For generating the results presented below, I consider that the farmers and the regulator have the same information on the costs of the abatement actions. In terms of the model presented in the previous chapter, this implies that I solve for the PS and trading solutions the following cost function: , , ̅ ∑ ∗ ∗ .The results are obtained for three levels of desired water quality improvements: 20%, 30%, and 40% reductions in the mean expected annual loadings of nitrogen and phosphorus. The PS and trading cost minimization problems are similar to the ones described by equation (19) to (21). However, since a second constraint is added, the solution is likely to be different if the new constraints is binding.
Next, I present the empirical assessment of the three policies with the assumption of cost symmetry. The set of point coefficients have been introduced previously. The field-level
requirements have been set according to random watershed configurations that achieve the same level of abatement for both pollutants. The outcomes for the performance standard and the trading program are obtained by using linear programming methods.
Table 4-25. Boone River Watershed: Multiple Pollutant Policy Approaches
Abatement Target/CAC Performance Standard Point‐Based‐Trading N P Total Cost N P Total Cost N P Total Cost 20% 20% 6.65 26.3 27.9 5.07 22 29.6 1.07 30% 30% 17.99 34.5 35.3 15.85 32.2 37.6 3.04 40% 40% 36.075 42.9 43.6 35.46 41.2 37.8 7.04
Table 4-25 summarizes the simulated outcomes under the three policies approach when both N and P are targeted for the Boone River Watershed. Under the CAC approach, while the abatement targets are met, the total costs are very high. Under a performance standard program, more reductions are obtained while the costs are lower than in the case of a command and
control program. Under a point-based trading, the costs are much lower, being on average about 20% of the costs under a command-and-control program. Both N and P abatement targets are over attained for both 20% and 30% targets. Interestingly, for 40% reductions in both N and P, under point-based trading, the N target is slightly over attained, while the P target is not attained. Table 4-26. Boone Watershed Single Pollutant Point‐Based Trading
Boone Watershed Single Pollutant Point‐Based Trading Abatement Nitrogen only Point‐Based Trading
Phosphorus only Point‐Based‐Trading
N/P N P Total Cost N P Total Costs 20% 22.0 29.6 1.07 12.6 19.3 0.37
30% 32.2 37.6 3.04 19.2 27.9 0.85 40% 41.2 37.8 7.04 27.4 36.7 1.90
Table 4-26 presents the simulated outcomes for the point-based trading scenarios where only one pollutant is targeted. Interestingly, the outcomes of a nitrogen point-based trading are similar to the outcomes of the trading policy that targets both N and P. Under phosphorus only point-based trading approach, the P abatement targets are on average underachieved by 2.5%, and the total costs are much lower than for nitrogen only point-based trading. However, the total costs are much lower. Qualitatively similar results are obtained for the RRW: the outcomes of the trading setting have the lowest costs and the outcomes of a nitrogen point-based trading approach are the same as the outcomes of a nitrogen and phosphorus point-based trading approach. (see Tables B-22 and B-23 in Appendix B)
Discussions
In this section, I extend the point-credit approximation procedure to multiple pollutant markets where the regulator seeks to achieve simultaneous reductions for multiple pollutants by using a system with a separate point market for each pollutant. The findings show that abatement
outcomes of a trading program that considers separate markets for both pollutants are achieved at lower costs relative the CAC or PS policies. However, there are no additional gains relative to the case where there is only a market for nitrogen, since by targeting N reductions significant higher reductions for P are obtained. A trading program for phosphorus only has the potential to achieve its phosphorus abatement goal at much lower costs, but the associated nitrogen
abatement levels are not met. The present findings show that there are no additional gains from focusing on both pollutants and the policy programs should be designed by focusing on the pollutant that raises the most interests.
Conclusions
In Chapter 3, I introduced a simple model of pollution and outlined the properties of three different policies under a linear approximation of the abatement function. In this chapter, I provided an empirical assessment of the tradeoffs between the cost efficiency and effectiveness across these policies given that a system of field-level point coefficients is used as a linear proxy for the abatement function. The outcomes of the policies were simulated under different set of assumptions: cost symmetry and cost heterogeneity, and single or multiple pollutants case. Three different levels of abatement were considered: 20%, 30%, and 40 % reductions in the baseline emissions. A robustness analysis for the single pollutant scenario was conducted by assuming a less precise point coefficients are used. The same single policy outcomes were compared to the outcomes of an emission based trading program where trading takes place according to the trading ratios defined by the delivery coefficients. Additionally, I tested whether the abatement outcomes are consistent under the historical weather distribution. Finally, the same set of policies was assessed assuming that two pollutants are simultaneously targeted.
Since I am interested in assessing how different policies perform under a linear approximation tot the abatement (water quality production) function, I assume that the
biophysical model is the “exact” representation of the complex water quality model. However, this is clearly not accurate. In reality, using a water quality model like SWAT introduces another level of approximation which is not the focus of my work.
Another note of caution arises from the assumption that the PS and trading outcomes achieves all gains from trade. However, as many authors have noted, when factors like the type of trading (sequential or bilateral), transaction costs or nonmonetary preferences are taken into account some of the gains of an efficient trade may not occurr (Atkinson and Tietenberg 1991; Stavins 1995; Shortle 2013).