et al., 2001; House et al., 2003; Friedlingstein et al., 2006). The differences in carbonfluxes between top-down inversion estimates and bottom-up model studies are consis-
tent with estimates due to environmental changes from process-based carbon cycle models, but the latter have been criticized since they do not account for residual ter- restrial sinks due to agricultural land management, and export of wood products, nor do they account for transport of carbonfromland areas to ocean via rivers (House et al., 2003). In general, the mean response of forest net primary productivity to elevated
estimates were applied to calculate net CO 2 emissions. The spatial distributions of deforestation and regrowth areas for two different years (1988 and 1994) were obtained through a visual analysis of sampled Landsat satellite images by the National Institute for Space Research (INPE). Major areas of regrowth were found in the Amazon forest (82.3×10 3 km 2 ) and in the Cerrado (17.7×10 3 km 2 ). The enhancement of regrowth due to environmental changes may be implicitly in- cluded in these estimates, since the satellite images capture only the net area changes due to LUC and thus cannot ex- clude the changes in forest areas modified by environmental factors (e.g. CO 2 fertilization-enhanced production rates of plants in re-growing forest, woody invasion in savanna-like cerrado). The mean estimates of above-ground carbon den- sities were calculated for each type of vegetation based on data gathered in over 2500 sampled sites, and these densities were overlaid on a vegetation type map. Thus this estimate does not include any time lag due to decay of biomass (i.e. wood products dumped in landfills or burned in incinerators and residuals after slash and burn). In addition to the de- forestation, selective harvest of timber occurs in Amazonia to exploit marketable tree species mainly along roads that are useful for log transport. The areas affected by selective logging can be later subject to deforestation or abandonment. Thus double counting of the carbon affected by selective log- ging can occur when the carbon stock changes due to the de- forestation are estimated from the differences between two different years and those due to selective logging are derived from independent methods (e.g. Nepstad et al., 1999; Asner et al., 2005). Because of the need for a more elaborate anal- ysis, CO 2 emissions from selective logging have not been explicitly included in this inventory.
Notes: n/a = no number is provided because the area of regeneration after harvest in the tropical region and part of the temperate region was not available. In addition, regeneration after selective cutting, as it is often used in the tropics, is difficult to capture with the FAO Definitional Scenario. It is assumed that recent area conversion rates [“recent” = for Annex I Parties AR late 1980s/early 1990s and for D 1980s (except for Canada and Russian Federation early 1990s); ARD in other regions 1980s] have applied since 1990, and will continue to do so until 2012. The IPCC Definitional Scenario includes transitions between forest and non-forest land uses under Article 3.3. For the purposes of this table, it is assumed that not only planting, but also other forms of stand establishment such as natural establishment, are considered AR activities. The FAO Definitional Scenario includes the harvest/regeneration cycle, because regeneration is defined as reforestation. Within the FAO Definitional Scenario, three accounting approaches are distinguished (see paragraph 25 and Section 3.3.2). Uptake rates are intended to span the range within which the average value for each region is expected to be. The lower bound of the estimated average annual stock change corresponds to the lower uptake rate in AR and the higher bound to the higher uptake rate. Trees have been assumed to grow according to a sigmoidal growth curve. Estimated area for conversion between non-forest and forest should be regarded as an upper limit for the temperate region total and the tropical region, because some countries may have reported plantations for 1990 but not for 1980, and because some of the plantations may not qualify as resulting from AR activities under the IPCC Definitional Scenario. Also, for tropical countries, the deforestation estimates are very uncertain and could be in error by as much as ±50%.
Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understanding regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory- based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimatesfrom the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimatesfrom the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469 – 687 g C m -2 yr -1 and total NPP in the range of 318 – 490 Tg C yr -1 for croplands in the Midwest in 2007 and 2008. The NPP estimatesfrom crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m -2 yr -1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m -2 yr -1 . MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species-specific crop information should be used in satellite-based and process-based models to improve carbonestimates for croplands.
Abstract. Reasons for the large uncertainty in landuse and land cover change (LULCC) emissions go beyond recog- nized issues related to the available data on land cover change and the fact that model simulations rely on a simplified and incomplete description of the complexity of biological and LULCC processes. The large range across published LULCC emission estimates is also fundamentally driven by the fact that the net LULCC flux is defined and calculated in differ- ent ways across models. We introduce a conceptual frame- work that allows us to compare the different types of models and simulation setups used to derive landusefluxes. We find that published studies are based on at least nine different def- initions of the net LULCC flux. Many multi-model synthe- ses lack a clear agreement on definition. Our analysis reveals three key processes that are accounted for in different ways: the landuse feedback, the loss of additional sink capacity, and legacy (regrowth and decomposition) fluxes. We show that these terminological differences, alone, explain differ- ences between published net LULCC flux estimates that are of the same order as the published estimates themselves. This has consequences for quantifications of the residual terres- trial sink: the spread in estimates caused by terminological differences is conveyed to those of the residual sink. Fur- thermore, the application of inconsistent definitions of net LULCC flux and residual sink has led to double-counting of fluxes in the past. While the decision to use a specific def- inition of the net LULCC flux will depend on the scientific application and potential political considerations, our analy- sis shows that the uncertainty of the net LULCC flux can be substantially reduced when the existing terminological con- fusion is resolved.
[ 1 ] Large-scale bottom-up estimates of terrestrial carbonfluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO 2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite-derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO 2 fluxes derived using the different 1 km resolution maps differ fromestimates made using the reference map. The ranges of these differences are 254 gC m 2 a 1 in Gross Primary Production (GPP); 133 gC m 2 a 1 in Net Primary Production (NPP); and 43 gC m 2 a 1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of 15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m 2 . The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.
Soil physico-chemical properties and soil microbial community characteristics were also found to differ between coniferous and broadleaved land uses following conversion to SRF. As expected soil acidity increased in the coniferous soils, but there was no change in pH between the control grassland and broadleaved soils. It is well known that growing conifers affects soil pH, by creating more acidic soil conditions due to the poorer quality of their litter inputs (Wedderburn & Carter, 1999; Peterken, 2001; Morsion et al., 2012) . These acidic conditions created under coniferous tree species can inhibit microbial activity and reduce decomposition rates leading to potential increases in soil C (Morison et al., 2012) . In this study, greater C concentrations were measured in the coniferous soils compared to the grassland control and broadleaved soils and, once PLFAs had been expressed per g C to account for differences in soil C, a reduction in total PLFA. However, biomass is not necessarily a direct measure of activity but related to a range of other factors including microbial community composition (Bardgett et al., 2008) . Differences in microbial composition were also observed with higher fungal PLFA concentrations per g C in broadleaved soils compared to both grassland control and coniferous soils. Other authors have observed greater fungal PLFA under coniferous species compared to broadleaved species (Hackl et al., 2005) . In contrast Priha et al. (2001) measured higher total PLFA and fungal PLFA in birch soil compared to pine or spruce soils. Nevertheless, these differences in soil physico-chemical and microbial characteristics may be important drivers of the GHG fluxes observed in this study.
data set. However, one might be concerned that we are by chance detecting actual laws or other relatively discrete changes. Also, there might be other features of the CPS wage data, such as state- specific time trends, that may also give rise to over-rejection. To address this issue, we replicate our analysis in an alternative Monte Carlo study where the data generating process is an AR(1) model with normal disturbances. The data is generated so that its variance structure in terms of relative contribution of state and year fixed effects matches the empirical variance decomposition of female state wages in the CPS. 18 We randomly generate a new data set and placebo laws for each simulation. By construction, wecan now be sure that there are no ambient trends and that the laws truly have no effect. In row 9, we assume that the auto-correlation parameter of the AR(1) model (ρ) equals .8. We find a rejection rate of 37 percent. In rows 10 through 14, we show that as ρ goes down, the rejection rates fall. When ρ is negative (row 14), there is under-rejection.
can attribute the difference between the baseline and our scenarios in terms of commodity prices, land-use, trade patterns, and GHG emissions to the various levels of the carbon tax. We adjust the cost of production of US agriculture, which we model through the different components of the Producer Price Index (PPI). An increase in the PPI from the carbon tax will affect crop and livestock producers. Adjustments in production quantities (i.e., crop area and livestock herd) allow us to assess the global effects of the carbon tax. We should note that weuse a simulation model to evaluate a reasonable pathway as opposed to using historical data in an econometric model; thus, there is inherent uncertainty about the actual evolution of agricultural markets including land-use, prices, and emissions. We only analyze one
During the negotiations leading up to the Kyoto Protocol and sub- sequently, there was considerable concern that credits issued for carbon sequestration would be subject to a risk of re-emission, due to either human action or natural events such as wildfires. This was called the permanence risk and it is unique to LULUCF projects under the Protocol. Eventually, Parties agreed that credits aris- ing from CDM afforestation and reforestation projects should be temporary, but could be re-issued or renewed every five years after an independent verification to confirm sufficient carbon was still sequestered within the project to account for all credits issued. This deals effectively with the permanence risk and guarantees that any losses of sequestered carbon for which credits have been issued will have to be made up through either additional sequestration elsewhere or through credits derived from non-LULUCF activi- ties. Two types of temporary credits were agreed: temporary CERs and long-term CERs. Some accounting issues relating to these credits are described in Section 5.5. There are additional issues in relation to pricing, restrictions on replacement, etc, that also need to be taken into account. The BioCarbon Fund has documenta- tion to guide project managers on these issues.
Consistent with the model of landowner behavior underlying the econometric analysis, crop and forest commodities are supplied inelastically. Thus, wecanuse crop and timber yields (per acre), to translate land-use changes into output changes. After aggregating output changes appropriately, corresponding price changes are computed using own-price demand elasticities estimated in previous econometric studies. 37 Changes in cropland area result in immediate changes in crop output, since crops are assumed to be harvested in the year they are planted. In the case of forests, timber harvests will be delayed for a period of years while the forest stand matures. We assume harvests on afforested lands are delayed for one optimal rotation period, after which time the forest is “fully regulated” and provides a constant annual timber flow. 38 All land originally in forest (at t=0) is also assumed to be fully regulated. 39 When these lands are converted to non-forest uses, we assume that 20% of the timber is merchantable.
As the foregoing illustrates, adopting a process that encourages harmony between forestry Licensees and adventure tourism operators is advisable when not faced with BC’s unique context. However, despite the significant differences and the complexities that the land claims in BC bring to the table, the government is no stranger to consultation and accommodation at this point in time. By bringing all of the stakeholders into the discussion and negotiation process—not just industry and government as is the case in Ontario—it is likely that some of the key successes of the Ontario method could be incorporated into a ‘made in BC’ approach. That approach could, for example, include processes similar to Ontario’s RSA, which would offer more detailed information to adventure tourism operators about the forestry activities planned for the land. Additionally, BC could look to the Ontario Guidelines as an example of how to provide greater guidance to tenure holders on Crown land and how those tenure holders can best navigate their competing interests. Finally, requiring tenure holders to actively seek public input on their plans for Crown land at several junctures would reflect a key element of the Ontario FMP process, and could support Crown consultation when the duty to consult and accommodate First Nations with interests in the land applies.
suggested a three percent decline in forested area from 2000 to 2050. This study is in general agreement with the newest release of the National Climate Assessment (2014) which has predicted an approximately two percent decline in forested areas in the Northeastern US from 2000 to 2050. Interestingly, a comparison of the 2001 with the 2011 State of Maine land cover maps (NLCD, USGS) depicts an increase in conifer trees, especially in the northwest and western part of the State, and a loss in both mixed and deciduous forests in the southern half of the State. Furthermore, in the southern half of the State, deciduous and mixed forest have been replaced by either grasslands/shrublands or herbaceous plants. In another study (Rustad et al.,2014) on North American forest and Eastern Canada, a reduction in suitable habitat for Conifer forest and an expansion of Deciduous forests was reported. In this study, we were interested into exploring the role of forest species composition on DOC flux and concentration. Different vegetation types will have different effects on fluvial DOC export by influencing hydrological cycles as well as impacting productivity rate and decomposition processes. For this reason, it is worth looking at the effects of possible future changes in forest extent and in changes in vegetation type distributions on the fluvial DOC. Four extreme vegetation change scenarios have been considered, which estimate that climate change and land cover changes will result in either the relative dominance of coniferous or deciduous vegetation types in the forested areas of the future.
Conclusions: Dedicated energy crops are not similar to the first generation feedstocks in the sense that they do not generate the level of market-mediated responses which we have seen in the first-generation feedstocks. The major market-mediated responses are reduced consumption, crop switching, changes in trade, changes in intensification, and forest or pasture conversion. These largely do not apply to dedicated energy corps. The landuse emissions for cel- lulosic feedstocks depend on what we assume in the emissions factor model regarding soil carbon gained or lost in converting land to these feedstocks. We examined this important point for producing bio-gasoline from miscanthus. Much of the literature suggests miscanthus actually sequesters carbon, if grown on the existing active cropland or degraded land. We provide some illustrative estimates for possible assumptions. Finally, it is important to note the importance of the new results for the regulatory process. The current California Air Resources Board carbon scores for corn ethanol and soy biodiesel are 19.8 and 29.1, respectively (done with a model version that includes irrigation). The new model and database carbon scores are 12 and 18, respectively, for corn ethanol and soy biodiesel. Thus, the cur- rent estimates values are substantially less than the values currently being used for regulatory purposes.
Integrated assessment models (IAM) are used to predict the landuse conversion for agriculture and pasture that is subsequently applied to full Earth system model simulations. IAMs used in the construction of the RCP scenarios assume that the continued increases in agricultural yield due to improved management, technol- ogy, and economic growth will reduce the rate of deforestation in the future, despite increases in population and shifts in diet [Riahi et al., 2011; Riahl et al., 2007; van Vuuren et al., 2011]. The combined historical and RCP8.5 estimates of current deforestation rates are less than those observed during the past two decades based on Food and Agriculture Organization estimates [Food and Agriculture Organization, 2010; Hurtt et al., 2011; Meyfroidt and Lambin, 2011], though more recent satellite estimates suggest even larger defores- tation rates [Hansen et al., 2013]. While the future yield projections are very sensitive to assumptions about the ability of humans to innovate both agricultural management methods and crop genetic attributes [e. g., Fischer et al., 2005], some evidence suggests that yield improvements may already be decreasing [Ray et al., 2012] and that agricultural crops may be more sensitive to temperature than previously estimated [Lobell et al., 2011]. On the other hand, estimates of net primary production and respiration in Earth system models assume a static response to temperature [e.g., Oleson et al., 2010], although there is evidence that plants can acclimate to higher temperatures on seasonal and interannual timescales [Kattge and Knorr, 2007; Atkin et al., 2008; Sendall et al., 2015]. This is consistent with other evidence that Earth system models may be overly pessimistic about the impact of higher temperatures on landcarbon [Frank et al., 2010; Keenan et al., 2013; Mahecha et al., 2010], and studies which include temperature acclimation indicate that including the effects of acclimation would increase the terrestrial carbon in Earth system models [Arneth et al., 2012; Atkin et al., 2008; King et al., 2006]. Thus, the IAM models used to predict future landuse conversion may be underestimating the threat to forests from deforestation, while Earth system models may be overestimat- ing the threat to forests from higher temperatures. Future studies should consider in more detail the incon- sistency in treatment of temperature on terrestrial ecosystems and the implications for LULCC and terrestrial carbon.
Received: 4 December 2014 / Accepted: 17 June 2016
# The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract A 2030 climate and energy policy framework was endorsed by the European Council in 2014. The main elements are a binding 40 % greenhouse gas (GHG) reduction target compared to 1990, a renewable energy share of 27 %, and an energy savings target of at least 27 % by 2030. In this paper, we assess the impact of these targets on the European landuse, landusechange, and forestry (LULUCF) sector using a Europe focused global landuse model linked with a detailed forest management model. We show that implementing a 40 % GHG emission reduction target by 2030 may only have a small negative impact on the domestic LULUCF sink if the additional biomass demand for energy is mostly met through ligno-cellulosic energy crops rather than forest removals. However, if the increased biomass demand were met through higher rates of forest harvest removals, a more negative impact on the LULUCF sink could be expected.
The 1990 Transportation Investment with 2010 LandUse scenario shows the effect of halting investment in new transportation infrastructure but permitting continued forecast growth patterns. It shows the extent to which people might be expected to change their behavior if faced with rising traffic congestion levels. Comparing this scenario to the 2010 Reference Scenario shows the extent to which planned infrastructure investment might alleviate traffic congestion, while stimulating VMT. Compared with the 2010 Reference Scenario, this produces 13% fewer AM peak hour County VMT, operating with 7% higher traffic congestion on County highways. This congestion was estimated to reduce average trip speed by 4% and to reduce average trip length by 7% to 9.48 miles, while slightly reducing average trip time for AM peak hour trips made by County residents. In other words, expanding lane- miles of capacity in Montgomery County by 20% between 1990 and 2010 would tend to produce almost 15% more AM peak hour VMT on County roads than would occur if capacity were held constant and congestion effects were permitted to discourage travel demand.
In addition, our calculations of net carbon loss due to permafrost thawing could be greatly influenced by the specification of the vertical distribution of SOC. To assess the influence of the choice of SOC profile data, we conducted one additional no-policy simulation using a biome-dependent SOC profile dataset (Jobb´agy and Jackson 2000 ), which includes a global summary of vertical SOC distribution of each biome (SOC profiles for two major biomes in the pan-Arctic, boreal forest and tundra, were shown in figure S5, available at stacks.iop.org/ERL/8/045003/mmedia ). Compared to the previous no-policy simulations with SOC profile data derived from Harden et al ( 2012 ), this additional no-policy simulation, during the 2000s, showed a 12% decrease in CH 4 source (from 53 to 47 Tg CH 4 yr −1 ) and a 20% increase in CO 2 sink (from −0.4 to −0 .5 Pg C yr −1 ), which together tripled the GHG sink (from −0.2 to −0.6 Pg CO 2 -eq. yr −1 ). However, in spite of the difference in the magnitude of carbon/GHG budgets, the changing patterns of NME, NEE and GWP from this additional no-policy simulation did not change in comparison with the previous simulations.
An alternative to a complete reliance on technological change is to start to implement schemes which are aimed at changing transport behaviour. The scale of such changes are likely to be large and to require considerable lifestyle adaptation, though the advantage of such changes is that they could, at least theoretically, be implemented on a quicker timescale than technological change. Another advantage is the potential for synergy in introducing measures that may reduce other transport related externalities, particularly congestion (Proost, 2000). However, major barriers exist to implementing such developments in particular the need (still) to take the potential impacts of climate change seriously at both a political and individual level and for government to be willing to take a lead in promoting and enforcing a more sustainable transport future. The Energy White Paper (DTI, 2003a) only considered technological change with respect to transport and made no mention of behavioural change. The 10 year plan for transport (DETR, 2000b) even if fully implemented will serve only to stabilise emissions from transport. There is considerable doubt as to whether many of the 10 year plan measures will be implemented within the time frame, especially the road user charging and work place parking levy schemes envisaged and the provision of sufficient rail capacity to carry the planned 50% increase in passenger miles (May et al, 2002). The Sustainable Development Commission (2003) estimate that the Governments 20% reduction target for CO 2 will not be met and consider savings from