Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022

Abstract

Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO₂ emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.

1. Introduction

The World Meteorological Organization (WMO) released the “2019 Annual Greenhouse Gas Bulletin”, showing that concentrations of major atmospheric greenhouse gas concentrations have surpassed historical records globally. The carbon dioxide, methane, and nitrous oxide concentrations were 413.2 ± 0.2, 1889 ± 2, and 332.0 ± 0.1 ppb, respectively, representing 148%, 260%, and 123% of pre-industrial (before 1750) levels. Consequently, due to the increasing global energy demand and worsening climate change [1], energy conservation and emission reduction have become considerable global challenges. Currently, more than 80% of the world’s energy demand is met by fossil fuels [2]. However, current extraction and use rates pose challenges such as fossil fuel depletion and increased environmental pollution. Therefore, the global energy focus is shifting toward a circular economy, emphasizing renewable energy and, particularly, biomass energy.

China’s vast territory, complex topography [3], and widespread mountainous regions contribute to land resource variations. The country encompasses diverse land types [4], including arable land, forests, and grasslands. Rapid developments in agriculture, forestry, animal husbandry, and fisheries have led to abundant and extensive biomass resources [5]. However, China’s large population and deepening urbanization process have resulted in substantial resource demands and consumption [6]. Annual residential energy consumption has exceeded 60 million tons of standard coal, while total energy consumption has surpassed 500 million tons of standard coal [7], posing considerable challenges for CO₂ emission reduction [8]. Against the backdrop of the dual-carbon strategy, China has formulated a series of planning and strategic goals [9]. On 29 January 2022, the National Development and Reform Commission and the National Energy Administration issued the 14th Five-Year Modern Energy System Plan [10]. This has emphasized the diversified use of biomass energy and context-specific development of bioenergy.

In recent years, local and international researchers have conducted extensive research on the importance of biomass energy as a renewable and low-carbon energy source [11]. Current studies often begin with an assessment or prediction of the potential of biomass resources starting from the overall biomass resource quantity. Wieruszewski and Mydlarz [12] accounted for the biomass energy potential and market in the European Union based on the latest recognition by EU member states. Lee and Park [13] estimated the potential of biomass resources in South Korea using geographic techniques and energy yield equations. Welfle [14] used biomass resource models to calculate the potential of biomass energy in Brazil, analyzing the availability of its trade resources. Developed countries such as Sweden, Denmark, and the United States have explored and experimented with the conversion and use of biomass energy. Pang et al. [15] assessed forest biomass energy potential in the United States using landscape simulation and ecological assessment (LECA) tools and the MESSAGE energy model. Wang et al. [16] combined statistical accounting and GIS technology to estimate the biomass resource potential of all types of woody cellulose biomass materials at a 1 km resolution in 2018. They constructed a transparent assessment framework that aligns with food security, the protection of forest and grassland areas, and biodiversity conservation. Lang et al. [17] calculated the biomass energy potential of 31 provinces in China from 2000 to 2020 and identified the spatial distribution and variation in biomass energy through hotspot analysis. Wu et al. [18] summarized the development of medieval biomass energy exploitation technologies. Zhang et al. [19] and Zhong et al. [20] conducted accounting and analysis of biomass energy in provinces and cities such as Beijing and Gansu.

In-depth studies on the technological aspects of biomass energy development and use have been conducted. The accounting of biomass resources for specific regions and provinces and particular years is relatively clear and detailed. As a major agricultural country, with the primary industry accounting for more than half of its economy, China possesses abundant resources of agricultural crop residues and livestock residues. However, there has been relatively little research on the accounting of agricultural crop residues and livestock residues. Therefore, focusing on agricultural crop residues and livestock residues, this study aims to calculate the total biomass resources and emission reduction potential from 2013 to 2022 across China. In-depth assessments of biomass energy potential and the further development of biomass energy as a zero-carbon energy alternative to traditional sources are of considerable practical importance. This provides a reference for researchers on the development and use of biomass energy and the assessment of biomass resources.

2. Data Sources and Research Methods

This study focused on mainland China, including 22 provinces (Hebei, Shanxi, Liaoning, Jilin, Heilongjiang, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Hainan, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, and Qinghai), five autonomous regions (Inner Mongolia, Guangxi, Tibet, Ningxia, and Xinjiang), and four municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing). Due to incomplete data availability, the Hong Kong Special Administrative Region, Macau Special Administrative Region, and Taiwan Province were not included in this study. This work focused on biomass resources from agricultural and livestock residues in China, estimating collectible biomass resource quantities. We analyzed the spatiotemporal distribution and influencing factors of biomass resource availability across provinces, integrating data on agricultural production, livestock farming, and total energy consumption, and calculated the emission reduction potential of biomass resources.

The primary data sources used in this study were (1) data for carbon emissions calculations, sourced from the China Carbon Accounting Database (CEADs) (https://www.ceads.net.cn/); (2) data used for estimating biomass resources from agricultural residues include data on crop yields, types, and areas, livestock and poultry species, and end-of-year inventories sourced from the China Statistical Yearbook (2013–2022); and (3) data on traditional energy consumption and coal equivalent coefficients for various crops, sourced from the China Energy Statistical Yearbook (2013–2022).

3. Research Methodology

3.1. Calculation of Biomass Energy Availability

3.1.1. Crop Residue Biomass Resources

Crop residues, such as stalks, leaves, and fruit shells, are primary biomass resources in rural areas, primarily used through combustion [21]. The quantity of crop residue-based resources, denoted as ${\mathrm{R}}_{\mathrm{c}}$, can be calculated as follows [22]:

$${\mathrm{R}}_{\mathrm{c}}={\sum }_{\mathrm{i}}^{\mathrm{I}}{\mathrm{R}}_{\mathrm{c},\mathrm{i}}={\sum }_{\mathrm{i}=1}^{\mathrm{I}}{\mathrm{Y}}_{\mathrm{i}}\times {\mathrm{S}}_{\mathrm{i}}\times {\mathsf{\lambda }}_{\mathrm{i}}$$

(1)

where i is the type of crop, i = 1, 2,…, I; Y_i is the annual yield (t) of the i-th crop; ${\mathrm{S}}_{\mathrm{i}}$ is the straw–grain ratio of the i-th crop referring to the ratio of straw to grain yield; and ${\mathsf{\lambda }}_{\mathrm{i}}$ is the coal conversion coefficient of the residues of the i-th crop in kg of standard coal per kg. The values of ${\mathrm{S}}_{\mathrm{i}}$ and ${\mathsf{\lambda }}_{\mathrm{i}}$ are shown in

Table 1. Straw–grain ratio and equivalent coal conversion factors. Data Source: Coal conversion factor was obtained from the “China Energy Statistical Yearbook” [23] and straw–grain ratio was obtained from “Temporal and spatial distribution, use status, and carbon emission reduction potential of straw resources in China” [24].

Varieties of Crops	Coal Conversion Factor	Straw–Grain Ratio
		National	Northeast China	North China Region	South China Region	Mongolia–Xinjiang Region	Qinghai–Tibet Region
Rice	0.429	1.00	1.1	1.07	1.02	—	—
Wheat	0.500	1.22	0.91	1.28	—	0.97	1.67
Corn	0.529	1.01	—	1.04	0.77	1.09	—
Other grains	0.500	1.06	0.97	0.85	1.27	1.23	0.97
Potato	0.446	0.16	0.04	0.13	0.2	0.42	0.5
Sweet potato	0.468	0.26	—	0.22	0.44	—	—
Peanut	0.527	1.26	0.73	1.27	1.61	—	—
Rapeseed	0.570	1.86	—	1.86	1.86	2.83	2.32
Soybean	0.543	1.19	0.93	1.46	1.45	—	—
Cotton	0.543	2.95	—	4.73	—	2.75	—
Sugarcane	0.494	0.06	—	—	0.06	—	—
Sugar beet	0.205	0.43	—	—	—	—	—

Notes: The Northeast region includes Heilongjiang, Jilin, and Liaoning; the North China region includes Beijing, Tianjin, Hebei, Henan, and Shandong; the South China region includes Fujian, Guangdong, Guangxi, and Hainan; the Mongolian-New region includes Inner Mongolia, Ningxia, Xinjiang, and Xinjiang; and the Qinghai–Tibet region includes Qinghai and Tibet.

.

3.1.2. Livestock Residue Resource Quantity

The quantity of livestock and poultry residue-based resources refers to the total amount of excrement generated by various types of livestock and poultry [22]:

$${\mathrm{R}}_{\mathrm{D}}={\sum }_{\mathrm{j}=1}^{\mathrm{J}}{\mathrm{n}}_{\mathrm{j}}{\mathrm{M}}_{\mathrm{j}}{\mathrm{T}}_{\mathrm{j}}{\mathsf{\lambda }}_{\mathrm{D}}$$

(2)

where ${\mathrm{R}}_{\mathrm{D}}$ is the total amount of excrement resources, n_j is the number of livestock, T_j is the rearing period in days (d), M_j is the daily excretion coefficient in kilograms per day per animal in kg per day per animal, and ${\mathsf{\lambda }}_{\mathrm{D}}$ is the coal conversion coefficient for livestock and poultry residues, which is 0.50 kg of standard coal per kilogram. Pig, cattle, sheep, and poultry inventory data were used as the basis for calculation.

Table 2. Excretion coefficients for pigs, cattle, sheep, and poultry in different regions.

Region	Pig	Cattle	Sheep	Poultry
North China	3.48	23.9	1.5	0.15
Northeast China	3.51	23.6	1.5	0.14
East China	3.65	23.2	1.5	0.18
Central South China	3.24	23.4	1.5	0.09
Southwest China	3.29	21.9	1.5	0.09

Notes: Excretion coefficients for pigs and sheep were derived from previous studies such as Bao’s assessment of livestock and poultry manure resources in China [25], whereas the excretion coefficients for cattle and poultry were obtained from the “The first national pollution sources survey of livestock and poultry breeding industry pollutant emission coefficient manual” [26].

shows the Mj values for different livestock.

3.2. Geographically and Temporally Weighted Regression (GTWR) Analysis Method

To further investigate the heterogeneity of various influencing factors in different regions, it is necessary to use the GTWR model to measure the influencing factors of biomass distribution across provinces at different times. GTWR adequately considers temporal and spatial variations, reflecting the differences in influencing factors among provinces over different years. The model describes the spatial and temporal effects by calculating regression coefficients related to explanatory variables. The formula is as follows [27]:

$${\mathrm{Y}}_{\mathrm{i}}=\mathsf{\beta }\left({\mathrm{u}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{i}},{\mathrm{t}}_{\mathrm{i}}\right)+\sum _{\mathrm{k}=1}^{\mathrm{p}}{\mathsf{\beta }}_{\mathrm{k}}\left({\mathrm{u}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{i}},{\mathrm{t}}_{\mathrm{i}}\right){\mathrm{X}}_{\mathrm{i}\mathrm{k}}+{\mathsf{\epsilon }}_{\mathrm{i}}$$

(3)

where ${\mathrm{Y}}_{\mathrm{i}}$ is the dependent variable, ${\mathrm{X}}_{\mathrm{i}\mathrm{k}}$ is the independent variable, $\mathsf{\beta }$ is the regression coefficient, i denotes cross-sectional units, t is time, ${\mathrm{u}}_{\mathrm{i}}$ and ${\mathrm{v}}_{\mathrm{i}}$ denote the longitude and latitude of each cross-sectional unit’s location, and ${\mathsf{\epsilon }}_{\mathrm{i}}$ is the random disturbance term satisfying classical assumptions including conditional mean zero, conditional homoscedasticity, mutual independence, and normal distribution.

3.3. Analysis of Biomass Energy Emission Reduction Potential

The calculation for carbon dioxide emissions from fossil fuel combustion is based on a method proposed by the Oak Ridge National Laboratory (ORNL), US Department of Energy [28], using energy carbon emission coefficients from the Intergovernmental Panel on Climate Change (IPCC) [29]:

$${\mathrm{C}\mathrm{O}}_2={\sum }_{\mathrm{i}=1}^8{\mathrm{K}}_{\mathrm{i}}{\mathrm{E}}_{\mathrm{i}} \times 44/12$$

(4)

where i is the type of energy, with a total of eight types; ${\mathrm{K}}_{\mathrm{i}}$ is the consumption of energy i, measured in standard coal equivalent (in 10,000 t); ${\mathrm{E}}_{\mathrm{i}}$ is the carbon emission coefficient of energy i (in 10,000 t of carbon per 10,000 t of standard coal); and 44/12 is the molar mass ratio of carbon dioxide to carbon. The conversion coefficients to standard coal and carbon emission coefficients for various types of energy are listed in

Table 3. Cost-effective standard coal coefficients and carbon emission coefficients for various types of energy sources.

Energy Types	Raw Coal	Coking Coal	Crude Oil	Gasoline	Kerosene	Diesel	Fuel Oil	Natural Gas
Conversion to Standard Coal (t standard coal/t)	0.7143	0.9714	1.4286	1.4714	1.4714	1.4571	1.4286	1.33
Carbon Emission Coefficient (10,000 t carbon/10,000 t standard coal)	0.7559	0.855	0.5857	0.5538	0.5714	0.5921	0.6185	0.4833

.

4. Results and Discussion

4.1. Spatiotemporal Evolution of Available Biomass Resources

In this study, we analyzed the spatiotemporal distribution of available biomass resources, including agricultural residue biomass resources and livestock residue biomass resources.

4.1.1. Biomass Resources from Agricultural Residues

In this study, we calculated the collectible amount of agricultural biomass resources in each province from 2013 to 2022. Figure 1 depicts the statistical chart of agricultural and livestock biomass resources over the decade. As shown in Figure 1, except for Guangxi, Hainan, Fujian, and Beijing, the biomass resources of crop residues in other provinces showed a fluctuating upward trend, with larger growth rates in provinces with larger inland plains. With economic growth and advancements in agricultural technology [30], especially in provinces with extensive non-coastal arable land, crop yields have increased [31]. However, due to various factors such as climate, policies, and pandemics, there is a trend of fluctuating changes.

Figure 2 illustrates the spatial distribution of biomass resources from crop residues over the past four years. Henan Province and Heilongjiang Province had the highest resource quantities over ten consecutive years, exceeding 35 million tons. Nationally, the resource distribution showed a pattern where northern provinces exceed southern ones and eastern provinces exceed western ones. This pattern aligns with findings from Zhang et al. [9], particularly in major grain-producing provinces in China where biomass resources are prominent. The three northeastern provinces, that is, Heilongjiang, Jilin, and Liaoning, alone contributed nearly 20% of the national biomass resources from crop residues. This is similar to the findings of Guo et al. [32] on biomass resource distribution in studies related to biomass resource power generation. Southern China has experienced rapid economic development [33], with a large population and extensive urban areas. Meanwhile, northern China has a long history of agricultural development, extensive arable land, and fertile soil [34], benefiting from superior natural conditions for agriculture. Eastern China benefits from favorable natural conditions such as terrain and climate, coupled with rapid economic development and higher agricultural technological [35] levels. From 2013–2022, biomass residue resources in the eastern and northern regions generally exceeded those in the western and southern regions, primarily due to the scale of agriculture constrained by natural conditions. The fluctuation and increase in resource quantities underscore the importance of economic and technological advancements in biomass resource development.

4.1.2. Biomass Resources from Livestock Residues

Figure 3 shows the statistical data for selected years, that is, 2013, 2016, 2019, and 2022. The calculation and statistical analysis of biomass resources from animal husbandry residues over the past decade reveal that theoretical collectible amounts showed fluctuating declining trends in seventeen provinces and municipalities, including Beijing, Tianjin, Liaoning, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Guangdong, Guangxi Zhuang Autonomous Region, Hainan, Chongqing, and Sichuan, with a relatively small decrease. In contrast, the theoretical collectible amounts in other provinces showed fluctuating upward trends, with significant increases in agro-pastoral transitional zones, including the Inner Mongolia Autonomous Region. China is the world’s largest livestock producer [36], with an extended livestock industry chain leading to increased value-added and growth in livestock numbers in pastoral areas.

Figure 3 illustrates the spatial distribution of biomass residue from livestock production over the past four years. Sichuan Province had the highest livestock biomass residue, averaging over 500 million tons of standard coal annually over the past decade, followed by Henan Province, with an annual average exceeding 430 million tons of standard coal. Yunnan, Shandong, Hunan, and Inner Mongolia also possessed substantial resources, exceeding 300 million tons of standard coal annually. Nationally, the northern provinces surpassed the southern provinces in resource distribution and the western provinces exceeded that of the eastern provinces. This is consistent with the findings of Zhang et al. [31] regarding the distribution of biomass resources in China in 2018. Southwestern China is dominated by mountainous terrain [37], with less land suitable for large-scale crop cultivation but more conducive to raising cattle, sheep, and poultry. Consequently, this region possesses abundant biomass residue from livestock production. Inner Mongolia and other areas lie in the agricultural and pastoral transitional zone of China [38], characterized by a temperate continental climate and extensive grasslands, which are favorable for livestock development. Therefore, these areas also exhibited significant biomass residue from livestock production.

4.2. Influencing Factors

We selected the per capita gross domestic product (PCGDP), the proportion of primary industry (PPI), the agricultural machinery quantity at year-end (AMQ), the crop planting area (CPA), and the number of large livestock at year-end (NLL) as influencing factors. The analysis examined their correlations with the distribution of biomass resources, and the computed correlation coefficients are presented in

Table 4. Regression coefficient statistics of different influencing factors in GTWR.

Influencing Factor	Mean	Std	Min	Max
$\mathrm{PCGDP}$	−0.0349	0.0556	−0.1129	0.1596
$\mathrm{PPI}$	−0.0895	0.0802	−0.2941	0.2095
AMQ	0.1926	0.1699	−0.8046	0.3506
CPA	0.5103	0.0997	0.2741	0.8771
NLL	0.4227	0.0476	0.3622	0.5892

.

Table 4 presents descriptive statistics of regression coefficients for influencing factors in GTWR, including the mean, standard deviation(std), maximum, and minimum values. The average size of the generated regression coefficients was CPA > NLL > AMQ > PPI > PCGDP. CPA and NLL had the highest positive regression coefficients, indicating that increasing crop planting area and year-end number of large livestock enhances biomass resource quantity. AMQ’s regression coefficient ranked second, suggesting that the quantity of agricultural machinery at year-end promotes biomass resource quantity. PCGDP and PPI coefficients were negative and relatively small, indicating their insignificant impact on biomass resource quantity.

Figure 4 shows the spatial distribution of PCGDP regression coefficients. PCGDP showed a positive correlation with the distribution of biomass resources in Xinjiang, Gansu, and Qinghai, with no significant effect. Meanwhile, it showed a negative correlation in other provinces. PCGDP had a greater impact in the northwest region compared to other provinces. With economic development, the sparse population and vast land in the northwest reflect the level of agricultural mechanization, leading to increased recyclable agricultural waste in the region. The high economic development in southeastern China, with a large urban area and a smaller PPI [39], reflects higher agricultural technology in terms of improved farming efficiency and reduced agricultural waste. Therefore, in economically developed areas, GDP development tended to inhibit biomass resources. The decreasing absolute values of PCGDP regression coefficients in 2016, 2019, and 2022 indicate that the economic impact on biomass resources is diminishing.

Figure 5 shows the spatial distribution of PPI regression coefficients. During the study period, the PPI impact on the distribution of biomass resources varied by province, positively correlating in Xinjiang and negatively correlating in other provinces. In regions such as Xinjiang in northwest China, agriculture remains the dominant industry. Therefore, the increase in the proportion of the primary sector [40] implies the development of large-scale agriculture, which provides more biomass resources. Therefore, the technological development in agriculture in the northwest region has a dual benefit for agricultural production and resource use. This indicates that as the PPI decreases and the proportion of secondary and tertiary industries increases with economic development and higher technological development, crop yields increase, thereby promoting an increase in biomass resources.

Figure 6 shows the spatial distribution of AMQ regression coefficients. From 2013 to 2022, AMQ’s impact on the distribution of biomass resources showed a positive correlation. The level of mechanization promotes agricultural development, thereby increasing biomass resources. The impact was more significant in the northeast, southwest, and southeast regions compared to the northwest, correlating with urban economic and technological development levels. The increasing values of AMQ regression coefficients in 2016, 2019, and 2022 indicate the growing influence of mechanization on agriculture [41].

Figure 7 shows the spatial distribution of CPA regression coefficients. CPA shows a positive and significant correlation with the distribution of biomass resources. Currently, globally, biomass energy industrialization is driven by crops such as wheat and corn. Therefore, the crop planting area directly impacts biomass resources, with a greater influence in the western regions than in the eastern regions. The main reason is that the eastern part of China is currently focused on industrial and high-tech development. Due to favorable hydrothermal conditions, agricultural production costs are lower compared to the northwest region, where the land is vast and sparsely populated. In recent years, the “Western Development” [42] policy support and improvement in agricultural levels have substantially increased the scale of agricultural development, thereby providing more raw materials for biomass resource use.

Figure 8 shows the spatial distribution of NLL regression coefficients. There is a significant and positive correlation between NLL and the biomass resource distribution. The technology for converting livestock manure into biogas is well-developed [43], making livestock numbers a direct source of biomass energy. This impact is particularly pronounced in Xinjiang, Tibet, Northeast China, and Jiangsu–Zhejiang, where NLL’s influence on biomass is more significant compared to other provinces. In Jiangsu and Zhejiang regions, there is an increasing correlation trend, potentially due to their relatively high level of biogas development and use. Xinjiang shows a stable high correlation, attributed to its developed animal husbandry. Therefore, livestock manure [44] will continue to be an important biomass resource with considerable development potential.

4.3. Reduction Potential of Biomass Resources in Each Province

According to the China Energy Yearbook (2013–2022), statistics on provincial energy consumption (Figure 9) reveal that China’s consumption of raw coal and coking coal is high, followed by gasoline and natural gas. Meanwhile, the consumption of crude oil and fuel oil is relatively low compared to other traditional energy types. Spatially, energy consumption was higher in northern regions than in southern regions and higher in eastern regions than in western regions. Coastal provinces along the Bohai Sea exhibited higher consumption of traditional energy sources.

After selection, in this study, we selected eight major energy sources with significant proportions in China’s energy structure, that is, raw coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas, to calculate the average expected reduction in CO₂ emissions from 2013 to 2022 by replacing traditional energy with biomass energy across provinces. The calculations showed that, nationwide, replacing raw coal and coke, which have higher carbon emission coefficients, with biomass energy yielded significant reductions in CO₂ emissions. The average CO₂ emission reduction from replacing coke was estimated at 11,478 million tons of standard coal across provinces. Regarding the spatial distribution of CO₂ emission reductions, Inner Mongolia, Sichuan, Shandong, and Henan exhibited the highest potential, mainly in regions abundant in biomass resources and areas with high consumption of traditional energy sources.

From 2013 to 2022, the average carbon emission reductions from replacing different traditional energy sources varied over time. The theoretical reduction was highest in 2016 and lowest in 2018, while the overall distribution remained consistent.

4.4. Biomass Resource Quantities and Carbon Emission Reduction Potential Zoning

Since 2012, China’s modern agricultural ecology has entered an innovative promotion stage [45]. By analyzing the average statistics of agricultural residual biomass resources from 2013 to 2022, we used ArcGIS10.8 software and a natural breakpoint classification method to form five categories of agricultural residual biomass energy emission reduction potential zones as shown in Figure 10 (left). The specific zoning results were as follows. Regions with abundant resources included Inner Mongolia, Heilongjiang, Shandong, Henan, and Sichuan. Relatively abundant resources included Hebei, Yunnan, and Hunan. Moderately abundant resources included Xinjiang, Tibet, Jilin, Liaoning, Gansu, Jiangsu, Anhui, Jiangxi, Hubei, Guizhou, and Guangxi. Limited resources included Shaanxi, Shanxi, Chongqing, Qinghai, Fujian, and Guangdong. Scarce resources included Beijing, Tianjin, Ningxia, Zhejiang, Shanghai, and Hainan.

In China’s soil zoning, Heilongjiang is characterized by black calcareous soil [46], with a thick layer of humus and rich organic matter content. Shandong and Henan are dominated by yellow-brown soil and brown soil, which are naturally fertile. Sichuan has unique purple soil [47] with rich mineral nutrients and relatively high fertility. These provinces have superior soil conditions, large areas suitable for cultivation, favorable conditions for agricultural development, and abundant biomass resources from crop residues. Although characterized by poorly developed desert soils, Inner Mongolia is not suitable for large-scale agricultural development. However, its rich grasslands provide excellent conditions for the development of animal husbandry, thus also offering abundant resources of livestock residue. The distribution of red soil and brick-red soil in Zhejiang, Shanghai, Guangdong, and other regions is also one of the reasons why biomass resources in agriculture and animal husbandry in these areas are relatively scarce.

As shown in the figure, regions with abundant resources were mostly distributed in northern China. Meanwhile, resource levels were lower in southern coastal areas. The northeast, southwest, and North China regions had relatively abundant biomass resources available. From 2013 to 2022, the proportion of the primary sector in economically developed southern regions gradually decreased [48], with GDP growth primarily driven by the development of industry and services. In contrast, northern regions, especially in the agricultural and pastoral areas rich in biomass resources, continued to rely mainly on animal husbandry as the pillar industry over time. Inner Mongolia is an example within the biomass-rich zones, due to the arid climate and unsuitable soil conditions for large-scale crop cultivation in northwestern China. However, with the development of agricultural technology and expansion of livestock scale over time, the region’s biomass resources primarily sourced from animal manure also showed an increasing trend. If extensively used, these resources could address local heating and other resource issues.

The zoning results of agricultural and livestock biomass resources can serve as theoretical indicators for the development of green agriculture in China. These results facilitate a better understanding and management of biomass resource distribution, enabling tailored strategies for development and use based on varying resource storage capacities, thus optimizing resource allocation [49].

We calculated the fitted values of theoretical CO₂ emission reductions by replacing traditional energy with biomass energy from 2013 to 2022 for each province. Using ArcGIS10.8 software and a natural breakpoint classification method, five categories of agricultural residual biomass energy emission reduction potential zones were formed, as shown in Figure 10 (right). The zoning map indicates that regions with higher emission reduction potential are predominantly located in southwestern and northeastern China, similar to the distribution of biomass resource zones.

4.5. Strategies and Recommendations

Based on the analysis of factors influencing biomass resources in this study, the development of agricultural technology impacts the quantity of biomass resources. With the development of the Chinese economy, urbanization rates continue to rise [50]. However, advancements in agricultural technology and mechanization will increase crop yields, thereby enhancing the availability of biomass resources. Additionally, it is noteworthy that China has a large population, making food security a critical issue [51]. Therefore, alongside urban expansion and economic development, it is crucial to strictly enforce the “red line” for arable land [52], ensuring sustainable agricultural development and stable crop cultivation and livestock breeding. Therefore, the government should maintain supervision and statistics on China’s agricultural industries, while supporting advancements in agricultural technology through policies and funding to promote sustainable agricultural development.

Based on the spatial and temporal distribution of biomass resources from agricultural residues in China, we propose that the development of bioenergy should adopt strategies tailored to local conditions and full use. According to the zoning of biomass resources quantity in the results of this study, biomass resources from agricultural residues in China are unevenly distributed. Southern economically developed regions have fewer resources. Meanwhile, the northern regions, as important bases of agricultural production, have abundant resources. The country has explicitly proposed strategies for clean biomass heating through on-site processing and conversion, local consumption [53], and distributed development and use in northern regions for winter heating. This is aimed at reducing the open burning of straw in rural areas and mitigating the contradiction between energy demand and atmospheric pollution [54] while constructing a distributed clean energy heating system in urban areas. Given the uneven use potential of agricultural and livestock residue biomass resources in different regions of China, the western region stands out for its abundant poultry and livestock manure resources. Compared to crop straw, manure contains a higher proportion of methane and volatile organic matter, making it more suitable for biogas production [55]. Investment in biogas construction during the Twelfth Five-Year Plan period has reached 14.2 billion yuan, leading to the rapid and sustained development of rural biogas projects under policy incentives. Therefore, in the southwestern region of China, policy guidance can promote a diversified development pattern focused on household biogas for gas supply and electricity generation, while encouraging pilot projects for large-scale bio-natural gas engineering [56].

Based on the emission reduction potential of biomass energy, we propose to enhance the efficiency of biomass resource use. First, raising awareness among farmers [57] and improving the system of resource use is crucial. Second, innovating resource conversion technologies to lower the cost of clean energy conversion is essential. Sweden is one of the world’s most advanced countries in the biomass industry [58], where biomass resources have become its primary source of energy consumption [59]. Supported by relevant policy actions, Sweden has gradually developed three main pathways for biomass resource use, that is, biomass heating, biomass electricity generation, and biomass transportation, primarily driven by abundant biomass resources and substantial heating demand. As a large agricultural and populous country, China also possesses abundant biomass resources. Particularly in the northern regions, similar to Sweden in terms of resources and climate conditions, China has considerable potential for biomass energy use. However, factors such as imprecise development paths, bottlenecks in cost and technology, a lack of mandatory and sustainable policy actions, and confusion in resource conversion and use standards [60] have contributed to the current inefficiency in biomass resource use in China.

China’s agriculture is primarily characterized by individual farming operations. To develop green agriculture, it is crucial to enhance farmers’ awareness of biomass resource use. Therefore, it is necessary to establish a sound system for resource use in rural areas [61]. Limited understanding among farmers about the use of biomass resources may lead to the unsustainable use of agricultural residues such as crop straw, resulting in air pollution and even degradation of arable land quality. Thus, policies should encourage rural households to adopt forms of green agriculture, promote talent and technology diffusion, guide farmers in the strategic use of biomass resources, and drive effective energy reform in rural China.

The results of the theoretical calculations of energy conservation and emission reduction reveal that China has abundant agricultural residue biomass resources. However, in practical terms, costs and technology pose major constraints on the development of biomass resources. Unlike developed countries that rely on strategies such as tax exemptions or subsidies [62], such approaches would impose considerable economic pressure on China, a populous developing country. Therefore, it is crucial to focus on reducing costs through increased biomass raw material production and innovation in energy conversion technologies [63]; for instance, improving oil extraction rates through the cultivation of new varieties of energy crops or enhancing the efficiency of physical and biochemical transformations of biomass resources. Despite the development of biomass heating industries since 2006, controversy regarding high-polluting fuels has restricted its scalability. Hence, continuous optimization and innovation in use methods [64] are essential to achieve large-scale use of zero-carbon energy.

5. Conclusions

This study focused on agricultural and livestock residues in China, showing the spatiotemporal changes in biomass resources from 2013 to 2022. We used GTWR to analyze the factors influencing biomass resources and proposed strategies and recommendations for the development of bioenergy. The main conclusions are outlined below.

From 2013 to 2022, the overall biomass resources of agricultural and livestock residues in China showed a fluctuating upward trend. The distribution indicates that the resources are more abundant in the north than the south and in the west than the east, mainly concentrated in China’s major grain cultivation areas and pastoral areas. The northeastern provinces exhibited higher crop residue biomass resources. Meanwhile, Sichuan Province and Inner Mongolia Autonomous Region showed increasingly prominent livestock residue biomass resources.

Analysis of the average biomass resources of agricultural residues from 2013 to 2022 revealed that regions with abundant resources were mostly located in northern China, while resource levels were lower in the southern coastal areas. The northeastern, southwestern, and North China regions had relatively abundant biomass resources. These areas were categorized into abundant, relatively abundant, moderately abundant, less abundant, and deficient resource zones based on biomass availability.

Biomass energy has a favorable effect on reducing carbon emissions compared to traditional energy sources such as raw coal and coke, which have higher carbon emission coefficients. On average, replacing coke with biomass in each province was estimated to achieve a CO₂ emissions reduction of 11,478 million tons of standard coal. Spatially, Inner Mongolia, Sichuan, and the Shandong–Henan region exhibited the greatest potential for CO₂ emission reductions, primarily located in regions abundant in biomass resources and high traditional energy consumption areas.

The average regression coefficients of factors influencing agricultural residue biomass resources were ranked as CPA > NLL> AMQ > PPI > PCGDP, indicating that increases in crop planting area and year-end livestock numbers significantly enhance biomass resources. The number of agricultural machinery at year-end also promoted biomass resources. In contrast, PCGDP and PPI coefficients were negative and relatively small, suggesting their insignificant impact on biomass resources.

The development of biomass energy should adopt tailored and integrated use strategies. Specifically addressing China’s predominantly individual-operated agricultural model, it is crucial to encourage and promote the participation of farmers in green agricultural practices [65]. Enhancing biomass energy efficiency in emission reductions should focus on expanding biomass production and innovating energy conversion technologies.