Canada's Emission Trends 2014: annex 1
Annex 1: The Contribution of the Land Use, Land-use Change and Forestry Sector and Modeling Methodologies
Importance of the Land Use, Land-use Change and Forestry Sector
The United Nations Framework Convention on Climate Change (UNFCCC) has recognized the important role of the Land Use, Land-use Change and Forestry (LULUCF) sector in addressing climate change. The LULUCF sector involves greenhouse gas (GHG) fluxes between the atmosphere and Canada’s managed lands, as well as those associated with land-use change.
Allowing countries to get “credits” for reductions of emissions from their land sector and/or increased removals provides an incentive to take the GHG impacts of human activities on the landscape into consideration when making land management decisions. Similarly, allotting “debits” for their land sector emission increases and/or reduced removals also provides an incentive.
As mentioned earlier in this report, the LULUCF sector includes the same four categories as shown in the 2013 Emissions Trends Report:
- Forest Land Remaining Forest Land: all forest that is “managed” for timber (e.g., harvesting) and non-timber resources (including parks), or subject to fire protection;
- Cropland Remaining Cropland: cultivated agricultural land;
- Forest Land Converted to Other Land Categories: permanent, human-induced conversion of forested land to other land use (to agricultural land, infrastructure, mines, etc.) where forest is not expected to re-grow; and
- Land Converted to Forest Land: land afforested through direct human activity (planting) and where the previous land use was not forest.
Over the last two decades, important changes have occurred in land management practices in Canada that have reduced GHG emissions or enhanced their removals from the atmosphere. For example, farmers have increasingly adopted no-till practices and reduced field area under summerfallow, which contribute to a higher rate of soil carbon sequestration. Beneficial management practices have also been adopted by the forestry sector, primarily through provincial policies and/or regulations in their areas of jurisdiction. Although these policies and regulations are aimed broadly at improving sustainability in the sector, they can also reduce GHG emissions and increase sequestration. They include increase in protected forest areas for conservation and biodiversity purposes; relatively more reliance on tree planting as opposed to natural regeneration; increasing use of improved seed stock for tree planting; and more and faster rehabilitation of harvest roads and landings. Recently, economic factors have had a large impact on the forest sector: it experienced a 43% decline in harvest levels between the peak year of 2004 and 2009, resulting in the lowest harvest since 1975, although harvests have recovered somewhat since 2009.
LULUCF Subsector Modeling Methodologies
The accounting approaches that Canada uses are largely consistent with the rules developed at the 2011 UNFCCC 17th Conference of the Parties in Durban, South Africa. Each subsector’s contribution to Canada’s 2020 target is estimated by comparing projected business-as-usual 2020 emissions/removals to 2005 emissions/removals, with the exception of Forest Land Remaining Forest Land, where 2020 projected emissions/removals are compared with a 2020 Reference Level based on historical data. The Reference Level approach has been internationally agreed to and is currently seen as the most scientifically credible approach to account for emissions and removals from Canada’s managed forests, considering Canada’s inventory methodology and forest structure.
Consistent with these accounting approaches, LULUCF projections have been modeled separately from the other sectors. Each LULUCF subsector has been projected using a different model/methodology as determined by the relevant federal government department subsector experts. Environment Canada, in partnership with Natural Resources Canada and Agriculture and Agri-Food Canada, develops and periodically updates projections of business-as-usual emissions and removals (i.e., in the absence of new policies aimed at contributing to mitigation) to 2020 for each subsector. Estimates for emissions and removals associated with management of Wetlands, Grasslands and Settlement land (other than those associated with forest conversion) have not been included, as data collection and modeling work are under development.
The Government of Canada’s work to analyze alternative accounting approaches is ongoing, and changes to the accounting approach may be made in the future. In particular, there remains uncertainty with respect to future approaches that may be included under a post-2020 climate change agreement.
Further detail on Canadian emissions trends and methodologies used are provided for each of the subsectors below.
Forest Land Remaining Forest Land
Provided by the Canadian Forestry Service of Natural Resources Canada
A Reference Level approach is used to measure changes in GHGs in the Forest Land Remaining Forest Land (hereafter, the managed forest) category. This approach measures countries’ progress in reducing forest emissions or increasing forest removals that can be attributed to changes in human activities/practices (e.g., harvesting, fertilization) over time, as the Reference Level approach allows factoring out of highly variable natural disturbance impacts.
The Reference Level is a baseline or expected business-as-usual scenario. This Reference Level scenario was established in 2011 by making assumptions about the human activities (primarily harvesting) that were expected to take place in the future in the forest if no additional policies were implemented and if the economic drivers remained similar to what they had been in the past. Under the Durban LULUCF agreement, once the Reference Level scenario is established, these underlying assumptions cannot be changed. The GHG emissions/removals resulting from these assumptions then can be calculated based on the state of the forest (e.g., the GHG impact of harvesting a hectare of forest in coastal British Columbia is different from harvesting a hectare of boreal forest in Ontario due to the differences in forest density, age and tree species).
The human activity that has the most impact on Canada’s forest emissions/removals is harvesting. Consequently, the core assumption underlying the Reference Level is the harvest volume: first, it assumes that harvest rates recover progressively between 2010 and 2012 from the low level in 2009 that resulted from the economic recession; then, it assumes that harvests each year from 2013 to 2020 will be the same as the average historical harvest volume for 1990-2009. This was considered the best estimate of Canada’s business-as-usual harvest level for the period 2013-2020 at the time when Canada established its Reference Level in 2011.Footnote 19
The projected 2020 contribution is estimated by comparing the GHG impacts of the Reference Level scenario with those of a second scenario based on the currently expected future harvest and other management activities. Updated harvest projections to 2020 were obtained from provincial and territorial governments in February 2014. The accounting contribution arises primarily from the difference in harvest level assumptions between the Reference Level and this updated future harvest scenario. As the Reference Level scenario and the updated harvest scenario differ only with regard to the level of human activities that they assume, the GHG impact of natural processes (i.e., carbon that is removed/emitted when trees naturally grow and die) and of natural disturbances (e.g., wildfire, insect infestations) are meant to cancel out when these two scenarios are compared to calculate the contribution.
All estimates for the managed forest (and for Land Converted to Forest Land, discussed below) were derived using Canada’s National Forest Carbon Monitoring Accounting and Reporting System. The system uses forest inventory data from the provinces and territories as well as from the National Forest Inventory, and includes detailed information on natural disturbance. Natural Resources Canada developed and maintains the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), a forest carbon dynamics estimation tool fully consistent with the Intergovernmental Panel on Climate Change (IPCC) inventory guidelines. Emissions from harvested wood products are calculated using the Carbon Budget Modelling Framework for Harvested Wood Products. With the CBM-CFS3 as its core, the system provides annual estimates of GHG emissions and removals affected by forest management, natural disturbances, and land-use change. This is the same system as the one used to produce forest-related emission/removal estimates from 1990 to 2012 for Canada’s 2014 National Inventory Report (NIR).
To produce the managed forest projections for 2020, the models are run to 2020 for both the Reference Level scenario and the scenario based on the currently expected future harvest and other management activities. Historical data on natural disturbances up to 2012 are included in the two scenarios; however, because future natural disturbances are unknown and unpredictable, the analysis for each scenario assumes no natural disturbances would occur from 2013 onward, apart from a minimum level of wildfire expected to occur every year (based on more than 50 years of historical data). Projected emissions from harvested wood products use the same general approach as used in managed forest estimates for the 2014 NIR, i.e., the pool of harvested wood products is assumed to start in 1990, with emissions occurring over time.
Forest Land Converted to Other Land Categories
Provided by Science and Risk Management Directorate, Environment Canada
Emissions associated with forest conversion to other land use are reported in Canada’s 2014 NIR under the LULUCF sector. Forest conversion is not a LULUCF category under UNFCCC reporting requirements, since it overlaps with the subcategories of land converted to cropland, land converted to wetlands and land converted to settlements; it is nevertheless reported as a memo item in the annual inventory submission. Emissions reported in this document are consistent with values reported in the NIR and are derived using identical data and methodology.
Historical estimates for forest land conversion are based on an earth observation sampling approach with resulting emissions calculated using the Carbon Budget Model of the Canadian Forest Sector. These estimates take into account land conversion activity from 1970 to 2012 and are developed for the specific activity (driver) that initiated the land conversion and by end land-use category (cropland, wetlands, settlements). Specific drivers include agriculture, urban and industrial expansion, hydroelectric development, transportation, oil and gas, and other resource extraction including mining, forestry roads and peat extraction.
The projected estimates for forest conversion are based on a business-as-usual scenario of forest conversion activity for the 2013 to 2020 period, using the best available knowledge of drivers, policies and practices, as documented in the NIR.
Emission estimates for projected forest conversion are developed using an empirical model; model parameters were derived by driver and ecological region based on the relationship between areas converted and resulting emissions as were reported in the NIR submissions for the years 2011 and 2012. All emission estimates for forest conversion use an instantaneous oxidation approach to represent the conversion of forest to harvested wood products, which is in keeping with the approach used for the development of estimates for Canada’s 2014 NIR.
The drivers of forest conversion in Canada are varied. In 2005, the largest driver was agriculture, followed by resource extraction, urban and industrial expansion, hydroelectric development, and transportation (Figure A.1). By 2020, resource extraction is projected to surpass agriculture as the largest driver of land conversion, due to the expansion of the oil and gas industry.
Figure A.1: Relative Contribution of Main Drivers of GHG Emissions from Forest Conversion in 2005 and Projected for 2020*
*For the specified year, the charts include all emissions from forest conversion since 1970, with the exception of conversion to harvested peat sites (Peat Extraction), which are included in historical estimates for 2005 but not available for the projections to 2020.
Note that the Urban and Industrial Expansion section includes industrial and commercial buildings, urban and municipal expansion, and recreation.
Text description of Figure A.1
Two pie charts are presented, the first for 2005 and the second for 2020.
2005: Agriculture: 40%; Urban and Industrial Expansion: 13%; Oil and Gas Resource Extraction: 18%; Other Resource Extraction: 13%; Hydroelectric Development: 11%; Transportation: 5%.
2020: Agriculture: 34%; Urban and Industrial Expansion: 11%; Oil and Gas Resource Extraction: 28%; Other Resource Extraction: 12%; Hydroelectric Development: 12%; Transportation: 3%.
Cropland Remaining Cropland
Provided by Agriculture and Agri-Food Canada (AAFC)
AAFC generated estimates for Cropland Remaining Cropland by using two models: the Canadian Regional Agricultural Model (CRAM) and the Canadian Agricultural Greenhouse Gas Monitoring Accounting and Reporting System (CanAG-MARS). CRAM was used to estimate the resource use patterns in the agriculture sector, which were then fed into CanAG-MARS to provide corresponding estimates of emissions/removals from Cropland Remaining Cropland.
CRAM is an economic model maintained by AAFC that provides a detailed characterization of agricultural activities in Canada. It is a static partial equilibrium model of the Canadian agriculture sector, which operates by maximizing consumer and producer surplus. CRAM’s features include coverage of all major cropping activities, livestock production and some processing, detailed provincial and/or sub-provincial breakdown of activities, and a detailed breakdown of cropping production practices including choice of tillage regime, use of summerfallow and stubble.
CRAM is directly calibrated to the 2011 Census of Agriculture, and all resource use patterns are the same as what is reported in the Census for that year. As CRAM is a static model, it does not provide any information on how the agriculture sector changes over time. In order to estimate future resource use patterns, a 2020 baseline was created where CRAM was aligned to the crop and livestock production estimates from AAFC’s 2014 Medium Term Outlook (MTO). The 2014 MTO provides a 10-year estimate of crop and livestock production from 2013 to 2023 based on the expected economic conditions that will impact the agriculture sector in Canada over that time period.
The CanAG-MARS model is maintained by AAFC, which reports on GHG sources and sinks resulting from changes in land use and land management practices in Canada’s agricultural sector. The estimation procedure follows a Tier 2 methodology under IPCC Good Practice Guidance for LULUCF. The model quantifies the annual change in soil organic carbon associated with land use or land management changes.
The amount of organic carbon retained in soil represents the balance between the rate of primary production (carbon transfer from the atmosphere to the soil) and soil organic carbon decomposition (carbon transfer from the soil to the atmosphere). How the soil is managed can determine whether the amount of organic carbon stored in soil is increasing or decreasing. The estimation procedure is based on the premise that changes in soil management influence the rate of soil carbon gains or losses for a period of time following a land management change. If there was no change in land management, then soil organic carbon is assumed to be at equilibrium and the change in carbon stock is deemed to be zero.
Carbon emissions and removals on mineral soils are estimated by applying country-specific, spatially disaggregated carbon emission and removal factors multiplied by the relevant area of land that undergoes a management change. The carbon factor represents the rate of change in soil carbon per unit area for each land management change as a function of time since the land management change occurred.
The 2011 and 2020 resource use patterns generated within CRAM were combined with activity data from past census periods dating back to 1951. Within the CanAG-MARS model, activity data is annualized assuming a constant rate of change between census periods and projection years. The data is linked to soil landscapes, and annual changes in land activities are estimated through a set of rule-based mechanisms. Factors are applied to the area of current and past land use or land management change activities to generate GHG emissions/removals for each inventory year.
Residual emissions occurring 20 years after the conversion of forest land to cropland were provided by Environment Canada, as AAFC does not have the capacity to estimate some components of this, such as the decay of woody biomass. These estimates were combined with the estimates generated by CRAM and CanAG-MARS to produce the final estimated Cropland Remaining Cropland emissions.
Land Converted to Forest Land
Provided by the Canadian Forestry Service of Natural Resources Canada
Projections were based on average historical rates of land being afforested as reflected in the NIR. As no new information is available, projections have not been updated from those published in the Emission Trends Report 2013. For that report, projections were based on the assumption that the 2000-2008 historical average provided the best representation of the business-as-usual scenario in each province, given that 2000-2008 is the most recent period for which afforestation activity data is available. This business-as-usual rate in the future totals about 2700 hectares per year for Canada as a whole. Data on creation of new forests for 2009-2012 are not available. Given the low levels of new forest creation, it is not possible to identify any trends in the activity, except that new forest creation appears to be lower than in the 1990s. Improvements in the data may be possible, as there are indications that some creation of new forest during the 2000s has not yet been reflected in the GHG inventory. Thus, the rate of new forest creation in the last decade may be underestimated.
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