The gains from differentiated policies to control stock pollution when producers are heterogeneous.

Some major pollution problems including ground and surface water contamination, soil erosion, buildup of pesticides resistance, and even climate changes are frequently stock externality problems caused by heterogeneous sources (Hoel and Karp 2002; Xepapadeas 1992a). Many of these are nonpoint source pollution problems, so that it is not feasible to directly measure the pollution originating from each source. With information about production, it is feasible to design incentives to correct the externality problems (Shortle and Horan 2001; Xepapadeas 1992b). However, the optimal corrective measures (e.g., taxes of observable activities) may have to vary among sources to address their heterogeneity and the dynamics of the pollution stock buildup and its damage. Implementation of such complex policies has been minimal because of technical difficulty of implementation and the high transaction costs of policy design (Abdalla et al. 2007). However, improvements in monitoring and information technologies reduce the costs of implementing policies that track behavior of individual agents. Health and food quality concerns have led to development and adoption of new technologies to increase trace-ability of food-production activities. Wireless-based technologies (like Tiger JILL and Pocket JILL (1)) are being adopted in California to comply with chemical application reporting requirements.

There are some notable policy examples in which information technologies are used to differentiate among drivers in the case of traffic pollution and other externalities. In Singapore, for example, smart cards are used to assess location-specific driving fees, as part of an effort to reduce pollution and congestion externalities. Yet, policy makers prefer solutions that are simple to understand and easy to implement. Fully differentiated policies may require high transaction costs in terms of time spent negotiating, or political costs to reach a consensus. These costs are likely to increase the more diverse affected populations are or the greater the variation over time (Dixit 1996). The transaction costs may explain why policy makers may prefer second-best policies that do not vary over space and time very much, if at all. However, as new technologies lower the cost of implementation and monitoring, policy makers should consider the introduction of more efficient, differentiated policies, comparing the gains from these policies with the extra transaction costs they may entail. Policy design will benefit from better modeling that determines more accurately first-best policies as well as second-best policies and, consequently, provides quantitative estimates of the welfare losses caused by implementing policies that do not take into full consideration variations over space or time or both.

This article introduces a modeling framework that addresses the buildup of a stock pollution caused by heterogeneous agents. To make the analysis more concrete, we cast this framework in terms of an agricultural production problem concerning lands with varying quality where residues of applied input have accumulated, and the stock is a source of damage. We consider two strategies to control pollution: reducing the application of variable inputs and adopting precision (conservation) technology. While this model directly applies to pollution problems emanating from agricultural production, it can be easily modified to control stock pollution problems in industries such as mining and energy generation. In all of these industries, technological change has resulted in an emergence of precision technologies that increase the efficiency of variable inputs such as water and fuel and reduce polluting residues (Khanna and Zilberman 1997). New technologies also enable better policy formation by governments. Through improving data availability (e.g., use of wireless communication), reducing computational cost, and better monitoring (e.g., geographic information system [GIS], remote sensing), agencies can link unobserved pollution to observed action and institute policies that include best management practices as well as incentives for adoption of conservation strategies and reduction of input use.

Policy makers can induce first-best outcomes through incentives or permits that vary over time and space. This policy requires intensive and costly monitoring and constant modification. The optimal policies are then compared with second-best policies that are constant over land quality or time or both. (2) The article provides an analytical framework that allows determining the varying and nonvarying part of the policies optimally, and identifying conditions when the associated efficiency losses of the second-best policies are relatively small compared to first-best policies.

There is a strand of literature that focuses on measuring the cost-effectiveness of differentiated versus nondifferentiated policy instruments when land is heterogeneous. However, the articles reach different conclusions about the differences in the relative efficiency of alternative policies. Helfand and House (1995) analyze, in a static setting, the efficiency of different regulatory instruments to reduce nitrate leaching when pollution sources are heterogeneous. They found that uniform instruments do not lead to large welfare losses relative to a socially optimal solution. In contrast, Claassen and Horan (2001) use a market equilibrium simulation model to explore the differences in the relative efficiency of uniform and nonuniform input taxes when market prices are endogenous and found that differences in the relative efficiency of uniform and nonuniform taxes can be quite considerable.

Fleming and Adams (1997) use a dynamic programming approach to assess the importance of spatial variability in the design of efficient policies to control pollution. They evaluate the costs to producers of two different types of nitrogen taxes, a uniform tax and a tax that varies by location, to achieve a determined groundwater quality goal and found that the gains from a spatially differentiated tax are rather modest. However, although they use a dynamic setting, the policies they evaluate are not the optimal dynamic taxes. The initial value of the tax is adjusted in constant increments per year until the previously determined standard is achieved.

To our best knowledge, the previous studies that considered space overlooked the time dimension of pollution control. A dynamic framework is essential when evaluating the efficiency of alternative second-best instruments to control stock pollution. Uniform and differentiated policies have different capacities to affect the evolution of technology adoption and exit decisions over time to correct the buildup of pollution stock, and the heterogeneity of cultivated land will change over time. Therefore, the performance of uniform policies may be considerably affected by the dynamics and severity of the environmental problem.

Since the magnitude of the efficiency losses of the second-best policies cannot be determined analytically, we employ a numerical example that allows us to rank the different policy options for the studied case. The example is based on the waterlogging problem caused by irrigated cotton production in the San Joaquin Valley of California. Our empirical analysis shows that the efficiency losses of the different second-best policies depend particularly on the length of the planning horizon and the initial level of the pollution stock. In situations with significant initial environmental degradation, the imposition of a static but spatially differentiated tax leads to a smaller efficiency loss (in one example, 15%) in comparison to a spatially uniform policy that is adjusted over time (loss of 36%). However, if the initial pollution stock is sufficiently low, the ranking of these two policies is reversed, that is, the dynamic spatially uniform policy outranks the static spatially differentiated policy. Thus, if the environmental policy can be differentiated only in one dimension--either space or time--the optimal choice of the dimension depends on the state of the initial degradation.

We found other interesting results. It is clear a priori that the performance of any spatially uniform policy depends on the heterogeneity of the land quality. However, contrary to intuition, our results demonstrate that efficiency losses of a dynamic spatially uniform policy may decrease with an increase in the initial heterogeneity of the land quality. This result is explained by the fact that an optimal dynamic but spatially uniform tax increases over time, which drives the lowest quality land out of production and homogenizes the quality of the land that remains in production.

This article is organized as follows. We first introduce the economic model and define the optimal outcome from a social point of view. Next, we contrast this result with the optimal outcome from a private point of view, describe alternative policies to encourage farmers to behave optimally, and identify conditions when these policies may replace first-best policies at low cost. The following section presents the empirical part of the article and discusses the obtained results. The article closes with a summary and conclusions.

The Economic Model