Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India

Abstract

Climate change and rapid urbanization currently pose major challenges for equitable development in megacities of the Global South, such as Delhi, India. This study considers how urban social inequities are distributed in terms of burdens and benefits by quantifying exposure through an urban heat risk index (UHRI), and proximity to greenspace through the normalized difference vegetation index (NDVI), at the ward level in Delhi. Landsat derived remote sensing imagery for May and September 2011 is used in a sensitivity analysis of varying seasonal exposure. Multivariable models based on generalized estimating equations (GEEs) reveal significant statistical associations (p < 0.05) between UHRI/NDVI and several indicators of social vulnerability. For example, the proportions of children (β = 0.922, p = 0.024) and agricultural workers (β = 0.394, p = 0.016) are positively associated with the May UHRI, while the proportions of households with assets (β = −1.978, p = 0.017) and households with electricity (β = −0.605, p = 0.010) are negatively associated with the May UHRI. In contrast, the proportions of children (β = 0.001, p = 0.633) and agricultural workers (β = 0.002, p = 0.356) are not significantly associated with the May NDVI, while the proportions of households with assets (β = 0.013, p = 0.010) and those with electricity (β = 0.008, p = 0.006) are positively associated with the May NDVI. Our findings emphasize the need for future research and policies to consider how socially vulnerable groups are inequitably exposed to the impact of climate change-related urban heat without the mitigating effects of greenspace.

Social vulnerability indicators to the Health Impacts of Climate Change: A Scoping Review

“…Language barriers were identified as a source of social vulnerability to climate change, with 15 articles addressing linguistic isolation or lack of fluency in the primary local language (22, 72, (28,41,47,53,59,168,171,187,188). Linguistic barriers thwart adaptive behaviors during extreme weather events as warnings are not fully understood (72,73,114,116), with the vulnerability particularly higher in linguistically heterogenous areas (85).…”

“…Studies have identified lack of access to green or blue space (38,39,45,48,49,58,62,67,69,95,105,112,162,188,190,191), building density and impervious surface (95,105,116,145,186,192,193), poor or polluted living environment (67,68,111,118,136), and the social distribution of access to public cooling facilities or urban heat refuges (52,189,194) as an social vulnerability factor that measures the aspect of living and built environment. Widely used indicators of green space include the normalized difference in vegetation index which measures that amount of vegetation vs non-vegetation surface coverage in an area (39,105,162,188), distance to parks and other green space (69), the relative abundance of impervious surfaces (e.g. the normalized difference built-up index) as a proxy for the lack of green space (188), and the percentage of non-green space (38).…”

“…Widely used indicators of green space include the normalized difference in vegetation index which measures that amount of vegetation vs non-vegetation surface coverage in an area (39,105,162,188), distance to parks and other green space (69), the relative abundance of impervious surfaces (e.g. the normalized difference built-up index) as a proxy for the lack of green space (188), and the percentage of non-green space (38). Green and blue space is associated with relatively lower temperatures compared to urban spaces with little or no green space or no access to blue space (39,49,67,112,162).…”

Unequal and unjust: The political ecology of Bangkok’s increasing urban heat island

“…Other studies have similarly found that areas in the US which were redlined and thus where non-white populations are overrepresented have higher land surface temperatures (Li et al, 2022;Wilson, 2020). Such situations recur in Asian cities (Mabon, 2020;Mitchell et al, 2021). Living in poor quality housing, combined with limited or non-existing cooling, results in lower-income households suffering more from heat waves (Harlan et al, 2007).…”

A Literature Review of Cooling Center, Misting Station, Cool Pavement, and Cool Roof Intervention Evaluations

“…As listed by the Environmental Protection Agency (USA EPA), these interventions may include: trees/vegetation, green and cool roofs, cool pavements, and broadly improved infrastructure that invests in 'greener' practices [11]. Cities are also investing in tackling disparities to heat exposures, some of which are the result of historic neighborhood disinvestment and discriminatory housing practices that affect low-income households and specific racial and ethnic groups (Hispanic and non-Hispanic/Black race/ethnicities) [12][13][14][15][16][17]. However, little is known about intervention efficacy or the optimal metrics to evaluate intervention impacts on temperature exposures and health.…”