Socially-differentiated urban metabolism methodology informs equity in coupled carbon-air pollution mitigation strategies
Insights from three Indian cities
Bibliographic Data
| ID | 15545850 |
|---|---|
| Authors | Ajay Singh Nagpure (0000-0002-4817-7329, University of Minnesota), Kangkang Tong (0000-0002-8515-4812, Princeton University), Anu Ramaswami (0000-0002-0476-2315, University of Minnesota, corresponding author) |
| Year | 2022 |
| Volume | 17 |
| Issue | 9 |
| Pages | 094025-094025 |
| Publication date | 2022-08-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac881e |
| OpenAlex | W4292421493 |
| Language | EN |
| Citations received | 1 |
| References cited | 5 |
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
Agricultural economics · Air pollution · Business · Civil engineering · Economics · Equity (law · Greenhouse gas · Natural resource economics · Political science · Renewable energy · Urban density · Urban metabolism · Urban planning · Engineering · Environmental Impact and Sustainability · Environmental Science · Sustainability and Ecological Systems Analysis · Urban Transport and Accessibility · Ecology
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |