TY - JOUR
T1 - Socially-differentiated urban metabolism methodology informs equity in coupled carbon-air pollution mitigation strategies
T2 - insights from three Indian cities
AU - Nagpure, Ajay Singh
AU - Tong, Kangkang
AU - Ramaswami, Anu
N1 - Funding Information:
This research work has been supported by the US National Science Foundation through a Partnership for International Research and Education (PIRE) Grant #1243535 and SRN Grant #1444745. We thank Emani Kumar, Ashish Rao-Ghorpade, Nagendran Nagarajan, Krishnan Sella, and Vandit Patel from ICLEI South Asia, India, Daqian Jiang, and Samuel Tabory from the University of Minnesota for their valuable assistance during data collection and writing.
Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - A differentiated urban metabolism methodology is developed to quantify inequality and inform social equity in urban infrastructure strategies aimed at mitigating local in-boundary PM2.5 and co-beneficially reducing transboundary greenhouse gas (GHG) emissions. The method differentiates community-wide local PM2.5 and transboundary GHG emission contributions by households of different income strata, alongside commercial and industrial activities. Applied in three Indian cities (Delhi, Coimbatore, and Rajkot) through development of new data sets, method yields key insights that across all three cities, top-20% highest-income households dominated motorized transportation, electricity, and construction activities, while poorest-20% homes dominated biomass and kerosene use, resulting in the top-20% households contributing more than three times GHGs as the bottom-20% homes. Further, after including commercial and industrial users, top-20% households contributed as much or more in-boundary PM2.5 emissions than all commercial OR all industrial emitters (e.g. Delhi’s top-20% homes contributed 21% of in-boundary PM2.5 similar to industries at 21%. These results enabled co-benefit analysis of various infrastructure transition strategies on the horizon, finding only three could yield both significant GHG and PM2.5 reductions (>2%-each): (a) Modest 10% efficiency improvements among top-20% households, industry and commercial sectors, requiring a focus on wealthiest homes; (b) Phasing out all biomass and kerosene use within cities (impacting poorest); (c) Replacing gas and diesel vehicles with renewable electric vehicles. The differentiated PM2.5 and GHG emissions data-informed social equity in the design of the three co-beneficial infrastructure transitions by: (a)-prioritizing free/subsidized clean cooking fuels to poorest homes; (b)-increasing electricity block rates and behavioral nudging for wealthiest homes; and, (c)-prioritizing electrification of mass transit and promoting electric two-wheelers ahead of providing subsidies for electric cars, where the free-rider phenomenon can occur, which benefits wealthiest homes. The methodology is broadly translatable to cities worldwide, while the policy insights are relevant to rapidly urbanizing Asia and Africa to advance clean, low-carbon urban infrastructure transitions.
AB - A differentiated urban metabolism methodology is developed to quantify inequality and inform social equity in urban infrastructure strategies aimed at mitigating local in-boundary PM2.5 and co-beneficially reducing transboundary greenhouse gas (GHG) emissions. The method differentiates community-wide local PM2.5 and transboundary GHG emission contributions by households of different income strata, alongside commercial and industrial activities. Applied in three Indian cities (Delhi, Coimbatore, and Rajkot) through development of new data sets, method yields key insights that across all three cities, top-20% highest-income households dominated motorized transportation, electricity, and construction activities, while poorest-20% homes dominated biomass and kerosene use, resulting in the top-20% households contributing more than three times GHGs as the bottom-20% homes. Further, after including commercial and industrial users, top-20% households contributed as much or more in-boundary PM2.5 emissions than all commercial OR all industrial emitters (e.g. Delhi’s top-20% homes contributed 21% of in-boundary PM2.5 similar to industries at 21%. These results enabled co-benefit analysis of various infrastructure transition strategies on the horizon, finding only three could yield both significant GHG and PM2.5 reductions (>2%-each): (a) Modest 10% efficiency improvements among top-20% households, industry and commercial sectors, requiring a focus on wealthiest homes; (b) Phasing out all biomass and kerosene use within cities (impacting poorest); (c) Replacing gas and diesel vehicles with renewable electric vehicles. The differentiated PM2.5 and GHG emissions data-informed social equity in the design of the three co-beneficial infrastructure transitions by: (a)-prioritizing free/subsidized clean cooking fuels to poorest homes; (b)-increasing electricity block rates and behavioral nudging for wealthiest homes; and, (c)-prioritizing electrification of mass transit and promoting electric two-wheelers ahead of providing subsidies for electric cars, where the free-rider phenomenon can occur, which benefits wealthiest homes. The methodology is broadly translatable to cities worldwide, while the policy insights are relevant to rapidly urbanizing Asia and Africa to advance clean, low-carbon urban infrastructure transitions.
KW - GHG footprints
KW - air pollution emission inventory
KW - co-benefits
KW - differentiated urban metabolism
KW - inclusive development
KW - inequality
KW - infrastructure
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U2 - 10.1088/1748-9326/ac881e
DO - 10.1088/1748-9326/ac881e
M3 - Article
AN - SCOPUS:85137649430
SN - 1748-9326
VL - 17
JO - Environmental Research Letters
JF - Environmental Research Letters
IS - 9
M1 - 094025
ER -