Land management explains major trends in forest structure and composition over the last millennium in California's Klamath Mountains.

SignificanceWe provide the first assessment of aboveground live tree biomass in a mixed conifer forest over the late Holocene. The biomass record, coupled with local Native oral history and fire scar records, shows that Native burning practices, along with a natural lightning-based fire regime, promoted long-term stability of the forest structure and composition for at least 1 millennium in a California forest. This record demonstrates that climate alone cannot account for observed forest conditions. Instead, forests were also shaped by a regime of frequent fire, including intentional ignitions by Native people. This work suggests a large-scale intervention could be required to achieve the historical conditions that supported forest resiliency and reflected Indigenous influence.

Damage to living trees contributes to almost half of the biomass losses in tropical forests.

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha-1 year-1 ; 95% confidence interval [CI] 2.36-5.25) of total AGB loss (8.72 Mg ha-1 year-1 ; CI 5.57-12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%-17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%-57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%-80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.

Geo-Spatial Economic Assessment of the Potential Development of Bioenergy Combined with Direct Air Carbon Capture (BEDAC) in the USA.

This study presents a geo-spatial and economic framework to localize future bioenergy power plants combined with direct air capture (BEDAC). This framework is applied to two regions in the USA to assess the optimal use of forest biomass and in situ carbon sequestration under three specific short-term sequestration targets. Results show that there are many locations that have both the necessary biomass and geology required for storage. The Southeast has greater potential for forestry biomass due to both the rate of growth and forested areas, but the sequestration potential is mostly limited to a CO2 solution in saline aquifers. The Pacific Northwest has more sequestration potential than the Southeast given the location of managed forests and storage sites in carbonate mineralization in bedrock. The two combined regions have a total potential sequestration of 9.3 GtCO2 for the next 20 years that can be achieved under an implicit carbon value of $249/tCO2.

Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images.

Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (p < 0.05), and 21 factors were significantly correlated with forest biomass (p < 0.01). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion.

Tallo: A global tree allometry and crown architecture database.

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Aboveground biomass increments over 26 years (1993-2019) in an old-growth cool-temperate forest in northern Japan.

Assessing long-term changes in the biomass of old-growth forests with consideration of climate effects is essential for understanding forest ecosystem functions under a changing climate. Long-term biomass changes are the result of accumulated short-term changes, which can be affected by endogenous processes such as gap filling in small-scale canopy openings. Here, we used 26 years (1993-2019) of repeated tree census data in an old-growth, cool-temperate, mixed deciduous forest that contains three topographic units (riparian, denuded slope, and terrace) in northern Japan to document decadal changes in aboveground biomass (AGB) and their processes in relation to endogenous processes and climatic factors. AGB increased steadily over the 26 years in all topographic units, but different tree species contributed to the increase among the topographic units. AGB gain within each topographic unit exceeded AGB loss via tree mortality in most of the measurement periods despite substantial temporal variation in AGB loss. At the local scale, variations in AGB gain were partially explained by compensating growth of trees around canopy gaps. Climate affected the local-scale AGB gain: the gain was larger in the measurement periods with higher mean air temperature during the current summer but smaller in those with higher mean air temperature during the previous autumn, synchronously in all topographic units. The influences of decadal summer and autumn warming on AGB growth appeared to be counteracting, suggesting that the observed steady AGB increase in KRRF is not fully explained by the warming. Future studies should consider global and regional environmental factors such as elevated CO2 concentrations and nitrogen deposition, and include cool-temperate forests with a broader temperature range to improve our understanding on biomass accumulation in this type of forests under climate change.

Replacing coal in Georgia's power plants with woody biomass to increase carbon benefit: A mixed integer linear programming model.

To combat climate change, reducing carbon emissions from coal consumption in the power sector can be an effective strategy. We developed a price-exogenous mixed integer linear optimization model satisfying both traditional timber demand in Georgia and its neighboring states (Alabama, Florida, North Carolina, South Carolina, and Tennessee) and additional bioenergy demand to replace coal in the power plants of Georgia for 50 years, maximizing social welfare. We used Forest Inventory & Analysis unit level yield of five forest types (planted softwood, natural softwood, upland hardwood, bottomland hardwood, and mixed forest), timber demand, and price information, and developed three scenarios. In the Baseline scenario, traditional annual timber demand (152 million tons of wood) was satisfied with no coal replacement. In Scenario 1, 100% coal (7.34 million tons annually) was replaced using pulpwood only, along with traditional demand. In Scenario 2, also with traditional demand, 100% coal was replaced using pulpwood and logging residues. It would require approximately 336 and 98 thousand acres of additional annual timberland harvested in Scenario 1 and Scenario 2, respectively, compared to Baseline (1280 thousand acres). During 50 years, a total of 9.3, 10.2, and 9.6 billion tons of timber was produced in Baseline, Scenario 1, and Scenario 2, respectively. About one-third of all torrefaction plants would be located in the central region of Georgia. The net change in stand carbon was positive in all three scenarios-the highest in Baseline (1330 million tons C), followed by Scenario 2 (1261 million tons C), and the lowest in Scenario 1 (872 million tons C). About 240 million tons of carbon was avoided by using biomass instead of coal in Scenario 1 and Scenario 2. In Baseline, with continued emission from coal usage in the power plant for 50 years (285 million tons C), net carbon benefit was 1046 million tons C. Replacing 100% of coal with both pulpwood and logging residues provided a net benefit of 1501 million tons C, about 43% higher compared to baseline.

Regionalized Life Cycle Greenhouse Gas Emissions of Forest Biomass Use for Electricity Generation in the United States.

This study presents a cradle-to-grave life cycle analysis (LCA) of the greenhouse gas (GHG) emissions of the electricity generated from forest biomass in different regions of the United States (U.S.), taking into consideration regional variations in biomass availabilities and logistics. The regional biomass supply for a 20 MW bioelectricity facility is estimated using the Land Use and Resource Allocation (LURA) model. Results from LURA and data on regional forest management, harvesting, and processing are incorporated into the GHGs, Regulated Emissions, and Energy Use in Technologies (GREET) model for LCA. The results suggest that GHG emissions of mill residues-based pathways can be 15-52% lower than those of pulpwood-based pathways, with logging residues falling in between. Nonetheless, our analysis suggests that screening bioenergy projects on specific feedstock types alone is not sufficient because GHG emissions of a pulpwood-based pathway in one state can be lower than those of a mill residue-based pathway in another state. Furthermore, the available biomass supply often consists of several woody feedstocks, and its composition is region-dependent. Forest biomass-derived electricity is associated with 86-93% lower life-cycle GHG emissions than the emissions of the average grid electricity in the U.S. Key factors driving bioelectricity GHG emissions include electricity generation efficiency, transportation distance, and energy use for biomass harvesting and processing.

Characterization and Cytotoxicity Assessment of the Lipophilic Fractions of Different Morphological Parts of Acacia dealbata.

Acacia dealbata biomass, either from forest exploitation or from the management of invasive species, can be a strategic topic, namely as a source of high-value compounds. In this sense, the present study aimed at the detailed characterization of the lipophilic components of different morphological parts of A. dealbata and the evaluation of their cytotoxicity in cells representative of different mammals' tissues. The chemical composition of lipophilic extracts from A. dealbata bark, wood and leaves was evaluated using gas chromatography-mass spectrometry (GC-MS). Terpenic compounds (representing 50.2%-68.4% of the total bark and leaves extracts, respectively) and sterols (60.5% of the total wood extract) were the main components of these extracts. Other constituents, such as fatty acids, long-chain aliphatic alcohols, monoglycerides, and aromatic compounds were also detected in the studied extracts. All the extracts showed low or no cytotoxicity in the different cells tested, demonstrating their safety profile and highlighting their potential to be used in nutraceutical or pharmaceutical applications. This study is therefore an important contribution to the valorization of A. dealbata, demonstrating the potential of this species as a source of high value lipophilic compounds.

Observed variation in soil properties can drive large variation in modelled forest functioning and composition during tropical forest secondary succession.

Censuses of tropical forest plots reveal large variation in biomass and plant composition. This paper evaluates whether such variation can emerge solely from realistic variation in a set of commonly measured soil chemical and physical properties. Controlled simulations were performed using a mechanistic model that includes forest dynamics, microbe-mediated biogeochemistry, and competition for nitrogen and phosphorus. Observations from 18 forest inventory plots in Guanacaste, Costa Rica were used to determine realistic variation in soil properties. In simulations of secondary succession, the across-plot range in plant biomass reached 30% of the mean and was attributable primarily to nutrient limitation and secondarily to soil texture differences that affected water availability. The contributions of different plant functional types to total biomass varied widely across plots and depended on soil nutrient status. In Central America, soil-induced variation in plant biomass increased with mean annual precipitation because of changes in nutrient limitation. In Central America, large variation in plant biomass and ecosystem composition arises mechanistically from realistic variation in soil properties. The degree of biomass and compositional variation is climate sensitive. In general, model predictions can be improved through better representation of soil nutrient processes, including their spatial variation.

Biosynthesis of Nutraceutical Fatty Acids by the Oleaginous Marine Microalgae Phaeodactylum tricornutum Utilizing Hydrolysates from Organosolv-Pretreated Birch and Spruce Biomass.

Polyunsaturated fatty acids (PUFAs) are essential for human function, however they have to be provided through the diet. As their production from fish oil is environmentally unsustainable, there is demand for new sources of PUFAs. The aim of the present work was to establish the microalgal platform to produce nutraceutical-value PUFAs from forest biomass. To this end, the growth of Phaeodactylum tricornutum on birch and spruce hydrolysates was compared to autotrophic cultivation and glucose synthetic media. Total lipid generated by P. tricornutum grown mixotrophically on glucose, birch, and spruce hydrolysates was 1.21, 1.26, and 1.29 g/L, respectively. The highest eicosapentaenoic acid (EPA) production (256 mg/L) and productivity (19.69 mg/L/d) were observed on spruce hydrolysates. These values were considerably higher than those obtained from the cultivation without glucose (79.80 mg/L and 6.14 mg/L/d, respectively) and also from the photoautotrophic cultivation (26.86 mg/L and 2.44 mg/L/d, respectively). To the best of our knowledge, this is the first report describing the use of forest biomass as raw material for EPA and docosapentaenoic acid (DHA) production.

Global-scale patterns of nutrient density and partitioning in forests in relation to climate.

Knowledge of nutrient storage and partitioning in forests is imperative for ecosystem models and ecological theory. Whether the nutrients (N, P, K, Ca, and Mg) stored in forest biomass and their partitioning patterns vary systematically across climatic gradients remains unknown. Here, we explored the global-scale patterns of nutrient density and partitioning using a newly compiled dataset including 372 forest stands. We found that temperature and precipitation were key factors driving the nutrients stored in living biomass of forests at global scale. The N, K, and Mg stored in living biomass tended to be greater in increasingly warm climates. The mean biomass N density was 577.0, 530.4, 513.2, and 336.7 kg/ha for tropical, subtropical, temperate, and boreal forests, respectively. Around 76% of the variation in biomass N density could be accounted by the empirical model combining biomass density, phylogeny (i.e., angiosperm, gymnosperm), and the interaction of mean annual temperature and precipitation. Climate, stand age, and biomass density significantly affected nutrients partitioning at forest community level. The fractional distribution of nutrients to roots decreased significantly with temperature, suggesting that forests in cold climates allocate greater nutrients to roots. Gymnosperm forests tended to allocate more nutrients to leaves as compared with angiosperm forests, whereas the angiosperm forests distributed more nutrients in stems. The nutrient-based Root:Shoot ratios (R:S), averaged 0.30 for R:SN , 0.36 for R:SP , 0.32 for R:SK , 0.27 for R:SCa , and 0.35 for R:SMg , respectively. The scaling exponents of the relationships describing root nutrients as a function of shoot nutrients were more than 1.0, suggesting that as nutrient allocated to shoot increases, nutrient allocated to roots increases faster than linearly with nutrient in shoot. Soil type significantly affected the total N, P, K, Ca, and Mg stored in living biomass of forests, and the Acrisols group displayed the lowest P, K, Ca, and Mg.

Early ecological outcomes of natural regeneration and tree plantations for restoring agricultural landscapes.

Mixed tree plantings and natural regeneration are the main restoration approaches for recovering tropical forests worldwide. Despite substantial differences in implementation costs between these methods, little is known regarding how they differ in terms of ecological outcomes, which is key information for guiding decision making and cost-effective restoration planning. Here, we compared the early ecological outcomes of natural regeneration and tree plantations for restoring the Brazilian Atlantic Forest in agricultural landscapes. We assessed and compared vegetation structure and composition in young (7-20 yr old) mixed tree plantings (PL), second-growth tropical forests established on former pastures (SGp), on former Eucalyptus spp. plantations (SGe), and in old-growth reference forests (Ref). We sampled trees with diameter at breast height (DBH) 1-5 cm (saplings) and trees at DBH > 5 cm (trees) in a total of 32 20 × 45 m plots established in these landscapes. Overall, the ecological outcomes of natural regeneration and restoration plantations were markedly different. SGe forests showed higher abundance of large (DBH > 20 cm) nonnative species, of which 98% were resprouting Eucalyptus trees, than SGp and PL, and higher total aboveground biomass; however, aboveground biomass of native species was higher in PL than in SGe. PL forests had lower abundance of native saplings and lianas than both naturally established second-growth forests, and lower proportion of animal dispersed saplings than SGe, probably due to higher isolation from native forest remnants. Rarefied species richness of trees was lower in SGp, intermediate in SGe and Ref and higher in PL, whereas rarefied species richness of saplings was higher in SG than in Ref. Species composition differed considerably among regeneration types. Although these forests are inevitably bound to specific landscape contexts and may present varying outcomes as they develop through longer time frames, the ecological particularities of forests established through different restoration approaches indicate that naturally established forests may not show similar outcomes to mixed tree plantings. The results of this study underscore the importance that restoration decisions need to be based on more robust expectations of outcomes that allow for a better analysis of the cost-effectiveness of different restoration approaches before scaling-up forest restoration in the tropics.

Optimal climate for large trees at high elevations drives patterns of biomass in remote forests of Papua New Guinea.

Our ability to model global carbon fluxes depends on understanding how terrestrial carbon stocks respond to varying environmental conditions. Tropical forests contain the bulk of the biosphere's carbon. However, there is a lack of consensus as to how gradients in environmental conditions affect tropical forest carbon. Papua New Guinea (PNG) lies within one of the largest areas of contiguous tropical forest and is characterized by environmental gradients driven by altitude; yet, the region has been grossly understudied. Here, we present the first field assessment of aboveground biomass (AGB) across three main forest types of PNG using 193 plots stratified across 3,100-m elevation gradient. Unexpectedly, AGB had no direct relationship to rainfall, temperature, soil, or topography. Instead, natural disturbances explained most variation in AGB. While large trees (diameter at breast height > 50 cm) drove altitudinal patterns of AGB, resulting in a major peak in AGB (2,200-3,100 m) and some of the most carbon-rich forests at these altitudes anywhere. Large trees were correlated to a set of climatic variables following a hump-shaped curve. The set of "optimal" climatic conditions found in montane cloud forests is similar to that of maritime temperate areas that harbor the largest trees in the world: high ratio of precipitation to evapotranspiration (2.8), moderate mean annual temperature (13.7°C), and low intra-annual temperature range (7.5°C). At extreme altitudes (2,800-3,100 m), where tree diversity elsewhere is usually low and large trees are generally rare or absent, specimens from 18 families had girths >70 cm diameter and maximum heights 20-41 m. These findings indicate that simple AGB-climate-edaphic models may not be suitable for estimating carbon storage in forests where optimal climate niches exist. Our study, conducted in a very remote area, suggests that tropical montane forests may contain greater AGB than previously thought and the importance of securing their future under a changing climate is therefore enhanced.

Predicting tree biomass growth in the temperate-boreal ecotone: Is tree size, age, competition, or climate response most important?

As global temperatures rise, variation in annual climate is also changing, with unknown consequences for forest biomes. Growing forests have the ability to capture atmospheric CO2 and thereby slow rising CO2 concentrations. Forests' ongoing ability to sequester C depends on how tree communities respond to changes in climate variation. Much of what we know about tree and forest response to climate variation comes from tree-ring records. Yet typical tree-ring datasets and models do not capture the diversity of climate responses that exist within and among trees and species. We address this issue using a model that estimates individual tree response to climate variables while accounting for variation in individuals' size, age, competitive status, and spatially structured latent covariates. Our model allows for inference about variance within and among species. We quantify how variables influence aboveground biomass growth of individual trees from a representative sample of 15 northern or southern tree species growing in a transition zone between boreal and temperate biomes. Individual trees varied in their growth response to fluctuating mean annual temperature and summer moisture stress. The variation among individuals within a species was wider than mean differences among species. The effects of mean temperature and summer moisture stress interacted, such that warm years produced positive responses to summer moisture availability and cool years produced negative responses. As climate models project significant increases in annual temperatures, growth of species like Acer saccharum, Quercus rubra, and Picea glauca will vary more in response to summer moisture stress than in the past. The magnitude of biomass growth variation in response to annual climate was 92-95% smaller than responses to tree size and age. This means that measuring or predicting the physical structure of current and future forests could tell us more about future C dynamics than growth responses related to climate change alone.

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.

As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality.

Variation of biomass and carbon pool with NDVI and altitude in sub-tropical forests of northwestern Himalaya.

In the present study, forests at three altitudes, viz., A1 (600-900 m), A2 (900-1200 m) and A3 (1200-1500 m) above mean sea level having normalised differential vegetation index (NDVI) values of N1 (0.0-0.1), N2 (0.1-0.2), N3 (0.2-0.3), N4 (0.3-0.4) and N5 (0.4-0.5) were selected for studying their relationship with the biomass and carbon pool in the state of Himachal Pradesh, India. The study reported maximum stem density of (928 trees ha-1) at the A2 altitude and minimum in the A3 and A1 with 600 trees ha-1 each. The stem densities in relation to NDVIs were observed in the order N5 > N3 > N4 > N1 > N2 and did not show any definite trend with increasing altitude. Highest stem volume (295.7 m3 ha-1) was observed in N1 NDVI and minimum (194.1 m3 ha-1) in N3 index. The trend observed for stem biomass at different altitudes was A3 > A1 > A2 and for NDVIs, it was N5 > N1 > N4 > N2 > N3. Maximum aboveground biomass (265.83 t ha-1) was recorded in the 0.0-0.1 NDVI and minimum (169.05 t ha-1) in 0.2-0.3 NDVI index. Significantly, maximum total soil carbon density (90.82 t C ha-1) was observed in 0.4-0.5 NDVI followed by 0.3-0.4 NDVI (77.12 t C ha-1). The relationship between soil carbon and other studied parameters was derived through different functions simultaneously. Cubic function showed highest r 2 in most cases, followed by power, inverse and exponential function. The relationship with NDVI showed highest r 2 (0.62) through cubic functions. In relationship between ecosystem carbon with other parameters of different altitudinal gradient and NDVI, only one positively significant relation was formed with total density (0.579) through cubic function. The present study thus reveals that soil carbon density was directly related to altitude and NDVIs, but the vegetation carbon density did not bear any significant relation with altitude and NDVI.

Atmospheric Emissions from Forest Biomass Residues to Energy Supply Chain: A Case Study in Portugal.

During the past decades, pressures on global environment and energy security have led to an increasing demand on renewable energy sources and diversification of the world's energy supply. The Portuguese energy strategy considers the use of Forest Biomass Residues (FBR) to energy as being essential to accomplish the goals established in the National Energy Strategy for 2020. However, despite the advantages pointing to FBR to the energy supply chain, few studies have evaluated the potential impacts on air quality. In this context, a case study was selected to estimate the atmospheric emissions of the FBR to the energy supply chain in Portugal. Results revealed that production, harvesting, and energy conversion processes are the main culprits for the biomass energy supply chain emissions (with a contribution higher than 90%), while the transport processes have a minor importance for all the pollutants. Compared with the coal-fired plants, the FBR combustion produces lower greenhouses emissions, on a mass basis of fuel consumed; the same is true for NOX and SO2 emissions.

Techno-economic analysis of CHP system supplied by waste forest biomass.

Poland, as for Europe, is a country with an average forest cover of approximately 30%. In these forests, more than 37M m3 of wood, mostly coniferous (over 80%), is harvested per year. In 2012, 4.2M m3 of sawn timber was produced (sawn timber without factory lumber). At the same time, in Poland there are over 8000 sawmills, whereas only about 700 of them saw over 90% of the harvested timber. So much fragmentation is a major cause of low sawmills innovation, particularly of those small ones. However, in recent years, a trend of development in this sector is noticeable, and it is through rationalisation of material and energy economy. One of the methods to increase the technical and economic effectiveness of enterprises involved in woodworking is to build in the combined heat and power system (CHP) plant with the ORC system into the existing infrastructure, which will be matched to the needs of the company. This article presents an analysis of the profitability of the investment based on the example of a medium-sized company sawing approximately 50,000 m3 of timber per year, and the economic analysis was performed for prices and costs valid in Poland. The analysis made for the 1650 kW(el) organic Rankine cycle (ORC) system, has resulted in a profitability index PI = 1.3, on the assumptions that the ORC system operates for 6000 h y(-1), will be purchased at the price of 4500 € kW(el)(-1) and at the price of electricity sales of 130 € MWh(-1).