Energy system transformations for the phase-out of fossil fuels towards 1.5°C future

Energy system transformations for the phase-out of fossil fuels towards 1.5°C future | 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 Physical Sciences - Article Energy system transformations for the phase-out of fossil fuels towards 1.5°C future Shotaro Mori, Siddharth Joshi, Volker Krey, Ken Oshiro, Oliver Fricko, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5698098/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The final decision of the 2023 United Nations Conference of the Parties (COP28) called for transitioning away from fossil fuels, sparking a growing interest in the full phase-out of fossil fuels1. Integrated assessment and energy system models have outlined energy system transformation pathways to limit global warming to 1.5°C2,3. However, pathways towards a full phase-out of fossil fuels, which may require additional efforts beyond those for the 1.5°C goal, remain unclear4. Here, we employ two global energy system models to explore energy system transformations, and the challenges and opportunities related to attaining zero-fossil (ZF) energy systems. Our results showed that reaching a ZF goal by 2050 would accelerate direct and indirect electrification, involving 1.6–1.8-fold increases in total power generation compared to the cost-optimal 1.5°C pathways. This transformation would inevitably increase cumulative energy supply investments within this century by up to 30% and require the rapid scaling of technologies such as solar and wind power, as well as electrolysers in the near term. Despite opportunities including reduced climate impacts and lower reliance on carbon dioxide removal from the energy and land use sectors, these challenges imply that international society must approach the transition towards ZF energy systems with strong determination. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Scientific community and society/Social sciences/Climate change/Climate-change mitigation Scientific community and society/Energy and society/Energy policy Scientific community and society/Energy and society/Energy supply and demand Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Limiting global warming to well below 2°C, and pursuing efforts to limit it to 1.5°C by the end of the century in line with the Paris Agreement, requires a rapid reduction of fossil fuel contributions to the energy system 5,6 . The final decision of the first “global stocktake” at the 2023 United Nations Conference of the Parties (COP28) highlighted the necessity for urgent action to address the climate crisis, given that the Parties are not on track to achieve the long-term goals of the Paris Agreement 1 . A key achievement of the global stocktake decision was that it called for all Parties to contribute to global efforts, particularly transitioning away from fossil fuels in energy systems. Although the language of the final decision has been described as weaker than a full phase-out, momentum is growing for the phase-out of fossil fuels to become a key focus of climate policies moving forward from COP28. The decarbonisation and defossilisation of energy systems could follow different energy transformation pathways. Scenarios from integrated assessment models (IAMs) and energy system models have provided insights into energy transformation pathways in line with the long-term goals of the Paris Agreement 2,3,7,8 , as reflected in assessment reports by the Intergovernmental Panel on Climate Change (IPCC), e.g. the Sixth Assessment Report (IPCC AR6) 9 . To obtain robust insights from previous studies, thousands of scenarios were submitted to the IPCC AR6 scenario database for analysis by IPCC Working Group III 10 . These scenario ensembles suggested that cost-optimal decarbonisation pathways involve replacing fossil fuel consumption primarily through direct electrification with renewable electricity, while also addressing CO 2 emissions from hard-to-abate fossil fuel consumption through incorporating carbon capture and storage (CCS) and offsetting via carbon dioxide removal (CDR) 4 . However, achieving what could be called a zero-fossil (ZF) energy system, beyond transitioning away from and phasing out fossil fuels, will likely require pathways that depart from these typical decarbonisation pathways. Such differences are likely to arise because ZF energy systems require the elimination of even hard-to-abate fossil fuel consumption, which would remain in a typical 1.5°C energy system 11,12 . Based on the outcomes of previous studies that focused on reducing residual fossil CO 2 emissions considering sustainability concerns about large-scale CDR deployment 13 , ZF energy systems are expected to rely on various measures such as deep electrification 14 , the utilisation of alternative fuels like biofuels and hydrogen-based energy carriers (e.g., hydrogen itself, ammonia, and synthetic hydrocarbon fuels) 15–17 , increasing energy efficiency in end-use sectors, and lowering energy service demand 18,19 . Considering the features of each measure, such as the impacts of large-scale bioenergy use on food security and biodiversity 13,20,21 and increased mitigation costs associated with the extensive use of hydrogen-based energy carriers 22,23 , determining how to integrate these measures to achieve ZF energy systems remains a critical question. Previous studies have lacked insight into the pathways for reaching ZF energy systems. In the context of exploring cost-optimal decarbonisation pathways aligned with the 2°C and 1.5°C goals of the Paris Agreement, there has been no explicit need to create ZF energy systems. A more technical factor is the lack of models that incorporate technology options for the full phase-out of residual fossil fuel consumption on the end-use side. Indeed, scenarios within the IPCC AR6 scenario database, which limit temperature increases to 1.5°C with 50% likelihood in 2100 (categories C1 and C2; see Methods), show primary energy supplies from fossil fuels of 115–334 EJ/yr in 2050 and 30–287 EJ/yr in 2100 (10 th to 90 th percentiles) 10 . Furthermore, there are no AR6 scenarios that achieve the full phase-out of fossil fuels at any point within this century 10 . Although some recent studies focusing on 100% renewable energy systems can be interpreted as a subset of ZF scenarios 24,25 , they have typically assumed a constrained set of available technologies (e.g., excluding nuclear energy or CDR), focused on limited numbers of sectors (e.g., power or energy supply), or relied on prescribed transformation pathways for energy end uses 14,26–28 . After COP28, societies may increase their focus on phasing out fossil fuels, which historically have been the main drivers of climate change. Furthermore, growing interest in hydrogen-based energy carriers and carbon capture, utilisation, and storage (CCUS) as measures to address residual fossil CO 2 emissions 29,30 has led to the emergence of models that include these potential enablers of ZF energy systems 16,17,31 . In this study, we investigated the extent to which the full phase-out of fossil fuels would differ from typical 1.5°C scenarios and identified the challenges and opportunities associated with their realisation. To explore two distinct illustrative pathways for ZF energy systems and gain robust insights, we employed two global energy system models: AIM-Technology (Asia–Pacific Integrated Model-Technology, hereinafter AIM) 32 , and MESSAGEix-GLOBIOM (Model for Energy Supply Strategy Alternatives and their General Environmental Impact combined with the Global Biosphere Management Model, hereinafter MESSAGEix) 33–35 . We defined the entire energy sector, including non-energy use, as the boundary of the energy system, and ZF was defined as the full phase-out of coal, oil, and natural gas commodities. A scenario-based approach was adopted to understand the diverse transition pathways to ZF energy systems, characterised primarily by the target year for achieving ZF. The ZF scenarios were labelled according to target years, from 2050 (ZF2050) to 2100 (ZF2100) in 10-year increments. These scenarios imposed upper limits on the primary energy supply from fossil fuels, along with emission constraints corresponding to a carbon budget of 500 GtCO 2 from 2018 to 2100, covering CO 2 emissions from all sectors 36 . Finally, we ran a model-specific 1.5°C scenario (Opt1.5C) that imposes only emission constraints, without setting limits on the primary energy supply from fossil fuels. The results were compared with those for the ZF scenarios, and the additional efforts required to achieve ZF energy systems were examined. Additionally, we used the C1 and C2 scenarios obtained from the AR6 scenario database 10 as examples of typical 1.5°C energy systems for comparison with the ZF scenarios. Fossil fuel phase-out in energy systems The primary energy mix of the ZF energy system in 2050 exhibited unique characteristics compared to those of the AR6 C1 and C2 scenarios (Fig. 1a). In 2050, fossil fuels accounted for 35% (188 EJ/yr) of the total primary energy supply according to AIM, and for 54% (275 EJ/yr) according to MESSAGEix, for the Opt1.5C scenarios (Fig. 1a, d). As energy systems approached ZF, the primary energy mix shifted away from the distribution of typical 1.5°C scenarios, including the Opt1.5C and AR6 C1 and C2 scenarios, due to the increasing replacement of fossil fuels with non-fossil energy, particularly non-biomass renewables. By contrast, in 2100, as deeper decarbonisation occurred, the primary energy mix of ZF scenarios closely resembled those of the Opt1.5C scenarios, highlighting the difficulty of achieving near- to mid-term transition to ZF energy systems (Extended Data Fig 1a). The cumulative fossil fuel primary energy supply from 2020 to 2100 was reduced to 34–43% of the Opt1.5C scenarios in the ZF2050 scenarios and to 64–70% even in the ZF2100 scenarios (Fig. 1e). Compared to the unique primary energy mix of the ZF energy system, the share of individual energy sources in the power generation mix did not significantly deviate from that of the AR6 C1 and C2 scenarios (Fig. 1b), because fossil fuels in the power sector are phased out to an extent that is nearly equal to a full phase-out, as observed in most previous studies 37 . In some typical 1.5°C scenarios, including the Opt1.5C scenario of MESSAGEix, natural gas power plants with CCS contribute as a bridging technology. However, even in the ZF2100 scenario, where the target year for ZF was set at the end of the century, the role of fossil power generation was limited by 2050 (Fig. 1f and Extended Data Fig. 4). In 2100, the power generation mixes of ZF scenarios and the Opt1.5C scenario were closely aligned within each model (Extended Data Fig. 1b). The final energy mix of the ZF energy system was characterised by a lower share of liquid and gaseous fuels and a higher share of non-hydrocarbon fuels, which mainly consist of electricity and hydrogen, compared to those in the Opt1.5C and the AR6 C1 and C2 scenarios (Fig. 1c). In the ZF2050 scenarios, the share of non-hydrocarbon fuels within total final energy consumption reached 67–80% by 2050. Notably, MESSAGEix exhibited a more pronounced transformation on the energy demand side compared to AIM. The main reason for this difference was the modelling of fuel switching on the end-use side, where AIM scenarios incorporate synthetic fuels produced from electricity, known as e-fuels, to some extent, whereas MESSAGEix scenarios do not. While the share of solid fuels decreased to 5–11% in the ZF2050 scenarios, it remained comparable to those in the AR6 C1 and C2 scenarios. Similar to the power generation mix, the final energy mix of the ZF scenarios and the Opt1.5C scenario within each model were closely aligned by 2100 (Extended Data Fig. 1c). Energy demand transformation Approaches to phasing out fossil fuels on the energy demand side showed some differences across models and scenarios (Fig. 2). In 2050, the Opt1.5C scenarios decarbonised end-use sectors primarily with electricity, accounting for 39–47% of the total final consumption, supported by biomass and hydrogen, while fossil fuel consumption, mainly oil products and natural gas, remained at 34–39% (Fig. 2a, b). In the ZF2050 scenario of AIM, the full phase-out of fossil fuels in end-use sectors by 2050 was achieved by replacing residual fossil fuel consumption with the expanded use of biomass, hydrogen, and synthetic fuels in the industry and transport sectors (Extended Data Fig. 2b, c). By contrast, in the ZF2050 scenario of MESSAGEix, deeper direct electrification in the industry and buildings sectors, expanded biomass use in the industry and transport sectors, and increased hydrogen use in the transport sector contributed to achieving ZF in end-use sectors by 2050 (Extended Data Fig. 2b–d). By 2100, the share of fossil fuels in total final consumption had already decreased to 5–12%, even in the Opt1.5C scenarios, such that the transition to ZF scenarios involved simply replacing this residual fossil fuel consumption with biomass or synthetic fuels (Fig. 2b). The development of indirect electrification through hydrogen-based energy carriers and biomass utilisation to achieve the full phase-out of fossil fuels by the middle of this century was a strategy commonly seen in both models (Fig. 2b). The trajectory of hydrogen penetration was similar across models. The Opt1.5C scenarios gradually reached hydrogen shares of 13–17% by the end of the century, while the ZF2050 scenarios achieved this level earlier, by the middle of the century. Synthetic fuels were a non-fossil option unique to AIM, accounting for 10% of the final consumption in ZF energy systems. By contrast, in the Opt1.5C scenario, their share in total final consumption remained around 1%, even by the end of the century, indicating that their use expands only under the extreme conditions of ZF scenarios. In the ZF2050 scenario of MESSAGEix, a sharp increase in direct electrification rates occurred to achieve the full phase-out of fossil fuels by 2050. By contrast, the increase in direct electrification rates from the Opt1.5C scenario to the ZF2050 scenario was more modest in AIM compared to MESSAGEix, likely because AIM had already reached near-maximum levels of direct electrification in its Opt1.5C scenario. As discussed later, in the ZF scenarios, almost all hydrogen was derived from electrolysis powered by renewable electricity (Extended Data Fig. 4b), reaffirming the effectiveness of hydrogen-based energy carriers in extending the application of renewables to sectors where direct electrification is challenging. The share of biomass in the total final consumption peaked at around 20% by the middle of the century, and then gradually declined to approximately 10% by 2100. This finding suggests that while biomass played a critical role in achieving ZF by the middle of the century, its contribution became smaller towards the end of the century due to growth in the total final consumption and biomass supply limitations. The differences in target years with respect to achieving a full phase-out of fossil fuels significantly influenced the pace of transitions in end-use sectors (Fig. 2b). Achieving a full phase-out of fossil fuels by the middle of this century requires realising an energy transition in end-use sectors earlier than transitions that gradually occurred towards the end of the century in the Opt1.5C scenarios. By contrast, pushing back the target year to the end of the century led to transitions in end-use sectors that more closely followed the pathways of the Opt1.5C scenarios, and proceeded at a more gradual pace. In particular, the trajectory of biomass shares indicates that the mid-century peak observed in the ZF scenarios can be avoided, implying that a rapid scale-up in energy crop production can also be avoided. Energy supply transformation Substantial increases in power and hydrogen generation by the middle of this century are among the most prominent characteristics of the ZF scenarios (Fig. 3). In 2100, total power generation in the ZF scenarios increased by approximately 10–20% compared to the Opt1.5C scenario, but power generation levels across all scenarios in this study, including the ZF scenarios, were within the range of the AR6 C1 and C2 scenarios (Fig. 3c). However, in 2050, significant increases in total power generation, particularly from solar and wind energy, were observed in the ZF scenarios (Fig. 3a, b). Notably, in the ZF2050 scenario of AIM, total power generation in 2050 increased 1.6-fold compared to the Opt1.5C scenario, reaching levels significantly exceeding the maximum observed in the AR6 C1 and C2 scenarios (Fig. 3c). Similarly, although the increase in absolute terms was not as pronounced, total power generation increased in the ZF2050 scenario of MESSAGEix, reaching 1.8-fold the level of the Opt1.5C scenario in 2050. The scale of solar and wind power generation in the ZF scenarios differed between models due to variations in assumptions about the availability of non-fossil power sources, such as nuclear and geothermal energy, and the extent to which energy service demands could be met with electricity or hydrogen on the end-use side. In the ZF2050 scenario of MESSAGEix, solar and wind power generation in 2050 was comparable to that in the Opt1.5C scenario of AIM. Total hydrogen generation, especially green hydrogen generation, also increased, particularly in the ZF2050 scenario of AIM, where it reached exceptionally high levels compared to typical 1.5°C scenarios (Fig. 3d). In the Opt1.5C scenario of MESSAGEix, fossil fuels and biomass with CCS accounted for a significant share of hydrogen generation. However, in the ZF scenarios, the phase-out of fossil fuels and the increased use of biomass in other hard-to-abate sectors resulted in hydrogen generation being dominated by green hydrogen (Extended Data Fig. 4b). The substantial increase in power generation by 2050 was driven by the need to support the phase-out of fossil fuels on the end-use side through both direct and indirect electrification (Fig. 3a, b). According to AIM, indirect electrification via hydrogen and synthetic fuels was the primary strategy for achieving the phase-out of fossil fuels in end-use sectors, leading to a substantial increase in green hydrogen demand, including for synthetic fuel production. As green hydrogen generation involves considerable energy losses 23 , the significant increase in hydrogen generation compared to typical 1.5°C scenarios contributed to power generation growth (Fig. 3a). In the ZF scenarios, the use of synthetic fuels increased; however, nearly all CO 2 sources for synthetic fuel production in 2050 were biomass-based. As a result, unlike in 2100, there was no additional power demand from direct air capture (DAC) in 2050 (Fig. 3a and Extended Data Fig. 3a). According to MESSAGEix, both direct electrification and indirect electrification via hydrogen were key strategies for achieving the ZF goal, and increased demand for electricity and hydrogen contributed to an increase in power generation (Fig. 3b). The rapid increase in power and hydrogen generation by the middle of the century under the ZF scenarios had a significant impact on scaling up supporting technologies such as solar and wind power, energy storage technologies, and electrolysers, particularly in the first half of the century (Fig. 3d). A comparison of annual net increases in installed capacity for these technologies between the Opt1.5C and ZF scenarios revealed that the ZF scenarios exhibited more uneven growth rates and sharper peaks in installation in the first half of the century compared to the Opt1.5C scenarios. The rapid upscaling observed in the ZF2050 scenarios surpassed that of the Opt1.5C scenarios and is therefore expected to become a critical bottleneck for achieving large-scale electricity and hydrogen supply, and consequently, the phase-out of fossil fuels through direct and indirect electrification. Pushing back the target year for the full phase-out of fossil fuels to the end of the century would stabilise upscaling in the first half of the century and delay the timing of its peaks. MESSAGEix, which makes more optimistic assumptions than AIM about the availability of nuclear power and the range of energy service demands that can be met by non-hydrocarbons, showed more moderate upscaling. Energy storage upscaling may have been influenced by power system representation in the models. In AIM, green hydrogen generation absorbed surplus electricity, resulting in lower energy storage capacity compared to MESSAGEix despite the higher level of power generation. Emission-related outcomes When fossil fuel phase-out was achieved, CO 2 emissions from the energy sector were reduced in the ZF scenarios compared to the Opt1.5C scenarios by 2060–2070 (Fig. 4 and Extended Data Fig. 5a). The degree of reduction from the Opt1.5C scenario varied depending on the model and target year. CO 2 emissions from the energy sector in 2050 were reduced by 6–68% in AIM and by 43–89% in MESSAGEix (Fig. 4a). In 2050, residual CO 2 emissions from fossil fuels and industry (CO 2 -FFI) in the Opt1.5C scenario ranged from 12 to 16 GtCO 2 /yr (Fig. 4b). In the ZF2050 scenario, the full phase-out of fossil fuels lowered residual CO 2 -FFI to 2–4 GtCO 2 /yr; residual CO 2 emissions from energy supply in AIM model included emissions from the utilisation of CO 2 captured from industrial processes. In the Opt1.5C scenario, both models used negative emissions from bioenergy with CCS (BECCS) in the energy sector by 2050, whereas in the ZF2050 scenario, no negative emissions were deployed in the energy sector. Additionally, in MESSAGEix, which endogenously determines CO 2 emissions from the agriculture, forestry, and other land use (AFOLU) sector, the reduction in residual CO 2 emissions from the energy sector led to a decrease in negative emissions from the AFOLU sector in 2050. In the latter half of the century, CO 2 emissions from the energy sector behaved differently between the AIM and MESSAGEix models, depending primarily on how emission constraints were imposed (Fig. 4a). In MESSAGEix, which applied cumulative carbon budgets for the entire century, deeper emissions reductions in the first half of the century led to higher CO 2 emissions from the energy sector in the ZF scenario than in the Opt1.5C scenario after 2070. By contrast, AIM, which applied annual carbon budgets, did not exhibit such a rebound. In any case, cumulative CO 2 emissions from the energy sector between 2020 and 2100 were reduced in the ZF scenarios of both models, particularly those that achieved ZF earlier. In the ZF scenarios, cumulative CO 2 emissions from the energy sector between 2020 and 2100 were reduced by 2–33% compared to the Opt1.5C scenario in AIM, and by 10–36% for that in MESSAGEix. When cumulative CO 2 emissions from the energy and AFOLU sectors were combined, reductions reached 2–44% in AIM and 7–32% in MESSAGEix. The scale and approach of CCUS differed between the Opt1.5C and ZF scenarios (Extended Data Fig. 5b). In ZF scenarios of MESSAGEix, the need for CCS decreased due to the reduction of residual emissions, resulting in smaller-scale CCS and CO 2 capture compared to the Opt1.5C scenarios over the entire century. In the Opt1.5C scenario of MESSAGEix, fossil fuel with CCS was deployed on a large scale, peaking around 2070, but its scale was significantly reduced in the ZF scenarios. By contrast, in the ZF scenarios of AIM, where CCU contributed to the phase-out of fossil fuels, CCS was decreased compared to the Opt1.5C scenario. As a result, the overall magnitude of CO 2 capture remained nearly the same as in the Opt1.5C scenario. In 2050, nearly all captured CO 2 , including that from fossil fuels, industrial processes, and biomass, was stored underground in the Opt1.5C scenario, whereas in the ZF scenario, almost all of it was utilised. Even in the latter half of the century, the contribution of CCU remained limited in the Opt1.5C scenario, whereas in the ZF scenarios, approximately 4 GtCO 2 /yr of CO 2 was utilised through bioenergy with CCU (BECCU) and direct air carbon capture and utilisation (DACCU). In both the Opt1.5C and ZF scenarios, the scale of DAC was 3–4 GtCO 2 /yr, suggesting that, unlike power and hydrogen generation technologies, the scalability of DAC is unlikely to pose a ZF scenario-specific bottleneck. Challenges and opportunities Energy transformations towards ZF energy systems highlight both challenges and opportunities from multiple perspectives (Fig. 5a–i). Phasing out even residual fossil fuel consumption in the Opt1.5C scenarios triggered upscaling of supporting technologies, such as solar and wind power (Fig. 5a), and increased the cumulative energy investments in energy supply sectors from 2020 to 2100 by 8–31% in AIM and 8–34% in MESSAGEix (Fig. 5b). Similarly, cumulative energy investments in energy demand sectors during the same period rose by 11–27% in AIM (Fig. 5d). In AIM, an earlier target year in the ZF scenarios was associated with greater stranded capacity of coal power plants by mid-century compared to the Opt1.5C scenario (Fig. 5c). By contrast, in MESSAGEix, which is a perfect-foresight model, the interaction between different ZF target years and the timing of investments in coal power plants was more complex. The stranded capacity showed the greatest increase in the ZF2070 scenario, and decreased in the ZF2050 scenario compared to the Opt1.5C scenario (Fig. 5c). In the ZF scenarios, non-hydrocarbon energy penetrated more deeply on the energy demand side compared to the Opt1.5C scenario (Fig. 5e). This result drove a shift from fossil fuel consumption technologies to electricity and hydrogen consumption technologies, which is anticipated to result in lifestyle changes. The phase-out of fossil fuels has a clear advantage, avoiding CCS and CDR as a consequence of deep reductions in CO 2 -FFI compared to the Opt1.5C scenario (Fig. 4). Cumulative geological CO 2 storage throughout the century was reduced by 37–46% in AIM and 52–77% in MESSAGEix compared to the Opt1.5C scenarios (Fig. 5f). Similarly, cumulative BECCS and DACCS deployment decreased by 35–42% in AIM and 39–74% in MESSAGEix (Fig. 5g). The energy transformation required for phasing out fossil fuels may have both positive and negative impacts on land use. In the ZF scenarios, biofuels were extensively utilised as non-fossil hydrocarbon fuels, resulting in increased primary energy supply from biomass and potentially greater pressure on the land use sector compared to the Opt1.5C scenarios (Fig. 5h). By contrast, in MESSAGEix, which considers interactions with the AFOLU sector, stronger emission reduction efforts in the energy sector under ZF scenarios alleviated the emission reduction burden in the AFOLU sector. Consequently, the need for negative emissions through measures such as afforestation was reduced (Fig. 5i). The implications of fossil fuel phase-out for the land use sector will need to be thoroughly evaluated in future studies. Discussion and conclusions We conducted a model intercomparison using two global energy system models to obtain robust insights into ZF energy systems and to illustrate two distinct representative pathways towards achieving such systems. The two models employed partially different strategies for phasing out fossil fuels, most notably with respect to the degree of penetration of non-hydrocarbon energy on the energy demand side. Nonetheless, the transformation towards ZF energy systems was characterised by substantial mid-century increases in power and hydrogen generation compared to typical 1.5°C scenarios, which could make the scalability of technologies such as solar and wind power, energy storage, and electrolysers a critical bottleneck in achieving ZF energy systems. Additionally, challenges observed in 1.5°C scenarios, such as increased cumulative energy investments and lifestyle changes due to rapid energy system transformation 22 , could be further amplified in ZF scenarios. The implication for stranded investments, a negative consequence of rapid energy system transformation 40,41 , varied across models and scenarios, reflecting differences in investment timing and the ZF target year. By contrast, ZF scenarios showed ancillary benefits such as lower peak and end-of-century temperatures, leading to reduced climate impacts, lower reliance on CCS, and a decreased burden of emission reductions in the land use sector compared to typical 1.5°C scenarios. Setting the target year to the end of this century, when near-ZF energy systems are achieved under typical 1.5°C scenarios, would reduce additional efforts but also diminish the benefits of the full phase-out of fossil fuels. Based on the fundamental premise that the full phase-out of fossil fuels is sufficient but not necessary to achieve the 1.5°C target, it is crucial to recognise the challenges and opportunities of ZF scenarios highlighted in this study and to evaluate whether the full phase-out should be the ultimate goal of climate policy. The increased energy investments in ZF scenarios reaffirm that if the primary objective of climate policy is to limit global warming to 1.5°C, then typical 1.5°C scenarios characterised by partial allowance of fossil fuels alongside abatement and removal through CCS and CDR are more cost-effective than ZF scenarios focused on the full phase-out of fossil fuels. When considering whether society should pursue the full phase-out of fossil fuels despite understanding that it is not a cost-optimal 1.5°C pathway, the quantitative and qualitative challenges and opportunities of ZF energy systems provide valuable insights for decision-making. The potential for greater increases in energy investments and lifestyle changes is likely to pose challenges to the socioeconomic viability of the full phase-out of fossil fuels. However, ZF scenarios achieve significantly greater reductions in CO 2 -FFI compared to 1.5°C scenarios, leading to lower peak and end-of-century temperatures, and consequently reduced climate impacts. Reduced reliance on CCS and CDR in the ZF scenarios may enhance their potential for broader societal acceptance, given the barriers to social acceptance associated with geological CO 2 storage 13,42,43 . Moreover, the straightforward concept of ZF scenarios, which uniformly phase out all fossil fuel consumption, may send a stronger signal to fossil fuel producers to cease investment in fossil fuel exploration, extraction, and transmission and distribution infrastructure compared to 1.5°C scenarios, which involve a degree of ambiguity by allowing a certain amount of residual fossil fuel consumption only in hard-to-abate sectors. The challenges and opportunities associated with the full phase-out of fossil fuels gradually diminish as the target year is extended further into the future. Some challenges and opportunities within and beyond the energy sector are complementary, where strengthening one effort can ease another. For example, if significant changes in human behaviours and lifestyles reduce energy service demand, as observed in previous studies 18,19 , then the energy investments required for ZF targets could decrease. Thus, it is important to first recognise that decarbonisation and defossilisation are not necessarily equivalent, and that mitigation pathways allowing for the limited use of fossil fuels in achieving climate targets should be widely understood by the public. With this understanding, society’s willingness to bear additional costs and embrace behavioural changes will determine whether a ZF energy system can become the ultimate goal, as well as when the target year for achieving it should be set, through considering the challenges and opportunities of the full phase-out of fossil fuels. The rapid upscaling of technologies required to meet the mid-century power generation and hydrogen generation increases in ZF scenarios, compared to 1.5°C scenarios, would likely become one of the most critical bottlenecks to achieving them. Therefore, ZF energy systems will require both policies that directly target the full phase-out of fossil fuels, such as limiting fossil fuel extraction, banning fossil fuel-consuming equipment, or phasing out fossil fuel incentives including subsidies, and complementary policies that strongly promote the market expansion and cost reduction of power and hydrogen generation technologies. The COP28 final decision highlighted both transitioning away from fossil fuels in energy systems and tripling renewable energy capacity as global efforts for the Parties to contribute, and the results of the present study emphasise the importance of the latter approach. CCUS and CDR are often debated with respect to their potential to delay the phase-out of fossil fuels. The COP28 final decision acknowledged the contributions of abatement technologies and transitional fuels, such as fossil power plants with CCS, blue hydrogen, and ammonia. While some 1.5°C scenarios, including the Opt1.5C scenario in MESSAGEix, suggested potential contributions from these bridging technologies, their role was limited in the ZF scenarios, including the ZF2100 scenario. Therefore, allowing fossil CCUS technologies could be regarded as a loophole, and may not align with the full phase-out of fossil fuels as the ultimate goal of global climate change mitigation policy. However, unlike fossil CCUS technologies, the contribution of non-fossil CCUS in the ZF scenarios indicates they are no longer installed as an excuse to retain fossil fuels, but rather as a serious contribution to climate change mitigation. As highlighted by the COP28 final decision, the phrase “transitioning away from fossil fuels in energy systems, in a just, orderly and equitable manner” underscores the importance of addressing the heterogeneous regional implications of a full phase-out of fossil fuels. While detailed national- and regional-scale analyses are beyond the scope of this study, the ZF scenarios showed that international fossil fuel trade, which persisted to some extent even by the end of the century in the Opt1.5C scenarios, was phased out and replaced by expanded international trade in biomass and hydrogen-based energy carriers (Extended Data Fig. 6). Although these findings are incomplete, they suggest significant impacts on current fossil fuel-exporting countries. To secure their cooperation, complementary policies supporting a just and equitable phase-out of fossil fuels will be essential. This study had several limitations. First, the representation of energy demand sectors by the models may have overlooked some bottlenecks associated with the full phase-out of fossil fuels from the energy demand side. These include sectors such as steel 17 , chemicals 44 , and aviation 45 , whose representation was simplified in one or both of the models used in this study. Nonetheless, our scenario analysis likely provides sufficient qualitative insights into potential energy system transformations, along with associated challenges and opportunities, for reducing fossil fuel consumption to nearly zero. Second, while the models used in this study provided a broad assessment of the interrelationships within the energy system under a full phase-out of fossil fuels, they also exhibited limitations in their representation of power systems. Both models have introduced innovative approaches to capture challenges related to integrating variable renewable energy (VRE), such as considering hourly dispatch for selected representative days during technology selection and incorporating constraints on system flexibility and capacity reserves 32,46 . However, other models are specifically designed for the detailed analysis of power systems by narrowing their focus to specific regions and sectors, and the models used in the present study fall short of such specialised models in terms of spatiotemporal resolution 47,48 . While the qualitative findings of our study are unlikely to be affected, a deeper understanding of the power system transformation required to support the increased power generation in the ZF scenarios will be necessary in future analyses. Third, we acknowledge that the two models used to explore ZF energy systems in this study may not have fully captured the entire solution space. Intercomparison of 1.5°C scenarios using two models, MESSAGEix and REMIND (REgional Model of Investment and Development) has been conducted previously 8 ; however, a broader model comparison involving more models would be desirable in the future. Future research could further analyse the sustainability implications of ZF energy systems, including the land use impacts and their potential heterogeneous regional effects. Declarations Acknowledgements Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the National Member Organization. S.F. acknowledges support from Japan Science and Technology Agency (JST) as part of Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE, grant number JPMJAP2331). S.M. acknowledges support from the Support for Pioneering Research Initiated by the Next Generation presented by the Division of Graduate Studies, Kyoto University (JST SPRING, grant number JPMJSP2110) and the Madume Research Encouragement Prize Award. S.J., O.F. and V.K. acknowledge support from the European Union’s Horizon Europe Research and Innovative Action Programme under Grant Agreement No. 101137582 (HYway) and Grant Agreement No. 101183367 (NEWPATHWAYS). Author contributions S.M., S.J., V.K. and S.F. conceptualised the research. S.M. contributed to scenario design. All authors participated in the interpretation of the results. K.O. and O.F. developed the model. S.M. conducted the analysis, created the figures, and wrote the first draft of the paper. S.M., S.J., V.K., T.H. and S.F. contributed to the final manuscript. References UNFCCC. 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(Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022). doi:10.1017/9781009157926.021. Hansen, T. A. Stranded assets and reduced profits: Analyzing the economic underpinnings of the fossil fuel industry’s resistance to climate stabilization. Renewable and Sustainable Energy Reviews 158 , 112144 (2022). Welsby, D., Price, J., Pye, S. & Ekins, P. Unextractable fossil fuels in a 1.5 °C world. Nature 597 , 230–234 (2021). Anderson, K. & Peters, G. The trouble with negative emissions. Science 354 , 182–183 (2016). Minx, J. C. et al. Negative emissions—Part 1: Research landscape and synthesis. Environ. Res. Lett. 13 , 063001 (2018). Zanon-Zotin, M. et al. Unaddressed non-energy use in the chemical industry can undermine fossil fuels phase-out. Nat Commun 15 , 8050 (2024). Dray, L. et al. Cost and emissions pathways towards net-zero climate impacts in aviation. Nat. Clim. Chang. 12 , 956–962 (2022). Luderer, G. et al. Assessment of wind and solar power in global low-carbon energy scenarios: An introduction. Energy Economics 64 , 542–551 (2017). Brown, T., Schlachtberger, D., Kies, A., Schramm, S. & Greiner, M. Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system. Energy 160 , 720–739 (2018). Brinkerink, M., Mayfield, E. & Deane, P. The role of spatial resolution in global electricity systems modelling. Energy Strategy Reviews 53 , 101370 (2024). Methods Energy system models We employed two global energy system models in this study, which are briefly described. AIM-Technology is a recursive dynamic energy system model covering all regions of the world. AIM-Technology includes 31 regions and various energy sectors including industry (steel, cement, and other industries), buildings (residential and commercial), transportation (passenger and freight), and energy supply (power generation, hydrogen generation, fossil extraction, and others). The model solves a linear programming problem at each time step to estimate the status of deployment, operation of energy technologies, and associated emissions by minimising the total energy system cost, which is represented as the sum of capital costs, operation and maintenance costs, energy costs, and emission costs for each technology. Constraints ensure that system requirements such as exogeneous energy service demands and emission caps are met. AIM-Technology operates with one-year time steps from 2005 to 2050 and five-year time steps from 2055 to 2100. Details of the model structure and mathematical formulation are provided in Oshiro & Fujimori (2022) 16 and Oshiro & Fujimori (2024) 32 . AIM-Technology is a technology-rich model that features detailed representations of energy supply and demand technologies across various energy sectors and sub-sectors. A comprehensive list of its technology options is available at the AIM-Technology documentation page (https://kenoshiro.github.io/AIM-Technology-doc/). AIM-Technology accounts for the extraction of fossil fuels such as coal (hard coal and lignite), crude oil, and natural gas. Resource potential is categorised into 12 different grades based on resource type and extraction cost. AIM-Technology includes an hourly resolution dispatch module, enabling detailed consideration of the variability in power supply from VREs such as solar and wind power, as well as variability in power demand. In this study, to account for seasonal variation in power supply from VREs and power demand, the model analysed the power supply–demand balance on an hourly basis for 12 representative days, one from each month. AIM-Technology models the production of liquid and gaseous synthetic hydrocarbon fuels from hydrogen and captured CO 2 . It includes hydrogen generation through electrolysis, biomass and coal gasification, and natural gas steam reforming. Furthermore, it enables CO 2 capture from large emission sources, such as power and hydrogen generation, oil refining, biomass liquefaction, steel and cement production, and furnaces, as well as from DAC. AIM-Technology accounts for trade in coal, crude oil, natural gas, oil products, biomass (solid and liquid), and hydrogen-based energy carriers (ammonia, synthetic fuels, and methylcyclohexane). AIM-Technology does not consider electricity trade. MESSAGEix–GLOBIOM 34,35 soft-links the energy system model MESSAGEix 33,49 with the land use model GLOBIOM 50,51 . MESSAGEix is a perfect-foresight energy system model covering all regions of the world. It includes 11 regions and various energy activities, such as energy extraction, energy conversion (electricity, heat, liquid fuels, gaseous fuels, and hydrogen), and final energy consumption (industry, buildings, and transportation). MESSAGEix solves a linear programming problem to estimate the least-cost portfolio, minimising total system costs expressed as the sum of capital costs, operation and maintenance costs, and costs for emissions and land use, while considering given service demands and emission constraints. Detailed formulations are available at the MESSAGEix documentation page (https://docs.messageix.org/) and in Sullivan et al. (2013) 52 . For time slices set at five-year intervals from 2025 to 2050 and ten-year intervals from 2055 to 2100, MESSAGEix optimises the total discounted system costs as the sum across these time slices. MESSAGEix estimates the macro-economic demand response based on energy system and service costs through iterative calculations with the single-sector macroeconomic module MACRO 53 . GLOBIOM, which is a partial-equilibrium land use model, provides MESSAGEix with information on land-use dynamics, such as the potential and costs of bioenergy and the opportunities and expenses for emission reductions in the AFOLU sector. To reduce computational costs, rather than running the full GLOBIOM model iteratively, the MESSAGEix model adopts an iterative process with a GLOBIOM emulator, which approximates land use-related results during the optimisation process. A detailed list of technology options available in MESSAGEix is available at the MESSAGEix documentation page (https://docs.messageix.org/projects/models/en/latest/). MESSAGEix covers extractions of coal, lignite, crude oil, and natural gas, with the potential of each resource graded according to varying extraction costs. MESSAGEix considers the reliability and flexibility requirements of the power system not by explicitly accounting for hourly power supply and demand, but by imposing a constraint that ensures sufficient dispatchable generator capacity in each time slice. The version of MESSAGEix used in this study includes hydrogen generation through coal and biomass gasification, natural gas steam reforming, and electrolysis, but does not include synthetic hydrocarbon production from captured CO 2 and hydrogen. Thus, all captured CO 2 is assumed to be stored underground. While CO 2 capture from large point sources is considered, DAC is not included. MESSAGEix assumes trade in solid, liquid and gaseous fuels, electricity, and liquid hydrogen. Scenarios In this study, multiple scenarios were modelled to understand diverse transition pathways for ZF energy systems. The ZF scenarios are labelled based on the target year for achieving full phase-out of fossil fuels (for example, ZF2050 indicates the complete elimination of fossil fuels by 2050), with constraints imposed on the upper limit of the fossil fuel primary energy supply, along with CO 2 emission constraints consistent with the 1.5°C target. The target years for ZF were set at 10-year intervals from 2050 (ZF2050) to 2100 (ZF2100). We employed a typical 1.5°C scenario (Opt1.5C) for comparison, without imposing the upper limits of primary supply of fossil fuels. The socioeconomic conditions are based on the middle-of-the-road Shared Socioeconomic Pathway (SSP2) 54 In AIM-Technology, the upper limits on the primary supply of fossil fuels each year were determined by the primary supply of fossil fuels for the same year in the Opt1.5C scenario and the reduction rate from the Opt1.5C scenario. First, the primary supply of fossil fuels in the Opt1.5C scenario without fossil phase-out constraints was obtained. The upper bounds of the primary supply of fossil fuels for ZF scenarios were obtained by multiplying this supply with the scenario-specific reduction pathways, expressed as values relative to the Opt1.5C scenario. The primary supply of fossil fuels was reduced linearly, starting from 100% of the Opt1.5C scenario level in 2030 and declining to 0% of the Opt1.5C scenario level by the target year. In MESSAGEix–GLOBIOM, which uses intertemporal optimisation, the pathways for phasing out fossil fuels are determined endogenously with greater flexibility compared to AIM-Technology, which is more myopic. Specifically, the upper limits on primary supply of fossil fuels each year are set to the levels for the same year in the Opt1.5C scenario before the target year, and to zero thereafter. In both models, the upper limits of fossil fuels were imposed by each region and fuel type (coal, crude oil, and natural gas). In the mitigation scenarios, we imposed CO 2 emission constraints consistent with 1.5°C temperature stabilisation, specifically limiting cumulative CO 2 emissions across all sectors from 2018 to 2100 to 500 GtCO 2 36 . First, in AIM-Technology, which is a recursive dynamic model, upper bounds on annual CO 2 emission were applied. The target emissions in AIM-Technology were CO 2 emissions from energy and industrial processes. Since AIM-Technology focuses only on the energy sector, the annual CO 2 emissions from energy and industrial processes, calculated under the condition of limiting cumulative CO 2 emissions across all sectors to 500 GtCO 2 , were obtained from the integrated assessment model AIM-Hub and used as emission constraints. Additionally, CO 2 emissions from the AFOLU sector were fixed based on the outputs of AIM-Hub. In MESSAGEix–GLOBIOM, which is an intertemporal optimisation model, an upper limit was imposed on cumulative CO 2 emissions from all sectors over the period 2018–2100. The target emissions in MESSAGEix–GLOBIOM include CO 2 emissions from all sectors. Unlike AIM-Technology, MESSAGEix–GLOBIOM endogenously determines CO 2 emissions from the AFOLU sector. We used the C1 and C2 scenarios obtained from the AR6 scenario database 10 as examples of typical 1.5°C energy systems for comparison with the ZF scenarios. We used the World v1.1 dataset. The C1 scenarios limit the temperature increase to 1.5°C, with no or limited overshoot, with a 50% likelihood. The C2 scenarios return warming to 1.5°C with a 50% likelihood after a high overshoot. The AR6 scenario database is described in IPCC (2022) 55 . Some indicators analysed in this study could not be directly obtained from the scenario database and were therefore calculated based on reported indicators. Regarding the shares in total final energy consumption shown in Fig. 2b, the final energy consumption of hydrocarbon fuels such as fossil fuels and biofuel was not disaggregated in detail for many scenarios. Therefore, it was calculated by fuel type (solid, liquid, and gaseous) as follows. Solid fuels were calculated directly using the final energy consumption of solid coal (“Final Energy|Solids|Coal”) and solid biomass (“Final Energy|Solids|Biomass”). Liquid fuels were disaggregated based on the secondary energy mix of liquid fuels in each scenario to approximate the final energy consumption of liquid fossil fuels and biofuels. Specifically, the share of liquid biomass (“Secondary Energy|Liquids|Biomass”) in the total secondary liquid energy (“Secondary Energy|Liquids”) was multiplied by the total final energy consumption of liquid fuels (“Final Energy|Liquids”) to estimate the final energy consumption of liquid biofuels. The remaining portion of the final energy consumption of liquid fuels was attributed to liquid fossil fuels. Scenarios that did not report secondary energy for liquid biomass (“Secondary Energy|Liquids|Biomass”) were excluded from this calculation. Gaseous fuels were calculated in the same manner as liquid fuels. The share of gaseous biomass (“Secondary Energy|Gases|Biomass”) in total secondary gaseous energy ((“Secondary Energy|Gases”) was multiplied by the total final energy consumption of gaseous fuels (“Final Energy|Gases”) to estimate the final energy consumption of gaseous biofuels. The remaining portion of the final energy consumption of gaseous fuels was attributed to gaseous fossil fuels. In cases where secondary energy for gaseous biomass (“Secondary Energy|Gases|Biomass”) was not reported, it was replaced with zero during the calculation. Data availability The scenario data generated in this study have been deposited in the Zenodo repository (https://zenodo.org/records/14515207). Code availability The source code used for scenario data analysis and figure production is provided in the Zenodo repository (https://zenodo.org/records/14515207). The source code of the AIM-Techology model is available at the GitHub repository (https://github.com/KUAtmos/AIMTechnology_core). The source code of the MESSAGEix-GLOBIOM model is available at the GitHub repository (https://github.com/iiasa/message_ix). The data of the MESSAGEix-GLOBIOM baseline scenario used in this study is available via Zenodo (https://doi.org/10.5281/zenodo.10514052). References for Methods section Messner, S. & Strubegger, M. User’s Guide for MESSAGE I1. (1995). Havlík, P. et al. Global land-use implications of first and second generation biofuel targets. Energy Policy 39 , 5690–5702 (2011). Lotze-Campen, H. et al. Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agricultural Economics 45 , 103–116 (2014). Sullivan, P., Krey, V. & Riahi, K. Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strategy Reviews 1 , 157–163 (2013). Messner, S. & Schrattenholzer, L. MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively. Energy 25 , 267–282 (2000). Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42 , 153–168 (2017). IPCC. Annex III: Scenarios and modelling methods [Guivarch, C., E. Kriegler, J. Portugal-Pereira, V. Bosetti, J. Edmonds, M. Fischedick, P. Havlík, P. Jaramillo, V. Krey, F. Lecocq, A. Lucena, M. Meinshausen, S. Mirasgedis, B. O’Neill, G.P. Peters, J. Rogelj, S. in IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Shukla, P. R. et al.) (Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022). doi:10.1017/9781009157926.022. Additional Declarations There is NO Competing Interest. Supplementary Files ExtendedData.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5698098","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":393775766,"identity":"363e7217-f3f2-4259-bf59-351da3d5aa64","order_by":0,"name":"Shotaro Mori","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0001-3515-8639","institution":"Kyoto University","correspondingAuthor":true,"prefix":"","firstName":"Shotaro","middleName":"","lastName":"Mori","suffix":""},{"id":393775767,"identity":"b5e00ac0-f19a-4a5d-9be5-e139b1c53261","order_by":1,"name":"Siddharth Joshi","email":"","orcid":"https://orcid.org/0000-0002-5746-3079","institution":"International Institute for Applied Systems Analysis","correspondingAuthor":false,"prefix":"","firstName":"Siddharth","middleName":"","lastName":"Joshi","suffix":""},{"id":393775768,"identity":"7ec27947-10a4-47e9-9c48-4e834d60646b","order_by":2,"name":"Volker Krey","email":"","orcid":"https://orcid.org/0000-0003-0307-3515","institution":"International Institute for Applied Systems Analysis","correspondingAuthor":false,"prefix":"","firstName":"Volker","middleName":"","lastName":"Krey","suffix":""},{"id":393775769,"identity":"5eaa0cb6-6bcc-45e4-8a0c-c49cc411832f","order_by":3,"name":"Ken Oshiro","email":"","orcid":"https://orcid.org/0000-0001-6720-409X","institution":"Kyoto University","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Oshiro","suffix":""},{"id":393775770,"identity":"8364690f-930f-471f-a6b4-37a516419beb","order_by":4,"name":"Oliver Fricko","email":"","orcid":"","institution":"International Institute for Applied Systems Analysis (IIASA)","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Fricko","suffix":""},{"id":393775771,"identity":"872070ee-5f25-4e3f-b74d-77271d2b0c9a","order_by":5,"name":"Takuya Hara","email":"","orcid":"","institution":"International Institute for Applied Systems Analysis (IIASA)","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Hara","suffix":""},{"id":393775772,"identity":"d13ec7cc-a9b9-46fa-8fa1-fc254e61a606","order_by":6,"name":"Shinichiro Fujimori","email":"","orcid":"https://orcid.org/0000-0001-7897-1796","institution":"Kyoto University","correspondingAuthor":false,"prefix":"","firstName":"Shinichiro","middleName":"","lastName":"Fujimori","suffix":""}],"badges":[],"createdAt":"2024-12-23 08:55:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5698098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5698098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73131405,"identity":"e7325e3e-8039-47d7-8549-e1a616fdeb5a","added_by":"auto","created_at":"2025-01-07 05:05:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":953141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFossil fuel phase-out and energy system transformation.\u003c/strong\u003e \u003cstrong\u003ea–c\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eEnergy mix for primary energy (\u003cstrong\u003ea\u003c/strong\u003e), power generation (\u003cstrong\u003eb\u003c/strong\u003e), and final energy (\u003cstrong\u003ec\u003c/strong\u003e) in 2050. Coloured symbols represent energy mixes by scenario and model. Grey symbols represent the energy mixes of the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) C1 and C2 scenarios for 2050. Black symbols represent the historical energy mix for 2020, based on the International Energy Agency (IEA) energy balance\u003csup\u003e38\u003c/sup\u003e. \u003cstrong\u003ed, e\u003c/strong\u003e, Annual primary energy supply from fossil fuels from 2020 to 2100 (\u003cstrong\u003ed\u003c/strong\u003e) and cumulative primary energy supply from fossil fuels from 2020 to 2100 (\u003cstrong\u003ee\u003c/strong\u003e). Box plots illustrate the primary energy supply from fossil fuels in the IPCC AR6 C1 and C2 scenarios for 2030, 2050, 2070, and 2100. \u003cstrong\u003ef\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eCumulative primary energy supply from fossil fuels by fuel type from 2020 to 2100. In this study, primary energy accounting was based on the direct equivalent method, which systematically reduces the contribution of non-combustible energy sources such as hydro, nuclear, solar, and wind energy compared to combustible fuels\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/981a59491e198b6ad4e7f146.png"},{"id":73131407,"identity":"c98396cb-3a7f-4fe7-aad1-8f95817b7df9","added_by":"auto","created_at":"2025-01-07 05:05:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1482044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTransformation to ZF energy systems in energy demand sectors\u003c/strong\u003e. \u003cstrong\u003ea\u003c/strong\u003e, Final energy consumption in the Opt1.5C and ZF2050 scenarios during the period 2020–2100. \u003cstrong\u003eb\u003c/strong\u003e, Shares in total final energy consumption in the Opt1.5C, ZF2100, and ZF2050 scenarios during the period 2020–2100. Box plots illustrate the shares in final energy of the IPCC AR6 C1 and C2 scenarios for 2030, 2050, 2070, and 2100.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/c4bf0aba896efffe023d051b.png"},{"id":73133298,"identity":"203d03c6-ea42-47d2-8bef-e8d2dd8dce7c","added_by":"auto","created_at":"2025-01-07 05:29:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":426644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnergy system transformation in energy supply sectors driven by a full phase-out of fossil fuels. a, b\u003c/strong\u003e, Power generation mixes and secondary energy flow in the Opt1.5C and ZF2050 scenarios of AIM-Technology (\u003cstrong\u003ea\u003c/strong\u003e) and MESSAGEix-GLOBIOM (\u003cstrong\u003eb\u003c/strong\u003e) in 2050. Bar plots on the left show power generation by energy source. Diagrams on the right show secondary energy flow associated with electricity and hydrogen-based energy carrier production. Grey shading represents energy losses including conversion, storage, and distribution and trade losses, curtailment, and electricity consumption for direct air capture (DAC). \u003cstrong\u003ec\u003c/strong\u003e, Total power and hydrogen generation in 2030, 2050, 2070, and 2100. Box plots illustrate power and hydrogen generation in the IPCC AR6 C1 and C2 scenarios for 2030, 2050, 2070, and 2100. \u003cstrong\u003ed\u003c/strong\u003e, Annual average net capacity increase in solar and wind power, energy storage technologies, and electrolyser by decade in the Opt1.5C, ZF2100, and ZF2050 scenarios.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/c109e591bcf4a6649c87c0b6.png"},{"id":73131409,"identity":"97938698-37cb-4fa8-951d-0dc85ce13818","added_by":"auto","created_at":"2025-01-07 05:05:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":507024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions in ZF energy systems.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e, CO\u003csub\u003e2\u003c/sub\u003e emissions from energy and industrial processes, including negative emissions originating from direct air carbon capture and storage (DACCS). Box plots illustrate CO\u003csub\u003e2\u003c/sub\u003e emissions in the IPCC AR6 C1 and C2 scenarios for 2030, 2050, 2070, and 2100. \u003cstrong\u003eb, c\u003c/strong\u003e, Sectoral CO\u003csub\u003e2\u003c/sub\u003e emissions in 2050 (\u003cstrong\u003eb\u003c/strong\u003e) and cumulative sectoral CO\u003csub\u003e2\u003c/sub\u003e emissions from 2020 to 2100 (\u003cstrong\u003ec\u003c/strong\u003e) in the Opt1.5C, ZF2100, and ZF2050 scenarios. Circles indicate net CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy and agriculture, forestry, and other land use (AFOLU) sectors; triangles indicate net CO\u003csub\u003e2 \u003c/sub\u003eemissions from energy sectors.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/17a4bd17ba098ec615fd429e.png"},{"id":73510830,"identity":"86dde9de-90b1-424d-93db-010f6c0976cf","added_by":"auto","created_at":"2025-01-10 16:43:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChallenges and opportunities for achieving the full phase-out of fossil fuels from multiple perspectives. a, \u003c/strong\u003ePercent changes in cumulative capacity additions of solar and wind power from 2020 to 2050 relative to the Opt1.5C scenario. \u003cstrong\u003eb\u003c/strong\u003e, Percent changes in cumulative energy investment in energy supply sectors from 2020 to 2100, discounted by 5% per year relative to the Opt1.5C scenario. \u003cstrong\u003ec\u003c/strong\u003e, Percent changes in the annual average stranded capacity of coal power plants from 2020 to 2050 relative to the Opt1.5C scenario, where stranded capacity is defined as the unused capacity of power plants in each period. \u003cstrong\u003ed\u003c/strong\u003e, Percent changes in cumulative energy investments in energy demand sectors from 2020 to 2100, discounted by 5% per year relative to the Opt1.5C scenario. Only AIM provided this indicator. \u003cstrong\u003ee\u003c/strong\u003e, Percent changes in cumulative non-hydrocarbon energy final consumption from 2020 to 2050 relative to the Opt1.5C scenario. \u003cstrong\u003ef\u003c/strong\u003e, Percent changes in cumulative geological CO\u003csub\u003e2\u003c/sub\u003e storage from 2020 to 2100 relative to the Opt1.5C scenario. \u003cstrong\u003eg\u003c/strong\u003e, Percent changes in cumulative CDR via BECCS and DACCS from 2020 to 2100 relative to the Opt1.5C scenario. \u003cstrong\u003eh\u003c/strong\u003e, Percent changes in cumulative primary energy supply from biomass 2020 to 2100 relative to the Opt1.5C scenario. \u003cstrong\u003ei\u003c/strong\u003e, Percent changes in cumulative negative CO\u003csub\u003e2\u003c/sub\u003e emissions from the AFOLU sector from 2020 to 2100 relative to the Opt1.5C scenario.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/ee9a1fd4cdf48032a156c684.png"},{"id":73732365,"identity":"3e1fa3c6-accf-4887-9dc7-23d9327f407a","added_by":"auto","created_at":"2025-01-14 06:06:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4240853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/03c9e8d6-3cb2-40cc-b318-ccd16623f6ff.pdf"},{"id":73133299,"identity":"85193f44-0286-43d6-b51b-937978234769","added_by":"auto","created_at":"2025-01-07 05:29:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":793174,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-5698098/v1/eb7c8785b83f44044b38fa55.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Energy system transformations for the phase-out of fossil fuels towards 1.5°C future","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLimiting global warming to well below 2\u0026deg;C, and pursuing efforts to limit it to 1.5\u0026deg;C by the end of the century in line with the Paris Agreement, requires a rapid reduction of fossil fuel contributions to the energy system\u003csup\u003e5,6\u003c/sup\u003e. The final decision of the first \u0026ldquo;global stocktake\u0026rdquo; at the 2023 United Nations Conference of the Parties (COP28) highlighted the necessity for urgent action to address the climate crisis, given that the Parties are not on track to achieve the long-term goals of the Paris Agreement\u003csup\u003e1\u003c/sup\u003e. A key achievement of the global stocktake decision was that it called for all Parties to contribute to global efforts, particularly transitioning away from fossil fuels in energy systems. Although the language of the final decision has been described as weaker than a full phase-out, momentum is growing for the phase-out of fossil fuels to become a key focus of climate policies moving forward from COP28.\u003c/p\u003e\n\u003cp\u003eThe decarbonisation and defossilisation of energy systems could follow different energy transformation pathways. Scenarios from integrated assessment models (IAMs) and energy system models have provided insights into energy transformation pathways in line with the long-term goals of the Paris Agreement\u003csup\u003e2,3,7,8\u003c/sup\u003e, as reflected in assessment reports by the Intergovernmental Panel on Climate Change (IPCC), e.g. the Sixth Assessment Report (IPCC AR6)\u003csup\u003e9\u003c/sup\u003e. To obtain robust insights from previous studies, thousands of scenarios were submitted to the IPCC AR6 scenario database for analysis by IPCC Working Group III\u003csup\u003e10\u003c/sup\u003e. These scenario ensembles suggested that cost-optimal decarbonisation pathways involve replacing fossil fuel consumption primarily through direct electrification with renewable electricity, while also addressing CO\u003csub\u003e2\u003c/sub\u003e emissions from hard-to-abate fossil fuel consumption through incorporating carbon capture and storage (CCS) and offsetting via carbon dioxide removal (CDR)\u003csup\u003e4\u003c/sup\u003e. However, achieving what could be called a zero-fossil (ZF) energy system, beyond transitioning away from and phasing out fossil fuels, will likely require pathways that depart from these typical decarbonisation pathways. Such differences are likely to arise because ZF energy systems require the elimination of even hard-to-abate fossil fuel consumption, which would remain in a typical 1.5\u0026deg;C energy system\u003csup\u003e11,12\u003c/sup\u003e. Based on the outcomes of previous studies that focused on reducing residual fossil CO\u003csub\u003e2\u003c/sub\u003e emissions considering sustainability concerns about large-scale CDR deployment\u003csup\u003e13\u003c/sup\u003e, ZF energy systems are expected to rely on various measures such as deep electrification\u003csup\u003e14\u003c/sup\u003e, the utilisation of alternative fuels like biofuels and hydrogen-based energy carriers (e.g., hydrogen itself, ammonia, and synthetic hydrocarbon fuels)\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e, increasing energy efficiency in end-use sectors, and lowering energy service demand\u003csup\u003e18,19\u003c/sup\u003e. Considering the features of each measure, such as the impacts of large-scale bioenergy use on food security and biodiversity\u003csup\u003e13,20,21\u003c/sup\u003e and increased mitigation costs associated with the extensive use of hydrogen-based energy carriers\u003csup\u003e22,23\u003c/sup\u003e, determining how to integrate these measures to achieve ZF energy systems remains a critical question.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies have lacked insight into the pathways for reaching ZF energy systems. In the context of exploring cost-optimal decarbonisation pathways aligned with the 2\u0026deg;C and 1.5\u0026deg;C goals of the Paris Agreement, there has been no explicit need to create ZF energy systems. A more technical factor is the lack of models that incorporate technology options for the full phase-out of residual fossil fuel consumption on the end-use side. Indeed, scenarios within the IPCC AR6 scenario database, which limit temperature increases to 1.5\u0026deg;C with 50% likelihood in 2100 (categories C1 and C2; see Methods), show primary energy supplies from fossil fuels of 115\u0026ndash;334 EJ/yr in 2050 and 30\u0026ndash;287 EJ/yr in 2100 (10\u003csup\u003eth\u003c/sup\u003e to 90\u003csup\u003eth\u003c/sup\u003e percentiles)\u003csup\u003e10\u003c/sup\u003e. Furthermore, there are no AR6 scenarios that achieve the full phase-out of fossil fuels at any point within this century\u003csup\u003e10\u003c/sup\u003e. Although some recent studies focusing on 100% renewable energy systems can be interpreted as a subset of ZF scenarios\u003csup\u003e24,25\u003c/sup\u003e, they have\u0026nbsp;typically\u0026nbsp;assumed\u0026nbsp;a constrained set of available technologies (e.g., excluding nuclear\u0026nbsp;energy\u0026nbsp;or CDR),\u0026nbsp;focused\u0026nbsp;on limited\u0026nbsp;numbers\u0026nbsp;of sectors (e.g., power or energy supply), or\u0026nbsp;relied\u0026nbsp;on prescribed transformation pathways for energy end uses\u003csup\u003e14,26\u0026ndash;28\u003c/sup\u003e. After COP28, societies may increase their focus on phasing out fossil fuels,\u0026nbsp;which\u0026nbsp;historically\u0026nbsp;have been\u0026nbsp;the main\u0026nbsp;drivers\u0026nbsp;of climate change. Furthermore, growing interest in hydrogen-based energy carriers and carbon capture,\u0026nbsp;utilisation,\u0026nbsp;and storage (CCUS) as measures to address residual fossil CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e29,30\u003c/sup\u003e has led to the emergence of models that include these potential enablers of\u0026nbsp;ZF\u0026nbsp;energy systems\u003csup\u003e16,17,31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we investigated the extent to which the full phase-out of fossil fuels would differ from typical 1.5\u0026deg;C scenarios and identified the challenges and opportunities associated with their realisation. To explore two distinct illustrative pathways for ZF energy systems and gain robust insights, we employed two global energy system models: AIM-Technology (Asia\u0026ndash;Pacific Integrated Model-Technology, hereinafter AIM)\u003csup\u003e32\u003c/sup\u003e, and MESSAGEix-GLOBIOM (Model for Energy Supply Strategy Alternatives and their General Environmental Impact combined with the Global Biosphere Management Model, hereinafter MESSAGEix)\u003csup\u003e33\u0026ndash;35\u003c/sup\u003e.\u0026nbsp;We defined\u0026nbsp;the entire energy sector, including non-energy use, as the boundary of the energy system, and\u0026nbsp;ZF was\u0026nbsp;defined as the full phase-out of coal, oil, and natural gas commodities. A scenario-based approach was adopted to understand the diverse transition pathways to\u0026nbsp;ZF\u0026nbsp;energy systems,\u0026nbsp;characterised\u0026nbsp;primarily by the target year for achieving\u0026nbsp;ZF. The ZF scenarios\u0026nbsp;were labelled\u0026nbsp;according to target years,\u0026nbsp;from 2050 (ZF2050) to 2100 (ZF2100) in 10-year increments. These scenarios\u0026nbsp;imposed upper limits on the primary energy supply from fossil fuels, along with emission constraints corresponding to a carbon budget of 500 GtCO\u003csub\u003e2\u003c/sub\u003e from 2018 to 2100, covering CO\u003csub\u003e2\u003c/sub\u003e emissions from all sectors\u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;Finally, we ran a model-specific 1.5\u0026deg;C scenario (Opt1.5C)\u0026nbsp;that\u0026nbsp;imposes only\u0026nbsp;emission\u0026nbsp;constraints,\u0026nbsp;without setting limits on the primary energy supply from fossil fuels. The results were\u0026nbsp;compared\u0026nbsp;with\u0026nbsp;those for the\u0026nbsp;ZF scenarios,\u0026nbsp;and\u0026nbsp;the additional efforts required to achieve\u0026nbsp;ZF\u0026nbsp;energy systems\u0026nbsp;were\u0026nbsp;examined. Additionally, we used the C1 and C2 scenarios obtained from the AR6 scenario database\u003csup\u003e10\u003c/sup\u003e as examples of typical 1.5\u0026deg;C energy systems\u0026nbsp;for comparison with the ZF scenarios.\u003c/p\u003e"},{"header":"Fossil fuel phase-out in energy systems","content":"\u003cp\u003eThe primary energy mix of the ZF energy system in 2050 exhibited unique characteristics compared to those of the AR6 C1 and C2 scenarios (Fig. 1a). In 2050, fossil fuels accounted for 35% (188 EJ/yr) of the total primary energy supply according to AIM, and for 54% (275 EJ/yr) according to MESSAGEix, for the Opt1.5C scenarios (Fig. 1a, d). As energy systems approached ZF, the primary energy mix shifted away from the distribution of typical 1.5\u0026deg;C scenarios, including the Opt1.5C and AR6 C1 and C2 scenarios, due to the increasing replacement of fossil fuels with non-fossil energy, particularly non-biomass renewables. By contrast, in 2100, as deeper decarbonisation occurred, the primary energy mix of ZF scenarios closely resembled those of the Opt1.5C scenarios, highlighting the difficulty of achieving near- to mid-term transition to ZF energy systems (Extended Data Fig 1a). The cumulative fossil fuel primary energy supply from 2020 to 2100 was reduced to 34\u0026ndash;43% of the Opt1.5C scenarios in the ZF2050 scenarios and to 64\u0026ndash;70% even in the ZF2100 scenarios (Fig. 1e).\u003c/p\u003e\n\u003cp\u003eCompared to the unique primary energy mix of the ZF energy system, the share of individual energy sources in the power generation mix did not significantly deviate from that of the AR6 C1 and C2 scenarios (Fig. 1b), because fossil fuels in the power sector are phased out to an extent that is nearly equal to a full phase-out, as observed in most previous studies\u003csup\u003e37\u003c/sup\u003e. In some typical 1.5\u0026deg;C scenarios, including the Opt1.5C scenario of MESSAGEix, natural gas power plants with CCS contribute as a bridging technology. However, even in the ZF2100 scenario, where the target year for ZF was set at the end of the century, the role of fossil power generation was limited by 2050 (Fig. 1f and Extended Data Fig. 4). In 2100, the power generation mixes of ZF scenarios and the Opt1.5C scenario were closely aligned within each model (Extended Data Fig. 1b).\u003c/p\u003e\n\u003cp\u003eThe final energy mix of the ZF energy system was characterised by a lower share of liquid and gaseous fuels and a higher share of non-hydrocarbon fuels, which mainly consist of electricity and hydrogen, compared to those in the Opt1.5C and the AR6 C1 and C2 scenarios (Fig. 1c). In the ZF2050 scenarios, the share of non-hydrocarbon fuels within total final energy consumption reached 67\u0026ndash;80% by 2050. Notably, MESSAGEix exhibited a more pronounced transformation on the energy demand side compared to AIM. The main reason for this difference was the modelling of fuel switching on the end-use side, where AIM scenarios incorporate synthetic fuels produced from electricity, known as e-fuels, to some extent, whereas MESSAGEix scenarios do not. While the share of solid fuels decreased to 5\u0026ndash;11% in the ZF2050 scenarios, it remained comparable to those in the AR6 C1 and C2 scenarios. Similar to the power generation mix, the final energy mix of the ZF scenarios and the Opt1.5C scenario within each model were closely aligned by 2100 (Extended Data Fig. 1c).\u003c/p\u003e"},{"header":"Energy demand transformation","content":"\u003cp\u003eApproaches to phasing out fossil fuels on the energy demand side showed some differences across models and scenarios (Fig. 2). In 2050, the Opt1.5C scenarios decarbonised end-use sectors primarily with electricity, accounting for 39\u0026ndash;47% of the total final consumption, supported by biomass and hydrogen, while fossil fuel consumption, mainly oil products and natural gas, remained at 34\u0026ndash;39% (Fig. 2a, b). In the ZF2050 scenario of AIM, the full phase-out of fossil fuels in end-use sectors by 2050 was achieved by replacing residual fossil fuel consumption with the expanded use of biomass, hydrogen, and synthetic fuels in the industry and transport sectors (Extended Data Fig. 2b, c). By contrast, in the ZF2050 scenario of MESSAGEix, deeper direct electrification in the industry and buildings sectors, expanded biomass use in the industry and transport sectors, and increased hydrogen use in the transport sector contributed to achieving ZF in end-use sectors by 2050 (Extended Data Fig. 2b\u0026ndash;d). By 2100, the share of fossil fuels in total final consumption had already decreased to 5\u0026ndash;12%, even in the Opt1.5C scenarios, such that the transition to ZF scenarios involved simply replacing this residual fossil fuel consumption with biomass or synthetic fuels (Fig. 2b).\u003c/p\u003e\n\u003cp\u003eThe development of indirect electrification through hydrogen-based energy carriers and biomass utilisation to achieve the full phase-out of fossil fuels by the middle of this century was a strategy commonly seen in both models (Fig. 2b). The trajectory of hydrogen penetration was similar across models. The Opt1.5C scenarios gradually reached hydrogen shares of 13\u0026ndash;17% by the end of the century, while the ZF2050 scenarios achieved this level earlier, by the middle of the century. Synthetic fuels were a non-fossil option unique to AIM, accounting for 10% of the final consumption in ZF energy systems. By contrast, in the Opt1.5C scenario, their share in total final consumption remained around 1%, even by the end of the century, indicating that their use expands only under the extreme conditions of ZF scenarios. In the ZF2050 scenario of MESSAGEix, a sharp increase in direct electrification rates occurred to achieve the full phase-out of fossil fuels by 2050. By contrast, the increase in direct electrification rates from the Opt1.5C scenario to the ZF2050 scenario was more modest in AIM compared to MESSAGEix, likely because AIM had already reached near-maximum levels of direct electrification in its Opt1.5C scenario. As discussed later, in the ZF scenarios, almost all hydrogen was derived from electrolysis powered by renewable electricity (Extended Data Fig. 4b), reaffirming the effectiveness of hydrogen-based energy carriers in extending the application of renewables to sectors where direct electrification is challenging. The share of biomass in the total final consumption peaked at around 20% by the middle of the century, and then gradually declined to approximately 10% by 2100. This finding suggests that while biomass played a critical role in achieving ZF by the middle of the century, its contribution became smaller towards the end of the century due to growth in the total final consumption and biomass supply limitations.\u003c/p\u003e\n\u003cp\u003eThe differences in target years with respect to achieving a full phase-out of fossil fuels significantly influenced the pace of transitions in end-use sectors (Fig. 2b). Achieving a full phase-out of fossil fuels by the middle of this century requires realising an energy transition in end-use sectors earlier than transitions that gradually occurred towards the end of the century in the Opt1.5C scenarios. By contrast, pushing back the target year to the end of the century led to transitions in end-use sectors that more closely followed the pathways of the Opt1.5C scenarios, and proceeded at a more gradual pace. In particular, the trajectory of biomass shares indicates that the mid-century peak observed in the ZF scenarios can be avoided, implying that a rapid scale-up in energy crop production can also be avoided.\u003c/p\u003e"},{"header":"Energy supply transformation","content":"\u003cp\u003eSubstantial increases in power and hydrogen generation by the middle of this century are among the most prominent characteristics of the ZF scenarios (Fig. 3). In 2100, total power generation in the ZF scenarios increased by approximately 10\u0026ndash;20% compared to the Opt1.5C scenario, but power generation levels across all scenarios in this study, including the ZF scenarios, were within the range of the AR6 C1 and C2 scenarios (Fig. 3c). However, in 2050, significant increases in total power generation, particularly from solar and wind energy, were observed in the ZF scenarios (Fig. 3a, b). Notably, in the ZF2050 scenario of AIM, total power generation in 2050 increased 1.6-fold compared to the Opt1.5C scenario, reaching levels significantly exceeding the maximum observed in the AR6 C1 and C2 scenarios (Fig. 3c). Similarly, although the increase in absolute terms was not as pronounced, total power generation increased in the ZF2050 scenario of MESSAGEix, reaching 1.8-fold the level of the Opt1.5C scenario in 2050. The scale of solar and wind power generation in the ZF scenarios differed between models due to variations in assumptions about the availability of non-fossil power sources, such as nuclear and geothermal energy, and the extent to which energy service demands could be met with electricity or hydrogen on the end-use side. In the ZF2050 scenario of MESSAGEix, solar and wind power generation in 2050 was comparable to that in the Opt1.5C scenario of AIM. Total hydrogen generation, especially green hydrogen generation, also increased, particularly in the ZF2050 scenario of AIM, where it reached exceptionally high levels compared to typical 1.5\u0026deg;C scenarios (Fig. 3d). In the Opt1.5C scenario of MESSAGEix, fossil fuels and biomass with CCS accounted for a significant share of hydrogen generation. However, in the ZF scenarios, the phase-out of fossil fuels and the increased use of biomass in other hard-to-abate sectors resulted in hydrogen generation being dominated by green hydrogen (Extended Data Fig. 4b).\u003c/p\u003e\n\u003cp\u003eThe substantial increase in power generation by 2050 was driven by the need to support the phase-out of fossil fuels on the end-use side through both direct and indirect electrification (Fig. 3a,\u0026nbsp;b).\u0026nbsp;According to\u0026nbsp;AIM, indirect electrification\u0026nbsp;via\u0026nbsp;hydrogen and synthetic fuels\u0026nbsp;was\u0026nbsp;the primary strategy for achieving the phase-out of fossil fuels in end-use sectors, leading to a substantial increase in green hydrogen demand, including for synthetic fuel production.\u0026nbsp;As\u0026nbsp;green hydrogen generation involves considerable energy losses\u003csup\u003e23\u003c/sup\u003e, the significant increase in hydrogen generation compared to typical 1.5\u0026deg;C scenarios contributed to power generation growth (Fig. 3a). In the ZF scenarios, the use of synthetic fuels increased; however, nearly all\u0026nbsp;CO\u003csub\u003e2\u003c/sub\u003e sources for synthetic fuel production in 2050\u0026nbsp;were\u0026nbsp;biomass-based. As a result, unlike in 2100, there\u0026nbsp;was\u0026nbsp;no additional power demand from\u0026nbsp;direct air capture\u0026nbsp;(DAC) in 2050 (Fig. 3a and Extended Data Fig. 3a).\u0026nbsp;According to\u0026nbsp;MESSAGEix, both direct electrification and indirect electrification via hydrogen\u0026nbsp;were\u0026nbsp;key strategies for achieving\u0026nbsp;the ZF goal, and increased demand for electricity and hydrogen contributed to\u0026nbsp;an increase\u0026nbsp;in power generation (Fig. 3b).\u003c/p\u003e\n\u003cp\u003eThe rapid increase in power and hydrogen generation by the middle of the century under the ZF scenarios had a significant impact on scaling up supporting technologies such as solar\u0026nbsp;and\u0026nbsp;wind power, energy storage technologies, and\u0026nbsp;electrolysers, particularly in the first half of the century (Fig. 3d). A comparison of annual net\u0026nbsp;increases\u0026nbsp;in installed capacity for these technologies between the Opt1.5C and ZF scenarios\u0026nbsp;revealed\u0026nbsp;that the ZF scenarios\u0026nbsp;exhibited\u0026nbsp;more uneven growth rates and sharper peaks in installation\u0026nbsp;in the first half of the century\u0026nbsp;compared to the Opt1.5C scenarios. The rapid\u0026nbsp;upscaling observed\u0026nbsp;in the ZF2050 scenarios\u0026nbsp;surpassed\u0026nbsp;that of the Opt1.5C scenarios and is\u0026nbsp;therefore\u0026nbsp;expected to become a critical bottleneck for achieving large-scale electricity and hydrogen supply, and consequently, the phase-out of fossil fuels through direct and indirect electrification. Pushing back the target year for\u0026nbsp;the\u0026nbsp;full phase-out of fossil fuels to the end of the century would\u0026nbsp;stabilise upscaling\u0026nbsp;in the first half of the century and delay the timing of its peaks. MESSAGEix, which\u0026nbsp;makes\u0026nbsp;more optimistic\u0026nbsp;assumptions than AIM about\u0026nbsp;the availability of nuclear power and the range of energy service demands that can be met by non-hydrocarbons, showed more moderate\u0026nbsp;upscaling. Energy\u0026nbsp;storage\u0026nbsp;upscaling may have been\u0026nbsp;influenced by power system representation in the\u0026nbsp;models. In AIM, green hydrogen generation absorbed surplus electricity, resulting in lower energy storage capacity compared to MESSAGEix despite the higher level of power generation.\u003c/p\u003e"},{"header":"Emission-related outcomes","content":"\u003cp\u003eWhen fossil fuel phase-out was achieved, CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector were reduced in the ZF scenarios compared to the Opt1.5C scenarios by 2060\u0026ndash;2070 (Fig. 4 and Extended Data Fig. 5a). The degree of reduction from the Opt1.5C scenario varied depending on the model and target year. CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector in 2050 were reduced by 6\u0026ndash;68% in AIM and by 43\u0026ndash;89% in MESSAGEix (Fig. 4a). In 2050, residual CO\u003csub\u003e2\u003c/sub\u003e emissions from fossil fuels and industry (CO\u003csub\u003e2\u003c/sub\u003e-FFI) in the Opt1.5C scenario ranged from 12 to 16 GtCO\u003csub\u003e2\u003c/sub\u003e/yr (Fig. 4b). In the ZF2050 scenario, the full phase-out of fossil fuels lowered residual CO\u003csub\u003e2\u003c/sub\u003e-FFI to 2\u0026ndash;4 GtCO\u003csub\u003e2\u003c/sub\u003e/yr; residual CO\u003csub\u003e2\u003c/sub\u003e emissions from energy supply in AIM model included emissions from the utilisation of CO\u003csub\u003e2\u003c/sub\u003e captured from industrial processes. In the Opt1.5C scenario, both models used negative emissions from bioenergy with CCS (BECCS) in the energy sector by 2050, whereas in the ZF2050 scenario, no negative emissions were deployed in the energy sector. Additionally, in MESSAGEix, which endogenously determines CO\u003csub\u003e2\u003c/sub\u003e emissions from the agriculture, forestry, and other land use (AFOLU) sector, the reduction in residual CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector led to a decrease in negative emissions from the AFOLU sector in 2050.\u003c/p\u003e\n\u003cp\u003eIn the latter half of the century, CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector behaved differently between the AIM and MESSAGEix models, depending primarily on how emission constraints were imposed (Fig. 4a). In MESSAGEix, which applied cumulative carbon budgets for the entire century, deeper emissions reductions in the first half of the century led to higher CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector in the ZF scenario than in the Opt1.5C scenario after 2070. By contrast, AIM, which applied annual carbon budgets, did not exhibit such a rebound. In any case, cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector between 2020 and 2100 were reduced in the ZF scenarios of both models, particularly those that achieved ZF earlier. In the ZF scenarios, cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy sector between 2020 and 2100 were reduced by 2\u0026ndash;33% compared to the Opt1.5C scenario in AIM, and by 10\u0026ndash;36% for that in MESSAGEix. When cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions from the energy and AFOLU sectors were combined, reductions reached 2\u0026ndash;44% in AIM and 7\u0026ndash;32% in MESSAGEix.\u003c/p\u003e\n\u003cp\u003eThe scale and approach of CCUS differed between the Opt1.5C and ZF scenarios (Extended Data Fig. 5b). In ZF scenarios of MESSAGEix, the need for CCS decreased due to the reduction of residual emissions, resulting in smaller-scale CCS and CO\u003csub\u003e2\u003c/sub\u003e capture compared to the Opt1.5C scenarios over the entire century. In the Opt1.5C scenario of MESSAGEix, fossil fuel with CCS was deployed on a large scale, peaking around 2070, but its scale was significantly reduced in the ZF scenarios. By contrast, in the ZF scenarios of AIM, where CCU contributed to the phase-out of fossil fuels, CCS was decreased compared to the Opt1.5C scenario. As a result, the overall magnitude of CO\u003csub\u003e2\u003c/sub\u003e capture remained nearly the same as in the Opt1.5C scenario. In 2050, nearly all captured CO\u003csub\u003e2\u003c/sub\u003e, including that from fossil fuels, industrial processes, and biomass, was stored underground in the Opt1.5C scenario, whereas in the ZF scenario, almost all of it was utilised. Even in the latter half of the century, the contribution of CCU remained limited in the Opt1.5C scenario, whereas in the ZF scenarios, approximately 4 GtCO\u003csub\u003e2\u003c/sub\u003e/yr of CO\u003csub\u003e2\u003c/sub\u003e was utilised through bioenergy with CCU (BECCU) and direct air carbon capture and utilisation (DACCU). In both the Opt1.5C and ZF scenarios, the scale of DAC was 3\u0026ndash;4 GtCO\u003csub\u003e2\u003c/sub\u003e/yr, suggesting that, unlike power and hydrogen generation technologies, the scalability of DAC is unlikely to pose a ZF scenario-specific bottleneck.\u003c/p\u003e"},{"header":"Challenges and opportunities","content":"\u003cp\u003eEnergy transformations towards ZF energy systems highlight both challenges and opportunities from multiple perspectives (Fig. 5a\u0026ndash;i). Phasing out even residual fossil fuel consumption in the Opt1.5C scenarios triggered upscaling of supporting technologies, such as solar and wind power (Fig. 5a), and increased the cumulative energy investments in energy supply sectors from 2020 to 2100 by 8\u0026ndash;31% in AIM and 8\u0026ndash;34% in MESSAGEix (Fig. 5b). Similarly, cumulative energy investments in energy demand sectors during the same period rose by 11\u0026ndash;27% in AIM (Fig. 5d). In AIM, an earlier target year in the ZF scenarios was associated with greater stranded capacity of coal power plants by mid-century compared to the Opt1.5C scenario (Fig. 5c). By contrast, in MESSAGEix, which is a perfect-foresight model, the interaction between different ZF target years and the timing of investments in coal power plants was more complex. The stranded capacity showed the greatest increase in the ZF2070 scenario, and decreased in the ZF2050 scenario compared to the Opt1.5C scenario (Fig. 5c). In the ZF scenarios, non-hydrocarbon energy penetrated more deeply on the energy demand side compared to the Opt1.5C scenario (Fig. 5e). This result drove a shift from fossil fuel consumption technologies to electricity and hydrogen consumption technologies, which is anticipated to result in lifestyle changes.\u003c/p\u003e\n\u003cp\u003eThe phase-out of fossil fuels has a clear advantage, avoiding CCS and CDR as a consequence of deep reductions in CO\u003csub\u003e2\u003c/sub\u003e-FFI compared to the Opt1.5C scenario (Fig. 4). Cumulative geological CO\u003csub\u003e2\u003c/sub\u003e storage throughout the century was reduced by 37\u0026ndash;46% in AIM and 52\u0026ndash;77% in MESSAGEix compared to the Opt1.5C scenarios (Fig. 5f). Similarly, cumulative BECCS and DACCS deployment decreased by 35\u0026ndash;42% in AIM and 39\u0026ndash;74% in MESSAGEix (Fig. 5g). The energy transformation required for phasing out fossil fuels may have both positive and negative impacts on land use. In the ZF scenarios, biofuels were extensively utilised as non-fossil hydrocarbon fuels, resulting in increased primary energy supply from biomass and potentially greater pressure on the land use sector compared to the Opt1.5C scenarios (Fig. 5h). By contrast, in MESSAGEix, which considers interactions with the AFOLU sector, stronger emission reduction efforts in the energy sector under ZF scenarios alleviated the emission reduction burden in the AFOLU sector. Consequently, the need for negative emissions through measures such as afforestation was reduced (Fig. 5i). The implications of fossil fuel phase-out for the land use sector will need to be thoroughly evaluated in future studies.\u003c/p\u003e"},{"header":"Discussion and conclusions","content":"\u003cp\u003eWe conducted a model intercomparison using two global energy system models to obtain robust insights into ZF energy systems and to illustrate two distinct representative pathways towards achieving such systems. The two models employed partially different strategies for phasing out fossil fuels, most notably with respect to the degree of penetration of non-hydrocarbon energy on the energy demand side. Nonetheless, the transformation towards ZF energy systems was characterised by substantial mid-century increases in power and hydrogen generation compared to typical 1.5\u0026deg;C scenarios, which could make the scalability of technologies such as solar and wind power, energy storage, and electrolysers a critical bottleneck in achieving ZF energy systems. Additionally, challenges observed in 1.5\u0026deg;C scenarios, such as increased cumulative energy investments and lifestyle changes due to rapid energy system transformation\u003csup\u003e22\u003c/sup\u003e, could be further amplified in ZF scenarios. The implication for stranded investments, a negative consequence of rapid energy system transformation\u003csup\u003e40,41\u003c/sup\u003e,\u0026nbsp;varied\u0026nbsp;across models and scenarios, reflecting differences in investment timing and the\u0026nbsp;ZF\u0026nbsp;target year.\u0026nbsp;By contrast, ZF scenarios showed ancillary benefits such as lower peak and end-of-century temperatures,\u0026nbsp;leading to reduced climate impacts, lower reliance on CCS, and a decreased burden of emission reductions in the land\u0026nbsp;use sector compared to typical 1.5\u0026deg;C scenarios. Setting the target year to the end of this century, when near-ZF\u0026nbsp;energy systems are achieved under typical 1.5\u0026deg;C scenarios, would reduce additional efforts but also diminish the benefits of the full phase-out of fossil fuels. Based on the fundamental premise that the full phase-out of fossil fuels is sufficient but not necessary to achieve the 1.5\u0026deg;C target, it is crucial to\u0026nbsp;recognise\u0026nbsp;the challenges and opportunities of ZF scenarios highlighted in this study and\u0026nbsp;to\u0026nbsp;evaluate whether the full phase-out should be the ultimate goal of climate policy.\u003c/p\u003e\n\u003cp\u003eThe increased energy investments in ZF scenarios reaffirm that if the primary objective of climate policy is to limit global warming to 1.5\u0026deg;C, then typical 1.5\u0026deg;C scenarios characterised by partial allowance of fossil fuels alongside abatement and removal through CCS and CDR are more cost-effective than ZF scenarios focused on the full phase-out of fossil fuels. When considering whether society should pursue the full phase-out of fossil fuels despite understanding that it is not a cost-optimal 1.5\u0026deg;C pathway, the quantitative and qualitative challenges and opportunities of ZF energy systems provide valuable insights for decision-making. The potential for greater increases in energy investments and lifestyle changes is likely to pose challenges to the socioeconomic viability of the full phase-out of fossil fuels. However, ZF scenarios achieve significantly greater reductions in CO\u003csub\u003e2\u003c/sub\u003e-FFI compared to 1.5\u0026deg;C scenarios, leading to lower peak and end-of-century temperatures, and consequently reduced climate impacts. Reduced reliance on CCS and CDR in the ZF scenarios may enhance their potential for broader societal acceptance, given the barriers to social acceptance associated with geological CO\u003csub\u003e2\u003c/sub\u003e storage\u003csup\u003e13,42,43\u003c/sup\u003e. Moreover, the straightforward concept of ZF scenarios, which uniformly phase out all fossil fuel consumption,\u0026nbsp;may send\u0026nbsp;a stronger signal to fossil fuel producers to cease investment in fossil fuel exploration, extraction, and transmission and distribution infrastructure compared to 1.5\u0026deg;C scenarios, which involve a degree of ambiguity by allowing a certain amount of residual fossil fuel consumption only in hard-to-abate sectors. The challenges and opportunities associated with the full phase-out of fossil fuels gradually diminish as the target year is extended further into the future. Some challenges and opportunities within and beyond the energy sector are complementary, where strengthening one effort can ease another. For example, if significant changes in human\u0026nbsp;behaviours\u0026nbsp;and lifestyles\u0026nbsp;reduce\u0026nbsp;energy service demand,\u0026nbsp;as\u0026nbsp;observed\u0026nbsp;in previous studies\u003csup\u003e18,19\u003c/sup\u003e,\u0026nbsp;then\u0026nbsp;the energy investments\u0026nbsp;required\u0026nbsp;for\u0026nbsp;ZF\u0026nbsp;targets could decrease.\u0026nbsp;Thus, it is important to first\u0026nbsp;recognise\u0026nbsp;that\u0026nbsp;decarbonisation\u0026nbsp;and\u0026nbsp;defossilisation\u0026nbsp;are not necessarily equivalent,\u0026nbsp;and that mitigation pathways allowing for the limited use of fossil fuels in achieving climate targets should be widely understood by the public. With this understanding,\u0026nbsp;society\u0026rsquo;s\u0026nbsp;willingness to bear\u0026nbsp;additional\u0026nbsp;costs and embrace\u0026nbsp;behavioural\u0026nbsp;changes will determine whether a\u0026nbsp;ZF\u0026nbsp;energy system can become\u0026nbsp;the\u0026nbsp;ultimate goal, as well as\u0026nbsp;when the target year for achieving it should be set,\u0026nbsp;through\u0026nbsp;considering the challenges and opportunities of the full phase-out of fossil fuels.\u003c/p\u003e\n\u003cp\u003eThe rapid upscaling of technologies required to meet the mid-century power generation and hydrogen generation increases in ZF scenarios, compared to 1.5\u0026deg;C scenarios, would likely become one of the most critical bottlenecks to achieving them. Therefore, ZF energy systems will require both policies that directly target the full phase-out of fossil fuels, such as limiting fossil fuel extraction, banning fossil fuel-consuming equipment, or phasing out fossil fuel incentives including subsidies, and complementary policies that strongly promote the market expansion and cost reduction of power and hydrogen generation technologies. The COP28 final decision highlighted both transitioning away from fossil fuels in energy systems and tripling renewable energy capacity as global efforts for the Parties to contribute, and the results of the present study emphasise the importance of the latter approach. CCUS and CDR are often debated with respect to their potential to delay the phase-out of fossil fuels. The COP28 final decision acknowledged the contributions of abatement technologies and transitional fuels, such as fossil power plants with CCS, blue hydrogen, and ammonia. While some 1.5\u0026deg;C scenarios, including the Opt1.5C scenario in MESSAGEix, suggested potential contributions from these bridging technologies, their role was limited in the ZF scenarios, including the ZF2100 scenario. Therefore, allowing fossil CCUS technologies could be regarded as a loophole, and may not align with the full phase-out of fossil fuels as the ultimate goal of global climate change mitigation policy. However, unlike fossil CCUS technologies, the contribution of non-fossil CCUS in the ZF scenarios indicates they are no longer installed as an excuse to retain fossil fuels, but rather as a serious contribution to climate change mitigation. As highlighted by the COP28 final decision, the phrase \u0026ldquo;transitioning away from fossil fuels in energy systems, in a just, orderly and equitable manner\u0026rdquo; underscores the importance of addressing the heterogeneous regional implications of a full phase-out of fossil fuels. While detailed national- and regional-scale analyses are beyond the scope of this study, the ZF scenarios showed that international fossil fuel trade, which persisted to some extent even by the end of the century in the Opt1.5C scenarios, was phased out and replaced by expanded international trade in biomass and hydrogen-based energy carriers (Extended Data Fig. 6). Although these findings are incomplete, they suggest significant impacts on current fossil fuel-exporting countries. To secure their cooperation, complementary policies supporting a just and equitable phase-out of fossil fuels will be essential.\u003c/p\u003e\n\u003cp\u003eThis study had several limitations. First, the representation of energy demand sectors by the models may have overlooked some bottlenecks associated with the full phase-out of fossil fuels from the energy demand side. These include sectors such as steel\u003csup\u003e17\u003c/sup\u003e, chemicals\u003csup\u003e44\u003c/sup\u003e, and aviation\u003csup\u003e45\u003c/sup\u003e, whose representation\u0026nbsp;was\u0026nbsp;simplified in one or both of the models used in this study. Nonetheless,\u0026nbsp;our\u0026nbsp;scenario analysis likely provides sufficient qualitative insights into potential energy system transformations, along with associated challenges and opportunities,\u0026nbsp;for\u0026nbsp;reducing fossil fuel consumption to nearly zero. Second, while the models used in this study provided a broad assessment of the interrelationships within the energy system under a full phase-out of fossil fuels, they also\u0026nbsp;exhibited\u0026nbsp;limitations in their representation of power systems. Both models have introduced innovative approaches to capture challenges related to integrating variable renewable energy\u0026nbsp;(VRE),\u0026nbsp;such as considering hourly dispatch for selected representative days during technology selection and incorporating constraints on system flexibility and capacity reserves\u003csup\u003e32,46\u003c/sup\u003e.\u0026nbsp;However,\u0026nbsp;other models\u0026nbsp;are\u0026nbsp;specifically designed for\u0026nbsp;the\u0026nbsp;detailed\u0026nbsp;analysis\u0026nbsp;of power systems by narrowing their focus to specific regions and sectors, and the models used in\u0026nbsp;the present\u0026nbsp;study\u0026nbsp;fall\u0026nbsp;short\u0026nbsp;of\u0026nbsp;such\u0026nbsp;specialised\u0026nbsp;models\u0026nbsp;in terms of spatiotemporal resolution\u003csup\u003e47,48\u003c/sup\u003e. While the qualitative findings of\u0026nbsp;our\u0026nbsp;study are unlikely to be affected, a deeper understanding of the power system transformation required to support the increased power generation in the ZF scenarios will be necessary in future\u0026nbsp;analyses. Third,\u0026nbsp;we acknowledge\u0026nbsp;that the two models used to explore\u0026nbsp;ZF\u0026nbsp;energy systems\u0026nbsp;in this study\u0026nbsp;may not\u0026nbsp;have\u0026nbsp;fully\u0026nbsp;captured\u0026nbsp;the entire solution space. Intercomparison\u0026nbsp;of\u0026nbsp;1.5\u0026deg;C scenarios using\u0026nbsp;two models,\u0026nbsp;MESSAGEix and\u0026nbsp;REMIND (REgional Model of Investment and Development) has been conducted\u0026nbsp;previously\u003csup\u003e8\u003c/sup\u003e; however, a broader model comparison involving more models would be desirable in the future. Future research could further analyse the sustainability implications of ZF energy systems, including the land use impacts and their potential heterogeneous regional effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003ePart of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the National Member Organization. S.F. acknowledges support from Japan Science and Technology Agency (JST) as part of Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE, grant number JPMJAP2331). S.M. acknowledges support from the Support for Pioneering Research Initiated by the Next Generation presented by the Division of Graduate Studies, Kyoto University (JST SPRING, grant number JPMJSP2110) and the Madume Research Encouragement Prize Award. S.J., O.F. and V.K. acknowledge support from the European Union\u0026rsquo;s Horizon Europe Research and Innovative Action Programme under Grant Agreement No. 101137582 (HYway) and Grant Agreement No. 101183367 (NEWPATHWAYS).\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eS.M., S.J., V.K. and S.F. conceptualised the research. S.M. contributed to scenario design. All authors participated in the interpretation of the results. K.O. and O.F. developed the model. S.M. conducted the analysis, created the figures, and wrote the first draft of the paper. S.M., S.J., V.K., T.H. and S.F. contributed to the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUNFCCC. 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The model solves a linear programming problem at each time step to estimate the status of deployment, operation of energy technologies, and associated emissions by minimising the total energy system cost, which is represented as the sum of capital costs, operation and maintenance costs, energy costs, and emission costs for each technology. Constraints ensure that system requirements such as exogeneous energy service demands and emission caps are met. AIM-Technology operates with one-year time steps from 2005 to 2050 and five-year time steps from 2055 to 2100. Details of the model structure and mathematical formulation are provided in Oshiro \u0026amp; Fujimori (2022)\u003csup\u003e16\u003c/sup\u003e and Oshiro \u0026amp; Fujimori (2024)\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAIM-Technology is a technology-rich model that features detailed representations of energy supply and demand technologies across various energy sectors and sub-sectors. A comprehensive list of its technology options is available at the AIM-Technology documentation page (https://kenoshiro.github.io/AIM-Technology-doc/). AIM-Technology accounts for the extraction of fossil fuels such as coal (hard coal and lignite), crude oil, and natural gas. Resource potential is categorised into 12 different grades based on resource type and extraction cost. AIM-Technology includes an hourly resolution dispatch module, enabling detailed consideration of the variability in power supply from VREs such as solar and wind power, as well as variability in power demand. In this study, to account for seasonal variation in power supply from VREs and power demand, the model analysed the power supply\u0026ndash;demand balance on an hourly basis for 12 representative days, one from each month. AIM-Technology models the production of liquid and gaseous synthetic hydrocarbon fuels from hydrogen and captured CO\u003csub\u003e2\u003c/sub\u003e. It includes hydrogen generation through electrolysis, biomass and coal gasification, and natural gas steam reforming. Furthermore, it enables CO\u003csub\u003e2\u003c/sub\u003e capture from large emission sources, such as power and hydrogen generation, oil refining, biomass liquefaction, steel and cement production, and furnaces, as well as from DAC. AIM-Technology accounts for trade in coal, crude oil, natural gas, oil products, biomass (solid and liquid), and hydrogen-based energy carriers (ammonia, synthetic fuels, and methylcyclohexane). AIM-Technology does not consider electricity trade.\u003c/p\u003e\n\u003cp\u003eMESSAGEix\u0026ndash;GLOBIOM\u003csup\u003e34,35\u003c/sup\u003e soft-links the energy system model MESSAGEix\u003csup\u003e33,49\u003c/sup\u003e with the land use model GLOBIOM\u003csup\u003e50,51\u003c/sup\u003e. MESSAGEix is a perfect-foresight energy system model covering all regions of the world. It includes 11 regions and various energy activities, such as energy extraction, energy conversion (electricity, heat, liquid fuels, gaseous fuels, and hydrogen), and final energy consumption (industry, buildings, and transportation). MESSAGEix solves a linear programming problem to estimate the least-cost portfolio, minimising total system costs expressed as the sum of capital costs, operation and maintenance costs, and costs for emissions and land use, while considering given service demands and emission constraints. Detailed formulations are available at the MESSAGEix documentation page (https://docs.messageix.org/) and in Sullivan et al. (2013)\u003csup\u003e52\u003c/sup\u003e. For time slices set at five-year intervals from 2025 to 2050 and ten-year intervals from 2055 to 2100, MESSAGEix optimises the total discounted system costs as the sum across these time slices. MESSAGEix estimates the macro-economic demand response based on energy system and service costs through iterative calculations with the single-sector macroeconomic module MACRO\u003csup\u003e53\u003c/sup\u003e. GLOBIOM, which is a partial-equilibrium land use model, provides MESSAGEix with information on land-use dynamics, such as the potential and costs of bioenergy and the opportunities and expenses for emission reductions in the AFOLU sector. To reduce computational costs, rather than running the full GLOBIOM model iteratively, the MESSAGEix model adopts an iterative process with a GLOBIOM emulator, which approximates land use-related results during the optimisation process.\u003c/p\u003e\n\u003cp\u003eA detailed list of technology options available in MESSAGEix is available at the MESSAGEix documentation page (https://docs.messageix.org/projects/models/en/latest/). MESSAGEix covers extractions of coal, lignite, crude oil, and natural gas, with the potential of each resource graded according to varying extraction costs. MESSAGEix considers the reliability and flexibility requirements of the power system not by explicitly accounting for hourly power supply and demand, but by imposing a constraint that ensures sufficient dispatchable generator capacity in each time slice. The version of MESSAGEix used in this study includes hydrogen generation through coal and biomass gasification, natural gas steam reforming, and electrolysis, but does not include synthetic hydrocarbon production from captured CO\u003csub\u003e2\u003c/sub\u003e and hydrogen. Thus, all captured CO\u003csub\u003e2\u003c/sub\u003e is assumed to be stored underground. While CO\u003csub\u003e2\u003c/sub\u003e capture from large point sources is considered, DAC is not included. MESSAGEix assumes trade in solid, liquid and gaseous fuels, electricity, and liquid hydrogen.\u003c/p\u003e\n\u003ch2\u003eScenarios\u003c/h2\u003e\n\u003cp\u003eIn this study, multiple scenarios were modelled to understand diverse transition pathways for ZF energy systems. The ZF scenarios are labelled based on the target year for achieving full phase-out of fossil fuels (for example, ZF2050 indicates the complete elimination of fossil fuels by 2050), with constraints imposed on the upper limit of the fossil fuel primary energy supply, along with CO\u003csub\u003e2\u003c/sub\u003e emission constraints consistent with the 1.5\u0026deg;C target. The target years for ZF were set at 10-year intervals from 2050 (ZF2050) to 2100 (ZF2100). We employed a typical 1.5\u0026deg;C scenario (Opt1.5C) for comparison, without imposing the upper limits of primary supply of fossil fuels. The socioeconomic conditions are based on the middle-of-the-road Shared Socioeconomic Pathway (SSP2)\u003csup\u003e54\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn AIM-Technology, the upper limits on the primary supply of fossil fuels each year were determined by the primary supply of fossil fuels for the same year in the Opt1.5C scenario and the reduction rate from the Opt1.5C scenario. First, the primary supply of fossil fuels in the Opt1.5C scenario without fossil phase-out constraints was obtained. The upper bounds of the primary supply of fossil fuels for ZF scenarios were obtained by multiplying this supply with the scenario-specific reduction pathways, expressed as values relative to the Opt1.5C scenario. The primary supply of fossil fuels was reduced linearly, starting from 100% of the Opt1.5C scenario level in 2030 and declining to 0% of the Opt1.5C scenario level by the target year. In MESSAGEix\u0026ndash;GLOBIOM, which uses intertemporal optimisation, the pathways for phasing out fossil fuels are determined endogenously with greater flexibility compared to AIM-Technology, which is more myopic. Specifically, the upper limits on primary supply of fossil fuels each year are set to the levels for the same year in the Opt1.5C scenario before the target year, and to zero thereafter. In both models, the upper limits of fossil fuels were imposed by each region and fuel type (coal, crude oil, and natural gas).\u003c/p\u003e\n\u003cp\u003eIn the mitigation scenarios, we imposed CO\u003csub\u003e2\u003c/sub\u003e emission constraints consistent with 1.5\u0026deg;C temperature stabilisation, specifically limiting cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions across all sectors from 2018 to 2100 to 500 GtCO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e36\u003c/sup\u003e. First, in AIM-Technology, which is a recursive dynamic model, upper bounds on annual CO\u003csub\u003e2\u003c/sub\u003e emission were applied. The target emissions in AIM-Technology were CO\u003csub\u003e2\u003c/sub\u003e emissions from energy and industrial processes. Since AIM-Technology focuses only on the energy sector, the annual CO\u003csub\u003e2\u003c/sub\u003e emissions from energy and industrial processes, calculated under the condition of limiting cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions across all sectors to 500 GtCO\u003csub\u003e2\u003c/sub\u003e, were obtained from the integrated assessment model AIM-Hub and used as emission constraints. Additionally, CO\u003csub\u003e2\u003c/sub\u003e emissions from the AFOLU sector were fixed based on the outputs of AIM-Hub. In MESSAGEix\u0026ndash;GLOBIOM, which is an intertemporal optimisation model, an upper limit was imposed on cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions from all sectors over the period 2018\u0026ndash;2100. The target emissions in MESSAGEix\u0026ndash;GLOBIOM include CO\u003csub\u003e2\u003c/sub\u003e emissions from all sectors. Unlike AIM-Technology, MESSAGEix\u0026ndash;GLOBIOM endogenously determines CO\u003csub\u003e2\u003c/sub\u003e emissions from the AFOLU sector.\u003c/p\u003e\n\u003cp\u003eWe used the C1 and C2 scenarios obtained from the AR6 scenario database\u003csup\u003e10\u003c/sup\u003e as examples of typical 1.5\u0026deg;C energy systems for comparison with the ZF scenarios. We used the World v1.1 dataset. The C1 scenarios limit the temperature increase to 1.5\u0026deg;C, with no or limited overshoot, with a 50% likelihood. The C2 scenarios return warming to 1.5\u0026deg;C with a 50% likelihood after a high overshoot. The AR6 scenario database is described in IPCC (2022)\u003csup\u003e55\u003c/sup\u003e. Some indicators analysed in this study could not be directly obtained from the scenario database and were therefore calculated based on reported indicators. Regarding the shares in total final energy consumption shown in Fig. 2b, the final energy consumption of hydrocarbon fuels such as fossil fuels and biofuel was not disaggregated in detail for many scenarios. Therefore, it was calculated by fuel type (solid, liquid, and gaseous) as follows. Solid fuels were calculated directly using the final energy consumption of solid coal (\u0026ldquo;Final Energy|Solids|Coal\u0026rdquo;) and solid biomass (\u0026ldquo;Final Energy|Solids|Biomass\u0026rdquo;). Liquid fuels were disaggregated based on the secondary energy mix of liquid fuels in each scenario to approximate the final energy consumption of liquid fossil fuels and biofuels. Specifically, the share of liquid biomass (\u0026ldquo;Secondary Energy|Liquids|Biomass\u0026rdquo;) in the total secondary liquid energy (\u0026ldquo;Secondary Energy|Liquids\u0026rdquo;) was multiplied by the total final energy consumption of liquid fuels (\u0026ldquo;Final Energy|Liquids\u0026rdquo;) to estimate the final energy consumption of liquid biofuels. The remaining portion of the final energy consumption of liquid fuels was attributed to liquid fossil fuels. Scenarios that did not report secondary energy for liquid biomass (\u0026ldquo;Secondary Energy|Liquids|Biomass\u0026rdquo;) were excluded from this calculation. Gaseous fuels were calculated in the same manner as liquid fuels. The share of gaseous biomass (\u0026ldquo;Secondary Energy|Gases|Biomass\u0026rdquo;) in total secondary gaseous energy ((\u0026ldquo;Secondary Energy|Gases\u0026rdquo;) was multiplied by the total final energy consumption of gaseous fuels (\u0026ldquo;Final Energy|Gases\u0026rdquo;) to estimate the final energy consumption of gaseous biofuels. The remaining portion of the final energy consumption of gaseous fuels was attributed to gaseous fossil fuels. In cases where secondary energy for gaseous biomass (\u0026ldquo;Secondary Energy|Gases|Biomass\u0026rdquo;) was not reported, it was replaced with zero during the calculation.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe scenario data generated in this study have been deposited in the Zenodo repository (https://zenodo.org/records/14515207).\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe source code used for scenario data analysis and figure production is provided in the Zenodo repository (https://zenodo.org/records/14515207). The source code of the AIM-Techology model is available at the GitHub repository (https://github.com/KUAtmos/AIMTechnology_core). The source code of the MESSAGEix-GLOBIOM model is available at the GitHub repository (https://github.com/iiasa/message_ix). The data of the MESSAGEix-GLOBIOM baseline scenario used in this study is available via Zenodo (https://doi.org/10.5281/zenodo.10514052).\u003c/p\u003e\n\u003ch2\u003eReferences for Methods section\u003c/h2\u003e\n\u003col start=\"49\"\u003e\n\u003cli\u003eMessner, S. \u0026amp; Strubegger, M. User\u0026rsquo;s Guide for MESSAGE I1. (1995).\u003c/li\u003e\n\u003cli\u003eHavl\u0026iacute;k, P. \u003cem\u003eet al.\u003c/em\u003e Global land-use implications of first and second generation biofuel targets. \u003cem\u003eEnergy Policy\u003c/em\u003e\u003cstrong\u003e39\u003c/strong\u003e, 5690\u0026ndash;5702 (2011).\u003c/li\u003e\n\u003cli\u003eLotze-Campen, H. \u003cem\u003eet al.\u003c/em\u003e Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. \u003cem\u003eAgricultural Economics\u003c/em\u003e\u003cstrong\u003e45\u003c/strong\u003e, 103\u0026ndash;116 (2014).\u003c/li\u003e\n\u003cli\u003eSullivan, P., Krey, V. \u0026amp; Riahi, K. Impacts of considering electric sector variability and reliability in the MESSAGE model. \u003cem\u003eEnergy Strategy Reviews\u003c/em\u003e\u003cstrong\u003e1\u003c/strong\u003e, 157\u0026ndash;163 (2013).\u003c/li\u003e\n\u003cli\u003eMessner, S. \u0026amp; Schrattenholzer, L. MESSAGE\u0026ndash;MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively. \u003cem\u003eEnergy\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 267\u0026ndash;282 (2000).\u003c/li\u003e\n\u003cli\u003eRiahi, K. \u003cem\u003eet al.\u003c/em\u003e The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 153\u0026ndash;168 (2017).\u003c/li\u003e\n\u003cli\u003eIPCC. Annex III: Scenarios and modelling methods [Guivarch, C., E. Kriegler, J. Portugal-Pereira, V. Bosetti, J. Edmonds, M. Fischedick, P. Havl\u0026iacute;k, P. Jaramillo, V. Krey, F. Lecocq, A. Lucena, M. Meinshausen, S. Mirasgedis, B. O\u0026rsquo;Neill, G.P. Peters, J. Rogelj, S. in \u003cem\u003eIPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e (eds. Shukla, P. R. et al.) (Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022). doi:10.1017/9781009157926.022.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5698098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5698098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The final decision of the 2023 United Nations Conference of the Parties (COP28) called for transitioning away from fossil fuels, sparking a growing interest in the full phase-out of fossil fuels1. Integrated assessment and energy system models have outlined energy system transformation pathways to limit global warming to 1.5°C2,3. However, pathways towards a full phase-out of fossil fuels, which may require additional efforts beyond those for the 1.5°C goal, remain unclear4. Here, we employ two global energy system models to explore energy system transformations, and the challenges and opportunities related to attaining zero-fossil (ZF) energy systems. Our results showed that reaching a ZF goal by 2050 would accelerate direct and indirect electrification, involving 1.6–1.8-fold increases in total power generation compared to the cost-optimal 1.5°C pathways. This transformation would inevitably increase cumulative energy supply investments within this century by up to 30% and require the rapid scaling of technologies such as solar and wind power, as well as electrolysers in the near term. Despite opportunities including reduced climate impacts and lower reliance on carbon dioxide removal from the energy and land use sectors, these challenges imply that international society must approach the transition towards ZF energy systems with strong determination.","manuscriptTitle":"Energy system transformations for the phase-out of fossil fuels towards 1.5°C future","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-07 05:05:01","doi":"10.21203/rs.3.rs-5698098/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5f27d99f-e213-4629-9aee-e63cf846a699","owner":[],"postedDate":"January 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":41970945,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":41970946,"name":"Scientific community and society/Social sciences/Climate change/Climate-change mitigation"},{"id":41970947,"name":"Scientific community and society/Energy and society/Energy policy"},{"id":41970948,"name":"Scientific community and society/Energy and society/Energy supply and demand"}],"tags":[],"updatedAt":"2025-05-30T13:41:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-07 05:05:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5698098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5698098","identity":"rs-5698098","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}