Scaling up Gas and Electric Cooking in Low- and Middle-Income Countries: Climate Threat or Mitigation Strategy with co-Benefits?

doi:10.21203/rs.3.rs-2011721/v2

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Scaling up Gas and Electric Cooking in Low- and Middle-Income Countries: Climate Threat or Mitigation Strategy with co-Benefits?

https://doi.org/10.21203/rs.3.rs-2011721/v2

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Nearly 3 billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths each year. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We use upstream and end-use emissions in a reduced-complexity emissions-based climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 mK global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM_2.5 by over 6 Mt (99%) by 2040, which would substantial lower health risks.

Climate Analysis and Modeling

clean cooking

LPG

energy transitions

climate impacts

Nearly 3 billion people, 40% of the world’s population, lack access to clean cooking fuels and technologies¹. Instead, they rely on polluting fuels like wood, charcoal, other solid biomass, kerosene, and coal². Commonly-used household stoves cannot burn these fuels efficiently and cleanly, resulting in exposure to products of incomplete combustion or “household air pollution” (HAP) that contributes to nearly 4 million premature deaths each year³. Polluting fuels also contribute to ambient air pollution⁴ and emit well-mixed greenhouse gases (GHGs) and short-lived climate forcers (SLCFs) that cause climate change⁵. Unsustainable harvesting of biomass fuels contributes to local environmental degradation⁶. Impacts of polluting fuel use fall disproportionately on women, adding to preexisting gender inequalities⁷. These challenges have persisted for decades despite thousands of programs and policies designed to facilitate a transition to cleaner cooking.

The global prioritization of clean cooking is reflected in the seventh Sustainable Development Goal (SDG7), which aims to achieve universal “access to affordable, reliable, sustainable and modern energy” by 2030⁸. 600 million people gained access to clean cooking fuels or technologies between 2010 and 2020¹. However, progress is uneven and some regions lag (SI Section 1). Over 75% of the population that gained primary access to clean cooking between 2010 and 2020 did so by adopting liquefied petroleum gas (LPG)⁹. LPG is well-suited to near-term scale-up¹⁰, and many LMIC governments have set ambitious targets to promote LPG adoption¹¹. Unlike electricity and piped natural gas, LPG does not require major investment in physical infrastructure for scale. It is clean at the point of use¹², and simple to use¹³. Electricity accounts for most of the remaining increase in primary access to clean cooking between 2010 and 2020. While access to electricity is increasing rapidly in LMICs, cooking with electricity is less common. The WHO estimates that just 10 LMICs have a majority of households using electricity as a primary cooking option⁹. Other clean cooking options like biogas, ethanol, solar thermal cookers, and biomass pellets burned in advanced stoves have gained traction in some niche markets, but globally they are far less prevalent than LPG and electric cooking.

Transitions from polluting to clean fuels and technologies results in large reductions in HAP exposure^14–16. However, the past growth and planned scale-up of LPG raises questions about trade-offs between mitigating the health impacts of polluting fuels and the potential climate impacts of large-scale increases in fossil fuel consumption. Though it is generally clean at the point of use, conventional LPG is a by-product of the petroleum and natural gas industries, with emissions arising from extraction, processing, and transport¹⁷. Grid-based electricity, completely clean at point of use, is derived primarily from fossil fuels in LMICs, and has similar issues^10,19.

Fuelwood, charcoal, and other biomass fuels are potentially renewable. However, a substantial fraction of current woodfuel consumption is unsustainable and causes declines in standing stocks of biomass⁶. In addition, biomass fuels emit more SLCFs than LPG or grid-based power (SI Section 8) and are detrimental for health due to the formation of products of incomplete combustion. Thus, a transition from polluting fuels to LPG or grid-based power could mitigate climate change through two mechanisms: lower consumption of unsustainable biomass and reduced SLCF emissions.

Previous work has quantified the global carbon footprint of biomass dependence at one point in time⁶, examined climate impacts of individual cooking choices⁵, and modeled the climate impacts of “shutting off” biomass demand^20,21. One analysis estimated that a transition to LPG in Cameroon resulted in net global cooling of 0.3-1.3 mK by 2100, depending on the levels of LPG adoption and sustainable biomass management²². Here we build on these previous studies by modeling LMIC-wide transitions to present the first global analysis of health and climate implications of transitions to clean cooking. We focus mainly on a transition to LPG, which is currently the dominant clean cooking option in most LMICs. However, we also include scenarios with increasing shares of grid-based electric cooking.

Future Household Energy Transitions

We use recent World Health Organization (WHO) analyses of cooking trends in LMICs to develop our business-as-usual (BAU) scenario⁹, which we compare to partial and full transitions to LPG and electricity for cooking. The scenarios are described in Table 1 with additional details provided in Methods and SI Sections 2 and 3. Figure 1 compares energy consumption, number of fuel users and energy consumed under BAU and a full transition to LPG (FT-LPG). By 2040, demand for polluting fuels like biomass, coal, and kerosene decrease by 25%, 96%, and 25% respectively. However, these patterns are not consistent across all regions. For example, BAU biomass demand decreases steeply in Asia/Pacific regions but increases in Africa. In addition, BAU charcoal consumption increases globally by over 125%, driven mainly by growth in urban consumption in Africa. Clean fuels also gain popularity under BAU: LPG consumption increases by 50% and electricity demand for cooking doubles.

Table 1: Descriptions of the scenarios developed for this analysis

Scenario name (abbreviation)	Description
Business as usual (BAU)	Future primary cooking fuels adoption rates follow recent country-specific trends as defined by WHO disaggregated by rural and urban populations⁹.
Intermediate transition (IT)	Adoption rates of LPG and electricity increase by 50% by 2040, displacing more polluting fuels than in BAU
Full transition to LPG (FT-LPG)	All polluting cooking fuels are displaced with LPG by 2040 but rate of electricity adoption proceeds as in BAU, resulting in an LPG dominant future.
Full transition to LPG and Electricity (FT-LE)	All polluting cooking fuels are displaced with grid-based electricity by 2040, but the rate of LPG adoption proceeds as in BAU, resulting in a balanced mix of LPG and electricity in the future.
Full transition to Electricity (FT-Elec)	All polluting cooking fuels and LPG are displaced with grid-based electricity by 2040, resulting in electricity dominant future.

In the FT-LPG scenario, polluting fuels are displaced by LPG, while demand for electricity increases at the BAU rate, which results in 125% increase in LPG consumption between 2018 and 2040 (Figure 1A). Other full transition scenarios show similar patterns but with more demand for electricity (Figure S4). In the intermediate transition (IT) scenario, demand for biomass and other polluting fuels decreases by less than 10%, while LPG and electricity consumption increase by a similar amount (Figure S4).

Changes in Emissions of Health-Damaging Pollutants

To gauge health impacts, we report changes in emissions of health-damaging pollutants including fine particulates (PM_2.5), carbon monoxide (CO), nitrogen oxides (NO_x) and sulfur oxides (SO_x). Here, and below, we refer to emissions from cooking fuels in the 77 countries included in our analysis. In 2018, approximately 7.6 Mt of PM_2.5 were emitted from cooking fuels (Figure 2A). Under the BAU scenario, this is projected to decline by 24%, driven by transitions from polluting fuels to LPG and electricity described above. Under the IT scenario, a modest increase in clean fuels adoption results in a 12% reduction of PM_2.5 emissions relative to BAU. Under full transitions to either LPG or electricity, PM_2.5 emissions from residential cooking are nearly eliminated.

CO emissions follow a similar pattern as PM_2.5 (Figure 2B). In 2018, we estimate that 71 Mt of CO was emitted from cooking fuels. Under the BAU scenario, CO emissions decrease 5% to 67 Mt by 2040 (CO declines less than PM_2.5 due to growth in charcoal consumption). In the IT scenario, 2040 CO emissions are ~10% lower than BAU, and in the full transitions to either LPG or electricity, CO emissions decrease by over 95%.

NO_x emissions result primarily from atmospheric nitrogen and increase with higher combustion temperatures²³. LPG, coal, and thermal power generation occur at higher temperatures than household biomass stoves and life-cycle NO_x emissions are higher from stoves using those fuels. Under the BAU scenario, NO_x emissions increase by ~20% between 2018 and 2040 and SO_x emissions decrease by a similar magnitude (Figure 2C and D). NO_x increases relative to BAU in both IT and FT-LPG scenarios because of increased LPG consumption and decreases in the FT-LE and FT-Elec scenarios, as LPG is displaced by electricity.

SO_x emissions are primarily from fuel-bound sulfur²⁴, which only occurs in trace amounts in biomass but can be several percent by weight in coal used in household stoves and power generation²⁵. Sulfur is also emitted from diesel-based transportation of household fuels. Therefore, SO_x emissions from household cooking occur mainly in countries where coal stoves and/or coal-fired power generation are common and follow a different pattern than NO_x. They are lower in all scenarios relative to BAU except for FT-Elec. In that pathway SO_x emissions are nearly 30% higher due to increased coal-based electricity consumption (see SI Section 10 for a discussion of regional variation in health-relevant emissions).

Emissions of Well-Mixed GHGs and SLCFs

Figure 3 shows fuel-specific emissions of well-mixed GHGs in 2018 and 2040 for each scenario. CO₂-equivalent units (CO₂e) represent the climate impacts of well-mixed GHGs (CO₂, CH₄, and N₂O). In 2018, total emissions of well-mixed GHGs from residential cooking was 1.14 Gt-CO2e. Under BAU, we project these to increase by 14%, to 1.30 Gt- CO₂e by 2040. Under IT, emissions decrease 1.2% relative to BAU (to 1.28 Gt- CO₂e) by 2040. More substantial decreases occur under the full-transition scenarios: 17%, 31%, and 47% for the FT-LPG, FT-LE, and FT-Elec scenarios respectively.

In 2018, there were ~1.2 Mt of BC emitted from household cooking. Emissions decrease to 1.0 Mt by 2040 in the BAU scenario. BC emissions decline by ~12% under the IT scenario and are nearly eliminated under full transitions to LPG and/or electricity. OC and NMVOC emissions follow similar patterns (see SI Section 10).

Impacts on Radiative Forcing and Temperature

Under BAU, compared to a scenario of zero emissions from cooking, household cooking emissions contribute to an increase in effective radiative forcing of roughly 39 mW/m² (-97-42 mW/m²; 95% CI) and 15 mK (-41-26 mK; 95% CI) warming by 2040. Our alternate intervention scenarios result in both reduced forcing and lower temperature (Figure 4). By 2040, median temperature is 0.3 mK lower in the IT scenario and 4.7-5.1 mK lower in the full transition scenarios (Figure 4A). However, the probability distributions for each scenario significantly overlap with zero (FT-E: -0.0238 - 0.0245, FT-LE: -0.0247 - 0.0267, FT-LPG: -0.0244 - 0.0271, IT: -0.0027 - 0.0035) so that we cannot state with certainty that the transitions would result in net cooling. Nevertheless, full transitions to LPG and/or electricity cause sustained reductions in annual emissions, resulting in median temperature pathways that will continue to diverge from BAU well into the future. Moreover, relative uncertainty in the temperature response should decrease in the future, due to the longer lifetimes of well-mixed GHGs and higher certainty associated with their effects.

Short-term climate forcing is dominated by aerosols with reductions in OC and BC resulting in net warming and cooling, respectively. Reductions in well-mixed climate GHGs like CO₂have minimal impact on temperature during the timeframe of our analysis. We note that cumulative CO₂e do not necessarily correspond well with their associated climate response for mixtures of long- and short-lived GHGs as in our transition scenarios ^26,27. However, changes in CO₂e (Figure 3) provide some indication of the long-term impacts of each scenario. For example, under the BAU scenario, cumulative CO₂e emissions are 27.7 Gt between 2018 and 2040. The three full transition scenarios reduce cumulative emissions of well-mixed GHGs by 2.3-4.3 Gt CO₂e over the simulation period (see SI Section 11 for more detailed discussion of GHG and SLCF emissions).

SLCFs dominate the projected climate impacts and associated uncertainty through 2040. Figure 5A shows the net temperature change and uncertainty (shown as the 5-95% confidence interval of an observationally constrained, perturbed parameter ensemble) between FT-LPG and BAU scenarios attributed to each pollutant. Figure 5C summarizes this for each scenario. BC and OC have the largest impacts as well as the largest confidence intervals due to uncertainties in climate response of aerosol emissions; CH₄, NMVOC, and CO also have substantial impacts due to their roles in tropospheric ozone formation. However, median response of the ensemble of runs for all scenarios shows that cooling due to decreases in emissions of pollutants that warm the climate (BC, CO, CH₄, and NMVOCs) overcomes warming arising from OC reductions. Net effect could be positive or negative based on associated uncertainty.

Figure 5B shows how regional emission reductions contribute to overall temperature change by 2040. The African region has the largest impact, driven by reductions in BC, CH_4, NMVOC, and CO from decreased demand for fuelwood and charcoal. Under BAU, the African region has substantial increases in charcoal and fuelwood consumption (Figure 1). Under the FT-LPG and other full transition scenarios, which phase out demand for charcoal and fuelwood, SLCF emissions are nearly eliminated.

Changes in PM_2.5, BC, and temperature are well-correlated because BC is a constituent of PM_2.5 and a key driver of short-term temperature change. Figure 6 shows that the modeled short-term temperature changes, though mostly driven by SLCF reductions, also scale with changes in CO₂e. These are reductions for all counties except Thailand and Myanmar (not shown). CO₂e is a more robust indicator of long-term temperature impacts. The generally linear relationship between ΔCO₂e and ΔT is moderated by several factors. For example, variation in the fraction of non-renewable biomass (fNRB), a geographically specific parameter characterizing the sustainability of woodfuel demand (see SI Section 7), has a strong influence on ΔCO₂e. Further, the varying relative contributions of CH₄ and CO₂ to total CO₂e reductions (Table S10) affects this relationship, as warming from CH₄is more strongly associated with annual emission changes than warming from CO₂, which is well-understood to be a function of cumulative emissions.

We have analyzed emissions and climate impacts of global transitions from BAU to clean residential cooking fuels in 77 countries. Over half of the population in our sample used either LPG (48%) or electricity (6%) as a primary cooking fuel in 2018. Our BAU scenario projects increases in the primary use of LPG (58%) and electricity (10%) by 2040, with polluting fuels decreasing in the same proportion. However, the rate of charcoal use doubles from 3% to 6% of the population, driven by expansion in Africa, where the number of people using charcoal as a primary fuel increases by nearly 220 million. Similarly, while global dependence on biomass decreases, in Africa the population using it increases by nearly 190 million.

Under BAU, overall emissions of most health-damaging pollutants decrease, though the reductions are modest. Health and environmental impacts decline by 2040, particularly in Asia, but we estimate over 700 million people across the region would still use polluting fuels. In addition, negative health and climate impacts will increase substantially in Africa as the use of polluting fuels expands. Increasing the pace of LPG/electricity cooking transitions will lead to large global benefits addressing these negative impacts. However, in the absence of major investments and shifts in policy, full transitions to such clean fuels by 2040 is only likely to occur in a handful of countries included in this analysis⁹. Achieving universal adoption of LPG and/or electricity for cooking would entail meeting multiple challenges²⁸^,²⁹ and require substantial investment³⁰. To date, investment has been slow, in part because of donor fears that financing expanded access to fossil-derived LPG sends the “wrong message” or will harm the climate. This analysis demonstrates that continuing along a BAU trajectory results in larger emissions of both SLCFs and well-mixed GHGs than a shift to LPG and/or grid-based (also largely fossil-derived) electricity. Moreover, despite large uncertainties in near-term temperature change (Figure 5), if we extend median trends into the future, the transitions from BAU pathways to LPG (and/or electricity) will likely result in net cooling while also essentially eliminating HAP emissions and associated health risks.

Incremental changes, illustrated by our IT scenario, result in modest reductions in climate forcers and negligible short-term temperature impact relative to BAU. In contrast, full transitions to LPG and/or electricity result in near elimination of the strongest SLCFs resulting in 4.7-5.2 mK near-term cooling compared to BAU as well as substantial reductions of well-mixed GHGs that would contribute to long-term cooling. The same transitions result in near elimination of PM_2.5. Under some scenarios, emissions of NO_x or SO_x increase, but the health benefits of zeroing out PM_2.5 emissions from household sources would far outweigh the risks of additional NO_x and SO_x. This is a rare example in which a fossil fuel-based energy transition can mitigate short- and long-term climate change while bringing large co-benefits: reduced health risks, time savings, and other social benefits associated with access to clean cooking options.

As with any complex modeling effort, our study has several limitations. Upstream emissions for most fuel pathways were taken from GREET, which is a life cycle model parameterized for fuels used in the US³¹. We made changes to reflect global conditions (SI Section 5), but the resulting emission factors may be inaccurate. The same applies to end-use emissions. In addition, we use total life cycle emissions as a proxy for health risk, but this does not consider that upstream emissions result in different exposures than end use emissions.

The fNRB data used in this analysis is the only available global estimate of woody biomass renewability. There are numerous sources of uncertainty in the models used to derive fNRB, which may over- or underestimate net CO₂ emissions from woodfuels⁶. In addition, fNRB is held constant in these scenarios; however, it is a function of woodfuel demand among other factors. In the full transition scenarios, as woodfuel demand decreases, fNRB should also decrease, leading to higher rates of regeneration and larger climate benefits from those transitions.

The FaIR model is a reduced-complexity climate model calibrated to an ensemble of observationally-constrained climate model simulations to estimate the range of plausible climate response³². However, impacts of SLCFs vary depending on source location ³³ and the radiative forcing response to emissions of aerosol species is highly uncertain.

There is increasing interest in cooking with electricity from decentralized sources ³⁴, which was not explored in this analysis. Off-grid options have become more feasible as the costs of renewable energy generation and storage have decreased and more efficient and inexpensive appliances like induction stoves and electric pressure cookers have become accessible ³⁵.

Conclusion

To our knowledge, this is the first study to examine emissions reductions and climate impacts of global clean cooking transitions in LMICs from polluting fuels to LPG and electricity. Under BAU conditions, HAP emissions decline modestly between 2018 and 2040, driven by a transition already underway in parts of Asia. However, in Africa, the population relying on polluting fuels will grow in the absence of substantial interventions. A full transition to LPG will lead to drastic reductions in emissions of nearly all climate-forcing and health-damaging pollutants, and possibly contribute to a small reduction in global temperature relative to the BAU scenario. Grid electricity, though it would require larger investment, would have a similar result. Thus, a rapid transition to LPG and/or electricity will reduce health risks for over 2 billion people, by reducing household exposures and a major contributor to ambient air pollution. Transitions are also likely result in a cooler cooling in the near and longer-term. Larger climate benefits could result from transitioning to clean fuels with lower life-cycle carbon emissions than conventional LPG and power grids under current stated policies. However, it is unclear if such low-carbon options (including bio-LPG) could be scaled up as quickly as standard LPG and stated policies for grid expansion could be.

Fuel Transition Scenarios

The analysis included LMIC countries with a population of at least 1 million households using polluting solid fuels and/or kerosene in 2018 (the baseline year) based on the WHO global database of primary fuels/ energy used for cooking⁹. The analysis includes over 2.6 billion polluting fuel users from 77 countries.

The Business as Usual (BAU) scenario assumes that past rates of change in primary household fuel choice are extended into the future. Past household cooking fuel choices were calculated for rural and urban areas of each country using WHO’s household cooking fuel choice database, which includes six fuel categories: Biomass (consists mainly of unprocessed firewood, but also includes crop residues and dung), Charcoal, Coal, Kerosene, Gas (includes LPG, natural gas, and biogas), and Electricity ³⁶. Average rates of change for each fuel are shown in the SI Section 2.

Simply applying past rates of change could result in the percentage of fuels used in a country exceeding 100%. To avoid this, we developed a “fuel hierarchy” based on the assumption that when households change primary cooking fuels, they are more likely to adopt cleaner fuels rather than more traditional/more polluting fuels³⁷. Accordingly, to simulate this, we applied growth rates in the following order: LPG; Electricity; Kerosene; Charcoal; Biomass; and Coal (assumptions used for the fuel hierarchy are provided in SI Section 3).

The Full Transition-LPG (FT-LPG) scenario makes two key assumptions: 1) households using polluting fuels (Kerosene, Coal, Charcoal and Biomass) transition steadily to LPG such that use of polluting fuels falls to zero by 2040 and 2) any electricity use at baseline evolves as in the BAU scenario. Therefore, by 2040, all cooking is done with either LPG or a mix of LPG and electricity. In some cases, the rate of decline in polluting fuel use is higher under BAU than would have occurred by a 20-year shift to LPG (for example, cooking with coal in China, which is rapidly declining). In these cases, we use the BAU rate instead.

The Full Transition to LPG and Electricity (FT-LE) scenario follows the same pattern as the FT-LPG scenario, but uses electricity as the main transition, while allowing BAU rates of LPG to continue. The Full Transition to Electricity (FTE) examines a hypothetical transition to 100% cooking with electricity (including LPG-using households transitioning to exclusive use of electricity by 2040). The Intermediate (IT) scenario assumes BAU rates for each fuel increase by 50%.

Emissions

Emissions from “upstream” processes like extraction, refining, and transportation of fossil-based cooking fuels (kerosene, LPG, and coal) were derived from GREET, a life cycle model developed by Argonne National Labs primarily for analyzing transportation fuels³¹. GREET was also used to estimate transportation emissions from charcoal production. GREET’s default parameters reflect current US conditions, but the model allows users to adjust inputs to reflect other markets. For the global analysis, we determined the dominant consumers of each fuel from our sample of 77 countries, as well as the major exporters supplying these countries and adjusted GREET accordingly. We describe specific approaches to each fuel in the SI Section 5.

For upstream emissions for charcoal production, the average emission factors for CO, NO_x, PM_2.5, SO_x, BC, OC, CH₄, N₂O, CO₂, and NMVOC were taken from previous compilations of field data ^17,38,39 and converted into a delivered energy basis using stove efficiency data ¹⁷. The values used are provided in Table S9. For biomass, we assumed that wood is locally collected and thus has no upstream emissions. The end use emission factors for biomass, charcoal, kerosene, coal and LPG for CO, NO_x, PM_2.5, SO_x, BC, OC, CH₄, N₂O, CO₂, and NMVOC were taken from previous compilations of lab and field data ^5,17,38,39.

Shares of electricity production and total production for different years were taken from the World Bank Development indicators ⁴⁰ with missing data supplemented by International Energy Agency (IEA) statistics ⁴¹. Future grid mixes were simulated using projections from the IEA’s World Energy Outlook (WEO) “Stated Policies” scenario through 2040 ⁴². These projections include grid mixes for all world regions and major electricity-producing countries in our sample (Brazil, Russia, India, China, and South Africa). Countries that were not explicitly included in WEO projections were assumed to follow the regional projection if it resulted in a cleaner grid over time. If the future regional mix was more polluting than the country’s own mix in 2020, we assumed that the country’s grid mix remained constant. Grid losses were accounted for using World Bank indicator data ⁴⁰.

Life cycle grid emissions per kWh of electricity produced from each type of power generation were estimated using GREET ³¹. Plant-specific emissions are included in GREET’s database with some tunable parameters. GREET’s default values assume a US-based grid and power plant feedstock. We changed some parameters for coal, as mentioned above. Parameters for other feedstocks were left at their default values. Grid projections and emission factors are shown in SI Section 4.

When woody biomass is harvested and burned as fuelwood or charcoal, much of the carbon in the wood is converted to CO₂, contributing to climate change. However, depending on the rate of harvest and specific land management practices in place, some or all of the woody biomasses can regenerate, which reduces the climate impact of the CO₂ emitted when wood and charcoal are burned. A previous study estimated the balance of woody biomass harvest and regrowth, labeled “fraction of non-renewable biomass” or fNRB ⁶. While these estimates cover most countries in the Global South, some countries included in our global sample were not included in the fNRB study. For these countries, a global average of 28.8% fNRB was used. Country-level fNRB values are included in SI Section 6.

For the global health impact assessment, we did not estimate contributions of emissions to specific health outcomes. Instead, we estimated changes in emissions of (and assumed exposure to) the following health-damaging pollutants: PM_2.5, CO, NO_x, and SO_x ⁴³. For national-level assessments of CCA priority countries, the ABODE model was used to translate changes in exposure into health impacts ⁴⁴. The results of these health models are discussed in a separate report.

Climate Impacts

We estimate climate impacts of the different scenarios using the FaIR v1.6.2 climate model, which is a simple emissions-based model that accounts for non-linearities in the carbon cycle and includes simplified processes representing greenhouse gas, aerosol, ozone, and other forcings from precursor emissions ^32,45,46. The multi-species configuration was used, with the RCP4.5 scenario as a baseline for the BAU scenario^47,48. RCP4.5 is a pathway for stabilization of radiative forcing by 2100, projecting somewhere in the region of 2.5-3^oC global mean temperature increase above pre-industrial by the end of this century, and is the closest RCP scenario to current global climate policy ⁴⁹. Uncertainty was calculated based on runs using parameters derived from a 2237 member ensemble developed for analysis in IPCC's Sixth Assessment Report⁵⁰. This ensemble is observationally constrained in order to span the assessed range in climate system uncertainty, including global temperature change (1850-2019), atmospheric CO₂ concentrations (1750-2014), change in ocean heat content (1971-2018), and assessments of equilibrium climate sensitivity, transient climate response and airborne fraction of CO₂ emissions^50,51. Differences in emissions between the BAU and other scenarios were calculated for CO₂, CH₄, N₂O, SO_x, CO, NMVOC, NO_x, BC, and OC, and added to the RCP4.5 baseline emissions trajectory. The resulting emissions trajectories were then run in the FaIR climate model to get the annual change in GHG concentrations, climate forcing, and temperature projected to 2040 from implementing each alternative scenario. To represent model uncertainties, we report the median and 5^th, 25^th, 75^th and 95^th percentile values of the ensemble simulations, with emissions for each scenario held constant. Therefore, uncertainty estimates do not represent uncertainties in scenarios, only in climate system response.

Acknowledgements

The authors gratefully acknowledge funding from the Clean Cooking Alliance (grant no. C-12881-1). Additionally, we would like to acknowledge the undergraduate students who helped with the project (Grace Gould, Jasmine Hayden-Lowe, Silas Huckins) as well as National Science Foundation (award no. 1743741).

Author contributions

AG and RB developed the concept, designed the research, and secured funding. EP and DP advised on research design. NL and CJS developed the FaIR model and heavily supported its use in this application. EF, AG, and RB developed BAU and alternate scenarios with input from EP, DP. EF and KL developed tools to manage and analyze scenario data. RB, EF, and AG compiled life-cycle emissions data for each cooking option. EF developed code to integrate emission factors and transition scenarios into FaIR with support from KL. EF, AG, EP, DP, RB, AGW and KL coauthored an internal report that forms the basis for this manuscript. EF, AG, RB, EP, DP, and AGW wrote the manuscript. NL and CJS provided additional text about FaIR output and related discussions.

Data Availability

All input, intermediate, and final output datasets are available for download from an open-access repository at this link: https://doi.org/10.5061/dryad.ttdz08m2c.

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Scaling up Gas and Electric Cooking in Low- and Middle-Income Countries: Climate Threat or Mitigation Strategy with co-Benefits?

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