Improving NO_<i>x</i> Cap-and-Trade System with Adjoint-Based Emission Exchange Rates

Cap-and-trade programs have proven to be effective instruments for achieving environmental goals while incurring minimum cost. The nature of the pollutant, however, affects the design of these programs. NO_<i>x</i>, an ozone precursor, is a nonuniformly mixed pollutant with a short atmospheric lifetime. NO_<i>x</i> cap-and-trade programs in the U.S. are successful in reducing total NO_<i>x</i> emissions but may result in suboptimal environmental performance because location-specific ozone formation potentials are neglected. In this paper, the current NO_<i>x</i> cap-and-trade system is contrasted to a hypothetical NO_<i>x</i> trading policy with sensitivity-based exchange rates. Location-specific exchange rates, calculated through adjoint sensitivity analysis, are combined with constrained optimization for prediction of NO_<i>x</i> emissions trading behavior and post-trade ozone concentrations. The current and proposed policies are examined in a case study for 218 coal-fired power plants that participated in the NO_<i>x</i> Budget Trading Program in 2007. We find that better environmental performance at negligibly higher system-wide abatement cost can be achieved through inclusion of emission exchange rates. Exposure-based exchange rates result in better environmental performance than those based on concentrations

Optimal Ozone Reduction Policy Design Using Adjoint-Based NO_<i>x</i> Marginal Damage Information

Despite substantial reductions in nitrogen oxide (NO_<i>x</i>) emissions in the United States, the success of emission control programs in optimal ozone reduction is disputable because they do not consider the spatial and temporal differences in health and environmental damages caused by NO_<i>x</i> emissions. This shortcoming in the current U.S. NO_<i>x</i> control policy is explored, and various methodologies for identifying optimal NO_<i>x</i> emission control strategies are evaluated. The proposed approach combines an optimization platform with an adjoint (or backward) sensitivity analysis model and is able to examine the environmental performance of the current cap-and-trade policy and two damage-based emissions-differentiated policies. Using the proposed methodology, a 2007 case study of 218 U.S. electricity generation units participating in the NO_<i>x</i> trading program is examined. The results indicate that inclusion of damage information can significantly enhance public health performance of an economic instrument. The net benefit under the policy that minimizes the social cost (i.e., health costs plus abatement costs) is six times larger than that of an exchange rate cap-and-trade policy

Optimal Ozone Control with Inclusion of Spatiotemporal Marginal Damages and Electricity Demand

Marginal damage (MD), or damage per ton of emission, is a policy metric used for effective pollution control and reducing the corresponding adverse health impacts. However, for a pollutant such as NO_<i>x</i>, the MD varies by the time and location of the emissions, a complication that is not adequately accounted for in the currently implemented economic instruments. Policies accounting for MD information would aim to encourage emitters with large MDs to reduce their emissions. An optimization framework is implemented to account for NO_<i>x</i> spatiotemporal MDs calculated through adjoint sensitivity analysis and to simulate power plants’ behavior under emission and simplified electricity constraints. The results from a case study of U.S. power plants indicate that time-specific MDs are high around noon and low in the evening. Furthermore, an emissions reduction of about 40% and a net benefit of about $1200 million can be gained for this subset of power plants if a larger fraction of the electricity demand is supplied by power plants at low-damage times and in low-damage locations. The results also indicate that the consideration of temporal effects in NO_<i>x</i> control policies results in a comparable net benefit to the consideration of spatial or spatiotemporal effects, thus providing a promising option for policy development