Combining Indicators: Maximizing Returns In Mexico’s Forex Strategies

Combining Indicators: Maximizing Returns In Mexico’s Forex Strategies – Development of reverse logistics modeling for end-of-life lithium-ion batteries and its impact on recycling reliability—a case study to support an end-of-life electric vehicle battery strategy in Canada

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Combining Indicators: Maximizing Returns In Mexico’s Forex Strategies

Combining Indicators: Maximizing Returns In Mexico's Forex Strategies

Using sensitivity analysis and spatial clustering to determine vulnerability to potentially toxic elements in a semiarid city in northwestern Mexico.

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By Jimena García-Burgos Jimena García-Burgos Scilit Google Scholar 1, Yosune Miquelajauregui Yosune Miquelajauregui Scilit Google Scholar 2, * , Elizabeth Vega Elizabeth Vega Scilit, Na Google Lite Preprints. org Google Scholar 4, Alejandro Ruz-Olivarez Alejandro Ruz-Olivarez Skilled Preprints. Martinez Scilit Google Scholar 8, Louise Hayes Louise Hayes Scilit Google Scholar 9, Lindsay Bramwell Lindsay Bramwell Scilit Google Scholar 4, Monica Jaimes-Palomera Monica Jaimes-Jamika Jaimes-Jaclit. Entwistle Jane Entwistle Scilit Google Scholar 4, Juan Carlos Núñez-Enríquez Juan Carlos Núñez-Enríquez Scilit Google Scholar 10, Antonio Portas M Richaral Scilit Google Scholar 9

Laboratorio de Ecología, Unidad de Biología de la Conservación, Parque Científico y Tecnológico de Yucatán, Universidad Nacional Autónoma de México (UNAM), Mérida 97302, Mexico

Combining Indicators: Maximizing Returns In Mexico's Forex Strategies

Laboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecology, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico

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Instituto de Ciencias de la Atmosfera y Cambio Climático, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico

Unitat de Investigación Medica en Epidemologia Clinica, UMAE Hospital de Pediatria Siclo XXI, Instituto Mexicano del Seguro Social (IMSS), Mexico City 06720, Mexico

Received: 5 September 2022 / Revised: 10 November 2022 / Accepted: 11 November 2022 / Published: 18 November 2022

Air pollution is one of the world’s most challenging global sustainability problems. Approximately 90% of the global population lives in areas that exceed acceptable air pollution levels, according to the World Health Organization’s air quality guidelines. However, socially disadvantaged groups are disproportionately located in areas exposed to high levels of air pollution. Understanding the relationship between risk exposure to air pollutants and the underlying socio-economic factors that determine risk is central to sustainable urban planning. The aim of this study was to investigate environmental inequalities in Mexico City, specifically the spatial association between air pollutants and socioeconomic status (SES) indicators. We propose that SES indicators are expected to relocate vulnerable individuals and groups to more polluted areas. To test this hypothesis, we used data from government records for 2017–2019 and performed spatial interpolations to examine the spatial distribution of pollution criteria. We conducted spatial autocorrelations of air pollutants and SES indicators using the bivariate Moran’s I index. Our findings provide strong evidence for spatial heterogeneity in air pollution exposure in Mexico City. We found that socially disadvantaged areas located in the southern periphery of Mexico City were exposed to higher ozone concentrations. Conversely, wealthy areas concentrated in the city center were exposed to higher concentrations of nitrogen dioxide and carbon monoxide. Our findings highlight the need for policy-based approaches that consider not only the geographic variability and meteorological dynamics associated with air pollution exposure, but also the management of socioeconomic risk factors aimed at reducing air pollution exposure and potential health impacts.

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Air pollution is recognized as one of the greatest global sustainability problems of the 21st century. According to the World Health Organization (WHO), about 4.2 million deaths per year are caused by air pollution [1]. Air pollution refers to the presence of small particles, chemical substances or gases in the atmosphere that are harmful to the health of human populations and other organisms [2]. Anthropogenic activities including transportation, waste incineration, solid fuel burning, industrial and agrochemical activities emit large amounts of air pollutants (hereafter “air pollutants”) such as carbon monoxide (CO), particulate matter (both PM and PM).

And volatile organic compounds in the presence of sunlight [3]. It is estimated that approximately 90% of the global population lives in areas where air pollution exceeds acceptable air pollution levels according to World Health Organization (WHO) air quality guidelines [4, 5]. However, socially disadvantaged groups, such as low socioeconomic status (SES), are disproportionately located in areas exposed to high levels of air pollution, which is referred to as environmental injustice or environmental inequality [6]. , 7, 8, 9].

Although the terms environmental injustice and environmental inequality are occasionally used interchangeably in the literature, they have distinct meanings [10, 11]. Environmental justice is an ethical concept with political connotations that includes distributive (i.e., the equitable distribution of resources and capabilities to implement strategies), procedural (i.e., the extent to which actors participate in decision-making processes) and precautionary (i.e., attitudes, values, and judgments regarding environmental risks) levels of justice [ 11, 12, 13]. Environmental inequality, on the other hand, is primarily a quantitative concept that involves measuring and comparing exposure risks among different individuals and groups [ 11 , 12 ]. In this paper, we focus on environmental inequality understood as disparities in air pollution exposure relative to SES indicators.

Combining Indicators: Maximizing Returns In Mexico's Forex Strategies

In the context of urban air pollution, exposure risks arise from hazards that include emissions from transport, industrial and domestic activities, forest fires and open burning [2]. However, in the vulnerability literature, it is emphasized that risk (i.e., the probability of an adverse event) emerges through interactions between environmental conditions and socio-political factors [ 14 , 15 ]. This analytical framework recognizes that stakeholders’ actions and responses to risks can also act as key determinants of risk [14, 15]. From this perspective, air pollution risk exposure in urban areas is not only the result of environmental risks, but also shaped by socio-political processes including infrastructure and technology investment decisions (i.e. urban settlements, roads, highways, urban services, technology [16]) as well as mitigation and adaptation decision-making [13]. .

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Environmental assessments are important analytical tools for effective exposure risk management, generally considered centrally planned tools aimed at supporting urban planning [17]. However, appropriate environmental assessments for sustainable urban planning should provide evidence of systematic inequalities in the distribution of risk exposure [8, 10, 17]. This need has encouraged the development of integrative approaches capable of addressing disparities in environmental risk exposure among individuals and groups with different SES [ 10 , 14 ]. For example, environmental inequality research in the context of air pollution has mainly been conducted in cities in North America, Europe, Asia, and Oceania [17]. The results of these studies consistently suggest that exposure to air pollution increases as SES decreases [ 18 , 19 , 20 , 21 , 22 ]. Only those from Europe [23] found an inverse relationship between SES and air pollution in historic city centers in Europe, which typically experience high levels of atmospheric pollution that are unaffordable to individuals of low SES [23]. However, studies on cities in industrialized countries in Africa, Latin America, and the Caribbean, which often face uncontrolled urban expansion, rampant population growth, increasing automobile emissions, and consequently high air pollution, are still scarce [17, 19, 24].

Despite these efforts, we still lack a complete understanding of the relationship between risk exposure to air pollution and the underlying socio-economic factors that determine risk. This study aims to fill this knowledge gap by assessing whether individuals and groups of low SES are exposed to a higher burden of air pollution exposure – in Mexico City – one of the most important economic centers of Latin America. Therefore, the aim of this study is to investigate environmental inequalities in Mexico City, specifically the spatial correlation between air pollutants and SES indicators. We present indicators of SES

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