Estimating quantity and equity of carbon emission from roads based on an improved LCA approach: the case of China

Purpose

Carbon emission from roads is an important contributor of a nation’s greenhouse gas emission that causes climate change. However, the existing life cycle assessment (LCA) analysis of road carbon emissions focus on project-level, ignoring regional differences. Significant challenges remain in developing regional road’s carbon emission mitigation strategies. This study estimates the quantity of carbon emissions from roads in China and calculated the regional equity of road carbon emissions.

Methods

An improved LCA approach, which considered the regional difference of raw materials’ carbon emissions, carbon emissions caused by traffic jam and road category, was applied to calculate the quantity of carbon emissions of roads. Sensitive analysis was conducted to find the key influential factors. Gini coefficient was used to calculate the equity degree of carbon emissions by roads based on the LCA results. The decomposition model of Gini coefficient is applied to analyze the causes of carbon emission differences.

Results

The total national carbon emissions by roads in 2019 increased by 2.2 times compared to 2009. Carbon emission from roads in the operation phase increased from 62% in 2009 to 83% in 2019. The functional unit for expressway in this study ranging from 1646 to 1794 t CO₂e/km in 31 provinces. An estimated uncertainty of plus or minus 4% of the traffic flow allocation between expressway and other roads makes an increase of 38% or a decrease of 15% of the life cycle emission. The overall Gini coefficient of carbon emissions from roads in China is under the warning line of 0.4. Outer inequity between regions contributes 88.83% of the whole inequity and the most developed three regions contribute 66.23%.

Conclusions

Large quantity of road construction in the past in China makes the burden of carbon emission transfer from the construction phase to the operation phase. Regional differences of raw materials’ carbon emissions, traffic jam, and road hierarchy are important factors influencing the LCA-based estimation of road carbon emission. To improve the national equity degree of road carbon emission, quota allocation of road carbon emission rights between regions and cross-regional carbon emission reduction policies would help.

     

Effects of advance and retreat of agricultural landscapes on Rana japonica and R. ornativentris

Paddy fields are essential habitats for frogs. We evaluated the impacts of both farmland consolidation including agricultural road improvement and farmland abandonment on the two Rana species using a model incorporating spatial autocorrelation. A sampling unit consists of several paddy fields that share a ditch and are isolated from other blocks by roads or other land covers. We surveyed 619 blocks in an area of about 1000 km² from the plain to the mountains of Toyota City in central Japan. Among them, 124 blocks included at least a flooded paddy field where frogs could lay eggs. R. ornativentris and R. japonica bred in 50 and 25 blocks, respectively. We constructed models to explain the presence/absence of two species by GLM (non-spatial model) and hierarchical Bayesian model with INLA (spatial model) that includes spatial autocorrelation as a random effect. Explanatory variables of the local scale were the altitude, location of the paddy field (yatsuda (valley bottom paddy fields) or non-yatsuda), farmland consolidated or not consolidated, and under cultivation or abandoned. Those of the landscape scale were areas of forest and paddy fields, and road density in 14 circles with different radius from 50 to 2000 m. Both species’ distribution had significant spatial autocorrelation. The spatial model had a higher discriminative ability than the non-spatial model. Farmland consolidation and the forest area in the 400 m radius had a positive effect on R. ornativentris. Altitude and road density in the 50 m radius had negative effects, cultivation had a positive effect, and farmland consolidation and yatsuda had no or negative effects on R. japonica. R. ornativentris was threatened by farmland abandonment, but the urbanization and/or farmland consolidation threatened R. japonica.

     

Predictive Modelling of Contagious Deforestation in the Brazilian Amazon

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.     

Modeling the spatial and temporal heterogeneity of deforestation‐driven carbon emissions: the INPE‐EM framework applied to the Brazilian Amazon

We present a generic spatially explicit modeling framework to estimate carbon emissions from deforestation (INPE‐EM). The framework incorporates the temporal dynamics related to the deforestation process and accounts for the biophysical and socioeconomic heterogeneity of the region under study. We build an emission model for the Brazilian Amazon combining annual maps of new clearings, four maps of biomass, and a set of alternative parameters based on the recent literature. The most important results are as follows: (a) Using different biomass maps leads to large differences in estimates of emission; for the entire region of the Brazilian Amazon in the last decade, emission estimates of primary forest deforestation range from 0.21 to 0.26 Pg C yr^?1. (b) Secondary vegetation growth presents a small impact on emission balance because of the short duration of secondary vegetation. In average, the balance is only 5% smaller than the primary forest deforestation emissions. (c) Deforestation rates decreased significantly in the Brazilian Amazon in recent years, from 27 Mkm² in 2004 to 7 Mkm² in 2010. INPE‐EM process‐based estimates reflect this decrease even though the agricultural frontier is moving to areas of higher biomass. The decrease is slower than a non‐process instantaneous model would estimate as it considers residual emissions (slash, wood products, and secondary vegetation). The average balance, considering all biomass, decreases from 0.28 in 2004 to 0.15 Pg C yr^?1 in 2009; the non‐process model estimates a decrease from 0.33 to 0.10 Pg C yr^?1. We conclude that the INPE‐EM is a powerful tool for representing deforestation‐driven carbon emissions. Biomass estimates are still the largest source of uncertainty in the effective use of this type of model for informing mechanisms such as REDD+. The results also indicate that efforts to reduce emissions should focus not only on controlling primary forest deforestation but also on creating incentives for the restoration of secondary forests.     

Spatial pattern of standing timber value across the Brazilian Amazon

The Amazon is a globally important system, providing a host of ecosystem services from climate regulation to food sources. It is also home to a quarter of all global diversity. Large swathes of forest are removed each year, and many models have attempted to predict the spatial patterns of this forest loss. The spatial patterns of deforestation are determined largely by the patterns of roads that open access to frontier areas and expansion of the road network in the Amazon is largely determined by profit seeking logging activities. Here we present predictions for the spatial distribution of standing value of timber across the Amazon. We show that the patterns of timber value reflect large-scale ecological gradients, determining the spatial distribution of functional traits of trees which are, in turn, correlated with timber values. We expect that understanding the spatial patterns of timber value across the Amazon will aid predictions of logging movements and thus predictions of potential future road developments. These predictions in turn will be of great use in estimating the spatial patterns of deforestation in this globally important biome.     

道路建设对西南地区景观格局的影响

为揭示西南地区道路建设对景观格局的影响,基于2015年土地覆被和道路数据,计算了研究区道路核密度与景观指数,在此基础上分析了道路建设的直接、间接和综合影响,探讨了影响的空间异质性和对不同土地覆被影响的特点,指出了各景观指数的适用性。结果表明：(1)道路建设使斑块密度增加,斑块离散化加剧,发生明显的破碎化趋势,同时道路核密度与PD、SHDI、SPLIT变化百分比呈显著正相关,与COHESION、CONTAG、PAFRAC呈显著负相关;(2)道路建设对景观指数的影响程度由大到小依次表现在：破碎化、斑块空间分布形态、聚集连通性,其中PD能很好的表征道路建设对景观破碎化的作用程度,SPLIT在识别破碎化程度高的区域更具优势;(3)不同景观指数具有不同适用性,PAFRAC适用于道路不发达区域,CONTAG适用于道路分布异质性较高的区域,DIVISION适用于人类活动强度不大的区域,COHESION、SPLIT适用于探究道路及伴随的人类活动共同作用的区域,PD、SHDI在探究道路的直接作用时适用性广泛;(4)探究不同土地覆被类型受道路建设的影响,林地优先选取PD、DIVISION;草地优先选取DI...     

Effects of environmental and anthropogenic drivers on Amur tiger distribution in northeastern China

We examined environmental and anthropogenic factors drive range loss in large mammals, using presence data of Amur tigers opportunistically collected between 2000 and 2012, and anthropogenic and environmental variables to model the distribution of the Amur tiger in northeastern China. Our results suggested that population distribution models of different subregions showed different habitat factors determining tiger population distribution patterns. Where farmland cover was over 50 km² per pixel (196 km²), distance was within 15 km to the railway in Changbaishan and road density (length per pixel) increased in Wandashan, the relative probability of Amur tiger occurrence exhibited monotonic avoidance responses; however, where distance was within 150 km of the Sino-Russia border, the occurrence probability of Amur tiger was relatively high. We analyzed the avoidance or preference responses of Amur tiger distribution to elevation, snow depth and Viewshed. Furthermore, different subregional models detected a variety of spatial autocorrelation distances due to different population clustering patterns. We found that spatial models significantly improved model fits for non-spatial models and made more robust habitat suitability predications than that of non-spatial models. Consequently, these findings provide useful guidance for habitat conservation and management.     

Life cycle assessment of the construction of an unpaved road in an undisturbed tropical rainforest area in the vicinity of Manu National Park,Peru

Purpose

The main goal of this study is to provide a thorough environmental sustainability analysis of the construction, traffic, and maintenance of a 45.6-km section of the ‘Manu Road’, an unpaved tropical road that is currently being built in the vicinity of Manu National Park, in the region of Madre de Dios, Peru.

Methods

Life cycle assessment (LCA) using a set of 18 different impact categories was selected to conduct the environmental analysis. Modelling of machinery and vehicle emissions, as well as dust emissions, was performed to account for site-specific characteristics in terms of road construction and traffic. Similarly, direct land use changes were modelled with a particular emphasis on the decay of deforested biomass during construction. A set of different scenarios for the production system were considered to account for uncertainty regarding vehicle transit, amount of deforested biomass, and emission standards.

Results and discussion

Construction, maintenance, and traffic of the Manu Road varied considerably depending on methodological assumptions. Deforestation due to direct land use changes appears to be the main environmental hotspot in terms of climate change, whereas in the remaining impact categories, traffic was the main carrier of environmental burdens.

Conclusions

To the best of our knowledge, this study is the first LCA that focuses on the construction, maintenance, and traffic in a tropical rainforest environment. Despite the low requirements in terms of materials and technology to build this road, its derived environmental impacts are relevant in terms of climate change and particulate matter formation due to deforestation and dust emissions, respectively. Unpaved roads represent a relevant proportion of the entire road network worldwide, especially in developing tropical countries, playing a crucial role in the transportation of raw materials. Furthermore, road infrastructure is expected to expand explosively in the decades to come. Therefore, we suggest that LCA studies can and should improve the planning of road infrastructure in terms of life cycle inventories.

     

Contextualizing ethanol avoided carbon emissions in Brazil

We propose to compare avoided emissions from ethanol use in Brazil with emissions caused by the use of fossil fuel, and by land use changes, specifically Amazon deforestation. The avoided emissions of CO₂ in Brazil due to ethanol use in 2008 ranged from approximately 9 to 12 Tg C yr^?1. These values are an order of magnitude higher than the amount of carbon that could be potentially sequestered in soils if sugarcane cultivation in Brazil switches completely to mechanized harvesting, and two orders of magnitude higher than the carbon emissions in soils cultivated with sugarcane and that undergo harvest with burning. In relation to fossil fuel emissions, ethanol avoided emissions are equivalent to 20–30% of the carbon emissions associated with the use of gasoline and diesel in the transportation sector, and to approximately 10% of the total use of fossil fuel in the country. When compared with the carbon emissions from Amazon deforestation ethanol avoided emissions are again one order of magnitude lower. We conclude that ethanol avoided emissions are relatively important within the transport sector, but are still incipient if compared with the emissions from total fossil fuel combustion and emissions from deforestation indicating that climate mitigation efforts in Brazil needs to focus outside of biofuel production. Consequently, we suggest that Brazil develop equally strong actions towards increased energy efficiency use in the country and, more importantly to drastically reduce carbon emissions associated with Amazon deforestation.     

Technoecological analysis of energy carriers for long‐haul transportation

Long‐haul transportation demand is predicted to increase in the future, resulting in higher carbon dioxide emissions. Different drivetrain technologies, such as hybrid or battery electric vehicles, electrified roads, liquefied natural gas and hydrogen, might offer solutions to this problem. To assess their ecological and economic impact, these concepts were simulated including a weight and cost model to estimate the total cost of ownership. An evolutionary algorithm optimizes each vehicle to find a concept specific optimal solution. A model calculates the minimum investment in infrastructure required to meet the energy demand for each concept. A well‐to‐wheel analysis takes into account upstream and on‐road carbon dioxide emissions, to compare fully electric vehicles with conventional combustion engines. Investment in new infrastructure is the biggest drawback of electrified road concepts, although they offer low CO₂ emissions. The diesel hybrid is the best compromise between carbon reduction and costs.     

基于格网的南四湖流域土地利用碳排放与其生态系统服务价值时空关系分析

社会发展引起的土地利用变化对生态系统服务和碳排放有显著影响,探讨碳排放与生态系统服务价值（ESV）的时空关联规律,对促进区域低碳绿色发展提供重要的理论和实践借鉴。为揭示土地利用变化下碳排放与ESV的时空关系,以南四湖流域为研究对象,利用2000-2018年5期土地利用数据,采用土地转移矩阵和空间自相关等方法,并引入了碳源、碳汇、净碳排放量、碳排放强度和ESV强度作为研究变量,探索了ESV和碳排放的时空演变特征及其空间关联规律。研究结果表明：19年内流域内各地类间发生了程度不同的转移,其中耕地和建设用地是变化最大的类型;ESV随土地间的相互转化而波动变化,但整体上是增加的,水体面积的增加是导致其增加的决定性原因。ESV强度呈现"东高、西低,湖区不变"的分布特点,这与土地利用方式有关,受自然和社会等多因素影响;流域的碳汇量要远低于碳源量,净碳排放量呈稳定增长态势,其中建设用地的碳排放起着主导作用,因此建设用地在碳减排方面具有较大潜力。碳排放强度在研究期间发生了明显的时空变化,最大值从21.61 t/hm²增长到101.42 t/hm²,增长了4.69倍,工业化和城镇化是其增长的驱动因素;碳排放强度和ESV强度具有空间负相关性,局部聚集现象明显,以高低聚集区为主转变为以低低聚集区为主,与地类面积和建设用地的碳排放系数有关;低高聚集区的范围和分布变化不大。总之,该流域在整体上面临着ESV和碳排放增加的趋势,根据它们之间的空间关联性,流域应采取有效措施来防止碳排放快速增长对周围区域生态环境带来负面影响,并构建生态良好的循环系统,以实现流域低碳经济。     

Carbon Pools and Emissions from Deforestation in Extra-Tropical Forests of Northern Argentina Between 1900 and 2005

We estimated carbon pools and emissions from deforestation in northern Argentine forests between 1900 and 2005, based on forest inventories, deforestation estimates from satellite images and historical data on forests and agriculture. Carbon fluxes were calculated using a book-keeping model. We ran 1000 simulations for a 105-year period with different combinations of values of carbon stocks (Mg C ha⁻¹), soil carbon in the top 0.2 m, and annual deforestation series. The 1000 combinations of parameters were performed as a sensitivity analysis that for each run, randomly selected the values of each variable within a predefined range of values and probability distributions. Using the simulation outputs, we calculated the accumulated C emissions due to deforestation from 1900 to 2005 and the annual emission as the average of the 1000 simulations, and uncertainties of our estimates as the standard deviation. We found that northern Argentine forests contain an estimated 4.54 Pg C (2.312 Pg C in biomass and 2.233 Pg C in soil). Between 1900 and 2005 approximately 30% of the forests were deforested, yielding carbon emissions of 0.945 (SD = 0.270) Pg C. Estimated average annual carbon emissions between 1996 and 2005, mostly from deforestation of the Chaco dry forests, were 20,875 (SD = 6,156) Gg C y⁻¹ (1 Gg = 10⁻⁶ Pg). These values represent the largest source of carbon from land-cover change in the extra-tropical southern hemisphere, between 0.9 and 2.7% of the global carbon emissions from deforestation, and approximately 10% of carbon emissions from the Brazilian Amazon. Deforestation, which has accelerated during the last decades as a result of modern agriculture expansion, represents a major national source of greenhouse gases and the second emission source, after fossil fuel consumption by fixed sources. We conclude that Argentine forests are an important carbon pool and emission source that need more attention for accurate global estimates, and seasonally dry forest deforestation is a key component of the Argentine carbon cycle. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010

Reducing emissions from deforestation and forest degradation (REDD+) is considered one of the most cost-effective strategies for mitigating climate change. However, historical deforestation and emission rates―critical inputs for setting reference emission levels for REDD+―are poorly understood. Here we use multi-source, time-series satellite data to quantify carbon emissions from deforestation in the Amazon basin on a year-to-year basis between 2000 and 2010. We first derive annual deforestation indicators by using the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) product. MODIS indicators are calibrated by using a large sample of Landsat data to generate accurate deforestation rates, which are subsequently combined with a spatially explicit biomass dataset to calculate committed annual carbon emissions. Across the study area, the average deforestation and associated carbon emissions were estimated to be 1.59 ± 0.25 M ha•yr⁻¹ and 0.18 ± 0.07 Pg C•yr⁻¹ respectively, with substantially different trends and inter-annual variability in different regions. Deforestation in the Brazilian Amazon increased between 2001 and 2004 and declined substantially afterwards, whereas deforestation in the Bolivian Amazon, the Colombian Amazon, and the Peruvian Amazon increased over the study period. The average carbon density of lost forests after 2005 was 130 Mg C•ha⁻¹, ~11% lower than the average carbon density of remaining forests in year 2010 (144 Mg C•ha⁻¹). Moreover, the average carbon density of cleared forests increased at a rate of 7 Mg C•ha⁻¹•yr⁻¹ from 2005 to 2010, suggesting that deforestation has been progressively encroaching into high-biomass lands in the Amazon basin. Spatially explicit, annual deforestation and emission estimates like the ones derived in this study are useful for setting baselines for REDD+ and other emission mitigation programs, and for evaluating the performance of such efforts.     

Carbon emissions from agricultural expansion and intensification in the Chaco

Carbon emissions from land‐use changes in tropical dry forest systems are poorly understood, although they are likely globally significant. The South American Chaco has recently emerged as a hot spot of agricultural expansion and intensification, as cattle ranching and soybean cultivation expand into forests, and as soybean cultivation replaces grazing lands. Still, our knowledge of the rates and spatial patterns of these land‐use changes and how they affected carbon emissions remains partial. We used the Landsat satellite image archive to reconstruct land‐use change over the past 30 years and applied a carbon bookkeeping model to quantify how these changes affected carbon budgets. Between 1985 and 2013, more than 142 000 km² of the Chaco's forests, equaling 20% of all forest, was replaced by croplands (38.9%) or grazing lands (61.1%). Of those grazing lands that existed in 1985, about 40% were subsequently converted to cropland. These land‐use changes resulted in substantial carbon emissions, totaling 824 Tg C between 1985 and 2013, and 46.2 Tg C for 2013 alone. The majority of these emissions came from forest‐to‐grazing‐land conversions (68%), but post‐deforestation land‐use change triggered an additional 52.6 Tg C. Although tropical dry forests are less carbon‐dense than moist tropical forests, carbon emissions from land‐use change in the Chaco were similar in magnitude to those from other major tropical deforestation frontiers. Our study thus highlights the urgent need for an improved monitoring of the often overlooked tropical dry forests and savannas, and more broadly speaking the value of the Landsat image archive for quantifying carbon fluxes from land change.     

北京城区道路系统路网空间特征及其与LST和NDVI的相关性

城市道路系统是人类活动最为频繁的场所之一,研究路网空间特征与相关环境指标的关系,可为定量分析城市建设过程对城市生态环境的影响以及为城市基础设施规划管理提供方法。对北京市五环内各级道路抽样调查及面向对象的高分辨率遥感影像识别,建立道路空间信息数据库,运用TM影像反演获得地表温度(LST)及归一化植被指数(NDVI),研究4种不同分析网格下城市路网特征与两者的空间相关性。结果表明,在较大分析网格下(4×4),路网空间指标与LST mean及NDVI mean相关性显著;中等网格下,除LST与NDVI的平均值外,路网空间指标还可与两者最小值建立相关性;在较小窗口下(32×32),除平均值与最小值,还可建立特定道路指标与LST max及NDVI max的联系;各路网指标中,单位面积道路总长与LST及NDVI相关性最强;加权道路结点数在8×8和32×32网格分析中,与NDVI的相关性高于该指标未加权值,而结点指标均不适合与LST max建立联系;在各分析网格下,4项道路指标均与NDVI mean呈极显著负相关,表明道路绿地建设规模还不足以对NDVI的强度及分布产生较大影响。     

Life cycle assessment of winter road maintenance

Purpose

Winter road maintenance in the Nordic climate is demanding due to challenging weather conditions, high precipitation, and icy conditions. As a leading country in the transition to low-emission transport, Norway must work to reduce their emissions while providing a safe level of service through winter maintenance operations. This article investigates the environmental impacts of winter road maintenance (WRM) in Norway both today and under a climate change scenario predicted for 2050.

Methods

Life cycle assessment (LCA) is used to evaluate the environmental impact of the functional unit “average winter road maintenance in Norway on national and county roads per km.lane.” The ReCiPe (hierarchy) method was used to identify and categorize emissions related to WRM to show how different factors affect the system and to reveal hidden emissions hotspots. Real-time data from WRM vehicles were used to determine how fuel consumption is affected by gradient and weather. Producers and operators provided other relevant information on WRM vehicles. Official reports supplied information on deicer quantities used and the total distance driven by WRM vehicles in Norway.

Results and discussion

The quantity of deicer used is the main source of emissions contributing toward all impact categories. The effect of deicer is likely to be even higher in certain impact categories. The environmental impact of the deicer after application is not included. The representation of WRM in existing emissions data is limited despite the considerable amount of deicer applied and the long distances that WRM vehicles travel. The results document how energy use throughout the system is another important source of emissions. Various parameters, such as road gradient, vehicle properties, driver behavior, and weather, affect the fuel consumption of WRM vehicles, with weather being the most important of these.

Conclusions

Significant potential for emissions reductions from WRM was found, and WRM operations should be included in cold-climate road LCA studies. The environmental impacts of deicer application are especially high compared to the mechanical clearing of roads and contribute strongly to impact categories such as terrestrial, freshwater, and human toxicity and to the formation of particulate matter.

     

Greenhouse gas emissions from forestry in East Norway

Purpose

So far no calculations have been made for greenhouse gas (GHG) emissions from forestry in East Norway. This region stands for 80 % of the Norwegian timber production. The aim of this study was to assess the annual GHG emissions of Norwegian forestry in the eastern parts of the country from seed production to final felling and transport of timber to sawmill and wood processing industry (cradle-to-gate inventory), based on specific Norwegian data.

Methods

The life cycle inventory was conducted with SimaPro applying primary and secondary data from Norwegian forestry. GHG emissions of fossil-related inputs from the technosphere were calculated for the functional unit of 1 m³ timber extracted and delivered to industry gate in East Norway in 2010. The analysis includes seed and seedling production, silvicultural operations, forest road construction and upgrading, thinning, final felling, timber forwarding and timber transport on road and rail from the forest to the industry. Norwegian time studies of forestry machines and operations were used to calculate efficiency, fuel consumption and transport distances. Due to the lack of specific Norwegian data in Ecoinvent, we designed and constructed unit processes based on primary and secondary data from forestry in East Norway.

Results and discussion

GHG emissions from forestry in East Norway amounted to 17.893 kg CO₂-equivalents per m³ of timber delivered to industry gate in 2010. Road transport of timber accounted for almost half of the total GHG emissions, final felling and forwarding for nearly one third of the GHG emissions. Due to longer road transport distances, pulpwood had higher impact on the climate change category than saw timber. The construction of forest roads had the highest impact on the natural land transformation category. The net CO₂ emissions of fossil CO₂ corresponded to 2.3 % of the CO₂ sequestered by 1 m³ of growing forest trees and were compared to a calculation of biogenic CO₂ release from the forest floor as a direct consequence of harvesting.

Conclusions

Shorter forwarding and road transport distances, increased logging truck size and higher proportion of railway transport may result in lower emissions per volume of transported timber. A life cycle assessment of forestry may also consider impacts on environmental categories other than climate change. Biogenic CO₂ emissions from the soil may be up to 10 times higher than the fossil-related emissions, at least in a short-term perspective, and are highly dependent on stand rotation length.     

Roads as nitrogen deposition hot spots

Mobile sources are the single largest source of nitrogen emissions to the atmosphere in the US. It is likely that a portion of mobile-source emissions are deposited adjacent to roads and thus not measured by traditional monitoring networks, which were designed to measure long-term and regional trends in deposition well away from emission sources. To estimate the magnitude of near-source nitrogen deposition, we measured concentrations of both dissolved inorganic nitrogen (DIN) and total dissolved nitrogen (inorganic + organic) (TDN) in throughfall (i.e., the nitrogen that comes through the forest canopy) along transects perpendicular to two moderately trafficked roads on Cape Cod in Falmouth MA, coupled with measurements of both DIN and TDN in bulk precipitation made in adjacent open fields at the same transect distances. We used the TDN throughfall data to estimate total nitrogen deposition, including dry gaseous nitrogen deposition in addition to wet deposition and dry particle deposition. There was no difference in TDN in the bulk collectors along the transects at either site; however TDN in the throughfall collectors was always higher closest to the road and decreased with distance. These patterns were driven primarily by differences in the inorganic N and not the organic N. Annual throughfall deposition was 8.7 (±0.4) and 6.8 (±0.5) TDN kg N ha^?1 year^?1 at sites 10 and 150 m away from the road respectively. We also characterized throughfall away from a non-road edge (power line right-of-way) to test whether the increased deposition observed near road edges was due to deposition near emission sources or due to a physical, edge effect causing higher deposition. The increased deposition we observed near roads was due to increases in inorganic N especially NH₄ ⁺. This increased deposition was not the result of an edge effect; rather it is due to near source deposition of mobile source emissions. We scaled these results to the entire watershed and estimate that by not taking into account the effects of increased gaseous N deposition from mobile sources we are underestimating the amount of N deposition to the watershed by 13–25 %.     

Nitrogen deposition in tropical forests from savanna and deforestation fires

We used satellite‐derived estimates of global fire emissions and a chemical transport model to estimate atmospheric nitrogen (N) fluxes from savanna and deforestation fires in tropical ecosystems. N emissions and reactive N deposition led to a net transport of N equatorward, from savannas and areas undergoing deforestation to tropical forests. Deposition of fire‐emitted N in savannas was only 26% of emissions – indicating a net export from this biome. On average, net N loss from fires (the sum of emissions and deposition) was equivalent to approximately 22% of biological N fixation (BNF) in savannas (4.0 kg N ha^?1 yr^?1) and 38% of BNF in ecosystems at the deforestation frontier (9.3 kg N ha^?1 yr^?1). Net N gains from fires occurred in interior tropical forests at a rate equivalent to 3% of their BNF (0.8 kg N ha^?1 yr^?1). This percentage was highest for African tropical forests in the Congo Basin (15%; 3.4 kg N ha^?1 yr^?1) owing to equatorward transport from frequently burning savannas north and south of the basin. These results provide evidence for cross‐biome atmospheric fluxes of N that may help to sustain productivity in some tropical forest ecosystems on millennial timescales. Anthropogenic fires associated with slash and burn agriculture and deforestation in the southern part of the Amazon Basin and across Southeast Asia have substantially increased N deposition in these regions in recent decades and may contribute to increased rates of carbon accumulation in secondary forests and other N‐limited ecosystems.     

Characterizing a tropical deforestation wave: a dynamic spatial analysis of a deforestation hotspot in the Colombian Amazon

Tropical deforestation is the major contemporary threat to global biodiversity, because a diminishing extent of tropical forests supports the majority of the Earth's biodiversity. Forest clearing is often spatially concentrated in regions where human land use pressures, either planned or unplanned, increase the likelihood of deforestation. However, it is not a random process, but often moves in waves originating from settled areas. We investigate the spatial dynamics of land cover change in a tropical deforestation hotspot in the Colombian Amazon. We apply a forest cover zoning approach which permitted: calculation of colonization speed; comparative spatial analysis of patterns of deforestation and regeneration; analysis of spatial patterns of mature and recently regenerated forests; and the identification of local‐level hotspots experiencing the fastest deforestation or regeneration. The colonization frontline moved at an average of 0.84 km yr^?1 from 1989 to 2002, resulting in the clearing of 3400 ha yr^?1 of forests beyond the 90% forest cover line. The dynamics of forest clearing varied across the colonization front according to the amount of forest in the landscape, but was spatially concentrated in well‐defined ‘local hotspots’ of deforestation and forest regeneration. Behind the deforestation front, the transformed landscape mosaic is composed of cropping and grazing lands interspersed with mature forest fragments and patches of recently regenerated forests. We discuss the implications of the patterns of forest loss and fragmentation for biodiversity conservation within a framework of dynamic conservation planning.