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Are Market-Based Environmental Laws Really the Best Thing Since Sliced Bread? A Case Study of Environmental Justice and The Toxics Release Inventory in North Carolina Jacy Gaige Urban Economics, 2010.
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Are Market-Based Environmental Laws Really the Best Thing Since Sliced Bread? A Case Study of Environmental Justice and The Toxics Release Inventory in North Carolina JacyGaige Urban Economics, 2010
October 2009 - Lindsay Graham and John Kerry form bipartisan team to push a climate bill in the Senate June 2010 – Landmark climate bill passes in the House mandating cuts in GHG through cap-and trade July 2010 – Harry Reid announces the Senate bill won't be put for a vote this term
Mass. v. EPA (2007) Holding that the Environmental Protection Agency must regulate Carbon Dioxide under the Clean Air Act because it is a pollutant that may “reasonably be expected to endanger public health or welfare”
What are “market-based environmental laws”? Green taxes, Cap-and-trade, information disclosure laws, and hybrids Pros: Efficiency, Incentives Cons: Distributional inequality Morality (right to pollute)
Toxics Release Inventory (1986) Factories of a certain size producing certain products 600 named chemicals, including Carcinogens and Persistent Bio-accumulative Toxins (PBTs) such as Mercury, Lead, and Dioxins Waste streams to air, water, and land + accidental spills The only requirement is to report
In 2008, releases included: 486 million pounds of Lead 6.2 million pounds of Mercury 5 million pounds of other PBT chemicals
From 2007 to 2008 total reported emissions in the U.S decreased by 257 million pounds, or 6% Since the first reporting year in 1988, emissions have decreased by a total of 65%
Why does distributional inequality matter? Concentrations Slower Action Symbolic power of the law But.... Causation Flexibility Incentives
Data: Census 2000, Block group data for Income, Race and Education 2008 Total On-site Emissions for all sites in North Carolina * Timing * Aggregation Process: Map, Buffer Analysis and Regression
Yi = α + β1x1 + β2x2 + β3x3 + ∑i Yi = total on-site emissions in pounds x1 = median household income x2 = percent of population that is white x3 = average number of years of education
Source | SS df MS Number of obs = 558 -------------+------------------------------ F( 3, 554) = 2.80 Model | 3.6117e+12 3 1.2039e+12 Prob > F = 0.0394 Residual | 2.3826e+14 554 4.3007e+11 R-squared = 0.0149 -------------+------------------------------ Adj R-squared = 0.0096 Total | 2.4187e+14 557 4.3424e+11 Root MSE = 6.6e+05 ------------------------------------------------------------------------------ sum_on_sit | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- avg_vari_1 | -582.9771 1526.642 -0.38 0.703 -3581.692 2415.738 lnavg_vari_2 | 296613.1 180326.7 1.64 0.101 -57594.58 650820.9 avg_vari_3 | -16780.81 5847.036 -2.87 0.004 -28265.88 -5295.735 _cons | -2744068 1797765 -1.53 0.127 -6275337 787200.6 ------------------------------------------------------------------------------