Thank you for visiting Nature. The browser version you are using has limited support for CSS. For the best experience, we recommend that you use a newer version of the browser (or turn off the compatibility mode in Internet Explorer). At the same time, to ensure continued support, we will display sites without styles and JavaScript.
Nature Communications Volume 12, Article Number: 6126 (2021) Cite this article
The growth of building stocks around the world has driven widespread material consumption and environmental impact. The future impact will depend on the level and speed of socio-economic development, as well as material use and supply strategies. Here, we assessed the material-related greenhouse gas (GHG) emissions and their emission reduction potentials of residential and commercial buildings in 26 regions of the world by 2060. For the middle route baseline scenario, the emissions related to construction materials will increase by 3.5 to 4.6 Gt CO2eq yr-1 in 2020-2060. Emissions in low-income and low- and middle-income regions will increase rapidly from 750 metric tons in 2020 (22% of the world) and 2.4 Gt (51%) in 2060, while absolute and relative emissions in high-income regions are declining. Simultaneous implementation of multiple material efficiency strategies in the high efficiency (HE) scenario can almost reduce baseline emissions by half. However, even in this case, the building materials industry needs to double its current emissions ratio to reach the 1.5 °C compatibility target.
Like food and clothing, housing is one of the most direct basic needs of human beings. Residential and commercial buildings account for more than one-third of global energy use and energy-related greenhouse gas emissions2. There are two main ways to reduce building-related emissions: (1) decarbonization/reducing the energy required by buildings in use; (2) decarbonization/reducing the production of building materials and energy. Environmental policy has traditionally focused on improving energy efficiency and renewable energy in the use phase, while ignoring material efficiency in buildings3,4. A policy approach that only focuses on emissions in use may miss important opportunities in construction5,6. In fact, there may also be an important trade-off between emissions before and during use, so energy-efficient buildings may require more building materials7,8,9. In 2018, more than half of the world’s concrete and bricks were consumed10 , About 40% of steel 11 and a large number of other metallic and non-metallic minerals 12.
Global trends indicate that the demand for new buildings will increase rapidly in the coming decades. This is mainly due to the growing population and increasing wealth all over the world (especially in Asia and Africa2,13), but also due to the demand for housing upgrades in highly urbanized areas14. Therefore, a large amount of material is required. In the past few decades, construction technology has made great strides. For example, buildings with less environmental impact (for example, using wood 15 or less metal to achieve the same structural characteristics 16), designed for a longer service life 17 or higher post-consumer recycling rate 18 can be constructed. However, despite these technological advances, less efficient construction practices are still widely used, especially in areas where demand is greatest19,20. These trends pose severe challenges to reducing greenhouse gas emissions from building materials and achieving global climate goals.
Only in the past decade has research on the environmental impact of building materials and mitigation strategies gained momentum. Research either focuses on residential building materials in a country17, 21, 22, 23, or represents a certain type of material 24, 25, 26 at a time. In addition, the calculation of emissions requires consistent scenarios of material requirements and process emission intensity6, and most studies involve only one of these aspects27,28. A recent study29 evaluated the climate impact of material efficiency strategies on residential buildings in nine large economies. Although valuable, this research ignores most of the emerging African and Asian regions (these regions represent a large part of the growing housing demand in the future2,13) and global non-residential buildings.
Here, we developed a global building material emissions model that integrates a dynamic material assessment model for estimating future building material demand and a forward-looking life cycle assessment (LCA) model for estimating material production emissions. We include 7 materials in 4 residential building types and 4 commercial building types in 26 regions around the world (see methods). We investigated the development of global greenhouse gas emissions from the production of residential and commercial building materials. We investigated the impact of major material efficiency strategies and the impact of these strategies on achieving climate goals (methods). We have found that greenhouse gas emissions related to building materials continue to increase globally, and emissions trends in different regions of the world are also very different. We observe significant emission reduction and material loop closure potential in the material efficiency strategies considered. We outline the important mitigation opportunities and challenges related to building materials to achieve global climate goals.
Our survey is based on the output of IMAGE30,31, the global comprehensive assessment model and the ecoinvent32 life cycle inventory database. Different shared socio-economic pathways (SSP) 33 are modeled in IMAGE, reflecting the possible future development of socio-economic parameters. We chose the “middle road” SSP2 route 34, which is expected to grow moderately in population and GDP. We use Socio-Economic 30, 31 and Energy Transition Scenario 35 under IMAGE-SSP2 respectively as inputs for the dynamic building material model and expected LCA. We explored two scenarios for the development of material requirements and emissions by 2060: the baseline scenario, given by the SSP2 baseline parameters from IMAGE, and the high efficiency scenario, assuming that several important material efficiencies derived from the literature are fully implemented. Strategy (see Table 1). The period from now to 2060 is characterized by an increase in population and the convergence of incomes in various economies 30,33, which has a huge impact on construction and material demand. It also provides enough time for the industry to develop and expand technology to achieve sustainable transformation36. The literature supporting the feasibility of these strategies usually provides goals for 2050, not 2060. In this case, we extrapolated these goals to 2060. For complete details on models, data, and scenarios, see the method supplement.
In the baseline scenario, greenhouse gas emissions related to building materials will continue to increase at a global average level of 0.7% yr−1 (from 3.5 to 4.6 Gt CO2eq yr−1) from 2020 to 2060. This trend is significantly different between different income groups (see Figure 1a, b). Low- and middle-income groups have the largest increase, from 750 Mt (22%) in 2020 to 2.4 Gt (51%) in 2060 (see Figure 1b), mainly due to the surge in population and economic development. For example, from 2020 to 2060, material-related emissions in India, other parts of South Asia, and Africa (excluding South Africa) will more than double. In contrast, the absolute value of high-income groups declined slightly, while their share of global emissions fell from 595 metric tons (17%) in 2020 and 530 metric tons (12%) in 2060. The middle and high income groups have similar trends (Figure 1c). Figure 1d shows a regional comparison of accumulated material-related greenhouse gases relative to GDP, highlighting the comparative economic challenges of adopting mitigation strategies. In general, high-income regions (such as the United States, Japan, and Western Europe) have relatively low emissions and therefore have a higher tolerance for deep decarbonization.
a The development of global greenhouse gas emissions of seven materials from 2020 to 2060. b Evolution of the percentage of greenhouse gas emissions for the three income groups during 2020-2060. c The development of emissions in the six major emission regions (by 2060) before 2020-2060, accounting for more than 60% of the total. d The expected cumulative greenhouse gas emissions of 26 regions in the world from 2020 to 2060 relative to the current GDP (the 2020 value from the IMAGE comprehensive assessment model, calculated at purchasing power parity).
China and India will remain the two largest emitters from 2020 to 2060, and India will become the largest emitter by 2053 (Figure 1c). The top six regional emitters in 2060 will all be located in Asia or Africa (Figure 1c). Overall, Asia will account for the majority of cumulative building material emissions from 2020-2060 (over 65%), followed by Africa with slightly more than 10%. Regarding material types, steel and concrete are still the largest sources of emissions, accounting for about two-thirds of the total, followed by bricks (18%) and aluminum (8%) (Figure 1a). Between 2020 and 2060, the share of metal-related emissions will drop slightly from 43% to 39%, which may be due to an increase in secondary metal production.
The mitigation potential of material efficiency strategies depends on the building stock in use in different regions, construction practices, and future technological, social and economic development. Figure 2 shows the emission reduction potential of each strategy at its high efficiency level during the period 2020-2060 (compared to the baseline value and when each strategy is independent of each other). Generally speaking, the emission reduction potential decreases from the top layer (building demand) to the middle layer (material demand) and the bottom layer (material supply). In other words, in terms of feasible interventions derived from the literature, reducing housing demand has greater potential to reduce impact than increasing material strength, and the latter has greater potential than improving material supply efficiency.
The three colors from left to right represent the three layers in the modeling framework: building demand, material demand, and material supply (see Supplementary Figure 1). These three methods roughly correspond to the general “avoid-shift-improve” emission mitigation framework 43. The whiskers represent the sensitive range of greenhouse gases in the high efficiency (HE) scenario (each strategy has a 20% change; for more details, please refer to the supplementary information). Please note that the scale of global, China and India is different from other regions, and the scale of "more intensive use" is different from other strategies.
On a global scale, more intensive use represents the greatest emission reduction potential of 56.8 Gt CO2eq, as it avoids a certain percentage of all materials at the same time. As a consumption-oriented strategy, more intensive use of building stock represents the possibility of decoupling the growth of construction demand from economic development20,44. It does not necessarily lead to lower happiness, and can be achieved by, for example, lowering the vacancy rate by 45,46, more shared offices47 and telecommuting48. Therefore, this strategy depends to a large extent on lifestyle and behavior changes20. This potential is particularly huge in rapidly urbanizing areas such as China and highly urbanized areas such as Western Europe, where the population will decrease and there will be opportunities to increase housing density45,49.
The extended lifespan reduces the demand for new buildings, reducing emissions by 6.6 Gt globally. The opportunities for life extension vary from region to region. For example, although some old buildings can extend their service life in areas with a short service life (such as China, Japan, and Southeast Asia), frequent demolition is often not because of the quality of the building, but because of the continuous changes in urban planning and land. Policies 50, 53, 52. The longer-lived buildings built today will only bring significant environmental returns in a few decades, and only planners ensure that the urban form is sustainable in the long term. Poor urban planning may lead to impoverished, unsustainable urban environments in trouble, which will need to be demolished and reorganized in the future.
Lightweight can potentially reduce 14.1 Gt CO2eq cumulatively. This can be achieved through the large-scale adoption of emerging technologies, including new structural designs53, type optimization54, additive structures (such as 3D printing)55, and the use of high-strength steel and aluminum5. Some adjustments to building regulations may be necessary for this lightweight transition. Depending on the technology and the level of adoption, the opportunities for weight reduction may be greater than those adopted in Table 1, for example, the reduction of concrete by 20% or more29,56. Through deployment-led learning, the current cost barriers to such implementation may decrease over time. Increasing the use of wood in buildings will reduce greenhouse gas emissions by 5.5 Gt CO2eq (due to the lower emission intensity of wood production) and provide long-term carbon storage 37,57. In a similar way, the secondary production of metals significantly reduces energy use and emissions, avoiding mining and early manufacturing emissions28. As waste materials become more and more after use, higher recycling and reuse are playing an increasingly important role in mitigation. The cumulative potential of greenhouse gases from 2020 to 2060 is 6.5 Gt (Figure 2). In order to realize the maximum recovery potential, rapid up-front industrial investment is required to develop new technologies and support infrastructure26,58.
In the material production phase, energy transition (decarbonization of energy used in the background LCA system) and efficiency improvement (reduction of energy in the prospect LCA system) have the combined potential to reduce 4.6 Gt CO2eq by 2060 (Figure 2). Due to energy intensity Different, the environmental impact of the two strategies differs depending on the type of material28. For example, due to the energy transition, the emission intensity of aluminum is expected to decrease significantly, while the impact on concrete is small. Therefore, when energy-intensive primary metals are increasingly replaced by low-energy secondary energy sources, the long-term effects of these two strategies will decrease26. This partly explains the different emission reduction potentials of different regions. For example, India believes that the energy transition (61 Mt) has greater emission reduction potential than China (56 Mt) (when the other five strategies are implemented separately, India sees smaller emissions reductions) because the latter sees Higher proportion of secondary energy. Metal. Another reason for this difference is that the Indian material manufacturing industry has drastically reduced its emission intensity from a deeper and faster energy transition.
Under the high-efficiency scenario where all material efficiency strategies (M1-M7) are applied at the same time, the cumulative greenhouse gas emissions related to building materials during 2020-2060 will be reduced by 78 Gt CO2eq (or 49%) (Figure 3). Please note that the total savings of a high-efficiency solution is not equal to the sum of the savings of each independent strategy, because the strategies can be mutually exclusive. In other words, we explicitly apply these strategies (M1-M7) in the model framework at the same time to avoid the potential savings of repeated calculations. The global growth trend in the baseline scenario reverses to a continuous decline during 2020-2060 (annual growth rate of -2.4%) (Figure 3). Between this scenario and the baseline, the regions with the greatest mitigation potential are China (28%), India (16%), Western Europe (6%), West Africa (5%) and the Middle East (5%) (in descending order).
a Compared with a 1.5/2 °C compatible mitigation path for greenhouse gas (GHG) emissions, the building materials sector shares 7.5% of the global carbon budget. b Compared with 1.5°C compatible mitigation pathways, the share of the building materials sector in the global carbon budget doubles the amount of carbon dioxide emissions. The green shaded band represents the sensitivity range of CO2 emissions in the HE scenario (defined as a 20% change in each strategy, see Supplementary Table 13 for more details). The other shaded areas represent the assessment range of the greenhouse gas emission path of the building materials industry, which meets the 2°C and 1.5°C climate targets stipulated by the IPCC respectively, and is used for the 33-67th percentile of TCRE (transient climate change for climate change). response). Cumulative carbon emissions (see method for details).
The climate goal requires deep decarbonization in all sectors59. The building materials we considered accounted for about 7.5% of global carbon dioxide emissions on average between 2015 and 2019. If the building materials industry is to maintain a 7.5% share of the available carbon budget of this century, the cumulative emissions of about 76 Gt CO2 emissions from 2020 to 2060 in the HE scenario are usually in line with the 2°C target (range 81-144 Gt, located in the first 33-67 percentiles) (see methods). For the 1.5 °C compatible path, the emission reductions in the HE scenario are not enough, and the emission limit for the period 2020-2060 is 25-57 Gt (33-67th percentile range). Figure 3a shows the HE scenario and the trajectory designed by the building materials industry to meet the 2°C and 1.5°C compatible paths, assuming an emission limit of 7.5% of the carbon budget. Figure 3b shows that to align the HE scenario with the 1.5 °C compatible path, the industry will need to double its emission allowances. We further see that the emission reduction strategy we are considering reaches its saturation point around 2060, and further strategies are needed to be consistent with the 1.5 and 2°C paths. The fact that some building materials are produced in sectors that are difficult to decarbonize, such as steel and cement production, presents major challenges.
There are many ways to bridge this emission reduction gap in the 1.5 °C compatible path and address the additional emission reductions required after 2060. First, we can assume that the strategic version of our investigation is more ambitious. However, it is questionable whether more intensive use, further extension of service life, and further increase in recycling or reuse rate are realistic. Second, we can consider other reduction strategies not included here. For example, wood stack 61 and brick reuse12 can reduce the use of primary materials, although these contributions may be small compared to steel and cement. At the material supply level, various carbon capture, utilization, and storage technologies, such as chemical absorption 62 and calcium recycling 63, can be used to reduce emissions from steel and cement production. These technologies and negative emission technologies that directly remove carbon emissions from the atmosphere are still in the early stages of development and face major technical and socio-economic obstacles64,65. Although substantial further developments may occur by 2060, we believe that they are complementary to existing and more predictable technologies (e.g., recycling) and regulatory developments (e.g., life extension), as widely emphasized in the literature That's 20,29. Finally, we can assume that it is too difficult to quickly reduce emissions of building materials in a 1.5°C compatible path, which means that sectors that are more prone to decarbonization should achieve faster and deeper emissions reductions.
In the past few decades, the outflow of building materials has increased from 1.5 Gt in 1980 to 6.5 Gt in 2020, of which more than 95% are non-metallic materials (especially concrete and bricks), and less than 5% are metal (supplementary image 3). Most of the non-metallic effluent, except for a small part that is recycled down as a basic material, is sent to landfill 12 as solid waste. For metals, although the recovery rate is already high, the inflow is much larger than the outflow. Primary production is still the main input of steel (80%), copper (76%) and aluminum (69%) (in the past ten years, supplementary image 3).
In the future, both outflow and inflow will be affected by housing demand and material usage strategies. Globally, the outflow to inflow ratio of construction materials will continue to increase in baseline and high-efficiency scenarios. Efficient scenarios will increase significantly, increasing material cycles and allowing more secondary production (Figure 4a). However, as with other models, there are significant differences in different regions (Figure 4b). In high-income and middle-to-high-income areas with large inventory but declining populations, the potential to end the metal cycle is relatively high, such as East Asia (ie, Japan, South Korea, and China), Europe, and North America, where there is a steady flow of outflows at the end of their life cycles , The inflow continues to decrease. Under the high-efficiency scenario, these regions may completely end the aluminum cycle between 2021 and 2060 (Figure 4b). In contrast, low-income and low-income regions, including most of Africa, South Asia and Southeast Asia, will face severe scrap shortages to end the cycle. This is not only due to the rapid increase in inflows driven mainly by population growth, but also due to the decrease in outflows from relatively small in-use stocks.
a Changes in the ratio of outflow to inflow over time under the two scenarios (2001-2020, 2021-2040, and 2041-2060, respectively). The shaded band represents the sensitive range of the outflow to inflow ratio in the high efficiency (HE) scenario (each strategy has a 20% change, see Supplementary Table 13 for more details). b During the period 2021-2060, the recycling output of the eight regions of the world accounted for the share of the total input of aluminum, steel and copper respectively (see the sub-regions in Supplementary Table 11). The whiskers represent the sensitive range of shares in the HE scenario. The black dots represent the share in the baseline scenario.
Some metal shortages in growing regions may be compensated by surpluses in shrinking regions. For example, transferring surplus aluminum scrap produced in East Asia to other Asian and African regions may significantly reduce the demand for primary aluminum production (a cumulative reduction of approximately 90 metric tons between 2041 and 2060), thereby reducing cumulative emissions of approximately 1 Gt of carbon dioxide equivalent. (In high-efficiency scenarios). It is worth noting that China, as the world's largest scrap metal importer66 for many years, may become a major exporter in the future due to shrinking inflows and a surge in outflows. In this context, China’s policy restrictions on solid waste imports in recent years may be the first sign of this development67. Post-consumer waste of bulk non-metallic materials is usually processed nearby and is mainly consumed by other infrastructure sectors (ie, downgraded recycling)46. If the amount of building demolition is expected to be very high in certain periods, infrastructure projects should keep this in mind, reduce the requirements for primary materials and use these secondary materials. To ensure that waste can be collected and converted into a wider range of valuable resources, it is important to understand "when and where and what types of materials can flow out of inventory"12,68,69. Inter-regional and inter-departmental cooperation can contribute to urban mining and future material production capacity planning.
Building emissions are often complicated by trade-offs in the building life cycle, especially implicit emissions (from the production of building materials) and operational emissions (from indoor energy use)9,20. Among the strategies considered in this study, more intensive use, more recycling, faster energy conversion, and improved production efficiency are unbalanced methods because they have no negative impact on energy use during building occupancy (more intensive Use also reduces operating energy consumption (70,71). For lightweight design, we only consider the opportunity to avoid excessive use of materials through improved design and technological development, which will not affect the thermal performance of the building, so indoor energy use will not be affected. For wood substitute materials, previous studies have confirmed the environmental benefits through case studies, taking into account the savings in the production phase and the potential losses during the operation phase15,72. In terms of extended service life, there are concerns that the standards of old buildings are often lower, so extending service life may increase operational energy requirements73. Although our analysis did not quantify this trade-off, we should emphasize that such an assessment should include a longer study period (far beyond 2060), because many buildings built today will be used until the end of this century. On the other hand, compared to earlier buildings, buildings today generally have higher energy performance, and many recent improvements have been made in building codes and standards (73 countries/regions established building codes in 2018)2,74. This means that the impact of extended service life on energy use will decrease (even negligible in low-energy buildings). In addition, most of the potential improvements in operational energy intensity are in electrical appliances, lighting, renewable energy, and human behavior, which do not necessarily depend on the main building structure and can be optimized at any time75,76. For example, in the Chinese construction industry, around half of energy savings by 2050 will come from improvements in lighting, equipment and appliances, fuel conversion, and renewable electricity77. The other half comes from space conditioning and heating, which requires updated equipment (such as coolers) and building renovation (such as enclosure upgrades). Some case studies report the environmental benefits of building renovation21,78. In general, the deployment of these strategies will not be hindered by the trade-off between pre-use and in-use emissions. This is not only due to net environmental benefits (over losses), but also because of the different characteristics between implied emissions and operational emissions, that is, operational emissions are generally easier to decarbonize and can often be mitigated during the life of the building.
A prominent obstacle to the widespread implementation of these strategies is the fragmentation of cross-sectoral policy design over time. For example, progressive urban planning and land policies driven by functional and/or aesthetic preferences may force the urban environment (including buildings, streets, or other infrastructure) to be rearranged or rezoned. This will increase the frequency of demolition and the risk of shortening the life of buildings (although they are in good physical condition)51. Due to political and economic interests, the lack of policy negotiation among stakeholders will lead to uncoordinated land urbanization and socio-economic development49,79. This may lead to faster urbanization of land than population, leading to "ghost towns" and higher vacancy rates, especially in areas with shrinking populations or exodus79,80. Policy choices to deal with high vacancy rates and underutilized building capacity also rely on a cross-sectoral package of policies, including upstream land resource management80 and downstream taxation of vacant and rental housing81. Another example is the split incentives faced by tenants and landlords in building operations. That is, those who bear the cost of reducing building efficiency (for example, tenants paying more for energy costs) are usually those who can't do anything, which can lead to the construction of low-quality buildings, leading to frequent renovation/demolition. As a result, policy makers are turning more toward multi-standard decision-making and stakeholder-related analysis82.
The second obstacle to these strategies is the investment required for infrastructure and technological development19. For example, for large-scale alloy separation by type 38, 83, secondary metal production is economically and technically challenging. This is especially important when we consider that the proportion of emissions in high-income and upper-middle-income regions may decrease as low-income and lower-middle-income regions increase. This has further exacerbated the global tension between the growth in housing demand and the investment needed to mitigate environmental impacts. Therefore, these strategies require cross-regional coordination in resource extraction, technology, and finance.
Despite these obstacles, more and more efforts have been made in recent years to improve material efficiency. In terms of waste management policies, the circular economy package has made many important progress, such as China's 3R principles (reduce, reuse and recycle)84 and the Circular Economy Action Plan (CEAP)85 adopted by the European Commission. Strategies such as lightweighting require more advanced technologies in highly developed regions, which highlights the importance of technology marketization and international cooperation to share best practices. Similarly, due to an increase in vacancy rates due to population decline, highly urbanized areas may first see higher occupational levels. The rise of the sharing economy has also created new opportunities for reducing occupancy rates. For example, as tried by the French Urban Renewal Project, parking lots are shared to avoid new infrastructure construction and emissions2.
Overall, we show that the growing demand for housing is driving a large amount of material-related greenhouse gas emissions, which are beginning to shift from high-income and upper-middle-income areas to low-income and lower-middle-income areas. By expanding material efficiency strategies on a global scale, nearly half of emissions can be avoided, although efficiency varies by region and strategy. However, when all observed material efficiency strategies are applied at the same time, the expected emissions of building materials are still higher than the emissions commensurate with the 1.5 °C climate target (if the remaining global carbon budget is allocated to each sector in proportion) . In order to achieve the 1.5°C target, building materials need to double their current carbon allowance share, which indicates the need to reduce emissions faster in industries that are easier to decarbonize. In the absence of fundamental changes in the manufacturing process, it seems necessary in the second half of this century to adopt negative emission technologies to offset the inevitable process-related emissions. This research may help policymakers better understand mitigation opportunities and challenges at the regional and global levels, and thus understand how upfront investments in facilities, guidelines, and cooperation are required.
We have developed a comprehensive global building material emissions model, which consists of a dynamic building material model and a forward-looking LCA model. This integrated model allows us to calculate the environmental impact of materials used to shelter the global population and explore the impact of different material use and supply strategies on emissions. We apply this model to study two scenarios defined by 7 key strategies in 26 global regions until 2060 (see the conceptual framework in Supplementary Figure 1). We include 4 types of residential buildings (detached houses, semi-detached houses, apartments and high-rise buildings) and 4 types of commercial buildings (office buildings, retail and warehouses, hotels and restaurants, and other commercial buildings) in urban and rural areas. building). By expanding the comprehensive building materials database 27,86, we include seven important building materials: steel, concrete, brick, aluminum, copper, glass and wood. IMAGE includes 26 regions (supplementary information), which we use as resolution to illustrate the heterogeneity of global results.
We have expanded the Dynamic Building Materials Evaluation Model (BUMA) to calculate building materials by region and year. BUMA is a dynamic model driven by groups and stocks, developed by Deetman et al. 27 on the basis of the open dynamic material system of Pauliuk and Heeren 87 and the floor space model of Daioglou et al. 31. In short, BUMA allows the conversion from the stock of building materials determined by the socio-economic parameters and the intensity of building materials to the inflow and outflow of materials under a specific life distribution. For this reason, we derive the main socio-economic determinants from the IMAGE platform and the material strength in the literature. The material strength of the global region is collected from documents 27,86. Since refractory clay bricks are widely used in building construction, they are further developed by adding clay bricks. For building life, we apply the Weibull distribution and relevant shape and scale parameters extracted from the literature. Complete details are provided in the supplementary information.
We use forward-looking LCA models to calculate the greenhouse gas emissions produced by each material type. According to the LCA program standardized by the International Organization for Standardization 88, we first choose "from cradle to door" as the scope of material production. Due to its coverage of global and high-resolution product categories, ecoinvent 3.6 database32 was selected as the Life Cycle Inventory (LCI) database. Distinguish the regional differences in material production as much as possible. The detailed information is shown in the supplementary information. We consider climate change as the main impact category and use global warming potential (100-year time frame)89. Finally, we use the Activity Browser (AB) software 90 to calculate the environmental impact of producing one kilogram of material from cradle to door in different scenarios. The activity browser implements the superstructure method 91 and greatly facilitates the modeling of future scenarios.
We investigated two scenarios with the same socioeconomic background (including population and GDP development) but different material intervention strategies applied. The main socio-economic assumptions are based on IMAGE's SSP. In order to maintain consistency, we choose the SSP2 baseline path to represent the “middle road” path, which is expected to have moderate population and GDP growth34. In the baseline scenario, historical trends in the construction industry around the world continue to a large extent. We use this scheme as a benchmark for understanding the emission reduction potential of any other strategy. The high-efficiency scenario represents a deep emission reduction path that simultaneously implements seven strategies. More details on the assumptions under each scenario and related uncertainty analysis can be found in the supplementary information.
To investigate the global importance of these interventions to climate goals, we also compared the baseline and HE scenarios with programmed mitigation paths compatible with 1.5 and 2 °C targets. Some industries, such as the power industry, are easier to decarbonize than the building materials industry60. Therefore, we evaluate the effectiveness of mitigation scenarios by comparing emissions related to building materials with the same proportional share of today’s global carbon budget and doubling the share of building materials. We followed four steps to generate a sectoral mitigation path consistent with the 1.5 and 2 °C carbon budgets. First, we derive the global carbon budget from the IPCC’s 1.5 °C Special Report59 (see Table 2.2 in Report 2.2), which shows the time from January 1, 2018 to when net zero carbon (or year 2100) is reached. The remaining carbon budget achieves the 1.5°C Paris Agreement target and the previous 2°C Cancun target. The carbon budget here is estimated for the 33rd, 50th, and 67th percentiles of TCRE (Transient Climate Response to Cumulative Carbon Emissions)92. Second, we subtract the carbon budget from the CO2 emissions in 2018 and 1993 to obtain the updated carbon budget after 2020. Third, we assume that the building materials sector will share the carbon budget in different proportions. Specifically, we explored two scenarios in which the building materials sector shared 7.5% of the budget (the average percentage of total anthropogenic carbon dioxide emissions from 2015 to 1994) or doubled the budget to 15.0%. We only considered CO2 emissions in this analysis (accounting for about 92% of the total greenhouse gas emissions of the industry), because other greenhouse gases have very different warming dynamics and only account for one part of the total greenhouse gas emissions of the building materials industry. Small part. Please note that in practice, multiple factors (for example, economic costs8) may affect the sharing of efforts (and carbon budget allocation) by sectors that achieve specific climate goals over a period of time. Finally, we use the method in the reference to calculate the mitigation rate under different carbon budgets. 95 (see equation 4 in reference 95).
Although the building materials database we use represents the best database in the world, it can be improved to provide higher geographic resolution (for example, using country-specific or even GIS-based data sets), higher resolution Coverage of building types and a wider range of material types. Materials not considered here (such as carpet, paint, and tile 96) represent further emissions on top of the materials examined here and may suggest different mitigation strategies. In addition, the process-based ecoinvent LCI database may underestimate some emission factors by truncating errors (excluding small processes that are difficult to quantify or small processes outside the defined system boundaries). The future development of the LCI database of mixed environmental flow coefficients (integrating bottom-up process data and top-down macroeconomic input-output data) may improve the completeness of the assessment91. Another improvement to the LCI database may include the use of dynamic sub-models to calculate the carbon sequestration effect of wood products to capture the time effect of slow and gradual carbon uptake in forests, as well as other important factors such as origin and crop rotation and harvest period 97. Similar The improvement can also include a dynamic sub-model to re-absorb CO2 into the concrete after the construction is completed25. Finally, it is worth noting that our results are not predictions of the future, but represent scenarios or ways in which efficiency strategies can be implemented to reduce emissions related to building materials. Performing a sensitivity analysis (see Figure 2-4 and Supplementary Information for more details) to understand the key interventions in the high-efficiency scenario further confirms the significant mitigation potential and challenges of achieving ambitious climate targets.
Data supporting dynamic materials and emission modeling can be obtained from the corresponding references and supplementary information. We also store them in the Zenodo repository in a form that is easy to use with our model code: https://doi.org/10.5281/zenodo.5171943. Since some of the data has been licensed, the energy system transition plan is not publicly available, but it can be obtained from the corresponding author upon reasonable request. This article provides source data.
The Python code used to generate material inflow, material outflow, and greenhouse gas emission results is provided on Zenodo98: https://doi.org/10.5281/zenodo.5171943.
Rao, ND & Min, J. A decent standard of living: a material prerequisite for human well-being. society. Hindi. Reservoir 138, 225–244 (2018).
International Energy Agency. 2019 Global State of Construction and Construction Report: Towards a zero-emission, efficient and resilient construction and construction industry. (2019).
Allwood, JM, Ashby, MF, Gutowski, TG & Worrell, E. Material efficiency: a white paper. resource. keep. Recycle. 55, 362–381 (2011).
Hertwich E. et al. Resource efficiency and climate change: a material efficiency strategy for a low-carbon future. Summary for policymakers. Report of the International Resource Group. (Nairobi, Kenya, 2019).
Dhar S., Pathak M. and Shukla PR The transformation of the Indian steel and cement industry in a sustainable 1.5 °C world. Energy policy. 137, 111104 (2020).
Milford, RL, Pauliuk, S., Allwood, JM & Müller, DB The role of energy and material efficiency in meeting carbon dioxide targets for the steel industry. environment. science. technology. 47, 3455–3462 (2013).
ADS CAS PubMed article Google Scholar
Dixit, MK, Fernández-Solís, JL, Lavy, S. & Culp, CH Need specific energy measurement protocols for buildings: review papers. renew. maintain. Energy Revision 16, 3730–3743 (2012).
Steininger KW, Meyer L., Nabernegg S., and Kirchengast G. The departmental carbon budget is used as an assessment framework for the built environment. Buildings and Cities 1, 337–360 (2020).
Venkatraj, V., Dixit, MK, Yan, W. and Lavy, S. assess the impact of operational energy reduction measures on implied energy. Energy construction. 226, 110340 (2020).
Herczeg M. et al. Resource efficiency in the construction sector. summary report. European Commission, DG Environment, Rotterdam (2014).
Müller DB, Wang T. and Duval B. The use of iron in social evolution. (ACS publication, 2011).
Klausman, F. et al. The global stock of socio-economic materials has increased by 23 times in the 20th century, and half of the resources are used every year. Process Natl. Akkad. science. America. 114, 1880–1885 (2017).
CAS PubMed PubMed Central Article Google Scholar
Kc, S. and Lutz, W. The human core of the shared socio-economic path: demographic scenarios of all countries by age, gender, and education level by 2100. worldwide. environment. Changes 42, 181–192 (2017).
PubMed PubMed Central article Google Scholar
Moura, MCP, Smith, SJ and Belzer, DB 120 US residential stock and floor space. PLoS ONE 10, e0134135 (2015).
PubMed PubMed Central article CAS Google Scholar
Heeren, N. etc. The environmental impact of buildings-what is important? environment. science. technology. 49, 9832–9841 (2015).
ADS CAS PubMed article PubMed Central Google Scholar
Allwood JM et al. Sustainable materials: Keep your eyes open. Cambridge, UK: UIT Cambridge Limited (2012).
Huang, T., Shi, F., Tanikawa, H., Fei, J. & Han, J. Material demand and environmental impact of Chinese construction and demolition based on dynamic material flow analysis. resource. keep. Recycle. 72, 91–101 (2013).
Blengini, GA. The life cycle, demolition and recycling potential of buildings: a case study in Turin, Italy. put up. environment. 44, 319–330 (2009).
International Energy Agency. Material efficiency in the clean energy transition. (International Energy Agency, Paris, 2019).
Hurtwich, EG, etc. Material efficiency strategies to reduce greenhouse gas emissions related to buildings, vehicles, and electronics-an overview. environment. Res. Wright. 14, 043004 (2019).
ADS CAS Article Google Scholar
Pauliuk, S., Sjöstrand, K. & Müller, DB Renovation of the Norwegian residential stock to meet the climate target of 2 degrees Celsius. J. Industrial ecology. 17, 542–554 (2013).
Giesekam, J., Barrett, J., Taylor, P. and Owen, A. Greenhouse gas emissions and mitigation options for materials used in construction in the UK. Energy construction. 78, 202–214 (2014).
Huang, B. et al. The use of building materials and related environmental impacts in China from 2000 to 2015. environment. science. technology. 52, 14006–14014 (2018).
ADS CAS PubMed article PubMed Central Google Scholar
Hu, M. etc. Steel in Chinese residential construction: dynamic analysis. resource. keep. Recycle. 54, 591–600 (2010).
Cao, Z. et al. The sponge effect and carbon reduction potential of the global cement cycle. Nat. Community. 11, 3777 (2020).
ADS CAS PubMed PubMed Central Article Google Scholar
Liu, G., Bangs, CE & Müller, DB Stock The dynamics and emission pathways of the global aluminum cycle. Nat. climate. Amendment 3, 338–342 (2013).
ADS CAS Article Google Scholar
Deetman S. et al. Simulate the global material inventory and flow of residential and service industry buildings in 2050. J. Clean. product. 245, 118658 (2020).
Van der Voet, E., Van Oers, L., Verboon, M. & Kuipers, K. Environmental impact of future metal demand scenarios: Methodology and applications of seven major metal cases. J. Industrial ecology. 23, 141–155 (2019).
IRP. Resource efficiency and climate change: a material efficiency strategy for a low-carbon future. Hertwich, E., Lifset, R., Pauliuk, S., Heeren, N. Report of the International Resource Group. (United Nations International Resource Group (IRP), Nairobi, Kenya, 2020).
Stehfest E., van Vuuren D., Bouwman L. and Kram T. use IMAGE 3.0 to comprehensively assess global environmental changes: model description and policy application. (Netherlands Environmental Assessment Agency (PBL), 2014).
Daioglou, V., van Ruijven, BJ & van Vuuren, DP Model prediction of household energy use in developing countries. Energy 37, 601–615 (2012).
Wernet, G. etc. ecoinvent database version 3 (part one): overview and methodology. internationality. J. Life cycle assessment. 21, 1218–1230 (2016).
Riahi, K. etc. Shared socio-economic approaches and their impact on energy, land use, and greenhouse gas emissions: an overview. Sphere. environment. Changes 42, 153–168 (2017).
Van Vuuren, DP, etc. Energy, land use and greenhouse gas emission trajectories under the green growth paradigm. Sphere. environment. Change 42, 237–250 (2017).
Mendoza Beltran, A. et al. When context is important: Use scenarios from comprehensive assessment models in forward-looking life cycle assessments. J. Industrial ecology. 24, 64–79 (2020).
Rockström, J. et al. Roadmap for rapid decarbonization. Science 355, 1269–1271 (2017).
ADS PubMed article PubMed Central Google Scholar
Churkina, G. etc. Building as a global carbon sink. Nat. maintain. 3, 269–276 (2020).
Allwood, JM, Cullen, JM & Milford, RL Options to reduce industrial carbon emissions by 50% by 2050. environment. science. technology. 44, 1888-1894 (2010).
ADS CAS PubMed article PubMed Central Google Scholar
Elshkaki, A., Graedel, T., Ciacci, L. & Reck, BK Resource demand scenarios for major metals. environment. science. technology. 52, 2491–2497 (2018).
ADS CAS PubMed article Google Scholar
World Steel Association. Overview: Energy use in the steel industry. https://www.worldsteel.org/publications/fact-sheets.html (2016).
Kuipers et al. E. Assess the environmental impacts associated with the global copper demand and supply scenario from 2010 to 2050. environment. Changes 49, 106–115 (2018).
Harpprecht C., van Oers, L., Northey, SA, Yang, Y. & Steubing, B. In the future, the supply of key metals and the impact of low-carbon technologies on the environment may be reduced. J. Industrial ecology. 1–17 https://doi.org/10.1111/jiec.13181 (2021).
Creutzig, F. etc. Seek demand-side solutions to mitigate climate change. Nat. climate. Amendment 8, 260–263 (2018).
Grubler, A. etc. The low energy demand scenario that meets the 1.5 °C target and the sustainable development goal does not require negative emission technologies. Nat. Energy 3, 515–527 (2018).
Gentili, M. and Hoekstra, J. Houses without people and people without houses: A cultural and institutional exploration of the Italian paradox. Hou. Studs. 34, 425–447 (2019).
Schiller, G., Gruhler, K. and Ortlepp, R. A continuous material flow analysis method for bulk non-metallic mineral building materials in the German construction industry. J. Industrial ecology. 21, 673–688 (2017).
Mi, Z. & Coffman, DM The sharing economy promotes a sustainable society. Nat. Community. 10, 1214 (2019).
ADS PubMed PubMed Central Article CAS Google Scholar
Cabrera Serrenho, A., Drewniok, M., Dunant, C. & Allwood, JM tests the greenhouse gas reduction potential of the UK's housing replacement strategy. resource. keep. Recycle. 144, 267–275 (2019).
Williams, S., Xu, W., Tan, SB, Foster, MJ & Chen, C. Ghost Towns in China: Identifying vacant cities through social media data. City 94, 275–285 (2019).
Cai, W., Wan, L., Jiang, Y., Wang, C. & Lin, L. Short-lived buildings in China: impact on water, energy and carbon emissions. environment. science. technology. 49, 13921–13928 (2015).
ADS CAS PubMed article PubMed Central Google Scholar
Liu, G., Xu, K., Zhang, X. & Zhang, G. Factors affecting the service life of buildings: an improved hedonic model. Habitat International 43, 274–282 (2014).
Wuyts, W., Miatto, A., Sedlitzky, R., and Tanikawa, H. Extending or Ending the Life of Japanese Residential Buildings: A Social Circular Economy Approach to Solving Short-term Construction Problems. J. Clean. product. 231, 660–670 (2019).
Carruth, MA, Allwood, JM & Moynihan, MC reduce the technical potential of metal demand through lightweight product design. resource. keep. Recycle. 57, 48-60 (2011).
Bendsoe MP and Sigmund O. Topology optimization: theory, methods and applications. (Springer Science and Business Media, 2013).
Ghaffar, SH, Corker, J. & Fan, M. Additive manufacturing technology and its implementation in construction as an ecologically innovative solution. Automation Structure 93, 1-11 (2018).
Hansemann G. et al. Lightweight reinforced concrete slab: 130 different 3D printed voids. CPT Worldwide-Construction Printing Technology 2021, 68 https://graz.pure.elsevier.com/en/publications/lightweight-enhanced-concrete-slab-130-different-3d-printed-voi (2021).
Arehart, JH, Hart, J., Pomponi, F. & D'Amico, B. Carbon sequestration and storage in the built environment. maintain. product. Consumption. 27, 1047–1063 (2021).
Dodoo, A., Gustavsson, L. and Sathre, R. The carbon impact of building material end-of-life management. resource. keep. Recycle. 53, 276–286 (2009).
Rogelj J. et al. A mitigation path compatible with 1.5 C in the context of sustainable development. (2018).
Davis, SJ etc. Net zero emission energy system. Science 360, eaas9793 (2018).
PubMed article CAS PubMed Central Google Scholar
Höglmeier, K., Steubing, B., Weber-Blaschke, G. & Richter, K. LCA-based wood utilization optimization with special consideration of the cascading use of wood. J. Environment. manage. 152, 158–170 (2015).
Pérez-Fortes, M., Moya, JA, Vatopoulos, K. & Tzimas, E. CO2 capture and utilization in the cement and steel industries. Energy Process 63, 6534–6543 (2014).
International Energy Agency. CCUS in the transition to clean energy. (International Energy Agency, Paris, 2020).
IRP. Resource efficiency and climate change: a material efficiency strategy for a low-carbon future. Hertwich, E., Lifset, R., Pauliuk, S., Heeren, N. Report of the International Resource Group. (United Nations International Resource Group (IRP), Nairobi, Kenya, 2020).
Anderson, K. and Peters, G. The problem of negative emissions. Science 354, 182–183 (2016).
ADS CAS PubMed article PubMed Central Google Scholar
Hu, X., Wang, C., Lim, MK & Koh, SCL International scrap metal trade characteristics and community evolution model. J. Clean. product. 243, 118576 (2020).
Dong, D. et al. China's artificial copper supply and demand scenario: the impact of the scrap import ban and the transformation of the circular economy. resource. keep. Recycle. 161, 104943 (2020).
Tanikawa, H. and Hashimoto, S. Urban inventory changes over time: using 4d-GIS for spatial material inventory analysis. put up. Reservoir Information 37, 483–502 (2009).
Stephan, A. and Athanassiadis, A. Towards a more circular construction industry: Estimate and spatialize current and future non-structural material substitution flows to maintain the urban building stock. resource. keep. Recycle. 129, 248–262 (2018).
Wilson, A. and Boehland, J. Small are beautiful American houses in size, resource utilization and environment. J. Industrial ecology. 9, 277–287 (2005).
Stephan, A. & Crawford, RH The relationship between house size and life cycle energy demand: the impact on building energy efficiency regulations. Energy 116, 1158–1171 (2016).
Sandin, G., Peters, GM and Svanström, M. Life cycle assessment of building materials: the impact of assumptions in end-of-life modeling. internationality. J. Life cycle assessment. 19, 723–731 (2014).
Grant, A. & Ries, R. The impact of establishing a service life model on life cycle assessment. put up. Reservoir Information 41, 168–186 (2013).
Bell, X. Wait. The evolution and future prospects of the energy intensity of the global construction sector from 1971 to 2060. J. Clean. product. 305, 127098 (2021).
International Energy Agency, Energy Efficiency 2018, IEA, Paris https://www.iea.org/reports/energy-efficiency-2018 (2018).
Sandberg, NH, Sartori, I., Vestrum, MI and Brattebø, H. Scenario analysis of future energy demand using a segmented dynamic housing stock model: Norwegian housing stock in 2016-2050. Energy construction. 146, 220–232 (2017).
Zhou, N., Khanna, N., Feng, W., Ke, J. & Levine, M. Scenarios of energy efficiency and carbon dioxide emission reduction potential of China's construction industry by 2050. Nat. Energy 3, 978 (2018).
ADS CAS Article Google Scholar
Itard, L. & Klunder, G. Compare the inventory of refurbished housing with the environmental impact of new buildings. put up. Reservoir Information 35, 252–267 (2007).
Sorace, C. & Hurst, W. Phantom urbanization and the pathology of ghost towns in China. J. Contemporary Asia 46, 304–322 (2016).
Reyes A. Mexico’s housing crisis: vacancy, restricted access, and deaf-mute policy response. internationality. J. Ulb. science. 25, 167–194 (2020).
Liberati, D. and Loberto, M. Taxation and housing market and search friction. J. House. economy. 46, 101632 (2019).
Guarini, MR, Battisti, F. & Chiovitti, A. Methodology for selecting multi-standard decision analysis methods in real estate and land management. Sustainability 10, 507 (2018).
Modaresi, R. & Müller, DB The role of cars in the future of aluminum recycling. environment. science. technology. 46, 8587–8594 (2012).
ADS CAS PubMed article PubMed Central Google Scholar
Huang, B. et al. China's construction and demolition waste management based on 3R principles. resource. keep. Recycle. 129, 36–44 (2018).
Plan CEA. For a cleaner and more competitive Europe. Available online: https://ec.europa.eu/environment/pdf/circular-economy/new_circular_economy_action_plan.pdf (2020).
Marinova S., Deetman S., van der Voet E. and Daioglou V. Global building materials database and analysis of residential building inventory from 1970 to 2050. J. Clean. product. 247, 119146 (2019).
Pauliuk S. & Heeren N. ODYM-an open software framework for studying dynamic material systems: principles, implementations and data structures. J. Industrial ecology. 24, 446–458 (2020).
Standardize IO. Environmental management: life cycle assessment; principles and framework. (International Organization for Standardization, 2006).
Qin D. et al. Climate Change in 2013: The Basis of Physical Science. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Stocker TF et al.), 5-14 (2014).
Steubing, B., de Koning, D., Haas, A. & Mutel, CL Active Browser-an open source LCA software built on the Brightway framework. Software Impact 3, 100012 (2020).
Steubing, B. & de Koning, D. Make the use of scenes in LCA easier: the superstructure method. International Journal of Life Cycle Assessment, 1-15 (2021).
Collins M. et al. Long-term climate change: predictions, promises and irreversibility. Climate Change-Natural Science Foundation in 2013: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2013).
Friedlingstein, P. et al. The 2019 global carbon budget. Earth system. science. Data 11, 1783–1838 (2019).
Friedlingstein, P. et al. The 2020 global carbon budget. Earth system. science. Data 12, 3269–3340 (2020).
Raupach, MR, etc. Sharing accumulated carbon emission allowances. Nat. climate. Change 4, 873–879 (2014).
ADS CAS Article Google Scholar
Crawford, RH, Stephan, A. & Prideaux, F. Built Environmental Performance (EPiC) database. Melbourne: University of Melbourne (2019).
Hoxha E. et al. Biochar in buildings: an important overview of the LCA method. Buildings and Cities. 1, 504–524 (2020).
Zhong X., Deetman S. & Aguilar-Hernandez GA Global greenhouse gas emissions from residential and commercial building materials and mitigation strategies to 2060, GloBUME (v1.0). Zenodo https://doi.org/10.5281/zenodo.5171943 (2021).
XZ thanks the China Scholarship Council for its support (No. 201806050096).
Environmental Science Institute (CML), Leiden University, 2333 CC, Leiden, Netherlands
Zhong Xiaoyang, Hu Mingming, Sebastiaan Deetman, Bernhard Steubing, Hai Xianglin, Glenn Aguilar Hernandez, Carina Harpprecht, Chunbo Zhang, Arnold Tukker and Paul Behrens
School of Management Science and Real Estate, Chongqing University, Chongqing 40045
Copernicus Institute for Sustainable Development, Utrecht University, 3584 CB, Utrecht, Netherlands
Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD, Delft, The Netherlands
German Aerospace Center (DLR), Institute for Network Energy Systems, Curiestreet 4, 70563, Stuttgart, Germany
Dutch Applied Science Research Organization TNO, 2595 DA, The Hague, Netherlands
Leiden University College The Hague, Leiden University, 2595 DG, The Hague, Netherlands
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
XZ and PB designed this study. XZ and SD developed dynamic material models. XZ, BS and CH conducted forward-looking LCA modeling. XZ conducted an analysis. XZ and PB explained the results. XZ drew numbers. XZ and PB prepared the paper. All authors participated in discussing the results and writing the paper.
Correspondence with Xiaoyangzhong or Paul Behrens.
The author declares no competing interests.
Peer review information Nature Communications thanks Karl Steininger, André Stephan and other anonymous reviewers for their contributions to the peer review of this work. Peer review reports are available.
The publisher states that Springer Nature remains neutral on the jurisdiction claims of published maps and agency affiliates.
Open Access This article has been licensed under the Creative Commons Attribution 4.0 International License Agreement, which permits use, sharing, adaptation, distribution and reproduction in any media or format, as long as you appropriately indicate the original author and source, and provide a link to the Creative Commons license , And indicate whether any changes have been made. The images or other third-party materials in this article are included in the article’s Creative Commons license, unless otherwise stated in the material’s credit line. If the article’s Creative Commons license does not include the material, and your intended use is not permitted by laws and regulations or exceeds the permitted use, you need to obtain permission directly from the copyright owner. To view a copy of this license, please visit http://creativecommons.org/licenses/by/4.0/.
Zhong, X., Hu, M., Dietman, S. etc. Global greenhouse gas emissions from residential and commercial building materials and mitigation strategies to 2060. Nat Commun 12, 6126 (2021). https://doi.org/10.1038/s41467-021-26212-z
DOI: https://doi.org/10.1038/s41467-021-26212-z
Anyone you share the following link with can read this content:
Sorry, there is currently no shareable link in this article.
Provided by Springer Nature SharedIt content sharing program
By submitting a comment, you agree to abide by our terms and community guidelines. If you find content that is abusive or does not comply with our terms or guidelines, please mark it as inappropriate.
Nature Communications (Nat Commun) ISSN 2041-1723 (online)