Socially-differentiated urban metabolism methodology informs equity in coupled carbon-air pollution mitigation strategies: insights from three Indian cities

Three case study cities, located in different geographies, are Delhi (National Capital Territory), Coimbatore (Tamil Nadu-State), and Rajkot (Gujarat-State). The cities varying by population sizes, employment, household income, expenditure, and levels of basic infrastructure services such as clean water and cooking fuels (table 1 and SI) and were selected for their diversity and availability of key infrastructure end-use data (e.g. residential, commercial, and industrial categories) (see SI and tables SI1–SI4).

2.1.1. Representing inequality in household infrastructure provision and use

2.1.2. Community-wide multi-sector data, incorporating households, commercial and industrial activities

2.1.3. Bottom-up metabolic model verification

To assess baseline GHG emissions associated with multiple infrastructure provisioning systems in a city, we applied a transboundary community-wide infrastructure-based carbon footprinting (Scope 1 + 2 + 3) approach, identified in a recent consensus article to be well-suited to inform community-wide zero-carbon urban infrastructure transitions [41]. Urban infrastructure provisioning systems included in this paper are energy supply, transportation, water, sanitation, MSW, and building construction materials (dominated by cement). The community-wide infrastructure-footprinting approach is consistent with advanced GHG protocols developed by practitioners [26, 42] and researchers [41, 43–45]. The method accounts for emissions arising from the use of a sector (e.g. energy use, mobility, building construction etc), and tracing lifecycle emissions across the use phase (e.g. using cooking fuels, driving a car, using electricity, constructing a home) to upstream/supply chain production of electricity, petrol fuels, construction materials at power plants, refineries, and cement factories, respectively, and further extraction of fuels/minerals from mining operations [42, 46, 47]. Aligning with the scope concept, emissions from in-boundary emission sources are called Scope 1. Scope 2 includes GHGs embodied in imported grid-supplied electricity, heat, steam, and/or cooling. Scope 3 includes transboundary lifecycle GHGs embodied in other upstream infrastructure supply chains serving cities. In this paper, we limit upstream Scope 3 GHGs to powerplants, cement factories, and oil refineries which substantially dominate life-cycle energy use and GHG emissions of producing electricity cement and petrol fuels [48]; further upstream accounting of GHGs was limited by data unavailability in India.

Equation (1) shows the computation of the transboundary infrastructure GHG emission footprints (${\text{TBI}}{{\text{F}}^{GHG}}$) differentiated by different users within cities k, including by different household segments and residential and commercial, as well as infrastructure sectors i.

$$\begin{align}{\text{TBI}}{{\text{F}}^{{\text{GHG}}}} &= \sum\limits_i {\sum\limits_k M } EFA{\text{ }}use{{\text{ }}_{i,k}}\nonumber\\ &\quad*(EF_{{\text{GHG}},i,k{\text{ }}use}^{\,{\text{IB}}} + EF_{{\text{GHG}},i,{\text{ production}}}^{\,{\text{IB or TB}}}){\text{ }}\end{align} \tag{ 1 }$$

where MEFA use represents direct community-wide material, energy flow, and use of various infrastructure i (such as VKT, water/wastewater, MSW generation, and burning) by user category k (tables 1–5). GHG emission factors are represented for in-boundary use activities, $EF_{{\text{GHG}},i,k{\text{ }}use}^{{\text{IB}}}$ (e.g. fossil fuel combustion), as well as $EF_{{\text{GHG}},i,{\text{ production}}}^{{\text{IB or TB}}}$ representing production of infrastructure services that may be produced inside or outside the city boundary. This methodology is standard and reported in several practitioners and research papers [43, 44, 46].

India-specific IPCC emission factors were used (tables SI7–SI11), except for biomass burning, which is assumed by the IPCC to have no net CO₂ emissions (carbon neutral). Following recent debates on the literature [49] and European Union guidance on wood-burning [50] given (un)sustainable regrowth of harvested biomass, we applied a factor of 25% to CO₂ emissions from wood-burning based on India data on the carbon content of firewood (table SI7) [51], with ∼75% assumed to be regrown [52].

To address co-beneficial local PM2.5 reduction, we quantify local PM2.5 emissions arising from the same community-wide infrastructure use activities in the GHG footprinting approach. Since PM2.5 pollution in cities is typically dominated by local/proximal sources [53, 54], we focused on in-boundary (Scope 1) PM2.5 emission sources using a well-established city-level emission inventory approach used in India [55] and by the US-EPA [56]. This PM2.5 inventory approach was appropriate to answer our question on local PM2.5 emission reduction and in- and trans-boundary GHG mitigation co-benefits from city-scale infrastructure strategies of interest to cities. Using similar notation as in equation (1) for sectors i and users k, in-Boundary PM2.5 emissions (IBE) were computed as:

$$\begin{equation}{\text{ }}IB{E^{{\text{PM}}2.5}} = \sum\limits_i {\sum\limits_k M } E{\text{ }}us{e_{i,k}}*EF_{{\text{PM}}2.5,i,k{\text{ }}use}^{{\text{IB}}}\end{equation} \tag{ 2 }$$

where EF now represents the PM2.5 emission factors for activities and fuel consumption for the different infrastructure sector. In cases of unavailability of local emissions factors, data from other South Asian countries [26, 44–48, 53, 54] were used and are available in SI (tables SI7–SI11).

Based upon baseline emissions computed in equations (1) and (2), we conducted a what-if analysis of 11 city-level policies that have potential for PM2.5 and GHG reduction (table 7), covering interventions in transportation, household energy use, industrial energy use, construction, and MSW sectors. The first ten policy strategies are derived from the Indian government's policy proposals detailed in the SI. In addition, we proposed one policy strategy (Policy 11) based on the results of the differentiated metabolism data developed in this paper.

The impact of these policies on PM2.5 and GHG reduction was quantified either by directly applying a reduction rate to the relevant flows or emission factors, with respect to baseline emissions, or implementing a fuel-switching model.

For modeling fuel-switching of cooking fuels in Policy 5, we computed equivalent energy 'delivered to the pot,' using stove efficiency and calorific value of fuels compiled by the EPA based on India-specific efficiencies [57], comparing LPG as a substitute for kerosene or biomass fuels including firewood and dung cake. Using firewood as an example, the amount of LPG ($LP{G_{\text{substitute}}}$) needed to substitute for the amount of firewood use in the baseline ($FW$) can be calculated as:

$$\begin{equation}LP{G_{{\text{substitute}}}} = \frac{{FW*C{V_{{\text{fw}}}}*{\mu _{{\text{stove, fw}}}}}}{{C{V_{{\text{LPG}}}}*{\mu _{{\text{stove,LPG}}}}}},\end{equation} \tag{ 3 }$$

$C{V_{{\text{fw}}}}$ is the calorific value of firewood and $C{V_{{\text{LPG}}}}$ is the calorifc value. ${\mu _{{\text{stove, fw}}}}$ is the wood stove efficiency and ${\mu _{{\text{stove, LPG}}}}$ is LPG stove efficiency. Table SI7 provides India-specific wood and stove parameters used in equation (3), derived from [51].

Results from the quantitative analyses were then used to inform equitable design of infrastructure policies and solutions.

Figure 2 demonstrates multiple infrastructure service provisioning by household income and figure 3 presents the differentiated local PM2.5 emission contributions by household income levels. The top 20% households (with highest income) have disproportionately large impacts on in-boundary PM2.5 emissions from transportation in Delhi and Coimbatore (50%–60% of total in-boundary household transportation emissions) relative to contributions from other income strata (figure 3). In contrast, PM_2.5 from cooking fuel used by the 20% lowest-income households is the largest in all three cities, ranging from 96%–99% of total cooking fuel-related PM_2.5 emissions in the residential sector. For total in-boundary PM_2.5 emissions from all infrastructure uses by households (figure 3), the top 20% (highest-income) and the bottom 20% (lowest-income) households by income make the following contributions to PM_2.5 emissions in the different cities: 42% (top-20%) vs. 14% (bottom-20%) in Delhi; 47% (top-20%) vs. 21% (bottom-20%) in Coimbatore largely due to more use of personal vehicles by high-income homes; while the trend is switched in Rajkot as 32% (top-20%) vs. 41% (bottom-20%) due to the prevalence of polluting cooking fuels in poorer homes, (figures 2 and 3).

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These results indicate that the largest contributors to in-boundary PM2.5 vary by city types. As income, wealth and infrastructure improve, often with city size, largest contributors to local PM2.5 transition from polluting cooking fuels to motorized transport. Our model results, derived for the first time from bottom-up data, yield results similar to overall city trends represented by others, e.g. [58].

When evaluating the share of PM2.5 emissions from households along with commercial and industrial users (figure 4), it is striking to observe that total in-boundary PM_2.5 emissions from the top 20% households (highest-income) can be equivalent or greater than total emissions from either all industrial users or all commercial users. For example, in Delhi, 21% of total PM_2.5 emissions are from the top 20% households, similar to all industrial activity (also contributing 21%), with these numbers being 28% in Coimbatore. In Rajkot, the top 20% of households contribute 18% of all emissions, comparing to 28%–36% from industry users and 8%–12% from commercial users.

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Our GHG results also show that the contribution to total trans-boundary emissions from top 20% households (highest income) is equivalent to or greater than the contribution from either industrial users or commercial sector (figure 5). For example, in Delhi, 25% of the total GHG emissions are contributed by the top 20% households, whereas the commercial and industrial sectors only contribute 24% and 19%, respectively. In Coimbatore, top 20% households contribute 25% of GHG emissions, while 40% of GHG emissions are from industrial users and 13% from the commercial sector.

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Out of the eleven strategies evaluated (table 7), only three (Policies 4, 5 & 11) yielded a significant reduction (>2%) of both GHG and PM_2.5 emissions in all three cities, meaning they have potential for GHG and PM_2.5 mitigation co-benefits (figure 6).

• (a)  
  Modest 10% efficiency improvements among the wealthiest 20% households as well as among industry and commercial sectors (Policy 11 in table 7), reduce 7.1%, 8.4%, 6.2% in-boundary PM2.5 emissions, and 6.4%, 7.6%, 6.7% GHG footprints in Delhi, Coimbatore, and Rajkot, respectively. Given that the highest-income households contributed a large proportion of community-wide PM2.5 and GHG emissions, focusing on energy efficiency and conservation among these households is important to achieve co-benefits. Equitable policy designs would address whether the highest-income households, who contribute the most to pollution, should receive incentives for energy conservation (inequitable) or if higher energy rates for higher energy users would be more equitable. In the latter scenario, the additional revenue generated can be earmarked to support low-income households, particularly those who are too poor to afford clean cooking fuels like LPG (discussed next). Furthermore, behavioral nudging using non-price incentives such as social norms [59, 60] can be more suitable to promote efficiency and conservation behaviors among wealthy households.
• (b)  
  Phasing out all biomass and kerosene use within cities from all users (households, commercial and industrial sectors) through fuel substitution to LPG (Policy 5 in table 7) _, reduces 11.8%, 58.4%, and 50.3% in-boundary PM2.5, and, 2.1%, 12.9%, and 11.5% GHG footprint in Delhi, Coimbatore, and Rajkot, respectively. This strategy would impact the poorest homes (figure 2), which already deal with a lack of infrastructure services. Subsidized or free access to clean cooking fuels to low-medium income households would be an important equity consideration for this policy; likewise, banning the use of firewood in industry must also consider that many industries, particularly those using firewood, such as food preparation, may disproportionately impact poorer workers. This policy is also expected to yield substantial health risk mitigation benefits for the impacted population [20, 57, 61] largely concentrated among the poorest households.
• (c)  
  Replacing all gas and diesel vehicles with renewable electric vehicles, is a highly ambitious future target, estimated to reduce 7.0%, 10.0%, and 13.0% of PM2.5 emissions and, 3.7%, 6.9%, and 7.2% of GHG footprints in Delhi, Coimbatore, and Rajkot, respectively. This strategy is expected to largely benefit the top 40% of households (figure 2). Shifting to electric vehicles is expected to already provide cost savings [62]; thus, market forces may suffice to enable this transition. Offering rebates for electric vehicles may create free ridership concerns while offering such rebates to high-income households owning cars can exacerbate inequities. More importantly, equity in electric charging infrastructure should be considered, prioritizing charging infrastructure for electric vehicles in middle-income groups using two-wheeler vehicles over electric car charging.

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Last, looking across policies addressing high levels of energy consumption by wealthy households, along with subsidies to promote LPG use among the poorest households, can be complementary. Together, they can advance equity and reduce both PM2.5 and GHG emissions from Indian cities.