In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

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.

In order to address severe siltation and enhance urban green spaces in Xianyang Lake, the research offers a sustainable solution by proposing an innovative integration of ecological dredging and landscape transformation. The key findings are as follows: Firstly, an ecological dredging mechanism was established by directly transporting sediment from Xianyang Lake to its central greenbelt, reducing dredging costs and environmental impact while creating a sustainable funding cycle through revenue from eco-tourism activities. Secondly, the landscape artistic conception of the central greenbelt was significantly improved by leveraging the natural distance between the lakeshore and the greenbelt, offering diverse viewing experiences and enhancing the cognitive abilities and urban life satisfaction of tourists. Thirdly, the project demonstrated substantial economic and social benefits, including revenue generation from paid activities like boat tours, increased public awareness of biodiversity through ecological education, and improved community well-being. The central greenbelt also enhanced the urban environment by improving air quality, mitigating the “heat island effect,” and providing habitats for wildlife. This integrated approach serves as a model for sustainable urban development, offering valuable insights for cities facing similar ecological challenges. Future research should focus on long-term monitoring to further evaluate the ecological and socio-economic impacts of such projects.

Information transparency is a basic principle of good governance that few studies in the literature have thoroughly examined. Riau Province in particular has a high record of land and forest conflicts that needs urgent response, yet environmental policies have mostly been scrutinized for its resource extraction and regulation aspects, not their aspect of information transparency. Low proactive disclosure of information from local governments is a recurring issue in Riau Province, so FITRA Riau initiated the Public Information Openness Index (IKIP) to cover the Riau Province and 12 regencies/cities. To address this research gap of governmental public bodies’ information transparency, this study conducted the novel substantive approach critical review to see the extent of local government’s transparency regarding their budgeting for one of Riau’s most prevalent issues, namely land and forest governance (TKHL). From March to September 2019, this study used a triangulation of data collected from information access tests, IKIP evaluation, and focus group discussion involving the Riau Information Commission, the Information Management and Documentation Officers (PPID) of the 12 regencies, and the Governor of Riau Province. After analyzing the four aspects of regulation, institution, budget, and TKHL information, results determined that the most open region in Riau Province is Indragiri Hulu, and the least open region is Kuantan Singingi. Information transparency is still limited in procedural terms, in which all regions have more or less fulfilled the administrative regulation demands but the substance of the public information across all aspects is too generic to truly inform the public of the regions’ TKHL.

The study intends to identify the existing implementation bottlenecks that hamper the effectiveness of the Ethiopian forest policy and laws in regional states by focusing on the Oromia Regional State. It attempts to address the question, “What are the challenges for the effective implementation of the federal forest policy and law in Ethiopia in general and Oromia Regional State in particular?”. The study followed a qualitative research approach, and the relevant data was collected through in-depth interviews from 11 leaders and experts of the policy, who were purposively selected. Furthermore, relevant documents such as the constitutions, forest policies and laws, and government documents were carefully reviewed. Based on this, the study found that there is the dichotomy between the provision of the constitution regarding the forest policy and lawmaking and the constitutional amendment on one hand and the push for genuine decentralization in the Ethiopian federal state on the other. To elaborate, the constitution is rigid for amendment, and it has given the power of forest policy and lawmaking to the federal government. On the other hand, the quest for genuine decentralization requires these powers to be devolved to the regional states. As the constitution is rigid, this may continue to be the major future challenge of the forest policy and lawmaking of the state. This demonstrates a conflict of interests between the two layers of governments, i.e., the federal and regional (Oromia Regional State) governments. Respecting and practicing the constitution may be the immediate solution to this pressing problem.

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.