Quantitative Modeling Techniques

This lesson provides comprehensive coverage of advanced quantitative methods for climate scenario analysis. Moving beyond basic scenario application, we’ll explore sophisticated modeling techniques that enable robust financial and strategic analysis under climate uncertainty.

Climate-Economy Modeling Fundamentals

Integrated Assessment Model Architecture

Climate Module Components

Radiative forcing: Relationship between GHG concentrations and radiative forcing
Climate sensitivity: Temperature response to radiative forcing changes
Carbon cycle: Atmospheric, oceanic, and terrestrial carbon cycle dynamics
Climate damages: Physical and economic damages from temperature and precipitation changes

Economic Module Components

Production functions: How climate affects economic production and productivity
Capital formation: Investment in productive capital and infrastructure
Technology progress: Endogenous and exogenous technological change
Sectoral interactions: Input-output relationships between economic sectors

Policy Module Components

Mitigation policies: Carbon pricing, regulations, subsidies, technology policies
Adaptation policies: Infrastructure investment, protection measures, ecosystem management
Welfare optimization: Optimization of social welfare under climate constraints
Distributional effects: Impacts on different regions, sectors, and income groups

Example: Simplified IAM Structure for Business Application

Business-Focused IAM Components:

Climate Module:
- Temperature pathway: User-defined (1.5°C, 2°C, 3°C scenarios)
- Physical impacts: Sector-specific damage functions
- Extreme events: Stochastic extreme event generation

Economic Module:
- Sectoral GDP: Production functions with climate sensitivity
- Energy system: Detailed energy supply and demand modeling
- Trade flows: International trade with climate and policy impacts

Business Module:
- Company operations: Asset-level climate exposure modeling
- Market dynamics: Supply, demand, and price formation
- Financial impacts: Revenue, costs, and investment implications

Sectoral Economic Models

Energy System Models

TIMES: Technology-rich, bottom-up energy system optimization model
MESSAGE: Medium to long-term energy supply strategy alternatives
GCAM: Global change assessment model with detailed energy-land-water interactions
IEA ETP: Energy technology perspectives with technology roadmaps

Land Use and Agriculture Models

GLOBIOM: Global partial equilibrium model of agriculture, forestry, and bioenergy
IMPACT: International model for policy analysis of agricultural commodities
MAgPIE: Model of agricultural production and its impact on the environment
FARM: Food and agricultural risk model with climate integration

Transport and Urban Models

URBAN: Urban development and transport system evolution models
MOVES: Motor vehicle emission simulator with technology and policy scenarios
SUMO: Simulation of urban mobility with electrification and automation
FREIGHT: Freight transport demand and modal shift models

Business-Level Integrated Models

Enterprise Climate Models

Asset-level modeling: Detailed modeling of individual assets and operations
Supply chain integration: Modeling of supply chain climate risks and opportunities
Market dynamics: Customer behavior, competitive responses, price formation
Financial integration: Translation of physical impacts to financial outcomes

Portfolio Climate Models

Multi-asset portfolios: Modeling climate impacts across diverse asset portfolios
Geographic diversification: Benefits and risks of geographic diversification
Sectoral exposure: Concentration risks in climate-sensitive sectors
Correlation analysis: Correlation of climate impacts across portfolio components

Example: Mining Company Integrated Model

Mining Company Climate Model Structure:

Physical Asset Layer:
- Mine sites: Production capacity, operating costs, climate exposure
- Processing facilities: Energy consumption, water requirements, efficiency
- Transport infrastructure: Rail, port, shipping logistics and vulnerabilities

Market Layer:
- Commodity demand: Steel, aluminum, battery mineral demand evolution
- Price formation: Supply-demand balance, cost curve positioning
- Trade flows: International trade patterns and policy impacts

Financial Layer:
- Revenue modeling: Volume × price with climate scenario impacts
- Cost modeling: Operating and capital costs with climate impacts
- Valuation: NPV analysis with climate-adjusted cash flows and discount rates

Advanced Stress Testing Methods

Climate Stress Testing Framework

Stress Testing Objectives

Resilience assessment: Understanding business model resilience under extreme scenarios
Vulnerability identification: Identifying key vulnerabilities and failure points
Capital adequacy: Assessing capital requirements under climate stress
Strategic planning: Informing strategic planning and risk management decisions

Stress Test Design Principles

Severe but plausible: Scenarios should be severe enough to test resilience but remain plausible
Forward-looking: Focus on potential future risks rather than historical patterns
Comprehensive coverage: Cover all material climate risks and transmission channels
Dynamic interactions: Account for feedback effects and system interactions

Regulatory Stress Testing

APRA Climate Vulnerability Assessment: Australian prudential regulator’s climate stress testing
Bank of England Climate Biennial Exploratory Scenario: UK central bank climate stress testing
ECB Climate Stress Test: European Central Bank climate and environmental stress testing
NGFS Scenarios for Central Banks: Network for Greening the Financial System stress testing guidance

Single-Factor Stress Testing

Physical Risk Stress Tests

Extreme weather events: Testing resilience to major hurricanes, floods, droughts, or heat waves
Chronic climate changes: Testing adaptation to gradual climate shifts over decades
Compound events: Testing response to multiple simultaneous climate hazards
Infrastructure failure: Testing response to climate-related infrastructure failures

Example: Cyclone Stress Test for Infrastructure Company

Cyclone Stress Test Scenario:

Event Characteristics:
- Category 5 cyclone impacting Queensland operations
- Wind speeds: 250+ km/h
- Storm surge: 4-6 meters above normal high tide
- Rainfall: 500mm in 24 hours

Asset Impacts:
- Port facilities: 60% capacity reduction for 6 months
- Transmission lines: 40% network damage requiring 12-month rebuild
- Corporate facilities: 25% of workforce unable to work for 3 months

Financial Stress Test:
- Revenue impact: -$200M over 12 months
- Insurance recovery: $150M (after deductible and coverage gaps)
- Repair costs: $300M incremental capital expenditure
- Business continuity: $50M additional operating costs
- Net financial impact: -$400M over 24 months

Transition Risk Stress Tests

Carbon price shocks: Testing response to rapid carbon price increases
Technology disruption: Testing response to accelerated clean technology deployment
Policy surprises: Testing response to unexpected policy changes or regulations
Market shifts: Testing response to rapid changes in customer preferences or competition

Multi-Factor Stress Testing

Correlated Risk Scenarios

Physical-transition combinations: Scenarios combining physical climate impacts with transition pressures
Geographic correlations: Multiple climate impacts occurring simultaneously across regions
Sectoral correlations: Climate impacts affecting multiple industry sectors simultaneously
Temporal correlations: Climate impacts clustering in time periods

Systemic Risk Analysis

Financial system contagion: How climate impacts spread through financial system connections
Supply chain cascades: How climate impacts cascade through supply chain networks
Infrastructure interdependencies: How climate impacts spread through infrastructure systems
Economic feedback loops: How climate impacts create economic feedback and amplification effects

Example: Systemic Risk Stress Test

Systemic Risk Scenario - Australian Energy Crisis:

Trigger Event: Extreme summer with record temperatures and bushfires

Cascade Effects:
1. Coal plant failures due to extreme heat
2. Transmission line damage from bushfires
3. Gas supply disruption from infrastructure damage
4. Electricity shortage and rolling blackouts
5. Manufacturing shutdowns and economic disruption
6. Insurance market stress from accumulated losses
7. Financial system stress from widespread business impacts

Quantified Impacts:
- Electricity prices: +300% during crisis period
- GDP impact: -2% in affected regions
- Business failures: +50% in energy-intensive sectors
- Insurance losses: $20B+ across multiple lines
- Financial system: 15% increase in non-performing loans

Reverse Stress Testing

Break-Point Analysis

Failure point identification: Identifying climate conditions that would cause business failure
Threshold analysis: Understanding critical thresholds for business model viability
Recovery analysis: Assessing ability to recover from severe climate impacts
Adaptation limits: Identifying limits to adaptation and transformation capacity

Scenario Back-Casting

Outcome definition: Define specific adverse outcomes to investigate
Pathway analysis: Identify potential pathways that could lead to those outcomes
Probability assessment: Assess likelihood of pathways given current knowledge
Prevention strategies: Develop strategies to prevent or mitigate adverse pathways

Bottom-Up vs Top-Down Approaches

Bottom-Up Modeling Approaches

Asset-Level Modeling

Physical vulnerability: Detailed assessment of individual asset climate vulnerability
Operational modeling: Modeling of operational performance under climate conditions
Maintenance and replacement: Climate impacts on asset maintenance and replacement cycles
Adaptation options: Specific adaptation options and their costs and benefits

Process-Level Modeling

Energy consumption: Detailed modeling of energy consumption by process and climate conditions
Water requirements: Process water requirements and availability under climate scenarios
Material efficiency: Climate impacts on material efficiency and waste generation
Quality control: Climate impacts on product quality and process control

Technology-Level Modeling

Performance curves: Technology performance as function of climate conditions
Degradation rates: Accelerated degradation under climate stress
Efficiency changes: Climate impacts on technology efficiency and output
Innovation responses: Technology innovation responses to climate challenges

Example: Solar Farm Bottom-Up Model

Solar Farm Climate Performance Model:

Physical Components:
- Solar panels: Temperature coefficients, degradation rates, extreme weather vulnerability
- Inverters: Temperature performance, failure rates, replacement schedules
- Mounting systems: Wind and hail resistance, thermal expansion effects
- Electrical systems: Cable performance, transformer efficiency, grid connection

Environmental Factors:
- Solar irradiance: Historical and projected solar resource availability
- Temperature: Module and ambient temperature impacts on performance
- Weather extremes: Hail, wind, dust storm impacts on generation
- Humidity and corrosion: Long-term degradation from environmental conditions

Performance Modeling:
- Energy output: Hourly generation modeling under climate scenarios
- Capacity factor: Annual average capacity factor changes
- Maintenance costs: Climate-driven changes in O&M requirements
- Replacement timing: Accelerated component replacement under climate stress

Financial Modeling:
- Revenue: Energy output × electricity prices
- Costs: O&M costs + replacement costs + financing costs
- Profitability: IRR and NPV under different climate scenarios

Top-Down Modeling Approaches

Macroeconomic Modeling

GDP impacts: Aggregate economic impacts of climate change on GDP growth
Sectoral shifts: Structural changes in economic composition due to climate impacts
Productivity effects: Climate impacts on overall economic productivity
Investment patterns: Changes in investment patterns and capital allocation

Industry-Level Modeling

Sectoral vulnerability: Aggregate vulnerability of industry sectors to climate change
Market dynamics: Supply and demand changes at industry level
Price formation: Industry-level price formation under climate scenarios
Competitive dynamics: Changes in industry structure and competition

Financial System Modeling

Credit portfolio impacts: Aggregate impacts on bank credit portfolios
Insurance market dynamics: Changes in insurance availability and pricing
Asset price effects: Climate impacts on asset valuations and price volatility
System stability: Overall financial system stability under climate scenarios

Hybrid Modeling Approaches

Multi-Scale Integration

Nested modeling: Detailed bottom-up models nested within top-down frameworks
Scaling factors: Statistical relationships for scaling between model levels
Consistency checking: Ensuring consistency between different modeling scales
Iterative refinement: Iterative refinement between bottom-up and top-down results

Cross-Sectoral Linkages

Input-output matrices: Economic input-output relationships between sectors
Supply chain networks: Physical supply chain connections and dependencies
Infrastructure interdependencies: Critical infrastructure interdependencies
Labor market connections: Labor market flows between sectors and regions

Model Validation and Calibration

Historical Validation Methods

Backtesting Approaches

Out-of-sample testing: Testing model performance on historical data not used in calibration
Rolling window validation: Testing model performance using rolling historical windows
Event studies: Testing model performance around specific historical climate events
Cross-validation: Statistical cross-validation techniques for model assessment

Pattern Recognition

Stylized facts: Ensuring models reproduce key stylized facts about climate-economy relationships
Historical analogues: Comparing model results with historical analogues and precedents
Extreme event representation: Ensuring models adequately represent extreme events
Trend reproduction: Ensuring models can reproduce historical trends and cycles

Example: Model Validation for Australian Agriculture

Agricultural Model Validation:

Historical Period: 1990-2020
Validation Metrics:
- Crop yield correlation: >0.8 for major crops (wheat, barley, canola)
- Drought impact magnitude: Model predicts 20-40% yield loss (actual: 25-45%)
- Regional variation: Model captures state-level differences in climate sensitivity
- Extreme events: Model captures 2002-2003 and 2018-2019 drought impacts

Validation Results:
- Temperature sensitivity: Model estimate 3%/°C vs observed 2.5%/°C
- Rainfall sensitivity: Model estimate 1.2%/10mm vs observed 1.0%/10mm
- Extreme heat: Model captures >35°C impacts on wheat yields
- Seasonal timing: Model captures importance of growing season rainfall

Model Adjustments:
- Recalibrate temperature sensitivity parameters
- Enhance extreme heat damage functions
- Improve seasonal rainfall weighting
- Add drought duration effects

Forward Validation Techniques

Expert Judgment Validation

Expert elicitation: Systematic elicitation of expert opinion on model assumptions and results
Delphi processes: Iterative expert consultation for model validation and improvement
Technical peer review: Independent technical review by modeling experts
Stakeholder validation: Validation of model assumptions and results with industry stakeholders

Stress Testing Validation

Extreme scenario testing: Testing model behavior under extreme scenarios
Boundary condition testing: Testing model behavior at boundary conditions
Sensitivity analysis: Testing model sensitivity to key assumptions and parameters
Robustness testing: Testing model robustness to alternative specifications

Uncertainty Quantification

Parameter Uncertainty

Confidence intervals: Statistical confidence intervals for model parameters
Bayesian updating: Bayesian approaches to parameter uncertainty
Ensemble modeling: Use of multiple model versions with different parameter sets
Monte Carlo analysis: Monte Carlo simulation of parameter uncertainty

Model Structure Uncertainty

Model comparison: Comparison of results across different model structures
Model averaging: Weighted averaging of results across multiple models
Structural sensitivity: Testing sensitivity to key structural assumptions
Alternative specifications: Testing alternative model specifications and approaches

Example: Uncertainty Quantification Framework

Climate-Economy Model Uncertainty Analysis:

Parameter Uncertainty:
- Climate sensitivity: 2.5-4.5°C (90% confidence interval)
- Damage function: ±50% uncertainty around central estimate
- Discount rate: 2-6% range reflecting ethical and empirical uncertainty
- Technology costs: ±30% uncertainty around learning curve projections

Model Structure Uncertainty:
- Damage function form: Quadratic vs exponential vs threshold functions
- Technology representation: Bottom-up vs top-down technology modeling
- Regional aggregation: Country-level vs global vs detailed regional models
- Time horizon: 2050 vs 2070 vs 2100 endpoint analysis

Uncertainty Propagation:
- Monte Carlo simulation with 10,000 parameter draws
- Model ensemble with 5 alternative structural specifications
- Scenario analysis across 6 climate-policy scenarios
- Sensitivity analysis for 20 key assumptions

Results Presentation:
- Central estimates with 90% confidence intervals
- Probability distributions for key outcomes
- Scenario comparison with uncertainty ranges
- Sensitivity indices for most important assumptions

Practical Implementation Guidelines

Model Selection Framework

Model Complexity vs Resources

Simple spreadsheet models: Quick analysis with limited resources
Intermediate econometric models: More sophisticated analysis with moderate resources
Complex integrated models: Comprehensive analysis requiring significant resources
Collaborative modeling: Partnering with research institutions for advanced modeling

Model Purpose Alignment

Strategic planning: Models optimized for long-term strategic decision support
Risk management: Models optimized for risk quantification and management
Investment analysis: Models optimized for project and portfolio evaluation
Regulatory compliance: Models meeting specific regulatory requirements

Capability Requirements

Technical skills: Required technical skills for model development and operation
Data requirements: Data availability and quality requirements
Computational resources: Hardware and software requirements for model operation
Ongoing maintenance: Requirements for model updates and maintenance

Model Implementation Process

Phase 1: Scoping and Design (Months 1-2)

Objective definition: Clear definition of modeling objectives and success criteria
Model specification: Technical specification of model structure and requirements
Resource planning: Planning of human, technical, and financial resources
Timeline development: Detailed timeline for model development and implementation

Phase 2: Development and Calibration (Months 3-6)

Model construction: Building of model components and integration
Data assembly: Collection and processing of required data
Calibration: Statistical calibration of model parameters
Initial validation: Initial validation testing and refinement

Phase 3: Validation and Testing (Months 6-9)

Comprehensive validation: Thorough validation using multiple approaches
Stress testing: Testing under extreme and boundary conditions
Sensitivity analysis: Comprehensive sensitivity and uncertainty analysis
Documentation: Comprehensive documentation of model and validation

Phase 4: Implementation and Training (Months 9-12)

User training: Training of model users and stakeholders
Integration: Integration with business processes and decision-making
Quality assurance: Implementation of ongoing quality assurance processes
Monitoring and updating: Systems for ongoing monitoring and model updates

Best Practices and Common Pitfalls

Modeling Best Practices

Start simple: Begin with simple models and add complexity gradually
Validate early: Validate model components early in development process
Document thoroughly: Maintain comprehensive documentation throughout development
Plan for updates: Design models for regular updates and improvements

Common Pitfalls to Avoid

Over-complexity: Building overly complex models that are difficult to understand and maintain
Under-validation: Insufficient validation leading to unreliable results
Data limitations: Ignoring data quality issues and limitations
User disconnect: Failing to engage users in model development and implementation

Quality Assurance Framework

Code review: Systematic review of model code and calculations
Version control: Proper version control and change management
Testing protocols: Systematic testing of model functionality and performance
User feedback: Regular collection and incorporation of user feedback

Summary

Advanced quantitative modeling techniques enable sophisticated climate scenario analysis that supports strategic decision-making and AASB S2 compliance:

Climate-economy models integrate physical climate science with economic analysis
Stress testing methods assess resilience under extreme climate scenarios
Bottom-up and top-down approaches provide different perspectives and levels of detail
Model validation ensures credibility through historical and forward validation
Implementation guidelines support practical model development and deployment
Best practices help avoid common pitfalls and ensure model quality

Sophisticated quantitative modeling provides the analytical foundation for robust climate risk assessment and strategic planning under uncertainty.

Key Takeaways

✅ Climate-economy models integrate physical climate impacts with economic and business analysis ✅ Stress testing methods assess business resilience under extreme climate scenarios ✅ Bottom-up approaches provide detailed asset-level analysis while top-down approaches capture system-wide effects ✅ Model validation requires both historical backtesting and forward validation techniques ✅ Uncertainty quantification is essential for credible analysis and decision-making ✅ Implementation success depends on proper scoping, validation, and user engagement

Modeling Approach Selection Framework

Business Need	Recommended Approach	Complexity Level	Resource Requirements
Strategic Planning	Integrated assessment models	High	Significant technical resources
Risk Management	Stress testing frameworks	Medium-High	Moderate technical + data
Investment Analysis	Bottom-up asset models	Medium	Specialized technical skills
Regulatory Compliance	Standardized stress tests	Medium	Compliance + technical expertise
Preliminary Assessment	Top-down sectoral models	Low-Medium	Basic analytical capabilities

Model Validation Hierarchy

Level 1: Technical Validation

Code verification: Mathematical and computational correctness
Unit testing: Individual component functionality
Integration testing: Component interaction verification

Level 2: Historical Validation

Backtesting: Performance on historical out-of-sample data
Pattern recognition: Reproduction of known stylized facts
Event studies: Performance around historical extreme events

Level 3: Expert Validation

Peer review: Independent expert review of methodology
Stakeholder validation: Industry expert assessment of assumptions
Sensitivity analysis: Robustness to alternative specifications

Level 4: Forward Validation

Stress testing: Performance under extreme scenarios
Cross-model comparison: Consistency with alternative models
Uncertainty quantification: Appropriate uncertainty representation

Practical Exercise

Quantitative Modeling Project: For your organization or a case study:

Define modeling objectives including specific questions to be answered and decisions to be supported
Select modeling approach based on complexity needs, available resources, and timeline
Design model structure including key components, data requirements, and integration points
Develop validation plan with historical validation, expert review, and stress testing components
Plan implementation process including development phases, resource allocation, and user training
Design quality assurance framework with documentation, testing, and update procedures

Focus on creating models that balance analytical sophistication with practical usability while meeting validation and uncertainty quantification requirements.