Multi-objective Optimization Based on Full LCEVA Life Analysis of Straw Energy Technologies

Multi-objective Optimization Based on Full LCEVA Life Analysis of Straw Energy Technologies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-objective Optimization Based on Full LCEVA Life Analysis of Straw Energy Technologies Zhen Chang, YongHui Li, Changmei Wang, YongCai Yu, Fang Yin, Wudi Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5628959/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, straw energy conversion technology has become a key research focus due to its potential to alleviate energy shortages and reduce environmental pollution. This paper systematically evaluates the economic, environmental, and social benefits of five main straw energy conversion technologies in Yunnan Province, based on a Life Cycle Energy Analysis (LCEA) and multi-objective linear programming. The technologies studied include straw biogas/biomethane, pellet fuel power generation, fuel ethanol, direct combustion power generation, and pyrolysis gas co-production. The results show that straw biogas/biomethane technology has the highest sustainability index, indicating its significant advantages in economic, environmental, and social benefits; therefore, it is recommended for prioritized development. In contrast, the sustainability indices of fuel ethanol and pyrolysis gas co-production technologies are 0.46 and 0.43, respectively, suggesting poor sustainability, and that their development should be restricted. If the policy goal is to enhance regional economic benefits, increasing the proportion of straw biogas/biomethane and pellet fuel power generation technologies is advisable, but i the focus is on environmental impact, straw biogas/biomethane technology should be the primary treatment method. The study further suggests that by improving the utilization rate of straw energy conversion, a win-win situation for economic growth and environmental protection can be achieved, providing strong support for Yunnan Province's transition to green, low-carbon development. The results provide a theoretical basis for local governments to formulate scientific policies. straw energization technology life cycle energy analysis multi-objective linear programming sustainability index energy utilization rate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Although fossil energy is currently the main product consumed to provide energy globally, its non-renewability and associated increasingly serious environmental pollution problems are driving the accelerated transformation of the global energy structure (Umar et al., 2021 ). In addition, agricultural production activities produce a large amount of straw and other biological waste every year, and they are traditionally treated through open burning or landfill, which not only wastes resources, but also causes serious environmental pollution and soil degradation problems (Toam et al., 2022). As such, straw energy technology has emerged as a process that converts agricultural waste into clean energy, such as biomass fuel, biogas and biochar; this can not only replace the use of fossil energy, but it also utilizes resource waste (Deora et al., 2022 ). As a clean and renewable energy technology, straw energization technology has shown great potential in addressing climate change, ensuring energy security, and promoting sustainable agricultural development (Pan et al., 2023 ; Babla et al., 2021 ). In recent years, major energy countries such as the United States, Brazil, and India have begun the large-scale utilization of straw resources to promote biomass energy production, reduce greenhouse gas (GHG) emissions, and cope with increasing resource wastage and environmental problems (Cuong et al., 2021 ; Borges et al., 2021 ; Usmani, 2020 ). Nevertheless, existing studies on the subject have mainly focused on conducting a single analysis of straw energy technology, such as that related to economic or carbon emission reduction benefits, and they have failed to comprehensively and systematically assess the integrated environmental, economic and social benefits. In addition, there has been a lack of multidimensional integrated analysis, which not only limits the optimization of the technology, but also negatively affects the diffusion of straw energy technology. There is thus an urgent need to establish a more comprehensive analytical framework to systematically assess and optimize the multidimensional benefits of straw energy technology and achieve the goal of providing a scientific reference basis for policy makers and technology promoters. Existing studies are limited in their assessment of straw energization technologies in several ways: first, most have conducted a qualitative analysis of single benefits, such as those pertaining to economics or the carbon emission reduction potential, and they lack systematic multidimensional comprehensive evaluations. For example, Ezz et al., ( 2023 ) conducted an economic analysis of the technology used to produce biogas from rice straw, while Wang et al., ( 2022 ) studied the path to achieve carbon emission reduction through zero-carbon fuel technology. However, these studies were mainly based on the optimization of a single indicator, and they failed to comprehensively consider the complex interactions among environmental, economic, and social benefits. Studies have shown that neglecting multi-dimensional comprehensive assessments may lead to policy orientation bias, which would make it difficult to truly achieve sustainable development goals (Elevarasan et al., 2022). Existing energy-value analysis methods are mainly biased towards qualitative analysis or simple parameter comparisons, and they usually only assess energy-value conversion for a single technology pathway and lack a systematic comparison of multiple technology combinations (Shie et al., ( 2014 ). This limitation prevents studies from revealing the interactions and potential synergies between different technology paths. In addition, existing studies lack quantitative analyses of the specific contributions of inputs and outputs in the energy-value conversion process, and they fail to assess in depth the actual impacts of various types of resources (e.g., hydroelectricity, diesel fuel, industrial water, equipment, and labor costs) in the implementation of the technologies. For example, some studies have only assessed the conversion rate of a single technology (Yu et al., ( 2019 ), while ignoring the applicability differences between technologies in different application scenarios. To address these issues, this study innovatively combined a life cycle energy analysis (LCEA) and the second generation of non-dominated sequential genetic algorithm (NSGA-II) to conduct a multi-objective study of five major straw energyization technologies in Yunnan Province. In this respect, biogas/biogas (SG), fuel ethanol (SP), pyrolysis carbon-gas cogeneration (SR), shaped fuel power generation (SPB), and direct-fired power generation (SFR) were evaluated for multi-objective optimization. Different from the traditional single-analysis method, this study systematically compared multiple technology pathways by constructing a comprehensive index system, including the energy-value conversion rate, economic benefits, environmental performance, and sustainability indexes, with the aim of screening out the optimal technology solutions. In particular, the impacts of various inputs (e.g., energy consumption, labor inputs, equipment costs, etc.) on the overall energy-value conversion efficiency were quantified in detail in the energy-value analysis, which provides a thorough quantitative analysis. In addition, a dynamic adjustment mechanism in multi-objective optimization in response to changes in environmental, economic and social benefits was introduced. This approach not only dynamically adjusts the priority of each objective according to different policy demands, but also adapts more flexibly to different application scenarios, thus improving the adaptability and effectiveness of multi-objective optimization. This innovation provides a new idea for optimizing the comprehensive benefits of energy technologies and negates the limitations of traditional methods in weight setting. Based on this, the marginal contributions of this paper include the following: first, a new multi-objective optimization evaluation framework is proposed, which is applicable to the systematic comparison and screening of multi-technology pathways; second, the energy-value conversion process of different inputs and outputs is finely quantified, and the optimal technology combinations under different resources and conditions are explored; and third, through the energy-value analysis combined with the multi-objective optimization model, a comprehensively coupled evaluation of the economic benefits, environmental performance. Furthermore, through the energy value analysis and use of the multi-objective optimization model, a comprehensive coupled assessment of economic benefits, environmental performance and sustainability indicators was conducted in this study, which provides a more scientific decision-making basis for policy formulation and technology promotion. 2. Method Research Methodology The primary aim of this study was to optimize the application of five straw energy technologies by analyzing their life-cycle energy values. To facilitate comparisons among technologies, the stover yield per hectare of the maize planting area was selected as the basic unit of analysis. The conversion and utilization of natural energy sources (i.e., solar, geothermal, topsoil loss, and rainwater energy, which are crucial for enhancing biomass yield and energy efficiency during the planting stage) were assessed using average data from Yunnan Province. A planting cycle of 100 days was assumed and the straw collection area was defined as a circle with an 88.25-km radius centered at the storage station. Collected straw was sealed, transferred, and stacked before transportation to processing plants using a tractor. The diesel fuel consumption during transportation was calculated at 0.06 L/ton-km (Da Silva et al., 2021 ), with a low heating value of 38.72 MJ/L for diesel (Liu et al., 2011 ). Life-cycle energy value analysis (LCEVA), a specific type of LCEA, includes an examination of the structures of energy-value flows, calculations of energy-value utilization, and the monetary ratios of the four technologies. The calculated energy-value monetary ratios were 17.7 and 82.3% for non-renewable feedback energy (FN) (Da Silva et al., 2021 ). The power structure of the input system was based on data from the National Bureau of Statistics, with thermal power and hydroelectric power accounting for 13% and 87% of the total energy input, respectively. In the optimization stage, the study adopted the life cycle energy value analysis method, selected the main energy efficiency indexes as the optimization objectives, and established a multi-objective planning model. In order to obtain the optimal technology combination, the second-generation non-dominated sequential genetic algorithm (NSGA-II) was used to solve the problem, and the preferred straw energyization technology combination was finally derived. As shown in Fig. 1 , the flowchart clearly demonstrates the whole research process from energy value analysis to multi-objective optimization, and finally proposes the optimization of straw energyization technologies. Through this optimization scheme, this study provides a scientific basis and innovative solutions for the efficient use of straw resources and the optimization of regional energy structure. Conducting a comprehensive evaluation of biomass energy utilization usually relies on the use of a series of systematic analysis methods, of which the life cycle assessment method and the energy value analysis method are two of the most common and widely used. In recent years, the Life Cycle Energy Value Analysis (LCEVA) method has emerged to more comprehensively assess the whole biomass energy utilization process, and this integrates the core advantages of the LCA and EVA methods to form a new evaluation framework. The life cycle energy value analysis method takes the whole life cycle of the product production process as the research object, and the structural function and ecological and economic benefits of the system are quantitatively analyzed using the energy value as the quantitative outline (Xu et al., ( 2024 ). Taking the straw biogas/biogas system as an example, its energy value diagram is shown in Fig. 2 . In practice, the solar contingency value is usually employed to measure the contingency value of a certain energy, and the contingency value of a single flow is shown below. Solar contingency values are often used in practical applications to assess the reliability of a particular energy source. The contingency value for a single energy flow is described as $$\:{U}_{i}=UQ{V}_{i}\times\:{f}_{i}$$ 1 , where U i is the total energy required to produce a specific quantity of a product or service, measured in terms of the solar equivalent, UQV i is the contingent energy required to produce one unit of a product or service, and f i is the flow rate in J .The flow in joules is expressed as the production of a unit of a product or service required for emergency energy, or the solar emergy joules (seJ) per joule of the product (i.e., seJ/J). This study builds on the results of Odum [17] and Law et al., (2019) [18], constructing an emergency energy consumption index system of straw energization technologies. The main emergency energy consumption indices include the economic yield ratio (EYR), environmental load ratio (ELR), and environmental sustainability index (ESI). The EYR is directly linked to the production efficiency of the system. Generally, more efficient production systems exhibit higher EYR values [19]. The ELR measures the environmental impact of a system, such that higher ELR values indicate greater environmental stress. In environmental assessments, 2 < ELR < 10 suggests that the environment is moderately stressed [20]. The ESI represents the ratio of the net acute output to environmental load. A higher ESI signifies that the system provides superior social and economic benefits relative to environmental pressures, reflecting high sustainability. An ESI below 1 signifies a consumer system where environmental resource extraction exceeds the return. An ESI above 1 indicates a sustainable system, highlighting efficient resource use and replenishment [21]. 3. Optimization Model for Straw Energization EYR and ELR were selected as the optimization objectives in this study, and the decision variables (X 1 , X 2 , X 3 , X 4 , and X 5 ) denote the proportions of straw biogas/biogas (SG), fuel ethanol (SP), pyrolysis C-gas cogeneration (SR), molded fuel power generation (SPB), and direct-fire generation (SFR), respectively, which are suitable for managing the amount of straw resources employed. The optimization objective functions are as follows, $$\:maxEYR={a}_{1}\ast\:{X}_{1}+{a}_{2}\ast\:{X}_{2}+...+{a}_{5}\ast\:{X}_{5},\:$$ 2 $$\:minELR={b}_{1}\ast\:{X}_{1}+{b}_{2}\ast\:{X}_{2}+...+{b}_{5}\ast\:{X}_{5}.$$ 3 Equations ( 2 ) and ( 3 ) yield the optimized EYR and ELR, respectively, where a 1 , a 2 , a 3 , a 4 , a 5 , b 1 , b 2 , b 3 , b 4 , and b 5 denote the EYR and ELR of each straw energization technology (from cultivation to collection, transportation, storage, and processing) for treating one ton of straw. Optimization is limited by some conditions [22]; up to 60% of straw can be collected and used as an energy source. The limiting conditions for the total amount of straw are as follows, $$\:{X}_{1}+{X}_{2}+...+{X}_{5}={\theta\:}_{1}$$ 4 , $$\:0\le\:{\theta\:}_{1}\le\:0.60\:$$ 5 , $$\:{X}_{1},{X}_{2},...,{X}_{5}＞0$$ 6 . As the labor force is an important resource in every region, it is essential to reasonably limit labor force resources to optimize straw energy in the model, as follows, \(\:{C}_{1}\ast\:{X}_{1}+{C}_{2}\ast\:{X}_{2}\) +...+ \(\:{C}_{5}\ast\:{X}_{5}\) ≤ \(\:{C}_{0}\) , (7) where C 1 , C 2 , C 3 , C 4 , and C 5 denote the labor force required to process each ton of straw by each straw energy technology and C 0 is expressed as the total labor force of the straw energy utilization industry in regional rural areas per year, calculated as 23.9 million people engaged in agriculture, forestry, animal husbandry, and fishery, according to the Yunnan Provincial Statistical Yearbook . Multi-objective optimization cannot simultaneously achieve a globally optimal solution for all objectives. Instead, the goal is to find a Pareto-optimal solution set, where Pareto optimality implies that improving one objective necessarily leads to a trade-off with another objective. To address such problems, evolutionary algorithms can be employed, including multi-objective genetic, particle swarm, and ant colony algorithms. In this study, NSGA-II was utilized to approximate the Pareto boundary and set. This approach was applied to solve the multi-objective optimization problem associated with straw energy technologies (Fig. 3 ). 4. Analysis of straw energy technology 4.1. Analysis of Straw Energization Technologies Initially, the unit contingency energy values for various substances and services were determined based on those reported in the literature [23,24]. Following the methodology of Brown et al. [25], these values were then converted into a baseline contingency energy value of 12 x 10 24 seJ/yr. Subsequently, the contingency energy flows for the five straw energy technologies were calculated (Table 1 ). Baseline data for the straw ethanol production system were sourced from the literature [26,27], and the baseline data for the straw pyrolysis–charcoal gas cogeneration system were obtained from both the literature [26] and additional research. Furthermore, baseline data for the straw direct combustion power generation system [27], the straw molded fuel power generation system [28], and the straw biogas/biogas production system [29] were sourced from the literature. To ensure a fair comparison of different straw energy technologies, consistent conditions for straw cultivation, collection, transportation, and storage were assumed. This approach enabled the evaluation of emergency energy consumption to be evaluated via indices for the five straw energy technologies (Table 2 ). Table 1 Energy inputs during corn cultivation Parameters Input Raw values Conversion ratio Solar value Renewable energy (R) Solar energy 1.57E + 13 J 1.00E + 00 1.57E + 13 Rainwater 1.77E + 10 J 1.82E + 04 3.22E + 14 Geothermal power 1.63E + 09 J 6.07E + 03 9.90E + 12 Subtotal 3.48E + 14 Non-renewable energy (N) Topsoil loss energy 8.73E + 09 J 7.40E + 04 6.46E + 14 Soil erosion energy 4.93E + 03 J 1.70E + 04 8.38E + 07 Subtotal 6.46E + 14 Renewable feedback energy (F R ) Water 3.00E + 06 g 6.64E + 05 1.99E + 12 Labor costs I (17.7%) ¥4.15E + 01 CNY 7.27E + 11 3.02E + 13 Torrent 3.25E + 03 g 5.11E + 08 1.66E + 12 Subtotal 3.38E + 13 Non-renewable feedback energy (F N ) Nitrogen fertilizer 1.32E + 04 g 4.60E + 09 6.07E + 13 Phosphate fertilizer 7.92E + 02 g 1.78E + 10 1.41E + 13 Potash 3.02E + 02 g 1.10E + 10 3.32E + 12 Compound fertilizer 1.71 + 04 g 2.13E + 08 3.64E + 12 Agrochemical 3.84E + 02 g 2.48E + 10 9.52E + 12 Diesel fuel 7.82E + 07 g 6.60E + 04 5.16E + 12 Cunning (old) 1.89E + 02 g 7.76E + 08 1.47E + 11 Labor costs II (82.3%) ¥1.93E + 02 CNY 7.27E + 11 1.40E + 14 Subtotal 2.37E + 14 Yield (Y) Stalk 4.31E + 06 g - 3.12E + 14 Table 2 Energy inputs for the collection, storage, transportation, and production of the straw biogas/biogas system Parameters Input Raw values Conversion ratio Solar value F R Labor costs I (17.7%) 9.20E + 00 (CNY) 7.72E + 11 6.69E + 12 Hydroelectricity 3.22E + 07 (J) 6.23E + 04 2.01E + 12 Subtotal 8.70E + 12 F N Equipment (in steel) 2.86E + 02 (g) 6.70E + 09 1.92E + 12 Diesel fuel 2.38E + 09 (J) 6.60E + 04 1.57E + 14 thermal power 7.52E + 07 (J) 2.00E + 05 1.50E + 13 Labor costs II (82.3%) 4.28E + 01 (CNY) 7.27E + 11 3.11E + 13 Subtotal 2.05E + 14 F R Industrial water 4.34E + 06 (g) 6.64E + 05 2.88E + 12 Hydroelectricity 416E + 06 (J) 6.23E + 04 2.59E + 11 Labor costs I (17.7%) 1.11E + 01 (CNY) 7.72E + 11 8.57E + 12 Engineering operations 1.22E + 08 (J) 2.56E + 06 3.13E + 14 Subtotal 3.25E + 14 F N Infrastructure investment 3.76E + 02 (CNY) 6.89E + 11 2.59E + 14 Instrumentation costs 1.59E + 03 (CNY) 6.89E + 11 1.09E + 15 Thermal power 3.65E + 06 (J) 2.00E + 05 1.24E + 12 Labor costs II (82.3%) 5.18E + 01 (CNY) 7.27E + 11 3.77E + 13 Subtotal 1.39E + 15 Y Straw biogas/biogas 9.11E + 10 (J) - - 4.2. EYR, ELR, and ESI Analysis In assessing the economic benefits of straw energyization technologies, as shown in Table 3 , there were significant differences between the performances of the five technologies. Except for the fuel ethanol technology (SP), the energy value yield rate (EYR) of the remaining four technologies was greater than 1, indicating that these technologies possess significant value-added effects of straw. Specifically, the ranking results of EYR were SG > SPB > SR > SFR > SP, among which the EYR of SG technology was as high as 11.32; this is mainly attributed to its lower non-renewable feedback energy value input in the production process, which demonstrates its absolute advantage in energy conversion efficiency. In contrast, the EYR of SP technology was only 0.97; this is mainly attributed to its reliance on a large amount of non-renewable feedback energy values (e.g., labor cost and thermal power consumption) in the production process, resulting in its poor economic performance. This finding is consistent with the study of Chandra et al. ( 2012 ), further confirming the potential of SG technology as a more economical and environmentally friendly method of utilizing waste biomass resources. In terms of environmental performance, the environmental loading ratios (ELR) of the five technologies showed significant differences in the order of SFR < SP < SPB < SG < SR. SFR showed the best environmental performance due to its lower non-renewable energy value input (62.05% of the total input). In contrast, SR technology provided the worst environmental performance with only 14.49% of renewable energy value inputs and 75.22% of non-renewable energy value inputs, which significantly increases the environmental burden. Further analysis of the sustainability index (ESI) of the five technologies showed that SG > SPB > SFR > SP > SR, among which the SG technology ranked first with an ESI of 3.52 due to its excellent energy output efficiency and sustainability. This indicates that SG technology not only excels in terms of economic efficiency, but it also makes a positive contribution in terms of environmental sustainability. This finding is consistent with the study of Bumharter et al. ( 2023 ), indicating that SG technology plays a key role in energy saving and emission reduction as well as agro-ecological and environmental protection. SG technology has been vigorously promoted and developed in Europe [32]. In contrast, the remaining four technologies had ESIs below 1, showing their shortcomings in long-term sustainability as consumption-based systems. This is in line with the study of Cao and Feng ( 2007 ), who noted that the unsustainability of these technologies mainly stems from the reliance on high ESI inputs (e.g., fossil fuels, chemicals, and mechanical equipment). These factors not only exacerbate environmental pressures, but also limit the sustainability potential of the system. Therefore, in order to improve the sustainability of these technologies, future research should focus on reducing the reliance on high energy-value inputs and exploring more environmentally friendly and long-term sustainable production methods. Such optimization will not only effectively reduce the environmental burden, but will also contribute to a green transition in the agriculture and energy sectors. Table 3 Comparative analysis of system emergy indicators Energy index Value type SFR SP SPB SR SG R Renewable energy (seJ) 3.77E + 14 3.48E + 14 3.78E + 14 3.48E + 14 3.48E + 14 N Non-renewable energy (seJ) 6.46E + 14 6.46E + 14 6.46E + 14 6.46E + 14 6.46E + 14 F R Renewable feedback energy (seJ) 1.31E + 14 2.00E + 14 1.03E + 14 8.80E + 13 3.71E + 14 F N Non-renewable feedback energy (seJ) 2.96E + 14 5.00E + 14 5.19E + 14 1.51E + 15 1.67E + 15 Y Yield (seJ) 5.85E + 14 6.79E + 14 1.42E + 15 3.41E + 15 2.31E + 16 EYR Energy yield ratio 1.36 0.97 2.28 2.14 11.32 ELR Environmental load ratio 1.84 2.09 2.42 4.94 3.20 ESI Environmental sustainability index 0.74 0.46 0.94 0.43 3.52 4.3. Optimization and Analysis of Straw Energization Scheme After coding and solving the NSGA-II using MATLAB v. [R2022a], the EYR and ELR values gradually converged by approximately the 150th generation (Fig. 4 a). Ultimately, 500 Pareto-optimal solutions satisfied the objectives and constraints (Fig. 4 b). Based on these results, the 50 solutions with the highest EYR values in the Pareto set were analyzed (Fig. 5 ). Figure 5 (a) shows the range of values for the SFR, SP, SPB, SG, and SR technologies when EYR was prioritized. SG achieved the highest EYR, with a range of [0.32, 0.60]. The ranking of EYR values for the other technologies was SP > SFR > SPB > SR, primarily because SG involves a relatively low consumption of nonrenewable energy, which accounts for only 2.80% of the output energy value and contributes to its significant economic benefits. In contrast, SR technology displayed an EYR close to zero, indicating poor performance in terms of energy value addition. Figure 5 (b) shows the range of solutions for SFR, SP, SPB, SG, and SR when ELR was the primary optimization objective. SG values ranged from [0.00056, 0.31662], whereas the proportion of SR proportion was near zero. Combining these findings with the EYR-focused results, it is evident that SR technology is not suitable for large-scale adoption, given its poor EYR and ELR performance. This aligns with the current challenges faced by SR technology utilization, including its high cost and technical difficulties with large-scale implementation. Chen et al. ( 2024 ) noted that SR technology often exhibits high equipment and operating costs. When employing the second-generation genetic algorithm to solve the multi-objective optimization model for straw energy technologies, the sums of the decision variables for the solutions with the most significant optimization effects tended to converge toward the minimum thresholds of the slack variables. Slack variable optimization was employed across the four constraints (Table 4 ) and the results showed that, as the scale of straw energy utilization decreased, the EYR, ELR, and ESI declined. This indicates that increased straw energization is economically and environmentally beneficial. As of 2020, the straw energization ratio of Yunnan was just 4.64%. Table 4 Impact of \(\:{\:\:\theta\:}_{1}\) -values on optimization results Range Scope of the decision variable \(\:{\:\:\theta\:}_{1}\) EYR ELR ESI [0.30,0.60] [0.3008,0.6000] 4.27 1.27 1.33 [0.20,0.60] [0.2008,0.6000] 4.18 1.20 1.30 [0.046,0.60] [0.0481,0.5999] 3.70 1.05 1.15 According to the model results, increasing the straw energization ratio to 20% would result in 12.97, 14.19, and 13.04% increases in EYR, ELR, and ESI, respectively. Further increasing the ratio to 30% would lead to gains of 15.40, 20.95, and 13.53% in these indicators, respectively. These results suggest that expanding the straw energization ratio would boost economic output and considerably improve environmental sustainability in Yunnan. Guo et al. ( 2021 ) similarly found that regional eco-efficiency improved as the straw energization ratio increased while production efficiency was maintained. Therefore, the outcomes of this study offer strong theoretical support for the practical promotion of straw energization, whose rate should be increased to achieve enhanced economic and ecological outcomes. 5. Discussion In this study, the energy value yield rate (EYR), environmental loading rate (ELR), and environmental sustainability index (ESI) of the five straw energization technologies were systematically analyzed, and the key findings were as follows: (1) SG technology outperformed the other technologies. In terms of energy efficiency (EYR = 11.32) and environmental sustainability (ESI = 3.52), SG technology performed well. Although its environmental loading rate ( ELR = 3.20) was slightly high, its potential for large-scale application was considered to be high due to its resource utilization and biomass conversion efficiency advantages. (2) SPB technology has a high promotional value. SPB technology also performed well in terms of economic (EYR = 2.28) and environmental benefits (ESI = 0.94); as such, its potential for promotion is high. (3) With respect to the limitations of inefficient technologies, comparatively speaking, SP and SR technologies had low EYRs and sustainability indexes, which were mainly limited by high EYR inputs and low energy conversion efficiencies; as such, they provide insufficient economic and environmental benefits. In addition, although SFR technology performed better in terms of its environmental burden (ELR = 1.84), its economic benefits (EYR = 1.36) remained limited. Among the optimization options for straw energyization technologies in Yunnan Province, the results of this study suggest that priority should be given to developing SG and SPB technologies. The analysis results show that SG technology provides the most outstanding performance in terms of energy efficiency (EYR = 11.32) and sustainability (ESI = 3.52), and its high efficiency biomass conversion rate and low consumption of non-renewable resources give it a significant advantage for use in large-scale applications. Especially in Yunnan Province, where straw resources are abundant and environmental protection requirements are high, SG technology should be the preferred development direction to fully utilize its potential in energy conversion and GHG emission reduction. In addition, SPB technology, with its good comprehensive benefits (EYR = 2.28, ESI = 0.94), also has an important popularization value. In contrast, the feasibility of using SR technology in the region is limited due to its higher resource consumption (ELR = 4.94) and lower environmental benefits (ESI = 0.43). Therefore, it is recommended to limit the application of SR technology without reducing its dependence on non-renewable resources or improving its energy conversion efficiency through technology optimization to avoid resource waste and environmental burden. To further promote the large-scale application of SG and SPB technologies, measures such as policy support, financial incentives and technological innovation should be implemented. In addition, international success stories, such as experiences in Europe and Brazil, can be drawn upon to gradually increase the rate of straw energyization in Yunnan Province. This will not only promote the coordinated development of regional economy and environmental protection, but also provide solid technological and policy support for realizing green and low-carbon development, and promote energy structure transformation and sustainable economic growth at the national level. As the rate of straw energyization in Yunnan Province increases, the energy value yield rate (EYR), environmental load ratio (ELR), and environmental sustainability index (ESI) will all increase significantly. These changes imply an increase in the energy utilization efficiency (EYR increase), a decrease in the dependence on non-renewable resources (ELR decrease), and an increase in overall system sustainability (ESI increase). Specifically, when the rate of straw energyization is increased to 20%, the EYR is expected to increase to 15.40%, the ELR will increase approximately to 20.95%, and the ESI will rise by nearly 13.53%. The increase in energy efficiency and environmental benefits is especially significant in the scenarios centered on SG and SPB technologies. Therefore, a gradual increase in the ratio of straw to energy will not only help promote economic growth, but will also significantly improve environmental protection and promote high-quality sustainable development in the region. To ensure the effectiveness and sustainability of the policy implementation, more specific support measures should be developed: for the development of SG technologies, financial incentives and technology subsidies can be used to reduce the initial costs; for SPB technologies, marketing strategies need to be strengthened to encourage local governments and enterprises to actively participate in the development and implementation of straw energy projects. 6. Conclusions With the increasing global demand for sustainable alternative energy sources, conducting a comprehensive assessment of the economic, environmental and social benefits of employing straw energy technologies was necessary. As such, this paper innovatively combined a Life Cycle Energy Analysis (LCEA) with the Multi-Objective Optimization Model (NSGA-II) to systematically evaluate five major straw energy technologies used in Yunnan Province, and application optimization strategies for different technologies were proposed. The method not only extends from a single-dimensional assessment of economic or environmental benefits to a comprehensive consideration of multi-dimensions, but it also provides a scientific basis for the selection of straw energyization technologies in a specific region. The core innovation of this study is the combination of the LCEA and NSGA-II algorithms, which improve upon the limitations of employing a single analytical method in existing studies. The LCEA accurately captured the inputs and outputs of various types of resources and energies in the production and conversion processes, while the NSGA-II provided an effective optimization pathway to balance the multiple objectives such as economic benefits and environmental sustainability. This enabled us to achieve a balance of environmental, economic and social benefits under a multi-objective optimization framework when analyzing the different straw energyization technologies, thus providing a more reliable and comprehensive basis for applying different technology combinations. Among the five technologies evaluated, biogas/biogas technology (SG) and molded fuel power generation (SPB) demonstrated significant potentials by virtue of their high energy value yield ratio (EYR) and environmental sustainability index (ESI). This finding was consistent with the findings of Wu et al. ( 2023 ) and Nie et al. ( 2023 ), who similarly recommended increasing the proportion of SG and SPB technologies used to capitalize on their advantages in energy conversion and environmental protection. SG technology is not only widely used in China, but it has also proved to be highly feasible and adaptable in Europe, as shown in the studies of Bumharter et al. ( 2023 ) and Magnusson et al. ( 2022 ). In addition, the successful dissemination experience of SPB technology in Brazil, as shown in the study by De Almeida et al. (2024), further demonstrates its sustainability and economic benefits in different agricultural resource-rich countries. Mentioning these success stories not only enhances the international context of this study, but it also provides important lessons for straw energy technology in Yunnan Province. Although direct-fired power generation (SFR) technology has a low energy yield, it still exhibits good environmental performance due to its reliance on renewable resources, and thus can be an important complement in the overall technology system. However, fuel ethanol (SP) and pyrolysis carbon gas cogeneration (SR) technologies have significant limitations in terms of high energy value inputs and environmental pressures, and should be promoted with caution at this stage. In particular, SP technology greatly reduces its economic and environmental benefits due to the high inputs of electricity and hydrochloric acid (90.20% of the total), while SR technology similarly exhibits a large environmental load due to the high inputs of iron and steel and cement, which is closely related to the limitations of the existing technology development level. Therefore, future research should focus on addressing the resource inputs of these technologies to improve their sustainability. According to the data of 2020, the ratio of straw energyization in Yunnan Province is only 4.64%. To further enhance economic benefits and environmental protection in the region, it is recommended that Yunnan Province should prioritize the development of SG and SPB technologies with the potential for scale-up. Gradually increasing the ratio of straw energyization will not only significantly improve the energy-yield ratio (EYR), environmental load ratio (ELR), and environmental sustainability index (ESI), but it will also promote the synergistic development of the economy and the environment. As shown in the studies of Elgarahy et al. ( 2024 ) and Kothari et al. ( 2020 ), straw energization not only improves the combustion efficiency, but it also effectively reduces environmental pollution, which provides the important opportunity to promote an energy revolution and national economic growth. Therefore, Yunnan Province should facilitate this process through specific policy support, such as providing subsidies to farmers and enterprises for straw collection and treatment, promoting market-based operation models for biomass energy technologies, and supporting related research and development (R&D) through a technology innovation fund. Guo et al. ( 2023 ) pointed out that the combination of financial incentives and technological support is an important driver for promoting the energy revolution, and this strategy also provides important references for other global agricultural resource-rich regions provide an important reference framework. This study provides a more systematic basis for technology selection through the life cycle energy value analysis method and multi-objective optimization model, and provides specific policy recommendations for other straw-rich regions in Yunnan Province and around the globe in terms of energy structure transformation and low-carbon economic development. With the deepening of technological progress and policy support, straw energy technology is expected to become an important part of sustainable energy development in the future. Future research needs to further explore how to optimize the resource use efficiency of inefficient technologies and assess the adaptability of technologies in a wider range of regions and application scenarios. Declarations Acknowledgments : We would like to express our sincere gratitude to Mr. Wudi Zhang for his invaluable guidance and insightful feedback throughout the course of this study. His expertise and advice were essential to both the conceptual development and successful completion of the research. We also extend our heartfelt thanks to Ms. Wang Changmei and Ms. Yin Fang from the Biomass Energy Group for their significant contributions to the experimental design, data analysis, and manuscript preparation. Their technical support and collaborative efforts were critical to achieving the objectives of this study. Additionally, we thank Invincible Zhang for his invaluable guidance and support during the research. We are also grateful to the teachers and students of the biomass group for their assistance. Furthermore, we would like to express our sincere thanks to Yonghui Li and Yongcai Yu of Plow Power Limited for generously providing the experimental data, which were crucial for the analysis in this research. Funding Statement : This research was jointly supported by the Yunnan Provincial Ten Thousand People Plan Industrial Technology Leaders Project (20191096), Yunnan Provincial Basic Research Special-Project (202401AT070120), Yunnan Provincial Key Laboratory of Rural Energy Engineering Fund (2022KF013), and the grassroots scientific research station of Zhang Wouwu Di expert of Kunming Roots Force Biotechnology Co. The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Statement: The data that support the findings of this study are openly available in the Yunnan Data Repository. Additionally, all relevant materials are available from the corresponding author upon reasonable request. References Umar M, Ji X, Kirikkaleli D, et al. 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Monitoring and evaluating the sustainability of Italian agricultural system. An emergy decomposition analysis. Ecol Model. 2014;271:132–48. Brown MT, Campbell DE, De Vilbiss C, et al. The geobiosphere emergy baseline: a synthesis. Ecol Model. 2016;339:92–5. Uihlein A, Schebek L. Environmental impacts of a lignocellulose feedstock biorefinery system: an assessment. Biomass Bioenergy. 2009;33(5):793–802. Tian W, Liao, Cuiping, Li, Li, et al. Life Cycle Energy Consumption and Greenhouse Gas Emissions Analysis of Corn Straw-Based Cellulosic Ethanol. Chin J Biotechnol. 2011;27(3):516–25. Wang H, Wang, Yajing, Bi Y, et al. Environmental Impact Assessment of Multi-Generation Mode of Straw Pyrolysis. J China Agricultural Univ. 2019;24(10):136–48. Wang H. (2016). Energy Value Analysis of Centralized Biogas Supply Engineering from Straw Based on Life Cycle Assessment. Master's thesis, Chinese Academy of Agricultural Sciences. Chandra R, Takeuchi H, Hasegawa T, et al. Improving biodegradability and biogas production of wheat straw substrates using sodium hydroxide and hydrothermal pretreatments. Energy. 2012;43(1):273–82. Bumharter C, Bolonio D, Amez I, et al. New opportunities for the European Biogas industry: A review on current installation development, production potentials and yield improvements for manure and agricultural waste mixtures. J Clean Prod. 2023;388:135867. González-Arias J, Baena-Moreno FM, Pastor-Pérez L, et al. Biogas upgrading to biomethane as a local source of renewable energy to power light marine transport: Profitability analysis for the county of Cornwall. Waste Manag. 2022;137:81–8. Cao K, Feng X. The emergy analysis of multi-product systems. Process Saf Environ Prot. 2007;85(5):494–500. Chen H, Dahlquist E, Kyprianidis K. Retrofitting biomass combined heat and power plant for biofuel production—A detailed techno-economic analysis. Energies. 2024;17(2):522. Guo X, Xiaona Y, Zhu W, et al. Ecological Efficiency Evaluation of Straw Energy Utilization Based on Data Envelopment Analysis. J China Agricultural Univ. 2021;26(3):1–9. Wu J, Atchike DW, Ahmad M. (2023) Crucial Adoption Factors of Renewable Energy Technology: Seeking Green Future by Promoting Biomethane. Processes, 11 (7). Nie Y, Shuiqing H, Han X, et al. Potential Assessment, Practical Challenges, and Optimization Paths for Energy Utilization of Corn Stalks in Concentrated Grain Production Areas. J Maize Sci. 2023;31(6):167–72. Bumharter C, Bolonio D, Amez I, et al. New opportunities for the European Biogas industry: A review on current installation development, production potentials and yield improvements for manure and agricultural waste mixtures. J Clean Prod. 2023;388:135867. Magnusson T, Zanatta H, Larsson M, et al. Circular economy, varieties of capitalism and technology diffusion: Anaerobic digestion in Sweden and Paraná. J Clean Prod. 2022;335:130300. De Moreira A, Rafael B, Júnio B, et al. Production of high-quality biogenic fuels by co-pelletization of sugarcane bagasse with pinewood sawdust and peanut shell. Biomass Convers Biorefinery. 2024;14(5):6797–820. Elgarahy AM, Eloffy MG, Priya AK et al. (2024). Biosolids management and utilizations: A review. J Clean Prod, 141974. Kothari R, Vashishtha A, Singh HM, et al. Assessment of Indian bioenergy policy for sustainable environment and its impact for rural India: Strategic implementation and challenges. Environ Technol Innov. 2020;20:101078. Guo W, Yang B, Ji J, et al. Green finance development drives renewable energy development: Mechanism analysis and empirical research. Renewable Energy. 2023;215:118982. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5628959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391830894,"identity":"8ed543f4-342e-4d12-913f-537886a3993b","order_by":0,"name":"Zhen Chang","email":"","orcid":"","institution":"Yunnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Chang","suffix":""},{"id":391830895,"identity":"501e6e8e-4ab9-4650-8436-85b92b569925","order_by":1,"name":"YongHui Li","email":"","orcid":"","institution":"Kunming Plow Power Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"YongHui","middleName":"","lastName":"Li","suffix":""},{"id":391830896,"identity":"802c2c75-118b-47d1-b38c-cf55c50c9eb8","order_by":2,"name":"Changmei Wang","email":"","orcid":"","institution":"Yunnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Changmei","middleName":"","lastName":"Wang","suffix":""},{"id":391830898,"identity":"3e11c7b5-2810-442a-ae43-c034936d3f2d","order_by":3,"name":"YongCai Yu","email":"","orcid":"","institution":"Kunming Plow Power Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"YongCai","middleName":"","lastName":"Yu","suffix":""},{"id":391830900,"identity":"cae60058-bd94-45b1-b86f-7d3586b404e8","order_by":4,"name":"Fang Yin","email":"","orcid":"","institution":"Yunnan Normal 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06:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5628959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5628959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71997296,"identity":"bbd7baaf-e111-4c6c-9dd3-fa3b67d2051b","added_by":"auto","created_at":"2024-12-20 12:41:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87449,"visible":true,"origin":"","legend":"\u003cp\u003eStraw energy technology scenarios and technology optimization models\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5628959/v1/589ff90225b274222add8188.jpeg"},{"id":71997295,"identity":"6f5f6dc8-bf4b-4876-8ed1-1a6708416f02","added_by":"auto","created_at":"2024-12-20 12:41:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46631,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy diagram of straw biogas/biogas system\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5628959/v1/c75a1fd7cbd9982c2a576263.png"},{"id":71997574,"identity":"abf5df97-c0b8-4765-82da-9c6678996451","added_by":"auto","created_at":"2024-12-20 12:49:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39789,"visible":true,"origin":"","legend":"\u003cp\u003eBasic flowchart of the NSGA-II algorithm\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5628959/v1/31331160b5b9a28c3f7ebdaf.jpeg"},{"id":71998927,"identity":"a8923818-67e9-497b-8d3f-e35d56582b64","added_by":"auto","created_at":"2024-12-20 12:57:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122882,"visible":true,"origin":"","legend":"\u003cp\u003eTarget optimization convergence of the (a) EYR and ELR and (b) Pareto front of the optimized solutions\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5628959/v1/5c9c1b4d83e2611db296bb2c.png"},{"id":71997308,"identity":"c2e5a071-b8c0-487a-871d-63302a61f5f9","added_by":"auto","created_at":"2024-12-20 12:41:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":62425,"visible":true,"origin":"","legend":"\u003cp\u003eOptimized results of straw energy utilization schemes prioritizing (a) EYR and (b) ELR\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5628959/v1/82dd2b75f8b7ee54eb6d4cfa.png"},{"id":79013234,"identity":"b1fcedb2-4517-4aa6-97ab-583b3710b3b2","added_by":"auto","created_at":"2025-03-22 12:01:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1147033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5628959/v1/51e03b63-f324-47eb-81aa-36b62268f239.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-objective Optimization Based on Full LCEVA Life Analysis of Straw Energy Technologies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlthough fossil energy is currently the main product consumed to provide energy globally, its non-renewability and associated increasingly serious environmental pollution problems are driving the accelerated transformation of the global energy structure (Umar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, agricultural production activities produce a large amount of straw and other biological waste every year, and they are traditionally treated through open burning or landfill, which not only wastes resources, but also causes serious environmental pollution and soil degradation problems (Toam et al., 2022). As such, straw energy technology has emerged as a process that converts agricultural waste into clean energy, such as biomass fuel, biogas and biochar; this can not only replace the use of fossil energy, but it also utilizes resource waste (Deora et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a clean and renewable energy technology, straw energization technology has shown great potential in addressing climate change, ensuring energy security, and promoting sustainable agricultural development (Pan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Babla et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent years, major energy countries such as the United States, Brazil, and India have begun the large-scale utilization of straw resources to promote biomass energy production, reduce greenhouse gas (GHG) emissions, and cope with increasing resource wastage and environmental problems (Cuong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Borges et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Usmani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ). Nevertheless, existing studies on the subject have mainly focused on conducting a single analysis of straw energy technology, such as that related to economic or carbon emission reduction benefits, and they have failed to comprehensively and systematically assess the integrated environmental, economic and social benefits. In addition, there has been a lack of multidimensional integrated analysis, which not only limits the optimization of the technology, but also negatively affects the diffusion of straw energy technology. There is thus an urgent need to establish a more comprehensive analytical framework to systematically assess and optimize the multidimensional benefits of straw energy technology and achieve the goal of providing a scientific reference basis for policy makers and technology promoters.\u003c/p\u003e \u003cp\u003eExisting studies are limited in their assessment of straw energization technologies in several ways: first, most have conducted a qualitative analysis of single benefits, such as those pertaining to economics or the carbon emission reduction potential, and they lack systematic multidimensional comprehensive evaluations. For example, Ezz et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted an economic analysis of the technology used to produce biogas from rice straw, while Wang et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied the path to achieve carbon emission reduction through zero-carbon fuel technology. However, these studies were mainly based on the optimization of a single indicator, and they failed to comprehensively consider the complex interactions among environmental, economic, and social benefits. Studies have shown that neglecting multi-dimensional comprehensive assessments may lead to policy orientation bias, which would make it difficult to truly achieve sustainable development goals (Elevarasan et al., 2022). Existing energy-value analysis methods are mainly biased towards qualitative analysis or simple parameter comparisons, and they usually only assess energy-value conversion for a single technology pathway and lack a systematic comparison of multiple technology combinations (Shie et al., (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This limitation prevents studies from revealing the interactions and potential synergies between different technology paths. In addition, existing studies lack quantitative analyses of the specific contributions of inputs and outputs in the energy-value conversion process, and they fail to assess in depth the actual impacts of various types of resources (e.g., hydroelectricity, diesel fuel, industrial water, equipment, and labor costs) in the implementation of the technologies. For example, some studies have only assessed the conversion rate of a single technology (Yu et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while ignoring the applicability differences between technologies in different application scenarios.\u003c/p\u003e \u003cp\u003eTo address these issues, this study innovatively combined a life cycle energy analysis (LCEA) and the second generation of non-dominated sequential genetic algorithm (NSGA-II) to conduct a multi-objective study of five major straw energyization technologies in Yunnan Province. In this respect, biogas/biogas (SG), fuel ethanol (SP), pyrolysis carbon-gas cogeneration (SR), shaped fuel power generation (SPB), and direct-fired power generation (SFR) were evaluated for multi-objective optimization. Different from the traditional single-analysis method, this study systematically compared multiple technology pathways by constructing a comprehensive index system, including the energy-value conversion rate, economic benefits, environmental performance, and sustainability indexes, with the aim of screening out the optimal technology solutions. In particular, the impacts of various inputs (e.g., energy consumption, labor inputs, equipment costs, etc.) on the overall energy-value conversion efficiency were quantified in detail in the energy-value analysis, which provides a thorough quantitative analysis. In addition, a dynamic adjustment mechanism in multi-objective optimization in response to changes in environmental, economic and social benefits was introduced. This approach not only dynamically adjusts the priority of each objective according to different policy demands, but also adapts more flexibly to different application scenarios, thus improving the adaptability and effectiveness of multi-objective optimization. This innovation provides a new idea for optimizing the comprehensive benefits of energy technologies and negates the limitations of traditional methods in weight setting.\u003c/p\u003e \u003cp\u003eBased on this, the marginal contributions of this paper include the following: first, a new multi-objective optimization evaluation framework is proposed, which is applicable to the systematic comparison and screening of multi-technology pathways; second, the energy-value conversion process of different inputs and outputs is finely quantified, and the optimal technology combinations under different resources and conditions are explored; and third, through the energy-value analysis combined with the multi-objective optimization model, a comprehensively coupled evaluation of the economic benefits, environmental performance. Furthermore, through the energy value analysis and use of the multi-objective optimization model, a comprehensive coupled assessment of economic benefits, environmental performance and sustainability indicators was conducted in this study, which provides a more scientific decision-making basis for policy formulation and technology promotion.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003e \u003cb\u003eResearch Methodology\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe primary aim of this study was to optimize the application of five straw energy technologies by analyzing their life-cycle energy values. To facilitate comparisons among technologies, the stover yield per hectare of the maize planting area was selected as the basic unit of analysis. The conversion and utilization of natural energy sources (i.e., solar, geothermal, topsoil loss, and rainwater energy, which are crucial for enhancing biomass yield and energy efficiency during the planting stage) were assessed using average data from Yunnan Province. A planting cycle of 100 days was assumed and the straw collection area was defined as a circle with an 88.25-km radius centered at the storage station. Collected straw was sealed, transferred, and stacked before transportation to processing plants using a tractor. The diesel fuel consumption during transportation was calculated at 0.06 L/ton-km (Da Silva et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with a low heating value of 38.72 MJ/L for diesel (Liu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Life-cycle energy value analysis (LCEVA), a specific type of LCEA, includes an examination of the structures of energy-value flows, calculations of energy-value utilization, and the monetary ratios of the four technologies. The calculated energy-value monetary ratios were 17.7 and 82.3% for non-renewable feedback energy (FN) (Da Silva et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The power structure of the input system was based on data from the National Bureau of Statistics, with thermal power and hydroelectric power accounting for 13% and 87% of the total energy input, respectively.\u003c/p\u003e \u003cp\u003eIn the optimization stage, the study adopted the life cycle energy value analysis method, selected the main energy efficiency indexes as the optimization objectives, and established a multi-objective planning model. In order to obtain the optimal technology combination, the second-generation non-dominated sequential genetic algorithm (NSGA-II) was used to solve the problem, and the preferred straw energyization technology combination was finally derived. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the flowchart clearly demonstrates the whole research process from energy value analysis to multi-objective optimization, and finally proposes the optimization of straw energyization technologies. Through this optimization scheme, this study provides a scientific basis and innovative solutions for the efficient use of straw resources and the optimization of regional energy structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConducting a comprehensive evaluation of biomass energy utilization usually relies on the use of a series of systematic analysis methods, of which the life cycle assessment method and the energy value analysis method are two of the most common and widely used. In recent years, the Life Cycle Energy Value Analysis (LCEVA) method has emerged to more comprehensively assess the whole biomass energy utilization process, and this integrates the core advantages of the LCA and EVA methods to form a new evaluation framework. The life cycle energy value analysis method takes the whole life cycle of the product production process as the research object, and the structural function and ecological and economic benefits of the system are quantitatively analyzed using the energy value as the quantitative outline (Xu et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taking the straw biogas/biogas system as an example, its energy value diagram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In practice, the solar contingency value is usually employed to measure the contingency value of a certain energy, and the contingency value of a single flow is shown below.\u003c/p\u003e \u003cp\u003eSolar contingency values are often used in practical applications to assess the reliability of a particular energy source. The contingency value for a single energy flow is described as\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{U}_{i}=UQ{V}_{i}\\times\\:{f}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the total energy required to produce a specific quantity of a product or service, measured in terms of the solar equivalent, \u003cem\u003eUQV\u003c/em\u003ei is the contingent energy required to produce one unit of a product or service, and f\u003csub\u003ei\u003c/sub\u003e is the flow rate in J .The flow in joules is expressed as the production of a unit of a product or service required for emergency energy, or the solar emergy joules (seJ) per joule of the product (i.e., seJ/J). This study builds on the results of Odum [17] and Law et al., (2019) [18], constructing an emergency energy consumption index system of straw energization technologies. The main emergency energy consumption indices include the economic yield ratio (EYR), environmental load ratio (ELR), and environmental sustainability index (ESI). The EYR is directly linked to the production efficiency of the system. Generally, more efficient production systems exhibit higher EYR values [19]. The ELR measures the environmental impact of a system, such that higher ELR values indicate greater environmental stress. In environmental assessments, 2\u0026thinsp;\u0026lt;\u0026thinsp;ELR\u0026thinsp;\u0026lt;\u0026thinsp;10 suggests that the environment is moderately stressed [20]. The ESI represents the ratio of the net acute output to environmental load. A higher ESI signifies that the system provides superior social and economic benefits relative to environmental pressures, reflecting high sustainability. An ESI below 1 signifies a consumer system where environmental resource extraction exceeds the return. An ESI above 1 indicates a sustainable system, highlighting efficient resource use and replenishment [21].\u003c/p\u003e"},{"header":"3. Optimization Model for Straw Energization","content":"\u003cp\u003eEYR and ELR were selected as the optimization objectives in this study, and the decision variables (X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e, X\u003csub\u003e3\u003c/sub\u003e, X\u003csub\u003e4\u003c/sub\u003e, and X\u003csub\u003e5\u003c/sub\u003e) denote the proportions of straw biogas/biogas (SG), fuel ethanol (SP), pyrolysis C-gas cogeneration (SR), molded fuel power generation (SPB), and direct-fire generation (SFR), respectively, which are suitable for managing the amount of straw resources employed. The optimization objective functions are as follows,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:maxEYR={a}_{1}\\ast\\:{X}_{1}+{a}_{2}\\ast\\:{X}_{2}+...+{a}_{5}\\ast\\:{X}_{5},\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:minELR={b}_{1}\\ast\\:{X}_{1}+{b}_{2}\\ast\\:{X}_{2}+...+{b}_{5}\\ast\\:{X}_{5}.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquations\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) yield the optimized EYR and ELR, respectively, where a\u003csub\u003e1\u003c/sub\u003e, a\u003csub\u003e2\u003c/sub\u003e, a\u003csub\u003e3\u003c/sub\u003e, a\u003csub\u003e4\u003c/sub\u003e, a\u003csub\u003e5\u003c/sub\u003e, b\u003csub\u003e1\u003c/sub\u003e, b\u003csub\u003e2\u003c/sub\u003e, b\u003csub\u003e3\u003c/sub\u003e, b\u003csub\u003e4\u003c/sub\u003e, and b\u003csub\u003e5\u003c/sub\u003e denote the EYR and ELR of each straw energization technology (from cultivation to collection, transportation, storage, and processing) for treating one ton of straw. Optimization is limited by some conditions [22]; up to 60% of straw can be collected and used as an energy source. The limiting conditions for the total amount of straw are as follows,\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{X}_{1}+{X}_{2}+...+{X}_{5}={\\theta\\:}_{1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e,\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:0\\le\\:{\\theta\\:}_{1}\\le\\:0.60\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e,\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{X}_{1},{X}_{2},...,{X}_{5}＞0$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eAs the labor force is an important resource in every region, it is essential to reasonably limit labor force resources to optimize straw energy in the model, as follows,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{1}\\ast\\:{X}_{1}+{C}_{2}\\ast\\:{X}_{2}\\)\u003c/span\u003e \u003c/span\u003e+...+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{5}\\ast\\:{X}_{5}\\)\u003c/span\u003e\u003c/span\u003e\u0026le;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{0}\\)\u003c/span\u003e\u003c/span\u003e, (7)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e denote the labor force required to process each ton of straw by each straw energy technology and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is expressed as the total labor force of the straw energy utilization industry in regional rural areas per year, calculated as 23.9\u0026nbsp;million people engaged in agriculture, forestry, animal husbandry, and fishery, according to the Yunnan Provincial Statistical Yearbook .\u003c/p\u003e \u003cp\u003eMulti-objective optimization cannot simultaneously achieve a globally optimal solution for all objectives. Instead, the goal is to find a Pareto-optimal solution set, where Pareto optimality implies that improving one objective necessarily leads to a trade-off with another objective. To address such problems, evolutionary algorithms can be employed, including multi-objective genetic, particle swarm, and ant colony algorithms. In this study, NSGA-II was utilized to approximate the Pareto boundary and set. This approach was applied to solve the multi-objective optimization problem associated with straw energy technologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Analysis of straw energy technology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Analysis of Straw Energization Technologies\u003c/h2\u003e \u003cp\u003eInitially, the unit contingency energy values for various substances and services were determined based on those reported in the literature [23,24]. Following the methodology of Brown et al. [25], these values were then converted into a baseline contingency energy value of 12 x 10\u003csup\u003e24\u003c/sup\u003e seJ/yr. Subsequently, the contingency energy flows for the five straw energy technologies were calculated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Baseline data for the straw ethanol production system were sourced from the literature [26,27], and the baseline data for the straw pyrolysis\u0026ndash;charcoal gas cogeneration system were obtained from both the literature [26] and additional research. Furthermore, baseline data for the straw direct combustion power generation system [27], the straw molded fuel power generation system [28], and the straw biogas/biogas production system [29] were sourced from the literature. To ensure a fair comparison of different straw energy technologies, consistent conditions for straw cultivation, collection, transportation, and storage were assumed. This approach enabled the evaluation of emergency energy consumption to be evaluated via indices for the five straw energy technologies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy inputs during corn cultivation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConversion ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSolar value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRenewable energy (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolar energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57E\u0026thinsp;+\u0026thinsp;13 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77E\u0026thinsp;+\u0026thinsp;10 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.22E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeothermal power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63E\u0026thinsp;+\u0026thinsp;09 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.07E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.90E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNon-renewable energy (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopsoil loss energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.73E\u0026thinsp;+\u0026thinsp;09 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.40E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil erosion energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.93E\u0026thinsp;+\u0026thinsp;03 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.38E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRenewable feedback energy (F\u003csub\u003eR\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00E\u0026thinsp;+\u0026thinsp;06 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.99E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor costs I (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026yen;4.15E\u0026thinsp;+\u0026thinsp;01 CNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTorrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25E\u0026thinsp;+\u0026thinsp;03 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.11E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.38E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eNon-renewable feedback energy (F\u003csub\u003eN\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen fertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32E\u0026thinsp;+\u0026thinsp;04 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.07E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphate fertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.92E\u0026thinsp;+\u0026thinsp;02 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.02E\u0026thinsp;+\u0026thinsp;02 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound fertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71\u0026thinsp;+\u0026thinsp;04 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgrochemical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84E\u0026thinsp;+\u0026thinsp;02 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.52E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiesel fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.82E\u0026thinsp;+\u0026thinsp;07 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.60E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCunning (old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89E\u0026thinsp;+\u0026thinsp;02 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.76E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor costs II (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026yen;1.93E\u0026thinsp;+\u0026thinsp;02 CNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYield (Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStalk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.31E\u0026thinsp;+\u0026thinsp;06 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.12E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy inputs for the collection, storage, transportation, and production of the straw biogas/biogas system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConversion ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSolar value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eF\u003csub\u003eR\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor costs I (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.20E\u0026thinsp;+\u0026thinsp;00 (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.72E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.69E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroelectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22E\u0026thinsp;+\u0026thinsp;07 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.23E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.70E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eF\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquipment (in steel)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86E\u0026thinsp;+\u0026thinsp;02 (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.70E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiesel fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38E\u0026thinsp;+\u0026thinsp;09 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.60E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethermal power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.52E\u0026thinsp;+\u0026thinsp;07 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor costs II (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.28E\u0026thinsp;+\u0026thinsp;01 (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.11E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eF\u003csub\u003eR\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.34E\u0026thinsp;+\u0026thinsp;06 (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroelectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416E\u0026thinsp;+\u0026thinsp;06 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.23E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.59E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor costs I (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11E\u0026thinsp;+\u0026thinsp;01 (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.72E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.57E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngineering operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22E\u0026thinsp;+\u0026thinsp;08 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.13E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eF\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfrastructure investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76E\u0026thinsp;+\u0026thinsp;02 (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.89E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.59E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstrumentation costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59E\u0026thinsp;+\u0026thinsp;03 (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.89E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThermal power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65E\u0026thinsp;+\u0026thinsp;06 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor costs II (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.18E\u0026thinsp;+\u0026thinsp;01 (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.77E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraw biogas/biogas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.11E\u0026thinsp;+\u0026thinsp;10 (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. EYR, ELR, and ESI Analysis\u003c/h2\u003e \u003cp\u003eIn assessing the economic benefits of straw energyization technologies, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there were significant differences between the performances of the five technologies. Except for the fuel ethanol technology (SP), the energy value yield rate (EYR) of the remaining four technologies was greater than 1, indicating that these technologies possess significant value-added effects of straw. Specifically, the ranking results of EYR were SG\u0026thinsp;\u0026gt;\u0026thinsp;SPB\u0026thinsp;\u0026gt;\u0026thinsp;SR\u0026thinsp;\u0026gt;\u0026thinsp;SFR\u0026thinsp;\u0026gt;\u0026thinsp;SP, among which the EYR of SG technology was as high as 11.32; this is mainly attributed to its lower non-renewable feedback energy value input in the production process, which demonstrates its absolute advantage in energy conversion efficiency. In contrast, the EYR of SP technology was only 0.97; this is mainly attributed to its reliance on a large amount of non-renewable feedback energy values (e.g., labor cost and thermal power consumption) in the production process, resulting in its poor economic performance. This finding is consistent with the study of Chandra et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), further confirming the potential of SG technology as a more economical and environmentally friendly method of utilizing waste biomass resources.\u003c/p\u003e \u003cp\u003eIn terms of environmental performance, the environmental loading ratios (ELR) of the five technologies showed significant differences in the order of SFR\u0026thinsp;\u0026lt;\u0026thinsp;SP\u0026thinsp;\u0026lt;\u0026thinsp;SPB\u0026thinsp;\u0026lt;\u0026thinsp;SG\u0026thinsp;\u0026lt;\u0026thinsp;SR. SFR showed the best environmental performance due to its lower non-renewable energy value input (62.05% of the total input). In contrast, SR technology provided the worst environmental performance with only 14.49% of renewable energy value inputs and 75.22% of non-renewable energy value inputs, which significantly increases the environmental burden. Further analysis of the sustainability index (ESI) of the five technologies showed that SG\u0026thinsp;\u0026gt;\u0026thinsp;SPB\u0026thinsp;\u0026gt;\u0026thinsp;SFR\u0026thinsp;\u0026gt;\u0026thinsp;SP\u0026thinsp;\u0026gt;\u0026thinsp;SR, among which the SG technology ranked first with an ESI of 3.52 due to its excellent energy output efficiency and sustainability. This indicates that SG technology not only excels in terms of economic efficiency, but it also makes a positive contribution in terms of environmental sustainability. This finding is consistent with the study of Bumharter et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), indicating that SG technology plays a key role in energy saving and emission reduction as well as agro-ecological and environmental protection. SG technology has been vigorously promoted and developed in Europe [32]. In contrast, the remaining four technologies had ESIs below 1, showing their shortcomings in long-term sustainability as consumption-based systems. This is in line with the study of Cao and Feng (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who noted that the unsustainability of these technologies mainly stems from the reliance on high ESI inputs (e.g., fossil fuels, chemicals, and mechanical equipment). These factors not only exacerbate environmental pressures, but also limit the sustainability potential of the system. Therefore, in order to improve the sustainability of these technologies, future research should focus on reducing the reliance on high energy-value inputs and exploring more environmentally friendly and long-term sustainable production methods. Such optimization will not only effectively reduce the environmental burden, but will also contribute to a green transition in the agriculture and energy sectors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of system emergy indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSFR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenewable energy (seJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.48E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.78E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.48E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.48E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-renewable energy (seJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.46E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003eR\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenewable feedback energy (seJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.80E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.71E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-renewable feedback energy (seJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.19E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.67E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYield (seJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.79E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.41E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.31E\u0026thinsp;+\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy yield ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental load ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental sustainability index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Optimization and Analysis of Straw Energization Scheme\u003c/h2\u003e \u003cp\u003eAfter coding and solving the NSGA-II using MATLAB v. [R2022a], the EYR and ELR values gradually converged by approximately the 150th generation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Ultimately, 500 Pareto-optimal solutions satisfied the objectives and constraints (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Based on these results, the 50 solutions with the highest EYR values in the Pareto set were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a) shows the range of values for the SFR, SP, SPB, SG, and SR technologies when EYR was prioritized. SG achieved the highest EYR, with a range of [0.32, 0.60]. The ranking of EYR values for the other technologies was SP\u0026thinsp;\u0026gt;\u0026thinsp;SFR\u0026thinsp;\u0026gt;\u0026thinsp;SPB\u0026thinsp;\u0026gt;\u0026thinsp;SR, primarily because SG involves a relatively low consumption of nonrenewable energy, which accounts for only 2.80% of the output energy value and contributes to its significant economic benefits. In contrast, SR technology displayed an EYR close to zero, indicating poor performance in terms of energy value addition.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b) shows the range of solutions for SFR, SP, SPB, SG, and SR when ELR was the primary optimization objective. SG values ranged from [0.00056, 0.31662], whereas the proportion of SR proportion was near zero. Combining these findings with the EYR-focused results, it is evident that SR technology is not suitable for large-scale adoption, given its poor EYR and ELR performance. This aligns with the current challenges faced by SR technology utilization, including its high cost and technical difficulties with large-scale implementation. Chen et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) noted that SR technology often exhibits high equipment and operating costs.\u003c/p\u003e \u003cp\u003e When employing the second-generation genetic algorithm to solve the multi-objective optimization model for straw energy technologies, the sums of the decision variables for the solutions with the most significant optimization effects tended to converge toward the minimum thresholds of the slack variables. Slack variable optimization was employed across the four constraints (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and the results showed that, as the scale of straw energy utilization decreased, the EYR, ELR, and ESI declined. This indicates that increased straw energization is economically and environmentally beneficial. As of 2020, the straw energization ratio of Yunnan was just 4.64%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\:\\theta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e-values on optimization results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScope of the decision\u003c/p\u003e \u003cp\u003evariable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\:\\theta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEYR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eELR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eESI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.30,0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[0.3008,0.6000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.20,0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[0.2008,0.6000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.046,0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[0.0481,0.5999]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the model results, increasing the straw energization ratio to 20% would result in 12.97, 14.19, and 13.04% increases in EYR, ELR, and ESI, respectively. Further increasing the ratio to 30% would lead to gains of 15.40, 20.95, and 13.53% in these indicators, respectively. These results suggest that expanding the straw energization ratio would boost economic output and considerably improve environmental sustainability in Yunnan. Guo et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) similarly found that regional eco-efficiency improved as the straw energization ratio increased while production efficiency was maintained. Therefore, the outcomes of this study offer strong theoretical support for the practical promotion of straw energization, whose rate should be increased to achieve enhanced economic and ecological outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eIn this study, the energy value yield rate (EYR), environmental loading rate (ELR), and environmental sustainability index (ESI) of the five straw energization technologies were systematically analyzed, and the key findings were as follows: (1) SG technology outperformed the other technologies. In terms of energy efficiency (EYR\u0026thinsp;=\u0026thinsp;11.32) and environmental sustainability (ESI\u0026thinsp;=\u0026thinsp;3.52), SG technology performed well. Although its environmental loading rate ( ELR\u0026thinsp;=\u0026thinsp;3.20) was slightly high, its potential for large-scale application was considered to be high due to its resource utilization and biomass conversion efficiency advantages. (2) SPB technology has a high promotional value. SPB technology also performed well in terms of economic (EYR\u0026thinsp;=\u0026thinsp;2.28) and environmental benefits (ESI\u0026thinsp;=\u0026thinsp;0.94); as such, its potential for promotion is high. (3) With respect to the limitations of inefficient technologies, comparatively speaking, SP and SR technologies had low EYRs and sustainability indexes, which were mainly limited by high EYR inputs and low energy conversion efficiencies; as such, they provide insufficient economic and environmental benefits. In addition, although SFR technology performed better in terms of its environmental burden (ELR\u0026thinsp;=\u0026thinsp;1.84), its economic benefits (EYR\u0026thinsp;=\u0026thinsp;1.36) remained limited.\u003c/p\u003e \u003cp\u003eAmong the optimization options for straw energyization technologies in Yunnan Province, the results of this study suggest that priority should be given to developing SG and SPB technologies. The analysis results show that SG technology provides the most outstanding performance in terms of energy efficiency (EYR\u0026thinsp;=\u0026thinsp;11.32) and sustainability (ESI\u0026thinsp;=\u0026thinsp;3.52), and its high efficiency biomass conversion rate and low consumption of non-renewable resources give it a significant advantage for use in large-scale applications. Especially in Yunnan Province, where straw resources are abundant and environmental protection requirements are high, SG technology should be the preferred development direction to fully utilize its potential in energy conversion and GHG emission reduction. In addition, SPB technology, with its good comprehensive benefits (EYR\u0026thinsp;=\u0026thinsp;2.28, ESI\u0026thinsp;=\u0026thinsp;0.94), also has an important popularization value. In contrast, the feasibility of using SR technology in the region is limited due to its higher resource consumption (ELR\u0026thinsp;=\u0026thinsp;4.94) and lower environmental benefits (ESI\u0026thinsp;=\u0026thinsp;0.43). Therefore, it is recommended to limit the application of SR technology without reducing its dependence on non-renewable resources or improving its energy conversion efficiency through technology optimization to avoid resource waste and environmental burden. To further promote the large-scale application of SG and SPB technologies, measures such as policy support, financial incentives and technological innovation should be implemented. In addition, international success stories, such as experiences in Europe and Brazil, can be drawn upon to gradually increase the rate of straw energyization in Yunnan Province. This will not only promote the coordinated development of regional economy and environmental protection, but also provide solid technological and policy support for realizing green and low-carbon development, and promote energy structure transformation and sustainable economic growth at the national level.\u003c/p\u003e \u003cp\u003eAs the rate of straw energyization in Yunnan Province increases, the energy value yield rate (EYR), environmental load ratio (ELR), and environmental sustainability index (ESI) will all increase significantly. These changes imply an increase in the energy utilization efficiency (EYR increase), a decrease in the dependence on non-renewable resources (ELR decrease), and an increase in overall system sustainability (ESI increase). Specifically, when the rate of straw energyization is increased to 20%, the EYR is expected to increase to 15.40%, the ELR will increase approximately to 20.95%, and the ESI will rise by nearly 13.53%. The increase in energy efficiency and environmental benefits is especially significant in the scenarios centered on SG and SPB technologies. Therefore, a gradual increase in the ratio of straw to energy will not only help promote economic growth, but will also significantly improve environmental protection and promote high-quality sustainable development in the region. To ensure the effectiveness and sustainability of the policy implementation, more specific support measures should be developed: for the development of SG technologies, financial incentives and technology subsidies can be used to reduce the initial costs; for SPB technologies, marketing strategies need to be strengthened to encourage local governments and enterprises to actively participate in the development and implementation of straw energy projects.\u003c/p\u003e "},{"header":"6. Conclusions","content":"\u003cp\u003eWith the increasing global demand for sustainable alternative energy sources, conducting a comprehensive assessment of the economic, environmental and social benefits of employing straw energy technologies was necessary. As such, this paper innovatively combined a Life Cycle Energy Analysis (LCEA) with the Multi-Objective Optimization Model (NSGA-II) to systematically evaluate five major straw energy technologies used in Yunnan Province, and application optimization strategies for different technologies were proposed. The method not only extends from a single-dimensional assessment of economic or environmental benefits to a comprehensive consideration of multi-dimensions, but it also provides a scientific basis for the selection of straw energyization technologies in a specific region. The core innovation of this study is the combination of the LCEA and NSGA-II algorithms, which improve upon the limitations of employing a single analytical method in existing studies. The LCEA accurately captured the inputs and outputs of various types of resources and energies in the production and conversion processes, while the NSGA-II provided an effective optimization pathway to balance the multiple objectives such as economic benefits and environmental sustainability. This enabled us to achieve a balance of environmental, economic and social benefits under a multi-objective optimization framework when analyzing the different straw energyization technologies, thus providing a more reliable and comprehensive basis for applying different technology combinations.\u003c/p\u003e \u003cp\u003eAmong the five technologies evaluated, biogas/biogas technology (SG) and molded fuel power generation (SPB) demonstrated significant potentials by virtue of their high energy value yield ratio (EYR) and environmental sustainability index (ESI). This finding was consistent with the findings of Wu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Nie et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who similarly recommended increasing the proportion of SG and SPB technologies used to capitalize on their advantages in energy conversion and environmental protection. SG technology is not only widely used in China, but it has also proved to be highly feasible and adaptable in Europe, as shown in the studies of Bumharter et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Magnusson et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the successful dissemination experience of SPB technology in Brazil, as shown in the study by De Almeida et al. (2024), further demonstrates its sustainability and economic benefits in different agricultural resource-rich countries. Mentioning these success stories not only enhances the international context of this study, but it also provides important lessons for straw energy technology in Yunnan Province. Although direct-fired power generation (SFR) technology has a low energy yield, it still exhibits good environmental performance due to its reliance on renewable resources, and thus can be an important complement in the overall technology system. However, fuel ethanol (SP) and pyrolysis carbon gas cogeneration (SR) technologies have significant limitations in terms of high energy value inputs and environmental pressures, and should be promoted with caution at this stage. In particular, SP technology greatly reduces its economic and environmental benefits due to the high inputs of electricity and hydrochloric acid (90.20% of the total), while SR technology similarly exhibits a large environmental load due to the high inputs of iron and steel and cement, which is closely related to the limitations of the existing technology development level. Therefore, future research should focus on addressing the resource inputs of these technologies to improve their sustainability. According to the data of 2020, the ratio of straw energyization in Yunnan Province is only 4.64%.\u003c/p\u003e \u003cp\u003eTo further enhance economic benefits and environmental protection in the region, it is recommended that Yunnan Province should prioritize the development of SG and SPB technologies with the potential for scale-up. Gradually increasing the ratio of straw energyization will not only significantly improve the energy-yield ratio (EYR), environmental load ratio (ELR), and environmental sustainability index (ESI), but it will also promote the synergistic development of the economy and the environment. As shown in the studies of Elgarahy et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Kothari et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), straw energization not only improves the combustion efficiency, but it also effectively reduces environmental pollution, which provides the important opportunity to promote an energy revolution and national economic growth. Therefore, Yunnan Province should facilitate this process through specific policy support, such as providing subsidies to farmers and enterprises for straw collection and treatment, promoting market-based operation models for biomass energy technologies, and supporting related research and development (R\u0026amp;D) through a technology innovation fund. Guo et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) pointed out that the combination of financial incentives and technological support is an important driver for promoting the energy revolution, and this strategy also provides important references for other global agricultural resource-rich regions provide an important reference framework. This study provides a more systematic basis for technology selection through the life cycle energy value analysis method and multi-objective optimization model, and provides specific policy recommendations for other straw-rich regions in Yunnan Province and around the globe in terms of energy structure transformation and low-carbon economic development. With the deepening of technological progress and policy support, straw energy technology is expected to become an important part of sustainable energy development in the future. Future research needs to further explore how to optimize the resource use efficiency of inefficient technologies and assess the adaptability of technologies in a wider range of regions and application scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eWe would like to express our sincere gratitude to Mr. Wudi Zhang for his invaluable guidance and insightful feedback throughout the course of this study. His expertise and advice were essential to both the conceptual development and successful completion of the research.\u003c/p\u003e\n\u003cp\u003eWe also extend our heartfelt thanks to Ms. Wang Changmei and Ms. Yin Fang from the Biomass Energy Group for their significant contributions to the experimental design, data analysis, and manuscript preparation. Their technical support and collaborative efforts were critical to achieving the objectives of this study.\u003c/p\u003e\n\u003cp\u003eAdditionally, we thank Invincible Zhang for his invaluable guidance and support during the research. We are also grateful to the teachers and students of the biomass group for their assistance.\u003c/p\u003e\n\u003cp\u003eFurthermore, we would like to express our sincere thanks to Yonghui Li and Yongcai Yu of Plow Power Limited for generously providing the experimental data, which were crucial for the analysis in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThis research was jointly supported by the Yunnan Provincial Ten Thousand People Plan Industrial Technology Leaders Project (20191096), Yunnan Provincial Basic Research Special-Project (202401AT070120), Yunnan Provincial Key Laboratory of Rural Energy Engineering Fund (2022KF013), and the grassroots scientific research station of Zhang Wouwu Di expert of Kunming Roots Force Biotechnology Co.\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003eThe data that support the findings of this study are openly available in the Yunnan Data Repository. Additionally, all relevant materials are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUmar M, Ji X, Kirikkaleli D, et al. The imperativeness of environmental quality in the United States transportation sector amidst biomass-fossil energy consumption and growth. J Clean Prod. 2021;285:124863.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToan NS, Phuong NTD, Thuy PT, et al. Effects of burning rice straw residue on-field on soil organic carbon pools: Environment-friendly approach from a conventional rice paddy in central Vietnam. Chemosphere. 2022;294:133596.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeora PS, Verma Y, Muhal RA et al. (2022). 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Renewable Energy. 2023;215:118982.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"straw energization technology, life cycle energy analysis, multi-objective linear programming, sustainability index, energy utilization rate","lastPublishedDoi":"10.21203/rs.3.rs-5628959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5628959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, straw energy conversion technology has become a key research focus due to its potential to alleviate energy shortages and reduce environmental pollution. This paper systematically evaluates the economic, environmental, and social benefits of five main straw energy conversion technologies in Yunnan Province, based on a Life Cycle Energy Analysis (LCEA) and multi-objective linear programming. The technologies studied include straw biogas/biomethane, pellet fuel power generation, fuel ethanol, direct combustion power generation, and pyrolysis gas co-production. The results show that straw biogas/biomethane technology has the highest sustainability index, indicating its significant advantages in economic, environmental, and social benefits; therefore, it is recommended for prioritized development. In contrast, the sustainability indices of fuel ethanol and pyrolysis gas co-production technologies are 0.46 and 0.43, respectively, suggesting poor sustainability, and that their development should be restricted. If the policy goal is to enhance regional economic benefits, increasing the proportion of straw biogas/biomethane and pellet fuel power generation technologies is advisable, but i the focus is on environmental impact, straw biogas/biomethane technology should be the primary treatment method. The study further suggests that by improving the utilization rate of straw energy conversion, a win-win situation for economic growth and environmental protection can be achieved, providing strong support for Yunnan Province's transition to green, low-carbon development. The results provide a theoretical basis for local governments to formulate scientific policies.\u003c/p\u003e","manuscriptTitle":"Multi-objective Optimization Based on Full LCEVA Life Analysis of Straw Energy Technologies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-20 12:41:36","doi":"10.21203/rs.3.rs-5628959/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f27aada4-26ef-48a5-bd21-d762082bc3b4","owner":[],"postedDate":"December 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-22T11:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-20 12:41:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5628959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5628959","identity":"rs-5628959","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}