GCAM Modeling of Bioenergy and Land Use Change: LUC from Bioenergy Grown in Different Regions of the US

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1 GCAM Modeling of Bioenergy and Land Use Change: LUC from Bioenergy Grown in Different Regions of the US 7 th EPA/USDA/AAFC Forestry and Agricultural GHG Modeling Forum Shepherdstown, WV June 3, 2014 Marshall Wise, Stephanie Waldhoff, Leon Clarke PNNL JGCRI PNNL-SA

2 Computing LUC Carbon Emissions Factors Bioenergy: Objectives To isolate and quantify the land use carbon emissions impact from specific biomass sources/crops, using GCAM. Contribute to a better understanding of the factors involved in biomass energy and land use emissions. Compare our results with the literature. Provide better understanding, diagnostics, and explanation for GCAM s land use change and emissions.

3 Computing LUC Carbon Emissions Factors Bioenergy : Study Approach Model global land use emissions impact from incremental switchgrass production in different US regions. Here we show results for AEZ 7 and AEZ 10 Simple numerical derivative approach is applied to GCAM to measure impact from a marginal additional quantity of bioenergy. Isolates impact of a specific amount of bioenergy grown in specific region Avoids confounding emissions with bioenergy expansion in following years and their corresponding emissions pulses. Avoids averaging of emissions impacts over different types of resources, where resources with zero emissions like residues would be penalized. As we will see specific numerical results are scenario dependent.

4 GCAM Description

5 Global Change Assessment Model: Design and Assumptions 14 Region Energy/Economy Model 151 Agriculture and Land Use Model GCAM is a dynamic-recursive global integrated assessment model that links Economic, Energy, Land-use, and Climate systems. Energy and economic modeling with 14 geopolitical regions. Agricultural and land use modeling with 18 agro-ecological zones (151 AEZ-geopolitical regions). Runs through 2100 in 5-year time-steps. Emissions of 16 greenhouse gases and shortlived species: CO 2, CH 4, N 2 O, halocarbons, carbonaceous aerosols, reactive gases, sulfur dioxide. Documentation available at: wiki.umd.edu/gcam

6 GCAM 3.0 Ag/Land Use Summary Crop yields, forests, pasture, terrestrial carbon, and other economic and physical land characteristics with data specific to each of 151 land regions. Includes modeling of all land cover both commercial cropland and forests and non-commercial classifications of forests, grasslands, pastures, and shrublands.

7 Ag/Land Use: Economic Modeling Approach GCAM is an economic equilibrium model, solves for the set of prices that equalize supplies and demands in all markets covered. Efficient allocation of resources theoretically related to optimization. GCAM is not a general equilibrium model (does include input/output structures but does not close the entire economy) Non-linear functions are used represent distributions of profits for competing uses of land (logit or logistics equations). These functions are readily calibrated. Future periods deviate from history to the extent that changes in key economic drivers (such as demand growth, yields, policies) change relative prices from history. These non-linear profit functions reflect diminishing returns to expansion. No technical need to place constraints on economic land allocation GCAM typically assumes global integrated market for ag products. Historical trade preferences are typically not calibrated and cast into future periods (as in an Armington approach). But all else equal, or in the absence of change in technology, policy or other drivers, trade patterns persist. Production among regions is based on comparative advantage relationships rather than on absolute yield advantage or on regional demand.

8 GCAM Land Nesting Structure Why is pasture in a separate node (unlike GTAP)? GCAM s Other Arable Land category contains idle cropland (like GTAP s cropland/pasture) GCAM includes non-commercial pasture and internationally, a vast amount of land is categorized as pasture. Much of this is low quality for agriculture. Conservative approach is to allow nest structure to specify lower elasticity into pasture where applicable.

9 Nesting Structure Is Only Part of the Picture Non-commercial nodes have economic value inferred in the calibration. Area of all nodes change with the exception of exogenously specified land use types (e.g., urban, tundra, desert). Cannot tell just by node placement how land will be displaced by an expansion of cropland. Exponents (i.e., elasticity) assumed between nodes is just as important as the placement within nodes. Using the same exponent above and below collapses nodes into one. The logit structure used by GCAM results in elasticities of land expansion that are not constant. Example: cropland expansion in a region with either very little forest or very high valued forest but plentiful pastureland. Cropland will expand into pasture at a higher rate than forest even though forest is closer to cropland in the nesting structure.

10 GCAM Land Allocation: Relationship of Logit Exponents to Own-Supply Elasticities Elasticities can be computed at each point, but By design, there is not a constant elasticity relationship with respect to changes in profit.

11 GCAM Approach to Intensification For the current version of the model (starting with GCAM 3.0 in 2011), in-year crop yields within each land use region are fixed, and intensification is implicitly modeled as the higher costs needed maintain those yields under expansion (diminishing returns to scale in terms of profit). Exogenous regional crop yield improvements are assumed for future model periods We are currently in process of adding capability to model multiple discrete options (practices or technologies) for producing the same crop as a more direct approach to economically model intensification. For example, Irrigated vs. rain-fed Mechanized, high fertilizer vs. low fertilizer

12 Bioenergy and LUC Modeling

13 U.S. AEZs

14 AEZ 7 and 10: 2005 Land Use Composition Regions show differences in extent of cropland, and amounts of land in forests, shrubland, and pasture in non-commercial uses. AEZ 7 less cropland, less forest, more pasture and shrubland. AEZ 10 lots of cropland and forest.

15 GCAM Results: LUC from an Incremental amount of Switchgrass in AEZ 7 and 10 Land use change for a 0.1 ExaJoule marginal increase in switchgrass produced in region starting in 2020 (no land policy)

16 AEZ 7 and 10: Net Cumulative Emissions Inside and Outside of the Region Although higher bioenergy crop yield assumed for AEZ 10, AEZ 10 emissions higher than AEZ 7 both within and outside the region due to Higher mix of forest displaced within region. More indirect emissions from cropland displaced.

17 GCAM LUC Carbon emissions in context to literature (though most studies are for biofuels) For GCAM - Assuming 50% conversion efficiency to fuels and 30 year averaging rule for comparison How do we interpret the range of results? And how do GCAM results under sensitivity assumptions compare?

18 Some Sources of Differences How does the study or modeling approach determine the mix of land use directly displaced by bioenergy? Is or how is non-commercial land included in the economic choice? Net direct emissions (+ or -) on the land on which the bioenergy is grown. Differences in carbon intensity assumptions for different uses of land. How are indirect and/or international LUC modeled? E.g., An Armington-trade approach to LUC emissions from biomass in the US may result in lower LUC as more displaced food crops would be grown in the US, where the yields are often higher than the rest of the world. Higher prices from increased US production also drive yield intensification and other market responses. Yield changes from Intensification and extensification. Are emissions marginal or averaged (either over crops or over time)? Consideration of time profile of emissions (averaging, discounting). Note: Hertel et al in BioScience contains a very nice overview of most of these factors for corn ethanol using GTAP.

19 Acknowledgements Acknowledgements: Research support was provided by the U.S. Department of Energy (DOE) Office of Climate and Environmental Science. Long-term support for GCAM development was provided by the Integrated Assessment Research Program in DOE s Office of Science, Integrated Assessment Research Program.

20 References CARB California Air Resources Board. Proposed Regulation to Implement the Low Carbon Fuel Standard: Volume 1. Staff Report: Initial Statement of Reasons. Dunn, J., S. Mueller, H. Kwon, M. Wang Land-Use Change and Greenhouse Has Emissions from Corn and Cellulosic Ethanol. Biotechnology for Biofuels. 6:51. EPA Renewable Fuel Standard Program (RFS2): Final Rule. U.S. Environmental Protection Agency. March Hertel T, Golub A, Jones A, O Hare M, Plevin R, Kammen D "Effect of U.S. maize ethanol on global land use and greenhouse gas emission: Estimating market mediated responses." BioScience. 60: Kyle, G. Page, Patrick Luckow, Katherine Calvin, William Emanuel, Mayda Nathan, and Yuyu Zhou GCAM 3.0 Agriculture and Land Use: Data Sources and Methods. Pacific Northwest National Laboratory. PNNL Melillo, Jerry M., John M. Reilly, David W. Kicklighter, Angelo C. Gurgel, Timothy W. Cronin, Sergey Paltsev, Benjamin S. Felzer, Xiaodong Wang, Andrei P. Sokolov, and C. Adam Schlosser Indirect Emissions from Biofuels: How Important? Science. December 2009: Wise, M., K. Calvin, P. Kyle, P. Luckow, Jae Edmonds (in Press). Economic and Physical Modeling of Land Use in GCAM 3.0 and an Application to Agricultural Productivity, Land, and Terrestrial Carbon. Climate Change Economics. In Press. Wise, M. and K. Calvin GCAM 3.0 Agriculture and Land Use: Technical Description of Modeling Approach. Pacific Northwest National Laboratory. PNNL

21 Additional Slides

22 The Global Change Assessment Model

23 Calculating Land Use CO 2 Emissions First, we determine the total change in carbon stock for each land type and region. Δ C Stock = [Land Area (t)]*[c density (t)] - [Land Area (t-1)]*[c density (t- 1)] Then, we allocate that change across time. Vegetation CO 2 emissions If the change in land area decreases the carbon stock (e.g., deforestation), then all carbon is released into the atmosphere instantaneously. If the change in land area increases the carbon stock (e.g., afforestation), then carbon accumulates slowly over time, depending on an exogenously specified mature age. The mature age varies by land type and region. Soil CO 2 emissions Whether carbon stock increases or decreases, we assume that the change is allocated evenly over a read in number of years. The number of years varies by region, but not by land type. In general, colder regions have longer soil carbon time scales.

24 Land Use Change Emission: Definitions Carbon emissions from land use change per unit of bioenergy - from any changes in land use across the globe that result from the bioenergy production. These are total emissions and include direct and indirect emissions. Direct emissions: net land use change (LUC) emissions in the place where the bioenergy is grown. Indirect emissions: LUC emissions that induced elsewhere by expansion of land to replace food crops or other production that was supplanted by the bioenergy crop Within the region of biomass growth, the distinction between direct and indirect emissions may be ambiguous, and the distinction is not as important as the total (also expressed by Wang et al 2011). What is important is to get the total right, not the categorization.

25 Example Model Hindcasting - Regional Wheat Harvested Land Area: USA Downward trend reflects general yield growth in all cases. GCAM annual land area results appear more volatile than the historical data.

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