CCMIntel
Our Methodology
Well-defined, appropriated methodologies behind every number
Every number on CCMIntel dashboards and reports follows a documented method, uses multiple independent sources and is reviewed by analysts and industry experts before it is published.
Market Size Estimation Methodology
We size each market bottom-up, by application, end-use industry and region, and cross-check it top-down against plant-level supply and trade. The result is one balanced number per product, region and year that reconciles demand, production and net trade.
Consumption = Production − Exports + Imports ± Stock change
Data sources
Process
- 1
Define scope
Product, grades, applications and regions
- 2
Supply side
Capacity × operating rate = production by plant
- 3
Trade flows
Import / export balance by country
- 4
Demand side
Consumption by application & end-use
- 5
Reconcile
Close the gap between supply-side and demand-side estimates
Quality checks
- Supply-side vs demand-side gap within tolerance
- Global imports ≈ global exports
- Benchmarked against company and association data
Outputs
- Market size (kt and USD) by region & application
- Supply–demand balance
- Historical series for the last 5 years
Market Forecast Methodology
Forecasts are built from the drivers of each application (end-use industry growth, GDP and population, substitution, regulation and sustainability trends) together with announced capacity. Analysts and industry experts review every forecast monthly.
Demandₜ₊₁ = Σ applications (Demandₜ × (1 + driver growth) × substitution factor)
Data sources
Process
- 1
Driver selection
Link each application to its growth drivers
- 2
Elasticities
Estimate demand response from historical data
- 3
Scenario build
Base, high and low cases
- 4
Capacity check
Test against supply additions and operating rates
- 5
Expert review
Monthly analyst and industry validation
Quality checks
- Back-testing against reported actuals
- Consistency with supply additions
- Scenario spread reviewed by experts
Outputs
- 5-year demand forecast by region & application
- CAGR and incremental volume
- Scenario ranges and key risks
Cash Cost of Production Methodology
Process-based cost models built from consumption norms per tonne of product for each technology. Regional raw material, utility and labour prices turn these norms into cash costs, benchmarked across the key producing countries.
Cash cost = Σ (raw material norm × price) + Σ (utility norm × price) + fixed costs − by-product credits
Data sources
Process
- 1
Process route
Select technology and plant configuration
- 2
Consumption norms
Raw materials, catalysts, utilities per tonne
- 3
Price inputs
Regional feedstock and utility prices
- 4
Fixed costs
Labour, maintenance, overheads
- 5
Credits & total
Subtract by-product credits; build cost stack
Quality checks
- Mass and energy balance check
- Benchmarked against producer disclosures
- Price inputs refreshed every month
Outputs
- Cost stack by country (USD/t)
- Raw material, utility and fixed cost split
- Margin vs market price
Cost Curve Analysis Methodology
Every plant's cash cost is ranked from lowest to highest against cumulative capacity. The curve shows who is competitive, which marginal producer sets the price floor, and which assets are at risk when the market weakens.
Price floor ≈ cash cost of the marginal producer needed to meet demand
Data sources
Process
- 1
Plant list
All operating plants with capacity
- 2
Plant cost
Apply cost models adjusted for scale & integration
- 3
Rank
Order plants by cash cost
- 4
Overlay demand
Locate the marginal plant
- 5
Interpret
Competitive position and closure risk
Quality checks
- Plant data reviewed against company information
- Sensitivity to feedstock prices
- Comparison with observed operating rates
Outputs
- Industry cost curve (USD/t vs cumulative kt)
- Quartile position of each plant
- Price-floor and closure-risk indicators
Price Assessment Methodology
Prices are tracked by region, grade and delivery basis from a broad base of market participants and published sources, then normalised to a common unit and currency to give consistent monthly and daily series.
Assessed price = weighted average of verified deals and offers, normalised to basis & currency
Data sources
Process
- 1
Collect
Deals, offers and bids by region
- 2
Verify
Cross-check with multiple participants
- 3
Normalise
Common basis, unit and currency
- 4
Assess
Weighted assessment by period
- 5
Publish
Monthly and daily series
Quality checks
- Outlier detection and exclusion
- Consistency with feedstock spreads
- Analyst sign-off for each period
Outputs
- Regional price series (monthly / daily)
- Spreads vs feedstock
- Price commentary and drivers
Price Forecast Methodology
Price forecasts combine feedstock cost pass-through, the supply–demand balance and cost-curve support. Scenarios show how prices could move under different feedstock, demand and capacity outcomes.
Priceₜ = feedstock cost + margin(operating rate) bounded by cost-curve floor
Data sources
Process
- 1
Feedstock outlook
Forecast key raw material prices
- 2
Balance
Project operating rates from supply & demand
- 3
Margin model
Relate margin to operating rate
- 4
Floor & ceiling
Cost-curve floor and import-parity ceiling
- 5
Scenarios
Base, high and low price paths
Quality checks
- Back-testing of past forecasts
- Arbitrage consistency between regions
- Monthly review with market experts
Outputs
- Price forecast by region (quarterly / annual)
- Margin outlook
- Scenario ranges and risks