Exploring negative emission potential of biochar to achieve carbon neutrality goal in China

The study addresses how biochar can contribute to China’s negative emissions needs for achieving carbon neutrality by 2060, as an alternative or complement to premature or costly NETs such as BECCS and DACCS. It situates the research within the context of increasing reliance on NETs to meet 1.5–2 °C climate goals and highlights the financial, technological, and sustainability limitations of BECCS and DACCS, including high costs, resource constraints, and competition with food and water. Biochar is presented as a technically proven NET with co-benefits for soil and yields, yet its climate mitigation role in China has been underappreciated. Prior assessments often overlook diverse biomass resources (forestry, grass residues, energy crops), heterogeneity in biochar properties, and spatial factors affecting yields and economics. The purpose is to produce a spatially explicit, nationally comprehensive estimate of biochar’s negative emission potential, costs, and priority deployment regions across multiple feedstocks, to inform a feasible, sustainable NET portfolio for China.

Global assessments underscore biochar’s potential as a negative emission option, but regional analyses often focus narrowly on specific biochar types or omit diverse biomass sources and spatial heterogeneity. In China, prior estimates predominantly emphasized agricultural residues, underestimating total potential by neglecting forestry and grass residues and energy crops. Variations in biochar physicochemical properties by feedstock and pyrolysis conditions, as well as soil texture and pH effects on yield responses, are frequently overlooked, biasing emissions and cost estimates. Recent advances in experimental data, assessment methods, and spatial datasets enable inclusion of biomass and spatial heterogeneity for more accurate, location-specific analyses. The study builds on this literature to address gaps and provide actionable deployment guidance.

The authors executed a spatially explicit assessment in several steps. (1) Biomass feedstock quantification: They estimated available biomass for 16 agricultural residues, 10 forestry residues, grass residues, and potential energy crops (C4 species such as miscanthus and sweet sorghum) using national statistics, spatial crop distribution (Harvard Dataverse), land use (RESDC), and MODIS NPP data. Energy crop suitability on marginal lands (shrub, unused lands; excluding intertidal zones, bottomlands, and nature reserves) was determined by environmental constraints (temperature, slope, precipitation) and literature-based yields; switchgrass was dropped due to low yield. (2) Scenarios of availability: Three scenarios were developed—Maximum Theoretical Potential (all biomass harvestable), Current Technical Potential (limited by current practices: 88% of agricultural and 28% of forestry residues collectable; energy crops and grass residues largely unavailable), and Sustainable Technical Potential (95% agricultural and 80% forestry residues minus competing uses for feed, rural energy and materials; grass residues only where livestock carrying capacity allows; energy crops restricted to unused land and shrub land and downscaled by soil quality). (3) Negative emission accounting: Using a unified empirical framework (Woolf et al.), they modeled slow pyrolysis at 550 °C with co-production of biochar and syngas. Negative emission potential equals the CO₂ fixed in biomass and preserved in biochar over 100 years, computed from feedstock availability, biomass-to-biochar conversion, biochar carbon content, and permanence. (4) Economic analysis: A cost–benefit analysis was performed for 20-year projects per 0.5° grid, including feedstock purchasing, storage, transport (biomass and biochar), pyrolysis CAPEX/OPEX, and application costs. Revenues comprise syngas sales (used for industrial steam/electricity) and crop yield improvements from biochar application at actual application rates inferred from biomass-to-biochar availability relative to an optimal 20 t ha⁻¹ reference. Net costs were derived from net present value apportioned per ton of CO₂ sequestered. (5) Spatial analysis: They mapped negative emission potentials and net costs at 0.5° resolution, incorporating soil texture and pH effects on yield responses and biomass abundance effects on specific investment costs; provinces were grouped into six regions. (6) Uncertainty: Monte Carlo simulations (10,000 iterations) assessed ranges and sensitivities for potentials and costs, with key sensitivities to byproduct income and feedstock purchasing costs.

• Biomass availability: Maximum theoretical feedstocks total 2.43 Gt yr⁻¹ (agricultural 0.99, energy crops 0.86, forestry 0.29, grass 0.29). Sustainable Technical Potential totals 1.73 Gt yr⁻¹; Current Technical Potential totals 0.81 Gt yr⁻¹.
• Negative emissions potential: 1.29 Gt CO₂ yr⁻¹ (maximum theoretical), 0.92 Gt CO₂ yr⁻¹ (sustainable technical), and 0.43 Gt CO₂ yr⁻¹ (current technical). The sustainable potential could meet most modeled NET demands for China’s carbon neutrality pathways, particularly when combined with managed forest sinks (~0.63 Gt CO₂ yr⁻¹).
• Feedstock contributions under sustainable scenario (selected): rice straw/hull 99 Mt CO₂ yr⁻¹, maize straw/cob 148, wheat straw 46, forestry residues 136, grass residues 21, miscanthus 208, sweet sorghum 148.
• Economics: Average net cost ≈ 90 $/tCO₂ nationally; by feedstock, agricultural and forestry residues ~60–96 $/tCO₂, energy crops and grass ~101–144 $/tCO₂. Without revenues from syngas and yields, gross costs rise to 142–273 $/tCO₂ (forestry lowest at ~142; rice 158; maize 162; wheat 168; sweet sorghum 245; miscanthus 270; grass 273).
• Revenue composition: Syngas sales offset ~32–57% of costs; yield improvements offset ~1–23% (lower in China due to already high fertilizer rates; herbaceous biochars show larger yield gains). Forestry and rice residues yield lower byproduct revenues due to higher carbon retained in biochar (forestry) and lower heating value (rice).
• Mitigation beyond removals: Total mitigation including fossil offset via syngas and soil GHG reductions is ~1.5× the pure negative emission component.
• Spatial patterns (sustainable scenario): Highest negative emission potentials in Central and South China (207 Mt CO₂ yr⁻¹), Southwest (194), North (161); Northwest lowest (87). Net costs vary regionally (~12–150 $/tCO₂). Regions rich in agroforestry residues have lower average costs: East China ~77.8 $/tCO₂; Central & South ~78.3; Southwest ~92.5 (more energy crops/grass); Northwest ~100.6 (sparser biomass); North ~110.4. Some grids with agricultural residues can yield net benefits due to high-value/high-yield crops (e.g., sugarcane in Guangxi, cereals in Henan and Shandong).
• Deployment priorities: Overlap of high potential and low cost suggests prioritizing Central & South China and East China; specific provinces highlighted include Guangxi, Henan, and Shandong. Initial deployment should focus on agricultural and forestry residues owing to favorable conversion rates and carbon content.

The findings demonstrate that biochar can provide substantial, near-term, and cost-competitive negative emissions within China compared to other NETs, addressing uncertainties around resource availability, costs, and siting by using a spatially explicit, multi-feedstock assessment. The sustainable potential (0.92 Gt CO₂ yr⁻¹) combined with managed forest sinks could satisfy the majority of negative emission needs under 1.5–2 °C-consistent pathways, alleviating dependence on premature or high-cost options like BECCS and DACCS. Economic analysis reveals that co-production of syngas is crucial to cost-effectiveness, with yield benefits offering additional but smaller contributions; spatial heterogeneity in biomass abundance and soil properties critically affects both potential and costs. The regional alignment of high potential and low costs provides actionable guidance for phased deployment and integration into IAM scenarios. Policy implications include the need for robust, transparent, locally adapted accounting and monitoring frameworks, and for incorporating biochar into national ETS and Article 6 mechanisms to close the gap between biochar costs and current carbon prices. Uncertainty analysis indicates a sustainable-scenario mean potential of ~1.07 Gt CO₂ yr⁻¹ (0.68–1.46) and mean net cost ~92 $/tCO₂ (−13 to 197), with byproduct revenues and feedstock prices as dominant sensitivities. Considering that total mitigation may be ~1.5× removals, biochar’s overall climate contribution could be even larger when life-cycle effects are included.

This study provides a comprehensive, spatially explicit quantification of biochar’s negative emission potential, economics, and deployment priorities in China across multiple feedstocks and scenarios. It shows that sustainable biochar deployment could deliver up to 0.92 Gt CO₂ yr⁻¹ at an average net cost of about 90 $/tCO₂, with particularly favorable opportunities in East China and Central & South China and immediate focus on agricultural and forestry residues. The work offers supply curves, regional cost–potential profiles, and methodological advances useful for policy design and IAM integration. Future research should broaden feedstock coverage (e.g., manures), explore technology pathways that optimize the trade-off between energy value and carbon sequestration, incorporate dynamics such as technological learning, economies of scale, and evolving carbon prices, and assess environmental and socioeconomic trade-offs (land-use change, food competition, biodiversity) and co-benefits (e.g., heavy metal adsorption) to inform sustainable, large-scale deployment.

Key limitations include: (1) Incomplete coverage of biomass resources—livestock manures and other potential feedstocks were not included, possibly underestimating potential. (2) Technology pathway choice prioritized co-production economics over maximal biochar yield; different process optimization could change the balance between energy co-products and sequestration. (3) Dynamics such as technological progress, economies of scale, and changing carbon prices were not fully modeled, potentially overestimating near-term costs. (4) Environmental and socioeconomic trade-offs from large-scale biomass use (land-use change emissions, food competition, biodiversity impacts) were not comprehensively assessed, though some co-benefits (e.g., soil contaminant adsorption) are noted. (5) Negative emission estimates focus on carbon sequestered in biochar and exclude broader mitigation via fossil displacement and soil GHG reductions, which are addressed separately.