H.
Cai
*a,
J.
Wang
b,
Y.
Feng
b,
M.
Wang
a,
Z.
Qin
a and
J. B.
Dunn
a
aSystems Assessment Group, Energy Systems Division, Argonne National Laboratory, Argonne, IL 60439, USA. E-mail: hcai@anl.gov
bEnvironmental Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
First published on 10th August 2016
Land use change (LUC)-induced surface albedo effects for expansive biofuel production need to be quantified for improved understanding of biofuel climate impacts. We addressed this emerging issue for expansive biofuel production in the United States (U.S.) and compared the albedo effects with greenhouse gas emissions highlighted by traditional life-cycle analysis of biofuels. We used improved spatial representation of albedo effects in our analysis by obtaining over 1.4 million albedo observations from the Moderate Resolution Imaging Spectroradiometer flown on NASA satellites over a thousand counties representative of six Agro-Ecological Zones (AEZs) in the U.S. We utilized high-spatial-resolution, crop-specific cropland cover data from the U.S. Department of Agriculture and paired the data with the albedo data to enable consideration of various LUC scenarios. We simulated the radiative effects of LUC-induced albedo changes for seven types of crop covers using the Monte Carlo Aerosol, Cloud and Radiation model, which employs an advanced radiative transfer mechanism coupled with spatially and temporally resolved meteorological and aerosol conditions. These simulations estimated the net radiative fluxes at the top of the atmosphere as a result of the LUC-induced albedo changes, which enabled quantification of the albedo effects on the basis of radiative forcing defined by the Intergovernmental Panel on Climate Change for CO2 and other greenhouse gases effects. Finally, we quantified the LUC-induced albedo effects for production of ethanol from corn, miscanthus, and switchgrass in different AEZs of the U.S. Results show that the weighted national average albedo effect is a small cooling effect of −1.8 g CO2 equivalent (CO2e) for a mega-Joule (MJ) of corn ethanol, a relatively stronger warming effect of 12.1 g CO2e per MJ of switchgrass ethanol, and a small warming effect of 2.7 g CO2e per MJ of miscanthus ethanol. Significant variations in albedo-induced effects are found among different land conversions for the same biofuel, and among different AEZ regions for the same land conversion and biofuel. This spatial heterogeneity, owing to non-linear albedo dynamics and radiation processes, suggests highly variable LUC-induced albedo effects depending on geographical locations and vegetation. These findings provide new insights on potential climate effects by producing biofuels through considering biogeophysical as well as biogeochemical effects of biofuel production and use in the U.S.
Broader contextIn this paper, we quantified land use change (LUC)-induced albedo effects for three major biofuels in the U.S., using a methodology that relies upon state-of-the-art satellite data products for albedo and vegetation observations and that estimates CO2 equivalent emissions of albedo effects per IPCC's recommendations. Our analysis indicates that the LUC-induced albedo effect is small for corn and miscanthus ethanol, but is significant for switchgrass ethanol, which is driven by the types, locations, and intensities of various land conversions to these biofuel feedstocks. The albedo effects quantified in this study, which has been generally overlooked in traditional LCA, fill in an important biofuel GHG analysis gap and shed light on a fuller picture of the climate impacts of major U.S. biofuels. We aspire that this work raises awareness among policy makers and other stakeholders regarding an additional effect that can be considered when estimating biofuel life-cycle greenhouse gas emissions and offers them insight into routes to estimating equivalent GHG emissions from albedo shifts stemming from LUC associated with large-scale expansion in production of biofuels in the U.S. and challenges associated with these estimations. |
A focal point in the debate about the potential climate change effects of biofuels has been GHG emissions from carbon stock changes stemming from land use change (LUC) associated with biofuel feedstock production, besides GHG emissions associated with direct activities for production of biomass feedstocks and biofuels, since 2008.4 Besides biogeochemical effects including soil organic carbon (SOC) changes, LUC also modifies the surface energy balance, moisture, latent heat fluxes, and momentum fluxes through biogeophysical mechanisms, such as surface albedo, evapotranspiration, and surface roughness.5–14 Of these biogeophysical factors, surface albedo has been considered a dominant effect at the global scale,15 particularly in the mid-latitude temperate and high-latitude boreal regions.16–18 Modification of surface albedo perturbs the radiation budget by modifying the absorption of incoming solar radiation, resulting in radiative forcings that need to be considered together with the radiative forcings due to GHG emissions through biogeochemical mechanisms. However, this LUC-induced albedo effect for biomass production has received limited focus until very recently.
In particular, albedo effects associated with forest systems for bioenergy production, especially those affected by seasonal snowfall, have been studied recently.16,19–27 Besides, albedo effects of hypothetical conversion of annual to perennial bioenergy crops28 and conversion from cropland and pasture to sugarcane in Brazil29 have been evaluated. The GHG emission-equivalent climate effects of both biogeochemical and biogeophysical factors including albedo effects for agricultural crops and forest systems were simulated, and biogeophysical effects were found to outperform the biogeochemical effects for agricultural ecosystems and Brazilian sugarcane systems.30 Climate impacts of changes in surface albedo from several LUC scenarios for cultivation of biomass for bio-based diesel fuel production varied significantly and could be substantial relative to those of petroleum-derived diesel.31 These studies suggested that LUC-induced albedo effects were potentially substantial and could even outweigh the biogeochemical effects evaluated in previous biofuel LCAs.
However, LUC-induced albedo effects of major biofuel production systems in the U.S. have yet to be examined in the context of life-cycle GHG emissions of such biofuels encouraged by RFS and LCFS. We aim to address this issue by quantifying the albedo effects of major biofuels including corn ethanol and cellulosic ethanol derived from switchgrass and miscanthus, in the U.S., and to integrate the albedo effects into life-cycle GHG intensities of these biofuels on a common basis. Corn ethanol has been produced on a mass scale in the U.S., reaching 14.8 billion gallons in 2015,32 whereas ethanol produced from cellulosic feedstocks is being promoted in the RFS1 and is predicted to cause significant GHG reductions.33 We evaluate the albedo effects associated with various LUC scenarios at the Agro-Ecological Zone (AEZ)34 geographical level that are simulated by the Global Trade Analysis Project (GTAP) model to produce 11.6, 7.0 and 7.0 billion gallons of corn, switchgrass, and miscanthus ethanol, respectively, a year, in accordance with assumed RFS induced production levels.35 We collected satellite measurement data for both surface albedo and surface land covers at high spatial resolution, covering most of the regions in the U.S. where corn, switchgrass, and miscanthus are grown or could be grown for ethanol production. The analysis on the AEZ level assures a consistent spatial resolution with our analysis of soil organic carbon (SOC) changes (biogeochemical effects) associated with the production of these biofuels,36,37 and thus enables the comparison and integration of albedo effects with SOC change effects resulting from the same LUC scenarios for these biofuel production systems in the U.S.
The quantified albedo effects in this analysis shed light on the key question of whether these biofuels still offer overall climate change benefits, and if so, the magnitude of the benefits, compared to petroleum fuels.
Fig. S1 (ESI†) presents a flowchart depicting our methodology quantifying LUC-induced albedo effects of biofuel production in the U.S. The details of this methodology are given below.
From | Forest | Grassland | Cropland/Pasture land | Shrubland |
---|---|---|---|---|
To | Cornfields | Cornfields | Cornfields | Cornfields |
a Positive numbers represent the number of hectares of original land cover that is converted to cornfields. Negative numbers represent reverse conversions. | ||||
AEZ 7 | 4436 | 340![]() |
224![]() |
−957 |
AEZ 8 | 7269 | 133![]() |
102![]() |
9662 |
AEZ 9 | 2066 | 10![]() |
64![]() |
−44 |
AEZ 10 | 132![]() |
82![]() |
403![]() |
47![]() |
AEZ 11 | 92![]() |
42![]() |
298![]() |
436 |
AEZ 12 | 23![]() |
14![]() |
74![]() |
6532 |
AEZ 13 | 273 | 11![]() |
1340 | 463 |
AEZ 14 | 1912 | 3518 | 278 | 3120 |
AEZ 15 | 73 | 214 | 0 | 127 |
AEZ 16 | 1 | 3 | 0 | 4 |
From | Forest | Forest | Grassland | SWG fields | Cropland/Pasture land | SWG fields | Shrubland | Shrubland |
---|---|---|---|---|---|---|---|---|
To | SWGb fields | Grassland | SWG fields | Grassland | SWG fields | Cropland/Pasture land | SWG fields | Grassland |
a Positive numbers represent the number of hectares of original land cover that is converted to switchgrass, grassland, or cropland/pasture land. Negative numbers represent reverse conversions. b Switchgrass. | ||||||||
AEZ 7 | 63![]() |
0 | 16![]() |
0 | 2![]() ![]() |
0 | −13![]() |
0 |
AEZ 8 | 0 | 34![]() |
0 | 1374 | 1![]() ![]() |
0 | 0 | 45![]() |
AEZ 9 | 824 | 32![]() |
0 | 0 | 340![]() |
0 | −17 | −693 |
AEZ 10 | 70![]() |
102![]() |
0 | 0 | 1![]() ![]() |
0 | 25![]() |
36![]() |
AEZ 11 | 84![]() |
100![]() |
0 | 0 | 1![]() ![]() |
0 | 394 | 470 |
AEZ 12 | 32![]() |
58![]() |
0 | 0 | 754![]() |
0 | 8931 | 16![]() |
AEZ 13 | 2752 | 16![]() |
0 | 0 | 0 | 39![]() |
4654 | 28![]() |
AEZ 14 | 2479 | 9430 | 0 | 0 | 0 | 10![]() |
4046 | 15![]() |
AEZ 15 | 83 | 666 | 0 | 0 | 0 | 0 | 145 | 1167 |
AEZ 16 | 1 | 5 | 0 | 0 | 0 | 0 | 6 | 26 |
From | Forest | Forest | Grassland | Cropland/Pasture land | Miscanthus fields | Shrubland | Shrubland |
---|---|---|---|---|---|---|---|
To | Miscanthus fields | Grassland | Miscanthus fields | Miscanthus fields | Cropland/Pasture land | Miscanthus fields | Grassland |
a Positive numbers represent the number of hectares of original land cover that is converted to miscanthus, grassland, or cropland/pasture land. Negative numbers represent reverse conversions. | |||||||
AEZ 7 | 15![]() |
0 | 17![]() |
1![]() ![]() |
0 | −3390 | 0 |
AEZ 8 | 2470 | 6168 | 0 | 638![]() |
0 | 3284 | 8200 |
AEZ 9 | 821 | 7512 | 0 | 230![]() |
0 | −17 | −159 |
AEZ 10 | 28![]() |
21![]() |
0 | 1![]() ![]() |
0 | 10![]() |
7647 |
AEZ 11 | 35![]() |
20![]() |
0 | 911![]() |
0 | 167 | 96 |
AEZ 12 | 14![]() |
13![]() |
0 | 275![]() |
0 | 3956 | 3671 |
AEZ 13 | 890 | 3868 | 0 | 0 | 10![]() |
1505 | 6543 |
AEZ 14 | 724 | 2229 | 0 | 0 | 2795 | 1182 | 3638 |
AEZ 15 | 28 | 158 | 0 | 0 | 0 | 49 | 277 |
AEZ 16 | 0 | 1 | 0 | 0 | 0 | 2 | 6 |
To reduce the temporal variability of albedo associated with inter-annual variability in phenology and local climate, we used eight-day MODIS albedo data in 2014, which was a climatically neutral year that may represent a long-term climatological mean of climatic parameters well. For example, the annual average precipitation in 2014 was about ±20% of the 30 year average over much of CONUS. Also, 2014 was neither an El Niño nor a La Niña year, which could otherwise have a significant impact on the climatic conditions over CONUS, affecting the vegetation growth conditions and thus their albedo effects.
For corn, we chose multiple counties within each AEZ where the county-level corn production is among the top 30% of all counties,37,48 of which the corn production accounted for about 71% of the total U.S. corn production in 2014.50 For switchgrass, miscanthus, cropland, grassland, forest, and shrubland, we chose the top 20% counties by growth area within each AEZ according to county-level summary statistics on the land cover acreages.48 For cropland, we first identified the most abundant crop type within each AEZ according to CDL county-level summary statistics on the cropland acreage by type;48 we then selected the top 20% counties by area for the major crop type in each AEZ. For forest, the dominant type of forest varies regionally, and can be either deciduous forest or evergreen forest, both of which we considered.
The CDL data do not classify miscanthus as a specific type of land cover, since there is no commercial growth of miscanthus in the U.S., and switchgrass acreage in the database is very limited. We assumed that the surface albedos of both switchgrass and miscanthus resemble those of grassland, as they are both herbaceous perennial grasses. We also assumed that pasture land has a similar surface albedo to grassland, and used grassland albedo observations as surrogates for those of pasture land, because of the lack of pasture-specific land cover classification. We assumed that the land use category of cropland/pasture land consists of 50% cropland and 50% pasture. Furthermore, soybean or winter wheat, which is the dominant type of cropland in the AEZs of interest, was used to represent cropland that is converted for cornfields for ethanol production, while the cropland converted for switchgrass and miscanthus growth was assumed to consist of 50% cornfields, and 50% soybean fields or 50% winter wheat fields.
We applied the geographical pairing technique to retrieve all the 500 meter-resolution albedo observations over identified clean pixels of the land cover of interest in these selected counties. Fig. S6 (ESI†) shows the county-level BSA retrieved for corn and forest as an example. We retrieved BSA and their data quality flags from those identified clean pixels from 10 tiles of the MCD43A3 and MCD43A2 datasets, to cover much of each AEZ. We employed Mira, a supercomputing system at Argonne National Laboratory,51 to meet the demanding computing requirements for pairing and retrieving albedo data over a variety of land cover types in dozens of counties within each AEZ, as shown in Table S1 (ESI†). Fig. 1 shows the geographical distributions of the counties with retrieved clean-pixel albedo data for each land cover type within each AEZ. With the large number of clean-pixel albedo observations retrieved from various locations for most land cover types within each AEZ, we greatly improved the spatial representation of the albedo observations for specific land cover types at the AEZ level, allowing investigation of spatial variation in the LUC-induced albedo effect associated with biofuel production.
![]() | ||
Fig. 1 Geographical distribution of counties with clean-pixel albedo observations retrieved for cornfields, switchgrass fields, miscanthus fields, cropland, forest, grassland, and shrubland in AEZ 7 to AEZ 12. Cropland is represented by winter wheat in AEZ 7 and soybean in other AEZs, as these are the most prevalent crop types in those AEZs, according to the cropland data layer.48 |
We used the Monte Carlo Aerosol, Cloud and Radiation (MACR) model53,54 to simulate the shortwave radiative forcing due to surface albedo changes over a specific type of land cover. The model was developed on the basis of the so-called Monte Carlo Independent Column Approximation approach,55 and simulated radiative fluxes, in W m−2, at the top of the atmosphere (TOA). The ESI,† Section S2 has details on the physical parameterization and validation of the model.
The Monte Carlo approach for solving the radiative transfer equation is advantageous over two-stream approximations, e.g.,56 as it calculates the atmospheric fluxes more accurately than can be done with for both clear and cloudy skies. In this study, MACR used an advanced radiative transfer coupled with spatially and temporally resolved meteorological and aerosol conditions. In the estimation of perturbations in global energy balance due to changes in land surface albedo, it is advantageous over previous studies that used an atmospheric transmittance factor, directly adopted estimates of the incoming solar radiation flux at surface level, or relied on simple empirical calculations.31,52 For instance, Cherubini et al. adopted a constant of 0.854 denoting the globally averaged annual fraction of upwelling shortwave radiation exiting a clear sky.52 The use of such a constant transmittance does not take into account spectral variations in absorption or reflection as well as its dependence on zenith angle.
MACR takes eight-day albedo data as one of the key inputs in simulating the resulting radiative fluxes at the TOA. The ESI,† Section S2 presents details on data for input parameters and methodology for radiation simulations performed by the model. Fig. S7 (ESI†) shows temporal variation in net radiation at the TOA over cornfields in AEZ 7 in 2014, as an example. Table S2 (ESI†) summarizes annual average diurnal net radiative flux for each land cover type within each AEZ predicted by MACR.
![]() | ||
Fig. 2 Temporal variation in eight-day BSA weighted by the number of selected clean pixels of various land covers in AEZs 7–12 in the U.S. in 2014. |
Fig. 2 shows that the BSA varies among different land covers within the same AEZ throughout the year, particularly in the off-season (October to March), which indicates a likely impact from snow events that increase the BSA significantly during the off-season as compared to the growing season (April to September). Besides, the albedo of the same land cover can vary significantly from one AEZ to another, showing a clear region-specific characteristic of albedo dynamics even for the same type of land cover.
Forest is shown to have the lowest albedo among the land covers during most of the year in AEZs 7, 8, and 9. However, shrubland has the lowest albedo during most of the year in AEZs 10 and 11, with a value about 0.03–0.06 lower than the shrubland albedo in AEZs 7, 8, and 9. Forest and shrubland exhibit the lowest albedo in AEZ 12. This result indicates the albedo effect of heterogeneous shrubland and forest cover conditions, e.g., the varying heights and densities of the vegetation in various regions. Forest in AEZs 7, 8, and 9 has lower albedo than forest in AEZs 10, 11, and 12, as shown in Fig. 2. This is a reflection of the dominance of evergreen forest in the forest systems in the western U.S. (AEZs 7, 8, and 9), and the dominance of deciduous forest in the forest systems in the eastern U.S. (AEZs 10, 11, and 12).48 The albedo of all land cover types exhibits a relatively smaller extent of fluctuation throughout the year in AEZ 12, as compared to the albedo dynamics in other AEZs. This result is mostly due to a difference in snowfall frequency among different AEZs. The warmer climate in AEZ 12 in the southeastern U.S., which has little snowfall, contributes to a relatively small annual variation in the albedo there.
Tables S3–S9 (ESI†) summarize the pixel count-weighted average albedo and the weighted standard deviation of albedo for the eight-day periods by AEZ and by land cover type.
Fig. 3 shows that for all land cover types in all AEZs, the eight-day average net radiation at the TOA increases from the beginning of the year, when the incoming radiation is not strong and the albedo is usually high, and reaches the peak at around the middle of the year, when the incoming radiation is strong and the albedo is typically low. Besides, the difference in the net radiation at the TOA among land covers can vary from as much as >60 W m−2 in some winter periods to as much as >50 W m−2 in some summer periods. However, this difference depends on the types of land covers and the region. The temporal variation patterns of the average net radiation at the TOA for all land covers are much smoother than the albedo variation patterns because in addition to albedo, the temporal variation patterns of other climatic factors, such as incoming solar radiation intensities, and other environmental factors, such as aerosol and ozone, could be affecting factors in the temporal variation patterns of the average net radiation at the TOA.
Tables S10–S18 (ESI†) summarize the pixel count-weighted average radiative fluxes and the weighted standard deviation of radiative fluxes for the eight-day periods by AEZ and by land cover type.
![]() | ||
Fig. 4 Albedo effects at the AEZ level, in g CO2e per m2 per year, of different LUC types associated with corn ethanol expansion scenarios. |
With Fig. 4 for corn ethanol as an example, the albedo effects of LUCs associated with corn ethanol production vary significantly within the same AEZ for the same land conversion, as indicated by the large error bars that represent the upper, or the 97.5 percentile, and lower bounds, or the 2.5 percentile, of the albedo effects in Fig. 4. Forest conversions to cornfields exhibit a cooling climate effect in AEZs 7, 11, and 12, where the cooling effect translates to an equivalent CO2 sequestration of as much as about 298 g CO2e per m2, on an area-weighted average (all numerical results hereafter are area-weighted averages, unless otherwise stated), in AEZ 11. This LUC, however, could result in a warming climate effect of about 60–66 g CO2e per m2 in AEZs 8 and 9. The large variation in the albedo effect stems from wide spatial variations in albedo over both the forests and cornfields within the AEZ region, as shown in Fig. 4. For a land conversion that converts forest to grassland, the resultant albedo effect can vary from a cooling effect of about −302 g CO2e per m2 in AEZ 7 to a warming effect of about 438 g CO2e per m2 in AEZ 9. Conversion from grassland to cornfields generally induces a cooling effect in all AEZs except in AEZ 7, where an average warming effect would persist. The greatest cooling effect would occur in AEZ 9, with an equivalent carbon sequestration of about −372 g CO2e per m2. A conversion of 50% of cropland, represented by either soybean fields in AEZs 7–11 or winter wheat fields in AEZ 12, and 50% of pasture land represented by grassland converted to cornfields would result in a warming effect of about 28 and 62 g CO2e per m2, on average, in AEZs 7 and 8, respectively, but a cooling effect of as much as −250 g CO2e per m2, on average, in AEZ 11 and a relatively smaller cooling effect in other AEZs. Conversion from shrubland to cornfields would exert a significant cooling effect in all AEZs, ranging from −179 g CO2e per m2 in AEZ 7 to −731 g CO2e per m2 in AEZ 10. Conversion from shrubland to grassland would also exert a significant cooling effect of as much as −631 g CO2e per m2 in AEZ 10 and a relatively smaller cooling effect in other AEZs, except in AEZ 11, where a warming effect of 42 g CO2e per m2 would be expected. This cooling effect is consistent with a counter warming effect for a shift from grassland to shrubland because of dark areas created by shrubland that allows extra sunlight to be absorbed.58 Therefore, the albedo effect is highly location-specific and land-cover-specific and varies significantly among various land covers within the same AEZs and among AEZs for the same land cover. We observed similar characteristics of the LUC-induced albedo effects associated with switchgrass and miscanthus ethanol (see ESI,† Fig. S8 and S9).
For corn ethanol in Fig. 5, the aggregated albedo effect for corn ethanol shows clear cooling effects in AEZs 10 and 11. Cooling effects range from −0.046 teragrams (Tg) CO2e in AEZ 8 to −1.2 Tg CO2e in AEZ 11, except in AEZ 7, where a warming effect of 0.24 Tg CO2e, on average, was estimated to be emitted, mostly owing to conversion from grassland to cornfields. In AEZ 11, where the most significant cooling albedo effect was noted, a conversion from cropland/pasture land to cornfields is the dominant cause, contributing to an equivalent sequestration of 0.75 Tg CO2e, on average, followed by an equivalent sequestration of 0.28 and 0.16 Tg CO2e for conversion from forest and grassland to cornfields, respectively. Conversion from shrubland to cornfields in AEZ 10 leads to a cooling effect of −0.35 Tg CO2e, followed by −0.12, −0.083, and −0.031 Tg CO2e for a conversion from cropland/pasture land, grassland, and forest to cornfields, respectively, in the region. The overall albedo effects in AEZs 8, 9, and 12 are relatively small, owing to offsetting albedo effects of different land conversions occurring in AEZs 8 and 9, and to a small land-conversion-specific albedo effect for conversion from cropland/pasture land to cornfields in AEZ 12, the dominant conversion there (see Table 1), as shown in Fig. 5. The total albedo effect from all the land conversions occurring in all AEZs results in a net cooling effect of −1.8 Tg CO2e, with all of the four types of land conversions associated with corn ethanol production contributing to a net cooling effect. Driven mostly by the very large variations in the land conversion-specific albedo effects in AEZ 7, particularly for conversion from cropland/pasture land to cornfields, which is the dominant type of LUC (accounting for 55% of all converted areas) in AEZ 7, the overall variation in the albedo effect for corn ethanol can vary from a cooling effect of as much as −5.0 Tg CO2e to a warming effect of 1.4 Tg CO2e.
For switchgrass ethanol (Fig. 6), a warming albedo effect, on average, is estimated in all the AEZs, ranging from 0.23 Tg CO2e in AEZ 7 to 2.09 Tg CO2e in AEZ 11, mostly owing to a shift from cropland/pasture land to switchgrass fields. Combining all AEZs leads to a total warming effect of 6.82 Tg CO2e, on average, for switchgrass ethanol. Note that the warming effect of the conversion from cropland/pasture land to switchgrass fields reflects the net albedo effect of converting 50% of the cropland/pasture land represented by corn/soybean fields or corn/winter wheat fields to 50% of the switchgrass fields represented by grassland. Again, large variations in the albedo effect are present in AEZs 7, 8, 10, and 11, resulting in a large variation in the total albedo effect of switchgrass ethanol, which varies from 1.79 to 11.8 Tg CO2e.
For miscanthus ethanol, the average albedo effect varies from a cooling effect in AEZs 7 and 11 to a warming effect in AEZs 8, 9, 10, and 12 (Fig. 7). A conversion from cropland/pasture land to miscanthus fields is the dominant cause of either the cooling or warming effect in each AEZ. Combining all AEZs results in a total warming effect of 1.5 Tg CO2e for miscanthus ethanol, dominated by the warming effect from a shift of cropland/pasture land to miscanthus fields, which more than offsets a cooling effect for conversion from forest to miscanthus fields in AEZ 11. Again, large variations in the albedo effect are present in all AEZs, resulting in a large variation in the total albedo effect of miscanthus ethanol, which varies from 0.049 Tg to 3.0 Tg CO2e.
The results of the albedo effects for corn, miscanthus, and switchgrass ethanol in g CO2e per MJ of ethanol are shown in Fig. 8. With conversion from cropland/pasture land, grassland, forest, and shrubland to cornfields, corn ethanol has a net cooling albedo effect of −1.8 g CO2e per MJ. When the spatial variation of albedo effects was considered, the LUC-induced albedo effect could range from a warming effect of 2.0 g CO2e per MJ to a cooling effect of −5.7 g CO2e per MJ for corn ethanol.
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Fig. 8 Total LUC-induced albedo effects, in g CO2e per MJ, of corn, switchgrass, and miscanthus ethanol in the U.S. |
A significant warming albedo effect of 12.1 g CO2e per MJ, on average, is found for switchgrass ethanol, as compared to life-cycle GHG emissions of about 17.3 g CO2e per MJ for switchgrass ethanol without albedo effects.59 The warming effect can vary from 3.2 to 21.0 g CO2e per MJ, mostly because of the spatial variation in the albedo effect of conversion from cropland/pasture land to switchgrass fields in various AEZs. The albedo warming effect more than offsets a GHG emission reduction of −3.5 g CO2e per MJ from increased SOC associated with the same LUCs for switchgrass ethanol production.37
Miscanthus ethanol has a relatively small warming albedo effect of about 2.7 g CO2e per MJ, on average, as compared to life-cycle GHG emissions of about −6.8 g CO2e per MJ.59 The warming effect can vary from 0.1 to 5.4 g CO2e per MJ, mostly because of the spatial variation in the albedo effect of conversion from cropland/pasture land to miscanthus fields in various AEZs. The albedo warming effect is relatively small, compared to a GHG emission reduction of −20.1 g CO2e per MJ from the increased SOC associated with the same LUCs for miscanthus ethanol production.37
The smaller total warming albedo effect for miscanthus ethanol as compared to switchgrass ethanol is caused by two factors: (1) a smaller amount of conversion from cropland/pasture land to miscanthus fields vs. conversion to switchgrass fields (the major land conversion associated with both biofuel feedstocks) (Fig. S12, ESI†), primarily caused by the considerably higher biomass yield for miscanthus than for switchgrass;35 and (2) the weaker warming albedo effect of this land conversion for miscanthus compared to that for switchgrass in all AEZs, except in AEZ 10 (Fig. S13, ESI†). As a result, the aggregated total albedo effects of the two biofuels differ (Fig. S14, ESI†), translating to a difference in the total albedo effect in g CO2e per MJ, given the same volumetric amount of ethanol production from the two feedstocks.
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Fig. 9 Integration of LUC-induced albedo effects with life-cycle GHG emissions, including LUC-induced SOC changes, for corn, switchgrass, and miscanthus ethanol. |
In this study, we considered multiple LUC types associated with biofuel production at the AEZ level to evaluate the aggregated albedo effects of ethanol from corn, switchgrass, and miscanthus. We found that distinctive albedo effects associated with different LUC scenarios, even in the same AEZs, contributed to the difference in aggregated albedo effects for ethanol from corn, switchgrass, and miscanthus. For example, in AEZs 9–12, conversion from cropland/pasture land to cornfields, the major land cover conversion type for corn ethanol production, could lead to cooling albedo effects, whereas conversion from this land cover to switchgrass would lead to warming albedo effects. Besides, the magnitudes of the various land cover conversions varied for the three feedstocks. For example, conversions from shrubland and forest to cornfields are two major LUC types besides conversion from cropland/pasture land to cornfields in AEZs 7, 10, 11, and 12, where the cropland/pasture conversion is the dominant LUC type for switchgrass and miscanthus. Therefore, variation in the types and magnitudes of different LUC scenarios that have distinct albedo effects results in significant differences in the overall albedo effects for ethanol from corn, switchgrass, and miscanthus.
Albedo effects are region-specific even for the same LUC scenarios. This finding was captured by the remarkable temporal and spatial variations in albedo and its radiative forcing from a large number of clean pixels that represent different locations of biomass production. This finding agrees with previous work.31 Increasing the geographical coverage of modelling biomass production systems is a key factor to better understanding the magnitude—and variability—of the albedo effect stemming from LUCs associated with biofuel production. However, it is noted that the land classification in the CDL data could be of low accuracy in some areas.61 Consideration of the uncertainty and biases of the CDL data62 is worth further effort and may add to the accuracy of our analysis.
The GTAP-simulated LUC results we applied in this study did not quantify variability and uncertainty in biofuel LUC modeling. The variations in LUC-induced albedo effects that we quantified for ethanol from corn, switchgrass, and miscanthus only reflected the variability in albedo dynamics from over one million location-specific sites, and did not represent any of the potential variability of LUC per se, which has yet to be understood quantitatively. It is worth noting that aggregated LUC-induced albedo effects for corn ethanol, as shown in Fig. 5, were less uncertain than those for switchgrass and miscanthus, as shown in Fig. 6 and 7, mainly because additional, less certain assumptions, e.g. biomass yields, yield elasticities, and price elasticities, were made in the GTAP LUC simulations for the latter two than for corn. Given the fundamental impacts of the types and scales of LUC results due to biofuel production on the resultant albedo effects, further investigation is warranted for improved understanding of the variability and uncertainty of LUC-induced albedo effects.
It is noted that the GTAP LUC results that we adopted in this study are just one set of the estimates of what could happen with LUC driven by expanded biomass production for biofuel production. For corn, alternative land transitions could be crop shifting. Fig. 10 shows the changes in radiative forcings due to albedo changes for a land transition from cropland, which is represented by soybean fields in AEZs 8–12 and winter wheat fields in AEZ 7, to cornfields, and for a land transition from cropland/pasture land to cornfields. A very similar albedo effect would be expected if winter wheat fields, instead of cropland/pasture land, were converted to cornfields in AEZ 7, and a small reduction in net radiative forcing would be expected in other AEZs if soybean fields, instead of cropland/pasture land, were converted to cornfields there. Other situations being equal, a slightly reinforced cooling albedo effect would be expected for corn ethanol if cropland, instead of cropland/pasture land, were converted to cornfields for corn ethanol production. For energy grasses like switchgrass and miscanthus, an alternative land transition could be conversion from pasture, resulting in very little albedo change and albedo climate effect.
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Fig. 10 Comparison of net radiative forcings in various AEZs, in W m−2, for land transitions from cropland to cornfields, in comparison to those from cropland/pasture land to cornfields. |
We considered only U.S. domestic LUC-induced albedo effects in this study, but did not consider any international LUC-induced albedo effects. International LUCs comprising conversions of forest land and cropland/pasture land to land growing corn, switchgrass, and miscanthus may cause relatively smaller albedo effects than do the domestic LUCs, while international conversion from grassland to these biomass feedstocks may cause relatively more significant albedo effects than do the domestic LUCs, on the basis of a comparison of the magnitudes of domestic and international LUCs from GTAP simulations for corn, switchgrass, and miscanthus ethanol production (Table S20, ESI†). Nevertheless, additional research efforts on quantifying international LUC-induced albedo effects are needed for better understanding of aggregate albedo effects globally.
Besides, the significant variation in LUC-induced albedo effects at the AEZ level partly stems from using potentially different site-specific locations of relevant land cover types associated with a given LUC at the AEZ level, which exhibit varied site-specific albedo dynamics in response to local solar zenith angles and phenology. Improved LUC assumptions that reflect the land use history at a finer spatial resolution than AEZ, if not at the exact same locations where the LUC occur, would be needed to reduce the variability and uncertainty of quantifying LUC-induced albedo effects for these biofuels.
We were limited to using year 2014 to represent the 30 year lifetime of LUCs as a result of ethanol production. Although year 2014 represents a climatically neutral year, other factors that impact the surface albedo—including crop phenology, biomass yields, solar radiation intensities, soil water contents, ambient aerosol concentrations, and others—can change over time, and thus could result in the albedo effects over a 30 year period different from what we estimated with a single year in this analysis. Long-term dynamic analysis of albedo effects could be helpful to improve the accuracy of the albedo effect results over a period of time.
Our analysis revealed the significant spatial variations in albedo-induced climate effects for the same land conversion types across different AEZs, or for different land conversion types within the same AEZs. Thus, analysis of albedo effects for biofuel production needs to consider the types, locations, and intensities of various land conversions to improve accuracy of such analysis. In particular, our analysis demonstrated the need for finer spatial resolution of biofuel LUC in reducing the variability and uncertainty of quantifying LUC-induced biofuel albedo effects.
AEZ | Agro-ecological zones |
BSA | Black-sky albedo |
CDL | Cropland data layer |
CO2e | CO2 equivalent |
CONUS | Contiguous U.S. |
GHG | Greenhouse gas |
GTAP | The global trade analysis project model |
GWP | Global warming potential |
LCA | Life-cycle analysis |
LCFS | Low Carbon Fuel Standard |
LUC | Land use change |
MACR | The Monte Carlo aerosol, cloud and radiation model |
MJ | Mega-Joule |
MODIS | Moderate resolution imaging spectroradiometer |
RFS | Renewable Fuel Standard |
SOC | Soil organic carbon |
Tg | Teragrams |
TOA | Top of the atmosphere |
USDA | U.S. Department of Agriculture |
WSA | White-sky albedo |
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ee01728b |
This journal is © The Royal Society of Chemistry 2016 |