Unveiling the environmental impact of earthquakes in Europe

Domain: neuroscience
Published: 2026-01-27
Editorial outcome: minor_revision

Abstract
Earthquakes represent a significant but often overlooked environmental burden in the construction sector, driven by post-disaster repairs and reconstruction that generate substantial carbon emissions. Here, we unveil the environmental toll of earthquakes in Europe by presenting a seismic risk map of embodied carbon associated with earthquake damage across residential, commercial, and industrial buildings. We develop a harmonised database of material quantities and carbon factors covering diverse construction materials and building types, which we integrate into a continental-scale probabilistic seismic risk model. Our analysis reveals that Europe’s building stock embodies nearly 14 billion tonnes of CO₂e, with seismic damage, based on over three million earthquake scenarios, contributing an average of 6.6 million tonnes annually. These values are comparable to the yearly emissions of millions of cars or tens of thousands of transatlantic flights. Our models and datasets offer a scalable, transferable tool to incorporate sustainability into disaster risk reduction and advance climate-resilient development.

Introduction
The construction sector stands as one of the largest contributors to environmental impacts, including greenhouse gas (GHG) emissions, raw material depletion, and waste generation1. Yet, in regions prone to natural hazards, its impact extends beyond routine construction and operations. Earthquakes, for example, can generate millions of tonnes of emissions during post-event recovery due to the vast volumes of debris, repair of damaged components, and reconstruction of collapsed buildings. Despite the growing global efforts to decarbonise the construction sector, life-cycle assessments (LCAs) have predominantly focused on planned construction impacts and operational energy consumption, rarely accounting for any disaster-related consequences. As a result, earthquake-induced emissions remain largely unquantified, limiting the integration of climate considerations into current disaster risk and resilience frameworks.

Several past disasters have underscored the severe environmental toll of earthquakes, typically quantified through embodied carbon, a globally recognised indicator of global warming potential. This metric captures the total GHG emissions, expressed as carbon dioxide equivalents (CO₂e), arising from material production, construction, use (excluding operational energy/water use), and end-of-life phases. For instance, the 2023 Türkiye-Syria earthquakes generated up to 210 million tonnes of debris, resulting in over 60 million tonnes of CO₂e from waste management2. Gonzalez et al.3 reported that the demolition and reconstruction of reinforced concrete buildings following the New Zealand’s 2010–2011 Canterbury earthquake sequence produced 300 thousand tonnes of CO₂e. Similarly, Pan et al.4 estimated that the reconstruction, debris removal, and land conversion following the 2011 Great East Japan earthquake and tsunami released about 26 million tonnes of CO₂e, equal to 2.1% of Japan’s total GHG emissions in 2010. These alarming impacts, coupled with the rising sustainability awareness worldwide, have prompted research to incorporate earthquake impacts into LCA frameworks5–8. Some studies have further proposed integrating these impacts alongside conventional risk metrics, such as economic losses and fatalities, to inform the design of rehabilitation strategies that enhance both structural resilience and environmental performance9–15. To build on these efforts, it is essential to consider environmental metrics like embodied carbon within the broader framework of seismic risk assessment.

Seismic risk assessment generally integrates three key components16: (i) a probabilistic seismic hazard model that defines the frequency of exceeding a range of ground-shaking intensity levels; (ii) a set of fragility and vulnerability models probabilistically characterising seismic damage and loss at different intensity levels; and (iii) an exposure model detailing the assets at risk, including their location, replacement value, quantity, and physical characteristics. Risk can be communicated to stakeholders and decision-makers in terms of average annual loss (AAL), which reflects the expected yearly loss from seismic damage and subsequent repairs, considering all potential earthquake scenarios derived probabilistically. To allow consistent comparisons across assets or regions, the AAL incurred by each asset is typically normalised by its replacement value, leading to average annual loss ratios (AALRs). Compared to traditional scenario-based assessments, which evaluate losses from single events with fixed magnitude, location, and rupture parameters, the AAL approach offers a more robust, holistic representation of long-term seismic risk.

While major progress has been achieved in seismic risk assessments for conventional loss types (e.g., repair costs, fatalities), quantification of embodied carbon related to earthquake damage remains limited, which hinders integrating sustainability criteria into disaster-resilience frameworks. The challenge is particularly evident at regional scales, where the diversity of construction types complicates the development of robust consequence models that translate various seismic damage levels (also called damage states) into embodied carbon. Most existing studies have derived such models for individual structural and non-structural building components, which are more suited for assessing single structures11,17–19. Only a few studies have extended these models to entire buildings18,20, with the goal of supporting portfolio-level risk assessments21. However, these efforts typically tackle a narrow range of building types and have not yet scaled to regional or national applications.

To address this research challenge, we estimate the embodied carbon associated with earthquake-induced damage across Europe’s residential, commercial, and industrial building stock. This begins with developing an embodied carbon exposure dataset that complements the latest European building exposure model22, facilitating environmental impact assessments of building portfolios at national or regional scales. The dataset is applicable to earthquakes and other hazards, and it offers a foundation for characterising the carbon footprint of Europe’s construction sector. To assemble this dataset, we collect, compile, and harmonise environmental-impact information from various European sources, creating an open-access database of embodied carbon values for different construction materials, structural/non-structural components, and building types. Using these data, we conduct a pan-European seismic risk assessment and produce a continental-scale map that conveys seismic risk in terms of the average annual embodied carbon (AAEC) associated with earthquake damage. All datasets and maps are publicly available via a GitHub repository23, with an additional interactive platform provided via the EPOS Built Environmental Data (BED) service at https://embodiedcarbon.builtenvdata.eu.

Results
European embodied-carbon data for building materials and components
Embodied carbon generated by seismic damage can be expressed as a fraction of the emissions from complete building replacement, as outlined in the “Methods” section. Reliable estimates of replacement carbon for different building types are therefore essential for performing risk assessments. Such estimates must capture emissions across multiple building life-cycle stages, including material production (modules A1-A3), construction (modules A4-A5), and end-of-life processes linked to the demolition of the damaged building (each module corresponds to a specific activity within one of the life-cycle stages, as described in the “Methods” section).

To address the material production stage (modules A1-A3), we derive embodied carbon factors (ECFs) for a range of construction materials and for structural and non-structural components prevalent in new constructions. This is based on an extensive collection and compilation of representative European environmental-impact data from published sources24–27. Table 1 lists a subset of the derived ECFs, expressed as average CO₂e per material or component unit. The full dataset, which is available in the GitHub repository23, includes both individual materials (e.g., steel rebar, ready-mix concrete) and their typical assemblies in structural or non-structural components (e.g., reinforced concrete beams, brick partitions). We acknowledge that this dataset is not exhaustive, but it cover the key components most likely to incur earthquake damage. For emissions from the construction stage (modules A4-A5) and end-of-life activities related to damaged buildings, we adopt simplified approaches recommended by existing guidelines28–30, as detailed in the “Methods” section.

We note that the ECFs in Table 1 are average values. The coefficients of variation, also provided in the same repository, range from 0.17 to 0.80, depending on the variability and consistency of the underlying data. For predominant construction materials like ready-mix concrete, cement, and hollow clay bricks, these coefficients are below 0.35, implying similar levels of uncertainty for building replacement.

Open-access database of embodied carbon for building replacement in Europe
We develop an open-access database providing detailed embodied carbon data on various building types, designed to serve as input for seismic risk assessments. The selected replacement types reflect Europe’s contemporary engineering practices, implicitly assuming that vulnerable structural systems (e.g., adobe, stone structures) are no longer viable for new construction. This approach aligns with the “Build Back Better” strategy31,32, aimed at enhancing community sustainability and resilience by integrating risk reduction measures into the post-disaster restoration of infrastructure and societal systems33. In this context, applying the “Build Back Better” principle means that the embodied carbon from seismic damage is expressed as a fraction of the emissions linked to a modern replacement rather than the original building, particularly when the latter is constructed with obsolete or seismically deficient materials. To streamline the modelling process, we assume that replacement buildings retain the same material category as the ones they substitute, but with improved structural systems and construction quality. For instance, adobe or stone buildings are assumed to be replaced with reinforced masonry structures, while older reinforced concrete buildings are rebuilt with concrete complying with current standards (see the “Methods” section).

To calculate the embodied carbon of building replacement, we derive average quantities of structural and non-structural components for each building type and occupancy category, based on simulated designs of archetype buildings. We then multiply these quantities by the corresponding average ECFs described in the previous section, capturing emissions from material production (modules A1-A3). Contributions from the construction stage (modules A4-A5), including pre-construction works, are subsequently added following the procedure illustrated in the “Methods” section. The resulting estimates represent average values; propagating the ECF uncertainty yields an overall coefficient of variation close to 0.35. The full dataset, featuring material quantities and replacement embodied carbon per unit floor area of relevant building types and occupancies, disaggregated by life-cycle stages and by structural versus non-structural contributions, is available via the GitHub repository23.

Figure 1a shows the replacement embodied carbon for different building types and occupancy categories in Europe, measured in kg CO₂e per square metre of floor area (see the “Methods” section for the definition of each building type). The individual shares of life-cycle phases in these estimates are consistent, irrespective of the construction material, lateral load-resisting system, or occupancy category. As expected, material/component production (modules A1-A3) consistently dominates the total amount of embodied carbon, accounting for at least 85%. Construction processes (modules A4-A5), including pre-construction demolition and disposal activities, contribute the remaining 15%, with negligible variations.

Our analysis reveals that the primary construction material is the most influential attribute in determining replacement embodied carbon, underscoring the key role of material selection in reducing environmental impacts. Considering A1-A3 modules only, we estimate average embodied carbon values of building replacement of 430 kg CO₂e/m² for reinforced masonry (MR), 375 kg CO₂e/m² for reinforced concrete (CR), 270 kg CO₂e/m² for steel (S), and 155 kg CO₂e/m² for timber (W) buildings. These variations stem from differences in quantities and ECFs of construction materials and building components. For example, reinforced masonry buildings feature thick walls constructed from high-impact clay brick units (0.20 kg CO₂e/kg), along with steel rebar and mortar, resulting in higher emissions than ready-mix concrete (0.11 kg CO₂e/kg). Conversely, hot-rolled steel and timber have larger ECFs per unit (0.69 and 0.25 kg CO₂e/kg, respectively). However, their application in structural components tends to be more material-efficient, requiring smaller cross-sectional dimensions than those used in concrete or masonry systems.

Beyond material choice, Fig. 1a indicates that the lateral load-resisting system can also affect the replacement embodied carbon. For instance, reinforced masonry buildings with concrete hollow blocks and ribbed concrete floors (MR+CBH/LWAL) exhibit up to 33% higher replacement embodied carbon than similar buildings with hollow clay bricks and timber composite floors (MR+CLBRH/LWAL/RWO/FW). Replacement embodied carbon is further influenced by occupancy category, which introduces significant variations in the type and quantity of construction materials and building components. Commercial and industrial buildings have the highest embodied carbon (Fig. 1a), up to 24% greater than residential buildings, due to their design requirements for supporting heavier loads and wider spans that lead to larger structural components or increased reinforcement. We observe an exception in steel residential buildings, which show larger embodied carbon due to their thicker external infill walls, compared to their commercial and industrial counterparts.

Figures 1b and c disaggregate the contributions of structural and non-structural components. In most residential building types, non-structural components dominate the carbon footprint due to the relatively smaller structural components. Reinforced masonry buildings deviate from this trend, with structural components representing up to 84% of the replacement embodied carbon due to the presence of thick, reinforced walls. In contrast, timber buildings show structural contributions as low as 36%. This shifts drastically in non-residential occupancies, especially industrial buildings, where structural components become the prevalent emission contributors, reflecting their enlarged size to support heavier loads and wider spans.

For comparison and verification, we look at the findings of existing studies, acknowledging potential differences in methodological assumptions and scope. The Embodied Carbon of European Buildings Database (EU-ECB-DB)34,35 reports mean A1-A3 embodied carbon values of 439 kg CO₂e/m² for masonry, 416 kg CO₂e/m² for reinforced concrete, and 264 kg CO₂e/m² for timber buildings. Our values align well with those for masonry and reinforced concrete but are notably lower for timber due to different data sources, ECF boundaries, and structural/non-structural component selection. For steel residential buildings, the database of embodied Quantity outputs (deQo)36 suggests a median value of 399 kg CO₂e/m², which exceeds our estimates as it covers contributions from the building use life-cycle stage. Other studies computed lower embodied carbon values37–40, likely due to the exclusion of foundations and some non-structural components, and the use of different ECFs. We recognise other global efforts to collect both synthetic and real-world data on embodied carbon35 and material use intensities41, but note that only a few provide both material quantities and embodied carbon data42.

Overall, our study delivers a harmonised database of material quantities and embodied carbon values of building replacement with consistent life-cycle stages, spanning a range of building types, structural systems, and occupancies. However, we acknowledge some limitations, such as the representativeness of selected building replacement types, the coverage of LCA modules, and the averaging of ECFs across multiple countries. Nevertheless, the dataset offers a coherent, meaningful basis for cross-comparisons among building types, occupancies, and regions, particularly in contexts where detailed building-level data are unavailable or fragmented. It also enhances exposure models of building portfolios, supporting applications like the integration of sustainability into regional-scale disaster risk management, the prioritisation of risk mitigation strategies, and policy development. Unlike detailed building-specific LCA studies, our work should be viewed as complementary, offering a scalable, portfolio-level framework aiding risk management, resilience planning, and adaptation strategies at territorial scales.

Embodied-carbon exposure data for European buildings
The latest European exposure model22, developed as part of the global exposure model of the Global Earthquake Model (GEM) Foundation32, provides information about the residential, industrial, and commercial building stock at the smallest available administrative level. It comprises data on building counts, locations, built-up area, replacement cost, number of occupants, and key vulnerability attributes, such as construction material, load-resisting systems, number of storeys, and design code level. These attributes are assigned based on nationally reported datasets, including housing census and surveys of commercial and industrial facilities, which offer valuable insights into building types, number of storeys, construction materials, and eras. The European exposure model covers ~157 million buildings and over 35 billion m² of built-up area, with a total replacement cost estimated at $60 trillion. Figure 2 depicts the spatial distribution of building counts within Europe, aggregated on a uniform 0.20° hexagonal grid.

Leveraging our embodied carbon database for various building types, we extend the European exposure model by integrating the environmental dimension, facilitating spatial estimates of embodied carbon associated with earthquake damage, repair, and reconstruction. To estimate the total building replacement embodied carbon, we multiply the per-building-type values (Fig. 1) by the respective built-up area of each asset in the exposure model. This evaluation helps understanding the fundamental role of the construction sector in regional carbon budgets and identifying opportunities to mitigate its environmental impacts. Accordingly, we present the first version of the exposure map in Fig. 3, showing the replacement embodied carbon of the European building stock. Our analysis estimates a total value of 14 billion tonnes of CO₂e; more than double Europe’s total CO₂e emissions in 2023 (7 billion tonnes of CO₂e)43. Notably, Germany, France, Italy, Türkiye, the United Kingdom, and Spain collectively account for over 60% of the total, with individual contributions ranging from 1.1 to 1.8 billion tonnes of CO₂e (see Table 2). Disaggregated values of replacement embodied carbon, separating structural (e.g., foundations, frames, floors, walls) and non-structural (e.g., partition walls, finishings, plumbing, heating, ventilation, air conditioning, lifts, ceilings) contributions are available in our GitHub repository23 at the first subnational administrative level of each European country.

Embodied-carbon seismic risk model for European buildings
In this study, we address the environmental impact of earthquakes by introducing the concept of AAEC. To streamline comparisons across different building types and regions, regardless of their size or material composition, we express AAEC as a fraction of the building’s replacement embodied carbon (average annual embodied carbon ratio, AAECR). This metric is conceptually analogous to the AALR, commonly used in economic seismic risk assessments.

Computing AAECR entails a comprehensive seismic risk assessment that integrates three key components: seismic hazard models, exposure information, and vulnerability functions. We derive the seismic hazard component from GEM’s global hazard mosaic44, which features a harmonised compilation of national and regional probabilistic seismic hazard models developed by various institutions in collaboration with the GEM Foundation. Vulnerability functions estimate seismic losses as a function of ground-shaking intensity and are typically derived by combining fragility curves with damage-to-loss (or consequence) models. Fragility curves characterise the probability of exceeding specific damage states given the shaking intensity, while damage-to-loss models translate these damage states into losses, expressed as ratios of a building’s replacement value. For conventional metrics like economic losses (repair costs) or disruption time, these models are well established in the literature45–49.

In contrast, deriving carbon-specific vulnerability functions remains challenging due to the scarcity of damage-to-carbon models. Existing studies have focused on a limited set of building types, mainly reinforced concrete and steel frames located in a few geographic regions18,20,50. Despite this limitation, Aljawhari et al.18 observed an almost one-to-one correlation between embodied carbon and repair cost ratios of seismic damage45 in a component-level risk assessment of nine reinforced concrete buildings in Italy. The authors developed both damage-to-carbon18 and damage-to-cost45 models for these buildings, confirming their close alignment. Similar remarks have been reported for comparable building types51,52, and for steel frames in China53. However, this correlation is not exact as economic losses involve labour costs, which are deemed irrelevant for carbon emissions. Hence, discrepancies might emerge, especially at lower damage levels, where labour constitutes a considerable fraction of repair costs. The correlation improves at large damage levels, where material replacement drives both repair cost and emissions. Further discussion on the applicability and limitations of this assumption, in addition to illustrative case studies, is provided in the Supplementary Discussion.

We adopt the above simplification by assuming that the AALR values, computed via vulnerability functions for economic losses, can serve as a proxy for AAECR. We acquire the AALR values for all European building assets by running the event-based probabilistic risk calculator of the OpenQuake Engine54,55, an open-source platform for seismic hazard and risk analyses. This calculator generates large sets of stochastic earthquake events over a specified time span, based on regional seismic hazard models. For each simulated event, ground-motion fields are computed using ground-motion models, capturing the spatial distribution of shaking intensity. Losses incurred by each building asset are computed through the relevant vulnerability function. The AAL for each building asset is obtained by dividing the aggregate losses from the whole event set by its time span, which is then normalised by the asset’s replacement cost to get the AALR. The AALR values for the entire globe, including Europe, disaggregated by component type (e.g., structural, non-structural) and occupancy (e.g., residential, industrial), are available as part of GEM’s global seismic risk model56.

Finally, we evaluate the AAEC by multiplying the AAECR (assumed equal to the AALR) for each building asset in Europe by its replacement embodied carbon, as further described in the “Methods” section. We emphasise that the resulting AAEC is a probabilistic estimate, derived from event-based risk analyses and averaged across millions of simulated earthquake scenarios over a long time span. This allows for capturing the full spectrum of damage outcomes, from slight damage to total collapse, rather than focusing on the consequences of a single deterministic event, where losses are unevenly distributed. This makes the AAEC a more robust metric for decision-making related to long-term risk mitigation and resilience planning.

Figure 4 presents the risk map of AAEC due to seismic damage for Europe, measured in tonnes of CO₂e. The map offers a spatially resolved visualisation of the environmental toll of earthquakes, pinpointing the regions where building sustainability is most challenged by seismic activity. When aggregated at the continental level, the total European AAEC exceeds 6.6 million tonnes of CO₂e, a figure equivalent to the overall 2021 carbon footprint of Norway’s manufacturing and construction sector, or Serbia’s entire transportation network in the same year43. To contextualise this further, this AAEC is comparable to the emissions from 33,000 one-way flights from Paris to New York carrying 200 passengers each, or the annual emissions of ~3.9 million diesel cars.

Figure 5 shows the AAEC values aggregated at the national level for 46 European countries, ranked in descending order. Six countries, Türkiye, Italy, Romania, Greece, Spain, and Bulgaria, represent almost 90% of Europe’s total AAEC. Türkiye leads by a significant margin, with an estimated 3.5 million tonnes of CO₂e per year, nearly triple of Italy’s total, the second largest contributor. This reflects Türkiye’s combination of high seismic hazard and large stock of vulnerable buildings. The majority of AAEC in Türkiye, Cyprus, and Greece stems from damage to reinforced concrete buildings (60–80%), while in Italy, Romania, Spain, Bulgaria, and Albania, masonry buildings account for over 60% of the total AAEC. Figure 5 also reports country-level AAECR values. Türkiye is again at the forefront, followed by Cyprus, Albania, Romania, and Greece. Despite having smaller building portfolios, Romania and Greece rank fourth and fifth in terms of AAECR due to their high seismic hazard. All AAEC values with structural and non-structural contributions are available in the GitHub repository23, aggregated at the first subnational administrative division for all European counties.

Table 2 offers a detailed breakdown of our analysis, including the total number of buildings, their replacement embodied carbon, and the AAEC with the respective contributions of structural components for the 46 European countries. In countries like Italy, Spain, Albania, and Bulgaria, structural components account for a larger share of the total replacement embodied carbon than non-structural components. This is due to the prevalence of masonry buildings, where the embodied carbon is driven by structural walls (see Fig. 1). Greece and Türkiye, on the other hand, exhibit a more balanced distribution for the two shares because of the substantial number of residential reinforced concrete buildings, where non-structural components like infill walls and partitions strongly contribute to the total emissions.

The AAEC values in Table 2 might appear modest in relative terms, but when viewed over a 100-year building lifespan, they become a critical share of the carbon footprint in regions with high seismic risk. In Romania, for example, the embodied carbon induced by earthquake damage and related repairs amounts to nearly 20% of that required for new construction. For countries like Türkiye, Greece, and Romania, this corresponds to millions of tonnes of CO₂e. As emissions accumulate across wider geographies and longer time horizons, their significance grows, highlighting the need to incorporate seismic risk into decarbonisation strategies and long-term sustainability planning.

Methods
Replacement embodied carbon vs average annual embodied carbon
The European standard EN-1597860 divides a building’s life cycle into four primary stages, each comprising several modules, as illustrated in Fig. 6: (i) the production stage (modules A1-A3); (ii) the construction stage (modules A4-A5); (iii) the operational stage (B1-B7); and (iv) the end-of-life stage (C1-C4). An additional stage, known as “beyond life” (module D) may be considered to capture environmental benefits beyond the building’s life-cycle boundaries (e.g., reuse, recycling, recovery of salvaged materials). Amongst the critical environmental impacts generated during different stages are GHG emissions, which are linked to global warming. These emissions can be collectively expressed as carbon dioxide equivalent, CO₂e (or may be simply referred to as “carbon”). Buildings generate both embodied and operational carbon during their life cycle. Embodied carbon comprises emissions associated with production, construction, use, and end-of-life (i.e., modules A1-A5, B1-B5, and C1-C4), while operational carbon refers exclusively to energy and water use (modules B6-B7). Emissions from the production stage alone can account for up to 50% of a building’s total life cycle61 and nearly 85% of the total embodied portion62.

Earthquake-induced damage introduces an additional, often overlooked source of embodied carbon, linked to repair and reconstruction activities that might occur during the building’s service life. These emissions affect the use stage (mainly modules B3-B5) and can be treated analogously to routine maintenance. In this context, the AAEC represents the expected yearly contribution from repairing earthquake damage and can be accumulated over the building’s life span by simple multiplication. As outlined earlier, our estimates of AAEC are derived from a probabilistic risk assessment framework, integrating millions of earthquake scenarios over a long period of time, capturing diverse seismic damage levels, from minor repairs to building collapse. Earthquake effects expressed as AAEC can therefore be interpreted as recurring, risk-driven use-stage emissions.

Our primary aim is to extend conventional seismic risk analyses by incorporating the embodied carbon associated with earthquake damage and repairs, reflected via the AAEC metric (see the “Results” section). This approach does not attempt to capture the entire life cycle of buildings, but instead focuses on damage-related impacts, similar to the treatment of economic losses in established risk assessment frameworks. To enable consistent comparisons across building types and regions, we express AAEC as a fraction of the building’s replacement embodied carbon (the emissions associated with constructing a modern substitute for a heavily damaged or collapsed building). Thus, AAEC pertains to existing structures, whereas replacement embodied carbon refers to hypothetical new buildings constructed after a destructive event.

We compute the replacement embodied carbon for a wide range of building types and occupancies, accounting for modules A1-A5 (cradle-to-practical-completion activities). Pre-construction demolition activities are included as part of module A5. The use stage (B modules) is excluded from this metric, as the building replacement is limited to reconstruction works only.

Identification of building replacement types
Europe’s building stock is vast and heterogeneous, containing many construction types that are no longer used in new developments due to their reliance on obsolete materials or high seismic vulnerability. Accordingly, when estimating replacement embodied carbon, we focus exclusively on building types compliant with current European engineering practices, following the “Build Back Better” strategy. A similar principle is adopted in GEM’s current exposure model32 for replacement cost estimates, which assumes that post-disaster reconstruction meet the minimum affordable housing standards of each country. This assumption can be appropriate for European countries, where national building codes/standards (e.g., Eurocode annexes), are legally enforced. For simplicity, we consider that replacement buildings retain the same material category as those they substitute, but with improved structural systems and modern materials (see the “Results” section). This reflects typical post-disaster reconstruction, where upgrades involve better seismic detailing rather than complete re-designs to meet architectural integration or urban regulatory constraints.

However, we recognise some limitations in this approach: (i) reconstruction does not always result in improved seismic performance; (ii) different structural systems can achieve similar seismic performance, yet produce varying carbon footprints; (iii) post-disaster reconstruction may be driven by factors like supply chain disruption or budget constraints, leading to the use of completely different materials (e.g., widespread use of prefabricated concrete upon Romania’s 1977 earthquake63). As such, our AAEC estimates should be interpreted as representative of plausible scenarios rather than precise forecasts. Nevertheless, the framework is flexible as users may refine results by selecting alternative building replacement types (see Fig. 1) or deriving more building-specific component take-offs and multiplying them by the corresponding ECFs.

Each building type in our analysis (both existing and replacement) is classified via unique strings according to GEM’s building taxonomy v3.364, which encodes the primary structural attributes relevant to seismic performance. These include, among others: (i) main construction material(s); (ii) lateral load-resisting system; (iii) height, expressed as number of stories; and (iv) occupancy type. Table 3 outlines the building types included within Europe’s exposure model and their potential replacements.

Quantity estimation of structural and non-structural components
Calculating the replacement embodied carbon warrants a quantification of all construction materials and individual components36,40,41,65, particularly those prone to earthquake damage. This becomes more difficult at the portfolio level, where building stock exhibits high variability in structural materials, geometric properties, and occupancy categories. In this context, we perform a simplified, practice-oriented quantity take-off approach consistent with a preliminary design stage. While this does not resolve the full complexity of portfolios, it offers reasonable accuracy suitable for regional-scale risk assessments30.

For structural components (i.e., frames, floors, foundations, and walls) of the building types listed in Table 3, we develop spreadsheets that perform tributary load distribution and simulated design using current building codes66,67. These calculations are based on simplified archetype geometries and consider gravity loads only. We do not consider seismic design as the associated increase in structural material quantities is unlikely to heavily impact building-level embodied carbon. The resulting quantities vary by material, structural system, and occupancy (i.e., residential, commercial, industrial). For cases where a simulated design is infeasible, we extract quantities from available construction drawings.

Non-structural components are grouped into four drift-sensitive categories (i.e., light partitions and finishings, heavy partitions and finishings, windows/glazing/doors, stairs) and four acceleration-sensitive categories (i.e., heating, ventilation, and air conditioning systems, electrical components, pipes, ceilings). We obtain their quantities from GEM’s building-specific inventory, which offers relevant estimates per square meter of built area based on geographic region, primary construction material, and occupancy. These estimates were developed through FEMA’s “Normative Quantity Estimation Tool”17, in conjunction with building surveys, blueprints, and expert judgement. All our quantity estimates, distinguished by individual component category, building type, and occupancy, are publicly available in our GitHub repository23.

Collection, compilation, and assembly of embodied-carbon data
The replacement embodied carbon combines emissions from the construction of new buildings (modules A1-A5) with the end-of-life activities related to demolishing damaged existing structures, which are considered under module A5.

For the production stage (A1-A3), we obtain embodied carbon data from environmental product declarations (EPDs). These are voluntary manufacturer-issued documents reporting product-specific environmental impacts at several life-cycle stages68. However, EPDs can involve substantial variability, even for identical products, due to differences in manufacturing processes, regional context, and data consistency (e.g., CO versus CO₂e). To tackle this, we collect and compile an extensive dataset of European EPDs from multiple sources, assuming similarity in products and related processes57,65,69. We prioritise the sources outlined below:
• The Embodied Carbon in Construction Calculator (EC3) Tool27: a global database reporting environmental-impact data, categorised by material/component type, geographic extent, and other relevant characteristics. The EC3 tool offers the required impacts as average values (with standard deviations), incorporating all EPD records in its database, along with access to individual EPDs from manufacturers;
• Recent peer-reviewed publications that compile multiple EPDs, offering averaged values (sometimes with standard deviations) for environmental impacts. We use these sources for materials and components not available in the EC3 tool at the time of writing;
• Manually collected EPDs, supplied by European manufacturers. For materials and components not covered by the above sources, we rely on additional EPDs. Overall, we compiled a dataset of >370 EPDs obtained from online databases like Eco Platform24, EPD-Hub25, and Ökobaudat26;
• Additional databases, such as the Inventory of Carbon and Energy (ICE)70.

We aggregate the collected data to derive average ECFs (see Table 1 in the “Results” section), depicting the embodied carbon per unit quantity of materials or components. Most of the embodied carbon data address exclusively the material production stage (A1-A3), as it is the largest emission contributor. For consistency, we only extract A1-A3 data from each EPD, even when some include other modules. We then multiply ECFs by the corresponding material or component quantities to generate the A1-A3 portion of replacement embodied carbon. We note that EPDs have a 5-year validity68 to adequately reflect prevalent manufacturing and construction practices. Manufacturers are expected to update EPDs upon expiration, which could change environmental-impact profiles, especially with better decarbonisation measures and cleaner production technologies. Thus, our ECF estimates are constrained by the temporal scope of the underlying data.

Comparison with previous studies shows reasonable variations. Pomponi and Moncaster71 analysed several EPDs worldwide from 2012 to 2016 and reported an average ECF for ready-mix concrete of 0.145 kg CO₂e/kg, while our estimate is 0.11 kg CO₂e/kg (assuming a concrete density of 2,400 kg/m³). For structural timber, their estimate (0.438 kg CO₂e/kg) exceeds our value (0.25 kg CO₂e/kg; 640 kg/m³), while our clay brick ECF (0.20 kg CO₂e/kg) aligns closely with both Pomponi and Moncaster’s71 (0.223 kg CO₂e/kg) and ICE70 (0.213 kg CO₂e/kg).

Estimating the contribution of module A4, which covers the transportation of components and materials to sites, is more challenging due to the high variability and lack of standardised data, especially for large-scale assessments. Therefore, we implement a simplified approach that multiplies the transported mass and distance (including empty returns) by a suitable transport carbon factor. Considering national manufacturers only, we assume that trucks (i.e., heavy-duty vehicles, HDVs) travel an average distance of 120 km from factory to site30, with a 70%72 increase to account for empty returns. We use a transport carbon factor of 0.053 kg CO₂/t·km, based on the European Environment Agency (EEA)28 estimates of 2019–2021 HDV emissions. Although this factor reflects CO₂ only, it is broadly representative of CO₂e for HDVs, where CO₂ is the dominant GHG73.

Module A5 reflects the embodied carbon associated with the construction stage, including pre-construction demolition (A5.1), construction activities (A5.2), and waste management (A5.3). Worker transport (A5.4) is excluded herein as it is typically considered as responsibility of individual employees30.

Pre-construction demolition (A5.1) corresponds to the end-of-life stage of the building being replaced (C1-C4). We compute the embodied carbon of demolition (C1) as 25% of the construction activities (A5.2), assuming standard demolition practices with limited deconstruction and recovery30. We treat waste transport (C2) similarly to module A4, but with a shorter travel distance of 50 km, given landfill sites are most likely local40. We increase this distance by 50% for empty returns29,72. For waste processing and disposal (C3-C4), we adopt an embodied carbon rate of 0.013 kg CO₂e/kg of waste29,74, considering a European-average scenario in which 90% of materials are recovered and 10% are landfilled75. We note that our end-of-life assumptions may not fully represent post-disaster demolition practices. In the aftermath of major earthquakes, for example, damaged materials are often unsuitable for reuse or recycling, leading to increased landfilling and unsustainable emergency waste disposal, as observed after the 2023 Türkiye-Syria earthquakes2.

For construction activities (A5.2), we compute on-site diesel machinery emissions from lifting building components to the building’s mid height by cranes. We first obtain the fuel consumption in litres (l) via the empirical relation76–78: fuel (l)=0.000037Mh+M/500+0.83, where M is the component mass (kg) and h is the lift height (m). Using a diesel energy content of 38 MJ/l and a carbon factor of 0.073 kg CO₂e/MJ, we convert fuel consumption to embodied carbon. This method only covers equipment fuel use and excludes other site activities (e.g., formwork). In module A5.3 (waste management), construction waste is estimated using component- and material-specific waste rates30. The resulting waste quantities are then multiplied by the appropriate factors for relevant modules, which depend on the disposal routes30.

Data sources and integration
This study combines multiple datasets to estimate the embodied carbon associated with seismic damage across Europe. As per Fig. 7, our analysis relies on two primary data sources: (i) building exposure and seismic risk models developed by GEM; and (ii) embodied carbon data derived from EPDs and scientific literature.

The GEM models for Europe form a part of a global initiative to develop comprehensive seismic hazard44, building exposure32, and vulnerability models79. The European component is based on the 2020 European Seismic Hazard Model (ESHM20) and the European Seismic Risk Model (ESRM20)80. These datasets are integrated by running probabilistic seismic risk analyses56 via the OpenQuake Engine54,55. The resulting risk metrics include AALs, expected fatalities, displaced populations, and collapsed buildings, among others. GEM’s global exposure model is publicly available via GitHub (https://github.com/gem/global_exposure_model/), aggregated at the first subnational administrative division for each country. GEM’s vulnerability functions are accessible through https://github.com/gem/global_vulnerability_model/, and the hazard model is hosted on the EFEHR portal (http://www.hazard.efehr.org/en/home/). Country-level summaries and risk metrics are also available through GEM’s risk profiles (https://github.com/gem/risk-profiles/). All data layers of GEM’s risk model can be downloaded as shapefiles for research and public-good, and can be explored as well via the interactive geoviewer (https://maps.openquake.org/map/grm-2023-1/#3/32.00/-2.00).

To provide more insight into our calculations, we use GEM’s exposure model to retrieve building assets and their built area at the finest available administrative level. Each asset constitutes a group of buildings with similar structural and occupancy attributes. From these attributes, we estimate component quantities based on building and occupancy types and then multiply them by suitable ECFs, which results in replacement embodied carbon. The AALRs incurred by building assets are derived from GEM’s risk model and used as proxies for the AAECRs. The absolute AAEC values of each asset are finally computed by multiplying the AAECRs by the corresponding replacement embodied carbon. The results are then aggregated at the country level (see Fig. 5 and Table 2). The complete process is depicted in Fig. 8.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Thank you for this opportunity, please see attached review document.\n(Remarks on code availability)\nWe reviewed the github site and only found images no code.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\n(Remarks on code availability)\nWe can not find access to the noted open source dataset of embodied carbon. The git-hub site only provides .jpgs of figures\nin the manuscript.", "reviewer_3": "The manuscript presents a well-written and timely contribution that integrates seismic risk assessment with environmental\nimpact analysis, a crucial yet often overlooked aspect in sustainable disaster risk reduction. The authors provide a complex\nframework, data-driven approach, developing a harmonized database and presenting the first seismic risk map based on\nembodied carbon losses across Europe. At this point, I commend the authors for their excellent work, as this manuscript\nprovides valuable insights and has greatly enhanced my understanding of the environmental impact of seismic risk.\nHowever, there are several areas where additional clarification and methodological refinement are needed to improve\nreadability, uncertainty quantification, and applicability across different parts of the manuscript. Hereinafter, are specific\ncomments and recommendations:\n1. The manuscript needs to clearly specify the methodology behind the embodied-carbon seismic risk map. The authors\nshould explicitly state whether an event-based or non-event-based approach was used in constructing the risk map. A\ndiscussion on uncertainty sources in the map is essential, particularly regarding exposure data limitations, hazard model\nassumptions, and consequence model approximations.\n2. The assignment of building typologies within the exposure dataset is inherently uncertain. The manuscript should\nacknowledge and discuss how misclassification of building types might impact the embodied carbon estimates. A more\ndetailed assessment of classification errors and their effect on results would improve the robustness of the study.\n3. The use of built-up area as a direct measure of embodied carbon oversimplifies real-world variations in material intensity.\nOlder buildings often use more materials per square meter, and regional differences in construction practices (e.g.,\nprefabrication adoption post-1977 Vrancea earthquake in Romania) further complicate this assumption. Variations in cement\nclinker composition across time periods also influence embodied carbon values. The authors should discuss these\nlimitations and, if possible, introduce adjustment factors for different construction periods to refine the embodied carbon\nestimations.\n4. The manuscript does not explicitly differentiate between full replacement and partial retrofitting scenarios following\nearthquake damage, and this might be a limitation. In many cases, buildings experiencing moderate damage undergo partial\nrepairs rather than complete reconstruction. This can be recommended to scenario case studies, and such discussion about\nthe retrofitting scenarios should be clearly reiterated in the discussion.\n5. The study would benefit from the inclusion of a consequence curve example, as demonstrated in Aljawhari et al. This\nwould help readers visualize how seismic damage correlates with embodied carbon losses, improving the accessibility of\nthe findings.\n6. The decision to exclude vulnerable buildings from the analysis needs further justification. These structures, due to their\nhigh likelihood of collapse and subsequent full replacement, contribute significantly to embodied carbon emissions. The\nauthors should specify what percentage of the dataset these excluded buildings represent. It would also be valuable to\nperform the analysis focusing exclusively on the most vulnerable buildings, as this aligns more closely with the full\nreplacement assumption used in the study.\n7. Furthermore, another possible limitation of the study is that replacement buildings use the same materials as the original\nones, but post-disaster reconstruction often leads to material substitutions. These shifts occur due to supply chain\ndisruptions, government incentives, and economic constraints (e.g., prefabricated concrete used post-1977 Romania\nearthquake). The manuscript should explore a sensitivity analysis examining alternative reconstruction materials, such as\ntimber replacing masonry or prefabricated concrete replacing reinforced concrete. This is particularly relevant for highly\nvulnerable buildings, as they are the most likely to be reconstructed with improved materials.\n8. Next, the manuscript applies uniform embodied carbon factors (ECFs) across Europe, but construction practices and\nmaterial sourcing vary significantly between regions. Differences in cement production carbon intensity (e.g., France vs.\nPoland), energy grid emissions, and local vs. imported material use introduce substantial variability. The authors should\ndiscuss how these regional disparities might impact the embodied carbon estimates and, if possible, introduce or propose a\nregion-specific ECF adjustments.\n9. The study reports coefficient of variation (CoV) values for ECFs, but this alone does not fully capture uncertainty in\nseismic carbon risk estimates. A sensitivity analysis on key parameters, such as transport distances, seismic reinforcement\nrequirements, recycling rates, and material intensity variations, would provide a clearer picture of the robustness of the\nresults. This is a suggestion not a requirement, given the length of the current manuscript, but a basic uncertainty\nassessment would strengthen the manuscript’s conclusions.\n10. The authors should acknowledge the dynamic nature of exposure, as new buildings are added every year, contributing\nto a growing stock of embodied carbon. Additionally, building age and retrofitting levels influence repair emissions, as older\nbuildings generally require more intensive material use. Again, this can be only mentioned and discussed in this manuscript,\nand in particular the vision to long-term applicability of such assessment for applied mitigation actions.\n11. The authors should ensure that the study is well-contextualized within the broader field by reviewing and integrating\nadditional relevant literature (various papers on the Turkish earthquakes), or acknowledge of other relevant datasets such as\nBath Inventory of Carbon and Energy (ICE). Finally, it is worth mentioning the link between this work and the input\ncomponents of the recent European Risk model (Crowley et al 2021), and properly cite the underlying hazard and\namplification if used in this study.\nTo conclude, I recommend a moderate revision prior to acceptance for publication. The manuscript is commendable for its\ninnovative approach and substantial insights; however, specific methodological clarifications, enhanced uncertainty\nquantification, and broader discussions on applicability and assumptions are required. I look forward to receiving the revised\nversion.\nBest wishes,\nLaurentiu Danciu\nETH Zurich\nReferences:\nhttps://doi.org/10.1016/j.rcns.2023.05.003\nhttps://link.springer.com/article/10.1007/s10518-023-01686-y\nhttps://journals.sagepub.com/doi/full/10.1177/87552930221082684"}, "author_response": "Manuscript NCOMMS-24-67383\nUnveiling the Environmental Impact of Earthquakes in Europe\nResponse to Reviewers’ comments\nBlue: Reviewers’ comments - Black: responses to Reviewers\nWe would like to sincerely thank the three Reviewers for their constructive and insightful\ncomments on our original submission. We have carefully addressed all suggestions and made\nvarious revisions to improve the overall quality and clarity of the manuscript. All modifications\nand additions are highlighted in track-changes mode in the revised manuscript. In addition, we\nmade several edits to improve the writing throughout the text. Below, we provide detailed,\npoint-by-point responses to each comment.\nReviewers #1 and #2\nOverview\nThis manuscript presents a novel approach to connect assessment of seismic damage to the\nenvironmental impact of the repair at a regional scale. The authors endeavour to extend the\nconcept of damage-to-cost AALR (annual average loss ratio), which is the Expected Annual\nRepair Cost divided by Replacement Cost, to the damage-to-carbon AAECR (annual average\nembodied carbon ratio), which is the AAEC divided by the replacement embodied carbon. Life\ncycle assessment (LCA) data is used to estimate the greenhouse gas emissions from replacing\nbuildings and whole building LCA methods are referenced. To accomplish the study aims,\nthree key tasks should be completed:\n1. Estimate AALR for all plausible earthquake scenarios across the European region\n2. Identify or develop a method to verify the correlation relationships between repair cost\nestimates and environmental impacts\n3. Estimate the environmental impact of damage and damage repair\nThe authors note that data and maps are available open source to enable others to replicate\nthe results. We were only able to find open source images of the maps, none of the background\ndata was found.\nWe sincerely thank the Reviewers for their positive and constructive feedback. We appreciate\nthe remark regarding data availability. At the time of the original submission, only the map\nlayers were available through the GitHub repository. We have now expanded the repository to\ninclude the background data used in this study, as detailed in other responses below (see\nhere). Furthermore, we have made the interactive web services publicly accessible via the\nEPOS Built Environment Data platform1.\n1 https://embodiedcarbon.builtenvdata.eu\nPage 1 of 32\nFindings\nThe findings daylight the environmental burden of earthquake damage and have the potential\nto help draw connections between seismic resilience and climate mitigation. Given the siloed\nnature of these domains, a bridging paper like this has the potential to help broaden\nunderstanding of and support for action in these two socially critical areas. Thus we feel that\nthe potential significance of the findings is high and worthy of consideration for publication after\naddressing the reviewer’s comments.\nIn conducting our review, we have brought disciplinary knowledge of structural engineering,\nseismic design, uncertainty analysis/propagation, and expertise in environmental life cycle\nassessment. Our review is focused on assessing if the paper adequately describes the tasks\nidentified above. For task one, the estimation of seismic damage, we are evaluating the\nmanuscript to assess if interdisciplinary audiences can understand and evaluate the methods\nused to generate this data. For tasks two and three, we have greater domain expertise and\nthus are critically evaluating the methods and data to assess the quality of the data and level\nof support for the conclusions as required for consideration by this journal. Our review is\nstructured to reflect this organization.\nTask 1: Estimation of seismic damage.\nThe authors appear to utilize AALR data compiled by others to predict the annual repair cost\nper region. For those without a detailed understanding of the datasets used, additional\nexplanatory information would be valuable. Ideally the authors would provide an open source\ncopy of the AAL economic data with the level of granularity identified. We interpret that you’ve\ntaken the repair cost data presented in Yepes-Estrada, C. et al. (2023) and reported online\nvia the Global Exposure Model. We understand that authors compiled data based on building\ntype for all of Europe. A section of the methods document and/or diagrams that capture the\nsources of data and the manipulation of that data for use in this study would be of great value.\nAs we don’t have the capacity to review and interpret all references, this paper could be greatly\nimproved with a high level summary of key factors behind the selected dataset.\nWe thank the Reviewers for this valuable suggestion. We fully agree that a high-level summary\nof key components would enhance the manuscript’s consistency and readability, making it\naccessible to readers from different scientific fields. To address this, we have expanded the\ndescription of the background datasets used in this work, adding a new section in “Methods”\ntitled “Data Sources and Integration”, and we have included a workflow diagram to clarify how\ndifferent data sources are integrated (see Figure 9, also shown below).\nAs per Figure 9, this study relies on two main data sources. First, we use the Global Earthquake\nModel (GEM) Foundation models for Europe (latest release v2023.1), which are part of the\nFoundation’s efforts to develop global models for seismic hazard1, building exposure2 and\nseismic vulnerability3. We utilise these models as input for performing seismic risk analyses4\nvia the GEM-developed open-source software OpenQuake, utilising state-of-the-art methods\nfor seismic hazard and risk assessments5,6. Seismic risk is typically expressed in terms of\nAverage Annual Loss (AAL), reflecting the expected annualised economic loss from seismic\ndamage and subsequent repairs. It is computed probabilistically considering all plausible\nearthquake scenarios at a given site. Relevant seismic risk metrics include fatalities, displaced\npopulation, and collapsed buildings, among others.\nPage 2 of 32\n[Figure Redacted]\nFigure 9. Data sources and integration overview. Overview o f data sources and the integration process used in\nthe embodied carbon and seismic risk assessment framework.\nIn our study, we use GEM exposure models to obtain information regarding the building stock,\nincluding their construction attributes, occupancy, and built areas at the smallest available\nadministrative division. The exposure models are available in a GitHub repository2, aggregated\nat the first administrative division for all countries. GEM’s vulnerability functions for Europe are\naccessible through a dedicated GitHub repository3, and the seismic hazard model is publicly\navailable in the EFEHR (European Facilities for Earthquake Hazard and Risk) portal4. We also\nuse the GEM’s risk model to retrieve the Average Annual Loss Ratios (AALRs) for each asset.\nSeveral risk metrics for all countries can be accessed through country profiles5. The underlying\ndatasets of GEM’s risk models are available as shapefiles for use in research and public-good\napplications, and can also be explored via the interactive geoviewer6.\nOn the other hand, the embodied carbon data and metrics developed in our manuscript are\nbased on extensive collection and compilation of relevant information from publicly-available\nenvironmental product declaration (EPD) databases and scientific literature. These sources\nprovide estimates of the embodied carbon associated with a variety of building materials and\nconstruction activities, which we gathered, processed, and compiled to integrate with GEM’s\n2 https://github.com/gem/global_exposure_model/\n3 https://github.com/gem/global_vulnerability_model/\n4 http://www.hazard.efehr.org/en/home/\n5 https://github.com/gem/risk-profiles/\n6 https://maps.openquake.org/map/grm-2023-1/#3/32.00/-2.00\nPage 3 of 32\nexposure and risk models to quantify the environmental impacts of earthquake-induced\ndamage. These data have now been included in the Github repository (see here).\nSome questions and specific comments that we have that this summary could address include:\n1. What are the specific assumptions behind the AALR model data used?\na) Does this inherently apply to the ‘Build Back Better’ scenarios mentioned in the\npaper?\nb) What are the limitations of these assumptions? How do these limitations impact\nthe ability to rigorously interpret the final results?\nWe thank the Reviewers for pointing out these issues. GEM’s exposure models (and,\nconsequently, resulting risk estimates) adopt a ‘‘build back better’’ approach when estimating\nbuilding replacement costs2. For example, if an old adobe masonry building collapses, its\nreplacement cost represents the economic value of the minimum affordable housing standard\nin the country (e.g., reinforced masonry house). We deem this approach suitable for Europe,\nwhere building codes are efficiently enforced. We implement a similar strategy to quantify the\nreplacement embodied carbon, considering that reconstructed buildings retain the same\nmaterial category, but with improved structural systems and material standards. We believe\nthis is a reasonable approach in contexts of urban or architectural regulatory constraints, where\nthe primary change involves seismic detailing rather than overall design. However, we\nacknowledge that our approach has the following limitations:\n(i) In some cases, reconstruction may not not lead to an improved structural performance.\nThis is unlikely to be an issue for European countries, but it can be relevant for other regions;\n(ii) Improved structural performance can be achieved via different structural systems (e.g.,\nreinforced concrete frames, confined masonry walls), and the choice of system may\naffect environmental estimates, given that each system may comprise distinct material\ntypes and quantities;\n(iii) Actual post-disaster reconstruction can sometimes utilise significantly different materials\ncompared to the original buildings due to supply-chain disruptions or economic constraints.\nWhile these limitations introduce some uncertainty in our carbon estimates, we note that the\ndifferences between different structural systems are not significant (see Figure 1 in the\nmanuscript). Hence, if some regions do not follow a “build back better” approach (i.e., an\nunreinforced stone masonry is replaced by similar construction), the overall results at the\nnational and continental scales would not be significantly affected.\nWe have included these considerations in the revised manuscript (see the section\n“Identification of Building Types”).\n2. What are the seismic probabilistic assumptions included in this data? We presume\nthere are assumptions based on time under consideration and maximum\nprobable/credible earthquakes being considered that influence the data. Knowing this\ninformation would inform users looking to interpret the findings.\nWe appreciate the Reviewers' request for additional clarification on our approach for seismic\nrisk assessment, which we have now provided in the section “Embodied-carbon Seismic Risk\nModel for European Buildings” of the revised manuscript.\nPage 4 of 32\nGEM’s risk estimates are obtained using the event-based probabilistic risk calculator of the\nOpenQuake Engine. First, the seismic hazard model is used to create an earthquake rupture\nforecast (ERF), i.e., a list of all potential earthquake ruptures that could occur in the region of\ninterest and their annual occurrence rates. The forecast is then adopted to generate a number\nof earthquake stochastic event sets (SESs), which constitute possible simulations of\nearthquakes within a pre-defined time period, considering their occurrence as time-\nindependent (Poisson’s process). For each rupture, ground motion fields (GMFs) are simulated\nto represent the spatial distributions of ground-shaking intensities like peak ground acceleration.\nThe aleatoric ground-shaking variability at each location is propagated via a Monte Carlo\napproach, which accounts for the natural randomness in ground motions. For epistemic\nuncertainties, such as the selection of a seismic source or ground motion models, logic trees\nare employed. To compute risk metrics, the GMFs affecting each building are used in\nconjunction with its vulnerability model class to compute the economic losses. This process is\nrepeated for each event in the SESs and the results are summarised into event loss tables\n(ELTs). The AAL is the sum of all resulting losses divided by the total number of years reflected\nin the SESs. This process can be perfrmed for individual buildings or for the entire region.\n3. Do the seismic damage cost estimates break out damage repair costs between\nstructural and non-structural components? In our review, the AALR data does not\nappear to have that level of detail yet your later evaluation of building baselines implies\nthat it does. As noted above, can you provide descriptions and/or diagrams that capture\nthe methods you’ve used to generate the background AALR data for this study? Can\nthe background data used in this analysis be published with the level of disaggregated\ndetail necessary to replicate or extend the study?\nWe thank the Reviewers for highlighting this aspect. The replacement cost in GEM’s exposure\nmodel covers three component types: structural (e.g., foundations, frames, floors, walls),\nnon-structural (e.g., staircase, partitions, facades, plumbing, electric system, HVAC, lifts,\nceilings), and contents (e.g., furniture, electronics). Similarly, GEM’s vulnerability models characterise\nseismic loss at different ground-shaking intensities for these three types. As a result, all the\nresulting risk metrics, including AALs and AALRs, can be disaggregated into separate\ncontributions depending on the type of building component and occupancy. The systematic\nprocess to compute AAL and AALR values is detailed in our response to the previous comment\nand throughout the manuscript. For our environmental metrics, including replacement\nembodied carbon (REC) and average annual embodied carbon (AAEC), we focus on structural\nand non-structural components only. Contents are excluded, given the the difficulty in\nsystematically identifying their type and quantity across different building classes.\nWe understand that presenting AALRs at each country level in Table 2 without showing the\ncorresponding structural and non-structural contributions, could be misleading. To clarify, we\nuse AALRs at the building asset level (not at the national level) to calculate their corresponding\naverage annual embodied carbon values, and then aggregate them at the country level. We\nhave revised the manuscript to reflect this distinction (see sections “Embodied-carbon Seismic\nRisk Model for European Buildings” and “Data Sources and Integration”).\nAdditionally, we have included the following material in the GitHub repository:\n• Description of the general approach;\nPage 5 of 32\n• European exposure map and data on the replacement embodied carbon, including:\no Quantities and embodied carbon factors for structural and non-structural\ncomponents across various building types and occupancies;\no Replacement embodied carbon per built area of different building types and\noccupancies, contribution of different life-cycle modules and structural/non-\nstructural component types;\no A “carbon class mapping” associating existing building types with carbon\nclasses used new constructions (as illustrated in Table 3 of the manuscript);\no Replacement embodied carbon aggregated at first subnational administrative\nlevel of each European country;\n• European seismic risk map and data on average annual embodied carbon, including\nthe aggregated values at first subnational administrative level of each European\ncountry, featuring structural and non-structural contributions.\n4. Lines 242-246, please provide more information about precision of the estimates. Is\nthe number of buildings from the European exposure model broken out by building\ntype and use or just average values?? EC values for different construction types can\nrange significantly and thus multiplying by single deterministic values appears counter\nto the probabilistic method used to estimate earthquake damage.\nWe thank the Reviewers for this comment. Additional information about GEM exposure model\nhas been included in the section on “Embodied-carbon Exposure Data for European\nBuildings”. This model covers buildings at the smallest available administrative level and it is\nbased on national data and statistics (e.g., housing census, surveys of commercial and industrial\nfacilities). It captures key attributes such as building type, primary construction materials, number\nof stories, and age of construction. We acknowledge the potential uncertainties associated\nwith building classification. However, in a previous study7, we evaluated the sensitivity of risk\nestimates due to such uncertainties. The analysis showed that their impact is limited because\nmisclassications tend to occur among building types with similar seismic vulnerability (e.g.,\nunreinforced masonry versus adobe buildings). As a result, due to the similarity in vulnerability\nand the averaging effect over large populations (i.e., rule of large numbers), these uncertainties\ndo not significantly affect the aggregated seismic risk outcomes.\nWe also agree that additional uncertainties are associated with the embodied carbon values\nacross different building types and materials, which we address as follows:\n• We gathered and compiled extensive embodied carbon data from available EPD\ndatabases and scientific literature, which incorporated a wide range of countries within\nEurope. This enabled us to relatively capture differences in manufacturing strategies\nand inter-regional product variations;\n• From these data, we derive embodied carbon factors (ECFs) expressing the amount of\nemissions per material unit quantity. We report the average values of ECFs in the\nmanuscript and we also provide their coefficients of variation on our GitHub repository,\nwhich range between 0.17 to 0.80;\n• To quantify the replacement embodied carbon, we multiply the derived ECFs with the\ncorresponding quantities of materials and structural/non-structural components needed\nfor each building type. We derive these quantities for our archetype buildings via\nsimulated design and inspection of available construction drawings;\nPage 6 of 32\n• This allows to adequately capture the variation accross different building types and their\ndiverse structural systems.\nWe acknowledge though that we only use the average values of replacement embodied carbon\nfor each building type/occupancy. However, this remains consistent with typical risk assessment\nprocedures to evaluate expected economic losses. Moreover, the use of average replacement\ncarbon may affect risk results when considering individual buildings, but it will not significantly\ninfluence aggregated estimates at national or regional levels due to the rule of large numbers.\nWe have added an explanation for these aspects in the ‘Discussion’ section.\n5. This study just focused on the seismic damage, can it be extended into other hazards\nlike flooding/tornado even multi-hazard? If so, what is the challenge?\nWe appreciate the Reviewers’ insight regarding the broader relevance of our work. In fact, our\nembodied carbon database is useful for forecasting the carbon footprint of future exposure, as\nwell as for evaluating the environmental impacts of natural hazards beyond earthquakes. This\nis because exposure models enriched with replacement embodied carbon can be easily used\nin conjunction with any models that predict vulnerability to other hazard types. Additionally, the\nuse of average annual loss ratios is applicable to other hazards, provided that that the loss\nmetric used (typically cost-based) can reasonably serve as a proxy for embodied carbon. A\nnew discussion of these aspects has been added to the “Discussion” section.\nTask 2: Correlation between repair costs and repair environmental impact.\nThe authors appear to have used the results of limited studies (Lines 275-280, references 15\nand 41) of concrete frame residential buildings in Italy to justify a direct correlation between\nenvironmental damage cost estimates and environmental impacts. Given the diversity of\nbuilding types and modes of seismic damage (structure, enclosure, finishes, systems etc.) in\nEurope, this seems to be an extraordinary level of simplification with limited justification.\nSeismic damage repair typically will require partial demolition and waste disposal, repair of\nexisting materials and replacement of materials and components. The environmental impact\nof labor is typically assumed to be zero while the impacts of transporting and processing waste\nand manufacturing, transporting and installing new materials can be tracked using LCA data\nand methods. While repair costs include labor and material parts, correlating repair costs that\nbundle labor and material is less credible than correlating repair material costs to\nenvironmental costs. Given the complexity of evaluating the massive diversity of buildings at\nthe continent scale, this does pose a significant challenge, and for high-level analysis, some\nsimplification is reasonable. We see this could be done in a range of manners.\n1. As done in the paper, propose a very simple relationship between cost of full building\nreplacement and repair and the environmental impact of replacement and repair. To\naddress this we feel the research team could follow one of the two approaches. In any\ncase, the paper would need to be more careful in stating the limitations of the study\nand not overstating the significance of the data.\na. Conduct or compile results of additional building studies to test this hypothesis\nand develop and publish (likely in a more technical journal) findings that can be\nreferenced to this paper. This could include the development a set of\nprototypical buildings (expanding beyond the one referenced study) that\naccurately represents the distribution of building types included in your study to\nPage 7 of 32\nanalyze the relationship between seismic damage repair costs and quantities\nof materials required for repairs.\nb. State this assumption as a significant source of uncertainty and a limitation to\nthe study. The sensitivity of the findings of your study to this uncertainty could\nbe done by varying the assumed relationship between cost and carbon Utilize\nenvironmentally extended economic input-output LCA for the sector under\nstudy which would rationalize the linear relationship between cost and carbon.\nSuggest separating between labor and material costs and/or clearly state the\nlimitations of the use of this data. (see FEMA P58).\nAdditional questions/comments about this task include the following:\n1. As noted above, the justification for direct relationship between cost and carbon is only\nbased on limited reinforced concrete or residential buildings in a single region, which\ndo not cover the types of buildings included in the study. We find this single source\ninsufficient to have a high level of confidence in this key assumption as noted above.\nWe thank the Reviewers for highlighting this critical aspect of our work. We agree that the\nassumption of a linear relationship between repair cost and embodied carbon requires scrutiny\nand more careful examination, particularly given the scarcity of prior research on earthquake-\nrelated environmental consequences. As suggested, we chose to consider a variation of\nOption “B”. Accordingly, we have now explicitly acknowledged that our assumption is a key\nuncertainty source and a limitation of the study, and we have revised the manuscript\naccordingly to provide a more cautious framing of such assumption (see section “Embodied-\ncarbon Seismic Risk Model for European Buildings”).\nWe have also cited additional scientific literature that adopted similar simplifications, other than\nthe previously cited work by Aljawhari et al.8, both in the main manuscript and in the\n“Supplementary Information”. For instance, Hasik et al.9 identify ‘repair-cost ratios’ as one valid\napproach to translate seismic damage into environmental impacts, by assuming a consistent\nratio between economic and environmental estimates. This method has been adopted in other\nstudies10–12, where earthquake damage and repair cost ratios were used to estimate the\ncorresponding environmental impacts, focusing on steel and reinforced concrete buildings and\nbridges in various regions. More recent studies13,14 confirmed as well the good correlation\nbetween economic and environmental loss ratios for reinforced concrete buildings in Italy,\nconsidering the effects of structural retrofitting. Ping and Wang15 drew similar conclusions for\nbuckling-restrained braced frames and self-centering braced frames.\nTo further support our approach, we provide extra analyses for comparing the ratios of\nembodied carbon and repair cost for a clay-brick masonry wall when subjected to different\nlevels of seismic damage, as suggested by the Reviewer. We select it as an example since\nsuch walls represent the main building components in masonry structures, which constitute\nthe main building type in Europe, together with reinforced concrete structures, accounting for\nup to 60% of the building stock. However, these extra analyses are presented in the\n“Supplementary Information”.\nPage 8 of 32\nThe Supplementary Table 1 displays the repair activities required for a brick masonry wall when subjected to different damage states (DSs), and\nthe corresponding unit costs (in €), adapted from Del Gaudio et al.16. The unit embodied carbon for each activity is also computed. For masonry\nwall construction, the embodied carbon includes the emissions associated with the individual clay bricks and the additional contributions of cement\nmortar, composed of a sand-cement ratio 3:1. Similarly, plastering involves a 5:1 sand-to-cement ratio. The volumes of each paint layer are\nestimated based on a spread ratio of 6.5 m2 per litre for water-based paints.\nSupplementary Table 1. Repair actions and unit costs for masonry wall damage states, adapted from Del Gaudio et al.16, with corresponding embodied carbon c alculated using\nthe current study’s database.\n[Table Redacted]\nPage 9 of 32\nTo depict how embodied carbon and repair cost correlate, we assess a typical masonry wall\npanel with a length (l ) of 4.00 m and a height (h ) of 2.75 m. The resulting repair costs and\nw w\nembodied carbon for each DS are summarised in the Supplementary Table 2, reported in both\nabsolute terms and normalised values. Our results show a close alignment between repair cost\nand embodied carbon ratios.\nSupplementary Table 2. Total repair costs and embodied carbon associated with various damage states of a\nmasonry wall.\nDamage state Embodied carbon Normalised repair Normalised\nRepair costs [€]\n(DS) [kgCO e] costs embodied carbon\nDS2 648 409 0.27 0.26\nDS3 2351 1580 1.00 1.00\nThe above observations are reflected in the Supplementary Figure 1(left), showing the\nrelationship between repair cost ratio and embodied carbon ratio for a low-code 5-storey\nreinforced concrete frame featuring unreinforced masonry infills and located in Italy. These\nresults were obtained by running a detailed component-based seismic loss assessment for the\nbuilding under a wide range of ground-shaking intensities, following the FEMA P-58\nmethodology17. The same observations can be made through Supplementary Figure 1(right),\nshowing the building-level vulnerability functions that express both the repair cost and\nembodied carbon ratios as a function of ground-shaking intensity.\n[Figure Redacted]\nSupplementary Figure 1. Repair costs vs embodied carbon. Realisations of embodied carbon loss ratios and\neconomic loss ratios for a reinforced concrete infilled frame8 (left); and vulnerability functions for repair costs and\nembodied carbon (right).\nWe also run additional analysis using the same methodology for a three-storey steel building\nused as an office. A variety of structural and non-structural components are considered. Their\nfragility, repair cost, and embodied carbon information are obtained from the default library of\nthe FEMA P-58 guidelines17, which are originally developed for the US. Although the embodied\ncarbon estimates in these guidelines are compiled using economic input-output information\nand did not come directly from explicit environmental product declarations, we still observe the\nclear correlation between embodied carbon and loss ratios in the Supplementary Figure 2.\nHowever, the dispersion is greater because the repair cost models used in FEMA P-58 offer\ndifferent prices based on the quantity of the materials being repaired.\nPage 10 of 32\nSupplementary Figure 2. Economic and environmental loss ratios. Realisations of embodied carbon loss ratios\nand economic loss ratios for a steel frame.\nGenerally, the main limitation in using repair cost ratios as a proxy for embodied carbon lies in\nthe inclusion of cost components, such as labour, technical services, shoring, and scaffolding,\nthat may contribute significantly to repair costs but have limited impacts on emissions. Based\non typical reconstruction cost breakdowns, material costs generally account for 60–80% of\ntotal repair expenditure, with the remaining share is attributed to labour and other services. It\nis also important to note that the proportion of material-related activities may differ between\nminor repair activities and major ones or full replacement scenarios. Specifically, when a\nbuilding sustains extensive damage or near-collapse conditions, where the replacement of the\nmajority of structural and non-structural components becomes necessary, both embodied\ncarbon and repair costs are dominated by the type and quantity of required materials. Under\nthese conditions, the correlation between the two metrics becomes stronger, making its use in\nrisk assessment contexts more defensible.\n2. In current practice, most people still talk about sustainability and resilience separately.\nAs addressed in lines 53-56, please articulate what you see as the challenge of\ncombining environmental impacts under the conventional risk metric framework. Is it\nthe development of the carbon-based consequence model and component inventory\nstudies that are still the most challenging? Could you elaborate more about why you\nselected to develop independent methods??\nWe appreciate the Reviewers’ comment. A key distinction that often goes unacknowledged is\nthat much of the current literature on sustainability-resilience interaction focus on design, rather\nthan assessment, leading to competing targets. For example, safer structural designs might\nrequire more material, increasing both initial costs and embodied carbon. Hence, researchers\nadopt multi-objective optimisation frameworks to explore potential trade-offs. In contrast, our\nstudy focuses on assessment, where embodied carbon is not the result of design decisions,\nbut rather a consequence of damage. The challenge is integrating this type of environmental\nconsequences into current risk frameworks, which requires consequence and vulnerability\nmodels linking damage and earthquake intensity to embodied carbon, respectively. Currently\navailable models are very scarce and might be limited in geographic and typological scope.\nGiven these constraints, we opted for a pragmatic alternative, which uses economic AALRs as\na proxy for their environmental counterparts (i.e., AAECRs), and then combines them with\nreliable estimates of replacement embodied carbon.\nPage 11 of 32\n3. We are having difficulty interpreting the information in Table 2 and imagining how we might\nreplicate your study methods for different regions or with different assumptions.\n1. Please provide an equation and/or diagram to explain the method of transferring data\nfrom the AALR data to the items in this table. We believe we understand the basic\nprocess but as written it is not yet clear. e.g.\ni. EC=Sum all building types (#bldgs typ1 x ECtype2 + #bldgs type 2 x ECtype2\n….#bldgs type’n’x ECtype’n’)\nii. How did you determine % structural contribution and % non-structural??\niii. How did you translate it to an annual amount??\n2. In Table 2, the AAECR column reflects the ratio of AAEC under seismic risk and initial\nEC consumption as a new replacement of the building. The value seems relatively\nsmall on a per building scale. Can you evaluate the importance of integrating seismic\nrisk into building scale assessments? On a per building scale in some locations would\nyou assess that the contribution of damage retrofit is not important at all considering\nthe full life cycle? For example if we are looking at a 100-year life span, a AAECR of 2\n‰ (e.g. Romania) would be insignificant (expect building lifespan repair carbon is only\n20% of new buildings in lifespan, while at 0.04 ‰ (Germany) is even less significant\n(over 100 years only 4 ‰ total damage which is calculated by product of average\nyearly ratio 0.04‰ and 100 year lifespan). Yet in aggregate over a region, the climate\nand cost impacts can still be significant-this is the key contribution of this research. Are\nwe interpreting the data in this table correctly??\nWe appreciate the Reviewers’ interpretation of Table 2, which we believe is accurate. We agree\nthat the methodology needs a clearer explanation. We have provided additional clarification in\na new section in “Methods” called “Data Sources and Integration”, and we created a flowchart\nclarifying how different data sources are integrated (see Figure 10, also shown below).\nFigure 10. Workflow for the assessment of replacement embodied carbon and average annual embodied\ncarbon per asset. Calculation workflow for estimating replacement embodied carbon (REC) and average annual\nembodied carbon (AAEC) of a given building asset, with a given occupancy type, exposed to seismic risk.\nThe GEM exposure model adopted in our work is composed of building assets and their\nattributes like building type, number of buildings, built area, replacement cost, occupancy,\nPage 12 of 32\nnumber of occupants and dwellings. The exposure attributes that are combined with our\nestimates of replacement embodied carbon are: (i) building type (ii) occupancy, and (iii) built\narea. Building type and occupancy allow to select the appropriate embodied carbon factors to\nbe used in conjunction with the corresponding built area, as detailed in Figure 1 and Table 3.\nMore detailed steps are outlined below:\n• For each building type and occupancy, we evaluate the embodied carbon factors\nassociated with its structural (foundations, frames, floors, walls) and non-structural\ncomponents (e.g., lifts, staircases, partition walls, windows, ceilings). We use for that\npurpose open-access sources on embodied carbon of materials/components (e.g., EPD\ndatabases, scientific papers/reports) in combination with our estimates of their quantities;\n• From the exposure model, we extract the built area per building type within each asset\nof a given occupancy. We multiply these areas with the corresponding embodied carbon\nfactors, disaggregated by structural and non-structural contributions, to obtain the\nreplacement embodied carbon per building class. In the end, we aggregate them to obtain\nthe total replacement embodied carbon;\n• For each building type of a given occupancy, we extract the AALR values\n(disaggregated by structural and non-structural contributions) from GEM’s latest risk\nmodel. By multiplying these ratios with the corresponding REC and summing the\nresults, we obtain the average annual embodied carbon due to seismic risk for each\nasset (also disaggregated);\n• To obtain summary values at the country level for both the replacement embodied\ncarbon (Figure 4) and the average annual embodied carbon (Figure 6 and Table 2), we\nsimply sum the results above for individual assets across all building types and\noccupancies (disaggregated by both structural and non-structural contributions).\nWe understand that displaying AALRs at the country level in Table 2 could be misleading and\nmay not exhaustively reflect the approach outlined above. We have clarified this aspect in the\nsection “Embodied-carbon Seismic Risk Model for European Buildings” and removed the\nAALRs at the country level from Table 2.\nLastly, we emphasise that the AAEC metric reflects an expected value, representing the\nannualised equivalent embodied carbon associated with the repair (or replacement) of\nbuildings following seismic damage. While per-building values may appear modest, the\naggregated national-scale impact is substantial, reaching millions of tons of CO₂e yearly for\nprone countries such as Turkey, Italy, Greece, and Romania. To our knowledge, this is the first\nstudy to provide such estimates at national and continental scales, while also identifying the\nconstruction types that contribute most significantly. Our findings underscore the urgent need for\ntwo complementary strategies: (i) promoting sustainable construction materials and practices\nto reduce REC, and (ii) implementing effective seismic risk mitigation measures to lower AAEC.\n3. Lines 379-381, authors propose a future advancement through the derivation of\nstructure-specific damage-to-carbon models (e.g., carbon-based consequence model).\nCompared with the traditional simulation-based risk analysis for the damage-to-loss\nmodels, what are the challenges for extending to damage-to-carbon models? Could\nyou elaborate more about this proposed method?\nPage 13 of 32\nAs noted in our previous responses, we acknowledge that developing consequence and/or\nmodels in terms of embodied carbon for various building types remains a significant challenge\nand is a key objective of our future research. In our view, the main difficulty lies in obtaining\ndetailed, component-level data across a wide range of building types, including accurate\nquantification of material use and access to corresponding EPDs. Another main challenge is\nestimating the embodied carbon at different levels of damage for individual structural and non-\nstructural components, which remains insufficient in the literature for most building classes.\nTask 3: Open-access embodied carbon database.\nThe authors present a method to estimate the embodied carbon of different building types\nusing engineering principles to estimate material quantities and a diverse source of life cycle\nassessment (LCA) data. While it does appear that they have built bottom-up models to\nestimate the embodied carbon of typical buildings that are of the same overall order of\nmagnitude as other studies, we feel that the current manuscript overstates the contributions of\nthis study. There are significant studies underway globally that both compile ‘synthetic’\n(modeled data as this team developed) and ‘wild’ (data collected from real-world buildings).\nPlease note that our team is in progress developing building scale benchmarking data from\nreal buildings in North America and is aware of other building scale benchmarking projects\nunderway that appear to be more detailed than the data outlined in this study. Based on the\ninformation provided within this paper’s documentation, we feel that the following statements\noverstate the significance of the contribution in this area and should be modified to reflect a\nmore considered view of the contribution.\n“Overall, our study delivers a highly comprehensive and harmonised database for the\nembodied carbon of building replacement, spanning over a wide range of building types,\nstructural systems, and occupancies. Built upon an extensive compilation of European\nembodied carbon information, our database ensures consistency across life-cycle stages.\nIt incorporates realistic estimates of material quantities, both for structural (with sub-structure)\nand non-structural components.”\n“Our data are essential for benchmarking the carbon footprint of the construction sector in\nEurope and forecasting future exposure projections””\n“In recognition of these research gaps, our study delivers comprehensive analyses of the\nembodied carbon of the European residential, commercial, and industrial building stock.”\nFor example:\nWe don’t find any evidence of the delivery of the database within the documentation provided.\nWe don’t see any evidence of the method used to harmonize the database.\nWe don’t see examples of the data regarding material quantities.\nWe sincerely thank the Reviewers for their valuable feedback. We recognise that there is a\nwide range of global initiatives dedicated to collecting both synthetic and real-world data on\nbuilding embodied carbon (e.g., Röck et al.18), as well as material-use intensities (e.g.,\nFishman et al.19). However, only a few open datasets are available that provide both material\nquantities and embodied carbon data, like those found in Benke et al.20. We have now revised\nthe manuscript, and in the “Open-access Database of Embodied Carbon for Building\nReplacement” section we recognise these other initiatives, and ensure that readers are aware\nthat our work is by no means the only effort of this kind. Should the Reviewers be aware of\nadditional relevant datasets/studies that we have not cited, we would be grateful for their\nsuggestions and pleased to include them in the manuscript.\nPage 14 of 32\nOur effort focused on designing archetypes for different building types/occupancies to estimate\nmaterial quantities and compiling EPDs from various databases and literature sources\ncovering a wide range of structural and non-structural components. We acknowledge the\nlimitations of our approach, particularly due to the variability of architectural configurations\nacross the building stock. Despite these challenges, we have made significant efforts to:\n• Differentiate building types based on key construction materials and structural systems,\nin addition to occupancy (i.e., residential, commercial, industrial). This approach goes\nbeyond the sole reliance on primary use type (e.g., single-family houses, multi-family\nhouses, offices) often found in the literature, which may not account for such aspects;\n• Provide material quantity estimates for a wide range of structural and non-structural\ncomponents of the most common building types in Europe, based on early-stage\ndesign processes, allowing for consistent cross-comparisons between building types;\n• Review a large number of open-access EPD databases and literature sources to obtain\nconsistent embodied carbon estimates for the typical structural and non-structural\ncomponents, disaggregated by LCA modules;\n• Public release of all datasets related to both replacement embodied carbon and average\nannual embodied carbon, now made available on GitHub, at the first subnational\nadministrative level of each country to promote transparency and reproducibility.\nWe refer to this as a harmonised database because it is aimed at making consistent estimates\nfor material quantities and embodied carbon factors across different building types,\noccupancies, construction materials, and LCA modules, allowing consistent comparisons.\nWe believe these contributions constitute an impactful advancement in the integration of embodied\ncarbon within seismic risk assessments. Our datasets and methods are designed to enhance\naccessibility and utility for the broader research community, enabling regional-scale assessments\nthat inform prioritisation strategies, policy development, and facilitate comparative analyses\nacross building portfolios. These objectives are distinct from building-specific life-cycle\nassessments or sustainable design studies, and we position our work as complementary to\nthese efforts. We wish to emphasise as well that that this work has applicability beyond\nearthquakes, provided that suitable hazard and vulnerability models are available.\nBased on the Reviewers’ comments, we have revised the manuscript to clarify the scope and\ndistinct contributions of our study in the section related to “Open-access Database of\nEmbodied Carbon for Building Replacement”.\nThe embodied carbon compilation is reasonable but not extensive.\nWe understand that the compilation of materials and components might not cover the entire\nrange of possibilities for every building type. Our selection of components is based on\nidentifying those most likely to sustain significant damage during earthquakes and representing\na substantial contribution to the replacement embodied carbon. Future efforts could further\nrefine this selection to encompass additional possibilities. We have acknowledged that in the\nrevised manuscript under the section “Collection and Processing of European Embodied Carbon\nData for Construction Materials, Structural and Non-structural Building Components”.\nPage 15 of 32\nThe full life cycle of buildings are not included but rather the study only focused on the embodied\ncarbon of replacing/repairing buildings due to seismic damage. For example the analysis doesn’t\ninclude Module B4/B5 nor does it include the original construction or final demolition.\nWe appreciate the Reviewers’ note. The main objective of our study is to quantify the embodied\ncarbon of seismic damage for Europe’s building stock, expressed as a ratio of the building’s\nreplacement embodied carbon. The replacement carbon does incorporate modules A1-A5 to\naccount for the construction processes of the new buildings. Regarding modules C1-C4, they\nare included as part of sub-module A5.1 (pre-construction demolition). This approach aligns\nwith the assumptions made when estimating the replacement cost of buildings.\nThe replacement embodied carbon does not include module B activities (e.g., B3-B5 for repair,\nmaintenance, refurbishment) as they pertain to damaged components only. While the\nreplacement embodied carbon represents the hypothetical reconstruction of a building after a\nsevere event, the AAEC refers to existing buildings, capturing the expected environmental\nburden due to seismic damage over time. In this context, we can interpret the AAEC as a\nprobabilistic, annualised contribution of seismic events to the embodied carbon impact during\na building’s use phase (i.e., part of module B), which conceptually aligns with LCA frameworks.\nThe revised manuscript has now reflected such aspects more clearly (see section “Collection,\nCompilation, and Assembly of Embodied Carbon Data”).\nWe don’t find the data as essential to benchmark carbon footprints of buildings.\nWe understand the Reviewers’ concern regarding the role of our data in benchmarking carbon\nfootprints of buildings. Our intention is not to present a definitive or “gold standard” dataset for\nembodied carbon benchmarking. Rather, our estimates, derived through a systematic methodology\ncombining detailed material quantity data and embodied carbon factors, are intended to provide a\nconsistent and harmonised reference framework. This supports comparative assessments across\ndifferent building types, occupancies, regions, and scenarios, enabling researchers and\npractitioners to evaluate relative differences and trends. We fully acknowledge the limitations of\nour dataset, including the representativeness of building types, the scope of included LCA\nmodules, and the averaging of embodied carbon factors across multiple European countries.\nDespite the above constraints, we believe our data add value by offering a transparent and\naccessible basis for comparison, especially in contexts where detailed building-level analyses\nare unavailable or inconsistent. Our approach to benchmarking should therefore be viewed as\nexploratory and comparative rather than prescriptive. It aims to facilitate consistency checks\nand relative performance evaluations among various methodologies and datasets. To reflect\nour intention and set appropriate expectations for users, we have clarified this distinction in the\n“Open-access Database of Embodied Carbon for Building Replacement” section.\nThe analysis does provide an effective order of magnitude estimate of the embodied carbon\nimpacts of seismic damage and repair. This alone is a significant contribution.\nWe appreciate the recognition of our effort in estimating the embodied carbon associated with\nseismic damage, and we hope that integrating environmental consequences into seismic risk\nmetrics becomes standardised in holistic decision-making in disaster risk management contexts.\nPage 16 of 32\nThe analysis conducted by this team does validate other studies (Lines 204-215) that capture the\noverall order of magnitude of ‘upfront’ embodied carbon (A1-A3) required to construct new buildings.\n1. We cannot find access to the noted open source dataset of embodied carbon. The git-\nhub site only provides .jpgs of maps. Can this data be provided as we believe this would\nhelp us interpret the findings??\nWe have now fully populated the GitHub repository with all the relevant datasets.\n2. Provide greater clarity on the LCA modeling assumptions behind key factors:\na. Life cycle stages included. Figure 7 is a very generic description of the life cycle\nstages used in conventional whole building LCA. For this paper a more detailed\nspecific system boundary diagram should be developed and both characterize\nhow seismic repair fits should within conventional LCA studies and address the\nspecific scope included in your study. For example, Lines 105-106, this paper\ndid not include modules B4-B5 which are the regular replacement/maintenance\nfor a building (different from hazard-based ones) yet your diagram shows this\nincluded. Please clarify.\nWe agree with the Reviewers that Figure 7 could be misleading as it was initially intended to\ndescribe the entire life cycle of an individual building. We have improved this figure (also shown\nbelow) as follows:\n• Adding AAEC within traditional building LCA under modules B3-B5, where it can be\ncalculated as an expected yearly estimate and multiplied by the building’s life span. We\nemphasise that AAEC is a probabilistic estimate, averaged across multiple earthquake\nscenarios occurring over a long time span. This approach accounts for a wide range\nof possible seismic damage levels, from slight to complete. This differs from analysing\nthe consequences of an individual earthquake event, where some buildings may incur\nminor damage while other can collapse. Accordingly, earthquake consequences are\nnot simply treated as an addition to the end-of-life phase;\n• A clear indication of the life cycle modules included in the evaluation of the replacement\nembodied carbon, which is subsequently used to quantify the AAEC.\n[Figure Redacted]\nFigure 7. Average annual embodied carbon in the building life cycle. Integration of average annual embodied\ncarbon (AAEC) into the building life cycle (after Magwood and Huynh, 202321). Icon credits: www.flaticon.com.\nPage 17 of 32\nb. Building physical scope included (e.g., interior partitions, and finishes included\nin the EC estimate)\nWe have now clarified throughout the text which components are included in the analysis,\nalong with some additional clarification on material quantities (see section “Collection and\nProcessing of European Embodied Carbon Data for Construction Materials, Structural and\nNon-structural Building Components”). For readers interested in further details, all relevant\ndatasets are now available on the GitHub repository.\n3. The authors mention the differences between structural and non-structural components\nand highlight them in Table 2 yet the range of embodied carbon estimates for these\nassemblies is not provided within the study nor could we find this background data\nwithin referenced open-source publication resources.\nWe have now included all the data on material quantities and embodied carbon factors for\nstructural and non-structural components for each building type/occupancy on GitHub.\n4. Lines 123-124 note that transportation and end of life are ‘highly varied and have scarce\ndata’ and thus filled with simplified placeholders. Later, in lines 151-155 draw conclusions\nbased on these rough assumptions.\na. The assumptions for end-of-life treatment for conventional construction seems less\nlikely to accurately reflect the end-of-life treatment after a natural disaster. Thus\nthe limited accuracy of the environmental impacts for the waste generated by\nearthquakes should be listed as a limitation.\nb. The end of life estimates reference an out of date version of RICS. The current\nversion has a range of end-of-life scenarios, none of which equal the 10% landfill,\n90% recycled percentage referenced in lines 533-535.\nWe thank the Reviewers for the insightful comment. We agree that relying on literature simplified\nfigures for transportation and end-of-life phases is a limitation in our analysis. However, we\ndeem them appropriate given the broad scope and geographic scale of the study. Specifically:\n• Our analysis estimates the average annual embodied carbon at a continent scale,\nacross a diverse building portfolio, rather than at the level of individual buildings. This\nenables comparative and large-scale assessments that support regional prioritisation\nof risk mitigation strategies and integration into multi-hazard risk frameworks;\n• As noted in previous studies (e.g., LETI, 2020), contributions from transportation and\nend-of-life phases are generally minor compared to other more dominant life-cycle\nstages, particularly modules A1-A3.\nWe also agree that our end-of-life assumptions may not fully capture the realities of post-disaster\nscenarios. In the aftermath of earthquakes, for instance, demolition and disposal often deviate\nfrom normative practices as damaged materials might be unsuitable for reuse or recycling,\nleading to increased landfilling or emergency disposal. Bektaş et al.22 highlighted that sustainable\nconstruction and waste management practices can significantly mitigate these impacts, as\nobserved after the 2023 Türkiye–Syria earthquake. We have added this limitation in the\n“Replacement Embodied Carbon vs Average Annual Embodied Carbon” section. We also\nclarified in the revised manuscript (see section “Collection, Compilation, and Assembly of\nEmbodied Carbon Data”) that end-of-life processes (modules C1-C4) are now included in our\nPage 18 of 32\nassessment as part of the sub-module A5.1 (pre-construction demolition), and modified Figure\n8 accordingly.\n[Figure Redacted]\nFigure 8. Workflow for the assessment of modules A1-A5. Workflow for estimating modules A1-A5 embodied\ncarbon per square meter of built area. Icon credits: www.flaticon.com.\nAccording to RICS (2023), “when any full or partial demolition or deconstruction of an existing\nstructure is required, to facilitate the construction of a new asset within the designated site\nboundary or an extension of the existing asset, the impacts associated with that demolition or\ndeconstruction must be included in the WLCA in sub-module A5.1 (pre-construction\ndemolition), including transport from site and waste processing of removed materials”.\nTherefore, we consider the following within module A5:\n• Module A5.1 (pre-construction demolition): this includes deconstruction and demolition\nactivities equivalent to the C1–C4 end-of-life impact of the existing built asset, as follows:\no Module C1 (deconstruction and demolition): estimated as 25% of A5.2 impacts,\nassuming standard demolition practices with limited deconstruction and recovery23;\no Module C2 (transport): treated similarly to module A4 but considering a reduced\ndistance, i.e., 50 km, reflecting likely proximity of disposal facilities24;\no Module C3-C4: as also reported in other works (e.g., Kitayama et al.25), in the absence\nof detailed information, these modules are grouped and the default emissions for\ndisposal to landfill (module C4) can be applied, as 0.013 kg CO /kg waste26;\n2\n• Module A5.2 (construction activities): we refine our estimation method for on-site\nenergy use, replacing the previous potential energy-based approach with an empirical\nmodel from the Athena Sustainable Materials Institute27, also used by Gu et al.28 and\nChen et al.29. This approach estimates diesel fuel use based on the mass (M) and\nheight (h) of building components lifted by cranes, as Fuel (L) = 0.000037 Mh + M/500\n+ 0.83. With a diesel energy content of 38 MJ/L and a carbon factor of 0.073 kg\nCO₂e/MJ, this allows conversion to embodied carbon. This method captures the\nequipment fuel use only, not other site activities (e.g., temporary works);\n• Module A5.3 (waste and waste management): previously estimated using a 3%\naverage waste rate across all components, we now adopt differentiated waste rates by\nmaterial/component per RICS23. These are applied to modules A1–A3, A4, C2, and C3-\nC4, accounting for disposal via landfill or incineration;\nPage 19 of 32\n• Module A5.4 (worker transport): excluded as transport of personnel is considered the\nresponsibility of individual employees rather than the project23,26.\nc. Lines 151-155, can you point to figures or data that is included to support the\nconclusions?\nWe have now included the embodied carbon values for each building type and occupancy,\ndisaggregated by LCA modules, in the GitHub repository.\nd. Lines 166-169, The 'size' mean 'mass' or volume. Please clarify, are they under the same standard of structural performance design after retrofitting?\nThe size refers to the cross-sectional dimensions of the components, and thus to their volume.\nAll archetype buildings are designed using simulated design based on current building codes\nor based on available construction drawings.\ne. Line 190, it is interesting to see the different material quantities/mass for the full\nreplacement, especially at the regional scales discussed here. You just provide\nthe EC directly. Could you also add different material quantities/mass used to\ngenerate your baselines? Similarly, in Line 221, reviewers did not see any\nfigures or results about material quantities. Please add this information or point\nto resources within the project dataset.\nWe have included CSV files and figures reporting mass per square meter of built area in the GitHub\nrepository, along with tables detailing the quantity of each component per square meter for each\nbuilding type. We have added multiple references to these outputs in the revised manuscript.\nf. Lines 204-215, it is not reasonable to compare your results with the reference\n33-35 because of different system boundaries, and physical scopes.\nWe are aware that other studies have assessed different life cycle phases and scopes, often\nwith varying levels of detail and assumptions. Nevertheless, we have provided comparisons\nwith available databases reporting average values of each database subset, mainly to verify\nwhether our estimates fall within reasonable ranges for similar building types. Hence, we have\nacknowledged these limitations in the revised version (see section “Open-access Database of\nEmbodied Carbon for Building Replacement”) to ensure clarity and transparency.\ng. Lines 520-526, can you provide any reference to support the method to\ncalculate A5?\nAs stated above, we have now enhanced our estimates of module A5 by incorporating additional\nreferences and refining our approach in addressing the construction and demolition phases.\nh. Line 248, 'benchmarking' requires a more nuanced definition that could include\nadditional context to validate the findings and report different statistical\nindicators (e.g., first quartile, mean, median...) This study reports\naverage/representative values. Suggest just using the term average predicted\nEC rather than benchmark.\nPage 20 of 32\nWe thank the Reviewer’s for the comment. Please refer to the initial part of our response to Task 3.\nFormat and other details\nThe following are editorial comments::\n1. Line 30, “ARTICLE” to “INTRODUCTION”\n2. Line 94, Please clarify your definition of pan-European?\n3. Line 96, AAEC is the terminology you defined? If not, please provide a reference?\n4. Lines 113, 123, and 434, this citation style is not common. Avoid () and put the citation\nbehind 'literature'\n5. Line 139, we are less familiar with the term ‘occupancy category’, perhaps replaced\nwith ‘primary building use type’. Or is that a term common in seismic performance?\n6. Line 139, please verify, is this ‘building type’ or ‘structural type’?\n7. Line 422, replace iii with IV\n8. Line 456, citation is wrong.\nWe have addressed all the format-related issues raised by the Reviewers. Regarding the\nadditional points:\n• In the context of this study, pan-European refers to the set of countries included in the\nEuropean seismic risk mosaic developed by the GEM Foundation;\n• AAEC is a new term we introduce to indicate the environmental consequences of\nearthquakes in terms of embodied carbon, analogous to other seismic risk metrics such\nas AAL (average annual loss) and AAF (average annual fatalities);\n• Occupancy vs. primary building use: we typically use the term 'occupancy', consistently\nwith GEM building exposure models and current practice of seismic risk assessments.\nHowever, we added that both terms are equivalent;\n• Building type vs. structural type: the former refers to the classification of buildings based\non specific attributes that contribute to their seismic vulnerability. GEM has developed\na comprehensive building taxonomy (see here) that is utilised throughout the\nmanuscript and in all datasets.\nConclusions\nWe find the premise of the study-to connect the environmental impact of seismic damage\nrepair on an annualized basis across all of Europe to be quite compelling and believe that the\nimpact of this method of contextualizing the data has the potential to spur action to improve\nseismic resilience and environmental performance. Visualizing the data as well as helping the\nreader understand the order of magnitude of environmental impacts in the context of other\nregional emission sources is an effective strategy to bridge between the disciplines of seismic\nresilience and environmental performance. This paper could be substantially improved by::\n1. Providing more context about AALR for those without seismic design domain expertise\nto understand the assumptions behind and limitations of the financial loss models as\naddressed in Task 1.\n2. Sensitivity analysis could be performed to test the assumption related to % repair cost\nand % environmental impact. Or other potential ways mentioned in Task 2 could be\ntried. While the details might be slightly different, the broader findings are likely to\nremain unchanged.\nPage 21 of 32\n3. Downplaying the significance of the contribution of the embodied carbon database.\nUnless the authors provide more detail on the data behind this data, we don’t feel it is\nactually as significant as they claim. It appears that more detailed reporting of the\nmaterial quantity generation could support more substantial claims in this area. We\nbelieve that foundational data documentation is likely better suited for a different journal\nthan Nature Communications. However if the building benchmarking data was\npresented in more detail elsewhere, that reference could be a valuable reference for\nthis paper.\n4. Address all the comments including Task 1-3 as well as the formatting items noted.\nWe commend the research team for taking on this important trans-disciplinary work. Thank\nyou for the opportunity to learn from and critique your work. We greatly appreciated the\nopportunity to conduct this peer review as a collaborative effort and welcome the opportunity\nto engage in future discussion on this topic.\nWe sincerely appreciate the Reviewers' thoughtful feedback, which we believe has significantly\nstrengthened the quality of the manuscript. We have carefully addressed all the points raised,\nboth in terms of content and formatting, and have made a concerted effort to ensure clarity\nand rigour throughout the text. We look forward to any further discussions and remain fully\nopen to continued engagement with other scholars working on similar or related themes.\nPage 22 of 32\nReviewer #3\nThe manuscript presents a well-written and timely contribution that integrates seismic risk\nassessment with environmental impact analysis, a crucial yet often overlooked aspect in\nsustainable disaster risk reduction. The authors provide a complex framework, data-driven\napproach, developing a harmonized database and presenting the 1st seismic risk map based\non embodied carbon losses across Europe. At this point, I commend the authors for their\nexcellent work, as this manuscript provides valuable insights and has greatly enhanced my\nunderstanding of the environmental impact of seismic risk.\nWe sincerely thank the Reviewer for their positive and spot on feedback, which has enhanced\nthe quality and clarity of the manuscipt. We appreciate the Reviewer’s recognition of our work’s\ncontribution to integrating seismic risk and environmental impact, and are glad it has enhanced\nunderstanding in this important area.\nHowever, there are several areas where additional clarification and methodological refinement\nare needed to improve readability, uncertainty quantification, and applicability across different\nparts of the submitted manuscript. Hereinafter, are specific comments and recommendations:\n1. The manuscript needs to clearly specify the methodology behind the embodied-carbon\nseismic risk map. The authors should explicitly state whether an event-based or non-\nevent-based approach was used in constructing the risk map. A discussion on\nuncertainty sources in the map is essential, particularly regarding exposure data\nlimitations, hazard model assumptions, and consequence model approximations.\nWe agree with the Reviewer that the original manuscript required more detailed discussion on\nthe risk assessment methodology and the underlying assumptions, limitations, and datasets\ninvolved in each component of seismic risk.\nTo clarify, the values presented in the seismic risk map are obtained by running the event-\nbased probabilistic risk assessment calculator in the OpenQuake Engine5,6, an open-source\nplatform for seismic hazard/risk analyses. This calculator generates large sets of stochastic\nearthquake events within a specified time span, based on the seismic hazard model of the\nregion of interest. For each event, ground-motion fields are generated via suitable ground-\nmotion models. The calculator computes the expected loss incurred by each asset in the\nexposure model by using the ground-shaking intensity at its location and the corresponding\nvulnerability function, which allows to generate losses and then compute metrics like AALR.\nWe have accordingly expanded the section “Embodied-carbon Seismic Risk Model for\nEuropean Buildings” to provide a clearer explanation of the risk assessment methodology and\nits components (seismic hazard, exposure, and vulnerability), the type of risk calculations\nperformed (event-based risk calculator using the OpenQuake Engine), the rationale behind the\nquantification of the average annual embodied carbon (AAEC), and how it is derived from AAL\nand AALR. We have further included new illustrative Figures (9-10) in the “Methods” section,\nwhich can effectively help clarify these aspects.\nWe also acknowledge that the accuracy of our results is bounded to a number of assumptions,\nwhich are broadly aligned with large-scale assessments of building portfolios. Seismic risk\nPage 23 of 32\nmodels inherently reflect the uncertainties embedded in their underlying components (i.e.,\nseismic hazard, exposure, and vulnerability models). These uncertainties arise from data\nlimitations, modelling assumptions, and regional variability, and they have been discussed in\nseveral studies1–3,30,31. For example, the exposure model we implement draws from available\nsurveys and national housing censuses, and it relies on national mapping schemes to assign\nbuilding types. Although this might lead to some misclassification of building types, they are\nunlikely to cause significant differences in the risk estimates, especially that misclassifications\nare often done between building types with similar vulnerability classes7. Other limitations\ninclude our significant reliance on environmental impact information pertaining to nations with\nhigh data availability (e.g., United Kingdom, Spain, France, Italy), and our use of average\nquantities of building components, besides assuming they are independent of their location.\nHowever, this is unlikely to affect AAEC estimates when the results are aggregated at regional\nlevels due to the rule of large numbers.\nAll above uncertainties and their impact on our risk estimates have now been discussed in\ndetail in the revised manuscript under the “Discussion” section. A further exploration of the\nlimitations of portfolio risk assessment, sources of uncertainty, with some focus on hazard and\nexposure, can be found in some literature references 1–3,30,31.\n2. The assignment of building typologies within the exposure dataset is inherently\nuncertain. The manuscript should acknowledge and discuss how misclassification of\nbuilding types might impact the embodied carbon estimates. A more detailed\nassessment of classification errors and their effect on results would improve the\nrobustness of the study.\nWe thank the Reviewer for raising this issue. As anticipated above, the GEM’s exposure\nmodel2 we adopt in our risk analyses relies on available housing census/surveys at the\nsmallest available administrative level. It also uses national mapping schemes to assign\nbuilding classes to each exposure asset that include information on construction materials and\nheight. This approach maintains uniformity and adjustability, but is inevitably affected by some\nepistemic uncertainties. However, we have investigated the impact of different building\nclassification assumptions in seismic risk analysis in an earlier study7, and concluded that its\nimpact is limited due to the fact that misclassifications are typically done between building\nclasses belonging to identical vulnerability classes. For example, with the available building\ndata from each country, misclassifications might occur between unreinforced masonry and\nadobe buildings, but the vulnerability of these buildings is not significantly different. As a result\n(and due to the rule of large numbers), when performing risk analysis at a large scale, these\nerrors do not cause significant differences in the seismic risk estimates. We have added a\ncomment on that in the “Discussion” section.\nWhen assigning replacement values to exposed assets, GEM models adopt a ‘‘build back\nbetter’’ approach. For example, if an adobe masonry building collapses, the replacement cost\nis estimated as the economic value corresponding to the minimum affordable housing standard\nin the country (e.g., a confined or reinforced masonry structure). We consider this approach\nappropriate for European countries, where building codes are generally legally enforced.\nAccordingly, we have applied a similar principle in estimating the replacement embodied\ncarbon. Specifically, we assume that reconstructed buildings retain the same material category\nas the original ones but with improved structural systems and materials consistent with modern\nPage 24 of 32\nstandards. We deem this assumption reasonable in European contexts, where architectural\npreservation or urban planning regulations constrain major alterations, and upgrades tend to\nfocus on structural detailing for seismic compliance rather than complete design changes.\nHowever, we recognise the limitations of this approach as post-disaster reconstruction does\nnot always lead to improved structural performance. Moreover, such improvement can be\nachieved by using different structural systems (e.g., reinforced concrete frames, reinforced\nmasonry walls), and each one involves varying material compositions and quantities. These\nvaruations are not necessarily reflected in economic costs, but they can notably affect carbon\nestimates. Furthermore, actual post-disaster reconstruction may involve significantly different\nmaterials due to supply-chain disruptions or economic constraints, introducing additional\nuncertainty to embodied carbon estimates. As such, our results should be interpreted as a\npossible scenario rather than exact predictions.\nWe have included these considerations in the revised manuscript (see the “Discussion” and\nthe “Identification of Building Types” sections) to improve the transparency of our work.\n3. The use of built-up area as a direct measure of embodied carbon oversimplifies real-\nworld variations in material intensity. Older buildings often use more materials per\nsquare meter, and regional differences in construction practices (e.g., prefabrication\nadoption post-1977 Vrancea earthquake in Romania) further complicate this\nassumption. Variations in cement clinker composition across time periods also\ninfluence embodied carbon values. The authors should discuss these limitations and,\nif possible, introduce adjustment factors for different construction periods to refine the\nembodied carbon estimations.\nWe thank the Reviewer for pointing out this challenge. We would like to clarify that we initially\ncompute the embodied carbon of building replacement by first performing a practice-oriented\nquantity take-off to estimate the quantities of structural and non-structural components of\ndifferent building types and occupancies, considering simplified archetype buildings with\ngeometries obtained through simulated design. These quantities are combined with the\ncorresponding embodied carbon factors (ECFs), and the overall carbon is normalised by the\ntotal floor area of the building. This variability across building types and occupancies provides\ngreater accuracy than using average embodied carbon estimates for generic buildings.\nWhile we agree that older constructions often used greater material quantities or distinct\nconstruction methods, we emphasise that our analysis is based on the “build back better”\napproach. In this framework, damaged buildings are assumed to be repaired or replaced using\ncontemporary building types and construction practices, rather than replicating the material\nintensities or techniques of the original structures. As such, our embodied carbon estimates\nreflect modern standards for replacement construction, regardless of the age or historical\ncharacteristics of the original buildings. The same principle applies to repairs, which are modelled\nusing current methodologies and techniques rather than those from the past. We believe this\nassumption reasonably addresses concerns related to variability in building age and construction\nera. We believe that our choices of building replacement types (summarised in Table 3)\nrepresent a plausible scenario.\nPage 25 of 32\nWe have now discussed the previous aspects in the revised manuscript under the\n“Identification of Building Types” section. Finally, all the underlying datasets (material and\ncomponent quantities, embodied carbon factors, exposure and risk data) are now publicly\navailable in our GitHub repository, allowing for easy access by the wider research community.\n4. The manuscript does not explicitly differentiate between full replacement and partial\nretrofitting scenarios following earthquake damage, and this might be a limitation. In\nmany cases, buildings experiencing moderate damage undergo partial repairs rather\nthan complete reconstruction. This can be recommended to scenario case studies, and\nsuch discussion about the retrofitting scenarios should be clearly reiterated in the\ndiscussion.\nWe thank the Reviewer for raising the important topic of repair and retrofit scenarios following\nearthquake events. To clarify, our approach does account for varying levels of seismic damage\n(also called damage states), ranging from slight to complete damage, and incorporates the\ncorresponding repair or replacement needs rather than considering only full replacement.\nSpecifically, the embodied carbon estimates reflect a damage ratio applied of the replacement\nembodied carbon, which allows us to capture partial repair or retrofit scenarios proportional to\nthe damage sustained by the building.\nWe have expanded the discussion in the manuscript (see section “Embodied-carbon Seismic\nRisk Model for European Buildings”) to highlight that our methodology considers a spectrum\nof damage and repair levels, rather than exclusively full reconstruction.\n5. The study would benefit from the inclusion of a consequence curve example, as\ndemonstrated in Aljawhari et al. This would help readers visualize how seismic damage\ncorrelates with embodied carbon losses, improving the accessibility of the findings.\nWe agree with the Reviewer that adding plots showing the correlation between seismic repair\ncosts and embodied carbon losses would be beneficial for the sake of clarity. We have now\nadded these in the “Supplementary Information”, including a plot showing embodied carbon\nand repair cost realisations obtained from detailed component-based risk assessment, and\nanother plot showing the vulnerability curves. These results show embodied carbon and repair\ncost ratios as a function of the ground-shaking intensity.\n6. The decision to exclude vulnerable buildings from the analysis needs further\njustification. These structures, due to their high likelihood of collapse and subsequent\nfull replacement, contribute significantly to embodied carbon emissions. The authors\nshould specify what percentage of the dataset these excluded buildings represent. It\nwould also be valuable to perform the analysis focusing exclusively on the most\nvulnerable buildings, as this aligns more closely with the full replacement assumption\nused in the study.\nWe thank the Reviewer for this observation. We wish to clarify that we did not exclude buildings\nwith high vulnerability from our risk assessment. The risk is computed based on the actual\nseismic performance of the existing building stock, including vulnerable structures. However,\nwhen expressing the average annual embodied carbon (AAEC), we normalise the results\nagainst the embodied carbon of the potential replacement building, in line with the “build back\nPage 26 of 32\nbetter approach”. This means, for example, that if an adobe masonry building collapses, the\nreplacement cost is estimated as the economic value corresponding to the minimum affordable\nhousing standard in the country (e.g., reinforced masonry structure). We apply the same\nanalogy for the replacement embodied carbon. We provided more detailed discussion on this\napproach in our response to Comment #2 and added several clarifications in the revised\nmanuscript (see the “Discussion” and the “Identification of Building Types” sections).\n7. Furthermore, another possible limitation of the study is that replacement buildings use\nthe same materials as the original ones, but post-disaster reconstruction often leads to\nmaterial substitutions. These shifts occur due to supply chain disruptions, government\nincentives, and economic constraints (e.g., prefabricated concrete used post-1977\nRomania earthquake). The manuscript should explore a sensitivity analysis examining\nalternative reconstruction materials, such as timber replacing masonry or prefabricated\nconcrete replacing reinforced concrete. This is particularly relevant for highly\nvulnerable buildings, as they are the most likely to be reconstructed with improved materials.\nWe thank the Reviewer for raising this important point. We acknowledge that the manuscript’s\nphrasing regarding the use of the “same main construction material in replacement buildings\nmay have caused some confusion. To clarify, our approach assumes that replacement\nbuildings will retain the general material category and lateral load-resisting system of the\noriginal structures, and in alignment with a “build back better” strategy. This does not imply\nthat replacements would replicate the same materials or structural characteristics of the\noriginal building, particularly on cases where the original building is obsolete. Instead,\nreplacement structures are assumed to use materials with enhanced performance and\nupdated production standards reflecting current technology and regulations. For example, an\nadobe or unreinforced masonry building would be replaced with reinforced masonry, as\ndetailed in Table 3 of the manuscript. We believe this assumption is reasonable, as a complete\nchange in construction materials is often not feasible due to practical constraints such as local\nmaterial availability, compatibility with existing construction practices, architectural or urban\ndesign considerations, and regulatory or heritage preservation requirements.\nNevertheless, we agree with the Reviewer that our assumption does not consider all realistic\npost-disaster reconstruction scenarios. In reality, replacement buildings might utilise entirely\ndifferent materials or lateral load-resisting systems, as pointed out by the Reviewer in\nreference to the post-1977 Romania earthquake. Our chosen replacement scenarios represent\none of many possible reconstruction pathways and are intended to balance simplicity, practicality,\nand consistency with standard approaches in large-scale seismic risk assessments2,4,5,31.\nImportantly, our framework is designed to be flexible. Researchers can modify the building\nreplacement assumptions to reflect alternative reconstruction hypotheses and re-run the\nanalysis to obtain updated embodied carbon estimates. For example, one could explore a\nscenario of replacing older reinforced concrete buildings with timber structures. We have\nclarified these aspects in the “Identification of Building Types” section.\nFurthermore, we provide the full database of the embodied carbon factors, both within the\nmanuscript and the open-access GitHub repository, which we compiled upon extensive data\ncollection. Hence, if users are interested in a building replacement type that is not covered by\nour manuscript, they may simply derive an estimate of the required material quantities and\nmultiply those with the corresponding ECFs to obtain the replacement carbon.\nPage 27 of 32\n8. Next, the manuscript applies uniform embodied carbon factors (ECFs) across Europe, but\nconstruction practices and material sourcing vary significantly between regions.\nDifferences in cement production carbon intensity (e.g., France vs. Poland),\nenergy grid emissions, and local vs. imported material use introduce substantial variability.\nThe authors should discuss how these regional disparities might impact the embodied carbon\nestimates and, if possible, introduce or propose a region-specific ECF adjustments.\nWe thank the Reviewer for highlighting this relevant aspect. We agree that our ECFs may not\nbe representative of all European countries homogeneously, due to regional differences in\nelectricity grid emissions, manufacturing practices, and material sourcing, in addition to data\nquality and availability. Additionally, we note that we assume all materials and components to\nbe produced and used in construction sites within Europe. This assumption aligns with the\ngeographical scope of the collected EPDs and other sources of embodied carbon data.\nTherefore, we have stated clearly in the manuscript that among the limitations of our analysis\nis that the environmental impact estimates rely heavily on information pertaining to countries\nwith high data availability like Italy, UK, Germany, France, and Spain (see “Discussion”\nsection).\nWhile we fully recognize the importance of incorporating region-specific environmental data to\nimprove the accuracy of embodied carbon estimates, the current availability of consistent and\nhigh-quality data across the entire region remains limited, which does not allow to develop\nreliable adjustment factors. We also encourage readers to apply regional modification factors\nif relevant and available to better reflect local conditions. Such adjustments can be directly\napplied to the AAEC values we report, providing greater flexibility for tailored analyses.\nAs an example, a recent report32 compiled LCA results from over 3,700 European buildings to\ndeduce their embodied carbon across key life-cycle stages (A1-A4, B4-B5, and C2-C4). It also\nclassified the buildings based on their occupancy (e.g., residential, industrial), and regional\ngroup, including: Eastern Europe (e.g., Poland, Romania, Greece), Western Europe (e.g., Italy,\nFrance, Germany, UK), and Northern Europe (e.g., Sweden, Denmark, Norway). While\nsimplified regional modification factors for replacement embodied carbon could be extracted\nfrom such data (see Table R1), it is important to note that these figures are indicative only as\nthey do not consider the building type and its structural system. We have added further\nclarifications for these aspects in the “Discussion” section.\nTable R1. Regional modification factors for replacement embodied carbon.\n[Table Redacted]\n9. The study reports coefficient of variation (CoV) values for ECFs, but this alone does\nnot fully capture uncertainty in seismic carbon risk estimates. A sensitivity analysis on\nkey parameters, such as transport distances, seismic reinforcement requirements,\nrecycling rates, and material intensity variations, would provide a clearer picture of the\nrobustness of the results. This is a suggestion, not a requirement, given the length of\nPage 28 of 32\nthe current manuscript, but a basic uncertainty assessment would strengthen the\nmanuscript’s conclusions.\nWe thank the Reviewer for this valuable suggestion regarding uncertainty and sensitivity\nanalysis. We agree that a comprehensive assessment of key parameters, such as transport\ndistances, reinforcement requirements, recycling rates, and material intensity variations, would\nprovide additional insights into the the results. However, we note that transportation generally\nrepresents a minor contribution to the total embodied carbon of seismic repair and replacement,\nas supported by existing literature33. Therefore, even substantial variations in transport-related\nassumptions are unlikely to significantly impact the AAEC estimates. A similar rationale applies\nto variations in assumed recycling rates, which generally have limited influence on overall\nembodied carbon outcomes at the scale of our analysis.\nRegarding aleatory uncertainty in seismic risk, the AAEC metric reflects an average annual\nloss over a long-term timeframe and across a large and diverse building portfolio. Due to the\nlaw of large numbers, variations at the individual building level tend to balance out when\naggregated at regional level, making the average loss metric relatively stable with respect to\nthis form of uncertainty. However, we acknowledge that our current framework has limitations\nin fully capturing and quantifying uncertainty, particularly with respect to sensitivity to input\nparameters and model assumptions. We have highlighted this point in the “Discussion” section\nand recognise that more comprehensive uncertainty and sensitivity analyses would enhance\nthe robustness and interpretability of future studies.\n10. The authors should acknowledge the dynamic nature of exposure, as new buildings\nare added every year, contributing to a growing stock of embodied carbon. Additionally,\nbuilding age and retrofitting levels influence repair emissions, as older\nbuildings generally require more intensive material use. Again, this can be only mentioned\nand discussed in this manuscript, and in particular the vision to long-term applicability of\nsuch assessment for applied mitigation actions.\nWe thank the Reviewer for highlighting the dynamic nature of exposure. We agree that\ntemporal changes in the building stock, driven by new construction, demolition and retrofitting,\nmay significantly influence risk assessments and affect their long-term applicability. While\nsome regions, particularly in parts of the Global South, are experiencing rapid urbanisation\nand expanding building inventories, many European countries are characterised by relatively\nstable or declining populations. This often results in slower rates of change, or even net\nreductions, in the building stock over time, which makes the results of our study remain\nrelevant. For future studies focusing on regions undergoing rapid development, especially in\nthe Global South, it will be essential to account for the dynamic nature of exposure, or at least\nto explicitly acknowledge it as a significant limitation. We fully agree with the Reviewer on the\nimportance of this consideration.\n11. The authors should ensure that the study is well-contextualized within the broader field\nby reviewing and integrating additional relevant literature (various papers on the\nTurkish earthquakes), or acknowledge of other relevant datasets such as\nBath Inventory of Carbon and Energy (ICE). Finally, it is worth mentioning the link between\nthis work and the input components of the recent European Risk model (Crowley et al\n2021), and properly cite the underlying hazard and amplification if used in this study.\nPage 29 of 32\nWe thank the Reviewer for highlighting important literature and datasets that help further\ncontextualise our work. In response, we have acknowledged and cited these contributions in\nthe revised manuscript. Finally, we have strengthened the link between our work and the input\ndatasets used in the latest European Seismic Hazard and Risk Models (see section “Data\nSources and Integration”).\nTo conclude, I recommend a moderate revision prior to acceptance for publication. The\nmanuscript is commendable for its innovative approach and substantial insights; however,\nspecific methodological clarifications, enhanced uncertainty quantification, and broader\ndiscussions on applicability and assumptions are required. I look forward to receiving the\nrevised version.\nWe thank the Reviewer for the constructive feedback and appreciation of the manuscript’s\ncontributions. In response, we have clarified the methodology, expanded on the assumptions\nand applicability, and improved the discussion on uncertainties. We hope the revised version\naddresses the concerns raised and look forward to further feedback.\nPage 30 of 32\nReferences\n1. Johnson, K. et al. Global Seismic Hazard Map. Preprint at\nhttps://doi.org/10.5281/zenodo.8409647 (2023).\n2. Yepes-Estrada, C. et al. Global building exposure model for earthquake risk assessment.\nEarthquake Spectra 39, 2212–2235 (2023).\n3. Martins, L. & Silva, V. Development of a fragility and vulnerability model for global seismic\nrisk analyses. Bulletin of Earthquake Engineering 19, 6719–6745 (2021).\n4. Silva, V. et al. Global Seismic Risk Map. Preprint at\nhttps://doi.org/10.5281/zenodo.8409623 (2023).\n5. Pagani, M. et al. Openquake engine: An open hazard (and risk) software for the global\nearthquake model. Seismological Research Letters 85, 692–702 (2014).\n6. Silva, V., Crowley, H., Pagani, M., Monelli, D. & Pinho, R. Development of the\nOpenQuake engine, the Global Earthquake Model’s open-source software for seismic\nrisk assessment. Natural Hazards 72, 1409–1427 (2014).\n7. Kalakonas, P., Silva, V., Mouyiannou, A. & Rao, A. 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Bektaş, N. & Jingjie, Z. Sustainable waste management strategies for earthquake debris:\nLessons from the 2008 China and 2023 Türkiye-Syria disasters. International Journal of\nDisaster Risk Reduction 116, 105153 (2025).\n23. RICS. Whole Life Carbon Assessment for the Built Environment RICS Professional\nStandard. www.rics.org (2023).\n24. Orr, J., Gibbons, O. & Arnold, W. A brief guide to calculating embodied carbon. The\nStructural Engineer 98, 22–27 (2020).\n25. Kitayama, S., Iuorio, O., Josa, I., Borrion, A. & Black, L. Determining the carbon footprint\nreduction of reusing lightweight exterior infill walls: A case study of a school building in\nthe United Kingdom. J Clean Prod 469, 143061 (2024).\n26. RICS. Whole life carbon assessment for the built environment: RICS professional\nstatement. (2017).\n27. Athena Sustainable Materials Institute. Maintenance, Repair, and Replacement Effects\nfor Building Envelope Materials. (2002).\n28. Gu, H. et al. Mass Timber Building Life Cycle Assessment Methodology for the U.S.\nRegional Case Studies. Sustainability 13, 14034 (2021).\n29. Chen, C. X. et al. Comparative Life Cycle Assessment of Mass Timber and Concrete\nResidential Buildings: A Case Study in China. Sustainability 14, 144 (2021).\n30. Pagani, M. et al. The 2018 version of the Global Earthquake Model: Hazard component.\nEarthquake Spectra 36, 226–251 (2020).\n31. Silva, V. et al. Development of a global seismic risk model. Earthquake Spectra 36, 372–\n394 (2020).\n32. One Click LCA. Embodied Carbon Benchmarks for European Buildings.\nhttps://oneclicklca.com/resources/research-embodied-carbon-benchmark-european-\nbuildings (2021).\n33. LETI. Embodied Carbon Primer - Supplementary Guidance to the Climate Emergency\nDesign Guide. (2020).\nPage 32 of 32"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for their thoughtful and thorough response to our review questions. We are pleased to see the\ninclusion of additional data and clarification of the findings. We believe that this helps ensure that their significant\ncontributions can both be well understood and readers have sufficient information in order to reproduce or extend the\nmethods to other regions.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "I would like to thank the authors for carefully addressing my earlier comments. The manuscript has improved significantly,\nparticularly in terms of clarity, methodological transparency, and contextualization within the wider literature. The results are\nwell supported by the analyses, and the study provides valuable insights into the environmental impact of earthquakes in\nEurope.\nI have no further concerns, and I recommend acceptance of this version.\nbest wishes,\nLaurentiu Danciu"}, "author_response": "Manuscript NCOMMS-24-67383A\nUnveiling the Environmental Impact of Earthquakes in Europe\nResponse to Reviewers’ comments\nBlue: Reviewers’ comments - Black: responses to Reviewers\nReviewer #1\nThank you to the authors for their thoughtful and thorough response to our review questions.\nWe are pleased to see the inclusion of additional data and clarification of the findings. We\nbelieve that this helps ensure that their significant contributions can both be well understood\nand readers have sufficient information in order to reproduce or extend the methods to other\nregions.\nReviewer #2\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3\nI would like to thank the authors for carefully addressing my earlier comments. The manuscript\nhas improved significantly, particularly in terms of clarity, methodological transparency, and\ncontextualization within the wider literature. The results are well supported by the analyses,\nand the study provides valuable insights into the environmental impact of earthquakes in\nEurope. I have no further concerns, and I recommend acceptance of this version.\nWe are grateful to Reviewers #1 and #2 for recognising the value of our work and for their\nconstructive feedback, which helped us render the manuscript more accessible to researchers\nacross different fields. Their input has contributed to strengthening the manuscript and\nensuring its wider applicability.\nWe also sincerely thank Reviewer #3 for highlighting the improvements in the presentation of\nour results. Their thoughtful comments helped us refine the study and better communicate its\ncontribution to understanding the environmental impacts of earthquakes in Europe."}]

Decision letter