Logging residues for charcoal production through forest management in the Brazilian Amazon: Economic gains and forest regrowth effects.

Sustainable forest management practices can potentially reverse loss of forest cover due to deforestation, while concomitantly preserving and maintaining biodiversity, and stimulating jobs, income, and forest services. Recent studies found that significant logging residues (i.e., leaves, branches, and buttress roots) suitable for bioenergy production were often left in the felling area, triggering risks of forest fires and increased CO2 emissions due to wildfires or decomposition processes. For impact assessment of forest management practices, we collected primary harvesting data and estimated net primary productivity (NPP) and net ecosystem exchange (NEE) for 13 forest plots in the Brazilian Amazon. We applied a process-based forestry growth model (BGC-Man) to analyze the impacts on forest dynamics of selective logging and removal of logging residues, subject to landscape, soil texture, and daily weather. We explored the following selective logging scenarios: the Legal Reserve (i.e., reference) scenario, a scenario with one cutting cycle over the whole period, and a scenario with three timber rotation periods of 30 years. Two of the later scenarios were complemented with harvesting of the woody logging residues (LR; Ø≥10 cm) for charcoal production. For each scenario, we computed forest NPP and NEE over a 120-year time horizon. Results suggest that using woody logging residues (i.e., 77% of total LR) for charcoal production would result in an economic gain equivalent to 24-46% of the timber price. Our findings indicate that under scenarios where LR were removed, forest NPP recovered to the reference level and even higher, while income and jobs from harvesting LR for charcoal production were generated. We conclude that sustainable forest management could enhance forest productivity and deliver economic benefit from otherwise unexploited logging residues

Entrepreneurship support institutions in Russia

The article presents historical and modern aspects of the theory of entrepreneurship, the role of entrepreneurship in the socio-economic life of the country. The paper considers the reasons: why the state should create conditions for the development of small and medium-sized businesses. The study gives criteria for defining small and medium-sized businesses in Russia and in the European Union. The author studies and presents the main indicators that characterize the degree of development of small and medium-sized businesses in terms of their involvement in the economy. The article gives the classification of the main measures to support small and medium-sized businesses in countries with a developed business sector. The paper considers the main measures of state support for entrepreneurship in Russia and assesses their effectiveness

Regional Variability and Driving Forces behind Forest Fires in Sweden

Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA, and are now an increasing threat in boreal Europe, where recent fire events in 2014 and 2018 drew attention to Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas, and to link fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database of 1 × 1 km2 homogenous grids, where monthly burned areas were derived from the MODIS FireCCI51 dataset. The database consists of various socio-economic, topographic-, forest-, and weather-related remote sensing products. To include new factors in the IIASA’s FLAM model, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden’s geographical diversity, fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and observed burned areas were well captured by the model. The result accuracy differs with respect to zone; the area under the curve (AUC) was 0.875 and 0.94 for zones with few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and their variability across Sweden provide valuable information to understand the reasons behind forest fires. The Fine Fuel Moisture Code, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other boreal regions and thus is highly policy-relevant. Visualization of our results is available in the Google Earth Engine Application

Regional Variability and Driving Forces behind Forest Fires in Sweden

Extreme forest fires have been a historic concern in forests in Canada, the Russian Federation, or the USA, and are now becoming an increasing threat in boreal Europe where recent fire events in 2014 and 2018 caught the attention of those in Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas and linking fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database at 1 × 1 km2 homogenous grid, where monthly areas burned in a forest were derived from the MODIS FireCCI51 dataset. Spatial factors, including camping sites, lakes, and roads, topographic features, including aspect, slope, and mean elevation, population density, forest management intensity, and forest stand volume, were collected from various sources and pre-processed. Monthly Fine Fuel Moisture Code (FFMC) values over 2011–2018 were calculated from daily weather data by IIASA’s FLAM model. To include new factors into FLAM, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden’s geographical diversity, the fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and optimized thresholds. Observed burned areas were well captured by the model. Result accuracy differs with respect to zones; area under the curve (AUC) was 0.875 and 0.94 for zones with a few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and its variability across Sweden provide important information to understand the factors behind forest fires. FFMC, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other Boreal regions

Exploring Drivers of Wildfires in Spain

Wildfires play a dual role in ecosystems by providing ecological benefits while posing catastrophic events; they also inflict non-catastrophic damage and yield long-term effects on biodiversity, soil quality, and air quality, among other factors, including public health. This study analysed the key determinants of wildland fires in Spain using openly available spatial data from 2008 to 2021, including fire perimeters, bioclimatic variables, topography, and socioeconomic datasets, at a resolution of 1 km2. Our methodology combined principal component analysis (PCA), linear regression analysis, and one-way analysis of variance (ANOVA). Our findings show that scrub/herbaceous vegetation (average 63 ± 1.45% SE) and forests (average 19 ± 0.76% SE) have been highly susceptible to wildfires. The population density exhibited a robust positive correlation with wildfire frequency (R2 = 0.88, p < 0.0001). Although the study provides insights into some fire-related climatic drivers over Spain, it includes only temperature- and precipitation-based variables and does not explicitly consider fuel dynamics. Therefore, a more advanced methodology should be applied in the future to understand the local specifics of regional wildfire dynamics. Our study identified that scrub/herbaceous areas and forests near densely populated regions should be prioritised for wildfire management in Spain, particularly under changing climate conditions

Forest Protection and Permanence of Reduced Emissions

Tropical forests are essential for climate change mitigation. As awareness grows over the use of credits from reduced emissions from deforestation and forest degradation (REDD+) and other nature-based climate solutions within both voluntary and compliance carbon markets, key concerns about the long-term durability of the reductions, or their permanence, arise for countries, corporations, regulators and policy makers. The paper seeks to analyze an efficient means of distribution and application of different policy pathways to slow down and stop deforestation and explore the longevity of reductions via modeling. The impact of policies like REDD+ most likely will have a time limitation. At some point tropical nations will take more responsibility to protect forests. REDD+ should constitute an initial intervention that will help tropical nations shock to a zero-deforestation trajectory. To establish conditions of permanence, we conduct numerical analyses using a model based on a cellular automata algorithm that learns from historical deforestation patterns and other spatial features in the Brazilian state of Mato Grosso. The model simulates future deforestation, first applying policy to reduce deforestation and then relaxing the policy intervention. Our simulations show that policies that are successful in reducing deforestation and related emissions from business as usual may have long-term positive consequences on an avoided deforestation trajectory even after potential policy reversals. Some accumulated gains could be lost but sizable benefits will remain, assuring permanence of emissions reduction during the policy implementation and potentially even after policies are relaxed. Hence, permanence depends both on the probability of policy reversals and the risk of emissions rebounding. Our results are important for advancing the understanding around the unsettled debate on the permanence of avoided emissions. Further, this paper argues that as policies to prevent deforestation or reduce emissions otherwise are reversible, permanence should be understood and discussed in a probabilistic and time-dependent framework

Forest protection and permanence of reduced emissions

Tropical forests are essential for climate change mitigation. With growing interest over the use of credits from reducing emissions from deforestation and forest degradation (REDD+) and other natural climate solutions within both voluntary and compliance carbon markets, key concerns about the long-term durability of the reductions, or their permanence, arise for countries, corporations, regulators, and policy makers. This paper seeks to analyze the longevity of emissions reductions from different policies to slow down and stop deforestation. To establish conditions of permanence, we conduct numerical analyses using a model based on a cellular automata algorithm that learns from historical deforestation patterns and other spatial features in the Brazilian state of Mato Grosso. First, we simulate increased law enforcement to curb deforestation at a jurisdictional scale from 2025 to 2034, followed by potential policy rollbacks from 2035 to 2050. Second, we consider alternative scenarios to avoid potentially legal deforestation coupled with reforestation. We find spatial and path dependence – a successful policy intervention may permanently change the deforestation trajectory even after potential policy reversals. Hence, permanence depends both on the probability of policy reversals and the risk of emissions overshooting. Our results are important for advancing the understanding around the unsettled debate on the permanence of avoided emissions. Further, this paper argues that as policies to prevent deforestation or reduce emissions otherwise are reversible, permanence should be understood and discussed in a probabilistic and time-dependent framework

Modeling Wildfire Dynamics in Latin America Using the FLAM Framework

The increasing frequency of wildfires caused by climate change poses a significant threat globally, particularly in Latin America – a region known for its critical ecosystems. Its vulnerability to climate change-induced wildfire threats, resulting from increasing temperatures and changing precipitation patterns, is uncertain, highlighting the need for comprehensive strategies such as incorporating advanced modeling and proactive measures to understand, manage, and conserve its ecological state in the face of threats posed by climate change, such as wildfires. This study utilizes the wildFire cLimate impacts and Adaptation Model (FLAM) by IIASA to provide a comprehensive analysis of past and projected wildfire dynamics in Latin America. FLAM is a process-based fire parameterization algorithm used to assess the impacts of climate, fuel availability, topography, and anthropogenic factors on wildfire characteristics. It is highly adjustable and adaptable, making it suitable to analyze past and future wildfire trends in diverse regions such as Latin America. We analyzed spatial and temporal wildfire patterns using MODIS satellite data alongside historical climate and anthropogenic data to calibrate FLAM. We generated projections of burned areas until 2100 under 3 RCP scenarios for Latin American as a whole, as well as for distinct sub-regions to better assess regional wildfire dynamics and climate change impacts. Moreover, we developed a scenario to explore the impacts of increased fire suppression efficiency on projected burned area and highlight the impacts of focusing mitigation and management efforts on areas identified as hotspots (high risk of wildfire). The study shows FLAM’s effectiveness in modeling historical wildfires and its sensitivity to the RCP scenarios in predicting wildfire trends in Latin America. Our analysis and results show how FLAM helps in evaluating the potential future changes in wildfire intensity, and geographic spread under various climatic scenarios. FLAM projected a dramatic rise in burned area until the end of the century across Latin America in line with observed trends, especially under severe climate change scenarios. Regions with the highest temperature rises are also prone to reduced precipitation, which further increase wildfire risks. The spatially-explicit projections highlight areas at higher risk of wildfire, enabling targeted and efficient fire management and mitigation strategies. Our study further showed the potential impact of adaptive measures, such as enhanced fire suppression efficiency in identified hotspots, in reducing annual mean burned area. Overall, this study provides critical insights into the relationship between climate change and wildfire dynamics using a state of the art model. It sets the foundation for further research on fires in Latin America and efficient management strategies which can be modelled by FLAM

НОВЫЙ ШАГ В УЛУЧШЕНИИ КАЧЕСТВА ЖИЗНИ БОЛЬНЫХ МУКОВИСЦИДОЗОМ: ТОБРАМИЦИН ДЛЯ ИНГАЛЯЦИОННОГО ВВЕДЕНИЯ

This article is dedicated to a monogenic polysystemic disease — mucoviscidosis; its course severity and prognosis are to a large extend determined by the degree of lung affection, infection and chronic inflammatory process development in the airways. The authors show the derangement of the quality of life in children with mucoviscidosis and examine approaches to the therapy adherence issues, which is important for the treatment effectiveness. On the example of their own study and international experience they show the effectiveness of the inhalation technique of the drug delivery to a pathological nidus.Статья посвящена моногенной полисистемной болезни — муковисцидозу, тяжесть течения которой и прогноз во многом определяются степенью поражения легких, а также инфицированием и развитием хронического воспалительного процесса в дыхательных путях. Авторы демонстрируют нарушение качества жизни у детей с муковисцидозом, а также рассматривают пути решения вопросов приверженности к терапии, что имеет немаловажное значение для эффективности лечения. На примере собственного исследования и международного опыта показана эффективность ингаляционного метода доставки лекарственного средства в патологический очаг

Moose or spruce: A systems analysis model for managing conflicts between moose and forestry in Sweden

Sweden has the world’s highest density of moose (Alces alces). Moose is not only a valuable game species; it also causes forest damages and traffic accidents. To avoid moose browsing, foresters respond by planting spruce (Picea abies) to an extent that reshapes the forest landscape with impacts on both production and biodiversity. To address this problem and maintain a healthy moose population in balance with the other interests, an adaptive management based on the knowledge and experiences of local hunters and landowners is advocated. However, the different stakeholders do not agree on what is an appropriate moose population, which leads to conflicts that are hard to resolve. A key problem is that it is very difficult to encompass and foresee long-term consequences of different options for moose hunting and forest management. This makes it challenging to form coherent strategies that integrate different sectorial interests at a national level. To address this issue, we have developed a systems analysis framework for integrated modeling of the moose population, forestry, and their interactions and consequences for biodiversity. We analyze the short and long-term consequences for multiple scenarios of moose hunting and forest management. Based on the results we elucidate and quantify the trade-offs and possible synergies between moose hunting and forest production. This analysis can be used to support better informed and more constructive discussions among the stakeholders in the Swedish forest sectors, and to support policies for long term sustainable forest and moose management