Location Decisions

ABSTRACT: Current practice in the control of urban air pollution concentrates on reducing emissions from both fixed and mobile sources directly, without consideration of the fact that (i) zonal restrictions and relocalization of activities -both directly polluting activities such as industrial sources and transport generating activities- affect emissions, and (ii) that the direct control of emissions impacts the costs of emitting sources, and this in turn affects location decisions. Thus the application of regulations that do not consider these effects can result in unexpected changes in emissions in different parts of the city, in response to underlying economic forces. Modeling this relationship is a difficult task because it involves relating the spatial distribution of sources that are responsive to economic conditions, with their emission and abatement cost characteristics. In response to this challenge, this research project presents a methodological proposition to determine an urban location/emission pattern, which can be used to predict the net cost and emission result of controls on location of activities and unit emissions. Specifically, implementing a self organizing neural network is proposed in order to model the relationship between local urban configuration parameters with the local characteristics of emissions and abatement costs. This model has been implemented for the 34 districts of Santiago and allows estimating changes in emissions of two pollutants (PM-10 and NOx) resulting from relocation of activities. The results show that zonal regulations imposing minor changes in urban location can change the emission pattern of the whole city. Thus, for long term environmental planning it is necessary to evaluate the impacts on emission of policies affecting location decisions.

1 Introduction

Since the beginning of history, cities appear to an observer as local population clusters defined by noticeable differences regarding land use, both qualitative as well as in intensity. These patterns reveal the presence of distinctive advantages provided by specific locations. Thus, for instance dockyards are near waterways, fortresses and garrisons are built on high ground, having brothels and taverns in their proximity. This happened long before urban planning boards and other regulatory efforts came into effect, suggesting that land use differentiation obey some self-organizing process far removed from state control.

Traditionally, cities have been the source for social and economic development. As centers for industry and trading, they harbor most of the wealth and political power. Nowadays, cities are the visible outcome of a process oriented to take advantage of a topology which minimizes transport and information costs between economic agents. Furthermore, the process appears to be successful; beginning from a critical mass cities show a tendency to grow, disregarding natural disasters there are no known examples of disappearance. This tendency is particularly evident in developing countries, leading to megapolis in a relatively brief period of time. Additionally, the elements conditioning this growth show no signs of decaying, therefore population growth in cities will continue to be higher than the overall demographic growth rate.

According to World Bank estimates, 80% of the economic growth in developing countries will take place in cities and towns, thus establishing a powerful incentive for urban migration. Moreover, urban benefits extend themselves further into better health care, more cultural opportunities, diversity, creativity, innovation and better quality of life.

However, there are negative externalities associated to this favorable setting. In effect, urban atmospheric pollution and traffic congestion are part of the landscape in most megapolis; stress and frustration have a definite negative impact on productivity and welfare. For instance, some quantitative estimates for these externalities based merely on wasted time, place annual values ranging from 272 to 1000 USD million for Bangkok.

Intuitively, it may be construed that these losses stem from inefficiencies in city morphology; an undesirable macro-behavior resulting from individual micro-motives, supposedly following a rational optimization process. This difficulty in reconciling individual rationality with the observed aggregate outcome was characterized by Thomas C. Schelling [8] more than two decades ago, leading to active Economics research in the area of artificial agents. As will be seen further down, this approach appears in most of the contemporary research on urban morphology, where the concept of artificial agents takes a formal structure based on cellular automata.

The regulatory response to deal with this problem consists in urban zoning, that is to prescribe