Rising Air-Conditioning Use Intensifies Global Warming

Domain: chemistry
Published: 2026-02-25
Editorial outcome: major_revision

Abstract
Global warming and socio-economic development are together prompting a surge in the use of air-conditioning (AC). Yet the technology that delivers thermal comfort also emits large quantities of greenhouse gases (GHG), exacerbating climate change. We quantify global AC-related GHGs and associated warming impact under five climate scenarios, separating the contributions of global warming and socio-economic development. In a middle-of-the-road scenario (SSP245), cumulative AC-related emissions reach 113.3 Gt CO2eq between 2010 and 2050, increasing global-mean temperature by 0.05°C (0.03°C-0.07°C), with only about 8.3% to climate-driven cooling demand. Income inequalities exacerbate disparities in AC use, substantially limiting access to cooling in lower-income regions. While rising incomes reduce this inequality, they increase emissions: income-driven AC growth adds 14–146 Gt CO2eq and a further 0.003–0.05°C of warming by 2050, even under SSP119. These results highlight the need for a rapid low-carbon cooling transition that balances total warming impacts with equitable cooling access.

Introduction
Excessive anthropogenic GHG emissions have already warmed the planet by 1.2 ± 0.1°C since the pre-industrial era1. The resulting rise in surface temperatures, together with more frequent and intense heatwaves, has caused nearly half a million heat-related excess deaths between 2000 and 20192,3. Air-conditioning is an effective and widely used adaptation measure for protecting people from hazardous heat4. In parallel, rapid economic expansion in emerging economies, growing urban populations, and falling prices of appliances have expanded access to AC-based space cooling, transforming latent thermal-comfort demand into an unprecedented surge in AC adoption4. Consequently, the joint influence of global warming and socio-economic development produced a four-fold surge in annual global air-conditioner sales from 1990 to 20164,5, with over half located in China and the United States6.

Although the widespread AC use facilitates human adaptation to hot and humid weather, it also brings about additional energy consumption and resulting GHG emissions5,7. Given that cooling largely relies on electricity, this strategy has led to a more than threefold increase in global electricity consumption for cooling from 1990 to 20164. Some regions (e.g., the United States and the Middle East) even have an AC demand accounting for over 50% of their peak electricity load on hot days8. Considering that our current power generation is mainly based on fossil fuels9,10, the increase in electricity use for air-conditioning could cause substantial GHG emissions in the near-term. Refrigerants used in air conditioners, such as hydrofluorocarbons (HFCs), chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), also contribute considerably to global warming if leaked to the air11. Consequently, expanding AC access presents a fundamental trade-off: it safeguards human well-being in hotter climates while simultaneously locking in additional warming impact through both energy and refrigerant pathways. Quantifying and mitigating this climate penalty is therefore essential to ensure that gains in thermal comfort do not undermine long-term mitigation goals.

There have been emerging discussions around the trade-offs between the thermal benefits of increasing AC use and its associated GHG footprints12,13. Yet no study has quantified how the interactions between socio-economic development and global warming across climate scenarios could shape future AC demand, nor how that demand, in turn, could feedback to global-mean temperature. Existing analyses have not investigated the respective contributions of socio-economic drivers (such as income growth and population) and climatic drivers to future AC uptake and total warming impact. More importantly, although current income-driven inequalities in AC use are well documented, there are only few studies focusing on systematic quantification of future cooling inequalities. The additional emissions and associated warming impact that would accompany low-income households attaining higher living standards are still unaccounted for14. In addition, existing research often fails to account for the influence of relative humidity (RH) on cooling demand and neglected regional variations in AC demand15. Finally, only a few studies have examined the cooling sector under high-emission scenarios (e.g. RCP8.5) at the regional and global scales within an integrated framework, utilizing detailed energy-system models and General Circulation models (GCMs)7,16,17. However, these studies overlooked the large uncertainties arising from limited GCM ensembles, and crucially, they have not explored the long-term impacts of low-carbon transition driven by stringent climate targets on the warming impact caused by AC use.

Aiming at the above research gap, our study seeks to quantify the total warming impact from AC use on global-mean temperature across scenarios, investigate the respective contributions of socio-economic development and global warming, and evaluate how narrowing income-driven cooling inequalities reshapes both emissions and warming potential. Therefore, we design an integrated approach that incorporates the improved cooling degree days (CDD) approach, the Global Change Analysis Model18,19 (GCAM), and the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC, https://magicc.org/)20–22. Our projection of future regional CDDs is enhanced by introducing the heat-index approach and multi-model simulation from 25 widely applied GCMs (see Supplementary Table 1), providing a more comprehensive and nuanced estimation of cooling demand across regions (see “Methods”). GCAM provides a series of detailed functions for cooling services and energy demands, leveraging the dynamic interactions between socio-economic development, complex energy systems and climate change to capture cooling energy demand at both global and regional scales. By incorporating projected CDDs across scenarios, we employ GCAM to estimate AC energy consumption for cooling and the associated GHG emissions under the five SSP-RCP scenarios (see “Methods”). We also quantify the respective contributing effects of socio-economic factors (e.g., floorspace, GDP, income, and population) and global warming (e.g., CDDs) to the trajectories of AC stock and energy consumption. Moreover, we use the well-calibrated climate emulator MAGICCv7.5.3 to quantify the possible ranges of additional warming effects from AC use (see “Methods”). To investigate the climate-driven feedback and socio-economic contributions of AC-induced global warming impacts, we construct simplified counterfactual experiments within our IAM framework across scenarios. Finally, we assess the inequality in cooling services using Lorenz curves and Gini coefficients based on cooling energy consumption and expenditure derived from the GCAM, and develop an econometric model to quantify these inequalities in the cooling gaps (the unmet need for air-conditioning due to a lack of income) through AC stock under different income levels within SSP-RCP scenarios (see Methods). Our study provides quantitative global insights on the impact of AC use with the interactions between the changing climate and socio-economic development, exploring the extent of warming impact and cooling vulnerabilities.

We employ two widely used scenario frameworks to build future climate simulations: the Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs). Following the scenarios highlighted by Working Group I of the IPCC Sixth Assessment Report (AR6)23,24, we use the SSP119 (referred as SSP1-RCP1.9), SSP126, SSP245, SSP370 and SSP585 scenarios as a range of future climate mitigation scenarios, matching with the climate projections in CMIP6 (Coupled Model Intercomparison Project Phase 6)25,26 and NASA NEX-GDDP-CMIP6 dataset. SSP126, SSP245, and SSP585 represent, respectively, a world on a sustainable pathway, medium development, and high dependence on fossil energy27,28. SSP119 and SSP370 emphasize scenarios, respectively, with lower emissions to achieve the 1.5°C target and significant developmental disparities between regions (see “Methods”)29,30.

Results
Impacts of global warming on cooling demand
Figure 1 shows global cooling demand trends under the five SSP-RCP scenarios. We account for the cooling demand consistent with the GCAM regional classification (see Supplementary Table 2). We use population-weighted Cooling Degree Days (CDDs) to measure historical and future cooling demand both globally and within regions using mean surface temperature and relative humidity gridded data (0.25° x 0.25°, ~25km), and global 1km downscaled population distribution gridded data. Gridded data of temperature and relative humidity come from the CMIP6 database26 and the NASA NEX-GDDP-CMIP6 dataset, and population distribution from the NASA Socioeconomic Data and Applications Center (SEDAC database)31,32.

Global cooling demand, measured in CDDs, is projected to increase by an average of 25% across climate models (−5% to 100%, ranging across 25 GCMs) by 2050 under the baseline scenario (SSP245) compared to 2010 (see Fig. 1a). By 2100, this increase is expected to reach 50% with a range of 12% to 160%. Due to the most stringent GHG emission control, global CDDs under the SSP119 scenario (Fig. 1c) show a slight increase, with a 4% rise in 2100 relative to 2010, remaining far below the other scenarios. Despite a 28% increase in global cooling demand from 2010 to 2050 under the SSP126 scenario, the widespread adoption of negative carbon technologies, similar to those in the SSP119 scenario, results in a lower global CDD in 2100 compared to 2050. By contrast, under high-emission scenarios (SSP370 and SSP585), global cooling demand is projected to increase considerably, particularly as GHG emissions accumulate, with demand in 2100 being substantially higher than in 2050 (with 48% and 66%, respectively).

From a regional perspective, higher-emission scenarios generally result in considerably higher cooling demand across all regions by the end of the century, while lower-emission scenarios lead to lower cooling demand. In contrast, by 2050, the expected rise in cooling demand under higher-emission scenarios does not emerge uniformly across all regions, suggesting an uneven distribution across regions in the mid-term projections. Several interconnected factors explain such discrepancies. First, regions responding most rapidly to climate change are primarily located in the high latitudes of the Northern Hemisphere, but they contribute relatively little to global CDDs21. For example, under the SSP119 scenario, cooling demand in Northern Hemisphere regions, such as Canada and Northern Europe, declines considerably by 2050; however, it does not fully offset the increases in other regions, contributing to a slight increase in global CDDs from 2010 to 2050. Second, ambitious mitigation policies in low-emission scenarios (SSP119 and SSP126) reduce aerosol concentrations29, which have previously exerted a short-lived cooling effect33,34. Consequently, diminishing aerosol-related cooling can lead to temperature rise and greater cooling demand in regions such as West Africa and South Asia35. Furthermore, demographic shifts can also affect regional cooling demand in the mid-term. For instance, under the SSP126 scenario, a relative increase in populations residing in warmer regions (e.g., Australia and New Zealand) contributes to higher cooling demand than under the baseline scenario. Conversely, projected population decline in regions such as Canada and Russia under the SSP370 scenario leads to reduced cooling demand compared to the baseline scenario. Taken together, these results highlight the intricate interplay between climate dynamics, aerosol forcing, and demographic changes that shape heterogeneous regional cooling demand in the mid-term.

Moreover, there is a considerable mismatch between regional cooling demand and economic development. The distribution of current and future cooling demand differs considerably across regions36: most of the cooling demand is concentrated in equatorial regions. Figure 1b shows that Southeast Asia have the highest CDDs of 6105 in 2050 under the baseline scenario, followed by Indonesia (6091) and the northern part of South America (5409). However, regions with substantial cooling demand (over 4000 CDDs), such as Pakistan, Western Africa, and South Asia, are often those with the lowest economic development37. Conversely, many high-income regions, such as Canada and European regions, typically exhibit cooling demand below 1000 CDDs. This economic divide is further highlighted by the fact that the five low-income regions, whose combined GDPs amounts to merely 14% of that of the five high-income regions, nevertheless account for 24.4% of global cooling demand, whereas these high-income regions contribute just 8.7%.

Air-conditioning stock and energy consumption
The extent to which global cooling demand could affect the AC stock is dependent on economic development9,38. Under the middle-of-the-road baseline scenario (SSP245), global AC stock is projected to be around 2.3 (1.9–3.4) billion units in 2050 (see Fig. 2a). The highest increases are found in the scenario that combines strong economic growth with high levels of global warming (SSP585), reaching 3.1 billion units in 2050. The sustainable development and “well below 2C” mitigation scenario (SSP126) also shows higher AC demand than the baseline, at 2.6 billion units. Due to slow economic development, the SSP370 scenario has the lowest AC stock (2.0 billion), even 14% lower than that under the SSP119 scenario.

Similarly to AC stock trends, global AC energy consumption is largely determined by economic development and cooling demand. Under the baseline scenario, global AC energy consumption is projected to increase by 135.7% in 2050 compared to 2010 (See Fig. 2b). Despite the variety of AC types, the use of air-conditioning is mostly powered by electricity39, with a very small portion provided by natural gas. AC electricity consumption contributes 8.0% of total energy consumption (TEC) globally in the building sector in 2050, reaching 4493 (3326–7225) TWh, aligning similarly with previous studies that estimate around 4000 TWh by 205040. Driven by high cooling demand and per capita GDP, the SSP585 scenario consumes the most AC-related electricity among all scenarios, ranging from around 5800 to 12,821 TWh, which accounts for up to 14.9% of TEC in the building sector globally. Even though the cooling demand in the SSP126 scenario is lower compared to the SSP370 scenario, higher per capita income still results in more AC electricity consumption by 2050, which aligns with previous studies indicating that income drives a large increase in cooling energy consumption under the SSP1 pathway41. Although the SSP370 scenario projects the lowest overall AC energy consumption, the reduction compared to the SSP119 scenario (~6%) is relatively smaller than the corresponding difference in AC stock. This diminished gap, beyond potential methodological mismatches between the stock and consumption modeling approaches (as discussed in Limitations), can be attributed to the interplay between various factors, including differences in cooling demand, usage patterns, and energy efficiency. For example, compared to the SSP119 scenario, the higher cooling demand projected under the SSP370 scenario lead to longer AC operating hours or more intensive usage (Unit energy consumption) (see Supplementary Fig. 2). Furthermore, the narrative of the SSP370 scenario assumes slower technological progress, implying lower building energy efficiency relative to the SSP119 scenario. The combination of potentially higher usage intensity and lower unit efficiency in SSP370 substantially offsets the reduction in aggregate energy consumption that would otherwise be expected from its considerably smaller AC stock.

Income growth has been found as the dominant driver of the potential surge in both installed AC stock and associated energy consumption. Our decomposition analysis shows that, averaged across the five SSP-RCP scenarios, the income effect alone contributes to 158% of the increase in the global AC stock and 190% of the increase in global AC energy consumption between 2010 and 2050 (see Supplementary Table 3 to 4). For the global AC stock, the next two largest growth drivers are the AC penetration intensity effect (captures changes in the number of installed AC units relative to the cooling demand) and population effect, which raise the stock by 74% and 40%, respectively. By contrast, the floorspace intensity effect produces the greatest decreasing effect, because aggregate economic output has expanded more rapidly than total floor area. These drivers exert similar effects on global AC energy consumption.

Because those income gains are unevenly distributed, their imprint on cooling demand is highly diversified regionally. Across all scenarios, AC stock and energy consumption of China and the United States far outweigh stock and consumption in other regions. With the additional contributions from Japan, the Middle East and Southeast Asia, Asia is projected to emerge as the leader in this aspect (see Fig. 2c,d). The decomposition analysis confirms these contrasts: the income effect results in 318.2% of the increase in AC stock and 395.5% of the increase in cooling energy consumption in China, whereas in the United States, it leads to only 73.2% and 64.2% increases, respectively. Similar disparities emerge elsewhere. In rapidly urbanizing economies such as Africa, India and Indonesia, lagging efficiency and steep income growth together drive the fastest proportional increases in both metrics. Although per capita levels in these regions remain below those in China due to income constraints, ongoing urban population growth and rising incomes suggest that future AC uptake and cooling energy consumption in these regions could exceed our current projections42.

Contribution of air-conditioning use to global warming
GHG emissions from AC use are projected to peak before 2040 under the ambitious climate mitigation scenarios (SSP119 and SSP126) but continue to increase until 2050 in other scenarios (see Fig. 3a). The emissions from AC use are largely determined by how clean the power generation supply is (see Supplementary Fig. 3), given that electricity is the main energy source. Under the baseline scenario, AC-related GHG emissions increase from 1.3 Gt CO2eq in 2010 to 3.8 (2.9–5.5) Gt CO2eq in 2050. The SSP585 scenario generates the largest GHG emissions. Considering the highest share of fossil fuel involved with power supply in all scenarios, GHG emissions from AC use under the SSP585 scenario reach about 8.5 (6.2–14.0) Gt CO2eq in 2050, approximately equivalent to the total CO2 emissions of the United States in the last two years43. The SSP370 scenario consumes less energy for air-conditioning relative to the baseline scenario, but still produces more GHG emissions, reaching 4.2 (3.1–5.9) Gt CO2eq in 2050. The underlying reason is similar: the assumed share of fossil fuels in the power supply under the SSP370 scenario (72.7%) is larger than under the baseline scenario (66.6%, including the share of CCS technology). However, when low-carbon energy dominates the power supply structure, the situation would be very different. For example, under the SSP126 scenario, GHG emissions from AC use decline by 30.5% (11.9%–46.6%) in 2050 relative to baseline, even with 10.2% higher energy consumption for air-conditioning. In contrast to the SSP585 scenario, the SSP119 scenario consumes the least amount of energy for air-conditioning with the associated GHG emissions peaking in 2035, generating less than half of the baseline GHG emissions in 2050.

Our findings show that there are substantial differences in AC-related emissions across scenarios, with the drivers of GHG emissions changing from electricity consumption to the leakage of air-conditioning refrigerants (see Fig. 3b). Global cumulative GHGs from AC use (see Fig. 3c) would reach 113.3 (83.8-168.7) Gt CO2eq between 2010 and 2050 under the baseline scenario. China and the United States alone account for almost 40% of annual emissions from AC use by 2050 across all scenarios, equivalent to the total emissions of the other top ten regions. Among all AC-related GHG emissions, refrigerant leakage is projected to become the primary source by 2050, accounting for up to 60% of total AC-related GHG emissions. The joint influence of global warming and socio-economic development markedly extends the working hours and operating units for air-conditioning. GHG emissions from electricity use and refrigerant leakage would correspondingly increase44. Decarbonization in power supply structure could offset some of these emissions. Deployment of renewable energy and negative-emission technologies restrains CO2 emissions from both residential and commercial cooling sectors between 2010 and 2050: under the SSP585 scenario, annual CO2 growth is limited to 3.1% and 3.4%, respectively, whereas under the most stringent scenario (SSP119), 2050 emissions fall below 2010 levels by 27.4% and 25.0%. By contrast, the shift to low-GWP refrigerants is progressing far more slowly, allowing non-CO2 GHGs from AC use to grow at more than twice the CO2 rate, sustaining annual increases of about 3%-7% across scenarios.

GHG emissions from AC use, in turn, would result in additional global-mean temperature increases by 2050, ranging from 0.02°C (the lower bound for the SSP119 scenario) to 0.11°C (the upper bound for the SSP585 scenario) across the five SSP-RCP scenarios. Under the baseline scenario, there would be an additional 0.05°C (0.03°C-0.07°C) temperature increase by 2050. The SSP585 scenario produces the most cumulative GHG emissions from AC use with the highest warming impact, bringing about an additional 0.07°C (0.05°C-0.11°C) temperature increase by 2050. In comparison, the SSP119 scenario would only see an additional temperature increase of 0.03°C (0.02°C-0.04°C). Given that the observed warming from 1998 to 2012 is about 0.05°C (−0.05°C-0.15°C) per decade45,46, this additional impact could accelerate global warming, reducing the likelihood of achieving the 1.5°C or 2°C temperature targets. Historical data further underscores the escalating economic damage associated with natural disasters under such warming conditions: the economic loss from the top 1% of single-event disasters reached 9.92 billion USD in 2010, more than doubling since 199847. Economic ramifications of this additional warming in production are far-reaching, indicating that global output could drop 2.5% in response to a temporary increase of 1°C48.

The total warming impact from AC use is attributable to the combined effects of socio-economic development and a climate-driven feedback loop. The socio-economic component captures the fundamental growth in cooling demand through population growth and rising affluence. The climate-driven warming feedback, in contrast, captures the additional, self-reinforcing loop that occurs as rising temperatures necessitate more cooling, which in turn generates more emissions. To separate global warming from socio-economic development, we construct a simplified counterfactual scenario analysis by fixing regional CDDs at their 2010-level on all future years to quantify their respective contributions of warming impact (see Supplementary Tables 7 to 11). Removing the climate-warming signal systematically lowers projected AC energy demand and the attendant GHG emissions in every scenario, with the gap widening over time. The climate-driven feedback is projected to cause no more than 0.01°C of additional warming by 2050 across all five SSP-RCP scenarios. In line with our previous analysis, socio-economic development dominates the additional warming impact from global AC use. For example, the climate-driven feedback peaks at about 0.01°C under the most warming scenario (SSP585), indicating that socio-economic development contributes ~0.06°C of warming. In contrast, under the lowest-warming SSP119 scenario, the differences between the two drivers are the greatest: the climate-driven feedback is projected to reach only 0.001°C, while the contribution from socio-economic development is projected to be more than 0.03°C.

Therefore, achieving a low-carbon transition is critical to mitigating the total warming impact associated with AC use. Such a transition not only suppresses the climate-driven warming feedback by slowing global warming itself, but also enables socio-economic development to proceed with substantially mitigated emissions through the deployment of clean energy supplies. Our results show that increased AC usage is projected to cause an average 101.7% increase of global direct cooling emissions from AC use between 2010 and 2050 (see Supplementary Table 5 to 6). Mitigation policies effectively drive the low-carbon transition of the power sector, particularly under the SSP119 scenario, achieving a 75.1% reduction in CO2 emissions. In contrast, the non-CO2 emission intensity effect results in increased non-CO2 emissions across all scenarios, indicating that the current low-GWP refrigerant transition is progressing slowly. Regional patterns, however, diverge sharply. In high-income regions with a high adoption of efficient, low-GWP refrigerants (e.g., the United States), the non-CO2 emission intensity effect contributes to over 90% of the emission reductions under low-emission scenarios. However, in regions with rapid growth in AC demand that primarily use high-GWP refrigerants (e.g., China), it contributes to a 362.1% increase in non-CO2 emissions even under the SSP119 scenario, ~1.6 times greater than the energy use effect.

Global inequality in the use of air-conditioning
Socio-economic development, especially household income, stands as the most important driver of global AC energy demand and the resulting GHG emissions. This means the profound global income disparities inevitably create a deeply unequal distribution of cooling access and its associated burdens. Although income growth from 2010 to 2050 reduces inequality in both cooling energy demand and expenditure, inequality remains high even after accounting for differences in regional cooling demand (CDD-adjusted Gini; Fig. 4). Higher income growth expands access to thermal comfort and narrows global inequalities in AC use, but they also increase global AC stock, as well as the associated energy consumption and GHG emissions. To further quantify the influence of rising incomes on global AC use, we employ an econometric model to estimate income-driven cooling gaps under three income-adjusted scenarios (see Fig. 5). Here, cooling gaps represent the difference between previously estimated AC stock under SSP-RCP scenarios and AC stock estimated derived from corresponding income-adjusted scenarios. Specifically, we build medium-income, high-income, and maximum-income scenarios within each SSP-RCP framework: raising the income of (1) populations in low-income regions to the average income of middle-income regions; (2) populations in low- and middle-income regions to the average income of high-income regions; (3) populations across all regions to the highest income level under the corresponding SSP-RCP scenario. Our results indicate that raising household incomes in low-income regions would increase the global AC stock by ~one million units and increase the operating hours by at least 40% by 2050. If all regions achieved the highest income level envisioned in each SSP framework, global AC stock would expand by tens of millions of units, driving an extra 0.003°C-0.05°C increase of global-mean temperatures even under the most stringent SSP119 scenario.

All Lorenz curves are constructed by ranking regions from lowest to highest per-capita GDP, allowing direct comparison with global economic inequality, following Zhao et al.49. Although the ratio of per-capita GDP by 2050 between the highest-income 10% and the lowest-income 10% of the global population remains larger than the corresponding ratio for energy consumption and expenditure for cooling. However, this conceals deeper, underlying inequalities. Based on the Gini coefficients, the distribution of energy consumption and expenditure for cooling is more unequal than GDP. Income growth helps to narrow these gaps. Based on SSP narratives, global economic inequality declines by 2050 in all scenarios, with the Gini coefficient shifting from high inequality towards less than 0.4 in 2050 (except for the SSP370 scenario, see Supplementary Table 12). As a result, the Gini coefficient for cooling energy consumption decreases to 0.47 and the Gini coefficients for cooling expenditure declines to 0.45 in 2050 under the baseline scenario. The lowest-income 10% of the population would consume about 2% of global cooling energy in 2050 and are linked to 3% of global cooling expenditure. In comparison, the highest-income 10% of the global population could have access to cheaper cooling service than the lowest-income 10% of the population, consuming around 18% of global cooling energy linked to 17% of global cooling expenditure. Scenario differences further reinforce this pattern. The SSP585 scenario, which features the highest income growth, has the most equal distribution of energy use and expenditure for cooling among the five scenarios. The highest-income 10% of the global population is expected to consume ~4 and 3 times more cooling energy and expenditure than the lowest-income 10% in 2050, with these differences being substantially lower than those observed in the baseline scenario. In contrast, the SSP370 scenario, characterized by slow and uneven economic development, shows the most unequal distribution, with the differences in cooling energy use and expenditure between the highest-income 10% and the lowest-income 10% projected to increase to 13- and 10-fold, respectively. Under low-emission scenarios, higher mitigation costs result in higher unit cost of cooling services relative to the other scenarios, which does not make cooling service more affordable for low-income people. Thus, achieving low-carbon targets contributes little to narrowing differences in energy use and expenditure for cooling relative to the SSP585 scenario.

However, such regional differences in energy use and expenditure for cooling become even more pronounced if regional differences in cooling demand are considered. Many high-income regions are located in high-latitude areas, where climate characteristics result in relatively low cooling energy consumption and expenditure. This geographic mismatch between cooling demand and economic development can obscure differences in cooling energy consumption and expenditure per unit of cooling demand. When we normalize by regional cooling demand, inequality intensifies. Specifically, under the baseline scenario, the differences in cooling energy consumption and expenditure per CDD between the highest-income 10% and the lowest-income 10% increase from 6- and 5-fold (without considering regional cooling demand differences) to 20- and 15-fold, respectively. Such differences are most pronounced under the SSP370 scenario, reaching ~8- and 29-fold in 2050, respectively. These results indicate that, relative to their climatic requirements, the lowest-income populations consume far less cooling energy than the highest-income populations, highlighting a substantial inequality in access to cooling services.

Higher incomes dramatically accelerate the expansion of the installed AC stock, yet regional differences in the underlying cooling demand modulate the scale of that growth. For example, regions with low climatic cooling demand tend to not require air conditioners even at high income levels. Canada is projected to have similar population size and per capita income to South Korea in 2050 across scenarios, while its average AC stock are projected to be ~14% of South Korea’s. That is because higher air temperature and relative humidity in summer would result in far greater demand for air conditioners in South Korea relative to Canada. We adopt the ratio of cooling gaps to the projected AC stock under corresponding income-adjusted scenarios to quantify the extent of inequalities in global AC stock. We find that cooling gaps would reach 94 (86-111) million units in 2050 for low-income regions, equivalent to ~45% of total AC stock under the medium-income scenarios. Income constraints on AC stock are the most pronounced under the SSP370 scenario, where its corresponding projected amounts of AC use can only meet 50% of those required for a medium-income lifestyle in low-income regions. Furthermore, cooling gaps under the high-income scenario would respectively reach 150 (126-166) and 117 (55-176) million units in 2050 for low-income and medium-income regions, equivalent to 56% and 27% of their income-adjusted AC stock. Most importantly, assuming that cooling access across regions were to become the same as the highest-income regions, global cooling gap in 2050 would increase to 0.7-1.1 billion units.

Accounting for the increased working hours of AC devices, income-adjusted scenarios are projected to drive a substantial surge in AC-related GHG emissions. We treat the income-induced change in unit energy consumption (see section 4 in SI) as a proxy for increased operating hours. Using the most stringent SSP119 scenario as an example, the cumulative GHG emissions between 2010 and 2050 are projected to increase by 19.1% under the medium-income scenario. Under the high-income scenario, cumulative AC-related GHG emissions from low- and medium-income regions are projected to increase by 107 Gt CO2eq each, exceeding the total AC-related emissions projected for the SSP126 scenario. Compared to the warming impact from the SSP119 scenario, additional GHG emissions under the medium-income scenario would cause an additional 0.003°C increase of global-mean temperatures, while a respective additional 0.015°C of warming under the high-income scenario. Under the maximum-income scenario, the cumulative GHG emissions between 2010 and 2050 would have a roughly 2-fold increase, surpassing the totals projected under the SSP585 scenario, with a further increase in temperature of 0.05°C.

Methods
Integrated assessment framework
This study develops an integrated assessment framework to quantify the total warming impact and inequalities resulting from AC use under various climate scenarios (see Fig. 6). For this scenario analysis, we utilize the SSP-RCP framework to simulate climate scenarios, matching the outputs of multiple GCM models from the CMIP6 database (see the Climate scenarios section). Subsequently, we quantify global and regional cooling demand across different scenarios using an improved population-weighted CDD approach, incorporating the heat-index approach (see the heat-index approach section). We integrate the improved regional CDDs as climate inputs into a widely used integrated assessment model GCAM19,66, enhancing the ability to project future AC energy consumption and associated GHG emissions. Additionally, by assessing AC-related impacts within GCAM across five IPCC-endorsed SSP-RCP scenarios, our findings provide a comprehensive outlook on future AC energy use and emissions under possible ranges of global warming trajectories and socio-economic development. Lastly, we assess the inequality in the use of air-conditioning using Gini coefficients and quantify the cooling gaps with an econometric model. The formulas used for the inequality assessment are presented below, with additional robustness analysis for the econometric model provided in the Supplementary Information. Finally, we employ the MAGICC model to evaluate the additional warming effects resulting from the GHG emissions associated with AC use. Detailed information is provided at the end of the “Methods” section.

Climate scenarios
In our research, we select five scenarios (SSP119, SSP126, SSP245, SSP370 and SSP585), combining Shared Socio-economic Pathways (SSPs) with Representative Concentration Pathways (RCPs) to depict future climate change scenarios. SSPs are narratives that describe plausible future global developments in demographics, economics, technology, and environmental factors without considering climate change or mitigation policies67. Each SSP outlines different challenges to mitigation and adaptation29: sustainable development (SSP1), middle of the road (SSP2), regional rivalry (SSP3), inequality (SSP4) and fossil-fueled development (SSP5). RCPs represent trajectories of GHGs and other forcing agents to guide modeling potential climate outcomes. The original RCP scenarios include four scenarios: RCP2.6 (very low forcing scenario), RCP4.5 and RCP6.0 (medium stabilization levels), RCP8.5 (a very high emissions scenario)30. In the IPCC AR623,24, two additional scenarios, RCP1.9 and RCP7.0, were introduced, representing global 1.5°C pathway and a high-emission baseline scenario, respectively. These five selected scenarios cover a wide range of possible climate outcomes and anthropogenic emissions pathways, and thereby encapsulate the uncertainties and complexities of projecting long-term climate and societal changes. Additionally, by using the same SSP-RCP scenarios recommended by the IPCC AR6, we ensure that our study is aligned with the global GCM community, strengthening the robustness of our findings. The main assumptions and warming outcomes of the SSP-RCP scenarios are shown in Supplementary Table 8. We acknowledge that the SSP4 scenario, which focuses on unequal development, is not included in our choice of five illustrative scenarios, meaning that the scenario characterized by significant inequalities in AC use has not been explicitly explored. However, even in the absence of SSP4, our analysis still captures key contrasts in inequalities between SSP3 (high inequality, high vulnerability) and SSP1 (low inequality, high resilience). While the inclusion of SSP4 may provide additional valuable insights, its exclusion does not diminish the broader relevance or robustness of our findings.

Heat-index approach
The heat-index approach, developed by Lans P. Rothfusz and Steadman (https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml), is widely used to capture the impacts of RH on regional thermal comfort levels68–70. We introduce the heat-index approach to extend the Cooling Degree Days approach by combining both air temperature and humidity, which is a prerequisite to accurately measure the cooling demand of each GCAM region. Existing studies have demonstrated that RH affects the value of perceived temperature a lot but have often been ignored in the long-term climate change modeling36. Based on the daily gridded data of near-surface air temperature and RH from the CMIP6 database and the NASA NEX-GDDP-CMIP6 database across SSP-RCP scenarios, we calculate the heat index in each grid for distinct conditions: temperatures below 80°F, temperatures above 80°F, RH below 13% with temperatures between 80°F and 112°F, and RH above 85% with temperatures between 80°F and 87°F. When the temperature is below 80°F, the Rothfusz regression model is deemed unsuitable, as it has not been validated for lower heat index values. We use a simplified function (Eq. (1)) to provide a reasonable approximation of the heat index at lower temperatures, where extreme heat stress is less of a concern:

HI = 0.5 × {T + 61.0 + [(T − 68.0) × 1.2] + (RH × 0.094)} (1)
below 80°F

Where HI represents the heat index value, also called the apparent temperature adjusted by relative humidity; T represents the near-surface air temperature in degrees F; and RH represents relative humidity in percent.

For temperatures exceeding 80°F, we utilize a multiple regression model developed by Lans Rothfusz (Eq. (2)), which was specifically designed to accurately model heat index values under high temperature and humidity conditions:

HI = −42.379 + 2.04901523 × T + 10.14333127 × RH
above 80°F
− 0.22475541 × T × RH − 0.00683783 × T × T (2)
− 0.05481717 × RH2 + 0.00122874 × T2 × RH
+ 0.00085282 × T × RH2 − 0.00000199 × T2 × RH2

Furthermore, when RH is below 13% with temperatures between 80°F and 112°F, a reduction adjustment is applied to account for the diminished perceived heat due to dry air:

Adjustment = [(13 − RH)/4] × √{[17 − |T − 95|]/17} (3)

Conversely, when RH exceeds 85% with temperatures between 80°F and 87°F, an increase adjustment is added to reflect the heightened perception of heat in humid conditions:

Adjustment = [(RH − 85)/10] × [(87 − T)/5] (4)

After the above steps, the heat index value represents the apparent temperature in each grid, which is used as the air temperature inputs for the Cooling Degree Days approach.

Population-weighted cooling degree days approach
Cooling Degree Days approach is widely used to measure the extent to which air-conditioning cooling could be required for a given period of time70,71. We have extended the Cooling Degree Days approach to the population-weighted Cooling Degree Days by taking into account both the apparent temperature adjusted by heat-index approach and future population distribution. The population distribution projections along the SSP framework comes from Socioeconomic Data and Applications Center (SEDAC database)32. Accordingly, population-weighted Cooling Degree Days approach provides more accurate projections of regional cooling demand for air conditioning under different future climate change and socioeconomic scenarios, contributing to a better understand the impacts of climate change on different regions. Population-weighted CDDs for 32 regions defined in GCAM is calculated using the following equations.

CDD day i,j = max(T mean,day i,j − T base,day i,j, 0) (5)

Where, i represents GCAM region i; j represents the grid cell j; CDD day i,j represents daily cooling degree days in grid cell j of region i; T mean,day i,j represents the daily heat index value in grid cell j of region i; T base,day i,j represents a given point (18°C in this paper based on numerous global CDD studies17,36,72), which is used for measuring the amount and duration of temperature being above this point. This choice is still a topic of considerable discussion for long-term global assessments. One limitation is that a uniform base temperature may not fully capture regional variations in thermal comfort thresholds, while this simplification facilitates global comparisons (see Limitations). Moreover, our choice of base temperature does not account for the influence of RH, which may create a degree of inconsistency with the heat-index approach. However, incorporating RH into the baseline temperature would introduce seasonal and regional variations, making global CDD aggregation and comparison less uniform and more complex. The current compromise strikes a balance between the need for regional specificity in the heat index approach and the practicality of calculating CDDs using a standardized baseline temperature. Furthermore, we conducted a sensitivity analysis using various base temperatures (see Supplementary Fig. 5) to quantify the impact of this parameter choice. While the results demonstrate considerable sensitivity of absolute CDD magnitudes to the selected base temperature, employing a consistent 18°C base temperature is crucial for maintaining comparability when assessing relative CDD trends across diverse regions and scenarios.

CDD year i,j = ∑ day CDD day i,j (6)

Where CDD year i,j represents yearly cooling degree days in grid cell j of region i after adding up all daily cooling degree days.

CDD i population-weighted,year = ∑ j (CDD year i,j × Pop year i,j) / ∑ j Pop year i,j (7)

Where CDD population-weighted,year represents population-weighted CDDs of region i; Pop year i,j represents the population amount in grid cell j of region i.

Global change analysis model (GCAM)
GCAM model is an open-source integrated assessment model, providing long-term simulation of economic, energy, land use, water resources and climate change interactions at global and regional levels19. In this study, we use GCAM model in its widely used version of 5.218 to match the same base year (2010) of climate outputs from 25 GCMs. Within GCAM, the world is divided into 32 energy-economic regions, simulating the equilibrium prices and quantities of various energy and GHG markets in each time period and in each region in five-year time steps from 2010 to 2100. Population and GDP are specified as exogenous parameters to drive a range of demand sectors in GCAM, aligned with the SSP narratives in our research. RCP scenarios are implemented by setting climate constraints or targets for designed years, with GCAM dynamically adjusting carbon prices to identify the least-cost pathway to meet climate goals.

In the GCAM, the effects of climate change on cooling expenditures and energy consumption for air-conditioning are estimated within the building sector modeling, accounting for improvements in air-conditioning efficiency and building energy efficiency (see details in ref. 17). Specifically, rates of shell conductivity improvements are set at 0.5% per year for industrialized countries and 0.8% per year for medium- and low-income regions, while AC technologies improve at rates ranging from 0.25% per year in industrialized countries to 0.58% per year in medium- and low-income regions over the century17. Cooling technologies in GCAM encompass various air-conditioning cooling technologies but exclude other methods such as fans.

As shown in Eq. (8), per capita expenditures for cooling (E t) are mainly driven by the levelized cost of the cooling services per unit floorspace in period t (P t) and the per-capita energy consumption of cooling services (C t). For convenience, it is useful to decompose C t into floorspace per capita (f t), and the energy consumption of cooling services per unit of floorspace (c t).

E t = C t P t = f t c t P t (8)

The demand for floorspace per capita is shown below.

f t = (s − a) × [1 − exp(− ln(2) / μ × I t)] + a (9)

Where f t shows future demand for per-capita floorspace in future time period t; s is the exogenous satiation level of per-capita floorspace, set to different values under various SSP scenarios, indicating the level of service demand at which increases in income do not lead to further demands for services; μ is the per-capita GDP at 50% of the satiation level, P t is the total levelized cost of the modeled energy services per unit floorspace in period t, t0 is the base year, and I t is per capita GDP in period t.

In modeling cooling energy consumption (Eq. (10)), GCAM and other IAMs share a focus on incorporating climatic variables, building characteristics, and socioeconomic factors73, yet they differ in approach and assumptions73,74. GCAM features an endogenous, bottom-up building sector that directly models these influences within a single framework, while other IAMs, such as AIM-Hub75 and MESSAGEix41 models, tend to rely on multi-model coupling and AC adoption assumptions to represent cooling energy demand. Additionally, the modeling structure also influences cooling energy consumption. For example, general equilibrium models, compared to partial equilibrium models, account for costs imposed by other sectors, often leading to higher costs and lower energy demand75.

c t = k × [1 − exp(− ln(2) / μ × I t)] × (CDD t × η t × R t + λ t × IG t) (10)

Where in period t, c t represents the demand of cooling services, CDD t shows global population-weighted CDDs; η t is the exogenous average building shell conductance, R t is the exogenous average floor-to-surface ratio of buildings, IG t is the internal gain heat from other building services, and λ t is an exogenous internal gain scaler.

On the basis of energy consumption for air-conditioning, we further calculate GHG emissions from AC use across regions. Energy-related GHG emissions, including CO2 emissions from electricity use and non-CO2 emissions from power transmission, are calculated based on different emission factors. The emission factors of power grid are derived by the amount of electricity generated each year and the associated emissions from GCAM model. Non-energy related GHG emissions from AC use, such as various AC refrigerants (C2F6, HFC134a, HFC143a, HFC32), are modeled in detail through various air-conditioning technologies for cooling in the GCAM (see details in http://jgcri.github.io/gcam-doc/v5.2/emissions.html). Additionally, given that the range of CDD values (maximum, mean, and minimum) derived from 25 GCM models under each SSP-RCP scenario, the energy consumption and emissions from air conditioning also exhibit corresponding ranges, thereby reducing uncertainty in the results. Global energy demand for air-conditioning during 2015-2050 across five SSP-RCP scenarios is shown in Supplementary Table 9.

Lorenz curve and Gini coefficient for inequality assessment of AC use
The Lorenz curve provides a graphical representation of income or wealth inequality within a population. It is typically depicted with the cumulative share of income or wealth (%) on the vertical axis, and the cumulative share of the population (%, ranked from lowest to highest income) on the horizontal axis. The Gini coefficient, a numerical measure of inequality derived from the Lorenz curve, ranges from 0 (perfect equality) to 1 (perfect inequality). The basic income Gini coefficient is calculated using the following formula:

G = 1 − ∑ n i=1 (P i+1 − P i) (Y i + Y i+1) (11)

Where G refers to economic development Gini coefficient, P i is the cumulative share of population of region i, Y i is the cumulative share of GDP of region i, and n is the number of regions. We rank the regions according to their GDP per capita to show the inequalities in economic development.

Here, we also apply the Lorenz curve to assess the inequality in cooling energy consumption and expenditure across different regions, based on data obtained from the GCAM under the five SSP-RCP scenarios. The analysis was based on key indicators derived from GCAM: cooling energy consumption, cooling energy expenditure, cooling energy consumption per CDD, and cooling energy expenditure per CDD. And following the method in Zhao et al.49., we use the same per capita GDP ranking order for the other Lorenz curves to compare the inequality in economic development with the inequality in the use of air-conditioning.

Empirical approach for future AC stock and quantifying the unmet income-driven cooling needs
We develop a reduced form of econometric model to forecast the number of AC units in use under a given scenario. The response function of AC stock is estimated for each region by applying panel Ordinary Least Squares (OLS) in logarithmic form. Following previous work from Diffenbaugh and Burke76, Davis, et al.38. and Colelli, et al.42., AC stock are determined as a function of CDDs, incomes, and urbanization rates. We adopt CDDs and the quadratic term of CDDs to capture the impacts of global warming on AC demand. Per capita GDP represents the regions’ income, and the proportion of urban population represent the urbanization rates. The region and year fixed effect estimation allows us to control for time-invariant and time-varying unobservable region characteristics that may be correlated to air condition demand. The empirical model takes the following form:

ln AC it = α 0 + β 1 ln CDD it + β 2 ln CDD it^2 + β 3 ln income it + β 4 ln urbanization it + β 5 ln pop it + λ i + δ t + ε it (12)

where i and t represent the region and year, respectively; dependent variable ln AC it is the natural logarithm of AC demand; income it is per capita GDP; urbanization it is urbanization rate of each GCAM regions; pop it is population of GCAM region i in period t. To avoid the effect of different scales of variables, we add 1 to all the variables and then take the natural logarithm. To mitigate the potential bias induced by outliers within the dataset, we employed a winsorizing procedure at the 1% and 99% distribution tails, replacing extreme observations with the corresponding values identified at these percentiles. Hence, the actual air-conditioning stock are estimated as follows:

AC it = e^ln AC it − 1 (13)

We estimate the coefficients of each variable based on data from 1990-2014. Furthermore, utilizing these coefficients as pivotal parameters, the forecast value of logarithmic value ln AC it of the demand for air conditioning is estimated based on the future CDDs, incomes and urbanization rates. Moreover, we have conducted robust analysis and regional heterogeneity analysis to ensure the reliability and uncertainties of our results (see Supplementary Fig. 6, Supplementary Table 10 to 11).

Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC)
Since long-term climate impacts depend on cumulative GHG emissions from AC use, we employ the climate emulator MAGICC in its version 7.5.3, which is the same version and calibration that was used by Working Group III of the IPCC Sixth Assessment Report24,77. MAGICC, a reduced complexity earth system model, is widely recognized for its satisfactorily representation of the global warming response to emissions. Numerous studies have validated its bias and precision, particularly for global-mean temperature estimates, in comparison to more complex climate models77,78. In our study, we set the climate sensitivity of the MAGICC model to 3.0°C, aligning with the IPCC AR6 report’s best estimate of equilibrium climate sensitivity, which ranges from 2.5°C to 4°C.

Our experimental design requires first calculating the emissions from AC use under global warming scenarios, followed by assessing the additional warming effects of these emissions within the given scenario. Exogenous constraints imposed to simulate climate change lead to an internal balancing in the IAM model that any increase in AC emissions, driven by projected CDDs, is offset by emission reductions in other sectors within GCAM, maintaining the integrity of the overall SSP-RCP emission pathway. In this case, we use MAGICC to calculate two temperature outcomes: first, the reference warming result corresponding to the standard SSP-RCP emission pathway; and second, the warming result from the same pathway with the addition of AC-related emissions as calculated by GCAM. The time step for GHG emissions obtained from the GCAM is five years, which does not match one-year time step in MAGICC, so we use a sliding average method to interpolate the emissions for the intermediate years before inputting these values into the MAGICC model. By comparing these two temperature outcomes, we can isolate the additional warming contribution from AC-related emissions. Given that GCAM calculates the range of air conditioning emissions for each SSP-RCP scenario, we utilize the maximum, mean, and minimum values of AC emissions within each scenario to repeat the above MAGICC calculation steps, obtaining a range of potential warming effects from AC use.

Limitations
Owing to limitations in modeling approaches and scenario design, uncertainties persist in our estimates of total warming impact and global inequalities in AC use. Firstly, our analysis adopted a fixed base temperature for CDD calculations across regions. While this approach allows for consistent comparisons across regions and scenarios, it does not account for the potential evolution of comfort thresholds over time. As global temperatures rise, behavioral and physiological adaptations may lead individuals to tolerate higher indoor temperatures before resorting to air conditioning, effectively increasing the base temperature that triggers cooling demand. To provide additional insight and characterize the uncertainty associated with this assumption, we performed a sensitivity analysis using different base temperatures, illustrating how variations in this parameter significantly affect CDD calculations. Secondly, there is a mismatch between the projected AC energy consumption from GCAM and the projected AC stock from our econometric model. GCAM accounts for technological advancements, energy efficiency improvements, and policy influences across regions over time, whereas our econometric model mainly focuses on socioeconomic factors and temperature. Consequently, our analysis’s projections of the AC stock do not fully capture the effects of future technological progress or regional disparities. Despite the mismatch, our findings indicate that the discrepancy between the model-predicted AC stock and the historical data during 2010–2014 is approximately -4.4%, with a range of -2.1% to -5.3%. Both models show consistent trends in the growth of AC stock and energy consumption driven by rising income, temperature, and population, which supports the validity of our overall findings. Lastly, our assessment does not incorporate high-resolution cooling loads analysis, such as monthly, daily or hourly series, which are crucial for understanding peak energy demand and infrastructure stress during periods of extreme temperatures. Regions with considerable seasonal variability, such as South Asia, may experience surges in cooling demand during specific months, placing substantial stress on energy infrastructure and necessitating effective peak load management strategies. Such improvements would provide deeper insights into the dynamics of cooling demand, inform more effective energy infrastructure planning and interventions aimed at mitigating climate impacts while addressing the diverse cooling needs across regions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Thanks for the opportunity to review this interesting paper on the effects of increasing air-cooling as an adaptation strategy.\nAs a first point, I would like to congratulate the authors on the efforts to build together such a robust methodology and\nparticularly on the social impacts deriving from these increasing energy needs. This social component is a crucial yet\nunderexplored aspect in more conventional IAM-based studies, which usually aggregate effects for a representative\nconsumer hiding potential social disparities across regions. It is also novel and very relevant to add the component of\nrelative humidity to the CDD assumptions. The figures are very insightful and condensing a lot of relevant results allowing\nthe reader to extract useful information from each of them. The structure of the paper is clear and intuitive and there are\nenough details and transparency provided in the Supporting Information to support the sound methodology.\nEven though there are relevant results provided and shown in the figures, it is sometimes not so straightforward to extract the\nrelevant information from the paragraphs in the description of the results. The wording selection is sometimes ambiguous\nand hard to follow. I try to point throughout my comments to some of these inconsistencies and provide suggestions.\nAdditionally, I would encourage the authors to provide additional justification for specific aspects of the study such as the\nscenario selection (along with additional details on its assumptions), the different formulas used for the relative humidity, and\nthe exclusion of the relative humidity in the base temperature and the sensitivity study detailed in S9.\nIn the following I outline several points I would suggest being addressed, acknowledged or justified:\nAbstract\nLine 42: “Its expanding” should probably read “its expansion”\nLine 45: “If the global can…” is probably a typo.\nLine 46: 0.02°C increase in which scenario?\nIntroduction\nLine 78 (and line 101): It is unclear what is referred to as “energy structures”. Could you please use an alternative wording or\nexplain in the text what do you mean with this?\nLine 92: “huge” sounds biased, I would suggest removing it.\nLine 108: The intro would be enhanced if the authors provided a justification of why they think they SSP-RCP scenario\nselection is fit for the purpose of the study (you mention this in line 690 of methods) and provided some data relative to\nrelevant assumptions such as renewable share in power generation (headlines of material shown in S4) and building\nrenovations rate.\nLine 109: The authors are making a good summary of the results, but this does no longer belong to the content that should\nappear in the introduction, but rather in the results or the discussion section.\nImpacts of global warming on cooling demand\nLine 142: It is unclear what this sentence means: “and the effect of climate model uncertainties covers current total global\nCDD levels.” I suspect the authors mean that current CDD levels fall within the max-min range of CDDs in 2100 under\nSPP119, but please clarify.\nLines 143-146: Concerning the following sentence “The differences in global mean cooling demand between 2050 and\n2100 would respectively reach 48.1% and 65.5% under high emission scenarios (SSP370 and SSP585), much higher than\nthat (2.1% and 0.6%) of ambitious mitigation scenarios (SSP119 and SSP126).”:\n• The choice of the term “differences” makes it difficult to assess whether the needs are higher or lower in 2100. I assume\nthey will be larger, but it will be convenient to explicitly say so.\n• Differences are indeed larger than in the most ambitious mitigation scenarios, using the term “higher” makes it more difficult\nto understand.\nLine 151: The authors give the following explanation to the fact that more ambitious scenarios may results in more cooling\ndemand: “due to stronger phasing out of aerosols, or demographic change. “. However, the example following that sentence\nonly refers to reasons related to urbanization. Unless there is another example given of how the phasing out of aerosols\nresults in this “counterintuitive” cooling demand differences across scenarios (which I strongly encourage to do), I would\nsuggest removing the “phasing out of aerosols” part.\nAir conditioning stocks and energy consumption\nLine 182: There does not seem to be any gray bar in Figure 2.\nLine 186: No need to repeat that uncertainties are coming from the different cooling demand from 25 GCMs, the reader\nalready got that in the previous results section.\nLine 195: “Similarly to” rather than “similar with”\nLine 198: If the sentence refers to 2050 it should clearly indicate so. It would also help the reader to clarify which cooling\nmechanisms or technologies would help to almost phase out fossil fuel use in the sector.\nLine 202: I am assuming “will consume the most electricity for air-conditioning” refers to SSP585 being the scenario with the\nscenario with the highest AC-related electricity consumption. If it is indeed the case, it should be better explained, as current\nwording suggests that ACs are the largest source of electricity consumption (which is not the case as later in the sentence\nthe 14.8% number clarifies)\nLine 209: I would recommend the authors to explicitly say where in the Supplementary Information this is discussed (S3, I\nsuppose).\nLine 224: Remove first “by”\nContribution of air-conditioning use to global warming\nLine 230: The fact that the order of the emission scopes in the stacked columns of Figure 3b change across scenarios\nmakes it difficult to compare them. E.g., Residential non-CO2 from refrigerant leaks is the first one shown for all scenarios\nexcept SSP126 where it is shown in the third place, or the CO2 from electricity consumption is the fourth one except in\nSSP126 where it is the last one.\nLine 230: In Figure 3c, the chart seems to indicate that emissions in SSP370 are higher than SSP245 and so is the\nadditional temperature increase if we look at the colored numbers (2.19C vs 1.99C). However, if we look at the colored\nvertical lines it seems that the SPP370-induced additional temperature increase is lower than in SPP245. If indeed the\ntemperature increase in SPP370 were lower than in SSP245 even if emissions are higher (and therefore the orange-colored\n2.19C in SSP370 is wrong), this could be due to the type of GHGs emitted (more non-CO2 refrigerant leaks with higher\nGWP), but Fig 3b does not seem to point in that direction as both scenarios seem to have more or less similar levels of non-\nCO2. In any case, if this is the case it should be explained in the paragraph starting in line 288.\nLine 242: “strong” is somehow ambiguous, I would suggest something clearer such as “ambitious”.\nGlobal inequality in the use of air-conditioning\nLine 329: The use of “GCAM regions” in the figure caption might confusingly the reader to think that these regions are\nindividually represented in the figure. I would suggest indicating that the values refer to global averages.\nLine 335: The Lorenz curves of cooling energy consumptions and expenditures are located for the most part to the left of the\nyellow curve, indicating that they are more equal than the GDP distribution. It is the cooling energy consumptions and\nexpenditures per CDD the ones which are more unequal.\nLine 339: In addition to Figure 4, it would help to have a table showing overall Gini coefficients by scenario as they are\nbrought up very often throughout the text.\nLine 351: Is it possible that the higher unit costs of cooling services are due only to higher mitigation costs and not to\nincreasing renewable shares? I would expect increasing renewable shares to decrease electricity bills.\nLine 362: It is not very clear what the figures compare (6.5 vs 20): It is the difference between 10% richest and 10% poorest\nin the baseline scenario between what and what?\nLine 366: Typo in Figure 5: “sceanrio”.\nDiscussion and conclusions\nLine 417: At least some of the most relevant limitations of the study explored in the respective Methods section would need\nto be highlighted here even if it is in a concise paragraph.\nLine 435: As mentioned above, if I am interpreting the Lorenz curves correctly, it is the cooling energy use and cooling\nexpenditure per CDD and not the cooling energy use and cooling expenditure the ones which are more unequal than GDP\ndistribution.\nLine 436: “Using alternative income-drive projections” is somehow difficult to understand, I would suggest dedicating a\nsentence to summarize the approach followed leading to results shown in Figure 5.\nLine 440: “everyone in this world” seems a bit too literary and not very scientific.\nLine 446: Noting that this study is successfully addressing distribution impacts of mitigation options in the residential and\ncommercial sectors and implications for the so-called “just energy transition”, I would suggest including in the conclusions\nrelative to how increasing policy costs (and potentially renewable shares in the electricity mix as suggested to review in the\ncomment of line 351) may increase inequalities as highlighted by the authors in line 351: “Under low-emission scenarios,\nhigher mitigation costs and involving share of renewable energy would result in higher unit cost of cooling services relative\nto the baseline scenario, which does not make cooling service more affordable for poor people.”\nMethods\nLine 690: It would help the reader to have a bit more context on what SSP119, SSP126, SSP245, SSP370 represent\nbeyond what is explained in lines 106-108. How are the different SSP narratives combined with the RCPs and what do the\nlatter depict?\nLine 727: “also called the apparent temperature” is already mentioned in the next sentence along with further specifications\n(“adjusted by relative humidity”), so no need to mention that here too.\nLine 728-741: Why are there different formulas used depending on the heat index value, temperature and/or RH levels?\nLine 760: It is very useful to do, as the authors have done in S9, a sensibility analysis on the base temperature on CDDs.\nHowever, considering the effort and space dedicated to deriving regionally disaggregated heat index values accounting not\nonly for temperature but also for RH levels, in my view, the study should dedicate some effort to explain why it is a sound\napproach to compare these detailed and regionally explicit heat index values with the same base temperature across all the\nregions, which also do not account for RH.\nLine 791: Explain the concept of “satiation” the first time you mention it in the parameter explanation of the formula (you\ncurrently do after the second time).\nLine 824: It is a bit confusing to the reader how do you isolate the effects of AC emissions in the different SSP-RCP scenario\nruns. From the current description, it seems that the runs “without AC emissions” completely neglect the “baseline”\nemissions associated to ACs, meaning the emissions from a scenario without increased global warming. In a scenario\nwithout increased global warming AC will still be used, but not that much, and these effects should be considered when\ncomparing with a scenario with AC emissions. It is the increasing AC use what this study is aiming to capture. If the study is\nindeed calculating only the increase, instead of “with/without AC emissions” it is perhaps more accurate to describe it as\n“increased AC emissions” and “baseline AC emissions” or something similar.\nSupplementary Material\nLine 33: In Figure S4 it is not clear whether the units of the horizontal bars are in kWh or kWh/household/yr. The figure\nseems to indicate they are in kWh but then the legend says it is per household.", "reviewer_2": "Response letter to Reviewers’ comments\nDear editor and reviewers,\nThank you very much for your consideration of our submission and your efforts in managing\nits review. We very much appreciate your valuable and insightful comments on our\nmanuscript. The manuscript has been revised following the reviewers’ comments. Please find\nour point-by-point response to the comments listed below.\nThe original comments are in black, our responses are given in blue, and the modifications\nmade to the manuscript are presented in italic and indented format.\nReviewer #1\nGeneral Comment: Thanks for the opportunity to review this interesting paper on the effects\nof increasing air-cooling as an adaptation strategy. As a first point, I would like to congratulate\nthe authors on the efforts to build together such a robust methodology and particularly on\nthe social impacts deriving from these increasing energy needs. This social component is a\ncrucial yet underexplored aspect in more conventional IAM-based studies, which usually\naggregate effects for a representative consumer hiding potential social disparities across\nregions. It is also novel and very relevant to add the component of relative humidity to the\nCDD assumptions. The figures are very insightful and condensing a lot of relevant results\nallowing the reader to extract useful information from each of them. The structure of the\npaper is clear and intuitive and there are enough details and transparency provided in the\nSupporting Information to support the sound methodology.\nEven though there are relevant results provided and shown in the figures, it is sometimes not\nso straightforward to extract the relevant information from the paragraphs in the description\nof the results. The wording selection is sometimes ambiguous and hard to follow. I try to point\nthroughout my comments to some of these inconsistencies and provide suggestions.\nAdditionally, I would encourage the authors to provide additional justification for specific\naspects of the study such as the scenario selection (along with additional details on its\nassumptions), the different formulas used for the relative humidity, and the exclusion of the\nrelative humidity in the base temperature and the sensitivity study detailed in S9.\nReviewer #2\nGeneral Comment: The study aims to quantify the feedback loop that is caused by the use of\nair-conditioning as an adaptation strategy to global warming. Using five SSP-RCP scenarios,\nthe study finds that AC use leads to an additional increase in GMST between 0.03°C and\n0.12°C, depending on the scenario. Based on the scenario analysis, the study finds that\nswitching to low-carbon energy sources in the power sector can be key to breaking this cycle.\nThe study also finds that, if income inequality is reduced, global AC stocks and warming will\nincrease further (another 0.02°C).\nThe magnitude of the additional warming caused by increased air-conditioning that this study\nfinds is clearly substantial, and thus important. However, the analysis suffers from several\nshortcomings, including a lack of clarity and justification of the chosen methods, missing\ndiscussions of key drivers and assumptions, and a limited analysis of the results (detailed\nbelow). The paper also suffers from a poor presentation of the findings and the analysis, which\nmakes it hard to appreciate the contributions that the study might offer and to assess the\nsignificance of the findings.\nAlthough the study addresses an important issue, in its current form, I would not recommend\nit for publication in Nature Communications. Because many of the shortcomings relate to\npresentation, this could potentially be solved with major revisions, but it is not clear whether\nit would. Please see my detailed comments below.", "reviewer_3": "The paper explores how global warming leads to increased air-conditioning (AC) usage, which in turn exacerbates\ngreenhouse gas emissions, creating a vicious cycle. It quantifies this climate feedback loop by examining energy system\nstructures under five SSP-RCP scenarios. Most sections of the paper are well-written, and I appreciate the innovative use of\nthe heat-index approach as well as the analysis of global inequality in AC usage. Overall, it is a valuable work of high\nquality. However, there are a few concerns in the “Empirical Approach for Future AC Stocks” section and related parts in the\nSupplemental Information that require attention. Here are some comments that I hope the authors find helpful to improve the\ncurrent manuscript.\n1) The paper briefly discusses the feedback effect of AC usage on global warming, but the quantitative analysis is\ninsufficient. For instance, the projected temperature increase of 0.03°C to 0.12°C may seem modest without additional\ncontext regarding its broader social and economic implications. A more detailed quantitative analysis of this feedback loop,\nincluding specific economic impacts, policy implications, and societal outcomes, would enhance the understanding of these\ntemperature changes' long-term significance.\n2) There is a conceptual inconsistency between the dependent variable (AC stock) and the independent variables (per\ncapita CDDs, per capita GDP, and urbanization rate). The dependent variable is a total quantity, while the independent\nvariables represent per capita measures. Although this aligns with Colelli et al. (2023), their study used the share of\nhouseholds with AC as the dependent variable. Clarifying this distinction would strengthen the analysis.\n3) The paper employs several models (e.g., GCAM, MAGICC) to forecast climate impacts and AC demand, yet the\nassumptions behind key parameters—such as economic growth and energy transition rates—are not clearly articulated.\nProviding more detailed explanations of these assumptions would enhance the credibility of the results.\n4) The analysis does not adequately address regional heterogeneity in the effects of the independent variables on AC\nstocks. For example, the impact of a one-unit increase in (log) income on AC stock may vary significantly between the\nUnited States and Africa. As highlighted by Colelli et al. (2023), AC ownership rates respond differently to changes in per\ncapita income and CDDs across regions. Including interaction terms between explanatory variables and country (or broader\nregional) fixed effects in Equation (11) could better capture this variation.\n5) The prediction of ACit may contain errors. The statement regarding the estimation of logarithmic values suggests that the\nauthors may have overlooked the constant term and fixed effects in Equation (11). Furthermore, using a model with year\nfixed effects for future predictions is inappropriate, as specific values for future year fixed effects cannot be obtained. An\nalternative approach would involve incorporating a time trend term.\n6) While the paper touches on the contribution of refrigerants (e.g., HFCs, CFCs) to global warming, it lacks a detailed\ndiscussion on the current status of refrigerant management in various regions. Expanding this discussion to include the\neconomic and technological challenges of transitioning to low-GWP refrigerants, especially in developing countries, would\nbe beneficial. A comparative analysis of global policies and strategies for addressing refrigerant leakage would add\nsignificant value.\n7) Although the paper acknowledges the role of behavioral changes in reducing AC usage, it does not explore how these\nchanges (e.g., energy-saving practices, pricing strategies) could impact energy consumption. Given the potential influence of\npolicy interventions on consumer behavior, an analysis of how such changes might affect energy consumption—particularly\nin regions with high cooling demands—would be insightful.\n8) The paper does not consider how seasonal temperature fluctuations might affect cooling demand. In regions with\nsignificant seasonal variability, such as South Asia, cooling demand may surge during specific months, stressing energy\ninfrastructure. An analysis of this seasonal variability and its implications for energy infrastructure and peak load\nmanagement would be valuable.\n9) Figure S5 suggests that CCS technology plays a crucial role in mitigating GHG emissions, particularly under the SSP119\nand SSP126 scenarios. However, the uncertainty surrounding CCS deployment and its technological and economic\nchallenges necessitate an exploration of how sensitive the results are to the assumption of widespread CCS adoption.\nWhat alternative mitigation strategies could be considered if CCS deployment is delayed or fails?\n10) S8 mentions that AC stocks were not significant predictors of CDDs in the regression model, which seems\ncounterintuitive. The availability of AC stocks should logically influence cooling demand. Please clarify the assumptions\nmade in the model and address the accuracy of these findings.\n11) The fixed base temperature for CDDs (e.g., 18°C) may not remain static as global warming progresses. For example,\npeople might currently turn on air conditioning when temperatures exceed 24°C, which may increase to 25°C ten years later.\nFactors such as bodily adaptation could lead to an increase in the base temperature for CDDs. This time-varying nature\nshould be considered in the “Limitations” section.\n12) In equation (12), ACit appears on both sides of the equation. The natural logarithm form of ACit needs to be denoted with\na distinct symbol (e.g., lnACit) to distinguish it from the level value of ACit."}, "author_response": "Response letter to Reviewers’ comments\nDear editor and reviewers,\nThank you very much for your consideration of our submission and your efforts in managing\nits review. We very much appreciate your valuable and insightful comments on our\nmanuscript. The manuscript has been revised following the reviewers’ comments. Please find\nour point-by-point response to the comments listed below.\nThe original comments are in black, our responses are given in blue, and the modifications\nmade to the manuscript are presented in italic and indented format.\nReviewer #1\nGeneral Comment: Thanks for the opportunity to review this interesting paper on the effects\nof increasing air-cooling as an adaptation strategy. As a first point, I would like to congratulate\nthe authors on the efforts to build together such a robust methodology and particularly on\nthe social impacts deriving from these increasing energy needs. This social component is a\ncrucial yet underexplored aspect in more conventional IAM-based studies, which usually\naggregate effects for a representative consumer hiding potential social disparities across\nregions. It is also novel and very relevant to add the component of relative humidity to the\nCDD assumptions. The figures are very insightful and condensing a lot of relevant results\nallowing the reader to extract useful information from each of them. The structure of the\npaper is clear and intuitive and there are enough details and transparency provided in the\nSupporting Information to support the sound methodology.\nEven though there are relevant results provided and shown in the figures, it is sometimes not\nso straightforward to extract the relevant information from the paragraphs in the description\nof the results. The wording selection is sometimes ambiguous and hard to follow. I try to point\nthroughout my comments to some of these inconsistencies and provide suggestions.\nAdditionally, I would encourage the authors to provide additional justification for specific\naspects of the study such as the scenario selection (along with additional details on its\nassumptions), the different formulas used for the relative humidity, and the exclusion of the\nrelative humidity in the base temperature and the sensitivity study detailed in S9.\nResponse: Thanks for your positive evaluation of our work. We have thoroughly revised the\nwording and structure of these paragraphs to enhance readability based on your comments\nand ensure that the key findings are clearly communicated. Specifically, we have improved the\nclarity of the results description, provided additional justification for our scenario selection,\nexpanded on the different formulas used for relative humidity, clarifying why specific formulas\nwere chosen and how they impact our results. Please see our detailed response below.\nComment 1.1.: Line 42: “Its expanding” should probably read “its expansion”\nResponse: Done.\nComment 1.2.: Line 45: “If the global can…” is probably a typo. Line 46: 0.02°C increase in\nwhich scenario?\nResponse: Thank you for your careful reading. We have revised the sentence as follows:\nLINE 47-49 IN THE REVISED MANUSCRIPT: if all regions can achieve full access to air-\nconditioning, the GHG emissions from additional AC energy use would still cause\nanother 0.02°C increase even under the SSP119 scenario.\nComment 1.3.: Line 78 (and line 101): It is unclear what is referred to as “energy structures”.\nCould you please use an alternative wording or explain in the text what do you mean with\nthis?\nResponse: We have replaced it with “energy mix in power generation” throughout the\nmanuscript.\nComment 1.4.: Line 92: “huge” sounds biased, I would suggest removing it.\nResponse: Done.\nComment 1.5.: Line 108: The intro would be enhanced if the authors provided a justification\nof why they think they SSP-RCP scenario selection is fit for the purpose of the study (you\nmention this in line 690 of methods) and provided some data relative to relevant assumptions\nsuch as renewable share in power generation (headlines of material shown in S4) and building\nrenovations rate.\nResponse: We have added a new paragraph in the Introduction section to include the reasons\nwhy we choose SSP119, SSP126, SSP245, SSP370 and SSP585 scenarios Details of assumptions\nare also included in the Methods section. Please see the paragraph below, which is in the\nrevised introduction.\nLINE 122-133 IN THE REVISED MANUSCRIPT: We employ two widely used scenario\nframeworks to build future climate simulations: the Representative Concentration\nPathways (RCPs) and Shared Socioeconomic Pathways (SSPs). Following the scenarios\nhighlighted by Working Group I of the IPCC Sixth Assessment Report (AR6) 1,2, we use\nthe SSP119 (referred as SSP1-RCP1.9), SSP126, SSP245, SSP370 and SSP585 scenarios\nas a range of future climate mitigation scenarios, matching with the climate\nprojections in CMIP6 (Coupled Model Intercomparison Project Phase 6) 3-5 and NASA\nNEX-GDDP-CMIP6 database 6. SSP126, SSP245 and SSP585 scenarios represent\nrespectively a world on a sustainable pathway, medium development, and high\ndependence on fossil energy 7,8. SSP119 and SSP370 emphasize scenarios respectively\nwith lower emissions to achieve the 1.5°C target and significant developmental\ndisparities between regions (see Methods) 9,10.\nComment 1.6.: Line 109: The authors are making a good summary of the results, but this does\nno longer belong to the content that should appear in the introduction, but rather in the\nresults or the discussion section.\nResponse: We have removed the summary from the Introduction and incorporated it into the\nDiscussion section.\nComment 1.7.: Line 142: It is unclear what this sentence means: “and the effect of climate\nmodel uncertainties covers current total global CDD levels.” I suspect the authors mean that\ncurrent CDD levels fall within the max-min range of CDDs in 2100 under SPP119, but please\nclarify.\nResponse: Yes. In our original manuscript, the 2010 CDD was calibrated using historical data\nfrom GCAM data system 11, approximately 69,335 CDDs . Due to the lack of high-resolution\nSSP119 data from NASA NEX GDDP, we employed CMIP6 data to calculate the mean, maximum,\nand minimum CDD across all models, as well as the mean, maximum, and minimum within\neach model (see text in Data Description section). By averaging these two approaches, we\nderived the SSP119 CDDs for 2050.\nHowever, in this revised version, we have refined our methodology by applying the widely\nused quantile mapping method 12,13 to calibrate the SSP119 data. This calibration yielded a\n2050 mean CDD of 69,587 days. We also performed robustness tests (e.g., RMSE) to validate\nthe calibration results, which are detailed in the Supplementary Information. To maintain data\nconsistency and comparability, we also recalculated historical data using NASA’s calibrated\nhistorical dataset, with historical global CDDs of 65,429 in 2010. Consequently, under the\nSSP119 scenario, the 2050 CDD shows a slow and slight increase, aligning more closely with\ncurrent levels within the uncertainty range.\nWe have revised the text as follows:\nLINE 158-161 IN THE REVISED MANUSCRIPT: Due to the most stringent GHG emission\ncontrol, global CDDs under the SSP119 scenario (Fig. 1c) show a slight increase from\n2010 to 2100, with a 4% rise in 2100 relative to 2010, which remains substantially\nlower than in other scenarios.\nWe have added robustness tests of calibrated SSP119 data in the Supplementary Information\nas follows:\nLINE 340-363 IN THE REVISED SUPPLEMENTARY INFORMATION (SI): In this study, we\nadopted a simplified bias-correction approach, quantile mapping method, to calibrate\nclimate variables under the SSP119 scenario based on calibrated history climate\nvariables from the NASA database and uncalibrated ones from the CMIP6 database.\nTo evaluate the calibration performance of the bias-adjusted data relative to data\nfrom the NASA database, we selected four widely used statistical test metrics for\nquantitative assessment of the results using mean near-surface air temperature in\n2100 under the SSP126 scenario as an example: (1) Mean bias is used to measure the\naverage difference between the calibration data and the reference data. If Mean Bias\nis a positive (negative) value, it means that the corrected data is higher (lower) than\nthe reference data overall. The closer the value is to 0, the smaller the overall average\ndeviation. (2) RMSE (Root Mean Squared Error) is used to measure the overall error\nsize between the calibration data and the reference data. The smaller the value, the\ncloser the corrected data is to the reference data overall. (3) Correlation coefficient is\nused to measure the consistency between the bias-adjusted data and the reference\ndata in terms of change trend, and the value range is [−1,1]. When the value is close\nto 1, it means that the two sets of data are highly consistent in trend.\nAfter applying our bias-adjustment procedure, all models exhibited markedly\nimproved agreement with NASA’s reference data. The mean bias was substantially\nreduced (e.g., from 1.911 K to −0.301 K for MIROC6), while the RMSE declined by over\nhalf in most cases. Correlation coefficients against the reference data also increased\nconsistently, reaching values above 0.995 for every bias-adjusted data. Overall, our\nbias correction method not only reduces systematic errors in the original model\noutputs but also enhances the representation of temporal variability and extreme\nevents.\nSupplementary Table 12. Robustness analysis results of quantile mapping method.. \nMean bias RMSE (°K) Correlation (%)\nModel OR BA OR BA OR BA\nCanESM5 -0.424 -0.386 3.630 1.723 0.978 0.995\nGFDL-ESM4 -0.828 -0.087 3.071 1.467 0.987 0.997\nIPSL-CM6A-LR -1.110 -0.111 3.715 1.617 0.979 0.996\nMIROC6 1.911 -0.301 4.087 1.656 0.980 0.996\nMPI-ESM1-2-LR -0.517 -0.118 3.183 1.448 0.984 0.997\nMRI-ESM2-0 0.472 -0.094 3.178 1.539 0.986 0.997\nNote: Each model RMSE and Correlation for the mean air temperature in 2100 under\nthe SSP126 scenario between NASA data and original simulation (OR, uncalibrated\ndata from CMIP6) and between NASA data and bias-adjustment simulation (BA).\nComment 1.8.: Lines 143-146: Concerning the following sentence “The differences in global\nmean cooling demand between 2050 and 2100 would respectively reach 48.1% and 65.5%\nunder high emission scenarios (SSP370 and SSP585), much higher than that (2.1% and 0.6%)\nof ambitious mitigation scenarios (SSP119 and SSP126).”: \n• The choice of the term “differences” makes it difficult to assess whether the needs are\nhigher or lower in 2100. I assume they will be larger, but it will be convenient to explicitly say\nso.\n• Differences are indeed larger than in the most ambitious mitigation scenarios, using the\nterm “higher” makes it more difficult to understand.\nResponse: Thank you for your insightful feedback. The revised sentences are as follows:\nLINE 164-167 IN THE REVISED MANUSCRIPT: In contrast, under high-emission\nscenarios (SSP370 and SSP585), global cooling demand is projected to increase\nconsiderably, particularly as GHG emissions accumulate, with demand in 2100 being\nsubstantially higher than in 2050 (with 48.1% and 65.5%, respectively).\nComment 1.9.: Line 151: The authors give the following explanation to the fact that more\nambitious scenarios may results in more cooling demand: “due to stronger phasing out of\naerosols, or demographic change. “. However, the example following that sentence only refers\nto reasons related to urbanization. Unless there is another example given of how the phasing\nout of aerosols results in this “counterintuitive” cooling demand differences across scenarios\n(which I strongly encourage to do), I would suggest removing the “phasing out of aerosols”\npart.\nResponse: We appreciate your careful reading of our manuscript and for highlighting this\nproblem. To address your concern, we have expanded our discussion to include a more\ncomprehensive analysis of how aerosol reduction, along with demographic changes,\ncontributes to the observed cooling demand differences across scenarios. The revised\nparagraph is as follows:\nLINE 172-191 IN THE REVISED MANUSCRIPT: Several interconnected factors explain\nthese discrepancies. First, regions responding most rapidly to climate change are\nprimarily located in the high latitudes of the Northern Hemisphere, but they contribute\nlittle to global CDDs 14. For example, under the SSP119 scenario, cooling demand in\nNorthern Hemisphere regions, such as Canada and Northern Europe, considerably\ndeclines by 2050, yet does not fully offset the increases in other regions, contributing\nto a slight increase of global CDDs from 2010 to 2050. Second, implementing strong\nmitigation policies in low-emission scenarios (SSP119 and SSP126) also reduces aerosol\nconcentrations 9, which have previously exerted a short-lived cooling effect 15,16.\nConsequently, diminishing aerosol-related cooling can lead to temperature rise and\ngreater cooling needs in regions such as West Africa and South Asia 17. Furthermore,\ndemographic shifts can also affect regional cooling demand in the mid-term. For\ninstance, under the SSP126 scenario, a relative increase in populations residing in\nwarmer regions (e.g., Australia and New Zealand) contributes to higher cooling\ndemand than under the baseline scenario. In contrast, population decline in regions\nsuch as Canada and Russia under the SSP370 scenario lead to reduced cooling demand\ncompared to the baseline scenario. Taken together, these results highlight the intricate\ninterplay between climate dynamics, aerosol forcing, and demographic changes that\nshape heterogeneous regional cooling demand in the mid-term.\nComment 1.10.: Line 182: There does not seem to be any grey bar in Figure 2.\nResponse: Thank you for noting this discrepancy. We have added the grey bar.\nComment 1.11.: Line 186: No need to repeat that uncertainties are coming from the different\ncooling demand from 25 GCMs, the reader already got that in the previous results section.\nResponse: Done.\nComment 1.12.: Line 195: “Similarly to” rather than “similar with”\nResponse: Done.\nComment 1.13.: Line 198: If the sentence refers to 2050 it should clearly indicate so. It would\nalso help the reader to clarify which cooling mechanisms or technologies would help to almost\nphase out fossil fuel use in the sector.\nResponse: Our intention was to convey that, despite the existence of different types of AC\nsystems, the global energy consumption for AC is predominantly supplied by electricity\n(approximately 99%), with natural gas accounting for about 1%. We did not mean to imply that\nspecific cooling mechanisms or technologies lead to an almost complete phase-out of fossil\nfuel use in the sector. To enhance clarity and avoid misunderstanding, we have revised the\nsentence as follows:\nLINE 226-227 IN THE REVISED MANUSCRIPT: Despite the variety of AC types, the use\nof air-conditioning is mostly powered by electricity 18, with a very small portion\nprovided by natural gas.\nComment 1.14.: Line 202: I am assuming “will consume the most electricity for air-conditioning” refers to SSP585 being the scenario with the scenario with the highest AC-related electricity consumption. If it is indeed the case, it should be better explained, as current\nwording suggests that ACs are the largest source of electricity consumption (which is not the case as later in the sentence\nthe 14.8% number clarifies)\nResponse: Yes. We have revised the text to highlight this distinction more clearly.\nLINE 230-233 IN THE REVISED MANUSCRIPT: Driven by high cooling demand and per\ncapita GDP, the SSP585 scenario consumes the most AC-related electricity among all\nscenarios, ranging from around 5,800 to 12,821 TWh, which accounts for up to 14.9%\nof TEC in the building sector globally.\nComment 1.15.: Line 209: I would recommend the authors to explicitly say where in the\nSupplementary Information this is discussed (S3, I suppose).\nResponse: We have revised the text in supplementary information. Based on current\nsupplementary structure, we have included an explicit figure reference to Supplementary\nFigure 3.\nComment 1.16.: Line 224: Remove first “by”\nResponse: Done.\nComment 1.17.: Line 230: The fact that the order of the emission scopes in the stacked\ncolumns of Figure 3b change across scenarios makes it difficult to compare them. E.g.,\nResidential non-CO2 from refrigerant leaks is the first one shown for all scenarios except\nSSP126 where it is shown in the third place, or the CO2 from electricity consumption is the\nfourth one except in SSP126 where it is the last one.\nResponse: We have reclassified the emission categories to ensure a uniform order across all\nscenarios in Figure 3b. The categories are now consistently ordered as follows for each\nscenario: CO emissions from residential AC use, CO emissions from commercial AC use, non-\n2 2\nCO emissions from residential AC use and non-CO emissions from commercial AC use. The\n2 2\nrevised figure is as follows:\nWe have revised the note accompanying Figure 3b as follows:\nLINE 270-280 IN THE REVISED MANUSCRIPT: Total GHG emissions associated with AC\nuse are categorized into four emission sources across two building types: residential\nand commercial. In residential buildings, CO₂ emissions from AC use comprise\napproximately 0.6% from natural gas consumption by gas-powered ACs and 99.4%\nfrom electricity consumption. In commercial buildings, CO₂ emissions account for\nabout 0.5% from natural gas consumption and 99.5% from electricity consumption.\nNon-CO emissions from AC use are from two sources: refrigerant leaks from AC use\n2\nand other non-CO emissions from power transmission. Regarding the types of AC\n2\nrefrigerants, the default settings of GCAM account for traditional refrigerants, such as\nHFC-134a and HFC-143a, and low-GWP refrigerant HFC-32. Non-CO emissions from\n2\nresidential and commercial AC use are primarily derived from AC refrigerants\n(approximately 98.9%), with a minor contribution from power transmission\n(approximately 1.1%).\nComment 1.18.: Line 230: In Figure 3c, the chart seems to indicate that emissions in SSP370\nare higher than SSP245 and so is the additional temperature increase if we look at the colored\nnumbers (2.19C vs 1.99C). However, if we look at the colored vertical lines it seems that the\nSPP370-induced additional temperature increase is lower than in SPP245. If indeed the\ntemperature increase in SPP370 were lower than in SSP245 even if emissions are higher (and\ntherefore the orange-colored 2.19C in SSP370 is wrong), this could be due to the type of GHGs\nemitted (more non-CO2 refrigerant leaks with higher GWP), but Fig 3b does not seem to point\nin that direction as both scenarios seem to have more or less similar levels of non-CO2. In any\ncase, if this is the case it should be explained in the paragraph starting in line 288.\nResponse: 2.19C and 1.99C, respectively, represents the reference warming temperature\nwithout the addition of AC emissions under the SSP370 and SSP245. Indeed, while the AC\nusage in the SSP370 scenario results in higher emissions compared to the SSP245 scenario,\nthe temperature increase attributed to these emissions appears lower in SSP370 when\nobserving the coloured vertical lines in Figure 3c. This seemingly counterintuitive result is due\nto two key factors:\n• Background GHG concentrations. The SSP370 scenario is characterized by higher GHG\nemissions, leading to a diminished climate sensitivity to additional GHG emissions.\n• Aerosol reduction. Aerosols, particularly sulphate aerosols, have a cooling effect on\nthe atmosphere by reflecting incoming solar radiation back into space. Under the\nSSP370 scenario, there is a slower reduction of aerosol emissions that partially\ncounteracts the warming from increased GHG emissions.\nIn response to your comment, we have added some text to explain it in the manuscript as\nfollows:\nLINE 336-341 IN THE REVISED MANUSCRIPT: Similarly to cooling demand trends by\n2050, the SSP370 scenario has higher AC-related emissions with lower additional\nwarming effects compared to the SSP245 scenario. This counterintuitive result arises\nbecause SSP370 has higher background GHG concentrations and a slower rate of\naerosol reduction, partially offsetting the warming effects from additional GHGs.\nComment 1.19.: Line 242: “strong” is somehow ambiguous, I would suggest something clearer\nsuch as “ambitious”.\nResponse: Done.\nComment 1.20.: Line 329: The use of “GCAM regions” in the figure caption might confusingly\nthe reader to think that these regions are individually represented in the figure. I would\nsuggest indicating that the values refer to global averages.\nResponse: To clarify, the Lorenz curves in the figure depict global distributions, with data\naggregated at the global level rather than representing individual GCAM regions. The ordering\nis based on the per capita GDP of GCAM regions from low to high. We have revised the figure\ncaption to explicitly as follows:\nLINE 389-395 IN THE REVISED MANUSCRIPT: Lorenz curves for cooling energy\nconsumption and expenditure attributable to per capita GDP (in 1990 US$ Purchasing\nPower Parity prices) at the global level in 2050. The yellow Lorenz curve represents\nglobal economic inequality, with data points ordered by the per capita income of\nGCAM regions from low to high. To facilitate a direct comparison with economic\ninequality, we use the same regional ordering for the other Lorenz curves to illustrate\nthe difference between global distribution of income and the distribution of cooling\nenergy consumption and expenditure.\nComment 1. 21. : Line 335: The Lorenz curves of cooling energy consumptions and\nexpenditures are located for the most part to the left of the yellow curve, indicating that they\nare more equal than the GDP distribution. It is the cooling energy consumptions and\nexpenditures per CDD the ones which are more unequal.\nResponse: In our analysis, we ordered all regions based on their per capita GDP from low to\nhigh for the yellow Lorenz curve representing global economic inequality. To facilitate a direct\ncomparison with economic inequality, we used the same regional ordering for the other\nLorenz curves depicting cooling energy consumption and expenditures. This approach follows\nthe methodology outlined in Zhao, et al. 19.\nHowever, because all Lorenz curves are ordered according to per capita GDP, the conventional\nmethod of comparing the positions of Lorenz curves (i.e., their proximity to the line of equality)\nto assess the degree of inequality is not directly applicable in this context. As a result, the\nLorenz curves for cooling energy consumption and expenditures appear to the left of the GDP\nLorenz curve, which might incorrectly suggest that these are more equally distributed than\nGDP. We have added sentences to clarify this as follows:\nLINE 398-404 IN THE REVISED MANUSCRIPT: All Lorenz curves are plotted with regions\nordered by per capita GDP from low to high to enable direct comparison with global\neconomic inequality, following the methodology of Zhao et al. 19. Although the\ndifferences in GDP between the richest 10% and the poorest 10% appear to be larger\nthan those in energy consumption and expenditure for cooling (Figure 4), this apparent\nequilibrium conceals deeper, underlying inequalities. Based on the Gini coefficients,\nwe find that the distribution of energy consumption and expenditure for cooling is more\nunequal than GDP.\nComment 1.22.: Line 339: In addition to Figure 4, it would help to have a table showing overall\nGini coefficients by scenario as they are brought up very often throughout the text.\nResponse: We have added a new table summarizing the overall Gini coefficients for each\nscenario. Considering the number of figures and tables already present in the main text, we\nhave placed this table in the Supplementary Information (SI) to maintain the manuscript's\nclarity and flow.\nLINE 282-284 IN THE REVISED SI:\nSupplementary Table 7. Gini coefficients of GDP, cooling energy consumption and\ncooling energy expenditure under different SSP scenarios.\nGini coefficient/Scenario SSP119 SSP126 SSP245 SSP370 SSP585\nGDP 0.31 0.31 0.37 0.44 0.29\nEnergy consumption 0.38 0.37 0.43 0.50 0.33\nEnergy consumption per 0.48 0.48 0.55 0.62 0.42\nCDD\nEnergy expenditure 0.37 0.36 0.42 0.49 0.33\nEnergy expenditure per 0.46 0.45 0.53 0.60 0.41\nCDD\nComment 1.23.: Line 351: Is it possible that the higher unit costs of cooling services are due\nonly to higher mitigation costs and not to increasing renewable shares? I would expect\nincreasing renewable shares to decrease electricity bills.\nResponse: Yes. Upon revisiting the GCAM results, we found that, for most regions, solar and\nwind generation costs in 2050 under the SSP119 scenario are much lower than that under the\nSSP585 scenario. The sentences have revised as follows:\nLINE 427-429 IN THE REVISED MANUSCRIPT: Under low-emission scenarios, higher\nmitigation costs result in higher unit costs of cooling services relative to the other\nscenarios, which does not make cooling services more affordable for poor people.\nComment 1.24.: Line 362: It is not very clear what the figures compare (6.5 vs 20): It is the\ndifference between 10% richest and 10% poorest in the baseline scenario between what and\nwhat?\nResponse: These figures represent the disparity between the richest 10% and the poorest 10%\nof the global population in the baseline scenario. Specifically, the ‘6.5-fold’ refers to the\ndifferences in total cooling energy consumption between the richest 10% and the poorest 10%\nwithout considering regional differences in cooling demand. When we account for climatic\nvariations by considering cooling energy consumption per CDD, this disparity increases to 20-\nfold. This means that, relative to their climatic requirements, the poorest populations have\nconsiderably less access to cooling energy compared to the richest populations. We have\nrevised the sentences in the paragraph as follows:\nLINE 440-445 IN THE REVISED MANUSCRIPT: Specifically, under the baseline scenario,\nthe differences in cooling energy consumption and expenditure per CDD between the\nrichest 10% and the poorest 10% increase from 7- and 5-fold (without considering\nregional differences in cooling demand) to 20- and 15-fold, respectively. Such\ndifferences are most pronounced under the SSP370 scenario, reaching approximately\n37.8- and 27.8-fold in 2050, respectively.\nComment 1.25.: Line 366: Typo in Figure 5: “sceanrio”.\nResponse: Done.\nComment 1.26.: Line 417: At least some of the most relevant limitations of the study explored\nin the respective Methods section would need to be highlighted here even if it is in a concise\nparagraph.\nResponse: We have added a paragraph to the Discussion section that outlines the most\nrelevant limitations of our modelling approach and methods. We have added a new paragraph\nas follows:\nLINE 497-525 IN THE REVISED MANUSCRIPT: This study quantifies the climate feedback\nloop caused by AC use, indicating that using air-conditioning as an adaptation strategy\nexacerbates climate change, causing an additional 0.03°C-0.12°C increase in global-\nmean temperature by 2050. These estimates underscore the critical need to address\nthe compounding effects of AC use on energy-emissions-temperature feedback, as this\nfeedback not only imposes billions of dollars in economic loss 20 but also accelerates\nglobal warming and further makes achieving global climate targets more difficult 21.\nHowever, the actual impact may be even more severe. First, our findings are likely\nconservative, as they are derived from a one-way evaluation under varying climate\nconditions. In an ideal scenario, GCMs would capture the additional warming effects\ninduced by AC use, as well as the heat-island effects associated with cooling systems\n22,23, thereby informing new estimates of regional cooling demand under elevated\ntemperature conditions, enabling these insights to be integrated into IAMs and\nultimately facilitating a continuously iterative modelling framework. However, such an\napproach requires extensive computational resources, sophisticated model calibration,\nand extensive interdisciplinary collaboration 24. This dynamic feedback loop would\nlikely reveal greater increases in cooling demand and associated emissions 25,26,\nindicating that the present analysis underestimates the full extent of these impacts.\nSecond, this study focuses exclusively on air-conditioning as the primary cooling\ntechnology due to limitations in modelling capabilities and data accessibility. This\nmeans that our study does not represent the heterogeneity of potential or available\ncooling devices and does not provide insights on the potential of adaptation, including\nthe use of alternative forms of cooling with lower (or higher) energy efficiency, causing\nuncertainties in cooling-related energy consumption and emissions. For example,\nwhile fans can reduce energy consumption by 76% compared to AC for the same\ncooling effect 27, other studies suggest that fan energy consumption is still substantial,\npotentially accounting for around 1.5% of TEC in the building sector globally in 2016 28.\nAs a result, our analysis likely underestimates the extent of the resulting feedback loop\nbut also ignores low energy-intensive alternatives.\nComment 1.27.: Line 435: As mentioned above, if I am interpreting the Lorenz curves\ncorrectly, it is the cooling energy use and cooling expenditure per CDD and not the cooling\nenergy use and cooling expenditure the ones which are more unequal than GDP distribution.\nResponse: Yes. We intend to convey that both the cooling energy use and expenditure and the\ncooling energy use and expenditure per CDD measures exhibit greater inequality compared to\nGDP distribution; however, the inequality is indeed more pronounced when considering the\nper CDD measures. We have added cooling energy use and cooling expenditure per CDD in the\nsentence.\nLINE 556-558 IN THE REVISED MANUSCRIPT: Our findings also highlight that large\ncontinuing inequalities in AC use result in cooling energy use and cooling expenditure\nper CDD across regions being far more unequal than the disparities in income.\nComment 1.28.: Line 436: “Using alternative income-drive projections” is somehow difficult\nto understand, I would suggest dedicating a sentence to summarize the approach followed\nleading to results shown in Figure 5.\nLine 440: “everyone in this world” seems a bit too literary and not very scientific.\nResponse: To enhance clarity, we have revised the sentence as follows:\nLINE 558-563 IN THE REVISED MANUSCRIPT: Due to affordability constraints in climate\nadaptation related to cooling, people in low-income regions currently have limited\naccess to air conditioning. Our analysis quantifies the potential increase in global AC\ndemand resulting from elevating incomes in these regions: an additional 94 million\nunits at medium-income levels, 150 million units at high-income levels, and up to over\n220 million units at highest-income levels.\nComment 1.29.: Line 446: Noting that this study is successfully addressing distribution impacts\nof mitigation options in the residential and commercial sectors and implications for the so-\ncalled “just energy transition”, I would suggest including in the conclusions relative to how\nincreasing policy costs (and potentially renewable shares in the electricity mix as suggested to\nreview in the comment of line 351) may increase inequalities as highlighted by the authors in\nline 351: “Under low-emission scenarios, higher mitigation costs and involving share of\nrenewable energy would result in higher unit cost of cooling services relative to the baseline\nscenario, which does not make cooling service more affordable for poor people.”\nResponse: We agree that it is important to highlight in the Conclusions how increasing policy\ncosts and higher renewable shares in the electricity mix under low-emission scenarios may\nexacerbate inequalities, particularly affecting the affordability of cooling services for poorer\npopulations. We have revised the Conclusions section to include a discussion on this critical\nissue as follows:\nLINE 563-572 IN THE REVISED MANUSCRIPT: Moreover, achieving low-emission\npathways would not notably reduce the inequalities in AC use compared to the high-\nemission pathway. High unit costs of cooling service due to stringent mitigation\npolicies may make cooling services less affordable for lower-income population,\npotentially exacerbating existing inequalities and undermining the goals of a ‘just\nenergy transition’. While ambitious climate mitigation itself reduces cooling gaps and\npotential contribution to global warming, targeted cooling policies are required to\nclose regional differences in cooling gaps. Ensuring equitable access to essential\nenergy services such as for cooling is crucial, especially for vulnerable groups who are\ndisproportionately affected by heat-related health risks 29.\nComment 1.30.: Line 690: It would help the reader to have a bit more context on what SSP119,\nSSP126, SSP245, SSP370 represent beyond what is explained in lines 106-108. How are the\ndifferent SSP narratives combined with the RCPs and what do the latter depict?\nResponse: We have added brief scenario descriptions in the Introduction section and a new\nsection ‘climate scenario’ in the Methods section to help readers better understand the\nscenarios employed in our study. The newly added section are shown as follows:\nLINE 813-829 IN THE REVISED MANUSCRIPT: In our research, we select five scenarios\n(SSP119, SSP126, SSP245, SSP370 and SSP585), combining Shared Socio-economic\nPathways (SSPs) with Representative Concentration Pathways (RCPs) to depict future\nclimate change scenarios. SSPs are narratives that describe plausible future global\ndevelopments in demographics, economics, technology, and environmental factors\nwithout considering climate change or mitigation policies 30. Each SSP outlines\ndifferent challenges to mitigation and adaptation: sustainable development (SSP1),\nmiddle of the road (SSP2), regional rivalry (SSP3), inequality (SSP4) and fossil-fuelled\ndevelopment (SSP5). Detailed descriptions are available in previous studies 9. RCPs\nrepresent trajectories of GHGs and other forcing agents to guide modelling potential\nclimate outcomes. The original RCP scenarios include four scenarios: RCP2.6 (very low\nforcing scenario), RCP4.5 and RCP6.0 (medium stabilization levels), RCP8.5 (a very high\nemission scenario) 10. In the IPCC AR6 1,2, two additional scenarios, RCP1.9 and RCP7.0,\nwere introduced, representing global 1.5°C pathway and a high-emission baseline\nscenario, respectively. The five selected scenarios are highlighted in AR6, covering a\nwide range of possible climate outcomes and anthropogenic emissions pathways, and\nthereby encapsulating the uncertainties and complexities of projecting long-term\nclimate and societal changes.\nComment 1.31.: Line 727: “also called the apparent temperature” is already mentioned in the\nnext sentence along with further specifications (“adjusted by relative humidity”), so no need\nto mention that here too.\nResponse: Done.\nComment 1.32.: Line 728-741: Why are there different formulas used depending on the heat\nindex value, temperature and/or RH levels?\nResponse: The approach to heat index calculation used here is based on established methods\nfrom the National Weather Service (NWS,\nhttps://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml), specifically the Rothfusz\nregression equation, with modifications to improve accuracy under certain conditions. The\nprimary reason for employing different formulas to calculate the heat index (HI) is that the\nrelationship between temperature, relative humidity (RH), and the perceived temperature (or\napparent temperature) is nonlinear and varies significantly under different climatic conditions.\nSpecifically: (1) simplified formula (Eq.(1)) for HI below 80°F. For temperatures below 80°F, the\nheat index is relatively straightforward to calculate because the effects of relative humidity on\napparent temperature are minimal at lower temperatures. (2) multiple regression model\n(Eq.(2)) for HI above 80°F. For temperatures above 80°F, the influence of relative humidity\nbecomes more pronounced and complex. At higher temperatures, the interaction between T\nand RH on apparent temperature is nonlinear, necessitating the use of a multiple regression\nmodel. (3) adjustments for specific RH ranges. For RH < 13% and T between 80 and 112°F (Eq.\n(3)): The formula subtracts an adjustment to account for the reduced impact of humidity on\napparent temperature in very dry conditions. For RH > 85% and T between 80 and 87°F (Eq.\n(4)): The formula adds an adjustment to account for the amplified effect of humidity in\nconditions where high RH and moderately high temperatures coexist.\nWe have revised the sentences as follows:\nLINE 854-874 IN THE REVISED MANUSCRIPT: Based on the daily grided data of near-\nsurface air temperature and RH from the CMIP6 database and the NASA NEX-GDDP-\nCMIP6 database (details in Data Description) across SSP-RCP scenarios, we calculate\nthe heat index in each grid for distinct conditions: temperatures below 80°F,\ntemperatures above 80°F, RH below 13% with temperatures between 80°F and 112°F,\nand RH above 85% with temperatures between 80°F and 87°F. When the temperature\nis below 80°F, the Rothfusz regression model is deemed unsuitable, as it has not been\nvalidated for lower heat index values. We use a simplified function (Eq.(1)) to provide\na reasonable approximation of the heat index at lower temperatures, where extreme\nheat stress is less of a concern:\nHI 0.5  T61.0\n\nT68.01.2\n\nRH0.094 (1)\nbelow80F\nWhere HI represents the heat index value, also called the apparent temperature\nadjusted by relative humidity; T represents the near-surface air temperature in degrees\nF; and RH represents relative humidity in percent.\nFor temperatures exceeding 80°F, we utilize a multiple regression model developed by\nLans Rothfusz (Formula 2), which was specifically designed to accurately model heat\nindex values under high temperature and humidity conditions:\nHI  -42.3792.04901523T10.14333127RH0.22475541TRH\nabove80F\n0.00683783TT0.05481717RH20.00122874T2RH (2)\n0.00085282TRH20.00000199T2RH2\nFurthermore, when RH is below 13% with temperatures between 80°F and 112°F, a\nreduction adjustment is applied to account for the diminished perceived heat due to\ndry air (Formula 3):\nAdjustment\n\n13RH/4\n\n \n\n17T95\n\n/17 (3)\nConversely, when RH exceeds 85% with temperatures between 80°F and 87°F, an\nincrease adjustment is added to reflect the heightened perception of heat in humid\nconditions (Formula 4):\nAdjustmentRH85/10 87T/5 (4)\n   \nComment 1.33.: Line 760: It is very useful to do, as the authors have done in S9, a sensibility\nanalysis on the base temperature on CDDs. However, considering the effort and space\ndedicated to deriving regionally disaggregated heat index values accounting not only for\ntemperature but also for RH levels, in my view, the study should dedicate some effort to\nexplain why it is a sound approach to compare these detailed and regionally explicit heat index\nvalues with the same base temperature across all the\nregions, which also do not account for RH.\nResponse: The selection of a uniform base temperature is based on standard practice in the\nliterature (references in CDD section and S9) and facilitates comparability across different\nregions and studies. Using a consistent base temperature ensures that CDDs are calculated in\na uniform manner, which is essential for aggregating results globally and maintaining\nconsistency in temporal and spatial analyses. Incorporating RH into the base temperature\nwould introduce variability not only between regions but also within regions across different\nseasons. This variability could lead to inconsistencies in the calculation of CDDs, making it\nchallenging to compare results and derive meaningful global aggregates. Additionally,\ndynamically adjusting the base temperature with RH could complicate the interpretation of\nCDDs and obscure the underlying patterns in cooling demand driven by climate change.\nWe acknowledge that using a uniform base temperature may not fully capture the\nheterogeneity in cooling needs across different climatic zones. To address this limitation, we\nconducted a new sensitivity analysis in the Supplementary Information to assess the impact\nof different base temperatures on our results. This analysis demonstrates that while varying\nthe base temperature changes the absolute values of CDDs across different years and regions,\nit does not alter the relative trend in CDD differences between regions. This finding supports\nthe robustness of using 18°C as a uniform base temperature across all regions, as it preserves\nthe comparability of CDD trends. However, we acknowledge that applying a single base\ntemperature that does not account for RH may not fully capture the relationship between\ntemperature, humidity, and cooling needs, especially in regions with varying humidity profiles.\nIn response to your suggestion, we have revised the manuscript to include a more detailed\nexplanation of our approach.\nSpecifically, we have added the following to the Methods section:\nLINE 896-909 IN THE REVISED MANUSCRIPT: One limitation is that a uniform base\ntemperature may not fully capture regional variations in thermal comfort thresholds,\nwhile this simplification facilitates global comparisons. Moreover, our choice of base\ntemperature does not account for the influence of RH, which may create\ninconsistencies with the heat-index approach described above. However,\nincorporating RH into the baseline temperature would introduce seasonal and regional\nvariations, making global CDD aggregation and comparison less uniform and more\ncomplex. The current compromise strikes a balance between the need for regional\nspecificity in the heat index approach and the practicality of calculating CDDs using a\nstandardized baseline temperature. Furthermore, we have conducted a sensitivity\nanalysis by selecting various base temperatures in Supplementary Information S9, and\nthe results support the robustness of using 18°C as a uniform base temperature across\nall regions, as it preserves the comparability of CDD trends.\nAnd we have added sentences in the Limitations section as follows:\nLINE 1090-1099 IN THE REVISED MANUSCRIPT: Firstly, our analysis adopted a fixed\nbase temperature for CDD calculations across regions. While this approach allows for\nconsistent comparisons across regions and scenarios, it does not account for the\npotential evolution of comfort thresholds over time. As global temperatures rise,\nbehavioural and physiological adaptations may lead individuals to tolerate higher\nindoor temperatures before resorting to air conditioning, effectively increasing the\nbase temperature that triggers cooling demand. To provide additional insight and\nrobustness analysis into the influence of this assumption, we have included an\nuncertainty analysis in the Supplementary Information (SI), illustrating how variations\nin the chosen base temperature affect CDD calculations.\nComment 1.34.: Line 791: Explain the concept of “satiation” the first time you mention it in\nthe parameter explanation of the formula (you currently do after the second time).\nResponse: Revised.\nComment 1.35.: Line 824: It is a bit confusing to the reader how do you isolate the effects of\nAC emissions in the different SSP-RCP scenario runs. From the current description, it seems\nthat the runs “without AC emissions” completely neglect the “baseline” emissions associated\nto ACs, meaning the emissions from a scenario without increased global warming. In a\nscenario without increased global warming AC will still be used, but not that much, and these\neffects should be considered when comparing with a scenario with AC emissions. It is the\nincreasing AC use what this study is aiming to capture. If the study is indeed calculating only\nthe increase, instead of “with/without AC emissions” it is perhaps more accurate to describe\nit as “increased AC emissions” and “baseline AC emissions” or something similar.\nResponse: Our aim is to quantify the climate feedback loop driven by AC use under global\nwarming, based on the premise that rising temperatures lead to massive use of air-\nconditioning for cooling, while the resulting emissions from AC use contribute to additional\nwarming, further intensifying global climate change. Thus, our experimental design requires\nfirst calculating the emissions from AC use under specific global warming scenarios, followed\nby assessing the additional warming effects of these emissions within the given scenario. In\nGCAM, the SSP-RCP scenarios are constrained by predefined concentrations and emission\nlimits for specific years based on the SSPs and RCPs narratives. When these exogenous\nconstraints are set, any increase in AC emissions due to projected CDDs would be offset by\nreductions in emissions from other sectors within GCAM to maintain the overall SSP-RCP\nemission pathway and vice versa. Thus, this internal balancing makes it hard for us to define\nand quantify the baseline emissions of AC use. In this case, our intention is not to account for\nthe emission gaps between baseline AC emissions and increased emissions due global\nwarming but to calculate total GHG emissions from AC use. To accurately capture the AC-\nrelated emissions without artificially adjusting emissions from other sectors, we calculated the\nAC emissions separately based on the projected CDDs and then added them to the standard\nSSP-RCP emissions for input into MAGICC. We isolated the warming contribution from AC-\nrelated emissions by comparing the temperature projections from MAGICC with and without\nthe additional AC emissions. This approach aligns with our goal of quantifying the climate\nfeedback loop driven by AC use, as well as the original intention of experimental design.\nWe have revised the sentences as follows:\nLINE 1038-1051 IN THE REVISED MANUSCRIPT: Our aim is to quantify the climate\nfeedback loop driven by AC use under global warming, based on the premise that rising\ntemperatures lead to massive use of air-conditioning for cooling, while the resulting\nemissions from AC use contribute to additional warming, further intensifying global\nclimate change. Thus, our experimental design requires first calculating the emissions\nfrom AC use under global warming scenarios, followed by assessing the additional\nwarming effects of these emissions within the given scenario. Exogenous constraints\nimposed to simulate climate change lead to an internal balancing in the IAM model\nthat any increase in AC emissions, driven by projected CDDs, is offset by emission\nreductions in other sectors within GCAM, maintaining the integrity of the overall SSP-\nRCP emission pathway. In this case, we use MAGICC to calculate two temperature\noutcomes: first, the reference warming result corresponding to the standard SSP-RCP\nemission pathway; and second, the warming result from the same pathway with the\naddition of AC-related emissions as calculated by GCAM.\nComment 1.36.: Line 33: In Figure S4 it is not clear whether the units of the horizontal bars\nare in kWh or kWh/household/yr. The figure seems to indicate they are in kWh but then the\nlegend says it is per household.\nResponse: Revised.\nReviewer #2\nGeneral Comment: The study aims to quantify the feedback loop that is caused by the use of\nair-conditioning as an adaptation strategy to global warming. Using five SSP-RCP scenarios,\nthe study finds that AC use leads to an additional increase in GMST between 0.03°C and\n0.12°C, depending on the scenario. Based on the scenario analysis, the study finds that\nswitching to low-carbon energy sources in the power sector can be key to breaking this cycle.\nThe study also finds that, if income inequality is reduced, global AC stocks and warming will\nincrease further (another 0.02°C).\nThe magnitude of the additional warming caused by increased air-conditioning that this study\nfinds is clearly substantial, and thus important. However, the analysis suffers from several\nshortcomings, including a lack of clarity and justification of the chosen methods, missing\ndiscussions of key drivers and assumptions, and a limited analysis of the results (detailed\nbelow). The paper also suffers from a poor presentation of the findings and the analysis, which\nmakes it hard to appreciate the contributions that the study might offer and to assess the\nsignificance of the findings.\nAlthough the study addresses an important issue, in its current form, I would not recommend\nit for publication in Nature Communications. Because many of the shortcomings relate to\npresentation, this could potentially be solved with major revisions, but it is not clear whether\nit would. Please see my detailed comments below.\nResponse: We sincerely appreciate your thorough evaluation of our manuscript and your\ninsightful comments. We are pleased to know that you recognize the importance of our study\nand the significance of our findings regarding the substantial additional warming caused by\nincreased air-conditioning use. We acknowledge the shortcomings you've identified\nconcerning the clarity and justification of our manuscript. Based on your comments, we have\nmade extensive revisions to address above concerns. We believe that these substantial\nrevisions have greatly improved the manuscript and have addressed the concerns you've\nraised.\nThank you once again for your valuable feedback. And we hope that the revised manuscript\nmeets the standards for publication in Nature Communications. We look forward to any\nfurther comments you may have.\nComment 2.1: The authors should clarify, in the main text, how the projections of AC stocks\nand energy consumption (Figure 2, L 175-227) are made. The fact that energy consumption is\nbased on GCAM, and that AC stocks are based on an econometric model, should be stated in\nthe main text, not just in the Methods. These two models are critical to the analysis and\nfindings. The Methods section can (and should) be referred to for more details on both.\nResponse: We have added the sentences in the Introduction section as follows:\nLINE 110-121 IN THE REVISED MANUSCRIPT: By incorporating projected CDDs across\nscenarios, we employ the GCAM to estimate AC energy consumption for cooling and\nthe associated GHG emissions under the corresponding SSP-RCP scenarios (Methods).\nAnd we use a well-calibrated climate emulator MAGICC v7.5.3 to quantify the possible\nranges of additional warming effects from AC use (Methods). Finally, we assess the\ninequality in cooling services using Lorenz curves and Gini coefficients based on cooling\nenergy consumption and expenditure derived from the GCAM, and develop an\neconometric model to quantify these inequalities in the cooling gaps (the unmet need\nfor air-conditioning due to income limitations) through AC stocks under different\nincome levels within SSP-RCP scenarios (Methods). Our study provides quantitative\nglobal insights on the impact of AC use with the changing climate, exploring the extent\nof feedback loop and cooling vulnerabilities.\nComment 2.2: Given that different IAMs make different assumptions, and have different\nmodel fingerprints (e.g. Dekker et al., 2023), the results may be quite sensitive to the IAM that\nis used. The authors should discuss how projections of AC energy consumption/expenditure\nin GCAM compare with projections from other IAMs that could have been used. And the\nchoice of model should be justified: Why was GCAM used (as opposed to a different IAM)? If\ncomparable AC projections are not available from other IAMs, this should be noted (this could\nin fact be the reason for choosing GCAM). In this case, a discussion of key drivers of AC energy\nconsumption in GCAM is still important.\nIdeally, the key drivers in GCAM could then be compared against other IAMs to say something\nabout how other IAMs might have given different results (or not).\nResponse: In response to your feedback, we have first added sentences in the Introduction\nsection to explain our rationale for selecting GCAM to model cooling energy consumption.\nThen, we include a comparison with outputs from other IAM models regarding cooling energy\nconsumption in the Results section. Additionally, we have added a detailed explanation of key\ndrivers of AC energy consumption in the GCAM and an analysis of differences in modelling\napproaches across various models.\nIn the Introduction section, we have added the following sentence:\nLINE 106-109 IN THE REVISED MANUSCRIPT: Moreover, GCAM constructs a series of\ndetailed functions for cooling services and energy demands, leveraging the dynamic\ninteractions between socio-economic development, complex energy systems and\nclimate change to capture cooling energy demand at both global and regional scales.\nIn the Results section, we have added some text to compare our cooling energy consumption\nfindings with those of other studies to ensure the robustness of our results. This focus is\njustified by the fact that CO emissions from cooling energy consumption are mainly\n2\ndetermined by the power generation mix; when cooling energy consumption is similar,\nemissions under the same power scenario also show minimal variation. We have added the\nfollowing text:\nLINE 227-230 IN THE REVISED MANUSCRIPT: AC electricity consumption contributes\n8.0% of total energy consumption (TEC) in global building sector in 2050, reaching\n4,493 (3,326-7,225) TWh, aligning similarly with previous studies that estimate around\n4,000 TWh by 2050 31.\nLINE 233-236 IN THE REVISED MANUSCRIPT: Even though the cooling demand in the\nSSP126 scenario is lower compared to the SSP370 scenario, higher per capita income\nstill results in more AC electricity consumption by 2050. Previous studies also indicate\nthat income drives a large increase in cooling energy consumption under the SSP1\npathway 32.\nIn the Methods section, we have adopted the literature you recommended in response to your\ncomments, emphasizing the key factors influencing AC energy consumption in GCAM. As\ncomparable projections of AC energy consumption at the required level of country detail from\nother IAMs are not available, we focused on GCAM to provide a detailed and nuanced analysis.\nWe have also explained the differences in modelling compared to other IAMs and discussed\nthe reasons for different results in energy consumption for cooling.\nLINE 950-959 IN THE REVISED MANUSCRIPT: In modelling cooling energy consumption\n(Eq. (10)), GCAM and other IAMs share a focus on incorporating climatic variables,\nbuilding characteristics, and socioeconomic factors 33, yet they differ in approach and\nassumptions 34. GCAM features an endogenous, bottom-up building sector that\ndirectly models these influences within a single framework, while other IAMs, such as\nAIM-Hub 35 and MESSAGEix 32 models, rely on multi-model coupling and AC adoption\nassumptions to represent cooling energy demand. Additionally, the modelling\nstructure also influences cooling energy consumption. For example, general\nequilibrium models, compared to partial equilibrium models, account for costs\nimposed by other sectors, often leading to higher costs and lower energy demand 35.\nComment 2.3: It is not clear from the presentation whether the projections of future energy\nfor cooling (L 780-782) is made based on GCAM model outputs, or in the GCAM model. It is\nalso unclear whether the additional improvements of air-conditioning and building energy\nefficiency (L 782-783) are done within the GCAM framework, for this study, or whether it is\ndone external to GCAM, and if so how. The presentation of these aspects needs to be clearer.\nI assume the equations (8-10) are equations in the GCAM model, but this could also be clearer,\nas the authors have also provided a reference to a web site for details.\nResponse: Future energy for cooling is calculated in the GCAM. And the additional\nimprovements of air-conditioning and building energy efficiency is considered in the GCAM.\nWe have clarified that how we project future cooling energy consumption and parameter\nsettings regarding the additional improvements of air-conditioning and building energy\nefficiency.\nWe have revised the paragraph as follows:\nLINE 927-963 IN THE REVISED MANUSCRIPT: In the GCAM, the effects of climate\nchange on cooling expenditure and energy consumption for air-conditioning are\nestimated within the building sector modelling, accounting for improvements in air-\nconditioning efficiency and building energy efficiency (see details in ref 36). Specifically,\nrates of shell conductivity improvements are set at 0.5% per year for industrialized\ncountries and 0.8% per year for developing countries, while AC technologies improve\nat rates ranging from 0.25% per year in industrialized countries to 0.58% per year in\ndeveloping countries over the century 36. Cooling technologies in GCAM encompass\nvarious air-conditioning cooling technologies but exclude other methods such as fans.\nAs shown in Eq. (8), per capita expenditures for cooling (Et) are mainly driven by the\nlevelized cost of the cooling services per unit floorspace in period t (Pt) and the per-\ncapita energy consumption of cooling services (Ct). For convenience, it is useful to\ndecompose Ct into floorspace per capita (ft), and the energy consumption of cooling\nservices per unit of floorspace (ct).\nE CP  f cP (8)\nt t t t t t\nThe demand for floorspace per capita is shown below.\n  \nln2  P \nf sa  1exp   I  t   a (9)\nt    t P  \n   t0  \nWhere ft shows future demand for per-capita floorspace in future time period t; s is\nthe exogenous satiation level of per-capita floorspace, set to different values under\nvarious SSP scenarios, indicating the level of service demand at which increases in\nincome do not lead to further demands for services; μ is the per-capita GDP at 50% of\nthe satiation level, β is the price elasticity of floorspace demand; a is an exogenous\ntuning parameter, Pt’ is the total levelized cost of the modeled energy services per unit\nfloorspace per unit floorspace in period t, t0 is the base year, and It is per capita GDP\nin period t.\nIn modelling cooling energy consumption (Eq. (10)), GCAM and other IAMs share a\nfocus on incorporating climatic variables, building characteristics, and socioeconomic\nfactors 33, yet they differ in approach and assumptions 34. GCAM features an\nendogenous, bottom-up building sector that directly models these influences within a\nsingle framework, while other IAMs, such as AIM-Hub 35 and MESSAGEix 32 models,\ntend to rely on multi-model coupling and AC adoption assumptions to represent\ncooling energy demand. Additionally, modelling structure also influences cooling\nenergy consumption. For example, general equilibrium models, compared to partial\nequilibrium models, account for costs imposed by other sectors, often leading to higher\ncosts and lower energy demand 35.\n  ln2\nI\n\nc kCDD R IG 1exp t  (10)\nt t t t c t    P  \n  \nt\nWhere in period t, ct represents the demand of cooling services, CDDt shows global\npopulation-weighted CDDs; ηt is the exogenous average building shell conductance,\nRt is the exogenous average floor-to-surface ratio of buildings, IGt is the internal gain\nheat from other building services, and λt is an exogenous internal gain scaler.\nComment 2.4: It is, overall, unclear what is new with respect to the GCAM model and AC\nenergy use, in this study, relative to other GCAM studies. It seems from the Methods section\nthat AC use is already built into the GCAM model. The authors should highlight the key\ndifferences more clearly, on a conceptual level, before presenting the details.\nResponse: Indeed, AC energy use is already built into the building module of GCAM. However,\nthe key innovations of our research lie in how we have enhanced the inputs to GCAM and the\napplication of the model to new climate scenarios, specifically:\n(1) We integrated the improved CDD into GCAM to simulate cooling energy consumption with\ngreater precision, considering the combined effects of climate change and population\ndynamics on cooling needs.\n(2) We applied GCAM to simulate AC emissions under the five SSP-RCP scenarios\nrecommended by the IPCC (SSP119, SSP126, SSP245, SSP370, and SSP585). By doing so, we\nprovide a comprehensive assessment of future AC energy use and emissions across a range of\nsocio-economic pathways and climate projections.\nTo address this comment, we have revised the first paragraph in the Methods section to\nhighlight these key differences more clearly:\nLINE 799-805 IN THE REVISED MANUSCRIPT: We integrate the improved regional CDDs\nas climate inputs into a widely used integrated assessment model GCAM 37,38,\nenhancing the ability to project future AC energy consumption and associated GHG\nemissions. Additionally, by assessing AC-related impacts within GCAM across five IPCC-\nendorsed SSP-RCP scenarios, our findings provide a comprehensive outlook on future\nAC energy use and emissions under possible ranges of global warming trajectories and\nsocio-economic development.\nComment 2.5: Based on the MAGICC section in the Methods, it seems GCAM is run twice for\neach SSP scenario, once with AC included, and once without, but this is not stated explicitly\nand so it remains unclear. Please clarify how “the SSP-RCP scenarios that do not account for\nAC-related emissions” (L 839) is run.\nThe authors should also clarify how the temperature outcome in each scenario is pre-\ndetermined by the SSP-RCP. Presumably, an increase in emissions from increased air-\nconditioning use leads to a reduction in other emissions in GCAM, because the model still\nneeds to meet the same RCP constraint. That is, the scenario analysis is premised on a\nbackcasting methodology. It is, presumably, the emissions that GCAM allocates to air-\nconditioning, as opposed to other means, in each SSP-RCP scenario, that represents the\n“additional emissions”. In literal terms, however, these emissions are not actually “additional”\ngiven the same RCP constraint is still applied. Please explain the logic here.\nResponse: Our aim is to quantify the climate feedback loop driven by AC use under global\nwarming, based on the premise that rising temperatures lead to more use of air-conditioning\nfor cooling, while the resulting GHG emissions from AC use contribute to additional warming,\nfurther amplifying global climate change.\nThus, our experimental design requires first calculating the emissions from AC use under\nspecific global warming scenarios, followed by assessing the additional warming effects of\nthese emissions within the given scenario.\nIn GCAM, under SSP-RCP scenarios with radiative forcing constraints, the model adjusts GHG\nemissions across sectors to meet the prescribed radiative forcing targets. This means that\nintegrating projected CDDs leads to negligible changes in overall concentrations\n(approximately a 0.25% variation annually). Thus, we only need to run GCAM once to obtain\nAC emissions under the warming scenario.\nClarification of GCAM and MAGICC runs: In our study, we ran GCAM only once for each SSP-\nRCP scenario to estimate the AC-related emissions. In this single GCAM run, we integrated the\nprojected CDDs, using the SSP-RCP constraints to simulate AC energy consumption and\nemissions. In contrast, we ran MAGICC twice, once with standard SSP-RCP emission pathways\n(we called it ‘without AC emissions’ in the previous version), once with SSP-RCP emissions +\nAC emissions (we called it ‘with AC emissions’).\nAddressing RCP constraints and emissions balancing in GCAM: In GCAM, the SSP-RCP\nscenarios are constrained by predefined concentration and emission limits for specific years\nbased on the SSP and RCP narratives. When these exogenous constraints are set, any increase\nin AC emissions due to projected CDDs would be offset by reductions in emissions from other\nsectors within GCAM to maintain the overall SSP-RCP emission pathway. This internal\nbalancing does not reflect the actual increase in total emissions caused by heightened AC use.\nTo accurately capture the AC-related emissions without artificially adjusting emissions from\nother sectors, we calculated the AC emissions separately based on the projected CDDs and\nthen added them to the standard SSP-RCP emissions for input into MAGICC. We isolated the\nwarming contribution from AC-related emissions by comparing the temperature projections\nfrom MAGICC with and without the additional AC emissions. This approach aligns with our\ngoal of quantifying the climate feedback loop driven by AC use, as well as the original intention\nof experimental design.\nWe have revised the MAGICC section to clarify our modelling approach as follows:\nLINE 1038-1059 IN THE REVISED MANUSCRIPT: Our aim is to quantify the climate\nfeedback loop driven by AC use under global warming, based on the premise that rising\ntemperatures lead to massive use of air-conditioning for cooling, while the resulting\nemissions from AC use contribute to additional warming, further intensifying global\nclimate change. Thus, our experimental design requires first calculating the emissions\nfrom AC use under global warming scenarios, followed by assessing the additional\nwarming effects of these emissions within the given scenario. Exogenous constraints\nimposed to simulate climate change leads to an internal balancing in IAM model that\nany increase in AC emissions, driven by projected CDDs, is offset by emission reductions\nin other sectors within GCAM, maintaining the integrity of the overall SSP-\nRCP emission pathway. In this case, we use MAGICC to calculate two temperature\noutcomes: first, the reference warming result corresponding to the standard SSP-RCP\nemission pathway; and second, the warming result from the same pathway with the\naddition of AC-related emissions as calculated by GCAM. The time step for GHG\nemissions obtained from the GCAM is five years, which is not matching one-year time\nstep in MAGICC, so we use a sliding average method to interpolate the emissions for\nthe intermediate years before inputting these values into the MAGICC model. By\ncomparing these two temperature outcomes, we can isolate the additional warming\ncontribution from AC-related emissions. Given that GCAM calculates the range of air\nconditioning emissions for each SSP-RCP scenario, we utilize the maximum, mean, and\nminimum values of AC emissions within each scenario to repeat the above MAGICC\ncalculation steps, obtaining a range of potential warming effects from AC use.\nComment 2.6: The description of the GCAM model, provided in the Methods section, should\nalso be referenced in the main text.\nResponse: In response, we have revised the manuscript to include explicit references to the\nMethods section wherever GCAM’s modelling framework is discussed.\nComment 2.7: Given the centrality of the SSP-RCP scenarios the authors should include a\ndescription of the SSP-RCP combinations that are used. This does not have to be very long but\nshould include warming levels and key socioeconomic (SSP) assumptions relevant to the\nstudy. This could be put in the Methods or the Supplementary Information\nResponse: We have added a small paragraph in the Introduction section to describe the\nnarratives of each scenario, added a table to show the warming levels and key socioeconomic\n(SSP) assumptions of each scenario in Methods section.\nThe revised paragraph is shown in Comment 1.5.\nSupplementary Table 8. Main assumptions in SSP-RCP scenarios.\nGlobal mean\nsurface air Mitigation Population in Renewable Energy\nScenario temperature targets in 2100** technology intensit Social\ny of CCS costs** acceptance\nanomalies* 2100* (billion) costs** services for CCS***\n(°C)\n**\n2041-2060:\nSSP119 1.7 (1.1, 2.4)\n(SSP1- 1.9 W/m2 6.9 low\nlow high low\nRCP1.9) 2080-2100:\n1.5 (1.0, 2.2)\nSSP126 2041-2060:\n(SSP1- 1.9 (1.2, 2.7) 2.6 W/m2 6.9 low\nlow high low\nRCP2.6) 2080-2100:\n2.0 (1.3, 2.8)\n2041-2060:\nSSP245\n2.1 (1.5, 3.0)\n(SSP2- 4.5 W/m2 9 medium\n2080-2100: medium medium medium\nRCP4.5)\n2.9 (2.1, 4.0)\n2041-2060:\nSSP370 2.3 (1.6, 3.2) 7.1 W/m2\n(SSP3- no mitigation 12.7 high low medium medium\nRCP7.0) 2080-2100: policies\n3.9 (2.8, 5.5)\nSSP585 2041-2060: 8.7 W/m2\n2.6 (1.8, 3.4)\n(SSP5- 2080-2100: no mitigation 7.4 medium high low medium\nRCP8.5) 4.8 (3.6, 6.5) policies\nNote: * represents data come from ref 1, and displayed are multi-model averages in\nparentheses, the 5%-95% ranges, for selected time periods compared to 1850-1900; **\nrepresents assumptions implemented in GCAM v5.2 (https://jgcri.github.io/gcam-\ndoc/v5.2/ssp.html); *** represents assumptions from ref 9.\nComment 2.8: In addition to this, the choice of SSP-RCP scenarios should be justified. The\nreasons for choosing the selected SSPs (L106-109) should be made clearer: is it just that these\nSSPs span the full global warming range? What about other key assumptions/dimensions\nrelevant to this study, including assumptions about socioeconomic development, inequality,\nand challenges to adaptation, which are all central to the SSP framework? To what extent are\nthe relevant dimensions for air- conditioning investigated spanned by the chosen SSPs? Five\nscenarios are unlikely to span all relevant dimensions: it is also important to acknowledge this,\nand to discuss dimensions that might not be fully captured by the five scenarios.\nA discussion and reflection around these kinds of aspects is missing from the paper. But the\nchoice of scenarios is an essential part of scenario analysis, and is key to assessing the\nrelevance, significance, and implications of the findings. Alternatively, the authors could\nacknowledge that the scenario choice is not based on a careful selection of socioeconomic\ndimensions and assumptions, but simply reflects the five scenarios used in the IPCC WGI\nreport (which were chosen because they span a wide range of emissions outcomes). The\nrelevance of these five scenarios, and the extent to which they span the key dimensions,\nand/or the potential limitations of this choice, should then be discussed.\nResponse: We selected five SSP-RCP scenarios primarily based on the scenarios developed and\nrecommended in the IPCC's Sixth Assessment Report (AR6). These scenarios span a broad\nrange of possible future emissions outcomes and provide a comprehensive framework for\nanalysing the potential impacts of climate change under various socioeconomic and policy\nconditions. Our choice of these specific scenarios was informed by their widespread use in the\nliterature and their alignment with the latest global climate models, including those from the\nCMIP6 and NASA NEX-GDDP-CMIP6 database. We agree that the five scenarios we have\nchosen do not span all possible dimensions of future socioeconomic development and\nadaptation. In particular, scenarios (SSP4) that address extreme forms of inequality or more\ndrastic technological advances may not be fully captured in our selected SSPs. Therefore, we\nhave added a discussion in the Climate Scenario section in Methods section, acknowledging\nthe limitations of our scenario selection.\nLINE 827-840 IN THE REVISED MANUSCRIPT: These five selected scenarios cover a wide\nrange of possible climate outcomes and anthropogenic emissions pathways, and\nthereby encapsulate the uncertainties and complexities of projecting long-term\nclimate and societal changes. Additionally, by using the same SSP-RCP scenarios\nrecommended by the IPCC AR6, we ensure that our study is aligned with the global\nGCM community, strengthening the robustness of our findings. The main assumptions\nand warming outcomes of the SSP-RCP scenarios are shown in Supplementary Table 8.\nWe acknowledge that the SSP4 scenario, which focuses on unequal development, is\nnot included in our choice of five illustrative scenarios, meaning that the scenario\ncharacterized by significant inequalities in AC use has not been explicitly explored.\nHowever, even in the absence of SSP4, our analysis still captures key contrasts in\ninequalities between SSP3 (high inequality, high vulnerability) and SSP1 (low inequality,\nhigh resilience). While the inclusion of SSP4 may have provided additional valuable\ninsights, its exclusion does not diminish the broader relevance or robustness of our\nfindings.\nComment 2.9: It is only explained in the Methods section that the AC stocks are based on\nthe authors own econometric model. This econometric model should be mentioned also in\nthe main text, as it is key to the calculations. The detailed description of this econometric\nmodel in the Methods section should also be referred to in the initial paragraphs of the\nMethods section.\nResponse: We have revised the main text to briefly introduce the econometric model used to\nestimate AC stocks. Additionally, we have updated the Methods section to reference this\nmodel in the initial paragraphs, providing readers with an understanding of its significance to\nthe study. The sentences we added in the Introduction section are as follows:\nLINE 114-119 IN THE REVISED MANUSCRIPT: Finally, we assess the inequality in cooling\nservices using Lorenz curves based on cooling energy consumption and expenditure\nderived from GCAM, and develop an econometric model to quantify these inequalities\nin the cooling gaps (the unmet need for air-conditioning due to income limitations)\nthrough AC stocks under different income levels within SSP-RCP scenarios (see\nMethods). \nComment 2.10: Like other key findings, it should be explained, at least briefly, in the main text\nhow the calculations of Lorenz curves and Gini coefficients are done (this could be just one\nsentence, explaining the essence, and use of the GCAM and econometric model). Like other\nsections, this section also lacks references to the methods and SI that could clarify the\ncalculations further. In fact, a clear explanation of the calculations of the impact of inequality\nis also missing from the Methods and the SI. It is, for example, unclear how the effects of\nmitigating inequality (L 325) are calculated. Figure 5 is also unclear: Among other things, the\nshading is not explained, and the caption refers back to itself (Figure 5). The lack of clarity and\nappropriate references overall makes this section confusing and difficult to interpret and\nassess.\nIn addition to this, there seem to be fundamental questions about potential inconsistencies\nthat are not addressed or discussed. If incomes were different, other aspects of emissions and\nadaptation and mitigation would be different too. Inequality is already an important aspect of\nthe SSP framework, and the authors could discuss how the methods applied can be made\nsense of with respect to the inequality that is already captured in the SSP framework.\nScenarios are useful in part because they provide internally consistent frameworks. The\nanalysis of the mitigation of inequalities appears to potentially break with these consistencies\n– although it is unclear, because the methodology is not sufficiently well explained. This is in\naddition to inconsistencies introduced in the calculations of AC stocks, which are mentioned\nbriefly in the methods section (under Limitations, referred to also below).\nResponse: We have added a paragraph in the Methods section explaining how we use the\nLorenz curve and Gini coefficient to assess inequality in AC use. Regarding the issue of\nmitigating inequality, we have added some text to clarify how these effects are calculated and\nhow the impact is modelled within our framework. We have revised the caption to provide a\nclearer explanation of the shading used in the figure.\nWe have added a sentence in the Introduction section for a brief explanation (see Comment\n2.9).\nWe have added a small section in Methods as follows:\nLINE 973-992 IN THE REVISED MANUSCRIPT:\nLorenz curve and Gini coefficient for inequality assessment of AC use\nThe Lorenz curve provides a graphical representation of income or wealth inequality\nwithin a population. It is typically depicted with the cumulative share of income or\nwealth (%) on the vertical axis, and the cumulative share of the population (%, ranked\nfrom lowest to highest income) on the horizontal axis. The Gini coefficient, a numerical\nmeasure of inequality derived from the Lorenz curve, ranges from 0 (perfect equality)\nto 1 (perfect inequality). The basic income Gini coefficient is calculated using the\nfollowing formula:\nn\nG 1P PY Y  (5)\ni1 i i i1\ni1\nWhere G refers to economic development Gini coefficient, Pi is the cumulative share of\npopulation of region i, Yi is the cumulative share of GDP of regional i, and n is the\nnumber of regions. We rank the regions according to their GDP per capita to show the\ninequalities in economic development.\nHere, we also apply the Lorenz curve to assess the inequality in cooling energy\nconsumption and expenditure across different regions, based on data obtained from\nthe GCAM under five SSP-RCP scenarios. The analysis was based on key indicators\nderived from GCAM: cooling energy consumption, cooling energy expenditure, cooling\nenergy consumption per CDD, and cooling energy expenditure per CDD. And following\nthe method in Zhao et al. 19, we use the same per-capita GDP ranking order for the\nother Lorenz curves to compare the inequality in economic development with the\ninequality in the use of air-conditioning.\nWe have revised our manuscript (L325) in the Results section for explaining how to evaluate\nthe warming effects of mitigating inequality in AC use:\nLINE 384-387 IN THE REVISED MANUSCRIPT: Finally, we use the previously calculated\nAC stocks and associated GHG emissions to estimate the additional emissions resulting\nfrom income-driven AC use. These emissions are then input into the MAGICC model to\nquantify the resulting warming effects.\nIn response to your comment on Figure 5, we revised the caption as follows:\nLINE 450-457 IN THE REVISED MANUSCRIPT: We have divided 32 GCAM regions into\nthree income groups, such as low-income, medium-income and high-income, based on\nthe World Bank definition in 2015 39 and van Ruijven, et al. 40 (details shown in\nSupplementary Information). The shading represents the proportion of cooling gaps to\ntotal AC stock for the corresponding income level within the SSP-RCP scenarios. A\nhigher proportion indicates a greater potential increase in air-conditioning units\nfollowing an income rise in the region. CAC, SAS, SAN and ETFA represent Central\nAmerica and Caribbean, Northern part of South America, Southern part of South\nAmerica and European Free Trade Association regions, respectively.\nAs for the potential inconsistencies you mention, our intention was not to modify the core\nassumptions of the SSP-RCP scenarios but to quantify the inequality in AC use through\nadjusting income levels within the existing scenario frameworks. Specifically, within each SSP-\nRCP scenario, we designed three hypothetical scenarios with higher income levels, and then\nestimated the effects of mitigating income inequality. By estimating the cooling gaps (the\nunmet AC stock) due to income insufficiency, we provide a nuanced view of how income\ninequality can influence technological adoption. To clarify this, we have revised the Results\nsection as follows:\nLINE 375-384 IN THE REVISED MANUSCRIPT: Furthermore, to quantify the income-\ndriven inequalities in AC use across regions, we employ the econometric model to\nestimate regional cooling gaps for various income levels (see Fig. 5). Here, cooling gaps\nrepresent the difference between previously estimated AC stock under SSP-RCP\nscenarios and AC stock estimated derived from corresponding income-adjusted\nscenarios. Specifically, we build three hypothetical income-adjusted scenarios within\neach SSP-RCP scenario: raising the income of (1) populations in low-income regions to\nthe average income of middle-income regions; (2) populations in low- and middle-\nincome regions to the average income of high-income regions; (3) populations across\nall regions to the highest income level under the corresponding SSP-RCP scenario.\nComment 2.11: AC stocks and energy consumption will generally increase with increased\ntemperatures. But they will also increase with increased income, even if temperatures stay\nthe same, and with increased population, even if temperature and income (per capita) stays\nconstant. The study would benefit from decomposing the increases in AC stocks and energy\nconsumption into such factors (temperature, income, population, and potentially also other\nfactors). This would help support the discussion in L 210-227 with concrete numbers.\nSimilarly, the analysis of the global warming impact would benefit from a quantitative\ndecomposition of the increased emissions into i) power generation structure and ii) increased\nAC use. Although the text discusses these factors, this is done only in qualitative terms. But it\nis obvious that emissions will increase more with AC use when there is more fossil fuels in the\npower mix, and that decarbonizing electricity supply will reduce emissions from AC use. The\ncontribution of this paper is the quantification of these impacts, which improves the\nunderstanding of the relative importance of different driving factors: is it the power mix, or is\nit the increased cooling demand (including leaking) that is the most important to GHG\nemissions? The example in L 303-306 comes closer to doing something like this, but a more\nthorough analysis of the different effects would significantly improve the overall analysis and\nfindings.\nResponse: As suggested, we have added a new section in Supplementary Information to\nconduct a decomposition analysis of AC stock, cooling energy consumption and associated\nemissions based on the LMDI approach. Moreover, we have also revised the paragraph in the\nResults section L210-227 and L303-306.\nThe new section in SI reads as follows:\nLINE 118-224 IN THE REVISED SI:\nS5. Decomposition analysis of AC stock, cooling energy consumption and associated\nemissions\nWe used an extended Kaya identity to investigate the key driving factors of cooling\nenergy consumption, AC stock and the associated emissions 41. The AC stock change\nbetween the base year 0 and the target year T are decomposed into five driving forces\nAC Stock\nas shown in Eq. (S2): AC efficiency effect (AC, ), cooling demand intensity\nCDD\nCDD Floorspace\neffect (CF, ), floorspace intensity effect (FI , ), income\nFloorspace GDP\nGDP\neffect (Income, ), population effect.\nPopulation\nAS(T0) AST AS0 AS(T0)AS(T0)AS(T0)AS(T0) AS(T0) S2\ntot AC CF FI Income Population\nThe effects of different driving forces of AC stock between the base year 0 and the\ntarget year T are calculated using the following equations:\nACT\nAS(T0)  L(T,0)ln( ) S3\nAC AC0\nCFT\nAS(T0)  L(T,0)ln( ) S4\nCF CF0\nFIT\nAS(T0)  L(T,0)ln( ) S5\nFI FI0\nIncomeT\nAS(T0)  L(T,0)ln( ) S6\nIncome Income0\nPopulationT\nAS(T0)  L(T,0)ln( ) S7\nPopulation Population0\nHere, L(T,0)(AST AS0)/(ln(AST)ln(AS0)), is a weighting factor called\nthe logarithmic mean weight.\nThe results show that the income effect is the dominant factor driving the increase in\nglobal AC stock (see Supplementary Table 3), contributing to an average of 158%\nincrease between 2010 and 2050 across all scenarios, followed by AC efficiency effect\n(74%) and population effect (40%). The AC efficiency effect reflects the quantity of air\nconditioners that can be deployed to meet one unit of cooling demand. With global\nwarming, in scenarios with rapid income growth (SSP585), cooling demand is easily\nmet, resulting in a significant rise in the global AC stock. However, stringent emission\nreduction policies facilitate improvements in cooling efficiency of air conditioners\n(SSP119 and SSP126), which partially offset the growth in the AC stock. Conversely,\nunder the SSP370 scenario with slow income growth, high cooling demand remains\nunmet as residents are unable to afford air conditioners, thereby limiting the increase\nin the global AC stock. The most important factor for decreasing the AC stock is the\nfloorspace intensity effect, causing an average of 125% decrease, everything else keep\nequal. This suggests that economic growth, improvements in building efficiency, and\ncompact urban form can effectively reduce the demand for floorspace per unit of GDP,\nthereby considerably reducing the demand for air conditioners. Similarly, as building\ninsulation improves, the cooling demand per unit of building area decreases, thereby\nreducing the demand for air conditioners. From a global perspective, the cooling\ndemand intensity effect leads to an average 53% decrease in the global AC stock. Using\nthe United States and China as examples, we find that income effect has respectively\ncaused a 60% and 270% increase in AC stock from 2010 to 2050 under the baseline\nscenario (SSP245). Under the SSP585 scenario, the income effect is most pronounced,\nresulting in a 102% increase in AC stock in the United States and a 439% increase in\nChina. The AC efficiency effect is the second most important factor of increasing\ncooling energy consumption in China, contributing to an average of 95% increase in\nAC stock. Similarly, in the United Sates, the AC efficiency effect leads to an average of\n40% increase in the AC stock, followed by the population effect with an average of 34%.\nIn China, the cooling demand intensity effect leads to an average increase of 17% in\nAC stock across all scenarios. In contrast, in the United States, the AC stock increases\nonly under the SSP370 scenario, while it declines under other scenarios. Floorspace\nintensity effect is the most retarding factor of AC stock for both China and the United\nStates, contributing to an average decrease of respectively 272% and 64% between\n2010 and 2050.\nSupplementary Table 3. Key drivers of the AC stock across scenarios.\nSSP119 SSP126 SSP245 SSP370 SSP585\nGlobal AC stock between 84% 103% 83% 58% 148%\n2010-2050\n△AC 98% 76% 60% 28% 107%\n△CF -78% -40% -46% -26% -74%\n△FI -135% -143% -118% -68% -159%\n△Income 170% 180% 127% 76% 238%\n△Population 29% 30% 60% 47% 36%\nAC stock between 2010- 51% 62% 54% 42% 102%\n2050 in the United\nStates\n△AC 71% 38% 27% 9% 57%\n△CF -56% -13% -13% 16% -41%\n△FI -71% -73% -51% -48% -77%\n△Income 73% 75% 60% 56% 102%\n△Population 34% 35% 32% 9% 61%\nAC stock between 2010- 137% 164% 124% 86% 225%\n2050 in China\n△AC 152% 101% 64% 32% 128%\n△CF -39% 37% 28% 28% 30%\n△FI -292% -311% -229% -170% -355%\n△Income 330% 351% 270% 200% 439%\n△Population -14% -15% -9% -3% -17%\nThe cooling energy consumption change between the base year 0 and the target year\nT are also decomposed into five driving forces: cooling efficiency effect ( EC ,\nCooling Energy\n), cooling demand intensity effect, floorspace intensity effect,\nCDD\nincome effect, population effect. Similar to the driving factors of AC stock, the income\neffect is the most important factor for driving the increase in global cooling energy\nconsumption (see Supplementary Table 4), contributing to an average of 190%\nincrease between 2010 and 2050 across all scenarios. The next important factor is the\ncooling efficiency effect with an average 139% increase of global cooling energy,\nfar surpassing the population effect. The cooling efficiency effect indicates that cooling\nenergy consumption per CDD is constrained by improvements in technological\nefficiency while being driven by rapidly increasing demand. The most important factor\nfor decreasing cooling energy consumption is the floorspace intensity effect, causing\nan average 148% decrease. Moreover, the cooling demand intensity effect leads to an\naverage 63% decrease in global cooling energy consumption. From a regional\nperspective, we find that the income effect has caused a 53% increase in cooling\nenergy consumption in the United States from 2010 to 2050 under the baseline\nscenario (SSP245), and a 342 increase in China. The cooling efficiency effect is the\nsecond most important factor of increasing cooling energy consumption in China,\nwhereas in the United States, it only contributes to an average of 7% increase across\nall scenarios. China's rapid economic growth and the widespread adoption of cooling\nequipment have significantly increased energy consumption per CDD, leading to an\nincrease in cooling energy consumption. In contrast, in the United States,\nimprovements in AC efficiency have resulted in a retarding effect under the mitigation\nscenarios. Furthermore, differences in income growth have led to a 49% decrease in\ncooling energy consumption under the SSP370 scenario, whereas a 96% increase under\nthe SSP585 scenario. Similarly, the cooling demand intensity effect in China, driven by\nthe growth in floorspace, has caused an over 35% increase in cooling energy\nconsumption across all scenarios except a 44% decrease in the SSP119 scenario, but\nan average 19% decrease in the United States. This is because rapid income growth\nleads to a substantial increase in floorspace, which reduces the cooling demand per\nunit of floorspace, and thereby decreases cooling energy consumption, and vice versa.\nAs the most important factor of decreasing cooling energy consumption, the\nfloorspace intensity effect contributes to an average 56% decrease in the United States,\nas well as an average 337% decrease in China, indicating that building utilization\nefficiency is the primary driver for reducing cooling energy consumption.\nSupplementary Table 4. Key drivers of cooling energy consumption across scenarios.\nSSP119 SSP126 SSP245 SSP370 SSP585\nGlobal Cooling 117% 160% 136% 103% 309%\nenergy\nconsumption\nbetween 2010-2050\n△EC 133% 129% 110% 70% 254%\n△CF -86% -46% -53% -30% -99%\n△FI -148% -164% -136% -78% -214%\n△Income 187% 207% 147% 88% 321%\n△Population 31% 35% 69% 54% 48%\nCooling energy 3% 16% 24% 10% 80%\nconsumption\nbetween 2010-2050\nin the United States\n△EC 19% -4% 0% -19% 38%\n△CF -46% -11% -12% 14% -39%\n△FI -58% -61% -46% -42% -72%\n△Income 59% 63% 53% 49% 96%\n△Population 28% 30% 28% 8% 57%\nCooling energy 191% 245% 234% 185% 437%\nconsumption\nbetween 2010-2050\nin China\n△EI 208% 171% 158% 116% 304%\n△CF -44% 43% 36% 35% 41%\n△FI -329% -364% -290% -217% -484%\n△Income 372% 411% 342% 255% 598%\n△Population -16% -18% -11% -4% -23%\nThe CO emissions of cooling energy consumption change between the base year 0 and\n2\nthe target year T are decomposed into two driving forces as shown in Eq. (S8): cooling\nemission intensity effect and cooling energy use effect.\nC(T0) CT C0 C(T0)C(T0) S8\ntot CI EU\nThe direct cooling emissions here represent the CO emissions associated with\n2\nelectricity consumption for air-conditioning, the cooling emission intensity effect\nreflects the impact of changes in the power generation mix and advancements in\npower generation technologies, such as reductions in coal consumption for power\ngeneration, on cooling emissions. As the stringency of mitigation targets increases,\nand the extent of electricity decarbonization improves, the cooling emission intensity\neffect contributes more significantly to emission reductions (see Supplementary Table\n5). Under the SSP119 scenario, the cooling emission intensity effect contributes a 75%\ndecrease in global direct cooling emissions between 2010 and 2050, with a 80%\ndecrease in the United States and a 129% decrease in China. In contrast, the SSP585\nscenario, characterized by rapid development of fossil fuel technologies and CCS but a\nrelatively low decarbonization in the power sector, still contributes a 23% decrease in\nglobal cooling direct emissions. Meanwhile, the rapid growth in income and high\ncooling demand significantly drive substantial increases in energy use intensity,\nresulting in the highest energy use effect among all scenarios, with a 236% increase in\nemissions globally. The SSP370 scenario relies heavily on fossil fuels, resulting in the\ncooling emission intensity effect contributing only 13% to emissions reductions.\nFurthermore, the slow economic growth in this scenario limits the ability of households\nto fully meet their cooling demand, leading to relatively low cooling energy\nconsumption. As a result, the energy use effect only drives a 69% increase in emissions,\nthe lowest among all scenarios.\nSupplementary Table 5. Key drivers of direct cooling emissions across scenarios.\nSSP119 SSP126 SSP245 SSP370 SSP585\nGlobal direct -28% 23% 61% 56% 213%\ncooling emissions\nbetween 2015-2050\n△CI -75% -43% -29% -13% -23%\n△EU 47% 66% 91% 69% 236%\nCooling emissions -79% -47% -9% 1% 62%\nbetween 2015-2050\nin the United States\n△CI -80% -58% -29% -9% -14%\n△EU 1% 11% 21% 10% 76%\nCooling emissions -42% 19% 71% 58% 179%\nbetween 2015-2050\nin China\n△CI -129% -22% -21% -13% -27%\n△EU 87% 41% 92% 71% 206%\nSimilarly, the non-CO GHG emissions are also decomposed into two effects: the non-\n2 2\nintensity effect quantifies the total non-CO emission intensity of refrigerant leakage\n2\nduring AC operation and non-CO GHGs associated with electricity consumption over\n2\na specified period. This effect reflects changes in the cooling efficiency of AC systems\nand the extent of the low-GWP transition in AC refrigerants. From a global perspective,\nthe non-CO emission intensity effect significantly increases non-CO emissions across\n2 2\nall scenarios, while the stringency of mitigation policies can partially offset this growth\neffect. However, the non-CO emission intensity effect shows more pronounced\n2\nvariation across regions. For instance, in the United States, the low-GWP refrigerants\ntransition and the improvements of cooling efficiency have significantly contributed to\nemission reductions through the non-CO emission intensity effect under the low-\n2\nemission scenarios, accounting for reductions of 98% and 92%, respectively. In contrast,\nin China, the rapid growth of AC demand and the widespread use of traditional high-\nGWP refrigerants have led to an increase in non-CO emissions, contributing to\n2\nincreases of 362% and 427%, respectively. The energy use effect is another important\nfactor for increasing non-CO emissions, contributing to 69% to 402% of global\n2\nincreases across scenarios. This effect is particularly pronounced in developing\ncountries, such as China, where it accounts for an increase ranging from 139% to 489%.\nSupplementary Table 6. Key drivers of indirect cooling emissions across scenarios.\nSSP119 SSP126 SSP245 SSP370 SSP585\nGlobal indirect 86% 126% 285% 339% 798%\ncooling emissions\nbetween 2015-2050\n△CI (non-CO2) 18% 42% 157% 223% 396%\n△EU 69% 84% 128% 116% 402%\nIndirect cooling -97% -86% -6% 40% 130%\nemissions between\n2015-2050 in the\nUnited States\n△CI (non-CO2) -98% -92% -27% 29% 38%\n△EU 1% 7% 21% 11% 92%\nIndirect cooling 502% 597% 783% 799% 1525%\nemissions between\n2015-2050 in China\n△CI (non-CO2) 362% 427% 577% 617% 1036%\n△EU 139% 169% 206% 181% 489%\nWe have revised the paragraph L210-227 as follows:\nLINE 243-264 IN THE REVISED MANUSCRIPT: We have investigated key drivers of AC\nstock and cooling energy consumption (see Supplementary Table 3 to 4) through\ndecomposition analysis. From 2010 to 2050, our results have shown that the income\neffect is the dominant factor for increasing AC stock and cooling energy consumption\nglobally, causing an average increase of 158.4% and 189.8% across scenarios,\nrespectively. Compared to developed regions, income growth exerts a more\npronounced effect on AC stock and cooling energy consumption in developing regions.\nFor instance, the income effect results in increases of 318.2% in AC stock and 395.5%\nin cooling energy consumption in China, whereas in the United States, it leads to only\n73.2% and 64.2% increases, respectively. Among the next significant drivers of increase,\nthe AC efficiency effect and population effect show similar increasing effects on global\nAC stock, causing average increases of 74.0% and 40.2%, respectively. Conversely, the\ncooling efficiency effect drives an average 139.0% increase in global cooling energy\nconsumption, far surpassing the influence of the population effect. Similarly, due to\nlower cooling technological levels in developing regions compared to developed\nregions, the growth rates of AC stock and cooling energy consumption in developing\nregions exceed those in developed regions under equivalent cooling demands. In\nregions like Africa, India and Indonesia, our results project substantial increases in AC\nstock and cooling energy consumption from 2010 to 2050. Although per capita levels\nin these regions remain lower than those in China due to income constraints, we\nanticipate that ongoing urban population growth and rising incomes will drive AC\nstock and cooling energy consumption in these regions are expected exceed our\ncurrent projections 42.\nWe have revised the sentences L303-306 as follows:\nLINE 352-364 IN THE REVISED MANUSCRIPT: Our decomposition results (see\nSupplementary Table 5 to 6) show that increased AC usage has caused an average\n101.7% increase of global direct cooling emissions from AC use between 2010 and\n2050. Mitigation policies have effectively driven the low-carbon transition of the\npower sector, particularly under the SSP119 scenario, achieving a 75.1% reduction in\nCO emissions. In contrast, the non-CO emission intensity effect results in increased\n2 2\nnon-CO emissions across all scenarios, indicating that current low-GWP refrigerant\n2\ntransition is progressing slowly. In developed regions with a high adoption of efficient,\nlow-GWP refrigerants, such as the United States, the non-CO2 emission intensity effect\ncontributes to over 90% of the emission reductions under low-emission scenarios.\nHowever, in regions with rapid growth in AC demand that primarily use high-GWP\nrefrigerants, such as China, it contributes to a 362.1% increasing effect of non-CO\n2\nemissions even under the SSP119 scenario, approximately 1.6 times greater than the\nenergy use effect.\nComment 2.12: It is unclear, in L98-106, whether the main contribution and novelty of the\npaper is the calculation of the warming impact from AC use, or the calculation of AC stocks\nand related inequalities – or both. This should be clarified. Whereas the abstract gives the\nimpression that the main purpose of the paper is to calculate the additional warming caused\nby the use of air-conditioning as an adaptation strategy, the stated research gap focuses on\nAC stocks and inequality: “our study seeks to assess the impact of using air- conditioning as\nan adaptation strategy on future AC stocks and income-driven global inequalities in AC usage”\n(L98-100). The impact on emissions and warming of AC use appears secondary: “Moreover,\nwe investigate the GHG emissions from AC energy consumption considering various energy\nstructures, and explored the additional warming effects…”.\nResponse: Thank you for your valuable comments. The most important contribution of our\nresearch is to quantify the climate feedback loop caused by AC use under different climate\nscenarios, followed by the We have revised the sentences in the Introduction section as\nfollows:\nLINE 98-119 IN THE REVISED MANUSCRIPT: Aiming at the above research gap, our\nstudy seeks to quantify this climate feedback loop from AC use and assess global AC\ninequalities by designing an integrated approach that incorporates the improved\ncooling degree days (CDD) approach, the Global Change Analysis Model 37,43 (GCAM),\nthe Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC,\nhttps://magicc.org/) 14,44,45. Our projection of future regional CDDs is enhanced by\nintroducing the heat-index approach and multi-model simulation from 25 widely\napplied GCMs, providing a more comprehensive and nuanced estimation of cooling\ndemand across regions (see Methods). Moreover, GCAM constructs a series of detailed\nfunctions for cooling services and energy demand, leveraging the dynamic interactions\nbetween socio-economic development, complex energy systems and climate change\nto capture cooling energy demand at both global and regional scales. By incorporating\nprojected CDDs across scenarios, we employ GCAM to estimate AC energy\nconsumption for cooling and the associated GHG emissions under the corresponding\nSSP-RCP scenarios (Methods). And we use a well-calibrated climate emulator MAGICC\nv7.5.3 to quantify the possible ranges of additional warming effects from AC use\n(Methods). Finally, we assess the inequality in cooling services using Lorenz curves and\nGini coefficients based on cooling energy consumption and expenditure derived from\nGCAM, and develop an econometric model to quantify these inequalities in the cooling\ngaps (the unmet need for air-conditioning due to income limitations) through AC\nstocks under different income levels within SSP-RCP scenarios (Methods). Our study\nprovides quantitative global insights on the impact of AC use with the changing climate,\nexploring the extent of feedback loop and cooling vulnerabilities.\nComment 2.13: Although the introduction discusses relevant literature, the authors should be\nclearer about exactly what part of the findings in the paper are novel and have not been done\nbefore, and what precisely the research gap is.\nResponse: Thank you for your insightful comment. We have revised the introduction to more\nexplicitly outline the research gap in the literature and highlight the novelty of our study.\nWe have revised the research gap part in the Introduction section as follows:\nLINE 79-97 IN THE REVISED MANUSCRIPT: There have been emerging discussions\naround the trade-offs between mitigation and adaptation 46,47, but none has quantified\nthis climate feedback loop caused by the adaptation strategy. Taking AC use as an\nexample of adaptation strategies, previous studies failed to explore the GHG emissions\nand associated warming feedback from AC use under various climate change scenarios.\nExisting research often failed to account for the influence of relative humidity (RH) on\ncooling demand and neglected regional variations in AC demand 48. Moreover, only a\nfew studies have examined the cooling sector under high-emission scenarios (e.g.\nRCP8.5) at the regional and global scales within an integrated framework, utilizing\ndetailed energy-system models and General Circulation models (GCMs) 36,49,50.\nHowever, these studies ignored the large uncertainties arising from limited GCM\nensembles, and crucially, they have not explored the long-term impacts of low-carbon\ntransition driven by ambitious climate targets on the climate feedback loop caused by\nAC use. Furthermore, previous studies rarely tackled regional differences in air-\nconditioning use induced by inequality in income 51, often focusing on the inequalities\nin AC adoption rates 52,53, which limits the exploration of the comprehensive effects of\nadaptation strategies to global warming. None of them have quantified inequalities in\ncooling needs using air-conditioning under different income levels, nor the additional\nGHG emissions and warming effects that would be brought about from raising income\nof low-income groups.\nComment 2.14: The authors note that there is a mismatch between AC energy consumption\nfrom GCAM and AC stocks from the OLS model. But they do not offer any analyses of this\nmismatch, or relevant numbers. The authors should at least discuss the potential impact, and\npreferably say something about the order of magnitude, and provide reasoning for why this\nmismatch should not be a significant concern.\nResponse: Given that GCAM is well calibrated with historical data, we compared the AC stock\nestimated by our OLS model with historical AC stock data from the IEA 28 for the period 2010-\n2014 to assess the potential impact of this mismatch. Our findings indicate that the\ndiscrepancy between the model-predicted AC stock and the historical data is approximately -\n4.4%, with a range of -2.1% to -5.3%. Additionally, we find that both models show consistent\ntrends in the growth of AC stocks and energy consumption driven by rising income,\ntemperature, and population, which supports the validity of our overall findings. We have\nadded some text for more discussion of this mismatch in the Limitation section as follows:\nLINE 1094-1105 IN THE REVISED MANUSCRIPT: Secondly, there is a mismatch between\nthe projected AC energy consumption from GCAM and the energy consumption implied\nby the AC stocks from our econometric model. GCAM accounts for technological\nadvancements, energy efficiency improvements, and policy influences across regions\nover time, whereas our econometric model mainly focuses on projecting AC stock\nbased on socioeconomic factors and temperature. Consequently, our analysis's\nprojections of the AC stock do not fully capture the effects of future technological\nprogress or regional disparities. Despite the mismatch, our findings indicate that the\ndiscrepancy between the model-predicted AC stock and the historical data during\n2010-2014 is approximately -4.4%, with a range of -2.1% to -5.3%. Both models show\nconsistent trends in the growth of AC stocks and energy consumption driven by rising\nincome, temperature, and population, which supports the validity of our overall\nfindings.\nComment 2.15: The ordering of Supplementary information items should follow the main text\n(i.e. the first SI Table that is mentioned in the text should be SI Text 1, not SI Table S2). And\nthe authors should ensure that all SI items, as well as the Methods sections, are referenced in\nthe main text. Several SI items are not referenced, which means that important explanations\nand aspects behind the results in the main text are not provided, and that the SI is not very\nuseful because information is given there without context.\nResponse: We have revised Supplementary Information labels as you suggested and ensure\nthat they are all referenced in the main text.\nComment 2.16: The number of significant figures is at times unnecessarily high given the\nunderlying uncertainties in this kind of scenario analysis. For example, in Line 424, it would be\nsufficient to write 59 to 311 GtCO2.\nResponse: Revised.\nComment 2.17: The “several trillions of dollars for cooling services” should ideally be\nsubstantiated with a simple calculation (in SI or Methods), or with a reference.\nRather than referring to 500 million units (L 441) it would be better to use the same measures\nas in the previous paragraph, to make them consistent and comparable. I suggest using energy\nuse and temperature increase, as these are more relatable to a wider audience.\nResponse: In response to feedback from another reviewer requesting revisions to the\nDiscussion section, we have restructured and revised that part of the manuscript by adding\nsome limitations and discussions and reducing the results descriptions. In the course of these\nrevisions, these two specific statements have been removed from the manuscript.\nComment 2.18: The last paragraph in the Discussion and Conclusion (L 447-472) seems overly\nfocused on what other studies have shown regarding aspects of emissions reductions that are\nnot assessed in this study (energy-saving, retrofitting, and low-GWP refrigerant use). While\nthese aspects are important to address, somewhere, the concluding section does not seem to\nbe the right place. The concluding section should be more closely related to the findings and\nthe implications of the findings. If this paragraph is kept as the last paragraph (which I would\nnot recommend), these other aspects need to be related to the paper findings. For example,\nwhat is the potential for emissions reductions from these other aspects relative to the\nprojected emissions increases from increased AC use found in the paper? Could these\nstrategies be sufficient to counteract the additional warming that this study finds from\nincreased AC use?\nResponse: In response to your suggestion, we have reduced the length of strategies that are\nnot implemented in this paper. And we have reorganized the last paragraph in the Discussion\nand Conclusions section to more closely align with our study's findings and their implications.\nWe have revised the manuscript as follows:\nLINE 526-555 IN THE REVISED MANUSCRIPT: Moreover, our study shows that achieving\nlow-carbon transitions can be the key to breaking this cycle. Cooling-related electricity\nconsumption is estimated to be approximately 3,600-12,821 TWh in 2050 across\nscenarios, amounting to 13%-47% of global total electricity consumption in 2022. A\nhigh share of renewable energy systems could lead to fewer GHG emissions while\nconsuming more AC-related electricity. Similarly, we also investigate the emission\nreduction potential of transitioning from HFC134a and HFC143a refrigerants to low-\nGWP alternatives such as HFC32 using GCAM. The most stringent scenario (SSP119)\nprojects that HFC32 will comprise 25% of all refrigerants by 2050, resulting in 84%\nlower emissions compared to the SSP585 scenario. Thus, when legislation is more\nstringent, adopting more efficient and low-GWP refrigerants, such as R-290 and R-\n1234yf, in place of HFC134a could contribute up to 90-99% emission reduction 54. In\naddition, building retrofits represent critical strategies for reducing cooling energy\nconsumption, particularly in regions with high cooling demands. Evidence shows that\nbuilding retrofits, such as using aerogels to improve insulation 55 and retrofit double\nglazing 56, can reduce annual cooling energy consumption by 7.5% in tropical regions.\nProviding subsidies for the adoption of energy-saving equipment can also improve the\nefficiency of cooling systems in buildings 57. Studies shows that the large-scale\napplication of air-to-air heat pumps in USA could provide 5%-9% emission reduction\nbased on current power system 58. The strategies discussed above require substantial\neconomic investment and extended timeframes, making them challenging to deliver\nshort-term impacts. Behavioural changes driven by policy interventions could play a\ncritical role in achieving short-term reductions in AC energy consumption 59. For\nexample, dynamic pricing strategies that raise electricity costs during peak hours and\ntemperature adjustment policies that increase cooling setpoints during peak demand\nperiods can incentivize users to shift their cooling demand to off-peak times,\npotentially reducing peak electricity loads by up to 20% 60. Additionally, environmental\neducation campaigns can promote energy-saving habits among the public, such as\nsetting thermostats at optimal levels or adopting passive cooling strategies 61, bringing\nabout 6%-20% energy-savings from AC use 62.\nComment 2.19: The Methods section should be referenced wherever relevant in the main\ntext. There are several sections in the main text where the reader is not pointed to where and\nhow results were calculated and how the analysis was undertaken (the Methods section is not\nreferenced anywhere in the main text).\nThe Methods section itself could also do with some internal referencing/signposting (to other\nparts of the Methods section) to make it much easier for the reader to find the relevant\ninformation. For example, L 693 says that “we first use the heat-index approach”, but it does\nnot state that this approach is explained further below. Please make this clear, for example by\nwriting “we first use the heat-index approach (explained below)”.\nThe schematic figure could also be improved and used more actively to provide a clear\nconceptual overview that explains how the analysis in the paper hangs together and supports\nthe findings. This could help clarify some of the methodological issues pointed to above. For\nexample, what is new in this study and what is already a part of GCAM? How is the global\nwarming with and without AC calculated and how does the RCP constraint and GCAM come\ninto this? The color coding is confusing, with MAGICC ending up partly in the green (with\nGCAM) and partly in the pink area (which presumably represents the calculation of Lorenz\ncurves and Gini coefficients).\nResponse: In response, we have added essential cross-references to the Methods section in\nthe main text, an added an Integrated Assessment Framework section in the Methods, and\nredesigned the schematic figure.\nSpecifically, the new section added in the Methods reads as follows:\nIntegrated assessment framework\nFigure 6 Research framework of this study. The red circle represents the climate\nfeedback loop caused by AC use. The content within the ellipse represents the key steps\nand components of the comprehensive assessment of air-conditioning, while the\ncontent within the boxes outlines the key methodologies employed to achieve the main\nobjectives of this study.\nLINE 792-811 IN THE REVISED MANUSCRIPT: This study develops an integrated\nassessment framework to quantify the climate-driven feedback loop and inequalities\nresulting from AC use under various climate scenarios (see Figure 6). For this scenario\nanalysis, we utilize the SSP-RCP framework to simulate climate scenarios, matching\nthe outputs of multiple GCM models from CMIP6 (see the Climate scenarios section).\nThe scenario narratives and main assumptions are outlined in the following section.\nSubsequently, we quantify global and regional cooling demand across different\nscenarios using an improved population-weighted CDD approach, incorporating the\nheat-index approach (see the Heat-index approach section). The GCAM is employed as\nthe core modelling tool 37,38, integrating the improved regional CDDs and SSP-RCP\nscenarios to simulate AC energy consumption and the associated emissions (see the\nGCAM section). We assess the inequality in the use of air-conditioning using Gini\ncoefficients (see the Lorenz Curve section) and quantify the cooling gaps with an\neconometric model. The formulas used for the inequality assessment are presented\nbelow, with additional robustness analysis for the econometric model provided in the\nSupplementary Information (see the Empirical approach section). Finally, we employ\nthe MAGICC model to evaluate the additional warming effects resulting from the GHG\nemissions associated with AC use. Detailed information is provided at the end of the\nMethods section.\nComment 2.20: L 45: Unclear subject. Who will reach access? The full “global population”?\nL46: Extra “an”: should be “still cause another 0.02…”\nResponse: We have revised the sentence as follows:\nLINE 47-49 IN THE REVISED MANUSCRIPT: Furthermore, if all regions can achieve full\naccess to air-conditioning, the GHG emissions from additional AC energy use would\nstill cause another 0.02°C increase even under the SSP119 scenario.\nComment 2.21: L 68-69: Consider rewording. It is inevitable that increasing air conditioning in\nthe past and today causes increased emissions (as long as power is based on some fossil fuels),\nbut it is not inevitable in the future (as we could have a clean power system).\nResponse: We have revised the sentences as follows:\nLINE 71-73 IN THE REVISED MANUSCRIPT: Considering that our current power\ngeneration is mainly based on fossil fuels, the increase in electricity use for air-\nconditioning will cause substantial GHG emissions in the near-term.\nComment 2.22: L 96: “and additional GHG emissions and warming effects that would be\nbrought” L 114: unclear what “mitigating such inequalities by increasing income” means.\nIncreasing the income of low-income groups, thereby reducing inequality, or of all, thereby\nincreasing total demand?\nResponse: We have revised the sentences as follows:\nLINE 95-97 IN THE REVISED MANUSCRIPT: None of them have quantified inequalities\nin cooling needs using air-conditioning under different income levels, nor the\nadditional GHG emissions and warming effects that would be brought about from\nraising income of low-income groups.\nComment 2.23: L 131: “possible ranges” What does this mean? These are the ranges from the\nResponse: Possible ranges mean wide ranges from the 25 GCM models. We have revised the\nsentences as follows:\nLINE 148-150 IN THE REVISED MANUSCRIPT: The bars on the right represent wide\nranges of global population-weighted CDDs in 2050/2100 from 25 GCM outputs\nComment 2.24: L 133: red should be increased and blue should be reduced? (it says the\nopposite)\nResponse: Yes. We have corrected this mistake in the revised manuscript\nComment 2.25: L 143: “the effect of climate model uncertainties covers current total global\nCDD levels” is unclear. Do you mean that the climate model uncertainties in 2050 are as large\nor larger than the absolute CDD today?\nResponse: In our original analysis, the 2010 CDD was calibrated using historical data from the\nGCAM database 11, approximately 69,335 CDDs. Due to the lack of high-resolution SSP119 data\nfrom NASA NEX GDDP, we initially employed CMIP6 data to calculate the mean, maximum,\nand minimum CDD across all models, as well as the mean, maximum, and minimum within each\nmodel (see text in Data Description section). By averaging these two approaches, we derived\nthe SSP119 CDDs for 2050, which had a mean of 62,226 CDDs with a range of 43,734 to 89,341\nCDDs. This supported our original statement that “the effect of climate model uncertainties\ncovers current total global CDD levels.”\nHowever, in response to your comments, we have refined our methodology by applying the\nwidely used quantile mapping method 12,13 to calibrate the SSP119 data. This calibration\nyielded a 2050 mean CDD of 69,587 days, ranging from 60,669 to 81857 CDDs, which reduces\na lot of uncertainties. We also performed robustness tests (e.g., RMSE) to validate the\ncalibration results, which are detailed in the Supplementary Information. To maintain data\nconsistency and comparability, we also recalculated the historical data using NASA’s calibrated\nhistorical dataset, with a historical CDD of 65,429 in 2010. Consequently, under the\nSSP119 scenario, the 2050 CDD shows a slow and slight increase, aligning more closely with\ncurrent levels within the uncertainty range.\nThe added section of robustness tests in Supplementary Information is shown in the response\nto Comment 1.7.\nComment 2.26: L 143-145: The language is a bit confusing. Maybe better to write “The\nincrease in global mean cooling demand between 2050 and 2100 is 48.1% and 65.5% in the\ntwo high emissions scenarios (SSP370 and SSP585, respectively), which is much higher than\nthe increase (2.1% and 0.6%) in the two most ambitious mitigation scenarios (SSP119 and\nSSP126, respectively)”. Two significant figures (for all of these) would also be more\nappropriate.\nResponse: In response, we have revised the sentence to enhance clarity and precision, and we\nhave adjusted the significant figures to two, as you recommended. In response, the sentence\nwe revised are shown in Comment 1.8.\nComment 2.27: L 147-152: I assume the “vice versa” means that a lower emissions scenario\nwould result in lower cooling demand across all regions in the long term. But it is unclear what\nscenarios the sentence “However, the variations in regional cooling demand for the year of\n2050 would be less pronounced” refers to. Is it the high emissions or the low emissions or\nboth? The link to the next sentence is confusing (L 150-152): that some regions will experience\nhigher cooling demand in low emissions scenarios than in high emission scenarios does not\nseem to follow from the previous sentences.\nResponse: To address your concerns, we have revised the sentences as follows:\nLINE 169-173 IN THE REVISED MANUSCRIPT: From a regional perspective, higher\nemission scenarios generally result in considerably higher cooling demand across all\nregions by the end of the century, while lower emission scenarios lead to lower cooling\ndemand. However, by 2050, the expected rise in cooling demand under high emission\nscenarios does not emerge uniformly across all regions, highlighting an inconsistency\nin the mid-term projections.\nComment 2.28: L 310-312: There are multiple differences between the different scenarios\nthat are explored. Explain how the 0.02C increase referred to here relates to inequality\nspecifically.\nResponse: We have added some text to explain how we calculated the 0.02C as follows:\nLINE 384-387 IN THE REVISED MANUSCRIPT: Finally, we use the previously calculated\nAC stocks and associated GHG emissions to estimate the additional emissions from\nincome-driven AC use. These emissions are then input into the MAGICC model to\nquantify the resulting warming effects.\nComment 2.29: L 336-338: Would increasing income levels overall reduce the differences? Or\nis it increasing income in low-income groups (this would obviously reduce the differences)?\nResponse: We have revised it as follows:\nLINE 407-408 IN THE REVISED MANUSCRIPT: Raising income of low-income groups\ncould help to reduce these disparities.\nComment 2.30: L 347-351: Break up the sentence starting with “Compared to the baseline\nscenario…” to make it clearer and more readable.\nResponse: The revised text now reads:\nLINE 417-427 IN THE REVISED MANUSCRIPT: Compared to the baseline scenario, the\nunit cost of cooling services under high-emission scenarios would be far lower. The\nSSP585 scenario, which features the highest income growth, has the most equal\ndistribution of energy use and expenditure for cooling among the five scenarios. The\nrichest 10% of the global population would consume approximately 4 and 3 times more\ncooling energy use and expenditure than the poorest 10% in 2050, with these\ndifferences being substantially lower than those observed in the baseline scenario. In\ncontrast, the SSP370 scenario, characterized by slow and uneven economic\ndevelopment, shows the most unequal distribution, with the differences in cooling\nenergy use and expenditure between the richest 10% and the poorest 10% projected\nto increase to 13- and 10-fold, respectively.\nComment 2.31: L 419: “it also leads to increase in global AC stocks” seems to be superfluous,\nsince the previous sentence already says “the use of air-conditioning for cooling is a crucial\nadaptation response” (it follows from this that AC stocks will increase).\nResponse: We have removed the sentence.\nComment 2.32: L 420: clarify what is meant by “accelerating warming”. Is it actually\naccelerating, or just increasing? What is shown is an increase in the global mean temperature,\nnot accelerated temperature increase.\nResponse: We have revised the sentences as follows:\nLINE 497-499 IN THE REVISED MANUSCRIPT: This study quantifies the climate feedback\nloop caused by AC use, indicating that using air-conditioning as an adaptation strategy\nexacerbates climate change, causing an additional 0.03°C-0.12°C increase in global-\nmean temperature by 2050.\nComment 2.33:\nL 444: need references for the “induced cooling demand according to various GCM\nprojections”\nFigure 2: The caption refers to a grey bar, but I cannot see a grey bar (I can see the black square\nin 2010).\nL 56: “half a million” or “a half million” L 66: “have an AC”\nL 75: No need to say ‘eventually’, as the feedback loop is already present. L 83: “investigate”\nL 94: “have” not “has”\nL 120, L 122, … (several other places): “gridded data” not “grided data”\nL 149, L336 (and elsewhere): I suggest using present tense to describe what is shown in the\nscenarios. So, “…cooling demand for the year of 2050 are less pronounced” rather than\n“…cooling demand for the year of 2050 would be less pronounced” and “cooling is more\nunequal” rather than “cooling would be more unequal”.\nL 209: reference to SI should refer to where in the SI (for example by numbering the SI text\nand including a table of contents in the SI).\nL 348: “cooling services”\nL 352: “..and higher share of renewable energy”? L 355: “regional gaps”\nL 429: “emphasizes”\nL 437: “income-driven”\nL 692: unfinished sentence.\nL 698 “the Cooling Degree Days…”\nL 702: “, and non-CO2 emissions from…” L 712: “to characterize”\nL 720: “ and is widely used to…” L 728: “to calculate”\nL 752: “a better understanding of” L 753: “is calculated using”\nL 760: “Supplementary”\nL 777: “and emission constraints” L 818: “that is”\nL 868: “uses”\nL1035: “Supplementary”\nFigure 5: Shading is not explained. Caption refers to itself “(see Figure 5)”\nResponse: Thank you for your careful reading and detailed comments. We have revised our\nmanuscript as you suggested.\nReviewer #3\nGeneral Comments: The paper explores how global warming leads to increased air-\nconditioning (AC) usage, which in turn exacerbates greenhouse gas emissions, creating a\nfeedback loop. It quantifies this climate feedback loop by examining energy system structures\nunder five SSP-RCP scenarios. Most sections of the paper are well-written, and I appreciate\nthe innovative use of the heat-index approach as well as the analysis of global inequality in AC\nusage. Overall, it is a valuable work of high quality. However, there are a few concerns in the\n“Empirical Approach for Future AC Stocks” section and related parts in the Supplemental\nInformation that require attention. Here are some comments that I hope the authors find\nhelpful to improve the current manuscript.\nResponse: Thank you for your constructive comments and positive feedback. We sincerely\nappreciate your thorough review of our manuscript and your positive feedback. We are\ndelighted that you find our work valuable and of high quality. We have carefully considered\nyour feedback and have made significant revisions to the manuscript: 1) We have revised the\n“Empirical Approach for Future AC Stocks” section to provide a clearer and more detailed\nexplanation of our methodology. 2) The related sections in the Supplemental Information\nhave been expanded to include additional data, figures, and explanations that support our\nempirical approach. 3) We have carefully addressed each of your specific comments (detailed\nbelow), making the necessary corrections and improvements to the manuscript.\nOnce again, we are grateful for your insightful comments and the time you have invested in\nreviewing our manuscript. Your feedback has been invaluable in helping us improve the\nquality and clarity of our work. We believe that the revisions have significantly strengthened\nthe manuscript, and we hope that it now meets your expectations.\nComment 3.1: The paper briefly discusses the feedback effect of AC usage on global\nwarming, but the quantitative analysis is insufficient. For instance, the projected temperature\nincrease of 0.03°C to 0.12°C may seem modest without additional context regarding its\nbroader social and economic implications. A more detailed quantitative analysis of this\nfeedback loop, including specific economic impacts, policy implications, and societal\noutcomes, would enhance the understanding of these temperature changes' long-term\nsignificance.\nResponse: Compared to the observed warming from 1998-2012 with 0.05°C (-0.05°C-0.15°C)\nper decade, our estimates of the climate feedback loop from AC use accelerate the timeline of\nglobal warming. We agree that a more detailed quantitative analysis about what the extent of\nthe climate feedback loop may affect socio-economic development across regions is very\nimportant, and we will focus on it in future work. To address your concerns, we have added\nsome text in the Results section to explain the significance of our estimates in climate change,\nemission targets and quantitative economic impacts as follows:\nLINE 341-351 IN THE REVISED MANUSCRIPT: Our estimated additional warming effects\nof 0.03°C-0.12°C due to AC use are considerable in the context of a changing climate.\nGiven that the observed warming from 1998 to 2012 is about 0.05°C (-0.05°C-0.15°C)\nper decade 63, this feedback loop could accelerate the timeline of global warming,\nreducing the likelihood of achieving the 1.5°C or 2°C temperature targets. Historical\ndata further underscores the escalating economic damage associated with natural\ndisasters under such warming conditions: the economic loss from the top 1% of single-\nevent disasters reached US$9.92 billion in 2010, more than doubling since 1998 64.\nAdditionally, economic ramifications of this additional warming in production are far-\nreaching, indicating that global output would drop 2.5% in response to a temporary\nincrease of 1°C 20.\nComment 3.2: There is a conceptual inconsistency between the dependent variable (AC\nstock) and the independent variables (per capita CDDs, per capita GDP, and urbanization rate).\nThe dependent variable is a total quantity, while the independent variables represent per\ncapita measures. Although this aligns with Colelli et al. (2023), their study used the share of\nhouseholds with AC as the dependent variable. Clarifying this distinction would strengthen\nthe analysis.\nResponse: Our primary goal is to capture the future AC stock change across different SSP-RCP\nscenarios to quantify global AC inequality, which necessitates using AC stock as the dependent\nvariable rather than a per-household ratio. To ensure consistency in the model, we have added\na population variable to align the total quantity of AC stock with the overall variables used in\nthe analysis, as you suggested.\nTo address your concern regarding conceptual consistency, we have revised our model to\ninclude the population explicitly. By incorporating population, we ensure that per capita\nmeasures can be effectively linked to the total AC stock, thus maintaining internal consistency\nin the regression framework. We have also updated parts of the manuscript’s conclusions\nbased on the adjusted AC stock values. This refinement clarifies our approach and strengthens\nthe analysis by aligning the total AC stock with per capita predictors in a logically consistent\nmanner. The coefficients of the new panel OLS model are as follows:\nlnAC\nlncdd -1.47690 (0.877)\n(lncdd)2 0.13863** (0.065)\nlnpgdp 0.50681** (0.198)\nlnurbanization 0.24636 (0.422)\nlnpop 0.61344* (0.316)\n_cons -4.91696 (6.672)\nCountry fixed effect Yes\nYear fixed effect Yes\nN 800\nadj. R2 0.993\nRobust standard errors in parentheses clustering at the National Level, * p<0.1, ** p<0.05, ***\np<0.01\nComment 3.3: The paper employs several models (e.g., GCAM, MAGICC) to forecast climate\nimpacts and AC demand, yet the assumptions behind key parameters—such as economic\ngrowth and energy transition rates—are not clearly articulated.\nProviding more detailed explanations of these assumptions would enhance the credibility of the results.\nResponse: In response, we have made significant revisions to the Methods section to provide\na more comprehensive explanation of the assumptions and key parameters used in the GCAM\nand MAGICC models.\nSpecifically, we have introduced a new subsection in the Methods part, which details the\nunderlying assumptions for each SSP-RCP scenario. The table of main assumptions in SSP-RCP\nscenarios reads as follows, shown in Supplementary Information:\nSupplementary Table 8. Main assumptions in SSP-RCP scenarios.\nGlobal mean\nsurface air Mitigation Population in Renewable Energy\nScenario temperature targets in 2100** technology intensit Social\ny of CCS costs** acceptance\nanomalies* 2100* (billion) costs** services for CCS***\n(°C)\n**\n2041-2060:\nSSP119 1.7 (1.1, 2.4)\n(SSP1- 1.9 W/m2 6.9 low\nlow high low\nRCP1.9) 2080-2100:\n1.5 (1.0, 2.2)\nSSP126 2041-2060:\n(SSP1- 1.9 (1.2, 2.7) 2.6 W/m2 6.9 low\nlow high low\nRCP2.6) 2080-2100:\n2.0 (1.3, 2.8)\n2041-2060:\nSSP245\n2.1 (1.5, 3.0)\n(SSP2- 4.5 W/m2 9 medium\n2080-2100: medium medium medium\nRCP4.5)\n2.9 (2.1, 4.0)\n2041-2060:\nSSP370 2.3 (1.6, 3.2) 7.1 W/m2\n(SSP3- no mitigation 12.7 high low medium medium\nRCP7.0) 2080-2100: policies\n3.9 (2.8, 5.5)\nSSP585 2041-2060: 8.7 W/m2\n2.6 (1.8, 3.4)\n(SSP5- 2080-2100: no mitigation 7.4 medium high low medium\nRCP8.5) 4.8 (3.6, 6.5) policies\nNote: * represents data come from ref 1, and displayed are multi-model averages in\nparentheses, the 5%-95% ranges, for selected time periods compared to 1850-1900;\n** represents assumptions implemented in GCAM v5.2 (https://jgcri.github.io/gcam-\ndoc/v5.2/ssp.html); *** represents assumptions from ref 9.\nAdditionally, the modelling description of GCAM we have revised is shown in Comment 2.3.\nFinally, we have revised the modelling description of MAGICC as follows to interpret how we\nestimate the additional warming effects from AC use. The revised text is shown in Comment\n2.5.\nComment 3.4: The analysis does not adequately address regional heterogeneity in the\neffects of the independent variables on AC stocks. For example, the impact of a one-unit\nincrease in (log) income on AC stock may vary significantly between the United States and\nAfrica. As highlighted by Colelli et al. (2023), AC ownership rates respond differently to\nchanges in per capita income and CDDs across regions. Including interaction terms between\nexplanatory variables and country (or broader regional) fixed effects in Equation (11) could\nbetter capture this variation.\nResponse: We acknowledge that incorporating interaction terms between explanatory\nvariables and country/region fixed effects, as suggested by Colelli et al. (2023), could provide\ndeeper insights into how these drivers vary across different parts of the world.\nIn our analysis, we initially attempted to classify countries by continent and introduce\ncontinent-level dummy variables interacting with our key predictors. However, the results\nwere not robust. For instance, our model projected a sustained decline in China’s future AC\nstock under the baseline scenario, AC stock in 2050 is projected to be 284.2 units, far below\nthe historical AC stock in 2010 (342.9 units), which contradicts current developments.\nThis inconsistency likely stems from data availability and quality constraints across regions,\nwhich undermined the reliability of the regional breakdown model.\nConsequently, we opted for a global-average approach to project AC stock, given the data\nlimitations. Nevertheless, to address the reviewer’s concern, we have added an appendix\ndetailing our preliminary tests for regional heterogeneity, including the methodologies\nemployed and a discussion of the resulting challenges. We hope this additional information\nclarifies our decision-making process and provides a foundation for future research that could\nincorporate more region-specific data.\nLINE 322-336 IN THE REVISED SI:\nSupplementary Figure 7. The regression coefficient plot of primary variables in the OLS\nmodel. The x-axis represents the coefficient values, while the y-axis denotes the five\nprimary variables. Colored circles of five different hues correspond to the regression\ncoefficients of the five regions; triangular symbols indicate the coefficients from the\nfull-sample regression, and error bars represent the 90% confidence intervals.\nIn this study, we have built a panel Ordinary Least Squares (OLS) model in logarithmic\nform to project future AC stocks and explore inequalities in AC use. To ensure the\nreliability and robustness of our results, we conduct regression analyses by grouping\n32 countries according to their respective continents, categorizing the samples into\nNorth America, Africa, South America, Europe, and Asia, and the results are shown in\nSupplementary Table 11 and Supplementary Figure 7. The table reveals that, despite\nvariations in the magnitudes of variables across groups, the significance and sign\ndirections of the coefficients remain consistent with those of the baseline model.\nThis indicates that the baseline model constructed using panel data encompassing 32\nregions demonstrates strong robustness. The figure presents the regression\ncoefficients for the five major regional groups, with all regression results controlling\nfor country-fixed effects and year-fixed effects.\nSupplementary Table 11. Coefficients of regional heterogeneity analysis.\nlnAC lnAC lnAC lnAC lnAC\n(North (Afri (Sou (Europ (Asi\nAmeric ca) th e) a) \na) Ame\nrica)\nlncdd - - - - -\n2.2503 10.5 6.04 4.296 3.84\n1* 442 643 57*** 553\n7\n(0.815) (4.9 (4.6 (0.952 (4.1\n95) 27) ) 84)\n(Lncdd)2 0.2110 0.74 0.48 0.439 0.29\n5 644 573 11*** 344\n*\n(0.091) (0.3 (0.3 (0.083 (0.2\n24) 08) ) 96)\nlnpgdp 1.0948 0.74 - 0.370 0.58\n8* 092 0.42 47** 892\n* 198 *\n(0.441) (0.3 (0.2 (0.124 (0.2\n35) 34) ) 76)\nlnurbani - - 1.00 - 0.23\nzation 3.2749 3.56 012 2.540 219\n0* 465 39\n(1.093) (2.0 (2.9 (1.478 (0.6\n97) 85) ) 16)\nlnpopu 6.1409 1.44 - 2.620 0.77\n1*** 655 2.89 45*** 997\n074\n(0.182) (1.7 (2.2 (0.554 (0.5\n00) 33) ) 82)\n_cons - 25.5 53.4 - 0.29\n62.390 881 289 11.11 022\n20*** 8 3 211**\n(2.582) (33. (42. (3.028 (16.\n980) 522) ) 722)\nCountry Y Y Y Y Y\nfixed\neffect\nYear Y Y Y Y Y\nfixed\neffect\nN 100 125 125 150 300\nadj. R2 0.999 0.98 0.99 0.998 0.99\n1 1 0\nNote: Robust standard errors in parentheses clustering at the National Level, * p<0.1, **\np<0.05, *** p<0.01\nComment 3.5: The prediction of ACit may contain errors. The statement regarding the\nestimation of logarithmic values suggests that the authors may have overlooked the constant\nterm and fixed effects in Equation (11). Furthermore, using a model with year fixed effects for\nfuture predictions is inappropriate, as specific values for future year fixed effects cannot be\nobtained. An alternative approach would involve incorporating a time trend term.\nResponse: We would like to clarify a few points: (1) Constant term inclusion. We have indeed\nincluded a constant term in our logarithmic estimation. Our prediction of AC stock accounts\nfor this constant, so we did not overlook it during the modelling process. (2) We have explored\nincorporating a time trend as suggested. We separately incorporated a time trend into the\noriginal OLS model and further added a time trend to the OLS model that includes the\npopulation variable. However, this approach led to extremely rapid growth in AC stock—far\nexceeding historical patterns. For instance, under the time trend model, some countries, such\nas the United States and China, would see AC stock double in only a few years, pushing the per\ncapita ownership rate beyond 100% by 2030. Such results are inconsistent with observed real-\nworld limits and historical trajectories. By contrast, year-fixed effects allow us to capture\nabrupt policy changes, economic crises, or other exogenous shocks that may not follow a\nsimple linear (or polynomial) trend. We recognize that projecting these year-fixed effects into\nthe future poses challenges, as the exact values of future shocks or policy changes are not\ndirectly observable. However, given data limitations, we believe that year-fixed effects better\nreflect historical variations and provide a more realistic representation of fluctuations in the\nAC stock.\nComment 3.6: While the paper touches on the contribution of refrigerants (e.g., HFCs, CFCs)\nto global warming, it lacks a detailed discussion on the current status of refrigerant\nmanagement in various regions. Expanding this discussion to include the economic and\ntechnological challenges of transitioning to low-GWP refrigerants, especially in developing\ncountries, would be beneficial. A comparative analysis of global policies and strategies for\naddressing refrigerant leakage would add significant value.\nResponse: We have added some text in the Results section to discuss the low-GWP refrigerant\ntransition both in developed and developing regions. The text reads as follows:\nLINE 356-364 IN THE REVISED MANUSCRIPT: In contrast, the non-CO emission intensity\n2\neffect results in increased non-CO emissions across all scenarios, indicating that the\n2\ncurrent low-GWP refrigerant transition is progressing slowly. In developed regions\nwith a high adoption of efficient, low-GWP refrigerants, such as the United States, the\nnon-CO emission intensity effect contributes to over 90% of the emission reductions\n2\nunder low-emission scenarios. However, in regions with rapid growth in AC demand\nthat primarily use high-GWP refrigerants, such as China, it contributes to a 375.8%\nincrease of non-CO emissions even under the SSP119 scenario, approximately 1.9\n2\ntimes greater than the energy use effect.\nWe have added some text in the Discussion and conclusion section as follows:\nLINE 531-537 IN THE REVISED MANUSCRIPT: Similarly, we also investigate the emission\nreduction potential of transitioning from HFC134a and HFC143a refrigerants to low-\nGWP alternatives such as HFC32. The most stringent scenario (SSP119) projects that\nHFC32 will comprise 25% of all refrigerants by 2050, resulting in 84% lower emissions\ncompared to the SSP585 scenario. Thus, when costs are favourable, adopting more\nefficient and low-GWP refrigerants, such as R-290 and R-1234yf, in place of HFC134a\ncould contribute up to 90-99% emission reduction 54.\nComment 3.7: Although the paper acknowledges the role of behavioral changes in reducing\nAC usage, it does not explore how these changes (e.g., energy-saving practices, pricing\nstrategies) could impact energy consumption. Given the potential influence of policy\ninterventions on consumer behavior, an analysis of how such changes might affect energy\nconsumption—particularly in regions with high cooling demands—would be insightful..\nResponse: We appreciate your suggestion to further analyse how energy-saving practices and\npolicy interventions, such as pricing strategies, could influence consumer behaviour and\nenergy demand, particularly in regions with high cooling needs. However, a quantitative\nanalysis of consumer behaviours may be beyond the scope of current studies. We have added\nsome discussions to talk about how the behaviour affect energy consumption. The text reads\nas follows:\nLINE 546-555 IN THE REVISED MANUSCRIPT: Behavioural changes driven by policy\ninterventions could play a critical role in achieving short-term reductions in AC energy\nconsumption 59. For example, dynamic pricing strategies that raise electricity costs\nduring peak hours and temperature adjustment policies that increase cooling setpoints\nduring peak demand periods can incentivize users to shift their cooling demand to off-\npeak times, potentially reducing peak electricity loads by up to 20% 60. Additionally,\nenvironmental education campaigns can promote energy-saving habits among the\npublic, such as setting thermostats at optimal levels or adopting passive cooling\nstrategies 61, bringing about 6%-20% energy-savings from AC use 62.\nComment 3.8: The paper does not consider how seasonal temperature fluctuations might\naffect cooling demand. In regions with significant seasonal variability, such as South Asia,\ncooling demand may surge during specific months, stressing energy infrastructure. An analysis\nof this seasonal variability and its implications for energy infrastructure and peak load\nmanagement would be valuable.\nResponse: We appreciate your suggestion and agree that analysing seasonal or monthly\nvariability and its implications for energy infrastructure and peak load management would\nprovide additional insights. Our analysis employs a population-weighted CDD approach, which\nconsiders annual variations in population distribution and climate patterns. While the\ncalculation primarily focuses on annual CDD, it captures key seasonal characteristics of cooling\ndemand (the CDD value is 0 when daily mean temperature is below 18 degrees), especially in\nregions where the majority of cooling needs occur during a specific period. Moreover, GCAM,\nMAGICC and the econometric analysis we used operate with a five-year or one-year time step,\nwhich limits its ability to capture intra-annual variations directly. While the monthly or daily\ncooling energy demand is not a focus of our paper, we recognize that regions with significant\nseasonal variability may face substantial additional challenges in managing energy\ninfrastructure and peak loads during periods of high cooling demand, such as insufficient\npower supply and associated high carbon emissions caused by flexible peak load regulation of\ncoal-fired power. We have added some text in the Limitation section addressing these\nchallenges as follows:\nLINE 1105-1114 IN THE REVISED MANUSCRIPT: Lastly, our assessment does not\nincorporate high-resolution cooling loads analysis, such as monthly, daily or hourly\nseries, which are crucial for understanding peak energy demand and infrastructure\nstress during periods of extreme temperatures. Regions with considerable seasonal\nvariability, such as South Asia, may experience surges in cooling demand during\nspecific months, placing substantial stress on energy infrastructure and necessitating\neffective peak load management strategies. Such improvements would provide deeper\ninsights into the dynamics of cooling demand, inform more effective energy\ninfrastructure planning and interventions aimed at mitigating climate impacts while\naddressing the diverse cooling needs across regions.\nComment 3.9: Figure S5 suggests that CCS technology plays a crucial role in mitigating GHG\nemissions, particularly under the SSP119 and SSP126 scenarios. However, the uncertainty\nsurrounding CCS deployment and its technological and economic challenges necessitate an\nexploration of how sensitive the results are to the assumption of widespread CCS adoption.\nWhat alternative mitigation strategies could be considered if CCS deployment is delayed or\nfails?\nResponse: First, we have updated the scenario assumption table in the Methods section to\ninclude detailed descriptions of CCS deployment assumptions for each SSP-RCP scenario. The\ntable is shown in previous response. Second, we added two paragraphs in the Supplementary\nInformation S5 to give a detailed description of differences in power generation mix across the\nfive scenarios, how to implement CCS technology in GCAM, the sensitivity analysis of CCS costs\nbased on previous studies, and the discussion of alternative mitigation strategies considered\nif CCS deployment is delayed or fails. The new paragraphs are shown below as follows:\nLINE 225-277 IN THE REVISED SI: Here, we present the energy mix in global power\ngeneration from 2015 to 2050 across scenarios, shown in Supplementary Figure 4.\nGiven that the use of air-conditioning primarily relies on electricity, understanding the\nenergy mix in global power generation under different scenarios is crucial for assessing\nAC-related emissions. Under the baseline scenario, fossil fuel still dominates global\npower generation. Although non-fossil energy has seen some development, the\nmedium mitigation targets and the pace of renewable energy adoption result in only\na modest decline in the share of fossil fuels, decreasing from 69.9% in 2015 to 65.1%\nin 2050. Similarly, with the deployment of carbon capture and storage (CCS)\ntechnologies beginning in 2025, fossil fuel-based power generation with CCS accounts\nfor only 7.6% of total fossil fuel electricity generation and 5.0% of total electricity\ngeneration by 2050. Under the high-emission scenarios (SSP370 and SSP585), the\nabsence of carbon removal technologies and mitigation targets leads to a continued\nreliance on fossil fuel-based power generation, which accounts for 72.7% and 75.2%\nof total electricity generation by 2050, respectively. Consequently, the lack of CCS\ntechnology to offset emissions from fossil fuel-based power generation in high-\nemission scenarios results in grid emission factors that are higher than those in the\nSSP245 scenario. In contrast, the SSP1 scenario emphasizes sustainable development\nand large-scale adoption of renewable energy, reducing fossil fuel-based power\ngeneration from its dominant role as the primary global electricity source. The fossil\nfuel-based power generation accounts for 47.5% under the SSP126 scenario and 35.0%\nunder the SSP119 scenario, respectively. Additionally, stringent mitigation policies\ndrive higher carbon prices and greater adoption of CCS technologies to meet emission\nreduction targets. As a result, by 2050, the share of CCS-equipped fossil fuel power\ngeneration increases to 10.2% under the SSP126 scenario and 24.2% under the SSP119\nscenario, respectively.\nThe adoption of CCS plays a pivotal role in achieving the ambitious mitigation targets\nunder SSP119 and SSP126 scenarios. In GCAM, the deployment of CCS technology is\nsimulated using supply curves, modelled as an add-on technology for fossil fuel-based\npower generation, including coal, refined liquids, and natural gas plants 65,66. CO2\nstorage reservoirs are disaggregated by region, type and grade and, as with other\nresources, can be depleted over time. GCAM includes conventional and CCS-equipped\npower plants, allowing them to compete based on cost and performance. The\ndeployment of CCS in the power sector is driven by the levelized cost of electricity,\nincluding capital cost, O&M cost, fuel cost and carbon price. Studies have conducted a\nsensitivity analysis of CCS in GCAM, indicating that technology competition in the\npower sector is primarily driven by fuel costs 66. When a carbon pricing scheme is\nintroduced, the lower carbon intensity of natural gas makes it more competitive than\nother fossil fuel-based power under equivalent conditions. At sufficiently high carbon\nprices, CCS technology also becomes economically competitive. Research has also\nexplored strategies for adjusting mitigation policies to achieve established targets in\nscenarios where CCS technology is limited in deployment, delayed, or entirely absent\n67. For example, in scenarios without CCS and nuclear power, achieving the RCP2.6\ntarget significantly increases capital investment requirements for the power sector\nacross most subregions of Latin America and the Caribbean, with a particular emphasis\non hydropower and wind energy. However, the large-scale deployment of renewable\nenergy also raises public concerns about the reliability and intermittency of power\nsupply. Additionally, Fuhrman, et al. 68,69 investigate the emission reduction potential\nof other negative emission technology portfolios, such as afforestation and direct air\ncapture (DAC) technology. While large-scale deployment of DAC can substitute for\nBECCS and afforestation in achieving negative emissions, it also significantly increases\nwater demand. The expansion of afforestation comes at the expense of grasslands,\npastures, and agricultural lands, with the largest global increase in forest area\nprojected to occur in the latter half of this century. While afforestation can serve as a\ncomplementary measure to CCS technologies, it is unlikely to substitute for them fully.\nSupplementary Figure 4. Energy mix in global power generation (Unit: EJ). Rooftop\nphotovoltaic is included in Solar. Hydrogen cogen represents hydrogen cogeneration\ntechnology. CCS technologies are applied within biomass, coal, gas and refined liquid\nplants in GCAM model. The deployment of CCS technology starts from 2025 under\nSSP119, SSP126 and SSP245 scenarios. In contrast, there would be no deployment of\nCCS technology under SSP370 and SSP585 scenarios.\nComment 3.10: S8 mentions that AC stocks were not significant predictors of CDDs in the\nregression model, which seems counterintuitive. The availability of AC stocks should logically\ninfluence cooling demand. Please clarify the assumptions made in the model and address the\naccuracy of these findings.\nResponse: Our econometric model was designed to estimate AC stocks as a function of key\npredictors, including CDD, CDD2, income, urbanization and population. The primary purpose\nof this model is to evaluate how these variables influence the adoption of AC units across\nregions and over time. To ensure the reliability of our model, we conducted additional tests to\nassess whether AC stocks and CDDs might influence each other (i.e., whether they exhibit\nendogeneity). To evaluate whether AC stocks influence CDDs or vice versa, we tested\nalternative regressions where CDD and CDD2 were treated as dependent variables, and AC\nstocks were included as predictors. These tests aimed to determine whether CDDs could be\nsignificantly predicted by AC stocks, which would suggest a reverse causality issue or\nendogeneity in our primary model. The results showed that AC stocks were not significant\npredictors of CDDs, indicating that the adoption of AC units does not directly influence climatic\nvariables such as temperature or CDDs. This finding supports the robustness of our primary\nmodel and suggests that the observed relationship between AC stocks and CDDs is\nunidirectional, with CDDs driving AC adoption rather than the reverse. To clarify these points,\nwe have updated the relevant sections in the Supplementary Information as follows:\nLINE 313-321 IN THE REVISED SI: To ensure the reliability of our model, we conducted\nadditional regressions to test whether AC stocks could serve as significant predictors\nof CDDs (see Supplementary Table 10). These tests indicated no significant causal\nrelationship between AC stocks and CDDs, confirming that CDDs drive AC stock but are\nnot directly influenced by AC availability. This supports the robustness of our primary\nmodel, where there is not a reversed causality between AC stocks and CDDs when we\nused CDD as a predictor of AC stock.\nSupplementary Table 10. Coefficient robust tests using CDD and CDD2.\nlnCDD (lnCDD)2\nlnAC 0.05085 1.06442\n(0.055) (0.657)\nlnincome -0.07983* -1.09129*\n(0.046) (0.625)\nlnurbanization -0.06278 -0.05215\n(0.150) (1.994)\nlnpop -0.72681*** -7.61677***\n(0.106) (1.350)\n_cons 16.79310*** 148.77275***\n(1.256) (15.483)\nCountry fixed effect Yes Yes\nYear fixed effect Yes Yes\nN 800 800\nadj. R2 0.997 0.997\nNote: Variables of CDD, (CDD)2, Income and Urbanization are shown in logarithmic\nform. Robust standard errors in parentheses clustering at the National Level, * p<0.1,\n** p<0.05, *** p<0.01\nComment 3.11: The fixed base temperature for CDDs (e.g., 18°C) may not remain static as\nglobal warming progresses. For example, people might currently turn on air conditioning\nwhen temperatures exceed 24°C, which may increase to 25°C ten years later. Factors such as\nbodily adaptation could lead to an increase in the base temperature for CDDs. This time-varying nature should be considered in the “Limitation” section.\nResponse: We agree that assuming a static base temperature (e.g., 18°C) does not fully capture\nhow behavioural and physiological adaptations to warming conditions may shift the threshold\nfor air-conditioning use over time. The text in the Limitation section has been revised as\nfollows:\nLINE 1085-1094 IN THE REVISED MANUSCRIPT: Firstly, our analysis adopted a fixed base\ntemperature for CDD calculations across regions. While this approach allows for\nconsistent comparisons across regions and scenarios, it does not account for the\npotential evolution of comfort thresholds over time. As global temperatures rise,\nbehavioural and physiological adaptations may lead individuals to tolerate higher\nindoor temperatures before resorting to air conditioning, effectively increasing the\nbase temperature that triggers cooling demand. To provide additional insight and\nrobustness analysis into the influence of this assumption, we have included an\nuncertainty analysis in the Supplementary Information (SI), illustrating how variations\nin the chosen base temperature affect CDD calculations.\nComment 3.12: In equation (12), ACit appears on both sides of the equation. The natural\nlogarithm form of ACit needs to be denoted with a distinct symbol (e.g., lnACit) to distinguish\nit from the level value of ACit.\nResponse: In response, we have revised the notation in the manuscript as follows:\nLINE 1006-1021 IN THE REVISED MANUSCRIPT:\nlnAC  lnCDD (lnCDD )2\nit 0 1 it 2 it\n(12)\n+ lnincome  lnurbanization  ln pop \n3 it 4 it 5 it i t it\nwhere i and t represent the region and year, respectively; dependent variable ACit is\nthe natural logarithm of air condition demand; income is per capita GDP;\nit\nurbanization is urbanization rate across all regions; pop is population of GCAM\nit i,t\nregion i in period t. To avoid the effect of different scales of variables, we add 1 to all\nthe variables and then take the natural logarithm. 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International Journal of Greenhouse\nGas Control 57, 34-41 (2017). https://doi.org/10.1016/j.ijggc.2016.11.026\nimpacts on renewable resources in Latin America and the Caribbean. Nat\nCommun 12, 1276 (2021). https://doi.org/10.1038/s41467-021-21502-y\ntechnologies in a +1.5 °C future. Nature Climate Change 10, 920-927 (2020).\nhttps://doi.org/10.1038/s41558-020-0876-z\ntechnologies in the shared socioeconomic pathways towards+ 1.5° C and+ 2° C\nfutures. Environmental Research Letters 16, 114012 (2021).\nDear reviewers,\nWe thank the reviewers for their further valuable feedback on our manuscript. We have\ncarefully considered the remaining concerns raised during this latest review round and have\nimplemented targeted revisions to the manuscript to specifically address them. Please find\nour detailed point-by-point responses to the comments from this latest review round below.\nThe original comments are in black, our responses are given in blue, and the modifications\nmade to the manuscript are presented in italic and indented format.\nReviewer #1\nGeneral Comment: Thank you for the careful consideration of my comments, the dedicated\nreplies and the successful implementation of the suggested changes. I have included a list of\nminor suggestions which I think would help to fine-tune the last details prior to publication.\nResponse: Thank you very much for your encouraging remarks and for taking the time to\nprovide an additional set of minor suggestions. We appreciate your recognition of the revisions\nwe have already implemented and value your commitment to helping us fine‑tune the\nmanuscript before publication. We have carefully reviewed each of your remaining comments\nand have incorporated all the suggested edits. Please see the marked‑up manuscript and the\ndetailed point‑by‑point response that follows this letter for confirmation of each adjustment.\nComment 1.1.: Line 172: In my view, the word “inconsistency” is confusing and should be\navoided in this context. The fact that there are regional disparities of cooling demand across\nthe world is not an inconsistency, it simply describes an uneven distribution across regions.\nResponse: Thank you for your valuable comment. We have revised the text to avoid this word\nand instead describe the regional disparities in cooling demand as an \"uneven distribution\nacross regions\" as you suggested.\nLINE 168-170 IN THE REVISED MANUSCRIPT: In contrast, by 2050, the expected rise in\ncooling demand under high emission scenarios does not emerge uniformly across all\nregions, suggesting an uneven distribution across regions in the mid-term projections.\nComment 1.2.: Line 206: Typo in the legends of Figure 2c) and d): The “c” of Africa is missing.\nResponse: Thank you for your careful reading. We have revised the Figure 2 as follows:\nAC efficiency and cooling efficiency as they are not intuitive or straightforward. Perhaps even\nthe short formula provided in the SI would help the reader.\nResponse: Thank you for your insightful comment regarding the terms “AC efficiency effect”\nand “cooling efficiency effect”. Following your suggestion, we have renamed the AC efficiency\neffect to “AC penetration intensity effect” in the revised manuscript to better reflect its\nmeaning (i.e., changes in AC stock relative to CDD) and to clearly distinguish it from the\n“Cooling Efficiency Effect”. And we have added a sentence in the main text to briefly explain\nthe concepts behind these two factors, shown as follows:\nLINE 254-257 IN THE REVISED MANUSCRIPT: Notably, we introduced two crucial\nfactors: the AC penetration intensity effect, which captures changes in the number of\ninstalled AC units relative to the cooling demand, and the cooling efficiency effect,\nwhich represents the energy consumed per unit of cooling demand.\nComment 1.4.: Line 264: I suspect the last part of the sentence contains a typo: “in these\nregions are expected (to?) exceed our current projections”\nResponse: Thank you for your careful reading. Revised.\nComment 1.5.: Line 282: By “origin” do you mean the dots? It is not clear what the “origin”\nrefers to, please clarify.\nResponse: Thank you for highlighting this unclear terminology. Yes, we were referring to the\ncoloured dots in Figure C.\nComment 1.6.: Line 353-355: The choice of the past tense such as “has caused” or “have\neffectively driven” imply that they happened in the past, but figures mentioned refer to\n2\nprojections. I would suggest “would cause”, “would effectively drive” or something along\nthose lines.\nResponse: Thank you for pointing out the incorrect verb tense usage. We agree with the\nsuggestion and have revised the manuscript accordingly, changing the tenses to conditional\nforms.\nComment 1.7.: Line 865: Typo in the word “combing”. I guess it should read “combining”.\nResponse: Thank you very much for your careful reading. Revised.\nComment 1.8.: Concerning the base temperature choice for the calculation of the CDDs and\ndisregarding the relative humidity, I can understand the argument of making global CDD\naggregation and comparison more uniform and less complex. I also think it is understandable\nas the authors point out that “The current compromise strikes a balance between the need\nfor regional specificity in the heat index approach and the practicality of calculating CDDs using\na standardized baseline temperature.” However, while acknowledging the authors’ effort the\nsensitivity analysis, it cannot be concluded that this analysis “the results support the\nrobustness of using 18°C as a uniform base temperature across all regions” as the authors\nhighlight, when the results in S9 show that “Further increases in the baseline temperature to\n22°C, 24°C, and 26°C lead to reductions of 33.0%, 46.3%, and 57.5%, respectively.” A sensitivity\nanalysis generally shows robustness when there are relatively small changes in the output\nvariable when introducing large changes in the input variable. I don’t think a global CDD\nreduction of up to 57.5% can be considered small in this context. I would therefore suggest\nthat the authors remove the conclusion of robustness drawn from the sensitivity analysis.\nResponse: Thank you for your insightful comment regarding our interpretation of the base\ntemperature sensitivity analysis. We agree with your assessment that the significant variations\nobserved in CDDs (up to 57.5% reduction) when altering the base temperature demonstrate\nsensitivity to this parameter, rather than robustness. Our intention was to show that while\nabsolute CDD values are sensitive, the use of a consistent baseline like 18°C allows for\nmeaningful comparisons of trends and relative changes across regions, and to quantify the\nmagnitude of the effect of this choice. Based on your comments, we have revised the\nmanuscript to remove the claims of “robustness” derived from this specific sensitivity analysis\nin the Methods part and the Limitations part. Specifically:\nLINE 927-932 IN THE REVISED MANUSCRIPT: Furthermore, we conducted a sensitivity\nanalysis using various base temperatures (see Supplementary Figure 6) to quantify the\nimpact of this parameter choice. While the results demonstrate considerable\nsensitivity of absolute CDD magnitudes to the selected base temperature, employing\na consistent 18°C base temperature is crucial for maintaining comparability when\nassessing relative CDD trends across diverse regions and scenarios.\nLINE 1118-1121 IN THE REVISED MANUSCRIPT: To provide additional insight and\ncharacterize the uncertainty associated with this assumption, we performed a\nsensitivity analysis using different base temperatures, illustrating how variations in\nthis parameter significantly affect CDD calculations.\nMoreover, we have added sentences in the SUPPLEMENTARY INFORMATION to further clarify\nthe reasoning behind this methodological choice as follows:\nLINE 312-324 IN THE REVISED SUPPLEMENTARY INFORMATION: Given the important\nimpact of base temperature on CDDs, the choice of 18°C as the base temperature in\n3\nour main analysis is justified by its widespread adoption in existing literature and its\nrepresentation of a relatively conservative estimate of cooling demand. By using a\nlower threshold like 18°C, we capture conditions across a broader climatic spectrum\nwhere cooling systems might be activated, whether due to ambient temperature,\nhumidity, building characteristics, or specific occupant needs. Moreover, this lower\nthreshold retains greater sensitivity to cooling requirements in regions with\ntraditionally lower cooling demand. As our analysis shows, the impact of base\ntemperature choice is most pronounced in these areas; using 18°C ensures their\npotential future cooling energy consumption under climate change scenarios is not\nprematurely underestimated by the choice of metric. Finally, 18°C serves as a widely\nutilized benchmark in numerous energy demand and climate impact studies,\nfacilitating comparability and consistency with a large body of existing literature.\nTherefore, while acknowledging the variability associated with base temperature\nselection, adopting 18°C allows for a more encompassing initial assessment of global\nand regional cooling degree days.\nComment 1.9.: Line 963: “per unit floorspace” is repeated.\nResponse: Thank you very much for your careful reading. Revised.\nGeneral Comment: The authors have made a thorough and extensive effort to improve the\npaper and respond to the reviewers’ comments. The introduction, methods, analysis and\nresults are now much more clearly motivated and explained. The presentation of the findings\nis also clear and easy to follow. I congratulate the authors on these efforts, which have led to\na much-improved paper.\nI am happy with most of the changes and responses to my comments. The information and\nclarifications that have been added are very helpful. But I have one remaining key concern\nregarding whether the additional increase in temperature from AC use – which is central to\nthe paper – is primarily due to climate change or due to other factors such as income and\npopulation growth. If the latter, it is misleading to refer to the increase in temperature caused\nby future increases in AC use as a “climate feedback loop”. Since this is the core contribution\nof the paper, this concern needs to be addressed before I can recommend the paper for\npublishing. In addition to this I have one smaller remaining comment, which has to do with\nthe relationship between AC energy consumption and AC stocks.\nResponse: Thank you very much for your positive assessment of our revised manuscript and\nfor acknowledging the improvements in motivation, methodology, and presentation. Your\ncomments have been invaluable in sharpening the focus and clarity of our work.\nTo address the reviewer’s remaining key concern regarding whether the observed\ntemperature increase from AC use should be characterized as a “climate feedback loop”, we\nhave revised the original terminology from “climate feedback loop” to “positive feedback\nbetween climate warming and AC use”, conducted additional counterfactual scenario\nsimulations using SSP245 as a baseline, and expanded our Discussion section to explicitly\naddress the interrelationship between climate change and socioeconomic development.\nPlease see the marked‑up manuscript and the detailed point‑by‑point response that follows\nthis letter for confirmation of each adjustment.\nComment 2.1: The impact of climate change on increasing AC use - the “climate feedback\nloop”: The decomposition analysis of AC stock and energy consumption, discussed in the main\ntext and detailed in the Supplementary Information (S5), shows that the income effect is the\ndominant factor, followed by population and AC efficiency/cooling efficiency. In other words,\na significant component of the increase in AC use by 2050 – and thus also AC emissions and\nadditional temperature increase – is explained by increasing income (per capita) and\npopulation by 2050. This increase has nothing to do with climate change because it would\nhappen even if there was no further climate change. It merely reflects increasing living\nstandards (for more people). That is, even in a scenario with no climate change, increasing\nincome and population would lead to an increase in AC use (along with an increase in many\nother technologies that make life more comfortable). Because the authors have not actually\nisolated the impact from increased temperature (or from increased CDD, which is derived\nfrom temperature) in the decomposition, it is difficult to compare this with the impacts from\nincreased income and population. The reason why I suggested decomposing the increases in\nAC stocks and energy consumption into temperature, income, population (and potentially\nother factors) in the original review (comment 2.11) was precisely to tell these key factors\napart.\nBy all accounts, it is only the additional warming from the increase in AC use caused by\nincreasing temperatures that can be termed a “climate feedback loop”. If the increase in AC\nuse is primarily, as the text and decomposition indicate, due to increased income and\npopulation, not due to increased cooling demand, it is misleading to refer to this as a “climate\nfeedback loop”. Concluding statements, such as “using air-conditioning as an adaptation\nstrategy exacerbates climate change, causing an additional 0.03°C-0.12°C increase in global-\nmean temperature by 2050” are then misleading. It appears incorrect to refer to an increase\nin living standards and associated technologies as ‘adaptation’ when it would take place\nirrespective of climate change. A large reason why so much of the increase in AC use is seen\nin China, for example, is because income growth in China across scenarios is very high. It is not\nbecause temperatures and thus adaptation needs in China increase faster than elsewhere.\nWhile the text explains, for example, that “the extent to which global cooling demand will\naffect the AC stocks is dependent on economic development” it does not sufficiently tease\napart the effects from economic development and climate change.\nThe effect from an increasing temperature will depend on the scenario. In a scenario with\nhigher population and income, each degree of warming will lead to a larger increase in AC use,\nbecause more people can afford it (and because there are more people). So population and\nincome are still relevant to the analysis of the consequences of adaptation. But one cannot\nattribute the entire increase in AC use and corresponding emissions to a climate adaptation\nfeedback loop - only the part that is caused by increased temperature can (and this may differ\nbetween scenarios).\nI can see two ways to resolve this issue. The first option is that the authors isolate the impact\nof climate change from the impacts of income/population growth on the temperature\nincrease from increased AC use and report this. This – which would amount to some fraction\nof the 0.06°C reported in the abstract and paper – could then be referred to as the “climate\nfeedback loop”. The remaining component would represent the temperature increase from\nincreased AC use due to increasing living standards. While the latter is independent of climate\nchange (it would happen even without climate change), the former is a consequence of\nclimate change (and would happen without increasing income and population). The\ninteractions between income, population, climate change, and other technological changes\nassumed, and their varied contributions to the temperature increase from increased AC use,\ncan then be discussed more clearly with respect to the section on inequality and solutions.\nThe other option is that the authors change the framing of the paper and clarify that the main\ncause of increased AC use in the future is not climate change, but income and population\ngrowth. The projected emissions increases from increasing AC use in the different scenarios is\nstill relevant, but it should not be referred to primarily as adaptation and as a “climate\nfeedback loop”, as much of it would take place without any further increases in temperature.\nIf this option is chosen, the use of the term “feedback loop” and mentions of conflicts or trade-\noffs between adaptation and mitigation (e.g. line 66, lines 76-78, lines 79-81) need to be\nadjusted and modified accordingly. For example, the sentence “our study provides\nquantitative global insights on the impact of AC use with the changing climate” could be\nmodified to “our study provides quantitative global insights on the impact of AC use with the\nchanging climate, income, and population.” Given that the quantification of the feedback loop\nis currently a key aim of the paper (e.g. lines 98-99), this reframing might reduce the overall\nimpact and strength of the paper’s findings. An analysis that separates out the climate impact\nfrom income and population impacts might indicate that the quantitative estimates of the\nclimate feedback offered in the paper are overestimates, not underestimates, as currently\nsuggested in the conclusion (line 514 and 523).\nResponse: We sincerely thank the reviewer for their critical feedback regarding the attribution\nof increased AC usage and its associated warming impact. We acknowledge the reviewer's\nvalid concern about whether the additional increase in temperature from AC use should be\nprimarily attributed to climate change or to other factors such as income and population\ngrowth.\nWhile we deeply appreciate these two thoughtful suggestions, we found that neither option\naligns perfectly with the core objectives and inherent methodological framework of our study.\nFirstly, performing a rigorous, quantitative isolation of the climate-change-specific impact\nacross all scenarios and regions presents substantial challenges within the architecture of\ncurrent IAMs like the one used here. Such models are designed primarily to explore the\ncomplex interactions between coupled climate-socioeconomic pathways (like the SSP-RCP\nframework). A precise attribution would necessitate a level of granular analysis akin to\ndedicated ‘detection and attribution’ studies often undertaken by the climate modelling\ncommunity (e.g., within IPCC assessments), operating at a different scale and with different\nmethodologies than typically feasible within a global IAM study based on scenario simulation\n1-3. Secondly, fundamentally shifting the paper's framing away from the feedback concept\nwould diverge from our primary research question. Our core objective was specifically to\ninvestigate and quantify the cycle wherein global warming necessitates adaptation (e.g.,\nincreased AC use), which in turn exacerbates the initial problem through associated emissions.\nWhile our decomposition analysis and new sensitivity analysis clearly show that\nsocioeconomic factors, especially income effect, are the dominant determinants of the scale\nof AC adoption and energy use, the initial trigger within the specific cycle we aimed to quantify\nis the increasing cooling demand driven by rising temperatures (originating from the\nanthropogenically altered climate baseline, as IPCC AR6 confirms). Therefore, while\nacknowledging the reviewer’s valid point about the dominant role of socioeconomics in the\noverall magnitude, we believe completely removing the ‘feedback loop’ or ‘adaptation\nstrategy’ framing neglects the crucial climate-driven initiation aspect of the cycle we intended\nto study.\nFurthermore, it is pertinent to emphasize that the phenomenon we term the ‘vicious cycle’—\nwhere warming drives AC use which causes further warming—is inherently a product of the\nconfluence of multiple factors: the availability of cooling technology (air conditioning),\nsocioeconomic development patterns (including power structure, AC adoption, usage\nintensity and so on), and the underlying climate factors (driving the initial demand). These\nelements are deeply intertwined in creating the dynamic we investigate. If the primary\nconcern is the rigorous attribution of the baseline warming itself, the IPCC AR6 has already\nprovided extensive and robust analysis establishing its predominantly anthropogenic origin 4.\nAR6 estimates the total human-caused global surface temperature increase from 1850–1900\nto 2010–2019 at 1.07°C [likely range 0.8°C–1.3°C], contrasting sharply with negligible\ncontributions from natural drivers [±0.1°C] and internal variability [±0.2°C] over the same\nperiod. Consequently, the contemporary climate baseline, including current CDDs, is itself\nlargely a product of these accumulated historical anthropogenic emissions. Cleanly\ndisentangling the effect of additional future warming from the impact of socioeconomic\ndevelopment operating within this already altered climate system therefore poses a significant\nmethodological challenge.\nTherefore, we propose an alternative revision strategy. Firstly, we have performed an\nadditional counterfactual scenario analysis using the SSP245 framework as a sensitivity\nanalysis. We simulated AC energy consumption and resulting emissions by fixing CDD at 2010\nlevels under the SSP245 scenario. By comparing this ‘counterfactual scenario’ simulation with\nthe original SSP245 results (which include evolving CDDs), we can estimate the contribution\nof changing climate conditions versus socioeconomic development to the projected increases\nunder this specific scenario. This analysis and its results have been added to the\nSupplementary Information as follows:\nLINE 353-363 IN THE REVISED SUPPLEMENTARY INFORMATION: Furthermore, we\nconstructed a counterfactual scenario under the SSP245 framework, in which climate\nchange impacts are excluded by fixing CDD at 2010 levels, to investigate the effect of\nCDD on increasing AC use, shown in Supplementary Table 12. We find that over time,\nthe consequences of neglecting CDD effects gradually become more pronounced. For\ninstance, in 2020, the changes in global AC energy use and global CO emissions in the\n2\ncounterfactual scenario are relatively small, with the only significant change being a\n5.6% reduction in global AC non-CO emissions. However, by 2050, this gap becomes\n2\nhard to overlook. In the counterfactual scenario, global AC energy use, CO emissions\n2\nand non-CO emissions are projected to be 16.4%, 18.1% and 15.7% lower than in the\nbrings about 8.4% losses in global cumulated AC GHG emissions.\nSupplementary Table 1. Results of the counterfactual scenario compared to the SSP245 scenario.\nGlobal AC energy Global AC CO2 Global AC non- Global cumulated AC GHG emissions\nuse use CO2 use during 1990-2050\nResults Compare Results Compare Results Compare Results Compare\n(TWh) d to the (Mt) d to the (Mt) d to the (Gt) d to the\nSSP245 SSP245 SSP245 SSP245\nscenario scenario scenario scenario\n2020 2455.2 0.6% 1324.2 -0.4% 469.6 -5.6%\n2025 2746.2 -3.9% 1423.3 -5.3% 778.1 -7.4%\n2030 3010.7 -7.2% 1487.3 -8.7% 1242.4 -8.1%\n2035 3228.3 -10.2% 1541.3 -11.5% 1493.2 -10.4%\n2040 3411.8 -12.1% 1527.5 -13.7% 1678.4 -11.7%\n2045 3595.5 -14.3% 1511.0 -16.0% 1705.9 -13.3%\n2050 3757.3 -16.4% 1492.3 -18.1% 1684.2 -15.7% 117.6 -8.4%\nSecondly, we have expanded our Discussion and conclusions section to explicitly acknowledge\nthat the current vicious cycle cannot be decoupled from broader socioeconomic development\nconsiderations. Specifically:\nLINE 515-527 IN THE REVISED MANUSCRIPT: It is essential to recognize that this\nfeedback mechanism is inherently coupled with socioeconomic development\ntrajectories. As demonstrated by our decomposition analysis, income effect serves as\nthe dominant driver, with climate change acting as both an initiating factor and\namplifying force in this cycle. This interrelationship aligns with IPCC findings that\ncontemporary warming is predominantly anthropogenic 4. The interaction between\nrising incomes and climate change creates particular concern. As global prosperity\nincreases, more households can afford cooling technologies, magnifying the emissions\nimpact of each incremental degree of warming. This dynamic poses a profound\nchallenge: the very socioeconomic development that enhances adaptive capacity\nsimultaneously intensifies climate impacts and mitigation burdens through increased\nenergy consumption. Without immediate intervention, this self-reinforcing cycle\nthreatens to undermine climate stabilization efforts and exacerbate warming beyond\ncurrent projections.\nLINE 552-554 IN THE REVISED MANUSCRIPT: Therefore, our study shows that breaking\nthis cycle demands immediate implementation of targeted mitigation measures,\nparticularly accelerated power sector decarbonization and widespread adoption of\nlow-GWP refrigerants.\nLINE 557-562 IN THE REVISED MANUSCRIPT: A high share of renewable energy systems\ncould substantially reduce or eliminate the emissions associated with increased AC-\nrelated electricity. Similarly, transitioning to low-GWP refrigerants can the direct\nclimate impact of AC systems themselves. For example, our results estimate low-GWP\nrefrigerants, such as HFC32, would comprise 25% of all refrigerants by 2050 under the\nSSP119 scenario, resulting in 84% lower emissions compared to the SSP585 scenario.\nThirdly, to address the reviewer’s concern, we have revised the original terminology from\n‘climate feedback loop’ to ‘positive feedback between climate warming and AC use’.\nSpecifically:\nLINE 35-36 IN THE REVISED MANUSCRIPT: Here, we quantify such a positive feedback\nbetween climate warming and AC use under the five SSP-RCP scenarios.\nLINE 76-77 IN THE REVISED MANUSCRIPT: Thus, increasing the use of AC as a main\nadaptation strategy to global warming can, in turn, induce additional warming effects,\ncausing a positive feedback between climate warming and AC use.\nLINE 97-98 IN THE REVISED MANUSCRIPT: Aiming at the above research gap, our study\nseeks to quantify the positive feedback between climate warming and AC use, as well\nas assess global AC inequalities, by designing an integrated approach that\nincorporates the improved cooling degree days (CDD) approach, the Global Change\nAnalysis Model (GCAM), the Model for the Assessment of Greenhouse Gas Induced\nClimate Change (MAGICC, https://magicc.org/).\nLINE 512-515 IN THE REVISED MANUSCRIPT: This study quantifies the positive\nfeedback between climate warming and AC use, indicating that increasing AC use to\nadapt to global warming further exacerbates climate change, causing an additional\n0.03°C-0.12°C increase in global-mean temperature by 2050.\nComment 2: Global AC use is a function of many factors including income, population,\ntechnology access, house size, and temperature. When either of these factors increase, AC\nuse will increase. But only an increase in AC use that is due to an increase in temperature\nrepresents adaptation to global warming. If this adaptation strategy leads to further increases\nin emissions and further warming, this rightly represents a “positive feedback loop between\nclimate warming and AC use” (the authors new term). An increase in global AC use due to an\nincrease in one of the other factors, however, cannot in general be said to represent\nadaptation to global warming. Many parts of the world would see temperatures outside of\npeople’s comfort zones for extended periods of time even without climate change, and AC\nhas been an important technology for increasing thermal comfort for decades. Increases in\nincome, population, technology access, house size, and other developmental factors would\nlead to increased AC use even without further climate change. The global inequality analysis\nin the paper itself demonstrates this very effect.\nWhile it is true, also, that some proportion of current AC use is attributable to the global\nwarming that has already taken place, this does not mean that any increase in AC use due to\nany one of the above factors can be interpreted and presented as adaptation to climate\nchange.\nResponse: We agree that only the portion of AC increase attributable to global warming\nshould be regarded as adaptation, and that conflating this with developmental drivers (e.g.,\nincome, population, technology access, house size) could lead to misinterpretation.\nIn response, we have made the following targeted revisions to ensure conceptual clarity and\nalignment with the reviewer’s concerns, specifically:\n(1) We revised the texts to ensure that increasing AC use is not just an “adaptation strategy”\nto climate change. For example, we described AC use as “one of the adaptation strategies to\nrising temperatures,” or “adaptation to hot-humid weather” to avoid implying that AC uptake\nis solely or primarily a response to climate change, rather than a broader means of achieving\nthermal comfort amid multiple drivers.\n(2) We have reframed the overall storyline of the paper to emphasize the joint influence of\nclimate warming and socio-economic development on the increasing global AC use. For\nexample, the introduction (LINES 53-64 IN THE REVISED MANUSCRIPT) has been revised as\nfollows:\nExcessive anthropogenic GHG emissions have already warmed the planet by 1.2 \n0.1°C since the pre-industrial era 1. The resulting rise in surface temperatures, together\nwith a marked escalation in the frequency and severity of heat-waves, has caused\nnearly half a million heat-related excess deaths between 2000-2019 2,3. The use of air\nconditioners is an effective and widely used adaptation measure of protecting people\nfrom hazardous heat 4. In parallel, rapid economic expansion in emerging regions,\ngrowing urban populations and falling appliance prices have collectively unlocked\naccess to AC-based space cooling, turning latent thermal-comfort demand into an\nunprecedented surge in AC adoption 4. Consequently, the joint influence of global\nwarming and socio-economic developments has driven a four-fold surge in annual\nglobal air-conditioner sales from 1990 to 2016 4,5, with over half located in China and\nthe United States 6.\nThe introduction section that previously described the increasing AC use due to global\nwarming causes the warming feedback loop (LINES 76-81 and LINES 102-106 IN THE\nREVISED MANUSCRIPT) has been revised as follows:\nConsequently, expanding AC access presents a fundamental trade-off: it safeguards\nhuman well-being in hotter weather while simultaneously locking in additional\nwarming potential through both energy and refrigerant pathways. Quantifying and\nmitigating this penalty are therefore essential to ensure that improvements in thermal\ncomfort do not inadvertently undermine long-term climate-mitigation goals.\nAiming at the above research gap, our study seeks to quantify the warming feedback\nfrom AC use on global-mean temperature across scenarios, investigate the respective\ncontributions of socio-economic development and climate warming in future AC use,\nand evaluate how narrowing income-driven cooling inequalities reshapes both\nemissions and warming potential.\nWe have also added text about how to implement decomposition analysis and\ncounterfactual analysis in the Introduction section (LINE 118-120 and LINES 122-126 IN THE\nREVISED MANUSCRIPT), shown below:\nWe also quantify the respective contributing effects of socio-economic factors (e.g,\nfloorspace, GDP, income, population) and climate warming (e.g. CDDs) to the\ntrajectories of AC stock and energy consumption.\nTo investigate the climate-driven and socio-economic contributions of this warming\nfeedback, we construct simplified counterfactual experiments within our IAM\nframework across scenarios.\nComment 3: The scenarios that are used in the paper to quantify the global warming impact\nof AC use, unfortunately, mix several different factors. For this reason, it is paramount that\nthe authors in their analysis of the results make an effort to disentangle the different effects\nin a way that is suited to answering the research questions. This does not have to be perfect\nto be informative. In fact, much of the current analysis, including the decomposition analysis\nand new sensitivity analysis already goes a long way to show what factors are more important\nfor the increase in AC use. The analysis clearly indicates that the dominant driver of future AC\nuse is income growth (and other socioeconomic factors), not global warming. But this is not\nproperly reflected in the discussion, conclusion, and key messages.\nResponse: To disentangle the different effects, we have now carried out more\ncounterfactual analysis in other SSP-RCP scenarios, please see detailed response in\nComment 1.\nWe fully concur that our analyses demonstrate income growth and other socio-economic\nfactors as the dominant drivers of future AC use, with global warming acting as an\nimportant but secondary amplifier. We realize that it is essential to emphasize this in the\ndiscussion, conclusion, and key messages of the previous version.\nTo address this, we have removed all text that implies all the warming feedback from AC\nuse can be entirely seen as a climate feedback loop. We have added a discussion on the\nrelative influences of climate warming and socio-economic development on AC use,\nincluding policy implications such as the critical role of decarbonizing the electricity sector\nto mitigate feedback amid dominant socio-economic drivers. We have revised the\nDiscussion and Conclusion section in the REVISED MANUSCRIPT (lines 529-537) as follows:\nThis study quantifies the warming feedback of 0.02°C-0.11°C by 2050 attributable to\nglobal AC use across five SSP-RCP scenarios, indicating that increasing AC use to adapt\nto rising temperatures further exacerbates climate change. The climate-driven AC use\ngrows stronger with climate warming, and the very warming feedback is below 0.01°C\nacross scenarios, comparing with the socio-economic driven feedback contributing\n0.03-0.06°C increase, this is smaller though not negligible. These estimates underscore\nthe critical need to address the compounding effects of AC use on energy-emissions-\ntemperature feedback, as this feedback not only imposes billions of dollars in economic\nloss 7 but also accelerates climate warming and further makes achieving the Paris\nAgreement goals more difficult 8.\nComment 4: The authors also recognize the importance of socioeconomic factors in their most\nrecent response, where they write: “While our decomposition analysis and new sensitivity\nanalysis clearly show that socioeconomic factors, especially income effect, are the dominant\ndeterminants of the scale of AC adoption and energy use, the initial trigger within the specific\ncycle we aimed to quantify is the increasing cooling demand driven by rising temperatures\n(originating from the anthropogenically altered climate baseline, as IPCC AR6 confirms)”. The\nauthors have, however, not demonstrated that the “initial trigger” of AC use is rising\ntemperatures from climate change. In fact, the observation that current AC use is highly\nuneven, with more AC use in richer and relatively less hot regions than in poorer and even\nhotter regions of the world suggests that the initial trigger of AC use is not rising temperatures,\nbut a demand for thermal comfort in regions with warm and hot weather by those who have\naccess and can afford it. The IEA report referred to in the paper (IEA, 2018) clearly shows that\nmany homes in hot countries have not yet purchased their first AC devices. Both this report\nand Mastrucci et al. (2021) show how increasing income and floorspace (i.e. increasing living\nstandards from economic development) will drive a sharp increase in future cooling demand.\nThere is currently a large unmet need for air-conditioning due to a lack of income and access.\nIt is the fulfilment of this need, not climate change, that is the main driver of the increase in\nfuture AC use and corresponding emissions. Referring to this increase in global AC use as\nadaptation to climate change is thus misleading. Even though “increasing the use of AC as a\nmain adaptation strategy to global warming can, in turn, induce additional warming effects,\ncausing a climate positive feedback loop between climate warming and AC use” (lines 77-79)\n– which is undoubtedly true – this is not what is quantified in the paper. What is quantified is\nthe total increases in AC use driven by a combination of income growth, population growth,\nother developmental factors, and temperature increase.\nWhile I fully recognize that global warming has already contributed to and will continue to\ncontribute to increasing AC demand, that does not imply that the additional temperature\nincrease from global warming is a significant factor in global AC demand. It might be\nsignificant, and it would be an important finding if the paper was able to demonstrate this,\nbut unfortunately it is not.\nInstead, the new counterfactual scenario analysis using the SSP245 framework as a sensitivity\nanalysis – which is actually very useful and informative, but unfortunately not referred to in\nthe main text – provides further evidence that global warming is not a particularly significant\nfactor in increasing AC use, with only a 8.4% difference in global cumulated AC GHG emissions.\nThese important results should be part of the main paper.\nResponse: We sincerely thank you for your continued guidance in refining the presentation\nof our findings, particularly regarding the accurate attribution of AC use drivers and the\nintegration of our counterfactual analysis. We agree that framing rising temperatures as\nthe “initial trigger” for AC adoption is misleading and appreciate the reviewer’s recognition\nof our SSP245 counterfactual analysis.\nTo address these concerns, we have made the following revisions to ensure our narrative\naligns with the evidence and clearly communicates the relative roles of socio-economic\nand climate drivers:\n(1) We have removed the claim that rising temperatures are the “initial trigger” for AC use.\nAnd we have substantially revised both the Introduction and the Abstract, reflecting our\nresponses to Comment 1 and Comment 2.\n(2) In the Results section, we have introduced the following revisions to the subsections on\nAC stock and AC energy consumption in the REVISED MANUSCRIPT (lines 263-285), as\ndetailed below:\nIncome growth has been found as the dominant driver of the coming surge in both\ninstalled AC stock and associated energy consumption. Our decomposition analysis\nshows that, averaged across the five SSP-RCP scenarios, the income effect alone\ncontributes to 158% of the increase in the global AC stock and 190% of the increase in\nglobal AC energy consumption between 2010 and 2050 (see Supplementary Tables 3\nand 4). For global AC stock, the next two largest growth drivers are the AC penetration\nintensity effect (captures changes in the number of installed AC units relative to cooling\ndemand) and population effect, which raise the stock by 74% and 40%, respectively.\nBy contrast, the floorspace intensity effect produces the greatest decreasing effect,\nbecause aggregate economic output has expanded more rapidly than total floor area.\nThese drivers exert similar effects on global AC energy consumption.\nBecause those income gains are unevenly distributed, their imprint on cooling demand\nis highly diversified regionally. Across all scenarios, AC stocks and energy consumption\nof China and the United States far outweigh stocks and consumption in other regions.\nWith the additional contributions from Japan, the Middle East and Southeast Asia, Asia\nis projected to emerge as the leader in this aspect (see Figures 2c and 2d). The\ndecomposition analysis confirms these contrasts: the income effect results in 318% of\nthe increase in AC stock and 396% of the increase in AC energy consumption in China,\nwhereas in the United States, it leads to only 73.2% and 64.2% increases, respectively.\nSimilar disparities emerge elsewhere. In rapidly urbanising economies such as Africa,\nIndia and Indonesia, lagging efficiency and steep income growth together drive the\nfastest proportional increases in both metrics.\n(3) We have revised the Results section on AC-related GHG emissions to incorporate the\nnew counterfactual scenarios, including detailed comparisons of climate-driven versus\nsocio-economic c"}]

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