Abstract
After a brief introduction summarising the dominant approach to development of risk maps and their relationship to the conceptual approach used in this project, the chapter details the empirical procedure used for calculating industrial hazardousness maps. This methodology measures the sum of potential hazard in a given geographical area, using an algorithm to extend the influence of the potential hazard of each industry to the surrounding area, also overlapping the effects of various industries within an area of influence. This allows the location of areas of potential hazardousness due to the cumulative effects of small and medium-sized firms in each area that had not been identified by previous methodologies based only on the size or potential impact of individual companies.
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Notes
- 1.
Environmental justice literature can be classified into two main streams: (a) Research focused on the pattern of environmental hazard distribution, in other words, whether vulnerable populations are disproportionately affected by environmental threats (e.g. US GAO 1983). (b) Analysis of temporal/spatial patterns of causality of environmental hazard, in other words whether vulnerable populations attract technological hazards or whether they follow industrial pollution in their search for jobs and access to infrastructure such as electricity and roads (e.g. Been and Gupta 1997).
- 2.
In Buenos Aires Province, LEC values are assessed by the Environmental Agency which classifies industries into categories (Type 1, Type 2, Type 3) according to their increasing LEC. However, LEC values were not available for other provinces in Argentina, nor for Spain and Bolivia. Therefore it was necessary to develop an alternative methodology that allows calculation of the weighting factor and categories. The methodology calculates level of complexity of each industry using an algorithm that estimates factors of emission per industrial sector (See Chapter 6).
- 3.
First category industries are considered harmless; therefore they are not generators. Second category includes small and medium size generators, Third category includes large generators.
- 4.
Mexico records emissions of conventional pollutants, sector of production and number of employees for approximately 6000 plants. However, no information on plant location is available.
- 5.
The algorithm produced by Dasgupta and Wheeler (2001) is based on mean real values of pollution emitted per province. However, these means come from aggregate data that do not take into account regional and local variations due to factors of regional/local governability. Consequently, on applying this algorithm to individual firms we assume the firm under analysis follows the behaviour of the average firm, thus we are estimating a potential rather than real hazardousness.
- 6.
The Pareto Principle applied to industrial risk assessment broadly states that the top 20% worst polluters of any population of industries cause 80% of the total population’s negative effect.
References
Aceves-Quesada, F., Díaz-Salgado, J., & López-Blanco, J. (2006). Vulnerability assessment in a volcanic risk evaluation in Central Mexico through a multi-criteria-GIS approach. Natural Hazards, 40(2), 339–356.
Bankoff, G., Frerks, G., & Hilhorst, D. (2004). Mapping vulnerability: Disasters, development and people. London: Earthscan.
Basta, C., Neuvel, J. M. M., Zlatanova, S., & Ale, B. (2007). Risk-maps informing land-use planning processes. A survey on the Netherlands and the United Kingdom recent developments. Journal of Hazardous Materials, 145, 241–249.
Been, V., & Gupta, F. (1997). Coming to the nuisance or going to the barrios? A longitudinal analysis of environmental justice claims. Ecology Law Quarterly, 24, 1–56.
Bosque Sendra, J., Díaz Muñoz, M., Gómez Delgado, M., Rodríguez Durán, A., & Rodríguez Espinosa, V. (2000). Sistemas de Información Geográfica y Cartografía de Riesgos Tecnológicos. El caso de las Instalaciones para la gestión de residuos en Madrid. Industria ymedio ambiente, 1, 315–325.
Bosque Sendra, J., Díaz Castillo, C., Díaz Muñoz, M., Gómez Delgado, M., Gónzalez Ferreiro, D., Rodríguez Espinosa, V., et al. (2004). Propuesta metodológica para caracterizar las áreas expuestas a riesgos tecnológicos mediante SIG. Aplicación en la Comunidad de Madrid. GeoFocus (Artículos) 4, 44.
Bowen, W. (2002). An analytical review of environmental justice research. What do we really know? Journal of Environmental Management, 29, 3–15.
Christou, M. D., & Mattarelli, M. (2000). Land-use planning in the vicinity of chemical sites: Risk-informed decision making at a local community level. Journal of Hazardous Materials, 78, 191–222.
Cozzani, V., Bandini, R., Basta, C., & Christou, M. D. (2006). Application of land-use planning criteria for the control of major accident hazards: A case-study. Journal of Hazardous Materials, 136, 170–180.
Dasgupta, S., & Wheeler, D. (2001). Small plants, industrial pollution and poverty. In R. Hillary (Ed.), Small and medium-sized firms and the environment (pp. 289–304). Sheffield: Greenleaf Publishing.
EPA – Environmental Protection Agency. (1998). Risk Assessment Forum. Guidelines for Ecological Risk Assessment, EPA/630/R-95/002F.
Fairhurst, S. (2003). Hazard and risk assessment of industrial chemicals in the occupational context in Europe: Some current issues. Food Chemical Toxicology Journal, 41, 1453–1462.
Lara-Valencia, F. A., Harlow, S., Lemos, M. C., & Denman, C. (2009). Equity dimensions of hazardous waste generation in rapidly industrialising cities along the United States-Mexico border. Journal of Environmental Planning and Management, 52(2), 195–216.
Lirer, L., & Vitelli, L. (1998). Volcanic risk assessment and mapping in the Vesuvian area using GIS. Natural Hazards, 17, 1–15.
Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature, International Journal of Geographical Information Science, 20(7), 703–726.
Meyer, V., Scheuer, S., & Haase, D. (2009). A multicriteria approach for flood risk mapping exemplified at the Mulde River, Germany. Natural Hazards, 1, 17–39.
Sengupta, S., & Patil, R. S. (1996). Assessment of population exposure and risk zones due to air pollution using the geographical information system. Computers, Environment and Urban Systems, 20(3), 191–199.
Ulberich, A. (2000). Niveles de Riesgo Ambiental Derivados de la Actividad Industrial. Estudio de Caso. Ciudad de Tandil, Buenos Aires, Argentina. Primer Congreso de la Ciencia Cartográfica y VIII Semana Nacional de la Cartografía. Buenos Aires.
US GAO. (1983). Siting of hazardous waste landfills and their correlation with racial and economic status of surrounding communities. Washington, DC: United States General Accounting Office.
Williamson, D., McLafferty, S., Goldsmith, V., Molenkopf, J., & McGuire, P. (1999). A better method to smooth crime incident data. Revista ArcUser, January–March, Chicago: ESRI Press.
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López, S.D., Vazquez-Brust, D.A. (2012). Evaluating the Firm’s Environmental Hazardousness: Methodology. In: Vázquez-Brust, D., Plaza-Úbeda, J., de Burgos-Jiménez, J., Natenzon, C. (eds) Business and Environmental Risks. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2742-7_4
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