approach. Several factors may influence this uncertainty. For example, the characteristics of agricultural pollution, including dispersion of harm and the inability to identify sources, could make the probability of a producer being sued and held liable very small under strict liability rules. A negligence rule may be more appropriate in these cases because it is not necessary to prove a producer’s contribution to damages. Liability based on compliance with
‘accepted management practices’ would be seen as the fairest, because producers would not be held liable unless they were not in compliance with acceptable practices.
Finally, the litigation process for liability may be expensive relative to other regulatory methods. This expense may prevent individuals from attempting to claim damages, letting polluters go unregulated (Shavell, 1987). Due to these considerations, liability rules are not likely to be first-best and are probably best suited to the control of pollution related to the use of hazardous materials, or to non-frequent occurrences such as accidental chemical spills (Menell, 1990;
Lichtenberg, 1992; Wetzstein and Centner, 1992).
point sources of water pollution (Elmore et al., 1985; Shortle, 1987;
Camacho, 1991; Letson et al., 1993; Malik et al., 1993; Rendleman et al., 1995; Faeth, 2000).
Point–non-point trading represents an innovative watershed-based approach to reducing non-point pollution while also improving the cost- effectiveness of the allocation of pollution load reductions between point and non-point sources (Elmore et al., 1985; Shortle, 1987; Camacho, 1991; Malik et al., 1993, 1994; Letson, 1992; Rendleman et al., 1995; Anderson et al., 1997; USDA and USEPA, 1998; Faeth, 2000; GLTN, 2000). Pilot point–
non-point trading programmes have been established for the Tar-Pamlico estuary in North Carolina, the Dillon Creek Reservoir in Colorado and Cherry Creek, Colorado. A number of other planned or pilot programmes are being developed (see Table 2.3), often in conjunction with the establishment of total maximum daily loads (TMDLs). Most of these programmes have identified agriculture as the primary non-point source for trading (Table 2.3).
There has been less activity in established US trading programmes than was originally anticipated. A recent assessment indicates that design flaws, rather than problems in the basic concept, are at fault (Hoag and Hughes- Popp, 1997). Given the increasing interest in point–non-point trading, it is important to gain a better understanding of what the programme design options are and how choices among these options can be made to promote cost-effective trading.
While there may be significant potential gains from reallocating pollution control between point and non-point sources, there are also significant chal- lenges in the design of point–non-point trading systems that can realize these gains. Trading between point and agricultural sources entails a fundamental departure from text book tradeable discharge markets (Shortle, 1987; Malik et al., 1993). Because non-point emissions cannot be monitored accurately at reasonable cost and are stochastic, a fundamental issue in the design of agricultural trading programmes is what farmers will trade. Point–non-point systems that have been developed to date involve point sources trading increases in emissions for reductions in estimated loadings from non-point sources (due to the stochastic nature of non-point pollution).21Existing and planned programmes work as follows. Point sources are provided with pollu- tion permits, such as through the NPDES system, that define allowable emissions or loadings for the permit holder. These sources would have the option of satisfying the permit on their own, or by purchasing additional allowances or credits from non-point sources. Thus, trading will transfer some control respon- sibility to non-point sources.22 However, under existing and planned programmes, agricultural (and other) non-point sources enter into such a commitment voluntarily and are compensated for their abatement efforts.
An alternative to trading mean loadings would be to trade inputs that are correlated with pollution flows (e.g. trading point source emissions permits for agricultural permits restricting the use of polluting inputs such as fertilizers).
Systems have also been proposed in which point source emissions could be
Environmental Instruments for Agriculture 55
56R.D. Horan and J.S. Shortle Table 2.3. Existing, pilot and planned point–non-point trading programmes in the United States.
Sources Pollutants Primary non-point PS/NPS trading
Programme involved traded sources ratio
Cherry Creek, Colorado PS/PS and Phosphorus Land use projects managed Range from 1.3:1 to 3:1
PS/NPS by Cherry Creek Basin
Water Quality Authority
Chesapeake Bay Program PS/NPS Nutrients Agriculture and urban Greater than 1:1 is suggested
(multi-state) to deal with uncertainty
Dillon Creek, Colorado PS/NPS and Phosphorus Urban, septic, ski areas 2:1
NPS/NPS
Fox-Wolf Basin 2000 Project, PS/NPS Nutrients Agriculture Not available
Wisconsin
Long Island Sound PS/PS and Nitrogen Not yet identified Not available
(multi-state) (eventually) (small % of total loads)
PS/NPS
Lower Boise River, Idaho PS/PS and Phosphorus Agriculture Site-specific with uncertainty
PS/NPS discount built in
Michigan (statewide) PS/NPS Nutrients and other Agriculture 2:1 with site-specific
factors
Red Cedar River, Wisconsin PS/NPS Phosphorus Agriculture Not yet available
Rock River, Wisconsin PS/NPS Phosphorus Agriculture Site-specific with a base ratio
of 1.75:1
Tar-Pamlico, North Carolina PS/NPS Nutrients Agriculture 3:1 for cropland,
2:1 for livestock Note: Preliminary analyses under way in Ohio, Texas, Maryland, Indiana, Illinois and Virginia.
This list is not exhaustive. Also, some changes are likely given the preliminary nature of some programmes.
Sources: Horan (forthcoming).
traded for reductions in the use of fertilizers and/or reductions of cropland in fertilizer-intensive uses (Hanley et al., 1997).
In addition to the question of what to trade, another fundamental issue in the design of any trading scheme is the rate at which non-point allowances are traded for point source allowances (Shortle, 1987; Letson, 1992; Malik et al., 1993). Because non-point inputs and estimated loadings are imperfect substitutes for point source emissions, trades should not occur at a ratio of one for one. Existing literature provides little guidance, but suggests that factors such as risk and relative contributions to ambient pollution are impor- tant in the design of first-best markets (Shortle, 1987; Malik et al., 1993).
Two types of trading system are outlined below. One involves trades of point source emissions for estimated non-point source loadings. The second involves trades of point source emissions for non-point source inputs.
Theoretical research has demonstrated that emissions-for-inputs (E-I) trading systems can be designed to provide greater economic efficiency (transactions costs aside) than emissions-for-estimated loadings (E-EL) trading schemes, because they are better able to manage the variability of non-point loads (Shortle and Abler, 1997). The reason, as discussed previously, is that estimated loadings are suboptimal as a basis for non-point pollution control.
However, under ‘real world’ conditions, an E-EL trading system may well out- perform an E-I system. We shall return to this issue later.
Emissions for estimated emissions trading
An emissions-for-estimated loadings trading system would consist of two categories of permits: point source permits, e, and non-point source permits, ^ ^r.
The former are denominated in terms of emissions while the latter are denominated in terms of estimated loadings. Firms must have a combination of both types at least equal to their emissions, in the case of point sources, or estimated loadings in the case of non-point sources. In existing programmes that include agricultural sources, agricultural sources are not required to have permits. Instead, these sources have an implicit, initial right to pollute, which is consistent with having permits equal to unregulated estimated load- ings levels. Trading occurs as non-point sources contract with point sources to reduce estimated loadings in exchange for a fee. Such contracts represent the only enforceable regulations on agricultural sources. However, point sources are ultimately held responsible for meeting water quality goals if they are not met through non-point source reductions (Malik et al., 1994).
In most existing programmes, permits are traded at a rate of 1:1 within source categories and a trading ratio, t = dr/d^ e,^ defines how many non-point permits substitute for one emissions permit for trades between source categories. This restriction of 1:1 trading within categories reduces cost- effectiveness when firms’ emissions (or loadings) have differential marginal environmental impacts, because uniform trading ratios do not give firms
Environmental Instruments for Agriculture 57
incentives to exploit differences in their relative marginal environmental impacts, as a differentiated system would (Tietenberg, 1995a,b). However, this restriction could provide a net economic gain if it reduces programme administrative and other transactions costs. The same is true for a uniform trading ratio that does not vary depending on the locations of sources involved in a trade. Most existing point–non-point programmes do operate with a single trading ratio, although the ratio is spatially differentiated for a few newer programmes such as the ones in Michigan and Idaho (GLTN, 2000).
Trading ratios for a cost-effective programme, given the 1:1 trading restriction within source categories, have been derived by Horan et al.
(2000b). As with other studies (e.g. Shortle, 1987; Malik et al., 1993), they found that this ratio can be greater than, less than, or equal to one. A ratio equal to one implies indifference at the margin between the source of control.
Ratios in excess of one imply a high cost of non-point control relative to point source control and thus a marginal preference for point source reductions.
The reverse is true for ratios less than one.
Little can be said a prioriabout the magnitude of an optimally set trading ratio, though theory suggests that factors such as the relative marginal contributions of point and non-point sources, the degree of environmental risk impacts, correlations between key environmental and cost relationships, and the overall level of heterogeneity associated with point and non-point source could all play a role (Horan et al., 2000b).
Emissions-for-inputs trading
Now consider an emissions-for-inputs (E-I) trading system. As above, we assume two main categories of permits: point (PS) and non-point (NPS). PS permits are denominated in terms of emissions as in the E-EL system. In contrast, NPS permits are differentiated further and denominated in terms of specific inputs. As with the E-EL system, we assume an efficiency-reducing restriction of 1:1 trading of permits within source categories, with trading ratios applicable for trades between source categories and for different inputs. Additional inefficiencies may arise for E-I trading systems where only a subset of inputs are traded, though this is likely to be a practical considera- tion because it will likely be difficult and costly to monitor all inputs (Shortle et al., 1998).
Trades involving pollution-reducing inputs (i.e. those inputs for which increased use reduces pollution) are characterized by some interesting features. Specifically, permits for these inputs may define minimumrequired input use in some situations. When this occurs, then cost-effective trading ratios involving these inputs will be negative (i.e. a reduction in emissions is traded for an increasein pollution-reducing inputs) and the economic effect will be to create an opportunity cost associated with reduced use of pollution- reducing inputs.
58 R.D. Horan and J.S. Shortle
Cost-effective E-I trading ratios, given the 1:1 trading restriction and the restrictions on the number of inputs requiring permits, have been derived by Horan et al.(2000b), though little can be said a prioriabout their magnitudes.
As with the E-EL ratio, the E-I ratios can be greater than, less than, or equal to one, and will likely be influenced by similar factors. One difference between E-I and E-EL trading, however, is the impact of input substitution. Specifically, if permits requiring an increase in pollution-reducing inputs also have the effect of increasing the producers’ demand for pollution-increasing inputs, then damages could increase as a result. In such cases, trading ratios involv- ing pollution-reducing inputs would be increased to encourage greater control of point sources and to encourage reduced use of pollution-increasing inputs. Accordingly, trading ratios involving pollution-reducing inputs will not necessarily be negative.
Some empirical results
Horan et al. (2000a) compared an E-EL system and two E-I systems for point–non-point trading in the Susquehanna River Basin in Pennsylvania, where trades are between farmers and municipal and industrial point sources of pollution. Uniform trading ratios are applied for each system. They found that trading programmes for which non-point permits are defined in terms of loadings are less costly than those based on input use and perform almost as well as the first-best approach. This result occurs largely because loadings are a better indicator of environmental pressures than are inputs. In contrast, programmes in which allowances are defined in terms of non-point inputs are more costly due to the restriction of uniform trading ratios within source categories. This result indicates that differential treatment among sources is likely to improve performance for input-based trading systems, but not for trading systems based on estimated loadings. Of course, the transactions costs associated with increased differentiation of trading ratios are also important to consider, especially given the large numbers of sources often associated with non-point pollution. However, such transactions costs might be justified if the transactions costs associated with the alternative, an estimated load- ings-based system, are large relative to those of input-based programmes.
A second result is that the choice of input permit bases can greatly affect the relative performance of input-based trading schemes. In particular, a trading system in which non-point permits are based solely on land use (i.e.
placing land in or out of production) does little, if anything, to reduce the expected social costs of pollution. This is because land use has a less direct impact on pollution relative to other inputs, and the effects of changes in land use on pollution depend largely on economic substitution and output effects.
This result raises important questions about the heavy reliance on current approaches that focus considerable attention on point sources and extensive margin decisions of non-point polluters (e.g. the CRP).
Environmental Instruments for Agriculture 59
Thirdly, the majority of control costs fall on non-point sources, indicating that having substantial point source controls relative to non-point controls yields excessive costs. Consider the emissions-for-land trading scheme: signifi- cant non-point controls are too costly to undertake in this system but, even so, little is optimally reallocated back to point sources. Instead, the optimal level of control is small and expected social costs are not reduced significantly.
Finally, the trading ratios for E-EL systems are much smaller than those found in existing markets, trading ratios for emissions-for-nitrogen systems are much larger than ratios currently applied in existing markets, and optimal ratios for each scheme can vary considerably depending on watershed characteristics.
This result suggests that there may be limitations to using existing markets for guidance for appropriate ratios. Moreover, it suggests that the manner in which environmental performance measures are defined is important. Specifically, as permits are defined for environmental performance measures that are closer to the field and farther from the location of damages (e.g. estimates of field losses), the magnitude of the trading ratio increases optimally.