Socioeconomic Determinants of Biomass Energy Transition in China: A Multiregional Spatial Analysis for Sustainable Development

Abstract

This study investigates the socioeconomic determinants governing biomass energy transitions in rural areas of Eastern China through a multiregional spatial analysis. Drawing on time-series data from national and local statistical yearbooks, screened and processed to ensure consistency, the research analyzes evolving rural energy consumption patterns across nine cities in Heilongjiang, Jiangsu, and Guangdong provinces. Biomass energy potential was estimated by integrating crop production and domestic waste data with region-specific residue-to-product ratios, calorific values, and conversion efficiencies. These estimates were further spatialized through GIS-based surplus–deficit modeling to reveal regional disparities in supply–demand balance. The analysis identifies a critical income threshold, whereby lower-income regions exhibit rapid growth in energy consumption until reaching a saturation point around RMB 13,000, while higher-income areas experience continued increases in energy demand beyond the capacity of biomass resources to supply. The findings emphasize that an integrated approach, incorporating agricultural residue and domestic waste utilization, is essential for facilitating sustainable energy transitions, particularly in economically advanced regions. Furthermore, the study develops a scalable framework that integrates socioeconomic and spatial variables into biomass energy planning, underscoring the need for regional transition strategies to address not only resource endowments but also demographic mobility, urbanization dynamics, and income-driven consumption behaviors.

1. Introduction

China’s GDP per capita passed USD 10,000 in 2019. According to statistics, the total energy consumption of China was 5.72 billion tons of standard coal in 2023, an increase of 5.7% over the previous year [1]. According to the International Energy Agency’s survey paper on biomass development in China in 2024, biofuels and waste accounted for 2.7% of total annual energy consumption in 2022 [2]. Figure 1 plots the evolution of energy consumption as well as the form of biomass power generation over a ten-year period. As the pandemic developed, the energy consumption of secondary and tertiary industries in China declined significantly and in stages [3]. At present, China’s economy is recovering gradually across various sectors, and the demand for electricity will increase over time [4]. Recent studies have shown that optimizing the regional deployment of sustainable biomass resources in China could achieve annual greenhouse gas emissions reductions of up to 1.0 Gt CO₂e, comparable to the total carbon sink of terrestrial ecosystems nationwide [5]. This significant mitigation potential highlights the critical role of biomass energy in supporting China’s broader energy transition objectives, offering a cost-effective and scalable pathway for reducing reliance on fossil fuels while enhancing energy security and sustainability.

This significant mitigation potential highlights the critical role of biomass energy in supporting China’s broader energy transition objectives, offering a cost-effective and scalable pathway for reducing reliance on fossil fuels while enhancing energy security and sustainability. Not only should the distribution of biomass feedstock and the technological potential of each region be fully taken into account, but the impact of technological differences, geography, demographics, and socio-economic heterogeneity among provinces and cities on energy demand should also be quantified and analyzed in concrete practice.

Literature shows that income is an important economic factor which affects energy consumption [6]. Some studies have revealed that households with a higher income consume more energy [7]. The data in some studies show that as income increases and reaches a stable level, the amount of energy consumed by households in their daily activities will gradually stabilize, a phenomenon that is consistent with the law of diminishing marginal utility [8]. Furthermore, evidence suggests that improvements in household income not only increase overall energy demand but also drive the transition from traditional biomass fuels toward modern energy sources. Recent findings in rural China show that higher household income reduces dependence on traditional biofuels such as firewood and agricultural residues, facilitating the adoption of cleaner and more efficient energy technologies [9]. Income will exert an impact on the energy consumption structure of rural households [10,11]. Villages with higher overall income and more financial assets have greater energy consumption efficiency, and more affluent households lean towards modern fuels and clean energy [12]. Wu et al. conducted a survey of the energy consumption of 3900 rural households in 12 provinces in China and concluded that the per capita energy consumption of residential buildings in villages and towns was much higher than that in urban areas. This survey found rural residents allocated a higher share of their income to energy purchase compared with urban residents’ expenditure on energy commodities [13]. However, the survey’s authors did not cover the impact of PCDI on per capita energy consumption, and they also asserted that biomass development in rural areas should primarily meet the energy demand for cooking, and did not discuss biomass power generation and the form of CHP. Adopting the LMDI algorithm, Meng et al. carried out a quantitative analysis of the drivers of increased domestic electricity consumption in China, identifying an increase in income as the most crucial factor in the growth of electricity consumption. With a focus on electricity consumption in urban households in China, this paper did not study electricity consumption in rural households from a micro perspective [14]. Ma et al. established an econometric model to analyze the impact of off-farm income on energy expenditure in rural households in China, and indicated that the increase in off-farm income played a key role in promoting the transition of energy consumption in rural households from traditional non-clean energy to efficient clean energy [15].

Previous studies have produced important findings on the drivers of household energy consumption in both urban and rural areas, as well as on the influence of economic factors on electricity and other energy use. However, few have systematically integrated PCDI with the spatial distribution of bioenergy demand and the regional suitability for bioenergy development. This integration enables a comprehensive understanding of the dynamic interactions among socioeconomic development, energy consumption behaviors, and resource availability. Building on this framework, the present study constructs an income-stratified energy consumption estimation model and a biomass surplus–deficit assessment model, informed by evaluations of agricultural biomass and waste-to-energy (WTE) potentials. By incorporating GIS-based spatial analysis, the study aims to reveal the impacts of PCDI on regional energy supply–demand dynamics and to spatially assess the suitability of sustainable bioenergy development under diverse economic and geographic conditions.

2. Materials and Methods

2.1. Research Framework and Data

All data applied in the study were obtained from national statistical yearbooks, local statistical yearbooks, and Chinese thermal design codes for civil buildings. Specifically, the 2024 National Statistical Yearbook was used, along with the statistical yearbooks of Guangdong Province, Jiangsu Province, and Heilongjiang Province covering the years from 2010 to 2023. The sample selection in the study was divided into two stages, to discuss the influence of rural PCDI on the suitability of bioenergy development at disparate economic development levels and where different geographical and climate conditions prevail. In these two stages, three provinces differing markedly in economic development levels and climate conditions were placed in two groups.

In the first stage, the PCDI data of rural residents in various areas was acquired from national statistical yearbooks of China. After splitting this data into five equal groups, 20% of the low-income group and 20% of the high-income group were removed, which meant 33 provinces with PCDI between RMB 10,391.6 and RMB 20,884.5 were left. Among these, 13 provinces are in the central winter heating areas, while the other 20 provinces are in non-central heating areas. To improve the representativeness and internal consistency of the dataset, the highest and lowest 20% income groups were excluded. Rural areas within these extreme high-income and low-income groups often exhibit distinct energy consumption behaviors, infrastructure conditions, and resource availabilities, which do not reflect the general patterns observed in most regions. Moreover, including these outliers could introduce significant bias and compromise the robustness and generalizability of the analysis.

As shown in Figure 2, three provinces that geographically reside in the eastern part of China were selected as cases for this study. The first is Heilongjiang Province in China’s northeast economic zone. Located in climate zone I, all cities of the province fall into central winter heating areas, with a rural PCDI of RMB 16,067, a median level among nationwide rural PCDI. The second is Guangdong Province in China’s east economic zone. Located in climate zone IV, the entire province falls outside the central winter heating areas, with a rural PCDI of RMB 20,143.4. The last province in the study is Jiangsu Province, of which the southern part is in climate zone III, and the northern part is partially in climate zone II, with a rural PCDI of RMB 24,198.5.

In 2023, Heilongjiang Province, Guangdong Province, and Jiangsu Province reported gross regional production (GRP) of, respectively, RMB 1588.39 billion, RMB 13,567.31 billion, and RMB 12,822.22 billion. Electricity consumption data further highlight the heterogeneity among the three provinces. As shown in Figure 2c, the electricity consumption of Guangdong Province and Jiangsu Province in 2023 was 850.2 billion kWh and 783.2 billion kWh, respectively. To ensure consistency in energy unit representation and to facilitate integrated analysis involving both electricity and thermal energy consumption, all electricity consumption data were converted into gigajoules (GJ), resulting in 306 × 10⁷ GJ and 281 × 10⁷ GJ for Guangdong and Jiangsu, respectively. These values are considerably higher than the total annual electricity consumption recorded in Heilongjiang Province. These values are considerably higher than the total annual electricity consumption recorded in Heilongjiang Province. Collectively, the three selected provinces differ markedly in terms of natural climate conditions, levels of economic development, and energy consumption structures. Such contrasts provide a robust foundation for analyzing region-specific biomass energy transition pathways under varying socioeconomic and climatic contexts.

In the second stage, three representative cities or counties—classified as high, middle, and low in terms of rural per capita disposable income (PCDI)—were selected from each of the three previously identified provinces. Specifically, Jiamusi City, Mudanjiang City, and Hegang City were chosen from Heilongjiang Province; Zhanjiang City, Maoming City, and Yangjiang City from Guangdong Province; and Changzhou City, Lianyungang City, and Yancheng City from Jiangsu Province. The selection of cities and counties was based on a stratified sampling approach according to rural PCDI levels. One city or county from each income group (high, middle, and low) was selected within each province to ensure representativeness across different economic strata. In addition to income stratification, regional representativeness was also considered by including areas from different climatic zones (centralized heating versus non-heating regions) and different geographic locations (Northeast versus South China). This approach aimed to capture the diversity of rural energy consumption patterns and to ensure that the selected samples reflected the socioeconomic and climatic variability relevant to the bioenergy transition analysis. These selected locations are geographically situated in either the northeastern or southern regions of China, providing a diverse spatial context for analysis.

Located within China’s designated winter heating belt, Heilongjiang Province displays a dual rural energy consumption pattern—space heating during the cold season and electricity use throughout the year. Evidence suggests that biomass-based combined heat and power (CHP) systems are particularly well-suited to address the integrated heating and electricity demands of rural households in cold-climate regions, including those in Heilongjiang [16].

By contrast, Jiangsu and Guangdong Provinces are situated in climate zones III and IV, respectively, where centralized winter heating is not typically required. Consequently, rural energy consumption in these regions is predominantly electricity-based, and was therefore assessed solely in terms of annual electricity usage.

In light of this diversity, the next step in the analysis involves evaluating the spatial distribution and availability of biomass resources across these provinces. By integrating multi-source datasets—including agricultural output statistics, land use records, and population data—we assess the theoretical and utilizable biomass potential at the city and county levels. This evaluation provides critical input for identifying high-priority areas for biomass deployment and for tailoring energy solutions to local resource endowments.

2.2. Estimation of Energy Consumption by Income

Based on the collection and synthesis of statistical data, and drawing on prior research by Weiss de Abreu and Riva et al. [17,18] concerning socioeconomic drivers, energy consumption demand, and rural energy use behaviors, this study assumes a nonlinear relationship between rural per capita disposable income (PCDI)—as a proxy for local economic development—and energy consumption. The shape of this relationship may vary depending on the level of economic development and actual energy use patterns.

Empirical observations in the study area suggest that rising rural PCDI is closely associated with increased per capita energy consumption. As economic conditions improve, residential infrastructure is upgraded and urbanization intensifies. These trends contribute to population migration toward more economically advanced urban centers, a reduction in rural household size, and a gradual increase in per capita housing construction area (PCHCA). Collectively, these factors significantly influence both the quantity and structure of rural energy consumption.

To estimate annual thermal energy consumption during the heating season, this study adopts the calculation method proposed by Ma et al. [19], based on the Design Code of District Heating Network (GJJ34-2002) [20]. In support of this analysis,

Table 1. Winter heating demand index for three cities in Heilongjiang Province.

Province	City/County	Design’s Heating Days (${\mathit{d}}_{\mathit{i}}$)	Average Outdoor Temperature During the Design’s Heating Period (°C)	Outdoor Temperature During the Design’s Heating Period	Design Heating Load Index (kW)
Heilongjiang	Mudanjiang	182	−7.3	−24	52.93
	Hegang	186	−7.3	−24	52.93
	Jiamusi	192	−7.3	−24	52.93

presents the key heating demand parameters used for the three sample cities in Heilongjiang Province—Jiamusi, Mudanjiang, and Hegang—including the number of heating days, average and designed outdoor temperatures, and the design heating load index. All parameter values were derived from Ma et al.’s empirical study and the national design standards issued in GJJ34-2002. The consistency of these values across the three cities reflects their shared classification within China’s severe cold climate zone and ensures methodological comparability in subsequent energy demand estimations. However, it is important to note that the original model does not account for intra-regional differences in energy demand arising from variations in economic development levels. This limitation is addressed in the present study by incorporating economic stratification into the energy consumption estimation framework. Refer to Formula (2) for detailed calculations:

$$E_x^p=0.0864RH{d}_i\times {\displaystyle \frac{T-T_O^i}{T-T_h^i}}$$

(1)

The energy demand of each city/county within the winter heating areas during the winter heating period can be worked out according to Formula (2).

$$E_d^p=\left[0.0864(R_a+R_b+\cdots +R_n)\times \left(P_a+P_b+\cdots +P_n\right)H{d}_i\times {\displaystyle \frac{T-T_O^i}{T-T_h^i}}\right]+E^Z$$

(2)

The energy demand of each city/county in the study area within the central winter heating areas during the non-heating period, and the annual energy demand of each city/county outside of the central winter heating areas can be worked out through Formula (3):

$$E^z=\left(P_a+P_b+\cdots +P_n\right)\times \left(e_1+e_1+\cdots +e_n\right)\times k$$

(3)

wherein $E_d^p$ is the total energy demand of each income group in the winter heating areas; $R_a$, $R_b, \cdots$, and $R_n$ are the PCHCAs of different income level groups; $P_a, P_b, \dots , \mathrm{a}\mathrm{n}\mathrm{d} P_n$ are the population sizes of different income groups; $H$ is the design heating load index during the heating period; $d_i$ is the number of heating days; T is the indoor temperature during the heating period; $T_O^i$ is the calculated average outdoor temperature during the heating period; $T_h^i$ is the calculated outdoor temperature during the heating period; $E^z$ is the total electricity demand of the study area; $e_1+e_1+\cdots +e_n$ are the per capita electricity demand of different income level groups; and k is the electric elasticity coefficient.

2.3. Calculation of Agricultural Biomass Availability

2.3.1. Calculation of Agricultural Biomass Potential

According to data on per unit area yield and the planting area of various types of crops provided in local agricultural statistical yearbooks, the annual average yield of various types of crops was calculated to work out the development trend of crop yield in the study area. The model is as follows:

$$C_y=\sum _{i=1}^5{F}_i{A}_i$$

(4)

wherein $C_y$ is the crop yield, with the unit of ton; i is the crop type;$F_i$ is the annual average yield of crop i per unit area, with the unit of ton/hm²; and $A_i$ is the annual average planting area of crop i, with the unit of hm².

According to the average per unit area yield of various types of crops in the study area known from previous calculations, five types of crops with higher average yield were chosen to undergo bioenergy conversion. Rice, tubers, soybean, sugarcane, and corn were selected in Guangdong Province. In Jiangsu Province and Heilongjiang Province, rice, wheat, corn, soybean, and tubers were the selected five crops. The straw yield can be calculated based on the crop yield and the corresponding residue-to-product ratio coefficient:

$$C_r=\sum _{i=1}^5{Q}_i{k}_i$$

(5)

wherein $C_r$ is the total yield of biomass straw in the study area, $Q_i$ is the total yield of crop i, and $k_i$ is the residue-to-product ratio coefficient of crop i.

According to the calculated straw yield of a crop and based on the calorific value, combustion efficiency, and straw collection coefficient of the crop, the total potential model for agricultural biomass in the study area can be obtained:

$$D_{bio}=K\times C\left(H_a{F}_a{I}_a+H_b{F}_b{I}_b+H_c{F}_c{I}_c+H_d{F}_d{I}_d+H_e{F}_e{I}_e+H_f{F}_f{I}_f\right)$$

(6)

wherein $D_{bio}$ is the total biomass potential in the study area; $K$ is the biomass combustion efficiency; $C$ is the straw collection coefficient; $H_a$, $H_b$, $H_c$, $H_d$, $H_e$, and $H_f$ represent the calorific values of straw of rice, tubers, soybean, sugarcane, corn, and wheat, respectively; $F_a$, $F_b$, $F_c$, $F_d$, $F_e$, and $F_f$ represent the forecasted per unit area yield of straw of the six types of crops, respectively; and $I_a$, $I_b$, $I_c$, $I_d$, $I_e$, and $I_f$ represent the planting area of the six types of crops, respectively.

Table 2. Crop yield and biomass potential of major crops in the study area.

Province	City/County	Total Production of Major Crops (1000 tons)	Straw Available (1000 tons)	Biomass Potential (TJ)
Heilongjiang	Mudanjiang	1612.2	2820.8	25,603.6
	Hegang	4507.8	7481.3	68,574.7
	Jiamusi	10,865.9	16,287.7	137,407.1
Jiangsu	Changzhou	535.9	699.8	5246.9
	Yancheng	6965.1	9694.6	77,091.1
	Lianyungang	3672.4	5066.7	40,013.5
Guangdong	Zhanjiang	11,607.5	2573.5	20,550.6
	Maoming	1990.5	1629.2	12,084.3
	Yangjiang	765.5	1165.8	9357.3

provides detailed estimates of agricultural biomass potential for electricity generation across the nine study cities, calculated using Equations (5)–(7).

2.3.2. Estimation of Waste-to-Energy (WTE) Potential from Domestic Solid Waste

The classified management and resource utilization of solid waste generated by rural residents in their daily lives help diminish pollution to the environment from garbage. Domestic waste output in a rural area presents a linear correlation with its population size, and an inverted U-shaped relationship with its PCDI, with the inflection point of PCDI being about USD 2500. As income rises, the consumption of daily necessities tends to increase, leading to higher levels of waste generation. However, once a certain income threshold is reached, residents are more likely to adopt environmentally conscious, low-carbon lifestyles, which in turn reduces the volume of waste produced [21].

Estimating the volume of household waste generated across income groups is essential for quantifying its biomass energy potential. This income-stratified approach enables a more accurate and regionally sensitive estimation of biomass availability from domestic waste. The specific formula used to calculate the annual total domestic waste output for each study area is presented below:

$$WTE=(S_0\times P_0+S_1\times P_1+S_2\times P_2) \times 365$$

(7)

wherein WTE is the annual total domestic waste output in the study area;$S_0$, $S_1$, and $S_2$ represent the per capita daily domestic waste output of areas with PCDI below RMB 17,500, between RMB 17,500 and RMB 22,300, and above RMB 22,300, respectively; and $P_0$, $P_1$, and $P_2$ represent the population size of areas with PCDI below RMB 17,500, between RMB 17,500 and RMB 22,300, and above RMB 22,300, respectively.

The values of S₀, S₁, and S₂ were determined based on the Technical Specifications of Domestic Pollution Control for Town and Village (HJ 574-2010) [22], which specify that the per capita domestic waste generation in rural areas ranges between 0.25 kg/day and 1.25 kg/day. Following the classification framework provided by [23], the present study stratified rural households by per capita disposable income (PCDI) and assigned corresponding waste generation rates within the official range: 1.25 kg/day for areas with PCDI below RMB 17,500, 0.75 kg/day for areas with PCDI between RMB 17,500 and RMB 22,300, and 0.25 kg/day for areas with PCDI above RMB 22,300. This assignment reflects the empirical relationship between household income and waste production patterns, where lower-income households tend to generate higher quantities of domestic waste due to consumption patterns, while higher-income households typically produce less waste as a result of more sustainable consumption behaviors.

Thermochemical technique and anaerobic digestion technique are practical measures for the energy conversion of domestic waste. Compared with anaerobic digestion technique, thermochemical techniques such as incineration, gasification, and pyrolysis demonstrate higher energy potential. The calculation method for the thermochemical power generation potential of rural domestic waste is as follows:

$$D_{wte}=WTE\times \left(1-q\right)\times 0.65\times BR\times \mu \times 41.67$$

(8)

wherein $D_{wte}$ is the total power generation potential of rural domestic waste; $q$ is the water content of domestic waste, which is set at 60% according to the study of Zhou et al. [24]; the incineration treatment rate of domestic waste is set at 65% in accordance with the document titled the Plan for Classification of Urban Domestic Waste and Development of Treatment Facilities during the 14th Five-Year Plan Period in China [25]; $BR$ is the net calorific value of domestic waste, which is set at 0.242 Kw/m³ according to the study of Chakraborty et al. [26]; $\mu$ is the conversion efficiency of the thermochemical process, which is set at 30% according to the standards of CJ/T20-1999 [27]; and parameter $41.67$ represents the treatment efficiency of 1000 tons of garbage per hour (1000 ton/24 h), with the unit of ton/h.

2.4. Assessment Model for Biomass Resource Surplus and Deficit

Based on the supply–demand relationship of biomass energy derived from the preceding analysis, this section introduces a biomass energy surplus assessment model to evaluate the suitability of bioenergy development in each study area. The model quantifies the balance between regional biomass potential and local electricity demand, as expressed by the following equation:

$$G_r={\displaystyle \frac{D-E^z}{E^z}}\times 100\%$$

(9)

wherein $G_r$ is the biomass potential gain/loss of the study area; $D$ is the total biomass potential of the area, which is the sum of $D_{bio}$ and $D_{wte}$; and $E^z$ is the electricity demand of the study area.

A positive Gr indicates that the biomass potential of the study area is greater than its total energy demand, and a higher value signifies higher suitability for bioenergy development in the area. A Gr of zero implies a balanced scenario, where biomass resources precisely meet local energy needs, making the area suitable for sustainable, stand-alone biomass deployment. A negative Gr denotes a biomass deficit, where local energy needs surpass available biomass resources. In such regions, biomass energy must be integrated with other renewable energy systems and possibly supplemented by conventional energy sources to meet demand reliably.

This model allows for a comparative and dynamic assessment of regional bioenergy suitability under varying socioeconomic and energy demand conditions, thereby offering actionable insights for planners and policymakers targeting sustainable rural energy transitions.

2.5. Nonlinear Modeling of Income-Driven Rural Energy Consumption Patterns

To explore the relationship between per capita disposable income (PCDI) and rural energy consumption patterns—including both electricity and thermal energy use—this study employed a non-linear curve-fitting approach. Specifically, income levels were fitted against per capita electricity consumption (PCEC) and estimated thermal energy demand to capture non-monotonic variations in energy use across income brackets. The fitting process involved polynomial regression modeling, with model selection based on residual minimization and goodness-of-fit criteria. Inflection points in the fitted curves were identified where the slope exhibited significant change, reflecting shifts in consumption behavior. All models were subjected to standard regression diagnostics, including significance tests (p-values < 0.05), residual normality checks, and heteroscedasticity analysis, to ensure the statistical validity and robustness of the identified relationships.

3. Results

The data on rural population and rural per capita disposable income (PCDI) used in this study were sourced from China’s national and municipal statistical yearbooks. Per capita electricity consumption (PCEC) was derived by dividing the total rural electricity consumption by the rural population in each study region. For regions with winter heating demand, thermal energy consumption was estimated based on regional heating data. Specifically, the thermal energy consumption index per unit of heated area during the winter season was calculated using the total quantity of urban central heating supply and the corresponding total heated floor area, as reported in China’s Urban Construction Statistical Yearbooks.

Given that the per capita housing construction area (PCHCA) varies significantly across income groups in rural China, with higher-income households generally possessing larger living spaces [28], and that housing area directly affects thermal energy consumption during winter, theoretical thermal energy demand was further estimated by incorporating data on population size, income-stratified PCHCA, and the regional heating energy index.

Although the rural areas included in this study are not yet comprehensively covered by central heating systems, national policy initiatives such as the Plan for Clean Winter Heating in Northern Regions (2017–2021), issued by the National Energy Administration of China [29], indicate a clear trajectory toward broader rural heating infrastructure deployment. Notably, Heilongjiang Province, which contains three of the selected cities (Jiamusi, Mudanjiang, and Hegang), is explicitly included among the 14 provinces targeted for clean winter heating expansion. According to the Plan, rural areas in Northern China were expected to achieve a clean heating rate exceeding 40% by 2021, with further expansion anticipated.

Based on the previously established methodology and multi-source datasets, this section presents the disaggregated energy demand metrics for each city and county within the three study provinces. These metrics allow for a comprehensive understanding of spatial and socioeconomic differences in rural energy consumption.

Table 3. Energy demand parameters in the study area.

Province	City/County	Number of Rural Residents (10,000 Persons)	Per Capita Disposable Income (RMB)	Per Capita Rural Electricity Consumption (GJ)	Thermal Energy Consumption per Heating Area in the Winter Heating Period (GJ/m2)	Thermal Energy Consumption per Capita During the Heating Period (GJ/Person)
Heilongjiang	Mudanjiang	108.2	20,045	1.81	0.42	29.71
	Hegang	15.5	16,466	1.38	0.54	30.15
	Jiamusi	110.1	19,196	3.74	0.43	13.62
Guangdong	Zhanjiang	376.43	20,693	2.18	-	-
	Maoming	348.45	19,621	2.18	-	-
	Yangjiang	177.6	19,841	2.53	-	-
Jiangsu	Changzhou	119.9	35,822	54.39 1	-	-
	Yancheng	240.84	23,670	12.82	-	-
	Lianyungang	177	19,237	7.09	-	-

¹ Calculated as 54.39 GJ based on rural population and electricity consumption data from the 2024 Changzhou Statistical Yearbook.

summarizes the core energy demand parameters across the selected rural regions. These data support the comparative analysis of rural energy needs under varying climatic and economic development conditions.

3.1. Simulation of the Impact of PCDI on Energy Demand

3.1.1. Relationship Between PCDI and Electricity Consumption

Differences in the PCEC curve shape for areas at particular rural PCDI levels were illustrated by fitting PCDI with rural electricity consumption in the study area. It can be seen from Figure 3 that the per capita rural electricity consumption of the areas at low rural PCDI levels such as Lianyungang City (c), Yangjiang City (f), and Hegang City (i) all demonstrate inflection points which lie between RMB 13,000 and RMB 15,300. The per capita rural electricity consumption of areas at high rural PCDI levels has not demonstrated an obvious inflection point. As shown in Figure 3b,e,i, the rural per capita electricity consumption in the three study cities at the median income level—Zhanjiang City, Changzhou City, and Mudanjiang City—rises rapidly with increasing income and does not exhibit a significant inflection point. It is noteworthy that these cities also display differences in slope values, reflecting regional disparities in economic development. The fitted slope for Zhanjiang City is 7.68, for Changzhou City is 12.49, and for Mudanjiang City is only 0.95.

3.1.2. Income-Driven Variability in Winter Thermal Energy Demand

A series of key parameters corresponding to rural areas with varying levels of per capita disposable income (PCDI)—including per capita housing construction area (PCHCA), the duration of the winter heating period, and the regional heating load index—were applied to Formulas (2) and (3) to estimate per capita thermal energy consumption during the heating season. These parameters reflect how economic conditions shape both residential energy needs and structural building characteristics. As illustrated in Figure 4, a clear nonlinear pattern emerges between PCDI and thermal energy consumption. In particular, the polynomial fitting results for Mudanjiang City and Hegang City in Heilongjiang Province exhibit similar parabolic curves. Across the three study cities located within the winter heating zone, the relationship between rural PCDI and per capita thermal energy consumption shows a distinct inflection point, with peak heating energy demand occurring at PCDI levels between approximately RMB 13,600 and RMB 14,500.

This suggests that while improved income levels lead to larger living spaces and greater energy use initially, beyond a certain threshold, improved energy efficiency, insulation, and possibly behavioral shifts begin to offset the upward trend in heating demand. These findings highlight the need to consider income-driven energy demand variation when designing biomass deployment strategies in cold-climate rural areas.

3.2. Distribution of Biomass and WTE Potentials Across Economic Regions

Based on the calculations of Equations (5)–(7), data on the potential of agricultural biomass for power generation were collected for nine cities in the study area. These data details were tabulated in Table 2. To examine the spatial distribution characteristics of biomass energy potential across regions with varying economic development levels, interpolated panel data for both agricultural biomass and waste-to-energy (WTE) potentials were analyzed. As shown in Figure 5, Jiamusi and Hegang cities in Heilongjiang Province, as well as Yancheng and Lianyungang cities in Jiangsu Province, exhibit substantial agricultural biomass potential. Notably, Lianyungang City demonstrates the highest straw resource density across all study areas, reaching 665.28 t/km². In contrast to the high biomass resource availability observed in Heilongjiang and Jiangsu—both provinces characterized by extensive agricultural activity and relatively lower urbanization rates—the three study cities in Guangdong Province show comparatively lower agricultural biomass potential. Among them, Zhanjiang City possesses the highest theoretical potential at 20,550.69 TJ, followed by Maoming City (12,084.31 TJ) and Yangjiang City (9357.31 TJ).

Based on data on per capita daily domestic waste generation in rural areas, this section utilizes Equations (8) and (9) to estimate the theoretical power generation potential of rural domestic waste for each of the selected cities. The resulting values are visualized using violin plots, as shown in the right-hand panel of Figure 5, where bar overlays indicate specific statistical values. This visualization enables a spatially comparative analysis of waste-to-energy (WTE) potential across regions with differing socioeconomic and demographic characteristics. As illustrated in Figure 5, Zhanjiang and Maoming cities in Guangdong Province demonstrate notably high WTE potential from rural domestic waste, with theoretical pyrolysis-based energy outputs of 4531.69 TJ and 4155.44 TJ, respectively. This high potential is primarily attributed to the large rural population base in these areas, coupled with relatively high per capita waste generation rates.

In contrast, Changzhou City in Jiangsu Province exhibits significantly lower rural WTE potential. This is largely due to its advanced level of urbanization, which has resulted in a reduced rural population and correspondingly lower volumes of domestic waste. Similarly, Hegang City in Heilongjiang Province reports the lowest WTE potential among all study cities, at only 184.84 TJ. This can be attributed to extensive rural depopulation in recent years, driven by large-scale outmigration to urban areas within and beyond the province, which has significantly reduced local waste generation capacity. In light of this limitation, an alternative energy strategy for Hegang could involve prioritizing the development of distributed photovoltaic (PV) systems and small-scale biomass combined heat and power (CHP) facilities. Additionally, where geothermal resources are available, their integration into rural heating solutions could further enhance energy sustainability. These approaches align with China’s national initiatives for promoting rural energy transformation, renewable energy deployment, and low-carbon development in support of the dual-carbon goals.

These findings underscore the critical influence of population dynamics, urbanization processes, and socioeconomic transitions on the availability of rural renewable energy resources. The evident spatial disparities in domestic WTE potential highlight the necessity for regionally adaptive, population- and economy-sensitive strategies. Such approaches are essential to support the development of decentralized renewable energy systems and to facilitate a sustainable rural energy transition.

3.3. Assessment of Surplus and Deficit of Biomass Energy

Based on the spatial analysis presented in Figure 6, Heilongjiang Province demonstrates a more favorable biomass potential gain/loss ratio compared to the other two study provinces. This outcome can be attributed to the province’s lower rural population density and abundant agricultural residue resources. Among the nine sample cities, Hegang demonstrates the highest biomass energy surplus, with a theoretical surplus ratio of 12.05—equivalent to approximately 1304% of its total rural energy demand. This result is derived from the surplus coefficient calculated using Equation (9), based on integrated data on local biomass availability and electricity consumption. In conjunction with the findings presented in Figure 3g and Figure 4c, the current level of agricultural biomass in Hegang is sufficient to meet both household electricity consumption and winter heating needs. Assuming that key factors such as population size, cultivated land area, and crop yields remain relatively stable over the short to medium term, this surplus is expected to persist or further improve.

A comparison of surplus coefficients across the study area, as visualized in Figure 6, reveals notable regional disparities. While Hegang exhibits a substantial surplus, Jiamusi and Mudanjiang report values of 3.06 and –0.2, respectively. This spatial imbalance suggests an opportunity for the inter-city optimization of biomass resource allocation. From a rural energy planning perspective, deploying a distributed biomass facility network could effectively leverage Hegang’s excess supply to support neighboring areas with limited biomass potential, thereby improving regional energy equity and enhancing the overall resilience of the rural energy transition.

In contrast, cities with higher rural PCDI levels generally exhibit higher annual per capita electricity consumption (PCEC), as shown in

Table 4. Power generation potential and energy demand indicators.

Province	City	Biomass Potential (TJ)	WTE Potential (TJ)	Annual per Capita Energy Demand (GJ)	Straw Resource Density (t/km2)	Estimated Annual Energy Demand (10,000 Mwh)
Jiangsu	Changzhou	5246.97	500.87	52.68	159.59	1843.75
	Yancheng	77,091.08	1866.89	12.82	572.59	857.63
	Lianyungang	40,013.55	1372.03	7.09	665.28	348.58
Guangdong	Zhanjiang	20,550.69	4531.69	2.03	203.91	214.37
	Maoming	12,084.31	2701.04	2.18	84.95	211.00
	Yangjiang	9357.31	1419.14	1.55	146.34	51.37
Heilongjiang	Mudanjiang	25,603.59	838.72	30.56	72.65	918.53
	Hegang	68,574.73	120.15	33.96	510.15	146.20
	Jiamusi	137,407.09	1321.23	30.84	501.78	952.18

. For example, Changzhou City, despite lacking central winter heating demand, demonstrates a per capita electricity consumption level that far exceeds that of the three study cities in Heilongjiang Province. This discrepancy can be attributed to a combination of factors, including a highly developed rural economy, dense rural population, and the rapid acceleration of electricity demand driven by improved living standards and lifestyle upgrades. However, the biomass potential gain/loss value in Changzhou remains negative, indicating that local biomass resources are currently insufficient to fully meet rural energy needs. While a portion of household electricity demand may be supported through biomass utilization, the city is expected to continue relying on coal-fired and conventional thermal power to satisfy the majority of its rural industrial and agricultural electricity consumption. This underscores the challenge of balancing increasing energy demand with renewable resource limitations—an issue central to the rural energy transition agenda.

Addressing this limitation calls for an integrated approach. One possibility is to coordinate biomass supply across regions by drawing surplus resources from adjacent areas, thereby relieving pressure on local supply chains. Another is to embed biomass within hybrid renewable energy systems that incorporate distributed photovoltaics or other low-carbon technologies, enhancing system resilience and reducing dependence on conventional thermal power. In parallel, improving biomass conversion efficiency and aligning resource allocation with demand profiles—such as prioritizing household over industrial use—can further elevate the contribution of bioenergy. Together, these measures offer a flexible and policy-aligned pathway to reconcile escalating energy demand with finite renewable resource availability in economically advanced rural regions.

Building on the previously established nonlinear relationship between PCDI and energy demand, energy consumption in the study area is projected to rise sharply during early economic growth, followed by a gradual stabilization or decline as income continues to increase and energy efficiency improves. With sustained policy support for cultivated land protection, coupled with targeted biomass resource development strategies at both the provincial and municipal levels, the biomass energy supply potential is expected to experience modest short-term variation, but to increase progressively as rural PCDI surpasses a critical threshold.

This evolving dynamic underscores not only the critical role of income-sensitive planning in bioenergy development, but also the broader imperative for integrated and adaptive renewable energy systems aligned with regional development priorities. Increasingly, the strategic deployment of biomass within rural energy infrastructures is being bolstered by national policy support. In particular, the Chinese government has implemented a suite of incentive mechanisms aimed at accelerating the uptake of clean rural energy technologies. These include capital subsidies for biomass-based combined heat and power (CHP) systems, tax incentives for agricultural waste valorization, and infrastructure investments under flagship initiatives such as the Clean Winter Heating Plan (2017–2021), with a strong focus on northern provinces. Designed to reduce implementation costs, mobilize investment, and expedite the shift from traditional combustion practices to low-carbon bioenergy solutions, these instruments provide critical policy leverage. Effectively aligning regional deployment strategies with such policy frameworks can enhance the scalability and operational feasibility of biomass systems, particularly in high-demand regions with limited endogenous resource endowments. Enhancing the biomass energy surplus in this context can play a pivotal role in advancing the energy transition in rural areas, reducing dependence on fossil fuels, and fostering a more resilient and sustainable rural energy future.

4. Conclusions

This study developed a multiregional spatial assessment framework to examine the socioeconomic determinants of biomass energy transition in rural China, with a particular focus on income levels, resource endowments, and regional consumption dynamics. Based on nine representative cities/counties across Heilongjiang, Jiangsu, and Guangdong Provinces—each with distinct climatic, economic, and demographic profiles—the analysis integrates statistical modeling, GIS-based spatial evaluation, and surplus-deficit estimation to identify region-specific patterns and planning implications. Three key findings emerged.

(1)

Nonlinear regression analysis revealed that rural energy demand exhibits a critical inflection point between RMB 13,000 and 15,000 in low- to middle-income regions. Beyond this threshold, per capita energy consumption tends to stabilize or decline, reflecting shifts in household behavior and infrastructure improvements. This income-sensitive pattern offers a novel empirical reference for energy demand forecasting and targeted policy interventions.

(2)

In low-income rural areas, energy consumption rises sharply after reaching a critical threshold (about 13,000 RMB) and then declines slowly. The results of the study suggest that the surplus of biomass energy improves significantly as the PCDI rises and targeted policy interventions (e.g., cropland protection policies and government subsidies to the biomass energy industry) are implemented.

(3)

Regions with superior economic development such as rural areas with PCDI levels above RMB 21,000 typically benefit from better financial conditions and more developed infrastructure, especially road transportation networks, which are conducive to the expansion of the bioenergy industry. Per capita energy consumption continues to rise sharply in rural areas with PCDIs between RMB 13,000 and 21,000. Here, transitioning to integrated systems that combine agricultural residues, domestic waste, and other renewables (e.g., photovoltaics) will be essential. Improved conversion efficiency and demand-responsive planning can further boost system performance.

This spatially explicit methodology demonstrates strong scalability for sustainable energy planning. By capturing how income levels, demographic structure, climate zones, and biomass resource potential interact to shape energy transitions, this study provides a generalizable framework for optimizing renewable energy strategies in data-scarce rural settings. The findings contribute to both scholarly understanding and practical decision-making toward China’s dual-carbon goals and broader rural sustainability agenda.

5. Discussion

The key contributions of this study lie in its simulation and analysis of the relationship between rural per capita disposable income (PCDI) and energy demand across regions with differing climate zones and socioeconomic development levels. A central finding of the research is the identification of RMB 13,000 as a critical threshold point for energy consumption behavior in rural areas with low to middle income levels. At this inflection point, the trajectory of per capita energy consumption shifts from a rapid increase to a more moderate or even declining trend. This result provides a novel empirical reference for future energy demand forecasting, particularly for regions undergoing economic transitions.

The significance of this threshold lies in its practical implications for mid- and long-term energy policy formulation. It enables more precise planning based on income segmentation, helping policymakers to tailor interventions, incentive structures, and technology deployments for regions at different stages of rural development. Moreover, it offers evidence-based guidance for investors in the bioenergy sector, particularly with regard to optimizing facility siting, investment timing, and expected return profiles in regions with contrasting socioeconomic dynamics. This study selected nine representative cities across three provinces with contrasting latitudinal positions, climate types, economic structures, and population characteristics. While the results are directly derived from these specific regions, the methodology and analytical framework have broader applicability. The research provides a generalizable approach for spatially explicit energy planning that integrates biomass resource availability, population distribution, and income-based energy consumption patterns.

To support this analysis, historical and current data were collected on agricultural biomass resources, population size, and PCDI levels. Using this data, the waste-to-energy potential from agricultural residues and domestic waste pyrolysis was calculated. These results were spatially visualized through ArcGIS, allowing for an intuitive understanding of regional energy resource disparities and the spatial clustering of bioenergy development potential. The calculated biomass energy surplus values—derived from a comparison of local biomass availability and rural energy demand—highlight areas with strong potential for renewable energy development in the medium and long term. These areas also present promising candidates for the strategic deployment of biomass power facilities, both for grid-connected bioenergy systems and off-grid rural applications.

With regard to waste-to-energy (WTE) potential, the study underscores the importance of domestic waste as a supplementary bioresource, particularly in low-income areas where agricultural residues alone are insufficient. However, technological and institutional barriers—such as high capital costs, limited access to technical expertise, and fragmented rural waste management systems—may constrain WTE deployment in under-resourced regions. Addressing these barriers will require sustained public investment, capacity building, and the design of modular or community-scale systems that reduce entry thresholds for rural stakeholders.

While the selected provinces capture a range of climatic zones and development levels, the findings may not fully reflect the heterogeneity of western or less-developed regions in China. Future research should expand the spatial scope to include regions that face unique energy access and resource allocation challenges in the research framework. Doing so will improve the generalizability and policy relevance of biomass energy transition models across China’s rural landscape.

Ultimately, this study demonstrates that effective bioenergy planning requires more than resource quantification; it must incorporate socioeconomic context, infrastructural feasibility, and spatial equity. Aligning biomass energy strategies with demographic dynamics, urban–rural migration patterns, and localized energy behaviors will be essential to achieving equitable and sustainable rural energy transitions under China’s dual-carbon policy agenda.