American Carbon Registry Sample Verification Report — Sensor-Based MRV Output

Document Version: 1.0 Date of Issue: December 15, 2024 Reporting Period: January 1, 2024 – December 31, 2024 Project ID: ACR-SENSOR-2024-001 Project Title: DaedArch IoT-Enabled Forest Carbon Sequestration Project Project Location: Willamette National Forest, Oregon, USA (Coordinates: 44.2167° N, 122.1667° W) Verification Body: [Verification Body Name] Lead Verifier: [Verifier Name], ISO 14065 Accredited Technical Review: [Technical Reviewer Name]

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1. PROJECT SUMMARY

1.1 Project Overview

This verification report documents the assessment of greenhouse gas (GHG) emission reductions and removals generated by the DaedArch IoT-Enabled Forest Carbon Sequestration Project during the reporting period January 1, 2024, through December 31, 2024. The project employs the DaedArch sensor-based Monitoring, Reporting, and Verification (MRV) platform to quantify carbon sequestration in a 5,000-hectare reforestation area within the Willamette National Forest, Oregon.

The project is registered under the American Carbon Registry (ACR) and adheres to the ACR Methodology for Improved Forest Management on Non-Federal U.S. Forestlands (Version 2.0, effective October 2020). This methodology requires demonstration of additionality, permanence, and conservative quantification of carbon benefits. The DaedArch platform provides continuous, ground-truth environmental data that exceeds the minimum monitoring requirements specified in ACR standards.

1.2 Project Proponent Information

Project Developer: DaedArch Carbon Solutions, LLC Registered Address: 1234 Carbon Lane, Portland, OR 97201 Contact Person: Dr. Elena Martínez, Director of Carbon Programs Email: [email protected] Phone: +1 (503) 555-0199

1.3 GHG Assertion

The Project Proponent asserts that during the reporting period, the project achieved verifiable net GHG emission reductions totaling 47,832 metric tonnes CO₂-equivalent (tCO₂e), calculated in accordance with ACR's quantification methodologies and incorporating DaedArch's sensor-derived biometric data, soil carbon measurements, and environmental parameter monitoring.

1.4 Verification Objective

The objective of this verification is to provide reasonable assurance that:

1. The GHG assertion is materially correct and is a fair representation of the GHG data and information;
2. The quantification methodologies conform to ACR's approved methodology requirements;
3. The DaedArch sensor-based MRV platform meets or exceeds ACR's data quality standards for accuracy, precision, completeness, and transparency;
4. Project implementation adheres to ACR's permanence and additionality requirements;
5. All material discrepancies, omissions, or misstatements have been identified and resolved.

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2. METHODOLOGY

2.1 Applicable ACR Methodology

This project operates under ACR Methodology for Improved Forest Management on Non-Federal U.S. Forestlands, Version 2.0 (ACR Methodology Document Reference: ACR-IFM-NFU-v2.0). This methodology establishes requirements for:

• Additionality Demonstration: Projects SHALL demonstrate that carbon sequestration activities exceed regulatory requirements and common practice benchmarks.
• Baseline Scenario: Projects SHALL establish a credible baseline representing carbon stocks absent project activities.
• Leakage Assessment: Projects SHALL quantify and account for activity-shifting leakage and market-effects leakage.
• Permanence: Projects SHALL establish legal mechanisms ensuring carbon stock maintenance for a minimum 40-year commitment period.
• Monitoring Requirements: Projects SHALL conduct monitoring at intervals specified in the methodology, with measurements traceable to internationally recognized standards.

2.2 DaedArch Sensor-Based MRV Platform Architecture

2.2.1 Platform Components

The DaedArch MRV platform integrates five core technological components:

1. IoT Sensor Network (ISN):

• Ground-deployed environmental sensors measuring biomass proxies, soil parameters, and microclimate conditions
• Deployment density: 1 sensor cluster per 10 hectares (500 total clusters)
• Sensor specifications: ISO 17025 calibrated, NIST-traceable measurement standards

1. Data Acquisition Layer (DAL):

• Real-time data collection via LoRaWAN and cellular backhaul
• Edge computing nodes for preliminary data validation
• Timestamp synchronization: GPS-disciplined oscillators (accuracy ≤1 millisecond)

1. Processing and Analytics Engine (PAE):

• Certified carbon quantification algorithms
• Allometric equation libraries (USDA Forest Service FIA database integration)
• Quality assurance/quality control (QA/QC) automated routines

1. Blockchain Audit Trail (BAT):

• Immutable record of all sensor readings, processing events, and data modifications
• Ethereum-compatible distributed ledger
• SHA-256 cryptographic hashing for data integrity verification

1. Verification Interface Module (VIM):

• ACR-compliant report generation
• API endpoints for third-party verification bodies
• Data export formats: JSON, XML, CSV (ISO 8601 timestamps)

2.2.2 Data Flow Architecture

` [IoT Sensors] → [LoRaWAN Gateway] → [Edge Node QA/QC] → [Central Processing] → [Carbon Quantification Engine] → [Blockchain Audit Trail] → [Verification Report Generation] `

Each data point SHALL be accompanied by metadata including:

• Sensor ID (UUID format per RFC 4122)
• Timestamp (ISO 8601 format: YYYY-MM-DDTHH:MM:SS.sssZ)
• Geographic coordinates (WGS 84 datum, decimal degrees to 6 decimal places)
• Measurement uncertainty (±σ at 95% confidence interval)
• Calibration certificate reference
• Chain-of-custody hash

2.3 Verification Methodology and Approach

This verification was conducted in accordance with:

• ISO 14064-3:2019 – Specification with guidance for the verification and validation of greenhouse gas statements
• ACR Standard Requirements and Procedures (Version 7.0)
• ACR Verification Guidance for Forestry Projects (2021 Edition)

2.3.1 Verification Level

This verification provides reasonable assurance, requiring verification body procedures to reduce verification risk to an acceptably low level. The materiality threshold is set at 5% of the total GHG assertion, consistent with ACR requirements.

2.3.2 Verification Activities

The verification team conducted the following activities:

1. Document Review (60 hours):

• Project Design Document (PDD) assessment
• Monitoring plan evaluation
• DaedArch platform technical specifications review
• Algorithm certification documentation review
• Legal permanence instruments verification

1. Data Validation (120 hours):

• Statistical sampling of sensor data (n=2,847 records, 95% confidence level)
• Cross-validation with traditional inventory measurements
• Blockchain audit trail integrity verification
• API endpoint testing and data retrieval validation
• QA/QC procedure effectiveness assessment

1. Site Visit (48 hours):

• Physical inspection of 25 sensor cluster installations (5% random sample)
• Sensor calibration certificate verification
• Ground-truth measurements using independent equipment
• Stakeholder interviews (n=12)
• Leakage activity assessment in surrounding areas

1. Calculation Verification (40 hours):

• Recalculation of carbon stock changes using verified sensor data
• Uncertainty analysis validation
• Baseline scenario assumptions verification
• Leakage deduction verification

1. Reporting (32 hours):

• Findings documentation
• Non-conformity and corrective action tracking
• Final report preparation

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3. MEASUREMENT DATA

3.1 Sensor Network Deployment and Performance

3.1.1 Sensor Types and Specifications

The DaedArch platform deployed five categories of sensors across the project area:

Table 3.1: Sensor Network Composition

| Sensor Type | Manufacturer | Model | Parameter Measured | Accuracy | Calibration Frequency | Quantity | |-------------|--------------|-------|-------------------|----------|----------------------|----------| | Dendrometer Bands | Ecomatik | DRL26 | Tree diameter increment | ±0.02 mm | Annual | 1,250 | | Soil Respiration Chambers | LI-COR | LI-8100A | CO₂ efflux | ±1% of reading | Semi-annual | 150 | | Soil Moisture/Temperature | Meter Group | TEROS 12 | VWC, temperature | ±3% VWC, ±0.5°C | Bi-annual | 500 | | PAR Sensors | Apogee | SQ-500-SS | Photosynthetically active radiation | ±5% | Annual | 200 | | Microclimate Stations | Campbell Scientific | CR1000X | Temperature, humidity, precipitation | ±0.2°C, ±2% RH | Annual | 25 |

All sensors meet or exceed the following ACR data quality requirements:

• Accuracy: Measurements SHALL be within manufacturer specifications, verified by NIST-traceable calibration.
• Precision: Repeated measurements SHALL demonstrate coefficient of variation ≤5% for critical carbon parameters.
• Completeness: Data capture SHALL exceed 95% for all sensors during the reporting period.
• Comparability: Sensor outputs SHALL be convertible to standard units (SI base units) with documented conversion factors.

3.1.2 Data Completeness Assessment

Table 3.2: Data Completeness by Sensor Category

| Sensor Category | Expected Data Points | Actual Data Points | Completeness (%) | ACR Threshold (%) | |-----------------|---------------------|-------------------|------------------|-------------------| | Dendrometer Bands | 15,000,000 | 14,685,432 | 97.9 | 95.0 | | Soil Respiration | 876,000 | 854,190 | 97.5 | 95.0 | | Soil Moisture/Temp | 26,280,000 | 25,934,760 | 98.7 | 95.0 | | PAR Sensors | 10,512,000 | 10,291,680 | 97.9 | 95.0 | | Microclimate | 1,314,000 | 1,296,720 | 98.7 | 95.0 |

Finding 3.1: All sensor categories exceeded ACR's 95% data completeness threshold. Data gaps were primarily attributable to scheduled maintenance (72%), wildlife interference (18%), and communication network outages (10%). Gap-filling procedures followed ACR-approved interpolation methods documented in Section 3.3.

3.2 Carbon Pool Quantification

3.2.1 Above-Ground Biomass (AGB)

DaedArch sensors enable continuous monitoring of tree diameter at breast height (DBH) through automated dendrometer bands. The platform applies species-specific allometric equations to convert diameter measurements to biomass estimates.

Calculation Methodology:

For each tree i, above-ground biomass is calculated as:

` AGB_i = exp(β₀ + β₁ × ln(DBH_i)) `

Where:

• AGB_i = Above-ground biomass (kg dry matter)
• DBH_i = Diameter at breast height measured by dendrometer (cm)
• β₀, β₁ = Species-specific allometric coefficients from Jenkins et al. (2003) National Biomass Equations

Table 3.3: Dominant Species Allometric Parameters

| Species | β₀ | β₁ | Sample Size (n) | R² | |---------|----|----|----------------|-----| | Douglas-fir (Pseudotsuga menziesii) | -2.2304 | 2.4435 | 387 | 0.96 | | Western Hemlock (Tsuga heterophylla) | -2.3480 | 2.3876 | 214 | 0.94 | | Western Redcedar (Thuja plicata) | -2.0336 | 2.2592 | 156 | 0.93 |

Carbon content is calculated using IPCC default factor: C = AGB × 0.47

Verification Finding 3.2: The verification team validated the allometric equations against ACR's approved biomass equation database. All equations are sourced from peer-reviewed literature and are appropriate for the project's geographic region and forest types. The 0.47 carbon fraction is consistent with IPCC Guidelines (Volume 4, Chapter 4).

Table 3.4: Above-Ground Biomass Carbon Pool Quantification

| Metric | Baseline (tC) | Reporting Period End (tC) | Change (ΔtC) | CO₂e (tCO₂e)* | |--------|---------------|---------------------------|--------------|--------------| | Total AGB Carbon | 412,634 | 456,891 | 44,257 | 162,276 |

*Conversion factor: tCO₂e = tC × 44/12

3.2.2 Below-Ground Biomass (BGB)

Below-ground biomass is estimated using IPCC default root-to-shoot ratios:

` BGB = AGB × R `

Where R = 0.24 for temperate/boreal forests (IPCC 2006, Table 4.4)

Table 3.5: Below-Ground Biomass Carbon Pool Quantification

| Metric | Baseline (tC) | Reporting Period End (tC) | Change (ΔtC) | CO₂e (tCO₂e) | |--------|---------------|---------------------------|--------------|--------------| | Total BGB Carbon | 99,032 | 109,654 | 10,622 | 38,947 |

3.2.3 Soil Organic Carbon (SOC)

DaedArch's soil respiration chambers measure heterotrophic respiration, which, combined with eddy covariance tower data (when available) and empirical models, enables estimation of net soil carbon changes. The platform employs the Rothamsted Carbon Model (RothC) calibrated with site-specific inputs.

Input Parameters:

• Soil temperature (continuous monitoring, hourly averages)
• Soil moisture (continuous monitoring, hourly averages)
• Clay content (initial soil survey: 23.4% ± 3.2%)
• Organic carbon inputs (modeled from litterfall and root turnover)

Table 3.6: Soil Organic Carbon Quantification

| Depth (cm) | Baseline (tC) | Reporting Period End (tC) | Change (ΔtC) | CO₂e (tCO₂e) | |------------|---------------|---------------------------|--------------|--------------| | 0-30 | 78,432 | 79,891 | 1,459 | 5,350 | | 30-100 | 134,567 | 135,234 | 667 | 2,446 | | Total | 212,999 | 215,125 | 2,126 | 7,796 |

Verification Finding 3.3: Soil carbon quantification methods align with ACR's conservative approach. The verification team notes that soil carbon changes are modest and within typical measurement uncertainty ranges. The use of continuous soil respiration monitoring represents a methodological improvement over periodic sampling, reducing temporal bias.

3.2.4 Dead Organic Matter (DOM)

Dead organic matter includes standing dead trees, down dead wood, and litter. DaedArch supplements sensor data with periodic physical surveys (frequency: annual for standing dead, biennial for down dead).

Table 3.7: Dead Organic Matter Carbon Quantification

| Pool | Baseline (tC) | Reporting Period End (tC) | Change (ΔtC) | CO₂e (tCO₂e) | |------|---------------|---------------------------|--------------|--------------| | Standing Dead | 8,934 | 9,123 | 189 | 693 | | Down