Greenwashing? The global rise of sustainability and forced housing displacement in Fortaleza, Brazil

Fortaleza is a lesser-known city of Latin America with over 3.5 million inhabitants, over one-third of whom reside in substandard housing. The city is currently undertaking transportation development projects that are driven by a broader discourse of sustainability, however the outcomes from these projects resemble those associated with urban renewal including slum clearance and public investment for private gain. This study analyzes whether transit projects rooted in sustainability are meeting the triple bottom line of social, economic, and environmental sustainability, with a focus on equitable impacts to both formal and informal settlements. Specifically, this thesis seeks to uncover why infrastructure investments justified as increasing the capacity for sustainability continue to result in the systematic displacement of low-income informal households. To examine the relationships between sustainable infrastructure investments and outcomes including forced displacement, a mixed-methods approach combining a spatial analysis to verify disproportionate impact and interview and document analyses are employed to reveal unique displacement threats. Findings suggest that these projects are causing disproportionate, negative impact on the urban poor of Fortaleza, and that the experiences of individuals who face that negative impact via threats of displacement manifest in patterns which can be corroborated by documentation. Further, findings suggest that these projects are justified and continued under the justification of sustainability, but social sustainability bias, a crucial pillar to broader sustainability, is missing from the picture. This is important for urban planners and policy makers, more generally, as the systemic biases against the urban poor and marginalized groups within the urban poor are not only being perpetuated, but are being further rationalized by a cooptation of an otherwise promising concept: sustainability

Is a clean river fun for all? Recognizing social vulnerability in watershed planning

Watershed planning can lead to policy innovation and action toward environmental protection. However, groups often suffer from low engagement with communities that experience disparate impacts from flooding and water pollution. This can limit the capacity of watershed efforts to dismantle pernicious forms of social inequality. As a result, the benefits of environmental changes often flow to more empowered residents, short-changing the power of watershed-based planning as a tool to transform ecological, economic, and social relationships. The objectives of this paper are to assess whether the worldview of watershed planning actors are sufficiently attuned to local patterns of social vulnerability and whether locally significant patterns of social vulnerability can be adequately differentiated using conventional data sources. Drawing from 35 in-depth interviews with watershed planners and community stakeholders in the Milwaukee River Basin (WI, USA), we identify five unique definitions of social vulnerability. Watershed planners in our sample articulate a narrower range of social vulnerability definitions than other participants. All five definitions emphasize spatial and demographic characteristics consistent with existing ways of measuring social vulnerability. However, existing measures do not adequately differentiate among the spatio-temporal dynamics used to distinguish definitions. In response, we develop two new social vulnerability measures. The combination of interviews and demographic analyses in this study provides an assessment technique that can help watershed planners (a) understand the limits of their own conceptualization of social vulnerability and (b) acknowledge the importance of place-based vulnerabilities that may otherwise be obscured. We conclude by discussing how our methods can be a useful tool for identifying opportunities to disrupt social vulnerability in a watershed by evaluating how issue frames, outreach messages, and engagement tactics. The approach allows watershed planners to shift their own culture in order to consider socially vulnerable populations comprehensively.Illinois-Indiana Sea Grant NA14OAR4170095Ope

Interview classification and definitions of social vulnerability.

<p>Interview classification and definitions of social vulnerability.</p

Measure used to operationalize the temporal definition of social vulnerability.

<p>Components (C) elicited for each decade. Component names connote the attributes with theoretical links to high vulnerability. The directional effect (DE) indicates whether the initial component scores needed to be reversed so that higher values were associated with greater vulnerability. The percent of the variance in the underlying data explained by each component is also provided. Full PCA results are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196416#pone.0196416.s001" target="_blank">S1 Table</a>.</p

Flow diagram indicating methodological framework and resulting measures of social vulnerability.

<p>Gray arrows and boxes show intermediate data processing steps. Black arrows and boxes connote results.</p

Summary of measures of vulnerability developed in this paper and their uses to inform planning.

<p>Summary of measures of vulnerability developed in this paper and their uses to inform planning.</p

Descriptive table of social of vulnerability measures corresponding to maps in Fig 2.

<p>Descriptive table of social of vulnerability measures corresponding to maps in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196416#pone.0196416.g002" target="_blank">Fig 2</a>.</p

Short-term risk prediction after major lower limb amputation: PERCEIVE study

This multicentre cohort study of 537 patients evaluated the accuracy of preoperative predictions of outcomes by healthcare professionals and several relevant risk prediction tools. Surgeons and anaesthetists predicted 30-day outcomes after major lower limb amputation more accurately than most risk prediction tools. The best performing method of predicting mortality was a tool that incorporated healthcare professional estimation of risk.Background The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. Methods The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. Results Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). Conclusion Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation