National climate action can ameliorate, perpetuate, or exacerbate international air pollution inequalities

Domain: chemistry
Published: 2026-01-26
Editorial outcome: major_revision

Abstract
Climate action ameliorates public health by reducing hazardous air pollutants alongside greenhouse gases, yet misguided mitigation efforts could induce imbalances in air pollution exchange across international borders. Despite its potential to endanger equality, the effects from climate action on transboundary air pollution are relatively unstudied. Here we show that stricter mitigation increases the fraction of co-benefits that originate externally in Africa by +8% in shared socioeconomic pathways (SSP) towards sustainability (SSP1) and by +53% for fragmentation (SSP3). The fraction of externally originating co-benefits is greater in developing countries (0.76 in SSP1-26) than developed (0.65), indicating that developing countries are more dependent on external action. Although co-benefits are maximized in the most ambitious scenario, SSP1-19 (1.32 million deaths avoided), their transboundary exchange between countries varies. These results suggest a need for climate policies that consider how inequalities in transboundary air pollution evolve across distinct socioeconomic trends and mitigation strategies in addition to total co-benefit estimates.

Introduction
Climate action has the potential to simultaneously alleviate the global health impacts from climate hazards and reduce the substantial health burden associated with air pollution 1–4. However, climate policy design that does not account for the geographic distribution of air pollution impacts and transboundary air pollution exchange risks perpetuating or exacerbating global inequalities in air pollution exposure 5,6. Integrating this consideration in climate policy design is necessary to ensure that current burdens of poor air quality do not continue into the future 7. With this in mind, the Intergovernmental Panel on Climate Change (IPCC) developed the Shared Socioeconomic Pathways 8 (SSPs) that represent different socioeconomic trends and challenges to mitigation and adaptation that could affect climate change, air pollution, and the inequality of their impacts.

Previous studies of the role of climate action on air pollution have explored how policy implementation could influence air quality and contribute to health impacts globally 2,9, regionally 10,11, and in individual countries 12,13 or cities 14. These studies have primarily focused on air pollution from particles less than 2.5 micrometers in diameter (PM 2.5) due to its large health burden 4. Climate action induces emission changes that affect the transboundary exchange of PM 2.5, which could heighten or relax international tensions; however, due to the complexity of modeling these interactions, few studies have examined how exchanges in PM 2.5 —and their associated health impacts—could vary across different climate futures. Specifically, considering an individual country, the balance between the benefits realized globally from emission reductions in that country compared to the benefits realized in a country from global emission reductions may differ dramatically across different socioeconomic trends and mitigation strategies. In turn, this could lead to shifting imbalances and inequalities in air pollution exchange. Studies that have explored this question generally demonstrate that policies that deepen global inequalities in emission patterns will subsequently worsen global air quality, especially in nations classified as developing 15,16. For example, one study 17 found that improving socioeconomic development was especially important for reducing the burden from poor air quality in low- and middle-income countries as classified based on income levels by the World Bank. In addition to atmospheric transport, trade imbalances in consumption and production also contribute to air pollution inequalities 18,19. Considering how inequalities in air pollution exchange could evolve across different SSPs for individual countries and regions remains insufficiently explored.

In the coming decades, climate action—or the lack thereof—will shape global efforts to mitigate and adapt to the consequences of climate change and poor air quality 20. Aggressive and equitable policies could catalyze socioeconomic trends that would ensure that fewer lives are lost from the hazards of climate change while also ameliorating the health burden and inequalities associated with air pollution. In this study, we consider three questions: (1) how might socioeconomic trends (SSPs) and climate mitigations (RCPs) affect the health burdens associated with PM 2.5 air pollution, (2) how could transboundary exchanges in these health burdens evolve across different climate futures, and (3) how could climate action address or worsen imbalances and inequalities in PM 2.5-related health impacts. We focus our assessment on the impact of changes in emissions—and not demographics—as they are the most directly controlled by air quality and climate mitigation policy.

To examine these questions, we employ the adjoint of the GEOS-Chem 21,22 chemical transport model (CTM) to conduct simulations that calculate the sensitivity of fine particulate matter (PM 2.5) exposure (i.e., population-weighted concentrations) in nearly every country of the world to its chemical precursor emissions. We integrate these sensitivities with air pollutant precursor emission projections across a diverse set of socioeconomic and climate mitigation scenarios 23 that are built by coupling SSP and RCP scenarios. We apply health impact assessment methods from the Global Burden of Disease 4 (GBD) 2019 Study and project baseline disease rates and population data into the future from the GBD Foresights 24 project to estimate the health impacts of these scenarios compared to a worst-case scenario (SSP3-Baseline). We identify not only the health impacts from specific scenarios but also—by leveraging the adjoint model source-receptor relationships—how each country contributes to impacts throughout the world in 2040. We compare these two quantities to characterize how transboundary air pollution relationships evolve across different SSPs and to identify how air pollution exposure inequalities could be exacerbated, perpetuated, or ameliorated through different socioeconomic trends. These adjoint modeling results enable us to explore the impacts of climate action across a larger geographic extent (only 25% of these sensitivities have been presented in prior analysis 25) and allow us to uncover information about climate action in the Global South—i.e., developing nations in Africa, Asia, and Latin America with shared histories of colonialism—a region that has historically been understudied. Crucially, we develop source-receptor matrices for nearly every individual country, facilitating the investigation of policy-related questions throughout the globe without selection or aggregation biases.

Results
Climate co-benefits and transboundary inequalities across different climate futures in 2040

Socioeconomic trends (SSPs) and climate mitigation strategies (RCPs) that improve upon the worst-case scenario, SSP3-Baseline, reduce PM 2.5 precursor emissions in 2040. For example, sustainable development with high climate mitigation (SSP1-19) would lower global nitrogen oxide (NO x) emissions by 73% in 2040. These emission reductions improve air quality and reduce PM 2.5-related deaths; however, the magnitude of these climate co-benefits varies by scenario (Table 1). In the most optimistic scenario (SSP1-19) in which there is sustainable socioeconomic development and a strong reduction in climate forcing that presents low challenges to mitigation and adaptation, emission reductions could avoid 1.32 million deaths (with a lower and upper bound of 0.95, 1.73). This is over four times the 0.32 million (0.24, 0.42) deaths avoided in SSP3-60, in which there is fragmented socioeconomic development, weak reductions to climate forcing, and high challenges to mitigation and adaptation. Conventional development (SSP5) and middle-of-the-road development (SSP2) towards RCP-45 result in climate co-benefits between these two extremes of 1.03 (0.75, 1.35) and 0.79 (0.58, 1.04) million deaths avoided, respectively, whereas trends towards inequality (SSP4) for RCP-45 closely match the results of the fragmentation scenario with 0.51 (0.37, 0.67) million deaths avoided.

There are two compounding factors that drive variability across these different climate futures: the degree of mitigation and the type of socioeconomic development. Represented by lower RCPs, stronger mitigation engenders greater co-benefits; however, the strength of this effect is mediated by the socioeconomic development pathway in which the action is implemented. Specifically, the effect of stronger mitigation is relatively weak in sustainable development—yielding 1.1 times as many co-benefits in SSP1-26 compared to SSP1-45—and stronger in more fragmented climate futures—there are 2.0 times as many co-benefits in SSP3-26 compared to SSP3-45. Similarly, socioeconomic development that is more equitable and fosters global cooperation generally leads to greater co-benefits compared to less equitable socioeconomic development, but this is dependent on the degree of climate mitigation. For example, for RCP-26, SSP1 results in 1.4 times as many co-benefits as SSP3 while in RCP-45, SSP1 results in 2.4 times as many co-benefits as SSP3. These results suggest that climate policy would ideally be designed to consider socioeconomic development alongside the degree of mitigation given these dependencies. Ultimately, in climate futures for which there are low challenges to both mitigation and adaptation the improvement to air quality will have substantial health benefits; however, our estimates suggest that challenges to adaptation are especially connected to the deaths avoided from climate action.

Past studies on the impact of climate action on air quality generally estimate the total magnitude of health benefits (as we do in the previous paragraphs); however, this neglects a crucial aspect of climate action with implications for equality: transboundary air pollution. To characterize disparities induced by changes to transboundary air pollution we define two metrics that are associated with air pollution inequality: transboundary fractions and exchanges (Fig. 1). When global climate action is adopted, a fraction of the co-benefits accrued by a country are attributable to external action (i.e., climate mitigation outside of its borders). We define a transboundary fraction (Fig. 1a) that quantifies the fraction of all co-benefits in a country that originate from action outside that country (refer to the “Methods” section for more details). The fraction can also be calculated for regions of aggregated countries. Higher transboundary fractions indicate that a country or region requires more regional or global cooperation to realize the benefits of climate action whereas a lower transboundary fraction positions a country or region as largely being in control of their own climate co-benefits. Transboundary fractions are calculated from the health impacts associated with PM 2.5-exposure. We discuss the second metric of exchanges (Fig. 1b) in detail in the next section of the results. For some analyses we group countries into one of six larger regions: South America (SA), Oceania (OC), North America (NA), Europe (EU), Asia (AS), and Africa (AF). The specific countries in each of these regions are provided in Supplementary Table 1 and Supplementary Fig. 1.

For the six regions we consider in this study, scenarios in which climate action is more aggressive (i.e., lower RCPs and SSPs with fewer challenges) achieve greater climate co-benefits (Fig. 2a). In fact, comparing SSP1-19 to SSP3-60, co-benefits are 279%, 410%, 412%, 449%, 548%, and 731% higher in Asia, Oceania, North America, Europe, Africa, and South America, respectively. Regardless of scenario, most of the global co-benefits occur in Asian countries. In SSP1-19, 79% of all co-benefits occur in Asia, placing it far ahead of other regions like Europe (9%), Africa (5%), North America (5%) and South America (3%). In SSP3-60, the Asian share of co-benefits grows (85%) while other regions such as Europe (7%), Africa (3%), North America (4%), and South America (1%) benefit proportionally less. This high share of co-benefits in Asia is not surprising and is attributable to two factors: (1) the population of Asia is large at present and by 2040 this young population will age and become more susceptible to PM 2.5-related health impacts; and (2) China and India are developing rapidly and these two countries account for a majority of the PM 2.5-related deaths in the region; thus, climate action in these countries could alleviate a great degree of the air pollution health burden. Specifically, co-benefits in these two countries alone make up between 52% (SSP1-19) and 63% (SSP3-60) of all global co-benefits.

Interestingly, although Asia makes up a dominant share of the total co-benefits from climate action, the same is not true for transboundary co-benefits (i.e., co-benefits originating from emissions outside of the region that they benefit) (Fig. 2b). In SSP1-19, Asia has the largest share of transboundary co-benefits (38%); however, this is only slightly ahead of other regions such as Europe (32%) and Africa (21%). In some scenarios, such as SSP1-45, Asia actually makes up a smaller percentage of transboundary co-benefits (32%) than Europe (36%) and only slightly more than Africa (22%) despite its much larger population. This is an unexpected result. As mentioned previously, rapid development and a large aging population position Asia as the primary beneficiary of climate action; however, this is not the case for transboundary co-benefits. Crucially, the major polluters and population centers of Asia in China and India are located in the eastern part of the continent distant from other regions; thus, a majority (62% in SSP4-45) of the benefits of climate action in the continent are geographically isolated from other regions.

Some regions are more affected by these transboundary co-benefits than others and the influence of external action is sensitive to socioeconomic trends (Fig. 2c) and mitigation strategies (Fig. 2d). Africa is the region most affected by regionally external climate action: on average 12% (9%, 19%) of its co-benefits originate from action in another region. This suggests that African countries are especially reliant on external action to maximize their climate co-benefits. This reliance is mediated by the degree of mitigation action. From RCP-45 to RCP-26, stronger mitigation induces proportionally more transboundary co-benefits across the different socioeconomic development pathways ranging from 8% more in SSP1 to 53% more in SSP3. This relationship between climate mitigation and transboundary co-benefits in Africa is likely attributable to development. The most aggressive climate mitigation scenarios necessitate stronger action in countries that have historically polluted more (e.g., countries in Europe); consequently, as these countries adopt stronger climate action, Africa benefits proportionally more. This is why, across most SSPs, the region with the second highest transboundary fraction—Europe—exhibits an inverse association between climate mitigation and transboundary co-benefits (e.g., a 5% lower transboundary fraction in SSP1-26 compared to SSP1-45). For the other regions, the transboundary fractions are low and on average 5%, 3%, 1%, and 1% for North America, Oceania, Asia, and South America, respectively. In these regions in which major population hubs are more geographically isolated from other regions, domestic climate action dominates co-benefits.

Surprisingly, less equal socioeconomic development increases the transboundary fraction for Africa. One reason why this could occur is that in more fragmented climate futures, there is less cooperative development. Consequently, in these less equal scenarios, African countries become even more sensitive to global action (67% higher in SSP3-26 than SSP1-26) as there is less local development and industrialization. This relationship is true across all of the RCPs; however, it is stronger in those with higher climate mitigation (i.e., RCP-26) than those with weaker mitigation. Specifically, comparing SSP3-45 to SSP1-45, the transboundary fraction is only 18% higher. Ultimately, the transboundary fraction in Africa is mediated by two competing phenomena: (1) more aggressive climate mitigation necessitates proportionally more involvement from developed nations and thus higher transboundary co-benefits and (2) less equal socioeconomic development further increases the disparity in industrialization and development in many Africa countries which forces them to be more dependent on external action to maximize co-benefits.

Transboundary fractions exhibit greater variability for countries than regions: e.g., there is a standard deviation of 0.29 for countries compared to the 0.04 for regions in SSP1-26. This variability in the transboundary fraction is influenced by the degree of development (Fig. 3). For SSP1-26, the transboundary fraction for the 20 countries with the lowest Human Development Index (HDI) 26 is on average 0.76 compared to 0.65 for the 20 highest HDI countries. Additionally, 90% of these low HDI countries are African nations with the two exceptions being Afghanistan (0.91) and Yemen (0.78). In contrast, the 20 countries with the highest HDI are primarily from Europe, North America, Oceania, and Asia; none of these countries are from Africa or South America. This suggests that developed nations receive proportionally fewer of their co-benefits from external action than developing nations and consequently that global cooperation benefits developing nations more than developed ones. The total co-benefit numbers are also included (Supplementary Fig. 2) to distinguish areas of high and low impact when interpreting the transboundary fractions.

There are several underlying factors driving the transboundary fractions: e.g., proximity to major polluters, geographic size, prevailing winds, and population density. Island nations, and those isolated by geographic features (e.g., Chile) tend to have more domestic pollution than nations proximate to high polluters (e.g., much of Europe). In the Northern Hemisphere, prevailing westerlies lead to more pollution transport to the East of major polluters in comparison to the West. Thus, it is important to consider that transboundary fractions can indicate equitable or inequitable policy outcomes when considered alongside the HDI of a country (Supplementary Fig. 3). Generally, countries with high transboundary fractions require more compensatory actions as they have less control of their air pollution burden, whereas countries with low transboundary fractions can adopt action more independently. However, given that geography and population density influence transboundary fractions, it is crucial to consider a dimension of development alongside this exchange. The greatest targets for compensatory action are those countries with high transboundary fractions and a low level of development. These countries will likely bear heavier health burdens in the future as industrialization and technological progress occur.

The global transboundary fraction (i.e., the sum of all external co-benefits over all co-benefits) is much lower than for most individual countries and it ranges from 0.23 (SSP3-60) to 0.28 (SSP1-19). However, this fraction is largely brought down from the inclusion of India and China who receive most of their co-benefits from internal action. For example, in SSP3-60 removing these two countries leads to a higher global transboundary fraction of 0.54. This explains why when calculating the average transboundary fraction across all countries there is a substantially higher value of 0.68 (SSP3-60) than the total global transboundary fraction. We note that these transboundary contributions are substantially greater than past analyses 27,28 of historical transboundary air pollution burdens, which found 14% and 12% of all PM 2.5-related deaths were attributable to transboundary pollution. This difference is likely attributable to three main factors: (1) these past studies focused on aggregated groups of countries whose boundaries are further from peak emissions locations whereas we perform this calculation for individual countries, (2) in this study we consider climate action whereas these previous studies consider all anthropogenic emissions; climate action is not adopted to the same degree globally and many of the highest emission reductions are implemented in a small set of developed nations, and (3) many of the targets of climate action are precursors of secondary PM 2.5 (specifically NO x and SO 2) and not primary PM 2.5 and thus this action targets pollution that is more likely to be transported greater distances due to its longer atmospheric lifetime.

These inequalities associated with transboundary air pollution are dependent on the socioeconomic development and climate mitigation future in which they take place. Considering HDI again, transboundary fractions decrease slightly for the 20 lowest HDI countries (−1%) while increasing substantially for the 20 highest (+32%) when transitioning from SSP1-26 to SSP1-45. In the inverse scenario, in which mitigation is held constant (RCP-26), but the socioeconomic development becomes less equal (from SSP1 to SSP3), transboundary fractions increase in the 20 highest HDI countries (+3%) and even more so in the 20 lowest HDI countries (+22%). This is consistent with our previous results at the regional scale and is likely attributable to the fact that stronger mitigation necessitates greater participation from developed nations and climate action taken in less equal socioeconomic development increases transboundary co-benefits in the developing world because they are more dependent on action outside of their borders due to weaker internal development and industrialization.

Compounding weaker mitigation (RCP-45) with less equal socioeconomic development (SSP3) does not exhibit additive effects of the two individually. For example, while Mexico is only slightly affected by changes to mitigation (−10%) and socioeconomic development (+1%) alone, their compounded effect greatly increases the transboundary fraction (+53%) indicating more contributions from external action than in SSP1-19. Thus, this again suggests the need to consider climate mitigation within the socioeconomic development framework for which it is implemented in.

The main results presented are for 2040; however, we additionally calculate the differences in transboundary fractions and co-benefits between the base year of study (2040) and 2030 (Supplementary Figs. 4 and 5) across SSP1-26 and SSP3-26. We find that in 2040 transboundary fractions are moderately higher in China, India, the US, and Brazil and lower in parts of Africa compared to SSP1 in 2030; however, these interannual changes are smaller than those attributable to socioeconomic changes. We additionally project changes in transboundary fractions from 2030 to 2050 across the set of scenarios (Supplementary Fig. 6) to identify regional-scale trends. Generally, transboundary fractions remain stable through time; however, in more equal SSPs (i.e., SSP1), transboundary fractions for Africa generally decrease from 2030 to 2050 (e.g., by 15% from 2030 to 2050 in SSP1-45) compared to less equal SSPs (i.e., SSP3) where transboundary fractions increase (e.g., increase by 30% from 2040 to 2050 in SSP3-45). We note that our estimates of co-benefits for 2030 lie outside of the lower-bound health estimate for 2040 indicating that the difference in these estimates is not captured by uncertainty in the health calculation alone.

Ultimately, these findings imply two outcomes: (1) the effects of climate action on transboundary air pollution are unique to socioeconomic development and mitigation strategies and these effects are not additive and (2) less developed countries receive greater transboundary co-benefits in less equal socioeconomic development as they are more dependent on external action due to weaker internal development and industrialization.

Climate co-benefit exchanges from transboundary air pollution

The exchange of climate co-benefits between pairs of countries (or regions) evolves in response to different socioeconomic development pathways and climate mitigation strategies. We characterize the intraregional and interregional exchange of climate co-benefits by calculating exchange metrics (Fig. 1b) that represent the air pollution co-benefits realized in one country that are attributable to climate action in another country. In our analyses, we consider exchanges in three ways: (1) in their absolute sense an exchange represents how emission reduction in one country induces co-benefits in another, (2) as net exchanges by differencing the two exchanges in a pair, and (3) the proportion of co-benefits contributed by one member of a pair to the total gross co-benefits exchanged between the pair to determine if a specific country dominates the exchange of air pollution co-benefits. In the latter approach, we note that exchanges in which there is a greater disparity—especially when the low HDI member of the pair is contributing more than the higher one—are key targets for compensatory action.

For most country pairs, one country contributes more co-benefits to the other than vice versa, leading to inequalities in exchange even in optimistic scenarios such as SSP1-26 (Fig. 4). Considering only exchanges with more than 30 co-benefits, for Europe, exchanges vary from being relatively balanced such as Germany-Sweden (1.06 times higher co-benefit contribution from Germany than Sweden), Russia-Romania (1.14), UK-Netherlands (1.14), and Ukraine-Poland (1.15) to very imbalanced such as Spain-Portugal (4.64), Czechia-Germany (4.58), and France-Spain (4.13). For Africa the most balanced exchange in SSP1-26 is between South Africa and Mozambique (1.27) and the most extreme is between Uganda-DRC (28.5) in which the effects of higher population, prevailing easterly trade winds, and a greater degree of development compound to contribute to substantial transboundary air pollution exchange inequality. For Asia, Pakistan-India (1.01), Thailand-Philippines (1.03), Thailand-Vietnam (1.11), and Pakistan-China (1.11) are the most balanced in SSP1-26 while China-Vietnam (21.0), Thailand-Myanmar (16.4), and India-Myanmar (12.8) are the most imbalanced. Between the two largest players in Asia, China and India, China contributes 1.8 times as many co-benefits to India as vice versa.

These exchange inequalities can be exacerbated or ameliorated through different socioeconomic development pathways. For Africa, fragmented socioeconomic development transforms the relatively equivalent exchange between South Africa and Mozambique in SSP1-26 to one in which South Africa contributes much more (3.39 times). For Asia, the previously equivalent Pakistan-India exchange in SSP1-26 has greater contributions from India (1.33) in SSP3-26. The inequal exchange between China and India is perpetuated in middle-of-the-road (1.79), fragmented (1.76), and inequality (1.82) development pathways; however, it notably increases through conventional development (2.07). Lastly, in Europe, fragmented development exacerbates the exchange inequality between Spain and Portugal (5.78) but partially reduces the inequality between France and Spain (3.67). Many of the European co-benefit exchanges increase in conventional development; this is likely owing to higher climate mitigation (RCP-26) paired with conventional development (SSP5) which necessitates greater climate action in developed countries (including many in Europe) compared to others socioeconomic pathways in which there is more even and sustainable development. When considering weaker climate mitigation as we include in Supplementary Figs. 7 and 8, we see that many of these increased exchanges are resolved.

Exchange inequalities between regions are also sensitive to the socioeconomic pathway in which the climate action takes place. In sustainable development, African countries contribute 887 more co-benefits to Europe than vice-versa; however, in fragmentation this dynamic weakens, and African countries instead contribute fewer (418), albeit still more, co-benefits to Europe than vice versa. The exchange between Asia and Africa is consistently imbalanced towards Asia: in sustainable development Asia contributes 1463 more co-benefits to Africa than vice versa; however, this net exchange decreases to 869 co-benefits in socioeconomic trends towards inequality. Ultimately, socioeconomic development dramatically affects interregional and intraregional exchange inequalities; however, this effect is not uniform. The same socioeconomic trend can simultaneously work to ameliorate some exchange inequalities while perpetuating or exacerbating others.

Comparing fragmented socioeconomic development to sustainable socioeconomic development results in some of the largest changes to exchange inequalities between regional and country pairs. For example, in SSP3-45, the exchange between Europe and Africa is relatively balanced: the former contributes 48% of the total co-benefits exchanged and the latter, 52% (Fig. 5a). Transitioning to more sustainable socioeconomic development leads to Africa—the region with the lower GDP today (as classified by the World Bank)—contributing a clear majority of co-benefits to the exchange (72%). This shift towards greater African contributions to Europe in a more sustainable world occurs because a more sustainable climate future necessitates growth in historically underdeveloped regions (i.e., countries with HDI < 0.7) and enables them to contribute more to climate action and subsequently greater contributions to co-benefits. Contrasting this with the transition from a middle-of-the-road to sustainable climate future (Fig. 5c), the exchange between Africa and Europe is essentially unaffected (from 29% to 28%, African favored). This suggests that fragmentation contributes to a balanced Africa and Europe exchange and through trends towards sustainable or middle-of-the-road development, Africa contributes more to Europe than vice versa due to a greater capacity to adopt more aggressive climate action. Generally, at the regional scale, changes to exchanges were mostly unaffected by this socioeconomic development excluding the exchange between South America and Africa which becomes less Africa dominated in sustainable development compared to both SSP3-45 and SSP2-45.

There is even greater sensitivity to socioeconomic development at the country-scale (Fig. 5b). When transitioning from fragmented (SSP3-45) to sustainable (SSP1-45) development there is generally more balance between country exchange pairs especially in the pairs that undergo the largest changes to exchanges: in the top ten pairs, the country with the higher current GDP (as classified by the World Bank) at present contributed 61% of co-benefits in SSP3-45 compared to 50% in SSP1-45. Nine of these ten pairs had more balanced exchange patterns (i.e., closer to 50%) in sustainable development futures compared to fragmentation futures. Crucially, sustainable development consistently pushed towards greater contributions from the lower GDP member of the exchange pair, ranging from a 32% lower TEC in the India-Nepal pair to 19% lower in Spain-Morocco. This supports previous findings that more sustainable futures will enable greater contributions to co-benefits from countries with lower GDPs due to greater development and industrialization. In contrast, in the transition from SSP2-45 to SSP1-45 (Fig. 5d), this trend towards more balance does not occur; average contributions from the higher GDP change only from 57% in SSP2-45 to 54% in SSP1-45 in the top 10 nations.

Another distinction in transitioning to SSP1-45 from SSP3-45 versus SSP2-45 is the regions in which the largest changes to exchange patterns occur. When comparing fragmentation to sustainable development, 7 of the 15 largest exchange shifts occur in Asia with the remaining 8 occurring in Africa (3), Europe (3), or interregional exchanges between Spain and North Africa (2). In contrast, when comparing the middle-of-the road scenario to sustainable development, only one of the largest exchanges includes an African country, not just in the top 15 but the entire top 50 (i.e., the Egypt-Ukraine exchange). Beyond this, the top 15 is largely made up of Asian (5), interregional (6), and European (3) exchanges. This suggests that fragmented socioeconomic development especially affects African exchanges compared to other socioeconomic development and exchange inequalities can be ameliorated by transitioning towards more sustainable development. The degree of climate forcing (Supplementary Fig. 9) also has implications for regional exchanges. In both sustainable development and fragmented development, the pairs that are most affected by the difference between an RCP-45 and RCP-26 future are largely those pairs that include developed nations in Europe and Asia. This supports our earlier finding that more aggressive climate mitigation requires more contributions from developed nations.

Methods
To assess the health benefits of socioeconomic trends and mitigation strategies, we integrate emission scenarios, adjoint modeling, and health data. First, we perform adjoint sensitivity calculations for nearly every country in the world to characterize the sensitivity of PM 2.5 exposure to emissions of its precursors. Then, we combine these sensitivities with a consistent set of projected SSP and RCP emissions 23 for 2040 to estimate how PM 2.5 exposure in these countries could change as a result of projected changes in emissions. We then estimate the health impacts associated with changes in PM 2.5 exposure using the GBD Foresights 24 project to project population and baseline disease rates into the future.

Emission scenarios for socioeconomic trends and mitigation strategies
We use a consistent set of gridded global emissions data that were generated using an integrated assessment framework 23. These data cover multiple socioeconomic assumptions (i.e., SSP1–SSP5), climate mitigation levels (RCP 1.9 Wm−2, 2.6 Wm−2, 3.4 Wm−2, 4.5 Wm−2, 6 Wm−2, and Baseline), and chemical species including black carbon (BC), ammonia (NH 3), nitrogen oxides (NO x), organic carbon (OC), and sulfur dioxide (SO 2). Emissions are provided for 2005 and then every ten years from 2010 to 2100; we extract the 2040 emissions for this study and also use the 2030 emissions for additional supplemental analyses. Emissions are separated by sectoral source in the dataset; we aggregate the emissions across these sectors excluding those associated with biomass burning (i.e., agricultural waste burning, deforestation, savanna burning). We include the following sectors: agriculture, aviation, residential and commercial, power plants, industry, international shipping, solvents, surface transportation, and waste. These emissions are generated at the 0.5°×0.5° resolution; we generate country masks at this resolution from SEDAC CIESIN 38 and, for each country, regrid the emissions to the model resolution (2°×2.5°) using the conservative regridding algorithm from the xESMF python library 39.

Air quality and adjoint modeling
We simulate the formation of PM 2.5 using the GEOS-Chem 22 chemical transport model, specifically, version 35 of the adjoint 21. We conduct global simulations at a horizontal resolution of 2°×2.5° with 47 vertical layers driven by GEOS-5 assimilated meteorology for 2010 from the National Aeronautics and Space Administration (NASA), Global Modeling and Assimilation Office 40. Our simulations contain two components: first, we perform the forward model simulation in which model sensitivities are propagated forward in time from emissions to form PM 2.5, second, we perform the adjoint calculation in which a reverse integration of the forward model is calculated to estimate the sensitivity of scalar cost-functions of country-scale population-weighted PM 2.5 to precursor emissions. We perform the simulations in 2-month increments run in parallel as discussed in our prior work 25. Overall, we perform these coupled calculations (i.e., the forward and adjoint simulations) for 168 countries amounting to around 2000 total 2-month simulations. This version of GEOS-Chem does not include fugitive dust and secondary organic aerosol which biases our results low as discussed in our prior work 25 and in the uncertainty analysis section.

To define the cost-functions of our adjoint simulation that are relevant for health outcomes, we integrate satellite-remote sensing derived estimates of PM 2.5 exposure 41 to downscale and rescale our simulated concentrations 36,42 to reduce resolution-based uncertainty in our health impact assessment. These data are available at 0.1°×0.1° and correspond to a global population-weighted PM 2.5 concentration of 32.6 μgm−3 with a variance of up to 33.9 μgm−3 in parts of Asia and Latin America, where uncertainty was highest. Additionally, we calculate the population-weighted average of these downscaled concentrations for each country to characterize the PM 2.5 exposure (i.e., how populations are exposed to PM 2.5). We use the cost-function definition for PM 2.5 from our prior work 36 and calculate sensitivities to BC, OC, NH 3, NO x, and SO 2 emissions. After calculating the cost-functions, the adjoint simulation calculates the linear sensitivity of population-weighted PM 2.5 exposure in a country to these emissions:

λ I,k,m = ∇ E I,k J m = ∂J m / ∂E I,k (1)

The adjoint sensitivities (λ I,k,m) of population-weighted PM 2.5 in country m to emissions from grid cell (I) and precursor species (k) are calculated by taking the gradient of the cost-function (J m) for country m. We employ adjoint simulations, as opposed to finite difference calculations from standard forward model simulations, to be more computationally efficient. To calculate the sensitivities for PM 2.5 exposure for each country and scenario for a single year would require 48,900 2-month simulations; this is over twenty times as many simulations as our adjoint approach. As employed here, the adjoint sensitivities are limited in that they represent the linear response to the cost-function from the model parameters (i.e., emissions), second order and non-linear effects are not captured through this approach. While the emission response of primary PM 2.5 is linear, there are still substantial non-linear effects of secondary inorganic aerosol that are not captured using this adjoint approach that are investigated in more detail in prior work 25.

Projecting changes in PM 2.5 exposure from emission scenarios
We combine the gridded emission projections from the SSP and RCP scenarios 23 with the gridded adjoint sensitivities for each country and each chemical precursor species, to estimate how projected changes in emissions in all grid cells contribute to changes in PM 2.5 exposure for each receptor country:

ΔJ I,m,s = λ I,k,m ΔE I,k,s (2)

ΔE I,k,s = E I,k,s − E I,k,SSP3-Baseline (3)

where ΔJ I,m,s is the contribution to the cost-function—i.e., PM 2.5 exposure in country m—from emissions in a 2°×2.5° grid cell, I for country m and scenarios. Specifically, we consider the emission delta, ΔE I,k,s, that is the difference between emissions from a scenario (s) E I,k,s compared to emissions from the SSP3-Baseline scenario in a chosen year (for this study, primarily 2040) E I,k,SSP3-Baseline. We note that we selected 2040 as the main year for our analysis because it was the first year in which SSP3 scenarios began to deviate from the SSP3-Baseline, it was the latest year for which we had GBD Foresights data (see next section), and approximately when global NO x emissions peak for the SSP3-Baseline scenario. In calculating these contributions to PM 2.5 exposure we identify how emissions from every grid cell—masking data from SEDAC CIESIN 38—identify how changes in emissions in specific countries or country blocs could influence air pollution in the future for each receptor country. Further details on this methodology can be found in our prior work 14,25,30,36,42–46.

Health impact calculation
We calculate the health impacts associated with changes in annual average PM 2.5 exposure associated from emission scenarios relative to the SSP3-Baseline following established methods 25. Briefly, we use relative risk tables developed in the GBD 2019 study 4 to relate specific PM 2.5 exposures to increased risks of premature death from ischemic heart disease, stroke, chronic obstructive pulmonary disease, and lung cancer. We combine these relative risks with national baseline disease rates and population data from the GBD 2019 study for a base year of 2010—the same as the adjoint sensitivities. For future years (i.e., 2040) we project these population and baseline disease rates using results from the GBD Foresights project 24 to project population and baseline disease rates into the future.

Mortality m = y0m [1 − 1/RRm] Popm (4)

Where y0m corresponds to the national baseline disease rate in country m projected to the year of analysis (i.e., 2040), RRm refers to the relative risk derived from a PM 2.5 exposure in country m and Popm corresponds to the population in country m projected to the year of analysis. For each grid cell, country, and scenario we perform this calculation twice: once calculating RRm using the baseline PM 2.5 exposure from the cost-function and a second time with the contribution (ΔJ I,m,s) removed from the cost-function. By taking the difference of these two premature deaths estimates we calculate the number of deaths avoided—relative to the SSP3-Baseline—from emission reductions in every grid cell (I), SSP/RCP scenario (s), and in each receptor country (m). We note that we perform this calculation for each distinct combination of health outcomes and age groups, following the GBD methodology, and then aggregate these estimates. Ultimately, this approach identifies how emissions from specific locations and scenarios affect every country for which we perform an adjoint sensitivity calculation.

Calculating transboundary fractions and exchanges in air pollution health impacts
To explore how inequalities in transboundary air pollution—and the associated co-benefits from climate emission scenarios—vary across different climate futures, we define a set of metrics. First, we consider the transboundary fraction:

TFm,s = Σ I≠m ΔJ I,m,s / Σ I ΔJ I,m,s (5)

The transboundary fraction (TFm,s) for country m and scenario s is equivalent to the ratio of the co-benefits contributed to country m from emissions outside of the country compared to the co-benefits contributed from everywhere, including the country. This is calculated by considering emission reductions that occur in grid cells that are not within m (I≠m), over the benefits in country m contributed by emission reductions from all grid cells. A transboundary fraction close to one indicates that country m receives most of its benefits through external emission reductions, whereas a ratio close to zero indicates that domestic emission reductions contribute to more benefits within m than reductions elsewhere. Next, we consider exchanges:

EXCm,n,s = Σ I∈m ΔJ I,n,s (6)

The exchange (EXCm,n,s) between countries or regions m and n for scenario s is equivalent to the amount of air pollution-related benefits in n that are attributable to emission reductions in m for scenario s. The net exchange refers to the difference between two exchanges in a country pair (i.e., EXCm,n,s - EXCn,m,s). Lastly, the contribution to total co-benefits exchanged, used in Fig. 5, refers to the percentage of total co-benefits exchanged between a pair attributable to one member of the pair:

TECm,n,s = EXCm,n,s / (EXCm,n,s + EXCn,m,s) (7)

Thus, the contribution to total co-benefits exchanged, TECm,n,s, compares the exchange contributed by country m to country n (EXCm,n,s) to the total gross co-benefits exchanged between the two countries (EXCm,n,s + EXCn,m,s). A TEC value of 0.5 indicates balance, while values above 0.5 indicate greater contributions from country m to country n, and values below 0.5 indicate greater contributions from country n to country m.

Uncertainty analysis
Uncertainty is introduced primarily by: (1) emission projections, (2) the GEOS-Chem forward simulation, (3) the adjoint sensitivity calculation, and (4) the health impact analysis.

Although it is impossible to quantify a specific uncertainty associated with emissions projected for the future—given that the true values are unknown—the emission projections 23 were tested against historical emissions from another inventory, the Community Emissions Data System (CEDS) 47 and they were found to be well correlated for SO 2 from all sectors in 2005 (R2=0.71). Additionally, the correlation with CEDS projections in 2050 for the energy sector was examined for both SO 2 (R2=0.44) and NO x (R2=0.70). The HTAP emissions 48 that drive our forward simulation have been compared to other inventories 49. Of the 12 regions considered in that analysis, HTAP had the minimum emissions of all bottom-up inventories in five of the regions for NO x (27% lower than average), none of the regions for SO 2, five of the regions for BC (44% lower than average), and three of the regions for OC (48% lower than average). Additionally, HTAP had the maximum emissions in none of the regions for NO x, two of the regions for SO 2 (39% higher than average), two of the regions for BC (21% higher than average), and two of the regions for OC (62% higher than average). This comparison suggests that the HTAP emissions have regional biases; however, compared to CEDS, the HTAP emissions of NO x are approximately 16% lower on a global scale. Given the degree of uncertainty in these emission projections, we exclusively consider the relative differences in projections as opposed to their absolute implications for our analysis.

GEOS-Chem has a strong track record of estimating atmospheric composition that compares well to observations 50,51. In an HTAP ensemble analysis 52 of the PM 2.5 health burden associated with intercontinental transport, the simulated PM 2.5 of the GEOS-Chem adjoint was compared to over 3000 global monitors. GEOS-Chem adjoint simulated concentrations fell within the range of ensemble members with a normalized mean error of 55.1% (ranged from NME=35.4% to NME=62.9%) and a correlation of 0.65 (ranged from R=0.63 to R=0.77). The GEOS-Chem adjoint had the most positive bias of the ensemble members (NMB=20.3%); however, in an absolute sense the bias was in the middle of the spread of ensemble members (ranged from NMB=−60.9% to NMB=20.3%). Additionally, all of the ensemble members underestimated the health burden of PM 2.5 compared to the GBD 2015 study (4.2 million premature deaths); thus, this potential high bias in the GEOS-Chem adjoint led to a more accurate estimate of health impacts (3.2 million premature deaths) than the multi-model mean (2.8 million premature deaths). We also note that a majority of the surface-level observations were located in North America, Europe, and China; thus, this analysis may not be representative of biases in other regions of the world.

The version of GEOS-Chem that is used in our analysis does not include anthropogenic fugitive dust and secondary organic aerosol; the prior was estimated 53 to increase global population-weighted PM 2.5 by around 2.9 μgm−3 and the latter was estimated 35 to make up between 15–30% of anthropogenic PM 2.5 in urban environments. Fugitive dust PM 2.5 is a primary species with a short atmospheric lifetime that mainly affects concentrations near where it is emitted. In contrast, secondary organic aerosol has a longer atmospheric lifetime enabling its transport across greater distances. For example, one study 37 simulated PM 2.5 transport for one UK city, and found that approximately 5% of the total PM 2.5 came from non-local secondary organic aerosol. In our previous work 36, we estimated that around 10% of PM 2.5 in DC, originated from non-local secondary organic aerosol. These past analyses suggest that the exclusion of SOA could lead to an underestimate of between 5–10% of non-local PM 2.5 that in turn would imply an underestimate of transboundary fractions and exchanges by this magnitude. However, we note that these past studies are for two locations in the Global North and not representative of the regional variation in this study and that the SOA contributions are calculated for urban areas that have higher local sources than rural areas.

Another source of uncertainty in our analysis is the application of local-linear sensitivities to inherently non-linear PM 2.5 formation. Although the primary components of PM 2.5 (i.e., black and organic carbonaceous aerosol) respond linearly to emissions, secondary inorganic aerosol does not respond linearly. Thus, this approach does not capture the second order effects of secondary inorganic aerosol. A previous study 30 explored the uncertainty associated with this local-linear assumption in response to SSP emission projections in Korea, and found that non-linearities contributed an underestimate of up to 57% in the PM 2.5-related health benefits from mitigation (although this uncertainty was lower for 2040).

Lastly, we quantify the uncertainty introduced from the health impact analysis by using upper and lower bound estimates of the different components of the health calculation (i.e., the relative risk, population estimates, and baseline disease rates) using the lower and upper bound values provided by the GBD study. This uncertainty is the only value included in the uncertainty bounds presented throughout the text given that previous work suggests that this is the largest contributor to uncertainty in our approach 54. We do not consider uncertainty in the projected changes to health data (i.e., the projected population and disease rates for 2040).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript evaluates how socioeconomic trends (SSPs) and climate mitigations (RCPs) might affect the health burdens associated with PM2.5 air pollution, and explores PM2.5 transboundary exchanges and associated environmental inequities across different climate scenarios. This manuscript is well organized and provides interesting results. However, several issues need to be addressed before the manuscript can be considered for publication.\n(1) The study involves the coupling of multiple data and models,including GEOS-Chem, Health impact calculation, and so on. The authors should consider the uncertainty introduced by each link and its impact on the final results to increase the credibility.\n(2) The PM2.5 transboundary exchanges include atmospheric transport and trade exchange, as should be noted in the text. And there are some studies that have revealed the impact of atmospheric transport on health and environmental inequities in historical years that the authors can compare the findings of this paper with them.\n(3) The manuscript defines two metrics to describe unfairness, the imbalance ratio and the exchange ratio, but their descriptions in the main text are too difficult to understand, such as Line538-555, Line54-56. The authors' intent is not understood until Figure S1.\n(4) Based on the impacts of climate action on PM2.5-related health and inequity analyzed in this paper, the authors can make specific recommendations for climate pathways or mitigation actions in future years.\n(5) What is the basis for the article's choice of 2040 as the study year? The results for 2030 are also presented in SI; is there any difference in the findings on inequality between the two years?\n(6) Confidence intervals are missing for some of the premature deaths.\n(7) What are the data sources for baseline incidence and population in 2040?national scale?\n(8) The font size of Figure 1 (a) (b) and Figure 3 (a) is too small. Consider enlarging or using abbreviations.", "reviewer_2": "Review Comments for NCOMMS-25-17271:\nWhile the manuscript presents a comprehensive assessment of transboundary PM2.5-related health co-benefits under various climate futures, and introduces metrics such as the imbalance ratio and exchange ratio, the overall methodological innovation is incremental rather than groundbreaking. The study builds upon previously published adjoint-based sensitivity analyses, extending the scale and scope, but without introducing novel modeling frameworks or substantially new insights.\nMajor concerns:\n1. The methodology is largely based on previous work. The modeling approach, including the use of the GEOS-Chem adjoint model for estimating sensitivities of PM2.5 exposure to precursor emissions, has been described in several earlier studies. The integration with health impact assessment methods and SSP-RCP emissions scenarios also follows a structure that has previously been applied. In the current manuscript, the methodology is scaled to a global level and applied to a broader set of scenarios. However, no substantial changes to the model, workflow, or conceptual framework are introduced. As such, the novelty of the methodology appears to be limited. For a journal that prioritizes conceptual or methodological advancement, this may not be sufficient.\n2. While the imbalance and exchange ratios are introduced as new indicators, they are derived directly from existing model outputs and are relatively straightforward in formulation. Their interpretation remains largely descriptive, and their implications are not developed in depth. The study shows that some regions receive more co-benefits than they contribute and that these patterns vary across climate scenarios. However, these observations tend to confirm existing assumptions about global inequalities rather than reveal new or unexpected findings. It is not shown how these indicators would be useful for designing future policy or guiding negotiations on climate action and air quality management.\n3. All results are presented for the year 2040. Since SSP-RCP pathways evolve differently over time, it is unclear whether the identified patterns of imbalance and exchange are consistent across other time points. A single-year analysis may be influenced by scenario-specific assumptions in that year and does not allow for understanding temporal trends or cumulative effects. Although additional figures for 2030 are provided in the supplement, the main analysis remains focused on one year, which limits the robustness of the conclusions.\n4. The version of GEOS-Chem used in this study does not include secondary organic aerosol formation, which can contribute significantly to PM2.5, especially in urban and vegetated regions. The lack of these components may lead to an underestimation of long-range transport and influence the imbalance and exchange results. Additionally, the adjoint sensitivities are based on linear approximations, which may not fully capture the nonlinear chemical reactions involved in PM2.5 formation, particularly in areas with high emissions of ammonia and nitrogen oxides. The limitations of the chemical representation are acknowledged briefly in the methods section, but their implications for the main findings are not discussed in detail.\n5. Although the manuscript touches on environmental justice and potential inequalities in the distribution of air pollution benefits, it is not made clear how the presented metrics could be applied in practice. There is a lack of connection between the model outputs and current international climate mechanisms or policy instruments. The imbalance ratio, in particular, is discussed without a clear normative interpretation. It remains uncertain whether a high or low ratio should be viewed as desirable or problematic, and how such findings could inform equitable burden sharing in climate action.\nSpecific Comments on Figures, Tables, and Technical Details:\n1. In lines 99–101, the main text states that SSP1 avoids 0.53 million deaths, but Table 1 reports 581,000 (i.e., 0.581 million) deaths for SSP1-19. While the values are close, the correspondence between the text and specific scenarios in Table 1 should be clarified to avoid confusion.\n2. The concept of the imbalance ratio is introduced around line 131, but its calculation is not explained until the Methods section (line 531 onward), and the visual schematic appears only in the supplement (Figure S1a).\n3. In Figure S5, the “Asia to Europe” exchange ratio under one of the SSP-RCP scenarios (likely around SSP2-34 or SSP2-45) spikes to a value exceeding 5, which is visually inconsistent with the rest of the dataset and other scenario curves. This point is not acknowledged or explained in the figure caption, nor is there any indication that the y-axis was clipped or rescaled to accommodate outliers.\n4. The caption for Figure 4 explains that values greater than 1 mean Region A contributes more to Region B’s benefits than vice versa. However, this phrasing could be misread as “Region A receives more benefit” rather than “Region A’s actions have greater effect”. Rephrase the caption to clarify directionality, e.g., “Values greater than one indicate that climate mitigation in Region A provides more benefits to Region B than the reverse.”\n5. Table 1 presents NOx emissions in teragrams and avoided deaths in thousands, but these units are not explicitly listed in the table headers. I think add units in parentheses to the headers for clarity (e.g., “Global NOx Emissions (Tg)”, “Global Deaths Avoided (Thousands)”) will improve current version.\n6. Oceania is excluded from Figure 1c on the grounds of having disproportionately high imbalance ratios due to small population size. However, no supporting data is shown to validate this exclusion. Please provide numerical values or variance ranges for Oceania in the supplement to justify its removal from the main analysis.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "This study involves a substantial amount of work. It presents regional inequality in the benefits of emission reduction under different SSP and RCP scenarios from a novel perspective, while also considering transboundary pollution transport. However, several issues still require the authors’ attention. Please make major revisions to the article.\nComments:\n1. The abstract is difficult to understand without reading the full manuscript. It requires appropriate revision for clarity.\n2. Table 1 does not present all scenarios, such as SSP3-Ba. The authors should clarify the reason for this omission.\n3. The reference to Figure 1 in line 116 seems inappropriate. In addition to providing Table S1, a spatial distribution map of the six regions should also be included.\n4. Why are Europe and Africa grouped into the same category? Is this classification unrelated to socioeconomic development levels? The rationale should be further explained.\n5. What does “IR” refer to in line 223? Its full name should be provided upon first appearance.\n6. What is the reason for the inclusion of the four specific regions in Figure 2a? Please clarify.\n7. The results presented in the manuscript are relatively limited. It is recommended to include spatial distribution and flow maps of health co-benefits under different scenarios.\n8. The manuscript primarily provides descriptive explanations of the observed phenomena, but lacks further analysis of the underlying causes. What specific factors lead to regional inequality in climate action benefits under different scenarios? Are these related to economic levels or population structures? Moreover, are there any suggestions for more equitable mitigation scenarios or compensatory mechanisms?"}, "author_response": "Response to Reviewers\n1.0 Guidance for this document:\nWe thank the editorial team and the reviewers for their valuable comments and suggestions for\nour manuscript entitled “National climate action can ameliorate, perpetuate, or exacerbate\ninternational air pollution inequalities” that we submitted to Nature Communications earlier this\nyear. We have endeavored to integrate these reviews and revise our manuscript to be less\ndescriptive, to more directly connect the quantitative results to conclusions, and to link our\nfindings to policy recommendations as discussed in our responses below. To differentiate\ncomments from the reviewers, the authors, and new additions to the text please refer to the key:\nElement Text Style\nReviewer Comment Italicized black text\nEditor Comment Italicized black text\nAuthor Comment Plain black text\nOriginal Manuscript Text Plain green text\nRevised Manuscript Text Plain blue text\nOriginal Line Number (in parentheses)\nNew Line Number [in brackets]\n2.0 NOTE TO ALL: In working through these revisions, we noticed an error in our original\nanalysis in which health benefits from specific endpoints were accidentally excluded from our\ntotal estimates. This led to an underestimate of co-benefits in the original manuscript of ~56%.\nThis mostly affected the total magnitude estimates (i.e., Table 1) and not the inequalities\nexplored in the paper; however, there were some minor impacts on inequalities due to different\nbaseline disease rates for different health outcomes across countries.\nAdditionally, given suggestions from the reviewers and editors, we have made major changes to\naspects of this analysis. We have revised the original “imbalance ratio” and “exchange ratio”\nindicators into forms that are hopefully more intuitive and have explained these in detail in a new\nFigure (Figure 1), now included in the main text. Using these revised metrics, we have updated\nall of the figures and adjusted the language of the results to more quantitively discuss inequalities\nand connect them to their causes and policy implications. While many of the ideas in the original\nmanuscript are included here as well, we have endeavored to more clearly connect them to the\nquantitative results.\n3.0 Reviewer Comments\n3.1 Reviewer #1:\nR1C0: The manuscript evaluates how socioeconomic trends (SSPs) and climate mitigations\n(RCPs) might affect the health burdens associated with PM2.5 air pollution, and explores PM2.5\ntransboundary exchanges and associated environmental inequities across different climate\nscenarios. This manuscript is well organized and provides interesting results. However, several\nissues need to be addressed before the manuscript can be considered for publication.\nWe thank Reviewer #1 for their positive comments; thank you for your suggested changes as we\nbelieve that these have greatly improved the quality of this work.\nR1C1: The study involves the coupling of multiple data and models, including GEOS-Chem,\nHealth impact calculation, and so on. The authors should consider the uncertainty introduced by\neach link and its impact on the final results to increase the credibility.\nThank you for raising this issue that we agree was missing in our original manuscript. To address\nthis, we have now: (1) quantified uncertainty in our health impact calculation, (2) updated Table\n1 and Figure 2 (previously Figure 1) to include uncertainty bounds (see section 4.0 of this\ndocument), (3) written a new uncertainty analysis section and added this to the methods, and (4)\nupdated the discussion to go into more detail on these uncertainties. Beyond the health\ncalculation, uncertainty is additionally introduced from the emission projections, chemical\ntransport model simulation, and in the adjoint sensitivity calculation. In previous work1 we found\nthat the health impact calculation was the leading source of uncertainty; however, we recognize\nthat this may not be the case when considering emission projections in the future. Please refer to\nour new updated “uncertainty analysis” section for our detailed thoughts on these uncertainties:\n[743-782]\nUncertainty analysis\nAssumptions and data that we use to estimate the health and equality impacts of climate action\nintroduce uncertainty into our results and conclusions. Specifically, uncertainty is introduced\nprimarily by: (1) emission projections, (2) the GEOS-Chem forward simulation, (3) the adjoint\nsensitivity calculation, and (4) the health impact analysis.\nAlthough it is impossible to quantify a specific uncertainty associated with emissions projected\nfor the future – given that the true values are unknown – the original dataset2 was tested against\nhistorical emissions from another inventory, the Community Emissions Data System (CEDS)3\nand the emissions were found to be well correlated for SO from all sectors in 2005 (R2=0.71).\nfound moderate correlation for both SO (R2=0.44) and NO (R2=0.70). Given the degree of\n2 x\nuncertainty in these emission projections, we exclusively consider the relative differences in\nprojections as opposed to their absolute implications for our analysis.\nAlthough GEOS-Chem makes assumptions and simplifications to simulate the formation and\ntransport of air pollution in a computationally tractable manner, it has a strong track record of\nestimating atmospheric composition that compares well to observations4,5. The version of the\nmodel that we use in this analysis does not include anthropogenic fugitive dust and secondary\norganic aerosol; the prior was estimated6 to increase global population-weighted PM by around\n2.5\n2.9 𝜇g m-3 and the latter was estimated7 to make up between 15%-30% of anthropogenic PM in\n2.5\nurban environments. Fugitive dust PM is a primary species with a short atmospheric lifetime\n2.5\nthat mainly affects concentrations near where it is emitted. In contrast, secondary organic aerosol\nhas a longer atmospheric lifetime enabling its transport across greater distances. Given the focus\nof\nour analysis on transboundary air pollution, it is likely that the exclusion of secondary organic\naerosol from the simulation will lead to an underestimate of transboundary air pollution. Another\nsource of uncertainty in our analysis is the application of local-linear sensitivities to inherently\nnon-linear PM formation. Although the primary components of PM (i.e., black and organic\n2.5 2.5\ncarbonaceous aerosol) respond linearly to emissions, secondary inorganic aerosol does not\nrespond linearly. Thus, this approach does not capture the second order effects of secondary\ninorganic aerosol.\nLastly, we quantify the uncertainty introduced from the health impact analysis by using upper\nand lower bound estimates of the different components of the health calculation (i.e., the relative\nrisk, population estimates, and baseline disease rates) using the lower and upper bound values\nprovided by the GBD study. This uncertainty is the only value included in the uncertainty bounds\npresented throughout the text given that previous work suggests that this is the largest contributor\nto uncertainty in our approach1. We do not consider uncertainty in the projected changes to health\ndata (i.e., the projected population and disease rates for 2040).\nAnd refer to our updates to the discussion section on uncertainty:\n(384-402)\nThere are many factors that could influence the unequal distribution of air pollution across\ndifferent socioeconomic trends and mitigation strategies beyond what is considered in this study.\nSpecifically, our adjoint-derived methodology calculates linear relationships between emissions\nand PM and while some components of PM (e.g., primary carbonaceous aerosol) are formed\n2.5 2.5\nlinearly, others (e.g., secondary inorganic aerosol) are formed through complex non-linear\nchemistry8. Our analysis relies on the simplified assumption that the pollution formation\nrelationships calculated in our simulation will be maintained in the future, although we expect\nchanges to climate9,10 and chemical11,12 environment would be additional modifiers to consider.\nAdditionally, our analysis neglects to include secondary organic aerosol formation that makes up\na substantial component of total PM , especially in heavily populated urban areas13. Given the\n2.5\nlonger atmospheric lifetime of this component, it is likely that we underestimate some of the\nlong-range exchange of air pollution. Additionally, this analysis is conducted for emissions\nscenarios in a single year – 2040 – so it represents a snapshot in time of the differences across\nthese socioeconomic trends and mitigation strategies. Given that the SSP-RCP emissions are\ndesigned to peak at different points across different countries and regions, it is likely that these\nimbalances and exchanges will differ at other points in the future. While in this study we do not\nexplore this in detail, a benefit of this adjoint methodology is its ability to rapidly assess different\nyears – as well as different scenarios – offline without additional simulations. To demonstrate\nthis, we include figures for an additional year (i.e., 2030) in the supplement (Figure S2-S5).\n[530-553]\nThe results presented in our work should be considered alongside sources of uncertainty. First,\nour adjoint-derived methodology calculates linear relationships between emissions and PM and\n2.5\nwhile some components of PM (e.g., primary carbonaceous aerosol) are formed linearly, others\n2.5\n(e.g., secondary inorganic aerosol) are formed through complex non-linear chemistry8.\nAdditionally, our estimates of co-benefit contributions rely on the simplified assumption that the\npollution formation relationships that we calculate for our base year (i.e., 2010) will be\nmaintained in the future. We anticipate that changes to climate9,10 and chemical11,12 environment\nin the future will modify transboundary air pollution and we suggest that feedbacks between\nclimate change and transboundary air pollution are explored in future work. Our analysis\nneglects to include anthropogenic fugitive dust and secondary organic aerosol formation that\ncontribute to PM , especially in heavily populated urban areas7. Given the longer atmospheric\n2.5\nlifetime of secondary organic aerosol, it is likely that we underestimate some of the long-range\nexchange of air pollution. Additionally, this analysis is conducted for emissions scenarios in a\nsingle year – 2040 – so it represents a snapshot in time of the differences between these\nsocioeconomic trends and mitigation strategies. Given that the SSP-RCP emissions are designed\nto peak at different points across different countries and regions, it is likely that these\ntransboundary fractions and exchanges will differ at other points in the future. While in this\nstudy we do not explore this in detail, a benefit of this adjoint methodology is its ability to\nrapidly assess different years – as well as different scenarios – offline without additional\nsimulations. To demonstrate this, we include figures for an additional year (i.e., 2030) in the\nsupplement (Figure S6-S9). Lastly, although we estimate uncertainty associated with the health\ncalculation, there is additional uncertainty in the projected changes to population, age\ndistribution, and baseline disease rates that are not captured in our analysis along with\nuncertainty on the air quality modeling side as we discuss in the methods.\nR1C2: The PM2.5 transboundary exchanges include atmospheric transport and trade exchange,\nas should be noted in the text. And there are some studies that have revealed the impact of\natmospheric transport on health and environmental inequities in historical years that the authors\ncan compare the findings of this paper with them.\nWe agree with the reviewer that transboundary exchanges of PM include both an atmospheric\n2.5\ntransport component and exchanges associated with trade (i.e., production and consumption\nrelationships). We have now clarified this in the text and cited some recent relevant studies:\n[63-64]\nAtmospheric transport drives some of this exchange; however, trade imbalances in consumption\nand production contribute to air pollution inequalities as well14,15.\nAdditionally, as you suggest we have now compared our analysis to some prior work. We note\nthat in our analysis we find a higher transboundary component than some past work; however,\nthis is likely due to the greater granularity (i.e., countries instead of regions) that our adjoint\nanalysis enables as we discuss in the text:\n[269-280]\nWe note that these transboundary contributions are substantially greater than past analyses16,17 of\nhistorical transboundary air pollution burdens, which found 14% and 12% of all PM -related\n2.5\ndeaths were attributable to transboundary pollution. This difference is likely attributable to three\nmain factors: (1) these past studies focused on aggregated groups of countries whose boundaries\nare further from peak emissions locations whereas we perform this calculation for individual\ncountries, (2) in this study we consider climate action whereas these previous studies consider all\nanthropogenic emissions; climate action is not adopted to the same degree globally and many of\nthe highest emission reductions are implemented in a small set of developed nations, and (3)\nmany of the targets of climate action are precursors of secondary PM (specifically NO and\n2.5 X\nSO ) and not primary PM and thus this action targets pollution that is more likely to be\n2 2.5\ntransported greater distances due to its longer atmospheric lifetime.\nR1C3: The manuscript defines two metrics to describe unfairness, the imbalance ratio and the\nexchange ratio, but their descriptions in the main text are too difficult to understand, such as\nLine538-555, Line54-56. The authors' intent is not understood until Figure S1.\nOur original descriptions of the imbalance and exchange ratios were lacking and were not placed\nin an appropriate area as you suggest (other reviewers have commented on this as well). To\naddress this, we have made major modifications. First, we have reworked the original indicators\ninto quantities that are hopefully more intuitive, and we have developed a new diagram\nexplaining the terms (originally Figure S1 now Figure 1) and moved this to the beginning of the\nresults section so that readers encounter it immediately (see section 4.0 of this document for the\nnew figure). Lastly, we have enhanced descriptions of the metrics throughout:\n(115-129)\nTo further explore how socioeconomic trends and mitigation strategies vary across different\ngeographies, we group all countries into one of six regions: South America (SA), Oceania (OC),\nNorth America (NA), Europe (EU), Asia (AS), and Africa (AF) (Figure 1). The specific countries\nin each of these regions are provided in the supplement (Table S1). We estimate the region-\nspecific co-benefits (i.e., deaths avoided from climate action) across all scenarios (Figure 1a) and\nthese same co-benefits normalized for each scenario (Figure 1b). Scenarios in which climate\naction is less aggressive – such as trends towards inequality and fragmentation and higher\nradiative forcing (i.e., higher RCPs) – lead to fewer health benefits from climate action. This is\nconsistent across the six regions with a majority of the health benefits occurring in Asian\ncountries, followed by Europe, Africa, North America, and then South America. In sustainable\nfutures, such as SSP1-19, co-benefits in Asian countries make up the largest percentage of the\nglobal co-benefits (72%), more so than Europe (13%), Africa (8%), North America (4%) and\nSouth America (3%). However, in fragmented futures, such as SSP3-60, the Asian share of co-\nbenefits grows (83%) and the share of other regions decreases relatively for Europe (8%), Africa\n(5%), North America (3%), and South America (1%).\n[148-160]\nPast studies on the impact of climate action on air quality generally estimate the total magnitude\nof health benefits (as we do in the previous paragraphs); however, this neglects a crucial aspect\nof climate action with implications for equality: transboundary air pollution. To characterize\ndisparities induced by changes to transboundary air pollution we define two metrics that are\nassociated with air pollution inequality: “transboundary fractions” and “exchanges” (Figure 1).\nWhen global climate action is adopted, a fraction of the co-benefits accrued by a country are\nattributable to external action (i.e., climate mitigation outside of its borders). We define a\n“transboundary fraction” (Figure 1a) that quantifies the fraction of all co-benefits in a country\nthat originate from action outside that country (refer to the methods section for more details).\nThe fraction can also be calculated for regions of aggregated countries. Higher transboundary\nfractions indicate a country or region that requires more regional or global cooperation to realize\nthe benefits of climate action whereas a lower transboundary fraction positions a country or\nregion as largely being in control of their own climate co-benefits.\n(237-244)\nThe benefits associated with climate action in one country are not exclusively realized within the\nsame country. To unpack how intraregional and interregional exchanges in climate action benefits\ncould differ across different socioeconomic trends and mitigation strategies, we\ncalculate “exchange ratios” that represent the transboundary exchange of air pollution health\nimpacts from one region to another. The exchange ratio is defined as the ratio of the benefits\noccurring in one country owing to emissions reductions in a second country, divided by the\nbenefits in the second country owing to emissions reductions in the first country, as shown in\nmore detail in the methods and schematically in the supplement (Figure S1b).\n[326-335]\nThe exchange of climate co-benefits between pairs of countries (or regions) evolves in response\nto different socioeconomic development pathways and climate mitigation strategies. We\ncharacterize the intraregional and interregional exchange of climate co-benefits by calculating\n“exchanges” (Figure 1b) that represent the air pollution co-benefits realized in one country that\nare attributable to climate action in another country. In our analyses, we consider exchanges in\nthree ways: (1) in their absolute sense an “exchange” represents how emission reduction in one\ncountry induces co-benefits in another, (2) as “net exchanges” by differencing the two exchanges\nin a pair, and (3) the proportion of co-benefits contributed by one member of a pair to the total\ngross co-benefits exchanged between the pair to determine if a specific country dominates the\nexchange of air pollution co-benefits.\n[710-723]\nTo explore how inequalities in transboundary air pollution – and the associated co-benefits from\nclimate emission scenarios – vary across different climate futures, we define a set of metrics.\nFirst, we consider the transboundary fraction:\n𝑇𝐹 = \n∑ 𝐼∉𝑚Δ𝐽𝐼,𝑚,𝑠\n(5)\n𝑚,𝑠\n∑ 𝐼Δ𝐽𝐼,𝑚,𝑠\nThe transboundary fraction (𝑇𝐹 ) for country 𝑚 and scenario 𝑠 is equivalent to the ratio of the\n𝑚,𝑠\nco-benefits contributed to country 𝑚 from emissions outside of the country compared to the co-\nbenefits contributed from everywhere, including the country. This is calculated by considering\nemission reductions that occur in grid cells that are not within 𝑚 (𝐼 ∉ 𝑚), over the benefits in\ncountry 𝑚 contributed by emission reductions from all grid cells. A transboundary fraction close\nto one indicates that country 𝑚 receives most of its benefits through external emission\nreductions, whereas a ratio close to zero indicates that domestic emission reductions contribute to\nmore benefits within 𝑚 than reductions elsewhere.\n(546-551)\nThe exchange ratio (𝐸𝑅 ) between country 𝑚 and 𝑛 for scenario 𝑠 is equivalent to the ratio\n𝑚,𝑛,𝑠\nbetween the benefits in 𝑚 from emission reductions in 𝑛 over the benefits in 𝑛 from emission\nreductions in 𝑚. An exchange ratio of 1 indicates that the two countries contribute equally to\nbenefits from climate action, a value greater than 1 indicates that 𝑚 receives more benefits from\n𝑛 than vice versa, and a value less than 1 indicates that 𝑛 receives more benefits from 𝑚 than\nvice versa. Schematic depictions of both ratios are included in the supplement (Figure S1).\n[723-741]\nNext, we consider exchanges:\n𝐸𝑋𝐶 = (6)\n𝑚,𝑛,𝑠\nThe exchange (𝐸𝑋𝐶 ) between countries or regions 𝑚 and 𝑛 for scenario 𝑠 is equivalent to\n𝑚,𝑛,𝑠\nthe amount of air pollution-related benefits in 𝑛 that are attributable to emission reductions in 𝑚\nfor scenario 𝑠. The “net exchange” refers to the difference between two exchanges in a country\npair (i.e., 𝐸𝑋𝐶 - 𝐸𝑋𝐶 ). Lastly, the “contribution to total co-benefits exchanged”, used in\n𝑚,𝑛,𝑠 𝑛,𝑚,𝑠\nFigure 5, refers to the percentage of total co-benefits exchanged between a pair attributable to\none member of the pair:\n𝑇𝐸𝐶 = \n𝐸𝑋𝐶𝑚,𝑛,𝑠\n(7)\n𝑚,𝑛,𝑠\n𝐸𝑋𝐶𝑚,𝑛,𝑠+ 𝐸𝑋𝐶𝑛,𝑚,𝑠\nThus, the contribution to total co-benefits exchanged, 𝑇𝐸𝐶 , compares the exchange\n𝑚,𝑛,𝑠\ncontributed by country 𝑚 to country 𝑛 (𝐸𝑋𝐶 ) to the total gross co-benefits exchanged\n𝑚,𝑛,𝑠\nbetween the two countries (𝐸𝑋𝐶 + 𝐸𝑋𝐶 ). A 𝑇𝐸𝐶 value of 0.5 indicates balanced\n𝑚,𝑛,𝑠 𝑛,𝑚,𝑠\ncontributions to co-benefits exchanged between countries, while values above 0.5 indicate\ngreater contributions from country 𝑚 and values below 0.5 indicate greater contributions from\ncountry 𝑛. Schematic depictions of these metrics are included Figure 1.\nR1C4: Based on the impacts of climate action on PM2.5-related health and inequity analyzed in\nthis paper, the authors can make specific recommendations for climate pathways or mitigation\nactions in future years.\nWe agree that there was a need in our original manuscript to better connect our results to specific\npolicy recommendations; this has also been raised by other reviewers and the editorial team. We\nhave now made major revisions throughout the text to address this and have dedicated a specific\nparagraph in the discussion for this purpose. Please refer to our responses to Reviewer #2 (R2C2)\nand (R2C5) and Reviewer #4 (R4C8) for details.\nR1C5: What is the basis for the article's choice of 2040 as the study year? The results for 2030\nare also presented in SI; is there any difference in the findings on inequality between the two\nyears?\nThank you for raising this issue as it was not discussed in the original manuscript, we choose the\nyear 2040 for three reasons: (1) it is the earliest year in which most of the SSP3 scenarios diverge\nfrom SSP3-Baseline, (2) it is the latest year for which we have GBD Foresights projections,\nand\n(3) it is approximately when global NO emissions peak. This was not mentioned in the original\nx\nmanuscript and so we have now updated the text to include this explanation:\n[670-673]\nWe note that we selected 2040 as the main year for our analysis because it was the first year in\nwhich SSP3 scenarios began to deviate from the SSP3-Basleline, it was the latest year for which\nwe had GBD Foresights data (see next section), and approximately when global NO emissions\nx\npeak for the SSP3-Baseline scenario.\nTo respond to your other comment regarding differences in the findings on inequality between\nthe two years, we agree that we do not give much detail on the differences in inequality between\n2030 and 2040. This was intentional, as quantifying and interpreting interannual changes in co-\nbenefits and inequality is a large undertaking and likely warrants an additional follow-up study;\nhowever, we have added some more detail to this by creating a new supplement Figure (Figure\nS2) that compares imbalance ratios between 2040 and 2030 in the style of Figure 3 (see section\n4.0) and have discussed it in the text:\n[303-308]\nAlthough we focus primarily on 2040 in our analysis, we additionally calculate the differences in\ntransboundary fractions between the base year of study (2040) and 2030 (Figure S2) across\nSSP1-26 and SSP3-26. We find that in 2040 transboundary fractions are moderately higher in\nChina, India, the US, and Brazil and lower in parts of Africa compared to SSP1 in 2030;\nhowever, these interannual changes are smaller than those attributable to socioeconomic changes.\nAdditionally, we refer to this as a potential source of uncertainty to consider in the discussion\n(see our response to R1C1).\nR1C6: Confidence intervals are missing for some of the premature deaths.\nThank you for pointing this out, this has now been resolved.\nR1C7: What are the data sources for baseline incidence and population in 2040? national\nscale?\nYou are correct, this has now been mentioned explicitly in the text:\n(508-510)\nWe combine these relative risks with baseline disease rates and population data from the GBD\n2019 study for a base year of 2010 – the same as the adjoint sensitivities.\n[687-689]\nWe combine these relative risks with national baseline disease rates and population data from the\nGBD 2019 study for a base year of 2010 – the same as the adjoint sensitivities.\nR1C8: The font size of Figure 1 (a) (b) and Figure 3 (a) is too small. Consider enlarging or\nusing abbreviations.\nThank you or this suggestion, we have now made major changes to Figure 2 (formerly Figure 1)\nand Figure 4 (formerly Figure 3) to improve readability (see section 4.0 of this document). We\nhave also enhanced the image resolution to retain more detail in the figures when zooming in.\n3.2. Reviewer #2:\nR2C0: While the manuscript presents a comprehensive assessment of transboundary PM2.5-\nrelated health co-benefits under various climate futures, and introduces metrics such as the\nimbalance ratio and exchange ratio, the overall methodological innovation is incremental rather\nthan groundbreaking. The study builds upon previously published adjoint-based sensitivity\nanalyses, extending the scale and scope, but without introducing novel modeling frameworks or\nsubstantially new insights.\nWe thank Reviewer #2 for their helpful comments and appreciate their insights. We believe that\nthrough incorporating your suggestions this manuscript has greatly improved. We address the\nspecific criticism on novelty in response to your next comment.\nR2C1: The methodology is largely based on previous work. The modeling approach, including\nthe use of the GEOS-Chem adjoint model for estimating sensitivities of PM2.5 exposure to\nprecursor emissions, has been described in several earlier studies. The integration with health\nimpact assessment methods and SSP-RCP emissions scenarios also follows a structure that has\npreviously been applied. In the current manuscript, the methodology is scaled to a global level\nand applied to a broader set of scenarios. However, no substantial changes to the model,\nworkflow, or conceptual framework are introduced. As such, the novelty of the methodology\nappears to be limited. For a journal that prioritizes conceptual or methodological advancement,\nthis may not be sufficient.\nThank you for raising this important point for discussion. We apologize that in the original\nversion of the manuscript the novelty of this work was not clearly stated. Through updating the\nfigures and reworking large sections of the results and discussion based on suggestions from\nyourself and other reviewers we believe that the novelty of this work is now clearer and more\nwell-articulated. We believe that there are many advancements in our analysis that positions it as\nan appropriate entry into the Justice and Equality in Decarbonization collection in Nature\nCommunications as we elaborate on below.\nWhile the underlying modeling approach that we have used here has indeed been described in\nour prior work, this is the first time that any detailed air quality modeling study has developed or\nused a source-receptor matrix for almost every individual country in the world. Regardless of the\ntechnical approach, we know of no prior work to achieve this, and thus the results here are\ntechnically and scientifically quite novel in that we resolve the long-range transport between\nalmost every pair of countries. Our approach enables probing of policy-related questions\nthroughout the globe without selection or aggregation biases, in contrast to previous studies that\nhave focused on a subset of countries or aggregations of countries, often to semi-continental or\ncontinental scales. Although recent studies18,19 have developed means of connecting air quality\nand climate effects of emissions controls to national impacts; these studies do not explore\ngeographic variation in both the policy implementation and health impacts as we do here. We\nfind, for example, a more significant role of atmospheric transport than prior studies16,20 owing to\nour ability to resolve individual countries. Further, for this study alone we include adjoint\nsensitivities calculated for 125 more countries than our previous study21 that focused just on the\nG20. This means that ~75% of these adjoint-based sensitivities are unique to this study and have\nnever been used in a scientific study before. Crucially, our prior analysis focused primarily on\nthe Global North (i.e., G20); however, there is a well-documented understudy bias of the Global\nSouth, and the results of our approach enable previously impossible analyses of inequalities in\nless developed nations as can be seen through many of our results and discussion that are focused\non African countries.\nSecond, we agree that fundamentals of our method are built upon prior work; however, we would\nargue that this is a positive aspect of our analysis. These are now established methods being\napplied to novel questions that have never been examined with this degree of detail but,\ncrucially, benefit from the comprehensive source-receptor specificity that this new set of adjoint\nsensitivities enable. This allow us to resolve previously undocumented details regarding\ninequality associated with climate action.\nLastly, we would note that none of the reviewers have compared our inequality analyses to other\nstudies, as no others exist -- further supporting the novelty of this work. We now explicitly call to\nattention the novelty of this work in the introduction:\n(86-88)\nWe identify not only the health impacts from specific scenarios but also – by leveraging the\nadjoint model source-receptor relationships – identify how each country contributes to impacts\nthroughout the world.\n[93-99]\nThese adjoint modeling results enable us to explore the impacts of climate action across a larger\ngeographic extent (only 25% of these sensitivities have been presented in prior analysis22), allow\nus to uncover novel information about climate action in the Global South that has historically\nbeen understudied, and, crucially, represent the first development of a source-receptor matrix for\nnearly every individual country facilitating the investigation of policy-related questions\nthroughout the globe without selection or aggregation biases.\nWe also have rewritten parts of the discussion text to emphasize this:\n(339-348)\nThrough the application of adjoint sensitivities in conjunction with SSP-RCP emission\nprojections, we characterize the benefits of climate action and consider how the exchange and\nimbalance relationships between countries and regions could evolve across different scenarios.\nThe approach developed in this work introduces methods to characterize how source receptor\nrelationships change in response to different challenges to mitigation and adaptation and offers a\nmeans to investigate how climate action could affect inequalities in the global distribution and\nexchange of air pollution. This progresses beyond prior analyses that focused on quantifying the\nmagnitude of benefits from emission reductions by investigating the complex exchange of air\npollution and health impacts between countries and regions.\n[457-465]\nWe integrate adjoint sensitivities with SSP and RCP emission projection to extend beyond past\nwork that estimated total co-benefits from climate action and consider how transboundary co-\nbenefits and exchange relationships between countries and regions could change across different\nclimate scenarios in 2040. We introduce novel capabilities for characterizing how country-\nspecific source receptor relationships worldwide change in response to different challenges to\nmitigation and adaptation. This enables us to investigate how climate action could affect\ninequalities in the global distribution and exchange of air pollution that progresses beyond prior\nanalyses that focused on quantifying the magnitude of benefits from emission reductions or\nexamined aggregated17 or limited22 source receptor relationships.\nR2C2: While the imbalance and exchange ratios are introduced as new indicators, they are\nderived directly from existing model outputs and are relatively straightforward in formulation.\nTheir interpretation remains largely descriptive, and their implications are not developed in\ndepth. The study shows that some regions receive more co-benefits than they contribute and that\nthese patterns vary across climate scenarios. However, these observations tend to confirm\nexisting assumptions about global inequalities rather than reveal new or unexpected findings. It\nis not shown how these indicators would be useful for designing future policy or guiding\nnegotiations on climate action and air quality management.\nBased on feedback from yourself and other reviewers it became clear that the old indicators (i.e.,\nimbalance and exchange ratios) were problematic and our interpretations of them could be\nimproved through more quantitative discussion that explored the implications of their values. To\naddress this, we have reworked the old indicators into a new set of simpler indicators (see also\nour response to R1C3) and now describe these in detail at the beginning of the results section\nalong with presenting them schematically and discussing their implications (see Figure 1):\n(133-141)\nWe define an “imbalance ratio” that compares the global benefits (deaths avoided) induced by\nclimate mitigation in a country compared to the benefits realized in that country from action\neverywhere (Figure 1c). The imbalance ratio is defined in the methods and explained\nschematically in the supplement (Figure S1a). We exclude the imbalance ratio from countries in\nOceania as they are much higher than the other five regions given their substantially smaller\npopulations. The regions fall into one of three categories: their countries contribute more benefits\nthan they receive regardless of scenario (i.e., imbalances greater than one), their countries\ncontribute less benefits than they receive regardless of scenario (i.e., imbalances less than one),\nor they have scenario-dependent imbalances (i.e., imbalances are both greater and less than one).\n[148-160]\nPast studies on the impact of climate action on air quality generally estimate the total magnitude\nof health benefits (as we do in the previous paragraphs); however, this neglects a crucial aspect\nof climate action with implications for equality: transboundary air pollution. To characterize\ndisparities induced by changes to transboundary air pollution we define two metrics that are\nassociated with air pollution inequality: “transboundary fractions” and “exchanges” (Figure 1).\nWhen global climate action is adopted, a fraction of the co-benefits accrued by a country are\nattributable to external action (i.e., climate mitigation outside of its borders). We define a\n“transboundary fraction” (Figure 1a) that quantifies the fraction of all co-benefits in a country\nthat originate from action outside that country (refer to the methods section for more details).\nThe fraction can also be calculated for regions of aggregated countries. Higher transboundary\nfractions indicate a country or region that requires more regional or global cooperation to realize\nthe benefits of climate action whereas a lower transboundary fraction positions a country or\nregion as largely being in control of their own climate co-benefits.\n(238-244)\nTo unpack how intraregional and interregional exchanges in climate action benefits could differ\nacross different socioeconomic trends and mitigation strategies, we calculate “exchange ratios”\nthat represent the transboundary exchange of air pollution health impacts from one region to\nanother. The exchange ratio is defined as the ratio of the benefits occurring in one country owing\nto emissions reductions in a second country, divided by the benefits in the second country owing\nto emissions reductions in the first country, as shown in more detail in the methods and\nschematically in the supplement (Figure S1b).\n[326-335]\nThe exchange of climate co-benefits between pairs of countries (or regions) evolves in response\nto different socioeconomic development pathways and climate mitigation strategies. We\ncharacterize the intraregional and interregional exchange of climate co-benefits by calculating\n“exchanges” (Figure 1b) that represent the air pollution co-benefits realized in one country that\nare attributable to climate action in another country. In our analyses, we consider exchanges in\nthree ways: (1) in their absolute sense an “exchange” represents how emission reduction in one\ncountry induces co-benefits in another, (2) as “net exchanges” by differencing the two exchanges\nin a pair, and (3) the proportion of co-benefits contributed by one member of a pair to the total\ngross co-benefits exchanged between the pair to determine if a specific country dominates the\nexchange of air pollution co-benefits.\nSecond, we agree that our interpretations of the old indicators were mostly descriptive, and that\nwe did not fully develop their implications. To address this, we have now discussed these\nindicators more quantitatively and further expand upon the implications of different values. We\nhave done this throughout the results text but highlight a few examples below:\n[209-218]\nAfrica is the region most affected by regionally external climate action: on average 12% (9%,\n19%) of its co-benefits originate from action in another region. This suggests that African\ncountries are especially reliant on external action to maximize their climate co-benefits.\nThis reliance is mediated by the degree of mitigation action. From RCP-45 to RCP-26,\nstronger\nmitigation induces proportionally more transboundary co-benefits across the different\nsocioeconomic development pathways ranging from 8% more in SSP1 to 53% more in SSP3.\nThis relationship between climate mitigation and transboundary co-benefits in Africa is likely\nattributable to development. The most aggressive climate mitigation scenarios necessitate\nstronger action in countries that have historically polluted more (e.g., countries in Europe) and\nso, as these countries adopt stronger climate action, Africa benefits proportionally more.\n[251-261]\nTransboundary fractions are more highly variable for countries than regions: e.g., there is a\nstandard deviation of 0.29 for countries compared to the 0.04 for regions in SSP1-26.\nThis\nvariability in the transboundary fraction is influenced by the degree of development (Figure 3).\nFor SSP1-26, the transboundary fraction for the twenty countries with the lowest Human\nDevelopment Index (HDI)23 is on average 0.76 compared to 0.65 for the twenty highest HDI\ncountries. Additionally, 90% of these low HDI countries are African nations with the two\nexceptions being Afghanistan (0.91) and Yemen (0.78). In contrast, the twenty countries with the\nhighest HDI are primarily from Europe, North America, Oceania, and Asia; none of these\ncountries are from Africa or South America. This suggests that developed nations receive\nproportionally fewer of their co-benefits from external action than developing nations and\nsubsequently that global cooperation benefits developing nations more than developed ones.\n[352-367]\nThese exchange inequalities can be exacerbated or ameliorated through different socioeconomic\ndevelopment pathways. For Africa, fragmented socioeconomic development transforms the\nrelatively equivalent exchange between South Africa and Mozambique in SSP1-26 to one in\nwhich South Africa contributes much more (3.39 times). For Asia, the previously equivalent\nPakistan-India exchange in SSP1-26 has greater contributions from India (1.33) in SSP3-26. The\ninequal exchange between China and India is perpetuated in middle-of-the-road (1.79),\nfragmented (1.76), and inequality (1.82) development pathways; however, it notably increases\nthrough conventional development (2.07). Lastly, in Europe, fragmented development\nexacerbates the exchange inequality between Spain and Portugal (5.78) but partially reduces the\ninequality between France and Spain (3.67). Many of the European co-benefit exchanges\nincrease in conventional development; this is likely owing to higher climate mitigation (RCP-26)\npaired with conventional development (SSP5) which necessitates greater climate action in\ndeveloped countries (including many in Europe) compared to other socioeconomic pathways in\nwhich there is more even sustainable development. When considering weaker climate mitigation\nas we include in the supplement (Figures S3 and S4), we see that many of these increased\nexchanges are resolved.\n[411-424]\nThere is even greater sensitivity to socioeconomic development at the country-scale (Figure 5b).\nWhen transitioning from fragmented (SSP3-45) to sustainable (SSP1-45) development there is\ngenerally more balance between country exchange pairs especially in the pairs that undergo the\nlargest change to exchanges: in the top ten pairs, the country with the higher GDP at present\ncontributed 61% of co-benefits in SSP3-45 compared to 50% in SSP1-45. Nine of these ten pairs\nhad more balanced exchange patterns (i.e., closer to 50%) in sustainable development futures\ncompared to fragmentation futures. Crucially, sustainable development consistently pushed\ntowards greater contributions from the lower GDP member of the exchange pair, ranging from\n32% lower in the India-Nepal pair to 19% lower in Spain-Morocco. This supports previous\nfindings that more sustainable futures will enable greater contributions to co-benefits from\npoorer nations due to greater development and industrialization. In the transition instead from\nSSP2-45 to SSP1-45 (Figure 5d), this trend towards more balance does not occur; average\ncontributions from the higher GDP change only from 57% in SSP2-45 to 54% in SSP1-45 in the\ntop 10 nations.\nThird, we also agree that our interpretations of the old indicators were mostly descriptive, and that\nwe did not fully develop their implications. In this revision we have taken care to specifically call out\nsurprising or unexpected results and include a few examples below:\n[194-205]\nInterestingly, although Asia makes up a dominant share of the total co-benefits from climate\naction, the same is not true for transboundary co-benefits (i.e., co-benefits originating from\nemissions outside of the region that they benefit) (Figure 2b). In SSP1-19, Asia has the largest\nshare of transboundary co-benefits (38%); however, this is only slightly ahead of other regions\nsuch as Europe (32%) and Africa (21%). In some scenarios, such as SSP1-45, Asia actually\nmakes up a smaller percentage of transboundary co-benefits (32%) than Europe (36%) and only\nslightly more than Africa (22%) despite its much larger population. This is an unexpected result.\nAs mentioned previously, rapid development and a large aging population position Asia as the\nprimary benefactor of climate action; however, this is not the case for transboundary co-benefits.\nCrucially, the major polluters and population centers of Asia in China and India are located in the\neastern part of the continent away from other regions; thus, a majority (62% in SSP4-45) of the\nbenefits of climate action in the continent are geographically isolated from other regions.\n[269-280]\nWe note that these transboundary contributions are substantially greater than past analyses16,17 of\nhistorical transboundary air pollution burdens, which found 14% and 12% of all PM -related\n2.5\ndeaths were attributable to transboundary pollution. This difference is likely attributable to three\nmain factors: (1) these past studies focused on aggregated groups of countries whose boundaries\nare further from peak emissions locations whereas we perform this calculation for individual\ncountries, (2) in this study we consider climate action whereas these previous studies consider all\nanthropogenic emissions; climate action is not adopted to the same degree globally and many of\nthe highest emission reductions are implemented in a small set of developed nations, and (3)\nmany of the targets of climate action are precursors of secondary PM (specifically NO and\n2.5 X\nSO ) and not primary PM and thus this action targets pollution that is more likely to be\n2 2.5\ntransported greater distances due to its longer atmospheric lifetime.\n[227-238]\nSurprisingly, less equal socioeconomic development increases the transboundary fraction for\nAfrica. One reason why this could occur is that in more fragmented climate futures, there is less\ncooperative development. Subsequently, in these less equal scenarios, African countries become\neven more sensitive to global action (67% higher in SSP3-26 than SSP1-26) as there is less local\ndevelopment and industrialization. This relationship is true across all of the RCPs; however, it is\nstronger in those with higher climate mitigation (i.e., RCP-26) than those with weaker\nmitigation. Specifically, from SSP3-45 to SSP1-45, the transboundary fraction only increases by\n18%. Ultimately the transboundary fraction in Africa is mediated by two competing phenomena:\n(1) more aggressive climate mitigation necessitates proportionally more involvement from\ndeveloped nations and thus higher transboundary co-benefits and (2) less equal socioeconomic\ndevelopment further increases the disparity in industrialization and development in many African\ncountries which forces them to be more dependent on external action to maximize co-benefits.\nLastly, we have now dedicated text specifically discussing how these indicators could be useful\nfor policy design in the discussion:\n[555-561]\nWith these uncertainties in mind, our results have clear relevance for climate policy design. First,\nthe specific inequality metrics we define, transboundary fractions and exchanges, have\nimplications for policy. In quantifying the fraction of co-benefits that are transboundary,\ncountries and groups can identify the scenarios that are most beneficial in specifically alleviating\ntransboundary air pollution external to their borders. Through quantifying exchanges between\ncountries or regions, they can identify the most important partnerships to address imbalanced\nexchanges of air pollution.\nR2C3: All results are presented for the year 2040. Since SSP-RCP pathways evolve differently\nover time, it is unclear whether the identified patterns of imbalance and exchange are consistent\nacross other time points. A single-year analysis may be influenced by scenario-specific\nassumptions in that year and does not allow for understanding temporal trends or cumulative\neffects. Although additional figures for 2030 are provided in the supplement, the main analysis\nremains focused on one year, which limits the robustness of the conclusions.\nWe agree that the patterns of imbalance and exchange will vary through time. First, we have now\ncreated a new figure (Figure S2) that directly compares 2030 and 2040 transboundary fractions\nand comment on this in the results. For this scenario we see that different years can influence\ntransboundary air pollution; however, generally this is a smaller effect than what we see from\ndistinct socioeconomic development.\n[303-312]\nAlthough we focus primarily on 2040 in our analysis, we additionally calculate the differences in\ntransboundary fractions between the base year of study (2040) and 2030 (Figure S2) across\nSSP1-26 and SSP3-26. We find that in 2040 transboundary fractions are moderately higher in\nChina, India, the US, and Brazil and lower in parts of Africa compared to SSP1 in 2030;\nhowever, these interannual changes are smaller than those attributable to socioeconomic changes.\nUltimately, these findings imply two key outcomes: (1) the effects of climate action on\ntransboundary air pollution are unique to socioeconomic development and mitigation strategies\nand these effects are not additive and (2) less developed countries receive greater transboundary\nco-benefits in less equal socioeconomic development as they are more dependent on external\naction due to weaker internal development and industrialization.\nThat being said, we agree that we need to make it clear that these results are most relevant for\n2040 and could deviate for different years in our analysis. To do this we have updated some text\nin the introduction and discussion:\n(86-88)\nWe identify not only the health impacts from specific scenarios but also – by leveraging the\nadjoint model source-receptor relationships – identify how each country contributes to impacts\nthroughout the world.\n[88-90]\nWe identify not only the health impacts from specific scenarios but also – by leveraging the\nadjoint model source-receptor relationships – identify how each country contributes to impacts\nthroughout the world in 2040.\n[457-460]\nWe integrate adjoint sensitivities with SSP and RCP emission projection to extend beyond past\nwork that estimated total co-benefits from climate action and consider how transboundary co-\nbenefits and exchange relationships between countries and regions could change across different\nclimate scenarios in 2040.\nWe also note that this was discussed briefly in the original manuscript and have kept this text in\nour uncertainty paragraph in the discussion:\n(395-399)\nAdditionally, this analysis is conducted for emissions scenarios in a single year – 2040 – so it\nrepresents a snapshot in time of the differences across these socioeconomic trends and mitigation\nstrategies. Given that the SSP-RCP emissions are designed to peak at different points across\ndifferent countries and regions, it is likely that these imbalances and exchanges will differ at\nother points in the future.\nR2C4: The version of GEOS-Chem used in this study does not include secondary organic aerosol formation, which can contribute significantly to PM2.5, especially in urban and vegetated regions. The lack of these components may lead to an underestimation of long-range transport and influence the imbalance and exchange results. Additionally, the adjoint sensitivities are based on linear approximations, which may not fully capture the nonlinear chemical reactions involved in PM2.5 formation, particularly in areas with high emissions of ammonia and nitrogen oxides. The limitations of the chemical representation are acknowledged briefly in the methods section, but their implications for the main findings are not discussed in detail.\nThank you for this comment and we agree that we could discuss the implications of these\nuncertainties in greater detail. We have now addressed this by developing an uncertainty analysis\n(see our response to R1C1). Additionally, we have adjusted some of the text in the discussion on\nthis point to better highlight these limitations:\n(389-395)\nOur analysis relies on the simplified assumption that the pollution formation relationships\ncalculated in our simulation will be maintained in the future, although we expect changes to\nclimate9,10 and chemical11,12 environment would be additional modifiers to consider.\nAdditionally, our analysis neglects to include secondary organic aerosol formation that makes up\na substantial component of total PM , especially in heavily populated urban areas13. Given the\n2.5\nlonger atmospheric lifetime of this component, it is likely that we underestimate some of the\nlong-range exchange of air pollution.\n[534-542]\nAdditionally, our estimates of co-benefit contributions rely on the simplified assumption that the\npollution formation relationships that we calculate for our base year (i.e., 2010) will be\nmaintained in the future. We anticipate that changes to climate9,10 and chemical11,12 environment\nin the future will modify transboundary air pollution and we suggest that feedbacks between\nclimate change and transboundary air pollution are explored in future work. Our analysis\nneglects to include anthropogenic fugitive dust and secondary organic aerosol formation that\ncontribute to PM , especially in heavily populated urban areas7. Given the longer atmospheric\n2.5\nlifetime of secondary organic aerosol, it is likely that we underestimate some of the long-range\nexchange of air pollution.\nR2C5: Although the manuscript touches on environmental justice and potential inequalities in\nthe distribution of air pollution benefits, it is not made clear how the presented metrics could be\napplied in practice. There is a lack of connection between the model outputs and current\ninternational climate mechanisms or policy instruments. The imbalance ratio, in particular, is\ndiscussed without a clear normative interpretation. It remains uncertain whether a high or low\nratio should be viewed as desirable or problematic, and how such findings could inform\nequitable burden sharing in climate action.\nThank you for raising this point. We have mostly responded to this concern in response to your\nsecond comment (R2C2). Beyond what we include there we have also now made it a point to\nbetter connect our outcomes to policy implications; for this please refer to our responses to R4C8\nand some additional examples below:\n[186-192]\nThis high share of co-benefits in Asia is not surprising and is attributable to two factors: (1) the\npopulation of Asia is large at present and by 2040 this young population will age and become\nmore susceptible to PM -related health impacts and (2) China and India are developing rapidly\n2.5\nand these two countries receive a majority of the PM -related deaths in the region, thus climate\n2.5\naction in these countries could alleviate a great degree of the air pollution health burden.\nSpecifically, co-benefits in these two countries alone make up between 52% (SSP1-19) and 63%\n(SSP3-60) of all global co-benefits.\n[490-499]\nFor both regions and countries, the fraction of co-benefits that are transboundary is closely tied\nto the scale of climate mitigation and the socioeconomic development pathway in which the\naction takes place. African countries exhibit higher transboundary fractions in high mitigation\nscenarios and less equal socioeconomic development. The former relationship occurs because\nhigh mitigation scenarios require more proportionate action from more economically developed\nareas. The latter relationship is due to the fact that more fragmented socioeconomic development\ninduces greater development disparities and subsequently more global dependence on external\naction throughout many countries in Africa and other developing areas of the world. This implies\na need for nuanced climate policy design that incorporates climate mitigation action in tandem\nwith projected socioeconomic development to ensure more equitable climate co-benefits.\n[561-575]\nSecond, there is clear evidence from our results that socioeconomic development and mitigation\naction are best considered in parallel rather than separately; the socioeconomic environment in\nwhich mitigation occurs dramatically influences both the total magnitude of co-benefits and the\ninequalities associated with transboundary air pollution. More fragmented socioeconomic\ndevelopment increases transboundary air pollution in developing countries (especially in Africa)\nand makes them more beholden to the decision-making of developed countries. Stronger climate\nmitigation, although overall beneficial, also increases the fraction of co-benefits that are\ntransboundary as the most aggressive climate action requires greater buy-in from historically\npolluting developed nations. From a socioeconomic development perspective, transitioning from\nfragmented to sustainable climate futures leads to more balanced exchanges of co-benefits which\nenables developing countries to contribute proportionally more to exchanges due to greater\nindustrialization and development. Ultimately, this suggests that sustainable socioeconomic\ndevelopment enables developing countries to participate more in global climate action, thus\nbenefitting both themselves and their wealthier and more developed neighbors via improved\ndomestic and foreign air quality.\nR2C6: In lines 99–101, the main text states that SSP1 avoids 0.53 million deaths, but Table 1\nreports 581,000 (i.e., 0.581 million) deaths for SSP1-19. While the values are close, the\ncorrespondence between the text and specific scenarios in Table 1 should be clarified to avoid\nconfusion.\nThank you for pointing this out. In the original manuscript we had calculated the average across\nall RCPs, but this was not mentioned in the text. We have now just included a single SSP-RCP\ncombination for each of the different socioeconomic development pathways:\n(97-109)\nThe SSP-RCP scenarios – and the socioeconomic trends and mitigation strategies they represent\n– project lower emissions in 2040 than the worst-case scenario (i.e., SSP3-Baseline) that result in\nimproved air quality and reduced health impacts; however, emission reduction impacts vary by\nscenario (Table 1). For example, socioeconomic trends towards sustainable development (SSP1),\nin which there are low challenges to mitigation and adaptation, avoid 0.53 million (ranging from\n0.49 – 0.58) deaths which is nearly double the 0.26 million (0.14 – 0.39) deaths avoided in\nfragmentation (SSP3) in which there are high challenges to mitigation and adaptation.\nConventional development (SSP5) and middle-of-the-road development (SSP2) have health\nbenefits between these two extremes: 0.47 (0.38 – 0.55) and 0.41 (0.35 – 0.53) million,\nrespectively, whereas trends towards inequality (SSP4) more closely match fragmentation with\n0.28 (0.19 – 0.40) million deaths avoided. Overall, while low challenges to both mitigation and\nadaptation have health benefits, our estimates suggest that challenges to adaptation are especially\nconnected to the deaths avoided from climate action.\n[110-119]\nIn the most optimistic scenario (SSP1-19) in which there is sustainable socioeconomic\ndevelopment and a strong reduction in climate forcing that presents low challenges to mitigation\nand adaptation, emission reductions could avoid 1.32 million deaths (with a lower and upper\nbound of 0.95, 1.73). This is over four times the 0.32 million (0.24, 0.42) deaths avoided in\nSSP3-60, in which there is fragmented socioeconomic development, weak reductions to climate\nforcing, and high challenges to mitigation and adaptation. Conventional development (SSP5) and\nmiddle-of-the-road development (SSP2) towards RCP-45 result in climate co-benefits between\nthese two extremes of 1.03 (0.75, 1.35) and 0.79 (0.58, 1.04) million deaths avoided,\nrespectively, whereas trends towards inequality (SSP4) for RCP-45 closely match fragmentation\nwith 0.51 (0.37, 0.67) million deaths avoided.\nWe also note that due to an error in our original analysis the magnitudes of the co-benefits were\nunderestimated hence the higher values in the updated text (see section 1.0 for details).\nR2C7: The concept of the imbalance ratio is introduced around line 131, but its calculation is\nnot explained until the Methods section (line 531 onward), and the visual schematic appears only\nin the supplement (Figure S1a).\nReviewer #1 had a similar comment (R1C3); we have addressed this by including the schematic\nin the main text as the new Figure 1 so that it is presented before we begin interpreting the\nindicators. Please refer to that comment for detailed changes related to this issue.\nR2C8: In Figure S5, the “Asia to Europe” exchange ratio under one of the SSP-RCP scenarios\n(likely around SSP2-34 or SSP2-45) spikes to a value exceeding 5, which is visually inconsistent\nwith the rest of the dataset and other scenario curves. This point is not acknowledged or\nexplained in the figure caption, nor is there any indication that the y-axis was clipped or\nrescaled to accommodate outliers.\nThank you for pointing out that we neglected to comment on this in the original text. We have\nupdated the supplemental figures in the styles of the main text figures now, so this visual\ninconsistency is no longer plotted. Out of curiosity we looked into this, and this is attributable to\nunique conditions in SSP2-45 in that major emission changes occur for India in 2030 (relative to\nSSP3-Baseline) but not as much for other countries.\nR2C9: The caption for Figure 4 explains that values greater than 1 mean Region A contributes\nmore to Region B’s benefits than vice versa. However, this phrasing could be misread as\n“Region A receives more benefit” rather than “Region A’s actions have greater effect”.\nRephrase the caption to clarify directionality, e.g., “Values greater than one indicate that climate\nmitigation in Region A provides more benefits to Region B than the reverse.”\nWe have now updated this figure and made major changes to this caption.\nR2C10: Table 1 presents NOx emissions in teragrams and avoided deaths in thousands, but\nthese units are not explicitly listed in the table headers. I think add units in parentheses to the\nheaders for clarity (e.g., “Global NOx Emissions (Tg)”, “Global Deaths Avoided (Thousands)”)\nwill improve current version.\nThank you for this suggestion; we have made this change (see section 4.0).\nR2C11: Oceania is excluded from Figure 1c on the grounds of having disproportionately high\nimbalance ratios due to small population size. However, no supporting data is shown to validate\nthis exclusion. Please provide numerical values or variance ranges for Oceania in the\nsupplement to justify its removal from the main analysis.\nThank you for this point. In the new metrics we no longer have this issue as we include Oceania\ndata in Figure 1 now; however, out of curiosity we plotted the old imbalance ratios and include\nthem below for reference:\n3.3 Reviewer #3:\nR3C0: I co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nThank you for your contributions to the revision of this manuscript. We appreciate the time that\nyou took to review and develop comments on our paper.\n3.4 Reviewer #4:\nR4C0: This study involves a substantial amount of work. It presents regional inequality in the\nbenefits of emission reduction under different SSP and RCP scenarios from a novel perspective,\nwhile also considering transboundary pollution transport. However, several issues still require\nthe authors’ attention. Please make major revisions to the article.\nThank you for your positive feedback and useful comments; we have endeavored to incorporate\nyour suggestions and believe that they have improved the quality of the manuscript.\nR4C1: The abstract is difficult to understand without reading the full manuscript. It requires\nappropriate revision for clarity.\nWe agree with you that the original abstract was difficult to interpret without reading the full\nmanuscript (partially due to the confusing old indicators). We have revised it to improve\ninterpretability:\n(18-32)\nClimate action improves air quality and health by reducing sources that co-emit greenhouse\ngases and air pollutants such as PM , yet ill-conceived mitigation could induce imbalances in\n2.5\nthe distribution and exchange of air pollution across international borders. Despite its potential to\nendanger equality, the future effects of climate action on transboundary PM are largely\n2.5\nunstudied. Here, we show that health co-benefits are maximized through sustainable\ndevelopment (581 thousand deaths avoided); however, imbalances in the transboundary\nexchange of PM -related health impacts vary across different climate-related emission scenarios\n2.5\nand regions. In sustainable futures, African countries contribute more co-benefits to Europe than\nvice-versa (805 net deaths avoided); however, in more fragmented futures this dynamic reverses\n(-356 net deaths avoided) and African countries are more dependent on European climate action.\nThis variation in transboundary air pollution across different climate futures underscores the\nneed to characterize how climate action – or lack thereof – could ameliorate, perpetuate, or\nexacerbate air pollution inequality beyond solely quantifying health co-benefits. Future climate-\nrelated policy design could consider these effects to rectify current and historical environmental\ninjustices.\n[18-33]\nClimate action redresses poor air quality and its health burden by reducing sources that co-emit\ngreenhouse gases and air pollutants such as PM , yet ill-conceived mitigation could induce\n2.5\nimbalances in the distribution and exchange of air pollution across international borders. Despite\nits potential to endanger equality, the future effects of climate action on transboundary PM are\n2.5\nlargely unstudied. Air quality co-benefits are maximized through sustainable development\ntowards RCP-19 (1.32 million deaths avoided) and minimized in fragmentation trends towards\nRCP-60 (0.32 million). Futures with stronger climate mitigation necessitate more aggressive\nclimate action from developed nations that increases the fraction of co-benefits that are\ntransboundary for Africa, ranging from 8% higher in SSP1 to 53% in SSP3. Additionally,\ncountries with lower human development indices have a greater fraction of their co-benefits\noriginate externally (0.76 in SSP1-26) than more developed countries (0.65) which positions\ndeveloping nations as more dependent on regional and global climate action than developed\ncountries. Co-benefit exchanges between countries are non-uniform, which suggests country-\nspecific analyses are necessary to develop climate policy that minimize transboundary effects.\nThese results support the need for nuanced policy design that considers how transboundary co-\nbenefits respond to regional differences, distinct socioeconomic trends, and mitigation strategies.\nR4C2: Table 1 does not present all scenarios, such as SSP3-Ba. The authors should clarify the\nreason for this omission.\nThank you for bringing this up, we include all scenarios in Table 1 except the SSP3-Baseline\nwhich we use as a reference point to calculate our health impacts. However, this was not\nexplained originally and so we have updated the Table 1 caption to reflect this:\n(111-112)\nTable 1. Global total NO emissions in 2040 (Tg), global deaths avoided in 2040, narrative\nx\ndescriptions, and challenges for all scenarios considered in this study.\n[139-145]\nTable 1. Details on the 24 future climate emission scenarios considered in this study across five\nshared socioeconomic pathways (SSPs) and six representative concentration Pathways (RCPs).\nGlobal NO emissions are presented in teragrams along with the global deaths avoided from\nx\nreductions in PM , with lower and upper bound health estimates included in brackets. The\n2.5\nnarratives and challenges associated with the SSPs are also included. All scenarios used in our\nanalysis are presented here with the exception of SSP3-Baseline, which is used as the baseline\nagainst which we calculate the climate co-benefits.\nR4C3: The reference to Figure 1 in line 116 seems inappropriate. In addition to providing Table\nS1, a spatial distribution map of the six regions should also be included.\nWe agree that the reference to Figure 1 (now Figure 2) was inappropriate in its original location\nand have now updated this:\n(115-117)\nTo further explore how socioeconomic trends and mitigation strategies vary across different\ngeographies, we group all countries into one of six regions: South America (SA), Oceania (OC),\nNorth America (NA), Europe (EU), Asia (AS), and Africa (AF) (Figure 1).\n[178-180]\nFor the six regions we consider in this study, scenarios in which climate action is more\naggressive (i.e., lower RCPs and SSPs with fewer challenges) achieve greater climate co-benefits\n(Figure 2a).\nAdditionally, we have taken your suggestion and included a spatial distribution map of the six\nregions in the supplement (Figure S1) that we have also included in section 4.0 of this document.\nR4C4: Why are Europe and Africa grouped into the same category? Is this classification\nunrelated to socioeconomic development levels? The rationale should be further explained.\nApologies, we are not entirely sure which aspect of our original analysis this refers to; however,\nwe believe that you are referring to what was originally presented in Figure 4 in which we\ncalculated exchange ratios between Europe and Africa. We have now removed this figure from\nour analysis and so we believe this is no longer an issue.\nR4C5: What does “IR” refer to in line 223? Its full name should be provided upon first\nappearance.\nThis “IR” originally referred to the “imbalance ratio” but we no longer use this term in this work\nto simplify our analysis (see R1C3 for details) and so this has been removed.\nR4C6: What is the reason for the inclusion of the four specific regions in Figure 2a? Please\nclarify.\nOriginally, Figure 2a referred to the scenario in which co-benefits were maximized (generally\nSSP1) but we have now overhauled this figure in response to your next comment and so we\nbelieve that this issue has now been resolved.\nR4C7: The results presented in the manuscript are relatively limited. It is recommended to\ninclude spatial distribution and flow maps of health co-benefits under different scenarios.\nThank you for this suggestion, we have endeavored to address this by updating all of our figures\nto provide more detail related to the inequalities borne by transboundary air pollution across the\ndifferent climate futures. Specifically, we have dramatically changed Figures 3 and 5 (formerly\nFigures 2 and 4) and specifically with the prior have updated it to better capture the spatial\ndistribution related to co-benefits, as you suggest. We have included all of the new main text\nfigures in section 4.0 of this document. We also experimented with developing flow maps of\nhealth co-benefits; however, given the amount of country relationships we consider, this looked\nmessy, so we decided not to include them in this version of the manuscript and instead use\nheatmaps to depict co-benefit exchanges in Figure 4.\nR4C8: The manuscript primarily provides descriptive explanations of the observed phenomena,\nbut lacks further analysis of the underlying causes. What specific factors lead to regional\ninequality in climate action benefits under different scenarios? Are these related to economic\nlevels or population structures? Moreover, are there any suggestions for more equitable\nmitigation scenarios or compensatory mechanisms?\nThank you for bringing up this important point. These echoes sentiments brought up by the other\nreviewers (for suggestions on more equitable scenarios or policies please refer to R2C2) and we\nhave sought to update our manuscript to provide fewer descriptive explanations and more\nquantitative analysis and have used these to discuss possible policy applications. We have also\nnow discussed the underlying causes of the observed phenomena and the specific factors leading\nto regional inequality and specifically comment on economic levels (including GDP in Figure 5)\nand population:\n(107-109)\nOverall, while low challenges to both mitigation and adaptation have health benefits, our\nestimates suggest that challenges to adaptation are especially connected to the deaths avoided\nfrom climate action.\n[121-137]\nThere are two compounding factors that drive variability across these different climate futures:\nthe degree of mitigation and type of socioeconomic development. Stronger mitigation (i.e., lower\nRCPs) engenders greater co-benefits; however, the strength of this effect is mediated by the\nsocioeconomic development pathway in which the action is implemented. Specifically, the effect\nof stronger mitigation is relatively weak in sustainable development – there are 1.1 times as\nmany co-benefits in SSP1-26 compared to SSP1-45 – and stronger in more fragmented climate\nfutures – there are 2.0 times as many co-benefits in SSP3-26 compared to SSP3-45. Similarly,\nsocioeconomic development that is more equitable and fosters global cooperation generally leads\nto greater co-benefits compared to less equitable socioeconomic development, but this is\ndependent on the degree of climate mitigation. For example, for RCP-26, SSP1 results in 1.4\ntimes as many co-benefits as SSP3 while in RCP-45 SSP1 results in 2.4 times as many co-\nbenefits as SSP3. These results suggest that climate policy would ideally be designed to consider\nsocioeconomic development alongside the degree of mitigation given these dependencies.\nUltimately, in climate futures for which there are low challenges to both mitigation and\nadaptation the improvement to air quality will have substantial health benefits; however, our\nestimates suggest that challenges to adaptation are especially connected to the deaths avoided\nfrom climate action.\n(127-129)\nHowever, in fragmented futures, such as SSP3-60, the Asian share of co-benefits grows (83%)\nand the share of other regions decreases relatively for Europe (8%), Africa (5%), North America\n(3%), and South America (1%).\n[184-192]\nIn SSP3-60 the Asian share of co-benefits grows (85%) while other regions such as Europe (7%),\nAfrica (3%), North America (4%), and South America (1%) benefit proportionally less. This high\nshare of co-benefits in Asia is not surprising and is attributable to two factors: (1) the\npopulation of Asia is large at present and by 2040 this young population will age and become\nmore susceptible to PM -related health impacts and (2) China and India are developing rapidly and\n2.5\nthese two countries receive a majority of the PM -related deaths in the region, thus climate\n2.5\naction in these countries could alleviate a great degree of the air pollution health burden.\nSpecifically, co-benefits in these two countries alone make up between 52% (SSP1-19) and 63%\n(SSP3-60) of all global co-benefits.\n[266-280]\nFor example, in SSP3-60 removing these two countries leads to a higher global transboundary\nfraction of 0.54. This explains why when calculating the average transboundary fraction across\nall countries there is a substantially higher value of 0.68 (SSP3-60) than the total global\ntransboundary fraction. We note that these transboundary contributions are substantially greater\nthan past analyses16,17 of historical transboundary air pollution burdens, which found 14% and\n12% of all PM -related deaths were attributable to transboundary pollution. This difference is\n2.5\nlikely attributable to three main factors: (1) these past studies focused on aggregated groups of\ncountries whose boundaries are further from peak emissions locations whereas we perform this\ncalculation for individual countries, (2) in this study we consider climate action whereas these\nprevious studies consider all anthropogenic emissions; climate action is not adopted to the same\ndegree globally and many of the highest emission reductions are implemented in a small set of\ndeveloped nations, and (3) many of the targets of climate action are precursors of secondary\nPM (specifically NO and SO ) and not primary PM and thus this action targets pollution\n2.5 X 2 2.5\nthat is more likely to be transported greater distances due to its longer atmospheric lifetime.\n[401-409]\nContrasting this with the transition from a middle-of-the-road to sustainable climate future\n(Figure 5c), the exchange between Africa and Europe is essentially unaffected (from 29% to\n28%, African favored). This suggests that fragmentation contributes to a balanced Africa and\nEurope exchange and through trends towards sustainable or middle-of-the-road development,\nAfrica contributes more to Europe than vice versa due to a greater capacity to adopt more\naggressive climate action. Generally, at the regional scale, changes to exchanges were mostly\nunaffected by this socioeconomic development excluding the exchange between South America\nand Africa which becomes less African dominated in sustainable development compared to both\nSSP3-45 and SSP2-45.\n[517-528]\nThe transition to more sustainable development does not always reduce inequality, e.g., for\nFrance and Spain the exchange in SSP3-26 is more balanced (3.67) than compared to SSP1-26\n(4.13). Ultimately, there is not a uniform relationship between socioeconomic development and\nexchange inequalities: the same transition could ameliorate some exchange inequalities while\nperpetuating or exacerbating others. However, generally a transition from a more fragmented\nclimate future to a more sustainable ones lead to greater balance: comparing SSP1-45 to SSP3-\n45, nine of the ten largest shifts in exchanges between countries enabled more balanced\nexchanges (i.e., where pairs of countries contribute even co-benefits to one another) and many of\nthese shifts were towards greater contributions from the lower GDP. This occurs because\nsustainable development fosters greater global cooperation; developing nations develop and\nindustrialize quicker and thus they have more potential to control emissions.\n[392-409]\nComparing fragmented socioeconomic development to sustainable socioeconomic development\nresults in some of the largest changes to exchange inequalities between regional and country\npairs. For example, in SSP3-45, the exchange between Europe and Africa is relatively balanced:\nthe prior contributes 48% of the total co-benefits exchanged and the latter, 52% (Figure 5a).\nTransitioning to more sustainable socioeconomic development leads to Africa – the region with\nthe weaker GDP today – contributing a clear majority of co-benefits to the exchange (72%). This\nshift towards greater African contributions to Europe in a more sustainable world occurs because\na more sustainable climate future necessitates growth in historically underdeveloped regions and\nenables them to contribute more to climate action and subsequently greater contributions to co-\nbenefits. Contrasting this with the transition from a middle-of-the-road to sustainable climate\nfuture (Figure 5c), the exchange between Africa and Europe is essentially unaffected (from 29%\nto 28%, African favored). This suggests that fragmentation contributes to a balanced Africa and\nEurope exchange and through trends towards sustainable or middle-of-the-road development,\nAfrica contributes more to Europe than vice versa due to a greater capacity to adopt more\naggressive climate action. Generally, at the regional scale, changes to exchanges were mostly\nunaffected by this socioeconomic development excluding the exchange between South America\nand Africa which becomes less African dominated in sustainable development compared to both\nSSP3-45 and SSP2-45.\n4.0 Tables and Figures\nTable 1. Details on the 24 future climate emission scenarios considered in this study across five\nshared socioeconomic pathways (SSPs) and six representative concentration Pathways (RCPs).\nGlobal NO emissions are presented in teragrams along with the global deaths avoided from\nx\nreductions in PM , with lower and upper bound health estimates included in brackets. The\n2.5\nnarratives and challenges associated with the SSPs are also included. All scenarios used in our\nanalysis are presented here with the exception of SSP3-Baseline, which is used as the baseline\nagainst which we calculate the climate co-benefits.\nSSP RCP Global Global Deaths Narrative Challenges\nNO Avoided\nx\nEmissions (Millions)\n(Tg) [Uncertainty]\n1 19 24.0 1.32 [0.95, 1.73] Sustainability Low challenges to adaptation\n26 31.8 1.26 [0.90, 1.64] and mitigation\n34 43.7 1.16 [0.83, 1.52]\n45 52.3 1.11 [0.80, 1.45]\nBa 55.4 1.08 [0.78, 1.42]\n2 19 33.7 1.22 [0.88, 1.59] Middle of the Intermediate challenges to\n26 45.8 1.13 [0.81, 1.47] Road adaptation and mitigation\n34 62.8 0.98 [0.71, 1.28]\n45 85.1 0.79 [0.58, 1.04]\n60 90.5 0.75 [0.55, 0.99]\nBa 102.4 0.67 [0.49, 0.87]\n3 26 61.9 0.90 [0.66, 1.17] Fragmentation High challenges to\n34 80.5 0.69 [0.51, 0.90] adaptation and mitigation\n45 102.1 0.46 [0.34, 0.60]\n60 113.0 0.32 [0.24, 0.42]\n4 26 57.8 0.91 [0.66, 1.18] Inequality High challenges to\n34 79.0 0.69 [0.51, 0.91] adaptation low challenges to\n45 98.1 0.51 [0.37, 0.67] mitigation\nBa 107.1 0.42 [0.30, 0.55]\n5 26 33.2 1.26 [0.91, 1.64] Conventional Low challenges to adaptation\n34 47.0 1.14 [0.82, 1.48] Development high challenges to mitigation\n45 58.3 1.03 [0.75, 1.35]\n60 62.0 1.00 [0.72, 1.30]\nBa 83.9 0.82 [0.60, 1.07]\nFigure 1. Schematics of the metrics used to quantify inequalities associated with transboundary\nair pollution across different climate futures. (a) Transboundary fractions (TF) characterize the\nextent of co-benefits in a country that come from external action; co-benefits are the premature\ndeaths avoided owing to reduced PM concentrations. (b) Exchanges (EXC) compare co-\n2.5\nbenefits from emission reductions between a pair of countries or regions; the contribution to total\nbenefits exchanged (TEC) indicates how much a country contributes to the total gross co-\nbenefits exchanged between two countries.\nFigure 2. (a) The total deaths avoided for each of the SSP-RCP scenarios relative to SSP3-\nBaseline in 2040 ordered from the most to least equitable scenarios. Colors indicate the receptor\nregion in which the co-benefits occurred (i.e., where the deaths were avoided). Socioeconomic\ntrends and mitigation strategies are included on the y-axis. Error bars refer to the lower and\nupper bound uncertainty from the health impact assessment for the total co-benefits. (b) The co-\nbenefits specifically attributable to external action for each of the receptor regions (i.e., the\ntransboundary co-benefits). The fraction of co-benefits that are transboundary broken down by\n(c) socioeconomic development type and (d) mitigation strategy.\nFigure 3. Transboundary fractions for individual countries in a sustainable and strong climate\nmitigation scenario (SSP1-26) (top left) and the percent difference in transboundary fractions for\neach country for a weaker RCP (SSP1-45) (top right), a less equitable SSP (SSP3-26) (bottom\nleft) and a scenario that has both weaker climate mitigation and less equal socioeconomic\ndevelopment (SSP3-45) (bottom right).\nFigure 4. Exchanges (EXC) of climate co-benefits within and between Europe, Africa, and Asia\nfor SSP1-26 in 2040 (left). Darker colors indicate greater co-benefits in the receptor country\nattributable to emission reductions in the source country; self-contributions (i.e., the diagonal)\nare excluded. Asian exchanges are a factor of ten greater than exchanges in Africa and Europe\nand the color-scales are logarithmic. Heatmaps of transboundary exchanges of climate action\nwithin Europe, Africa, and Asia relative to SSP1 in 2040 for RCP-26 for SSP2, SSP3, SSP4,\nand SSP5 (right). The color-scales are linear. Interregional exchanges (i.e., Africa to Europe and Asia\nto Africa) are provided in an absolute sense – not relative to SSP1.\nFigure 5. The change in the percentage of co-benefits exchanged (TEC × 100%) between (a)\nregional and (b) country pairs that is contributed by the higher GDP (first name) from SSP3-45\nto SSP1-45 in 2040. For the regional exchanges, the arrow points from the less equal scenario\n(SSP3-45) to the more equal scenario (SSP1-45) and the color of the dot indicates the higher\nregional contributor. For the country exchanges, the arrow is the same but the black dot\nrepresents SSP3-45 and the white dot represents SSP1-45 and the background shading indicates\nin which region the exchange occurs as labelled above Figure 5a; grey indicates exchange\nbetween different regions. Panels (c) and (d) are the same as (a) and (b), respectively, but for\nmiddle-of-the-road development (SSP2 vs SSP1) instead of fragmentation development.\nFigure S1. Regional assignments for all countries considered, as either a source or receptor, in\nthis work in map form.\nFigure S2. Transboundary fractions for individual countries for a sustainable and strong climate\nforcing scenario (SSP1-26) in 2030 (top left) and the percent difference in transboundary\nfractions for each country for the same scenario in 2040 (SSP1-26) (top right), a less equitable\nSSP in 2030 (SSP3-26) (bottom left) and the less equitable SSP in 2040 (bottom right).\n5.0 References\n1. Nawaz, M. O. & Henze, D. K. Premature Deaths in Brazil Associated With Long-Term\nExposure to PM2.5 From Amazon Fires Between 2016 and 2019. GeoHealth 4,\ne2020GH000268 (2020).\n2. Fujimori, S., Hasegawa, T., Ito, A., Takahashi, K. & Masui, T. Gridded emissions and land-use\ndata for 2005–2100 under diverse socioeconomic and climate mitigation scenarios. Sci. Data\n5, 180210 (2018).\n3. Hoesly, R. M. et al. Historical (1750–2014) anthropogenic emissions of reactive gases and\naerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev. 11, 369–\n408 (2018).\n4. Heald, C. L. et al. Atmospheric ammonia and particulate inorganic nitrogen over the United\nStates. Atmospheric Chem. Phys. 12, 10295–10312 (2012).\n5. Henze, D. K., Seinfeld, J. H. & Shindell, D. T. Inverse modeling and mapping US air quality\ninfluences of inorganic PM precursor emissions using the adjoint of GEOS-Chem.\n2.5\nAtmospheric Chem. Phys. 9, 5877–5903 (2009).\n6. Philip, S. et al. Anthropogenic fugitive, combustion and industrial dust is a significant,\nunderrepresented fine particulate matter source in global atmospheric models. Environ. Res.\nLett. 12, 044018 (2017).\n7. Nault, B. A. et al. Secondary organic aerosols from anthropogenic volatile organic compounds\ncontribute substantially to air pollution mortality. Atmospheric Chem. Phys. 21, 11201–\n11224 (2021).\n8. Thunis, P. et al. Non-linear response of PM to changes in NO and NH emissions in the Po\n2.5 x 3\nbasin (Italy): consequences for air quality plans. Atmospheric Chem. Phys. 21, 9309–9327\n(2021).\n9. Wiel, K. van der, Selten, F. M., Bintanja, R., Blackport, R. & Screen, J. A. Ensemble climate-\nimpact modelling: extreme impacts from moderate meteorological conditions. Environ. Res.\nLett. 15, 034050 (2020).\n10. Clarke, B., Otto, F., Stuart-Smith, R. & Harrington, L. Extreme weather impacts of climate\nchange: an attribution perspective. Environ. Res. Clim. 1, 012001 (2022).\n11. Turnock, S. T. et al. Historical and future changes in air pollutants from CMIP6 models.\nAtmospheric Chem. Phys. 20, 14547–14579 (2020).\n12. Kumar, P. Climate Change and Cities: Challenges Ahead. Front. Sustain. Cities 3, (2021).\n13. Nault, B. A. et al. Anthropogenic Secondary Organic Aerosols Contribute Substantially to\nAir Pollution Mortality. https://acp.copernicus.org/preprints/acp-2020-914/ (2020)\ndoi:10.5194/acp-2020-914.\n14. Nansai, K. et al. Consumption in the G20 nations causes particulate air pollution resulting in\ntwo million premature deaths annually. Nat. Commun. 12, 6286 (2021).\n15. Fang, D. & Chen, B. Inequality of air pollution and carbon emission embodied in inter-\nregional transport. Energy Procedia 158, 3833–3839 (2019).\n16. Chen, L. et al. Inequality in historical transboundary anthropogenic PM2.5 health impacts.\nSci. Bull. 67, 437–444 (2022).\n17. Zhang, Q. et al. Transboundary health impacts of transported global air pollution and\ninternational trade. Nature 543, 705–709 (2017).\n18. Renna, S., Granella, F., Aleluia Reis, L. & Schulz-Antipa, P. CLAQC v1.0 – Country Level\nAir Quality Calculator: an empirical modeling approach. Geosci. Model Dev. 18, 2373–2408\n(2025).\n19. Eastham, S. D., Monier, E., Rothenberg, D., Paltsev, S. & Selin, N. E. Rapid Estimation of\nClimate–Air Quality Interactions in Integrated Assessment Using a Response Surface Model.\nACS Environ. Au 3, 153–163 (2023).\n20. Zhang, Q. et al. Transboundary health impacts of transported global air pollution and\ninternational trade. Nature 543, 705–709 (2017).\n21. Nawaz, M. O. et al. A Source Apportionment and Emission Scenario Assessment of PM2.5‐\nand O3‐Related Health Impacts in G20 Countries. GeoHealth 7, e2022GH000713 (2023).\n22. Nawaz, M. O. et al. A Source Apportionment and Emission Scenario Assessment of PM2.5-\nand O3-Related Health Impacts in G20 Countries. GeoHealth 7, e2022GH000713 (2023).\n23. United Nations Development Programme. Human Development Index (HDI). (2025)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors’ detailed response, and most of the issues have been adequately addressed. Regarding the\nuncertainty in the results, the authors acknowledge in their discussion that uncertainties arise from various aspects such as\nemission inventories, model simulations, and health impact assessments, significantly enhancing the rigor\nand transparency of the analysis. We believe the robustness of the results has been markedly improved and recommend\nacceptance of this manuscript.\nThis work dose not present major innovations about the methodological framework. Extensive literature has already\ndiscussed the uncertainty of chemical transport models and the propagation of model uncertainties. It is suggested that the\nauthors refer to some methodological studies to provide a more substantive quantification of the uncertainties in their results.\nAnd, there is a minor issue that the equation on line 725 appears to be incomplete.", "reviewer_2": "The figures play a central role in communicating the results; however, I have identified several issues regarding figure\nformatting, consistency, and clarity that require substantial revision prior to publication. Addressing these issues will\nsubstantially improve the quality and impact of the visual presentation in this manuscript. Below, I provide detailed\ncomments for each figure:\nGeneral Figure Issues:\n1. There is a lack of consistency in font style and size across figures, with some using Arial and others Times New Roman.\n2. Legends and labels are sometimes incomplete or formatted in a non-standard way.\n3. Axis labels, panel annotations, and units are not always clearly presented.\n4. In-figure legends and explanations are missing in some multi-panel figures, making interpretation less accessible.\nSpecific Figure Comments:\nFigure 2: 1. All textual elements, including axis labels, titles, legends, and color bars, are set in Times New Roman, while\nFigures 1, 4, and 5 use Arial. I recommend standardizing all figures to use Arial and adopting consistent font sizes. For\nexample, use 14 pt for titles and 12 pt for axis labels and legends throughout the manuscript; 2. The color legend for\ncontinents in panels (a) and (b) does not follow standard formatting. Please revise using standard legend boxes with clear\nlabels for each region; 3. The x-axis in panel (c) is incomplete, as only partial scenario labels such as 19 and 26 are shown.\nThis may be confusing to readers. Please ensure that all axis labels and tick marks are fully represented.\nFigure 3: 1. All elements in this figure are presented in Times New Roman. For consistency, please change all fonts to Arial\nso that all figures follow the same style; 2. The upper color bar does not include a unit. Please add “Transboundary Fraction (unitless)” or\n“Transboundary Fraction (%)” as appropriate. For the lower color bars, make sure the unit “percentage (%)” is\nclearly indicated; 3. Currently, the figure does not display panel labels or scenario flow arrows. I recommend adding clear\npanel labels, such as (a), (b), (c), and (d), in the upper left of each map. It would also be helpful to include arrows to illustrate\nthe direction of scenario changes; 4. The figure caption should be expanded to provide a distinct explanation for each panel,\nincluding a description of the scenarios being compared and the direction of change.\nFigure 4: 1. I recommend restructuring the figure legend so that each panel “(a), (b), etc.” is described separately.\nFor each\nheatmap, the legend should clearly state the main variable displayed, the numerical range or color scale, and the main\nfindings or notable patterns. Moreover, heatmaps can be improved through the addition of markers (e.g., bold boxes, stars)\nto identify top contributors or outliers.\nFigure 5: 1. The font size for country names and axis labels is too small. Please increase font size and consider bolding\nlabels for clarity; 2. Please add explicit in-panel legends explaining the meaning of black/white dots and arrow directions,\nrather than relying solely on the main caption.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Manuscript number: NCOMMS-25-17271A\nTitle: National climate action can ameliorate, perpetuate, or exacerbate international air pollution inequalities by Nawaz et\nal.\nThe authors have made thorough and thoughtful revisions in response to the initial round of comments. The manuscript has\nbeen significantly improved through the incorporation of additional analyses, clarification of methodologies, and enhanced\ndiscussion of results and implications. The introduction of “transboundary fractions” and “exchanges” as well as the\ninclusion of uncertainty analyses strengthen the paper’s contributions to understanding how\nclimate action may affect international air pollution inequalities.\nDespite these substantial improvements, several issues require further attention to fully improve the manuscript’s quality.\n(1)The focus on 2040 is well-justified, but the analysis remains a single-time-slice comparison. The supplemental analysis of\n2030 is appreciated, but the manuscript should more explicitly discuss the limitations of not capturing temporal evolution,\nespecially since emission pathways and socioeconomic assumptions evolve complexly. A brief discussion on how key\ninequalities might trend over time (e.g., towards 2050) would strengthen the manuscript.\n(2)The revised metrics (transboundary fractions and exchanges) are much clearer now. I suggest the authors present some\ndetailed discussions about their implications for climate justice or policy design. The authors might also more explicitly link\nmetric values to equitable policy outcomes, e.g., whether certain values indicate need for compensation, cooperation, or\nindependent action.\nLine 22. Abstract should distinguish between background and original results. To improve clarity of the abstract, please\nexplicitly introduce key results with phrases such as \"our results reveal that...\" or \"we find that...\".\nLine 63-64. The sentence on atmospheric transport and trade is somewhat abrupt. Consider integrating it more smoothly or\nexpanding with a phrase like “In addition to atmospheric transport, trade imbalances also contribute…”\nLine 148-160. Please clarify whether this fraction is calculated based on mass of PM₂.₅ or health impacts (deaths).\nLine 251-261. The correlation between transboundary fraction and HDI is insightful. Have the authors considered also\ncontrolling for geographic size or population density? Smaller or coastal countries may have inherently different\ntransboundary fractions regardless of development status.\nLine 303-308. The comparison between 2030 and 2040 is useful. Please include in the main text whether the differences\nare statistically significant or within uncertainty bounds.\nLine 530–553. The uncertainty paragraphs in the Methods and Discussion are somewhat repetitive. Consider rephrasing or\ncross-referencing to avoid duplication.\nFigure 3. The maps are effective, but I suggest consider adding a panel showing the absolute number of transboundary co-\nbenefits (deaths avoided) in addition to the fraction. This would help distinguish between high-fraction but low-impact\nregions vs. high-impact regions\nThe authors have done a great job revising the manuscript and addressing most of the initial concerns. The paper presents a\nvaluable and timely analysis of how climate action may affect international air pollution inequalities. With the additional\nclarifications and discussions suggested above, the manuscript will be suitable for publication in Nature Communications."}, "author_response": "Response to Reviewers\n1.0 Guidance for this document:\nWe thank the editorial team and the reviewers for their second review of our manuscript entitled\n“National climate action can ameliorate, perpetuate, or exacerbate international air pollution\ninequalities” that we submitted to Nature Communications earlier this year. We have endeavored\nto integrate this second round of comments. To differentiate text from the reviewers, the authors,\nand new additions to the text please refer to the key:\nElement Text Style\nReviewer Comment Italicized black text\nAuthor Comment Plain black text\nOriginal Manuscript Text Plain green text\nRevised Manuscript Text Plain blue text\nOriginal Line Number (in parentheses)\nNew Line Number [in brackets]\nAll of the figures that are mentioned in response to reviewer comments can be found at the end\nof this document.\n2.0 Reviewer Comments\n2.1 Reviewer #1:\nR1C0: I appreciate the authors’ detailed response, and most of the issues have been adequately\naddressed.\nThank you for spending the additional time that you have taken to review our revised manuscript\nand for your positive comment.\nR1C1: Regarding the uncertainty in the results, the authors acknowledge in their discussion that\nuncertainties arise from various aspects such as emission projections, chemical transport model\nsimulations, and adjoint sensitivity calculations. However, the current revision still limits the\nquantification of uncertainty mainly to the health impact calculations. In the GEOS-Chem\nanalysis, simply stating that “transboundary pollution may be underestimated” is insufficient.\nReaders may wang to know what this implies for the final estimates of mortality.\nThis work dose not present major innovations about the methodological framework. Extensive\nliterature has already discussed the uncertainty of chemical transport models and the\npropagation of model uncertainties. It is suggested that the authors refer to some methodological\nstudies to provide a more substantive quantification of the uncertainties in their results.\nThank you for raising this issue and we agree with you that in the revised manuscript most of the\nquantitative discussion of uncertainty is on the health impact calculation. Given that similar\nmethodological frameworks have been used before, as you suggest, we can better quantify\nuncertainty in the other steps of our analysis by referencing past studies. To address this, we have\nrevised the uncertainty analysis section, replacing qualitative descriptions of uncertainty in our\nanalysis with quantitative ones. Specifically, we have added additional detail about how our\nemissions compare to other bottom-up inventories, included uncertainty estimates of the GEOS-\nChem adjoint of PM and its related health burden from HTAP analyses, identified the\n2.5\nuncertainty introduced by the linear adjoint calculation from a past study projecting PM from\n2.5\nSSP emission scenarios, and estimated how the absence of SOA could bias our results and\ntransboundary fractions. In response to R4C8 we have also rephrased some of the text to be less\nrepetitive and more concise. We have included the updated uncertainty analysis section below:\n[890-960]\nUncertainty is introduced primarily by: (1) emission projections, (2) the GEOS-Chem forward\nsimulation, (3) the adjoint sensitivity calculation, and (4) the health impact analysis.\nAlthough it is impossible to quantify a specific uncertainty associated with emissions projected\nfor the future – given that the true values are unknown – the emission projections49 were tested\nagainst historical emissions from another inventory, the Community Emissions Data System\n(CEDS)50 and they were found to be well correlated for SO from all sectors in 2005 (R2=0.71).\nfor both SO (R2=0.44) and NO (R2=0.70). The HTAP emissions51 that drive our forward\n2 x\nsimulation have been compared to other inventories52. Of the twelve regions considered in that\nanalysis, HTAP had the minimum emissions of all bottom-up inventories in five of the regions\nfor NO (27% lower than average), none of the regions for SO , five of the regions for BC (44%\nx 2\nlower than average), and three of the regions for OC (48% lower than average). Additionally,\nHTAP had the maximum emissions in none of the regions for NO , two of the regions for SO\nx 2\n(39% higher than average), two of the regions for BC (21% higher than average), and two of the\nregions for OC (62% higher than average). This comparison suggests that the HTAP emissions\nhave regional biases; however, compared to CEDS, the HTAP emissions of NO are\nx\napproximately 16% lower on a global scale. Given the degree of uncertainty in these emission\nprojections, we exclusively consider the relative differences in projections as opposed to absolute\ndifferences for our analysis.\nGEOS-Chem has a strong record of estimating atmospheric composition that compares well to\nobservations53,54. In an HTAP ensemble analysis55 of the PM health burden associated with\n2.5\nintercontinental transport, the simulated PM of the GEOS-Chem adjoint was compared to over\n2.5\n3000 global monitors. GEOS-Chem adjoint simulated concentrations fell within the range of\nensemble members with a normalized mean error of 55.1% (ranged from NME=35.4% to\nNME=62.9%) and a correlation of 0.65 (ranged from R=0.63 to R=0.77). The GEOS-Chem\nadjoint had the most positive bias of the ensemble members (NMB=20.3%); however, in an\nabsolute sense the bias was in the middle of the spread of ensemble members (ranged from\nNMB=-60.9% to NMB=20.3%). Additionally, all of the ensemble members underestimated the\nhealth burden of PM compared to the GBD 2015 study (4.2 million premature deaths); thus,\n2.5\nthis potential high bias in the GEOS-Chem adjoint led to a more accurate estimate of health\nimpacts (3.2 million premature deaths) than the multi-model mean (2.8 million premature\ndeaths). We also note that a majority of the surface-level observations were located in North\nAmerica, Europe, and China; thus, this analysis may not be representative of biases in other\nregions of the world.\nThe version of GEOS-Chem that is used in our analysis does not include anthropogenic fugitive\ndust and secondary organic aerosol; the prior was estimated56 to increase global population-\nweighted PM by around 2.9 𝜇g m-3 and the latter was estimated36 to make up between 15%-\n2.5\n30% of anthropogenic PM in urban environments. Fugitive dust PM is a primary species\n2.5 2.5\nwith a short atmospheric lifetime that mainly affects concentrations near where it is emitted. In\ncontrast, secondary organic aerosol has a longer atmospheric lifetime enabling its transport\nacross greater distances. For example, one study38 simulated PM transport for one UK city, and\n2.5\nfound that approximately 5% of the total PM came from non-local secondary organic aerosol.\n2.5\nIn our previous work37, we estimated that around 10% of PM in DC, originated from non-local\n2.5\nsecondary organic aerosol. These past analyses suggest that the exclusion of SOA could lead to\nan underestimate of between 5 – 10% of non-local PM that in turn would imply an\n2.5\nunderestimate of transboundary factions and exchanges by this magnitude. However, we note\nthat these past studies are for two locations in the Global North and not representative of the\nregional variation in this study and that the SOA contributions are calculated for urban areas that\nhave higher local sources than rural areas.\nAnother source of uncertainty in our analysis is the application of local-linear sensitivities to\ninherently non-linear PM formation. Although the primary components of PM (i.e., black\n2.5 2.5\nand organic carbonaceous aerosol) respond linearly to emissions, secondary inorganic aerosol\ndoes not respond linearly. Thus, this approach does not capture the second order effects of\nsecondary inorganic aerosol formation. A previous study31 explored the uncertainty associated\nwith this local-linear assumption in response to SSP emission projections in Korea, and found\nthat non-linearities contributed an underestimate of up to 57% in the PM -related health\n2.5\nbenefits from mitigation (although this uncertainty was lower for 2040).\nAdditionally, we have made some minor changes to the discussion to reflect the new quantitative\nnumbers from the uncertainty analysis\n[650-668]\nThe results presented in our work should be considered alongside sources of uncertainty. First,\nour adjoint-derived methodology calculates linear relationships between emissions and PM and\n2.5\nwhile some components of PM (e.g., primary carbonaceous aerosol) are formed linearly, others\n2.5\n(e.g., secondary inorganic aerosol) are formed through complex non-linear chemistry30; previous\nwork31 has suggested that this non-linearity could lead to underestimates of PM -related health\n2.5\nimpacts up to 57%. Additionally, our estimates of co-benefit contributions rely on the simplified\nassumption that the pollution formation relationships that we calculate for our base year (i.e.,\n2010) will be maintained in the future. We anticipate that changes to climate32,33 and\nchemical34,35 environment in the future will modify transboundary air pollution and we suggest\nthat feedbacks between climate change and transboundary air pollution are explored in future\nwork. Our analysis neglects to include anthropogenic fugitive dust and secondary organic aerosol\nformation that contribute to PM , especially in heavily populated urban areas36. Previous\n2.5\nstudies37,38 indicate that for urban locations, between 5 and 10% of PM originates from non-\n2.5\nlocal SOA. We suggest that future work is done to quantify the transboundary fractions of SOA\nglobally.\nR1C2: And, there is a minor issue that the equation on line 725 appears to be incomplete.\nThank you for catching this mistake. This has now been fixed:\n(725)\nBefore:\n𝐸𝑋𝐶 = (6)\n𝑚,𝑛,𝑠\n[870]\nAfter:\n𝐸𝑋𝐶 = ∑ Δ𝐽\n𝑚,𝑛,𝑠 𝐼∈𝑚 𝐼,𝑛,𝑠\n(6)\n2.2 Reviewer #2:\nR2C0: The figures play a central role in communicating the results; however, I have identified\nseveral issues regarding figure formatting, consistency, and clarity that require substantial\nrevision prior to publication. Addressing these issues will substantially improve the quality and\nimpact of the visual presentation in this manuscript. Below, I provide detailed comments for each\nfigure:\nThank you for your detailed comments on our manuscript. We agree that through improving the\nformatting, consistency, and clarity of our figures we can increase the quality of our manuscript,\nand we appreciate your suggestions on this matter. Please see our responses below:\nGeneral Figure Issues:\n1. There is a lack of consistency in font style and size across figures, with some using Arial and\nothers Times New Roman.\n2. Legends and labels are sometimes incomplete or formatted in a non-standard way.\n3. Axis labels, panel annotations, and units are not always clearly presented.\n4. In-figure legends and explanations are missing in some multi-panel figures, making\ninterpretation less accessible.\nWe appreciate your summary of the main issues with the figures. In response to your comments,\nwe have now updated the figures to use a consistent font type (i.e., Arial). We have adjusted the\nlegends and labels based on your suggestions (please refer to our figure-specific comments\nbelow for details). Additionally, we have included the axis labels, annotations, and units based on\nyour suggestions and included in-figure legends to improve interpretability. For specific changes,\nplease see our responses below:\nR2C1: Figure 2: 1. All textual elements, including axis labels, titles, legends, and color bars, are\nset in Times New Roman, while Figures 1, 4, and 5 use Arial. I recommend standardizing all\nfigures to use Arial and adopting consistent font sizes. For example, use 14 pt for titles and 12 pt\nfor axis labels and legends throughout the manuscript; 2. The color legend for continents in\npanels (a) and (b) does not follow standard formatting. Please revise using standard legend\nboxes with clear labels for each region; 3. The x-axis in panel (c) is incomplete, as only partial\nscenario labels such as 19 and 26 are shown. This may be confusing to readers. Please ensure\nthat all axis labels and tick marks are fully represented.\nThank you for your detailed comments on Figure 2. First, we have taken your suggestion here\nand changed the figure font throughout from Times New Roman to Arial; in doing so, we now\nhave a consistent font type in our manuscript figures. In terms of the font size, we have now\nstandardized this to use 14 pt for titles, 12 pt for axis labels and legends, and 10 pt for tick labels.\nWe have also changed the color legend to follow standard formatting and put this in a box in\nFigure 2b. For the x-tick labels for panel (c), we now include “RCP” in front of the RCP number\nto avoid confusing readers. We appreciate your last comment; however, we have opted to group\nsimilar panels together to avoid unnecessary text on the figures and to enable quick comparison\nbetween different scenario. Specifically, given that the panels share a common y-axis, we hide\nthe y-labels and y-tick marks to give the figure a cleaner appearance. For your reference, we\nhave included the updated version of Figure 2 at the bottom of this document.\nR2C2: Figure 3: 1. All elements in this figure are presented in Times New Roman. For\nconsistency, please change all fonts to Arial so that all figures follow the same style; 2. The\nupper color bar does not include a unit. Please add “Transboundary Fraction (unitless)” or\n“Transboundary Fraction (%)” as appropriate. For the lower color bars, make sure the unit “percentage (%)” is\nclearly indicated; 3. Currently, the figure does not display panel labels or scenario flow arrows. I recommend adding clear\npanel labels, such as (a), (b), (c), and (d), in the upper left of each map. It would also be helpful to include arrows to illustrate\nthe direction of scenario changes; 4. The figure caption should be expanded to provide a distinct explanation for each panel,\nincluding a description of the scenarios being compared and the direction of change.\nIn response to R2C1, we have now updated the font types of all figures from Times New Roman\nto Arial to maintain consistency. Thank you for pointing out that the units of the colorbars are\nunclear; we have added “unitless” to the colorbar label and rewritten the other labels to more\nclearly indicate the “%”. We agree that adding panel labels enables us to describe the specific\nscenarios more clearly in the figure caption; so, based on your suggestion, we have now included\npanel labels. We appreciate the suggestion to include arrows to illustrate the direction of scenario\nchanges; however, we have opted to demonstrate the direction of the scenario changes on the\nsubplots directly (e.g., “SSP3-45 – SSP1-26”) to indicate the difference between the two\nscenarios. We have now also mentioned this explicitly in the caption and, based on your earlier\ncomment, we have updated the panel figure captions to describe this for each subplot. For your\nreference, we have included the updated version of Figure 3 at the bottom of this document.\nR2C3: Figure 4: 1. I recommend restructuring the figure legend so that each panel “(a), (b),\netc.” is described separately. For each heatmap, the legend should clearly state the main\nvariable displayed, the numerical range or color scale, and the main findings or notable\npatterns. Moreover, heatmaps can be improved through the addition of markers (e.g., bold boxes,\nstars) to identify top contributors or outliers.\nThank you for your comment on Figure 4. In response to your suggestion, we have now added\nsubplot panels grouping the four sections of the figure by their common colorbars. With this\nchange, we can now more clearly state the variables displayed and the numerical range. For your\npoint on the main findings and notable patterns, we opted to address the main findings and\nnotable patterns of the figure in the main text instead of in the caption, e.g.:\n(394-397)\nFor example, in SSP3-45, the exchange between Europe and Africa is relatively balanced: the\nprior contributes 48% of the total co-benefits exchanged and the latter, 52% (Figure 5a).\nTransitioning to more sustainable socioeconomic development leads to Africa – the region with\nthe weaker GDP today – contributing a clear majority of co-benefits to the exchange (72%).\nIn response to the final suggestion to identify top contributors in the figure we have added white\nstars to indicate contribution relationships that are within the top 5% highest contributions for\neach heatmap. For your reference, we have included the updated version of Figure 4 at the\nbottom of this document.\nR2C4: Figure 5: 1. The font size for country names and axis labels is too small. Please increase\nfont size and consider bolding labels for clarity; 2. Please add explicit in-panel legends\nexplaining the meaning of black/white dots and arrow directions, rather than relying solely on\nthe main caption.\nThank you for your comments on Figure 5. In response to these comments, we have increased\nthe font size for the country names and country labels and bolded the text to improve readability.\nWe have also added explicit in-panel legends to explain the scenarios that the black and white\ndots represent and the regions that the shading indicates. For your reference, we have included\nthe updated version of Figure 5 at the bottom of this document.\n2.3 Reviewer #3\nR3C0: I co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nThank you again for your contributions to this manuscript. We appreciate the time that you have\ntaken to review our manuscript for a second time.\n2.4 Reviewer #4\nR4C0: The authors have made thorough and thoughtful revisions in response to the initial round\nof comments. The manuscript has been significantly improved through the incorporation of\nadditional analyses, clarification of methodologies, and enhanced discussion of results and\nimplications. The introduction of “transboundary fractions” and “exchanges” as well as the\ninclusion of uncertainty analyses strengthen the paper’s contributions to understanding how\nclimate action may affect international air pollution inequalities.\nWe thank you for your positive comments and detailed suggestions on our revised manuscript.\nWe have endeavored to address these concerns below.\nR4C1: The focus on 2040 is well-justified, but the analysis remains a single-time-slice\ncomparison. The supplemental analysis of 2030 is appreciated, but the manuscript should more\nexplicitly discuss the limitations of not capturing temporal evolution, especially since emission\npathways and socioeconomic assumptions evolve complexly. A brief discussion on how key\ninequalities might trend over time (e.g., towards 2050) would strengthen the manuscript.\nThank you for raising this comment on the temporal evolution of inequalities. We agree that\nmore care is needed when discussing our results given that they are tied to just a single year\nwhereas the inequalities associated with air pollution exchange will naturally evolve over time.\nTo address this comment, we have three responses. First, we have performed an additional\nanalysis for 2050 and now compare the trends in transboundary fractions at the regional level\nfrom 2030 to 2050 in a new supplemental figure (Figure S6). For your reference, we have\nincluded Figure S6 at the bottom of this document. Second, we have now updated the paragraph\nafter Figure 2 to discuss these new results\n[364-374]\nThe main results presented are for 2040; however, we additionally calculate the differences in\ntransboundary fractions and co-benefits between the base year of study (2040) and 2030 (Figure\nS4 and S5) across SSP1-26 and SSP3-26. We find that in 2040 transboundary fractions are\nmoderately higher in China, India, the US, and Brazil and lower in parts of Africa compared to\nSSP1 in 2030; however, these interannual changes are smaller than those attributable to\nsocioeconomic changes. We additionally project changes in transboundary fractions from 2030\nto 2050 across the set of scenarios (Figure S6) to identify regional-scale trends. Generally,\ntransboundary fractions remain stable through time; however, in more equal SSPs (i.e., SSP1),\ntransboundary fractions for Africa generally decrease from 2030 to 2050 (e.g., by 15% from\n2030 to 2050 in SSP1-45) compared to less equal SSPS (i.e., SSP3) where transboundary\nfractions increase (e.g., increase by 30% from 2040 to 2050 in SSP3-45).\nLastly, we modified the discussion section of the manuscript to more explicitly call out the\nlimitations of our focus on a single year:\n[670-684]\nGiven that the SSP-RCP emissions are designed to peak at different points across different\ncountries and regions the results presented in this work should be considered only as a snapshot\nof the inequalities in the year 2040 as the transboundary fractions and exchanges will differ at\ndifferent points in the future. In this study we do not explore the evolution of inequalities in\ndetail; however, a benefit of this adjoint methodology is its ability to rapidly assess different\nyears – as well as different scenarios – offline without additional simulations. To demonstrate\nthis, we include figures for an additional year (i.e., 2030) in the supplement (Figure S10-S13).\nAdditionally, we compare the transboundary fractions and co-benefits in 2030 to those in 2040\n(Figure S4 and S5) at the national-scale and estimate the regional trends in transboundary\nfractions from 2030 to 2050 (Figure S6); these additional results suggest that less equal SSPs\nlikely will exacerbate transboundary fractions through time than more equal ones. Lastly,\nalthough we estimate uncertainty associated with the health calculation, there is additional\nuncertainty in the projected changes to population, age distribution, and baseline disease rates\nthat are not captured in our analysis along with uncertainty in the air quality modeling that we\ndiscuss in detail in the methods.\nR4C2: The revised metrics (transboundary fractions and exchanges) are much clearer now. I\nsuggest the authors present some detailed discussions about their implications for climate justice\nor policy design. The authors might also more explicitly link metric values to equitable policy\noutcomes, e.g., whether certain values indicate need for compensation, cooperation, or\nindependent action.\nWe are glad to hear that the revised metrics are easier to interpret. We agree that we can further\nexplore the implications of the metrics for policy design. We have taken your suggestion and\ndeveloped a new figure (Figure S3) to link the transboundary fraction values to specific policy\nconcerns through considering an additional dimension of HDI. This figure has been included at\nthe end of this document. We now discuss this supplemental figure in a new paragraph after\nFigure 3 and specifically call out values that imply compensation, cooperation, and independent\naction:\n[314-322]\n… it is important to consider that transboundary fractions can indicate equitable or inequitable\npolicy outcomes when considered alongside the HDI of a country (Figure S3). Generally,\ncountries with high transboundary fractions require more compensatory actions as they have less\ncontrol of their air pollution burden, whereas countries with low transboundary fractions can\nadopt action more independently. However, given that geography and population density\ninfluence transboundary fractions, it is crucial to consider a dimension of development alongside\nthis exchange. The greatest targets for compensatory action are those countries with high\ntransboundary fractions and a low level of development. These countries will likely bear heavier\nhealth burdens in the future as industrialization and technological progress occur.\nWe explored this in the earlier manuscript with Figure 5; however, we did not clearly articulate\nthe implications of the results in this figure. We have modified the text to more clearly lay this\nout:\n[415-422]\nIn our analyses, we consider exchanges in three ways: (1) in their absolute sense an “exchange”\nrepresents how emission reduction in one country induces co-benefits in another, (2) as “net\nexchanges” by differencing the two exchanges in a pair, and (3) the proportion of co-benefits\ncontributed by one member of a pair to the total gross co-benefits exchanged between the pair to\ndetermine if a specific country dominates the exchange of air pollution co-benefits. In the latter\napproach, we note that exchanges in which there is a greater disparity – especially when the low\nHDI member of the pair is contributing more than the higher one – are key targets for\ncompensatory action.\nR4C3: Line 22. Abstract should distinguish between background and original results. To\nimprove clarity of the abstract, please explicitly introduce key results with phrases such as \"our\nresults reveal that...\" or \"we find that...\".\nThank you for this comment, we have now updated the abstract to more clearly distinguish our\nresults from other studies:\n[18-34]\nClimate action redresses poor air quality and its health burden by reducing sources that co-emit\ngreenhouse gases and air pollutants such as PM , yet ill-conceived mitigation could induce\n2.5\nimbalances in the distribution and exchange of air pollution across international borders. Despite\nits potential to endanger equality, the future effects of climate action on transboundary PM are\n2.5\nlargely unstudied. We find that air quality co-benefits are maximized through sustainable\ndevelopment towards RCP-19 (1.32 million deaths avoided) and minimized in fragmentation\ntrends towards RCP-60 (0.32 million). Our results suggest that futures with stronger climate\nmitigation necessitate more aggressive climate action from developed nations that increases the\nfraction of co-benefits that are transboundary for Africa, ranging from 8% higher in SSP1 to 53%\nin SSP3. Additionally, we show that countries with lower human development indices have a\ngreater fraction of their co-benefits originate externally (0.76 in SSP1-26) than more developed\ncountries (0.65) which positions developing nations as more dependent on regional and global\nclimate action than developed countries. We also find that co-benefit exchanges between\ncountries are non-uniform, which suggests country-specific analyses are necessary to develop\nclimate policy that minimize transboundary effects. Our results support the need for nuanced\npolicy design that considers how transboundary co-benefits respond to regional differences,\ndistinct socioeconomic trends, and mitigation strategies.\nR4C4: Line 63-64. The sentence on atmospheric transport and trade is somewhat abrupt.\nConsider integrating it more smoothly or expanding with a phrase like “In addition to\natmospheric transport, trade imbalances also contribute…”\nThank you for this suggestion, we have now updated this sentence:\nBefore:\n(63-64)\nAtmospheric transport drives some of this exchange; however, trade imbalances in consumption\nand production contribute to air pollution inequalities as well.\nAfter:\n[67-69]\nIn addition to atmospheric transport, trade imbalances in consumption and production also\ncontribute to air pollution inequalities19,20\nR4C5: Line 148-160. Please clarify whether this fraction is calculated based on mass of PM₂.₅\nor health impacts (deaths).\nThese fractions refer to health impacts (deaths). We have now clarified this in the text:\n[168-169]\nTransboundary fractions are calculated from the health impacts associated with PM -exposure.\n2.5\nR4C6: Line 251-261. The correlation between transboundary fraction and HDI is insightful.\nHave the authors considered also controlling for geographic size or population density? Smaller\nor coastal countries may have inherently different transboundary fractions regardless of\ndevelopment status.\nYou raise a good point as there are a number of variables that drive this correlation (geographic\nsize, population density, prevailing wind direction, how isolated major population centers are\nfrom other countries). We explored controlling for population density; however, ultimately\nsettled on performing this analysis using the total health impacts as these are the most policy-\nrelevant number. We note that we discussed this briefly in the discussion:\n(476-479)\nThis proportionally weaker transboundary influence in Asia is a product of population density:\nmajor populations in Africa and Europe are in closer proximity to extraregional polluters,\nwhereas in Asia most of the high population is concentrated in the Eastern part of the continent,\naway from other regions.\nHowever, we agree that it would be useful to comment on some of the overarching trends in\nmore detail. We have added text after Figure 2 to address this:\n[275-280]\nThere are several underlying factors driving the transboundary fractions: e.g., proximity to major\npolluters, geographic size, prevailing winds, and population density. Island nations, and those\nisolated by geographic features (e.g., Chile) tend to have more domestic pollution than nations\nproximate to high polluters (e.g., much of Europe). In the Northern Hemisphere, prevailing\nwesterlies lead to more pollution transport to the East of major polluters in comparison to the\nWest. Thus, it is important to consider that transboundary fractions…\nWe then tie this into the earlier discussion in response to R4C2.\nR4C7: Line 303-308. The comparison between 2030 and 2040 is useful. Please include in the\nmain text whether the differences are statistically significant or within uncertainty bounds.\nWe have now added this to the main text:\n[XXX]\nWe note that our estimates of co-benefits for 2030 lie outside of the lower-bound health estimate\nfor 2040 indicating that the difference in these estimates is not captured by uncertainty in the\nhealth calculation alone.\nR4C8: Line 530–553. The uncertainty paragraphs in the Methods and Discussion are somewhat\nrepetitive. Consider rephrasing or cross-referencing to avoid duplication.\nWe have now reworded these paragraphs to be more concise and added quantitative examples to\nthe uncertainty analysis in response to R1C1.\nR4C9: Figure 3. The maps are effective, but I suggest consider adding a panel showing the\nabsolute number of transboundary co-benefits (deaths avoided) in addition to the fraction.\nThis would help distinguish between high-fraction but low-impact regions vs. high-impact regions\nThank you for this suggestion. We agree that a figure presenting the absolute co-benefits would\nbe useful for comparison against the transboundary fractions. We initially experimented with\nadding this as a new panel to Figure 3 but preferred it as a separate figure, so we have now added\nthis to the supplement (Figure S2) that we include at the bottom of this document. We\nadditionally now point to this figure in the main text:\n[271-273]\nThe total co-benefit numbers are also included (Figure S2) to distinguish areas of high and low\nimpact when interpreting the transboundary fractions.\nR4C10: The authors have done a great job revising the manuscript and addressing most of the\ninitial concerns. The paper presents a valuable and timely analysis of how climate action may\naffect international air pollution inequalities. With the additional clarifications and discussions\nsuggested above, the manuscript will be suitable for publication in Nature Communications.\nThank you again for the positive comments and your useful suggestions."}, {"round": 3, "reviewer_comments": {"reviewer_1": "We appreciate the authors' thorough additions and revisions addressing the quantification of uncertainties. By citing multiple\nrelevant studies in the revised manuscript, the authors have provided a quantitative discussion of uncertainties in key\naspects such as emission inventories, model simulations, and health impact assessments, significantly enhancing the rigor\nand transparency of the analysis. We believe the robustness of the results has been markedly improved and recommend\nacceptance of this manuscript.", "reviewer_2": "The authors have addressed all previous reviewer comments, including methodological clarifications, improved uncertainty\nquantification, figure revisions, and clearer interpretation of results. The revised manuscript presents a significant and timely\ncontribution to the literature and is suitable for publication in its current form. Hence, I recommend acceptance.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "I thank the authors for their thorough and thoughtful responses to my previous comments, as well as for the corresponding\nrevisions made to the manuscript. The revisions have addressed all my concerns satisfactorily. The manuscript is now\nsignificantly improved, clearer, and well-supported. I have no further suggestions."}, "author_response": ""}]

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