Contributions of countries without a carbon neutrality target to limit global warming

Contributions of countries without a carbon neutrality target to limit global warming | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Contributions of countries without a carbon neutrality target to limit global warming Wei Li, Jiaxin Zhou, Philippe Ciais, Thomas Gasser, Jingmeng Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3847798/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Bioenergy with carbon capture and storage (BECCS) is a key negative emission technology in future climate mitigation. Some countries have made no commitment to carbon neutrality, but they are viewed as potential candidates for BECCS. Here we analyze the contribution of these countries with respect to BECCS and ask the question of how much would be lost for global climate change mitigation if these countries decide not to adopt BECCS. The cooling effect due to carbon-dioxide removal (CDR) through switchgrass cultivation and carbon capture in these countries is largely counterbalanced by its biophysical warming, but the net effect is still an extra cooling. These countries play a more important role in the low-warming scenario than the overshoot scenario, despite the inequality of temperature change among countries. Our study highlights the importance of efforts from all countries in global climate mitigation. Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling Earth and environmental sciences/Environmental sciences/Environmental impact Figures Figure 1 Figure 2 Figure 3 Introduction Bioenergy with carbon capture and storage (BECCS) has been widely used by integrated assessment models (IAMs) in future climate mitigation scenarios (Harper et al., 2018; Krause et al., 2018). It is projected to remove 150 ~ 1200 GtCO 2 from the atmosphere by 2100 for limiting warming to 1.5°C (Rogel et al., 2018). The net carbon-dioxide removal (CDR) capacity of BECCS is mainly determined by bioenergy crop yields (Li et al., 2020), cultivation area (Cai et al., 2011), the CCS efficiency, and land-use change (LUC) carbon emissions (Smith et al., 2013; Boysen et al., 2017; Read et al., 2008; Bui et al., 2018). In addition to the biogeochemical cooling from the reduced CO 2 concentration (Wang et al., 2023), large-scale cultivation of bioenergy crops alters the land surface properties (e.g., albedo, evapotranspiration), leading to biophysical temperature changes (Wang et al., 2021). Both CDR and the biophysical effects of bioenergy cultivation show strong spatial variations (Wang et al., 2021; Wang et al., 2023). In particular, bioenergy cultivation in one region can affect the climate of others by causing changes in atmospheric circulation. However, unlike IAMs assuming a global coordinated mitigation starting this decade, currently, only 130 countries have set a target of achieving net zero or carbon neutrality (CN, hereafter, CN countries, Fig. 1 ), despite of varying degrees of progress (Figure S1 , Methods). There are still more than 50 countries without a CN target (non-CN countries), altogether representing about 11% of global anthropogenic CO 2 emissions (Friedlingstein et al. 2021). It remains unclear to what extent CDR and temperature change would be lost if non-CN countries do not implement BECCS while CN countries do. Here, we use an Earth system model (ESM) with an explicit representation of bioenergy crops (Li et al., 2018; Wang et al., 2021) to simulate the contribution of non-CN countries to the global temperature change in future BECCS scenarios. We consider two BECCS scenarios where BECCS is the main CDR option: 1) a low-warming scenario based on Shared-Socioeconomic Pathway (SSP) 2 and Representative Concentration Pathway (RCP) 2.6 (hereafter, the low-warming scenario) and 2) an overshoot scenario based on SSP5 and RCP3.4 (hereafter, the overshoot scenario). The global cultivation maps in these two scenarios are derived from the IAM MAgPIE (Popp et al., 2014), which implements bioenergy crop cultivation globally in both CN and non-CN countries (Fig. 1 ) based on cost minimization principle and suitable land use types. The cultivation area of bioenergy crops in the low-warming scenario in 2100 is about half of that in the overshoot scenario (Fig. 1 ), because substantial BECCS will be implemented after 2040 to offset the overshoot emissions in the latter scenario (Hurtt et al., 2020). We assumed a typical lignocellulosic bioenergy crop, switchgrass, over the BECCS regions (Fig. 1 ). Switchgrass is explicitly described in the land surface model with parameters calibrated from field data (Li et al., 2018). The net CDR is the sum of harvested biomass, CCS loss and LUC emissions caused by the bioenergy crop cultivation (Eq. (S1) in Methods), and it is further translated into biogeochemical air temperature change using the OSCAR ESM emulator (Methods, Gasser et al., 2017). The biophysical air temperature change (Figure S5) is simulated by the coupled ESM (Methods). The net air temperature change is thus the sum of biogeochemical and biophysical temperature change (Eq. ( 1 ) in Methods, Wang et al., 2023). We assume that the CN countries would cultivate bioenergy crops in order to realize the carbon neutrality commitment, on the top of which, non-CN countries may or may not cultivate bioenergy crops (Methods). Results Contribution of non-CN countries at the global scale The non-CN countries account for 14% and 20% of the global total bioenergy crop cultivation area under the low-warming and overshoot scenarios (408 Mha and 803 Mha, respectively, Fig. 1 and Figure S2). Their cumulative CDR until 2100 is non-negligible, reaching 9 PgC and 20 PgC for the two scenarios. The corresponding proportions of global total CDR from BECCS in non-CN countries are 17% and 20%, higher than their proportions of cultivation area. In terms of biogeochemical temperature changes resulting from CDR, the contribution of non-CN countries is even more pronounced. The biogeochemical effects from CDR of additional cultivation in these non-CN countries will reduce global average temperature by 0.03 ℃ and 0.05 ℃ (30% and 27% of the total reduction) in the low-warming and overshoot scenarios (Figure S11). Despite the biogeochemical cooling effects, the overall biophysical effect of further switchgrass cultivation in non-CN countries is warming in both scenarios. Under the low-warming scenario, the biophysical effects of cultivation in the non-CN countries contribute a temperature increase of 0.02 ℃ (from 0.03 ℃ when only cultivation in CN countries to 0.05 ℃ when cultivation in all countries). Under the overshoot scenario, by contrast, switchgrass cultivation in CN countries will cool the lands by 0.01 ℃ through biophysical feedbacks. However, the biophysical effect of cultivation in non-CN countries will increase the temperature by 0.04 ℃, leading to an overall increase of 0.03 ℃ with cultivation in all countries. Combining the biogeochemical effects from CDR and the biophysical effects, the net air temperature change over lands is -0.03 and − 0.15 ℃ in the low-warming and overshoot scenarios with switchgrass cultivation only implemented in the CN countries. Cultivation in the non-CN countries will further contribute a cooling effect of 0.01 ℃ and 0.02 ℃ in these two scenarios, because its biogeochemical cooling effect (-0.03 and − 0.05 ℃) is partly counterbalanced by biophysical warming effect (0.02 and 0.04 ℃). The overall contribution of non-CN countries to the net temperature reduction is 25% and 12% under the low-warming and overshoot scenarios, implying that the non-CN countries play a more important role in mitigating climate in the low-warming scenario than the overshoot scenario. Contribution of non-CN countries in each region At the regional scale, the air temperature changes show strong variations (Fig. 2 a). Assuming that switchgrass is cultivated in the CN countries, further cultivation in the non-CN countries leads to an extra cooling (or nearly zero) effect in most regions under the two scenarios. However, it causes extra warming in western Europe and Eurasia in both scenarios, and in Eastern Asia and South Asia only in low-warming scenario, implying more challenges in controlling temperature increase in these regions. We also find that the extra air temperature change and the additional cultivation area in the non-CN countries are decoupled geographically. For instance, there is no additional cultivation area in Pacific developed region (Fig. 2 b and c), but the temperature of this region would be reduced substantially if cultivation occurs in remote non-CN countries (Fig. 2 a). In the low-warming scenario, additional cultivation area in the non-CN countries is primarily located in Africa, South and central America, Western Europe, and Eurasia (Fig. 2 b). However, further cultivation of switchgrass in the non-CN countries leads to significant warming effects in Western Europe and Eurasia, primarily contributed by the biophysical warming effect (Figure S12). Additionally, in the low-warming scenario, although the cultivation area in the non-CN countries in North America is marginal, it exhibits noticeable reduction in net air temperature after further cultivation in the non-CN countries, primarily attributed to the biophysical cooling effect (Figure S12). In the overshoot scenario, the cultivation area of non-CN countries is lower in Eurasia, South Asia, and Western Europe but higher in Africa (Fig. 2 c). However, after additional switchgrass cultivation in the non-CN countries, the net air temperature change in Africa remains relatively small (Fig. 2 a), despite the higher CDR contributed by the non-CN countries (Figure S10). Further cultivation in the global non-CN countries induces a strong biophysical warming effect in Western Europe (Figure S10), leading to a net air temperature increase (Fig. 2 a). Temperature changes in countries We further analyze the net air temperature change in the non-CN countries with the largest cultivation area (e.g., Democratic Republic of the Congo, Mexico, and Paraguay in the low-warming scenario; Iran, Republic of Côte d'Ivoire, and Cameroon in the overshoot scenario, Fig. 3 a, b), and the temperature changes in the CN countries (e.g., Afghanistan, Nepal, and Ukraine in the low-warming scenario; Bhutan, Bulgaria, and Hungary in the overshoot scenario) that are most affected (i.e., largest absolute temperature change) by the additional cultivation in non-CN countries (Fig. 3 c, d). In the low-warming scenario, 7 out of the top 10 non-CN countries experience an extra warming with switchgrass cultivation in the non-CN countries, and the warming magnitude in these 7 countries (e.g., Belarus) is much larger than the cooling magnitude in the remaining 3 countries with an extra cooling (orange arrows in Fig. 3 a). By contrast, 7 out of the top 10 CN countries show a temperature reduction with additional cultivation in the non-CN countries (Fig. 3 c), indicating further benefits of cooling in these CN countries. In the overshoot scenario, 4 and 3 out of the top 10 non-CN countries show an extra moderate cooling and warming after additional cultivation in all non-CN countries, and the temperature change in other countries is minor (Fig. 3 b). However, the impacts of further cultivation in the non-CN countries on the top 10 most affected CN countries are very strong in the overshoot scenario, ranging from 0.58 to 1.13 ℃ (except Bhutan) driven by the biophysical effects via atmospheric teleconnection (Fig. 3 b, d, Fig. 2 c). It should be noted that some non-CN countries (e.g., Iran and Cameroon) and CN countries (e.g., Russia) have large cultivation area, but the CDR is low due to lower biomass yields in regions with unfavorable climate conditions (Figure S7, Figure S8b, c). Discussion Our results are based on simulations from the ESM with explicit processes for bioenergy crops (Li et al., 2018; Wang et al., 2021). However, there are some uncertainties due to the simulation set-up and missing processes in the model (Text S5). For example, the amounts of CDR in different bioenergy crop cultivation scenarios were calculated using the response curves of various carbon pools derived from the offline simulations. It ignores the impact of future climate change on the bioenergy crop biomass production (Text S5.1). The CCS efficiency may also vary spatially, and thus we added a sensitivity test using different levels of CCS efficiency (Text S5.1). As expected, the CDR will increase if the CCS efficiency becomes higher (Figure S13). In addition, BECCS has other costs such as post-harvest processing such as baling and pelleting (Negri et al., 2021), transportation from the cultivation area to processing plants, pyrolysis plants and power plants (Fajardy et al., 2020; Negri et al., 2021; Sultana et al., 2011), and its conversion to available energy (Negri et al., 2021). All these additional economic constraints are not explicitly considered in our study. Despite uncertainties in our CDR estimates arising from the idealized assumptions, our results show that additional cultivation of switchgrass in non-CN countries would induce an overall significant biogeochemical cooling effect. Although this cooling effect will be partly offset by its biophysical warming effect, the net effect is cooling at the global scale (Fig. 1 a). Therefore, taking the biophysical effects into account, the contribution of additional cultivation in non-CN countries to global air temperature reduction will be weakened but still a net cooling effect, implying the non-negligible role of these countries in mitigating climate change. At the regional scale, some non-CN countries (mostly developing countries such as Mexico, Poland and Paraguay) suffer an extra warming while some CN countries gain extra cooling from cultivation in the non-CN countries, which may aggravate the inequality between the CN and non-CN countries. In addition, the relative contribution of non-CN countries to the global and regional temperature reduction is greater in the low-warming scenario than that in the overshoot scenario. Therefore, avoiding the overshooting of temperature will not only reduce cost for climate change mitigation but also strength the effectiveness of implementing BECCS in the non-CN countries. The implementation of bioenergy crop cultivation is not likely synchronized across countries, and a delayed implementation may lead to a decrease in CDR and ultimately reduce the effectiveness of BECCS as a climate mitigation strategy (Text S5; Xu et al., 2022). Our study provides a framework for assessing the roles of non-CN countries in land-based climate mitigation options such as afforestation, and using bioenergy crop cultivation as an example, demonstrates the importance of their efforts in global climate mitigation. Methods Simulation scenario design The status of carbon neutrality target for each country is downloaded from https://zerotracker.net/ , and there were 136 countries with a carbon neutrality target but at different degrees of progress by the end of November 2021 (achieved, in law, in policy document, declaration / pledge, proposed / in discussion, Figure S1 ). Switchgrass is assumed to be cultivated synchronously in all CN countries or in both CN and non-CN countries. In order to separate the contribution of non-CN countries to the biophysical temperature change, we ran two sets of simulations: bioenergy crop is cultivated 1) in the CN countries only and 2) in both CN and non-CN countries. Their difference is thus the contribution of non-CN countries. We designed four bioenergy crop cultivation scenarios based on two bioenergy crop cultivation maps and either cultivating in the CN countries only or in both CN and non-CN countries and a reference scenario without bioenergy crop cultivation (Table S1 , Text S3). The contribution of non-CN countries is calculated as the difference between the scenario with switchgrass cultivated in both CN and non-CN countries and the scenario with switchgrass only cultivated in the CN countries. The cultivation maps (Figure S2) were the BECCS scenarios from the integrated assessment model of MAgPIE (Hurtt et al., 2020), in which BECCS serves as the main negative emission technology to limit global warming (Text S1). Estimation of CDR Following Wang et al. (2023), the offline simulations for the carbon dynamics were performed using ORCHIDEE-MICT-BIOENERGY, a dynamic vegetation model with an explicit representation of bioenergy crops (Li et al., 2018) (Text S2). In the offline simulations, ORCHIDEE-MICT-BIOENERGY simulated the changes in biomass and soil carbon pools resulting from the conversion of different vegetation types to bioenergy crops. Response curves for LUC types (from forest, grass, pasture, and cropland to switchgrass) were derived from these offline simulations, used for calculating CDR (including harvested biomass, LUC carbon emissions and CCS loss, Text S2.1) under bioenergy crop cultivation scenarios. Besides, the CDR from bioenergy crops relies on regular harvests, impacting soil fertility (Li et al., 2021). We replenished nitrogen loss through fertilizer application, considering GHG emissions from fertilizer production and N 2 O emissions. The study accounts for CO 2 reduction, fertilizer-related emissions, and N 2 O emissions, estimating soil nitrogen loss and applied fertilizer amounts in different scenarios (Text S2.2). Estimation of the temperature change The CDR were further translated into biogeochemical temperature changes using the compact ESM (OSCAR, Gasser et al., 2017; Text S2.3). OSCAR simulated temperature changes related to CDR processes and GHG emissions from fertilization, considering modeling uncertainties with a sample size of 2000. Global biogeochemical cooling effects were calculated by aggregating regional outputs. The biophysical temperature changes were simulated by the coupled land-atmosphere model IPSL-CM (Boucher et al., 2020), in which ORCHIDEE-MICT-BIOENERGY serves as the land surface model (Wang et al., 2021), LMDz (v6) served as the atmosphere model (Hourdin et al., 2006; Contoux et al., 2012; Text S3). Ocean and sea-ice models were not activated. The simulations, spanning 50 years with 2014 atmospheric CO 2 levels (Sitch et al., 2015; Peng et al., 2015), employed a spatial resolution of 1.26° × 2.5°. The study conducted five coupled simulations, including switchgrass cultivation scenarios in the CN and non-CN countries under the low-warming and overshoot scenarios, and a reference simulation without bioenergy crops (Table S1 ). The simulations reached a steady state between the fifth and tenth years for switchgrass, and results from the last decade (41st to 50th years) were analyzed for biophysical effects. The cultivation map in 2100 was used for the simulations of biophysical effects. The net air temperature change (ΔT net ) induced by switchgrass cultivation in this study includes 1) biogeochemical effects from the CDR of BECCS and the fertilization related greenhouse gas emissions (Text S2) and 2) biophysical effects from the changed local energy budget and the altered atmosphere circulation (Text S3): $${{\Delta }\text{T}}_{\text{n}\text{e}\text{t}}={{\Delta }\text{T}}_{\text{b}\text{g}\text{c}}+{{\Delta }\text{T}}_{\text{b}\text{p}\text{h}}$$ 1 The subscript represents the air temperature contributed by the biogeochemical effects (“bgc”) or the biophysical effects (“bph”). Declarations Acknowledgments This study was funded by the National Natural Science Foundation of China (grant number: 42175169, 72348001, to W.L.), the National Key R&D Program of China (grant number: 2019YFA0606604, to W.L.), the Tsinghua University Initiative Scientific Research Program (grant number: 202230041, to W.L.). Data availability All data are available in the main text or the supplementary information. Author contributions W.L., J.W. and P.C. designed the study, J.Z., W.L. and J.W. carried out the modeling and analysis. J.Z., W.L. and J.W. wrote the first draft. P.C., T.G., Z.L., L.Z., M.H., J.H., M.S., L.L., X.H. contributed to the interpretation of the results, the draft revision and the computational tools. Competing interests The authors declare no competing interests. References Harper AB, Powell T, Cox PM et al (2018) Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nat Commun 9:2938 Krause A, Pugh TAM, Bayer AD et al (2018) Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts. Glob Change Biol 24:3025–3038 Rogelj J, Popp A, Calvin KV et al (2018) Scenarios towards limiting global mean temperature increase below 1.5°C. 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1","display":"","copyAsset":false,"role":"figure","size":286589,"visible":true,"origin":"","legend":"\u003cp\u003eBioenergy crop cultivation maps under the low-warming \u003cstrong\u003e(a)\u003c/strong\u003e and overshoot \u003cstrong\u003e(b)\u003c/strong\u003e scenarios and contributions of the CN and non-CN countries to the global total bioenergy crop cultivation area, net carbon-dioxide removal (CDR), biophysical air temperature change (ΔT\u003csub\u003ebph\u003c/sub\u003e) and net air temperature change (ΔT\u003csub\u003enet\u003c/sub\u003e). Blue bars represent changes when cultivating switchgrass in the CN countries only, and yellow bars represent further changes when cultivating switchgrass in both CN and non-CN countries. Arrows represent the directions of changes. The non-CN countries with a cultivation area \u0026gt; 1 ha are marked in red. Note the scales in the bar plot are different between (\u003cstrong\u003ea\u003c/strong\u003e) and (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3847798/v1/de874362547839111d26f8c7.png"},{"id":49789594,"identity":"32159069-95b3-4638-b564-60ef09b6485e","added_by":"auto","created_at":"2024-01-18 04:50:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":237253,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of the CN and non-CN countries to net air temperature change at the regional scale\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e), and the cultivation area in the CN and non-CN countries in each region under the low-warming (\u003cstrong\u003eb\u003c/strong\u003e) and overshoot (\u003cstrong\u003ec\u003c/strong\u003e) scenarios. In (\u003cstrong\u003ea\u003c/strong\u003e), blue bars represent the net air temperature change when cultivating switchgrass in the CN countries only, and orange bars represent the temperature changes after further cultivation in the non-CN countries. In (\u003cstrong\u003eb\u003c/strong\u003e) and (\u003cstrong\u003ec\u003c/strong\u003e), blue bars indicate the cultivation area in the CN countries within each region, and orange bars indicate the further cultivation area in the non-CN countries.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3847798/v1/7514b21a372ba1ecc391fb04.png"},{"id":49789596,"identity":"50ac81b9-57f5-4fec-88b3-eb7e3d465c18","added_by":"auto","created_at":"2024-01-18 04:50:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178128,"visible":true,"origin":"","legend":"\u003cp\u003eThe cultivation area and net air temperature change in the top ten non-CN countries with the largest cultivation area and the top ten CN countries with the maximum net air temperature change under the low-warming (\u003cstrong\u003ea\u003c/strong\u003eand \u003cstrong\u003ec\u003c/strong\u003e) and overshoot (\u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e) scenarios. Blue arrows refer to the net air temperature change when cultivating switchgrass in the CN countries only, and orange arrows indicate the temperature changes after further cultivation in the non-CN countries. The directions of arrows represent increase and decrease in air temperatures. Black dots in \u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003ed \u003c/strong\u003eindicate the cultivation area in each country. DRC and CIV are the Democratic Republic of the Congo and the Republic of Côte d'Ivoire.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3847798/v1/0d4976ece2b45f7e9e3e7d01.png"},{"id":73251404,"identity":"491db77a-d38d-4940-8300-d040b14b9474","added_by":"auto","created_at":"2025-01-08 08:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1031717,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3847798/v1/8093b1f6-89d1-4637-846d-367cee82e658.pdf"},{"id":49789597,"identity":"f9099963-c729-4eb7-a735-2ae175452f9c","added_by":"auto","created_at":"2024-01-18 04:50:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1964436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"NCSIregionalBECCS240109.docx","url":"https://assets-eu.researchsquare.com/files/rs-3847798/v1/0f374ee93ec82cc8afa04d4c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Contributions of countries without a carbon neutrality target to limit global warming","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBioenergy with carbon capture and storage (BECCS) has been widely used by integrated assessment models (IAMs) in future climate mitigation scenarios (Harper et al., 2018; Krause et al., 2018). It is projected to remove 150\u0026thinsp;~\u0026thinsp;1200 GtCO\u003csub\u003e2\u003c/sub\u003e from the atmosphere by 2100 for limiting warming to 1.5\u0026deg;C (Rogel et al., 2018). The net carbon-dioxide removal (CDR) capacity of BECCS is mainly determined by bioenergy crop yields (Li et al., 2020), cultivation area (Cai et al., 2011), the CCS efficiency, and land-use change (LUC) carbon emissions (Smith et al., 2013; Boysen et al., 2017; Read et al., 2008; Bui et al., 2018). In addition to the biogeochemical cooling from the reduced CO\u003csub\u003e2\u003c/sub\u003e concentration (Wang et al., 2023), large-scale cultivation of bioenergy crops alters the land surface properties (e.g., albedo, evapotranspiration), leading to biophysical temperature changes (Wang et al., 2021). Both CDR and the biophysical effects of bioenergy cultivation show strong spatial variations (Wang et al., 2021; Wang et al., 2023). In particular, bioenergy cultivation in one region can affect the climate of others by causing changes in atmospheric circulation. However, unlike IAMs assuming a global coordinated mitigation starting this decade, currently, only 130 countries have set a target of achieving net zero or carbon neutrality (CN, hereafter, CN countries, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), despite of varying degrees of progress (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Methods). There are still more than 50 countries without a CN target (non-CN countries), altogether representing about 11% of global anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions (Friedlingstein et al. 2021). It remains unclear to what extent CDR and temperature change would be lost if non-CN countries do not implement BECCS while CN countries do.\u003c/p\u003e \u003cp\u003eHere, we use an Earth system model (ESM) with an explicit representation of bioenergy crops (Li et al., 2018; Wang et al., 2021) to simulate the contribution of non-CN countries to the global temperature change in future BECCS scenarios. We consider two BECCS scenarios where BECCS is the main CDR option: 1) a low-warming scenario based on Shared-Socioeconomic Pathway (SSP) 2 and Representative Concentration Pathway (RCP) 2.6 (hereafter, the low-warming scenario) and 2) an overshoot scenario based on SSP5 and RCP3.4 (hereafter, the overshoot scenario). The global cultivation maps in these two scenarios are derived from the IAM MAgPIE (Popp et al., 2014), which implements bioenergy crop cultivation globally in both CN and non-CN countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) based on cost minimization principle and suitable land use types. The cultivation area of bioenergy crops in the low-warming scenario in 2100 is about half of that in the overshoot scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), because substantial BECCS will be implemented after 2040 to offset the overshoot emissions in the latter scenario (Hurtt et al., 2020). We assumed a typical lignocellulosic bioenergy crop, switchgrass, over the BECCS regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Switchgrass is explicitly described in the land surface model with parameters calibrated from field data (Li et al., 2018). The net CDR is the sum of harvested biomass, CCS loss and LUC emissions caused by the bioenergy crop cultivation (Eq. (S1) in Methods), and it is further translated into biogeochemical air temperature change using the OSCAR ESM emulator (Methods, Gasser et al., 2017). The biophysical air temperature change (Figure S5) is simulated by the coupled ESM (Methods). The net air temperature change is thus the sum of biogeochemical and biophysical temperature change (Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in Methods, Wang et al., 2023). We assume that the CN countries would cultivate bioenergy crops in order to realize the carbon neutrality commitment, on the top of which, non-CN countries may or may not cultivate bioenergy crops (Methods).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eContribution of non-CN countries at the global scale\u003c/h2\u003e \u003cp\u003eThe non-CN countries account for 14% and 20% of the global total bioenergy crop cultivation area under the low-warming and overshoot scenarios (408 Mha and 803 Mha, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure S2). Their cumulative CDR until 2100 is non-negligible, reaching 9 PgC and 20 PgC for the two scenarios. The corresponding proportions of global total CDR from BECCS in non-CN countries are 17% and 20%, higher than their proportions of cultivation area. In terms of biogeochemical temperature changes resulting from CDR, the contribution of non-CN countries is even more pronounced. The biogeochemical effects from CDR of additional cultivation in these non-CN countries will reduce global average temperature by 0.03 ℃ and 0.05 ℃ (30% and 27% of the total reduction) in the low-warming and overshoot scenarios (Figure S11).\u003c/p\u003e \u003cp\u003eDespite the biogeochemical cooling effects, the overall biophysical effect of further switchgrass cultivation in non-CN countries is warming in both scenarios. Under the low-warming scenario, the biophysical effects of cultivation in the non-CN countries contribute a temperature increase of 0.02 ℃ (from 0.03 ℃ when only cultivation in CN countries to 0.05 ℃ when cultivation in all countries). Under the overshoot scenario, by contrast, switchgrass cultivation in CN countries will cool the lands by 0.01 ℃ through biophysical feedbacks. However, the biophysical effect of cultivation in non-CN countries will increase the temperature by 0.04 ℃, leading to an overall increase of 0.03 ℃ with cultivation in all countries.\u003c/p\u003e \u003cp\u003eCombining the biogeochemical effects from CDR and the biophysical effects, the net air temperature change over lands is -0.03 and \u0026minus;\u0026thinsp;0.15 ℃ in the low-warming and overshoot scenarios with switchgrass cultivation only implemented in the CN countries. Cultivation in the non-CN countries will further contribute a cooling effect of 0.01 ℃ and 0.02 ℃ in these two scenarios, because its biogeochemical cooling effect (-0.03 and \u0026minus;\u0026thinsp;0.05 ℃) is partly counterbalanced by biophysical warming effect (0.02 and 0.04 ℃). The overall contribution of non-CN countries to the net temperature reduction is 25% and 12% under the low-warming and overshoot scenarios, implying that the non-CN countries play a more important role in mitigating climate in the low-warming scenario than the overshoot scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContribution of non-CN countries in each region\u003c/h3\u003e\n\u003cp\u003eAt the regional scale, the air temperature changes show strong variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Assuming that switchgrass is cultivated in the CN countries, further cultivation in the non-CN countries leads to an extra cooling (or nearly zero) effect in most regions under the two scenarios. However, it causes extra warming in western Europe and Eurasia in both scenarios, and in Eastern Asia and South Asia only in low-warming scenario, implying more challenges in controlling temperature increase in these regions. We also find that the extra air temperature change and the additional cultivation area in the non-CN countries are decoupled geographically. For instance, there is no additional cultivation area in Pacific developed region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and c), but the temperature of this region would be reduced substantially if cultivation occurs in remote non-CN countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eIn the low-warming scenario, additional cultivation area in the non-CN countries is primarily located in Africa, South and central America, Western Europe, and Eurasia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). However, further cultivation of switchgrass in the non-CN countries leads to significant warming effects in Western Europe and Eurasia, primarily contributed by the biophysical warming effect (Figure S12). Additionally, in the low-warming scenario, although the cultivation area in the non-CN countries in North America is marginal, it exhibits noticeable reduction in net air temperature after further cultivation in the non-CN countries, primarily attributed to the biophysical cooling effect (Figure S12).\u003c/p\u003e \u003cp\u003eIn the overshoot scenario, the cultivation area of non-CN countries is lower in Eurasia, South Asia, and Western Europe but higher in Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). However, after additional switchgrass cultivation in the non-CN countries, the net air temperature change in Africa remains relatively small (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), despite the higher CDR contributed by the non-CN countries (Figure S10). Further cultivation in the global non-CN countries induces a strong biophysical warming effect in Western Europe (Figure S10), leading to a net air temperature increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTemperature changes in countries\u003c/h2\u003e \u003cp\u003eWe further analyze the net air temperature change in the non-CN countries with the largest cultivation area (e.g., Democratic Republic of the Congo, Mexico, and Paraguay in the low-warming scenario; Iran, Republic of C\u0026ocirc;te d'Ivoire, and Cameroon in the overshoot scenario, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b), and the temperature changes in the CN countries (e.g., Afghanistan, Nepal, and Ukraine in the low-warming scenario; Bhutan, Bulgaria, and Hungary in the overshoot scenario) that are most affected (i.e., largest absolute temperature change) by the additional cultivation in non-CN countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d). In the low-warming scenario, 7 out of the top 10 non-CN countries experience an extra warming with switchgrass cultivation in the non-CN countries, and the warming magnitude in these 7 countries (e.g., Belarus) is much larger than the cooling magnitude in the remaining 3 countries with an extra cooling (orange arrows in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). By contrast, 7 out of the top 10 CN countries show a temperature reduction with additional cultivation in the non-CN countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), indicating further benefits of cooling in these CN countries. In the overshoot scenario, 4 and 3 out of the top 10 non-CN countries show an extra moderate cooling and warming after additional cultivation in all non-CN countries, and the temperature change in other countries is minor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). However, the impacts of further cultivation in the non-CN countries on the top 10 most affected CN countries are very strong in the overshoot scenario, ranging from 0.58 to 1.13 ℃ (except Bhutan) driven by the biophysical effects via atmospheric teleconnection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, d, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). It should be noted that some non-CN countries (e.g., Iran and Cameroon) and CN countries (e.g., Russia) have large cultivation area, but the CDR is low due to lower biomass yields in regions with unfavorable climate conditions (Figure S7, Figure S8b, c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results are based on simulations from the ESM with explicit processes for bioenergy crops (Li et al., 2018; Wang et al., 2021). However, there are some uncertainties due to the simulation set-up and missing processes in the model (Text S5). For example, the amounts of CDR in different bioenergy crop cultivation scenarios were calculated using the response curves of various carbon pools derived from the offline simulations. It ignores the impact of future climate change on the bioenergy crop biomass production (Text S5.1). The CCS efficiency may also vary spatially, and thus we added a sensitivity test using different levels of CCS efficiency (Text S5.1). As expected, the CDR will increase if the CCS efficiency becomes higher (Figure S13). In addition, BECCS has other costs such as post-harvest processing such as baling and pelleting (Negri et al., 2021), transportation from the cultivation area to processing plants, pyrolysis plants and power plants (Fajardy et al., 2020; Negri et al., 2021; Sultana et al., 2011), and its conversion to available energy (Negri et al., 2021). All these additional economic constraints are not explicitly considered in our study.\u003c/p\u003e \u003cp\u003eDespite uncertainties in our CDR estimates arising from the idealized assumptions, our results show that additional cultivation of switchgrass in non-CN countries would induce an overall significant biogeochemical cooling effect. Although this cooling effect will be partly offset by its biophysical warming effect, the net effect is cooling at the global scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Therefore, taking the biophysical effects into account, the contribution of additional cultivation in non-CN countries to global air temperature reduction will be weakened but still a net cooling effect, implying the non-negligible role of these countries in mitigating climate change. At the regional scale, some non-CN countries (mostly developing countries such as Mexico, Poland and Paraguay) suffer an extra warming while some CN countries gain extra cooling from cultivation in the non-CN countries, which may aggravate the inequality between the CN and non-CN countries. In addition, the relative contribution of non-CN countries to the global and regional temperature reduction is greater in the low-warming scenario than that in the overshoot scenario. Therefore, avoiding the overshooting of temperature will not only reduce cost for climate change mitigation but also strength the effectiveness of implementing BECCS in the non-CN countries. The implementation of bioenergy crop cultivation is not likely synchronized across countries, and a delayed implementation may lead to a decrease in CDR and ultimately reduce the effectiveness of BECCS as a climate mitigation strategy (Text S5; Xu et al., 2022). Our study provides a framework for assessing the roles of non-CN countries in land-based climate mitigation options such as afforestation, and using bioenergy crop cultivation as an example, demonstrates the importance of their efforts in global climate mitigation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSimulation scenario design\u003c/h2\u003e \u003cp\u003eThe status of carbon neutrality target for each country is downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zerotracker.net/\u003c/span\u003e\u003cspan address=\"https://zerotracker.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and there were 136 countries with a carbon neutrality target but at different degrees of progress by the end of November 2021 (achieved, in law, in policy document, declaration / pledge, proposed / in discussion, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Switchgrass is assumed to be cultivated synchronously in all CN countries or in both CN and non-CN countries. In order to separate the contribution of non-CN countries to the biophysical temperature change, we ran two sets of simulations: bioenergy crop is cultivated 1) in the CN countries only and 2) in both CN and non-CN countries. Their difference is thus the contribution of non-CN countries.\u003c/p\u003e \u003cp\u003eWe designed four bioenergy crop cultivation scenarios based on two bioenergy crop cultivation maps and either cultivating in the CN countries only or in both CN and non-CN countries and a reference scenario without bioenergy crop cultivation (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Text S3). The contribution of non-CN countries is calculated as the difference between the scenario with switchgrass cultivated in both CN and non-CN countries and the scenario with switchgrass only cultivated in the CN countries. The cultivation maps (Figure S2) were the BECCS scenarios from the integrated assessment model of MAgPIE (Hurtt et al., 2020), in which BECCS serves as the main negative emission technology to limit global warming (Text S1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstimation of CDR\u003c/h3\u003e\n\u003cp\u003eFollowing Wang et al. (2023), the offline simulations for the carbon dynamics were performed using ORCHIDEE-MICT-BIOENERGY, a dynamic vegetation model with an explicit representation of bioenergy crops (Li et al., 2018) (Text S2). In the offline simulations, ORCHIDEE-MICT-BIOENERGY simulated the changes in biomass and soil carbon pools resulting from the conversion of different vegetation types to bioenergy crops. Response curves for LUC types (from forest, grass, pasture, and cropland to switchgrass) were derived from these offline simulations, used for calculating CDR (including harvested biomass, LUC carbon emissions and CCS loss, Text S2.1) under bioenergy crop cultivation scenarios.\u003c/p\u003e \u003cp\u003eBesides, the CDR from bioenergy crops relies on regular harvests, impacting soil fertility (Li et al., 2021). We replenished nitrogen loss through fertilizer application, considering GHG emissions from fertilizer production and N\u003csub\u003e2\u003c/sub\u003eO emissions. The study accounts for CO\u003csub\u003e2\u003c/sub\u003e reduction, fertilizer-related emissions, and N\u003csub\u003e2\u003c/sub\u003eO emissions, estimating soil nitrogen loss and applied fertilizer amounts in different scenarios (Text S2.2).\u003c/p\u003e\n\u003ch3\u003eEstimation of the temperature change\u003c/h3\u003e\n\u003cp\u003eThe CDR were further translated into biogeochemical temperature changes using the compact ESM (OSCAR, Gasser et al., 2017; Text S2.3). OSCAR simulated temperature changes related to CDR processes and GHG emissions from fertilization, considering modeling uncertainties with a sample size of 2000. Global biogeochemical cooling effects were calculated by aggregating regional outputs.\u003c/p\u003e \u003cp\u003eThe biophysical temperature changes were simulated by the coupled land-atmosphere model IPSL-CM (Boucher et al., 2020), in which ORCHIDEE-MICT-BIOENERGY serves as the land surface model (Wang et al., 2021), LMDz (v6) served as the atmosphere model (Hourdin et al., 2006; Contoux et al., 2012; Text S3). Ocean and sea-ice models were not activated. The simulations, spanning 50 years with 2014 atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels (Sitch et al., 2015; Peng et al., 2015), employed a spatial resolution of 1.26\u0026deg; \u0026times; 2.5\u0026deg;. The study conducted five coupled simulations, including switchgrass cultivation scenarios in the CN and non-CN countries under the low-warming and overshoot scenarios, and a reference simulation without bioenergy crops (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The simulations reached a steady state between the fifth and tenth years for switchgrass, and results from the last decade (41st to 50th years) were analyzed for biophysical effects. The cultivation map in 2100 was used for the simulations of biophysical effects.\u003c/p\u003e \u003cp\u003eThe net air temperature change (ΔT\u003csub\u003enet\u003c/sub\u003e) induced by switchgrass cultivation in this study includes \u003cb\u003e1)\u003c/b\u003e biogeochemical effects from the CDR of BECCS and the fertilization related greenhouse gas emissions (Text S2) and \u003cb\u003e2)\u003c/b\u003e biophysical effects from the changed local energy budget and the altered atmosphere circulation (Text S3):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${{\\Delta }\\text{T}}_{\\text{n}\\text{e}\\text{t}}={{\\Delta }\\text{T}}_{\\text{b}\\text{g}\\text{c}}+{{\\Delta }\\text{T}}_{\\text{b}\\text{p}\\text{h}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe subscript represents the air temperature contributed by the biogeochemical effects (\u0026ldquo;bgc\u0026rdquo;) or the biophysical effects (\u0026ldquo;bph\u0026rdquo;).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (grant number: 42175169, 72348001, to W.L.), the National Key R\u0026amp;D Program of China (grant number: 2019YFA0606604, to W.L.), the Tsinghua University Initiative Scientific Research Program (grant number: 202230041, to W.L.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available in the main text or the supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.L., J.W. and P.C. designed the study, J.Z., W.L. and J.W. carried out the modeling and analysis. J.Z., W.L. and J.W. wrote the first draft. P.C., T.G., Z.L., L.Z., M.H., J.H., M.S., L.L., X.H. contributed to the interpretation of the results, the draft revision and the computational tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eHarper AB, Powell T, Cox PM et al (2018) Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nat Commun 9:2938\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKrause A, Pugh TAM, Bayer AD et al (2018) Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts. 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Environ Sci Technol 57:2474\u0026ndash;2483\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWang J, Li W, Ciais P et al (2021) Global cooling induced by biophysical effects of bioenergy crop cultivation. Nat Commun 12:7255\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFriedlingstein P et al (2022) Global Carbon Budget 2021. Earth Syst Sci Data 14:1917\u0026ndash;2005\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi W, Yue C, Ciais P et al (2018) ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model. Geosci Model Dev 11:2249\u0026ndash;2272\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePopp A, Rose SK, Calvin K et al (2014) Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options. 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Nature 609(7926):299\u0026ndash;306\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHourdin F, Musat I, Bony S et al (2006) The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Clim Dyn 27:787\u0026ndash;813\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eContoux C, Ramstein G, Jost A (2012) Modelling the mid-Pliocene Warm Period climate with the IPSL coupled model and its atmospheric component LMDZ5A, Geosci. Model Dev 5:903\u0026ndash;917\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSitch S, Friedlingstein P, Gruber N et al (2015) Recent trends and drivers of regional sources and sinks of carbon dioxide.Biogeosciences 12, 653\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePeng S, Ciais P, Maignan F et al (2015) Sensitivity of land use change emission estimates to historical land use and land cover mapping. Global Biogeochem Cycles 31:626\u0026ndash;643\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3847798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3847798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBioenergy with carbon capture and storage (BECCS) is a key negative emission technology in future climate mitigation. Some countries have made no commitment to carbon neutrality, but they are viewed as potential candidates for BECCS. Here we analyze the contribution of these countries with respect to BECCS and ask the question of how much would be lost for global climate change mitigation if these countries decide not to adopt BECCS. The cooling effect due to carbon-dioxide removal (CDR) through switchgrass cultivation and carbon capture in these countries is largely counterbalanced by its biophysical warming, but the net effect is still an extra cooling. These countries play a more important role in the low-warming scenario than the overshoot scenario, despite the inequality of temperature change among countries. Our study highlights the importance of efforts from all countries in global climate mitigation.\u003c/p\u003e","manuscriptTitle":"Contributions of countries without a carbon neutrality target to limit global warming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 04:50:32","doi":"10.21203/rs.3.rs-3847798/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0e011be7-f615-4ff5-9e27-5526872df57c","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28206717,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling"},{"id":28206718,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-01-08T08:05:35+00:00","versionOfRecord":{"articleIdentity":"rs-3847798","link":"https://doi.org/10.1038/s41467-024-55720-x","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-01-07 05:00:00","publishedOnDateReadable":"January 7th, 2025"},"versionCreatedAt":"2024-01-18 04:50:32","video":"","vorDoi":"10.1038/s41467-024-55720-x","vorDoiUrl":"https://doi.org/10.1038/s41467-024-55720-x","workflowStages":[]},"version":"v1","identity":"rs-3847798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3847798","identity":"rs-3847798","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}