyzed the factors influencing their willingness to sell online. The study found that age, education level, gender, household size, and access to online selling information significantly influenced their willingness to sell online. The findings highlight the potential benefits of online selling for street vendors, such as increased sales and a broader customer base. The study recommends that governments provide training and workshops on online selling, develop educational programs, distribute educational materials, and create marketing strategies to support street vendors in transitioning to online platforms.

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.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.