Pareto-Optimal Environmental Decision Making

5.1.1.Decision-making at Different Scales

“Contemporary environmental decision-making is faced with a twofold challenge: the complexity of the physical domain in which decisions are taken, where most of the processes are dynamic, spatially-distributed and highly non-linear, and the heterogeneousness of the socio-economic-ecologic context they affect, which usually involves multiple (often many), conflicting objectives.” (Castelletti et al., 2010)

There are no panaceas for resolving most environmental issues, and the use of spatial information does not promise to be one. Environmental decisions at all scales are made in some degree through a Pareto-optimizing decision process¹. A household must determine the value of the trade-off between living more efficiently in the city and the comforts and affordability of commuting from the suburbs. A government agency must determine how many citizens it can

1Wilfredo Pareto (1848-1923) developed the theory of resource allocation in which no decision can make an individual better off without making at least another worse off.

help through any given policy and how many citizens will be negatively affected by that same policy.

“Computationally-onerous simulations are the only feasible way of analysis, while the multi-objective nature of the problem entails the combined use of optimization techniques and appropriate tools for the visualization of the associated Pareto frontier.” (Castelletti et al. 2010)

Finding the best policy decision, through Pareto-optimization where the most benefit in all objectives is achieved through the compromise of each, is only possible by heavy empirical calculation. The use of spatial metrics to map the optimized empirical data into data that is informative and easily communicated is essential to finding the optimal balance of objectives and strategies.

“A-priori methods are based on the elicitation and articulation of the decision-maker (DM) preference structure and its subsequent use to transform the multi-objective (MO) problem into a Single-Objective (SO) problem” (Castelletti et al., 2010)

Take multi-faceted problems and turn them into one single problem, that is, take all the variables and narrow them down to each decision where the most benefit is made for each of those variable conditions.

It is up to policymakers and city planners to create the most Pareto-optimal decisions regarding the development of regions so that households are given viable, sustainable options rather than forced to make the trade-off themselves. Despite the power of government agencies and institutions to shape the lifestyles of individuals through laws, incentives, and public projects, the day-to-day decisions that impact the environment are still at the household level. Except

certain laws that directly prohibit or demand an individual's action, such as murder, sending children to school or paying taxes, most government policies are designed to inform and direct the individual to make decisions that are socially optimal. Policies such as a gas tax increases the consumer level price for fossil fuels to a level that was calculated to reduce the overall consumption of gasoline for the benefit of the environment as well as the tax base while producing what is calculated to be a reasonable hardship on the individual. The Pareto optimal level of taxation is determined by the dynamic between individual consumption habits and priorities with intended national levels of consumption. Mirroring taxes, subsidies are also used to influence individual decisions to increase consumption of goods or services that naturally would not be at an optimal price point. Agriculture and energy are the two sectors with the most notable subsidy policies across the globe.

The use of taxes and subsidies, as well as other unconventional strategies, are examples of Real world implementations of Castelletti’s transformation of multi-objective problems into a single-objective problem. That is, the various objectives of reducing the use of fossil fuels - reduc-tion in greenhouse gas emissions, reducreduc-tion in dependence on foreign sources - is reduced to a single objective at the individual level - how much is the individual willing to pay for gasoline at the pump?

Policies regarding urban sustainability are also multi-objective problems. The potential set of tools for policymakers is diverse - ranging from zoning laws, direct investment, taxes, and subsidies - all requiring and equally diverse toolset of knowledge or “actionable intelligence.”

5.1.2.Modeling sustainability decisions - a viable next step beyond observa-tion?

The goal of empirical approaches to Pareto-Optimization are to minimize the conflicts that may occur when an intervention, however well intended, may cause undesirable effects on another sector. Caparros-Midwood et al. (2015) provide the example of European efforts to miti-gate greenhouse gas emissions by implementing the paradigm of denser cities associated with lower transportation energy use. The urban densification was found to increase the heat island effect in cities, which increased flood risk by reducing surface permeability and lowered health levels for residents. Echenique et al. (2012) suggest that compact cities only cause minor reductions in travel distances and that the benefits of shorter commutes were outweighed by fewer housing options and increased congestion. Both papers’ intentions are not to disparage the paradigm of city den-sification, but to call attention to the necessity of finding the proper Pareto-optimal balance of sustainability objectives that may complement and offset each other in the short and long term.

Caparros-Midwood et al. (2015) determine the following multi-objective spatial Pareto-Optimization approach:

1. Define the set of sustainability objectives to be optimized within the framework 2. Apply a simulated annealing algorithm to generate spatial configurations of new

de-velopment that meet the sustainability objectives

3. Use a sorting procedure to extract the Pareto-optimal subset of solutions that perform best in at least one sustainability objective