Impact Assessment Approaches
Before recommending a carbon pricing policy, you need to assess its likely impacts. This lesson introduces the main approaches used to evaluate carbon pricing options, from quick screening to comprehensive modeling.
Why Impact Assessment Matters
Impact assessment serves multiple purposes:
Informing design: Understanding likely impacts helps choose between design options.
Building support: Evidence-based analysis can persuade skeptics and build coalitions.
Anticipating problems: Assessment identifies potential issues before implementation.
Setting expectations: Realistic projections help manage stakeholder expectations.
Enabling comparison: Systematic assessment allows comparison across policy options.
Impact assessment is not about predicting the future with certainty. It is about understanding the likely direction and magnitude of effects, identifying uncertainties, and informing decisions under uncertainty.
Types of Impacts to Assess
A comprehensive assessment considers multiple impact categories:
Emissions impacts: How will the policy affect greenhouse gas emissions? By how much? When?
Economic impacts: What are the costs and benefits? How does it affect GDP, employment, investment?
Sectoral impacts: Which industries are most affected? How will they respond?
Distributional impacts: Who bears the costs? Who benefits? Are impacts progressive or regressive?
Competitiveness impacts: How are trade-exposed industries affected? Is there leakage risk?
Revenue impacts: How much revenue will be generated? How predictable is it?
Co-benefits: What air quality, health, or other benefits result from emissions reductions?
The Assessment Spectrum
Different situations call for different levels of analytical rigor:
Quick screening:
- High-level order-of-magnitude estimates
- Useful for initial option comparison
- Can be done quickly with limited resources
- May use rules of thumb or analogies to other jurisdictions
Intermediate analysis:
- Spreadsheet-based calculations
- Sector-level impacts
- Some sensitivity analysis
- Weeks to months of work
Comprehensive modeling:
- Full economic models (CGE, macroeconometric)
- Detailed sectoral analysis
- Extensive sensitivity and scenario analysis
- Months to years of work
| Level | Time | Resources | Precision | Use case |
|---|---|---|---|---|
| Quick screening | Days | Low | Low | Initial options, political discussions |
| Intermediate | Weeks-months | Moderate | Moderate | Policy design, stakeholder engagement |
| Comprehensive | Months-years | High | Higher | Final decisions, legislation |
Quick Screening Methods
When you need rapid initial estimates:
Emissions price response: Estimate emissions reductions using assumed price elasticities.
Revenue calculation: Emissions base multiplied by tax rate gives approximate revenue.
Benchmarking: What happened in similar jurisdictions?
Expert judgment: Convene experts to provide structured estimates.
Quick screening example:
A country considering a $20/ton carbon tax wants initial estimates:
Emissions covered: 200 million tons/year Assumed elasticity: -0.2 (10% price increase = 2% emissions reduction) Current energy price effect: $20/ton adds ~15% to coal cost, ~10% to gas
Quick estimates:
- Emissions reduction: roughly 3-5% in first year
- Revenue: roughly $4 billion/year initially (200M x $20)
- Declining over time as emissions fall
This took an afternoon, not months. It is imprecise but directionally useful.
Intermediate Analysis
When more rigor is needed:
Bottom-up sectoral analysis: Examine each major sector's emissions profile, abatement options, and costs.
Cost curve analysis: Build marginal abatement cost curves showing reduction potential at different prices.
Financial analysis: Model impacts on representative firms in key sectors.
Household impact analysis: Calculate direct and indirect impacts on household budgets.
Revenue modeling: More sophisticated projection of revenue over time.
Comprehensive Modeling Approaches
For major policy decisions, more sophisticated tools are used:
Computable General Equilibrium (CGE) models:
CGE models represent the entire economy as a system of interacting markets.
Strengths:
- Captures economy-wide interactions
- Shows how effects ripple through supply chains
- Can model structural change
Weaknesses:
- Requires significant expertise and data
- Results depend heavily on assumptions
- May miss short-term dynamics
Macroeconometric models:
Estimate relationships using historical data.
Strengths:
- Based on observed behavior
- Better for short to medium term
- Can incorporate policy detail
Weaknesses:
- Assumes past relationships continue
- May not capture structural breaks
- Less suitable for long-term analysis
Energy system models:
Detailed representation of the energy sector.
Strengths:
- Technology-rich representation
- Can model energy transition pathways
- Captures energy sector dynamics well
Weaknesses:
- Energy-focused, may miss broader economy
- Requires detailed technology data
- Complex and resource-intensive
Selecting an appropriate model depends on several factors:
What questions are you asking?
- Economy-wide impacts: CGE
- Short-term macroeconomic effects: Macroeconometric
- Energy sector transition: Energy system models
- Household impacts: Microsimulation
What data is available?
- CGE models need input-output tables
- Macroeconometric models need time series
- Energy models need technology data
What resources do you have?
- CGE models require specialized expertise
- Some models are proprietary and expensive
- Building capacity takes time
What timeline are you analyzing?
- Short-term: Macroeconometric may be better
- Long-term structural change: CGE may be better
Who is your audience?
- Technical audience: Detailed modeling appropriate
- Political audience: Simpler analysis may communicate better
Many jurisdictions use multiple approaches to triangulate.
Key Parameters and Assumptions
Model results depend heavily on assumptions about:
Price elasticities: How responsive are emissions to carbon prices? Literature shows wide ranges.
Abatement costs: What does it cost to reduce emissions in different sectors?
Technology assumptions: What technologies are available? At what cost?
Baseline emissions: What would emissions be without the policy?
Behavioral responses: How do firms and households actually respond to price signals?
International context: What are competitors doing? What are global energy prices?
Be transparent about key assumptions. Conduct sensitivity analysis to show how results change when assumptions change. Decision-makers need to understand uncertainty, not just point estimates.
Sensitivity and Scenario Analysis
Single-point estimates are not enough:
Sensitivity analysis: Vary key parameters one at a time to see how results change.
Scenario analysis: Construct coherent alternative futures (high growth, low growth, technology breakthrough).
Monte Carlo analysis: Vary many parameters simultaneously based on probability distributions.
Stress testing: Test extreme but plausible scenarios.
Practical Considerations
Data availability: Do you have the data needed for the analysis you want?
Time constraints: When is the decision needed? Match analysis to timeline.
Expertise: Do you have in-house expertise or need external support?
Credibility: Will the analysis be seen as credible by stakeholders?
Communication: Can results be communicated to non-technical audiences?
Impact assessment is like weather forecasting. Short-term forecasts can be quite accurate. Longer-term forecasts become increasingly uncertain. But even uncertain forecasts are better than no forecast at all, as long as you communicate the uncertainty honestly.
Common Pitfalls
False precision: Presenting results with more decimal places than the analysis supports.
Omitting uncertainty: Failing to communicate ranges and sensitivities.
Confirmation bias: Designing analysis to support predetermined conclusions.
Ignoring interactions: Missing how carbon pricing interacts with other policies.
Static analysis: Ignoring dynamic responses and adaptation over time.
Overconfidence in models: Models are tools, not crystal balls.
Building Assessment Capacity
Start simple: Begin with methods matching your current capacity.
Learn by doing: Each assessment builds experience for the next.
Engage academics: Universities can contribute expertise and credibility.
Use international support: PMI, World Bank, and others offer technical assistance.
Build gradually: Develop more sophisticated capacity over time.
Looking Ahead
Impact assessment provides the quantitative foundation for policy recommendations. The next lesson examines economic modeling for carbon pricing in more depth.