Figures (6)  Tables (4)

    • Figure 1. 

      Schematic illustration of biochar used for soil carbon neutralization.

    • Figure 2. 

      Schematic diagram of priming effect in biochar-soil system.

    • Figure 3. 

      The effects of feedstock and pyrolysis temperature on biochar properties and its applications impacting soil properties regulating the soil health and GHG emissions.

    • Figure 4. 

      Microbial functional gene abundance and metabolic pathway enrichment in biochar-amended soils relative to other organic amendments. (a) Synthesis of published studies shows that biochar amendment significantly increases the mean abundance of key functional genes for mercury reduction (merA), denitrification (nirK, nirS, nosZ), and methane oxidation (pmoA) compared to manure, rice straw, wood chips, and unamended control soils. (b) Enrichment of microbial metabolic pathways—including those for carbon fixation, nitrogen metabolism, and aromatic compound degradation—varies with biochar type, defined by feedstock (e.g., maize straw, wheat straw, wood chips, peanut shells) and pyrolysis temperature (400 vs 800 °C). Higher temperature biochar (800 °C) generally induce distinct metabolic profiles compared to those produced at 400 °C, reflecting how feedstock properties and pyrolysis conditions shape microbial functional potential in amended soils. (c) Mechanistic pathways of biochar in assisting microbial growth.

    • Figure 5. 

      An illustration depicting the data flow diagram elucidating the experimental procedure and data processing pathway for the 13C-labeled biochar.

    • Figure 6. 

      Comparative evaluation of carbon accounting methodologies for biochar. Radar plot showing relative performance scores (0 = low, 10 = high) across six evaluation criteria. Each method displays characteristic strengths: IPCC for standardized direct emission estimates, LCA for comprehensive lifecycle analysis, and IOA for economic-environmental linkage assessment. The visualization highlights the methodological trade-offs necessary for selecting appropriate carbon assessment approaches in biochar research and policy development.

    • Pyrolysis temperature Specific surface
      area (m2 g−1)      		 Porosity
      (cm3 g−1)      		 H/C atomic
      ratio      		 O/C atomic
      ratio      		 Dominant carbon structure Primary functional groups
      250–350 °C (Low) 10–50 0.01–0.05 1.0–1.4 0.4–0.7 Amorphous, aliphatic-C –OH, –COOH, –CH3
      400–500 °C (Medium) 100–400 0.05–0.15 0.5–0.8 0.2–0.4 Mixed aliphatic/aromatic Quinones, phenols
      600–700 °C (High) 400–800 0.15–0.30 0.3–0.5 0.05–0.15 Condensed aromatic-C Graphitic domains, π-π*
      > 800 °C (Very high) 800–1,200 0.30–0.50 < 0.3 < 0.05 Highly graphitic, turbostratic Conjugated π-electrons
      * H/C and O/C ratios are indicative of aromaticity and stability: lower values correspond to higher carbon stability. Data were synthesized from multiple feedstocks including wood, straw, and manure sources[51].

      Table 1. 

      Key physicochemical properties of biochar as a function of pyrolysis temperature

    • Aspect Life cycle assessment (LCA) Input-output analysis (IOA) Hybrid approach (LCA + IOA)
      System boundary Cradle-to-gate or cradle-to-grave. Includes: (1) Feedstock collection, (2) Transportation, (3) Pyrolysis, (4) Application. Excludes indirect economic effects. Economy-wide. Captures all sectoral interconnections. Includes direct and indirect emissions from all related industries (mining, manufacturing, services). Combines: (1) Process-specific LCA for pyrolysis, (2) IOA for upstream supply chains (steel for reactors, electricity grid).
      Data requirements Primary process data (e.g., pyrolysis energy use, transportation distances). Secondary data from Ecoinvent/GaBi databases. High resolution but limited scope. National/regional input-output tables (e.g., USEEIO, EXIOBASE). Sectoral monetary flows and emission factors. Broad but aggregated. Integrated dataset: Process inventories + IO tables. Requires data alignment between physical and monetary units.
      Carbon accounting results (example) Net sequestration: −0.8 to −1.2 t CO2e per t biochar (including: −2.8 t from carbon stability, +0.5 t from production, +0.3 t from transport). Net economy-wide impact: −0.3 to +0.2 t CO2e per t biochar (includes market-mediated effects: fuel substitution, land-use changes, sectoral shifts). Net impact: −0.6 to −0.9 t CO2e per t biochar. Captures both engineering precision and economy-wide ripple effects.
      Key differences in results Consistently shows negative emissions (−0.5 to −1.5 t CO2e per t). Ignores market effects (e.g., increased fertilizer demand). Sensitive to carbon stability factor (0.7–0.9). Can show positive emissions in some scenarios due to economic rebound effects. Captures sectoral displacement (e.g., reduced coal use). Highly sensitive to regional economic structure. Intermediate results between LCA and IOA extremes. Accounts for key supply chain nodes with precision. Can identify policy leakage (emissions shifting to other sectors).
      Error ranges and uncertainty ±25%–40%
      • Process data variability (e.g., pyrolysis efficiency: ±15%).
      • Carbon stability uncertainty (±20%).
      • Allocation methods (mass vs energy: ±10%).      		 ±50%–100%
      • Sector aggregation error (e.g., 'chemical industry' includes diverse processes).
      • Price vs physical unit misalignment.
      • Temporal lag in IO tables (two to five years).      		 ±30%–50%
      • Hybridization errors (mismatch between process and IO data).
      • Boundary selection bias (which processes get detailed LCA).
      • Double-counting risk between LCA and IOA components.
      Optimal application scenarios Technology comparison (slow vs fast pyrolysis). Project financing (carbon credit verification). Process optimization (identifying emission hotspots). Regional policy planning (subsidy impact assessment). National carbon budgeting (economy-wide decarbonization pathways). Trade analysis (import/export embodied carbon). Strategic decision-making for large-scale deployment. Carbon pricing scheme design. International reporting (UNFCCC, IPCC Tier 3 methods).
      Limitations Truncation error (omits distant supply chain effects). Static analysis (no market feedback). Data intensive for site-specific studies. Low technological resolution (cannot distinguish pyrolysis types). Homogeneity assumption (all products in a sector are identical). Complex implementation (requires specialized expertise). Computationally intensive. Limited standardized frameworks.
      Validation methods Sensitivity analysis (Monte Carlo). Peer-reviewed databases (Ecoinvent). Third-party verification (ISO 14044). Cross-regional comparison (comparing different IO tables). Historical data back-testing. Sectoral disaggregation (using make/use tables). Convergence testing (LCA vs IOA results). Scenario analysis (high/low biochar adoption). Expert elicitation (Delphi method).

      Table 2. 

      Comparison of life cycle assessment (LCA) and input-output analysis (IOA) methods for carbon accounting in biochar projects

    • SectorRole in biochar systemKey IO relationshipsEnvironmental link
      AgricultureFeedstock supplier → Provides biomass residuesSells biomass to biochar sector; Purchases biochar for soil amendmentProvides carbon-negative feedstock; Reduces field burning emissions
      Biochar
      production      		Core processing → Converts biomass to stable carbonPurchases from multiple sectors; Sells to agriculture/energy/wasteDirect pyrolysis emissions; Creates net carbon sink via stable C
      EnergyEnergy provider → Powers pyrolysis;
      Can use syngas byproduct      		Sells electricity to biochar sector; May purchase syngas fuelEnergy source emissions offset by renewable syngas utilization
      TransportLogistics network → Moves feedstock and final productServes all sectors in supply chain; Major cost componentTransport emissions partially offset by reduced fertilizer transport needs
      Waste
      management      		Feedstock source → Agricultural/forestry wastesProvides low-cost inputs; Reduces waste disposal needsAvoids landfill CH4 emissions; Converts waste to value
      ManufacturingEquipment supplier → Pyrolysis reactors, handling systemsCapital investments; Technology developmentEmbodied carbon in equipment offset by long-term sequestration

      Table 3. 

      Biochar's position in environmental input-output analysis

    • Method Applicable scenarios Key advantages Main limitations Ref.
      IPCC emission coefficient method Initial screening of biochar systems
      Policy-level carbon accounting
      Standardized reporting for compliance
      Rapid assessment of direct emissions
      Comparison across standardized protocols      		 Internationally recognized standard
      Simple calculation procedure
      Low data requirements
      Consistent and comparable results
      Fast implementation time
      Well-established for direct emissions      		 Only accounts for direct emissions
      Cannot capture indirect emissions (transport, manufacturing)
      Uses generic emission factors that may not be region-specific
      No economic linkages considered
      Static assessment without temporal dynamics
      May underestimate total carbon footprint      		 [8,132,133]
      Life cycle assessment (LCA) Comprehensive product carbon footprint
      Technology comparison (e.g., different pyrolysis methods)
      Sustainability certification
      Eco-design optimization
      'Cradle-to-grave' system analysis      		 Complete system boundary coverage
      Captures direct and indirect emissions
      Identifies environmental hotspots
      Multi-impact assessment (not just climate)
      Supports decision-making for process optimization
      Dynamic modeling possible      		 Data-intensive and time-consuming
      Subjective system boundary definition
      Complex modeling requirements
      Allocation issues for co-products
      Results sensitive to methodological choices
      Regional specificity challenges      		 [122]
      Input-output analysis (IOA) Regional carbon budgeting
      Supply chain analysis
      Economic-environmental policy planning
      Sectoral emission analysis
      Macro-scale carbon footprint assessment      		 Captures economic interdependencies
      Avoids system boundary truncation
      Consistent sectoral data framework
      Suitable for policy analysis
      Time-series analysis capability
      Good for regional/national scales      		 Aggregated sector-level data (lacks product specificity)
      Static coefficients (assumes fixed relationships)
      Limited micro-scale applicability
      Data lag issues
      Cannot accurately assess temporal dynamics
      Regional data availability constraints      		 [134,135]

      Table 4. 

      Comparative analysis of carbon assessment methods for biochar systems