0GCB Bioenergy (2015), doi: 10.1111/gcbb.12305
An integrated assessment of the potential of agricultural and forestry residues for energy production in China
JI GAO1, AIPING ZHANG1, SHU KEE LAM2, XUESONG ZHANG3,4, ALLISON M. THOMSON5, ERDA LIN1, KEJUN JIANG6, LEON E. CLARKE3, JAMES A. EDMONDS3, PAGE G. KYLE3, SHA YU3, YUYU ZHOU7 and SHENG ZHOU8
1 Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China, 2Crop and Soil Sciences Section, Faculty of Veterinary and Agricultural Sciences, the University of Melbourne, Melbourne, Vic. 3010, Australia, 3Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD 20740, USA, 4Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824, USA, 5Field to Market, The Alliance for Sustainable Agriculture, 777 N Capitol St. NE, Suite 803, Washington, DC 20002, USA, 6Energy Research Institute (ERI), Beijing 100038, China, 7Department of Geological & Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA, 8Institutes of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
Abstract
Biomass has been widely recognized as an important energy source with high potential to reduce greenhouse gas emissions while minimizing environmental pollution. In this study, we employ the Global Change Assessment Model to estimate the potential of agricultural and forestry residue biomass for energy production in China. Potential availability of residue biomass as an energy source was analyzed for the 21st century under different climate policy scenarios. Currently, the amount of total annual residue biomass, averaged over 2003-2007, is around 15 519 PJ in China, consisting of 10 818 PJ from agriculture residues (70%) and 4701 PJ forestry residues (30%). We estimate that 12 693 PJ of the total biomass is available for energy production, with 66% derived from agricultural residue and 34% from forestry residue. Most of the available residue is from south central China (3347 PJ), east China (2862 PJ) and south-west China (2229 PJ), which combined exceeds 66% of the total national biomass. Under the reference scenario without carbon tax, the potential availability of residue biomass for energy production is projected to be 3380 PJ by 2050 and 4108 PJ by 2095, respectively. When carbon tax is imposed, biomass availability increases substantially. For the CCS 450 ppm scenario, availability of biomass increases to 9002 PJ (2050) and 11 524 PJ (2095), respectively. For the 450 ppm scenario without CCS, 9183 (2050) and 11 150 PJ (2095) residue biomass, respectively, is projected to be available. Moreover, the implementation of CCS will have a little impact on the supply of residue biomass after 2035. Our results suggest that residue biomass has the potential to be an important component in China's sustainable energy production portfolio. As a low carbon emission energy source, climate change policies that involve carbon tariff and CCS technology promote the use of residue biomass for energy production in a low carbon-constrained world.
Keywords: bioenergy, carbon tax, carbon capture and storage, climate policy, integrated assessment, residue biomass
Received 21 May 2015; accepted 3 August 2015
Introduction
China's energy consumption has been soaring due to rapid increase in population and economic growth over the last decade. Its total energy consumption has increased from 44 022 PJ in 2001 to 110 055 PJ in 2013. Since 2011, China has been the largest energy consumer
Ji Gao and Aiping Zhang contributed equally to this work and should be considered co-first authors.
Correspondence: Erda Lin, tel. +8610 82105998, fax +8610 82105998, e-mail: lined@ami.ac.cn; Kejun Jiang, tel. +8610 63908587, fax +8610 63908478, e-mail: kjiang@eri.org.cn
with oil and natural gas dependency rates of approximately 60% and 33%, respectively (Shi, 2013). The International Energy Agency (IEA) estimates that with an 80% oil dependency rate, China will overtake the United States to become the world's largest oil-demanding country by 2035 (IEA, 2010). Meanwhile, China has overtaken the United States as the world's largest carbon emitter since 2007 and is projected to account for half of the increase in global CO2 emissions through 2035 (IEA, 2011). In December 2009, China's State Council announced that China will reduce its carbon intensity per unit of GDP by 40-45% by 2020, compared with 2005. Energy security, environmental health and greenhouse
© 2015 The Authors. Global Change Biology Bioenergy Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License,
which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 1
gases (GHGs) mitigation have been a major impediment to China's sustainable development. Bioenergy is often regarded as an environmentally acceptable and more efficient alternative for energy production (IEA, 2007). China's abundance in biomass resources accentuates the potential of using biomass to promote its development in renewable energy in a carbon-constrained world.
Unlike fossil fuel, biomass energy generates low or even net-zero carbon emissions because CO2 is recycled during the life cycle of using biomass for energy production. Therefore, temporary and permanent carbon storage based on biogenic sources is thought of as a key way to achieve low CO2 concentrations and mitigate climate change (Guest et al., 2013). Bioenergy with carbon capture and storage (Bio-CCS) can lead to negative carbon emissions (IEA, 2011). It could potentially have a 33% share of overall mitigation by the end of the century (Klein et al., 2011) and is important to mitigating global warming.
Although large-scale production may incur negative impacts such as increasing food price, accelerating soil erosion and runoff, decreased farmland productivity, and loss of wildlife habitat and biodiversity (Pimentel, 1994; Cramer, 2007), biomass can be environmentally friendly and renewable when used in a sustainable and responsible manner (Gustavsson et al., 2007). Most of the studies on biomass energy were focused on energy production potential, energy conversion technologies, and associated environmental, political and financial problems (Liao et al., 2004; Elmore et al., 2008; Sun et al., 2011; Cui & Wu, 2012; Li et al, 2012; Yu et al, 2012; He et al., 2013). However, previous studies have not examined the potential of biomass as a sustainable energy source in China with a global Integrated Assessment Modeling Framework.
In this study, we estimated the energy potential of agricultural and forestry residue biomass and quantity of residue retention, as well as their spatial distribution in China. We employed the Global Change Assessment Model (GCAM) to simulate the future potential of residue biomass from agricultural and forestry residues for energy production in China in response to global and national energy demand and climate change policies. Results obtained here would improve the understanding of how the development of residue biomass for energy production can help China achieve climate change mitigation goals and contribute to global mitigation efforts.
Materials and methods
Current availability of residue biomass
Determining potential availability of agricultural residues. The four main categories of residue biomass for energy produc-
tion are agriculture, forestry, municipal solid wastes (MSW) and emerging energy crops. Agricultural residues refer to field (e.g., straw, stalks, stubble, leaves and seed pods) and processed (e.g., husks, seeds, bagasse, molasses and roots) residues from a variety of crops. Agricultural residues are used as fertilizer, forage, raw material for producing paper and generating energy for cooking and heating.
The total amount of agricultural residues was calculated using the estimated ratios of agricultural biomass residue to agricultural product in China (Bi et al., 2008; Bi 2010; Table 1). Not all residue biomass was available due to residue retention and loss during transportation and storage, and subsequent processing. These factors were taken into consideration when estimating the maximum available supply of residue biomass. We used the collectable and usable coefficient (Table 1) of agricultural residues (the ratio of collectable and usable residues to the aboveground biomass of crop) to estimate the maximum available supply of biomass residue (Bi et al., 2008). We also calculated the total potential energy supply by agricultural residues based on their heating values on a dry mass basis. For each crop, we also estimated a residue retention fraction (Table 1) as the amount of residue to be retained for erosion control and nutrient cycling. The yields of main agricultural products of different crops averaged over 2003-2007 are presented in Table 1 and used as the baseline crop yields data for GCAM, for which simulation starts from year 2005 through the end of the 21st century.
Determining the potential availability of forest residues. Forestry residues refer to wastes associated with the processing of forest products including logging residues, wood-processing residues and tending/thinning residues (Cai et al., 2012). Logging residues originate from the harvesting operations and include stumps, roots, leaves, off-cuts, branches and sawdust. These residues are left on forestland. Wood-processing residues, or primary mill residues, are generated when processing roundwood at a sawmill, veneer mill, plywood mill or pulp mill. These residues include discarded logs, bark, sawdust and shavings (Liao et al., 2004; Yuan, 2002). Tending/thinning residues are derived from the processing of tending and thinning of different forests and afforestation activities such as stumping, thinning and pruning. Forest residues are used for generating heat, electricity, liquid fuels and solid fuels (Tan et al., 2010; MOA, 1998).
The total biomass production from logging and tending/ thinning residues varies with forest type, location, and tree density and growth rate. The amount of forest residues was estimated by multiplying biomass yields by collectable coefficients of biomass (Table 2). Forests were divided into five categories according to the Forest Law of the People's Republic of China: timber stands, protection forest, economic forest, forest for special uses and firewood forest. In addition, residues from other kinds of forest were evaluated based on the number of trees, their productivity and collectability. In this category, we included sparse forest, shrubs, sipang forest and bamboo forest. Notably, orchards, urban greening forest and hedgerow may produce large amounts of biomass due to annual pruning, which might be a potential bioenergy source. Firewood was assumed to be entirely harvested and currently used for
Table 1 The theoretical maximum energy potential and energy potential availability of agricultural residues in 2003 and 2007
a> P. a-
en o 3
Residue to Average Crop Low Maximum Energy
product production heating energy potential
ratio (104 t) over Sown area Water value Collectable Retention potential availability
Type of residue (RPR) (2003-2007)* (103 ha)f content (%) (KJ kg-1) coefficient (t ha"1) (PJ) (PJ) Source
Rice Straws 0.94 17761.68 28318.10 6.00™ 14059.00 0.83 1.01 2354.78 1954.47 [1] Niu & Liu
(1984)
- Husks 0.21 17761.68 28318.10 9.00™ 13067.00 0.95 0.00 487.39 463.02
Wheat Straws and stalks 1.30 9872.99 22749.90 13.50 14766.00 0.65 1.97 1895.20 1231.88
Corn Stalks 1.10 13787.72 26762.52 15.00 14356.00 0.90 0.57 2177.30 1959.57
- Cobs 0.21 13787.72 26762.52 9.70 14359.00 0.90 0.00 415.75 374.18
Other grains Straws and stalks 1.27 996.31 3917.91 11.35 14384.00 0.86 0.44 181.32 156.39
Millet Straws 1.40 173.67 897.19 13.50 14569.00 0.85 0.41 35.42 30.11
Sorghum Stalks 1.60 236.75 618.52 10.20 15105.00 0.90 0.61 57.22 51.50
Barley Straws and stalks 1.09 316.60 826.68 10.40™ 13720.00 0.85 0.63 47.35 40.25 [2] Lv & Wang
(1998)
Others Straws and stalks 1.09 269.29 1575.52 11.30 14142.00 0.85 0.28 41.51 35.28
Beans Straws stalk 1.60 2048.21 12505.55 5.10 14788.50 0.56 1.15 484.64 271.40
leaves pod
Gram Straws stalk pod 1.60 90.46 735.45 10.30 14615.00 0.56 0.87 21.15 11.85
Small red bean Straws stalk pod 1.60 32.98 219.59 10.30 14548.00 0.56 1.06 7.68 4.30
Soy beans Straws stalk 1.60 1538.99 9310.13 10.30 15079.00 0.56 1.16 371.30 207.93
leaves pod
Others Straws stalk pod 1.60 385.79 2240.38 10.30 14912.00 0.56 1.21 92.05 51.55
Tubers Stem and leaves 0.77 3209.70 8924.16 11.80 14125.50 0.73 0.75 349.11 254.85
Potato Stem and leaves 0.96 1361.79 4528.09 11.30 13498.00 0.73 0.78 176.46 128.82
Sweet potato Stem and leaves 0.63 1847.91 4396.07 12.30 14753.00 0.73 0.72 171.75 125.38
Cotton Stalk and torus 5.00[31 641.08 5521.40 15.00 14979.00 0.86 0.81 480.13 412.91 [3] Cui et al.
(2008)
Oils crop - 2.32 2832.62 14775.12 9.92 14775.12 0.78 0.98 971.04 757.41
Peanut Stalks 1.50 1360.35 4472.96 10.23™ 15033.00 0.83 0.78 306.75 254.60 [4] Nan et al.
(2008)
- Peanut hull 0.28 1360.35 4472.96 7.80 15682.00 0.70 0.00 59.73 41.81
Rapes Stem, leaves, pod 2.87[51 1183.85 6679.35 10.78™ 14142.00 0.64 1.83 480.50 307.52 [5] Xie et al.
(2011)
Sesame Stem, leaves, pod 2.80 62.82 590.89 10.85™ 15491.00 0.83 0.51 27.25 22.62
Sunflower Residues after 2.80 156.99 925.69 Air drying 15021.00 0.86 0.66 66.03 56.79
Sunflower seed
harvest
Flax (linseed) Hemps blade tips 2.01151 37.61 393.60 Air drying 15439.00 0.74 0.50 11.67 8.64
Others (average) Stem, leaves, pod 2.00 30.99 1712.63 Air drying 15491.00 0.85 0.05 9.60 8.16
en en M en en
w en 'n O
O w 2Z w yd O
(continued)
Table 1 (continued)
Residue to Average Crop Low Maximum Energy
product production heating energy potential
ratio (104 t) over Sown area Water value Collectable Retention potential availability
Type of residue (RPR) (2003-2007)* (103 ha)f content (%) (KJ kg-1) coefficient (t ha"1) (PJ) (PJ) Source
Fiber crops"'1 Sticks, sheath, leaves, hempshell 2.86 93.01 310.10 Air drying 15491,00[71 0.84 1.37 41.20 34.61 [6] Liu (1986); [7] Jing (2006)
Jute and hemp Sticks sheath 1.90 9.11 33.62 Air drying 15491.00 0.87 0.67 2.68 2.33
Flax (Linum) Sticks sheath 1.10 50.75 123.86 Air drying 15491.00 0.82 0.81 8.65 7.09
Cannabis sativa Sticks sheath 3.00 4.69 15.53 Air drying 15491.00 0.86 1.27 2.18 1.87
Ramie Leaves hempshell 6.50 27.11 133.97 Air drying 15491.00 0.84 2.10 27.30 22.93
Others (like - 1.90 1.36 3.11 Air drying 15491.00 0.85 1.24 0.40 0.34
jute and hemp)
Sugar Sugar bagasse 0.23 10262.47 1631.71 Air drying 15350 0.97 0.00 355.74 343.31
- Sugar cane stalk 0.10 10262.47 1631.71 Air drying 14902 0.76 1.53 152.93 115.80
Sheath
Sugarcane Bagasse 0.24 9535.30 1421.16 Air drying 15491.00 0.97 0.48 354.51 343.87
- Cane stalk sheath 0.10 9535.30 Air drying 13816.00 0.70 - 131.74 92.22
Sugar beet Bagasse 0.04 727.17 210.55 13.50 13500.00™ 0.90 0.14 3.93 3.53 [8] Wang & Liu (1984)
- Stems leaves 0.10 727.17 210.55 Air drying 14235.00 0.75 - 10.35 7.76
Tobacco Stems leaves 1.60 243.95 1249.10 3.51 11300.00191 0.95 0.16 44.11 41.90 [9] Zhang et al. (2012)
Flue-cured Stems leaves 1.60 220.92 1136.71 3.51 11300.00191 0.95 0.16 39.94 37.94
Tobacco
Others Stems leaves 1.60 23.03 112.39 Air drying 11300.00191 0.95 0.16 4.16 3.96
Vegetables and Vine stems shell 0.10 62453.90 19681.70 Air drying 13498.001111 0.50 1.59 843.00 421.50
Melons
Total 10817.9 8419.02
Residue to product ratio, water content, collectable coefficient and low heating value determined following Bi (2010) except [1] to [9].
Residue retention fraction means the minimum to return the field and determined by sown area, collectable coefficient, residue to product ratio (RPR) and crop production. * and f come from NBS (2003-2007).
Table 2 The theoretical energy potential availability of forestry residues from 2004 to 2008
Forest area Product Heating Energy
(104 ha) yield Collectable Water value Retention potentia
Type of residue (2004-2008)* (kg ha-1)f coefficient} content (KJ kg-1) t ha-1 PJ yr-1
Timber stands Wood chips, sawdust, needle leaves, bark, branches, cone 6007.44 3750 0.50 Dry weight 18600.0 1.31 2095.09
Protection forest Wood chips, sawdust, bark, branches 8194.68 3750 0.20 Dry weight 18600.0 0.53 1143.16
Forest for special Wood chips, sawdust, 1182.14 1875 0.10 Dry weight 18600.0 0.13 41.23
uses bark, branches
Firewood forest Total train 174.73 3750 1.00 Dry weight 16747.0 0.00 109.73
Bamboo forest Wood chips, sawdust, bark, branches 538.10 1875 0.10 Dry weight 17672.1 0.13 17.83
Economic forest Wood chips, sawdust, bark, branches 2041.00 1875 0.10 Dry weight 18600.0 0.13 71.18
Sparse forest Wood chips, sawdust, bark, branches 482.22 1875 0.50 Dry weight 18600.0 0.66 84.09
Shrubbery Bark, branches 5365.34 938 0.50 Dry weight 18600.0 0.33 468.04
Sipang forest Wood chips, sawdust, bark, branches 1121054.00 (104zhu) 2 (kg zhu-1) 0.50 Dry weight 18600.0 0.00 208.52
City greening Wood chips, sawdust, 400.00[1] 1625 0.10 Dry weight 18600.0 0.11 12.09
forest; bark, branches
Hedgerow
Orchard Fruitwood, pruning coconut shell, chestnut shell, walnut shell, etc. 996.66[2] 1875 0.10 Dry weight 18600.0 0.13 34.76
Mill Lath, slab, woodshaving 10675.27 540 (kg m-3) 0.34 Dry weight 19500.0[3] 0.00 382.20
Waste wood 2000.00™ 250 (kg m-3) 0.34 Dry weight 19500.0[3] 0.00 33.15
products
Total 4701.06
*Comes from SFA (2009) except [1] Cai et al, 2012 and [2] NBS (2004-2008), f and } determined following MOA, 1998 and Lu, 1997; Heating value derives from MOA, 1998 except [3] Zhang et al., 2008. Residue retention fraction determined by forest area, collectable coefficient, product yield based on assumption of the 30% availability of forest residues (MOA, 1998).
heating in rural areas. For wood-processing residues, the available amount was estimated based on the average annual production of roundwood in 2005 and 2009, which included net imported roundwood. These residues collectively accounted for ca. 34.4% of the total roundwood production (MOA, 1998). For forest residues, we also estimated (i) the maximum available supply of residue biomass based on the coefficients of collectable residues, (ii) the total potential energy supply from forestry residues according to their heating values (Table 2) and (iii) a residue retention value (Table 2).
While agricultural residues and forestry residues were the major focus of our study, MSW and energy crops were also considered and discussed. Note that the information provided here only reflects the gross amount of residues and energy potentials, which were derived based on the assumption that all the residues were economically exploitable and fully utilized.
The potential of residue biomass in the future
To better describe the interrelations between agriculture, food, bioenergy and climate change and understand the potential
role of this energy resource in the future, the residue availability parameters particularly derived for China, as introduced in the above section, were incorporated into the Global Change Assessment Model (GCAM) to simulate future availability of residue biomass for bioenergy production in response to global mitigation policies.
The GCAM is a long-term partial equilibrium model with 32 energy/economy regions and 283 agro-ecological zones (AEZs). Besides, it also includes a reduced form carbon cycle and climate module and runs from 1990 to 2100 in 5-year time step. GCAM was designed to estimate the long-term changes in the global energy/economy, agriculture/land use and water use and further explore the interactions between sectors (Kim et al., 2006). It will serve for understanding the potential ramifications of climate mitigation actions. GCAM has been used to investigate the potential roles of specific policy measures and different energy technologies such as bioenergy, CCS (carbon capture and storage), nuclear energy and other technologies used in different sectors Clarke et al., 2007a; (Thomson et al., 2011). We used the standard release of GCAM 3.0 with a thorough representation of bioenergy, agriculture and land
use as described in (Wise et al., 2009; Wise & Calvin 2011; Wise et al., 2014). GCAM can model three types of commercial biomass energy including dedicated energy crops, municipal solid waste and residue biomass (Wise et al., 2009; Luckow et al., 2010; Kyle et al., 2011). Biomass energy production from dedicated crops is mainly dependent on the availability and characteristics of land resources, technology options for production, competing land uses as well as bioenergy price in the context of energy markets. Potential energy production from residue biomass depends on crop production, harvest index and price of bioenergy. Potential production is also influenced by population and income. Carbon fluxes associated with terrestrial ecosystems were simulated in 15 different carbon pools (Wise et al., 2009), which inform bioenergy production under a carbon-constrained world.
For this analysis, the GCAM was used to simulate future bioenergy production from residue biomass under a reference scenario and two policy scenarios with and without CCS that are targeted at 450 ppm atmospheric concentration of CO2 by the end of the 21st century. The reference scenarios (Business as Usual) do not have greenhouse gas emissions constraints or taxes. For the policy scenario with carbon tax, we assumed that carbon emissions from the terrestrial ecosystems, fossil fuel and industrial sources are equally charged with a carbon price starting in 2020 and increasing at 5% per year through 2100. This scenario is noted as UCT (Universal Carbon Tax) (Edmonds et al., 2008; Wise et al., 2009). The carbon price pathway was set to limit atmospheric CO2 concentration to 450 ppm. In the other policy scenario, bioenergy with CCS detailed in Clarke et al. (2007b) was also considered, which has been shown as an effective technology to greatly reduce CO2 emissions for achieving low CO2 concentration targets. The policy scenario without CCS would be of higher cost. We used different carbon price starting in 2020 at approximately 76 $ t-1 C-1 (in 2005$) without CCS and 129 $ t-1 C-1 (in 2005$) with CCS. Future crop productivity needs be considered for projecting the energy production from residue biomass in the future. In the reference scenario, change in crop yield was based on FAO projection until 2050 to ensure global food security (Briunsma, 2009). Consistent with the historical trend, we assumed yields increase at a slower growth rate in the developed countries, but a relatively high yield growth rate in the developing countries. For instance, in China, the crop yield increase rates are 0.83%,0.62% and 0.35% for 2020, 2035 and 2050, respectively (Kyle et al., 2011). After 2050, the annual agriculture productivity changes converge to 0.25% for all crops and regions in the world. Global population growth pathway was inherited from United Nation's 2011 (Eom et al., 2012). Chinese population and GDP growth was described in Jiang et al. (2009), peaking in around 2035 and decreasing thereafter due to population aging and low birth rate. We assumed that GDP increases at a fast growth rate in China before 2030, and changes to a lower growth rate close to other developed countries beyond that (Jiang et al., 2009; Zhou et al., 2013). In this study, we used the same social and macroeconomic drivers, including population, labor productivity and changes in crop productivity, for all the scenarios.
Results
Current availability of agricultural residues
The total amount of agricultural residues and available energy supply are about 10 818 PJ and 8419 PJ per year, respectively (Table 1). This energy supply is roughly 8% of the annual energy consumption (105 952 PJ) of China in 2012. The energy potential of rice residues (including rice husks) is the greatest (around 2418 PJ), followed by corn residues (including corn cobs) of 2334 PJ and wheat straws (1232 PJ). These three crop residues combined account for ca. 71% of the total potentially available energy supply. The total processing crop residues, including rice husks, corn cobs, sugar bagasse and peanut hull, account for approximately 1319 PJ and 1223 PJ, respectively, which dominantly represent about 12% and 15% of the total residue availability.
The potential availability of crop residues for energy production in China was also analyzed spatially in Table 3. South central China has the highest potential for crop residue-based energy production of ca. 2419 PJ, followed by east China with ca. 2198 PJ. North-east China has the lowest potential of ca. 648 PJ. Other districts, including south-west China, north-west China and north China, combined have the potential to provide crop residue-based energy production of more than 3156 PJ. In China, Henan, Shandong, Jiangsu, Guangxi, Sichuan, Hubei and Heilongjiang are the top seven provinces in terms of potential availability of agricultural residues, occupying 46.5% of the total availability. Rice residues are mainly available over the central south China, east China and south-west China, accounting for 86.7% (2098 PJ) of the national rice residue potential. Wheat residue is mainly available in north China, east China and south central China, which collectively account for about 80.5% of the total of 1232 PJ. Henan, Shandong and Anhui provinces in located in these three districts account for about 70.1% of the total wheat residue availability. The energy production potential of corn residues is distributed mainly over north-west China and north China, amounting to 51.4% of the total national availability. The availability of the two root crops, viz. sugarcane and sugar beet, is the highest in Guangxi, Yunnan and Guangdong of central south and south-west of China, accounting for 82.9% of the national energy production potential of 459 PJ (Table 3).
Current availability of forest residues
The total amount of forest residues for energy production was estimated at ca. 4274 PJ per year (Table 4) based on the data of the seventh National Forestry
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Survey (2004-2008), when forest area reached 195 x 106 ha and covered 20 .4% of China's land . The energy potential of timber stands is the greatest (around 2095 PJ), followed by protection forest of 1143 PJ . These four main forestry residues combined account for ca 73 . 6% of the total potentially available energy supply . The total amount of residues from other forest types is about 778 48 PJ accounting for ca 16 6% of the total potentially available energy supply . The total wood-processing residues account for approximately 415 PJ, which represents about 8 9% of the total residue availability The residues of orchards, urban greening forest and hedgerow are of the lowest potential of ca 46 9 PJ accounting 0 99% of the total forestry residues The energy potential of logging (including tending/thinning residues) and mills residues was about 4286 PJ and 415 PJ, respectively, with north-east China, north China and south-west China having the largest amount of forest residues availability. The top five provinces in terms of forest residue availability are Yunnan, Heilongjiang, Inner Mongolia, Sichuan and Guangxi, which combined account for 41 4% of total logging residue
Combined agricultural and forest residue availability
The total energy potential from all sources is about 12 693 PJ per year (Table 5), with agricultural residues contributing about 8419 PJ each year . The total energy potential of forest residues is ca . 4274 PJ each year (excluding city greening forest; hedgerow, mills and waste wood) . Agricultural residues alone contribute more than 66% of the national energy potential of biomass residues The spatial distribution of the potential availability of biomass residues for energy production is shown in Tables 5 and 6 The total residue availability was the highest in south central China (3347 PJ), followed by the east China and south-west China with 2862 and 2229 PJ, respectively These three regions collectively account for over 66% of the national residue availability
Future residue biomass availability under different scenarios
The total bioenergy potential from agricultural residues, forest residues and mills will reach 17 660, 21 710 and 21 980 PJ by 2050 under BAU, CCS450 and N0CCS450, respectively, and 17 320, 21 180 and 21 640 PJ by the end of the 21st century, as a result of an increase in food demand, agriculture productivity and crop price The energy potential under the reference scenario is lower than that of the two policy scenarios (Table 6)
To project bioenergy production in the future, bioen-ergy price was calculated within the GCAM based on energy demand and competition with other energy
Table 4 The spatial distribution of the theoretical energy potential availability of forestry residues from 2004 to 2008 (PJ)
a> P. a-
en o 3
Timber stands Protection forest Forest for special uses Firewood forest Bamboo forest Economic forest Sparse forest Shrubbery Sipang forest Orchard Total
Product yields (kg ha-1) 3750 3750 1875 3750 1875 1875 1875 938 2 1875
Collectable coefficient 0.5 0.2 0.1 1.0 0.1 0.1 0.5 0.5 0.5 0.1
Heating value KJ kg-1 18 600 18 600 18 600 16 747 17672.1 18 600 18 600 18 600 18 600 18 600
Beijing 0.88 4.13 0.12 0.08 0.00 0.57 0.04 3.32 0.36 0.27 9.76
Tianjin 0.24 0.61 0.02 0.00 0.00 0.13 0.03 0.17 0.24 0.13 1.55
Hebei 23.03 28.37 0.36 5.36 0.00 3.18 1.46 8.97 70.70 3.82 145.24
Shanxi 8.10 19.07 0.42 0.19 0.00 1.58 3.14 10.36 3.34 0.96 47.18
Inner Mongolia 148.02 154.20 5.28 0.00 0.00 0.69 11.84 61.32 1.61 0.17 383.14
North China 180.27 206.38 6.20 5.63 0.00 6.16 16.50 84.14 76.24 5.36 586.87
Liaoning 44.47 26.23 0.47 20.23 0.00 4.26 0.99 5.12 2.13 1.09 105.00
Jilin 128.10 44.41 1.39 0.81 0.00 0.31 2.15 1.38 0.76 0.25 179.57
Heilongjiang 186.15 169.93 5.52 1.60 0.00 0.50 2.84 0.56 1.95 0.14 369.18
North-east China 358.72 240.57 7.38 22.64 0.00 5.08 5.98 7.07 4.83 1.49 653.75
Shanghai 0.03 0.12 0.08 0.00 0.01 0.08 0.00 0.03 0.47 0.09 0.91
Jiangsu 18.40 2.38 0.16 0.08 0.12 1.03 0.06 0.10 8.37 0.64 31.35
Zhejiang 79.66 21.88 0.29 0.00 2.59 3.92 0.67 2.74 4.13 1.04 116.92
Anhui 63.40 10.93 0.25 2.18 1.07 1.98 1.23 3.06 7.22 0.36 91.68
Fujian 130.63 21.51 1.17 2.26 3.29 3.53 1.59 1.81 0.73 1.91 168.44
Jiangxi 127.03 50.18 1.21 6.04 2.82 4.20 0.78 1.73 1.95 1.04 196.97
Shandong 29.83 9.33 0.13 0.00 0.00 3.43 1.17 0.74 10.56 2.56 57.75
East China 448.98 116.34 3.29 10.55 9.91 18.17 5.51 10.20 33.44 7.64 664.04
Henan 43.97 19.22 0.60 1.51 0.07 1.78 1.12 5.36 20.88 1.43 95.94
Hubei 42.63 49.50 0.50 10.25 0.50 1.94 2.18 12.56 4.90 0.94 125.89
Hunan 152.33 36.58 0.87 1.61 2.08 5.52 2.40 12.38 3.36 1.50 218.63
Guangdong 152.75 25.97 1.79 2.11 1.35 4.55 2.43 5.81 0.92 3.44 201.12
Guangxi 195.87 28.35 1.16 5.43 0.99 6.87 2.68 21.50 2.41 2.99 268.24
Hainan 8.28 5.10 0.83 0.00 0.05 3.16 0.04 0.33 0.37 0.58 18.74
Central south China 595.83 164.71 5.75 20.91 5.04 23.82 10.85 57.95 32.84 10.87 928.57
Chongqing 18.13 16.22 0.46 0.40 0.41 0.67 2.55 8.90 13.76 0.63 62.14
Sichuan 116.68 103.77 2.86 3.05 1.61 3.47 8.73 63.49 22.67 1.61 327.93
Guizhou 37.75 35.07 0.83 9.25 0.44 1.68 3.29 10.92 3.51 0.40 103.14
Yunnan 241.81 81.67 5.52 22.31 0.30 5.81 9.03 32.90 6.16 0.82 406.34
Tibet 49.34 81.59 3.98 0.35 0.00 0.02 5.04 74.55 0.67 0.00 215.56
South-west China 463.72 318.31 13.65 35.36 2.76 11.65 28.64 190.77 46.78 3.46 1115.10
Shaanxi 45.49 53.18 1.15 14.05 0.12 4.05 5.02 16.04 2.97 2.86 144.94
Gansu 1.32 19.86 2.35 0.00 0.00 0.90 2.99 29.93 5.85 1.25 64.46
Qinghai 0.25 1.74 0.78 0.00 0.00 0.01 1.18 28.43 0.50 0.02 32.90
Ningxia 0.08 0.71 0.20 0.00 0.00 0.16 0.36 3.31 0.65 0.18 5.65
Xinjiang 0.42 21.35 0.49 0.60 0.00 1.20 7.06 40.20 4.41 1.62 77.35
North-west China 47.57 96.85 4.96 14.65 0.12 6.31 16.61 117.91 14.38 5.93 325.30
Total 2095.09 1143.16 41.23 109.73 17.83 71.18 84.09 468.04 208.52 34.76 4273.62
The theoretical energy potential availability of forestry residues did not include mill and waste products because of no available data at province level.
en en w en en
O 'n yd
tri en
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Table 5 The total theoretical energy potential availability of agriculture and forestry residues (PJ)
Forestry residues Agriculture residues Total
Beijing 9 76 15.82 25 58
Tianjin 1 55 28 11 29 67
Hebei 145 .24 474.78 620 02
Shanxi 47. 18 155 .97 203.15
Inner Mongolia 383. 14 259 53 642.66
North China 586.87 934.20 1521.07
Liaoning 105.00 265 95 370.95
Jilin 179 . 57 369 67 549.24
Heilongjiang 369 18 471 96 841.14
North-east China 653.75 1107.58 1761.33
Shanghai 0 91 19.92 20.83
Jiangsu 31 35 484 67 516.02
Zhejiang 116. 92 135 .93 252.86
Anhui 91 68 453 40 545.09
Fujian 168.44 108 . 72 277.17
Jiangxi 196.97 273.06 470.03
Shandong 57 75 721. 86 779.61
East China 664.04 2197.57 2861.60
Henan 95 94 795 71 891.65
Hubei 125.89 406 12 532.01
Hunan 218. 63 427 33 645.96
Guangdong 201 12 264 36 465.48
Guangxi 268 24 482 28 750.52
Hainan 18. 74 42 84 61.58
Central south China 928.57 2418.64 3347.21
Chongqing 62 14 148 .54 210.68
Sichuan 327 93 480 17 808.10
Guizhou 103. 14 175 .66 278.80
Yunnan 406 34 294 13 700.47
Tibet 215.56 15.52 231.08
South-west China 1115.10 1114.03 2229.12
Shaanxi 144. 94 159.08 304.02
Gansu 64 46 128.50 192.96
Qinghai 32 90 19.36 52.26
Ningxia 5 65 44 21 49.86
Xinjiang 77 35 296 42 373.78
North-west China 325.30 647.58 972.88
Total 4273 62 8419.59 12693.21
Table 6 The theoretical maximum energy potential under different scenario (EJ)
2020 2035 2050 2065 2080 2095
BAU 16.70 CCS450 18.71 NOCCS450 19.16 17 . 46 17 . 66 20 65 21 71 21 08 21 98 17. 77 22 09 22 45 17 . 63 21 87 22 33 17.32 21.18 21.64
sources . Figure 1 shows the bioenergy prices for BAU and two limitation concentration scenarios . Growth of bioenergy market prices over time is enhanced by carbon price in a perspective of economics . Note that the
2005 2020 2035 2050 2065 2080 2095 Year
Fig. 1 Bioenergy prices along two alternative UCT CO2 concentration target pathways (index year 2005 = 1.0) . Growth of bioenergy market prices over time is enhanced by carbon price under climate policy scenarios in a perspective of economics
carbon price was not added to the price of bioenergy based on the assumption of zero carbon emissions from bioenergy production (Wise et al., 2009) . Bioenergy price under the policy scenarios is much than that in the reference scenario after 2035. Among the two policy scenarios, bioenergy price under the NOCCS 450 ppm scenario has a competitive advantage compared to the CCS 450 ppm mitigation scenario after 2065, as evidenced by the trends of more bioenergy production from energy crop until the carbon prices are very high
In the reference scenario without carbon tax, more and more residue biomass from agriculture and forestry becomes available along with increase in energy demand and energy prices and reaches a projected output of approximately 3380 PJ yr"1 by 2050 and 4108 PJ yr"1 by 2095 (Fig. 2) . Under the UCT scenarios, the carbon price is charged for the emission of CO2 This intensifies the demand and increases price of residue biomass for energy and further decreases the use of fossil fuels The total bioenergy production from residue biomass is 9000 and 9180 PJ by 2050 under CCS 450 ppm and NOCCS 450 ppm, respectively, and 11 520 and 11 150 PJ by the end of the century, respectively (Fig 2)
Figure 3 shows the carbon prices calculated within the GCAM that are required to drive a fundamental transformation of the global economy To achieve the 450 ppm CO2 concentration targets, the policy scenario without CCS will require higher carbon prices than the policy scenario with CCS, especially toward the end of
2020 2035 2050 2065 2080 2095 Year
Fig. 2 The total available supply of residue biomass for energy under different scenarios from 2020 to 2095 (EJ). More and more residue biomass from agriculture and forestry becomes available along with increase in energy demand and energy prices, and the carbon price is charged under climate policy scenarios, which further intensifies the demand and increases price of residue biomass for energy.
the century. For example, the 2095 carbon price for the CCS 450 ppm scenario is 5004 $ t"1 C"1, which is much higher than the carbon price of 2955 $ t"1 C"1 under NOCCS 450 ppm in 2095. However, the two policy scenarios do not differ substantially from each other in terms of supply of residue biomass after 2035 (Fig. 2).
Figure 4 shows the total bioenergy production (including residue biomass, energy crop and MSW) increases substantially over time under all three climate policy scenarios, but with higher bioenergy production under the CCS 450 ppm CO2 scenario than that under the other two scenarios after 2050. The NOCCS 450 ppm CO2 scenario projects more bioenergy production compared with the other two scenarios before 2050.
Figure 5 shows the proportion of bioenergy production from residue biomass over time in the total bioen-ergy production. In the reference scenario, bioenergy production from energy crops accounts for about 65% of the total bioenergy production after 2035, as a result of no CO2 emission limitation. In the CCS 450 ppm CO2 scenario, residue biomass meets nearly half all the bioenergy production in 2035, 53% by mid-century and 40% by the end of the century. In the NOCCS 450 ppm CO2 scenario, residue biomass contributes ca. 60% of all the total bioenergy production in 2035, 55% by 2050 and
Fig. 3 Carbon price pathway under different climate policy scenario (2005$). The carbon prices calculated within the GCAM that are required to drive a fundamental transformation of the global economy. The policy scenario without CCS will require higher carbon prices than the policy scenario with CCS, especially toward the end of the century for achieving the 450 ppm CO2 concentration targets.
Fig. 4 The total bioenergy production (including residue biomass, energy crop and MSW) under different scenarios (EJ). In the future, the total bioenergy production substantially shows an increases, but with higher bioenergy production with the CCS scenario than that under two other scenarios after 2050, and the NOCCS scenario accounts for a great proportion in bioenergy production compared with two other scenarios before 2050.
Fig. 5 The share of residue biomass and energy crop in the total bioenergy production under different scenarios. In the CCS scenario, residue biomass meets nearly half all the bioen-ergy production in 2035, 53% by mid-century and 40% by the end of the century. In the NOCCS scenario, residue biomass accounts for about 60% of all the total bioenergy production in 2035, 55% by 2050 and 48% by the 2095. Bioenergy production from energy crops contributes to about 65% of the total bioen-ergy production after 2035 in the BAU.
48% by the 2095. Total bioenergy production will contribute about 31% of the total energy production by 2050 and 35% by 2095. These results show that more biomass energy from residue biomass will be produced under climate policies without carbon tax and additional land and that trade-off between energy prices competitiveness, options of low carbon technology (CCS) and climate policy (carbon tax) is required for bioenergy production.
Discussion
We evaluated the energy potential of agricultural and forest residues in China and found that the total potential is about 12 693 PJ per year under current conditions. This is close to 10% of the total primary energy demand of China in 2013 (110 055 PJ). However, it is important to note these estimated values may be affected by the availability and reliability of data on crop species, harvest index, location, soil properties and seasonal variation (Liao et al, 2004; Zhou et al, 2011). The potential of agricultural residues as a bioenergy source is complicated by their numerous alternative uses including feeding, fodder, fertilizer, household fuels and industrial fuels. Currently, agricultural residues are mainly used for forage (24.5%), industry materials (3.9%), base
material for edible mushrooms (2.3%), biogas (0.85%), direct field restoration (14.1-14.6%), direct combustion by farmers (24.9-30.7%), whereas the rest are lost during collection (15%), being discarded or directly burnt (12.3-20.5%) in the field (Bi et al., 2008; Wang et al, 2010). At present, the collectable and utilizable amount of agriculture residues as a bioenergy resource is estimated to be around only 23.9%. In addition, the actual availability is also limited by economic, social, environmental, institutional and policy incentives, logistical considerations, infrastructural and technological constraints, and availability of skilled personnel (Bi, 2010; Okello et al, 2013).
Overall, most of residue biomass should be returned to the field for improving soil fertility through maintaining soil organic matter and soil structure. The reasonable residue incorporation rate of 3.0-4.5 t ha"1 has been reported to slightly increase soil organic carbon and crop yield for rice and wheat and 4.5-6.0 t ha"1 for corn in China. The amount of residue retention was 1 911 721 x 104 t, accounting for 22.7% of the total residue biomass in 2008. If the amount of residues returned directly to fields (92 x 106 t accounting for 10.9% of the total residue biomass in 2008) is also considered, the amount of residue retention represents one-third of the total residue biomass in 2008, when a residue retention ratio of 2.33 t ha"1 is used (Bi, 2010). Note that this residue retention ratio is lower than the desired residue retention ratio for maintaining sustainable agroecosys-tems, which is an important factor in collecting residue biomass for energy production.
Not all forest residues are harvestable. Some of them must be retained for maintaining nutrient levels and preventing soil erosion. This study identified that the logging and processing forest residues would potentially provide 4701 PJ of energy. Significant variation in the potential is observed, as influenced by numerous factors such as forest type, collectable fraction and geographical location. For example, the average yield of firewood forest in the southern mountain area is as high as 7.5 t ha"1, but only 3.75 t ha"1 in the North Mountain area. The yield shrub forest is 0.75 t ha"1 over the country, with a collectable coefficient of 0.2 in the mountain area and 0.5 in the plains area (Yuan, 2002). Logging residues are usually located in remote regions, leading to difficulties for collecting and utilizing them. The amount of forest residues available used for renewable energy production is also affected by technical, ecological and environmental factors. In fact, the potential for renewable energy production from logging residues and wood-processing residues is estimated to be about 1286-1607 PJ and 228.5 PJ, respectively, accounting for 30-37.5% and 55% of logging residues and wood-processing residues in China (MOA, 2006).
In the future, climate policy is a key factor affecting the supply of residue biomass. Imposing carbon tax is projected to be an effective way to reduce CO2 emissions and mitigate climate change (Wise et al., 2009). Terrestrial carbon storage has been thought to be a low cost method to address the climate change. For example, soil carbon on croplands is a key component of terrestrial carbon storage. In China, croplands (over 130 M ha) contain 730 (329-1095) Tg C in the topsoil and are estimated to have sequestrated carbon at a rate of about 24.3 (11.0-36.5) Tg C yr"1 over the last 30 years (Yu et al., 2012, 2013). Residue removal may lead to the loss of soil organic carbon, which can be minimized by improved management practices such as nitrogen fertilizer application, straw retention and incorporation and conservation tillage. These practices have been estimated to increase soil organic carbon from 38.5 Mg C ha"1 in 2010 to 56.9 Mg C ha"1 in 2050 on China's croplands (Yu et al., 2013), which translate to $2929 ha"1 in 2010 and $18 711 ha"1 in 2050 with the carbon price under the CCS 450 ppm scenario. The carbon sequestration potential through optimal management is estimated to be approximately 2.39 Pg C over the next 40 years nationally (Smith et al., 2007; Yu et al., 2013). Nevertheless, the carbon sequestration potential in China after 2050 requires further evaluation under different future climate scenarios and management practices because soil carbon may reach a new equilibrium after 84 years of improved management practices and fertilizer amendment (Yan et al., 2007; Yu et al., 2013).
In this study, GCAM allows farmers to allocate the amount of residues to be retained on the field or to be removed for energy production, based on an economic assessment on carbon price, carbon stocks, the cost and benefit of the bioenergy (Wise et al., 2009). Notably, imposing climate policies, such as carbon tax, can be difficult as it requires monitoring and evaluating terrestrial carbon emissions and stocks. Solutions to these barriers may require huge amounts of money to identify the landowners and transfer the decrease or increase of carbon stocks (Calvin et al., 2014). One limitation of this study is that projected future utilization of residue biomass depends on a series of assumptions within GCAM including crop productivity, economic growth and land policy. Residue biomass production seems to be highly sensitive to future changes in crop productivity that may reduce land-use change emissions under the climate policy scenario (Wise et al., 2009). In addition, the increasing demand for food to feed the rising world population may further limit residue availability (Gregg & Izaurralde, 2009; Gregg & Smith, 2010). As well, this will likely decrease unit mass collection cost and shift the supply curve accordingly. Dedicated energy crops
will occupy more available agricultural land in order to achieve higher biomass yields while reducing production cost to compete with residue biomass by 2095. If crop yields increase only slightly or remain stable in the future, less residue biomass would be harvested because a higher proportion of the residue must be left in fields to maintain soil quality and reduce erosion. Management options that may increase residue removal rate include the practice of conservation tillage, better crop rotation and the introduction of catch crops. In addition, under a UCT regime, all carbon emissions to be taxed are simulated as the best policy to limit the CO2 concentration. Forests with a higher below ground storage of carbon will be preferable due to their efficiency in limiting carbon emissions from land-use change. This implies that land polices limit the conversion from forests to bioenergy production and stress food production from agricultural lands (Calvin et al., 2014).
In summary, China is the largest developing agricultural country in the world. Agricultural and forest residues in China have considerable potential to be available as a bioenergy source to provide ca. 10% of its total primary energy consumption in 2013. Accurate projection and successful utilization of residue biomass for energy production requires a comprehensive and multifactorial assessment. The integrated assessment results indicate that residue biomass for energy production could play an important role in mitigating the climate change. The production of bioenergy should be achieved in a sustainable way through optimal land management practices by conserving soil quality to enhance interactive economic, environmental and social purposes.
Acknowledgements
This work was supported by the Ministry of Science and Technology of the People's Republic of China (2013BAD11B03 and 2012CB955801) and the National Natural Science Foundation of China (71373142).
References
Bi YY (2010) Study of straw resources evaluation and utilization. Chinese Academy
of Agriculture Science in China PHD Dissertation. Bi YY, Wang DL, Gao CY, Wang YH (2008) Straw Resources Evaluation and Utilization
in China. Chinese Agricultural Sciences and Technology, Beijing. Briunsma J (2009) The Resource Outlook to 2050: By How Much Do Land, Water, and Crop Yields Need to Increase by 2050? Expert Meeting on How to Feed the World in 2050. Food and Agriculture Organization of the United Nations, Rome. Cai F, Zhang L, Zhang CH (2012) Potential of woody biomass energy and its availability in China. Journal of Beijing Forestry University (Social Sciences), 1, 103 107. Calvin K, Wise M, Kyle P, Patel P, Clarke L, Edmonds J (2014) Trade-offs of different land and bioenergy policies on the path to achieving climate targets. Climatic Change, 123, 691 704.
Clarke L, Edmonds J, Jacoby H, Pitcher H, Reilly J, Richels R (2007a) CCSP Synthesis and Assessment Product 2.1. Part A: Scenarios of Greenhouse Gas Emissions
and Atmospheric Concentration. U.S. Government Printing Office, Washington, DC.
Clarke L, Lurz J, Wise M, Edmonds J, Kim S, Smith S, Pitcher H (2007b) Model Documentation for the MiniCAM Climate Change Science Program Stabilization Scenarios: CCSP Product 2.1a. PNNL Technical Report. PNNL-16735.
Cramer J (ed.) (2007) Testing Framework for Sustainable Biomass: Final Report from the Project Group "Sustainable Production of Biomass". Energy Transition's Interdepartmental Programme Management (IPM), The Netherlands.
Cui H, Wu R (2012) Feasibility analysis of biomass power generation in China. Energy Procedia, 16, 45 52.
Cui M, Zhao LX, Tian YS et al. (2008) Analysis and evaluation on energy utilization of main crop straw resources in China. Transactions from the Chinese Society of Agricultural Engineering, 12, 291 295.
Edmonds J, Clarke L, Lurz J, Wise M (2008) Stabilizing CO2 concentrations with incomplete international cooperation. Climate Policy, 8, 355 376.
Elmore AJ, Shi X, Gorence NJ, Li X, Jin H, Wang F, Zhang X (2008) Spatial distribution of agricultural residue from rice for potential biofuel production in China. Biomass and Bioenergy, 32, 22 27.
Eom J, Calvin K, Clarke L et al. (2012) Exploring the future role of Asia utilizing a scenario matrix architecture and shared socio-economic pathways. Energy Economics, 34(Supplement 3), S325 S338.
Gregg JS, Izaurralde RC (2009) Effect of crop residue harvest on long-term crop yield, soil erosion and nutrient balance: trade-offs for a sustainable bioenergy feedstock. Biofuels, 1, 69 83.
Gregg J, Smith S (2010) Global and regional potential for bioenergy from agricultural and forestry residue biomass. Mitigation and Adaptation Strategies for Global Change, 15, 241 262.
Guest G, Bright RM, Cherubini F, Stromman AH (2013) Consistent quantification of climate impacts due to biogenic carbon storage across a range of bio-product systems. Environmental Impact Assessment Review, 43, 21 30.
Gustavsson L, Holmberg J, Dornburg V, Sathre R, Eggers T, Mahapatra K, Marland G (2007) Using biomass for climate change mitigation and oil use reduction. Energy Policy, 35, 5671 5691.
He G, Bluemling B, Mol APJ, Zhang L, Lu Y (2013) Comparing centralized and decentralized bio-energy systems in rural China. Energy Policy, 63, 34 43.
IEA (2007) Bioenergy Project Development & Biomass Supply. International Energy Agency, Paris.
IEA (2010) World Energy Outlook 2010. International Energy Agency, Paris.
IEA (2011) Combining Bioenergy with CCS Reporting and Accounting for Negative Emissions under UNFCCC and the Kyoto Protocol. International Energy Agency, Paris.
Jiang KJ, Zhuang X, Liu Q (2009) China's low-carbon scenarios and roadmap for 2050. Sino-Global Energy, 24, 28 30.
Jing WR (2006) Power Generation Utilizing Biological Energy Sources. North China Power Engineering Co., Ltd, Beijing.
Kim SH, Edmonds J, Lurz J, Smith S, Wise M (2006) The object-oriented energy climate technology systems (ObjECTS) framework and hybrid modeling of transportation in the MiniCAM Long-term, global integrated assessment model. The Energy Journal Special Issue: Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down, 2, 63 91.
Klein D, Bauer N, Bodirsky B, Dietrich JP, Popp A (2011) Bio-IGCC with CCS as a long-term mitigation option in a coupled energy-system and land-use model. Energy Procedia, 4, 2933 2940.
Kyle GP, Luckow P, Calvin K, Emanuel W, Nathan M, Zhou Y (2011) GCAM 3.0 Agriculture and Land Use: Data Sources and Methods. PNNL-21025. Pacific Northwest National Laboratory, Richland, WA.
Li Q, Hu S, Chen D, Zhu B (2012) System analysis of grain straw for centralised industrial usages in China. Biomass and Bioenergy, 47, 277 288.
Liao CP, Yan YJ, Wang CZ, Huang H-T (2004) Study on the distribution and quantity of biomass residues resource in China. Biomass and Bioenergy, 27, 111 117.
Liu TF (1986) Technology Economic Manual. LiaoNing Peopes's Publishing House, ShengYang.
Lu N (1997) Introduction of New Energy, pp. 152 153. China Agriculture Press, Beijing.
Luckow P, Wise MA, Dooley JJ, Kim SH (2010) Large-scale utilization of biomass energy and carbon dioxide capture and storage in the transport and electricity sectors under stringent CO2 concentration limit scenarios. International Journal of Greenhouse Gas Control, 4, 865 877.
Lv ZW, Wang JL (1998) Evaluation of the nutritional value on Highland barley straw in Tibet. Shaanxi Journal of Agricultural Sciences, 2, 5 8.
Ministry of Agriculture (MOA) of the P.R.C Project Expert Team (1998) Assessment of Biomass Resource Availability in China. China Environmental Science Press, Beijing.
Ministry of Agriculture (MOA) of the P.R.C Project Expert Team (2006) Preliminary research of wood energy development potential in China. China Forestry Industry, 1, 12 21.
National Bureau of Statistics (NBS) of China of the P.R.C (2003 2007) China Statistical Year Book. China Statistics Press, Beijing.
Nan ZD, Yang M, Han W et al. (2008) Fast pyrolysis oil crops straw and characteristics of bio-oil. Chinese Journal of OH Crop Sciences, 30, 501 505.
National Bureau of Statistics of China of the P.R.C (2004 2008) China Statistical Yearbook. China Statistics Press, Beijing.
Niu RF, Liu TF (1984) Agricultural Technology Economic Manual, Revised edn. China Agriculture Press, Beijing.
Okello C, Pindozzi S, Faugno S, Boccia L (2013) Bioenergy potential of agricultural and forest residues in Uganda. Biomass and Bioenergy, 56, 515 525.
Pimentel D (1994) Renewable energy: economic and environmental issues. Bioscience, 44, 536 547.
Shi D (2013) Changes in global energy supply landscape and implications to China's energy security. Sino-Global Energy, 18,1 7.
Smith JO, Smith P, Wattenbach M et al. (2007) Projected changes in the organic carbon stocks of cropland mineral soils of European Russia and the Ukraine, 1990 2070. Global Change Biology, 13, 342 356.
State Forestry Administration (SFA) of the P.R.C (2009) The Seventh National Forest Resources Inventory and the Status of Forest Resources (2004-2008). State Forestry Administration (SFA) of the P.R.C, Beijing.
Sun J, Chen J, Xi Y, Hou J (2011) Mapping the cost risk of agricultural residue supply for energy application in rural China. Journal of Cleaner Production, 19,121 128.
Tan T, Shang F, Zhang X (2010) Current development of biorefinery in China. Biotechnology Advances, 28, 543 555.
Thomson A, Calvin K, Smith S et al. (2011) RCP4.5: a pathway for stabilization of radiative forcing by 2100. Climatic Change, 109, 77 94.
Wang XZ, Liu YM (1984) Study of beet residue utilization. Heilongjiang Animal Science and Veterinary Medicine, 5,13 15.
Wang YJ, Bi YY, Gao CY (2010) The assessment and utilization of straw resources in China. Agricultural Sciences in China, 9, 1807 1815.
Wise M, Calvin KV (2011) GCAM 3.0 Agriculture and Land Use: Technical Description of Modeling Approach. Pacific Northwest National Laboratory. PNNL-20971.
Wise MK, Calvin A, Thomson L et al. (2009) Implications of limiting CO2 concentrations for land use and energy. Science, 324, 1183 1186.
Wise MA, Calvin KV, Kyle GP, Luckow P, Edmonds JE (2014) Economic and physical modeling of land use in GCAM 3.0 and an application to agricultural productivity, land, and terrestrial carbon. Climate Change Economics, 5, 1450003.
Xie GH, Han DQ, Wang XY, Lu RH (2011) Harvest index and residue factor of cereal crops in China. Journal of China Agricultural University, 16, 1 8.
Yan H, Cao M, Liu J, Tao B (2007) Potential and sustainability for carbon sequestration with improved soil management in agricultural soils of China. Agriculture, Ecosystems & Environment, 121, 325 335.
Yu H, Wang Q, Ileleji KE, Yu C, Luo Z, Cen K, Gore J (2012) Design and analysis of geographic distribution of biomass power plant and satellite storages in China. Part 1: straight-line delivery. Biomass and Bioenergy, 46, 773 784.
Yu Y, Huang Y, Zhang W (2013) Projected changes in soil organic carbon stocks of China's croplands under different agricultural managements, 2011 2050. Agriculture, Ecosystems & Environment, 178, 109 120.
Yuan ZH (2002) Research and development on biomass energy in China. Interna-Honal Journal of Energy Technology and Policy, 2,108 144.
Zhang XL, Lv W, Zhang CH et al. (2008) China Forest Energy. China Agriculture Press, Beijing.
Zhang SH, Yang ZX, Wang XH, Chen HP (2012) Experiment on agglomeration characteristics during fluidized bed combustion of tobacco stem. Transactions of the Chinese Society for Agricultural Machinery, 43, 97 101.
Zhou XP, Wang F, Hu H, Yang L, Guo P, Xiao B (2011) Assessment of sustainable biomass resource for energy use in China. Biomass and Bioenergy, 35, 1 11.
Zhou S, Kyle GP, Yu S et al. (2013) Energy use and CO2 emissions of China's industrial sector from a global perspective. Energy Policy, 58, 284 294.
