Emergy, Environmental and Economic (3E) Assessment of Biomass Pellets from Agricultural Waste

Abstract

Biomass pellets are increasingly recognized as a cost-effective and sustainable renewable energy source worldwide. However, comprehensive sustainability assessments of their production processes are scarce. To address this gap, three distinct scenarios in Northeast China were evaluated using emergy, economic, and environmental analysis methods: corn single production, corn–pellet co-production, and pellet production. A modified method for calculating the environmental loading rate (ELR) was proposed, which accounts for the environmental benefits associated with replacing coal with biomass pellets for heating. The results showed that corn–pellet co-production demonstrates superior energy efficiency compared to corn-only production, but presents a contrasting economic profile. The ELR for corn single production and corn–pellet co-production are 1.57 and 1.63, respectively, with corresponding emergy sustainability indices (ESI) of 0.89 and 0.84. After applying the modified method, the ELR and ESI for corn–pellet co-production were adjusted to 0.84 and 1.63, respectively, and the ESI of pellet production increased from 8.24 to 21.15. Furthermore, processing corn straw into biomass pellets for heating can reduce heating costs by approximately USD 254.26/hm² and reduce emissions of SO₂, NO_x, CO, PM_2.5, and CO₂ by 9.12, 19.82, 580.31, 65.86, and 13,060.66 kg/hm², respectively. Sensitivity analysis revealed that transportation distance and renewable electricity have a greater impact on pellet production than corn–pellet co-production. The ESI for pellet production decreases from 21.15 to 14.02 as transport distance increases from 20 km to 100 km, while it rises to 57.81 as the proportion of renewable energy in the power supply increases from 0% to 100%.

1. Introduction

The growing global demand for energy, driven by industrialization and urbanization, has placed significant pressure on the environment. Over 86% of global energy consumption is still reliant on traditional fossil fuels, which emit pollutants such as SO₂, NO_x, PAHs, CO, and CO₂, exacerbating environmental degradation and contributing to climate change [1,2,3]. To mitigate these environmental impacts and protect human health, transitioning to renewable energy sources such as wind, solar, geothermal, and biomass is considered one of the most promising solutions to reduce dependence on fossil fuels [4]. Among these, bioenergy has become the world’s fourth-largest energy source, following oil, coal, and natural gas, accounting for an estimated 11.6% of global final energy consumption, or 44 exajoules (EJ), with more than half of this coming from renewable sources [5]. Some practices have proven that utilizing crop straw for biomass-pellet production can not only alleviate rural energy poverty but also contribute significantly to rural development and sustainable agricultural practices [6].

Research on biomass pellets has been primarily focused in five key areas. First, advancements in pellet production processes aim to optimize energy consumption during pelleting, minimize friction during ejection, and increase the volumetric energy density of pellets [7,8,9,10]. Second, the influence of different raw materials, such as size and moisture content, on the performance of combustion and pollutant emissions of biomass pellets has been extensively studied [11,12,13,14]. Third, innovations in stove design and air-supply systems have been explored to enhance combustion efficiency and reduce emissions [15,16,17]. In addition to experimental approaches, modern analytical methods employing mathematical modeling and simulation are being utilized to study the temperature and flow dynamics during pellets’ fuel-combustion processes in a certain stove [18,19,20]. Finally, methods such as Life-Cycle Assessment (LCA) and economic analysis are employed to evaluate the economic and environmental benefits of pellet production. These studies have significantly advanced the understanding of the biomass pellet, focusing on its technical, environmental, and economic properties, as well as its application [21,22,23]. Biomass, as the raw material for pellet fuel production, heavily relies on natural resources such as sunlight, rainwater, and soil nutrients for its formation. However, LCA primarily focuses on the environmental impacts of various emissions from the production process on the ecosystem, failing to reflect the natural resources’ contribution to pellet production, such as the ecological efficiency and sustainability of the production process, which makes the study of pellet production incomplete. Additionally, the process of comparing and analyzing the extent of environmental impacts in LCA carries a certain degree of subjectivity.

The emergy analysis methodology is able to convert any form of resources such as energy, financial, and human labor into solar emjoules (sej) by multiplying their solar transformity [24]. This enables the application of emergy analysis methods to quantify and scrutinize the intricate interplay between human economic systems and the natural environment within a unified metric framework [25,26]. Such an approach aids in harmonizing the ecological equilibrium with economic progress, thereby fostering the rational utilization of natural resources and the attainment of sustainable development. This distinctive feature positions emergy analysis as a powerful tool in the sustainability assessment in the other production process of other economic products that depend on natural resources. For instance, Fabrizio et al. [27] applied emergy analysis to evaluate the ecological sustainability of biodiesel production systems. Sun et al. [28] employed emergy theory to assess the ecological sustainability of a biogas production system based on the case of the Weilai energy company. Morandi et al. [29] evaluated the sustainability performance of miscanthus as an energy crop from the field to the plant.

To address this research gap, the present study employs a case-study approach to conduct a thorough sustainability assessment of the entire process, from straw to pellet fuel, using a 3E (emergy, economic, and environmental) analysis framework. The key contributions of this paper are as follows: (1) A modified ELR calculation method that reflects the environmental and economic benefits of replacing traditional fossil fuels by pellet is proposed to more accurately assess the environmental impact of biomass energy. (2) The energy efficiency, economic viability, and sustainability of three different scenarios of corn single production, corn–pellet co-production, and biomass-pellet production are qualitative compared. (3) The impacts of transportation distance and renewable electricity on the results of sustainability of pellet production are examined. (4) A quantitative analysis is conducted to evaluate the economic and environmental benefits of using biomass pellets for heating purposes.

2. Methods

The emergy evaluation process generally involves several key steps: defining the research scale and emergy baseline, data collection, drawing the emergy system diagram, preparing the emergy analysis table for inputs and outputs, establishing the emergy indices system, and conducting system evaluation and analysis.

2.1. Research Scale

Corn, the second largest agricultural cash crop in the world in terms of area planted, has the most abundant straw production [30]. Therefore, a case study on the utilization of corn stalk for the production of pellet fuel are evaluated by emergy analysis in this paper. There are three distinct scenarios in the pellet production process as shown in Figure 1. The first scenario, corn single production (CSP), represents a basic agricultural system where corn is cultivated for its grain, and the corn straw is combusted on the open field. In this scenario, there is no secondary use for the straw, and the process focuses solely on corn production. The second scenario, corn–pellet co-production (CPCP), builds upon the first by incorporating the collection and processing of corn straw into biomass pellets. In this scenario, after corn is harvested, the straw is collected and transported to a pellet plant, where it is converted into pellets that can be used as a renewable energy source for heating and other purposes in rural areas. To gain a deeper understanding of the pellet production process itself, the third scenario, pellet production (PP), is examined. This scenario focuses exclusively on the pellet-manufacturing phase and is also considered a sub-system of the CPCP.

The input and output flow within the emergy are categorized as follows: renewable environmental resource emergy (R), nonrenewable environment resource emergy (N), purchased resource emergy (F), purchased renewable resource emergy (F_R), purchased nonrenewable resource emergy (F_N), and product energy (P). The yield (Y) represents the sum of total input emergy flows. The relationship between input and output emergy is elucidated by Equations (1) and (2).

$$F=F_{\mathrm{R}}+F_{\mathrm{N}}$$

(1)

$$Y=R+N+F$$

(2)

2.2. Emergy Baseline

The emergy baseline, a fundamental parameter to the calculation of solar transformity, represents the total annual solar emergy supporting the Earth’s biosphere system, encompassing the emergy of direct solar radiation on the Earth’s surface, geothermal energy from the Earth’s interior, and tidal energy. The emergy baseline of 9.44 × 10²⁴ sej/yr was first given by Odum in 1996, and through this baseline, the solar transformity of the main energies and substances in nature and human socio-economic systems was calculated [26]. This baseline was updated in 2000, where it was revised to 15.83 × 10²⁴ sej/yr [31]. In 2010, Brown and Ulgiati further refined this to 15.2 × 10²⁴ sej/yr [32]. The most recent calculation by Brown and Ulgiati yielded a value of 12.1 × 10²⁴ sej/yr in 2016 [33]. These fluctuations in the emergy baseline are primarily attributed to advancements in calculation methods and improvements in the accuracy of the Earth’s internal emergy cycle. Odum et al. [26] emphasized that emergy studies should rely on the latest emergy baseline, which is why the value of 12.1 × 10²⁴ sej/yr is adopted in this study. To convert the solar transformity of various substances and energies under different emergy baselines, the following is applied:

$$T{r}_{\mathrm{new}}=T{r}_{\mathrm{old}}\times (L_{\mathrm{new}}/L_{\mathrm{old}})$$

(3)

where Tr_new and L_new is the solar transformity and emergy baseline of one substance or energy, respectively. Tr_old and L_old denotes the solar transformity and emergy baseline which calculated by Odum et al. in 1996.

2.3. Emergy Indices

Numerous indices are commonly employed in emergy analysis to assess the environmental impact, economic efficiency, and sustainability of different systems [34,35,36]. These include emergy yield ratio (EYR), environmental loading rate (ELR), emergy sustainable indices (ESI), renewable energy input ratio (RIR), emergy self-support ratio (ESR), emergy investment ratio (EIR), and so on. The definition and meaning of each index are listed in

Table 1. Common indices for emergy analysis.

Category	Indicator	Formula	Meaning and Applying
Eco-economy indices	EYR	EYR = Y/F	EYR is the ratio of yield emergy to purchased emergy and is used to evaluate the output efficiency of the system. Higher EYR indicates that the system is more economically efficient with greater yield emergy from fewer economic inputs.
	EIR	EIR = F/(R + N)	EIR is the ratio of purchased emergy and natural resource emergy. A low EIR implies that the investment of system is economical.
	ESR	ESR = (R + N)/Y	ESR is used to evaluate the natural environmental support capacity to the system. A high ESR means the system has a high degree of self-sustainability and internal natural resource development, and a low dependence on purchased emergy.
Environment indices	ELR	ELR = (FN + N)/(R + FR)	ELR represents the pressure exerted on the environment by the system. A high ELR means that the greater the utilization of non-renewable resources by the system, the greater the environmental impacts caused.
	RIR	RIR = (R + FR)/Y	RIR indicates the ability of the system to utilize renewable resources. Higher values of RIR indicate a higher utilization of renewable resources by the system.
Sustainability index	ESI	ESI = EYR/ELR	ESI is applied to the evaluation of sustainability of a system. The higher the ESI, the more sustainable the system is, i.e., with higher productivity and less environmental stress.
Others	Tr	Tr = Y/P	Tr is the solar transformity of one product. Indicates how much input is required to obtain the product. A high Tr represents a product with a high level of emergy in the system, meaning that more natural resources and services are consumed for the production of product.

.

2.3.1. Correlation Analysis of Emergy Indices

In the emergy analysis indicator system, there are obvious correlations among some indices. According to the principle that each index used in emergy analysis is independent of each other, they should be grouped and simplified to avoid repetitive evaluation of the system.

For eco-economic indices, EYR, EIR, and ESR can be written as:

$$ESR=1-1/EYR$$

(4)

$$1/ESR=1+EIR$$

(5)

Equations (4) and (5) indicate that there is a direct correlation between EYR, ESR, and EIR, which has an overlapping effect on the eco-economic evaluation. By analyzing the frequency of use in existing cases of emergy analysis and its similarity to the output/input ratio of the primary evaluation index in classical economics, EYR is selected as the eco-economic evaluation index. In contrast, ESR and EIR focus on the relationship between purchased emergy or total input emergy and the emergy from natural resource inputs.

For environment indicators, RIR and ELR can be expressed as follows:

$$ELR=1-1/RIR$$

(6)

From Equation (6), it is obvious that ELR and RIR share the same function in system environment evaluation. The ELR is more appropriate in view of the intuitiveness and clarity of the evaluation of the environmental pressure level [34], while RIR focuses on expressing the system’s efficiency in utilizing renewable resources. Therefore, ELR is selected to evaluate environmental stress of the system in this paper.

2.3.2. The Joint Transformity and the Weighted Average of Transformity

In co-production systems, the conventional concept of transformity as a measure of efficiency may not be directly applicable [35]. This is because allocating input emergy between co-products can lead to output emergy exceeding input emergy, which goes against the emergy algebra rule 4 (i.e., the total output emergy cannot be greater than input emergy). Other methods include the proportional allocation method [31]. However, in the case of corn grain and corn stalk (the latter used for pellet production), both share the same biosphere inputs during cultivation, making it difficult to precisely assign individual inputs to each product. To address this challenge, two indices introduced by Bastiaoniand and Marchettini [37] are adopted in this study for CPCP scenario: joint transformity Tr_j and the weighted average of transformity Tr_ave, which can be calculated by Equation (7) and Equation (8), respectively.

$$T{r}_{\mathrm{j}}={\displaystyle \frac{E{m}_{\mathrm{cp}}}{E_{\mathrm{c}}+E_{\mathrm{p}}}}$$

(7)

$$T{r}_{\mathrm{ave}}={\displaystyle \frac{E_{\mathrm{c}}}{E_{\mathrm{c}}+E_{\mathrm{p}}}}T{r}_{\mathrm{c}}+{\displaystyle \frac{E_{\mathrm{p}}}{E_{\mathrm{c}}+E_{\mathrm{p}}}}T{r}_{\mathrm{p}}={\displaystyle \frac{E{m}_{\mathrm{c}}+E{m}_{\mathrm{p}}}{E_{\mathrm{c}}+E_{\mathrm{p}}}}$$

(8)

where Em_cp denotes the total emergy needed for co-production. E_c and E_p denote the energy content of corn and pellets, respectively. Tr_c and Tr_p denote the transformity of corn and pellets in independent production, respectively. Em_c and Em_p are the emergy needed in corn and pellets’ independent production, respectively. Co-production is deemed more efficient if the joint transformity Tr_j is lower than the weighted average of transformity Tr_ave. This approach allows for a more accurate representation of the emergy flows and transformations within the co-production system, ensuring adherence to emergy algebra principles while providing insights into the relative contributions of each product to the overall emergy balance.

2.3.3. Modified ELR

In the traditional emergy analysis method, the environmental impact of the pollutants emitted by the system is not considered. Xiang [38] et al. proposed an improved methodology for calculating ELR, which measures the load on the environment caused by the overexploitation of local non-renewable resources and environmental emissions in excess of environmental capacity. Zhang et al. [39] revised the ELR calculation to reflect the environment and economic benefits of replacing traditional fossil fuels with renewable energy sources. In summary, the modified ELR calculation method takes into account the reduced emissions of various pollutants from pellet heating as a replacement for traditional coal heating, converting these reductions into treatment costs and incorporating them into the emergy analysis of pellet production, as follows:

$$ELR=(F_{\mathrm{N}}+N)/(R+F_{\mathrm{R}}+F_{\mathrm{CN}}+F_{\mathrm{CR}})$$

(9)

where F_CN is the emergy of coal saved by the biomass-pellet fuel, F_CR is the emergy of the saved treatment cost through decreased emissions. For the CSP, the open burning of straw produces pollutants that are emitted directly into the atmosphere, so the saved treatment cost is negative.

F_CN and F_CR can be calculated as:

$$m_{\mathrm{c}}={\displaystyle \frac{LH{V}_{\mathrm{p}}\times m_{\mathrm{p}}\times {\eta }_{\mathrm{p}}}{LH{V}_{\mathrm{c}}\times {\eta }_{\mathrm{c}}}}$$

(10)

$$F_{\mathrm{CN}}=E_{\mathrm{c}}\times T{r}_{\mathrm{c}}$$

(11)

$$F_{\mathrm{CR}}=\left[{\displaystyle \sum _{i=1}^n(E{F}_{\mathrm{c},i}\times m_{\mathrm{c}}+E{F}_{\mathrm{s},i}\times m_{\mathrm{s}}-E{F}_{\mathrm{p},i}\times m_{\mathrm{p}}-E{F}_{\mathrm{e},i}\times P_{\mathrm{e}}-E{F}_{\mathrm{t},i}\times S_{\mathrm{t}})}\times E{E}_i\right]\times T{r}_{\mathrm{m}}$$

(12)

where m_coal is the mass of coal saved; LHV_p and LHV_c are the lower heating values of biomass pellet and coal; η_p and η_c are the thermal efficiency of the pellet heating stove and the coal heating stove; E_c is the energy of coal saved; Tr_c is the solar transformity of coal, EF_c,i, EF_s,i, EF_p,i, EF_e,i, and EF_t,i, are the emission factors of ith pollutants (SO₂, NO_x, CO, PM_2.5 and CO₂) for the coal, corn stalk, corn-stalk pellets, electricity, and truck; m_p is the mass of pellet fuel produced by one hectare of corn straw; and P_e is the power consumed in pellet production. S_t is the distance of truck for biomass transportation; EE_i is the treatment cost of ith pollutant, and the values of PM_2.5, SO₂, NO_x, CO₂ and CO are 0.02, 0.87, 1.16, 3.3 × 10⁻³, and USD 0.14/kg, respectively [40]; Tr_m is the solar transformity of monetary value. The emission factors of corn-stalk pellets are analyzed by a laboratory testing system [41], and others are collected from the literature, which are shown in

Table 2. Emission factors of different fuel and sector.

Category	Emission Factor a (g/kg)					Reference
	SO2	NOx	CO	PM2.5	CO2
Corn stalk b	0.31	2.57	68.61	8.35	1350.00	[42]
Coal c	1.78	2.05	61.05	3.73	2497.23	[43]
Corn-stalk Pellets d	0.03	1.24	28.06	0.68	1492.14
Electricity e	0.20	0.49	2.18	0.41	852.79	[44]
Truck	\	9.66	3.11	0.04	893.4	[45]

^a The unit of emission factor for electricity is g/kWh, and for the truck it is g/km. ^b Corn stalk burned in open fields. ^c Coal burned in the coal heating stove under a steady-state conditions. ^d Corn-stalk pellets burned in the biomass pellets’ heating stove under a steady-state conditions. ^e The pollutants generated by the thermal power plant.

.

2.4. Environmental and Economic Assessment

Emergy analysis provides only a qualitative description of the ecological efficiency and sustainability of using agricultural waste for pellet fuel production. To quantitatively characterize the advantages of pellet fuel as a substitute for coal in heating applications, the concepts of economic benefit (ECB), environmental benefit (ENB), and pollutant net emission (PNE) are introduced, defined as follows:

$$ECB=m_{\mathrm{c}}{c}_{\mathrm{c}}-m_{\mathrm{p}}{c}_{\mathrm{p}}$$

(13)

$$EN{B}_i=F_{\mathrm{CR}}/T{r}_{\mathrm{m}}$$

(14)

$$PN{E}_i=m_{\mathrm{c}}{\displaystyle \sum E{F}_{\mathrm{c},i}+}{m}_{\mathrm{s}}{\displaystyle \sum E{F}_{\mathrm{s},i}-}{m}_{\mathrm{p}}{\displaystyle \sum E{F}_{\mathrm{p},i}}-P_{\mathrm{e}}{\displaystyle \sum E{F}_{\mathrm{e},i}}-S_{\mathrm{t}}{\displaystyle \sum E{F}_{\mathrm{t},i}}$$

(15)

where ECB is the cost saved by using corn-stalk pellets to substitute coal; ENB_i is the environment benefit of total pollutants’ emission reduction from pellet fuel to substitute coal.

3. Case Study

A case study was conducted in Northeast China to perform emergy analysis based on field surveys and data collection. The whole process of CPCP is shown in Figure 2. The yield per hectare of corn and corn straw was 6130 kg and 6375 kg, respectively. The collection factor of straw is 0.85. The total distance for biomass transportation includes field collection and pellet fuel sales, which is approximately 20 km or 5 km per trip, the average distance between farmland and village town. Diesel-fueled trucks with a loading capacity of 3000 kg and fuel consumption of 0.15 L/km at full load were employed for transportation, with an empty-to-loaded fuel consumption ratio of 0.75. Straw filtration and water evaporation have a mass loss rate of about 30%. After the process of filtering and the evaporation of water, the final mass of corn stalk available for processing into biomass-pellet fuel is 3793 kg. The pellet fuel produced complies with ISO 17225: 2021 [46], and the average diameter, length, bulk density, mechanical durability and breakage rage of the pellets are 8 mm, 30 mm, 630 kg/m³, 98.2%, 2.0%. The other characteristics of the corn-stalk pellets are shown in

Table 3. The mechanical characteristics and chemical composition of the corn-stalk pellets.

Parameter	Lower Heating Value/(MJ/kg)	Industrial Analysis			Elemental Analysis a
		Moisture Content/%	Ash Content/%	Volatile Content/%	N Content/%	S Content/%	Cl Content/%
Corn-stalk pellets	16.80	8.46	5.21	70.31	0.77	0.06	0.25

^a The heavy metal content complies with the requirements of the ISO 17225: 2021 standard [46].

. The fuel cost of coal and corn-stalk pellets are USD 116/t and USD 58/t (which have a government subsidy of USD 29/t).

With the exception of meteorological and topsoil loss data, which are derived from the report and the literature, all other data are derived from field research. The input and output data of CSP and CPCP are summarized in

Table 4. Emergy inputs and outputs data of CSP and CPCP.

NO.	Items	Data	Units	Transformity (sej/Unit)	Reference	Emergy (sej/hm2)
Renewable environment resource emergy (R) a						6.15 × 1014
1 AB b	Sunlight	1.85 × 1013	J/hm2	1.00	[26]	1.85 × 1013
2 AB	Rain (gravitational)	3.02 × 109	J/hm2	1.28 × 104	[26,47]	3.87 × 1013
3 AB	Rain (chemical)	2.67 × 1010	J/hm2	2.31 × 104	[26,47]	6.15 × 1014
4 AB	Wind	6.76 × 107	J/hm2	1.28 × 103	[26,47]	8.66 × 1010
Nonrenewable environment resource emergy (N)						1.52 × 1015
5 AB	Topsoil loss	1.90 × 1010	J/hm2	8.01 × 104	[33,48]	1.52 × 1015
6 AB	Irrigation water	8.89 × 109	g/hm2	2.24 × 105	[33,37]	1.99 × 1015
Purchased resource emergy (F)						1.09 × 1016
6 AB	Seed	7.34 × 108	J/hm2	1.42 × 105	[33]	1.04 × 1014
7 AB	Labor in crop production c	9.80 × 102	USD/hm2	6.67 × 1012 d	[33]	8.17 × 1015
8 B	Labor in pellet production	38.20	USD/hm2	6.67 × 1012	[33]	2.55 × 1014
9 B	Labor in biomass transportation e	1.45	USD/hm2	6.67 × 1012	[33]	9.66 × 1012
10 AB	Nitrogen fertilizer	1.80 × 105	g/hm2	4.87 × 109	[33]	8.77 × 1014
11 AB	Phosphate fertilizer	5.00 × 104	g/hm2	5.00 × 109	[33]	2.50 × 1014
12 AB	Potassium fertilizer	3.00 × 104	g/hm2	1.41 × 109	[33]	4.23 × 1013
13 AB	Complex fertilizer	4.00 × 104	g/hm2	3.59 × 109	[33]	1.44 × 1014
14 AB	Pesticide	2.30 × 104	g/hm2	2.05 × 109	[33]	4.72 × 1013
15 AB	Machinery in cultivation	6.40 × 104	g/hm2	3.55 × 109	[33]	2.27 × 1014
16 AB	Diesel of cultivation machine f	5.48 × 109	J/hm2	8.46 × 104	[33]	4.63 × 1014
17 B	Machinery in baling	1.60 × 104	g/hm2	3.55 × 109	[33]	5.68 × 1013
18 B	Diesel for binding machine	9.93 × 108	J/hm2	8.46 × 104	[33]	8.40 × 1013
19 B	Machinery in pelleting	1.82 × 104	g/hm2	3.55 × 109	[33]	6.46 × 1013
20 B	Diesel for biomass transportation	5.88 × 108	J/hm2	8.46 × 104	[33]	4.97 × 1013
21 B	Electricity for pelleting g	1.26 × 109	J/hm2	2.05 × 105	[33]	2.58 × 1014
FCN B	The emergy of coal saved by the biomass-pellet fuel	8.50 × 1010	J/hm2	5.13 × 104	[33]	4.36 × 1015
FCR A	The emergy of the saved treatment cost through decreased emissions	−1.14 × 102	USD/hm2	6.67 × 1012	[33]	−7.58 × 1014
FCR B	The emergy of the saved treatment cost through decreased emissions	−1.59 × 102	USD/hm2	6.67 × 1012	[33]	1.06 × 1015
Products energy (P)
22 AB	Corn	9.99 × 1010	J/hm2
23 B	Corn-stalk pellets h	6.37 × 1010	J/hm2

^a According to the emergy theory, only the maximum emergy input of the same nature is taken to avoid double calculation, because wind, rain (chemical), rain (gravitational), and sunlight are all conversion forms of solar energy. ^b AB means the emergy input of this item exists in CSP and CPCP. B means the emergy input only exists in CPCP. ^c Based on the National Bureau of Statistics of China, the average salary of agricultural workers is CNY 20,492. ^d Based on the constant price, exchange ratio of USD to CNY = 6.8974 CNY/USD. ^e The cost of labor for truck drivers is CNY 0.5/km. ^f The lower heating value of diesel is 36.5 MJ/L. ^g Coal electricity accounts for about 70% of the national electricity generation in China. Based on the specific power structure of the study area, the electricity here is calculated to be generated solely from coal. ^h The lower heating value of corn-straw pellets is 16.8 MJ/kg.

, and the data of PP are summarized in

Table 5. Input and output data of PP.

NO.	Items	Data	Units	Transformity (sej/Unit)	Ref	Emergy (sej)
Purchased resource emergy (F)
1	Straw bales a	6.77 × 1010	J	5.00 × 104	[33]	3.39 × 1015
2	Labor in pellet fuel production	16.5	USD	6.67 × 1012	[33]	1.10 × 1014
3	Labor for biomass transportation	1.45	USD	6.67 × 1012	[33]	9.66 × 1012
4	Machinery in pelleting	1.82 × 104	g	3.55 × 109	[33]	6.46 × 1013
5	Diesel for biomass transportation	5.88 × 108	J	8.46 × 104	[33]	4.97 × 1013
6	Electricity for pelleting	1.26 × 109	J	2.05 × 105	[33]	2.58 × 1014
Products (P)
7	Corn-stalk pellets	6.37 × 1010	J	5.94 × 104		3.88 × 1015

^a The moisture content of straw bales is about 15% and the lower heating value is 12.5 MJ/kg.

. The solar transformity of each energy or substance are collected from the literature and all are converted under the emergy baseline of 12.1 × 10²⁴ sej/yr by Equation (3).

4. Results and Discussion

Based on data from the literature and case studies, the input emergy and emergy indices of different scenarios are obtained and compared. Subsequently, an analysis is conducted on the impact of transportation distance and renewable electricity on the emergy indices of pellet fuel production.

4.1. Composition of Emergy Inputs

The composition of emergy inputs is elucidated in Figure 3. In comparison to CSP, the CPCP extends the industrial chain by utilizing agricultural waste—corn straw—for pellet fuel production, thus enhancing the overall utilization of straw. Therefore, CSP and CPCP exhibit identical emergy inputs during the cultivating process, with values of R and N amounting to 6.15 × 10¹⁴ and 3.51 × 10¹⁵ sej/hm², respectively. Here, R denotes the emergy inputs derived from local natural resources such as wind, sun, and precipitation, and N signifies the emergy associated with topsoil depletion and irrigation water.

In terms of emergy proportions across the different scenarios, R remains minimal in all cases. Specifically, the proportion of R is 10% for CSP, 4% for CPCP, and 0% for PP. In the PP scenario, there is no input of R or N, and the F accounts for 100% of the inputs, which is further subdivided into 10.8% of F_N and 89.2% F_R. The increase in F_R input allocation from 30% in CSP to 33.9% in CPCP highlights the greater reliance on renewable purchased resources in the co-production process. Furthermore, the exclusive reliance on F inputs in PP underscores that pellet fuel production is essentially a purely economic system from the aspects of pellet production plants, devoid of direct natural resource utilization, reflecting the industrial nature of pellet manufacturing.

4.2. Transformity Evaluation in CSP and CPCP

The results of transformity and emergy evaluations are summarized in

Table 6. Transformity and emergy values in CSP and CPCP.

Items	CSP	CPCP
Emergy input (sej/hm2)	1.44 × 1016	1.52 × 1016
Energy in product (sej/J)	9.99 × 1010	1.64 × 1011
Transformity in independent production (sej/J)	1.45 × 105	9.31 × 104
Trj (sej/J)	9.31 × 104
Trave (sej/J)	1.81 × 105

. The transformity in independent production for CPCP is 9.31 × 10⁴ sej/J, lower than CSP which is 1.25 × 10⁵ sej/J. This suggests that with congruous resources emergy inputs, the emergy effluents of CPCP surpass those of CSP. Moreover, the value of Tr_ave and Tr_j is 1.81 × 10⁵ and 9.31 × 10⁴ sej/J, respectively. The Tr_ave is approximately 95% larger than the Tr_j. This difference can be interpreted as follows: the biosphere’s past work in CPCP can be attenuated by 95% to obtain the same quantity of outputs, in energy terms, in the same proportions. This significant reduction highlights the higher energy efficiency of CPCP, implying that the process of producing pellet fuel from straw is a more efficient method of generating emergy. Therefore, yielding greater economic and ecological benefits compared to CSP.

4.3. Emergy-Based Indicators and Sensitivity Analysis

4.3.1. Comparison of Emergy Indices

The emergy indices for different scenarios are outlined in

Table 7. Emergy indices, ECB, and ENB of the different scenarios.

Items	EYR		ELR		ESI
	Traditional	Modified	Traditional	Modified	Traditional	Modified
CSP	1.40	\	1.57	1.82	0.89	0.77
CPCP	1.37	\	1.63	0.84	0.84	1.63
PP	1.00	\	0.12	0.05	8.24	21.15

. The EYR for CSP and CPCP is 1.40 and 1.37, respectively. This indicates that CSP is more economically efficient in terms of energy utilization. Combined with the insight discussed in the previous section, it can be concluded that CPCP demonstrates better ecological efficiency than CSP. However, the economic perspective presents a contrasting picture. Through the conventional computation approach, the ELR for CPCP is 1.63, exceeding that of CSP at 1.57, illustrating that the extension of the straw industry chain for pellet production amplifies the environmental burden. Nevertheless, when the ELR is calculated using the modified approach, the ELR for CPCP decreases to 0.84, while the ELR for CSP increased to 1.82. This suggests that CPCP could derive positive environmental benefits by reducing pollutants emitted from coal use and open-field straw burning.

The ESI for CSP and CPCP are 0.89 and 0.84, respectively, under the traditional calculation method. Both of which are below than 1, indicating that neither system achieves sustainable development under conventional practice. As mentioned earlier, the cultivation of corn leads to topsoil loss and water consumption, contributing to the long-term degradation of arable land. This is consistent with the findings in Ruiz et al.’s study [48], which indicate that the bioenergy system performs worse in terms of land use. Therefore, scholars have proposed several applications such as using organic fertilizers, returning the straw to the field, and utilizing the water-saving irrigation equipment to realize the sustainable development of agriculture. By improving the methodology, the ESI for CSP was reduced to 0.77, while the ESI for CPCP increased to 1.63, which is greater than 1. This indicated that the CPCP can achieve sustainable development when considering the environmental benefits of pelletized fuels as an alternative to coal for heating. Although pellet production from straw does not directly address soil-erosion issues, it offers a solution to the energy needs of rural households by providing clean energy for heating and cooking, and the positive environmental benefits of replacing bulk coal outweigh the negative environmental benefits of soil erosion.

The PP has a very low ELR of 0.05 in modified method, which means that it has very little impact on the environment, whereas the ESI of 21.15 above 10 represents undeveloped, echoing the previous section’s conclusion that there is a lack of utilization of natural resources in pellet production. This can be improved by using the electricity generated by natural resources such as wind power and photovoltaics.

4.3.2. Economic and Environment Benefit

The conversion of corn straw into biomass pellets for heating purposes yields significant economic and environmental benefits which are shown in

Table 8. The ECB, ENB, and PNE of different scenarios.

Items	PNE (kg/hm2)					ECB (USD/hm2)	ENB (USD/hm2)
	SO2	NOx	CO	PM2.5	CO2
CSP	1.98	16.38	437.39	53.23	8606.25	−474.23	−113.76
CPCP	−9.12	−19.82	−580.31	−65.86	−13,060.66	254.26	159.76

. Environmentally, the process substantially decreases emissions of major air pollutants, with reductions of SO₂ by 9.12 kg/hm², NO_x by 19.82 kg/hm², CO by 580.31 kg/hm², PM_2.5 by 65.86 kg/hm², and CO₂ by 13,060.66 kg/hm². The environmental benefit of producing pellets from corn straw is valued at USD 159.76/hm². This aligns with the results of the LCA conducted by Song et al. [49], which demonstrated that utilizing pellet fuel derived from corn straw can reduce lifecycle greenhouse gas emissions when replacing coal combustion. Economically, pellet fuel can reduce the use of coal by about 4.09 t/hm², and the total cost of coal is USD 474.23/hm². After deducting the expenditure on purchasing pellet fuel, residentials can reduce their heating costs by about USD 254.26 hm². These findings underscore the dual advantages of biomass pellets as a sustainable alternative to conventional fossil fuels, offering both cost effectiveness and environmental protection.

4.4. Sensitivity Analysis

Since biomass is characterized by low energy density and dispersed resources, the long-distance transportation of it will increase the utilization cost, thus limiting its resourcefulness [40]. Moreover, many countries are making great efforts to develop renewable energy for the energy transition, and the unit cost of electricity generation is falling. So, the influence of two more important factors on the emergy indices, transportation distance and the proportion of renewable electricity in rural grid, is discussed. Since straw is usually handled within the county, the variation in straw transport distances from 20 to 100 km (5 to 25 km per trip—25 km per trip is usually the distance from county to village). The renewable electricity shares in a grid vary from 0% to 100%. It is assumed that there are no environmental pollutant emissions from renewable electricity. The cost of thermal power and renewable power is USD 0.12/kWh and USD 0.03/kWh [50]. The solar transformity of renewable power is different with the thermal power, which is 1.13 × 10⁵ sej/J [24].

4.4.1. Transportation Distance

Figure 4 demonstrates the differential response patterns of emergy indices between CPCP and PP under varying straw transportation distances using the modified emergy accounting methodology. With the increases in straw transportation distance under the modified calculation method, the analysis reveals that transportation distance exhibits minimal influence on CPCP’s emergy indices, attributable to the relatively insignificant proportion (2.27% at maximum transportation radius of 100 km) of biomass transportation emergy in total system emergy inputs (1.52 × 10¹⁶ sej/hm²). This observation aligns with Odum’s emergy hierarchy principle, where marginal input components demonstrate limited systemic impact on aggregated indices [26]. Meanwhile, CPCP’s integrated production model buffers transportation impacts through internal emergy recycling, while PP’s complete external dependence creates linear input–output vulnerability. This supports the emergy theory postulating that system complexity inversely correlates with external sensitivity [51].

The PP system presents contrasting characteristics where purchased resources constitute 100% of energy inputs, resulting in stable EYR values (Y = F) across transportation gradients. However, ESI display significant distance-dependent degradation. Specifically, when transportation distance extends from 20 km to 100 km, these indices decrease substantially to 14.02, USD 210.27/hm², and USD 158.59/hm². The deterioration mechanism involves two synergistic effects: (1) Exponential growth in pellet production costs following the distance–cost correlation model, and (2) amplified environmental externalities from transportation emissions, particularly greenhouse gases and particulate matter. This distance–impact relationship corroborates findings from Terasa et al.’s LCA study of pellets production [52], which identified positive correlations between transportation radius and multiple environmental impact categories, including climate-change potential and fossil-fuel depletion potential.

4.4.2. Renewable Electricity Proportion in Grid

According to Figure 5, the results show that the utilization of renewable energy sources expands the use of natural resources while reducing the input of non-renewable emergy components of purchased emergy and energy bills of pellet production. It not only improves EYR but also reduces the environment impact. The increase in the share of renewable electricity is not considerable for the sustainability of CPCP; ESI only increases from 1.63 to 1.72, which is similar to the result obtained for transportation distance, namely that the electricity emergy accounts for a minor share of total input emergy, about 1.70%. Conversely, PP exhibits exponential sustainability gains (ESI surge from 21.15 to 57.81) under 100% renewable electrification. The differential response stems from PP’s structural dependence on electricity emergy, constituting about 35.3% of total input emergy and more than 50% of production cost when raw material inputs and variance are not considered, creating critical leverage points for system optimization. Pellet fuel plants can adapt to energy development trends in rural areas and build solar photovoltaic facilities on its own, or wind-power facilities in conjunction with surrounding villages and towns with the support of policies and financial subsidies by the government. In addition, rural residents will also benefit by spending less on energy use, with the ECB increasing to USD 294.74/hm² when the renewable electricity proportion is 100% in grid. Even when pellet fuel is not subsidized by the government, the ECB is still greater than 0 at USD 74.78/hm².

5. Conclusions

This study provides valuable insights into the sustainability assessment of utilizing agricultural waste for pellet fuel production and proposes a novel ELR calculation method that incorporates the contribution of renewable energy in replacing traditional coal for pollutant emission reduction. Key findings include:

(1)

CPCP demonstrates higher energy efficiency compared to CSP, while CSP exhibits greater economic efficiency. The ESI for CPCP and CSP are both below 1, with values of 0.84 and 0.89, respectively, indicating that neither system is sustainable in the traditional sense.

(2)

The ESI of PP is notably higher at 8.24, indicating better sustainability. However, PP relies entirely on purchased emergy inputs and does not utilize renewable resources.

(3)

The ESI of CSP, CPCP, and PP by the modified calculation method are 0.77, 1.63, and 21.15, respectively, which are greater than traditional calculation method.

(4)

The impacts of transportation distance and the proportion of renewable electricity on emergy indices are minimal for CPCP, but significantly influences PP, with the sustainability diminishing notably with increases in biomass transport distance and the reduction in renewable electricity shares.

(5)

The production of biomass pellets from corn straw for heating can reduce heating fuel costs by approximately USD 254.26/hm². Additionally, air pollutant emissions are significantly reduced, with decreases of 9.12 kg/hm² for SO₂, 19.82 kg/hm² for NO_x, 580.31 kg/hm² for CO, 65.86 kg/hm² for PM_2.5, and 13,060.66 kg/hm² for CO₂.

Future research should prioritize investigating the investment and development of renewable power infrastructure to better understand the impact of renewable electricity adoption on the economic viability and profitability of biomass-pellet fuel production.