Modelling the policies of optimal straw use for maximum mitigation of climate change in China from a system perspective

Renewable and Sustainable Energy Reviews 55 (2016) 789–810

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Modelling the policies of optimal straw use for maximum mitigation of climate change in China from a system perspective Guobao Song n, Jie Song, Shushen Zhang Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China

art ic l e i nf o

a b s t r a c t

Article history: Received 10 August 2014 Received in revised form 12 August 2015 Accepted 26 October 2015 Available online 5 December 2015

Understanding the competitive uses of straw resources as substitutes for fertilizer nutrients, forage and fossil energy is critical to maximize the mitigation of climate change. Focusing on the mitigation of global warming, we developed an uncertainty model for the optimal use of straw in China based on available studies of life-cycle assessment that have determined the advantages of energy savings and reductions of greenhouse gases (GHGs) of 14 conversion technologies. The current pattern of straw use has saved China 0.75 EJ of energy and has reduced GHGs by 270.76 Mt CO2e on average annually. Among all competitive uses, the use of straw as forage was most environmental friendly, followed by the uses of straw as alternative sources of nutrients and energy. Simulated scenarios for policies of competing straw uses suggested that joint decision-making among administrative departments was vital to maximize the national mitigation of global warming (i.e. 1.84–2.26 EJ of energy saved or reductions of GHGs of 464.89– 568.61 Mt CO2e annually) by considering all straw uses comprehensively instead of overemphasizing a single use. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Climate-change mitigation Policy decision Life-cycle assessment Uncertainty optimization China

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790 2.1. Optimization system for straw use in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790 2.2. Optimization under uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 2.3. LCAs for different straw uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 2.3.1. Substitutes for fertilizer nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 2.3.2. Substitutes for commercial forage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 2.3.3. Substitutes for fossil energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 2.4. System boundaries and assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 3.1. Comparison of LCA results for conversion technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 3.2. Net-weighted rates of energy savings and GHG reduction for each straw use. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 3.3. Total benefits and contributions from different straw uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 3.4. Scenario analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 3.5. Strengths and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796

Abbreviations: ABC, ammonium bicarbonate; AS, ammonium sulfate; MAP, monoammonium phosphate; DAP, diammonium phosphate; SSP, single superphosphate; TSP, triple superphosphate; MOP, potassium chloride; SOP, potassium sulfate; Nmachine, nutrient use of straw returned to farmland by machinery; Fsilage, forage use of straw by silage technology; Fammonification, forage use of straw by ammonification technology; Funtreated, forage use of straw by direct feeding without treatment; Ppure, pure combustion for power; Pcofiring, co-firing 15% biomass and 85% hard coal for power; Pgasification, gasification and combustion for power; P&Hdigestion, anaerobic digestion for heat and power generation; Hstove(TSC), traditional household cook-stove burning for heat; Hstove(ISC), improved energy-saving cook-stove burning for heat; Hboiler, combustion in biomass boiler for heat; Hgasification, gasification and combustion for heat; Hh-digestion, household anaerobic digestion and combustion for heat; HL-digestion, large-scale anaerobic digestion and combustion for heat; IPCC, Intergovernmental Panel on Climate Change. n Corresponding author. E-mail address: [email protected] (G. Song). http://dx.doi.org/10.1016/j.rser.2015.10.136 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Pattern of straw use and constraints for optimizing policy decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A1. Pattern of straw use in China (Xi). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A2. Estimates of amount of straw used by various conversion technologies (xi,j) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A3. Constraints for optimizing policy decisions (Ai (ai) or Bi (bi)). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix B. Substituting commercial fertilizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B1. Estimate of macronutrients in 1 t of mixed straw (Nci). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2. Environmental impact of straw-returning machines (EftM and GftM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3. SOC improvement by returning straw to farmland (GftSOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B4. Energy input (Efti) and GHG emission (Gfti) for fertilizers in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix C. Substituting commercial forage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C1. Impacts of straw-based forage production (Erfrj and Grfrj) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C2. Impacts of corn-based forage production (Ecfr and Gcfr) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C3. Nutrient effect (EN and GN) and SOC improvement (GfrSOC) from dung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C4. Composite of straw forages (Wfrj) and substitution rate for corn forage (Sbrj) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix D. Substituting fossil energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D1. Schematic of life-cycle assessment for bioenergy production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D2. Efficiency uncertainties of bioenergy conversion technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D3. Effects of bioenergy technologies on global-warming mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix E. Sensitivity and optimal uses of straw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix F. Supplementary material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Straw contributes to the mitigation of climate change in multiple ways [1,2]. By leaving straw in the field in both tilled and untilled farming systems, soil fertility can be maintained by the addition of nutrients and soil organic carbon (SOC) [3–5]. Straw can also be used to feed cattle, especially in mixed crop-livestock systems in developing countries [6]. Crop residue is also a promising alternative to fossil energy due to its carbon neutrality, which can contribute to the mitigation of climate change [7–9]. Different users compete for the limited straw resources. Policy makers are challenged by the dilemma of quantitatively allocating limited resources among different straw users to maximize the environmental benefits of energy savings or reductions in greenhouse gases (GHGs) [10]. Life-cycle assessment (LCA) is an effective methodology for quantifying environmental impacts caused by products or services [11], including global-warming potential specified by flows of energy and GHGs. Numerous LCA studies have recently been published on bioenergy based on the raw material feedstock of straw, with the dual challenges of the energy crisis and GHG reduction [12–15]. LCA, combined with optimization, has been effectively used for the planning of bioenergy production, by both prioritizing the input of biomass for specific energy types and optimizing the size and location of a power plant [16,17]. Available reviews [18–20] have also summarized the environmental benefits of various systems of bioenergy from a LCA perspective, highlighting the two issues of result uncertainty and competition among multiple straw users, on which this study focuses. Uncertainty is an inherent deficiency of LCA methodology originating from various processes. It cannot be avoided completely but can be analyzed by established methodologies, such as Monte Carlo simulation [21,22], meta-analysis [23] or fuzzy evaluation [24]. Resource competition among various straw users for bioenergy, fertilizer nutrients and livestock forage has been frequently highlighted [8,18,25]. An LCA-based system simulation has rarely been conducted to help policy makers to optimally allocate straw resources among various users for the maximum mitigation of climate change, although the environmental impacts of straw used as energy, forage and fertilizer have been compared [26].

796 796 796 796 797 798 798 800 800 801 801 801 803 803 803 804 804 804 804 806 808 808

The rapid development of China is leading to energy shortages, farmland degradation and increasing demand for animal-based foods. The central government has vowed to cut GHG emissions by 40–50% per unit of GDP by 2020 relative to the 2005 level (Central Government, PRC) [27]. These challenges are closely linked to straw management. The Ministry of Agriculture (MOA) reported that approximately 800 Mt of straw was produced annually in China, with the majority used in an environmentally friendly way. Unfortunately, a large amount of straw (210 Mt) was directly burnt in the fields (Section A1 in Appendix A), leading to substantial environmental burdens such as air pollution [28,29]. Multiple administrations are responsible for straw uses as nutrients, forage and energy, policy-making for multiple straw uses compete to each other in practice. A study is thus needed to analyze the problem of the optimal allocation of straw resources among multiple competitive users to maximize the mitigation of climate change. We quantified the net rates and uncertainties of climatechange mitigation of 14 conversion technologies based on a series of reviewed and developed LCAs by comparing corresponding reference systems to provide equivalent products or services. We then developed an optimization model with uncertainties to maximize energy saving or GHG reductions by allocating straw resources as substitutes for fertilizer, commercial forage and fossil energy. We also simulated and compared scenarios for various policies of straw use.

2. Materials and methods 2.1. Optimization system for straw use in China The system of straw use in China was constructed in accordance with the pattern of straw uses (Fig. 1, Section A2 in Appendix A). The life-cycle inventories of commercial fertilizers and forages (reference systems 1 and 2) were investigated in this study. Commercial fertilizer plays two important roles: as a necessary input to grow corn as raw material for forage production and as a nitrogen source for accelerating microbial activities during straw silaging and ammonification.

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

2.2. Optimization under uncertainty The optimal objective of straw-use systems is set to achieve the maximum energy saving (E) or maximum GHG reduction (G) in China by combining policies that reduce straw burning in fields and reallocate the straw to different users by various conversion technologies. The objective function and linear constraints are thus defined as: Max obj :

E¼

nX ¼4

Ei  X i

or

G¼

i¼1

s:t:

A i r X i r Bi ; X i ¼

nX ¼4

Gi  X i

ð1Þ

i¼1 m X

xij

and

ai r xi r bi

ð2Þ

791

developed into the standard software package OptQuest and has successfully been integrated into various simulation tools [31]. Crystal Ball (Oracle) was used in this study because of its excellent compatibility with Microsoft Excel spreadsheets [32], based on which the uncertainties originating from 121 LCA variables were quantified by Monte Carlo simulations with 5000 trials. 2.3. LCAs for different straw uses The final units were standardized as GJ t  1 for net energy savings and kg CO2e t  1 for GHG reductions, when 1 t of straw resource was used as substitutes for fertilizer nutrients, commercial forage and fossil energy converted by various routes.

j¼0

where i (1, 2, 3 and 4) is the index of straw use as a substitute for commercial fertilizers, forage and fossil energy in addition to the straw wasted by burning, and j (1 to 14) is the index of conversion technology. For each straw use, the net rate of energy saving (Ei) and GHG reduction (Gi) is calculated by a weighting method based on the LCAs for different technologies of straw conversion (Section 2.3). Xi is the amount of straw used for purpose i, with boundaries (Ai and Bi) determined by both the potentials of straw consumers and straw-use policies. xi, j is a decision variable representing the amount of straw converted by technology j for purpose i, and the optimal tonnes of xi, j depend on the net effect of global-warming mitigation from corresponding technology j under the uncertainty. Ei and Gi during optimization are thus dynamic according to the shares of xi, j in Xi. An official data gap, however, remains. The initial values of Xi and xi, j and the constraints of Ai, Bi, ai, j and bi, j were thus estimated from the published literature and fragmented materials. See Section A3 of Appendix A for detailed information. The tabu search heuristic algorithm was used during optimization to search for the best solution under the uncertainty. Compared to the various current optimization methods [30], this algorithm efficiently incorporates the uncertainty, has been

2.3.1. Substitutes for fertilizer nutrients Machines fueled by diesel are usually used to return straw to farmland. The negative effect of GHG emissions from diesel engines and the positive effects of the straw nutrients and SOC improvement were included in the LCA components to assess the net rates of both energy saving (E1) and GHG reduction (G1) for each tonne of straw returned to the fields. These rates were calculated as: 8 3 X > > > Nci U Ef t i  Ef t M > E1 ¼ > < i¼1 ð3Þ 3 X > > > > G ¼ Nc U Gf t  Gf t þ Gf t M SOC i i > : 1 i¼1

where Nci is the nutrient contents of nitrogen (N), phosphorus (P2O5) and potassium (K2O) in 1 t of mixed straw, indexed by i, and GftSOC is the GHG reduction achieved from SOC improvement. Diesel-energy input (EftM) to, and GHG emissions (GftM) from, straw-returning machines were calculated based on the parameters of popular machines available in Chinese markets (Sections B1–B3 in Appendix B).

Fig. 1. Straw uses and reference systems. X1–X4 were used for the analysis, X5 and X6 were excluded due to their low amounts and X7 was beyond policy control.

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G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

China is the largest and least efficient consumer of fertilizer [33,34], with N, P2O5 and K2O consumption accounting for 31.3, 29.9 and 19% of the global consumption, respectively [35]. The Chinese native coefficients for energy input (Efti) and GHG emissions (Gfti) for fertilizer production, however, are seldom reported from an LCA perspective. We systematically assessed the coefficients using basic building blocks [36], with emphasis on ammonia synthesis because of its intensive energy input and GHG emission resulting from the coal-dominant raw materials in China. The LCAs of other processes, including the mining, beneficiation and transport of phosphorus ore, production of sulfuric acid, synthesis of final products and packaging and application of fertilizers, were all reviewed and summarized. The accumulated energy inputs and GHG emissions per tonne of N, P2O5 and K2O nutrients were calculated by the weighting method based on the structure of fertilizer consumption and nutrient contents for China (Sections B4 in Appendix B).

Joules, with a conversion coefficient of 3.6 GJ MWh  1, to compare the environmental benefits of all straw uses for energy production, animal feeding and maintenance of soil fertility (Sections D1 and D2 in Appendix D). The environmental benefits of each bioenergy technology were assessed, with the net weighted rates of energy saving (E3) and GHG reduction (G3) calculated for further analysis as: 8 nX ¼ 10 > > > Energyk  W k > E3 ¼ > < k ð5Þ nX ¼ 10 > > > > G ¼ GHG  W 3 k k > :

2.3.2. Substitutes for commercial forage LCA components included straw-based forage production, commercial forage production, the nutrient effect of animal dung and SOC improvement. The corresponding net rates of energy saving (E2) and GHG reduction (G2) were calculated as: 8 m ¼ 3 X >  > > E2 ¼ > Ecf r USbrj  Erf r j þ EN U Wf r j > > < j¼1 ð4Þ m ¼ 3 X >  > > > G2 ¼ Gcf r U Sbrj  Grf r j þ GN þ Gf r SOC U Wf r j > > : j¼1

2.4. System boundaries and assumptions

where j is the index of methods for straw-based forage production, including silaging (j¼1), ammonification (j¼2) and the direct feeding of untreated straw to livestock after collection from fields (j¼ 3). The transport of straw and animal dung, straw chopping and the production of auxiliary materials were included for assessing the energy saving (Erfrj) and GHG reduction (Grfrj) (Section C1 in Appendix C). We conducted an LCA using corn as the raw material to estimate the energy input (Ecfr) and GHG emission (Gcfr) for commercial forage (i.e. reference system 2). The LCA included the planting of corn, field management, input of commercial fertilizer, feed processing in mills and the transport of both corn raw materials and corn-based forage products (Section C2 in Appendix C). The palatability and digestibility of commercial forage are much higher than those of straw-based forage, so we assumed that the commercial forage would be completely digested, whereas the undigested fiber in dung from straw-based forage was returned to farmland, leading to a GHG reduction by SOC improvement (GfrSOC). The macronutrients in animal excrement and urine could be used as substitutes for equivalent amounts of fertilizer nutrients, leading to energy saving (EN) and GHG reduction (GN) (Section C3 in Appendix C). The proportion of strawbased forage j consumed is represented by Wfrj, with Sbrj as the substitution rate for commercial forage, which was estimated based on the balance of dry materials and water content in the raw materials for straw-based forage production by different methods (Section C4 in Appendix C). 2.3.3. Substitutes for fossil energy Ten conversion routes for bioenergy production were assessed based on an available study contributed by Lu [27], with three improvements and modifications. First, the straw was regarded as a crop by-product, and straw production was excluded from consideration. Second, in contrast to a single efficiency of energy conversion by different technologies, more Chinese native publications were reviewed to quantify the uncertainties of LCA studies. Third, the units for power and heat were standardized as

k

where Energyk and GHGk are the accumulated environmental impacts of technology k per tonne of straw consumed, and Wk is the weight equivalent of the proportion of straw used by technology k (Section D3 in Appendix D).

We analyzed 21 kinds of straw, assuming moisture contents of 15% under natural drying conditions. The preferential use of each kind of straw was not considered to simplify the competition among the various straw users, and all types of straws were equally likely to be used as substitutes for fertilizer, forage or fossil energy. Straw was assumed to be carbon neutral, implying that the amount of fixed atmospheric carbon used in photosynthesis was equivalent to the emissions from completely burnt straw, with an average emission factor of 1361 kg CO2e t  1 (range: 1261–1558 kg CO2e t  1) [37–39]. The carrying capacity of a truck for transporting straw and animal dung was assumed to be 8 t, with a transport distance of 10 km. Emission factors for diesel, coal, natural gas and kerosene were obtained from IPCC 2006, and a more reasonable emission factor for electricity was used (1040 kg CO2e MWh  1) [40]. The uncertainties from 105 LCA components were included in this study, with probability distributions fitted or defined according to the IPCC guidelines [41]. Among all uncertain variables, the nutrient contents of N, P2O5 and K2O contained in straw were normally distributed, and the tractor power and work efficiency of straw-returning machines were fitted as lognormal and Weibull distributions, respectively. All other variables, including the increase in SOC content, ammonia synthesis, conversion efficiencies of the bioenergy technologies and energy input for forage processing in mills, were assumed to follow a Beta PERT distribution [42,43], which tends to be smoother than a triangular distribution and can be defined by the minimum, maximum and average as the highest probability values obtained from literature reviews.

3. Results and discussion 3.1. Comparison of LCA results for conversion technologies In contrast to the assumption of carbon neutrality for straw, from fixing atmospheric carbon dioxide to emission when completely burnt as waste, the LCA results of all 14 conversion technologies for the different straw uses led to varying environmental benefits, despite the uncertainties (Fig. 2). The use of straw for energy production is generally regarded as the best use for reducing the effect of global warming. Feeding livestock straw-based forage produced by the Fsilage method, however, unexpectedly maximized the rate of climate-change mitigation among all conversion technologies. This finding is important, especially with the dietary change that has occurred in

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

China. Of the other two means of straw-based forage production, Funtreated performed better than Fammonification, mainly because of the extra input of urea, which was produced by ammonia synthesis that had drastic environmental effects. Fammonification, despite its low effectiveness in GHG reduction, is becoming increasingly popular with Chinese farms because ammonified straw is more digestible and palatable than untreated straw. Hgasification and Pgasification, as alternates to fossil energy, had the highest rates of energy saving, with averages of 5.23 and 2.48 GJ, respectively. They were, however, inferior to Pco-firing and HLdigestion in reducing GHGs. Furthermore, Hstove(TSC), Hstove(ISC) and Hboiler were comparable to each other in reducing GHG emission with similar rates at 260 kg CO2e t  1 but differed slightly in energy saving, with rates ranging from 0.18 to 0.76 GJ t  1. Hhdigestion, the energy producer at the household level, performed better than its counterparts Hstove(TSC) and Hstove(ISC) as well as Hboiler, Ppure and P&Hdigestion at the commercial level.

793

The use of straw as a substitute to fertilizers as a nutrient supplier was not as attractive for mitigating climate change as other technologies, such as Fsilage, Hgasification, Pco-firing, HL-digestion, Funtreated and Hh-digestion (Fig. 2). Its ecological function of maintaining soil fertility over the long term by improving SOC content, however, was vitally important [44]. In addition to Nmachine, a farming system without tillage (not considered here) could play a much greater role in mitigating climate change by avoiding diesel inputs for agricultural machines. 3.2. Net-weighted rates of energy savings and GHG reduction for each straw use The net rates of energy saving and GHG reduction in China were calculated by the weighting method per tonne of straw used for livestock feeding, nutrient improvement and energy generation (Fig. 3a–b) using the LCA results (Fig. 2), straw-use pattern (Fig. 1) and estimated composite of various conversion routes (Table A.1 in Appendix A). The net weighted rates of climate-change mitigation by the three categories of straw uses were not only determined by the Table 1 Scenarios developed by combining the extents of implementation of bans on burning straw, the promotion of the three straw uses as nutrients, forage and energy and the technological levels of ammonia synthesis.

Fig. 2. Life-cycle assessments of the 14 conversion technologies for alternative straw uses. The 95% confidence ellipses were produced by Monte Carlo simulations to illustrate the uncertainties.

Scenario

Nutrient (Mt) Forage (Mt) Energy (Mt) Burning (Mt) Levela

Base scenario (SB) SP1 (50) SP2 (50) SP3 (50) SP1 (100) SP2 (100) SP3 (100) ST1 (100) ST2 (100) ST3 (100) ST4 (100)

99.62

206.67

125.86

210.67

D

149.43↑b 99.62 99.62 149.43↑ 99.62 99.62 99.62 99.62 99.62 99.62

206.67 310.00↑ 206.67 206.67 310.00↑ 206.67 206.67 206.67 206.67 206.67

125.86 125.86 188.79↑ 125.86 125.86 188.79↑ 125.86 125.86 125.86 125.86

105.03↓b 105.03↓ 105.03↓ 0.00↓ 0.00↓ 0.00↓ 0.00↓ 0.00↓ 0.00↓ 0.00↓

D D D D D D D C B A

a Technical levels of ammonia synthesis. A, B, C and D represent the present production levels in China: (49 (31–101) GJ tNH3  1, 5220 (5080– 5340) kg CO2e tNH3  1); a cleaner production level (46 (28–98) GJ tNH3  1, 4931 (3001–10,504) kg CO2e tNH3  1); an average international level (37 (28– 58) GJ tNH3  1, 2700 (1600–3800) kg CO2e tNH3  1) and an advanced international level (33 (28–38) GJ tNH3  1, 1650 (1200–2100) kg CO2e tNH3  1), respectively. b ↑ and ↓ represent increased and decreased tonnes of straw, respectively, by different policies.

Fig. 3. Net-weighted rates of (a) energy saving and (b) reduction of greenhouse gases following alternative methods of straw use, with subscripts of 1, 2 and 3 representing use purposes for nutrient, forage and energy, respectively.

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Fig. 4. Environmental benefits and contribution composite by different purposes. (a) The total mitigation effect of climate change under patterns of straw use in China and (b) contributions of different straw uses to maintain soil fertility, feed livestock and produce energy.

Fig. 5. Optimization of straw uses to maximize the mitigation of climate change under the straw-use policies. (a) and (b) represent the policy scenarios to reduce a half of burnt straw; (c) and (d) represent scenarios to ban straw burning completely; and (e) and (f) ban straw burning completely in addition to under different technological levels of ammonia synthesis referring to Table 1.

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

advantages of their corresponding conversion technologies, but were closely related to their popularity of market shares. For example, Fsilage, Hgasification and Pco-firing had the highest environmental performances (Fig. 2) but unsatisfactory market shares. The advantages of Fsilage, Hgasification and Pco-firing were thus offset when straw was used as a substitute for commercial forage and fossil energy, leading to a discounted environmental benefit, with rates of energy saving of 2.79 GJ t  1 and GHG reductions of 834.37 kg CO2e t  1 for forage uses and of 0.46 GJ t  1 and 319.66 kg CO2e t  1 for energy uses. Uncertainties for the straw uses propagated and accumulated along the LCA chains. Sensitivity analysis showed that ammonia synthesis, the mechanized return of straw to fields and the increase in SOC content contributed most to the uncertainty of climate-change mitigation for the use of straw as a substitute for commercial fertilizers. The uncertainty when straw was used as livestock forage mainly resulted from ammonia synthesis, urea synthesis and straw chopping. Moreover, Hstove(ISC) played a significant role in the generation of uncertainty when straw was used for energy generation and heat production by both a traditional stove Hstove(TSC) and an anaerobic digester (Fig. E.1 in Appendix E). 3.3. Total benefits and contributions from different straw uses The system of straw use has benefited China greatly in the mitigation of climate change. The average (uncertainty: 95% confidence limits) annual amount of reduced GHGs and conserved energy has reached 270.76 (228.08–296.70) Mt and 0.75 (0.52– 1.05) EJ, respectively (Fig. 4a). Of the three categories of straw uses, the substitution of straw for commercial forage contributed the most, followed by the use of straw as farmland nutrients by adding straw to fields. In contrast, the use of straw as an alternative to fossil energy contributed the least, with a predicted 58.24 PJ of energy savings and 40.18 Mt of GHG reductions (Fig. 4b), based on the surveyed and estimated pattern of straw uses. Our estimates differed from others. Our estimate of conserved energy of 0.75 EJ y  1 (95% confidence interval: 0.58–0.94 EJ y  1) was much less optimistic than previous estimates of 5.24–10.49 EJ by lowering heating values [45–47]. Similarly, the overestimation of the energy potential of straw, which would harm the national energy strategy and make bioenergy policies unrealistic, was demonstrated with the help of remotely sensed images [48]. Different methods lead to varying estimates, so the global energy potential for straw feedstock (5–27 EJ y  1) [49], in our opinion, may be discounted if the LCA method was used from a system perspective. 3.4. Scenario analysis Multiple administrative departments in China are responsible for the management of straw resources, with the MOA most dominant. Feed production by the promotion of Fsilage and Fammonification is governed by the Department of Animal Production of the MOA, conservation tillage by Nmachine is overseen by the Department of Farm Mechanization of the MOA and the development of rural energy is led by the Association of Rural Energy Industry in China, a non-governmental organization directed by the MOA to promote the substitution of Hstove(ISC) for Hstove(TSC). The development of modern bioenergy, such as Pgasification, Ppure and Hgasification, is governed by the National Energy Administration, which is subordinate to the National Development and Reform Commission, and straw burning is supervised by the Ministry of Environmental Protection. To increase the adaptability of the country to climate change, policy makers from multiple administrative departments have a shared aim of promoting comprehensive straw use and reducing

795

straw waste by burning [50]. With the planning of conservation tillage [51], the development of the forage industry [52] and the promotion of bioenergy [53], however, different policy makers usually overemphasized the importance of their own jurisdiction and inadvertently omitted the limitation of straw resources. The competition for resources among users still exists in practice [54,55], so inter-departmental cooperation and the development of combined policies are critical for maximizing the environmental benefits from the system of straw uses. Isolated policies of straw use made by each administrative department without considering other straw uses may adversely lead to an unintended deviation from the original objectives. We illustrated the impacts of straw-use policies from multiple administrations on the mitigation of national climate change by developing ten scenarios as example (Table 1) for uncertainty optimization to maximize national energy savings or alternative GHG reductions for the current pattern of straw use in China. These predefined scenarios indicated that maximum energy saving or GHG reduction in China could be achieved by optimization under uncertainty to allocate straw resources among the conversion technologies (Fig. 5). The simulations demonstrated that the present pattern of straw use in China is not the most beneficial, either for saving energy or reducing GHGs. The maximum energy potential in scenario SB would be 1.48 EJ, if promoting Hgasification and HL-digestion to replace Hstove(TSC), or 373.63 Mt CO2e of GHG reduction could be alternatively achieved by promoting the technologies of Pco-firing, Pgasification, Hboiler and Hh-digestion, in addition to producing more silage. Scenario SP2(50) conserved 2.03 EJ of energy or reduced GHGs by 490.28 Mt CO2e (Fig. 4a–b), but these achievements did not completely depend on the initial policies that substituted straw for livestock forage. Instead, a corresponding energy policy was also needed to promote the use of 128.16 Mt of straw compared to the present amount of 125.86 Mt (Table E.1 in Appendix E). If the energy application of straw is not included in the initial policy, an additional 2.30 Mt of unburned straw would likely be stored adjacent to ponds in rural areas, resulting in water pollution, because of a lack of effective straw consumption or corresponding conversion technologies. In the initial scenarios SP1(50) and SP3(50), we predicted an energy saving of 1.86 EJ or a GHG reduction of 864.89– 868.26 Mt CO2e. Stronger policies that ban straw burning and promote the use of straw by multiple stakeholders will generally be more environmentally beneficial. A complete ban of straw burning in scenarios SP1(100), SP2(100) and SP3(100) accompanied by effective policies for alternative straw uses can conserve 2.21– 2.26 EJ of energy or reduce GHG emissions by 557.67– 563.21 Mt CO2e annually, which are three- or two-fold higher, respectively, than in the current scenario (Fig. 5c–d and Table E.2 in Appendix E). As a necessary additive, nitrogen fertilizer (urea) originating from ammonia has an indirect but significant influence on system performance in global-warming mitigation. With the technological advances of ammonia synthesis, the environmental benefits from the system of straw use in China would gradually be reduced, with the net energy potential decreasing from 2.26 to 1.84 EJ, or with GHG reduction gradually decreasing from 568.61 to 495.59 Mt CO2e. Policy makers must correspondingly encourage the use of more straw for forage and energy purposes to mitigate climate change as much as possible (Fig. 5e–f and Table E.3 in Appendix E). Straw resources are important resources in China [56]. In particular, with high-meat diets becoming more popular, farmlands becoming increasingly degraded and cellulosic ethanol becoming available at commercial scales, the competing requirements for

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straw resources will inevitably intensify in the foreseeable future [57,58]. Policies that overemphasize straw use for a single purpose at a large scale should thus be reviewed with caution. Bioenergy is a technology in demand and has demonstrated environmental benefits in the United States and the European Union, such as in Denmark, Germany and the UK [59,60]. The commercial development of modern bioenergy technologies in China, such as Ppure and Pco-firing, however, despite recent large investments, has been difficult and remains in its early stages [61]. The slow development is generally attributed to low costeffectiveness or technological constraints, but we nevertheless assert that its root cause lies in the present system of agricultural production in China. Farmers under this system are contracted to manage the land that is divided into small parcels in household units (i.e. 0.3 ha per farmer). Farming in some rural areas is even dependent on livestock for labor, which is in stark contrast with the systems in developed economies such as that in the United States where farmers use a high degree of mechanization to cultivate as much as 63.7 ha per capita [62]. Straw is abundantly available in China, but more than 153 million head of cattle are waiting to be fed, 121 Mha of farmland require nutrient supplementation and hundreds of millions of rural farmers depend on straw to meet their daily energy requirements. A dilemma of rich biomass but a shortage of feedstock compromise the development of bioenergy in China [63]. Except on some industrialized farms with redundant straw resources, the large-scale modern use of straw feedstock as bioenergy (such as Ppure and Pco-firing) is difficult to promote in China [64–66], and its performance is lower than expected and investment risks are high [67], despite policy of Feed-In-Tariff implemented [68]. In contrast, most farm households have little mechanization, so localized energy techniques, such as Hh-digestion and Hstove(ISC), have instead proven to be more suitable in China, especially in undeveloped areas with low population densities [69,70]. Modern straw-based bioenergy, however, should not be disregarded completely. Urbanization has been vigorously promoted in China by the central government [71], and increasing numbers of people are expected to migrate from rural villages to cities and towns. Additional farmland will thus become available to fewer farmers, and the mechanization of farming and the industrialization of livestock feeding will become necessary in the near future to improve efficiency. Straw-based bioenergy on a large scale could function as an important safeguard of national energy security at that time. 3.5. Strengths and limitations This study has two strengths. The framework of the straw-use system of multiple conversion routes and competition among multiple straw users for resources was primarily established in this study to investigate the potential of mitigating climate change at a national level. LCAs were integrated with Crystal-Ballsupported optimization under uncertainties to illustrate examples of policy simulations, and all original data and models are shared with the readership for replicating our results or investigating more scenarios of straw-use policies by modifying the associated parameters (Appendix F). Limitations are unavoidable. Fourteen LCAs for conversion routes and 10 LCAs associated with fertilizer and commercial forage (i.e. native Chinese reference systems) were included to quantify the mitigation of climate change from a system perspective. Large amounts of data were thus reviewed from available sources instead of from our original step-by-step studies. The development of bioenergy, especially the technologies of Ppure, Pcofiring, Pgasification and P&Hdigestion, is still at an early stage, so the amount of data for straw use in these technologies is limited.

Estimates were conducted by reviewing the available fragmented material to quantify the market shares of each conversion route, leading to the uncertainties in this study. If more official data were available, rerunning the optimization model with updated parameters in the original model would produce a more accurate result. Additionally, we did not include the spatial dimension in our study, so LCA-based spatial optimization along with spatial distributions of farmland and livestock should be further investigated using geographic information systems.

4. Conclusions Bioenergy dependent on straw feedstock is an attractive contribution to national energy security, but the competition for straw resources as fertilizer nutrients and animal forage needs to be highlighted in China, which is challenged by diet change for more animal-derived food and the pressure to mitigate climate change. An analysis of the system of straw use in China, with uncertainties, estimated an energy saving of 0.75 EJ y  1 and GHG reductions of 270.76 Mt CO2e y  1 on average. A further environmentally beneficial saving of 1.84–2.26 EJ y  1 of energy or reductions of 464.89– 568.61 Mt CO2e y  1 of GHGs is also possible if the cooperative policy makers from multiple administrative departments optimally allocate the limited straw resources and reduce straw waste by burning. We expect that our study can support a national mitigation of, and adaptation to, climate change from a new perspective.

Acknowledgments We gratefully acknowledge the valuable comments from the two anonymous reviewers. Special thanks are given to Dr. William Blackhall for improving the English. Appreciation should be given to Samik Raychaudhuri from Oracle for his advice on the development of the optimization model under uncertainty. This work was supported by the Key Laboratory of Industrial Ecology and Environmental Engineering, MOE (KLIEEE-11-06) and Fundamental Research Funds for the Central Universities of China (DUT14LAB17).

Appendix A. Pattern of straw use and constraints for optimizing policy decisions A1. Pattern of straw use in China (Xi) See Fig. A.1 A2. Estimates of amount of straw used by various conversion technologies (xi,j) Little data for the amount of straw converted by various technologies was available, so we estimated the pattern of straw use based on both MOA reports and fragmented material from various sources. Ppure and Pgasification are two typical technologies in China that use straw as biomass stock. The total installed capacity of Ppure is approximately 618 MW, which consumes 2.32 Mt of straw annually [74]. Approximately fifty power stations are currently operating under Pgasification, with a total installed capacity of 100 MW [75,76]. The typical rate of straw consumption for Pgasification is 14,100 t MW  1 y  1 [77,78], leading to an annual requirement of 1.41 Mt of straw for Pgasification. Other bioenergy technologies, such as P&Hdigestion and Pco-firing, are often hampered

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Fig. A.1. Pattern of crop-residue use in China. Data in black were reported by MOA surveys for 2009–2010 [72]. Data in red were calculated using the ratio of residue/grain [73] for 21 kinds of crop straw with data for grain yield from 2010. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table A.1 Estimates of straw-use patterns by various technologies and the constraints for optimization. Constraint Variable Technique

s.t.1

3 P i¼1

Fig. A.2. Structure of energy consumption in rural China [86].

by difficulties in supervision, because some power plants cheat the government for subsidies by directly burning coal for energy production instead of burning biomass, leading to their current negligible market shares of P&Hdigestion and Pco-firing. Biogas from anaerobic fermentation, including Hh-digestion and HL-digestion, are two other important bioenergy technologies in China. The number of household digesters was 35,070,000 in 2009 and reached 40,000,000 by the end of 2010. The total straw requirement for Hh-digestion is 12.27 Mt, with 0.35 t y  1 used by a typical household [79,80]. HL-digestion has also been progressively developed in China, with 56,900 biogas projects constructed in 2009 and 72,700 developed by the end of 2010 [81,82]. Biogas production is currently dominated by livestock and poultry manure, and the single use of straw for raw material is still premature. The development report from the Chinese biogas industry (2011) and the viewpoints of specialists [83,84] indicate that the total level of HL-digestion that uses only straw as biomass feedstock is less than 50 in China. The straw requirement for HL-digestion is optimistically estimated to be 0.10 Mt, with a typical rate of straw consumption of 730 t y  1 per 1000 m3 of digester capacity [85]. In addition, 221.08 million households in rural areas of China still use biomass to meet their daily energy requirements [86]. Hstove(TSC) and Hstove(ISC) are two important methods for using straw, with thermal efficiencies of 15 and 35%, respectively [81]. The amounts of straw used for Hstove(TSC) and Hstove(ISC) are 81.56 Mt and 27.96 Mt, respectively, with an assumed 50% market share for Hstove(ISC). The amounts of straw-based forage that is not treated, silaged or ammonified are 115.73, 51.67 and 39.27 Mt, respectively [72,87].

s.t.2 s.t.3 s.t.4 s.t.5 s.t.6 s.t.7 s.t.8 s.t.9 s.t.10 s.t.11 s.t.12 s.t.13 s.t.14 s.t.15 s.t.16 s.t.17 s.t.18

Xi

X1 (x10) X2 x20 x21 x22 X3 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 X4 (x40)

Mmachine Fsilage Fammonization Funtreated Ppure Pco-firing Pgasification P&Hdigestion Hstove(TSC) Hstove(ISC) Hboiler Hgasification Hh-degestion HL-egestion Bopenly

Present amount (Mt)

Ai or ai (Mt)

Bi or bi (Mt)

432.15

432.15

642.81

99.62 206.63 51.671 39.27 115.73 125.86 2.32 0.00 1.41 0.00 81.56 27.96 0.00 0.24 12.27 0.10 210.67

99.62 206.63 51.67 39.27 0.00 125.86 2.32 0.00 1.41 0.00 0.00 27.96 0.00 0.24 12.27 0.10 0.00

519.74 439.92 250.75 189.16 115.73 321.41 120.00 20.00 20.00 40.00 81.56 113.18 15.00 20.00 39.69 82.62 210.67

A3. Constraints for optimizing policy decisions (Ai (ai) or Bi (bi)) The straw-survey report by MOA [72] indicated that the total amount of straw used for nutrients, forage and energy (X1 þX2 þ X3) is 432.15 Mt, with 210.67 t of straw wasted by field burning (X4). If all straw burned was instead comprehensively used, the maximum potential of the straw would be 642.81 Mt (s. t.1). Approximately 99.62 Mt of straw was returned to 23.87 Mha of farmland as nutrients, with an average of 4.27 t ha  1 y  1. Based on this standard, the maximum straw requirement for 121 Mha of farmland in China [88] is estimated at 519.74 Mt (s.t.2). The demand for straw-based forage is determined by the number of ruminants without considering imports and exports. The total number of cows and buffaloes in China was 106.26 million in 2010 [89], with an average forage consumption of 4.14 t y  1. The maximum annual straw requirement for forage is thus approximately 439.92 Mt (s.t.3). For the long term, more untreated straw (x22) will be silaged (x20) and ammonified (x21) to improve its palatability and digestibility, and the maximum requirement of treated straw forage by these two methods is estimated at approximately 250.75 and 189.16 Mt, respectively (s.t.4–6). The potential amount of straw required for energy production is limited to a maximum of 50% of the total available straw (321.41 Mt) [90–92] (s.t.4). The Mid- and Long-term Program of

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Renewable Energy Development in China [93] reported that the installed power capacity using straw as feedstock will be 300 MW by 2020, but achieving the expected goal will be difficult [94]. The straw requirement, assuming various energy conversion technologies, has not been accurately calculated, because the development of bioenergy has been slow in China. In this study, the annual maximum requirement for straw for power generation is estimated to be 200 Mt, with Ppure, Pco-firing, Pgasification and P&Hdigestion consuming 120, 20, 20 and 40 Mt, respectively (s.t.8– 11). The average energy requirement of a rural household is approximately 500 kg of standard coal equivalent per year, equivalent to the calorific value of 1.0 t of straw [95]. The maximum amount of straw used by Hstove(ISC) is estimated to be 113.18 Mt (s.t.12–13), based on the structure of energy consumption in rural China (Fig. A.2). We estimated that Hbioler and Hgasification required less than 15 and 20 Mt of raw materials, respectively (s.t.14–15). If all rural households in China used biogas produced by Hh-digestion and HLdigestion, the maximum annual straw demand would be 39.69 and 82.62 Mt, respectively, assuming the use of typical biogasproduction equipment (s.t.16–17) [79,80,85]. All constraints (Ai, Bi) and the ranges of decision variables (ai, bi) for the system of straw use are summarized in Table A.1.

summarized by Song [73]; Yj is the grain yield reported in the official statistics year book (2011) [96]; Wj is the mass proportion of crop straw j in 1 t of mixed straw; Nuj,i is the amount of nutrient

Table B.2 Macronutrients (Nci) in 1 t of mixed straw. Nutrient

N

P2O5

K2O

Amount (kg t  1)

10.04

3.15

17.75

Table B.3 Energy input (EftM) to and GHG emission (GftM) from straw-returning machines. Energy input (GJ t  1) GHG emission (kg CO2e t  1) 0.29

Table B.4 SOC improvement by returning straw to farmland (GftSOC).

Appendix B. Substituting commercial fertilizers B1. Estimate of macronutrients in 1 t of mixed straw (Nci) Nci ¼ αi 

21 X

21.50

Energy input (GJ t  1) GHG reduction (kg CO2e t  1) 512 (120–532)a

0.00

ðRj =Gj Þ  Yj  Nuj;i  Wj

ðB:1Þ

j¼1

where Nci is the amount (kg) of nutrient nitrogen (N, i¼ 1), phosphorus (P2O5, i¼2) and potassium (K2O, i¼3); Rj/Gj is the ratio of residue to grain for crop j with the uncertainty

a Amount of SOC increase by 0.597 (0.14– 0.62) tC ha  1 y  1 from returning straw to fields in China [101]. An average of 4.27 t of straw was returned to 1 ha of farmland in 2010 [72], equivalent to 512 (120– 532) kg CO2 ha  1 y  1 fixed by the cropland.

Table B.1 Nutrients in various crop straw determined using the residue to grain ratio. Crop

Maize Wheat Rice Legumes Potatoes Millets Sorghum Minor cereals Cotton Sugar cane Sugar beets Groundnut Rapeseed Sesame Benne Sunflowers Fiber crops Ramie Bhang Flax Tobacco Total

Grain yield (Mt)

163.97 115.12 195.10 19.30 29.95 1.23 1.68 4.47 6.38 115.59 7.18 14.71 13.66 0.62 0.32 1.96 0.08 0.21 0.01 0.09 3.07 694.70

Ri/Gi

1.33 1.35 1.16 1.86 0.72 1.52 1.58 1.14 4.97 0.27 0.44 1.42 2.64 2.79 2.50 2.81 2.03 6.27 2.30 1.51 1.19

Straw (Mt)

218.08 155.41 226.32 35.90 21.57 1.87 2.65 5.10 31.69 31.21 3.16 20.89 36.05 1.74 0.80 5.50 0.16 1.33 0.03 0.13 3.65

Wi (%)

27.15 19.35 28.18 4.47 2.69 0.23 0.33 0.63 3.95 3.89 0.39 2.60 4.49 0.22 0.10 0.68 0.02 0.17 0.00 0.02 0.45

Composite of 1 t of straw (kg)

271.52 193.48 281.77 44.69 26.85 2.33 3.30 6.34 39.46 38.85 3.93 26.00 44.89 2.16 0.99 6.84 0.19 1.65 0.04 0.16 4.54

Nutrient content (Nuj,i, %)

Nutrients in 1 t of straw (g)

N

P

K

N

P

K

0.96 0.67 0.91 1.94 2.96 0.21 0.38 0.97 1.26 1.03 1.08 1.69 0.64 0.47 1.08 0.70 1.35 1.02 0.73 1.23 1.68

0.17 0.09 0.13 0.16 0.20 0.09 0.20 0.04 0.16 0.13 0.23 0.15 0.14 0.07 0.09 0.05 0.03 0.03 0.07 0.09 0.12

1.24 1.09 1.89 1.28 3.84 2.05 2.19 0.15 1.02 1.14 0.98 1.01 1.86 0.43 0.52 2.03 0.42 0.18 0.67 0.71 1.26

2606.38 1296.32 2563.94 867.01 794.64 4.89 12.56 61.54 497.39 400.20 42.48 439.48 287.33 10.12 10.76 48.00 2.73 16.72 0.21 2.08 76.41 10,041.18

461.55 174.13 366.28 71.51 53.69 2.09 6.61 2.54 63.16 50.51 9.05 39.01 62.85 1.51 0.90 3.43 0.06 0.49 0.02 0.15 5.46 1374.99

3366.58 2108.93 5325.11 572.05 1030.89 47.71 72.37 9.52 402.65 442.94 38.54 262.65 835.06 9.26 5.18 139.19 0.85 2.95 0.19 1.20 57.31 14,731.12

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Fig. B.1. LCA schematic for fertilizer-related processes. Dotted lines represent heat recovery.

Table B.5 Energy input and GHG emission for ammonia synthesis at different technical levels. Technical levela

Energy input (GJ t  1 NH3  1)

GHG emission (kg CO2e t  1 NH3  1)

Present Chinese level Cleaner Chinese level Average international level Advanced European level

49(31–101) [102,103] 46(28–98) [104] 37(28–58) [105]

5220(5080–5340) [40,100] 4931(3001–10504)b 2700(1600–3800) [105]

33(28–38) [105]

1650(1200–2100) [106]

a Four technical levels are presented for further scenario analysis to investigate the influence of technical improvements in fertilizer production on the mitigation of national climate change from the perspective of straw-use systems. b Calculated by multiplying energy input by the emission factor 107.18 kg CO2e GJ  1.

Table B.6 Energy input and GHG emissions associated with the mining, beneficiation and transport of phosphorous ore. Process

Energy input (GJ t  1) GHG emission (kg CO2e t  1)

Mining and beneficiation Rail transportation Road transportation Total

0.39 [107,108] 0.12a 0.74a 1.25

95.80 [108] 8.92 55.02 159.74

a Phosphorus ore in China is transported from mines to fertilizer plants by trains and trucks, with transport distances reaching 810 km [109]. The transport distance by railway and road was assumed to account for 80 and 20%, with energy coefficients of 0.10 and 2.50 MJ t  1 km  1, respectively [110].

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G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

Table B.7 Accumulated results for producing 1 t of sulfuric acid from both sulfur and sulfur– iron ores. Raw materiala

Main process

Energy input (GJ)

GHG emission (kg CO2e)

Sulfur ore (67%)

Mining & beneficiation Transportation Acid production Heat recovery Total

1.40b

405b

1.49c 2.05 [113]  3.23 [114] 1.71

110c 592 [113]  512 [114] 595

1.84b

532b

1.95c 2.05 [113]  2.63 [114] 3.21 2.21

145c 592 [113]  417 [114] 852 680

Sulfur–iron ore (33%)

Mining & beneficiation Transportation Acid production Heat recovery Total

Weighted result

i in straw j cited in the special handbook of experimental study [97] and αi is a coefficient for converting a kilogram of nitrogen, phosphorus and potassium into a standardized kilogram of N (α1 ¼1.00), P2O5 (α2 ¼2.29) and K2O (α3 ¼1.21). The calculated results are summarized in Tables B.1 and B.2. B2. Environmental impact of straw-returning machines (EftM and GftM) Ef t M ¼

1 P  3600  A  W EE  1000000

ðB:2Þ

Gf t M ¼ E  EF diesel

a The amounts of sulfuric acid made from sulfur ore and sulfur–iron ore in China in 2009 were 27.96 and 13.85 Mt, accounting for 67% and 33%, respectively [111]. b Energy input for mining and beneficiating sulfur ore or sulfur–iron ore was 0.81 GJ t  1 [112], and 1.73 t of sulfur–iron ore (20% sulfur content) or 2.27 t of sulfur ore (15% sulfur content) needed to be mined and beneficiated to produce 1 t of sulfuric acid [113]. c The transport distance for sulfur or sulfur–iron ore is approximately 1481 km [109], and the energy coefficients for railways and roads are 0.10 and 2.50 MJ t  1 km  1, respectively [110], with transport distances accounting for 80% and 20% respectively.

ðB:3Þ

where EftM and GftM are the consumption of diesel energy and GHG emission by straw-returning machines, respectively; A (4.27 t ha  1) is the average tonnes of straw returned to 1 ha of farmland in China [72]; W is the work efficiency of a strawreturning machine, with a value of 0.50 (uncertainty: 0.15–0.97) ha h  1; P is the tractor power requirement, with a value of 51.83 (range: 11.00–85.00) kW, summarized according to the parameters [98] of agricultural machines recommended by MOA [99]; EE is the tractor engine efficiency, assumed to be 30% (range: 25–40%), and EF is the mobile diesel emission factor [100]. The average results are presented in Table B.3. B3. SOC improvement by returning straw to farmland (GftSOC) See Table B.4

Table B.8 Energy input and GHG emissions for the final synthesis of various fertilizers. Final synthesis

Energy input (GJ t  1)

GHG emission (kg CO2e t  1)

Urea (N) ABC (N) MAP (P2O5) DAP (P2O5) SSP (P2O5) TSP (P2O5) MOP (K2O) SOP (K2O)

7.03[115] 4.47 [40] 3.51 [116] 4.10 [116] 0.44 [116] 5.27 [116] 5.00 [107] 1.40 [107]

1308.07a 202.40a 653.11a 762.89a 81.87a 980.59a 340.00 [107] 100.00 [107]

a The GHG emission was calculated on the basis of assumed structure of energy input, including 60% power, 20% coal, 10% diesel and 10% natural gas [40], with a weighted GHG emission factor of 05.46 (54.86–289) kg CO2e GJ  1 [100].

Table B.9 Energy input and GHG emission for fertilizer packaging, transport and application. N

Packaginga Transportb Applicationa Total

P2O5

K2O

Energy (GJ t  1)

GHG (kg CO2e t  1)

Energy (GJ t  1)

GHG (kg CO2e t  1)

Energy (GJ t  1)

GHG (kg CO2e t  1)

2.60 1.56 1.60 5.76

751.40 115.98 118.96 986.34

2.60 3.19 1.50 7.29

751.40 237.17 111.52 1100.09

1.80 1.35 1.00 4.15

520.20 100.37 74.35 694.92

a Energy inputs for the packaging and application of fertilizer were cited from Gellings [117], with an emission factor of 1040 kg CO2e MkWh  1 (i.e. 289 kg CO2e GJ  1) for packaging [40]. b The average concentrations of N, P2O5 and K2O in fertilizers in China provided by the International Fertilizer Industry Association [118] are 51, 25 and 59%, respectively, with 1376 km of total transport distance [109] and coefficients of energy input for railways and roads of 0.10 and 2.50 MJ t  1 km  1, respectively [110].

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

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Table B.10 Summary of energy input and GHG emission associated with each kind of fertilizer. Fertilizer

Nitrogen

Urea

ABC

AS

Phosphate MAP

DAP

SSP

TSP

Others P2O5

Potash

MOP

SOP

Other K2O a

Main process

Energy input (GJ t  1)

GHG emission (kg CO2e t  1)

Nutrient consumption (t)a wij (%)

Ammonia synthesis Urea synthesis N fertilizer package, transportation & application Total Ammonia synthesis ABC synthesis N fertilizer package, transportation & application Total Ammonia synthesis Sulfuric acid production N fertilizer package, transportation & application Total

61.78 15.28 5.76 82.83 59.52 25.25 5.76 90.53 282.33 36.83 5.76 324.93

6581.74 2843.63 986.34 10,411.71 6340.68 1143.50 986.34 8470.52 30,077.14 11,333.33 986.34 42,396.82

22,302,000

71.44

8,556,000

27.41

358,000

1.15

Ammonia synthesis Phosphate ore mining, beneficiation & transportation Sulfuric acid production MAP synthesis P2O5 fertilizer package, transportation & Total Ammonia synthesis Phosphate ore mining, beneficiation & transportation Sulfuric acid production DAP synthesis P2O5 fertilizer package, transportation & Total Phosphate ore mining, beneficiation & transportation Sulfuric acid production SSP synthesis P2O5 fertilizer packaging, transportation application Total Phosphate ore mining, beneficiation & transportation Sulfuric acid production TSP synthesis P2O5 fertilizer packaging, transportation application Total Considered SSP data

61.25 4.38

6525.00 559.09

2,198,181

19.08

4,444,819

38.58

2,871,000

24.92

219,000

1.90

1,787,000

15.51

4,350,000

92.85

150,000

3.20

185,000

3.95

6.19 3.51 application 7.29 82.61 61.74 4.38

1904.00 653.11 1100.09 10,741.29 6577.20 559.09

6.41 4.10 application 7.29 83.91 4.65

1972.00 762.89 1100.09 10,971.27 594.23

&

&

Production K2O fertilizer packaging, transportation & application Total Production K2O fertilizer packaging, transportation & application Total Considering as SOP data

5.53 0.44 7.29

1700.00 81.87 1100.09

17.91 4.63

3476.19 591.04

5.08 5.27 7.29

1564.00 980.59 1100.09

22.27 17.91

4235.72 3476.19

5.00 4.15

340.00 694.92

9.15 1.40 4.15

1034.92 100.00 694.92

5.55 5.55

794.92 794.92

Fertilizer consumption and nutrient contents were used and cited from the database of the International Fertilizer Industry Association [119].

See Fig. B.1 and Tables B.5–B.11 Table B.11 Accumulated Efti and Gfti per tonne of nutrients in China by the weighting method based on the structure of fertilizer consumption. Nutrient

N

P2O5

K2O

Energy input (GJ t  1) GHG emission (kg CO2e t  1)

87.71 9662.70

55.80 7768.78

8.89 1017.76

B4. Energy input (Efti) and GHG emission (Gfti) for fertilizers in China The LCA schematic of building blocks associated with fertilizer along with energy input (GHG emission) from each block is summarized as follows.

Appendix C. Substituting commercial forage C1. Impacts of straw-based forage production (Erfrj and Grfrj) See Fig. C.1 and Tables C.1–C.4 Energy input (Echopping) and GHG emission (Gchopping) for chopping straw were calculated as: Echopping ¼

1 P  3600  W EE  1000000

Gchopping ¼ E  EF

ðC:1Þ ðC:2Þ

802

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

Fig. C.1. Schematic of life-cycle assessment for processes related to straw-based forage.

Table C.1 Energy input and GHG emissions associated with the transport of straw and animal dunga. Material

Energy input (GJ t  1)

GHG emission (kg CO2e t  1)

Crop residue Manure

0.037 0.078

2.73 5.75

a An average of 2.1 t of manure can be produced for providing nutrients from 1 t of straw with 15% moisture as raw material for feeding livestock, based on feeding experiments in China [120]. Energy inputs and GHG emissions for manure transportation were estimated using an emission factor for diesel automobiles.

Table C.4 Accumulated Erfrj and Grfrj associated with straw-based forage production. Method

Process

Energy input (GJ t  1)

GHG emission (kg CO2e t  1)

Amount consumed (Mt)

Silage

Straw transportation Chopping Urea production Salt production Limestone production Formaldehyde production Manure transportation Total

0.1110

8.20

51.67

0.0738 0.5715 0.2328 0.0080

21.32 71.84 17.91 0.46

0.2985

9.39

0.2340

17.29

1.5295

146.41

0.0444

3.28

0.0295 2.0574 0.0936

8.53 258.63 6.92

2.2249

277.35

0.0370

2.74

0.0780

5.76

0.1150

8.50

Table C.2 Energy input (Echopping) and GHG emission (Gchopping) for chopping 1 t of straw. Echopping (GJ t  1) Gchopping (kg CO2e t  1) 0.025

7.11

Table C.3 Energy input and GHG emission associated with the production of auxiliary additivesa.

Formaldehyde Salt Limestone Urea a

Energy input (GJ t  1)

GHG emission (kg CO2e t  1)

19.9 1.94 0.53 38.10

625.69 149.26 30.95 4789.39

45 and 5 kg of urea usually need to be added as a nitrogen source for straw ammonification and silaging, respectively. Other additives, including 5 kg of formaldehyde, 40 kg of salt and 5 kg of limestone, are also added during silaging to improve the palatability and antisepsis of straw-based forage. GHG factors for the additives were cited from the Gabi database [121].

Ammonification

Straw transportation Chopping Urea production Manure transportation Total

Untreated straw Straw transportation Manure transportation Total

Fig. C.2. LCA schematic for corn-based forage production (reference system 2).

39.27

115.73

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

Table C.5 Energy input and GHG emission associated with corn planting and field management.

803

Table C.10 Accumulated energy input (Ecfr) and GHG emission (Gcfr) associated with commercial forage production.

Energy input (GJ t  1) GHG emission (kg CO2e t  1)

Process

Ecfr (GJ t  1)

Gcfr (kg CO2e t  1)

0.73 [122,123]

Planting & field management Fertilizer input Transportation Forage processing Total

0.73 3.08 0.75 0.09 4.65

78.93 354.41 55.41 25.87 514.61

78.93 [122,123]

Table C.6 Energy input and GHG emission for fertilizer input to produce 1 t of corn.

1

Nutrient input (kg t ) Energy input (GJ t  1)a GHG emission (kg CO2e t  1)a

N

P2O5

K2O

Total

28.47 [124] 2.50 275.10

9.08 [124] 0.51 70.54

8.62 [124] 0.08 8.77

– 3.08 354.41

Table C.11 Energy saving (EN) and GHG reduction (GN) from dung nutrients after feeding livestock with 1 t of straw-based forage. N

P2O5

K2O

Total

a

Calculated from the LCA results for N, P2O5 and K2O production, packaging, transport and application presented in Table B.11 in Appendix B.

Table C.7 Energy input and GHG emission associated with corn and forage transporta.

)

a

8.73 0.77 84.36

a

2.83 0.16 21.99

a

13.18 0.12 13.41

– 1.04b 119.76b

a Calculated based on a standard that 38.00 kg N, 12.33 kg P2O5 and 57.36 kg K2O are excreted in 8703 kg of dung by a single head of cattle fed with straw-based forage, based on the survey and experiment conducted by the National Agro-Tech Extension and Service Center of China [120,127]. b Calculated from the LCA-derived energy input and GHG factors presented in Table B.11 in Appendix B.

Energy input (GJ t  1) GHG emission (kg CO2e t  1) 0.75

Dung nutrients (kg t EN (GJ t  1) GN (kg CO2e t  1)

1

55.41

a

Total transport distance by diesel truck for corn and forage products was assumed to be 300 km, with an energy input coefficient of 2.50 MJ t  1 km  1 [110].

Table C.12 Environmental impacts of SOC improvement by undigested fiber in dung (GfrSOC). Energy saving (GJ t  1) GfrSOC (kg CO2e t  1)

Table C.8 Power consumption and GHG emission associated with feed millsa.

0.00

Efficiency (t h  1)

Grinding Mixing Pelleting Cooling Total Average Average (kWh t  1) (GJ t  1)

5 10 20

37 90 220

7.5 15 30

55 110 220

11 22 44

110.5 22.1 237 23.7 514 25.7

a The digestibility rate of crude fiber in straw-based forage is 60%, with 37% organic carbon excreted in the form of animal dung [127]. By returning the undigested organic carbon from 1 t of straw forage to fields, 148 kg of SOC (equivalent to 542 kg CO2e) can be fixed in the soil, which is consistent with the results from [101] and the Intergovernmental Panel on Climate Change.

0.0795 0.0852 0.0924

a Summarized from Li [125]; the average power input for producing 1 t of animal forage was 23.8 kWh t  1, which is comparable to the output of 28 kWh t  1 reported by Liu [126] from the Chinese Academy of Agricultural Sciences. We assumed an average electricity input (uncertainty) of 24.87 (23.8– 28) kWh t  1 by jointly considering the two references.

Table C.13 Composite of straw forages (Wfrj) and substitution rate (Sbrj) of all three kinds of straw-based forages for corn-based forage.

Table C.9 Energy input and GHG emission associated with the processing of 1 t of corn forage. Energy input (GJ t  1) GHG emission (kg CO2e t  1) 0.09

25.87

where W is the work efficiency of an electrical crop cutter (4.27 (0.22–23.10) t h  1); P is the machinery power requirement (8.75 (1.30–36.80) kW), summarized from 63 types of electrical cutters popular in the Chinese market [99]; EE is the output efficiency of a tractor engine (i.e. 30% with an uncertainty of 25–40%) and EF is the electricity emission factor in China (1040 kg CO2e MWh  1) [40]. C2. Impacts of corn-based forage production (Ecfr and Gcfr) See Fig. C.2 and Tables C.5–C.10

542.00a

Wfrj (%) Sbrj (t t  1)

Silage

Ammonification

Untreated

25 [72,87] 1.40a

19 [72,87] 0.34a

56 [72,87] 0.20a

a Based on the mass balance of dry materials; 1 t of naturally dried straw (15% moisture) is equivalent to 2.80 t of silage (70% water content) or 1.13 t of ammonified forage (25% moisture), and 1 t of silaged, ammonified and untreated straw are equivalent to 0.50, 0.30 and 0.20 t of commercial forage, respectively.

C3. Nutrient effect (EN and GN) and SOC improvement (GfrSOC) from dung See Tables C.11 and C.12 C4. Composite of straw forages (Wfrj) and substitution rate for corn forage (Sbrj) See Table C.13

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Appendix D. Substituting fossil energy D1. Schematic of life-cycle assessment for bioenergy production

D2. Efficiency uncertainties of bioenergy conversion technologies See Tables D.1 and D.2

See Fig. D.1 D3. Effects of bioenergy technologies on global-warming mitigation See Table D.3

Fig. D.1. Schematic of life-cycle assessment for bioenergy produced from straw-based biomass feedstock.

Table D.1 Uncertainty in conversion efficiency associated with various bioenergy techniques from an LCA perspective. Main technology

Installed capacity (MW)

Conversion coefficient

Average

Range

Ppure (MWh t  1)

6.0

0.67, 0.70, 0.78, 0.77 [128,129] 0.71, 0.91, 1.01, 1.00 [130–132] 0.63, 0.70, 0.69 [129] 0.71, 0.79, 0.78 [133] 0.89 [133] 0.74 [134] 0.91[135] 0.91[136] 0.20 [26], 0.15 [137] 0.41[26], 0.50 [137] 5.50 [138], 5.00 [139]

0.78

0.67–1.00

0.78

0.63–0.91

0.18 0.46 5.20

0.15–0.20 0.41–0.50 5.00–5.50

25.0 Pgasification (MWh t  1)

1.0 3.0

Hstove(TSC) (GJ t  1) Hstove(ISC) (GJ t  1) Hgasification (GJ t  1)

4.0 2.0 5.5 6.0 – – –

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

805

Table D.2 Summary of energy input and GHG emission for various bioenergy techniques by using straw-based feedstock. Energy input (MWh t  1 or MJ t  1)a

Energy

Technology

Process

Power generation

Ppure

Carbon fixation by photosynthesis Transportation Heat drying and pulverization Electricity generationa Total Carbon fixation by photosynthesis Transportation Heat drying and pulverization Electricity generation Total energy input Carbon fixation by photosynthesis Transportation Heat drying and pulverization Electricity generation Total energy input Carbon fixation by photosynthesis Transportation Large-scale anaerobic digestion Transporting & spreading biogas waste Electricity Generation Total energy input

Pcofiring

Pgasification

P&Hdigestion

Heat generation

Hstove(TSC)

Hstove(ISC)

Hboiler

Hgasification

Hh-digestion

HL-digestion

Carbon fixation by photosynthesis Heat generation Total energy input Carbon fixation by photosynthesis Heat generation Total energy input Carbon fixation by photosynthesis Transportation Heat drying and pulverization Heat generation Total energy input Carbon fixation by photosynthesis Transportation Heat generation Total energy input Carbon fixation by photosynthesis Transportation Anaerobic digestion Heat generation Total energy input Carbon fixation by photosynthesis Transportation Anaerobic digestion Transport & spreading biogas waste Heat generation Total energy input

GHG emission (kg CO2e t  1)b

0 261.00 1768.00  0.78 2029.00 0 174.00 884.00  0.78 1058.00 0 16.00 265.20  0.67 281.20 0 29 144 41  0.33 214.00

 1360.62 19.87 118.10 1400.00 177.35  1360.62 13.25 118.10 906.40  322.87  1360.62 1.56 25.21 1306.06  27.79  1360.62 3.02 39.98 3.3 993.93 1320.39

0  180.00  180.00 0  460.00  460.00 0 90 514  1400  796.00 0 27  5304  5277.00 0 0.00 22.00  2100.00  2078.00 0 29.00 144.00 41.00  2300.00  2086.00

 1360.62 1191.55  169.07  1360.62 1164.28  196.34  1360.62 5.45 118.1 1109.95  127.12  1360.62 2.02 1130.75  227.85  1360.62 0.00 33.25 719.40  607.97  1360.62 3.02 39.98 3.30 923.21  391.11

a Negative values (in bold italic fonts) represent heat or power outputs, and the “Total energy input” (in bold fonts) represents the accumulated energy consumption of all processes to generate energy or heat by a specific conversion technology. b The negative value  1360.62 kg CO2e t  1 is the amount of carbon fixed in 1 t of straw by photosynthesis; other negative values (in bold fonts) represent the net GHG reductions when 1 t of straw is used for energy.

Table D.3 Net rates of energy saving (Energyk), GHG reduction (GHGk) and weight (Wk) associated with various bioenergy technologies. Conversion technology

Energyk (GJ t  1)a

GHG emission (kg CO2e t  1)

Reference system GHG (kg CO2e)

GHGk (kg CO2e t  1)

Wk (%)

Ppure (x20) Pcofiring (x21) Pgasification (x22) P&Hdigestion (x23) Hstove(TSC) (x24) Hstove(ISC) (x25) Hboiler (x26) Hgasification (x27) Hh-degestion (x28) HL-egestion (x29)

0.78 1.75 2.52 0.97 0.18 0.46 0.80 5.28 2.08 2.09

177.35  322.87  27.79  320.39  169.07  196.34  127.12  227.85  607.97  391.11

583.91 583.91 583.91 247.04 18.21 46.53 80.52 533.77 210.19 211.00

406.56 906.78 611.70 567.43 187.28 242.87 207.64 761.62 818.16 602.11

1.84 0.00 1.12 0.00 64.80 22.21 0.00 0.19 9.75 0.08

a

The power unit MWh t  1 is standardized to an energy unit as GJ t  1, with a conversion coefficient of 3.6 GJ MWh  1.

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G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

Appendix E. Sensitivity and optimal uses of straw See Fig. E.1 and Tables E.1–E.3

Fig. E.1. Sensitivity analysis for net rates of energy saving and GHG reduction achieved from different straw uses.

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

Table E.1 Optimization results when burned straw is decreased by 50%.

Table E.2 Optimization results when straw burning is banned completely.

807

808

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

Table E.3 Indirect impact of ammonia synthesis on optimal combined policies.

Appendix F. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.rser.2015.10.136.

References [1] Lal R. Soil carbon sequestration impacts on global climate change and food security. Science 2004;304:1623–7. [2] Smith P, Martino D, Cai Z, Gwary D, Janzen H, Kumar P, McCarl B, Ogle S, O'Mara F, Rice C, Scholes B, Sirotenko O, Howden M, McAllister T, Pan G, Romanenkov V, Schneider U, Towprayoon S, Wattenbach M, Smith J. Greenhouse gas mitigation in agriculture. Philos Trans R Soc Lond B: Biol Sci 2008;36:789–813. [3] Wang JZ, Wang XJ, Xu MG, Feng G, Zhang WJ, Lu CA. Crop yield and soil organic matter after long-term straw return to soil in China. Nutr Cycl Agroecosyst 2015;102:371–81. [4] Zhang L, Wu WL, Wei YP, Hu KL. Effects of straw return and regional factors on spatio-temporal variability of soil organic matter in a high-yielding area of northern China. Soil Tillage Res 2015;145:78–86. [5] Karlen DL, Lal R, Follett RF, Kimble JM, Hatfield JL, Miranowski JM. Crop residues: the rest of the story. Environ Sci Technol 2009;43:8011–5. [6] Herrero M, Thornton PK, Notenbaert AM, Wood S, Msangi S, Freeman HA, Bossio D, Dixon J, Peters M, Steeg JV, Lynam J, Rao PP, MacMillan S, Gerard B, McDermott J, Seré C, Rosegrant M. Smart investments in sustainable food production: revisiting mixed crop-livestock systems. Science 2010;327:822–5. [7] Erakhrumen AA. Growing pertinence of bioenergy in formal/informal global energy schemes: necessity for optimising awareness strategies and increased investments in renewable energy technologies. Renew Sustain Energy Rev 2014;31:305–11. [8] Lal R. World crop residues production and implications of its use as a biofuel. Environ Int 2005;31:575–84. [9] Goldemberg J. Ethanol for a sustainable energy future. Science 2007;315:808–10. [10] Tilman D, Socolow R, Foley JA, Hill J, Larson E, Lynd L, Pacala S, Reilly J, Searchinger T, Somerville C, Williams R. Beneficial biofuels – the food, energy, and environment trilemma. Science 2009;325:270–1. [11] Guinee JB, Heijungs R, Huppes G, Zamagni A, Masoni P, Buonamici R, Ekvall T, Rydberg T. Life cycle assessment: past, present, and future. Environ Sci Technol 2011;45:90–6.

[12] Hammond J, Shackley S, Sohi S, Brownsort P. Prospective life cycle carbon abatement for pyrolysis biochar systems in the UK. Energy Policy 2011;39:2646–55. [13] Liu HT, Polenske KR, Xi YM, Guo J. Comprehensive evaluation of effects of straw-based electricity generation: a Chinese case. Energy Policy 2010;38:6153–60. [14] Wang CB, Zhang LX, Yang SY, Pang MY. A hybrid life-cycle assessment of nonrenewable energy and greenhouse-gas emissions of a village-level biomass gasification project in China. Energies 2012;5:2708–23. [15] Sastre CM, González-Arechavala Y, Santos AM. Global warming and energy yield evaluation of Spanish wheat straw electricity generation – a LCA that takes into account parameter uncertainty and variability. Appl Energy 2015;15:900–11. [16] Steubing B, Zah R, Ludwig C. Heat, electricity, or transportation? the optimal use of residual and waste biomass in Europe from an environmental perspective Environ Sci Technol 2012;46:164–71. [17] Steubing B, Ballmer I, Gerber L, Maréchal F, Zah R, Ludwig C. An environmental optimization model for bioenergy plant sizes and locations for the case of wood-derived SNG in Switzerland. Sweden: World Renewable Energy Congress; 2011. [18] Biofuels. At what cost? a review of costs and benefits of EU biofuel policies Manitoba, Canada: The International Institute for Sustainable Development (IISD) and GSI; 2013 [accessed 09.14]http://www.iisd.org/gsi/sites/default/ files/biofuels_subsidies_eu_review.pdf. [19] Cherubini F, Bird ND, Cowie A, Jungmeier G, Schlamadinger B, WoessGallasch S. Energy and greenhouse gas-based LCA of biofuel and bioenergy systems: key issues, ranges and recommendations. Resour Conserv Recycl 2009;53:434–47. [20] Cherubini F, Stromman AH. Life cycle assessment of bioenergy systems: state of the art and future challenges. Bioresour Technol 2011;102:437–51. [21] Luo SH, Ma HW, Luo SHL. Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method. Sci Total Environ 2005;3:23–33. [22] Raychaudhuri S. Introduction to Monte Carlo Simulation. In: Proceedings of winter simulation conference 2008. Miami, FL; 2008. p 91–100. [23] Brandão M, Heath G, Cooper J. What can meta-analyses tell us about the reliability of life cycle assessment for decision support? J Ind Ecol 2012;16: S3–7. [24] Clavreul J, Guyonnet D, Tonini D, Christensen TH. Stochastic and epistemic uncertainty propagation in LCA. Int J Life Cycle Assess 2013;18:1393–403. [25] David Styles D, Gibbons J, Williams AP, Stichnothe HH, Robert-Chadwick D, Robert-Healey J. Cattle feed or bioenergy? Consequential life cycle assessment of biogas feedstock options on dairy farms Glob Change Biol 2014;7:1– 16.

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

[26] Lu W, Zhang TZ. Life-Cycle implications of using crop residues for various energy demands in China. Environ Sci Technol 2010;44:4026–32. [27] Central Government, PRC. China's policies and actions for addressing climate change, 〈http://www.gov.cn/english/official/2011-11/22/content_2000272_8. htm〉; 2009 [accessed 08.15]. [28] Yamaji K, Li J, Uno I, Kanaya Y, Irie H, Takigawa M, Komazaki Y, Pochanart P, Liu Y, Tanimoto H. Impact of open crop residual burning on air quality over central eastern China during the Mount Tai Experiment 2006 (MTX2006). Atmos Chem Phys 2010;10:7353–68. [29] Qu CS, Li B, Wu HS, Giesy JP. Controlling air pollution from straw burning in China calls for efficient recycling. Environ Sci Technol 2012;46:7934–6. [30] Meyer AD, Cattrysse D, Rasinmäki J, Orshoven JV. Methods to optimise the design and management of biomass-for-bioenergy supply chains: a review. Renew Sustain Energy Rev 2014;31:657–70. [31] Glover FK, James P, Manuel L. The OptQuest approach to crystal ball simulation optimization. Boulder, Colorado: University of Colorado; 2015 [accessed 02.15]. [32] Crystal Ball at Oracle Corporation Website, 〈www.oracle.com/us/products/ applications/crystalball/index.html〉 [accessed 03.15]. [33] Guo JH, Liu XJ, Zhang Y, Shen JL, Han WX, Zhang WF, Christie P, Goulding KWT, Vitousek PM, Zhang FS. Significant acidification in major Chinese croplands. Science 2010;327:1008–10. [34] Ju XT, Xing GX, Chen XP, Zhang SL, Zhang LJ, Liu XJ, Cui ZL, Yin B, Christie P, Zhu ZL, Zhang FS. Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proc Natl Acad Sci 2009;106:3041– 6. [35] Heffer P. Assessment of fertilizer use by crop at the global level. Paris, France: International Fertilizer Industry Association; 2010. [36] Kongshaug G. Energy consumption and greenhouse gas emissions in fertilizer production. In: Proceedings of IFA technical conference. Marrakech, Morocco 〈www.fertilizer.org〉; 1998. [37] Li XH, Wang SX, Duan L, Hao JM, Li C, Chen YS, Yang L. Particulate and trace gas emissions from open burning of wheat straw and corn stover in China. Environ Sci Technol 2007;41:6052–8. [38] Cao GL, Zhang XY, Gong SL, Zhang FC. Investigation on emission factors of articulate matter and gaseous pollutants from crop residue burning. J Environ Sci 2008;20:50–5. [39] Zhang HF, Ye XN, Cheng TT, Chen JM, Yang X, Wang L, Zhang RY. A laboratory study of agricultural crop residue combustion in China: emission factor and emission inventory. Atmos Environ 2008;42:8432–41. [40] Kahrl R, Li YJ, Su YF, Tennigkeit T, Wilkes A, Xu JC. Greenhouse gas emissions from nitrogen fertilizer use in China. Environ Sci Policy 2010;13:688–94. [41] Frey C, et al. Chapter 3 uncertainties. In: Draft 2006 IPCC guidelines for national greenhouse gas inventories. IPCC and WMO. Switzerland, 〈www. ipcc.ch/meetings/session25/doc4a4b/vol1.pdf〉; 2006. p. 6–65 [accessed 01.15]. [42] Davis R. Teaching project simulation in Excel using PERT-Beta distributions. Inf Trans Educ 2008;8:139–48. [43] Charnes J. Financial modeling with crystal ball and excel. Hoboken: John Wiley & Sons Inc.; 2012. p. 245. [44] Stavi I, Lal R. Agriculture and greenhouse gases, a common tragedy. A review. Agron Sustain Dev 2013;33:275–89. [45] Jiang D, Zhuang DF, Fu JY, Huang YH, Wen KG. Bioenergy potential from crop residues in China: availability and distribution. Renew Sustain Energy Rev 2012;16:1377–82. [46] Shen L, Liu LT, Yao ZJ, Liu G, Lucas M. Development potentials and policy options of biomass in China. J Environ Manag 2010;46:539–54. [47] Li JF, Hu RQ, Song YQ, Shi JL, Bhattacharya SC, Salam PA. Assessment of sustainable energy potential of non-plantation biomass resources in China. Biomass Bioenergy 2005;29:167–77. [48] Smith WK, Cleveland CC, Reed SC, Norman LM, Running SW. Bioenergy potential of the United States constrained by satellite observations of existing productivity. Environ Sci Technol 2012;46:3536–44. [49] Hoogwijk M, Faaij A, Van den BR, Berndes G, Gielen D, Turkenburg W. Exploration of the ranges of the global potential of biomass for energy. Biomass Bioenergy 2003;25:119–33. [50] National implementation plan for straw comprehensive use, 〈http://www. sdpc.gov.cn/zcfb/zcfbghwb/200902/W020140220607191602912.pdf〉 [accessed 02.15] [in Chinese]. [51] National construction engineering plan for conservation tilling, 〈http://www. moa.gov.cn/zwllm/zcfg/nybgz/200908/t20090828_1340481.htm〉 [accessed 02.15] [in Chinese]. [52] National development plan for feed industry, 〈http://www.moa.gov.cn/gov public/XMYS/201110/t20111012_2355459.htm〉 [accessed 03.15] [in Chinese]. [53] National bioenergy development plan, 〈http://zfxxgk.nea.gov.cn/auto87/ 201212/t20121228_1568.htm〉 [accessed 03.15] [in Chinese]. [54] Yan LZ, Cheng SK, Min QW. Utilizations of crop straw and its driving forces in typical rural areas. Chin J Eco-Agric 2006;14:196–8 [in Chinese]. [55] Gao CY, Wang YJ, Li BY, Bi YY. Research on the shortage and surplus of straw resources in China. J Agric Mech Res 2010;4:209–12 [in Chinese]. [56] Han JY, Mol APJ, Lub YL, Zhang L. Small-scale bioenergy projects in rural China: lessons to be learnt. Energy Policy 2008;36:2154–62. [57] Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C. Food security: the challenge of feeding 9 billion people. Science 2010;327:812–8.

809

[58] Dixon J, et al. Feed, food and fuel: competition and potential impacts on small scale crop-livestock-energy farming systems. CGIAR system wide livestock programme, project report. SLP, Addis Ababa, Ethiopia; 2010. p. 114. [59] Scarlat N, Dallemand JF, Monforti-Ferrario F, Banja M, Motola V. Renewable energy policy framework and bioenergy contribution in the European Union – an overview from National Renewable Energy Action Plans and Progress Reports. Renew Sustain Energy Rev 2015;51:969–85. [60] Pazheri FR, Othman MF, Malik NH. A review on global renewable electricity scenario. Renew Sustain Energy Rev 2014;31:835–45. [61] Hu YA, Chen HF. Development and bottlenecks of renewable electricity generation in China: a critical review. Environ Sci Technol 2013;47:3044–56. [62] Li YL, Tang QX. Research on current situation and countermeasures of China's modern agriculture. Res Agric Mod 2009;30:641–5 [in Chinese]. [63] Zhang PD, Yang YL, Tian YS, Yang XT, Zhang YK, Zheng YH, Wang LS. Bioenergy industries development in China: dilemma and solution. Renew Sustain Energy Rev 2009;13:2571–9. [64] Wang ZJ, Zhou CX. Key problems of power generation by direct burning of crop residues. Sichuan Electr Power Technol 2006;29:82–5 [in Chinese]. [65] Zhai JH, Zhou HQ. Situation, problems and policies of energy use of crop straw in Heilongjiang Provinces. J Northeast Agric Univ 2013;11:20–4 [in Chinese]. [66] Huang JT, Wang XL, Xu T. Related questions research on biomass generation using agricultural and forest residue in China. J Shenyang Inst Eng 2008;4:7– 14 [in Chinese]. [67] Zhao XG, Feng TT, Ma Y, Yang YS, Pan XF. Analysis on investment strategies in China: the case of biomass direct combustion power generation sector. Renew Sustain Energy Rev 2015;42:760–72. [68] Gosens J. Biopower from direct firing of crop and forestry residues in China: a review of developments and investment outlook. Biomass Bioenergy 2015;73:110–23. [69] Song ZL, Zhang C, Yang GH, Feng YZ, Ren GX, Han XH. Comparison of biogas development from household and medium and large-scale biogas plants in rural China. Renew Sustain Energy Rev 2014;33:204–13. [70] Li CJ, Liao YC, Wen XX, Wang YF, Yang F. The development and countermeasures of household biogas in northwest grain for green project areas of China. Renew Sustain Energy Rev 2015;44:835–46. [71] Bai XM, Shi PJ, Liu YS. Realizing China's urban dream. Nature 2014;509:158– 60. [72] Straw resources survey and evaluation report of China. Department of Science, Technology & Education (MOA) Beijing, 〈www.fjagri.gov.cn/upload/ File/20110217103019.pdf〉; 2010 [accessed 03.13] [in Chinese]. [73] Song GB, Che L, Yang YG, Lyakurwa F, Zhang SS. Estimation of crop residue in China based on a Monte Carlo analysis. Chin J Popul Resour Environ 2014;12:88–94. [74] He X, Liu X, Song LL. The important role of biomass energy industry development in rural environmental protection in China/HE. China Biogas 2011;29:31–4 [in Chinese]. [75] Wang ZC. Industry development strategy for power generation of crop residue gasification. China High-tech Enterp 2011;10:1–5 [in Chinese]. [76] Fully localized biomass gasification power generation system (Brief Report). Electr Power Constr 2009;30:78 [in Chinese]. [77] Risk analysis report of biomass power generation in China. China Economic Herald of National Development and Reform Commission and Investment Consulting Co. Ltd. of Beijing Century Future Beijing, 〈wenku.baidu.com/ view/a92f195d312b3169a451a4fe.html〉; 2010 [accessed 05.13] [in Chinese]. [78] Life cycle analysis of power generation based on agricultural and forestry biomass – promotion of Chinese renewable energy program. Chinese Government, World Bank and Global Environment Facility. Guangzhou, 〈www. cresp.org.cn/uploadfiles/73/1153/a1-b1-cs-2007-005-d1-cn.pdf〉; 2009 [accessed 01.14] [in Chinese]. [79] Zhang SR, Yue JZ, Liu SY, Lu ZG, Han YJ, Lv HQ. Test on anaerobic fermentation with differently treated wheat straw. China Biogas 2010;28:21–33 [in Chinese]. [80] Huang RY, He WN, Tang HJ, Yi Y, Luo XJ, Yuan YX, Liao YZ. Straws biogas fermentation with bacterium additive pretreatment. China Biogas 2008;26 (4):24–6 [in Chinese]. [81] Chen XF, Zhang WH, Liu GQ. The development and application of biomassfed household stove in China, 〈http://www.ehome.gov.cn/Article/Upload Files/201003/2010031113005455.pdf〉 [accessed 01.14] [in Chinese]. [82] Hao GR. China biogas project development status and prospects. China Anim Husb Bull 2011;10:28–31 [in Chinese]. [83] Report on the development status of Chinese biogas industry. Environmental protection research institute of light industry. Beijing, China, 〈www.cnred. org.cn/manageInfo.do?action ¼ showColumn&type ¼article&typeId ¼ 100〉; 2011 [accessed 02.14] [in Chinese]. [84] Web of rural development website, 〈www.nczfj.com/yzzz/20103931.html〉 [accessed 01.14] [in Chinese]. [85] Li YF. Operation model and management experiences of biogas digester engineer in Qing county of Hebei province (Special Reports). Agric Eng Technol 2010;8:9–13 [in Chinese]. [86] Report of main statistical data of the second agriculture census. Chinese National Statistics Bureau. Beijing, 〈www.stats.gov.cn/tjsj/tjgb/nypcgb/ qgnypcgb/200802/t20080226_30464.html〉; 2008 [accessed 06.14] [in Chinese].

810

G. Song et al. / Renewable and Sustainable Energy Reviews 55 (2016) 789–810

[87] Development of animal-forage and food security in China. China feed industry Website, 〈http://www.3dfeed.cn/xxzx/keji/ztls/wttt/2009312/ 21829.html〉 [accessed 08.13] [in Chinese]. [88] The situation of farmland resources in China. Supervisor of State Land, 〈http://www.gjtddc.gov.cn/zzjg/ldzc/zdc/lxs/fcjj/200907/t20090706_ 505592.htm〉 [accessed 01.14] [in Chinese]. [89] China rural statistical yearbook. Department of Rural & Social Economic Survey of National Bureau of Statistics. Beijing; 2011 [in Chinese]. [90] Zhang PD, Yang YL, Li GQ, Li XR. Energy potentiality of crop straw resources in China. Renew Energy Resour 2007;25:80–3 [in Chinese]. [91] Chen L, Zhao LX, Dong BC, Wan XC, Gao XX. The status and trends of the development of biogas plants for crop straws in China. Renew Energy Resour 2010;28:145–8 [in Chinese]. [92] Cui M, Zhao LX, Tian YS, Meng HB, Sun LY, Zhang YL, Wang F, Li BF. Analysis and evaluation on energy utilization of main crop straw resources in China. Trans CSAE 2008;24:291–6 [in Chinese]. [93] Planning for mid- and long-term program of renewable energy development in China. National Energy Administration, 〈http://www.nea.gov.cn/ 131215784_11n.pdf〉 [accessed 11.14] [in Chinese]. [94] Hu YA, Cheng HF. Development and bottlenecks of renewable electricity generation in china: a critical review. Environ Sci Technol 2013;47:3044–56. [95] Tian YS. Current situation of China's rural energy development and prospects for the 12th Five Year Plan. Energy China 2011;33:13–6 [in Chinese]. [96] National Bureau of Statistics of China. China statistical yearbook (2011). Beijing: China Statistics Press; 2011 [in Chinese]. [97] National Agro-Tech extension and service center, China. China's organic fertilizer nutrients experiment notes. Beijing: China Agricultural Press; 1999 [in Chinese]. [98] Product of agricultural machinery website, 〈http://www.nongjitong.com/ product〉 [accessed 12.12] [in Chinese]. [99] National recommended list of mechanical equipment for agricultural production. Proposed by Department of Agriculture Mechanization and Administration (MOA). Beijing, China, 〈http://www.qtnw.gov.cn/ztzj/njjbt/ 201403/P020140305424444845758.pdf〉; 2010 [accessed 11.12] [in Chinese]. [100] Emission Factor from IPCC Database (EFDB) Website, 〈http://www.ipccnggip.iges.or.jp/EFDB/main.php〉 [accessed 11.13]. [101] Jin L, Li Y, Gao QZ, Liu YT, Wan YF, Qin XB, Shi F. Estimate of carbon sequestration under crop land management in China. Sci Agric Sin 2008;41:734–43 [in Chinese]. [102] Cao L, Zhang WF, Gao L, Ma WQ, Zhang FS. Energy consumption in ammonia production in China and energy-saving potential. J Chem Fertil Ind 2008;35:20–4 [in Chinese]. [103] Yu ZF. Review of energy-saving technology in synthetic ammonia industry. Nitrogen Fertil Technol 2010;31:1–6 [in Chinese]. [104] Cleaner Production Standard – Nitrogenous Fertilizer Industry (HJ/T1882006). 2013 Ministry of Environmental Protection. Beijing, China, 〈www.zhb. gov.cn/image20010518/7369.pdf〉 [accessed 12.13] [in Chinese]. [105] Energy efficiency and CO2 emissions in ammonia production, feeding the earth. International Fertilizer Industry Association. Paris, 〈www.fertilizer.org/ ifa/content/download/23001/329425/version/1/file/2009_climate_change_ brief.pdf〉 [accessed 12.12, now available in 〈http://www.fertilizer.org/〉 for registered members]. [106] A review of greenhouse gas emission factors for fertilizer production. For IEA Bioenergy Task38. 2004 Cooperative Research Centre for Greenhouse Accounting, Research and Development Division, State Forests of New South Wales. New South Wales 〈http://ecite.utas.edu.au/87108/1/WoodCo wie2004_EmissionsFertiliser.pdf〉; 2004 [accessed 12.12]. [107] Energy consumption and greenhouse gas emissions in fertilizer production. In: Proceedings of IFA Technical Conference. Marrakech, Morocco, 〈www.fer tilizer.org〉; 28 September–1 October, 1998 [accessed 12.12]. [108] Mining Industry of the Future, Energy and Environmental Profile of the U.S. Mining Industry. U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Washington. Washington, DC, 〈http://infohouse.p2ric.org/ ref/46/45410.pdf〉; 2002 [accessed 01.13]. [109] Year Book of China Transportation & Communication 2009. Ministry of Transport. China Transportation Press. Beijing; 2009 [in Chinese]. [110] 2050 China Energy and CO2 Emission Report (2050CEACER). Development Research Center of the State Council, Bureau of Energy of National Development and Reform Commission, Institute of Nuclear and New Energy Technology, Tsinghua University. Science Press. Beijing; 2007 [in Chinese]. [111] Qi Y. China’s sulfuric acid and phosphate fertilizer production in 2009. Sulfuric Acid Ind. 2010;2:1–5 [in Chinese]. [112] Design criterion for energy saving of iron and steel industry (GB50632-2010). Ministry of Housing and Urban-Rural Development of the PRC & State General Administration for Quality Supervision and Inspection and Quarantine; 2010 [in Chinese].

[113] Index system of clean production evaluation in Sulfuric acid industry (tentative standard). National Development and Reform Commission. Beijing, China, 〈http://jns.miit.gov.cn/n11293472/n11295091/n11477141/n12841251. files/n12841153.pdf〉; 2007 [accessed 02.13] [in Chinese]. [114] Entrance conditions for ammonium phosphate production industry (draft 2011 Ministry of Industry and Information Technology) 〈www.miit.gov.cn/ n11293472/n11293832/n11293907/n11368223/13665391.html〉; 2011 [accessed 02.13] [in Chinese]. [115] National Standard of the People's Republic of China-The norm of energy consumption per unit Product of Urea. Department of Resource Conversion and Environmental Protection of NDRC, 〈http://www.cnfia.com.cn/d/201009/09/201009090808267.doc〉; 2010 [accessed 02.13] [in Chinese]. [116] Index system of clean production evaluation in Phosphate fertilizer industry (tentative standard). National Development and Reform Commission. Beijing, China, 〈http://jns.miit.gov.cn/n11293472/n11295091/n11477141/n12841251. files/n12840288.pdf〉; 2010 [accessed 02.13] [in Chinese]. [117] Gellings CW, Parmenter KE. Energy efficiency in fertilizer production and use, 〈http://www.eolss.net/sample-chapters/c08/e3-18-04-03.pdf〉 [accessed 01.12]. [118] International Fertilizer Industry Association Website, 〈www.fertilizer.org〉 [accessed 02.12]. [119] Statistics database of fertilizer consumption in International Fertilizer Industry Association, 〈http://www.fertilizer.org/Statistics〉 (accessed 07.13). [120] Guo ST. Animal production based on crop residues – chinese experiences. Room, FAO, 〈www.fao.org/DOCREP/005/Y1936E/y1936e00.htm#Contents〉; 2002 [accessed 02.13]. [121] PE International Website, 〈www.gabi-software.com/china/index〉. [122] Ding WW, Yuan L, Tang XY, Xiao ZY. Analysis of net energy production in the corn ethanol life cycle. J Harbin Eng Univ 2010;31:773–9 [in Chinese]. [123] Ou XM, Zhang XL, Chang SY, Guo QF. LCA of bio-ethanol and biodiesel path ways in China. Acta Energy Sol Sin 2010;31:1246–50 [in Chinese]. [124] Li ZY, Wang X, Wei J, Ma WQ. Life cycle assessment of fertilization in corn production in different regions of China. Acta Sci Circumst 2010;30:1912–20 [in Chinese]. [125] Li YF. Automatic control cooling system for energy saving in pellet feed processing. Mod Agric Equip 2003;3:62–4 [in Chinese]. [126] Liu JP. Cost control measure for animal feed processing. Jiangxi Feed 2002;5:28–9 [in Chinese]. [127] China's organic fertilizer nutrients experiment notes. National Agro-Tech Extension and Service Center, China. China Agricultural Press. Beijing; 1999. [128] Feng C, Ma XQ. Life cycle assessment of the straw generation by direct combustion. Acta Energiae Sol Sin 2008;29:711–5 [in Chinese]. [129] He Z, Wu CZ, Yin XL. Carbon cycle analysis of biomass power generation system. Acta Energiae Sol Sin 2008;29:705–10 [in Chinese]. [130] Liu JW, Tian BH, Zhang PD, Li XJ. Life cycle assessment on straw directly combustion for power generation system. Renew Energy Resour 2009;27:102–6 [in Chinese]. [131] Wang SM. Technical and economic analysis of straw power generation engineering (Master Degree Thesis). Baoding: Heibei Agriculture University; 2008 [in Chinese]. [132] Lin L, Zhao DQ, Li L. Environmental impact analysis of biomass power generation system based on life cycle assessment. Acta Energiae Sol Sin 2008;29:618–22 [in Chinese]. [133] Jia YJ, Yu Z, Wu CZ. Life cycle assessment of a 4 MW biomass integrated gasification gas engines-steam turbine combined cycles power plant. Acta Energiae Sol Sin 2004;25:56–62 [in Chinese]. [134] Cui HR, Ai N. Assessment of life cycle of straw gasification power generation system. Technol. Econ. 2010;29:70–4 [in Chinese]. [135] Luo YH, Lou B, Ding LX. Energy analysis of biomass gasification power generation under the clean development mechanism. Acta Energiae Sol Sin 2010;3:1124–8 [in Chinese]. [136] Wu CZ, Zhou ZQ, Ma LL, Yin XL, Chen HP. Economic analysis of biomass gasification and power generation projects. Acta Energiae Sol Sin 2009;30:368–73 [in Chinese]. [137] Zhang WH, Chen XF, Liu XY, Liu GQ. Advance and application of household cooking stove with improved efficiency in China. Chem Ind Eng Prog. 2009;28:516–20 [in Chinese]. [138] Cao GQ, Zhang ZY. Externality, influence factors and development counter measures for straw gasification system for gas supply. Renew Energy Resour 2008;26:38–42 [in Chinese]. [139] Li DK, Sun L, Cui YB. Technical assessment on rural cooking gas supply system for straw gasification technology. Trans Chin Soc Agric Eng 1999;1:1–6 [in Chinese].