Evaluating Emissions Trading Using a Nearest (Polluting) Neighbor Estimator Meredith Fowlie (Michigan and NBER) Stephen Holland (UNC at Greensboro and NBER) Erin Mansur (Yale and NBER)

Why study emissions trading? Emissions trading is preferred for environmental regulation  Supported by many constituencies (& many countries)  Not a tax, but can raise revenue Emissions trading will be part of regulations to control greenhouse gas emissions Many unanswered questions about emissions trading  Can it reduce emissions above and beyond those achieved by traditional regulation?  Can it reduce emissions across industries?  Is it equitable? (Does it harm susceptible populations?)

Why do we study RECLAIM? RECLAIM is a large regional market trading NOx emissions in Southern California  Over 10 years of high volume trading  Many facilities (~350) across different industries RECLAIM marked many “firsts”  First CAT for urban air pollution  First mandatory CAT supplanting existing CAC  First CAT across heterogeneous sources  First CAT to be challenged on grounds of environmental injustice

Have others studied RECLAIM’s effectiveness? Two approaches study RECLAIM’s effectiveness by comparing actual emissions with:  predicted emissions based on ex ante emissions factors and economic trends (the initial allocation) (SCAQMD annual reports)  adjusting this prediction for emissions trends (EPA) Disagreements over success of program  Stavins (2007): clear success, 60% emissions reductions  Green etal (2007): “spectacular” failure

Our contributions We exploit the incomplete coverage of the program and use a nearest neighbor matching estimator to estimate counterfactual emissions for each facility We combine our matching results with regression techniques to analyze covariates of the emissions reductions We find:  significant reductions in emissions (~20%)  abatement mostly from larger facilities (consistent with scale economies)  no correlation with demographic characteristics (no support for environmental justice concerns)

The Four RECLAIM Counties

Multi-Industry Regulation Share of Initial NOx Emissions for RECLAIM Facilities by SIC4

Reclaim trading credit allocation

Permit allocation and aggregate emissions

RECLAIM Emissions, Permits, & Prices

Our Data We combine emissions data from two main sources:  CA ARB data on emissions from all facilities in California  RECLAIM data on emissions (used to validate and update ARB data) Our sample  Construct a balanced sample of facilities polluting from early 1990s through early 2000s.  Accounts for about 70% of stationary source emissions in region. Data issues  Self reporting (accuracy; non-report some years)  Selection? Entry and exit affected by type of regulation.

Did RECLAIM reduce aggregate emissions?

Changes in distribution of NOx emissions Table shows means and (standard deviations).

Difference-in-Differences

What is our empirical strategy? Construct a control group for each RECLAIM facility i using n i (=6) nearest neighbors  “exact” match on 4-digit SIC codes  nearest match on Period 1 emissions  Nonattainment in 1993 Compare differences in Period 3 v. Period 2 emissions at RECLAIM (treated) facilities versus control facilities  SATT: sample average treatment effect on the treated w/ quadratic bias adjustment

Bias adjustment x0x0 x1x1 linear bias adjustment quadratic bias adjustment y1y1 y0y0 A B C

Did facilities in RECLAIM reduce emissions?

Does the reduction in differences come from… an increase in Period 2 emissions? No. Period 2 emissions did not increase relative to the control.

Bias adjustment: none and linear

Does the reduction in differences come from… differences in Southern CA economy? No. No evidence of general decrease in emissions in the L.A. region.

Does the reduction in differences come from… spillovers to control group? No. Main result is quite robust to alternative control groups.

Does the reduction in differences come from… only large (or small) facilities? No. Reductions at both large and small facilities.

Does the reduction in differences come from… anything else? No. Main result is robust to excluding large generators and using 2-digit SIC’s.

Does the reduction in differences come from… our choice of sample window?

Controlling for economic shocks Main result is robust to controlling for payroll, employment and establishments.

Preliminary kernel matching results

RECLAIM and environmental justice EJ advocates routinely oppose CAT  CAC had specific mechanisms for dealing with EJ, CAT does not  However, MAC likely lower in disadvantaged neighborhoods RECLAIM much criticized on EJ grounds  LA is exceptionally diverse (see maps)  NO x is non-uniformly mixed pollutant (hotspots?)  RECLAIM indirectly implicated in controversy over mobile source emissions reduction credits

How can we analyze RECLAIM’s effects on disadvantaged communities? Map changes in emissions at each facility relative to that facility’s counterfactual emissions  Compare with black/Hispanic neighborhoods Mixed regression: What are correlates of emissions reductions?  Use (weighted) fixed effects estimator on the subsample of treated facilities and (matched) controls  Include facility/zip-level demographic controls as covariates  Include group fixed effects

What are correlates of emissions reductions? LHS: Change in NOx Emissions from Period 2 to Period 3 Notes: Group fixed effects not shown. Significance is noted at the 10% (*), 5% (**) and 1% (***) levels.

What are correlates of emissions reductions? LHS: Change in NOx Emissions from Period 2 to Period 3 Notes: Group fixed effects not shown. Significance is noted at the 10% (*), 5% (**) and 1% (***) levels.

Conclusions We use a nearest neighbor matching estimator to analyze RECLAIM and find:  significant reductions in emissions (~20%) reduction is robust  larger emissions reductions from larger facilities  no correlation with demographic characteristics These emissions reductions occurred despite the many design and implementation mistakes of RECLAIM Caveats  Not measuring cost effectiveness  Not addressing selection  Unobserved economic shocks not common across firms

Demographic Summary Statistics

Economic Trends: Employment

Economic Trends: Payroll

Poverty in Southern California