Quantifying the Transformational Requirements of the Electricity Sector Under Uncertain Expectations of Carbon Removal

Quantifying the Transformational Requirements of the Electricity Sector Under Uncertain Expectations of Carbon Removal | 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 Quantifying the Transformational Requirements of the Electricity Sector Under Uncertain Expectations of Carbon Removal Pingjian Yang, SANDYLOVE AFRANE, Jeffrey Ampah, HUMPHREY ADUN, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4761526/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Mar, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Decarbonization and carbon dioxide removal (CDR) are crucial for achieving global climate targets. The power sector is pivotal in this process, yet the role of CDR in deep decarbonization and its implications on the sector have been underexplored. Using a global multi-sector model, we quantify the effects of CDR on the transformation of the power sector under different CDR reliance levels by 2050 — high (4–10 Gt/yr), moderate (2.5-5 Gt/yr), and low (≤ 1Gt/yr)—aligned with 1.5°C and 2°C climate targets. We show that BECCS is essential for future electricity demands, particularly in Asia and Central America. High CDR pathways could require 10–20% of electricity consumption in South America and Australia/New Zealand for carbon removal. Major economies like China, the US, and India face significant investment needs, risking stranded assets worth up to US $ 165–225 billion by mid-century under low CDR compared to high CDR. Regions heavily dependent on coal, such as China and India, face greater stranding costs, while gas-dependent regions like the Middle East and Russia have relatively lower costs. Global mitigation efforts with limited CDR require a 15% reduction in committed emissions compared to high-CDR scenarios, with the most pronounced reduction of 65% anticipated for India. Earth and environmental sciences/Environmental social sciences Scientific community and society/Energy and society Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The Paris Agreement aims to limit global warming to well below 2°C above pre-industrial levels, with an aspirational goal of limiting the increase to 1.5°C by 2100 1,2 . Central to achieving these targets is the concept of the remaining carbon budget (RCB), which refers to the maximum amount of anthropogenic net carbon dioxide (CO 2 ) that can be emitted while still having a likely chance of limiting warming to these levels 3 . Current estimates indicate that the RCB will be exhausted within the next five years 4,5 , highlighting the urgent need for rapid and unprecedented decarbonization across all sectors. The electricity sector, in particular, plays a critical role in this transition. It is essential not only for decarbonizing other sectors reliant on fossil fuel-generated electricity but also for providing the green electricity needed to produce green hydrogen, which is crucial for the deep decarbonization of hard-to-abate sectors like heavy industry and aviation 6,7 . However, the rapid decarbonization of the electricity sector could present a highly disruptive transition. Technically, the expansion of the grid to accommodate high shares of variable renewable energy 8 , the development of large-scale storage solutions to manage the variability of wind and solar power 9,10 , and the extraction of critical minerals required for renewable technologies 11,12 all pose significant transition challenges. Socio-economically, the shift away from fossil fuels could lead to substantial job losses in the fossil fuel industry 13,14 . In addition, there is also the issue of stranded assets. Stranded assets refer to investments in fossil fuel infrastructure that may become obsolete or uneconomical as the world transitions to cleaner energy sources 15 . According to the International Panel on Climate Change (IPCC), unburned fossil fuels and stranded fossil fuel infrastructure are projected to have a discounted global value of approximately USD 1–4 trillion from 2015 to 2050 in scenarios that aim to limit global warming to around 2°C. The estimated value increases further for pathways targeting a 1.5°C warming limit 2 . Given these challenges, a critical question arises: is it possible to achieve the Paris Agreement's climate targets without incurring the negative socio-economic and technical consequences associated with rapid electricity sector decarbonization? The answer may lie in carbon removal (CDR) technologies. CDR technologies will play a pivotal role in mitigating climate change by removing CO 2 from the atmosphere and can offset emissions to achieve specific climate targets. Examples of these technologies include Direct Air Capture with Carbon Storage (DACCS), Bioenergy with Carbon Capture and Storage (BECCS), afforestation, biochar, and enhanced weathering 16 . These technologies can slow the rate of decarbonization needed in the short term by offsetting emissions later 17,18 , thus acting as backstop technologies to cut emissions in a cost-effective manner 19 . For instance, Ampah et al. 20 show that if the availability of future CDR could reach at least 8 GtCO 2 /yr by 2050 instead of 1 GtCO 2 /yr, the cost of mitigation to reach net zero by 2050 could be reduced from US $ 2700/tCO 2 to less than US $ 1500/tCO 2 . Similarly, Fuhrman et al. 17 reveal that China and the rest of the world could get to net zero by 2060 at a marginal cost of over US $ 800/tCO 2 in the absence of DAC but its availability would reduce this cost to between US $ 200–400/tCO 2 . Based on these characteristics and roles, CDR technologies could address one of the major issues associated with rapid and deep decarbonization: stranded assets. Despite the potential of CDR to mitigate the premature retirement of existing power plants, quantitative studies that investigate the impact of these technologies on stranded assets in the power sector remain limited. Existing literature has primarily focused on quantifying the capacity and cost of stranded assets resulting from decarbonization efforts aligned with the Paris Agreement. Binsted et al. investigated the implications of the stranded assets in Latin America and the Caribbean’s climate goals under the Paris Agreement 15 . Similar studies have also been conducted elsewhere such as those from Iyer et al. 21 , Ou et al. 22 , Auger et al. 13 , Lu et al. 23 , von Dulong et al. 24 , Afrane et al. 25 among many others. While the existing studies on decarbonization and stranded assets in the electric power sector offer valuable contributions, the relationship between CDR technologies and asset stranding in the electricity sector requires similar attention. Moreover, the feasibility of achieving the necessary scale of CDR—up to 10 gigatonnes (Gt) of CO 2 removal per year by mid-century 26 —faces substantial technical, socio-economic, environmental, and political barriers 27,28 , necessitating a deeper examination of their role in the power sector transformation. To date, only Pradhan et al. 29 have made an initial attempt to quantify the relationship between CDR and stranded assets. Our study builds on their efforts in several ways. Firstly, considering the uncertainties surrounding the future success of CDR, we deploy various future scales of novel CDR technologies, ranging from 1 to 10 GtCO 2 by 2050, to assess their impact on the electricity sector transformation and stranded assets. Secondly, while Pradhan et al. 29 limited their analysis to achieving a 2°C warming target, we evaluate CDR deployment consistent with both 1.5°C and 2°C targets to understand the differing roles of CDR in mitigating disruptive transformations in the electricity sector under these climate scenarios. Thirdly, most global analyses provide valuable insights for policymakers but often overlook regional differences and sectoral transformational requirements needed across different countries and regions. This study offers a global analysis while highlighting how different regions and countries respond to varying levels of CDR deployment in their electricity sectors. Furthermore, different countries may prioritize different types of CDR technologies based on their unique resources. Countries with abundant land and biomass may favor BECCS and biochar, while those with suitable geological sites but limited land may prefer DACCS 20 . Recent studies have suggested that co-deploying various CDR types may have fewer negative impacts compared to relying on a single technology 30,31 . While Pradhan et al. 29 focused solely on the role of DACCS, we examine six different CDR types—afforestation/reforestation (AR), BECCS, biochar, enhanced rock weathering (ERW), DACCS, and direct ocean capture and carbon storage (DOCCS)—to assess how their full availability might affect global and regional electricity sector transformations compared to scenarios with fewer available options. Lastly, we consider the concept of committed emissions, which refer to the future CO 2 emissions associated with existing and planned power plants 32 . While CDR technologies could reduce the need for aggressive electricity sector transformations and mitigate stranded assets, they might also allow for higher committed emissions from power plants than if these technologies were absent. We explore this trade-off of CDR technologies by assessing the committed emissions tied to different CDR success rates under various climate targets, providing a comprehensive view of their potential impact on the power sector. Table 1 provides a general summary of the scenarios explored in this study. Table 1 Scenario description Scenario name Climate pathway Total net CO 2 trajectory CDR options Novel CDR amount 1.5 C_HIGH 1.5 o C Peak before 2025 and linearly decline to net zero by mid-century. Annual net negative emission of 9 GtCO 2 /yr from 2055 to 2100 BECCS, DACCS, DOCCS, Biochar, ERW Novel CDRs reach 10 GtCO 2 /yr by 2050 1.5 C _MODERATE BECCS and DACCS Novel CDRs reach 5 GtCO 2 /yr by 2050 1.5 C_LOW DACCS Novel CDR reaches 1 GtCO 2 /yr by 2050 2 C_HIGH 2 o C Peak before 2025 and linearly decline to net zero before 2070. Annual net negative emission of 4 GtCO 2 /yr from 2070 to 2100 BECCS, DACCS, DOCCS, Biochar, ERW Novel CDRs reach 4 GtCO 2 /yr by 2050 2 C_MODERATE BECCS and DACCS Novel CDRs reach 2.5 GtCO 2 /yr by 2050 2 C_LOW DACCS Novel CDR reaches less than 1 GtCO 2 /yr by 2050 From a modeling perspective, our approach to modeling CDR uncertainty differs from those commonly seen in existing IAM studies. Unlike most previous studies where CDR is endogenously deployed 18,33,34 , we explicitly set caps on the amount of negative emissions available for deployment in each modeling period. The problem with endogenously deployed CDR is that it offers a cost-effective approach to reaching climate targets by lowering the carbon price 35 , which in turn reduces the pace of emission reductions. This allows residual emissions to be offset later at a cheaper mitigation cost 17–19 . This approach can lead to the perpetuation of fossil fuels and higher levels of residual emissions 20 , causing the 1.5°C carbon budget to be overshot for several decades before returning to safer levels towards the end of the century. Our approach avoids this situation by forcing emission reductions and removals to be pursued separately without one undermining the other as advocated in recent studies 36,37 . This method allows us to assess the implications of varying CDR levels on power sector transformation under different climate targets. By addressing these critical aspects, we aim to offer new insights into the understanding of how CDR technologies can facilitate the global power sector's transformation while mitigating the socio-economic and technical challenges associated with rapid decarbonization. Results and discussion CDR deployment Understanding the complex interaction between CDR pathways and the global electric power system requires examining the roles and contributions of various CDR approaches in achieving climate mitigation goals. Figure 1 a illustrates the development and distribution of CDR methods across different modeled scenarios. LUC plays a significant role, especially in the LOW scenario, which relies solely on LUC for negative emissions until the last decade (2040–2050) when DACCS emerges. In scenarios with a broader portfolio of novel CDR approaches, such as the HIGH scenario, the contribution of LUC diminishes considerably by 2050, replaced by BECCS and DACCS as these technologies become more mature and cost-effective 38 . Although DACCS deployment is relatively delayed, it becomes a principal CDR approach under the 1.5°C pathway, with gross removals reaching 3 to 3.3 GtCO 2 /yr by 2050 in the MODERATE and HIGH scenarios, respectively. The 2°C pathway's less stringent requirements may allow for a further delay in DACCS deployment, likely towards the end of the century, providing time for technological advancements and cost reductions. Due to cost-effectiveness, the modeling assumes that high-temperature electric heating DACCS systems are deployed at a much lower scale compared to natural gas-based systems across all scenarios. Despite the limited deployment of the fully electric system, its energy-intensive nature poses major implications for electricity demands. BECCS deployment is expected to begin relatively early, around 2030. Under the HIGH scenario for both 1.5°C and 2°C pathways, BECCS would become the predominant source of negative emissions by 2050, delivering 4.6 and 2.9 GtCO 2 /yr, respectively. The 1.5°C and 2°C pathways show notable differences in BECCS deployment across sectors. In the 1.5°C pathway, BECCS-electricity dominates over BECCS-liquids, reflecting a prioritization of deep decarbonization in the power sector and an emphasis on electrification as a key mitigation strategy. Conversely, the 2°C pathway has a higher share of BECCS-liquids relative to BECCS-electricity, indicating a reduced emphasis on electrification and sector coupling. While BECCS and DACCS are expected to be the most widely deployed forms of novel CDR, enhanced weathering and biochar also play significant roles in providing negative emissions under the HIGH scenario. In the 1.5°C pathway, gross removals from enhanced weathering and biochar could reach 1.8 and 0.5 GtCO 2 /yr by 2050, respectively. The regional variations in the distribution and contribution of various CDR approaches are mainly driven by resource availability, domestic climate targets, and policy incentives. Figure 1 b shows the percent share of various CDR approaches in each country/region by 2050 under the most ambitious and optimistic CDR scenario (1.5 C_HIGH). Canada, Russia, Eastern Europe, and Southern/Western Africa will continue to rely heavily on LUC by 2050, with minor contributions from novel CDR technologies. The US, Australia/New Zealand, and several parts of South and Central America are expected to lean towards DACCS, while BECCS is anticipated to be the dominant source of negative emissions in several regions across Asia, Europe, and Eastern/Northern Africa. Enhanced weathering will play a major role in contributing to negative emissions in South and Southeast Asia, and biochar deployment will make significant contributions in India, Indonesia, and many parts of Africa due to favorable climatic conditions 30 . Transformations in the global electricity system Electricity generation The interactions between CDR pathways and the electricity sector will determine how the scale of CO₂ removal impacts the sector’s composition (in terms of electricity production) and the broader decarbonization effort. While most CDR approaches typically increase electricity demand, BECCS offers a way to remove CO₂ while also contributing to energy supply including electricity. Figure 2 a shows the transformation in global electricity supply under varying CDR scenarios for the 1.5℃ and 2℃ climate pathways. With minimal CDR deployment and no BECCS contribution, the LOW scenario requires the highest level of electricity generation, predominantly from renewables and nuclear energy. Under the 1.5℃ pathway, the LOW scenario sees an almost complete phase-out of unabated fossil fuels by 2050, with renewables and nuclear energy increasing nearly seven-fold and substantial deployment of long-duration energy storage, such as hydrogen, for renewable energy balancing. The MODERATE and HIGH scenarios offer greater flexibility in transforming the electricity mix by allowing continued use of fossil fuels, particularly gas (with and without CCS). In these scenarios, BECCS helps share the emissions reduction burden, enabling the electricity system to leverage gas as a 'transition fuel' due to its cost-effectiveness and lower emissions intensity compared to coal and oil 39 . Our results indicate a 47%-56% increase in electricity generation from gas (mainly with CCS) by 2050 relative to 2020 under the MODERATE and HIGH scenarios. Electricity generation from BECCS is projected to account for 2%-4% of the total generation in 2050. The integration of BECCS and overall greater CDR deployment could displace generation from nuclear and renewables by 5%-15% compared to the LOW scenario due to cost-effectiveness. The significant potential role of BECCS in meeting future electricity demands while contributing to negative emissions carries important regional implications. Under the most ambitious climate target and optimistic CDR assumptions, China, the US, and India are projected to lead in electricity generation from BECCS by 2050, with outputs ranging from 1.0 to 2.2 EJ/yr (Fig. 2 b). When considering BECCS' proportional contribution to regional electricity generation, India is expected to maintain a significant reliance on this technology, alongside Mexico and some parts of Central and Southern America. In these regions, BECCS is projected to account for 5%-10% of total electricity generation by 2050 (Fig. 2 c). This highlights BECCS's potential importance in their decarbonization strategies, driven by factors such as biomass resource availability, existing bioenergy infrastructure, and policy support. However, large-scale BECCS deployment may pose trade-offs and sustainability challenges surrounding issues such as land-use competition, biodiversity impacts, and water resource management for these regions 40–42 . Electricity prices The rapid energy transition is projected to increase electricity prices due to the higher initial costs of integrating renewable energy sources and the anticipated rise in carbon pricing mechanisms, which make fossil fuel-based energy generation more expensive. Figure 3 a shows the impact of various CDR deployment levels on global electricity price changes under 1.5°C and 2°C pathways. Achieving the 1.5°C target with limited CDR necessitates a swifter and more profound decarbonization of the power sector, resulting in higher electricity prices for consumers. The 1.5°C scenarios generally show higher initial price increases compared to 2°C scenarios. The 1.5°C_LOW scenario, which anticipates the most abrupt energy transition, shows the most significant price increase by 2050, diverging sharply from other scenarios. The 1.5°C_HIGH scenario exhibits the most dramatic price changes, starting with a relatively high price increase in 2025 and ending with the most significant price decrease in 2050. This trajectory suggests that aggressive climate mitigation strategies required for the 1.5°C target may incur higher near-term costs. However, the anticipated mid-century deployment of CDR technologies could potentially lead to significant long-term reductions in electricity prices. The electricity supply sector consistently shows higher price increases across all scenarios compared to the demand sectors, particularly in the early years. Among the demand sectors, the building and transport sectors generally exhibit lower price changes compared to the industry sector. Regional differences in current electricity mixes, resource potential, technological capacities, and transition requirements manifest as divergent regional price impacts, particularly in scenarios pushing the boundaries of rapid change or limited technology availability. Figure 3 b shows the percent change in electricity prices by 2050 relative to 2020 across different countries/regions. The 1.5°C_LOW scenario, which requires the most rapid and aggressive shift to renewable energy deployment, sees some of the highest percentage increases in electricity prices. South Africa and India, in particular, experience a 35–45% increase in electricity prices under this scenario, primarily due to the high costs associated with rapidly phasing out their coal-heavy power system. The US alongside several countries in Asia and Europe also face high price increases of 20%-30% under the 1.5℃_LOW scenario. The HIGH scenarios (both 1.5°C and 2°C), with greater CDR availability, help mitigate price shocks across various countries/regions. Canada, the Middle East, and some parts of South America and Africa are expected to witness decreased electricity prices of about 5%-20% under these optimistic scenarios. Consumption in final energy On the demand side, the deployment of CDR technologies could significantly impact the electrification of end-use sectors as a mitigation strategy across different climate pathways, as shown in Fig. 4 a. The LOW scenario sees the highest level of electrification in the three end-use sectors. Achieving the 1.5°C climate target with limited CDR deployment to offset emissions from the industry and transport sectors necessitates increased electrification in these sectors by about 28% and 16%, respectively, compared to the scenario with full deployment of CDR options. Most CDR approaches such as DACCS will inevitably interact with the electricity system primarily as consumers of energy, highlighting that while these technologies offer potential solutions for mitigating climate change, they also introduce additional energy demands that must be met. As CDR technologies become more widely deployed towards mid-century, electricity consumption for CDR processes is projected to increase considerably, reaching approximately 1–9 EJ by 2050. Under the 1.5°C pathway, electricity consumption for CDR typically increases with higher CDR deployment. However, for the 2°C pathway, the LOW scenario exhibits higher electricity consumption for CDR compared to the MODERATE and HIGH scenarios. This is likely because the 2°C pathway minimizes reliance on energy-intensive approaches like DACCS when other CDR options are available. In the MODERATE scenario, DACCS is projected to contribute minimally to negative emissions, resulting in nearly non-existent electricity consumption for CDR by 2050. In contrast, in the LOW scenario, DACCS remains the only deployable novel CDR technology, making some level of electricity consumption inevitable. The large-scale deployment of CDR technologies needed to meet climate targets at national and regional levels will significantly impact electricity consumption in various countries and regions. Figure 4 b shows the percentage of electricity consumption for CDR in each country/region’s final energy use across modeled scenarios. In scenarios relying heavily on energy-intensive CDR approaches (1.5℃_MODERATE and HIGH), several parts of South America and Australia/New Zealand will see a significant portion (about 10%-20%) of their electricity consumption dedicated to CDR processes. These regions may be well-positioned to utilize their abundant renewable energy resources to support CDR deployment while minimizing additional emissions 43,44 . Meanwhile, the 2°C_MODERATE pathway, which assumes minimal overall CDR electricity consumption, sees zero electricity consumption for CDR in several countries across Africa, Europe, and Southern Asia. Capacity additions and investment costs To drive down the power sector's emissions towards zero levels by mid-century, any new generation capacity added globally over the coming decades must be dominated by low or zero-carbon technologies. Fossil fuel-based additions, unless fitted with CCS, would create long-lived, emissions-intensive capital stocks incompatible with long-term climate goals 32 . Figure 5 a shows how varying levels of CDR deployment can influence capacity additions in the global electricity sector under the 1.5°C and 2°C climate targets. In the 1.5°C pathway, the CDR-constrained scenarios (LOW and MODERATE) necessitate continuous capacity expansion throughout the modeled period to offset potential stranded assets and accommodate the inflexibility and higher reserve margins demanded by stringent mitigation efforts. Meanwhile, the HIGH scenario under 1.5℃, along with all CDR scenarios under the 2°C pathway, reaches peak capacity additions between 2036 and 2040. Decarbonizing electricity supply while meeting projected global demand under the LOW scenario requires cumulative capacity additions which are about 5%-10% higher than the MODERATE scenario and 7%-22% above the HIGH scenario. Capacity additions for unabated coal will cease completely after 2035 under LOW and after 2040 under MODERATE and HIGH in the 1.5°C pathway. The deployment of CCS technology for retrofitting existing fossil fuel plants begins steadily after 2025 under all scenarios, reaching a cumulative capacity of 1450–2800 GW by 2050. Wind and solar capacity additions are projected to increase substantially across all scenarios, accounting for 64–66% of total capacity additions over the modeled period. Additions of nuclear capacity will account for 12–15% of the total. Cumulative BECCS capacity under the MODERATE and HIGH scenarios could reach 160–380 GW, offsetting some of the need for renewables and nuclear capacity relative to the BECCS-exclusive LOW scenario. The transition towards zero or low-carbon power infrastructure hinges not only on technological advancement but also on the ability to mobilize and channel investments at an unprecedented scale. Figure 5 b quantifies the capital requirements for deploying new capacity under modeled scenarios. We find that the full deployment of CDR options could reduce cumulative investment costs by up to 10% and 18% compared to the MODERATE and LOW scenarios, respectively. Investment in solar and wind capacity is projected to reach a cumulative of US $ 17–25 trillion by 2050, representing 45–50% of total investment costs. Nuclear and BECCS, being more expensive, will have disproportionately higher shares of investment costs relative to their shares in capacity additions. The HIGH scenario may require up to US $ 1.3 trillion more investments in BECCS compared to the MODERATE scenario. The regional variations in cumulative capacity additions and related costs under the most ambitious climate target and optimistic CDR assumptions are shown in Fig. 5 c and d . China's extensive energy demand and the need to decarbonize its emissions-intensive power sector significantly amplify its future capacity needs. China's cumulative capacity additions for renewables and nuclear will account for approximately 23% of the world’s total. This translates into an investment cost of about US $ 7.5 trillion, roughly equivalent to 42% of the country’s gross domestic product (GDP) in 2022 45 . The US, India, and Europe also see substantial investments in renewables and nuclear power, cumulating to US $ 3-3.5 trillion. China leads again in investments in bioenergy and CCS technologies, followed by India, the US, and the Middle East. Driven primarily by its biomass resource availability 46,47 , India's projected capacity investments significantly favor BECCS, potentially accounting for approximately 25% of its total investment costs. Meanwhile, the Middle East's sustained reliance on gas and oil, even in the long term, necessitates substantial investments in fossil CCS technologies, projected to account for about 40% of the region’s total capacity investments. Stranded capacity and costs Climate policies aimed at deep decarbonization pose a significant risk of rendering high-emission power sector assets, such as fossil fuel-based plants, economically unprofitable or obsolete before the end of their natural operational lifetimes 48,49 . The impact of varying levels of CDR deployment on asset stranding in the electric power sector under the 1.5°C and 2°C climate pathways is illustrated in Fig. 6 a. Our modeled scenarios project about 1140–2200 GW of existing power plants becoming stranded from 2016 to 2050. Expanding CDR deployment helps mitigate the overall magnitude and pace of the required premature retirements, particularly under the 1.5°C pathway. Specifically, the HIGH scenario reduces stranded asset capacity by up to 15% and 25% compared to the MODERATE and LOW scenarios, respectively. Conventional coal-fired power plants account for approximately 55%-70% of the total stranded capacity across modeled scenarios. In absolute terms, the LOW scenario sees the highest stranding of coal assets with about 40 and 130 GW more capacity stranding relative to the MODERATE and HIGH scenarios under the 1.5°C pathway, respectively. Interestingly, the HIGH scenario projects a larger share of coal in its total stranded capacity compared to the LOW and MODERATE scenarios. This is because the share of gas in total stranded capacity is significantly reduced under the HIGH scenario which enables the continued use gas as a “transition fuel”. In comparison, the LOW and MODERATE scenarios, particularly under 1.5°C, require earlier stranding of all emission-intensive assets including gas-fired plants. The MODERATE and HIGH scenarios foresee earlier stranding of conventional bioenergy assets due to the potential for retrofitting these plants with CCS technology. Transitioning conventional bioenergy plants to BECCS facilities aligns these assets with stringent climate mitigation goals, incentivizing the early stranding of these plants to facilitate their conversion. The premature retirement of conventional power plants from 2016 to 2050 could result in cumulative stranding costs of about US $ 3.6 to 6.8 trillion across modeled scenarios. The HIGH scenario project approximately 5%-10% and 17%-20% lower stranding costs than the MODERATE and LOW scenarios, respectively. Notably, the share of stranding costs for coal power plants is disproportionately larger relative to their share of stranded capacity. This could be attributed to the significantly higher capital expenditures associated with coal power plants and their slower depreciation rates, which stem from their longer assumed operational lifespans 50 . This observation highlights significant economic challenges associated with the potential phase-out of conventional coal power plants compared to gas and oil in the context of deep decarbonization. As countries and regions adopt increasingly ambitious climate policies, such as carbon pricing mechanisms, emissions performance standards, or outright phase-out plans, the economic and regulatory environment for emission-intensive power generation could become highly unfavorable. Figure 6 b and c illustrate the regional variations in potential stranded assets and associated costs by 2050 under the 1.5°C_HIGH scenario. China faces the highest levels of prematurely retired assets globally, driven by the need to decarbonize its coal-heavy power sector. China's projected premature retirements reach a cumulative total of 480 GW by 2050, resulting in stranding costs of about US $ 1.9 trillion. Existing coal assets account for 98% of the total stranded capacity, equivalent to approximately 40% of the country’s current coal capacity 51 . The US and India rank as the second and third countries most at risk for stranded assets, respectively with nearly identical cumulative stranded capacity. However, India's stranding costs are about 35% higher than those of the US. This discrepancy arises from the differing composition of their stranded assets: in India, about 95% of the total stranded capacity is due to coal assets, whereas in the US, coal accounts for only 55% of the total stranded capacity. Since coal has much higher associated stranding costs compared to gas and oil, India's total stranding costs are substantially greater than those of the US, despite the US having a slightly higher overall stranded capacity. Other countries, such as South Africa and Indonesia, would also experience high potential stranding costs due to their coal-heavy power sectors. In contrast, Russia and the Middle East would see disproportionately lower stranding costs, primarily from gas and oil assets. Committed emissions The continued operation of fossil fuel-based power plants over their typical lifetimes would result in a substantial amount of committed emissions 52 , posing challenges to both near- and long-term climate goals. By accounting for the expected lifetimes and utilization rates of existing fossil fuel-based power plants, we estimate future "locked-in" emissions if these assets operate as intended (REFERENCE/REF), comparing them with modeled emissions under various CDR deployment scenarios consistent with the 1.5°C and 2°C climate pathways. Figure 7 a shows that the continued operation of existing power plants over their full remaining lifetimes would result in approximately 495 GtCO 2 in the coming decades, with about 75% of these emissions attributed to coal assets. For a 50% likelihood of limiting global warming to 2°C 53,54 , these committed emissions would consume approximately 43% of the remaining carbon budget and nearly exhaust the budget for the 1.5°C-consistent scenario. The premature retirement or retrofitting of existing infrastructure before the end of its expected lifetime could mitigate committed emissions linked to these assets and help align the power sector with decarbonization targets. Our modeled results indicate that the 1.5°C and 2°C pathways require up to 55% and 40% reductions in committed emissions, respectively relative to the REF scenario. The prospect of large-scale CDR deployment by mid-century could potentially offset committed emissions from existing long-lived assets, reducing the immediate pressure for aggressive premature retirements to meet climate targets. Consequently, scenarios with multi-gigatonnes expectations of CDR enable longer operational lifetimes for emissions-intensive assets, resulting in higher committed emissions compared to scenarios with limited CDR deployment. Under our modeled 2°C pathway, the LOW scenario requires reducing committed emissions by 8% and 12% relative to the MODERATE and HIGH scenarios, respectively. For the 1.5°C pathway, mitigation efforts with LOW CDR deployment necessitate reducing committed emissions by 5% and 15% compared to the MODERATE and HIGH scenarios, respectively. Across all modeled scenarios, the majority of potential emissions reductions are projected to come from the premature retirements of coal assets, with reductions of 35–55% compared to the REF scenario. This is attributable to the high emissions intensity of coal compared to natural gas and oil, as well as the significant share of coal in the existing global power generation mix. Figure 7 b illustrates the committed emissions estimated for various countries/regions under REF compared to the modeled scenarios. China leads in committed emissions from operational power plants at 216 GtCO 2 , accounting for approximately 40% of the global total. Given that about 97% of China's commitments originate from coal, achieving climate goals will require reductions of approximately 60–100 GtCO 2 through extensive stranding of coal assets. The US, Europe, and India collectively account for one-third of global committed emissions. Despite India having the lowest committed emissions among these three regions, it requires the most significant reductions across modeled scenarios, particularly under the most ambitious 1.5°C_LOW scenario. India's higher proportion of emissions from coal generators, combined with its limited potential for CDR deployment under the LOW (no BECCS) scenario, necessitates an aggressive approach to stranding existing assets to meet stringent mitigation targets. The Middle East, Africa, and Latin America and the Caribbean (LAC) contribute the least to global committed emissions. In the Middle East and LAC, natural gas predominates, accounting for roughly one-quarter of global committed emissions from gas-fired generators. Our modeled projections for the Middle East suggest the most modest reductions in committed emissions across all regions, likely due to its minimal reliance on coal-fired power generation and the associated committed emissions. Conclusion This study provides a comprehensive global analysis of how varying levels of CDR deployment could impact the transformation of the electric power sector under 1.5°C and 2°C climate pathways. Our findings indicate that most CDR approaches typically interact with the electricity system as energy consumers, potentially accounting for 1%-4% of global electricity demand by 2050. In contrast, BECCS offers a way to remove CO 2 while contributing approximately 2%-4% to electricity supply. Limiting novel CDR deployment to about 1 GtCO 2 by 2050, without BECCS, necessitates up to $ 9 trillion more investment in renewables and nuclear compared to a pathway with up to 10 GtCO 2 of CDR from a diverse portfolio, including BECCS. The aggressive scaling of renewables and nuclear under the LOW CDR scenario results in higher electricity prices for consumers, especially under the 1.5°C climate pathway. More importantly, our findings indicate that large-scale CDR deployment can alleviate some socio-economic and technical challenges of rapid decarbonization by reducing stranded assets in the power sector. The HIGH CDR scenario could decrease stranded asset capacity by up to 25% compared to the LOW scenario, saving approximately half a trillion US dollars in stranding costs. However, this benefit comes with a trade-off: greater CDR availability extends the operational lifetimes of emissions-intensive assets, increasing projected committed emissions from the power sector. The high CDR pathway could result in about 15% higher committed emissions under the 1.5°C target and 12% higher under the 2°C target, compared to the constrained CDR scenario. This raises important considerations for policymakers regarding the balance between relying on CDR deployment as an emission-offsetting strategy and prioritizing more aggressive premature retirements of emissions-intensive assets. While CDR technologies offer a potential solution for achieving climate targets more gradually, their feasibility at the necessary scale remains uncertain due to substantial technical, socio-economic, environmental, and political barriers 27,55–57 . Regionally, the impacts of varying CDR deployment levels are significant but diverse. In optimistic CDR scenarios (HIGH CDR), regions like Mexico, India, and some parts of Central and South America will see substantial shares of their electricity supply derived from BECCS. Conversely, regions such as Brazil, Argentina, and Australia/New Zealand will need to utilize their renewable energy resources to support a significant portion of their electricity consumption dedicated to CDR processes. Despite optimistic CDR deployment, the scale and speed of power sector transformation required under both climate targets remain substantial across all regions. Major economies like China, the U.S., and India face multi-trillion-dollar investment requirements, primarily in expanding renewable and nuclear capacity. Paradoxically, these countries are most at risk for stranded assets, translating into stranding costs of up to US $ 0.5-2 trillion by mid-century, even under the most optimistic CDR assumptions. Generally, regions with higher coal dependence face significantly higher stranding costs compared to regions relying more on gas and oil, particularly under the HIGH scenario, which further delays gas stranding. Future research can enhance our understanding of the challenges and opportunities linked to the rapid decarbonization of the power sector required under the Paris Agreement and the integration of novel CDR technologies. Key research directions should include: ( 1 ) Developing integrated assessment models that capture the complex socio-economic impacts of power sector decarbonization, such as job displacement, economic development, and inequities among affected populations; and ( 2 ) Exploring policy frameworks, support mechanisms, and international cooperation strategies to facilitate a just and equitable transition, particularly for the Global South. ( 3 ) Quantifying the implications of varying degrees of government commitment to asset stranding, especially considering that countries in the Global South, which have recently acquired carbon-intensive assets, may be less willing to strand their assets than those in the Global North. Methods The primary objective of this study is to examine the impact of CDR reliance on the transformation of the electric power sector, with a particular focus on stranded assets and committed emissions linked to the technologies within this sector. To achieve this, we employed a modified version of the Global Change Assessment Model (GCAM) 30 . This enhanced model incorporates five novel CDR approaches, and it allows users to set separate and explicit targets for the amount of CO 2 to be removed at any given time 20 . Model Description GCAM is one of the leading Integrated Assessment Models (IAMs) in climate change research. GCAM has been extensively used to investigate the interconnections between the global economy, energy systems, land use, and the environment 58 . It is a market equilibrium model with a global scope, operating from 1990 to 2100 in five-year time steps. GCAM can analyze how changes in factors such as population, income, or technology costs may impact crop production, energy demand, land use changes and water withdrawals 59 . It can also explore how variations in one region's energy demand influence energy, water, and land use in other regions. The core of GCAM represents the entire world but is constructed with varying levels of resolution for different systems. In the current release version, the energy-economy system is divided into 32 global regions, land use is subdivided into over 300 subregions, and water resources are tracked across 233 basins worldwide 59 . Over the past two decades, GCAM has been utilized in virtually every major climate, energy, and economics assessment, highlighting its significance in climate change mitigation research 59 . For a detailed model description, including modeling equations and parameters, refer to the Supplementary Information. Scenario Formulation In this study, we model three different levels of future CDR reliance under two climate targets. CDR technologies are broadly categorized into land-based (conventional) CDR, such as afforestation/reforestation (AR) and CCS-CDR, and novel (technological) CDR, which includes BECCS, DACCS, DOCCS, biochar, and enhanced rock weathering (EW). Existing studies indicate that current global CDR totals approximately 2 GtCO 2 /yr, with 99.9% derived from conventional CDR 60 . However, conventional CDR approaches face challenges related to additionality, permanence, and reversibility 27 . Consequently, novel CDR methods are expected to play a more significant role in offsetting residual emissions as they become more cost-effective and technologically mature 38 . The success of future negative emissions from novel CDR technologies remains uncertain from a technical standpoint 27,57 , and there are environmental and sustainability concerns associated with these methods 20,61 . Against this backdrop, we model three possible pathways for future CDR efforts. HIGH CDR Scenario : In this scenario, we assume that near-term emissions reductions will be insufficient, necessitating large-scale CDR by the mid-term to counterbalance residual emissions, achieve net zero, and eventually sustain net negative emissions to return temperatures to safe levels after a temporary overshoot. This scenario deploys five novel CDR technologies without any annual removal limits. MODERATE CDR Scenario : Here, we assume that challenges to the deployment of several novel CDR approach with lower Technology Readiness Levels (TRL) and mitigation potential will persist. The focus will be on BECCS and DACCS, the two most researched novel CDR methods, which have TRLs of about 6 and mitigation potentials of up to 11 GtCO 2 /yr and 40 GtCO 2 /yr, respectively 2 . To limit negative impacts, their combined deployment is capped at 5 GtCO 2 /yr of negative emissions by 2050. LOW CDR Scenario : This scenario assumes that novel CDR technologies will not meet expectations, and the focus will shift to decarbonization. Due to sustainability issues such as land competition associated with BECCS 61 , the emphasis will be on DACCS, which is expected to provide not more than 1 GtCO 2 /yr of negative emissions by 2050. This will mainly offset residual emissions from recalcitrant sectors such as aviation, shipping, and agriculture. In all three scenarios, negative emissions from conventional CDR are deployed endogenously. All three CDR scenarios are modeled under two climate pathways: 1.5°C (high overshoot) and 2°C. These pathways align with scenarios assessed by the IPCC in its Sixth Assessment Report (AR6). According to these scenarios, the 5th-95th percentile range for the net zero year for 1.5°C (high overshoot) is between 2045 and 2070 (with a 50th percentile of 2055–2060), while for 2°C, it is from 2055 onwards (with a 50th percentile of 2070–2075). Additionally, for 1.5°C (high overshoot), the 5th-95th percentile for total net CO 2 emissions from 2020 to the year of net zero is 530–930 GtCO 2 , and for 2°C, it is 640–1160 GtCO 2 2 . Consistent with these existing pathways, we model a 1.5°C target where CO 2 emissions peak before 2025 and decline linearly to net zero before 2055, with a total carbon budget of 780 GtCO 2 . For the below 2°C target, CO 2 emissions also peak before 2025 but decline linearly to net zero before 2070, with a total carbon budget of 1095 GtCO 2 . It is important to note that we do not directly constrain emissions from land use change; instead, we apply the same pricing to these emissions as we do to emissions from fossil fuels and industry. This method enhances the role of conventional CDR in offsetting residual emissions, particularly in scenarios where novel CDR methods do not achieve sufficient negative emissions. It also aids in reducing deforestation rates by enabling a quick shift from net positive land use change (LUC) emissions to net negative LUC emissions by as early as 2025. Furthermore, in all our modeled pathways, we do not directly constrain non-CO 2 greenhouse gas (GHG) emissions, such as methane, nitrous oxides, and fluorinated gases. Instead, the model reduces these emissions by applying marginal abatement cost (MAC) curves. The emission intensities of these non-CO 2 gases decrease as GHG prices rise, with these prices linked to carbon prices through global warming potential (GWP) values using a 100-year time horizon (GWP-100) 38,62 . Estimating stranded assets in the electric power sector We follow Ou et al. 22 to evaluate yearly capacity additions, premature retirements, and related capital expenditures in the electric power sector. In this process, GCAM tracks the capital stock, technological specifications, and vintage of electrical power plants throughout the entire lifespan of each technology. Two different mechanisms can bring about the retirement of electrical power plants. That is, power plants retiring naturally or profit-induced retirement. When power plants reach the end of their designed operational lifespan they retire naturally. On the other hand, a power plant can be pre-maturely retired when its continued operation becomes financially unviable, resulting in stranded assets and this falls under profit-induced retirement (as defined in Eq. 1 ). $$\:{G}_{T,V,r}\left(t\right)={G}_{T,V,r}\left(t-1\right)\times\:\left[1-{f}_{T,V,r}^{N}\left(t\right)\right]\times\:\left[1-{f}_{T,V,r}^{P}\left(t\right)\right]$$ 1 Where \(\:{G}_{T,V,r}\left(t\right)\) denotes electricity generation for technology \(\:T\) and vintage \(\:V\) in region \(\:r\) during modeling period \(\:t\) . \(\:{f}_{T,V,r}^{N}\left(t\right)\) and \(\:{f}_{T,V,r}^{P}\left(t\right)\) are the fraction of natural and profit-induced retirement in modeling period \(\:t\) . The Plutus package in R 63 was employed to analyze capacity and capital stock turnover in the power sector, following a three-stage process 22 . Under each scenario, the package extracts technology- and vintage-specific electricity generation from each country's output. Next, the natural retirement fraction \(\:{f}_{T,V,r}^{N}\left(t\right)\) for each new fleet's vintage is estimated. Finally, Plutus compares scenario electricity generation output with the projected natural retirement trajectory from Step 2 to determine premature retirement and stranded assets. Stranded assets are calculated by multiplying premature retirement by the corresponding technology's capital cost. Estimating committed emissions Our calculation of current committed emissions is based on currently operating power plants. In this context, committed emissions are those emissions that will continue to occur over the remaining operational life of a fossil fuel-based power generator 52 . Our calculation for committed emissions of existing coal, oil, and gas plants follows Eq. 2 48 where F represents CO 2 emissions measured in tCO 2 yr − 1 and it is expressed as the product of plant capacity (in GW) and utilization rate E/C (in %) and carbon intensity of electricity generated F/E (in g kWh − 1 ). E is the electricity output (GWh yr − 1 ). Constant utilization rates and emission factors are used in our calculations as shown in Table 2 . $$\:\varvec{F}=\varvec{C}\:\times\:\left(\frac{\varvec{E}}{\varvec{C}}\right)\:\times\:\:\frac{\varvec{F}}{\varvec{E}}$$ 2 The various capacities of existing coal, oil, and gas power plants are obtained from Global Energy Monitor ( https://globalenergymonitor.org/ ). Table 2 Global fossil fuel power generating capacity operating in 2023 Technology Capacity (GW) a Lifetime (Years) b Utilization rate (%) c Carbon intensity (g kWh − 1 ) b Coal 2153 37 61 930 Oil 1810 32 28 640 Gas 152 35 39 427 a : Obtained from Global energy monitor ( https://globalenergymonitor.org/ ) b : Obtained from González-Mahecha et al. 48 c : Obtained from Pfeiffer et al. 32 Declarations Competing Interest The authors declare no competing interests. Code and Data availability GCAM is an open-source community model available at https://github.com/JGCRI/gcam-core/releases . The version of GCAM and other input files used in this work will be made available in a public GitHub repository upon publication. Supplementary information Supplementary information contains additional information about study method and model. Author contributions G.M, P.Y, J.C, S.A, and H.Z., conceived and designed the research. S.A and J.D.A designed and developed the scenarios. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4761526","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329896316,"identity":"1f536081-b3e1-4118-beb2-65158e477c9a","order_by":0,"name":"Pingjian Yang","email":"data:image/png;base64,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","orcid":"","institution":"Chinese Research Academy of Environmental Sciences","correspondingAuthor":true,"prefix":"","firstName":"Pingjian","middleName":"","lastName":"Yang","suffix":""},{"id":329896317,"identity":"ce1807c0-9320-4f42-93a7-0f3f388736e1","order_by":1,"name":"SANDYLOVE AFRANE","email":"","orcid":"","institution":"TIANJIN UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"SANDYLOVE","middleName":"","lastName":"AFRANE","suffix":""},{"id":329896318,"identity":"a33362db-fcb6-48c2-b4fb-461853855066","order_by":2,"name":"Jeffrey Ampah","email":"","orcid":"https://orcid.org/0000-0002-0985-151X","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Ampah","suffix":""},{"id":329896319,"identity":"b631e6ab-99ac-40ce-baa5-66e8201b776a","order_by":3,"name":"HUMPHREY ADUN","email":"","orcid":"https://orcid.org/0000-0002-8593-814X","institution":"Near East University","correspondingAuthor":false,"prefix":"","firstName":"HUMPHREY","middleName":"","lastName":"ADUN","suffix":""},{"id":329896320,"identity":"b13d2823-57a5-42ed-8303-83707192b32a","order_by":4,"name":"Jianlin Chen","email":"","orcid":"","institution":"City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Chen","suffix":""},{"id":329896321,"identity":"bdd8f3b3-ac0c-4559-9bd1-7a236ccd150a","order_by":5,"name":"Hongyang Zou","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Hongyang","middleName":"","lastName":"Zou","suffix":""},{"id":329896322,"identity":"3b9a38db-20ba-48ac-adb4-d1b251c4526b","order_by":6,"name":"Guozhu Mao","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Guozhu","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2024-07-18 09:40:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4761526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4761526/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02190-8","type":"published","date":"2025-03-22T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64897075,"identity":"f8040f19-0bef-4929-b467-c143c8ce4463","added_by":"auto","created_at":"2024-09-20 07:15:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":575012,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Negative emissions by different CDR methods across modeled scenarios (b) Regional variations in the share of different CDR methods in 2050 under the most ambitious and optimistic CDR scenario (1.5℃_HIGH). Here, in panel b, our analysis focuses exclusively on the 1.5°C_HIGH scenario, as it represents the most extensive deployment of CDR technologies among all scenarios considered in this study. For a comprehensive overview of regional negative emissions in 2050 across all scenarios, see\u003cstrong\u003e supplementary figure S1\u003c/strong\u003e. \u003cstrong\u003eBECCS\u003c/strong\u003e; bioenergy with carbon capture and storage. Depending on the sector, BECCS technologies capture a pre-defined fraction of CO2 that would be emitted into the atmosphere while producing electricity, hydrogen, or refined liquids. Here, the CO\u003csub\u003e2\u003c/sub\u003e removed from the atmosphere by bioenergy crops during photosynthesis is not counted to avoid double counting. \u003cstrong\u003eEW\u003c/strong\u003e refers to enhanced weathering, and \u003cstrong\u003eDOCCS\u003c/strong\u003e refers to direct ocean capture and carbon storage. We deploy three different archetypes for direct air capture (\u003cstrong\u003eDAC\u003c/strong\u003e), i.e., electricity-consuming DAC (elec), natural gas-consuming DAC (NG), and low-temperature heatpump DAC. All DAC systems are coupled with carbon capture and storage (DACCS). \u003cstrong\u003eLUC\u003c/strong\u003e refers to land use change and it represents the difference between positive and negative emissions from land use. Australia and New Zealand are combined as one region and called Australia_NZ; FTA refers to Free Trade Association; South America is divided into 2 i.e., South America Northern (with suffix as N) and Southern America Southern (with suffix as S). Central America denotes countries in Central America and the Caribbean.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/c8d6618efbae29296f27fc1c.png"},{"id":64897079,"identity":"15e015ba-16fc-4733-86a4-abd57f3787d5","added_by":"auto","created_at":"2024-09-20 07:15:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":437163,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Electricity generation by fuel. Higher requirements for emission reduction as CDR availability reduces increases the rate of electricity generation. Scenarios with limited CDR availability such as LOW and MODERATE have significant increases in zero-carbon electricity generation. By 2050, unabated coal, biomass, and refined liquids electricity generation are almost completely phased out under 1.5℃_LOW. \u003cstrong\u003eCCS:\u003c/strong\u003e carbon capture and storage (b) Regional variation in BECCS-electricity. Regions with higher populations and economic growth such as China, the US, and India, have higher demands for electricity which affects the amount of electricity generated from BECCS compared to other regions (c) Regional share of BECCS in total electricity generation. The proportional contribution of BECCS to regional electricity generation is most pronounced in Mexico, India, and some parts of Central and Southern America. Here, panels b and c exclusively depict the 1.5°C_HIGH scenario, as this scenario shows the highest demand for BECCS and BECCS-derived electricity. It should be noted that regional BECCS-electricity generation patterns demonstrate modest variations across different scenarios. For a comparative analysis of alternative scenarios, see \u003cstrong\u003esupplementary figures S3 and S4\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/1aadb381a3769b94f44f1ac6.png"},{"id":64898404,"identity":"11382bc5-7067-4dce-973c-09e644b6c4af","added_by":"auto","created_at":"2024-09-20 07:31:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":479413,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Evolution of electricity price changes across different sectors and climate mitigation scenarios from 2025 to 2050. The graph employs a dual visualization approach: (1) Continuous lines depict the aggregate electricity price changes across all sectors for six distinct scenarios, differentiated by color coding. The scenarios consist of combinations of temperature targets (1.5°C and 2°C) and CDR assumptions (LOW, MODERATE, and HIGH). (2) Discrete markers, distinguished by shape, represent sector-specific electricity price changes for Building (circle), Electricity (diamond), Industry (triangle), and Transport (square) sectors. (b) Percentage change in electricity prices across regions by 2050 relative to 2020 levels. Coal-dependent countries/regions such as South Africa, India, and China face higher electricity price increases due to the extent of their energy transition compared to other countries/regions.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/d8ea34901eb174cb8f2d40f5.png"},{"id":64897078,"identity":"8921a133-29d9-4459-8d3c-61c7992a29d0","added_by":"auto","created_at":"2024-09-20 07:15:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":548489,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Electricity consumption by end-use sectors and CDR technologies. Higher demands in building and industry sectors are attributable to the relative ease and availability of options to decarbonize these sectors with electricity compared to transport which has major difficult-to-electrify sub-sectors such as aviation and shipping (b) Share of CDR in electricity consumption by region. The electricity demand for CDR is predominantly attributable to DACCS technology, with the highest demand observed under the 1.5℃_HIGH scenario. Notably, the regions with the highest DACCS deployment such as the US and China (\u003cstrong\u003esupplementary figure S11\u003c/strong\u003e) do not necessarily allocate disproportionately large shares of their electricity to CO\u003csub\u003e2\u003c/sub\u003e removal. South America and Australia appear more susceptible to higher relative electricity consumption for this purpose. The MODERATE CDR pathway under 2℃ has the lowest electricity demands for CO\u003csub\u003e2\u003c/sub\u003e removal across all regions, likely due to the relatively higher reliance on BECCS.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/1cbcba8ac9af0d91ed1fb24b.png"},{"id":64897076,"identity":"8e4b75b5-ed8a-40cf-a39c-9b3a49926ee4","added_by":"auto","created_at":"2024-09-20 07:15:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":879612,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Global capacity additions and (b) investment costs. The bars depict cumulative capacity additions and costs aggregated over five-year model intervals. Stringent requirements under 1.5℃_LOW and MODERATE scenarios necessitate increasing global capacity additions over the entire 30-year period, whereas the remaining scenarios reach peak additions in the middle of the modeled period. (c) Regional cumulative capacity additions and (d) investment costs over the 30-year period under the most ambitious climate target and optimistic CDR assumptions (1.5℃_HIGH). \u003cstrong\u003eCCS\u003c/strong\u003e: carbon capture and storage. CCS here includes both fossil fuel-based capacity that is equipped with CCS as well as bioenergy CCS.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/815f3a58ed399a24541b774d.png"},{"id":64897832,"identity":"4a9f4269-ad36-4c04-a5cb-3e8321ecc3ad","added_by":"auto","created_at":"2024-09-20 07:23:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":773818,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Global stranded capacity and (b) stranding costs. The bars depict cumulative stranded capacity and costs aggregated over five-year model intervals. The HIGH CDR pathway could save up to US$ 1.3 trillion in stranding costs over the next three decades compared to the LOW CDR scenario (c) Regional cumulative stranded assets and (d) stranding costs over the 30-year period under the most ambitious climate target and optimistic CDR assumptions (1.5℃_HIGH). Countries and regions with a higher dependence on coal will incur disproportionately higher stranding costs compared to those that rely more on gas and oil.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/93abce280c317f3706b5cb49.png"},{"id":64897077,"identity":"b99060d9-c0c1-49b6-b814-d127a0966bf2","added_by":"auto","created_at":"2024-09-20 07:15:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":449705,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Global committed emissions from existing power plants in a reference scenario (REF) compared to our modeled scenarios. Under the 1.5℃ pathway, committed emissions are projected to decrease by 221, 250, and 263 GtCO\u003csub\u003e2\u003c/sub\u003e in the HIGH, MODERATE, and LOW scenarios, respectively. Meanwhile, the 2℃ pathway sees reductions of 154, 169, and 195 GtCO\u003csub\u003e2\u003c/sub\u003e in the same CDR scenarios (b) Regional variations in committed emissions under the reference scenario (REF) compared to our modeled scenarios. Other Asia represents all Asian countries including Australia and New Zealand and excluding China and India. LAC represents Latin America and the Caribbean, i.e., South America, Central America, and countries in the Caribbean. The horizontal lines represent the fraction of committed emission reduction relative to the REFERENCE scenario for the HIGH (blue), MODERATE (red), and LOW (green) scenarios.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/141ff8dae3c89b6779154b7a.png"},{"id":79034135,"identity":"a462986a-b48a-4a72-a436-8768764f2da9","added_by":"auto","created_at":"2025-03-23 07:05:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5105235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/3a58c2e6-aeda-4d41-a220-ce58c30e3774.pdf"},{"id":64897083,"identity":"a39b0e05-f7d0-45a7-9843-f16200f143f0","added_by":"auto","created_at":"2024-09-20 07:15:33","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34782662,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4761526/v1/4f3bcc97424a8edadfd1cc1e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Quantifying the Transformational Requirements of the Electricity Sector Under Uncertain Expectations of Carbon Removal","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Paris Agreement aims to limit global warming to well below 2\u0026deg;C above pre-industrial levels, with an aspirational goal of limiting the increase to 1.5\u0026deg;C by 2100 \u003csup\u003e1,2\u003c/sup\u003e. Central to achieving these targets is the concept of the remaining carbon budget (RCB), which refers to the maximum amount of anthropogenic net carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) that can be emitted while still having a likely chance of limiting warming to these levels \u003csup\u003e3\u003c/sup\u003e. Current estimates indicate that the RCB will be exhausted within the next five years \u003csup\u003e4,5\u003c/sup\u003e, highlighting the urgent need for rapid and unprecedented decarbonization across all sectors. The electricity sector, in particular, plays a critical role in this transition. It is essential not only for decarbonizing other sectors reliant on fossil fuel-generated electricity but also for providing the green electricity needed to produce green hydrogen, which is crucial for the deep decarbonization of hard-to-abate sectors like heavy industry and aviation \u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the rapid decarbonization of the electricity sector could present a highly disruptive transition. Technically, the expansion of the grid to accommodate high shares of variable renewable energy \u003csup\u003e8\u003c/sup\u003e, the development of large-scale storage solutions to manage the variability of wind and solar power \u003csup\u003e9,10\u003c/sup\u003e, and the extraction of critical minerals required for renewable technologies \u003csup\u003e11,12\u003c/sup\u003e all pose significant transition challenges. Socio-economically, the shift away from fossil fuels could lead to substantial job losses in the fossil fuel industry \u003csup\u003e13,14\u003c/sup\u003e. In addition, there is also the issue of stranded assets. Stranded assets refer to investments in fossil fuel infrastructure that may become obsolete or uneconomical as the world transitions to cleaner energy sources \u003csup\u003e15\u003c/sup\u003e. According to the International Panel on Climate Change (IPCC), unburned fossil fuels and stranded fossil fuel infrastructure are projected to have a discounted global value of approximately USD 1\u0026ndash;4 trillion from 2015 to 2050 in scenarios that aim to limit global warming to around 2\u0026deg;C. The estimated value increases further for pathways targeting a 1.5\u0026deg;C warming limit \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven these challenges, a critical question arises: is it possible to achieve the Paris Agreement's climate targets without incurring the negative socio-economic and technical consequences associated with rapid electricity sector decarbonization? The answer may lie in carbon removal (CDR) technologies. CDR technologies will play a pivotal role in mitigating climate change by removing CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere and can offset emissions to achieve specific climate targets. Examples of these technologies include Direct Air Capture with Carbon Storage (DACCS), Bioenergy with Carbon Capture and Storage (BECCS), afforestation, biochar, and enhanced weathering \u003csup\u003e16\u003c/sup\u003e. These technologies can slow the rate of decarbonization needed in the short term by offsetting emissions later \u003csup\u003e17,18\u003c/sup\u003e, thus acting as backstop technologies to cut emissions in a cost-effective manner \u003csup\u003e19\u003c/sup\u003e. For instance, Ampah et al. \u003csup\u003e20\u003c/sup\u003e show that if the availability of future CDR could reach at least 8 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050 instead of 1 GtCO\u003csub\u003e2\u003c/sub\u003e/yr, the cost of mitigation to reach net zero by 2050 could be reduced from US\u003cspan\u003e$\u003c/span\u003e2700/tCO\u003csub\u003e2\u003c/sub\u003e to less than US\u003cspan\u003e$\u003c/span\u003e1500/tCO\u003csub\u003e2\u003c/sub\u003e. Similarly, Fuhrman et al. \u003csup\u003e17\u003c/sup\u003e reveal that China and the rest of the world could get to net zero by 2060 at a marginal cost of over US\u003cspan\u003e$\u003c/span\u003e800/tCO\u003csub\u003e2\u003c/sub\u003e in the absence of DAC but its availability would reduce this cost to between US\u003cspan\u003e$\u003c/span\u003e200\u0026ndash;400/tCO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eBased on these characteristics and roles, CDR technologies could address one of the major issues associated with rapid and deep decarbonization: stranded assets. Despite the potential of CDR to mitigate the premature retirement of existing power plants, quantitative studies that investigate the impact of these technologies on stranded assets in the power sector remain limited. Existing literature has primarily focused on quantifying the capacity and cost of stranded assets resulting from decarbonization efforts aligned with the Paris Agreement. Binsted et al. investigated the implications of the stranded assets in Latin America and the Caribbean\u0026rsquo;s climate goals under the Paris Agreement \u003csup\u003e15\u003c/sup\u003e. Similar studies have also been conducted elsewhere such as those from Iyer et al. \u003csup\u003e21\u003c/sup\u003e, Ou et al. \u003csup\u003e22\u003c/sup\u003e, Auger et al. \u003csup\u003e13\u003c/sup\u003e, Lu et al. \u003csup\u003e23\u003c/sup\u003e, von Dulong et al. \u003csup\u003e24\u003c/sup\u003e, Afrane et al. \u003csup\u003e25\u003c/sup\u003e among many others. While the existing studies on decarbonization and stranded assets in the electric power sector offer valuable contributions, the relationship between CDR technologies and asset stranding in the electricity sector requires similar attention. Moreover, the feasibility of achieving the necessary scale of CDR\u0026mdash;up to 10 gigatonnes (Gt) of CO\u003csub\u003e2\u003c/sub\u003e removal per year by mid-century \u003csup\u003e26\u003c/sup\u003e \u0026mdash;faces substantial technical, socio-economic, environmental, and political barriers \u003csup\u003e27,28\u003c/sup\u003e, necessitating a deeper examination of their role in the power sector transformation. To date, only Pradhan et al. \u003csup\u003e29\u003c/sup\u003e have made an initial attempt to quantify the relationship between CDR and stranded assets. Our study builds on their efforts in several ways.\u003c/p\u003e \u003cp\u003eFirstly, considering the uncertainties surrounding the future success of CDR, we deploy various future scales of novel CDR technologies, ranging from 1 to 10 GtCO\u003csub\u003e2\u003c/sub\u003e by 2050, to assess their impact on the electricity sector transformation and stranded assets. Secondly, while Pradhan et al. \u003csup\u003e29\u003c/sup\u003e limited their analysis to achieving a 2\u0026deg;C warming target, we evaluate CDR deployment consistent with both 1.5\u0026deg;C and 2\u0026deg;C targets to understand the differing roles of CDR in mitigating disruptive transformations in the electricity sector under these climate scenarios. Thirdly, most global analyses provide valuable insights for policymakers but often overlook regional differences and sectoral transformational requirements needed across different countries and regions. This study offers a global analysis while highlighting how different regions and countries respond to varying levels of CDR deployment in their electricity sectors. Furthermore, different countries may prioritize different types of CDR technologies based on their unique resources. Countries with abundant land and biomass may favor BECCS and biochar, while those with suitable geological sites but limited land may prefer DACCS \u003csup\u003e20\u003c/sup\u003e. Recent studies have suggested that co-deploying various CDR types may have fewer negative impacts compared to relying on a single technology \u003csup\u003e30,31\u003c/sup\u003e. While Pradhan et al. \u003csup\u003e29\u003c/sup\u003e focused solely on the role of DACCS, we examine six different CDR types\u0026mdash;afforestation/reforestation (AR), BECCS, biochar, enhanced rock weathering (ERW), DACCS, and direct ocean capture and carbon storage (DOCCS)\u0026mdash;to assess how their full availability might affect global and regional electricity sector transformations compared to scenarios with fewer available options. Lastly, we consider the concept of committed emissions, which refer to the future CO\u003csub\u003e2\u003c/sub\u003e emissions associated with existing and planned power plants \u003csup\u003e32\u003c/sup\u003e. While CDR technologies could reduce the need for aggressive electricity sector transformations and mitigate stranded assets, they might also allow for higher committed emissions from power plants than if these technologies were absent. We explore this trade-off of CDR technologies by assessing the committed emissions tied to different CDR success rates under various climate targets, providing a comprehensive view of their potential impact on the power sector. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a general summary of the scenarios explored in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScenario description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal net CO\u003csub\u003e2\u003c/sub\u003e trajectory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDR options\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDR amount\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5 C_HIGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.5\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePeak before 2025 and linearly decline to net zero by mid-century. Annual net negative emission of 9 GtCO\u003csub\u003e2\u003c/sub\u003e/yr from 2055 to 2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBECCS, DACCS, DOCCS, Biochar, ERW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDRs reach 10 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5 C _MODERATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBECCS and DACCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDRs reach 5 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5 C_LOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDACCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDR reaches 1 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 C_HIGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePeak before 2025 and linearly decline to net zero before 2070. Annual net negative emission of 4 GtCO\u003csub\u003e2\u003c/sub\u003e/yr from 2070 to 2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBECCS, DACCS, DOCCS, Biochar, ERW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDRs reach 4 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 C_MODERATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBECCS and DACCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDRs reach 2.5 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 C_LOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDACCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovel CDR reaches less than 1 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom a modeling perspective, our approach to modeling CDR uncertainty differs from those commonly seen in existing IAM studies. Unlike most previous studies where CDR is endogenously deployed \u003csup\u003e18,33,34\u003c/sup\u003e, we explicitly set caps on the amount of negative emissions available for deployment in each modeling period. The problem with endogenously deployed CDR is that it offers a cost-effective approach to reaching climate targets by lowering the carbon price \u003csup\u003e35\u003c/sup\u003e, which in turn reduces the pace of emission reductions. This allows residual emissions to be offset later at a cheaper mitigation cost \u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. This approach can lead to the perpetuation of fossil fuels and higher levels of residual emissions \u003csup\u003e20\u003c/sup\u003e, causing the 1.5\u0026deg;C carbon budget to be overshot for several decades before returning to safer levels towards the end of the century. Our approach avoids this situation by forcing emission reductions and removals to be pursued separately without one undermining the other as advocated in recent studies \u003csup\u003e36,37\u003c/sup\u003e. This method allows us to assess the implications of varying CDR levels on power sector transformation under different climate targets.\u003c/p\u003e \u003cp\u003eBy addressing these critical aspects, we aim to offer new insights into the understanding of how CDR technologies can facilitate the global power sector's transformation while mitigating the socio-economic and technical challenges associated with rapid decarbonization.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCDR deployment\u003c/h2\u003e \u003cp\u003eUnderstanding the complex interaction between CDR pathways and the global electric power system requires examining the roles and contributions of various CDR approaches in achieving climate mitigation goals. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea illustrates the development and distribution of CDR methods across different modeled scenarios. LUC plays a significant role, especially in the LOW scenario, which relies solely on LUC for negative emissions until the last decade (2040\u0026ndash;2050) when DACCS emerges. In scenarios with a broader portfolio of novel CDR approaches, such as the HIGH scenario, the contribution of LUC diminishes considerably by 2050, replaced by BECCS and DACCS as these technologies become more mature and cost-effective \u003csup\u003e38\u003c/sup\u003e. Although DACCS deployment is relatively delayed, it becomes a principal CDR approach under the 1.5\u0026deg;C pathway, with gross removals reaching 3 to 3.3 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050 in the MODERATE and HIGH scenarios, respectively. The 2\u0026deg;C pathway's less stringent requirements may allow for a further delay in DACCS deployment, likely towards the end of the century, providing time for technological advancements and cost reductions. Due to cost-effectiveness, the modeling assumes that high-temperature electric heating DACCS systems are deployed at a much lower scale compared to natural gas-based systems across all scenarios. Despite the limited deployment of the fully electric system, its energy-intensive nature poses major implications for electricity demands. BECCS deployment is expected to begin relatively early, around 2030. Under the HIGH scenario for both 1.5\u0026deg;C and 2\u0026deg;C pathways, BECCS would become the predominant source of negative emissions by 2050, delivering 4.6 and 2.9 GtCO\u003csub\u003e2\u003c/sub\u003e/yr, respectively. The 1.5\u0026deg;C and 2\u0026deg;C pathways show notable differences in BECCS deployment across sectors. In the 1.5\u0026deg;C pathway, BECCS-electricity dominates over BECCS-liquids, reflecting a prioritization of deep decarbonization in the power sector and an emphasis on electrification as a key mitigation strategy. Conversely, the 2\u0026deg;C pathway has a higher share of BECCS-liquids relative to BECCS-electricity, indicating a reduced emphasis on electrification and sector coupling. While BECCS and DACCS are expected to be the most widely deployed forms of novel CDR, enhanced weathering and biochar also play significant roles in providing negative emissions under the HIGH scenario. In the 1.5\u0026deg;C pathway, gross removals from enhanced weathering and biochar could reach 1.8 and 0.5 GtCO\u003csub\u003e2\u003c/sub\u003e/yr by 2050, respectively.\u003c/p\u003e \u003cp\u003eThe regional variations in the distribution and contribution of various CDR approaches are mainly driven by resource availability, domestic climate targets, and policy incentives. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb shows the percent share of various CDR approaches in each country/region by 2050 under the most ambitious and optimistic CDR scenario (1.5 C_HIGH). Canada, Russia, Eastern Europe, and Southern/Western Africa will continue to rely heavily on LUC by 2050, with minor contributions from novel CDR technologies. The US, Australia/New Zealand, and several parts of South and Central America are expected to lean towards DACCS, while BECCS is anticipated to be the dominant source of negative emissions in several regions across Asia, Europe, and Eastern/Northern Africa. Enhanced weathering will play a major role in contributing to negative emissions in South and Southeast Asia, and biochar deployment will make significant contributions in India, Indonesia, and many parts of Africa due to favorable climatic conditions \u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTransformations in the global electricity system\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eElectricity generation\u003c/h2\u003e \u003cp\u003eThe interactions between CDR pathways and the electricity sector will determine how the scale of CO₂ removal impacts the sector\u0026rsquo;s composition (in terms of electricity production) and the broader decarbonization effort. While most CDR approaches typically increase electricity demand, BECCS offers a way to remove CO₂ while also contributing to energy supply including electricity. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows the transformation in global electricity supply under varying CDR scenarios for the 1.5℃ and 2℃ climate pathways. With minimal CDR deployment and no BECCS contribution, the LOW scenario requires the highest level of electricity generation, predominantly from renewables and nuclear energy. Under the 1.5℃ pathway, the LOW scenario sees an almost complete phase-out of unabated fossil fuels by 2050, with renewables and nuclear energy increasing nearly seven-fold and substantial deployment of long-duration energy storage, such as hydrogen, for renewable energy balancing. The MODERATE and HIGH scenarios offer greater flexibility in transforming the electricity mix by allowing continued use of fossil fuels, particularly gas (with and without CCS). In these scenarios, BECCS helps share the emissions reduction burden, enabling the electricity system to leverage gas as a 'transition fuel' due to its cost-effectiveness and lower emissions intensity compared to coal and oil \u003csup\u003e39\u003c/sup\u003e. Our results indicate a 47%-56% increase in electricity generation from gas (mainly with CCS) by 2050 relative to 2020 under the MODERATE and HIGH scenarios. Electricity generation from BECCS is projected to account for 2%-4% of the total generation in 2050. The integration of BECCS and overall greater CDR deployment could displace generation from nuclear and renewables by 5%-15% compared to the LOW scenario due to cost-effectiveness.\u003c/p\u003e \u003cp\u003eThe significant potential role of BECCS in meeting future electricity demands while contributing to negative emissions carries important regional implications. Under the most ambitious climate target and optimistic CDR assumptions, China, the US, and India are projected to lead in electricity generation from BECCS by 2050, with outputs ranging from 1.0 to 2.2 EJ/yr (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). When considering BECCS' proportional contribution to regional electricity generation, India is expected to maintain a significant reliance on this technology, alongside Mexico and some parts of Central and Southern America. In these regions, BECCS is projected to account for 5%-10% of total electricity generation by 2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This highlights BECCS's potential importance in their decarbonization strategies, driven by factors such as biomass resource availability, existing bioenergy infrastructure, and policy support. However, large-scale BECCS deployment may pose trade-offs and sustainability challenges surrounding issues such as land-use competition, biodiversity impacts, and water resource management for these regions \u003csup\u003e40\u0026ndash;42\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eElectricity prices\u003c/h2\u003e \u003cp\u003eThe rapid energy transition is projected to increase electricity prices due to the higher initial costs of integrating renewable energy sources and the anticipated rise in carbon pricing mechanisms, which make fossil fuel-based energy generation more expensive. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows the impact of various CDR deployment levels on global electricity price changes under 1.5\u0026deg;C and 2\u0026deg;C pathways. Achieving the 1.5\u0026deg;C target with limited CDR necessitates a swifter and more profound decarbonization of the power sector, resulting in higher electricity prices for consumers. The 1.5\u0026deg;C scenarios generally show higher initial price increases compared to 2\u0026deg;C scenarios. The 1.5\u0026deg;C_LOW scenario, which anticipates the most abrupt energy transition, shows the most significant price increase by 2050, diverging sharply from other scenarios. The 1.5\u0026deg;C_HIGH scenario exhibits the most dramatic price changes, starting with a relatively high price increase in 2025 and ending with the most significant price decrease in 2050. This trajectory suggests that aggressive climate mitigation strategies required for the 1.5\u0026deg;C target may incur higher near-term costs. However, the anticipated mid-century deployment of CDR technologies could potentially lead to significant long-term reductions in electricity prices. The electricity supply sector consistently shows higher price increases across all scenarios compared to the demand sectors, particularly in the early years. Among the demand sectors, the building and transport sectors generally exhibit lower price changes compared to the industry sector.\u003c/p\u003e \u003cp\u003eRegional differences in current electricity mixes, resource potential, technological capacities, and transition requirements manifest as divergent regional price impacts, particularly in scenarios pushing the boundaries of rapid change or limited technology availability. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the percent change in electricity prices by 2050 relative to 2020 across different countries/regions. The 1.5\u0026deg;C_LOW scenario, which requires the most rapid and aggressive shift to renewable energy deployment, sees some of the highest percentage increases in electricity prices. South Africa and India, in particular, experience a 35\u0026ndash;45% increase in electricity prices under this scenario, primarily due to the high costs associated with rapidly phasing out their coal-heavy power system. The US alongside several countries in Asia and Europe also face high price increases of 20%-30% under the 1.5℃_LOW scenario. The HIGH scenarios (both 1.5\u0026deg;C and 2\u0026deg;C), with greater CDR availability, help mitigate price shocks across various countries/regions. Canada, the Middle East, and some parts of South America and Africa are expected to witness decreased electricity prices of about 5%-20% under these optimistic scenarios.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConsumption in final energy\u003c/h2\u003e \u003cp\u003eOn the demand side, the deployment of CDR technologies could significantly impact the electrification of end-use sectors as a mitigation strategy across different climate pathways, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea. The LOW scenario sees the highest level of electrification in the three end-use sectors. Achieving the 1.5\u0026deg;C climate target with limited CDR deployment to offset emissions from the industry and transport sectors necessitates increased electrification in these sectors by about 28% and 16%, respectively, compared to the scenario with full deployment of CDR options. Most CDR approaches such as DACCS will inevitably interact with the electricity system primarily as consumers of energy, highlighting that while these technologies offer potential solutions for mitigating climate change, they also introduce additional energy demands that must be met. As CDR technologies become more widely deployed towards mid-century, electricity consumption for CDR processes is projected to increase considerably, reaching approximately 1\u0026ndash;9 EJ by 2050. Under the 1.5\u0026deg;C pathway, electricity consumption for CDR typically increases with higher CDR deployment. However, for the 2\u0026deg;C pathway, the LOW scenario exhibits higher electricity consumption for CDR compared to the MODERATE and HIGH scenarios. This is likely because the 2\u0026deg;C pathway minimizes reliance on energy-intensive approaches like DACCS when other CDR options are available. In the MODERATE scenario, DACCS is projected to contribute minimally to negative emissions, resulting in nearly non-existent electricity consumption for CDR by 2050. In contrast, in the LOW scenario, DACCS remains the only deployable novel CDR technology, making some level of electricity consumption inevitable.\u003c/p\u003e \u003cp\u003eThe large-scale deployment of CDR technologies needed to meet climate targets at national and regional levels will significantly impact electricity consumption in various countries and regions. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb shows the percentage of electricity consumption for CDR in each country/region\u0026rsquo;s final energy use across modeled scenarios. In scenarios relying heavily on energy-intensive CDR approaches (1.5℃_MODERATE and HIGH), several parts of South America and Australia/New Zealand will see a significant portion (about 10%-20%) of their electricity consumption dedicated to CDR processes. These regions may be well-positioned to utilize their abundant renewable energy resources to support CDR deployment while minimizing additional emissions \u003csup\u003e43,44\u003c/sup\u003e. Meanwhile, the 2\u0026deg;C_MODERATE pathway, which assumes minimal overall CDR electricity consumption, sees zero electricity consumption for CDR in several countries across Africa, Europe, and Southern Asia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCapacity additions and investment costs\u003c/h2\u003e \u003cp\u003eTo drive down the power sector's emissions towards zero levels by mid-century, any new generation capacity added globally over the coming decades must be dominated by low or zero-carbon technologies. Fossil fuel-based additions, unless fitted with CCS, would create long-lived, emissions-intensive capital stocks incompatible with long-term climate goals \u003csup\u003e32\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea shows how varying levels of CDR deployment can influence capacity additions in the global electricity sector under the 1.5\u0026deg;C and 2\u0026deg;C climate targets. In the 1.5\u0026deg;C pathway, the CDR-constrained scenarios (LOW and MODERATE) necessitate continuous capacity expansion throughout the modeled period to offset potential stranded assets and accommodate the inflexibility and higher reserve margins demanded by stringent mitigation efforts. Meanwhile, the HIGH scenario under 1.5℃, along with all CDR scenarios under the 2\u0026deg;C pathway, reaches peak capacity additions between 2036 and 2040. Decarbonizing electricity supply while meeting projected global demand under the LOW scenario requires cumulative capacity additions which are about 5%-10% higher than the MODERATE scenario and 7%-22% above the HIGH scenario. Capacity additions for unabated coal will cease completely after 2035 under LOW and after 2040 under MODERATE and HIGH in the 1.5\u0026deg;C pathway. The deployment of CCS technology for retrofitting existing fossil fuel plants begins steadily after 2025 under all scenarios, reaching a cumulative capacity of 1450\u0026ndash;2800 GW by 2050. Wind and solar capacity additions are projected to increase substantially across all scenarios, accounting for 64\u0026ndash;66% of total capacity additions over the modeled period. Additions of nuclear capacity will account for 12\u0026ndash;15% of the total. Cumulative BECCS capacity under the MODERATE and HIGH scenarios could reach 160\u0026ndash;380 GW, offsetting some of the need for renewables and nuclear capacity relative to the BECCS-exclusive LOW scenario.\u003c/p\u003e \u003cp\u003eThe transition towards zero or low-carbon power infrastructure hinges not only on technological advancement but also on the ability to mobilize and channel investments at an unprecedented scale. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb quantifies the capital requirements for deploying new capacity under modeled scenarios. We find that the full deployment of CDR options could reduce cumulative investment costs by up to 10% and 18% compared to the MODERATE and LOW scenarios, respectively. Investment in solar and wind capacity is projected to reach a cumulative of US\u003cspan\u003e$\u003c/span\u003e 17\u0026ndash;25 trillion by 2050, representing 45\u0026ndash;50% of total investment costs. Nuclear and BECCS, being more expensive, will have disproportionately higher shares of investment costs relative to their shares in capacity additions. The HIGH scenario may require up to US\u003cspan\u003e$\u003c/span\u003e 1.3 trillion more investments in BECCS compared to the MODERATE scenario.\u003c/p\u003e \u003cp\u003eThe regional variations in cumulative capacity additions and related costs under the most ambitious climate target and optimistic CDR assumptions are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec \u003cb\u003eand d\u003c/b\u003e. China's extensive energy demand and the need to decarbonize its emissions-intensive power sector significantly amplify its future capacity needs. China's cumulative capacity additions for renewables and nuclear will account for approximately 23% of the world\u0026rsquo;s total. This translates into an investment cost of about US\u003cspan\u003e$\u003c/span\u003e 7.5 trillion, roughly equivalent to 42% of the country\u0026rsquo;s gross domestic product (GDP) in 2022 \u003csup\u003e45\u003c/sup\u003e. The US, India, and Europe also see substantial investments in renewables and nuclear power, cumulating to US\u003cspan\u003e$\u003c/span\u003e 3-3.5 trillion. China leads again in investments in bioenergy and CCS technologies, followed by India, the US, and the Middle East. Driven primarily by its biomass resource availability \u003csup\u003e46,47\u003c/sup\u003e, India's projected capacity investments significantly favor BECCS, potentially accounting for approximately 25% of its total investment costs. Meanwhile, the Middle East's sustained reliance on gas and oil, even in the long term, necessitates substantial investments in fossil CCS technologies, projected to account for about 40% of the region\u0026rsquo;s total capacity investments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStranded capacity and costs\u003c/h2\u003e \u003cp\u003eClimate policies aimed at deep decarbonization pose a significant risk of rendering high-emission power sector assets, such as fossil fuel-based plants, economically unprofitable or obsolete before the end of their natural operational lifetimes \u003csup\u003e48,49\u003c/sup\u003e. The impact of varying levels of CDR deployment on asset stranding in the electric power sector under the 1.5\u0026deg;C and 2\u0026deg;C climate pathways is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea. Our modeled scenarios project about 1140\u0026ndash;2200 GW of existing power plants becoming stranded from 2016 to 2050. Expanding CDR deployment helps mitigate the overall magnitude and pace of the required premature retirements, particularly under the 1.5\u0026deg;C pathway. Specifically, the HIGH scenario reduces stranded asset capacity by up to 15% and 25% compared to the MODERATE and LOW scenarios, respectively. Conventional coal-fired power plants account for approximately 55%-70% of the total stranded capacity across modeled scenarios. In absolute terms, the LOW scenario sees the highest stranding of coal assets with about 40 and 130 GW more capacity stranding relative to the MODERATE and HIGH scenarios under the 1.5\u0026deg;C pathway, respectively. Interestingly, the HIGH scenario projects a larger share of coal in its total stranded capacity compared to the LOW and MODERATE scenarios. This is because the share of gas in total stranded capacity is significantly reduced under the HIGH scenario which enables the continued use gas as a \u0026ldquo;transition fuel\u0026rdquo;. In comparison, the LOW and MODERATE scenarios, particularly under 1.5\u0026deg;C, require earlier stranding of all emission-intensive assets including gas-fired plants. The MODERATE and HIGH scenarios foresee earlier stranding of conventional bioenergy assets due to the potential for retrofitting these plants with CCS technology. Transitioning conventional bioenergy plants to BECCS facilities aligns these assets with stringent climate mitigation goals, incentivizing the early stranding of these plants to facilitate their conversion.\u003c/p\u003e \u003cp\u003eThe premature retirement of conventional power plants from 2016 to 2050 could result in cumulative stranding costs of about US\u003cspan\u003e$\u003c/span\u003e3.6 to 6.8 trillion across modeled scenarios. The HIGH scenario project approximately 5%-10% and 17%-20% lower stranding costs than the MODERATE and LOW scenarios, respectively. Notably, the share of stranding costs for coal power plants is disproportionately larger relative to their share of stranded capacity. This could be attributed to the significantly higher capital expenditures associated with coal power plants and their slower depreciation rates, which stem from their longer assumed operational lifespans \u003csup\u003e50\u003c/sup\u003e. This observation highlights significant economic challenges associated with the potential phase-out of conventional coal power plants compared to gas and oil in the context of deep decarbonization.\u003c/p\u003e \u003cp\u003eAs countries and regions adopt increasingly ambitious climate policies, such as carbon pricing mechanisms, emissions performance standards, or outright phase-out plans, the economic and regulatory environment for emission-intensive power generation could become highly unfavorable. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb \u003cb\u003eand c\u003c/b\u003e illustrate the regional variations in potential stranded assets and associated costs by 2050 under the 1.5\u0026deg;C_HIGH scenario. China faces the highest levels of prematurely retired assets globally, driven by the need to decarbonize its coal-heavy power sector. China's projected premature retirements reach a cumulative total of 480 GW by 2050, resulting in stranding costs of about US\u003cspan\u003e$\u003c/span\u003e 1.9 trillion. Existing coal assets account for 98% of the total stranded capacity, equivalent to approximately 40% of the country\u0026rsquo;s current coal capacity \u003csup\u003e51\u003c/sup\u003e. The US and India rank as the second and third countries most at risk for stranded assets, respectively with nearly identical cumulative stranded capacity. However, India's stranding costs are about 35% higher than those of the US. This discrepancy arises from the differing composition of their stranded assets: in India, about 95% of the total stranded capacity is due to coal assets, whereas in the US, coal accounts for only 55% of the total stranded capacity. Since coal has much higher associated stranding costs compared to gas and oil, India's total stranding costs are substantially greater than those of the US, despite the US having a slightly higher overall stranded capacity. Other countries, such as South Africa and Indonesia, would also experience high potential stranding costs due to their coal-heavy power sectors. In contrast, Russia and the Middle East would see disproportionately lower stranding costs, primarily from gas and oil assets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCommitted emissions\u003c/h2\u003e \u003cp\u003eThe continued operation of fossil fuel-based power plants over their typical lifetimes would result in a substantial amount of committed emissions \u003csup\u003e52\u003c/sup\u003e, posing challenges to both near- and long-term climate goals. By accounting for the expected lifetimes and utilization rates of existing fossil fuel-based power plants, we estimate future \"locked-in\" emissions if these assets operate as intended (REFERENCE/REF), comparing them with modeled emissions under various CDR deployment scenarios consistent with the 1.5\u0026deg;C and 2\u0026deg;C climate pathways. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea shows that the continued operation of existing power plants over their full remaining lifetimes would result in approximately 495 GtCO\u003csub\u003e2\u003c/sub\u003e in the coming decades, with about 75% of these emissions attributed to coal assets. For a 50% likelihood of limiting global warming to 2\u0026deg;C \u003csup\u003e53,54\u003c/sup\u003e, these committed emissions would consume approximately 43% of the remaining carbon budget and nearly exhaust the budget for the 1.5\u0026deg;C-consistent scenario. The premature retirement or retrofitting of existing infrastructure before the end of its expected lifetime could mitigate committed emissions linked to these assets and help align the power sector with decarbonization targets. Our modeled results indicate that the 1.5\u0026deg;C and 2\u0026deg;C pathways require up to 55% and 40% reductions in committed emissions, respectively relative to the REF scenario.\u003c/p\u003e \u003cp\u003eThe prospect of large-scale CDR deployment by mid-century could potentially offset committed emissions from existing long-lived assets, reducing the immediate pressure for aggressive premature retirements to meet climate targets. Consequently, scenarios with multi-gigatonnes expectations of CDR enable longer operational lifetimes for emissions-intensive assets, resulting in higher committed emissions compared to scenarios with limited CDR deployment. Under our modeled 2\u0026deg;C pathway, the LOW scenario requires reducing committed emissions by 8% and 12% relative to the MODERATE and HIGH scenarios, respectively. For the 1.5\u0026deg;C pathway, mitigation efforts with LOW CDR deployment necessitate reducing committed emissions by 5% and 15% compared to the MODERATE and HIGH scenarios, respectively. Across all modeled scenarios, the majority of potential emissions reductions are projected to come from the premature retirements of coal assets, with reductions of 35\u0026ndash;55% compared to the REF scenario. This is attributable to the high emissions intensity of coal compared to natural gas and oil, as well as the significant share of coal in the existing global power generation mix.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb illustrates the committed emissions estimated for various countries/regions under REF compared to the modeled scenarios. China leads in committed emissions from operational power plants at 216 GtCO\u003csub\u003e2\u003c/sub\u003e, accounting for approximately 40% of the global total. Given that about 97% of China's commitments originate from coal, achieving climate goals will require reductions of approximately 60\u0026ndash;100 GtCO\u003csub\u003e2\u003c/sub\u003e through extensive stranding of coal assets. The US, Europe, and India collectively account for one-third of global committed emissions. Despite India having the lowest committed emissions among these three regions, it requires the most significant reductions across modeled scenarios, particularly under the most ambitious 1.5\u0026deg;C_LOW scenario. India's higher proportion of emissions from coal generators, combined with its limited potential for CDR deployment under the LOW (no BECCS) scenario, necessitates an aggressive approach to stranding existing assets to meet stringent mitigation targets. The Middle East, Africa, and Latin America and the Caribbean (LAC) contribute the least to global committed emissions. In the Middle East and LAC, natural gas predominates, accounting for roughly one-quarter of global committed emissions from gas-fired generators. Our modeled projections for the Middle East suggest the most modest reductions in committed emissions across all regions, likely due to its minimal reliance on coal-fired power generation and the associated committed emissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive global analysis of how varying levels of CDR deployment could impact the transformation of the electric power sector under 1.5\u0026deg;C and 2\u0026deg;C climate pathways. Our findings indicate that most CDR approaches typically interact with the electricity system as energy consumers, potentially accounting for 1%-4% of global electricity demand by 2050. In contrast, BECCS offers a way to remove CO\u003csub\u003e2\u003c/sub\u003e while contributing approximately 2%-4% to electricity supply. Limiting novel CDR deployment to about 1 GtCO\u003csub\u003e2\u003c/sub\u003e by 2050, without BECCS, necessitates up to \u003cspan\u003e$\u003c/span\u003e9 trillion more investment in renewables and nuclear compared to a pathway with up to 10 GtCO\u003csub\u003e2\u003c/sub\u003e of CDR from a diverse portfolio, including BECCS. The aggressive scaling of renewables and nuclear under the LOW CDR scenario results in higher electricity prices for consumers, especially under the 1.5\u0026deg;C climate pathway.\u003c/p\u003e \u003cp\u003eMore importantly, our findings indicate that large-scale CDR deployment can alleviate some socio-economic and technical challenges of rapid decarbonization by reducing stranded assets in the power sector. The HIGH CDR scenario could decrease stranded asset capacity by up to 25% compared to the LOW scenario, saving approximately half a trillion US dollars in stranding costs. However, this benefit comes with a trade-off: greater CDR availability extends the operational lifetimes of emissions-intensive assets, increasing projected committed emissions from the power sector. The high CDR pathway could result in about 15% higher committed emissions under the 1.5\u0026deg;C target and 12% higher under the 2\u0026deg;C target, compared to the constrained CDR scenario. This raises important considerations for policymakers regarding the balance between relying on CDR deployment as an emission-offsetting strategy and prioritizing more aggressive premature retirements of emissions-intensive assets. While CDR technologies offer a potential solution for achieving climate targets more gradually, their feasibility at the necessary scale remains uncertain due to substantial technical, socio-economic, environmental, and political barriers \u003csup\u003e27,55\u0026ndash;57\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegionally, the impacts of varying CDR deployment levels are significant but diverse. In optimistic CDR scenarios (HIGH CDR), regions like Mexico, India, and some parts of Central and South America will see substantial shares of their electricity supply derived from BECCS. Conversely, regions such as Brazil, Argentina, and Australia/New Zealand will need to utilize their renewable energy resources to support a significant portion of their electricity consumption dedicated to CDR processes. Despite optimistic CDR deployment, the scale and speed of power sector transformation required under both climate targets remain substantial across all regions. Major economies like China, the U.S., and India face multi-trillion-dollar investment requirements, primarily in expanding renewable and nuclear capacity. Paradoxically, these countries are most at risk for stranded assets, translating into stranding costs of up to US\u003cspan\u003e$\u003c/span\u003e0.5-2 trillion by mid-century, even under the most optimistic CDR assumptions. Generally, regions with higher coal dependence face significantly higher stranding costs compared to regions relying more on gas and oil, particularly under the HIGH scenario, which further delays gas stranding.\u003c/p\u003e \u003cp\u003eFuture research can enhance our understanding of the challenges and opportunities linked to the rapid decarbonization of the power sector required under the Paris Agreement and the integration of novel CDR technologies. Key research directions should include: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Developing integrated assessment models that capture the complex socio-economic impacts of power sector decarbonization, such as job displacement, economic development, and inequities among affected populations; and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Exploring policy frameworks, support mechanisms, and international cooperation strategies to facilitate a just and equitable transition, particularly for the Global South. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Quantifying the implications of varying degrees of government commitment to asset stranding, especially considering that countries in the Global South, which have recently acquired carbon-intensive assets, may be less willing to strand their assets than those in the Global North.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe primary objective of this study is to examine the impact of CDR reliance on the transformation of the electric power sector, with a particular focus on stranded assets and committed emissions linked to the technologies within this sector. To achieve this, we employed a modified version of the Global Change Assessment Model (GCAM) \u003csup\u003e30\u003c/sup\u003e. This enhanced model incorporates five novel CDR approaches, and it allows users to set separate and explicit targets for the amount of CO\u003csub\u003e2\u003c/sub\u003e to be removed at any given time \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel Description\u003c/h2\u003e \u003cp\u003eGCAM is one of the leading Integrated Assessment Models (IAMs) in climate change research. GCAM has been extensively used to investigate the interconnections between the global economy, energy systems, land use, and the environment \u003csup\u003e58\u003c/sup\u003e. It is a market equilibrium model with a global scope, operating from 1990 to 2100 in five-year time steps. GCAM can analyze how changes in factors such as population, income, or technology costs may impact crop production, energy demand, land use changes and water withdrawals \u003csup\u003e59\u003c/sup\u003e. It can also explore how variations in one region's energy demand influence energy, water, and land use in other regions. The core of GCAM represents the entire world but is constructed with varying levels of resolution for different systems. In the current release version, the energy-economy system is divided into 32 global regions, land use is subdivided into over 300 subregions, and water resources are tracked across 233 basins worldwide \u003csup\u003e59\u003c/sup\u003e. Over the past two decades, GCAM has been utilized in virtually every major climate, energy, and economics assessment, highlighting its significance in climate change mitigation research \u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor a detailed model description, including modeling equations and parameters, refer to the Supplementary Information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eScenario Formulation\u003c/h2\u003e \u003cp\u003eIn this study, we model three different levels of future CDR reliance under two climate targets. CDR technologies are broadly categorized into land-based (conventional) CDR, such as afforestation/reforestation (AR) and CCS-CDR, and novel (technological) CDR, which includes BECCS, DACCS, DOCCS, biochar, and enhanced rock weathering (EW). Existing studies indicate that current global CDR totals approximately 2 GtCO\u003csub\u003e2\u003c/sub\u003e/yr, with 99.9% derived from conventional CDR \u003csup\u003e60\u003c/sup\u003e. However, conventional CDR approaches face challenges related to additionality, permanence, and reversibility \u003csup\u003e27\u003c/sup\u003e. Consequently, novel CDR methods are expected to play a more significant role in offsetting residual emissions as they become more cost-effective and technologically mature \u003csup\u003e38\u003c/sup\u003e. The success of future negative emissions from novel CDR technologies remains uncertain from a technical standpoint \u003csup\u003e27,57\u003c/sup\u003e, and there are environmental and sustainability concerns associated with these methods \u003csup\u003e20,61\u003c/sup\u003e. Against this backdrop, we model three possible pathways for future CDR efforts.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHIGH CDR Scenario\u003c/b\u003e: In this scenario, we assume that near-term emissions reductions will be insufficient, necessitating large-scale CDR by the mid-term to counterbalance residual emissions, achieve net zero, and eventually sustain net negative emissions to return temperatures to safe levels after a temporary overshoot. This scenario deploys five novel CDR technologies without any annual removal limits.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMODERATE CDR Scenario\u003c/b\u003e: Here, we assume that challenges to the deployment of several novel CDR approach with lower Technology Readiness Levels (TRL) and mitigation potential will persist. The focus will be on BECCS and DACCS, the two most researched novel CDR methods, which have TRLs of about 6 and mitigation potentials of up to 11 GtCO\u003csub\u003e2\u003c/sub\u003e/yr and 40 GtCO\u003csub\u003e2\u003c/sub\u003e/yr, respectively \u003csup\u003e2\u003c/sup\u003e. To limit negative impacts, their combined deployment is capped at 5 GtCO\u003csub\u003e2\u003c/sub\u003e/yr of negative emissions by 2050.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLOW CDR Scenario\u003c/b\u003e: This scenario assumes that novel CDR technologies will not meet expectations, and the focus will shift to decarbonization. Due to sustainability issues such as land competition associated with BECCS \u003csup\u003e61\u003c/sup\u003e, the emphasis will be on DACCS, which is expected to provide not more than 1 GtCO\u003csub\u003e2\u003c/sub\u003e/yr of negative emissions by 2050. This will mainly offset residual emissions from recalcitrant sectors such as aviation, shipping, and agriculture. In all three scenarios, negative emissions from conventional CDR are deployed endogenously.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAll three CDR scenarios are modeled under two climate pathways: 1.5\u0026deg;C (high overshoot) and 2\u0026deg;C. These pathways align with scenarios assessed by the IPCC in its Sixth Assessment Report (AR6). According to these scenarios, the 5th-95th percentile range for the net zero year for 1.5\u0026deg;C (high overshoot) is between 2045 and 2070 (with a 50th percentile of 2055\u0026ndash;2060), while for 2\u0026deg;C, it is from 2055 onwards (with a 50th percentile of 2070\u0026ndash;2075). Additionally, for 1.5\u0026deg;C (high overshoot), the 5th-95th percentile for total net CO\u003csub\u003e2\u003c/sub\u003e emissions from 2020 to the year of net zero is 530\u0026ndash;930 GtCO\u003csub\u003e2\u003c/sub\u003e, and for 2\u0026deg;C, it is 640\u0026ndash;1160 GtCO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003e. Consistent with these existing pathways, we model a 1.5\u0026deg;C target where CO\u003csub\u003e2\u003c/sub\u003e emissions peak before 2025 and decline linearly to net zero before 2055, with a total carbon budget of 780 GtCO\u003csub\u003e2\u003c/sub\u003e. For the below 2\u0026deg;C target, CO\u003csub\u003e2\u003c/sub\u003e emissions also peak before 2025 but decline linearly to net zero before 2070, with a total carbon budget of 1095 GtCO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eIt is important to note that we do not directly constrain emissions from land use change; instead, we apply the same pricing to these emissions as we do to emissions from fossil fuels and industry. This method enhances the role of conventional CDR in offsetting residual emissions, particularly in scenarios where novel CDR methods do not achieve sufficient negative emissions. It also aids in reducing deforestation rates by enabling a quick shift from net positive land use change (LUC) emissions to net negative LUC emissions by as early as 2025. Furthermore, in all our modeled pathways, we do not directly constrain non-CO\u003csub\u003e2\u003c/sub\u003e greenhouse gas (GHG) emissions, such as methane, nitrous oxides, and fluorinated gases. Instead, the model reduces these emissions by applying marginal abatement cost (MAC) curves. The emission intensities of these non-CO\u003csub\u003e2\u003c/sub\u003e gases decrease as GHG prices rise, with these prices linked to carbon prices through global warming potential (GWP) values using a 100-year time horizon (GWP-100) \u003csup\u003e38,62\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEstimating stranded assets in the electric power sector\u003c/h2\u003e \u003cp\u003eWe follow Ou et al. \u003csup\u003e22\u003c/sup\u003e to evaluate yearly capacity additions, premature retirements, and related capital expenditures in the electric power sector. In this process, GCAM tracks the capital stock, technological specifications, and vintage of electrical power plants throughout the entire lifespan of each technology. Two different mechanisms can bring about the retirement of electrical power plants. That is, power plants retiring naturally or profit-induced retirement. When power plants reach the end of their designed operational lifespan they retire naturally. On the other hand, a power plant can be pre-maturely retired when its continued operation becomes financially unviable, resulting in stranded assets and this falls under profit-induced retirement (as defined in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{G}_{T,V,r}\\left(t\\right)={G}_{T,V,r}\\left(t-1\\right)\\times\\:\\left[1-{f}_{T,V,r}^{N}\\left(t\\right)\\right]\\times\\:\\left[1-{f}_{T,V,r}^{P}\\left(t\\right)\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{T,V,r}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes electricity generation for technology \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T\\)\u003c/span\u003e\u003c/span\u003e and vintage \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e in region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e during modeling period \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{T,V,r}^{N}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{T,V,r}^{P}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e are the fraction of natural and profit-induced retirement in modeling period \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Plutus package in R \u003csup\u003e63\u003c/sup\u003e was employed to analyze capacity and capital stock turnover in the power sector, following a three-stage process \u003csup\u003e22\u003c/sup\u003e. Under each scenario, the package extracts technology- and vintage-specific electricity generation from each country's output. Next, the natural retirement fraction \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{T,V,r}^{N}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e for each new fleet's vintage is estimated. Finally, Plutus compares scenario electricity generation output with the projected natural retirement trajectory from Step 2 to determine premature retirement and stranded assets. Stranded assets are calculated by multiplying premature retirement by the corresponding technology's capital cost.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEstimating committed emissions\u003c/h2\u003e \u003cp\u003eOur calculation of current committed emissions is based on currently operating power plants. In this context, committed emissions are those emissions that will continue to occur over the remaining operational life of a fossil fuel-based power generator \u003csup\u003e52\u003c/sup\u003e. Our calculation for committed emissions of existing coal, oil, and gas plants follows Eq.\u0026nbsp;2 \u003csup\u003e48\u003c/sup\u003e where F represents CO\u003csub\u003e2\u003c/sub\u003e emissions measured in tCO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and it is expressed as the product of plant capacity (in GW) and utilization rate E/C (in %) and carbon intensity of electricity generated F/E (in g kWh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). E is the electricity output (GWh yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Constant utilization rates and emission factors are used in our calculations as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{F}=\\varvec{C}\\:\\times\\:\\left(\\frac{\\varvec{E}}{\\varvec{C}}\\right)\\:\\times\\:\\:\\frac{\\varvec{F}}{\\varvec{E}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe various capacities of existing coal, oil, and gas power plants are obtained from Global Energy Monitor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://globalenergymonitor.org/\u003c/span\u003e\u003cspan address=\"https://globalenergymonitor.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlobal fossil fuel power generating capacity operating in 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapacity (GW)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifetime (Years)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUtilization rate (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCarbon intensity (g kWh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003ea\u003c/b\u003e: Obtained from Global energy monitor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://globalenergymonitor.org/\u003c/span\u003e\u003cspan address=\"https://globalenergymonitor.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eb\u003c/b\u003e: Obtained from Gonz\u0026aacute;lez-Mahecha et al. \u003csup\u003e48\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003ec\u003c/b\u003e: Obtained from Pfeiffer et al. \u003csup\u003e32\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCode and Data availability\u003c/h2\u003e \u003cp\u003eGCAM is an open-source community model available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/JGCRI/gcam-core/releases\u003c/span\u003e\u003cspan address=\"https://github.com/JGCRI/gcam-core/releases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The version of GCAM and other input files used in this work will be made available in a public GitHub repository upon publication.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSupplementary information\u003c/strong\u003e \u003cp\u003eSupplementary information contains additional information about study method and model.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eG.M, P.Y, J.C, S.A, and H.Z., conceived and designed the research. S.A and J.D.A designed and developed the scenarios. S.A. led the modeling and wrote the first draft of the paper. S.A, J.D.A and H.A co-led parts of the assessment and contributed to the analysis of data. G.M, J.C, and P.Y supervised the research. All authors provided feedback throughout the work and contributed to the writing of the article.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are grateful to the National Key R\u0026amp;D Program of China (2023YFE0113100), for supporting the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (Eds.)]. (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. ([P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. 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JOSS 6, 3212 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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-4761526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4761526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDecarbonization and carbon dioxide removal (CDR) are crucial for achieving global climate targets. The power sector is pivotal in this process, yet the role of CDR in deep decarbonization and its implications on the sector have been underexplored. Using a global multi-sector model, we quantify the effects of CDR on the transformation of the power sector under different CDR reliance levels by 2050 \u0026mdash; high (4\u0026ndash;10 Gt/yr), moderate (2.5-5 Gt/yr), and low (\u0026le;\u0026thinsp;1Gt/yr)\u0026mdash;aligned with 1.5\u0026deg;C and 2\u0026deg;C climate targets. We show that BECCS is essential for future electricity demands, particularly in Asia and Central America. High CDR pathways could require 10\u0026ndash;20% of electricity consumption in South America and Australia/New Zealand for carbon removal. Major economies like China, the US, and India face significant investment needs, risking stranded assets worth up to US\u003cspan\u003e$\u003c/span\u003e165\u0026ndash;225\u0026nbsp;billion by mid-century under low CDR compared to high CDR. Regions heavily dependent on coal, such as China and India, face greater stranding costs, while gas-dependent regions like the Middle East and Russia have relatively lower costs. Global mitigation efforts with limited CDR require a 15% reduction in committed emissions compared to high-CDR scenarios, with the most pronounced reduction of 65% anticipated for India.\u003c/p\u003e","manuscriptTitle":"Quantifying the Transformational Requirements of the Electricity Sector Under Uncertain Expectations of Carbon Removal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 07:15:28","doi":"10.21203/rs.3.rs-4761526/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"874a16ab-8877-490f-8862-e3be787a6b1f","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34918134,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":34918135,"name":"Scientific community and society/Energy and society"}],"tags":[],"updatedAt":"2025-03-23T07:05:45+00:00","versionOfRecord":{"articleIdentity":"rs-4761526","link":"https://doi.org/10.1038/s43247-025-02190-8","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-03-22 04:00:00","publishedOnDateReadable":"March 22nd, 2025"},"versionCreatedAt":"2024-09-20 07:15:28","video":"","vorDoi":"10.1038/s43247-025-02190-8","vorDoiUrl":"https://doi.org/10.1038/s43247-025-02190-8","workflowStages":[]},"version":"v1","identity":"rs-4761526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4761526","identity":"rs-4761526","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}