Increased Global Tensions Jeopardize Climate Targets

Increased Global Tensions Jeopardize Climate Targets | 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 Research Article Increased Global Tensions Jeopardize Climate Targets Wenjie Dong, Qi Ran, Fei Liu, Rong Deng, Jie Yang, Kaixi Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3958885/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The Sixth Assessment Report of the Intergovernmental Panel on Climate Change highlights the reliance on optimistic scenarios such as shared socioeconomic pathways SSP1-1.9 and SSP1-2.6 to achieve the 1.5°C or 2°C climate targets by the end of this century [1] . However, these scenarios have not quantitatively assessed the impact of global tensions (measured as global military expenditure as a percentage of GDP) on CO 2 emissions. Our research reveals that events such as the 2001–2011 War on Terror and the 2021 prelude to the Russian–Ukrainian war have led to a 0.041% (95% CI: 0.030–0.052) kg/USD increase in CO 2 emission intensity (CO 2 emissions per unit of GDP) for each 1% rise in global tensions. This increment accounts for 30% of the total change in CO 2 emission intensity from 1995 to 2021. In the case of escalating global tensions, with the global military expenditure ratio exceeding thresholds of 11% (for SSP1-1.9) or 23% (for SSP1-2.6), the global surface temperature increase would fail to return below 1.5°C after the initial overshoot in SSP1-1.9, and the 2°C climate target would become unattainable in SSP1-2.6 by the end of this century. These findings underscore the potential of escalating global tensions to undermine climate targets, emphasizing the critical need for a more peaceful international environment to effectively limit global warming to 1.5°C or 2°C. Climatology Sociology Figures Figure 1 Figure 2 Figure 3 Introduction The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) indicates that meeting the 1.5°C or 2°C climate targets by the end of the century is achievable under the optimistic shared socioeconomic pathways SSP1-1.9 and SSP1-2.6 scenarios [1] , emphasizing on sustainable development, rapid decarbonization, and strong international cooperation. The emission inventories used in IPCC modeling of temperature rises include the production, residential, and transport sectors but do not distinctly categorize the military sector. This sector is heavily dependent on fossil fuels and has extensive, complex supply chains [2-5] . Because of the voluntary and inconsistent nature of military fuel use data reporting to the United Nations Framework Convention on Climate Change (UNFCCC) [6] , there is significant uncertainty in accurately quantifying global military and conflict emissions. Consequently, most SSP scenarios have not quantitatively integrated the potential impacts of increased global tensions or conflicts on temperature projections. The global landscape since 2022 has experienced considerable complexity and instability, which has been unparalleled since the conclusion of the Cold War. Events such as the ongoing Russo–Ukrainian war and the Israeli–Palestinian conflict have profound effects on political and security dynamics in Eurasia. These conflicts could exacerbate climate change vulnerability, impede climate mitigation efforts, and obstruct multilateral climate action [7, 8] . Addressing the gap in understanding the interplay between sociopolitical risks and climate change mitigation is vital. Quantifying the impact of heightened global tensions or conflicts on the climate is crucial for developing effective policy responses and promoting global cooperation in addressing both climate change and geopolitical challenges. Research framework Herein, we employ historical data and statistical methods to quantify the impact of global military expenditure as a percentage of GDP (hereafter referred to as the military expenditure ratio) on CO 2 emission intensity. This analysis further explores the effects of escalating global tensions on achieving climate targets (Extended Data Fig. 1). The global military expenditure ratio serves as a proxy for the level of global tension. Given that technological advancements are expected to significantly enhance carbon emission efficiency [9] , variations in CO 2 emission intensity over time reflect efforts to improve energy efficiency and mitigate climate change. We apply correlation analysis and linear regression to several decades of historical data to investigate and quantify the relationship between the global military expenditure ratio and CO 2 emission intensity. This study includes a significance test and an uncertainty analysis of their relationship. In addition, we examine the composition of war-related emissions and assess the impact of unaccounted emissions from wars on the anthropogenic CO 2 emission inventory. This evaluation aids in understanding how these emissions influence the estimation of the relationship between the military expenditure ratio and CO 2 emission intensity. Finally, we utilize the established relationship between the military expenditure ratio and emission intensity, along with the correlation between annual CO 2 emissions and the annual change in global surface temperature (GST) derived from IPCC AR6 data, to modify the future GST increase projections in the SSP scenarios, considering various levels of global tension. Detailed descriptions of all data sources, statistical methods, significance tests, and uncertainty analyses are provided in the Methods section. Quantification of the relationship between the global military expenditure ratio and CO2 emission intensity Following the conclusion of the Cold War in 1991, the global military expenditure ratio experienced a marked decline over the next five years, dropping from an average of 4.5% during 1960–1990 to approximately 2.5% by 1995 [10] . This reduction was significantly contributed by major nations such as the United States, Russia, the United Kingdom, and France (Extended Data Fig. 3). After 1995, the global military expenditure ratio exhibited modest fluctuations near 2.3%. The variance in this ratio from 1990 to 1994 (0.13) was considerably larger than that between 1995 and 2021 (0.02), reflecting the distinct characteristics of these eras. This study emphasizes the changes in the military expenditure ratio after 1995. The trends in the global military expenditure ratio from 1995 to 2021 are associated with regional conflicts and wars (Fig. 2a). As the United States initiated the war on terror following the September 11 attacks, the global military expenditure ratio increased by 0.34%. This increase was primarily attributed to Operation Ending Freedom (2001–2014) and the War in Iraq (2003–2011), referred to as Phase II. During these conflicts, the military expenditure ratio of the United States increased by 1.7% in 2011 compared with 2001, whereas Iraq’s ratio increased by 1% in 2010 relative to 2004. The 2007–2008 financial crisis, the most severe global economic downturn since the Great Depression, is suspected to have contributed to a significant rise in the global military expenditure ratio between 2007 and 2009. Following the Global Financial Crisis, there was a downward trend in global military expenditure (Phase III), which reversed after 2019 (Phase IV). This period coincided with the COVID-19 outbreak and the prelude to the Russian–Ukrainian war. During the study period, the CO 2 emission intensity exhibited a consistent downward trend (Extended Data Fig. 2), which is primarily driven by technological progress and industrial structure optimization [9, 11] . To focus on the variations beyond this trend, we analyze the fluctuations after detrending (represented by the pink line in Fig. 1a). The detrended global CO 2 emission intensity demonstrates lagging yet synchronous fluctuations with global military expenditure across the four identified phases (compare the pink and blue lines in Fig. 1a). The three-year weighted average military expenditure is shown in Fig. 1a (red line) because of the strong correlation between the military expenditure ratio of the previous two years and the current year, and the CO 2 emission intensity of the current year (Extended Data Table 1). A significant positive correlation exists between the three-year weighted global military expenditure ratio and the detrended CO 2 emission intensity, as evidenced by a Pearson correlation coefficient of 0.84, surpassing the 95% confidence level (Fig. 1b). An increase (or decrease) of 1% in the global military expenditure ratio leads to an increase (or decrease) of 0.041 (95% CI: 0.030–0.052) kg/USD in CO 2 emission intensity (see Methods). This change represents 30% of the total change in emission intensity from 2021 to 1995. Despite the overall reduction in global CO 2 intensity due to technological advancements (Extended Data Fig. 2), the escalation of global tensions is anticipated to hinder this reduction trend. The influence of the military expenditure ratio on emission intensity underscores the detrimental impact of heightened global conflicts on climate change mitigation efforts. The global military expenditure ratio significantly influences CO 2 emission intensity, primarily by impacting total CO 2 emissions (Extended Data Fig. 4.1 vs . Extended Data Fig. 4.2). There are four principal sources of CO 2 emissions associated with warfare: i. operational emissions (originating from military bases and operations); ii. military industry (including the production of vehicles, weapons, and equipment); iii. post-conflict reconstruction; and iv. destruction of carbon reservoirs [4, 12] . Most military activities are characterized by high carbon intensity [4, 13-15] . However, current accounting for war-related emissions typically includes only sources i and ii, often overlooking the others. Scientists for Global Responsibility (SGR) [3] estimated that the total military carbon footprint constitutes approximately 5.5% (CI: 3.3%–7%) of global emissions, including operational emissions (source i) and upstream emissions from the supply chain (source ii). As one of the largest military entities, the US military emitted about 4,310 million metric tons of greenhouse gases (sources i and ii) from 2001, with the onset of the Afghanistan invasion, until fiscal year 2018 [4] . Emission sources i and ii are included in total emission inventories and directly related to GDP, indicating that the time series of CO 2 emission intensity in this study has accounted for the effects of war-related emissions from these two sources. Sources iii and iv are connected to the actual outbreak of war. Given the limited number of regional wars over the past 30 years, emissions from post-war reconstruction are considerably smaller than those from sources i and ii [2, 3, 12, 16] . Furthermore, most emissions from reconstruction are included in the emission inventory and categorized under the construction sector. Therefore, in quantifying the relationship between the military expenditure ratio and CO 2 emission intensity, we do not separately consider emissions from post-conflict reconstruction. However, note that this emission source could significantly affect the CO 2 emission accounting in large-scale wars. Carbon emissions resulting from the destruction of carbon reservoirs are not directly related to GDP and are consequently excluded from global emission accounting. Extended Data Table 2 illustrates the CO 2 emissions arising from the combustion of carbon-containing materials during wars. These emissions are expected to increase the total CO 2 emissions, thereby augmenting CO 2 emission intensity. If emissions from the destruction of carbon reservoirs are incorporated into the analysis of the relationship between emission intensity and military expenditure ratio (assuming an additional emission of 320 million tons of CO 2 annually during Phase II owing to reservoir destruction), the CO 2 emission intensity will increase by 0.044 kg/USD (95% CI: 0.032–0.056) for each 1% increase in military expenditure ratio. This increase of 0.044 kg/USD represents a modest 1% increment from the original value of 0.04 kg/USD. Such a change is within the 95% confidence interval of the original regression coefficient, suggesting that historical CO 2 emission intensity accounts for most of the impacts of war-related emissions during 1995 and 2021. Impacts of global tensions on future GST projections Using the established relationship between the global military expenditure ratio and CO 2 emission intensity (Fig. 1b), we project the effects of varying global tensions on emission pathways across the five baseline SSPs, as illustrated in Fig. 2. The baseline SSPs do not specifically delineate the global military expenditure ratio. Therefore, we hypothesize that the ratio of 2014 represent those for 2015–2099 in each baseline SSP (approximately 2.3%). Reflecting on historical changes in the military expenditure ratios of global and major combatants since World War II [10, 17] , we developed a series of sensitivity scenarios. These scenarios are based on each baseline SSP and contemplate variations in the global military expenditure ratio ranging from −2% to 30% (incrementing in 1% steps) annually from 2024 to 2099. The results selected for scenarios with −2%, 5%, and 10% deviations in military expenditure ratio compared to the baselines are presented in Fig. 2. Acknowledging that economic development is influenced by a complex array of factors and that GDP is not necessarily affected by fluctuations in global tensions (Extended Data Fig. 4.2), we assume that the GDP trajectories in these sensitivity scenarios align with those of their respective baseline SSPs. Increases (or decreases) in the military expenditure ratio correspondingly increase (or reduce) annual CO 2 emissions relative to the baseline (Fig. 2a). The impact of variations in the military expenditure ratio on cumulative CO 2 emissions is more evident in SSP1-1.9 and SSP1-2.6 compared to higher emission pathways (Fig. 2b). Given the near-linear relationship between global surface temperature increase since 1850–1900 and cumulative CO 2 emissions [1] , variations in the global military expenditure ratio could significantly influence GST changes by affecting CO 2 emissions. We estimate the GST changes under different levels of global tensions, using the relationship between annual changes in GST increase relative to 1850–1900 (℃) and annual CO 2 emissions (Fig. 3 and Extended Data Fig. 5; see Methods). SSP1-1.9 and SSP1-2.6 represent the “Green Road” scenarios, aligning with socioeconomic pathways aimed at achieving the 2015 Paris Agreement target of limiting global warming to well below 2°C (preferably 1.5°C) above pre-industrial levels. In the baseline scenario SSP1-1.9, GST is projected to exceed 1.5°C around 2035 but is expected to return below 1.5°C after 2070. However, an increase in the global military expenditure ratio prolongs the time required to reduce GST increase below 1.5°C following the initial overshoot. For example, a 5% rise in military expenditure ratio compared with the SSP1-1.9 baseline would delay achieving the 1.5°C climate target by an additional 13 years, resulting in a 0.05℃ increase in GST at the end of this century (refer to Extended Data Table 3). If the global military expenditure ratio increases beyond 11%, it becomes unfeasible to meet the 1.5°C climate target (after the overshoot) by the end of the century, with GST potentially rising by over 0.11℃ by 2099. Conversely, in scenarios where global military expenditure decreases by 2% compared to the SSP1-1.9 baseline, humanity could potentially reach the goal of restricting the GST increase below 1.5℃ three years ahead of the original schedule. SSP1-2.6 shares the same socioeconomic framework as SSP1-1.9 but exhibits higher radiative forcing values owing to the different mitigation strategies. In this scenario, if the global military expenditure ratio increases by less than 23%, the rise in GST can still be confined to under 2°C during this century. Notably, the temperature overshoot associated with a 23% increase in the expenditure ratio is negligible. However, if the global military expenditure ratio increases beyond 23%, the 2°C climate target would become unattainable by the end of this century, resulting in a GST increase and exceeding 0.28°C by 2099 compared with the baseline SSP1-2.6. In medium- or high-emission scenarios such as SSP2-4.5, SSP3-7.0, and SSP5-8.5, the impact of changes in military expenditure ratio on GST increase projections is comparatively less pronounced than in SSP1-1.9 and SSP1-2.6 scenarios (Extended Data Fig. 7 and Extended Data Table 3). In scenarios with already high emissions, variations in military spending have a more muted effect on future GST trends. Conclusions and discussion This research uncovers a statistically significant positive correlation between the global military expenditure ratio and detrended global CO 2 emission intensity from 1995 to 2021. Specifically, a 1% shift in the global military expenditure ratio corresponds to a 0.041 (95% CI: 0.030–0.052) kg/USD change in global CO 2 emission intensity. Although the proposed methodology does not necessitate segregating military-related emissions within the total CO 2 emissions, it does not fully account for emissions from post-conflict reconstruction and the destruction of carbon reservoirs. These factors could have a more pronounced impact in large-scale conflicts. Consequently, the established relationship between the global military expenditure ratio and CO 2 emission intensity might underestimate the influence of significant war outbreaks on climate change. Scenarios SSP1-1.9 and SSP1-2.6 are crucial for humanity’s efforts to achieve the 1.5°C and 2°C climate targets, respectively. By applying polynomial fitting to the relationship between annual CO 2 emissions and the annual change in GST increase relative to 1850–1990, we assessed the impact of variations in the global military expenditure ratio on GST projections across the five SSP scenarios. The present findings suggest that escalating global tensions, leading to a military expenditure ratio exceeding 11% (or 23%), will result in missing the critical opportunity to prevent the climate system from reaching hazardous levels of greenhouse gasses. If emissions from the destruction of carbon reservoirs and post-war reconstruction were comprehensively considered, the threshold for the military expenditure ratio jeopardizing climate targets would likely be lower. Enhancing transparency and effectiveness in the national reporting of military fuel use within the UNFCCC reporting framework is imperative to foster research on military emissions and their impact on climate change. The results emphasize that peace and technological advancement are pivotal in combating climate change. Therefore, elevated global tensions and conflicts pose significant risks to climate objectives. Limiting global warming to 1.5°C or 2°C and mitigating the threat of extreme temperatures necessitate a more harmonious international climate and strong international cooperation and collaboration. Declarations Data availability: Global and national military expenditure are available at https://milex.sipri.org/sipri. Historical global GDP is provided at https://data.worldbank.org/indicator/NY.GDP.MKTP.KD. Global GDP projections across the five SSP scenarios are provided at https://doi.org/10.57760/sciencedb.01683. Annual anthropogenic CO 2 emissions used for caculating CO 2 emission intensity, are available at https://ourworldindata.org/co2-dataset-sources. The GST rise since 1850-1900 and annual anthropogenic CO 2 emissions across the five SSP scenarios, used for estimating the relationship between CO 2 emissions and GST increase, can be found at https://ipcc-browser.ipcc-data.org/. Acknowledgments: We acknowledge Prof. Tong Jiang's team at the Institute of Disaster Risk Management, Nanjing University of Information Science and Technology, for providing the Gridded datasets for economy under Shared Socioeconomic Pathways. Funding: This work was supported by: National Natural Science Foundation of China (U21A6001, 42175173) to W. D. National Natural Science Foundation of China (No. 42261144687) to W. D. Author contributions: W. D. conceived the research and designed the study. W. D. and Q. R. wrote the first version of the manuscript. Q. R. compiled data, performed the research and prepared graphs. F. L. contributed to the analysis of the relationship between global tensions and CO 2 emission intensity. F. L., J. C. and W. Y. participated in the review and editing of the manuscript. R. D., J. Y., K. W. and X. W. contributed to the programming of statistical methods used in this study. D. Z., C. L. and W. L. provided the data and literatures on war-related emissions. All co-authors interpreted the results. Competing interests: Authors declare that they have no competing interests. References IPCC, Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 2021. Lin, H-C. & Burton, D., INDEFENSIBLE: The true cost of the global military to our climate and human security, in Tipping Point North South’s Transform Defence. 2020. Parkinson, S. & Cottrell, L., Estimating the Military’s Global Greenhouse Gas Emissions. 2022, Scientists for Global Responsibility (SGR) and the Conflict and Environment Observatory (CEOBS). Crawford, N.C., Pentagon Fuel Use, Climate Change, and the Costs of War. 2019: Watson Institute, Brown University. Clark, B., Jorgenson, A.K., and Kentor, J. Militarization and Energy Consumption. International Journal of Sociology, 2010. 40 (2): p. 23-43. Cottrell, L., Military Emissions Are a Black Box. Green European Journal 2023. Klerk, L., Shlapak, M., Shmurak, A. et al., Climate Damage caused by Russia’s war in Ukraine. 2023. Brown, O., Froggatt, A., Gozak, N. et al., The consequences of Russia’s war on Ukraine for climate action, food supply and energy security. 2023. Xie, Z., Wu, R., and Wang, S. How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression. Journal of Cleaner Production, 2021. 307 : p. 127133. World bank, Military expenditure (% of GDP). 2023. Yang, J., Cheng, J., and Huang, S. CO 2 emissions performance and reduction potential in China’s manufacturing industry: A multi-hierarchy meta-frontier approach. Journal of Cleaner Production, 2020. 255 : p. 120226. Michaelowa, A., Koch, T., Charro, D. et al., Military and conflict-related emissions: kyoto to glasgow and beyond. 2022, Perspectives Climate Group: Freiburg, Germany,. Huang, L. et al., Carbon emission of global construction sector. Renewable and Sustainable Energy Reviews, 2018. 81 : p. 1906-1916. Rajaeifar, M.A. et al., Decarbonize the military - mandate emissions reporting. Nature, 2022. 611 (7934): p. 29-32. CEOBS (Conflict and Environment Observatory), A Framework for Military Greenhouse Gas Emissions Reporting. 2022. Matsunaga, H., The Reconstruction of Iraq after 2003: Learning from Its Successes and Failures. 2019, Washington, DC: World Bank: MENA Development Report Series. Our World in Data (based on COW & SIPRI 2018), Military expenditure as a share of GDP. https://ourworldindata.org/grapher/military-expenditure-as-a-share-of-gdp-long Methods Data sources . This study necessitates the integration of data and methodologies from diverse sources and scientific disciplines. We utilize global military expenditure from the Stockholm International Peace Research Institute [18] and global GDP [19] (USD at constant 2015 prices) from 1995 to 2021 to obtain the global military expenditure as a percentage of GDP. CO 2 emission intensity (kg/USD) during 1995 and 2021 is computed by dividing historic anthropogenic CO 2 emissions (from fossil fuels and industry, excluding land-use change) sourced from [20, 21] by global GDP [22] (USD in constant 2017 of PPP). Annual anthropogenic CO 2 emissions for five SSP scenarios during 2015–2100 are derived from data in Figures SPM.4 and SPM.10 (v20210809) [1, 23, 24] . Global surface temperature changes since 1850–1900 for five SSPs during 2015–2099 are obtained from data in Figure SPM.8 (v20210809) in IPCC AR6 [1, 25] . Global GDP projections (USD in constant 2010 of PPP) for the five SSPs are provided by Jiang et al. [26] Quantification of the relationship between military expenditure ratio and CO 2 emission intensity. We use the historic data from 1995 to 2021 to quantify the relationship between CO 2 emission intensity and global military expenditure ratio. The CO 2 emission intensity time series was linearly detrended before analysis. Acknowledging the potential noncontemporaneous effects of the global military expenditure ratio on CO 2 emission intensity, we compute their lagged Pearson correlation coefficients (Extended Data Table 1). This analysis reveals that the military expenditure ratio of the current year significantly influences CO 2 emission intensity for the current and subsequent two years. Consequently, we use the ratios of correlation coefficients with lags of 0, 1, and 2 years as weights for the current and preceding two years to calculate the three-year weighted averages of the global military expenditure ratio. These three-year weighted average ratios serve as explanatory variables, and the detrended CO 2 intensity as the dependent variable, in constructing our linear regression model (Fig. 1b; Eq. 1). Significance analysis. The time series of detrended CO 2 emission intensity and the weighted global military expenditure ratio from 1995 to 2021 conform to a normal distribution (Extended Data Fig. 6). Therefore, we employ Student’s t -test to assess the statistical significance of the correlation coefficient between these two series. The effective sample size of the N years of data is calculated using Eq. 2.1 [27] , accounting for their temporal correlation. The 95% confidence interval (CI) of the regression coefficient ( m ) was determined using Equation 2.2. Quantification of the relationship between CO 2 emissions and GST changes. The IPCC AR6 exhibits a near-linear relationship between cumulative CO 2 emissions and the increase in GST, as the transient climate response (TCRE) to cumulative CO 2 emissions remains constant. This relationship holds true during periods of net positive global CO 2 emissions; however, there is limited evidence for the quantitative application of TCRE to estimate temperature evolution under conditions of net negative CO 2 emissions [24] . Both the SSP1-1.9 and SSP1-2.6 scenarios anticipate net negative annual CO 2 emissions after 2050, rendering the linear relationship inapplicable. Consequently, we estimated the evolution of GST changes by constructing a relationship between annual CO 2 emissions and the annual change in GST increase using polynomial fitting for the five SSPs (Extended Data Fig. 5). 18. SIPRI Military Expenditure Database 2023. Retrieved from https://milex.sipri.org/sipri. 19. USD at constant 2015 prices. World Bank. Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.KD. 20. Friedlingstein, P. et al., Global Carbon Budget 2023. Earth Syst. Sci. Data, 15, 5301-5369, https://doi.org/10.5194/essd-15-5301-2023, 2023. 21. Ritchie, H., Rosado, P. and Roser, M. CO₂ and Greenhouse Gas Emissions. 2023, OurWorldInData.org. 22. GDP, PPP (constant 2017 international $). World Bank. Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD 23. Rogelj, J., Smith, C., Plattner, G.-K. et al., Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.4 (v20210809), N.E.C.f.E.D. Analysis, Editor. 2021. 24. Rogelj, J., Trewin, B., Haustein, K. et al., Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.10 (v20210809), N.E.C.f.E.D. Analysis, Editor. 2021. 25. Fyfe, J., Fox-Kemper, B., Kopp, R., Garner, G. Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.8 (v20210809). 2021, NERC EDS Centre for Environmental Data Analysis. 26. Tong Jiang, Buda Su, Yanjun Wang, et al. Gridded datasets for population and economy under Shared Socioeconomic Pathways[DS/OL]. V1. Science Data Bank, 2022[2024-01-11]. DOI:10.57760/sciencedb.01683. 27. Bretherton, C.S., et al., The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field %J Journal of Climate. 1999. 12 (7): p. 1990-2009. Additional Declarations The authors declare no competing interests. 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University","correspondingAuthor":false,"prefix":"","firstName":"Jieming","middleName":"","lastName":"Chou","suffix":""},{"id":273279658,"identity":"de6042bf-0654-4867-a8ac-500690b1648e","order_by":11,"name":"Wenping Yuan","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Wenping","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2024-02-15 14:16:19","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3958885/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3958885/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51335904,"identity":"32fe53a0-726d-4f09-85ca-eb611eefb1b8","added_by":"auto","created_at":"2024-02-19 19:22:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":624969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between global military expenditure ratio and global CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emission intensity.\u003c/strong\u003e \u003cstrong\u003ea,\u003c/strong\u003e Time series of detrended CO\u003csub\u003e2\u003c/sub\u003e emission intensity (kg/USD in constant 2017 of purchasing power parity; blue line), military expenditure as a percentage of GDP (pink line) and three-year weighted average global military expenditure ratio (red line) between 1995 and 2021. Military expenditure ratios for 1995 and 1996 have not been weighted. The research period is segmented into four phases, displaying a relatively peaceful phase (light-green background) and a phase of frequent regional wars (light-orange background). The boxes below exhibit the major characters or wars of this phase. \u003cstrong\u003eb,\u003c/strong\u003e Scatter plot of the weighted military expenditure ratio and detrended CO\u003csub\u003e2\u003c/sub\u003e emission intensity from 1995 to 2021, and the fitted line (black line) for the two variables with a slope of 0.041 (95% CI: 0.030–0.052).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3958885/v2/47556c4533127ab6f2957a22.png"},{"id":51335905,"identity":"ca735d30-aa24-438d-8073-1faacf212116","added_by":"auto","created_at":"2024-02-19 19:22:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":670370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions in the five baseline SSPs and the sensitivity scenarios with various global tensions.\u003c/strong\u003e\u0026nbsp;\u003cstrong\u003ea,\u003c/strong\u003e Annual CO\u003csub\u003e2\u003c/sub\u003e emissions from 2015 and 2099 under baseline SSP scenarios (blue lines) and sensitivity scenarios when global military expenditure (%GDP) decreases by 2% (green lines) or increases by 5% (orange lines) or 10% (red lines) compared with the baseline. We assume equal variations in the military expenditure ratio of each year in the sensitivity experiment relative to the baseline. \u003cstrong\u003eb,\u003c/strong\u003e Cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions since 1850 in 2030, 2050, and 2099, under the baseline SSP scenarios (blue bars) and sensitivity scenarios, when the global military expenditure (% of GDP) decreases by 2% (green bars) or increases by 5% (yellow bars) or 10% (red lines) compared with the baseline.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3958885/v2/cbfb5719466c6fbd167a7c35.png"},{"id":51336200,"identity":"2b8304fc-7eca-41cd-a7f1-c0af03ece0ee","added_by":"auto","created_at":"2024-02-19 19:30:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":396308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe projections of GST increase in the baseline SSPs and the sensitivity scenarios with various global tensions.\u003c/strong\u003e \u003cstrong\u003ea, b,\u003c/strong\u003e Projections of global surface temperature change relative to 1850–1900 in the SSP1-1.9 (\u003cstrong\u003ea\u003c/strong\u003e) and SSP1-2.6 (\u003cstrong\u003eb\u003c/strong\u003e) baselines and the sensitivity scenarios considering variations in global military expenditure ratio.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3958885/v2/2e29016eb38740b52d2ae91e.png"},{"id":52090583,"identity":"57002de1-886e-4036-96f9-02169e203717","added_by":"auto","created_at":"2024-03-06 13:55:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1028193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3958885/v2/de92511f-e605-46e5-9c04-1af91ed11c26.pdf"},{"id":51335902,"identity":"43a704d9-8ad2-41e3-80eb-38d00e6f9703","added_by":"auto","created_at":"2024-02-19 19:22:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4932564,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-3958885/v2/a0897ebae903cc3cbfd7a113.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Increased Global Tensions Jeopardize Climate Targets","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) indicates that meeting the 1.5\u0026deg;C or 2\u0026deg;C climate targets by the end of the century is achievable under the optimistic shared socioeconomic pathways SSP1-1.9 and SSP1-2.6 scenarios\u003csup\u003e[1]\u003c/sup\u003e, emphasizing on sustainable development, rapid decarbonization, and strong international cooperation. The emission inventories used in IPCC modeling of temperature rises include the production, residential, and transport sectors but do not distinctly categorize the military sector. This sector is heavily dependent on fossil fuels and has extensive, complex supply chains\u003csup\u003e[2-5]\u003c/sup\u003e. Because of the voluntary and inconsistent nature of military fuel use data reporting to the United Nations Framework Convention on Climate Change (UNFCCC)\u003csup\u003e[6]\u003c/sup\u003e, there is significant uncertainty in accurately quantifying global military and conflict emissions. Consequently, most SSP scenarios have not quantitatively integrated the potential impacts of increased global tensions or conflicts on temperature projections.\u003c/p\u003e\n\u003cp\u003eThe global landscape since 2022 has experienced considerable complexity and instability, which has been unparalleled since the conclusion of the Cold War. Events such as the ongoing Russo\u0026ndash;Ukrainian war and the Israeli\u0026ndash;Palestinian conflict have profound effects on political and security dynamics in Eurasia. These conflicts could exacerbate climate change vulnerability, impede climate mitigation efforts, and obstruct multilateral climate action\u003csup\u003e[7, 8]\u003c/sup\u003e. Addressing the gap in understanding the interplay between sociopolitical risks and climate change mitigation is vital. Quantifying the impact of heightened global tensions or conflicts on the climate is crucial for developing effective policy responses and promoting global cooperation in addressing both climate change and geopolitical challenges.\u003c/p\u003e"},{"header":"Research framework","content":"\u003cp\u003eHerein, we employ historical data and statistical methods to quantify the impact of global military expenditure as a percentage of GDP (hereafter referred to as the military expenditure ratio) on CO\u003csub\u003e2\u003c/sub\u003e emission intensity. This analysis further explores the effects of escalating global tensions on achieving climate targets (Extended Data Fig. 1). The global military expenditure ratio serves as a proxy for the level of global tension. Given that technological advancements are expected to significantly enhance carbon emission efficiency\u003csup\u003e[9]\u003c/sup\u003e, variations in CO\u003csub\u003e2\u003c/sub\u003e emission intensity over time reflect efforts to improve energy efficiency and mitigate climate change. We apply correlation analysis and linear regression to several decades of historical data to investigate and quantify the relationship between the global military expenditure ratio and CO\u003csub\u003e2\u003c/sub\u003e emission intensity. This study includes a significance test and an uncertainty analysis of their relationship.\u003c/p\u003e\n\u003cp\u003eIn addition, we examine the composition of war-related emissions and assess the impact of unaccounted emissions from wars on the anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emission inventory. This evaluation aids in understanding how these emissions influence the estimation of the relationship between the military expenditure ratio and CO\u003csub\u003e2\u003c/sub\u003e emission intensity. Finally, we utilize the established relationship between the military expenditure ratio and emission intensity, along with the correlation between annual CO\u003csub\u003e2\u003c/sub\u003e emissions and the annual change in global surface temperature (GST) derived from IPCC AR6 data, to modify the future GST increase projections in the SSP scenarios, considering various levels of global tension. Detailed descriptions of all data sources, statistical methods, significance tests, and uncertainty analyses are provided in the Methods section.\u003c/p\u003e"},{"header":"Quantification of the relationship between the global military expenditure ratio and CO2 emission intensity","content":"\u003cp\u003eFollowing the conclusion of the Cold War in 1991, the global military expenditure ratio experienced a marked decline over the next five years, dropping from an average of 4.5% during 1960\u0026ndash;1990 to approximately 2.5% by 1995\u003csup\u003e[10]\u003c/sup\u003e. This reduction was significantly contributed by major nations such as the United States, Russia, the United Kingdom, and France (Extended Data Fig. 3). After 1995, the global military expenditure ratio exhibited modest fluctuations near 2.3%. The variance in this ratio from 1990 to 1994 (0.13) was considerably larger than that between 1995 and 2021 (0.02), reflecting the distinct characteristics of these eras. This study emphasizes the changes in the military expenditure ratio after 1995.\u003c/p\u003e\n\u003cp\u003eThe trends in the global military expenditure ratio from 1995 to 2021 are associated with regional conflicts and wars (Fig. 2a). As the United States initiated the war on terror following the September 11 attacks, the global military expenditure ratio increased by 0.34%. This increase was primarily attributed to Operation Ending Freedom (2001\u0026ndash;2014) and the War in Iraq (2003\u0026ndash;2011), referred to as Phase II. During these conflicts, the military expenditure ratio of the United States increased by 1.7% in 2011 compared with 2001, whereas Iraq\u0026rsquo;s ratio increased by 1% in 2010 relative to 2004. The 2007\u0026ndash;2008 financial crisis, the most severe global economic downturn since the Great Depression, is suspected to have contributed to a significant rise in the global military expenditure ratio between 2007 and 2009. Following the Global Financial Crisis, there was a downward trend in global military expenditure (Phase III), which reversed after 2019 (Phase IV). This period coincided with the COVID-19 outbreak and the prelude to the Russian\u0026ndash;Ukrainian war.\u003c/p\u003e\n\u003cp\u003eDuring the study period, the CO\u003csub\u003e2\u003c/sub\u003e emission intensity exhibited a consistent downward trend (Extended Data Fig. 2), which is primarily driven by technological progress and industrial structure optimization\u003csup\u003e[9, 11]\u003c/sup\u003e. To focus on the variations beyond this trend, we analyze the fluctuations after detrending (represented by the pink line in Fig. 1a). The detrended global CO\u003csub\u003e2\u003c/sub\u003e emission intensity demonstrates lagging yet synchronous fluctuations with global military expenditure across the four identified phases (compare the pink and blue lines in Fig. 1a). The three-year weighted average military expenditure is shown in Fig. 1a (red line) because of the strong correlation between the military expenditure ratio of the previous two years and the current year, and the CO\u003csub\u003e2\u003c/sub\u003e emission intensity of the current year (Extended Data Table 1).\u003c/p\u003e\n\u003cp\u003eA significant positive correlation exists between the three-year weighted global military expenditure ratio and the detrended CO\u003csub\u003e2\u003c/sub\u003e emission intensity, as evidenced by a Pearson correlation coefficient of 0.84, surpassing the 95% confidence level (Fig. 1b). An increase (or decrease) of 1% in the global military expenditure ratio leads to an increase (or decrease) of 0.041 (95% CI: 0.030\u0026ndash;0.052) kg/USD in CO\u003csub\u003e2\u003c/sub\u003e emission intensity (see Methods). This change represents 30% of the total change in emission intensity from 2021 to 1995. Despite the overall reduction in global CO\u003csub\u003e2\u003c/sub\u003e intensity due to technological advancements (Extended Data Fig. 2), the escalation of global tensions is anticipated to hinder this reduction trend. The influence of the military expenditure ratio on emission intensity underscores the detrimental impact of heightened global conflicts on climate change mitigation efforts.\u003c/p\u003e\n\u003cp\u003eThe global military expenditure ratio significantly influences CO\u003csub\u003e2\u003c/sub\u003e emission intensity, primarily by impacting total CO\u003csub\u003e2\u003c/sub\u003e emissions (Extended Data Fig. 4.1 \u003cem\u003evs\u003c/em\u003e. Extended Data Fig. 4.2). There are four principal sources of CO\u003csub\u003e2\u003c/sub\u003e emissions associated with warfare: i. operational emissions (originating from military bases and operations); ii. military industry (including the production of vehicles, weapons, and equipment); iii. post-conflict reconstruction; and iv. destruction of carbon reservoirs\u003csup\u003e[4, 12]\u003c/sup\u003e. Most military activities are characterized by high carbon intensity\u003csup\u003e[4, 13-15]\u003c/sup\u003e. However, current accounting for war-related emissions typically includes only sources i and ii, often overlooking the others. Scientists for Global Responsibility (SGR)\u003csup\u003e[3] \u003c/sup\u003eestimated that the total military carbon footprint constitutes approximately 5.5% (CI: 3.3%\u0026ndash;7%) of global emissions, including operational emissions (source i) and upstream emissions from the supply chain (source ii). As one of the largest military entities, the US military emitted about 4,310 million metric tons of greenhouse gases (sources i and ii) from 2001, with the onset of the Afghanistan invasion, until fiscal year 2018\u003csup\u003e[4]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEmission sources i and ii are included in total emission inventories and directly related to GDP, indicating that the time series of CO\u003csub\u003e2\u003c/sub\u003e emission intensity in this study has accounted for the effects of war-related emissions from these two sources. Sources iii and iv are connected to the actual outbreak of war. Given the limited number of regional wars over the past 30 years, emissions from post-war reconstruction are considerably smaller than those from sources i and ii\u003csup\u003e[2, 3, 12, 16]\u003c/sup\u003e. Furthermore, most emissions from reconstruction are included in the emission inventory and categorized under the construction sector. Therefore, in quantifying the relationship between the military expenditure ratio and CO\u003csub\u003e2\u003c/sub\u003e emission intensity, we do not separately consider emissions from post-conflict reconstruction. However, note that this emission source could significantly affect the CO\u003csub\u003e2\u003c/sub\u003e emission accounting in large-scale wars.\u003c/p\u003e\n\u003cp\u003eCarbon emissions resulting from the destruction of carbon reservoirs are not directly related to GDP and are consequently excluded from global emission accounting. Extended Data Table 2 illustrates the CO\u003csub\u003e2\u003c/sub\u003e emissions arising from the combustion of carbon-containing materials during wars. These emissions are expected to increase the total CO\u003csub\u003e2\u003c/sub\u003e emissions, thereby augmenting CO\u003csub\u003e2\u003c/sub\u003e emission intensity. If emissions from the destruction of carbon reservoirs are incorporated into the analysis of the relationship between emission intensity and military expenditure ratio (assuming an additional emission of 320 million tons of CO\u003csub\u003e2\u003c/sub\u003e annually during Phase II owing to reservoir destruction), the CO\u003csub\u003e2\u003c/sub\u003e emission intensity will increase by 0.044 kg/USD (95% CI: 0.032\u0026ndash;0.056) for each 1% increase in military expenditure ratio. This increase of 0.044 kg/USD represents a modest 1% increment from the original value of 0.04 kg/USD. Such a change is within the 95% confidence interval of the original regression coefficient, suggesting that historical CO\u003csub\u003e2\u003c/sub\u003e emission intensity accounts for most of the impacts of war-related emissions during 1995 and 2021.\u003c/p\u003e"},{"header":"Impacts of global tensions on future GST projections","content":"\u003cp\u003eUsing the established relationship between the global military expenditure ratio and CO\u003csub\u003e2\u003c/sub\u003e emission intensity (Fig. 1b), we project the effects of varying global tensions on emission pathways across the five baseline SSPs, as illustrated in Fig. 2. The baseline SSPs do not specifically delineate the global military expenditure ratio. Therefore, we hypothesize that the ratio of 2014 represent those for 2015\u0026ndash;2099 in each baseline SSP (approximately 2.3%). Reflecting on historical changes in the military expenditure ratios of global and major combatants since World War II\u003csup\u003e[10, 17]\u003c/sup\u003e, we developed a series of sensitivity scenarios. These scenarios are based on each baseline SSP and contemplate variations in the global military expenditure ratio ranging from \u0026minus;2% to 30% (incrementing in 1% steps) annually from 2024 to 2099. The results selected for scenarios with \u0026minus;2%, 5%, and 10% deviations in military expenditure ratio compared to the baselines are presented in Fig. 2. Acknowledging that economic development is influenced by a complex array of factors and that GDP is not necessarily\u0026nbsp;affected\u0026nbsp;by fluctuations in global tensions (Extended Data Fig. 4.2), we assume that the GDP trajectories in these sensitivity scenarios align with those of their respective baseline SSPs. Increases (or decreases) in the military expenditure ratio correspondingly increase (or reduce) annual CO\u003csub\u003e2\u003c/sub\u003e emissions relative to the baseline (Fig. 2a). The impact of variations in the military expenditure ratio on cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions is more evident in SSP1-1.9 and SSP1-2.6 compared to higher emission pathways (Fig. 2b).\u003c/p\u003e\n\u003cp\u003eGiven the near-linear relationship between global surface temperature increase since 1850\u0026ndash;1900 and cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e[1]\u003c/sup\u003e, variations in the global military expenditure ratio could significantly influence GST changes by affecting CO\u003csub\u003e2\u003c/sub\u003e emissions. We estimate the GST changes under different levels of global tensions, using the relationship between annual changes in GST increase relative to 1850\u0026ndash;1900 (℃) and annual CO\u003csub\u003e2\u003c/sub\u003e emissions (Fig. 3 and Extended Data Fig. 5; see Methods). SSP1-1.9 and SSP1-2.6 represent the \u0026ldquo;Green Road\u0026rdquo; scenarios, aligning with socioeconomic pathways aimed at achieving the 2015 Paris Agreement target of limiting global warming to well below 2\u0026deg;C (preferably 1.5\u0026deg;C) above pre-industrial levels. In the baseline scenario SSP1-1.9, GST is projected to exceed 1.5\u0026deg;C around 2035 but is expected to return below 1.5\u0026deg;C after 2070. However, an increase in the global military expenditure ratio prolongs the time required to reduce GST increase below 1.5\u0026deg;C following the initial overshoot. For example, a 5% rise in military expenditure ratio compared with the SSP1-1.9 baseline would delay achieving the 1.5\u0026deg;C climate target by an additional 13 years, resulting in a 0.05℃ increase in GST at the end of this century (refer to Extended Data Table 3). If the global military expenditure ratio increases beyond 11%, it becomes unfeasible to meet the 1.5\u0026deg;C climate target (after the overshoot) by the end of the century, with GST potentially rising by over 0.11℃ by 2099. Conversely, in scenarios where global military expenditure decreases by 2% compared to the SSP1-1.9 baseline, humanity could potentially reach the goal of restricting the GST increase below 1.5℃ three years ahead of the original schedule.\u003c/p\u003e\n\u003cp\u003eSSP1-2.6 shares the same socioeconomic framework as SSP1-1.9 but exhibits higher radiative forcing values owing to the different mitigation strategies. In this scenario, if the global military expenditure ratio increases by less than 23%, the rise in GST can still be confined to under 2\u0026deg;C during this century. Notably, the temperature overshoot associated with a 23% increase in the expenditure ratio is negligible. However, if the global military expenditure ratio increases beyond 23%, the 2\u0026deg;C climate target would become unattainable by the end of this century, resulting in a GST increase and exceeding 0.28\u0026deg;C by 2099 compared with the baseline SSP1-2.6. In medium- or high-emission scenarios such as SSP2-4.5, SSP3-7.0, and SSP5-8.5, the impact of changes in military expenditure ratio on GST increase projections is comparatively less pronounced than in SSP1-1.9 and SSP1-2.6 scenarios (Extended Data Fig. 7 and Extended Data Table 3). In scenarios with already high emissions, variations in military spending have a more muted effect on future GST trends.\u003c/p\u003e"},{"header":"Conclusions and discussion","content":"\u003cp\u003eThis research uncovers a statistically significant positive correlation between the global military expenditure ratio and detrended global CO\u003csub\u003e2\u003c/sub\u003e emission intensity from 1995 to 2021. Specifically, a 1% shift in the global military expenditure ratio corresponds to a 0.041 (95% CI: 0.030\u0026ndash;0.052) kg/USD change in global CO\u003csub\u003e2\u003c/sub\u003e emission intensity. Although the proposed methodology does not necessitate segregating military-related emissions within the total CO\u003csub\u003e2\u003c/sub\u003e emissions, it does not fully account for emissions from post-conflict reconstruction and the destruction of carbon reservoirs. These factors could have a more pronounced impact in large-scale conflicts. Consequently, the established relationship between the global military expenditure ratio and CO\u003csub\u003e2\u003c/sub\u003e emission intensity might underestimate the influence of significant war outbreaks on climate change.\u003c/p\u003e\n\u003cp\u003eScenarios SSP1-1.9 and SSP1-2.6 are crucial for humanity\u0026rsquo;s efforts to achieve the 1.5\u0026deg;C and 2\u0026deg;C climate targets, respectively. By applying polynomial fitting to the relationship between annual CO\u003csub\u003e2\u003c/sub\u003e emissions and the annual change in GST increase relative to 1850\u0026ndash;1990, we assessed the impact of variations in the global military expenditure ratio on GST projections across the five SSP scenarios. The present findings suggest that escalating global tensions, leading to a military expenditure ratio exceeding 11% (or 23%), will result in missing the critical opportunity to prevent the climate system from reaching hazardous levels of greenhouse gasses. If emissions from the destruction of carbon reservoirs and post-war reconstruction were comprehensively considered, the threshold for the military expenditure ratio jeopardizing climate targets would likely be lower. Enhancing transparency and effectiveness in the national reporting of military fuel use within the UNFCCC reporting framework is imperative to foster research on military emissions and their impact on climate change. The results emphasize that peace and technological advancement are pivotal in combating climate change. Therefore, elevated global tensions and conflicts pose significant risks to climate objectives. Limiting global warming to 1.5\u0026deg;C or 2\u0026deg;C and mitigating the threat of extreme temperatures necessitate a more harmonious international climate and strong international cooperation and collaboration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobal and national military expenditure are available at https://milex.sipri.org/sipri. Historical global GDP is provided at https://data.worldbank.org/indicator/NY.GDP.MKTP.KD. Global GDP projections across the five SSP scenarios are provided at https://doi.org/10.57760/sciencedb.01683.\u003c/p\u003e\n\u003cp\u003eAnnual anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions used for caculating CO\u003csub\u003e2\u003c/sub\u003e emission intensity, are available at https://ourworldindata.org/co2-dataset-sources. The GST rise since 1850-1900 and annual anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions across the five SSP scenarios, used for estimating the relationship between CO\u003csub\u003e2\u003c/sub\u003e emissions and GST increase, can be found at https://ipcc-browser.ipcc-data.org/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Prof. Tong Jiang\u0026apos;s team at the Institute of Disaster Risk Management, Nanjing University of Information Science and Technology, for providing the Gridded datasets for economy under Shared Socioeconomic Pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by:\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (U21A6001, 42175173) to W. D.\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (No. 42261144687) to W. D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW. D. conceived the research and designed the study. W. D. and Q. R. wrote the first version of the manuscript. Q. R. compiled data, performed the research and prepared graphs. F. L. contributed to the analysis of the relationship between global tensions and CO\u003csub\u003e2\u003c/sub\u003e emission intensity. F. L., J. C. and W. Y. participated in the review and editing of the manuscript. R. D., J. Y., K. W. and X. W. contributed to the programming of statistical methods used in this study. D. Z., C. L. and W. L. provided the data and literatures on war-related emissions. All co-authors interpreted the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIPCC, Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 2021.\u003c/li\u003e\n\u003cli\u003eLin, H-C. \u0026amp; Burton, D., INDEFENSIBLE: The true cost of the global military to our climate and human security, in Tipping Point North South\u0026rsquo;s Transform Defence. 2020.\u003c/li\u003e\n\u003cli\u003eParkinson, S. \u0026amp; Cottrell, L., Estimating the Military\u0026rsquo;s Global Greenhouse Gas Emissions. 2022, Scientists for Global Responsibility (SGR) and the Conflict and Environment Observatory (CEOBS).\u003c/li\u003e\n\u003cli\u003eCrawford, N.C., Pentagon Fuel Use, Climate Change, and the Costs of War. 2019: Watson Institute, Brown University.\u003c/li\u003e\n\u003cli\u003eClark, B., Jorgenson, A.K., and Kentor, J. Militarization and Energy Consumption. International Journal of Sociology, 2010. \u003cstrong\u003e40\u003c/strong\u003e(2): p. 23-43.\u003c/li\u003e\n\u003cli\u003eCottrell, L., Military Emissions Are a Black Box. Green European Journal 2023.\u003c/li\u003e\n\u003cli\u003eKlerk, L., Shlapak, M., Shmurak, A. et al., Climate Damage caused by Russia\u0026rsquo;s war in Ukraine. 2023.\u003c/li\u003e\n\u003cli\u003eBrown, O., Froggatt, A., Gozak, N. et al., The consequences of Russia\u0026rsquo;s war on Ukraine for climate action, food supply and energy security. 2023.\u003c/li\u003e\n\u003cli\u003eXie, Z., Wu, R., and Wang, S. How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression. Journal of Cleaner Production, 2021. \u003cstrong\u003e307\u003c/strong\u003e: p. 127133.\u003c/li\u003e\n\u003cli\u003eWorld bank, Military expenditure (% of GDP). 2023.\u003c/li\u003e\n\u003cli\u003eYang, J., Cheng, J., and Huang, S. CO\u003csub\u003e2\u003c/sub\u003e emissions performance and reduction potential in China\u0026rsquo;s manufacturing industry: A multi-hierarchy meta-frontier approach. Journal of Cleaner Production, 2020. \u003cstrong\u003e255\u003c/strong\u003e: p. 120226.\u003c/li\u003e\n\u003cli\u003eMichaelowa, A., Koch, T., Charro, D. et al., Military and conflict-related emissions: kyoto to glasgow and beyond. 2022, Perspectives Climate Group: Freiburg, Germany,.\u003c/li\u003e\n\u003cli\u003eHuang, L. et al., Carbon emission of global construction sector. Renewable and Sustainable Energy Reviews, 2018. \u003cstrong\u003e81\u003c/strong\u003e: p. 1906-1916.\u003c/li\u003e\n\u003cli\u003eRajaeifar, M.A. et al., Decarbonize the military - mandate emissions reporting. Nature, 2022. \u003cstrong\u003e611\u003c/strong\u003e(7934): p. 29-32.\u003c/li\u003e\n\u003cli\u003eCEOBS (Conflict and Environment Observatory), A Framework for Military Greenhouse Gas Emissions Reporting. 2022.\u003c/li\u003e\n\u003cli\u003eMatsunaga, H., The Reconstruction of Iraq after 2003: Learning from Its Successes and Failures. 2019, Washington, DC: World Bank: MENA Development Report Series.\u003c/li\u003e\n\u003cli\u003eOur World in Data (based on COW \u0026amp; SIPRI 2018), Military expenditure as a share of GDP. https://ourworldindata.org/grapher/military-expenditure-as-a-share-of-gdp-long\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e This study necessitates the integration of data and methodologies from diverse sources and scientific disciplines. We utilize global military expenditure from the Stockholm International Peace Research Institute\u003csup\u003e[18]\u003c/sup\u003e and global GDP\u003csup\u003e[19]\u003c/sup\u003e (USD at constant 2015 prices) from 1995 to 2021 to obtain the global military expenditure as a percentage of GDP. CO\u003csub\u003e2\u003c/sub\u003e emission intensity (kg/USD) during 1995 and 2021 is computed by dividing historic anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions (from fossil fuels and industry, excluding land-use change) sourced from\u003csup\u003e[20, 21]\u003c/sup\u003e by global GDP\u003csup\u003e[22]\u003c/sup\u003e (USD in constant 2017 of PPP). Annual anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions for five SSP scenarios during 2015\u0026ndash;2100 are derived from data in Figures SPM.4 and SPM.10 (v20210809)\u003csup\u003e[1, 23, 24]\u003c/sup\u003e. Global surface temperature changes since 1850\u0026ndash;1900 for five SSPs during 2015\u0026ndash;2099 are obtained from data in Figure SPM.8 (v20210809) in IPCC AR6\u003csup\u003e[1, 25]\u003c/sup\u003e. Global GDP projections (USD in constant 2010 of PPP) for the five SSPs are provided by Jiang et al.\u003csup\u003e[26]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of the relationship between military expenditure ratio and CO\u003csub\u003e2\u003c/sub\u003e emission\u003csub\u003e\u0026nbsp;\u003c/sub\u003eintensity.\u003c/strong\u003e We use the historic data from 1995 to 2021 to quantify the relationship between CO\u003csub\u003e2\u003c/sub\u003e emission intensity and global military expenditure ratio. The CO\u003csub\u003e2\u003c/sub\u003e emission intensity time series was linearly detrended before analysis. Acknowledging the potential noncontemporaneous effects of the global military expenditure ratio on CO\u003csub\u003e2\u003c/sub\u003e emission intensity, we compute their lagged Pearson correlation coefficients (Extended Data Table 1). This analysis reveals that the military expenditure ratio of the current year significantly influences CO\u003csub\u003e2\u003c/sub\u003e emission intensity for the current and subsequent two years. Consequently, we use the ratios of correlation coefficients with lags of 0, 1, and 2 years as weights for the current and preceding two years to calculate the three-year weighted averages of the global military expenditure ratio. These three-year weighted average ratios serve as explanatory variables, and the detrended CO\u003csub\u003e2\u003c/sub\u003e intensity as the dependent variable, in constructing our linear regression model (Fig. 1b; Eq. 1).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"710\" height=\"303\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance analysis.\u003c/strong\u003e The time series of detrended CO\u003csub\u003e2\u003c/sub\u003e emission intensity and the weighted global military expenditure ratio from 1995 to 2021 conform to a normal distribution (Extended Data Fig. 6). Therefore, we employ Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test to assess the statistical significance of the correlation coefficient between these two series. The effective sample size of the \u003cem\u003eN\u003c/em\u003e years of data is calculated using Eq. 2.1\u003csup\u003e[27]\u003c/sup\u003e, accounting for their temporal correlation. The 95% confidence interval (CI) of the regression coefficient (\u003cem\u003em\u003c/em\u003e) was determined using Equation 2.2.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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width=\"715\" height=\"455\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of the relationship between CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions and GST changes.\u003c/strong\u003e The IPCC AR6 exhibits a near-linear relationship between cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions and the increase in GST, as the transient climate response (TCRE) to cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions remains constant. This relationship holds true during periods of net positive global CO\u003csub\u003e2\u003c/sub\u003e emissions; however, there is limited evidence for the quantitative application of TCRE to estimate temperature evolution under conditions of net negative CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e[24]\u003c/sup\u003e. Both the SSP1-1.9 and SSP1-2.6 scenarios anticipate net negative annual CO\u003csub\u003e2\u003c/sub\u003e emissions after 2050, rendering the linear relationship inapplicable. Consequently, we estimated the evolution of GST changes by constructing a relationship between annual CO\u003csub\u003e2\u003c/sub\u003e emissions and the annual change in GST increase using polynomial fitting for the five SSPs (Extended Data Fig. 5).\u003c/p\u003e\n\u003cp\u003e18. SIPRI Military Expenditure Database 2023. Retrieved from https://milex.sipri.org/sipri.\u003c/p\u003e\n\u003cp\u003e19. USD at constant 2015 prices. World Bank. Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.\u003c/p\u003e\n\u003cp\u003e20. Friedlingstein, P. et al., Global Carbon Budget 2023. Earth Syst. Sci. Data, 15, 5301-5369, https://doi.org/10.5194/essd-15-5301-2023, 2023.\u003c/p\u003e\n\u003cp\u003e21. Ritchie, H., Rosado, P. and Roser, M. CO₂ and Greenhouse Gas Emissions. 2023, OurWorldInData.org.\u003c/p\u003e\n\u003cp\u003e22. GDP, PPP (constant 2017 international $). World Bank. Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD\u003c/p\u003e\n\u003cp\u003e23. Rogelj, J., Smith, C., Plattner, G.-K. et al., Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.4 (v20210809), N.E.C.f.E.D. Analysis, Editor. 2021.\u003c/p\u003e\n\u003cp\u003e24. Rogelj, J., Trewin, B., Haustein, K. et al., Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.10 (v20210809), N.E.C.f.E.D. Analysis, Editor. 2021.\u003c/p\u003e\n\u003cp\u003e25. Fyfe, J., Fox-Kemper, B., Kopp, R., Garner, G. Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.8 (v20210809). 2021, NERC EDS Centre for Environmental Data Analysis.\u003c/p\u003e\n\u003cp\u003e26. Tong Jiang, Buda Su, Yanjun Wang, et al. Gridded datasets for population and economy under Shared Socioeconomic Pathways[DS/OL]. V1. Science Data Bank, 2022[2024-01-11]. DOI:10.57760/sciencedb.01683.\u003c/p\u003e\n\u003cp\u003e27. Bretherton, C.S., et al., The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field %J Journal of Climate. 1999. \u003cstrong\u003e12\u003c/strong\u003e(7): p. 1990-2009.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sun Yat-sen University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3958885/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3958885/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Sixth Assessment Report of the Intergovernmental Panel on Climate Change highlights the reliance on optimistic scenarios such as shared socioeconomic pathways SSP1-1.9 and SSP1-2.6 to achieve the 1.5°C or 2°C climate targets by the end of this century\u003csup\u003e[1]\u003c/sup\u003e. However, these scenarios have not quantitatively assessed the impact of global tensions (measured as global military expenditure as a percentage of GDP) on CO\u003csub\u003e2\u003c/sub\u003e emissions. Our research reveals that events such as the 2001–2011 War on Terror and the 2021 prelude to the Russian–Ukrainian war have led to a 0.041% (95% CI: 0.030–0.052) kg/USD increase in CO\u003csub\u003e2\u003c/sub\u003e emission intensity (CO\u003csub\u003e2\u003c/sub\u003e emissions per unit of GDP) for each 1% rise in global tensions. This increment accounts for 30% of the total change in CO\u003csub\u003e2\u003c/sub\u003e emission intensity from 1995 to 2021. In the case of escalating global tensions, with the global military expenditure ratio exceeding thresholds of 11% (for SSP1-1.9) or 23% (for SSP1-2.6), the global surface temperature increase would fail to return below 1.5°C after the initial overshoot in SSP1-1.9, and the 2°C climate target would become unattainable in SSP1-2.6 by the end of this century. These findings underscore the potential of escalating global tensions to undermine climate targets, emphasizing the critical need for a more peaceful international environment to effectively limit global warming to 1.5°C or 2°C.\u003c/p\u003e","manuscriptTitle":"Increased Global Tensions Jeopardize Climate Targets","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-02-19 19:22:46","doi":"10.21203/rs.3.rs-3958885/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-02-16 20:16:48","doi":"10.21203/rs.3.rs-3958885/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00534a14-f420-411e-ae76-9d615685a501","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28810478,"name":"Climatology"},{"id":28810479,"name":"Sociology"}],"tags":[],"updatedAt":"2024-02-16T20:16:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-19 19:22:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-3958885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3958885","identity":"rs-3958885","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}