مالیات بر علف کش خطر مشخص شده به منظور کاهش آلودگی زمین و آب های سطحی: ارزیابی یکپارچه اقتصاد محیط زیست

Public policy toward pesticide use in agriculture can benefit from data coming from models that integrate ecological and economic constraints into cropping decisions and pesticide use. Herein we use such a model to focus on the environmental and economic effectiveness of a specific set of tools used to promote sustainable agriculture with less pesticide runoff — incentive-based instruments created by risk-indexed herbicide input-taxes. We measure risk by health advisory levels and by an ecological economic simulation model that estimates predicted exposure levels. We explore whether this innovative solution of herbicide input-taxes does better at reducing losses to farm net returns, and surface and groundwater loadings than quantity restrictions. Using the integrated CEEPES model, our results suggest that risk-indexed input taxes by information about individual herbicide exposure levels can be a cost-effective tool to reduce predicted groundwater exposures. No single policy, however, was efficient at simultaneously improving groundwater and surface water quality. Instead we construct an efficient policy set. We find exposure-induced taxes were most efficient for small percentage reductions in overall exposure, bans were efficient for medium reductions, and flat taxes were efficient for high reductions.

A major research question in ecological economics is ‘what regulatory or incentive-based instruments are most appropriate for assuring sustainability?’ (Costanza et al., 1991, p. 15). This question is especially relevant when considering the goal of sustainable agriculture, in which high productivity levels are maintained by many actions including pest control. People control pests such as weeds, diseases, nematodes and insects because they are constraints that reduce net returns in agricultural production. They maintain financial returns by controlling pests through pesticides including herbicides, insecticides, and rodenticides. Pesticides are estimated to be a good investment — $1 spent on a pesticide yields an average $3–6 savings in reduced crop damage (see Headley (1968) and Carrasco-Tauber (1990)). People like good investments, as revealed by the estimate that about 2.5 million tons of 55 000 pesticide products are applied annually worldwide (Pimentel et al., 1992), in which 80% are used in developed economies like Canada and the United States.1 The United States Department of Agriculture (1991) estimates that people use pesticides on about 92% of the corn acres, 95% of the acres in the six largest cotton states, and 95% of the soybean acres. Use of the popular pesticide atrazine during the 1980s, for example, was estimated at nearly eighty million pounds of active ingredient, accounting for about 12% of total herbicide use (United States Environmental Protection Agency, 1990). For corn alone, atrazine use is estimated at nearly 60 million pounds of active ingredient over 64% of all treated acres (Center for Agricultural and Rural Development, 1993). But pesticides are also perceived to pose a risk to human and environmental health including toxicity to non-target organisms such as pollinators and wildlife, environmental contamination of soil, water, and air affecting ecosystem functions such as nutrient cycles, selection of resistant pests, and acute and chronic toxicity to humans. Pesticides are best viewed as part of an overall pest control strategy aimed at providing abundant food at reasonable prices — an objective with which few would disagree given the broader goal of sustainable agriculture. But when pesticides threaten the sustainability of human and environmental health due to their persistence, mobility, and toxicity to non-target species, the public often asks policymakers to rethink how these inputs are used.2 The public push for a more sustainable agriculture asks these decision makers to better understand the nature of pesticide risks to humans and natural resources, how people perceive and react to these risks, and how specific public policy tools can help or hinder private actions. Understanding which policy tools work best for risky choices under both economic and ecology constraints can provide additional information to help policymakers promote effective sustainable agricultural — more food and less pesticide risk for more people. The World Health Organization (1990) estimated that over 3 million cases of acute pesticide poisoning occur annually worldwide, including 735 000 cases of long-term chronic impacts, and 37 000 cases of cancer. In the U.S., pesticides as a source of non-point pollution have also been accused of damaging an estimated 16% (206 179 miles) of the rivers in 40 surveyed states and 20% (5.4 million surface acres) of lakes. In addition, one or more of 46 pesticides have been detected in the groundwater of 26 states. Based on this evidence, the final report of the U.S. Congress on section 319 of the Clean Water Act states that ‘…information indicates very clearly that non-point source pollution has caused severe damage to aquatic communities nationwide and has destroyed the aesthetic values of many of our treasured recreational waters’ (United States Environmental Protection Agency, 1992, 1–2). Effective policies to promote sustainable agriculture through reduced pesticide pollution require information on how alternative policy tools affect the economic and environmental relationships involved in crop or livestock production. In general, three general management tools exist to address the human and environmental risks associated with pesticide use: technological restrictions such as improved pesticide application methods or biological pest control; cooperative institutions to share information on costs, damages, and technology; and economic incentives to change producer behavior and raise revenues. But monitoring difficulties and random weather shocks complicate the task of deciding which instrument to select to such a degree that the typical emission-based policies promoted by economists are often impractical. Pesticides are a non-point source of pollution because there are many diffuse sources of pollution that are extremely costly to identify or monitor. Designing an incentive to alter a producer's pollution control strategy requires a significant amount of information on the marginal costs and benefits of control, including the environmental fate and transport systems and the value of life. Often a producer has private information on his own costs of pollution control or choice of control strategies, and if the regulator is uninformed, the producer can take advantage of this asymmetry to gain additional net returns. First-best incentive systems — those that would meet both economic and environmental objectives of efficiency and effectiveness — still require a significant amount of information on behavior that might not be feasible due to high costs or political unacceptability or both (see Hanley et al., 1997 and Shortle and Abler, 1999). In response, pragmatic policymakers and researchers have offered up a novel second-best policy tool to promote the goal of sustainable agriculture — input taxesindexedby riskiness as measured by health advisory levels or ecological economic simulation models that construct ‘non-point production functions’ to predict the fate and transport of pollutants. The open question is whether these indexed input charges can outperform more traditional policy options like command and control quantity restriction.3 Herein we examine how on-farm economic and water quality indicators are impacted by a set of corn and sorghum herbicide input taxes indexed by either: (1) the U.S. Environmental Protection Agency's (EPA) human Health Advisory Level (HAL) benchmark; (2) the EPA's aquatic advisory benchmark; (3) predicted chronic exposure values for 1.2 m (meter) groundwater; and (4) predicted chronic exposure values by tillage–herbicide combinations for 1.2 meters (m) groundwater. Focusing on corn and sorghum in the Iowa region, we compare the indexed input taxes to the baseline policies of an atrazine ban, a triazine ban, and a flat input tax.4 We use the Comprehensive Environmental Economic Policy Modeling System (CEEPES) to construct these alternative input taxes that target herbicide characteristics and tillage practices. We generate trade-off frontiers to compare the effects of each policy tool on producer net returns and measures of groundwater and surface water quality.5 Our results suggest that no policy tool is globally efficient for simultaneously improving both groundwater and surface water quality. While input taxes indexed by 1.2 m groundwater exposures is an effective tool to improve groundwater quality, an atrazine ban is equally effective for medium reductions in herbicide loadings. Since no one policy works for all goals, we define an efficient policy set that shows which policies achieve different levels of water quality improvement cost-effectively. Giving equal weights to improvements in groundwater and surface water quality, the exposure-based taxes are most efficient to produce small improvements in water quality; flat taxes are most efficient for larger improvements; and an atrazine ban is most efficient for intermediate improvements.

Ecological economic principles promote the ideal of global sustainability. Such broad principles become more concrete from specific case studies that explore how to make the abstract operational on the ground. Herein we examine one such case study — the design of environment-indexed incentive policies to reduced pesticide use in agriculture. We use the integrated economic-ecological CEEPES model to consider how alternative risk-indexed incentivepolicies affect economic and environmental indicators of well-being, and to explore how these indexed policies compare to a traditional command and control herbicide ban. The risk-indexed incentive tax targets specific herbicides and herbicide–tillage practices based on predicted groundwater exposure levels from the CEEPES model. Our results suggest that indexed taxes can be an effective and cost-efficient tool to reduce predicted groundwater exposure. We also find no significant advantage exists to fine tune the index to include herbicide–tillage combinations: the results are similar regardless of whether we target herbicides alone or we target herbicide–tillage combinations. This occurs because individual herbicides have more effect on groundwater quality than do tillage practices. Finally, we observed that environmental advisory benchmarks alone are not useful to construct effective tax policies. No single policy tool dominated the other options for reducing groundwater exposure, surface water acute exposure, and surface water aquatic exposure. Different tools were more effective than others depending on the context. We used this data to construct an efficient policy set describing the best tool for the context using an equal weight on decreases in 1.2 m groundwater chronic exposure, surface water acute exposure, and surface water aquatic exposure. With the efficient policy set, we show both the achievable tradeoffs, and the policies that can achieve specific exposure levels. Our results indicate that exposure tax policies are most cost-efficient to achieve small percentage reductions in overall exposure, bans are most cost-efficient for moderate reductions, and flat taxes are most cost-efficient for high reductions. This suggests that the usefulness of information about predicted exposures depends on the desired reductions in exposure levels. For large reductions, for instance, crude tools like bans or flat taxes are most cost-effective. Several possible extensions to our work may be useful for future policy analysis. First, our results for combined surface water and groundwater effects are subject to change depending on the relative weights placed on changes in groundwater and surface water exposure values. Future research should focus on developing weighting schemes based on social or policy preferences. Second, our framework allows for the comparison of any number of non-point pollution policies. Policies that could be added to our analysis include targeting based on measures of surface water quality and targeting based on geographical characteristics. Finally, our analysis was restricted to the corn and sorghum weed control decisions modeled in WISH. Adding additional crops to the WISH model could expand our approach to cover all herbicide use in the region.