Cost Effectiveness Analysis

Cost effectiveness analysis (CEA) is a technique for comparing the relative value of various strategies.

In its most common form, a new strategy is compared with current practice (the “low-cost alternative") in the calculation of the cost-effectiveness ratio (CE ratio):
The result might be considered as the "price" of the additional outcome purchased by switching from current practice to the new strategy. If the price is low enough, the new strategy is considered cost-effective. Thus, this approach assesses the most efficient way to meet targets for human health improvement, ecological protection etc.
 
In the development of European air pollution strategies cost-effectiveness has been the main concept used. The tool used for the calculation of the most cost-effective solutions has been the RAINS modelexternal link, opens in new window (Regional Air Pollution INformation and Simulation). In this model the costs have been attributed to various control measures on a country basis. The effectiveness is considered not in relation to emissions but rather in relation to environmental impact.
 
In the RAINS model cost effectiveness to evaluate costs of emission reduction follows the concept of marginal costs (MC). MC relates to the extra costs for an additional measure to the marginal abatement of that measure (compared to the abatement of the less effective option. RAINS uses the concept of marginal costs for ranking the available abatement options according of their cost effectiveness (into so called “national cost curves").

The general functional form for the calculation of the MC used in the RAINS model takes the following form:

Based on the RAINS model figures in Table 1 are used to calculate the marginal cost of different technologies to be used to abate emissions of sulphur in Sweden according to the NOC (Non Control Strategy) scenario.

Table 1: Activity-specific abatement costs and removed emissions / category for the hypothetical NOC scenario sulphur emissions in Sweden. For details about the acronyms see IIASA and the RAINS model (Source: www.iiasa.ac.at)
Activity Sector Technology Unit cost (€ / ton) Removed emissions (kton)
NOF* PR_SINT SO2PR1 420 0.58
NOF PR_OT_NFME SO2PR1 423.5 8.4
NOF PR_SUAC SO2PR1 424 4.49
NOF PR_REF SO2PR1 466.67 3.18
NOF PR_LIME SO2PR1 473.68 0.18
HC1 IN_BO LSCO 513.52 0.26
HC1 IN_OC LSCO 513.52 2.55
To estimate cost effective measures that can be implemented to reach optimal emission reductions the RAINS model uses optimization procedure. The optimization approach of the RAINS model reflects the overall environmental policy objectives by specifying constraints on the maximum deposition at each grid cell. A cost effective cooperative solution is then obtained by finding a pattern of emissions that minimize total emission control costs, measured in a common currency (Euro) for the countries involved that meet the specified constraints on deposition. Thereby, spatial environmental standards (targets) are formulated as constraints of the optimization problem:

The theoretical formulation of the optimization procedure is as follows (Forsund (2000)).

For the EU countries the functional form of the objective function is:
The optimization procedure in RAINS consists of the following steps:
  • set target values for the environmental impacts
  • minimize costs for achieving these targets
  • read off the optimal emission levels, costs and control strategies for all pollutants and countries.
Since the objective function and all constraints are linear functions in the decision variables, the problem can be solved using linear programming (LP) methods.
 
A linear programming problem may be defined as the problem of maximizing or minimizing a linear function subject to linear constraints. The constraints may be equalities or inequalities. Here is a simple example.
 
Find numbers x1 and x2 that maximize the sum x1 + x2 subject to the constraints:
x1 ≥ 0, x2 ≥ 0, and
x1 + 2x2 ≤ 4
4x1 + 2x2 ≤ 12
-x1 + x2 ≤ 1

In this problem there are two unknowns, and five constraints. All the constraints are inequalities and they are all linear in the sense that each involves an inequality in some linear function of the variables. The first two constraints, x1 ≥ 0 and x2 ≥ 0, are special. These are called non-negativity constraints and are often found in linear programming problems. The other constraints are then called the main constraints. The function to be maximized (or minimized) is called the objective function. Here, the objective function is x1 + x2.

Updated: 2009-04-17
NEWS
2009-06-18

Project conclusions available online


General conclusions from the SOCOPSE project are now available online.  
2009-04-03

SOCOPSE Final Conference


"Future Approach to Priority and Emerging Substances in European Waters."
2009-04-03

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Draft substance reports for Atrazine, Cadmium, Isoproturon, Mercury, PBDE, TBT, HCB, PAH, DEHP and...

FINANCIAL SUPPORT
Topics addressed: FP6-2005-Global-4, Topic: II. 3.1 Source control of priority substances
Project duration: 2006-2009
Contract no.: 037038
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