Climate Benchmarks — User Guide
Compare performance and correlations of climate-related investment benchmarks
Contents
Overview
Essential Concepts
The Benchmarks
Correlation Analysis
Chart Controls
Applications
Data Updates
Usage Guidelines
Essential Concepts
These terms appear in the tables and charts on this page:
Stranded Assets (SA)
Fossil fuel reserves and infrastructure that may lose value as the world transitions to clean energy. The SA benchmark represents companies with significant exposure to oil, gas, and coal producers whose reserves may become uneconomic under climate policies.
Why it matters: Use the correlation matrix to see how other benchmarks relate to SA. Negative correlation with SA indicates a fund or benchmark tends to gain when fossil fuel assets decline, providing a hedge against climate transition risk. Positive correlation indicates shared exposure to fossil fuel risk.
SPY-XLE
The S&P 500 minus the energy sector. This is a long SPY, short XLE portfolio that shows market performance without fossil fuel exposure.
Why it matters: Compare climate funds to SPY-XLE to see if they outperform simply by avoiding energy. High correlation to SPY-XLE suggests the fund is mainly avoiding energy rather than adding climate-specific value.
Emissions Factor (EMIT)
A long-short portfolio capturing carbon emissions intensity. Long SPY, short emissions-weighted S&P 500.
Why it matters: Shows the return difference between low and high carbon intensity stocks. Positive EMIT performance means low-carbon stocks outperformed.
Oil Beta Factor (OIL)
A long-short portfolio capturing sensitivity to oil price movements. Long market, short lowest oil-beta stocks.
Why it matters: Measures oil price exposure separate from climate transition risk. High OIL correlation indicates a fund's returns are sensitive to oil price movements.
Subsidy Factor (SUB)
A long-short portfolio measuring clean energy vs fossil fuel performance. Long ICLN, short XLE/KOL blend.
Why it matters: Tracks the clean energy transition trade. Positive SUB correlation indicates a fund benefits when renewables outperform fossil fuels.
Correlation Matrix
Table showing how benchmarks move together. Values range from -1 (move opposite) to +1 (move together). Near zero means little relationship.
Why it matters: Use correlations to find diversification. Benchmarks with low or negative correlations provide different risk exposures.
Climate Transition Risk
The financial risk arising from policy changes, technological shifts, or market sentiment changes that affect high-carbon assets. Companies heavily invested in fossil fuels face potential devaluation as the economy shifts toward lower emissions.
Why it matters: Negative correlation between a fund and Stranded Assets suggests the fund hedges transition risk. Positive correlation means the fund is exposed to the same risks as fossil fuel companies.
The Benchmarks
The correlation matrix and charts include seven benchmarks. Each captures different aspects of climate-related investment risk and performance.
ACWI — MSCI All Country World Index
A market-capitalization-weighted index designed to measure equity market performance across developed and emerging markets globally. ACWI covers approximately 85% of the global investable equity opportunity set, including large and mid-cap stocks across 23 developed and 24 emerging markets.
Construction: Market-cap weighted across approximately 2,900 constituents. U.S. equities typically represent 60-65% of the index weight, which explains its high correlation with SPY. The index is rebalanced quarterly and reconstituted semi-annually.
When to use: Compare global climate funds against ACWI to assess whether they outperform or underperform the broad global equity market. High correlation between a fund and ACWI suggests the fund behaves like a global equity portfolio.
SPY — S&P 500 ETF
An exchange-traded fund tracking the S&P 500 Index, representing 500 of the largest U.S. publicly traded companies. SPY is the most liquid equity ETF globally and serves as the primary benchmark for U.S. large-cap equity performance.
Construction: Market-cap weighted index of 500 U.S. large-cap stocks selected by a committee based on market capitalization, liquidity, domicile, public float, sector classification, financial viability, and length of time publicly traded. Reconstituted quarterly.
When to use: Compare U.S.-focused climate funds to SPY to measure domestic market performance. SPY provides the baseline for understanding whether a fund's returns come from general market exposure or climate-specific positioning.
SA — Stranded Assets
A long-short portfolio designed to capture exposure to fossil fuel companies whose reserves and infrastructure may lose substantial value as the world transitions to clean energy. These 'stranded assets' face devaluation risk from climate policy, carbon pricing, technological disruption, and shifting consumer preferences.
Construction: Long the broad market (SPY), short a blend of energy and coal exposure. When fossil fuel companies underperform the broad market, SA rises.
When to use: SA serves as a climate transition risk proxy. Positive SA returns indicate fossil fuels are underperforming the market. Negative correlation with SA indicates a fund or benchmark tends to gain when fossil fuel assets decline, providing a hedge against climate transition risk.
SPY-XLE — Market Minus Energy
A synthetic benchmark representing the S&P 500's performance excluding the energy sector. This isolates what the U.S. equity market would return if it had zero exposure to fossil fuel companies, allowing analysts to separate 'avoiding energy' from 'active climate positioning.'
Construction: Computed as a long position in SPY minus a short position in XLE (Energy Select Sector SPDR Fund). XLE tracks the energy sector of the S&P 500, including oil, gas, and consumable fuels companies. The result is a daily return series equivalent to the S&P 500 with energy sector returns removed.
When to use: Compare climate funds to SPY-XLE to determine whether their performance comes from simply avoiding energy stocks or from active climate factor exposure. High correlation with SPY-XLE suggests the fund's climate positioning is primarily about energy avoidance.
EMIT — Emissions Factor
A long-short portfolio that captures the return difference between low-carbon and high-carbon intensity stocks within the S&P 500. EMIT isolates the 'emissions factor,' the systematic return component associated with carbon efficiency.
Construction: Long SPY (standard S&P 500), short an emissions-weighted version of the S&P 500. Companies are assigned weights based on their SIC sector's total Scope 1 and 2 emissions. The portfolio rebalances annually on January 1st when S&P 500 constituents and emissions data are updated. When low-emissions sectors outperform high-emissions sectors, EMIT produces positive returns.
When to use: EMIT measures whether carbon efficiency is rewarded in the market. Positive correlation with EMIT suggests a fund benefits when low-carbon stocks outperform. Use EMIT to assess exposure to the emissions factor independent of sector allocation effects.
OIL — Oil Beta Factor
A long-short portfolio that captures the return spread between stocks with high and low sensitivity to oil price movements. OIL isolates the 'oil beta factor,' measuring how much of an equity's return can be attributed to oil price exposure.
Construction: Long the broad market (SPY), short an equal-weighted portfolio of the lowest oil-beta U.S. equities (bottom 25%). Oil betas are estimated using a 60-month rolling two-factor regression (market and oil factors). The short leg rebalances monthly as oil betas are re-estimated. When high oil-beta stocks outperform low oil-beta stocks, OIL produces positive returns.
When to use: OIL measures exposure to oil price risk in equity portfolios. Positive correlation with OIL indicates a fund benefits when oil-sensitive stocks outperform. Use OIL to assess whether climate funds have successfully reduced oil price exposure compared to broad market benchmarks.
SUB — Subsidy Factor
A long-short portfolio that captures the return spread between clean energy and fossil fuel stocks. SUB measures the relative performance of renewable energy investments versus traditional oil, gas, and coal companies, reflecting shifts in energy subsidies and policy support.
Construction: Long ICLN (iShares Global Clean Energy ETF) at 100% weight, short a blend of fossil fuels: 30% XLE (Energy Select Sector SPDR) and 70% KOL (VanEck Coal ETF). When clean energy outperforms fossil fuels, SUB produces positive returns.
When to use: SUB tracks the clean energy transition trade. Positive SUB returns indicate clean energy is outperforming fossil fuels. Use SUB to assess whether climate funds capture the renewable energy premium or remain exposed to fossil fuel underperformance.
Correlation Analysis
The correlation matrix reveals how benchmarks move relative to each other, helping you understand diversification potential and risk relationships.
Reading the Correlation Matrix
The matrix displays correlation coefficients between each pair of benchmarks. Here's how to interpret what you see:
- The Correlation Scale
Values range from -1 to +1. Colors typically range from red (negative) through white (zero) to green (positive). The diagonal is always +1 (a benchmark perfectly correlates with itself).
- Finding Any Pair
To find the correlation between two benchmarks, locate one in the row headers and the other in the column headers. The intersecting cell shows their correlation.
- Changing Time Periods
Use the dropdown above the matrix to select calculation periods (1Y, 3Y, 5Y, exponentially weighted, or Max). Different periods reveal how relationships evolve. The exponentially weighted option applies decaying weights to historical data, meaning recent observations matter more than older ones. Use it when you suspect correlations are shifting or want a view that adapts more quickly to current market conditions.
What to Expect
Some correlations are predictable; others reveal important dynamics:
- ACWI ↔ SPY: Typically 0.85–0.98
Very high correlation because U.S. equities dominate ACWI by market cap. These move nearly in lockstep and don't diversify each other.
- SA ↔ EMIT: Variable, often 0.3–0.6
Moderate positive correlation. Both capture climate-related factors but from different angles. SA tracks fossil fuel assets while EMIT captures carbon intensity spreads.
- SPY-XLE ↔ SA: Often low or negative
These capture opposite exposures. SPY-XLE excludes energy; SA is concentrated in energy. When oil rises, SA gains while SPY-XLE's energy exclusion becomes a drag.
Correlations Change Over Time
Don't assume today's correlations will hold tomorrow:
- Crisis Convergence
During market stress, correlations often spike toward +1. Assets that normally diversify may move together in sell-offs, reducing hedging effectiveness.
- Regime Changes
Policy shifts, energy price shocks, or climate events can structurally change correlations. The correlation between SA and broad markets has evolved as climate policy has intensified.
- Compare Multiple Periods
Always check 1Y, 3Y, and 5Y correlations. If they differ substantially, the relationship is unstable. Weight recent correlations more heavily for short-term decisions.
Key Relationships to Watch
- High Positive Correlation (near +1)
Benchmarks move together. ACWI and SPY typically show high correlation since U.S. stocks dominate global indices.
- Low or Negative Correlation (near 0 or below)
Benchmarks provide diversification. SA and SPY-XLE often show lower correlation, capturing different risk exposures.
- Climate vs. Traditional Benchmarks
Compare SA and EMIT correlations with SPY to see how climate factors relate to broad market movements.
Using Correlations
- Finding Diversification
Low correlations between benchmarks indicate they respond differently to market conditions. Combining them may reduce portfolio risk.
- Identifying Risk Factors
High correlation with SA suggests exposure to climate transition risk. High correlation with EMIT indicates carbon intensity sensitivity.
- Benchmark Selection
Choose comparison benchmarks based on what question you're asking. Use SA for hedging analysis, SPY-XLE for energy-exclusion analysis.
Chart Controls
The page displays two time series charts showing benchmark performance and risk over time.
Available Charts
- Benchmark Levels Chart
Shows cumulative performance of all benchmarks, normalized to start at 100. Compare how different benchmarks performed over your selected time period.
- Benchmark Volatility Chart
Shows annualized volatility for each benchmark over time, estimated using a GJR-GARCH model. Unlike static historical averages, these estimates capture volatility clustering, where elevated volatility today predicts elevated volatility tomorrow. Spikes indicate periods of market stress.
Chart Controls
- Date Range: Use the preset buttons (6M, 1Y, 2Y, etc.) or the date pickers to select a time period.
- Quick Presets: Select common time periods with one click
- Hover: See exact values for any date by hovering over the chart
Applications
Climate Investment Analysis
- Benchmark Selection
Choose the right comparison: ACWI for global funds, SPY for U.S. funds, SA for climate hedging analysis, SPY-XLE to isolate energy-exclusion effects, OIL to assess oil price sensitivity, SUB to measure clean energy transition positioning.
- Performance Attribution
Use correlations to understand what's driving returns. High SPY-XLE correlation suggests energy avoidance; low SA correlation suggests active climate positioning. High OIL correlation indicates oil price sensitivity; high SUB correlation indicates the fund benefits when clean energy outperforms fossil fuels.
- Risk Assessment
Check volatility charts during stress periods. Compare benchmark behavior during market disruptions to understand risk characteristics.
Portfolio Construction
- Diversification Analysis
Use the correlation matrix to find benchmarks with low correlations. Combining uncorrelated assets reduces overall portfolio risk.
- Climate Hedging
Look for negative correlation with SA. Assets that gain when stranded assets fall provide protection against climate transition risk.
- Benchmark Optimization
Compare multiple time periods to ensure relationships are stable. Short-term correlations may differ from long-term patterns.
Data Updates
Data Refresh Schedule
- Daily Correlation Updates
The correlation matrix recalculates daily using the latest market data. Timestamp below the matrix shows the last update.
- Benchmark Rebalancing
SA and SPY-XLE maintain fixed long-short weights through daily rebalancing. EMIT rebalances annually on January 1st to reflect changes in S&P 500 constituents and updated SIC-level emissions data. OIL rebalances monthly as oil betas are re-estimated using a 60-month rolling window. SUB maintains fixed ETF weights with the underlying ETFs managing their own constituent updates.
- Methodology Consistency
Calculation methods remain consistent over time to ensure historical comparisons are valid.
Usage Guidelines
Best Practices
- Choose the Right Benchmark
Use ACWI for global performance, SPY for U.S. performance, SA for climate hedging, SPY-XLE for energy-exclusion effects, EMIT for carbon intensity exposure, OIL for oil price sensitivity, SUB for clean energy transition positioning.
- Check Multiple Time Periods
Correlations change over time. Compare 1Y, 3Y, and 5Y periods to see if relationships are stable or evolving.
- Align with Investment Horizon
Short-term traders focus on recent correlations. Long-term investors should emphasize longer calculation periods.
Integration with Other V-Lab Tools
- Link to Climate Analysis
After selecting benchmarks here, go to the analysis pages for specific funds to compare them to your chosen benchmarks.
- Compare Across Pages
Use insights from benchmark correlations to inform your analysis of individual climate funds.
- Portfolio Risk Context
Understanding benchmark relationships helps assess how climate fund additions affect overall portfolio risk.
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