Quantifying the global climate feedback from energy-based adaptation

Domain: earth_science
Published: 2025-04-26
Editorial outcome: minor_revision

Abstract
Many behavioral responses to climate change are carbon-intensive, raising concerns that adaptation may cause additional warming. The sign and magnitude of this feedback depend on how increased emissions from cooling balance against reduced emissions from heating across space and time. We present an empirical approach that forecasts the effect of future adaptive energy use on global average temperature over the 21st century. We estimate that energy-based adaptation will lower global mean surface temperature in 2099 by 0.07 to 0.12 °C relative to baseline projections under Representative Concentration Pathways 4.5 and 8.5. This cooling avoids 0.6 to 1.8 trillion U.S. Dollars ($2019) in damages, depending on the baseline emissions scenario. Energy-based adaptation lowers business-as-usual emissions for 85% of countries, reducing the mitigation required to meet their unilateral Nationally Determined Contributions by 20% on average. These findings indicate that while business-as-usual adaptive energy use is unlikely to accelerate warming, it raises important implications for countries’ existing mitigation commitments.

Introduction
Human adaptation will be increasingly critical for moderating harms and exploiting opportunities under climate change1. Recent studies highlight that climate adaptation requires significant changes in energy use; increased energy consumption has been shown to reduce excess mortality and protect well-being in homes, workplaces, and schools under extreme temperatures2–10. Energy use is carbon-intensive: cooling demand alone comprised 10% of recent global electricity consumption and is expected to rise substantially during the 21st century11. This raises the question of whether adaptation to climate change may itself induce additional warming, a phenomenon we call the Climate Adaptation Feedback (CAF). The CAF is the anthropogenic analog to geophysical climate feedback mechanisms (e.g., declines in oceanic CO2 uptake or albedo) that can amplify global climate change12. It captures how behavioral responses to climate change may be maladaptive by ultimately increasing future global mean surface temperatures (GMST)10,13,14.

Prior work has noted and made progress toward assessing the potential feedback between energy used for adaptation and the climate. Recent analyses quantify the responsiveness of historical energy demand to weather and use such estimates to project future global energy consumption under climate change8,15,16. However, these studies do not assess the impacts of these changes in energy consumption on global temperatures. One study does estimate a version of the CAF using a regional Integrated Assessment Model (IAM), modeling adaptation as changes in the productivity of different fuels used as inputs in production, and finds a positive feedback in which adaptive energy use raises global mean surface temperature17. This analysis assumes regional planners set optimal policy for energy use, adaptation, and mitigation18 and it captures key general equilibrium channels, such as how adaptation affects prices, expectations, and investment.

This paper develops a data-driven framework for quantifying the CAF driven by adaptive energy consumption. We calculate the CAF over the course of the 21st century by combining high-resolution, subnational projections of energy consumption responses to anthropogenic climate change with country- and energy-specific CO2 emissions intensities. Our calculation accounts for energy consumption implications of all behaviors and investments that individuals, communities, and firms undertake in response to temperature change across all non-transport sectors including residential, commercial, industrial, and agricultural sectors8, covering 142 countries or nearly 80% of current global CO2 emissions19. It is built from state-of-the-art, globally-comprehensive, empirical estimates of how energy demand responds to changing temperatures8. These estimates incorporate heterogeneous effects of a changing climate on energy consumption across fuels and locations2,3,8,9,15,20–22, as well as how income growth and climate change will alter these responses over time.

In contrast to prior assessments of the CAF17, we do not impose the structure of an IAM. Instead, we assess how adaptation to climate change will affect future temperatures using empirically-estimated energy demand responses and exogenous future trajectories of baseline warming, economic growth, and population (see “Methods”). Relative to earlier analyzes, our study increases the spatial resolution of energy demand responses used to forecast changes in emissions, includes the response to the full distribution of daily temperature realizations rather than restricting inputs to realizations of local extremes, reports uncertainty from both climatological and statistical sources, and accounts for country-level heterogeneity in emissions intensities and their dynamics for electricity and other fuels. However, our statistically-based method necessarily foregoes modeling general equilibrium channels that are captured by IAMs, making the two approaches complementary.

Prior research and media attention have predominantly focused on the risks posed by behavioral feedbacks that would lead to a positive CAF, such as increased demand for air conditioning raising emissions from the electricity sector4,11,17,23. While this specific channel will undoubtedly play an important role, Fig. 1 illustrates that both the sign and magnitude of the CAF stemming from all forms of energy-based adaptation to climate change are unclear. While climate change will lead to warming of daily temperatures in all locations (Fig. 1a, b), the resulting response in energy consumption will be highly heterogeneous. A higher frequency of realized hot days will increase electricity demanded for cooling services in locations that are already warm (Fig. 1d), but warming will simultaneously lower demand for heating in locations that currently experience a large number of cold days (Fig. 1c). This heterogeneity interacts with variation in the CO2 intensities of electricity and other sources of energy, leading to substantial differences in the response of emissions from adaptation to temperature change across countries.

For example, Canada and Sweden will both likely experience declines in heating demand under climate change. However, Canada’s electricity sector emits 15 times more carbon dioxide per Gigajoule (CO2 × GJ−1) than does Sweden’s, while its other fuels sector emits 2 times more. These differences lead to differential emissions responses to changing daily temperatures (Fig. 1e). Similarly, energy-based adaptation in India and Brazil will lead to increased demand for electricity due to more energy being required for refrigeration and indoor temperature control, but the dominance of coal in India implies much larger increases in emissions than in Brazil, where hydropower is the primary source of electricity (Fig. 1f). Thus, the change in future global CO2 emissions due to energy-based adaptation, and by extension the sign and magnitude of the CAF, are a priori unknown. A positive CAF implies behavioral adaptations increase global CO2 emissions on net (Fig. 1g), raising projected rates of warming (Fig. 1h). However, if future declines in emissions due to reduced demand for heat from other fuels dominate the additional future emissions due to higher electricity demand, the CAF will be negative.

We develop a framework to sign and quantify the net effects of the forces illustrated in Fig. 1. Specifically, we define the CAF in any given year as the difference in global mean surface temperature between a baseline value (e.g., projected warming under the Representative Concentration Pathway 8.5 (RCP8.5) emissions trajectory) and one accounting for future energy-based adaptation (e.g., projected warming under RCP8.5 plus the net change in emissions from adaptation) (see “Methods”). We note that while energy demand responses to warming in specific sectors and by specific energy sources will be heterogeneous, as illustrated in Fig. 1, the sign and magnitude of the CAF depend only on the net change in resulting emissions across all sectors and sources. Therefore, we focus our analysis on the aggregate emissions changes resulting from all measurable energy-based adaptations.

Results
Empirical estimation of the global climate adaptation feedback
We find a negative global CAF that declines over time, moderating rates of warming under future anthropogenic climate change. Fig. 3a plots point estimates (solid green line) and 90% confidence intervals (shaded green area) for the annual CAF over the 2020–2099 horizon under our baseline SSP2-RCP8.5 scenario. In 2099, under RCP8.5 the CAF is −0.12 °C; changes in energy consumption driven by adaptation lead to a 0.12 °C lower GMST relative to baseline. Under RCP4.5, our more moderate scenario for emissions and projected warming, the magnitude of the CAF in 2099 falls to −0.07 °C. This projected reduction in warming in 2099 under RCP8.5 alone is equivalent to six years of recent warming at the observed 0.018 °C × yr−1 rate between 1981–201934 and is 25 times larger than that implied by a back-of-the-envelope calculation in ref. 8. Under the SSP2-RCP8.5 scenario, our estimated CAF implies that adaptive energy consumption is predicted to lower the change in GMST in 2099 from 4.27 °C to 4.15 °C, relative to the pre-industrial climate. Using the Data-driven Spatial Climate Impact Model (DSCIM) built by the Climate Impact Lab, we estimate that the decrease in warming due to the CAF lowers the present value of cumulative damages from climate change between 2020 and 2099 by 1.8 trillion ($2019 USD) (see “Methods”). The analog in avoided damages is 0.6 trillion for the RCP4.5 scenario. Accounting for both climatological and statistical uncertainty in 2099 yields a 90% confidence interval for the CAF of −0.35 to 0.073 °C, and the CAF is robust to iteratively leaving out the adaptation of individual countries when forecasting cumulative emissions reductions in 2099 (Supplementary Note 5).

A limitation of our benchmark approach is that it implicitly assumes the effects of adaptive emissions on the GMST pathway do not themselves affect future adaptation. Paths for future changes in energy consumption are fixed in the sense that the demand responses generated by temperature trajectories under each SSP-RCP are determined before we calculate the CAF at a given horizon. We relax this assumption to construct a dynamic version of the CAF, which allows for historical adaptation to affect our projections for adaptive energy use from that point forward (see “Methods”). Allowing for such dynamic linkages leads to a negligible difference, as shown by the dashed green line in Fig. 3a; the two CAFs are indistinguishable given the degree of statistical uncertainty. Our point estimates imply that accounting for dynamic adaptation effects decreases the magnitude of the CAF in 2099 by 0.8%.

We also recalculate the CAF relaxing the assumption of fixed emissions factors. The long dashed lime green line in Fig. 3a shows the CAF under the assumption that historical global trends in emissions intensity for both fuels between 2000 and 2018 continue for all countries through 2099, an assumption taken in prior related work on global climate damages35,36. Allowing each sector to continue to decarbonize at historical rates lowers the CAF to −0.13 °C under SSP2-RCP8.5. As emissions from other fuels have remained relatively unchanged on a unit basis when compared to the substantial reduction in the emissions intensity of electricity generation over the past two decades, extrapolation of this trend lowers emissions from the electricity sector and amplifies the mechanisms leading to a negative CAF (Supplementary Note 3).

Decomposition of the global climate adaptation feedback
Figure 3b illustrates how several factors contribute to the sign and magnitude of the benchmark CAF reported by the first bar. First, adaptation-induced changes in electricity consumption, which are largely driven by increased demand for cooling under rising temperatures4,8,37, lead to a positive value of 0.06 °C (second bar). This electricity effect has been the focus of prior discussions of energy-based adaptation11,23. However, a negative CAF emerges when we add changes in demand for other fuels, whose value of −0.18 °C (third bar) more than offsets the positive component from electricity, due to substantial projected declines in demand for heating under climate change. These findings highlight the importance of accounting for all forms of energy demand that will change in response to climate change. Moreover, they reinforce results in the previous literature demonstrating that energy demand responses to climate change differ in sign and magnitude across distinct sectors and fuel sources15.

Heterogeneity in CO2 emissions intensity also plays an important role. Using a constant global CO2 emissions intensity—a simple average across countries and fuels (fourth bar)—results in a CAF that is 37% smaller in magnitude than our benchmark estimate, which allows CO2 emissions intensities to vary across countries and fuels. This result implies that emissions are higher per unit of energy in countries with larger energy-based adaptive responses to climate change. Our estimates of the CAF are largely invariant across socioeconomic scenarios, but depend heavily on the magnitude of baseline greenhouse gas emissions. Fig. 3c shows point estimates for the 2099 CAF under alternative SSP-RCP scenarios, demonstrating that the CAF under RCP8.5 is roughly double that under RCP4.5, due to greater baseline warming leading to larger energy savings from fewer cold days. Although SSP scenarios change total population and levels of income across countries, which can shape the total energy response to daily temperatures8, we find that within an RCP, global CAF values differ little across SSP scenarios. This is due to the fact that a majority of variation in projected income and population levels across SSPs38 occurs outside of countries with high historical emissions, which as we show below comprise the primary source of adaptive behavior driving the CAF.

Country-level heterogeneity
There is substantial heterogeneity in the magnitude of adaptation-induced CO2 emission changes across countries. Fig. 4a shows both a map and histogram of country-level cumulative adaptation-induced CO2 emissions changes by 2099 under SSP2-RCP8.5 (denoted as E in the “Methods” section). While 85% of countries experience CO2 emissions reductions, of those countries, the 5th and 95th percentiles of cumulative adaptation-induced emission changes by 2099 are −0.027 and -6.24 GtCO2, respectively. For the remaining 15% of countries that experience increases in emissions, magnitudes are small, with the 5th and 95th percentile range estimated at 0.0024 to 0.5 GtCO2. The net changes in emissions can be decomposed into country-level changes in emissions from electricity, which are positive in most countries (Supplementary Fig. 1), and in emissions from other fuels, which are negative in virtually all countries (Supplementary Fig. 2). The case of India is particularly striking, as it exhibits large adaptation-induced declines in CO2 emissions by end-of-century, despite facing substantial increases in exposure to extreme heat in future years39. This result is driven partially by the demand responses from ref. 8, which estimate that electricity demand will increase by 4.1 Exajoules (EJs) by 2099, while cumulative demand for other energy will fall by 7.0 EJs, due to electricity-temperature demand responses being relatively flat for much of the world. This result is also influenced by heterogeneous emissions intensities: other fuels in India are substantially more emissions-intensive, implying that demand responses translate into an increase of only 0.9 GTCO2 emissions from electricity compared to a decline of 2.7 GTCO2 from other fuels. This decomposition is detailed in Supplementary Note 4.

Implications for mitigation policy
Heterogeneity in country-level adaptation-induced CO2 emissions can inform the fairness and stringency of countries’ Nationally Determined Contributions (NDC) from the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC). One interpretation of reduced CO2 emissions from energy-based adaptation is the accrual of “free” abatement. Unlike typical CO2 abatement, which results from climate mitigation policies designed to directly curtail emissions, this abatement emerges solely as a consequence of behavioral adjustments unprompted by mitigation policies. However, the resulting emissions reductions from these adaptations have global benefits identical to those induced by environmental policy and can influence international negotiations and country-level mitigation benchmarks. The magnitude and distribution of this free abatement has two important features relevant for global climate policy.

First, Fig. 4b shows that for the 121 countries projected to experience adaptation-induced CO2 declines, the magnitude of CO2 cumulative abatement by 2099 is strongly correlated with historical emissions. For example, while Eritrea is responsible for less than 0.03 billion tons of CO2 emissions cumulatively since 1970, we project that it will accrue only 0.14 billion tons of free abatement in the coming century. In contrast, the United States is responsible for more historical emissions than any other nation on earth, but is projected to benefit from over 10 billion tons of adaptation-induced abatement without any mitigation policy. This correlation implies that while the countries responsible for most greenhouse gas emissions to date are often targeted for aggressive mitigation in international climate negotiations40, they are projected to receive substantially more free abatement during the 21st century, relative to today’s developing economies. To ensure equity principles are fairly upheld in climate negotiations, these findings suggest that historically large emitters should increase the stringency of their mitigation targets to appropriately account for the CAF.

Second, we find that the magnitude of adaptation-induced abatement is large relative to existing mitigation targets. To illustrate this, we report each country’s cumulative adaptation-induced abatement in 2050 as a fraction of that country’s cumulative required abatement in 2050 under its NDC41–43. When this ratio takes a value of one, the entirety of a country’s obligations under its NDC will be met without any mitigation policy; that is, the projected gap between a country’s baseline emissions and its NDC is met entirely by our estimate of adaptation-induced abatement. Fig. 4c shows a histogram of these values, displaying the share of mitigation stipulated under each country’s NDC that is realized through adaptation-induced abatement for each of the 63 countries with long-term commitments cataloged by ref. 43. Similar to the the full sample, 82% of the countries in this subsample are projected to experience adaptation-induced abatement. For these 52 countries, adaptation-induced abatement will on average reduce gaps between baseline emissions and their NDCs by 20% in 2050. Several countries are projected to undergo emissions reductions from adaptation that are larger than the mitigation commitments implied by their NDCs, as shown by share values that exceed 1. This highlights that NDC commitments, which already have been shown to be insufficient to meet the Paris Agreement goal of no more than 2 °C increase in global average temperatures relative to pre-industrial44, are even weaker than previously assessed: once the CAF is accounted for, many countries need to undertake little mitigation policy to achieve their targets.

Methods
Constructing the climate adaptation feedback
Our paper develops and implements a methodology to quantify the extent to which changes in future energy use driven by adaptation to anthropogenic climate change (ACC) will alter greenhouse gas (GHG) emissions and in turn affect climate change. We call the difference between global mean surface temperature (GMST) with versus without adaptation-induced energy consumption at time horizon τ the “Climate Adaptation Feedback”, or CAFτ. CAFτ depends on the baseline scenario of emissions and socioeconomic conditions, defined for our purposes as a combination of a Representative Concentration Pathway (RCP) of global anthropogenic GHG emissions54–58 and a Shared Socioeconomic Pathway (SSP) of projected national populations, incomes, and other socioeconomic characteristics59–61. The following calculations fix our baseline SSP2-RCP8.5 scenario to avoid notational clutter, but we repeat the processes below for each SSP-RCP we consider (results for all scenarios are displayed in Fig. 3).

Consider two projections of future warming, one that accounts for adaptive changes in energy use and one that does not. Denote projected GMST in period t as TA t when adaptive energy use is accounted for, and as TN t when it is not. Normalize time periods such that t=0 is the year 2020 and let Δ denote the time difference operator between period t=0 and t=τ. With this notation, we define the CAF at horizon τ as Eq. (1):

CAFτ = ΔTAτ − ΔTNτ : (1)

Equation (1) is the difference in GMST change at horizon τ due to adaptive changes in energy use around the world. When the CAF is positive, adaptation exacerbates warming globally. When the CAF is negative, adaptation dampens warming.

To construct CAFτ, we first calculate the change in global CO2 emissions due to adaptation-induced energy use in each period through horizon τ. At the local scale, emissions from adaptive energy use depend on how ACC changes local temperature distributions as well as how different temperature realizations affect energy demand (see Fig. 1). Because local temperature changes, energy use responses, and the CO2 intensity of energy consumption vary substantially across space, we conduct this step at the country level before aggregating globally to compute the global CAF. Specifically, for each year t and country i, the CO2 emissions generated by energy-based adaptation are given by Eq. (2):

Ei,t = Σh Fh i ΔJi h,t = Σh Fh i Σp∈i [Jh(TN p,t, Xp,t) − Jh(TN p,0, Xp,t)] (2)

where h indicates final consumption of either electricity or an aggregate of consumption across all other fuels, including natural gas, oil shale and oil sands, biofuels, and others, as detailed in ref. 8, the source of our energy use projections. In this expression, p indicates one of ~ 25,000 global subnational regions with approximately internally-homogeneous historical temperatures, which are shown as Eq. (2) defined by ref. 8. Each Fh i is the CO2 emissions factor for a given energy type h ∈ {electricity, other fuels} for country i, measured in units of tCO2 × GJ−1 of fuel consumed. Construction of Fh i is detailed below. We fix Fh i to the observed 2010-2018 averages for each country-fuel pair to avoid projecting future changes in CO2 intensities while reflecting current differences in countries’ energy mixes.

The underbraced object in Eq. (2) represents the impact of climate change on adaptation-induced total energy use in region p. It is defined as the difference in total energy use in GJ between a future climate affected by ACC and a future with stable temperatures representative of the current (t=0) climate. This projected change in energy consumption depends critically on the “dose-response” functions Jh(⋅), which are constructed by ref. 8 using historical energy consumption data and standard climate econometric tools. These functions relate energy consumption in each fuel category h to daily temperature, capturing the energy consumption that results from all behaviors and investments that individuals and firms undertake in response to temperature change.

As detailed in ref. 8, these energy demand response functions depend primarily on the realization of future daily temperatures within a given impact region, denoted by the vector TN p,t, under a given RCP scenario. The dose-response functions also include higher-order terms of daily grid cell-level temperature realization along with a set of covariates summarized by Xp,t, which include projections of GDP per capita and population specific to a SSP scenario and long-run averages of cooling and heating degree days under each temperature trajectory. These covariates allow for the response of energy consumption to daily temperature realizations to vary based on how the economic resources and climatology of a given location change in the future. This ensures the CAF calculation captures the extensive margin of adaptation that takes place over long periods and may be especially important in developing economies, where income growth is likely to lead to substantial increases in cooling and heating technology adoption4,8,9. The empirical estimation of such extensive margin effects is conducted in ref. 8 by interacting short-run variation in weather with long-run variation in income and climate in a country-by-year panel regression. This model results in the estimation of heterogeneous dose-response functions in which energy demand sensitivity to daily realizations of temperature differs based on long-run average income and climate. The econometric procedure employed follows previously-developed methods (e.g., ref. 62) to recover nonlinear energy demand responses to temperature at a grid cell-by-day level, which can be applied to predict climate change impacts at the scale of ~25,000 regions, even though more aggregated outcome data were employed (see Methods of ref. 8 for details). More generally, these interaction terms allow for the model to identify the heterogeneity in the response of local energy demand to temperature realizations caused by variation in income and climate over space and time. We show the quantitative importance of the extensive margin in shaping the CAF in Supplementary Note 2.

Two sources of uncertainty enter into Eq. (2). The first is climate model uncertainty: for a given emissions scenario, there is uncertainty over future local temperature realizations TN p,t. We account for this uncertainty by utilizing all 33 Global Climate Model (GCM) projections included in the Surrogate Model Mixture Ensemble (SMME) employed by ref. 58 (CMIP5)63 built from the climate models. The second is statistical uncertainty in the empirical estimates of the energy-temperature dose-response functions Jh(⋅); this uncertainty is captured by ref. 8 through application of the statistical Delta Method, creating a Gaussian distribution of predicted impacts for each of the 33 climate model projections. To combine both sources of uncertainties, we follow ref. 8 in constructing the mixture distribution of these 33 Gaussian distributions using Newton’s method.

There are multiple possible sources of empirically-based energy-temperature demand responses that we could have used to estimate Eq. (2). In selecting a source, we sought to identify estimates that: (i) account for demand responses to changes across the entire temperature distribution, not just extreme temperatures; (ii) are differentiated between electricity and other fuels, allowing for a different effect of temperature on the demand for both types of energy; (iii) incorporate extensive margin responses of energy demand to changes in temperature over time; and (iv) allow for different localities to have different responses to temperature change. While other multi-country energy-temperature demand estimates exist (e.g., ref. 15), to our knowledge ref. 8 represents the only study meeting all four of these criteria.

Summing the results from Eq. (2) over time and across space, we write the cumulative change in global CO2 emissions between years 0 and τ caused by adaptation-induced energy use as Eq. (3):

Eτ = Στ t=0 Σi Ei,t (3)

To convert cumulative emissions from adaptation, Eτ, to changes in future GMST, we estimate a relationship between projected future emissions and warming in the absence of adaptation using the forecasts generated by the 33 GCM projections described above. Below we discuss this relationship in detail, how it relates with the transient climate response to cumulative carbon emissions (TCRE) in the climate science literature, and we show that it is well-approximated by a linear coefficient. We denote this linear relationship between emissions and GMST with the slope coefficient β. This implies that the GMST change between years 0 and τ due to adaptation is: ΔTAτ = ΔTNτ + βEτ. Rewriting this expression in terms of the climate adaptation feedback definition in Eq. (1) gives us Eq. (4):

CAFτ = βEτ : (4)

For each SSP-RCP combination, we obtain a point estimate for CAFτ from Eq. (4), as well as a 90% confidence interval that account for both climate uncertainty across GCMs and statistical uncertainty in the energy response functions, as discussed above.

Valuation of the climate adaptation feedback
To convert our estimates of the CAF into dollar value of avoided damages, we use the Climate Impact Lab’s Data-driven Spatial Climate Impact Model (DSCIM)29. This model includes climate change damages to mortality, coastal storms and sea level rise, labor, energy, and agriculture. Mortality risk is monetized using the U.S. EPA VSL with a value of life years lost adjustment and an income elasticity of one, following ref. 29. DSCIM assigns a monetary value to the damages from global warming in every year along a baseline socioeconomic and climatic trajectory. Avoided damages due to the negative CAF are calculated as the difference between predicted damages in the baseline scenario and in the scenario inclusive of the CAF, for each year between 2020 and 2099. When discounting damages, the DSCIM model generates a stochastic discount factor (SDF) for all future periods based on the Ramsey rule calculated along the exogenous consumption pathway for a given socioeconomic scenario, and after incorporating consumption losses from baseline warming. Specifically, we use a coefficient of relative risk aversion equal to η=2, and we discount future values using Ramsey discounting with η=2 and a pure rate of time preference of ρ=0.0001. We convert the avoided consumption losses due to the CAF into a present value in 2019 equivalents using this SDF.

Comparison with the integrated assessment model approach
Unlike prior literature, we do not use an Integrated Assessment Model (IAM) to estimate the effect of adaptive energy demand on global temperatures. This abstracts from fully specifying a demand system wherein agents balance the benefits of using energy for adaptation against the costs of foregoing income for other forms of consumption. Our data-driven approach avoids making structural assumptions governing the channels through which adaptation to climate change occurs, which an IAM framework usually imposes. This abstention from using an IAM of course poses a tradeoff in that our empirically-based CAF omits general equilibrium channels that may affect energy demand, as highlighted in the Discussion section.

The benchmark study to date examining the potential for feedback between energy used for adaptation and global temperatures17 uses the WITCH IAM18 framework. WITCH is a multi-region IAM based on an augmented Ramsey model of optimal savings designed to assess the welfare and temperature effects of climate policy in a dynamic setting18. Agents in the model consist of regional planners who select sequences of consumption, savings, and energy use in their local economy so as to maximize residents’ welfare18. The model allows for both a non-cooperative formulation, where up to 17 regional planners maximize the welfare of their populations in a global extensive game, as well as a cooperative formulation under which a single social planner maximizes welfare across all regions globally. Reference 17 use the non-cooperative formulation of the model as their laboratory for examining the effects of adaptation on energy demand and temperatures. While the non-cooperative formulation is the closest version of the model to a competitive equilibrium, unlike in a decentralized economy, regional planners in the non-cooperative formulation still internalize some climate externalities. These planners choose mitigation and/or abatement efforts to maximize regional welfare; the Nash equilibrium in the non-cooperative formulation of WITCH is a second-best optimal solution, as opposed to a model of outcomes under laissez-faire18.

To use the WITCH model to estimate the effects of adaptation on energy use, ref. 17 modify the model to allow climate change to affect the productivity of different fuels used as intermediate inputs for producing the modeled final consumption good. Adaptation inside of this alternate specification is taken to be how energy use changes in response to the productivity shocks driven by future climate change17. The authors then calculate aggregated demand in the alternative (baseline) model specifications that include (exclude) the adaptive energy demand induced by the productivity shocks for three scenarios: no climate policy, a 2.5 °C limit on warming, and a <2 °C limit on warming. For each scenario, ref. 17 solve both specifications of the model in the non-cooperative mode and raise carbon taxes until the resulting Nash equilibrium yields a level of aggregate emissions that coincides with the policy goal in the scenario. Reference 17 quantify the additional energy demand (and in turn, emissions) induced by adaptation as the difference between aggregate energy demand in the alternative specification with adaptation and a baseline version of the WITCH model where adaptation is absent and energy-specific productivity levels are temperature-invariant.

In contrast, our data-driven approach is agnostic regarding the underlying structure of the economy, relying instead on empirical estimates of the historical response of energy demand to temperature fluctuations. Our methodology trades the structure of an IAM for increased spatial granularity and a greater ability to account for both statistical uncertainty in future demand responses as well as model uncertainty in how future temperature dynamics will respond to emissions. To do so, we use existing forecasts from ref. 8 that predict how future climate change will affect energy demand based on realized historical adaptation to intra-annual temperature changes. This avoids measuring adaptation as the change in energy used by regional social planners or assuming that the shifts in demand are driven by productivity shocks and/or changes in relative prices. At the same time, the lack of structure precludes us from accounting for future government policy or voluntary actions that may alter how energy demand responds to climate change beyond historical patterns. In that sense, we sacrifice the ability to estimate what the CAF would be in a world where global policy governing emissions or carbon prices change (cf. ref. 17).

Data
We obtain point estimates and 90% confidence intervals for projections of adaptation-induced energy use (i.e., the underbraced terms in Eq. (2)) at the country-year-fuel level for two emissions scenarios and four socioeconomic scenarios directly from ref. 8.

We obtain projection-specific annual series of GMST between 2020 and 2099 under the RCP4.5 and RCP8.5 pathways directly from ref. 8. The SMME employed by ref. 8 generates 33 projections of annual GMST under each RCP scenario. These global averages correspond directly with the impact-region specific daily temperature realizations that drive future ΔJh i,t s under each model run in ref. 8.

We obtain five-year country-level GDP per capita and population projections for the 2020–2099 period under each SSP scenario from the International Institute for Applied Systems Analysis (IIASA) model59–61 and from the Organization for Economic Co-operation and Development (OECD) Env-Growth model. For projections under each SSP scenario, we take the average between these two model outputs.

To convert adaptation-induced final energy consumption of energy source h ∈ {electricity, other fuels} to CO2 emissions, we need energy source-specific CO2 emissions intensities that account for heterogeneity in the mix of primary fuels (e.g, coal, natural gas, and renewables) in each country. For example, electricity in Poland is mostly generated using coal, while in Costa Rica it comes almost exclusively from renewables, each with very different resulting CO2 emissions intensities.

For each country i and year t, let r ∈ Hh i,t index the primary fuels used to generate final consumption of energy source h. The final energy source CO2 emissions intensity, Fh i, is the weighted average of primary fuel CO2 emissions intensities, fh i,r,t, across primary fuels r used to produce final energy source h, where each weight, ωh i,r,t, is the total amount of energy fuel r contributes to final consumption of energy type h in year t. To account for year-to-year fluctuations in primary energy use, we take this average over 2010–2018 values. Country-level final energy source CO2 emissions intensities are calculated as Eq. (5):

Fh i = Σ2018 t=2010 Σr∈Hh i,t fh i,r,t ωh i,r,t / Σ2018 t=2010 Σr∈Hh i,t ωh i,r,t (5)

We obtain primary fuel CO2 emissions intensities fh i,r,t from the International Energy Agency (IEA) Emissions Intensities Report for the year 202164. We use the IEA World Energy Balances65. The WEB catalogs country-level primary fuel consumption at the sector level which we aggregate to form our final energy use sectors. Electricity is one such sector (i.e., code ELOUTPUT). For other fuels, we follow ref. 8 for consistency and pool together the industrial, residential, commercial and public services, agricultural, fishing, and other sectors not elsewhere specified (i.e., codes TOTIND, RESIDENT, COMMPUB, AGRICUL, FISHING, and ONONSPEC respectively).

To construct the globally constant emissions-weighted average CO2 intensity across final energy sources and countries used in Fig. 3b, we compute Eq. (6):

F = Σn i=1 Σh E2019 i,h Fh i / Σn i=1 E2019 i (6)

where the weights E2019 i are set equal to 2019 GHG emissions measured in CO2-equivalents from ref. 66.

We obtain country-level baseline CO2 emissions pathways and Nationally Determined Contributions (NDCs) from ref. 67. Reference 67 provide two sets of NDCs for most countries: a more stringent “conditional" NDC path (in the sense that the pathway is conditioned on action by other countries) and an “unconditional" NDC path, both available only under SSP1 and SSP5. We use the more stringent conditional NDCs along with baseline CO2 emissions projections under the SSP5 scenario, both for the year 2050, to construct the ratio of cumulative adaptation-induced CO2 emissions reduction over cumulative CO2 emissions reduction under NDCs by 2050 in Fig. 4c.

Estimating the GMST-cumulative CO2 relationship
A key challenge to quantifying the CAF is that available emissions intensity data only apply to CO2 emissions, while energy-based adaptation is likely to feedback into climate change via other greenhouse gases as well. Specifically, the IEA data detailed above does not contain emissions intensities for non-CO2 greenhouse gas emissions from final consumption outside of the electricity and heat and power sectors. To address this data limitation, we construct an empirical relationship between GMST and cumulative CO2 emissions that includes any changes in non-CO2 emissions which covary with CO2 emissions. Such a relationship is similar to, but not the same as, the transient climate response to cumulative emissions of CO2 (TCRE), which is the direct (causal) effect of cumulative CO2 emissions on GMST change and has been documented in the climate science literature and shown to be well-approximated by a linear relationship12,68–70. However, our empirical relationship additionally includes the effects of non-CO2 greenhouse gases on GMST, to the extent that these gases correlate with CO2 emissions in historical data.

To illustrate this approach, suppose the change in GMST over time horizon τ, ΔTτ, responds to cumulative CO2 emissions, ECτ O2 and cumulative emissions of another GHG, Eother τ, as given by Eq. (7):

ΔTτ = ρECτ O2 + αEother τ (7)

In this expression, ρ is the TCRE—the direct effect of changing CO2 emissions on GMST holding cumulative emissions of all other GHGs constant. However, due to IEA data limitations, we cannot estimate projected changes in Eother τ due to adaptive energy use. Instead, we can estimate the same regression omitting the effects of other GHG emissions in Eq. (8):

ΔTτ = βECτ O2 + errorτ (8)

Since future emissions of CO2 and other GHGs are likely to be positively correlated, β can be expressed as Eq. (9):

β = ρ + α cov(ECτ O2, Eother τ) / var(ECO2 τ) > ρ (9)

The coefficient β is therefore our object of interest; it is the projected GMST change from an observed increase in cumulative carbon emissions. This coefficient combines the direct effect of a unit increase in cumulative CO2 emissions and the indirect effect that accounts for the covariance between CO2 and the other GHG emissions that are inputs into the SMME used to forecast future temperature pathways. When that covariance is positively correlated, one would expect β to exceed the TCRE, or ρ.

In practice, to estimate β, we use variation in GMST and cumulative CO2 emissions (in the absence of adaptation) across RCP4.5 and RCP8.5 and the 33 GCM predictions drawn from the SMME based on CMIP5. Letting s index the 66 RCP-GCM combinations, we estimate Eq. (10):

ΔTN τ,s = βEN τ,s + errorτ,s (10)

using the temperature and emissions time series generated by the ensemble of models in CMIP5 (N=5,200). Our estimate of β from Eq. (10) is 2.2 × 10−3 °C × GTC−1 (p(|T|>|t|) < 0.01). To examine whether our linearity assumption is valid, Fig. 2d shows a scatter plot between ΔTN τ,s and EN τ,s along with a flexible relationship estimated using a local polynomial function with an Epanechnikov kernel and a rule-of-thumb bandwidth71,72 that reveals any data-driven nonlinearities. We do not detect any nonlinearities. As a point of comparison, our estimate for β is 1.4 times the median estimate for the TCRE (or ρ) detected in the literature, although well within confidence intervals for the TCRE70. This is consistent with cumulative emissions of CO2 and other GHGs being positively correlated.

A dynamic climate adaptation feedback
As discussed in the main text, our baseline estimate for the CAF takes projected changes in emissions from adaptation as given by the calculations in ref. 8. That is, we assume that the additional climate change due to the CAF does not itself lead to additional adaptive energy demand. In doing so, the estimated CAF outlined above implicitly assumes adaptive changes in emissions have no concurrent effects on the GMST pathway that determines future adaptation. This is a strong assumption if emissions changes due to adaptation have large immediate effects on the GMST path each year after they enter the atmosphere. In this section, we develop a dynamic version of the CAF and show that, in practice, the result is nearly identical to the approximated value we center in our analysis.

Specifically, we account for the dynamics of energy-based adaptation by updating the original projections of Ei,t from Eq. (2) at each time horizon to account for how historical adaptive emissions through time t−1 will have affected the GMST pathway that year. This is implemented by iteratively updating the temperature pathway at each horizon relative to a given RCP baseline to account for temperature change due to the CAF, and then using this distance from the baseline to adjust projected adaptive energy use from that point forward. Starting in 2021 (the second projection year from the 8 data we use), we adjust the baseline projected temperature pathway each year to account for the cumulative effects of emissions from adaptation. We then update each country-year-fuel emissions tuple in that year to account for the adjusted GMST pathway. We repeat this procedure out to 2099 and recalculate the cumulative emissions changes to form an estimate of the CAF that accounts for concurrent dynamics between adaptation use and GMST change.

Specifically, we begin with the set of 66 projected time series of emissions changes computed under our baseline SSP2-RCP8.5 scenario from Eq. (2). These forecasts are country-level changes in emissions for each climate model m given by Eq. (11):

Eh i,t,m = Fh i Σp∈i [Jh(TN p,m,t, Xp,t) − Jh(TN p,m,0, Xp,t)] (11)

in year t for fuel h in country i under climate model m. We use these country-level sets of projected horizon-t emissions changes to estimate, for each country-fuel-year combination, the reduced form response function in Eq. (12):

Eh i,t,m = αh i,t ΔGMST t,m + γh i,t ΔGMST2 t,m + εh i,t,m (12)

Equation (12) capture the additional emissions due to adaptation for each i, t, and h, as a quadratic function of changes in GMST. We estimate Eq. (12) separately for each (i, t, h) tuple under the SSP2-RCP8.5 combination to form a time series of estimated α^h i,t and γ^h i,t coefficients for each country-fuel combination. From these estimates we construct time-country-fuel impulse response functions (IRFs): each IRF (for an i, t, h pair) gives the estimated additional change in emissions from energy-based adaptation induced by marginal changes in projected GMST derived from fuel h and country i at the time horizon t. This object is the derivative of Eq. (12) with respect to ΔGMST, shown in Eq. (13):

Θ^h i,t = ∂Eh i,t / ∂ΔGMST t = α^h i,t + 2γ^h i,t ΔGMST t (13)

These impulse responses give, by fuel-horizon-country, the local effects on concurrent emissions from adaptive energy use due to additional warming. We use this to project how prior temperature changes from adaptation will affect contemporary adaptive energy use relative to the baseline pathway. Starting in 2021, we update the values of emissions from energy-based adaptation using a first-order Taylor expansion around their baseline levels. For each country-fuel-year, we define an updated series of emissions using Eq. (14):

gEh i,t = Eh i,t + Θ^h i,t × β^ ΔfE t−1 (14)

where ΔfE t−1 is the cumulative emissions change due to energy-based adaptation between 2021 and year t−1 accounting for the dynamic effects of adaptation as defined in Eq. (15):

ΔfE t−1 = Σt−1 s=2021 Σi Σh Eh i,s (15)

and β^ is our mapping between emissions and temperature as described in Eq. (10) in Methods. Starting in 2021, we calculate the cumulative change in emissions due to adaptation while accounting for how adaptive emissions in turn affect future use for adaptation through the GMST channel. The first term in Eq. (14) captures baseline emissions from adaptation as calculated in Eq. (2) and used in the main text. The second term accounts for how the effects of past emissions from adaptation on the pathway of GMST in turn affect emissions in that year.

We iterate over the process in Eq. (14) over all country-fuel pairs at each horizon through 2099, updating the series for cumulative emissions and temperature each period. The iterative procedure ensures the baseline temperature path (under RCP8.5) is adjusted to reflect the cumulative level of adaptive emissions changes each year. We then recalculate the CAF with concurrent dynamics using the updated energy changes due to adaptation through year 2099. We track the dynamic CAF each year using Eq. (16):

CAFdyn τ = β^ Στ t Σi Σh gEh i,t (16)

The dashed line in Fig. 3a displays the time series of our baseline CAFτ without dynamic emissions-temperature linkages and the dynamic version CAFdyn τ incorporating concurrent emissions-GMST linkages over the 2021-2099 period under the baseline SSP2-RCP8.5 scenario.

The concurrent emissions-GMST linkages channel decreases cumulative emissions and temperature changes (in magnitude) attributable to adaptation. This occurs because of the negative nature of the CAF: lower emissions from declines in heating use outweigh additional emissions from increased cooling at each horizon. This in turn lowers cumulative emissions relative to baseline each year and with it our forecast for ΔGMST. These lower temperature levels in turn result in smaller future changes in emissions from adaptation through the IRFs, as declines in temperature lead to smaller declines in consumption of other fuels and smaller increases in emissions from electricity. This is indicated by an increasing gap between CAFτ and CAFdyn τ over time; the larger the cumulative negative effect from the CAF, the lower the updated temperature series is relative to baseline. This further decreases emissions changes from adaptation (in magnitude) each year, further lowering the temperature. However, empirically, by 2099, CAF2099 is −0.12 °C while CAFdyn 2099 is −0.119 °C. Given the minor consequence played by such dynamic emissions-GMST linkages, in our main text we emphasize CAFτ over CAFdyn τ.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
Emissions intensities and annual consumption at the country-by-fuel level are taken from the International Energy Agency’s Emissions Intensities Report and World Energy Balance datasets, respectively. The raw data are publicly available from the IEA. The supplementary information required for a bottom-up replication of all analysis are publicly available in the Zenodo repository associated with the paper at https://doi.org/10.5281/zenodo.10476310.

Code availability
A permanent reference to the specific version of the computer code used to produce the analysis presented in this manuscript is archived at https://doi.org/10.5281/zenodo.14894953. The active repository for the paper is publicly available on GitHub at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This work provides a framework for quantifying the implications of energy used for adaptation on global GHG emissions and mitigation goals.\nThis is a timely and relevant work, that focuses on a channel of anthopogenic emissions often understudied. The paper is\nalso concise and well written.\nThe methodology is well described, and robust as for the method directly proposed in this work for the computation of the\nCAF. The only important remark I have concerns the possible limitations of one key input used to compute the CAF: the\nenergy demand elasticities, taken from Rode et al., 2021. My concern derives from the following points:\n1) the sectoral aggregation of energy demand performed in Rode et al., 2021 may mask several different responses of\nopposite sign that add up to zero in the estimated elasticities, eg consumption of fossil fuels by buildings for heating and by\nthe industrial sector for cooling (as documented eg in De Cian and Sue Wing, 2019; Schleypen et al., 2019).\n2) the elasticities estimated by Rode et al., 2021 do not provide an indication of the long-run adpative adjustments of actors\n(the so called \"extensive margin\"), as they are estimated exploiting contemporaneous energy-demand and weather co-\nvariation. The works that have estimated the extensive margin in energy demand, although with different methods, come to a\nsimilar conclusion: i.e. long-term elasticities differ (and are typically higher) higher than short-term ones (De Cian and Sue\nWing, 2019; Auffhammer 2022).\nMy concern derives from the fact that works that adopt sectorally explicit coefficients for the calibration of the energy demand\nfor adaptation (and its tradeoff with mitigation), reach opposite conclusions with respect to this work (see an Ruijven et al.,\n2019; Colelli et al. 2022).\nMy suggestion to the authors would therefore be to clearly state in the discussion section the limitations that follow from the\nadoption of Rode et al., 2021, particularly as for point 1) and 2). Similarly, a more extensive discussion of the authors'\nexplanations to why this manuscript finds opposite conclusions to the few other comparable studies (Colelli et al. 2022; Van\nRuijven et al., 2019) is needed.\nA sensitivity analysis of the results based on elasticities from different sources would be ideal in order to really convince the\nreadership that the strong conclusions reached by the manuscript do not depend on one specific empirical work used for\ncalibration.\nMinor comments:\nI found it hard to investigate heterogeneity acorss regions with the scale of Figure 4-a, which collapses a lot of variation in\nthe lowest colour interval making India look very similar to the US. Furthermore, I would suggest to produce two maps: one\nbased on the cooling channel and one based on the heating channel (i.e. providing a decomposition of the CAF).\nI would suggest to discuss regional heterogeneity more thoroughly in the results section. One example would be clarifying\nwhy India is a country with a large negative CAF despite (as the author mention):a) its currently carbon-intensive power mix;\nb) that energy demand changes should come mostly from additional electricity for cooling than from lower fossil demand\nfrom avoided heating.\nReferences not cited in the manuscript:\nAuffhammer, M. (2022). Climate Adaptive Response Estimation: Short and long run impacts of climate change on residential\nelectricity and natural gas consumption. Journal of Environmental Economics and Management, 114, 102669.\nSchleypen, J.R., Dasgupta, S., Borsky, S., Jury, M., Ščasný, M., Bezhanishvili, L. (2019). D2.4 Impacts on Industry, Energy,\nServices, and Trade. Deliverable of the H2020 COACCH project.", "reviewer_2": "Dear Editor\nThis paper analyzes to what extent changes in energy demand due to climate change impacts may alter the magnitude of\nglobal warming. While this question is interesting and relevant, it is not very clear to me what this article really adds to the\nexisting literature.\nThe main conclusion of this study, namely the fact that climate change may globally decrease energy demand and GHG\nemissions (because it will reduce demand for heating more than it will increase demand for air conditioning) has already\nbeen analyzed in different papers, with approaches close to the one done here. Much work remains to be done on this\nsubject, though, as these existing studies have many limitations, but none of their research gaps is addressed here.\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope and increasing the\naccuracy of prior efforts to capture individual components of the CAF\" without further explanations. I have a hard time being\nconvinced by this strong statement. I encourage the authors to give more details, and especially analyze what their paper\nadds for instance to the many papers published through the Energya project (e.g. https://www.nature.com/articles/s41467-\n022-32471-1 https://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nMoreover, the authors derive very detailed figures from their analysis (e.g. on the reduced economic costs of climate\nchange), even though their assumptions are extremely simplistic. Their hypothesis that countries will keep the same\nemission factors as today is especially strange, in the context of prospective scenarios. It is also slightly incoherent with the\nSSP framework, as SSP scenarios include hypotheses on technological changes, notably in energy production. Changing\nthe energy mix of a country would deeply impact the results presented here.\nMoreover, along a similar idea, the authors use fixed energy consumption responses to daily variations in temperature. This\nis again a simplistic approximation, as this response will a priori change drastically over time, due to the increase in air\nconditioning equipment rate across the world (see e.g. https://www.iea.org/reports/the-future-of-cooling), to changes in\nheating and cooling technologies, and, more generally, to changes in buildings insulation and to the adoption of varying\nclimate change adaptation strategies (see e.g. https://iopscience.iop.org/article/10.1088/1748-9326/abc044).", "reviewer_3": "Review of “The Climate Adaptation Feedback”\nSummary\nThis paper studies the so-called “Climate Adaptation Feedback” (CAF), namely whether adaptation to anthropogenic climate\nchange results in a positive or negative contribution to anthropogenic greenhouse gas emissions. In particular, the paper\nleverages recently published research to study the CAF from global energy consumption. It estimates quite a substantial\nnegative feedback. That is, adaptation to climate change reduces emissions from energy consumption on net, due to\nemissions from reduced heating demand outweighing emissions from increased cooling demand.\nI like this paper a lot. It makes a crisp point with a nice conceptual framing, substantiated by some incredibly detailed\nmodelling. Therefore, I would like to see it published in Nature Communications. However, first I would like to table some\nqueries about modeling choices.\nComments\n• I am not entirely happy about the assumption of fixed emissions factors at their 2010-18 averages. The authors defend this\nchoice as avoiding “the inconsistency that would result from changing emissions factors while maintaining an SSP-RCP that\nfixes baseline emissions”. I understand this point, but it seems surmountable, for example by generating scenario-specific\nforecasts for emissions factor changes. Alternatively, a simpler approach that seems to me to still be safe enough would be\nto use longer-term historical trends in emissions factors as the basis for projecting presumably declining trends in the future\nthat are fixed across scenarios. That is to say, even if you worry about the inconsistency, I don’t think you need to assume\nfixed emissions factors. Whatever happens to climate policy from now on, it seems reasonable to suppose that renewables\nare here to stay, will continue to diffuse and assume a rising share of at least the electricity generation mix, and will come\ndown in cost.\n• I am also not entirely happy with the choice of RCP8.5 as the baseline scenario. I think few people regard RCP8.5 as a\nplausible baseline anymore. It tends to lead to bigger climate impact projections, which might explain its continuing\npopularity in the literature, but developments in policy and technology make the most likely scenario of future emissions\nlower. RCP8.5 is outside even the 90% confidence interval of the RFF-SPs, I think. RCP4.5 would be the SSP that best fits a\ncurrent policies scenario. If you want a higher emissions baseline, then I would not go beyond RCP7.0.\n• My last comment on modeling choices relates to the comparison of the CAF with countries’ NDCs. I am not sure this is\ncurrently set up as a fair comparison, because the NDCs prescribe emissions reductions in 2030, yet the CAF is quantified\nup to 2050. This makes the CAF look larger in relation to the NDCs than it probably is. However, I may have misunderstood\nhow the underlying literature on the NDCs, which this paper relies on, deals with post-2030 emissions reductions. Yet, if this\nliterature essentially freezes climate action in 2030, I would say my point stands.\n• The authors rightly steer the reader towards interpreting the results in terms of a ‘battle’ between declining emissions from\nspace heating and increasing emissions from AC, yet technically the modeling includes all uses of energy in the economy\noutside transportation. The authors may want to square that circle by discussing more directly how heating/cooling drives the\nnumbers. For example, it might be useful to report how much of non-transportation energy consumption goes to space\nheating and AC.\n• While cumulating discounted damages from 2020 to 2099 is a perfectly natural thing to do from an economic point of view,\nit is a little difficult to put the resulting $1.8 trillion in context. Most people would think of comparing it with current GDP, but\nthat is a flow and this is more like a change in a stock. Could you put this number in context?\n• The fact that the CAF varies little across SSP scenarios is at first unintuitive, given how much uptake of AC is determined\nby income (see also Davis et al., 2021). It would be helpful to have a slightly deeper explanation of this result.\n• The quantification of the heterogeneity in adaptation-induced emissions changes across countries would be more\nconvincing if it controlled for country size (normalizing by population or GDP) – at present this result seems quite strongly\ninfluenced by country size but size seems of secondary interest.\n• I would like the authors to consider whether the language in the final paragraph of the discussion exaggerates the extent to\nwhich current thinking and policy separates mitigation and adaptation. I do agree with this point to a certain extent, but I think\nequally any climate policy expert worth their salt would agree that (i) a good climate policy does both adaptation and\nmitigation and (ii) there are interactions between the two.\nMinor comments\n• Line 37: “incorporates” should be “incorporate”.\n• Line 226: “avoids 1.8 trillion [OF…] in present value terms”\n• Figure 1: perhaps you want to add an explanation of panels (e) and (f), for completeness?\n• Line 347: I think ACC means anthropogenic climate change, but I didn’t remember seeing it spelled out previously.\n• Estimating the GMST-cumulative CO2 relationship: in general, I liked this approach a lot, but I did wonder about the\nestimate of beta that is 1.4 times the median estimate of the TCRE. In particular, I wanted to get a handle on how the\nindirectly estimated contribution of non-CO2 GHGs compares with other estimates.\nDavis, L., Gertler, P., Jarvis, S. and Wolfram, C., 2021. Air conditioning and global inequality. Global Environmental Change,\n69, p.102299."}, "author_response": "We thank Reviewer 1 for their insightful comments and suggestions. We believe that by addressing each\nof them, the paper has been much improved. This document provides a guide to the nature and location of\nthe changes prompted by your comments. Specifically, we reproduce each of your comments italicized in\ngray and provide our replies in black. Excerpts from the paper are indented with the line numbers noted.\nFigures that are part of this document are prefixed with the letter “R” in order to distinguish them from\nfigures in the paper.\nThis work provides a framework for quantifying the implications of energy used for adaptation on global\nGHG emissions and mitigation goals. This is a timely and relevant work, that focuses on a channel of\nanthopogenic [sic] emissions often understudied. The paper is also concise and well written. The\nmethodology is well described, and robust as for the method directly proposed in this work for the\ncomputation of the CAF. The only important remark I have concerns the possible limitations of one key\ninput used to compute the CAF: the energy demand elasticities, taken from Rode et al., 2021. My concern\nderives from the following points:\n1) the sectoral aggregation of energy demand performed in Rode et al., 2021 may mask several different\nresponses of opposite sign that add up to zero in the estimated elasticities, eg consumption of fossil fuels\nby buildings for heating and by the industrial sector for cooling (as documented eg in De Cian and Sue\nWing, 2019; Colelli et al. 2022).\nThe reviewer is correct that energy demand across different sectors and/or fuels is likely to exhibit\ndifferent responses to climate change, as has been elucidated in prior work (De Cian and Sue Wing 2019;\nColelli et al. 2022). However, the goal of our analysis is to define and quantify the “climate adaptation\nfeedback” – that is, how total energy demand responses to climate change will alter global greenhouse gas\n(GHG) emissions, ultimately changing global average temperatures. Because GHGs are rapidly mixed in\nthe global atmosphere and lead to the same global warming regardless of where and from what source they\nwere emitted, the value of the CAF depends only on the aggregate energy demand changes under climate\nchange, and not on the individual sectoral components. It is critical that the aggregate energy demand\nresponse to changes in the climate be econometrically unbiased, but a decomposition into sectoral and\nfuel-specific effects has no bearing on the value of the CAF. Therefore, we define and focus our analysis\non the concept of the CAF, which depends only on the aggregate response of energy demand to\ntemperature change.\nHowever, we do agree with the reviewer that it is interesting to understand how different fuel sources may\ndifferentially contribute to the aggregate CAF value. We therefore ensure that these differential effects are\nconceptually clear in Figure 1, where we diagrammatically define the CAF and illustrate how it is\ncomposed of heterogeneous demand responses from different fuels used in both heating and cooling. We\nrevisit the critical distinction between fuel sources in our empirical analysis in Figure 3, where we break\ndown the total CAF to show that there is a warming feedback caused by increased electricity consumption\n(which is largely used for cooling) but also a cooling feedback caused by decreased other fuels\nconsumption (which are largely used for heating).\nTo ensure that our focus on the CAF, as opposed to sectoral responses, is clear, and to highlight the fact\nthat the CAF combines responses of opposite sign originating from heterogeneous underlying demand\nresponses, we have amended the text in two places. First, in the section defining the CAF and introducing\nour methodology, we now highlight that the warming or cooling potential of energy-based adaptation\ndepends only on the aggregate changes in demand. The new text reads (lines 96-106; edited text in red):\n“We develop a framework to sign and quantify the net effects of the forces illustrated in\nFigure 1. Specifically, we define the CAF in any given year as the difference in global mean\nsurface temperature (GMST) between a baseline value (e.g., projected warming under the\nRepresentative Concentration Pathway 8.5 (RCP8.5) emissions trajectory) and one\naccounting for future energy-based adaptation (e.g., projected warming under RCP8.5 plus\nthe net change in emissions from adaptation) (see Supplementary Materials Section 5.1).\nWe note that while energy demand responses to warming in specific sectors and by specific\nenergy sources will be heterogeneous, as illustrated in Figure 1, the sign and magnitude of\nthe CAF depend only on the net change in resulting emissions across all sectors and sources.\nTherefore, we focus our analysis on the aggregate emissions changes resulting from all\nmeasurable energy-based adaptations.”\nSecond, in the Results section, we point to well-established literature (cited by the reviewer) that,\nconsistent with our findings, shows the CAF is composed of fuel-specific responses that point in two\ndifferent directions. The new text reads (lines 176-186; edited text in red):\n“Figure 3b displays how several factors contribute to the sign and magnitude of the\nbenchmark CAF (first bar). First, adaptation-induced changes in electricity consumption,\nwhich are largely driven by increased demand for cooling under rising temperatures (4; 36;\n8), lead to a positive value of 0.06℃ (second bar). This electricity effect has been the focus\nof prior discussions of energy-based adaptation (11; 21). However, a negative CAF emerges\nwhen we add changes in demand for other fuels, whose value of -0.18℃ (third bar) more\nthan offsets the positive component from electricity, due to substantial projected declines in\ndemand for heating under climate change. These findings highlight the importance of\naccounting for all forms of energy demand that will change in response to climate change.\nMoreover, they reinforce results in the previous literature demonstrating that energy demand\nresponses to climate change differ in sign and magnitude across distinct sectors and fuel\nsources (16).”\n2) the elasticities estimated by Rode et al., 2021 do not provide an indication of the long-run adpative [sic]\nadjustments of actors (the so called [sic] \"extensive margin\"), as they are estimated exploiting\ncontemporaneous energy-demand and weather co-variation. The works that have estimated the extensive\nmargin in energy demand, although with different methods, come to a similar conclusion: i.e. long-term\nelasticities differ (and are typically higher) higher than short-term ones (De Cian and Sue Wing, 2019;\nAuffhammer 2022).\nWe agree wholeheartedly with the reviewer that accounting for extensive margin changes in energy\ndemand responses to weather variation is of first order importance when generating climate change impact\nprojections. We recognize that our manuscript was unclear on whether and how such adaptive responses\n1\nare accounted for in our analysis; in this revision, we have remedied this lack of clarity. Importantly, the\nelasticities we use from Rode et al. (2021a) do include extensive margin adjustments. Specifically, these\nelasticities account for both long-term adjustments due to a gradually evolving climate (e.g., the increased\nadoption of air conditioning in today’s temperate climates as they warm in the future) and due to growing\nincomes (e.g., the increased adoption of air conditioning in today’s developing economies as they\nexperience economic growth in the future). To execute this extensive margin adjustment, Rode et al.\n(2021) estimate regression models that interact long-run average climate and long-run average GDP per\ncapita with local short-run variation in the weather (see equation (3) of the Methods section in Rode et al.\nfor details), an approach analogous to the two-step methods used in Auffhammer (2022) and Colelli et al.\n(2023). These econometric estimates are then used in combination with climate and socioeconomic\nprojections to estimate how energy demand responses to daily weather realizations will change due to\nextensive margin adjustments.\nWe apologize that this critical feature of our analysis was unclear in the first version of the manuscript. We\nhave remedied this with updated text and with a new analysis that demonstrates the quantitative\nimportance of accounting for the extensive margin. We detail these changes here.\nFirst, in Section 2 of the paper, where we define the CAF and briefly describe its methodology, we now\nclarify that extensive margin adjustments to energy demand are accounted for in our estimates. We\nspecifically link to prior literature cited by the reviewer on the importance of this feature. The new text\nnow reads (lines 114-119; edited in red):\n“The estimated dose-response functions vary across space and time, accounting for the fact\nthat average incomes and baseline climates shape the sensitivity of energy use to\ntemperature changes, for example through changing the adoption and efficiency of energy-\nintensive technologies. Accounting for such extensive margin adjustments in the energy\nsector has been shown to have first-order impacts on projected impacts of climate change\non energy demand (4; 9; 16; 21).”\nWe additionally clarify how we incorporate these extensive margin adjustments in the Methods section,\nhighlighting that the interaction terms estimated in the Rode et al. (2021) demand response functions\nrepresent extensive margin responses. Updated text now reads (lines 447-459; edited text in red):\n“As detailed in ref. (8), these energy demand response functions depend primarily on the\nrealization of future daily temperatures within a given impact region, denoted by the vector\n𝑇𝑁 , under a given RCP scenario. The dose-response functions also include higher-order\n𝑝,𝑡\nterms of daily grid cell-level temperature realization along with a set of covariates\nsummarized by 𝑋 , which include projections of GDP per capita and population specific\n𝑝,𝑡\nto a SSP scenario and long-run averages of cooling and heating degree days under each\ntemperature trajectory. These covariates allow for the response of energy consumption to\ndaily temperature realizations to vary based on how the economic resources and climatology\nof a given location change in the future. This ensures the CAF calculation captures the\nextensive margin of adaptation that takes place over long periods and may be especially\nimportant in developing economies, where income growth is likely to lead to substantial\nincreases in cooling and heating technology adoption (4; 8; 9). We show the quantitative\nimportance of the extensive margin in shaping the CAF in Supplementary Information\nSection 2.”\nSecond, we have added to the paper an analysis that quantifies the critical role of the extensive margin in\ngenerating an accurate CAF. Specifically, we use an alternative set of energy demand projections under\nclimate change from Rode et al. (2021a) that do not account for how local responses to temperature\nrealizations change over time. That is, these estimates assume that energy demand response functions (or\n“elasticities”, as the reviewer refers to them) are held fixed into the future. Figure R.1 displays the result,\nwhich shows the importance of accounting for the extensive margin when calculating the CAF (in this\nexample, for an SSP2-RCP4.5 scenario). We find that the CAF would be only 20 percent as large without\nthese dynamic extensive margin adjustments. This figure has been added to a new section in the\nSupplementary Information (as Figure S3) in the revised manuscript. This new Supplementary section\nreads (SI Section 2, lines 28-49; new text in red):\n“Accounting for the Extensive Margin of Adaptation\nThis section demonstrates the importance of the extensive margin of adaptive energy use\nfor constructing an accurate estimate of the Climate Adaptation Feedback (CAF). In the\nbaseline CAF calculation shown in the main text, estimates of energy demand changes under\nclimate change are constructed using dose-response functions from ref. (1) that account for\nboth long-term adjustments due to gradually evolving climate (e.g., the increased adoption\nof air conditioning in today’s temperate climates as they warm in the future) and due to\ngrowing incomes (e.g., the increased adoption of air conditioning in today’s developing\neconomies as they experience economic growth in the future). To account for such extensive\nmargin adjustments, ref. (1) estimate regression models that interact long-run average\nclimate and long-run average GDP per capita with local short-run variation in the weather\n(we direct the reader to the Methods section in ref. (1) as well as their Supplementary\nInformation for details), an approach analogous to the two-step method used in ref. (2).\nThese econometric estimates are then used in combination with climate and socioeconomic\nprojections to estimate how energy demand responses to daily weather realizations will\nchange due to extensive margin adjustments.\nHere, we quantify the importance of accounting for such extensive margin effects by\ncomputing the CAF using a set of demand responses from ref. (1) that do not include\nextensive margin adjustments. Specifically, Figure S3 displays the baseline CAF for the\nSSP2-RCP4.5 scenario in blue, as well as a counterfactual CAF in red for the same scenario\nbut without extensive margin adjustments. We find that by 2100, the CAF would be only\n20 percent as large without these dynamic extensive margin adjustments.\ntext (blue line) to an alternative in which all energy demand responses to future temperature realizations are\nfixed at 2001-2010 levels (red line). This alternative shows what the CAF would be were extensive margin\nadjustments in energy demand responses to temperature ignored. Both estimates use the SSP2-RCP4.5\nsocioeconomic and emissions scenario.”\nThis treatment of the extensive margin, as estimated by Rode et al. (2021), is standard in the literature\n(e.g., De Cian and Sue Wing, 2019; Auffhammer, 2022; Colelli et al., 2023). However, this approach is not\nwithout its limitations. In particular, future technological innovation and/or rates of adoption may evolve\nin ways that are not well captured by the two-factor (long-run climate and income) model estimated in\nhistorical data by Rode et al. (2021) (or, equivalently, by Auffhammer, 2022 or Colelli et al., 2023). For\nexample, if the relative price of cooling technologies falls in the future, our CAF estimate could be too\nlow, understating positive emissions responses from future cooling-based adaptation. However, it is also\npossible that energy efficiency in cooling and/or heating technologies may improve with economic growth\nat rates faster than those observed historically, pushing the CAF in the opposite direction. It is incredibly\ndifficult to forecast these out-of-sample responses beyond what can be motivated by historical data. We\nhave updated the text in our Discussion to openly acknowledge and discuss these challenges, and we detail\nthem in Supplementary Information Section 2. Specifically, we speak to the differential short- and long-\nrun elasticities of energy use that are demonstrated in the literature (Viguié 2021; Davis et al. 2021;\nAuffhammer 2022). Updated text in the Discussion Section now reads (lines 322-334; edited text in red):\n4\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 20; 21). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nThese limitations are also discussed at the end of the new Supplementary Information Section 2 (SI\nSection 2, lines 54-62; new text in red):\n“The treatment of the extensive margin for adaptation in ref. (1) is standard in the literature\n(e.g., refs. (2; 3)). However, this approach is not without its limitations. In particular, future\ntechnological innovation and/or rates of adoption may evolve in ways that are not driven by\nthe two factors (long-run climate and income) used in the estimations of refs. (1), (2), and\n(3), among others. For example, if the relative price of cooling technologies falls in the\nfuture, our CAF estimate may be too low and understate the increase in future cooling\ndemand and emissions driven by warming climates. However, it is also possible that energy\nefficiency in cooling and/or heating technologies may improve with economic growth at\nrates faster than those observed historically, pushing the CAF in the opposite direction.”\nMy concern derives from the fact that works that adopt sectorally explicit coefficients for the calibration of\nthe energy demand for adaptation (and its tradeoff with mitigation), reach opposite conclusions with\nrespect to this work (see an Ruijven et al., 2019 [sic]; Colelli et al. 2022). My suggestion to the authors\nwould therefore be to clearly state in the discussion section the limitations that follow from the\nadoption of Rode et al., 2021, particularly as for point 1) and 2). Similarly, a more extensive discussion of the\nauthors' explanations to why this manuscript finds opposite conclusions to the few other comparable studies\n(Colelli et al. 2022; Van Ruijven et al., 2019) is needed.\nWe thank the reviewer for highlighting that we did not sufficiently discuss how our findings fit into the\nbroader literature. We have amended this in two ways. First, we added to the Introduction additional\ncontext for our framework. Specifically, the second half of the second Introduction paragraph now reads\n(lines 36-52; edited text in red):\n“It is built from state-of-the-art, globally-comprehensive, empirical estimates of how energy\ndemand responds to changing temperatures (8). These estimates incorporate heterogeneous\neffects of a changing climate on energy consumption across fuels and locations (2; 3; 8; 9;\n16; 17; 18; 19), as well as how income growth and climate change will alter these responses\nover time.\nTo our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nWe note that these citations point to prior research adjacent to this topic, namely that of De Cian and Sue\nWing (2019), Van Ruijen et al. (2019), Viguié (2021), Colelli et al. (2022) and Colelli et al. (2023),\nmodulo the order of citations in the excerpt above.\nIn the Discussion section, we have also added the following material explicitly stating that some prior\nresearch has found opposite conclusions to ours, and helping the reader understand the key differences\nbetween our study and this prior work. Updated text now reads (lines 284-298; edited text in red):\n“One existing study used computational general equilibrium methods to endogenize\nthe dynamic relationship between climate change and energy demand (22). The\nauthors find, in contrast to our results, evidence of a positive climate feedback from\nadaptation. However, this study relies on incomplete and coarse empirical estimates\nof energy demand responses, which are likely responsible for divergent findings.\nFor example, our CAF is built from 24,378 heterogeneous empirically-derived fuel-\nspecific energy demand responses representing subnational units across the globe,\nwhile there are just two aggregate fuel-specific demand responses in ref. (22)\nrepresenting two large global regions. Similarly, our estimates include demand\nresponses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22).\nMoreover, we account for country-level heterogeneity in emissions intensities, as\nopposed to heterogeneity across only 17 global macro-regions. Notwithstanding,\nour sensitivity analysis outlined in Methods Section 5.4 computes a dynamic\nversion of the CAF, finding a nearly identical result to the baseline estimate shown\nin Figure 3.”\nA sensitivity analysis of the results based on elasticities from different sources would be ideal in order to\nreally convince the readership that the strong conclusions reached by the manuscript do not depend on\none specific empirical work used for calibration.\nWe thank the reviewer for this comment. It prompted us to realize the paper was not clear enough as to\nwhy we selected the estimates from Rode et al. (2021) to derive our estimate of the CAF. We have\nremedied this in the updated draft, as detailed below.\nIn order to construct a comprehensive and accurate CAF, estimates of the temperature-energy demand\nfunction (what the reviewer calls elasticities) must satisfy four key criteria: (1) The estimates must take\ninto account possible changes across the entire temperature distribution, not just specific temperature\nthresholds (like <0 ℃ or >30 ℃), because climate change will alter the entire distribution of daily\ntemperatures; (2) The estimates must be differentiated between electricity and other fuels, allowing for a\ndifferent effect of temperature on the demand for both types of energy. This differentiation is important\nsince electricity typically has a lower carbon intensity than other fuels, and responds most strongly to\ncooling demand (whereas other fuels responds more strongly to heating demand); (3) The estimates must\nincorporate extensive margin responses of energy demand to changes in temperature over time (the\nimportance of this is discussed above); and (4) The estimates must allow for different localities to have\ndifferent responses to temperature change. This is necessary because climate change will have different\nimpacts across space, and because energy demand in each location depends on local energy infrastructure\nand policy (e.g., subsidies).\nTo our knowledge, the only empirical estimates of the temperature-energy demand function that satisfy\nthese 4 criteria are generated by Rode et al. (2021). This paper represents the most recent and\ncomprehensive global analysis of energy demand responses to climate change published to date.\nImportantly, prior studies that do not meet all criteria are very likely to generate different results for the\nCAF because they omit key aspects of the energy-temperature adaptive response. For example, De Cian\nand Sue Wing (2019) represents a possible set of empirical estimates that could be used in place of Rode et\nal. (2021), as the authors estimate multi-country energy-temperature demand responses. However, this\npaper: excludes the effect of temperature realizations in the open interval (12.5, 27.5) degrees C; estimates\njust two sets of fuel-specific demand responses (one for temperate and one for tropical regions, as opposed\nto the 24,378 heterogeneous sets of fuel-specific responses estimated in Rode et al.); relies on data from 6-\n49 countries, depending on the fuel and sector (vs. 149 countries in Rode et al.); and includes natural gas\nresponses to extreme cold only for the commercial sector (vs. all non-transport sectors in Rode et al.). This\nlack of comprehensiveness will bias estimates of the total emissions, and therefore warming, resulting\nfrom energy-based adaptation.\nTo demonstrate the importance of just one of these 4 criteria, Figure R.2 shows how the distribution of\ncountry-level average daily temperatures within the [12.5℃, 27.5℃] interval is a crucial aspect of future\nclimate change. The figure compares the global frequency of country-level daily average temperatures\nacross 160 countries in 2006 with forecasts for average daily countries in 2100 under RCP8.5 using the\nACCESS1-3 model (Copernicus, 2021). In 2006, average daily temperature readings in 160 countries were\nbetween the 12.5℃ and 27.5℃ degree interval on 58.6 percent of country-days. In 2100, this interval is\nforecast to contain only 37.8℃ percent of country days – such large changes in exposure to these common\ntemperatures has important implications for total energy use. The conditional mean of days realized in this\ninterval falls under a changing climate from 22.3℃ to 20.7℃; this difference in conditional means is both\nstatistically significant [P(T > t) < 0.0001] and large magnitude. This difference extends to higher\nmoments; a Kolmogorov-Smirnov test rejects the null hypothesis of equal distributions in 2006 and 2100\nof average daily temperatures on this interval at the 1 percent level. The estimates in De Cian and Sue\nWing (2019) do not account for the shift in the distribution of temperature realizations that account for\nalmost 40 percent of country-days across all countries, and over 50 percent of realizations in places that\ncomprise a large share of energy demand such as the United States or China.\nrealizations in 2006 while red bars show forecasts under RCP8.5 from the ACCESS1-3 model in the year\n2100 (Copernicus 2021). The 160 countries are drawn from the shapefiles for each ISO-3 code taken from\nthe GADM database (https://gadm.org).\nIn the paper, we now explicitly list these four criteria and justify our choice of Rode et al. (2021) for inputs\ninto the CAF calculation. Specifically, in the Methods section 5.1 we write (lines 471-480; new text in\nred):\n“There are multiple possible sources of empirically-based energy-temperature demand\nresponses that we could have used to estimate Equation 2. In selecting a source, we sought\nto identify estimates that: (1) account for demand responses to changes across the entire\ntemperature distribution, not just extreme temperatures; (2) are differentiated between\nelectricity and other fuels, allowing for a different effect of temperature on the demand for\nmultiple types of energy; (3) incorporate extensive margin responses of energy demand to\nchanges in temperature over time; and (4) allow for different localities to have different\nresponses to temperature change. While other multi-country energy-temperature demand\nestimates exist (e.g., ref. (16)), to our knowledge ref. (8) represents the only study meeting\nall four of these criteria.”\nBecause no other study meets all 4 criteria above, we do not conduct sensitivity analyses in which we\nsubstitute alternative energy-temperature demand responses for Rode et al. (2021) estimates. However, we\ndo perform several sensitivity analyses in the paper to probe the consistency of results that rely on Rode et\nal. (2021) demand responses (in addition to showing confidence intervals for the CAF that account for\nstatistical uncertainty in the Rode et al. (2021) estimates themselves). These include: a) a version of the\nglobal emissions at each time horizon to account for the effects of historical adaptive emissions through\ntime (Figure 3); b) a version of the CAF that projects changes in fuel-specific emissions intensities (Figure\n3); c) versions of the CAF for distinct socioeconomic and emissions scenarios (Figure 3); and d) a version\nof the CAF that shuts down the extensive margin of the energy-temperature demand response (Figure S3).\nMinor comments:\nI found it hard to investigate heterogeneity acorss regions with the scale of Figure 4-a, which collapses a\nlot of variation in the lowest colour interval making India look very similar to the US. Furthermore, I\nwould suggest to produce two maps: one based on the cooling channel and one based on the heating\nchannel (i.e. providing a decomposition of the CAF).\nWe thank the reviewer for these suggestions, both of which we have implemented. First, we updated the\ncolor scale in Figure 4a to highlight the large variation across countries in adaptation-induced CO\n2\nemissions. As the reviewer pointed out, the previous color scale masked much of this variation. The new\nfigure is:\nFigure R.3 (Figure 4a in main text). International heterogeneity in adaptation-induced cumulative CO2 emissions. The map and\nhistogram display country-level cumulative adaptation-induced CO2 emissions in 2099 measured in GtCO2 (𝜉\n2099\nin Equation (3)).\nWe also follow the reviewer’s second recommendation, making two maps that separate emissions driven\nby electricity (largely used for cooling) from those driven by other fuels (largely used for heating). Figures\nR.4 and R.5 below show the results. These figures are now in the Supplementary Information and\n9\ndescribed in the main text, as detailed below. In line with intuition, most countries are estimated to\nexperience fairly large increases in emissions from increasing electricity demand under climate change, as\nshown in Figure R.4. However, emissions from increasing electricity demand are offset by the near-\nuniversal decline in demand for other fuels driven by warmer temperatures globally, as shown in Figure\nR.5. Most countries experience more-than-offsetting emissions declines from other fuels, as shown in the\nmain text and Figure R.3.\nFigure R.4 (Figure S1 in Supplementary Information): The map displays the country-level cumulative\nadaptation-induced CO2 emissions in 2099 due to the electricity component of the CAF. This is measured in\nGtCO2, as in main text Figure 4a, and corresponds to the electricity component of Equation (3) in the main\ntext.\nFigure R.5 (Figure S2 in Supplementary Information): The map displays the country-level cumulative\nadaptation-induced CO2 emissions in 2099 due to the other fuels component of the CAF. This is measured in\nGtCO2, as in main text Figure 4a, and corresponds to the electricity component of Equation (3) in the main\ntext.\nThese new figures are discussed and cited in the main text on lines 206-213 (edited text in red):\n10\n“While 85% of countries experience CO emissions reductions, of those countries, the 5th\n2\nand 95th percentiles of cumulative adaptation-induced emission changes by 2099 are -\n0.027 and -6.24 GtCO , respectively. For the remaining 15% of countries that experience\n2\nincreases in emissions, magnitudes are small, with the 5th and 95th percentile range\nestimated at 0.0024 to 0.5 GtCO . These net changes in emissions can be decomposed into\n2\ncountry-level changes in emissions from electricity, which are positive in most countries\n(Supplementary Figure S1), and in emissions from other fuels, which are negative in\nvirtually all countries (Supplementary Figure S2).”\nI would suggest to discuss regional heterogeneity more thoroughly in the results section. One example\nwould be clarifying why India is a country with a large negative CAF despite (as the author mention):a)\n[sic] its currently carbon-intensive power mix; b) that energy demand changes should come mostly from\nadditional electricity for cooling than from lower fossil demand from avoided heating.\nWe appreciate the suggestion - the reviewer is correct that there is a great deal of regional heterogeneity in\nthe emissions effects of energy-based adaptation (as shown in Figure 4a of the main text), and that we can\ndo more to discuss and explain these differences across space, particularly with respect to India. In\nresponse, we have added text in the Results section that discusses regional heterogeneity, with a particular\nfocus on India (noting that we have limited space in the main text and view it as important to maintain a\ndiscussion of this heterogeneity in the context of Nationally Determined Contributions, as shown in Figure\n4c). We have also added a new Supplementary Information section detailing our investigation of the\nresults in India.\nIn the results section of the main text, the updated text now reads (lines 187-192; edited text in red):\n“Heterogeneity in CO emissions intensity also plays an important role. Using a constant\n2\nglobal CO emissions intensity -- a simple average across countries and fuels (fourth bar)\n2\n-- results in a CAF that's 37% smaller in magnitude than our benchmark estimate, which\nallows CO emissions intensities vary across countries and fuels. This result implies that\n2\nemissions are higher per unit of energy in countries with larger energy-based adaptive\nresponses to climate change.”\nAnd later at lines 203-223 (edits again in red):\n“…There is substantial heterogeneity in the magnitude of adaptation-induced CO\n2\nemission changes across countries. Figure 4a shows both a map and histogram of country-\nlevel cumulative adaptation-induced CO emissions changes by 2099 under SSP2-RCP8.5\n2\n(this is denoted as 𝜉 in Equation (3)). While 85% of countries experience CO\n2099 2\nemissions reductions, of those countries, the 5th and 95th percentiles of cumulative\nadaptation-induced emission changes by 2099 are -0.027 and -6.24 GtCO , respectively.\nsmall, with the 5th and 95th percentile range estimated at 0.0024 to 0.5 GtCO . These net\n2\nchanges in emissions can be decomposed into country-level changes in emissions from\nelectricity, which are positive in most countries (Supplementary Figure S1), and in\nemissions from other fuels, which are negative in virtually all countries (Supplementary\ninduced declines in CO emissions by end-of-century, despite facing substantial increases\n2\nin exposure to extreme heat in future years (38). This result is driven partially by the\ndemand responses from ref. (8), which estimate that electricity demand will increase by\n4.1 Exajoules (EJs) by 2099, while cumulative demand for other energy will fall by 7.0\nEJs, due to electricity-temperature demand responses being very low for lower-income\npopulations. This result is also influenced by heterogeneous emissions intensities: other\nfuels in India are substantially more emissions-intensive, implying that demand responses\ntranslate into an increase of only 0.9 GTCO emissions from electricity compared to a\n2\ndecline of 2.7 GTCO from other fuels. This decomposition is detailed in Supplementary\nFor the specific case of India, we have added an entirely new Supplementary Information section, as we\nthink this case study is both important quantitatively for the global CAF (given India’s size), and that it\nhelps elucidate the mechanisms behind the CAF. This new material in the Supplementary Information\nreads (SI Section 4, lines 86-123; new text in red):\n“A Case Study of India\nFigure 4a in the main text shows that India exhibits large adaptation-induced declines in\nCO emissions by end-of-century, despite facing substantial increases in exposure to\n2\nextreme heat in future years (6). In this section, we decompose this result, showing that it\nis driven both by heterogeneous demand responses across electricity (largely used for\ncooling) and other fuels (largely used for heating), as well as heterogeneous emissions\nintensities of these two fuel types. Specifically, we show our forecasts for how adaptation\nto climate change will affect energy demand (Figure S5) and in turn, emissions (Figure S6)\nfor India under our baseline SSP2-RCP8.5 scenario.\nFigure S5 shows that by 2099, adaptation to climate change will induce an additional 5\nExajoules (EJs) of electricity demand relative to a counterfactual demand absent\nadaptation. This effect is almost 50% larger in magnitude than total annual electricity\nconsumption in India today (c.f. Figure 2b in ref. (1)), representing a large increase in\ndemand for cooling. However, Figure S5 also shows that the increased demand for\nelectricity is offset by lower demand for other fuels in a future under climate change. The\ndashed line in Figure S5 shows that other fuels consumption is projected to fall by over 6\nEJs by 2099, or about half of aggregate consumption today. This result is driven by a key\nfinding of the ref. (1) paper, which is that for lower-income populations, electricity demand\nis essentially unresponsive to high temperatures, while other fuels (including biomass\nburning, among other sources) are used by such populations to respond to moderate to cold\nconditions. Even though India is projected to experience substantial economic growth\nunder the SSP2 socioeconomic scenario, its relatively low level of baseline income inhibits\na stronger electricity demand response (1).\nThis larger (in magnitude of EJs) demand effect on other fuels is compounded by the fact\nthat on a unit basis, other fuels consumption in India is more carbon intensive than is\n12\nelectricity. Figure S6 shows the associated effects on emissions from the changes in\ndemand plotted in Figure S6. India's other fuels consumption emits 1.38 kgCO per kWh\n2\nof energy produced, almost double the factor of 0.8 kgCO per kWh for electricity. These\n2\ntwo facts combine to produce the large (negative) change in cumulative emissions for India\nin 2099, shown in main text Figure 4a.\nFigure R.6 (Figure S5 in Supplementary Information): This figure shows the mean value of projected\nannual changes in energy demand from electricity (solid line) and other fuels (dashed line) due to adaptation\nto climate change in India between 2020 and 2099. Values are averaged over projections under an SSP2-\nRCP8.5 scenario.\nFigure S5 into carbon dioxide emissions using emissions factors for India. CO2 emissions due to energy-\nbased adaptation are shown for electricity (solid line) and other fuels (dashed line).”\nReferences:\nAuffhammer, Maximilian. \"Climate Adaptive Response Estimation: Short and long run impacts of climate\nchange on residential electricity and natural gas consumption.\" Journal of Environmental Economics and\nManagement 114 (2022): 102669.\nColelli, Francesco Pietro, Johannes Emmerling, Giacomo Marangoni, Malcolm N. Mistry, and Enrica De\nCian. \"Increased energy use for adaptation significantly impacts mitigation pathways.\" Nature\nCommunications 13, no. 1 (2022): 4964.\nColelli, Francesco Pietro, Ian Sue Wing, and Enrica De Cian. \"Air-conditioning adoption and electricity\ndemand highlight climate change mitigation–adaptation tradeoffs.\" Scientific Reports 13.1 (2023): 4413.\nCopernicus Climate Change Service, Climate Data Store, (2021): In situ total column ozone and ozone\nsoundings from 1924 to present from the World Ozone and Ultraviolet Radiation Data Centre. Copernicus\nClimate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.99842490 (Accessed on 28-\n02-2024).\nEnvironmental Change, 69, p.102299\nDe Cian, Enrica, and Ian Sue Wing. \"Global energy consumption in a warming climate.\" Environmental\nand Resource Economics 72 (2019): 365-410.\nRode, Ashwin, Tamma Carleton, Michael Delgado, Michael Greenstone, Trevor Houser, Solomon Hsiang,\nAndrew Hultgren et al. \"Estimating a social cost of carbon for global energy consumption.\" Nature 598,\nno. 7880 (2021): 308-314.\nVan Ruijven, Bas J., Enrica De Cian, and Ian Sue Wing. \"Amplification of future energy demand growth\ndue to climate change.\" Nature Communications 10, no. 1 (2019): 2762.\nViguié, V., S. Juhel, T. Ben-Ari, M. Colombert, J. D. Ford, L. G. Giraudet, and D. Reckien. \"When\nadaptation increases energy demand: A systematic map of the literature.\" Environmental Research Letters\n16, no. 3 (2021): 033004\n15"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for their detailed work and replies, which I consider satisfactory for the purpose of accepting the paper for\npublication.\nI only have a few minor edits I would suggest, to enhance the clarity of some key points of the work:\n- While the authors correctly specify that the spatial resolution of energy demand responses is much larger than in previous\nworks (~25,000 subnational localities), I would find it useful if the authors included a brief explanation on the source of such\nheterogeneity, namely the possibility to identify locality-specific shocks thanks to the interaction coefficients between\ntemperature exposure, income and average climate estimated in a single econometric equation by Rode at al.\n- The in depth case study on India is very interesting and useful. A key point that drives this works' result, stressed indeed by\nthe authors in the SI - is:\n\"This result is driven by a key finding of the ref. (1) paper, which is that for lower-income populations, electricity demand\nis essentially unresponsive to high temperatures, while other fuels (including biomass burning, among other sources) are\nused by such populations to respond to moderate to cold conditions. Even though India is projected to experience\nsubstantial economic growth under the SSP2 socioeconomic scenario, its relatively low level of baseline income inhibits a\nstronger electricity demand response (1).\"\nIn fact, the authors note that the fossil fuels reductions they project are \"about half of aggregate consumption today\".\nFirst, I think the above-mentioned comment provided by the authors could be moved to the discussion section, as it can help\nreaders to understand the underlying dynamics driving the results.\nSecond, the specific result on India's fuel reductions suggests that a great amount of energy is already being used today to\nheat Indian homes - or for other heating-related energy uses. If possible, it would be very useful to add in the SI section a\nrough calculation/statistic of present-day fossil fuel consumption for heating services in the country (eg exploiting IEA or\nother institutional data), to put the projected reductions into perspective.\n(Remarks on code availability)", "reviewer_2": "Dear Editor\nWhile I acknowledge the efforts made by the authors, I remain unconvinced by their response and by their results. In this\npaper, they simulate with an econometric approach how changes in temperature due to climate change may impact energy\ndemand. They then use country-specific carbon intensities to translate these energy demand variations into carbon emission\nvariations.\nMy first remark is about the novelty of this approach, and what it adds to the literature. The authors explain that their work is\nnovel because, compared to existing papers:\n1. it assesses energy demand variations caused by changes in temperature with a much finer spatial resolution (25,000\nsubnational localities in 146 countries) and a much finer temperature resolution (energy demand response to the full\ndistribution of daily temperature realizations, instead of commonly used heating-degree days and cooling-degree days)\n2. it goes beyond estimating energy demand variations, and computes variations in GHG emissions\nRegarding point 1, assessing an energy demand response with an extremely fine resolution does not necessary lead to a\nbetter assessment, because of the risk of over-fitting. This is especially concerning because the authors find conclusions\nopposite to the rest of the literature, and justify this difference by the fact that they precisely compute more response\nfunctions. The authors should try to find out why using their data leads to such results. They should try to do their analysis\nwith temperature thresholds, see if the results are different, and explain why assessing an energy-demand response to the\nfull distribution of daily temperature realizations could leads to better predictions than using the commonly used temperature\nthresholds. They should also assess the spatial heterogeneity of the response functions, and see for instance if some\nspecific locations are driving the results. Simply saying that, because they have more data points and do many more\nregressions, they are more correct that other papers, is not correct.\nRegarding point 2, the authors use an extremely crude, and, to my sense, misleading, approach to translate energy demand\nvariations into GHG emissions variations. Compared to the first version of the manuscript, the authors do not suppose any\nmore that countries emission factors will remain constant over the XXIst century. However, they now suppose that countries\nemission factors will evolve following the current average global trend in emission intensity. Such a scenario does not make\nany sense to me: the global trend in emission intensities hides a wide variety of changes at each country level. Moreover,\neven if the authors were using country-specific trends, it would be strange to rely on such trends. Most countries have indeed\nalready begun to implement deep changes in their electricity production technologies, for instance, so current trends are not\nparticularly representative of what is expected over the next decades. The authors state that there are \"no existing country-\nspecific projections of future emissions factors up to 2100\". This assumption is partly false, as this is specifically what\nIntegrated Assessment models are providing (see for instance https://unfccc.int/topics/mitigation/workstreams/response-\nmeasures/modelling-tools-to-assess-the-impact-of-the-implementation-of-response-measures/integrated-assessment-\nmodels-iams-and-energy-environment-economy-e3-models or https://www.carbonbrief.org/qa-how-integrated-assessment-\nmodels-are-used-to-study-climate-change/ ). This is for instance what is done in ref. 22 of the paper, in which \"fuel and\ntechnology choices are optimized intertemporally, under a set of constraints, including carbon and other energy prices\".\nMore globally, the discrepancy between the extremely detailed regressions of point 1 and the overly simplistic factors used\nfor point 2 is also of concern. Even if point 1 uses very detailed data, the overall level of confidence of any scientific result is\nalways determined by the weakest point of the analysis. The fact that the energy demand response function relies on very\nfine data cannot be used as a pretext to hide the fact that simplistic hypotheses on emission factors are one of the main\ndrivers of the results.\nAnother general comment is the fact that the authors often tend to exaggerate their contribution. The term \"Climate\nAdaptation Feedback\", especially, is rather misleading. As stated by the authors, they do not capture future shifts in energy\ndemand responses used to adapt to climate change driven by factors outside the two-factor model of Ref. 8 (changes in\nincome and temperature) and they do not take into account any potential changes in building insulation levels, in heating or\ncooling technologies, or, more generally, any specific climate change adaptation strategies (urban greening etc.). For the\nsake of clarity I would recommend using a more specific term, such as \"heating and cooling demand if countries keep\npresent technologies and emission factors\".\n(Remarks on code availability)", "reviewer_3": "I think the authors have taken adequate steps to address my concerns and therefore from my perspective the paper can\nproceed to publication.\n(Remarks on code availability)"}, "author_response": "We thank Reviewer 2 for their insightful comments and suggestions, which we think have substantially\nimproved the paper. This document provides a guide to the nature and location of the changes prompted by\nyour comments. Specifically, we reproduce each of your comments italicized in gray and provide our\nreplies in black. Excerpts from the paper are indented with the line numbers noted. Figures that are part of\nthis document are prefixed with the letter “R” in order to distinguish them from figures in the paper.\nThis paper analyzes to what extent changes in energy demand due to climate change impacts may alter the\nmagnitude of global warming. While this question is interesting and relevant, it is not very clear to me\nwhat this article really adds to the existing literature.\nThe main conclusion of this study, namely the fact that climate change may globally decrease energy\ndemand and GHG emissions (because it will reduce demand for heating more than it will increase demand\nfor air conditioning) has already been analyzed in different papers, with approaches close to the one done\nhere. Much work remains to be done on this subject, though, as these existing studies have many\nlimitations, but none of their research gaps is addressed here.\nWe thank the reviewer for making clear that we did not sufficiently clarify the key contributions of the\nanalysis, relative to the broader literature. We have amended the text to better situate our paper within the\nliterature and to clarify the novel aspects of our analysis.\nMost importantly, the goal of our paper is to introduce and define the Climate Adaptation Feedback\n(CAF), to explain its importance, and to provide the first global estimate of the CAF using the most recent,\ncomprehensive, and accurate projections of energy demand responses to climate change. To our\nknowledge, just one prior paper (Colelli et al., 2022) estimates a similar object, computing the warming\ninduced by adaptive energy use under climate change. We detail below how our paper expands the scope\nand improves the accuracy of that prior result. In addition, our analysis uncovers substantial regional\nheterogeneity in the CAF and uses such heterogeneity to assess the implications of adaptive energy use for\nmitigation policy – specifically, the Nationally Determined Contributions under the Paris Agreement.\nOur textual changes are in two places. First, we added to the Introduction additional context for our\nframework. Specifically, the second half of the second Introduction paragraph now reads (lines 36-52;\nedited text in red):\n“It is built from state-of-the-art, globally-comprehensive, empirical estimates of how energy\ndemand responds to changing temperatures (8). These estimates incorporate heterogeneous\neffects of a changing climate on energy consumption across fuels and locations (2; 3; 8; 9;\n16; 17; 18; 19), as well as how income growth and climate change will alter these responses\nover time.\nTo our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nAs the reviewer points out, our analysis still has important limitations. To ensure these are fully discussed,\nand to help the reader understand the key differences between our study and the only prior analogous\nanalysis, we have added text to the Discussion section. Updated text in the Discussion Section now reads\n(lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\responses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nincluded\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited text in red):\n“To our knowledge, this work represents the first definition and calculation of the CAF. In\nso doing, it builds on prior efforts to empirically estimate how energy demand responds to\nwarming temperatures (16; 20; 21), as these inputs are critical to estimating the adaptation\nfeedback. It also expands the scope and improves the accuracy of the only existing estimate\nof the global warming effects of energy-based adaptation (22) by: dramatically increasing\nthe spatial resolution of energy demand responses (from 2 macro-regions to ~25,000\nsubnational localities); leveraging energy demand responses estimated on a comprehensive\nsample of 146 countries (instead of <50 countries); including the energy demand response\nto the full distribution of daily temperature realizations, instead of only assessing the effects\nof temperature events in the tails of the distribution; and accounting for country-level\nheterogeneity in emissions intensities of electricity and other fuels (instead of using common\nemission factors across regions).”\nIn our short methodological summary in the main text, we write (lines 107-119; edited text in red):\n“We implement this framework by leveraging newly-available high-resolution projections\nof future energy changes in response to local temperature realizations from ref. (8). These\ncausal “dose-response” functions represent the change in the use of final energy sources --\nelectricity and all other fuels -- in response to variations in daily temperature, pooling energy\nconsumption across residential, commercial, industrial, and agricultural end-uses (excluding\ntransportation). Any adaptive actions taken by individuals, firms, or public agencies across\na broad spectrum of sectors, such as the use of air conditioning or space heating, are included\nin these estimates. The estimated dose-response functions vary across space and time,\naccounting for the fact that average incomes and baseline climates shape the sensitivity of\nenergy use to temperature changes, for example through changing the adoption and\nefficiency of energy-intensive technologies. Accounting for such extensive margin\nadjustments in the energy sector has been shown to have first-order impacts on projected\nimpacts of climate change on energy demand (4; 9; 16; 21).”\nIn the Results section, we put our estimate of a negative CAF in context of a broader literature (lines 180-\n186; edited text in red):\n“However, a negative CAF emerges when we add changes in demand for other fuels, whose\nvalue of -0.18℃ (third bar) more than offsets the positive component from electricity, due\nto substantial projected declines in demand for heating under climate change. These findings\nhighlight the importance of accounting for all forms of energy demand that will change in\nresponse to climate change. Moreover, they reinforce results in the previous literature\ndemonstrating that energy demand responses to climate change differ in sign and magnitude\nacross distinct sectors and fuel sources (16).”\nAs mentioned above, in the Discussion section we help the reader understand the key differences between\nour study and the only prior analogues (lines 279-298; edited text in red):\n“Our analysis has several limitations. First, we define the CAF relative to a fixed SSP for\nsocioeconomic conditions and fixed RCP for baseline global emissions. The advantage of\nthis approach is that SSPs and RCPs are widely used in climate projections and do not\nalready account for emissions arising from energy-based adaptation (44). As discussed\nabove and in Methods Section 5.4, this abstracts from a full characterization of the dynamic\ninterplay between changing adaptive energy demand and climate change. One existing study\nused computational general equilibrium methods to endogenize the dynamic relationship\nbetween climate change and energy demand (22). The authors find, in contrast to our results,\nevidence of a positive climate feedback from adaptation. However, this study relies on\nincomplete and coarse empirical estimates of energy demand responses, which are likely\nresponsible for divergent findings. For example, our CAF is built from 24,378\nheterogeneous empirically-derived fuel-specific energy demand responses representing\nsubnational units across the globe, while there are just two aggregate fuel-specific demand\nresponses in ref. (22) representing two large global regions. Similarly, our estimates include\ndemand responses to the full distribution of daily temperature realizations, instead of only\nthe effects of temperature events in the tails of the distribution in ref. (22). Moreover, we\naccount for country-level heterogeneity in emissions intensities, as opposed to heterogeneity\nacross only 17 global macro-regions. Notwithstanding, our sensitivity analysis outlined in\nMethods Section 5.4 computes a dynamic version of the CAF, finding a nearly identical\nresult to the baseline estimate shown in Figure 3.”\nAlong with later in the updated manuscript (lines 322-334; edited text in red):\n“Third, our analysis is somewhat limited in its coverage of energy-based adaptations. We\nomit possible feedbacks arising from transportation-based adaptation due to a paucity of\nempirical estimates for how transportation-related GHG emissions respond to a warming\nclimate (e.g., ref. (48) provide such estimates, but only for the United States). Our estimates\nmay also fail to account for long-run changes in the response of energy demand driven by\nshifts in preferences or technological change, leading us to not fully capture some facets of\nthe extensive margin for adaptation (9; 21; 22). If the price of electricity falls drastically in\nthe future relative to that of other energy, we may underestimate electricity demand\nresponses which would bias our CAF estimate downward. Conversely, if end-uses for\nelectricity become more efficient in a way previously unexplained by income growth, our\nCAF will overstate future emission from electricity. More generally, our estimates may fail\nto capture future shifts in energy demand responses used to adapt to climate change driven\nby factors outside the two-factor model in ref. (8).”\nTo justify the novelty of their work, the authors say that their study is \"dramatically expanding the scope\nand increasing the accuracy of prior efforts to capture individual components of the CAF\" without further\nexplanations. I have a hard time being convinced by this strong statement. I encourage the authors to give\nmore details, and especially analyze what their paper adds for instance to the many papers published\nthrough the Energya project (e.g. https://www.nature.com/articles/s41467-022-32471-1\nhttps://www.nature.com/articles/s41467-019-10399-3 or\nhttps://www.sciencedirect.com/science/article/pii/S0378778822004868?via=ihub).\nWe agree with the reviewer that this statement exaggerated our contribution – we have removed it from the\nrevised manuscript, along with any other analogous claims. As detailed in our response to the prior\ncomment, we have clarified our contribution relative to the existing literature throughout the text. Here we\nlist the multiple places in the updated manuscript in which we cite, discuss, and compare prior literature to\nour analysis (noting that some of these were also mentioned above in response to the related prior\ncomment).\nIn the Introduction, we write (lines 41-52; edited"}]

Decision letter