Research Progress and Future Trends in Biomass Energy Spatial Planning

Abstract

Biomass energy, as a renewable and abundant source of clean energy, offers strong support for mitigating the environmental crises caused by fossil fuel consumption and realizing global carbon neutrality goals. Research on biomass energy spatial planning is inherently complex and interdisciplinary. Although existing studies span a wide range of spatial scales and thematic focuses, there remains a lack of review that constructs the research framework from a holistic perspective, systematically synthesizing existing literature, identifying research hotspots, and analyzing evolving trends. To address this gap, this research employs CiteSpace to visualize the research trends of the field. Thereby, grounded in "energy landscapes" theory, this research constructs the "resource–supply chain–demand–optimization" spatial operational logic and corresponding biomass energy spatial planning research framework. It reviews existing literature on potential assessment, supply chain, energy demand, and spatial optimization of supply–demand alignment, to clarify the interconnections among research themes, methods, and subfields, enhance the practical feasibility of biomass energy assessment and spatial planning, and improve the scientific rigor and applicability of optimization strategies. Finally, the research outlines future research directions, emphasizing the need to integrate energy planning with spatial planning. Through scientifically guided planning and rational allocation of biomass resources, the added spatial value of renewable energy can be fully leveraged to support sustainable development.

Graphical abstract

Keywords

Renewable Energy / Energy Landscapes / Potential Assessment / Supply Chain / Supply–Demand Alignment / Biomass Combined Heat and Power Plant

Highlight

	· Develops a research framework of biomass energy spatial planning
	· Proposes the "resource–supply chain–demand–optimization" biomass energy spatial operational logic
	· Reviews the recent developments and future directions of biomass energy spatial planning research

1 Introduction

The rapid growth of the global economy has long relied on fossil fuels, leading to a series of environmental crises. Currently, greenhouse gas emissions from power plants account for approximately 35% of the total global emissions^[1]. As a clean energy source, biomass energy offers the advantage of zero or even negative carbon emissions^[2], making it a key contributor to the transition toward low-carbon energy systems and the realization of global carbon neutrality goals. According to the International Energy Agency (IEA), biomass energy accounts for 55% of the world's renewable energy supply and over 6% of the total global energy consumption^[3], positioning it as a critical component of the national energy strategy of China. Biomass energy refers to the energy stored in biomass carriers through photosynthesis, by which solar energy is converted into biological matter^[4]. These carriers include agricultural, forestry, and industrial residues, giving biomass a distinctive advantage in storage and making it one of the most widely promotable renewable energy sources^[5].

Carbon neutrality has become not only a major driving force for territorial spatial planning but also a key criterion for evaluating its core outcomes. The transition to renewable energies significantly influences land-use functions and environmental conditions^[6]. In response, planners and designers have increasingly engaged in related research, leveraging their interdisciplinary strengths to provide scientific support for spatial analysis and policy-making in renewable energy deployment. With the spatial restructuring guided by urban planning, the medium- and long-term goals of renewable energy development have gradually shifted from centralized power generation to distributed systems. This transition fosters a closer spatial integration between newly constructed infrastructure and densely populated areas, thereby reshaping urban landscapes^[7]. Consequently, energy solutions can no longer be separated from spatial considerations^[8]. Future energy transitions should integrate spatial and energy planning—particularly by coupling biomass energy supply and demand within specific regions and landscapes.

Thomas Blaschke et al. introduced the concept of landscape into the energy domain, giving rise to the theory of energy landscapes^[9]. An energy landscape refers to a complex spatiotemporal configuration that connects energy supply, demand, and infrastructure within a given landscape context^[10]. This framework emphasizes the integration of actual and potential energy sources, transportation pathways, and localized energy demand, while also considering synergies with other ecosystem products and services^[11]. Within this theoretical context, spatial planning for biomass energy is a form of strategic planning that leverages energy modeling and spatial analysis to examine the current status, spatial heterogeneity, and temporal dynamics of biomass energy resources, and to coordinate resource distribution, transportation conditions, and demand-side layout for optimized spatial allocation of biomass conversion facilities. Such planning contributes to reducing transportation and processing costs while balancing ecological protection and resource efficiency. It also supports decision-making in territorial spatial planning, offering a critical pathway for maximizing the benefits and minimizing the ecological impacts of biomass energy development^[12]. For instance, Seolhee Cho et al. proposed a strategic planning model for optimizing biomass-based hydrogen energy networks, identifying both optimal investment timing and regional allocation^[13]. At present, Geographic Information System (GIS) technology—owing to its strengths in spatial mapping and analysis—has become a key tool for biomass resource potential assessment and energy infrastructure site selection^[14][15]. Junnian Song et al. projected the trade-offs and contributions of three types of agricultural residues to sustainable development in 31 provincial regions of China by 2030, based on the environmental, economic, and social impacts of biomass energy and a multi-resource–technology–output life cycle framework^[16].

Although recent years have seen considerable progress in biomass energy policies and technologies development^[17][18], there is still an absence of comprehensive reviews through the lens of spatial planning. Existing studies lack systematic synthesis, research hotspot identification, and analysis of research trends, highlighting the need for an integrative framework that connects biomass energy studies with territorial spatial planning. To address this gap, this research utilizes CiteSpace to visualize and analyze research dynamics in the field, and constructs a research framework of biomass energy spatial planning framework on the basis of the theory of energy landscapes. This study incorporates multi-scale and multi-spatial analysis approaches, with particular emphasis on small and micro-scale spatial contexts, to clarify the interconnections among research themes, methods, and subfields, enhance the practical feasibility of biomass energy spatial assessment and planning, improve the scientific rigor and applicability of optimization strategies, and ultimately support the geographic planning of future energy development while increasing the social acceptance of energy landscapes.

2 Research Overview

2.1 Bibliometric Analysis of Current Research

This study utilized the Web of Science (WoS) Core Collection database to retrieve literature by searching the topic terms of "biomass energy spatial planning," "bioenergy spatial planning," and "energyscape." The search was limited to articles and review papers published in English from January 1, 2015 to December 31, 2024, yielding a total of 236 records. To ensure the quality and relevance of the dataset, unrelated publications were excluded, resulting in 194 retained articles. To further enhance the representativeness of the dataset and cover the research development over a decade, an additional 22 highly relevant publications—though not fully matched by the topic terms—were manually included, leading to 216 valid records for analysis. Subsequently, descriptive statistics were conducted using built-in analysis tools of WoS, covering publication year, research area, citation count, etc. CiteSpace was then employed to perform international collaboration network analysis, keyword co-occurrence analysis, cluster detection, and timeline visualization.

2.1.1 General Characteristics and Evolution of the Research Field

The total number of publications in the field of biomass energy spatial planning remains relatively low, with annual outputs showing noticeable fluctuations. Publication peaks occurred in 2018 and 2022, while the overall citation count has steadily increased over the past decade (Fig.1). This field demonstrates broad disciplinary coverage, spanning 46 WoS categories. Among them, "Energy & Fuels" accounts for the largest proportion, representing 45.83% of the total publications.

2.1.2 International Collaboration Analysis

Betweenness centrality (BC) is a network metric to assess a node's bridging ability for information flow within a network structure^[19]. The international collaboration network on biomass energy spatial planning and related data indicates that the United States of America (USA), Germany, and the Netherlands have both high publication output and the highest BC values, 0.24, 0.20, and 0.19, respectively, suggesting their key roles as international collaboration hubs. In contrast, China, despite its high publication volume, exhibits a relatively low BC value (0.02), implying that its influence within the global collaboration network remains limited. Early contributors to this field include Finland, Germany, Denmark, Ireland, and Croatia. China, the USA, Germany, and Italy have emerged as major contributors in terms of cumulative research output and consistent publishing, providing a theoretical foundation for the development of this field. However, in terms of collaboration pathways, China and the USA, although both are central nodes, show limited direct cooperation and remain largely disconnected. China's collaborations are more closely linked with Finland and Sweden. The overall network reveals that European and North American countries tend to form more multinational research collaborations, with Germany, the USA, and the Netherlands maintaining the most linkages. In contrast, East Asian countries show limited interconnectivity and have yet to establish a cohesive cooperation mechanism. Additionally, several Asian and Middle Eastern countries (e.g., Malaysia, Iran) have also appeared in the collaboration network, indicating a trend toward diversification and regional diffusion in this field's development (Fig.2).

2.1.3 Keyword Clustering Analysis

Keyword clustering focuses on intrinsic similarities of the keywords, such as semantic meaning, co-occurrence frequency, and word vector similarity. By selecting "Keyword" as the node type and using default parameter settings, a keyword co-occurrence network was generated (Fig.3). Excluding "biomass," "bioenergy," and "energy" in the topic terms, the ten keywords with the highest BC values are: optimization, renewable energy, availability, systems/system, life cycle assessment, spatial analysis, crop residues, land use, model, and management. The findings indicate that the research hotspots of biomass energy spatial planning are primarily focused on the optimization of land-use patterns, the assessment of biomass resource availability and optimized allocation planning, modeling rely on spatial analysis and life cycle assessment, and efficient management and comprehensive layout to support renewable energy systems.

Keyword co-occurrence analysis helps identify the core areas of research attention, while clustering analysis further uncovers the structural relationships among keywords, enabling the construction of the internal logic and evolutionary pathways of research themes, offering insights into emerging directions. This research applied clustering and timeline visualization functions of CiteSpace (Fig.4), using the log-likelihood ratio (LLR) algorithm for clustering. To ensure the interpretability of cluster labels, three labeling strategies—title terms, keywords, and subject categories—were compared, and title terms were finally selected as the basis for naming clusters. The software automatically selected the top seven cluster labels as the output, with a modularity (Q) value of 0.42 and a silhouette (S) value of 0.76, indicating a relatively high degree of structural homogeneity and internal consistency and suggesting that the clustering results are reliable. The seven identified cluster labels are: spatiotemporal assessment, suitable site, marginal area, sustainable energy potential, using algal cultivation, bioenergy market, and landscape framework. These clusters align the connotation of energy landscapes, suggesting that research in this field emphasizes spatial layout, spatiotemporal analysis, potential assessment, management processes, and marketing and management. In terms of temporal distribution, Cluster #0 spatiotemporal assessment emerged the earliest and has remained active the longest, reflecting its role as a core and persistent research direction. In the early development stage of this field (2015 ~ 2017), studies mainly focused on fundamental methodological development and model construction. Since 2018, the research emphasis has shifted from theoretical exploration toward practical applications. After 2020, increasing attention has been paid to facility layout optimization and multi-dimensional spatial matching, presenting a trend of transitioning from isolated factor analysis to systemic integration and region-wide spatial coordination in this field.

2.2 Spatial Operational Logic of Biomass Energy From the Energy Landscape Perspective

The industrial processes encompassed by energy landscapes cover the dimensions of energy demand, supply, and flow, the description of which enables the identification of coupled pathways among energy production, conversion, distribution, and consumption; highlight constraints related to spatial planning, policy-making, and social acceptance; and reveal critical linkages between energy systems and other socioeconomic systems^[20]. Among the high BC value keywords identified in the co-occurrence network, "life cycle assessment" (LCA) refers to the evaluation of a product or service throughout its entire life cycle—from raw material extraction, production, and use to disposal^[21]. Following this logic and building upon existing studies on biomass energy supply chains^[22][23], this research divides the operation cycle into distinct stages: initial feedstock collection at the source site, establishment of biomass collection hubs, preprocessing, processing at biomass combined heat and power (CHP) plants^①, end-use of biofuels, forming multi-type transmission networks (logistics and electricity distribution), and establishing dynamic supply–demand relationship based on deployment feedback. Thereby, this research extracted the spatial operational logic of "resource–supply chain–demand–optimization" (Fig.5).

① This research focuses on biomass CHP plants as representative conversion nodes, as they serve both energy conversion and end-use supply functions within the biomass energy supply chain. Given the representativeness and widespread application of CHP plants, the research did not separately analyze other types of biomass conversion facilities (e.g., biogas plants, ethanol factories), but instead incorporated them within the CHP plant.

2.3 Biomass Energy Spatial Planning Research Framework

To systematically understand biomass energy spatial planning and to capture the production–consumption processes of biomass energy across multiple spatial scales, this research integrates the spatial operational logic of biomass systems with current and future scenario analysis (near- and long-term). A research framework is established on the "resource–supply chain–demand–optimization" logic, consisting of four core components: resource potential assessment, biomass energy supply chain analysis, energy demand characterization, and spatial supply–demand alignment (Fig.6).

3 Research Progress

3.1 Biomass Energy Potential Assessment

Assessing the development potential of biomass energy and accurately characterizing its spatial distribution are fundamental prerequisites for defining energy utilization pathways and formulating development plans^[24]. Due to limitations in data availability, most existing studies focused on national and regional scales^[25]. The diversity of biomass feedstock and conversion technologies increases the complexity of potential assessments—biomass feedstock includes agricultural residues, forestry residues, municipal solid waste, and animal manure^[26], which can be converted through various technologies into electricity, heat, and a range of bio-based products (e.g., bioethanol, biomethane, biogas)^[27].

Existing biomass energy potential assessment can be categorized into three types: first, theoretical and technical potential assessment; second, demand-driven assessment, including economic potential and implementation potential; third, integrated modeling assessment that links theoretical potential to sustainable implementation potential^[28][29]. At the national level, existing research has estimated the potential of available biomass in China^[30] and assessed the potential contribution of biomass resources to emission reduction targets^[31]. Integrated modeling assessment is more prevalent at regional to village scales. These studies typically incorporate potential assessments with refined planning and management, emphasizing the optimization of policy-making according to the spatial heterogeneity of resource distribution within specific regions^[32][33].

To meet the goal of promoting the efficient and coordinated development of land resources within territorial spatial planning, biomass energy potential assessments should be integrated with land-use configurations. For instance, due to the land use conflict of food crops and energy crops, the production potential of marginal land^②[34] has received growing attention^[35]. Such land is often suitable for cultivating non-food energy plants such as starchy crops, sugar-rich plants, and woody oil plants^[36]. The conversion technologies associated with these biomass sources are relatively mature and have demonstrated preliminary economic and environmental competitiveness compared with fossil fuels. As a result, research on suitability analysis^[37], estimation of energy crop cultivation areas, and corresponding potential assessments of marginal land has become prominent^[38].

② Marginal land refers to the land with limited potential for agricultural use due to constraints such as poor soil quality, insufficient water and thermal resources, and unfavorable topography, typically exhibiting low economic returns and fragile ecosystems (source: Ref. [34]). It was automatically captured as "marginal area" by CiteSpace in previous keyword clustering analysis.

Biomass energy potential assessment can provide essential data to delineate resource utilization limits for territorial spatial functional zoning, and serve as a basis for energy infrastructure spatial planning. However, current studies still face several challenges, including quantitative estimation difficulties, conversion technology bottlenecks, and insufficient policy support^[29]. Due to varying interpretations of biomass among researchers, differences in resource classification and assessment approaches and the absence of localized or standardized key parameters have led to low comparability across studies^[28]. Moreover, many assessments assume uniform yield and spatial distribution within a given area, neglecting the variety of biomass types^[38]. For example, crop straws exhibit strong seasonality, while most studies rely on annual-scale data that overlook the cyclical fluctuations in resource availability^[39].

3.2 Biomass Energy Supply Chain

The biomass energy supply chain refers to the process by which feedstock is collected, processed, and converted into usable fuel products^[40][41]. Relevant research primarily focuses on improving economic efficiency or achieving multi-objective optimization within complex systems to support optimal decision-making^[42]. As the supply chain is closely linked to user demands and stakeholder interests^[43], its design and outcomes directly influence the social acceptance of biomass energy. From a geospatial planning perspective, biomass energy supply chain studies mainly address the site selection and the number of energy facilities, their processing, conversion, and production capacities, as well as the allocation and transportation of biomass between different processing facilities^[44].

3.2.1 Biomass Energy Feedstock Collection and Preprocessing

Research on biomass feedstock collection largely addresses the processes occurring before the biomass reaches the preprocessing facilities^[45], with most conducted at regional to village scales. The collected biomass is typically stored at the source site, the biomass conversion plant, or designated collection hubs^[46]. As intermediate points between the source sites and the CHP plants, the location of collection hubs should prioritize the areas with high biomass yield and short transportation distances^[47]. Most studies are conducted at the village scale and concentrate on optimizing collection hub locations to streamline logistics, reduce transportation costs, and enhance overall efficiency^[48], thereby supporting the development of biomass resource collection models and energy facility site selection strategies^[49].

During the collection stage, biomass feedstock is preprocessed to be converted into higher-density energy carriers, improving energy conversion efficiency. Additionally, adding preprocessing facilities can enhance storage and transportation efficiency^[50]. Previous studies have identified optimal site selection strategies for preprocessing facilities by combining resource availability assessments with suitability analyses of potential locations^[51].

3.2.2 Biomass Energy Transportation

Logistics research in the context of biomass energy must consider transportation methods, costs, and road network accessibility to enhance the feasibility and effectiveness of decision-making. Among the various transportation methods, road transport currently dominates, while multimodal transport systems—combining road, rail, waterway, and pipeline transport—are emerging as a growing trend^[52][53]. Within the biomass energy supply chain, inbound transportation refers to the movement of feedstock to CHP plants, whereas outbound transportation involves the delivery of final products from these plants to end users. Existing studies have predominantly focused on the inbound segment of the supply chain^[54].

Research on transportation costs within the biomass supply chain involves the transportation route development based on multi-models and feasibility analyses, vehicle type and capacity selection, schedule delivery, overall logistics costs and operation time reduction, and environmental impacts mitigation^[55]. Scholars have identified a range of potential factors influencing transportation costs, including feedstock volume and type, moisture content, round-trip distance, and regional conditions. However, conclusions across studies often vary^[54]. For example, Marc Schröder et al. found that vehicle fuel consumption and emissions are key contributors to transportation costs by incorporating road slope and three-dimensional routing models^[56]. In contrast, Rajdeep Golecha et al. emphasized the curvature factor (the ratio of actual transport distance to the Euclidean distance) and biomass yield density as the primary determinants of transportation cost^[57].

3.2.3 Site Selection and Network Layout of Biomass Combined Heat and Power Plants

The construction and operation of biomass CHP plants are widely recognized as one of the most efficient approaches to developing biomass energy^[58]. The site selection of biomass CHP plants should take into account local climatic and environmental conditions, address challenges related to resource supply and environmental risks, and avoid adverse impacts on natural environment. Furthermore, it should also align with local urban development plans and ecological protection requirements. A range of approaches can be employed for comprehensive site assessment, including multi-criteria decision analysis^[59], risk assessment^[60], environmental impact assessment^[61], and fuzzy logic evaluation^[62].

The biomass raw material collection distance threshold (BCDT) refers to the maximum road distance between a source site and a CHP plant, which is commonly used in feedstock accessibility study. An increase in BCDT expands the choice of potential destinations that a given source site can supply, allowing greater flexibility of biomass distribution strategies^[63].

Most studies on the biomass CHP plant site selection are conducted at regional or village spatial scales. Regional-scale research typically focuses on evaluating overall conditions of the selected sites; whereas village-scale studies aim to provide practical and detailed planning or logistics solutions through suitability or optimality analyses, the quantitative indicators of which commonly include resource availability, supply chain cost, and greenhouse gas emission from transportation processes^[44]. In village-scale spatial planning, the configuration of CHP plant network forms the basis of site selection. The total power output of the plants is determined by the regional distribution of energy demand, while the heat transmission threshold (T) defines the spatial relationship between the service coverage of a CHP plant and the surrounding communities. Given the irregularity of road networks between towns, a road nonlinearity coefficient (N) is introduced to calculate the average service radius of a CHP plant (T/N)^[64]. The community distribution density and community population also influence the overall CHP network configuration (Fig.7).

Overall, biomass energy supply chain research can provide scientific support for territorial spatial planning in terms of resource distribution, processing procedure, transportation routing, facility site selection, and logistics cost analysis. However, current studies still face several limitations. First, numerical inaccuracies and spatial deviations in potential assessments can directly affect the construction of an integrated and effective supply chain system. Second, planning and design commonly rely on a fixed number of facilities to cover end-use locations, which often fails to reflect the actual optimal cost configuration, thereby limiting applicability^[65]. Finally, the multi-tiered structure of the supply chain may result in mismatches between resource endowment and infrastructure capacity, making policy implementation difficult. In addition, inconsistencies across different governance levels—such as differences in indicator systems, policy priorities, and administrative capacity—can lead to poor coordination between decision-making processes.

3.3 Biomass Energy Demand

Biomass energy demand is closely related to factors such as price, gross domestic product (GDP), and population^[66]. Spatial-related energy demand research involves two dimensions: the spatial distribution of demand and demand intensity. Estimating and forecasting the related factors are highly challenging due to the influence of dynamic market conditions and policy changes. Relevant studies cover multiple spatial scales, including national, regional, municipal/county, and village levels. However, in practical modeling, differences in data resolution, boundary definitions, and planning objectives across these administrative levels make them difficult to match with the biomass supply chain analysis^[16].

3.3.1 Spatial Distribution of Biomass Energy Demand

The spatial imbalance of biomass energy demand presents significant challenges for the site selection of CHP plants and the spatial configuration of urban energy systems. At the macro scale, seasonal fluctuations in demand caused by latitudinal variation are particularly pronounced—heating and cooling needs represent major components of energy demand^[67]. Chenshuo Ma et al. compared 50 cities and counties across different latitudes where winter heating is required. They found that during non-heating periods, latitude and per capita feedstock consumption by biomass CHP plants had a linear negative correlation, whereas during heating periods, the relationship followed an inverse S-shaped curve.^[68] Moreover, the spatial distribution of biomass energy demand becomes increasingly complex as the scale of analysis expands. For example, Sandra Venghaus et al. demonstrated that the contribution of biomass energy to improving rural residents' quality of life was significantly more pronounced at the regional level than that at the individual level^[69].

3.3.2 Biomass Energy Demand Intensity

Energy demand intensity is normally measured by the amount of energy consumed per unit of GDP^[70]. Population is a fundamental determinant of energy demand^[71], and its spatial distribution and migration patterns offer crucial insights into the characteristics and intensity of energy use, encompassing population growth^[72], urban migration^[73], and changes in population density^[74]. In addition, demographic characteristics (e.g., education level^[74], age structure^[75]) also play significant roles in energy demand analysis. For example, in designing biomass CHP plant networks, variables such as the size and density of population, and other demographic trends can be applied to determine the capacity and operational efficiency of biomass CHP plants^[76].

The scale, structure, and spatial distribution of energy demand directly affect the strategy and spatial layout in territorial spatial planning. At the national level, it is essential to coordinate the spatial relationship between energy production and consumption areas; at the regional level, the planning should promote supply–demand balance; and at the municipal/county and village levels, adjustments to the layout of energy infrastructure (e.g., heating, electricity, transportation systems) are required. Currently, the demand-side research faces several limitations. First, the assessments at small scales often involve a high degree of subjectivity. Second, many energy demand models tend to hypothesize that there is a simple linear relationship between energy consumption and economic activity, overlooking nonlinear dynamics and the influence of multidimensional factors, thereby failing to capture the complexity of the energy system^[77]. Third, existing studies offer limited analysis on the combined effects of policy, economic, and technological drivers. For instance, estimating demand based solely on population metrics results in an oversimplified, homogeneous, and uniform result.

3.4 Optimization of Spatial Biomass Energy Supply–Demand Alignment

Spatial supply–demand alignment analysis of biomass energy can identify areas of potential resource shortages or surpluses under dynamic spatiotemporal conditions. This enhances the resilience of territorial spatial planning towards uncertainties in future energy markets and serves as a key approach to exploring sustainable pathways in the bioeconomy^[78]. Based on the analysis of supply–demand relationships and scenario simulation results, the spatial optimization of energy facility layout and supply chain pathways can be conducted to improve overall system efficiency and adaptability.

The demand feedback represents the response mechanism to future demand changes in the model, enabling a dynamic feedback loop from forecasting to planning. This research area involves statistical data and distribution characteristics from both the supply and demand sides, including spatial matching patterns and the energy surplus coefficient^[39]. Existing studies primarily focus on the regional scale, with some extending to the national or global scale to explore, from a macro perspective, the potential of biomass energy as a substitute for fossil fuels^[79] and to provide a reference for development suitability. The energy surplus coefficient is defined as the ratio between the energy-convertible potential of biomass and the actual energy demand, serving as an intuitive indicator of regional energy self-sufficiency. This coefficient enables the assessment of various biomass conversion technologies, covering the forms of solid, liquid, and gaseous biomass^[80]. It also highlights the comparative advantages and disadvantages of biomass energy versus other renewable energy sources in production and conversion, thus contributing to the development of integrated and complementary sustainable energy systems^[81]. However, current supply–demand alignment analyses face limitations in spatial and temporal alignment due to constraints in data availability and methodological applicability. Future research should take into account resources, technologies, policies, and public participation to achieve the efficient and sustainable development and utilization of biomass energy.

Scenario simulation research aims to forecast planning and decision-making outcomes under varying sets of driving factors. The temporal settings of future scenarios are typically aligned with national energy strategies^[82], carbon peaking targets^[83], and climate change projection models^[84]. In addition, to avoid the risk of ecological deficits, spatial planning should coordinate the supply and demand of key ecosystem services such as carbon storage, biodiversity, water cycling, and soil health^[85]. Scenario simulation provides a useful tool for anticipating trade-offs among multiple ecosystem services under different development trajectories, thereby informing the design of ecological protection or restoration initiatives to compensate for potential environmental losses.

4 Research Trends and Future Perspectives

4.1 Global Research Trends

This research further refines the global landscape of biomass energy research by examining the top ten countries and regions in terms of publication volume. For each, the five keywords with the highest BC values were identified as key research hotspots, while persistent clustering themes were extracted to indicate emerging trends (Tab.1). China and the USA, as the leading contributors, both place strong emphasis on feedstock assessment and greenhouse gas emission mitigation strategies. However, the USA focuses more on modeling frameworks and process analysis, whereas China emphasizes engineering application and adaptive development. In European collaboration networks, research is more about ecological sustainability and spatial optimization. In terms of research hotspots, combining the operational logic of resource–supply chain–demand–optimization, it is clear that research on feedstock potential is the most well-developed. Countries such as China, the USA, and Italy frequently feature keywords related to resource identification and potential assessment (e.g., "crop residues," "energy crops"). Supply chain studies are also relatively advanced, with keywords such as "life cycle assessment," "transportation," "logistics," and "bioenergy facility" appearing in the literature from China, the USA, the Netherlands, Australia, and Spain. In contrast, research on demand-side response and system optimization remains underdeveloped. Related keywords tend to be broad and conceptual, lacking specificity. This gap highlights the necessity and relevance to construct the four-tier framework in this research, which extends the existing research logic and enhance the value of future planning. Globally, biomass energy research is shifting toward a more integrated paradigm, spanning multiple dimensions and spatial scales. Countries such as the Netherlands, Germany, and Australia highlight the increasing importance of spatial analysis and data platforms in decision-making processes. China, Italy, Australia, and England are advancing research at the intersection of renewable resource acquisition and ecosystem functionality. Meanwhile, China, the USA, the Netherlands, Brazil, and Australia are expected to maintain a strong focus on goal-oriented spatial planning, emphasizing refined dynamic supply–demand alignment and regulatory adaptability. Together, these developments reflect a broader transition from static layouts to responsive, systematic biomass energy planning.

4.2 Future Perspectives

Building upon the aforementioned research hotspots, current findings, and emerging trends, this study proposes five key directions for future research in biomass energy spatial planning.

1) Developing databases and precision accounting systems. Advancements in data technologies and modeling methods will provide a robust foundation for energy planning and policy-making across multiple spatial scales. Future studies should prioritize the dynamic monitoring and spatial variation analysis of biomass resources. This includes establishing databases capable of real-time data tracking and updates, capturing the socioeconomic impacts of biomass energy, and providing interpretable indicators. In addition, it is crucial to develop standardized assessment frameworks to ensure the consistency, comparability, and accuracy of biomass resource accounting.

2) Optimizing multi-scale decision-making models. Future research should focus on the development of multi-scale decision-making models that incorporate regional heterogeneity to optimize biomass energy supply chains. Promoting cross-scale coordination is essential to ensure policy coherence and consistency across various governance levels. This includes integrating long-term policy shifts and climate change scenarios into decision-making processes to enhance continuity, and expanding multi-objective planning approaches that operate across scales.

3) Enhancing energy demand assessment and supply–demand alignment. To enable more accurate biomass energy planning, a comprehensive and systematic demand assessment framework should be developed to support effective supply–demand alignment. This includes in-depth analysis of the variations in energy use types and regional demand levels associated with different biomass conversion approaches, comparative studies of demand disparity across multiple renewable energy resources. The relationship between biomass energy and ecosystem services should also be considered. Multi-level scheduling and coordination mechanisms can be applied to flexibly adjust production plans and supply chain configurations, thereby improving supply–demand alignment.

4) Establishing dynamic optimization and intelligent decision-making systems for supply–demand forecasting. Future research can incorporate machine learning and artificial intelligence technologies to enhance the forecasting capacity of energy planning under conditions of uncertainties and dynamic changes, using historical and real-time data. Dynamic dispatch systems should also be established to enable real-time adjustment of strategies in biomass production, transmission, storage, and consumption. Furthermore, mechanisms for renewable energy load shifting and peak regulation should be developed to improve resource utilization efficiency.

5) Integrating biomass energy spatial planning with territorial spatial planning. Territorial spatial planning can provide regulatory constraints and strategic guidance for biomass energy planning, while biomass energy planning can inform territorial planning through its policy coordination frameworks. Database interoperability between the two domains should be promoted. Future research should emphasize on their integration to avoid the occupation of protected functional zones, balance resource exploitation with land use, and enhance the resilience and adaptability of territorial spatial planning systems.

5 Conclusions

This research systematically reviews the core of biomass energy spatial planning and develops an integrated research framework encompassing biomass potential assessment, supply chain analysis, energy demand evaluation, supply–demand alignment, and future scenario simulation grounded in the theory of energy landscapes. This paper explores the fundamental operation mechanisms of biomass energy—from production and transportation to end use—and summarizes the current research dynamics and characteristics of the field on the basis of the bibliometric analysis. Compared with earlier studies that tended to focus on isolated aspects, this research establishes a comprehensive analytical framework with the resources–supply chain–demand–optimization operational logic, integrating multiple spatial scales and temporal dimensions (past, present, and future) to explore the spatial optimization of biomass energy deployment in a more refined manner.

In terms of practice, this study offers several insights for the discipline of planning and design. Optimizing biomass energy layout within territorial spatial planning to improve land-use efficiency and promote the integration of biomass development with urban–rural systems; supporting refined energy planning through GIS-based spatial analysis to enhance the scientific rigor of facility site selection; advancing eco-friendly energy design by applying the theory of energy landscapes; and establishing multi-objective optimization models that balance economic, environmental, and social benefits to support sustainable energy planning. Furthermore, this study provides a scientific approaches for biomass energy spatial configuration, offering valuable references for government agencies and energy enterprises, particularly in policy formulation, agricultural waste utilization, and rural clean energy development.

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