Moriah Bostian

Spatial Targeting of Agri-Environmental Policy Using Bilevel Evolutionary Optimization

In this study we describe the optimal designation of agri-environmental policy as a bilevel optimization problem and propose an integrated solution method using a hybrid genetic algorithm. The problem is characterized by a single leader, the agency, that establishes a policy with the goal of optimizing its own objectives, and multiple followers, the producers, who respond by complying with the policy in a way that maximizes their own objectives. We assume that the leader has perfect knowledge of policy outcomes for all parameterizations of agri-environmental policy. We use a hybrid genetic algorithm to simulate perfect knowledge of all policy outcomes in a bilevel optimization. Our hybrid genetic algorithm integrates a biophysical model (Soil and Water Assessment Tool; SWAT) with an economic model (profit maximization; DEA). The Soil and Water Assessment Tool (SWAT) is included to specify agency environmental objectives, and Data Envelopment Analysis (DEA) is used to model producer behavior in response to agri-environmental policy. We applied the resulting integrated modeling system to the analysis of an input tax on fertilizer in the Calapooia watershed in Oregon, USA. Application of the incentive policy at different geographical resolutions showed that bilevel optimization is effective for calculating optimal spatial targeting of agri-environmental policy. Surprisingly, the presented algorithm found multiple different policy configurations that achieved nearly identical results for the upper level (agency) objectives. This observation raises the possibility that additional objectives could incorporate equity, equality of outcome, and policy initiatives such as support for small farms at no additional cost.

Incorporating Data Envelopment Analysis solution methods into bilevel multi-objective optimization

This study illustrates the use of Data Envelopment Analysis (DEA) solution methods in bilevel optimization problems. Here, we show that DEA and bilevel optimization can also be used together as part of an integrated solution framework. We work with a policy-oriented problem in which the regulators multi-objective optimization problem at the upper level is constrained by the profit-maximizing decisions of individual firms at the lower level. Each firms response to the policy is a prori unknown to the regulator, and depends on the underlying production technology. Rather than assuming a common production relationship across firms, DEA allows us to model each firms response to the policy without imposing a functional form on the production technology. We use DEA to estimate the technology facing each producer, based on observed practices of other producers. Doing so endogenizes the cost of responding to prospective policies at the lower level, providing a more realistic solution set at the upper level. Our application addresses the design of a policy to reduce fertilizer runoff from agriculture, an important problem in environmental economics. We employ a multi-objective bilevel evolutionary algorithm to solve for the approximate optimal frontier.

Handling practicalities in agricultural policy optimization for water quality improvements

Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (i) a large number of decision variables, (ii) at least two inter-dependent levels of optimization between policy makers and policy followers, and (iii) uncertainty in decision variables and problem parameters. Given agricultural and economic data from the Raccoon watershed in central Iowa, we formulate a bilevel multi-objective optimization problem that accommodates objectives of both policy makers and farmers. The solution procedure then explicitly accounts for the nested nature of farm-level management decisions in response to agri-environmental policy incentives constructed by policy makers. We specifically examine the spatial targeting of a fertilizer-reduction incentive policy while seeking to maximize farm-level productivity while generating mandated water quality improvements using this framework. We test three different evolutionary optimization algorithms - m-BLEAQ, NSGA-II, and SPEA2 - and show that m-BLEAQ is well suited for handling the bilevel optimization problems and the considered practicalities.

Valuing Water Quality Tradeoffs at the Farm Level: An Integrated Approach

This study evaluates the tradeoff between agricultural production and water quality for individual producers using an integrated economic-biophysical hybrid genetic algorithm. We apply a multi-input, multi-output profit maximization model to detailed farm-level production data from the Oregon Willamette Valley to predict each producers response to a targeted fertilizer tax policy. Their resulting production decisions are included in a biophysical model of basin-level soil and water quality. We use a hybrid genetic algorithm to integrate the economic and biophysical models into one multi-objective optimization problem, the joint maximization of farm profits and minimization of Nitrate runoff resulting from fertilizer usage. We then measure the tradeoffs between maximum profit and Nitrogen loading for individual farms, subject to the fertilizer tax policy. We find considerable variation in tradeoff values across the basin, which could be used to better target incentives for reducing Nitrogen loading to agricultural producers.

Network Representations of Pollution-Generating Technologies

We update developments on modeling technology including unintended outputs and show how these can, at least to a large extent, be incorporated in a network model framework. Recently there have been ...

Time Substitution for Environmental Performance: The Case of Sweden Manufacturing

We apply recent advances in time substitution modeling to examine the environmental performance of firms in Sweden’s pulp and paper industry for the years 2002-2008. Our data allow us to estimate the optimal reallocation of environmental investments, expenditures and energy use to simultaneously maximize production output and minimize emissions reductions in the years immediately before and after the implementation of the European Union Emissions Trading Scheme. We find some evidence of overall productivity decline when considering both emissions and output objectives, due primarily to technological decline, and that cumulative dynamic inefficiency outweighs static inefficiency. A comparison of optimal investment time paths to observed investment levels indicates that firms could have improved their performance by reallocating environmental investments to early periods and production-oriented investment to later periods.