Assessing Domestic Forest Capacity for Cross-Laminated Timber in Korean Urban Housing
Description
RESEARCH HYPOTHESIS: This study tests whether Korea's domestic forests can adequately support cross-laminated timber (CLT) adoption in apartment construction through 2050, given constraints from forest accessibility, sustainability requirements, and processing capacity. WHAT THE DATA SHOWS: 26-year projections (2025-2050) of CLT demand and supply for Korean apartment construction under multiple scenarios. Key findings: (1) Annual apartment demand of 200-460k units driven by replacement cycles despite demographic decline; (2) CLT requirements of 0.32-4.39 million m³/year under three adoption scenarios (15%/35%/50% ceilings); (3) Domestic supply potential of 0.26-0.93 million m³/year; (4) Self-sufficiency ranging from 18% (accelerated adoption) to 110% (conservative adoption); (5) Cumulative carbon mitigation of 31-187 Mt CO₂ by 2050. NOTABLE FINDINGS: Forest accessibility is the most restrictive constraint, reducing theoretical harvest from 16.4 million m³/year to <1 million m³/year. Moderate adoption (35% ceiling) achieves 37% self-sufficiency with 93.5 Mt CO₂ mitigation. Import dependence is inevitable for medium-to-high adoption pathways. DATA CONTENTS: Five primary tables with annual values (2025-2050): - Table A1: CLT demand by adoption scenario (Conservative/Moderate/Accelerated) - Table A2: CLT supply under resource constraints (Low/Medium/High) - Table A3: Self-sufficiency ratios (supply/demand) - Table B1: Annual carbon mitigation - Table B2: Cumulative carbon benefits HOW DATA WAS GATHERED: Demand: Based on Statistics Korea (2023) demographic projections and 35-year apartment replacement cycles. CLT volumes calculated from 105 m² average floor area × 0.15 m³/m² structural intensity. Supply: Sequential constraints applied to Korea Forest Service (2023) national forest inventory: species suitability (69%), age-class (77%), sustainability (80% of increment), accessibility (12.5%→18.5%), processing efficiency (30%→38%). Adoption scenarios: Logistic S-curves converging to policy-realistic ceilings by late 2030s, consistent with international CLT diffusion patterns. Carbon: Biogenic storage (0.9 tCO₂/m³) + substitution effects (1.5 tCO₂/m³) from Korean species studies and international LCA. HOW TO INTERPRET: Units: Volume (m³), Carbon (tCO₂), Percentages (%). Self-sufficiency >100% = surplus, <100% = import required. All projections use 2025 baseline. Results support scenario planning for import strategies, forest management priorities, and adoption incentives. REPLICATION & EXTENSION: Framework transferable to other countries with aging forests and apartment-dominated housing. Parameters adjustable for alternative demographic/policy scenarios. Enables cross-country comparisons of resource adequacy. DATA SOURCES: Statistics Korea (2023) Population Projections; Korea Forest Service (2023) Statistical Yearbook; Land and Housing Research Institute (2025) Housing Demand Analysis; MOLIT (2023) Housing Statistics; Author calculations.
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Steps to reproduce
STEPS TO REPRODUCE: 1. DATA COLLECTION Obtain baseline data from Korean government sources: - Statistics Korea (kostat.go.kr): Population and Household Projections 2020-2070 - Korea Forest Service (kfss.forest.go.kr): Statistical Yearbook 2023 for forest inventory - MOLIT (stat.molit.go.kr): Housing construction statistics - LHRI (2025): Housing demand analysis report 2. DEMAND MODELING (Excel-based) Step 2.1: Calculate annual apartment construction Formula: Demand = New_households + Replacement_demand Where Replacement = Stock_built_(year-35) / 35 Apply demographic projections for 2025-2050 Step 2.2: Convert to CLT volume Formula: CLT_demand = Units × 105 m²/unit × 0.15 m³/m² Step 2.3: Apply adoption scenarios Use logistic S-curve: P(t) = P_max / (1 + e^(-k(t-t0))) Parameters: t0=2032, k=0.4 Ceilings: Conservative 15%, Moderate 35%, Accelerated 50% Final demand: CLT_demand × P(t) 3. SUPPLY MODELING (Sequential constraints) Step 3.1: Start with forest increment = 16.4 million m³/year (KFS 2023) Step 3.2: Apply multipliers sequentially - Species (0.69): Larch and red pine proportion - Age-class (0.77): Stands ≥30 years - Sustainability (0.80): IPCC harvest limit - Accessibility: 0.125 (2025) → 0.185 (2050) linear - Processing efficiency: 0.30 (2025) → 0.38 (2050) linear Formula: Supply(t) = 16.4 × 0.69 × 0.77 × 0.80 × Acc(t) × Proc(t) Generate three scenarios: - Low: 50% slower parameter improvement - Medium: baseline improvement - High: 50% faster improvement 4. SELF-SUFFICIENCY & IMPORTS Self_sufficiency = (Supply / Demand) × 100% Import_requirement = max(0, Demand - Supply) 5. CARBON ACCOUNTING Annual: C = CLT_volume × (0.9 + 1.5) tCO₂/m³ Where 0.9 = storage, 1.5 = substitution effect Cumulative: Sum of annual values 6. SCENARIO MATRIX Generate 3×3 matrix: 3 demand scenarios × 3 supply scenarios = 9 combinations 7. DATA COMPILATION Create Excel tables: - A1: Demand (26 years × 3 scenarios) - A2: Supply (26 years × 3 scenarios) - A3: Self-sufficiency ratios - B1: Annual carbon - B2: Cumulative carbon SOFTWARE: Microsoft Excel 2021 Functions: SUM, EXP, MAX, MIN, IF No specialized software required VALIDATION: - Compare modeled 2015-2023 housing to historical data (±5% target) - Verify forest harvest against KFS sustainable limits - Cross-check demographics with Statistics Korea - Ensure mass balance: supply + imports = demand KEY PARAMETERS (adjustable for replication): - Service life: 35 years (range: 30-40) - Floor area: 105 m² (range: 85-120) - CLT intensity: 0.15 m³/m² (range: 0.12-0.18) - Adoption ceilings: 15%/35%/50% - Accessibility: 12.5%→18.5% - Processing: 30%→38% REPRODUCIBILITY: Deterministic formulas, no randomness. Fully reproducible with identical inputs. Transparent Excel formulas enable auditing. Framework transferable to other countries by substituting local demographic and forest data. COMPUTATIONAL NEEDS: Standard desktop computer. Processing time <5 minutes for full 26-year projection across all scenarios.
Institutions
- Pusan National UniversityKumjeong-ku