Rwanda's natural forest is under pressure due to increasing rural population growth and subsistence farming. The Rwanda Ministry of Agriculture has therefore introduced agroforestry technology in forest-dependent communities to minimize the pressures on the forests and improve local people's livelihoods. This new technology has, however, not seen massive adoption in the country. This study examined the socioeconomic and environmental benefits of the agroforestry technology introduced particularly in Nyamagabe District and the challenges facing its adoption. Results showed that the agroforestry practices are contributing to an increase in income of agroforestry adopters compared to non-agroforestry adopters, and are improving soil fertility, reducing deforestation, and conserving soil and water in the district. However, results showed that, due to lack of skills and technical know-how, capital and quality seeds, some farmers are declining to adopt the new agroforestry practices. The respondents perceived that by providing subsidies to farmers, regular training, and informal education, establishing tree nurseries to improve the production of quality seeds, and also involving farmers in decision-making will increase agroforestry adoption. The government and other stakeholders should consider the views expressed by the farmers and take the necessary steps to address these challenges facing agroforestry technology adoption.

Reducing emissions from deforestation and forest degradation, and the role of conservation, sustainable forest management and enhancement of forest carbon stocks (REDD+) in developing countries requires a National REDD+ Strategy (NRS) to ensure effectiveness, efficiency and equity. So far, only a few countries have submitted their NRS to the United Nations Framework Convention on Climate Change (UNFCCC) to progress to the implementation phase of REDD+. To compare the NRS of eight countries from Africa and the Asia-Pacific region, we used content analysis to assess whether these countries have paid attention to the REDD+ design components and adhered to the UNFCCC REDD+ rules. Our results demonstrate that all eight countries have paid considerable attention to REDD+ activities, finance, measurement, reporting and verification (MRV), and safeguard systems, and most countries have not adhered to the UNFCCC REDD+ rules on scale including the definition of national and subnational forests, subnational projects to be nested into national systems, and subnational activities to be verified by experts. REDD+ countries must develop definitions for national and subnational forests to enhance forest monitoring and they must develop technical and institutional infrastructure for MRV and safeguard systems, to receive results-based payments, and for the sustainability of REDD+ projects.

This study provides an assessment of dominant drivers of deforestation and forest degradation across 29 REDD+ participant countries and total funds disbursed from the Forest Carbon Partnership Facility to 15 participant countries since they signed their respective Readiness Preparation Proposals (R-PPs), as well as what they spent the grants on. This study aims to provide information for policymakers and donors on the need to invest in REDD+, as well as the need for participant countries to channel funds to key activities for effectively and efficiently implementing REDD+. The assessment is based on participant countries’ R-PPs and FCPF readiness fund progress reports, using a content analysis approach. The expansion of agriculture, which includes both commercial and subsistence agriculture, was the predominant driver of deforestation, while logging, such as industrial and small-scale exploitation, was the main driver of forest degradation in most participating countries. In addition, we observed that the 15 participant countries received a total of USD 702.36 million for REDD+ readiness preparation. However, most countries have received less than USD 10 million for their readiness-related activities. We observed a huge disparity regarding the funds received between countries from sub-Saharan Africa and their Asian and Latin American counterparts, particularly Indonesia and Peru, respectively. Furthermore, almost all countries have spent their funds on policy and strategy development, stakeholder and consultation events, and public awareness creation. Relatively small amounts have been spent on capacity building and training. Thus, international donors need to focus on strengthening institutional capacities and building effective forest governance structures in countries participating in REDD+, and they should also provide platforms to engage with participant countries to tackle the main deforestation and forest degradation drivers. We suggest further studies to identify high-performing countries that have received lower-than-average funding, consider their deforestation pressures, the size of forests, and the emission reduction potential.

Forest practices for mountainous areas can enhance the scenery value and function of forests. However, forest scenery management is rarely implemented except for conservation areas and public forests. In this study, we first used the viewshed analysis to extract visible and invisible zones from the surface areas of ordinary mountains in Korea, and then we used spatial aesthetic analysis to interpret the human-recognized characteristics on the visible zones of mountain scenery. Finally, based on the results of both analyses, reasonable guidelines for forest practice planning were proposed to improve the scenery of ordinary mountains. The result shows that the viewshed analysis made it possible to extract visible and invisible areas from the surface areas of ordinary mountains, and to determine the scale of zoning for forest practices to improve mountain scenery. In addition, using spatial aesthetic analysis, it was possible to explain the characteristics of mountain scenery according to distance and elevational differences between viewpoint and target, and to suggest a treatment target and direction for forest practices to improve the mountain scenery. This study is meaningful in that the viewshed and spatial aesthetic analyses were applied to evaluate the current scenery of ordinary mountains and to present guidelines for forest practice planning to promote their own scenery values.

Bang Kachao, the largest green area in the Bangkok metropolitan area, delivers significant ecosystem services to sustain society free of charge. It is therefore difficult to achieve socially optimal services because of inefficient allocation of resources, over-consumption, and negative externalities resulting from market failures. This study’s purpose is to assess consumers’ willingness to pay (WTP) for enhancing ecosystem services from the Bang Kachao Green Area and to investigate factors influencing the WTP of Bangkok residents. A choice experiment was applied by interviewing 200 respondents living in the Bangkok metropolitan area. The data were collected between July and September 2016 and analyzed using a conditional logit model. The results reveal that the respondents are willing to pay 42 USD per year to improve the ecosystem services in Bang Kachao. The respondents demand clean air the most, followed by food, recreation, and bird diversity. The government of Bangkok may take proactive steps to promote agroforestry and ecotourism in Bang Kachao. A Payment for Ecosystem Services (PES) scheme may ensure the provision of ecosystem services in Bang Kachao.

Under climate change and human's dominant influence, wildfires have been increasing in frequency and scale, highlighting the demand of effective wildfire prediction and response. While prior research often maps high-risk areas, a few studies predict wildfire occurrences at specific dates and locations. This study aims to develop a year-round daily wildfire prediction model using machine learning, targeting Gangwon State in the Republic of Korea, and examine major influencing factors. We integrate meteorological elements (e.g., temperature, humidity, precipitation), forest-related variables (e.g., coniferous forest ratio, forest growing stock volume), and socioeconomic indicators (e.g., agricultural and cemetery land ratios) to identify salient predictors. We compare multiple algorithms, including Logistic Regression, XGBoost, and Random Forest, and use SHAP (SHapley Additive exPlanations) to enhance interpretability. The Extra Tree model achieves the highest AUC (0.839), and Random Forest demonstrates the best recall (0.828). SHAP results confirm that meteorological factors-especially relative humidity, precipitation, and temperature-are crucial, with forest- and socioeconomic variables also showing consistent effects. Applying a machine learning-based approach to daily wildfire prediction, integrating climate, environmental, and anthropogenic factors nationwide, and refining the temporal and spatial resolution of input data helps to advance wildfire prevention and response strategies in practice.