Exploring the feasible net-zero transition pathway in China considering energy system flexibility

Domain: neuroscience
Published: 2026-04-11
Editorial outcome: major_revision

Abstract
Net-zero energy transition is projected to accelerate the replacement of fossil fuels with renewables, leaving system flexibility resources increasingly scarce. Here, we present a sub-annual energy-environment-economy model with endogenous hourly energy demand profiles and power balance dynamics including power dispatch, storage operations and demand-side response that co-optimizes supply- and demand-side flexibility, to map feasible transition pathways for China. The results show that compared with coarser timeslice representative of common modelling practice, sub-annual representation tightening flexibility needs with a high variable renewable energy and high electrification energy system. Results reveal steeper load ramps and frequent evening price strikes, identify increased activities in thermal power, nuclear power, hydrogen production, and energy storage. Accounting for temporal variability in supply and demand, the cost-optimal solution exhibits marginal abatement costs that are over 9% higher, but incorporating demand-side flexibility measures can mitigate cost growth and delineates least-regret portfolios for reliable, affordable decarbonization. Incentives for demand-side response such as load time-shifting and vehicle-to-grid can reduce investment in pumped hydro by 23% and yield more than a threefold cost-benefit ratio. The study highlights enhanced modelling of temporal dynamics within future energy model development and incentive-compatible market mechanism design for dispatchable resource development.

Introduction
In alignment with the Paris Agreement, key nations have updated their nationally determined contributions (NDCs) and declared strategies targeting mid-century net-zero emissions, necessitating swift energy system transition1. China, the top emitter of greenhouse gases in total amount2, is undergoing a rapid transformation, driven by the massive variable renewable energy (VRE) deployment and accelerated electrification. The increased variability in supply and demand is making the flexibility challenge more urgent for the purposes of enhancing energy security and facilitating the low-carbon transition3-7. Fragmented regulatory frameworks limited economic incentives and insufficient market mechanisms pose significant obstacles to the redistribution of load in a manner that aligns with power output, consequently constraining system flexibility8. Failing to adequately quantify system flexibility may result in the misallocation of resources toward unfeasible or misguided endeavors.

Multi-sector energy system model and integrated Assessment Model (IAM) are designed to provide a quantitative description of coupled human and natural system processes and their interactions, having been widely applied in long-term planning studies in energy and broader domains. Some national energy system model framework, such as TIMES-family, employ coarse sub-annual timeslices (timeslices by season and daily period)9, and several global IAMs operate at annual with limited operational detail, although optional timeslices exist10,11. Coarse timeslice modeling, however, often cannot fully capture diurnal ramps, peak load hours, or the operational synergies from demand-side flexibility. Without considering the challenges of power system flexibility, their proposed transition pathways casts doubt on the practicability12-18. The power system-focused models have detailed temporal resolution but fall short in projecting load patterns due to the lack of a comprehensive energy system perspective, consequently necessitating the external setting of future load projections19-26. Some studies have attempted to link IAMs to power system models. While these studies have partially addressed shortcomings associated with temporal resolution, they have encountered difficulties in accurately portraying the intricate interactions between energy storage, sector coupling, and demand-side response, which are recognized as critical to energy system flexibility27-31. Building hierarchically nested representative timeslices within the IAM framework facilitates the depiction of transition dynamics with higher temporal resolution, concurrently avoiding unnecessary computational requirements32-34. However, most studies concentrate on power dispatching, ignoring the immense potential within energy demand sectors35-37.

In this study, we enhanced the modeling of temporal dynamics in an energy-environment-economy model, China TIMES 2.0, using 56 representative timeslices nested at the annual-seasonal-weekly-daily-hourly to assess energy transition under operational constraints and flexibility enhancement measures. This approach establishes an integrated framework that aligns long-term strategic planning with the complexities of short-term operational dynamics. Based on industry-level energy consumption pattern modeling, the amplitude and shape of the power load curve are optimized endogenously. Additionally, simulations encompass the functioning of energy storage, variations in energy service demands across diverse time scales, and the application of demand-side response tools, including load time-shifting, orderly electric vehicle (EV) charging, and vehicle-to-grid (V2G) integration. Our findings indicate that effective coordination between orderly energy consumption, energy storage and power dispatching are pivotal in rectifying demand-supply imbalances. Rapidly rising electricity demand for EV charging and green hydrogen production should be directed to accommodate fluctuations in renewable energy generation. Active demand-side response strategies such as V2G and load time-shifting can significantly curtail peak load and alleviate ramping issues, thereby circumventing the need for energy storage investment, and preventing volatile price oscillations. This signifies the key in subsidizing demand-side response measures.

Results
Assessing the energy transition at the sub-annual level
China aims for net-zero emissions by 2060 amid rising energy demand and sectoral transition (Fig. 1). China is projected to peak its fossil fuel and industrial CO₂ emissions between 2025 and 2030, then carbon emissions will decrease rapidly to achieve carbon neutrality by 2060. Coal use will decline sharply post-2030, with renewables dominating power generation after 2035. In this study, we propose four hourly scenarios with different flexibility management measures and climate ambition: NDC, CN60, CN60-noLM, and CN60-LM, which represent the baseline reference, the net-zero scenario with orderly energy consumption (flexible load adjustment for EV charging and hydrogen production), the net-zero scenario maintaining current energy consumption patterns and the net-zero scenario with active supply-demand interactions (introduction of demand-side response measures), respectively. For comparison, we also present three scenarios considering coarser temporal resolutions: CN60-noTS, CN60-DN, and CN60-noHour, corresponding to no sub-annual modeling (most multi-sector energy system models in China), only considering day and night timeslices (China TIMES 1.0 model), and considering seasonal-weekly-daily modeling but without hourly modeling (common practice in national TIMES models).

Fig. 2 compares outcomes under different temporal granularity and flexibility management across mitigation strategy, energy demand, energy supply, dispatchable resources, and cost. Fig. 3 compares the results with prominent IAMs under a harmonized scenario framework with the same carbon budget constraints and net-zero targets. The finer temporal resolution reveals operational constraints that are not visible at coarse resolution which tighten feasible pathways for high VRE shares and fast electrification unless flexibility is enhanced accordingly. In addition, the distinct trajectories of thermal power, green hydrogen, and energy storage in China TIMES 2.0, which differ from other IAMs, clearly demonstrate the unique impact of system flexibility on long-term planning. We identify renewable resource curtailment and load shedding might both occur due to supply-demand mismatches. By 2060, electricity consumption in the transportation, industrial, and building sectors would decline by up to 7%, 5%, and 2%, respectively, compared to the annual scenario. When temporal variability is explicitly represented, the cost-optimal solution exhibits marginal abatement costs that are over 9% higher and total final energy about 3% lower by 2060 relative to a coarse-timeslice baseline. These differences arise because the model internalizes flexibility requirements and operational constraints that are muted at the annual scenario.

The discrepancies in the technology portfolio are more pronounced than those observed in aggregated pathways. Due to the difficulty of VRE integration, fossil fuel with carbon capture and storage (CCS) would play a prominent role before net-zero is achieved. Thermal power capacity in 2055 could increase by 16-21% in the CN60-DN and CN60-noHour scenarios compared to the CN60-noTS scenario. For hourly scenarios, the increase would be 30-34%. From the hourly perspective, we identify thermal power achieves a shift from electricity provider to flexibility provider, mitigating the possible shocks of rapid decommissioning. On the other hand, renewables proportion among energy mix would decrease by 2.2% for CN60-nohour and 3.0% for CN60. Comparing the three hourly scenarios, the demand-side measures have cross-sectoral impacts, in particular significant impacts on power transformation and energy storage development. The coarse time granularity masks the impact of different flexibility measures. Under the CN60-LM scenario, which considers active demand-side flexibility, photovoltaics (PV) generation would decrease by 2% compared to the annual-scale model, while in the CN60-noLM scenario, which does not include demand-side flexibility measures, PV generation would decrease by 12%. For scenarios considering 2-timeslice (CLN60-DN) and 32-timeslice (CN60-noHour) scenarios, PV generation would decrease by 3% and 4%, respectively. Demand for dispatchable resources would rise sharply with considerable differences between scenarios, with pumped hydro growing by 43-135% under hourly scenarios, and CN60-noHour and CN60-DN would grow by 90% and 46% respectively. Grid-connected hydrogen production would increase by more than 45% when seasonal factors are taken into account, but only by 27% when only diurnal variations are considered.

Flexibility scarcity leading to power plant repositioning
During periods of ramping, generation output variations, net load fluctuations, and peak load hours, flexibility scarcity issues are likely to arise. China has historically concentrated its flexibility resources on the supply side, primarily through thermal power and hydropower executing dispatch instructions. Risks associated with system flexibility are progressively accumulating, particularly following the expedited decommissioning of thermal power plants after 2040. Under the CN60 scenario, an annual coal-fired power plant (CFPP) retirement averages 26 gigawatts (GW) during the 2030-2040. This rate quadruples during 2040-2050, with almost all unabated CFPPs phased out by 2050 (Fig. 4a). As VRE expands, the proportion of dispatchable units will gradually decrease, thereby weakening the system flexibility. CFPP flexibility retrofits can significantly improve the dispatch performance of existing assets, partially alleviating the lack of flexibility (Supplementary Notes 2 and Supplementary Figure 16-17). The net load curve, the difference between the real load and VRE output, which is an indicator of power system flexibility, would be negative at noon after 2050 with two increasingly steep load ramps (Supplementary Fig. 6).

With the rapid spread of VRE, it is essential to rapidly scale up low-carbon flexibility resources. Nuclear, hydro and bioenergy with CCS (BECCS) power are expected to become increasingly valuable dispatchable resources of responding to rapid power load and supply fluctuations (Fig. 5c). Nuclear power, traditionally employed to meet baseload, but its flexible operation has gained momentum in both research38 and policy39 perspectives. Hydropower has gained significant importance in the high-VRE power system and is poised to become a central pillar of flexibility. Its operational focus is anticipated to transition from catering to peak loads to bolstering ramping capacity and accommodating nighttime loads, with over 35% of electricity demand at night being met by hydro.

Seasonal pattens in PV and wind output reshape system balance configuration (Fig. 5d). In summer, PV will dominate, requiring massive electricity storage during midday hours and low-load operation capabilities for thermal power plants. In winter, wind dominance will increase intraday power load fluctuations and multi-hour deficits. These dynamics will increase the demand for fast-ramping capacity, short-duration storage, and demand flexibility to contain curtailment and maintain adequacy.

Orderly demand easing power imbalance
It is anticipated that there will be an increase in total electricity consumption, rising from 7.6 petawatt-hours (PWh) in 2020 to 17.6 PWh by 2060 (Fig. 1d). This surge is coupled with an increase in peak load, projected to grow from 1.0 terawatt (TW) in 2019 to at least 2.5 TW in 2060. Peak loads could be even further in case power load fluctuates unpredictably.

Our study delineates the seasonal and intraday variations in power loads, a crucial aspect for integrating more challenging-to-dispatch power systems. A thorough examination of power load characteristics is conducted to investigate the flexibility resources embedded in energy consumption patterns. By comparing the CN60 and CN60-noLM scenarios, a discernible shift is evident in the consumption patterns of adaptive loads, including EV charging and hydrogen production. Under the CN60 scenario, results show EV would shift from charging primarily at night to charging during the day, utilizing PV power and alleviating excess electricity supply. Model results suggest adoption of fast-charging devices would increase dramatically, projected to increase from 15% in 2019 to 80% by 2060 in CN60 while remaining stable in CN60-noLM. Comparing year 2060 and year 2020 (Supplementary Figure 6a), the load profile for CN60 not only shows an increase in value, but also a change in shape from relatively flat to a significant daytime peak, attributed to the effect of orderly load. In the CN60-noLM scenario without demand-side participation, the shape of the load curve remains unchanged, thereby requiring higher storage capacity and activity (Fig. 6a).

By 2060, hydrogen is projected to account for approximately 7% of final energy consumption in China, with the majority originating from grid-connected hydrogen production facilities if considering flexibility demands (Fig. 7c). Compared with the CN60-noLM scenario, strategic hydrogen production in the CN60 scenario has the potential to utilize surplus daytime electricity, thereby mitigating rather than exacerbating power imbalances during peak demand periods. Furthermore, the conversion and storage of liquefied hydrogen present a viable method for long-duration energy storage.

Space heating and cooling are major contributors to seasonal variations in power load. Nowadays, fossil fuels and district heating (mostly fossil fuels) are the primary sources of the heating demand, while cooling demand is almost entirely met by electricity. Significant increases in winter electricity consumption are expected in the future due to the growing popularity of electric heating technology, thereby narrowing seasonal differences in electricity consumption (Fig. 5b). Hydrogen produced in spring and autumn is stored for use in summer and winter, thereby achieving cross-seasonal energy storage (Fig. 7d).

Addressing the flexibility crisis through demand-supply interaction
The degree of interaction between supply and demand determines the extent of the flexibility risk. Some demands such as EV charging can be economically optimized by adjusting its energy consumption behavior, and response to price fluctuations. Additional incentives could promote load time-shifting in BF-BOF steelmaking, chemicals, paper and cement industries. The CN60-LM scenario introduces load time-shifting and V2G across all economic sectors with differential incentives (Fig. 6b).

In the CN60-LM scenario, time-shifting and V2G technologies are projected to gain widespread adoption after 2045. Pumped hydro capacity is expected to reach 285 GW by 2060, a level deemed feasible with a consistent annual growth rate of 3% (Fig. 4d). In the CN60 scenario, it is necessary to invest pumped hydro facilities in nearly all eligible candidate sites (368 GW). Even worse, the CN60-noLM scenario exhausts the potential of pumped hydro and requires the intensive construction of other long-term storage facilities (417 GW).

V2G technology has emerged as a promising approach for managing peak load. V2G could shift as much as 7% of the peak load in 2060, which would result in substantial reductions in investments required for power balancing and thereby effectively lower the marginal cost of electricity (Fig. 8e). The result of sensitivity analysis of different V2G application rates can be found in Supplementary Notes 3 and Supplementary Figures 18-19.

The energy price increase resulting from emission reduction could incentivize demand-side load reduction, which is also an important means of demand-side response. By 2060, the energy service demands in the industrial sector in the net-zero scenarios are expected to decline by 7-15% compared to the NDC scenario, while transport turnover is projected to decline by 6%, except for aviation and navigation, which would drop by 15%. These figures illustrate the adaptive shifts in production mode and lifestyle in response to transformative pressures.

Financial implications of a feasible energy transition to net-zero
Considering system operation constraints at the hourly level (CN60), the total system cost would rise by about 4% compared to the CN60-noTS scenario. Enhanced temporal granularity scenarios show more demand on flexibility resources, and the differences between scenarios are mainly in the areas of electricity, hydrogen and energy storage (Supplementary Fig. 13). Net-zero transition in the CN60 scenario requires an increment annual investment by 50% compared to the NDC scenario (Fig. 8). Under the CN60 scenario, annual investment in VRE, energy storage, BECCS and hydrogen needs to be approximately 250, 30, 12 and 44 billion US dollars (USD) by 2060. Energy storage expenses would be 24% larger in the CN60 compared to the CN60-noTS by 2060.

Demand-side response exerts a substantial influence on energy storage expenditures. A 10% reduction in expenses is observed for the CN60-LM scenario relative to the CN60 scenario, while the CN60-noLM scenario demonstrates an expense increase of more than 12% compared to the CN60 scenario. Specifically, the CN60-LM scenario shows a 23% decrease in investment for pumped hydro attributable to active demand-side response measures compared to the CN60 scenario. Furthermore, Incentives for V2G technology, amounting to 4.9 billion USD, could lead to a large saving in power system investment by 14 billion USD.

The marginal costs of electricity show significant variations across different scenarios. The cost fluctuations are more pronounced within the day. Excess power generation during midday hours results in lower marginal costs. In contrast, generation falls sharply in the evening while demand remains high, leading to a spike in prices. More active demand-side response in the CN60-LM scenario can potentially decrease tariff volatility and cut peak hour tariffs in half, dropping from 0.135 to 0.072 USD kWh-1.

A significant reduction in the cost of hydrogen from renewables (green hydrogen) is anticipated. During periods of high solar power availability, green hydrogen price could potentially drop to as low as 2 USD kg-1 H2, lower than that of conventional hydrogen produced from fossil fuels (gray hydrogen). During nighttime, the cost of green hydrogen is projected to hover 3 USD kg-1 H2, aligning closely with the cost of fossil fuel-based hydrogen integrated with CCS (blue hydrogen). Based on an estimated 5000 hours of annual operating hours, green hydrogen produced during daylight hours is already cost-effective in the net-zero era, even without considering its contribution to system flexibility.

Methods
China TIMES 2.0 model overview
China TIMES 2.0 is designed as a reconstructed energy-environment-economy model adapted to the deep decarbonization and long-term sustainable transition in China (Fig. 9). With modeling capabilities for water system, land system and air quality system, China TIMES 2.0 is emerging as one of active national-level IAMs for China in the international climate change arena43. Building upon the China MARKAL44-46 and China TIMES47-51 models, which have been widely used in climate change mitigation and energy strategy initiatives, China TIMES 2.0 recalibrates the base year to 2019, with a model horizon spanning the entire century (2019 to 2100). Model outputs for 2020-2023 were calibrated against official statistics. Distinctively, the model achieves flexible time steps, with a one-year period until 2030, a five-year period from 2030 to 2060, and several 15-year periods thereafter. China TIMES 2.0 models the entire energy system at the sub-annual level, constructing 56 timeslices by stratifying the selection of representative time periods according to the annual-seasonal-weekly-daily-hourly nesting (Fig. 10), thus increasing cross-timescale carving capability as much as possible while taking into account computational efficiency. As a bottom-up linear planning framework, China TIMES 2.0 portrays the whole energy system process of energy extraction, energy processing, energy transmission, energy distribution, and energy end-use. The built-in emulator is connected to the GLOBIOM model, thus covering the main greenhouse gas emissions such as CO2, CH4 and N2O from the FFI as well as the agriculture, forestry and other land use (AFOLU) sector52.

Vast existing and future technologies empower the model to provide path projections with technical details. The energy demand for heating, cooling, hot water, cooking, refrigerators, electrical appliances, and data centers in the building sector is divided into 15 sub-areas based on five climate zones and three building types (urban, rural, commercial)53. The model classifies existing building envelopes from 1980 according to five generations of building standards and adds modeling of ultra-low and near-zero energy building envelopes. Old buildings can reduce their heating and cooling demands through envelope retrofits. From traditional energy-intensive technologies such as direct solid biomass combustion and incandescent lighting to advanced low-carbon technologies such as ground-source heat pumps and natural gas-hydrogen blended combustion, more than 50 energy end-use technologies and nearly 20 new and retrofit options for envelopes provide staunch support for building energy simulation in different building types and regions. Industrial production such as Na2CO3, NaOH, NH3, C2H4, cement54,55, glass, steel56-59, paper, and non-ferrous are modeled at the process level. To meet the need for deep decarbonization, fuels such as coal, oil, gas, hydrogen, biomass, and electricity are widely introduced, making up more than 100 industrial energy technologies. Benchmark, baseline and historical values are set for all technical processes to encourage to increase energy efficiency60. As for transportation sector, the model covers gasoline, diesel, electricity, hydrogen, natural gas, LPG, biofuel and carves motorcycles, low-speed three or four wheelers, light-duty vehicles, medium-sized buses, large buses, cabs, mini-trucks, small trucks, medium trucks, large trucks, normal trains, high-speed trains, subways, aviation, navigation, and pipelines, which are used to meet the passenger and freight transportation needs in different distances (intracity, intercity, and international)61.

The model also designed various energy supply and transmission technologies. Electricity and heat production are highlighted separately (see next section). Technologies such as fossil fuel extraction and trade, hydrogen production from fossil fuels (with or without CCS) and water electrolysis, coal washing, oil refining (with or without CCS), coking (with or without CCS), coal to oil or gas, biomass to oil or gas, water supply (groundwater, surface water, desalination, wastewater treatment), direct air capture storage (DACS) are represented in the upstream sector.

Electricity and heat production
China TIMES 2.0 model clusters existing power and heating units into 100 representative technologies with vintage based on plant-level data for coal, natural gas and nuclear power, as well as renewable energy data by technology category from China Electricity Council (CEC) and considers 186 future technologies that could be built new or retrofitted on existing units62,63. For the determination of the cooling type of each unit, information from the CEC is mainly used and cross-referenced with Google satellite maps on a plant-by-plant basis.

For thermal power, technologies are classified according to fuel type (biomass, coal, gas, oil, biomass-coal blending), unit size (1000 megawatts class, 600 megawatts class, 300 megawatts class, 100 megawatts class, small units), cooling method (air cooling, once-through cooling, recirculating cooling), steam pressure (ultra-supercritical, supercritical, subcritical, high-pressure), and whether combined heat and power (CHP) units64-66. The model portrays the ongoing CFPP retrofit plan. Flexibility retrofit, CCS retrofit, biomass-coal blending retrofit, and BECCS retrofit can be performed on compatible units. At the same time, units are able to gain better regulation capacity through additional investment (500 Chinese Yuans kW-1)67. Taking the ultra-supercritical unit as an example, the minimum load level after the retrofit dips from 40% to 20%. For nuclear energy, the model considers pressurized water reactors and determines a capacity limit based on the availability of coastal locations68. The model also portrays new types of nuclear reactors, such as the high-temperature gas-cooled reactor, which have better safety features and are assumed to be built inland, and therefore avoid the location constraint. All thermal power plants and nuclear reactors are set up with parameters such as ramp-up and ramp-down costs, start-stop costs, ramp-up and ramp-down rate constraints, minimum output levels, to more realistically reflect the operation of the power system20,69.

Renewable energy is expected to experience extremely rapid growth, because of its nature, it may pose risks of power system security. Run-of-river and regulated hydropower are portrayed separately in the model, and both provide supply curves with varying costs, thus reflecting the value of regulation resources. Offshore wind and onshore wind are treated differently according to the real data in 2018 representing the hour-by-hour output going forward70. Similarly, centralized PV, distributed PV, building-integrated PV (BIPV), and centralized solar power (CSP) set future sub-annual operations based on hourly data in 201870. We pre-processed the raw data for 2018 to find the hourly PV and wind generation and load as a percentage of the total for the year. In this way, we extracted the seasonal and intraday variations that characterize renewables and load, and excluded the influence of trend changes on the results. In addition, wave energy, tidal energy, geothermal energy, are also modeled. Biomass-fired power is one of the alternative choices to mitigate high-emitted CFPPs and can be equipped with CCS technology to contribute valuable negative emissions. There are fuel substitution technologies for almost every size of CFPPs, and a variety of combustion types such as biomass combustion, biomass gasification, and biomass-coal blending, as well as options for units equipped with CCS. In the model, we introduce the constraint of the peak load reserve margin, in which we require that the total credible capacity exceeds 10% of the peak load at each timepoint. The contribution of each technology to the peak load is described in Supplementary Table 1. The constraints on the capacity of the power plant are described in Supplementary Table 2.

Future load profile generation
As China is still in its rapid electrification progress, electricity is meeting energy service demands in expanding areas. As a result, future electricity load curves are expected to change significantly due to the addition of new electrical appliances. In this study, we propose a method to determine the time distribution of energy use according to behavioral patterns and construct energy use profiles for different energy service demands, thus avoiding exogenous prediction of future load curve. In this paper, according to the mandatory national standard issued in 2021, energy use for lighting, hot water, and cooking in different rooms is split by time of a day. The space cooling and heating demand is differentiated for different seasons, and they are also modeled by time of a day71. For electricity use for transportation, this study mainly considers the timing and mode of EV charging. In view of the various categories of EV chargers, fast charging and slow charging are portrayed according to the New Energy Vehicle Industry Development Plan (2021-2035), which corresponds to different time distribution of energy use in a day (Supplementary Table 3). Data center operations often require consistency, and their energy consumption cannot vary dramatically in a day. Electricity consumption from industry is used as the residue in the load profile to match historical load curves and thus complete the calibration for each sector. For the specific energy use curves for each demand, refer to Supplementary Fig. 14.

Energy storage and sectoral coupling
Given the unregulated nature of VRE, it is physically hard to achieve a real-time supply-demand balance in a power system with a high share of VRE, thus requiring energy storage. China TIMES 2.0 portrays electrical energy storage technologies such as pumped hydro, CAES, liquid flow batteries (VRB and Zn-Br), lithium-ion batteries (NMC, NCA, LFP, LTO), lead-acid batteries, molten salt batteries (Na-S and zebra), superconducting magnetic energy storage, and flywheel energy storage72. China TIMES 2.0 considers the current policy and sets a lower limit for energy storage development (10% of VRE capacity) and sets a lower limit for future pumped hydro capacity in accordance with the Medium- and Long-Term Development Plan for Pumped Hydro (2021-2035). In addition to the energy storage, China TIMES 2.0 also includes technological options for sectoral coupling. For non-ferrous metals smelting, EAF steelmaking processes, seawater desalination, and water electrolysis for hydrogen production, these technologies can be regulated more easily and flexibly, thus allowing them to be worked in an orderly manner within a certain range, bridging the gap between power supply and demand in real time. Furthermore, liquefied hydrogen storage and hydrogen-natural gas blending are also portrayed, thus maximizing the exploitation of flexibility resources.

Active demand-side response measures
In a market environment, where demand responds to both price changes and incentive signals, demand-side response measures are considered to have enormous potential to help balance the grid. All energy service demands can respond to price changes at each timeslice (annual, seasonal, weekly, daily, hourly) by means of price elasticity of demand which helps us model the curtailable load. Over a longer time scale, the price elasticity of demand can act to cut demand due to price increases resulting from the deployment of abatement technologies. Thus, it can represent the consumer behavior shift.

For all energy service demands, the energy demand can be time-shifted through incentives, thus reducing mismatch between supply and demand. EV charging demand can not only be shifted but can be reversed to supply power to the grid through V2G technology. Based on the TIMES open-source framework73,74, the China TIMES 2.0 model models load shifting as a special bi-directional energy storage technology that is able to represent the maximum allowed deviations from nominal demand loads, maximum advance or delay for meeting the shifted loads, and the value per hour shifted by setting the storage capacity, the storage time, and the operating cost, respectively. The model conservatively assumes a subsidy of approximately 4 US cents kWh-1 hour-1 of shifting, whether advanced or delayed, based on a subsidy of 1 Chinese Yuan kWh-1 for three-hour load shifting in Yunnan Province.

Scenario setting
In this study, four hourly scenarios are proposed. The NDC, as a reference, contains energy and climate policies issued until 2020 and considers the climate target of carbon peaking by 2030 in the updated NDCs. Carbon intensity reduction of more than 65% from 2005 and VRE capacity exceeding 1200 GW targets are bounded in the NDC scenario. The scenario is the cost-optimal solution within physical and technical feasibility constraints. The CN60, CN60-noLM and CN60-LM scenarios all address the carbon neutrality commitment of China (FFI CO2 net-zero by 2060) and correspond to distinct levels of demand-side participations. In addition, we propose a scenario CN60-noTS without sub-annual constraints, which removes the seasonal, weekly, and daily variability carve-outs of renewable energy outputs and loads (assumed to be averaged distributions) and relaxes the power unit flexibility constraints. CN60-DN represents the fluctuations in renewable energy and load considering only daytime and nighttime, which is the practice of the China TIMES 1.0 model48. CN60-noHour scenario depicts four seasons, weekdays and weekends, and four time periods in a day, for a total of 32 timeslices, similar to the leading national TIMES model.

For all net-zero scenarios, we set the cumulative carbon emissions for 2019-2100 under RCP2.6 assumption based on the carbon budget allocation principle (230 billion tons of CO2). The global carbon budget allocation is calculated according to the Equal Cumulative Per Capita (ECPC) methodology, with a historical allocation starting point in 1990, and a global pathway that selects the median pathway of the IPCC Sixth Assessment Report Category C4 (limit warming in 2 degrees with >50% probability). It also assumes that China follows the path of the NDC scenario until 2030, to reflect the inertia of the energy system and the institutional constraints. Price elasticity of demand and early retirement of power plant and industrial capacity are also allowed in all net-zero scenarios. For NDC and all net-zero scenarios, we set capacity limits for coal, nuclear, wind, and PV power plants based on policy and resource potential. For all end-use sectors, we assumed no further increase in the share of fossil fuel. In addition, for all scenarios, we require total carbon emissions to decline monotonically after peaking (no rebound).

The difference in assumptions between main scenarios centers on the difference in the degree of demand-side contribution to system flexibility. CN60-noLM represents a scenario where no demand-side response measures are implemented, and the energy user consumes energy in the same way as they do presently. This represents the case where energy prices do not affect demand-side behavior at all. CN60 is an intermediate scenario that envisions the electricity demand for transportation and hydrogen production adapting to variations in online electricity generation capacity. In this scenario, energy prices have an impact on demand that can be easily regulated, such as hydrogen production and EV charging, and users regulate the time distribution of energy use in response to price signals. Meanwhile, CN60-LM is a supply-demand interaction scenario that introduces load time-shifting across a wide range of industries alongside V2G technology and home energy storage feed-in. In this scenario, most of the energy service demands are capable of demand-side response with price incentives, and in particular, EV batteries, and home energy storage can be dispatched to balance system supply and demand.

For comparison, we introduced six prominent global IAMs. China TIMES and these six IAMs worked together on a simulation of the Glasgow+ scenario, guided by the ENGAGE project proposal. This task aims at exploring the consistency of mid-century strategies and policies with the overall objectives of the Paris Agreement. The protocol integrates national and global pathways into a coherent set of low-carbon mid-century strategies (Supplementary Table 4), assessing national mid-century strategies and their consistency with global pathways to 1.5 and 2°C warming levels and identifying the most effective policies in different countries and sectors. The proposal does not mandate harmonization of input assumptions across model groups due to differences in calibration data sources for national and global models. Based on the model input information voluntarily reported by the teams, China TIMES model is in the middle of the models in terms of the demand assumptions for steel, cement, and residential and commercial floor space. The GDP assumptions are on the high side of the models, and the population assumptions are on the low side of the models. Despite the differences in demand assumptions, for the trajectory trends and quantity differences in power sector emissions, storage, and thermal power identified by the comparisons in this paper, they can be accounted for by the implications of sub-annual level modeling.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper proposes the China TIMES 2.0 model, which significantly enhances the temporal granularity of traditional IAMs by introducing a nested temporal resolution and 56 representative timeslices. This framework provides a technical foundation for capturing energy system flexibility requirements (e.g., intraday fluctuations, seasonal variations). Overall, this paper demonstrates innovation and advances the existing literature. However, revisions are required before acceptance. Here are some comments I would incorporate into the paper.\n1) The main contribution of this paper is the presentation of a sub-annual modeling framework within a national IAM. However, the authors do not sufficiently elaborate on this advantage in the results section. In this regard, the most important result is Extended Data Fig. 1. It can be observed that most subplots in this figure show that there is no significant differentiation between the proposed model and other annual IAM models. In fact, for some indicators, the trends are almost identical between the paper's model and other models. The authors should expand their discussion on model comparisons to better highlight the advantages of their proposed model.\n2) On page 11, the authors mention that \"Similarly, centralized PV, distributed PV, building-integrated PV (BIPV), and centralized solar power (CSP) set future sub-annual operations based on hourly data in 2018\". Given that the model’s base year is 2019, using 2018 data for PV and wind hourly generation profiles introduces potential temporal misalignment. If updated data are available, the inputs should be harmonized. If not, a justification for using 2018 data must be added.\n3) Model validation against recent trends. The model starts planning pathways from 2019. Given that actual data for the past five years (2020-2024) may be available for indicators such as energy consumption, CO2 emissions, and renewable energy generation, a key question arises: which modeling pathway best aligns with recent trends? Addressing this question would strengthen the model’s empirical credibility and its relevance to real-world transitions.\n4) Parameter sensitivity analysis. The model relies on numerous predefined parameters. To assess robustness, the authors should: (a) Identify key parameters that distinguish this model from others (e.g., demand response elasticity, storage cost assumptions). (b) Conduct sensitivity analyses (e.g., Monte Carlo simulations) to quantify how uncertainties in these parameters affect outcomes (e.g., marginal abatement costs, technology portfolios).\n5) Page 6. The text cites \"Fig. 5d\", but Figure 5 does not include a panel labeled \"d\".", "reviewer_2": "The paper should be improved by addressing the following comments.\n1. Whether the model covers all factors and contingencies, response to special circumstances such as extreme weather, policy changes, etc. There is a need for further in-depth analysis of technological development uncertainties, such as the expansion of the scale of renewable energy from the point of view of renewable energy systems, the rate of decline in the cost of renewable energy technologies, and breakthroughs in energy storage technologies.\n2. Feedback on how the impacts of different policies and market mechanisms on the flexibility of the energy system will feed into forecasts and be accurately predicted.\n3. How is the balancing of peak load shifting for V2G with the utilization of renewable energy sources such as PV during daytime peaks considered.\n4. The sub-annual time model constructed is divided into: annual-seasonal-weekly-daily-hourly, whether energy flexibility is taken into account for over-seasonalization, and why not annual-monthly-weekly-daily-hourly and consider a more detailed division of time even down to the minute.", "reviewer_3": "This paper sets out to improve the temporal resolution of the China TIMES model, arguing that such models typically fail to, due to limited representation, capture the necessary flexibility needed for higher renewable systems. In doing so, the study makes a valuable contribution by enhancing the time-slice granularity of the China-TIMES model, strengthening its capability to represent time-varying electricity load curves, proactive demand response, and the interaction between demand and supply. The authors present comprehensive results illustrating China's energy transition under its net-zero targets, considering system flexibility and financial implications.\nHowever, there is a question as to whether the analysis is novel enough to meet the high bar for acceptance in a journal such as Nature Communications. There are other examples of IAM and energy system models running at higher temporal resolution. Therefore, further development of the argument regarding novelty is required.\nFurthermore, the paper needs to bring stronger clarity to the difference between the ‘standard’ model and improved model. It was not clear from the scenario descriptions as to the key scenarios that were being compared, although this could be partly deciphered from Figure 2. Also, key results need to feature in the abstract – to headline the main findings. Additional information is needed in the main paper (incl. Methods) as to what the ‘noTS’ scenario means. This is not described; if it is simply a scenario that has no timeslices (TS) at all then this is less interesting, but if it was the previous version of China TIMES that had some more aggregated TS then that would be a useful comparison.\nWith the model effectively demonstrating the role of transportation time-shifting load and the potential of various short- and long-term storage technologies in electricity supply - both of which are critical components of your results - it is necessary to understand the prior baseline before the improvements were made.\nThere is also the question of scenario design. While the study constructs three net-zero scenarios with varying levels of demand-side participation, the overarching net-zero constraints seem to dominate the energy transition dynamics, thereby weakening the impact of demand-side management assumptions. As a result, the authors should clarify their scenario assumptions or revise the scenario design to align more closely with this granular modelling approach. This would help better illustrate the marginal impacts of the sub-annual analysis on the national net-zero transition.\nIn general, some of the language could be tightened up, and would benefit from further proof reading.\nAbstract:\nThere is a need for some headline figures in here that underpin the main arguments about improved temporal resolution. What would you consider to be the main findings in quantitative terms?\nMain:\nLine 37. Define what you mean by ‘IAM’. Many modellers would view China TIMES as an integrated energy systems model, not an IAM.\nLine 41-42. Why does this result in ‘casting doubts’?\nLine 64-65. You state that “Our findings indicate that current IAM model does not capture well the challenges and opportunities of accessing large-scale VRE and electrical appliances.” What ‘model’ are you referring to – China TIMES or IAMs in general? Is this context to your research rather than your finding?\nLine 66-71. The energy demand temporal disaggregation proposed in this paper does not seem to directly address the obstacles mentioned - \"fragmented regulatory frameworks, limited economic incentives, and insufficient market mechanisms.” Could you clarify what the proactive demand response strategies refer to in your scenarios or modelling? Additionally, what policy recommendations can be derived from this analysis?\nResults:\nEnergy Transition:\nLine 76-81. Despite some description in Methods, more elaboration is needed here prior to reading the results. This includes for ‘noTS’, which is not described. What does ‘orderly energy consumption’ mean?\nFigure 1 would benefit from including the ‘noTS’ scenario for completeness - although perhaps may not show key differences due to the results being presented at an annual level.\nLine 82-95. Most modelling results presented here, along with the corresponding figures in your supplementary document, indicate no pronounced differences among your three CN60 scenarios. As I understand, China's key energy transition trends—such as the emissions peak, energy consumption peak, and declining trajectory—are primarily determined by your net-zero constraints or carbon budget constraints. Could you clarify how your different demand- and supply-side strategies (key modelling assumptions) applied in your scenarios influence China's net-zero transition pathway?\nAdditionally, what new insights does your modelling improvement provide for China net zero transition?\nAs shown in Figure 2, there is a significant difference between sub-annual and annual modelling analyses across the aspects you mentioned. However, this impact remains generally consistent across the three CN60 scenarios (e.g., sectoral electricity demand). As I understand, these variations cannot be attributed to the effects of more granular and flexible demand-side management on the net-zero transition. Instead, they likely reflect structural uncertainties in this TIMES modelling results. Therefore, I am not convinced that this sub-annual analysis demonstrates any substantial impact on the energy transition itself. Additionally, in your comparison with other modelling analyses, I recommend clarifying the key differences in scenario assumptions between your analysis and other harmonized scenarios - particularly regarding demand-side input assumptions, which may contribute more significantly to the differences you present than the variations in time slices.\nLine 108-124. Can you be a bit more precise as to the scenarios that are being compared in this section?\nFlexibility:\nLine 136-148. As shown in Figure 3c and 3d, the role of nuclear and hydropower storage shows generally consistent patterns in both NDC scenario and net zero scenarios. You then mention “However, cost and security considerations still limit its contribution to flexibility”. Could you clarify if you impose any constraints on the power sector, or if it is the model choice based you carbon budget constraints? If it is, I would recommend showing your fundamental assumptions around the power sector in the SI.\nLine 141. Can you evidence the statement – ‘its flexible operation has gained momentum in both research and policy perspectives’? What does this mean?\nLine 169-170. Can you state what you are comparing to when discussing the EV charging shift?\nLine 174-175. You state that “Concurrently, the load profile undergoes a transformation from a relatively flat distribution to one characterized by daytime peaks, thereby aligning more effectively with the PV power generation”. Which load profile do you mean? Could you refer to the corresponding figure here?\nLine 185-186. You write that “Nowadays, fossil fuels and district heating are the primary sources for satisfying the heating demand, while electricity almost exclusively fulfils cooling needs.” Is that district heating also fuelled by fossil fuels (e.g. Gas) in China?\nDemand-supply interaction:\nThe figures cited in the text are inconsistent with the actual figures. Figure 5b is electricity consumption in the transportation sector on a summer workday, while Figure 5d is missing. Figure 6e is marginal electricity supply costs for different time periods on a summer workday.\nLine 224-230 (line 459-468). Provide more detail on how demand shifting is achieved within the modelling framework. Price elasticity reducing demand level is not shifting the load. Is demand shifting to that described by Li et al. - https://www.sciencedirect.com/science/article/pii/S0306261918310237. More elaboration is needed.\nFinancial implications:\nAs with other sections, clarity on understanding the additional implications of improved temporality on system costs would be useful.\nDiscussion:\nDevelop this section more towards the implications of the results for decision makers, and for the modelling community.\nLine 273-274. ‘Most economical’ compared to what other options?\nLine 279-289. While interesting, are these results pertinent to the main argument of the paper?\nScenario setting:\nThe scenarios are distinct but need more clarification of common assumptions and differentiated assumptions, specific questions include:\nLine 470-423. In your NDC scenario, does it include over 65% reduction in carbon intensity and other renewable related target?\nLine 476-477. The statement “We set the cumulative carbon emissions for 2019-2100 under RCP2.6 assumption based on the carbon budget allocation principle (230 GtCO2)”, needs reference or clarification of the allocation principle, and why is it applied for all your scenarios?\nSupplementary document:\nSI Figure 2, why does coal use rebound in 2060 in urban areas within the hot summer–cold winter zone under your NDC scenario? However, in the same scenario and climate zone, coal use continues to phase down after 2030.\nSI Figure 6 lacks legend\nSI Figure 15 needs more elaboration around data source", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Referees\nNCOMMS-25-08417-T\nJune 25, 2024\nReviewer 1\nGeneral Comment:\nThis paper proposes the China TIMES 2.0 model, which significantly enhances the\ntemporal granularity of traditional IAMs by introducing a nested temporal resolution\nand 56 representative timeslices. This framework provides a technical foundation for\ncapturing energy system flexibility requirements (e.g., intraday fluctuations, seasonal\nvariations). Overall, this paper demonstrates innovation and advances the existing\nliterature.\nResponse:\nThank you very much for reviewing this paper and for your valuable comments, we\nwill improve the paper based on the suggestions you have provided, hoping that it\nwill convey our intentions more clearly to the reader.\nComment 1.1:\nThe main contribution of this paper is the presentation of a sub-annual modeling\nframework within a national IAM. However, the authors do not sufficiently elaborate\non this advantage in the results section. In this regard, the most important result is\nExtended Data Fig. 1. It can be observed that most subplots in this figure show that\nthere is no significant differentiation between the proposed model and other annual\nIAM models. In fact, for some indicators, the trends are almost identical between the\npaper's model and other models. The authors should expand their discussion on\nmodel comparisons to better highlight the advantages of their proposed model.\nResponse:\nThank you very much for your valuable comments. In this paper, we discuss three\ncomparisons: 1) a comparison between China TIMES and other IAMs under the\nharmonized scenario proposal; 2) a comparison of scenarios in which China TIMES\nconsiders sub-annual modeling or not; and 3) a comparison of scenarios in which\nChina TIMES considers different degrees of demand-side management.\nFor the first comparison, the Extended Data Figure 1 (Figure 3 in the revised version)\nthat you referenced, all models optimize pathways given the net-zero constraint in\n2060 and the same cumulative carbon emissions cap. As a result, the models show\nnearly identical trends in indicators such as CO emissions, renewable energy share,\n2\nand electrification rate, which suggests that the models are largely harmonized.\nHowever, due to the unique role of thermal power, green hydrogen, and energy storage\nfor system flexibility, their trajectory projections have been differentiated from other\nmodels. Take thermal power as an example, thermal power that offers flexibility,\nenabling reduced investment in storage and avoiding stranded assets in existing units\nby 2040, also leads to a delay in the decline of emissions in the power sector.\nFor the second comparison, Figs. 1, 2 and Supplementary Fig. 13 show the impacts\nfrom considering sub-annual modeling especially in terms of dispatchable resources,\nPV capacity, hydrogen and storage investments and welfare losses.\nFor the third comparison, it can be seen from Figs. 4-8 and Supplementary Fig. 13 that\ndemand-side management measures can have an impact on cross-sectoral energy\nand financial flows, due to the clear impact on energy demand in the transportation\nsector, investments in energy storage such as pumped hydro, marginal electricity and\nhydrogen prices, etc.\nIn order to further demonstrate the novelty of our paper, we have revised the results\nsection, while we have redrawn Fig. 1 and Supplementary Fig. 13 with the addition of\nthe CN60-noTS scenario, which illustrates the impacts of sub-annual modeling from\nthe perspective of energy and financial flows, respectively.\nChanges/additions to the manuscript:\n(Manuscript, Line 93-99) For comparison, we also present a scenario without sub-\nannual constraints, CN60-noTS, which removes the seasonal, weekly, and daily\nvariations in renewable energy output, load carve-outs (assuming an average\ndistribution) and relaxes the power plant flexibility constraints. Energy storage and\ndemand-side response measures are also not considered. Such a scenario is\nequivalent to the scenario of a traditional annual-level IAM model without embedded\nsub-annual level inscriptions.\n(Manuscript, Line 118-127) Fig. 3 compares the results with prominent IAMs under\nharmonized scenario framework. China TIMES and global IAMs ran the Glasgow+\nscenario under the ENGAGE project protocol to extend the net-zero target to all\ncountries based on income level, after considering countries already proposing a net-\nzero target (Supplementary Table 4). All models set the same carbon budget\nconstraints according to the equal cumulative per capita method, which ensures that\nthe outputs are comparable. The converging trends in carbon emission pathways,\nrenewables share, and electrification rates show that the models are largely\nharmonized. However, due to the unique role of thermal power, green hydrogen, and\nenergy storage for system flexibility, their trajectory projections have been\ndifferentiated from other models.\n(Manuscript, Line 143-145) Thermal power achieves a shift from electricity provider\nto flexibility provider, mitigating the possible shocks of rapid decommissioning, which\nis unique to the sub-annual perspective.\n(Manuscript, Line 147-151) Comparing the three sub-annual scenarios, the demand-\nside measures have cross-sectoral impacts, in particular significant impacts on power\ntransformation and energy storage development. Energy storage is an important\nsource of system flexibility, both for frequency and voltage support as well as for\nenergy transfer.\n(Manuscript, Fig.1) add CN60-noTS scenario\nFig. 1 China's energy system decarbonization pathway\n(Supplementary Information, Supplementary Fig.13) add CN60-noTS scenario\na\nb c\nd\ne\nSupplementary Fig. 13 Energy transition cost in electricity, hydrogen and storage for\nChina during 2020-2100.\nComment 1.2:\nOn page 11, the authors mention that \"Similarly, centralized PV, distributed PV,\nbuilding-integrated PV (BIPV), and centralized solar power (CSP) set future sub-\nannual operations based on hourly data in 2018\". Given that the model’s base year is\n2019, using 2018 data for PV and wind hourly generation profiles introduces potential\ntemporal misalignment. If updated data are available, the inputs should be\nharmonized. If not, a justification for using 2018 data must be added.\nResponse:\nThank you very much for your question. In view of the limitation of data access, the\ncurrent data on load, wind power and PV annual hourly levels are all referred from\nthe published paper in Nature Communications, and the statistics department does\nnot regularly publish the hourly generation and load information, so it is not yet\npossible to obtain the relevant data for 2019.\nFor this reason, instead of feeding the raw data directly into the model, we pre-\nprocessed the data to find the hourly PV and wind generation and the load as a\npercentage of the annual total. In this way, we extract seasonal and intraday fluctuation\ncharacteristics of renewable energy and load. The actual values imported into the\nmodel are those scaled by the full year 2019 totals.\nChanges/additions to the manuscript:\n(Manuscript, Line 494-497) We pre-processed the raw data for 2018 to find the\nhourly PV and wind generation and load as a percentage of the total for the year. In\nthis way, we extracted the seasonal and intraday variations that characterize\nrenewables and load, and excluded the influence of trend changes on the results.\nComment 1.3:\nModel validation against recent trends. The model starts planning pathways from\n2019. Given that actual data for the past five years (2020-2024) may be available for\nindicators such as energy consumption, CO2 emissions, and renewable energy\ngeneration, a key question arises: which modeling pathway best aligns with recent\ntrends? Addressing this question would strengthen the model’s empirical credibility\nand its relevance to real-world transitions.\nResponse:\nThank you very much for your question. The calibration of the model is the biggest part\nof the workload in the development of an energy system model or IAM model, and our\nmodel is indeed one of the few models that have updated the base year calibration to\nnear 2020 (the vast majority of the IAM models in the comparison are still calibrated to\n2015 as their base year). At the same time, the vast majority of IAM models do not\nreport data for every year, but usually on a 5-year cycle. In our model, the paths of all\nscenarios up to 2030 are aligned with the NDC scenario to reflect the inertia of current\npolicy. Currently the official published energy balances are only updated to 2022,\notherwise we have compared the data related to 2023.\nVariable Unit Model Statistic Diff\nCO Emissions (2023)1 Mt 11316.52 11605.93 -2.5%\nFinal Energy (2022)2 PJ 103137.47 99546.38 3.6%\nHydro power generation (2023)3 TWh 1324.43 1285.90 3.0%\nNuclear power generation (2023)3 TWh 432.09 434.70 -0.6%\nSolar power generation (2023)3 TWh 587.99 584.20 0.6%\nWind power generation (2023)3 TWh 913.38 885.90 3.1%\nHydro power capacity (2023)3 GW 421.54 422.37 -0.2%\nNuclear power capacity (2023)3 GW 56.91 56.91 0.0%\nSolar power capacity (2023)3 GW 609.49 610.48 -0.2%\nWind power capacity (2023)3 GW 441.34 441.44 0.0%\nAlthough our model is not tuned specifically for short-term forecasting, but rather\nfocuses on long-term path planning, we have forecast errors of less than 4% on the\ncarbon, energy and power generation indicators you are interested in, which is a good\nreflection of the reality of the situation.\nChanges/additions to the manuscript:\n(Manuscript, Line 418-419) Model outputs for 2020-2023 were calibrated against\nofficial statistics.\nComment 1.4:\nParameter sensitivity analysis. The model relies on numerous predefined\nparameters. To assess robustness, the authors should: (a) Identify key parameters\nthat distinguish this model from others (e.g., demand response elasticity, storage cost\nassumptions). (b) Conduct sensitivity analyses (e.g., Monte Carlo simulations) to\nquantify how uncertainties in these parameters affect outcomes (e.g., marginal\nabatement costs, technology portfolios).\nResponse:\nThank you very much for your question. Modeling requires a lot of assumptions about\nfuture techno-economic parameters. For this reason, we show important cost\nassumptions in Supplementary Table 5.\nWe know that sensitivity analysis is very important. However, given the computational\nscale of the model (10 hours for a single scenario on a 64-core CPU workstation),\nlarge-scale sensitivity analysis is difficult at this stage, and we have a dedicated\npaper for parameter uncertainty (https://www.nature.com/articles/s41467-021-27671-\n0). Therefore, we performed 1000 rounds of Monte Carlo simulations using the same\nconstraints but without the refined time resolution scenario (i.e., CN60-noTS). We set\nthe uncertainty range for wind power costs, PV costs, nuclear power costs, BECCS\ncosts, energy storage costs, and hydrogen energy costs to be 70%-143% of the\nintermediate scenario (Log-Normal distribution), and the uncertainty range for wind\npower installation growth, PV installation growth, nuclear power installation growth,\nand price elasticity to be 70%-130% of the intermediate scenario (Normal\ndistribution). The cumulative carbon budget for 2020-2100 is 200-250 Gt (uniform\ndistribution). In addition, we set up the On-Time Action Scenario (CN60) and the\nfollowing three figures illustrate the net-zero transition emission trajectories, marginal\nabatement costs, and power capacity under uncertainty perturbations in the input\nparameters.\nChanges/additions to the manuscript:\n(Manuscript, Line 627-628) Main parameter assumptions are listed in the\nSupplementary Table 5. Source Data are provided with this paper.\n(Supplementary Information, Supplementary Table 5) Add table.\nTechnology Unit 2025 2030 2060 2100\nPower Plant\nCoal without CCS USD/kW 547 545 535 535\nCoal with CCS USD/kW 1031 1000 913 913\nGas without CCS USD/kW 550 550 550 550\nGas with CCS USD/kW 904 897 897 897\nNuclear (PWR) USD/kW 1946 1897 1805 1805\nNuclear (HTGR) USD/kW 2571 2286 1829 1805\nBiomass without CCS USD/kW 1220 1102 735 735\nBiomass with CCS USD/kW 1916 1726 1136 1136\nHydro USD/kW 1066 1066 1066 1066\nOnshore Wind USD/kW 602 542 439 439\nOffshore Wind USD/kW 1287 1030 667 667\nSolar PV USD/kW 377 339 247 247\nSolar CSP USD/kW 2531 2405 1782 1371\nEnergy Storage\nPumped Hydro USD/kW 908 896 833 754\nBattery storage LFP USD/kWh 119 110 69 37\nBattery storage NMC USD/kWh 126 117 73 39\nHydrogen alkaline USD/kW 246 232 205 205\nHydrogen PEM USD/kW 1043 580 213 213\nHydrogen SOEC USD/kW 1712 1384 387 387\nDemand-Side Response\nLoad time-shifting USD/kWh·hour 0.04 0.03 0.02 0.02\nV2G feed-in cost USD/kWh 0.08 0.06 0.04 0.04\nSupplementary Table 5 Power plant construction, energy storage construction,\nhydrogen electrolyzer construction and demand side response costs.\nComment 1.5:\n5) Page 6. The text cites \"Fig. 5d\", but Figure 5 does not include a panel labeled \"d\".\nResponse:\nWe are very sorry for the issue with our figure citation, which has been corrected in\nthe revised version of the manuscript.\nChanges/additions to the manuscript:\n(Manuscript, Line 246) these interactions significantly diminish the reliance on power-\nbased energy storage, such as pumped hydro storage (Fig. 4d).\n(Extended Data Fig. 2) Rename to Fig. 4.\nReviewer 2\nComment 2.1:\nWhether the model covers all factors and contingencies, response to special\ncircumstances such as extreme weather, policy changes, etc. There is a need for\nfurther in-depth analysis of technological development uncertainties, such as the\nexpansion of the scale of renewable energy from the point of view of renewable energy\nsystems, the rate of decline in the cost of renewable energy technologies, and\nbreakthroughs in energy storage technologies.\nResponse:\nThank you very much for your question. The scenario setting and optimization\nsimulation of our model adopts a multi-layer temporal nested structure, which achieves\nthe simulation coverage of the whole year. Among which the most refined hourly\nsimulation, we selected the load and renewable energy output curves of a typical\nweekday in summer for 24 consecutive hours, which is used to reflect the daily\ncondition of the energy system operation. In order to ensure a correct estimation of\nenergy supply and demand throughout the year, the current simulations are likelihood\nscenarios rather than extreme scenarios. Therefore, we are mainly dealing with a “high\nprobability - low risk” scenario for the future development of the energy system. To\ncope with small probability-high risk situations, we retain a larger margin of safety while\nsatisfying normal operation by adding reserve constraints, credible output capacity\nconstraints, peaking constraints, and start-stop constraints.\nIn response to policy and technology uncertainty, this is indeed a constant theme in\nlong-term path planning research. We have quantified these factors you mentioned\nusing Monte Carlo analysis in our Nature Communications paper in 2022\n(10.1038/s41467-021-27671-0). Due to the heavy computational effort associated with\nenhanced temporal resolution, it is impractical to perform large-scale scenario\nsimulations in this framework. However, in response to your question, we performed\nMonte Carlo simulations using the same constraints but without the refined time\nresolution scenario (i.e., CN60-noTS). We set the uncertainty range for wind power\ncosts, PV costs, nuclear power costs, BECCS costs, energy storage costs, and\nhydrogen energy costs to be 70%-143% of the intermediate scenario (Log-Normal\ndistribution), and the uncertainty range for wind power installation growth, PV\ninstallation growth, nuclear power installation growth, and price elasticity to be 70%-\n130% of the intermediate scenario (Normal distribution). The cumulative carbon\nbudget for 2020-2100 is 200-250 Gt (uniform distribution). In addition, we set up the\nOn-Time Action Scenario (CN60) and the 2025 Early Action Scenario (CN60E) to\nrepresent different policy strengths. The results show that in 2060, the proportion of\nrenewable energy in the primary energy supply of the energy system is 60-66%,\nthe electrification rate is 56-59%, and the installed capacity of PV and wind power is 5.6-\n6.8 TW and 2.6-3.5 TW, respectively. More data and result figures can be seen in our\nresponse to Comment 1.4. It can be seen that our results remain robust under\nuncertainty perturbations.\nChanges/additions to the manuscript:\n(Manuscript, Line 403-404) While richer simulations of uncertainty and extreme\nscenarios are difficult to perform due to computational capacity limitations, the insights\ngarnered here provide a nuanced understanding of the multifaceted challenges and\nprospects pertaining to energy system flexibility in China, offering valuable lessons that\ncould be beneficial for other developing nations navigating towards a net-zero future.\nComment 2.2:\nFeedback on how the impacts of different policies and market mechanisms on the\nflexibility of the energy system will feed into forecasts and be accurately predicted.\nResponse:\nThank you very much for your question. In our modelling, we have carved out policies\nfor energy and climate change that have been released by the end of 2020. For\nexample, we consider no new coal power after 2030, wind and PV capacity exceeding\n1,200GW in 2030, and a reduction in carbon intensity of more than 65% in 2030\ncompared to 2005. These policies directly determine the future direction of energy\ntransition and climate change mitigation.\nIn order to describe the ongoing coal power “Three Retrofits” plan, our model is\ndedicated to the technologies of coal power biomass-coal blending retrofit, flexibility\nretrofit, and CCS retrofit. The model optimizes the transition options for different coal\npower units, i.e., continued service, early retirement, and retrofit, by considering the\ntechnical economics (age, efficiency, cost, etc.). For retrofits, multiple retrofits can be\nperformed simultaneously, or a particular retrofit can be performed separately.\nThe model aims at cost minimization and does not include subsidies at the level of\npower system operation. In 2025, the government has eliminated all renewable power\nsubsidies and incentives such as price and quantity guarantees, i.e., all power sources\n(including thermal and renewable power) are fully traded in the electricity market.\nFor demand-side response, the current level of subsidy varies from province to\nprovince in China, and given that we are a national model, we selected the demand-\nside response rate for load shifting in Yunnan Province, which has a moderate\ndemand-side response subsidy, as a basis (1 CNY/kWh for 3-hour load time-shifting),\nand set the subsidy for load time-shifting to 4 US cents/kWh·hour. Therefore, in model\noptimization, if the cost reduction due to load time-shifting is higher than the required\nsubsidy, the pair is operated to the extent that it can be time-shifted.\nChanges/additions to the manuscript:\n(Manuscript, Line 474-479) The model portrays the ongoing CFPP retrofit plan.\nFlexibility retrofit, CCS retrofit, biomass-coal blending retrofit, and BECCS retrofit can\nbe performed on compatible units. At the same time, units are able to gain better deep\nregulation capacity through additional investment (500 CNY/kW)63. Taking the ultra-\nsupercritical unit as an example, the minimum load level after the retrofit dips from 40%\nto 20%.\n(Manuscript, Line 558-565) Based on the TIMES open-source framework69,70, the\nChina TIMES 2.0 model models load shifting as a special bi-directional energy storage\ntechnology that is able to represent the maximum allowed deviations from nominal\ndemand loads, maximum advance or delay for meeting the shifted loads, and the value\nper hour shifted by setting the storage capacity, the storage time, and the operating\ncost, respectively. The model conservatively assumes a subsidy of approximately 4\nUS cents/kWh per hour of shifting, whether advanced or delayed, based on a subsidy\nof 1 CNY/kWh for three-hour load shifting in Yunnan Province.\nComment 2.3:\nHow is the balancing of peak load shifting for V2G with the utilization of renewable\nenergy sources such as PV during daytime peaks considered.\nResponse:\nThank you very much for your question. In terms of the mechanism of the model, China\nTIMES model uses a cost minimization optimization approach. During the midday\nhours, the supply exceeds the demand due to the higher renewable energy output,\nand thus the marginal cost of electricity for that time period decreases, which guides\nmore demand to be concentrated in this time period.\nTaking the transportation sector as an example, we are a priori given two charging time\nprofiles, fast charging and slow charging. For the CN60 scenario, we only consider the\nshift between fast and slow charging profiles, and for the CN60-LM scenario, we\nconsider load time shifting and V2G. Load time shifting is to shift a portion of the\ncharging demand from other time periods other than noon to the noon time for charging,\nwhere there is only a charging process between the EV and the grid, and the SOC of\nthe batteries is only on the rise. V2G technology, on the other hand, treats the EV as\nan energy storage technology, which is connected to the grid for both charging and\ndischarging during the day, and the SOC of its battery remains constant after\nfluctuations.\nThe difference in the cost of electricity over the course of the day due to the\nconcentration of PV output at noon, which directs the model to seek the least costly\nway to meet demand, involves both the supply-side derating of other adjustable power\nsources as well as the demand-side use of demand-side response measures to\nincrease load during that time of day.\nComment 2.4:\nThe sub-annual time model constructed is divided into: annual-seasonal-weekly-daily-\nhourly, whether energy flexibility is taken into account for over-seasonalization, and\nwhy not annual-monthly-weekly-daily-hourly and consider a more detailed division of\ntime even down to the minute.\nResponse:\nThank you very much for your question about our time nested structure tradeoffs. In\nour model, pumped hydro, CAES, and hydrogen storage all function as both intraday\nand long-duration storage. Undoubtedly, more fine-grained simulations (considering\neach month as well as taking into account the minute scale) would enable the checking\nof the short-term/ very-short-term system operational state at more time periods, but\nthis would significantly increase the computational burden of the model. At this stage,\nour model has about 200 million non-zeroes, and with the application of Gurobi's multi-\nthreaded Barrier method for solving, it takes about 10 hours and about 100 GB of\nmemory, which is close to the bottleneck of the computational power of desktop\nworkstations.\nOn the other hand, the focus of energy transition and climate change mitigation\nresearch is not to provide much insight into power system operational decisions, but\nrather to improve the feasibility of energy system planning by taking into account\noperational constraints, and thus to guide “big inertia” energy systems towards no-\nregrets investments.\nAt present, our division of seasons takes into account to a large extent the dry and wet\nseasons of hydropower, the seasonal differences in wind and solar resources, and the\nseasonal differences in space heating and cooling demand, and basically reflects the\ncharacteristics of power supply and demand in different months.\nHourly-level simulations that can reflect major flexibility aspects such as peak-\nshaving (2h or more), demand side response (1h-3h), thermal power start/stop (4-12\nhours), and energy storage operation (1-6 hours). Minute-scale simulation mainly\naddresses aspects such as frequency regulation, load tracking, ramp control, spike\nloads, etc., which are often generated due to uncertainty in energy use behavior or\nnatural conditions, and are not mainly dominated by socio-economic development and\nchanges in production mode and lifestyle, etc. For minute-scale simulations, the use\nof power system-specific models and scenario-generation techniques can provide\ngreater explanatory power for these extreme scenarios.\nChanges/additions to the manuscript:\n(Manuscript, Line 637-639) The model has about 200 million non-zero values, and\napplying the Barrier method of the Gurobi 11 software to solve it requires about 10\nhours of computation and about 100 GB of memory on a workstation with 64 CPU\ncores.\nReviewer 3\nGeneral Comment:\nThis paper sets out to improve the temporal resolution of the China TIMES model,\narguing that such models typically fail to, due to limited representation, capture the\nnecessary flexibility needed for higher renewable systems. In doing so, the study\nmakes a valuable contribution by enhancing the time-slice granularity of the China-\nTIMES model, strengthening its capability to represent time-varying electricity load\ncurves, proactive demand response, and the interaction between demand and supply.\nThe authors present comprehensive results illustrating China's energy transition under\nits net-zero targets, considering system flexibility and financial implications.\nHowever, there is a question as to whether the analysis is novel enough to meet the\nhigh bar for acceptance in a journal such as Nature Communications. There are other\nexamples of IAM and energy system models running at higher temporal resolution.\nTherefore, further development of the argument regarding novelty is required.\nFurthermore, the paper needs to bring stronger clarity to the difference between the\n‘standard’ model and improved model. It was not clear from the scenario descriptions\nas to the key scenarios that were being compared, although this could be partly\ndeciphered from Figure 2. Also, key results need to feature in the abstract – to headline\nthe main findings. Additional information is needed in the main paper (incl. Methods)\nas to what the ‘noTS’ scenario means. This is not described; if it is simply a scenario\nthat has no timeslices (TS) at all then this is less interesting, but if it was the previous\nversion of China TIMES that had some more aggregated TS then that would be a\nuseful comparison.\nWith the model effectively demonstrating the role of transportation time-shifting load\nand the potential of various short- and long-term storage technologies in electricity\nsupply - both of which are critical components of your results - it is necessary to\nunderstand the prior baseline before the improvements were made.\nThere is also the question of scenario design. While the study constructs three net-\nzero scenarios with varying levels of demand-side participation, the overarching net-\nzero constraints seem to dominate the energy transition dynamics, thereby weakening\nthe impact of demand-side management assumptions. As a result, the authors should\nclarify their scenario assumptions or revise the scenario design to align more closely\nwith this granular modelling approach. This would help better illustrate the marginal\nimpacts of the sub-annual analysis on the national net-zero transition.\nIn general, some of the language could be tightened up, and would benefit from further\nproof reading.\nResponse:\nThank you very much for your constructive comments. First of all, the main novelty of\nthis paper is the significant extension of the sub-annual (up to hourly) modeling of the\nIAMs/energy system model, especially in the portrayal of the demand side, which\novercomes the problem of exogenous load curves in previous studies and enables the\nintegrated simulation of power dispatch, load characteristic changes, and demand\nresponse measures in the same model. More responses about new insights and\nimprovements to our model can be found in the subsequent response to Comment 3.8.\nIn our response to Comment 3.18, we also provided recommendations for\npolicymakers and the modeling community based on the results.\nSecond, you mentioned the missing distinction between sub-annual models and the\noriginal models without sub-annual modeling. And there is a lack of key take-away\nfindings. For this reason, we have revised the summary in our response to Comment\n3.1 to include quantitative analyses for renewable energy, thermal power, dispatchable\nresources, and demand-side response. In this study, CN60-noTS, relaxes all sub-\nannual constraints and assumes that all supply and demand are equally distributed\nthroughout the year. To address the issue of model differences, we present the three\nsub-annual scenarios (CN60-noLM, CN60-LM, and CN60) and the annual scenario\n(CN60-noTS) differences at the beginning of the results section, and we show the\nresults for the CN60-noTS scenario in Fig. 1 and Supplementary Fig. 13, which are\ndetailed in the responses to the Comment 3.6, 3.7 and 3.17.\nThird, you mentioned the baseline regarding energy storage and demand-side\nresponse. Modeling with the goal of cost minimization does not result in significant\nactivity and investment in energy storage in scenarios that include only annual-level\nconstraints. The capacity and investment in CN60-noTS regarding energy storage is\ndue to the consideration of the current policy renewable energy mandate to equip\nstorage (10% of installed renewable energy) and the planned construction capacity of\npumped hydro. For details, see the response to Comment 3.10. It is not difficult to see\na significant rise in demand for energy storage in the scenarios that consider sub-\nannual balancing. Demand side response mainly responds to peak loads and therefore\nits main operating dynamics cannot be portrayed in models that do not consider sub-\nannual level modeling or even hourly level modeling.\nFourth, you provided suggestions for our scenario design. As you note, the net-zero\nconstraint and the carbon budget assumptions have a strong impact on the model\nresults, which is to be expected. However, how net zero is achieved is something that\nneeds to be carefully assessed. By setting up three scenarios with different levels of\ndemand-side participation, we show how the energy system, especially electricity,\nhydrogen, energy storage and demand behavior may change even with the same\nclimate goals. The importance of improving the temporal resolution during the\nmodeling of net-zero transition pathways is also reinforced by the comparison with\nscenarios that do not take into account sub-annual level modeling. To this end, we\nhave further elaborated on the scenario setting and inter-scenario comparisons in both\nthe main text and the methodology section, as detailed in the responses to Comment\n3.5, 3.21, 3.22, 3.23.\nFinally, we have carefully proofread the entire text in response to your questions to\nminimize ambiguity, opacity, and categorical statements, and some of the changes can\nbe found in the responses to Comment 3.2, 3.3, 3.4, 3.9, 3.11, 3.12, 3.13, 3.14,\n3.15, 3.17, 3.19, 3.24, 3.25, and 3.26.\nWe hope that our revisions will help you and the readers to better discover the\ncontribution of this study to China's net-zero transition and modeling community.\nComment 3.1:\nAbstract: There is a need for some headline figures in here that underpin the main\narguments about improved temporal resolution. What would you consider to be the\nmain findings in quantitative terms?\nResponse:\nThank you very much for your advice. We have included quantitative data on\nrenewable energy, thermal power, dispatchable resources, and demand side\nmanagement measures in the revised abstract.\nChanges/additions to the manuscript:\n(Manuscript, Line 21-27) China-based case study finds that, compared to the annual-\nlevel mitigation scenario, the renewables share would decrease by 4% by 2060, while\nthermal power capacity would increase by 18%; dispatchable resources (energy\nstorage, grid-connected hydrogen and electric vehicles) would grow faster. Incentives\nfor demand-side response such as load time-shifting and vehicle-to-grid can reduce\ninvestment in pumped hydro by 23% and yield more than a threefold cost-benefit ratio.\nComment 3.2:\nLine 37. Define what you mean by ‘IAM’. Many modellers would view China TIMES as\nan integrated energy systems model, not an IAM.\nResponse:\nThank you very much for bringing this up. It is true that there are different\nunderstandings of the definition of an IAM model, especially for national IAMs, which\noften do not directly integrate their results with climate models in the same way as\nglobal IAMs. Your suggestion for adding an IAM definition is apt, and it allows the\nreader to get a more intuitive sense of the modeling improvements presented in this\npaper.\nAdditionally, according to the definition of IAMC, the China TIMES model belongs to\nthe category of process-based optimization models at the national level, which focuses\non the energy system and also involves the quantitative description of the interactions\nbetween energy, water, land, air quality and other domains. For research on the\nenergy-food-water-air quality connection, you can follow our paper in Nature\nSustainability (10.1038/s41893-024-01400-z).\nChanges/additions to the manuscript:\n(Manuscript, Line 45-47) Integrated Assessment Models (IAMs) are designed to\nprovide a quantitative description of key human and earth system processes and their\ninteractions, which usually includes a portrayal of energy, land, water, climate, and\nother systems.\n(Manuscript, Line 413-414) With modeling capabilities for water system, land system\nand air quality, China TIMES 2.0 is becoming a representative national IAM for China.\nComment 3.3:\nLine 41-42. Why does this result in ‘casting doubts’?\nResponse:\nThank you very much for your question. In China, the current LCOE of wind power and\nPV with storage is already lower than the LCOE of coal power, which means coal\npower can't compete with renewable energy in terms of economy in the dimension of\nelectricity generation. Therefore, if models do not have the capacity to simulation at\nthe sub-annual level, the role of coal power for grid integration, peaking shelving,\ncapacity reserve, etc., cannot be fully charactered. Thus, without considering the role\nof coal power for flexibility, coal power is deemed to be cost-effective to exit quickly in\nthe near term in the face of carbon reduction pressures. Disorganized renewable\nenergy expansion and coal power exit have already forced time-sharing blackouts in\nsome Chinese provinces in 2022. As a result, a transition pathway that does not take\ninto account power system flexibility has been recognized by policymakers as having\na feasibility concern.\nChanges/additions to the manuscript:\n(Manuscript, Line 52-53) Without considering the challenges of power system flexibility,\nsuch pathways casts doubt on the practicability of their proposed transition pathways\nComment 3.4:\nLine 64-65. You state that “Our findings indicate that current IAM model does not\ncapture well the challenges and opportunities of accessing large-scale VRE and\nelectrical appliances.” What ‘model’ are you referring to – China TIMES or IAMs in\ngeneral? Is this context to your research rather than your finding?\nResponse:\nThank you very much for pointing out where our language is not rigorous enough, we\nwill try to tighten up the description. IAM modeling here refers to most current IAMs or\nenergy system models on an annual scale.\nChanges/additions to the manuscript:\n(Manuscript, Line 75-76) Most IAMs or energy models are typically modeled on an\nannual scale, making it difficult to capture well the challenges and opportunities of\naccessing large-scale VRE and electrical appliances.\nComment 3.5:\nLine 66-71. The energy demand temporal disaggregation proposed in this paper does\nnot seem to directly address the obstacles mentioned - \"fragmented regulatory\nframeworks, limited economic incentives, and insufficient market mechanisms.” Could\nyou clarify what the proactive demand response strategies refer to in your scenarios\nor modelling? Additionally, what policy recommendations can be derived from this\nanalysis?\nResponse:\nThank you very much for your question, the sentence you quoted we recognize as\nforming the context of the flexibility challenge, not our findings, and we have adjusted\nit to the first paragraph of the introduction.\nThe proactive demand response stands for proactive measures that provide\nspecialized incentives such as load time shifting, V2G, etc., and corresponds to the\nrelevant technologies in the CN60-LM scenario later. We will refine the text to make\nour intentions clearer.\nTwo policy insights can be gleaned from this study.1) Orderly energy consumption\n(especially EV and hydrogen production demand), energy storage, and power dispatch\nwill be important in the net-zero transition, and this paper quantifies their size and\noperating modes.2) Demand-side response measures, such as V2G, load time-shifting,\nwhich are highly cost-effective for peaking loads, should be incentivized.\nChanges/additions to the manuscript:\n(Manuscript, Line 39-42) Fragmented regulatory frameworks, limited economic\nincentives and insufficient market mechanisms pose significant obstacles to the\nredistribution of load in a manner that aligns with power output, consequently\nconstraining system flexibility.\n(Manuscript, Line 78-85) Our findings indicate that effective coordination between\norderly energy consumption, energy storage and power dispatching are pivotal in\nrectifying demand-supply imbalances. Rapidly rising electricity demand for EV\ncharging and green hydrogen production should be directed to accommodate\nfluctuations in renewable energy generation. Proactive demand response strategies\nsuch as V2G and load time-shifting, can significantly curtail peak load and alleviate\nramping issues, thereby circumventing the need for energy storage investment, and\npreventing volatile price oscillations. This signifies the key in subsidizing demand-side\nresponse measures.\nComment 3.6:\nLine 76-81. Despite some description in Methods, more elaboration is needed here\nprior to reading the results. This includes for ‘noTS’, which is not described. What does\n‘orderly energy consumption’ mean?\nResponse:\nThank you very much for your advice. We have added explanations to CN60 and\nCN60-LM indicating the most important characteristics of the changes. Where CN60\nordered energy demand mainly means that hydrogen production can be regulated over\ntime and EV charging can freely choose between fast and slow charging modes (but\nnot load time shifting); CN60-LM considers demand-side response measures such as\nload time shifting for almost all energy service demands and adds V2G technology\ncarve-outs.\nFor the CN60-noTS scenario, we have also enriched the description by stating the\nmain sub-annual constraints and technical carve-outs that we remove.\nChanges/additions to the manuscript:\n(Manuscript, Line 88-99) In this study, we propose four sub-annual scenarios: NDC,\nCN60, CN60-noLM, and CN60-LM, which represent the baseline reference, the net-\nzero scenario with orderly energy consumption (flexible load adjustment for\ntransportation and hydrogen production), the net-zero scenario maintaining current\nenergy consumption patterns and the net-zero scenario with proactive supply-demand\ninteractions (introduction of demand-side response for almost all energy service\ndemands and V2G technology), respectively. For comparison, we also present a\nscenario without sub-annual constraints, CN60-noTS, which removes the seasonal,\nweekly, and daily variations in renewable energy output, load carve-outs (assuming an\naverage distribution) and relaxes the power plant flexibility constraints. Energy storage\nand demand-side response measures are also not considered. Such a scenario is\nequivalent to the scenario of a traditional annual-level IAM model without embedded\nsub-annual level inscriptions.\nComment 3.7:\nFigure 1 would benefit from including the ‘noTS’ scenario for completeness - although\nperhaps may not show key differences due to the results being presented at an annual\nlevel.\nResponse:\nThank you very much for your suggestion, we have added the CN60-noTS scenario in\nFigure 1.\nChanges/additions to the manuscript:\n(Manuscript, Figure 1) modify figure.\nComment 3.8:\nLine 82-95. Most modelling results presented here, along with the corresponding\nfigures in your supplementary document, indicate no pronounced differences among\nyour three CN60 scenarios. As I understand, China's key energy transition trends—\nsuch as the emissions peak, energy consumption peak, and declining trajectory—are\nprimarily determined by your net-zero constraints or carbon budget constraints. Could\nyou clarify how your different demand- and supply-side strategies (key modelling\nassumptions) applied in your scenarios influence China's net-zero transition pathway?\nAdditionally, what new insights does your modelling improvement provide for China net\nzero transition?\nAs shown in Figure 2, there is a significant difference between sub-annual and annual\nmodelling analyses across the aspects you mentioned. However, this impact remains\ngenerally consistent across the three CN60 scenarios (e.g., sectoral electricity\ndemand). As I understand, these variations cannot be attributed to the effects of more\ngranular and flexible demand-side management on the net-zero transition. Instead,\nthey likely reflect structural uncertainties in this TIMES modelling results. Therefore, I\nam not convinced that this sub-annual analysis demonstrates any substantial impact\non the energy transition itself. Additionally, in your comparison with other modelling\nanalyses, I recommend clarifying the key differences in scenario assumptions between\nyour analysis and other harmonized scenarios - particularly regarding demand-side\ninput assumptions, which may contribute more significantly to the differences you\npresent than the variations in time slices.\nResponse:\nThank you very much for your enlightening comments. Overall, we will respond to your\ncomments in three ways: a comparison of the impacts of the sub-annual model\nscenarios and the annual model scenarios on the transition paths; a comparison\nbetween the three sub-annual scenarios with varying degrees of demand-side\nparticipation; and a comparison of the assumptions of the models proposed in this\nstudy with those of other comparative models.\n1. First of all, as you said, the most critical influences on the net-zero transition pathway\nare the carbon budget and the net-zero target. Within a given carbon constraint, we,\nmodel both supply and demand sub-annually to address the feasibility concerns arising\nfrom the current change in energy system landscape under the rapid development of\nrenewable energy in China.\nFor the supply side, 1) we consider the output curves of renewables, the\nregulation performance of thermal, hydro and nuclear power, which changes the\nchoice of model optimization. In the model without sub-annual modeling, since PV\nalready has a clear cost advantage, without additional constraints, almost all future\nelectricity demand will shift to be supplied by PV, which is clearly a large gap with reality.\n2) We consider diverse energy storage needs and achieve endogenous\noptimization of the model for energy storage capacity expansion and operation\nsimulation. Without sub-annual modeling, energy storage will not be applied based\non cost considerations, and most energy system models or IAMs currently use a given\nproportion of renewable energy with storage, while such an exogenously given\ncoefficient is still questionable; 3) We consider the coordination between green\nhydrogen production and the power system. When sub-annual modeling is not\nconsidered, the green hydrogen cost are annual average cost, which are significantly\nhigher than the cost using midday affluent power. Also. orderly green hydrogen\nproduction is an important complement to system flexibility, an operational state that is\nnot captured in annual-scale models.\nFor the demand side, 1) we consider the real-time matching of supply and demand\non a sub-annual basis. When sub-annual modeling is not considered, supply-\ndemand matching is built on an annual scale, thus ignoring many sub-annual\nmismatches that pose feasibility concerns; 2) we consider the temporal distribution\nfor each type of energy service demand and endogenously generate load curves\nat different periods in the future. Existing power system-specific models’ future load\nprofiles are often held constant or exogenously given, but changes in the load curves\nwith electricity substitution and behavioral changes can have impacts on the pathways;\n3) We consider demand-side response measures such as demand curtailment\nand demand shifting. The costs and benefits of demand-side response are portrayed\nby making full use of multi-timeslice modeling of elastic demand, load time-shift,\nand V2G, and setting appropriate subsidies according to the real situation.\nThrough sub-annual modeling, we were able to obtain some special results, some\nexamples are as follows: the shift of thermal power from providing power to providing\nflexibility rather than a quick exit provides a path for a smooth transition of thermal\npower; for renewables, hydropower and wind power received more attention; and for\nenergy storage, we identified a clear correlation between the pumped hydro capacity\nand the level of demand-side participation, whereas the battery storage capacity is\nmainly associated with the PV and wind power capacities are correlated. Charging\nmodes will tend to favor the midday fast charging option to take advantage of daytime\nsolar energy.\n2. Secondly, as you say, if you look at electricity consumption by sector, there is little\ndifference between the three sub-annual scenarios. This is due to the need to meet\nexogenously given energy services demand regardless of the level of demand side\nparticipation. But looking at the three scenarios' dispatchable resource, wind and PV\nratios, and energy storage investments, you can see significant differences. This also\nillustrates the cross-sectoral impact of demand-side measures, making a significant\ndifference to the transformation of the electricity sector in particular. Thus, the\ndifferences in the power sector expansion and operating modes, investments, and\ntransition burdens required to meet demand in the three scenarios that essentially fix\nthe end-use sector transition trend and carbon emissions are precisely what demand-\nside participation creates.\n3. Since the ENGAGE project does not harmonize the demand inputs for each model\ngroup, the demand input assumptions used by each model may vary. We reviewed the\nraw data submitted to the database, and no fewer than three models reported data in\nGDP, population, cement production, steel production, and floor space of the\nresidential and commercial houses. See the figure below for details.\nFor socio-economic parameters, China TIMES model considers China's policy goal of\ndoubling its GDP in 2035 compared to 2020, and sets an average annual growth rate\nof 3% after 2040. Our model takes into account the fact that China is already in a trend\nof population decline, which is slightly less overall than the other models. Calculations\nshow that the main driver of demand, GDP per capita, will be on the high side of all\nmodels, reaching the policy target of $30,000 per person in 2050.\nThere are large differences in the base year due to the different sources of calibration\ndata for the different models, but in terms of trends, the China TIMES model is basically\nin the middle of the models in terms of indicators such as iron and steel, cement, and\nfloor space of the residential and commercial houses.\nAs can be seen in Figure 2, there are some uncertainties between the different models,\nwhere the main differences between China TIMES and the other models are reflected\nin the emission reduction pathways of the power sector, thermal capacity, and energy\nstorage ratio. These items show significant differences in trends that can be explained\nnot only by the size of demand, but due to the sub-annual level modeling that exploits\nthe easy-to-overlook value of thermal power. Hydrogen energy, on the other hand,\nenters an accelerated period of development after 2040, with electrification rates\nshowing a clear bottleneck after 2040, because power balance becomes more difficult.\nChanges/additions to the manuscript:\n(Manuscript, Line 143-145) Thermal power achieves a shift from electricity provider to\nflexibility provider, mitigating the possible shocks of rapid decommissioning, which is\nunique to the sub-annual perspective.\n(Manuscript, Line 147-151) Comparing the three sub-annual scenarios, the demand-\nside measures have cross-sectoral impacts, in particular significant impacts on power\ntransformation and energy storage development. Energy storage is an important\nsource of system flexibility, both for frequency and voltage support as well as for\nenergy transfer. Energy storage is an important source of system flexibility, both for\nfrequency and voltage support as well as for energy transfer.\n(Manuscript, Line 175-176) Hydropower has gained significantly in importance in the\nsub-annual perspective and is poised to become a central pillar of flexibility in the\npower supply side.\n(Manuscript, Line 205-206) Benefiting from cost differences across time due to sub-\nannual modeling, under the CN60 scenario, EV would shift from charging primarily at\nnight to charging during the day, thereby utilizing PV power.\n(Manuscript, Line 608-624) For comparison, we introduced six prominent global IAMs.\nChina TIMES and these six IAMs worked together on a simulation of the Glasgow+\nscenario, guided by the ENGAGE project proposal. This task aims at exploring the\nconsistency of mid-century strategies and policies with the overall objectives of the\nParis Agreement. The protocol integrates national and global pathways into a coherent\nset of low-carbon mid-century strategies, assessing national mid-century strategies\nand their consistency with global pathways to 1.5/2°C warming levels and identifying\nthe most effective policies in different countries and sectors. The proposal does not\nmandate harmonization of input assumptions across model groups due to differences\nin calibration data sources for national and global models. Based on the model input\ninformation voluntarily reported by the teams, China TIMES model is in the middle of\nthe models in terms of the demand assumptions for steel, cement, and residential and\ncommercial floor space. The GDP assumptions are on the high side of the models, and\nthe population assumptions are on the low side of the models. Despite the differences\nin demand assumptions, for the trajectory trends and quantity differences in power\nsector emissions, storage, and thermal power identified by the comparisons in this\npaper, they can be accounted for by the implications of sub-annual level modeling.\nComment 3.9:\nLine 108-124. Can you be a bit more precise as to the scenarios that are being\ncompared in this section?\nResponse:\nThank you very much for your advice. We have added the ENGAGE project protocol\non scenario design in the text. The detailed net-zero target year for each country is\nshowed in Supplementary Table 4.\nChanges/additions to the manuscript:\n(Manuscript, Line 119-127) China TIMES and global IAMs ran the Glasgow+ scenario\nunder the ENGAGE project protocol to extend the net-zero target to all countries based\non income level, after considering countries already proposing a net-zero target\n(Supplementary Table 4). All models set the same carbon budget constraints according\nto the equal cumulative per capita method, which ensures that the outputs are\ncomparable.\n(Supplementary Information, Supplementary Table 4) Add table.\nRegion Countries Net-zero year\nEastern Africa Burundi, Comoros, Djibouti, Eritrea, Ethiopia, 2070\nKenya, Madagascar, Mauritius, Reunion,\nRwanda, Sudan, Somalia, Uganda\nNorthern Africa Algeria, Egypt, Western Sahara, Libya, 2045\nMorocco, Tunisia\nSouthern Africa Angola, Botswana, Lesotho, Mozambique, 2070\nMalawi, Namibia, Swaziland, Tanzania,\nZambia, Zimbabwe\nWestern Africa Benin, Burkina Faso, Central African 2060\nRepublic, Cote d’Ivoire, Cameroon,\nDemocratic Republic of the Congo, Congo,\nCape Verde, Gabon, Ghana, Guinea, Gambia,\nGuinea-Bissau, Equatorial Guinea, Liberia,\nMali, Mauritania, Niger, Nigeria, Senegal,\nSierra Leone, Sao Tome and Principe, Chad,\nTogo\nArgentina Argentina 2040\nAustralia/NZ Australia, New Zeeland 2040\nBrazil Brazil 2040\nCanada Canada 2040\nCentral America and Aruba, Anguilla, Netherlands Antilles, Antigua 2045\nthe Caribbean & Barbuda, Bahamas, Belize, Bermuda,\nBarbados, Costa Rica, Cuba, Cayman\nIslands, Dominica, Dominican Republic,\nGuadeloupe, Grenada, Guatemala,\nHonduras, Haiti, Jamaica, Saint Kitts and\nNevis, Saint Lucia, Montserrat, Martinique,\nNicaragua, Panama, El Salvador, Trinidad and\nTobago, Saint Vincent and the Grenadines\nCentral Asia Armenia, Azerbaijan, Georgia, Kazakhstan, 2045\nKyrgyzstan, Mongolia, Tajikistan,\nTurkmenistan, Uzbekistan\nChina China 2060\nColombia Colombia 2040\nEU-12 Bulgaria, Cyprus, Czech Republic, Estonia, 2040\nHungary, Lithuania, Latvia, Malta, Poland,\nRomania, Slovakia, Slovenia\nEU-15 Andorra, Austria, Belgium, Denmark, Finland, 2040\nFrance, Germany, Greece, Greenland,\nIreland, Italy, Luxembourg, Monaco,\nNetherlands, Portugal, Sweden, Spain, United\nKingdom\nEastern Europe Belarus, Moldova, Ukraine 2050\nEuropean Free Trade Iceland, Norway, Switzerland 2040\nAssociation\nEurope Non-EU Albania, Bosnia and Herzegovina, Croatia, 2053\nMacedonia, Montenegro, Serbia, Turkey\nIndia India 2060\nIndonesia Indonesia 2050\nJapan Japan 2040\nMexico Mexico 2040\nMiddle East United Arab Emirates, Bahrain, Iran, Iraq, 2040\nJordan, Kuwait, Lebanon, Oman,\nPalestine, Qatar, Saudi Arabia, Syria, Yemen\nPakistan Pakistan 2070\nRussia Russia 2060\nSouth Africa South Africa 2050\nNorthern South America French Guiana, Guyana, Suriname, 2050\nVenezuela\nSouthern South America Bolivia, Chile, Ecuador, Peru, Paraguay, 2040\nUruguay\nSouth Asia Afghanistan, Bangladesh, Bhutan, Sri Lanka, 2065\nMaldives, Nepal\nSoutheast Asia American Samoa, Brunei Darussalam, Cocos 2050\n(Keeling) Islands, Cook Islands, Christmas\nIsland, Fiji, Federated States of Micronesia,\nGuam, Cambodia, Kiribati, Lao Peoples\nDemocratic Republic, Marshall Islands,\nMyanmar, Northern Mariana Islands,\nMalaysia, Mayotte, New Caledonia, Norfolk\nIsland, Niue, Nauru, Pacific Islands Trust\nTerritory, Pitcairn Islands, Philippines, Palau,\nPapua New Guinea, Democratic People’s\nRepublic of Korea, French Polynesia,\nSingapore, Solomon Islands, Seychelles,\nThailand, Tokelau, Timor Leste, Tonga,\nTuvalu, Viet Nam, Vanuatu, Samoa\nSouth Korea South Korea 2050\nTaiwan Taiwan 2040\nUSA United States of America 2050\nSupplementary Table 4 Net-zero CO target year based on official submissions,\n2\naggregated to the GCAM regions, for Glasgow+ scenario.\nComment 3.10:\nLine 136-148. As shown in Figure 3c and 3d, the role of nuclear and hydropower\nstorage shows generally consistent patterns in both NDC scenario and net zero\nscenarios. You then mention “However, cost and security considerations still limit its\ncontribution to flexibility”. Could you clarify if you impose any constraints on the power\nsector, or if it is the model choice based you carbon budget constraints? If it is, I would\nrecommend showing your fundamental assumptions around the power sector in the\nSI.\nResponse:\nThank you very much for your question. In order to make the results of the optimization\nmodel realistic and policy-guiding, we have set some upper limit and growth rate\nconstraints on capacity expansion in the power sector based on resource endowment,\nplease refer to Supplementary Table 2.\nFor the nuclear power you mentioned, we have considered 2 major types of nuclear\npower: for pressurized water reactor (PWR) type, it is only allowed to be built along the\ncoast of China, given the security considerations; for Generation IV nuclear power\ntechnology (such as high-temperature gas-cooled reactors (HTGR)), it is allowed to be\nbuilt inland. According to previous study (10.1016/j.accre.2018.05.002) coastal and\ninland nuclear capacity is capped at 218GW and 273GW. Since Generation IV nuclear\npower technology is significantly more expensive than PWR, it is less likely to be used\nuntil 2050, given the cost considerations.\nFor the pumped hydro you mentioned, we follow the target of the Medium and Long\nTerm Development Plan for Pumped Hydro (2021-2035) issued by the National Energy\nAdministration (NEA), and we set the lower limit of pumped hydro at 60GW and\n120GW in 2025 and 2030, and according to the current medium and long term planning\nlayout and potential assessment, we set the upper limit at 420GW in 2060, and the\nupper limit at 500GW in 2100.\nChanges/additions to the manuscript:\n(Manuscript, Line 507-508) The constraints on the capacity of the power plant are\ndescribed in Supplementary Table 2.\n(Manuscript, Line 534-538) China TIMES 2.0 considers the current policy and sets a\nlower limit for energy storage development (10% of installed renewable energy) and\nsets a lower limit for future pumped hydro capacity in accordance with the Medium-\nand Long-Term Development Plan for Pumped Hydro (2021-2035).\n(Supplementary Information, Supplementary Table 2) Add table.\nTechnology Unit 2025 2030 2060 2100\nCoal power plant capacity upper limit GW 1300 1350 1350 1350\nNuclear power capacity upper limit GW 200 200 350 400\nHydropower capacity upper limit GW 445 500 615 700\nWind power capacity lower limit GW 600 1000 / /\nWind power capacity upper limit GW / / 3500 5500\nSolar power capacity lower limit GW 1000 2000 / /\nSolar power capacity upper limit GW / / 6800 8000\nPumped hydro capacity lower limit GW 60 120 / /\nPumped hydro capacity upper limit GW / / 420 500\nSupplementary Table 2 Power capacity limit assumptions.\nThe coal power capacity upper limit is set based on existing coal power construction projects\nfrom Global Energy Monitor database, assuming that no new coal power units will be built after\n2030 according to the policy requirement; it is assumed that only coastal nuclear power\nconstruction will be permitted before 2030, and inland nuclear power (only Generation IV\nreactors) will be allowed to be built according to the 14th Five-year Development Plan; The\nupper limit for wind and solar capacity is based on the National Energy Administration(NEA)’s\nguidance of 200 GW of PV and 80 GW of wind per year in the short term, and on the results of\nthe wind resource assessment in the long term. we set the lower limit of pumped hydro at 60GW\nand 120GW in 2025 and 2030 according to the Pumped Hydro Plan issued by NEA, and\naccording to the long-term planning layout and potential assessment reported by NEA, we set\nthe upper limit at 420GW in 2060, and the upper limit at 500GW in 2100.\nComment 3.11:\nLine 141. Can you evidence the statement – ‘its flexible operation has gained\nmomentum in both research and policy perspectives’? What does this mean?\nResponse:\nThank you for your question. The issue of flexibility for nuclear power has been studied\nand promoted in academia, industry, and by policymakers. In terms of academic\nresearch, the article published in Nature Energy considers the techno-economics of\nflexible nuclear power and its adaptability to renewable power systems\n(10.1038/s41560-022-00979-x). National Development and Reform Commission of\nChina and National Energy Administration issued guidance in January 2024 (Ref. 35)\nstating that it “explores nuclear power peaking shaving and studies the feasibility of\nsafe participation of nuclear power in power system regulation.” According to our field\nresearch, the current nuclear power plant in Jiangsu province has already been\nperforming regular output reduction regulation during the midday hours in summer.\nChanges/additions to the manuscript:\n(Manuscript, Line 171-174) Nuclear power, traditionally employed to meet baseload,\nbut its flexible operation has gained momentum in both research34 and policy35\nperspectives. Some nuclear power is already undergoing regular power regulation in\nthe midday of summer.\nComment 3.12:\nLine 169-170. Can you state what you are comparing to when discussing the EV\ncharging shift?\nResponse:\nThank you very much for your question, the issue of EV behavior is indeed receiving\nmore attention at the moment. We mentioned in “Future load profile generation” of the\nMethod section that we refer to the New Energy Vehicle Industry Development Plan\n(2021-2035) for the EV charging time distribution, and we showed the details in\nSupplementary Table 3. We further explain in the main text that we carved out two\ntypes of charging modes, and corresponded to intensive charging during the day, and\nslow charging at night.\nChanges/additions to the manuscript:\n(Manuscript, Line 203-205) Fast and slow charging devices are modeled according to\nthe EV development plan, with charging taking place at midday and night, respectively\n(Supplementary Table 3).\n(Supplementary Information, Supplementary Table 3) Add table.\nDemand H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24\nCommercial 2.4% 2.4% 2.4% 2.4% 2.4% 2.6% 3.1% 3.9% 5.1% 5.7% 6.0% 5.5% 5.4% 5.8% 5.7% 5.7% 5.9% 5.7% 4.9% 4.6% 4.3% 3.2% 2.8% 2.5%\nCooling 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 6.0% 6.0% 6.0% 6.0% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 3.0% 3.0% 1.7%\nCooking 2.1% 2.1% 2.1% 2.1% 2.1% 4.8% 6.5% 4.8% 3.4% 4.8% 8.3% 9.2% 6.5% 4.8% 2.5% 3.0% 6.1% 8.3% 4.8% 3.0% 2.5% 2.1% 2.1% 2.1%\nHeating 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 5.3% 5.3% 4.5% 4.5% 4.5% 4.5% 4.5% 4.5% 4.5% 4.5% 4.5% 4.5% 4.5% 5.3% 5.3% 3.3% 3.3% 3.3%\nLighting 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 1.4% 4.8% 7.5% 7.5% 7.5% 6.7% 6.7% 7.5% 7.7% 7.7% 8.4% 5.6% 6.0% 4.4% 4.4% 1.4% 0.6% 0.6%\nData Center 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2%\nOthers 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 2.0% 3.8% 5.5% 6.9% 7.0% 5.4% 5.7% 6.8% 6.9% 6.9% 6.5% 6.3% 4.6% 4.3% 3.6% 3.3% 2.2% 1.8%\nHot water 3.6% 3.5% 3.5% 3.5% 3.6% 3.7% 3.8% 3.9% 4.0% 4.1% 4.3% 4.4% 4.6% 4.7% 4.8% 4.9% 4.9% 4.8% 4.7% 4.6% 4.5% 4.2% 3.9% 3.7%\nRural 3.0% 3.0% 3.0% 3.0% 3.2% 4.6% 5.0% 4.0% 3.5% 3.7% 4.4% 4.4% 4.1% 4.2% 4.1% 4.2% 4.9% 5.6% 5.5% 5.8% 5.5% 4.3% 3.8% 3.3%\nHeating 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 3.9% 3.9% 3.9% 3.9% 3.9% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 3.9% 3.9% 2.5% 2.5%\nCooling 2.1% 2.1% 2.1% 2.1% 2.1% 4.8% 6.5% 4.8% 2.5% 3.9% 9.2% 7.4% 4.8% 3.4% 2.5% 3.0% 7.4% 10.1% 7.9% 2.5% 2.5% 2.1% 2.1% 2.1%\nHot water 4.9% 4.9% 4.9% 4.9% 4.9% 4.9% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 4.9% 4.9% 4.9% 4.9% 4.9% 4.9% 4.9%\nRefrigeration 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2%\nLighting 0.7% 0.7% 0.7% 0.7% 1.7% 9.9% 10.4% 3.4% 1.9% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 2.6% 4.4% 6.6% 14.9% 15.4% 7.7% 5.7% 2.7%\nCooking 2.7% 2.7% 2.9% 3.1% 3.5% 4.0% 5.7% 5.5% 3.5% 3.7% 3.5% 5.7% 5.3% 3.9% 3.6% 3.6% 4.3% 6.1% 5.9% 5.6% 4.8% 4.2% 3.5% 2.9%\nAppliances 3.6% 3.5% 3.5% 3.5% 3.6% 3.7% 3.8% 3.9% 4.0% 4.1% 4.3% 4.4% 4.6% 4.7% 4.8% 4.9% 4.9% 4.8% 4.7% 4.6% 4.5% 4.2% 3.9% 3.7%\nUrban 3.0% 3.0% 3.0% 3.0% 3.2% 4.5% 4.8% 3.9% 3.5% 3.7% 4.3% 4.3% 4.1% 4.3% 4.2% 4.2% 4.9% 5.6% 5.5% 5.9% 5.5% 4.4% 3.8% 3.3%\nHeating 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 3.9% 3.9% 3.9% 3.9% 3.9% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 6.8% 3.9% 3.9% 2.5% 2.5%\nCooling 2.1% 2.1% 2.1% 2.1% 2.1% 4.8% 6.5% 4.8% 2.5% 3.9% 9.2% 7.4% 4.8% 3.4% 2.5% 3.0% 7.4% 10.1% 7.9% 2.5% 2.5% 2.1% 2.1% 2.1%\nHot water 4.9% 4.9% 4.9% 4.9% 4.9% 4.9% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 4.9% 4.9% 4.9% 4.9% 4.9% 4.9% 4.9%\nRefrigeration 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2%\nLighting 0.7% 0.7% 0.7% 0.7% 1.7% 9.9% 10.4% 3.4% 1.9% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 2.6% 4.4% 6.6% 14.9% 15.4% 7.7% 5.7% 2.7%\nCooking 2.7% 2.7% 2.9% 3.1% 3.5% 4.0% 5.7% 5.5% 3.5% 3.7% 3.5% 5.7% 5.3% 3.9% 3.6% 3.6% 4.3% 6.1% 5.9% 5.6% 4.8% 4.2% 3.5% 2.9%\nAppliances 3.6% 3.5% 3.5% 3.5% 3.6% 3.7% 3.8% 3.9% 4.0% 4.1% 4.3% 4.4% 4.6% 4.7% 4.8% 4.9% 4.9% 4.8% 4.7% 4.6% 4.5% 4.2% 3.9% 3.7%\nTransport 6.9% 7.4% 7.0% 6.2% 5.2% 4.2% 3.4% 2.8% 2.4% 2.0% 1.8% 1.7% 1.7% 1.7% 1.7% 1.9% 2.3% 2.9% 3.6% 4.6% 5.8% 6.9% 7.8% 8.0%\nFast 1.7% 5.1% 4.7% 2.8% 1.1% 0.3% 0.7% 2.0% 3.9% 6.0% 7.7% 8.9% 9.1% 8.5% 7.1% 5.3% 3.4% 1.9% 1.3% 1.6% 3.0% 4.7% 5.6% 3.6%\nSlow 7.8% 7.8% 7.5% 6.8% 5.9% 4.9% 3.9% 3.0% 2.1% 1.4% 0.8% 0.5% 0.3% 0.5% 0.8% 1.4% 2.1% 3.0% 4.1% 5.1% 6.2% 7.3% 8.1% 8.7%\nSupplementary Table 3 The percentage of energy consumption in different hours\nof the day in the building and transportation sectors.\nThe percentage of energy use by time period in the building sector refers to the national\nstandard GB 55015-2021 “General specifications for building energy efficiency and\nrenewable energy use”. The percentage of energy use by time period in the\ntransportation sector refers to the “New Energy Vehicle Industry Development Plan\n(2021-2035)”. The data is processed by spline smoothing.\nComment 3.13:\nLine 174-175. You state that “Concurrently, the load profile undergoes a transformation\nfrom a relatively flat distribution to one characterized by daytime peaks, thereby\naligning more effectively with the PV power generation”. Which load profile do you\nmean? Could you refer to the corresponding figure here?\nResponse:\nThank you very much for bringing up the confusing points. What we are trying to\nconvey here is that future loads, guided by prices, will be more concentrated when\nrenewables are generating large amounts of electricity, and that the comparison is to\nthe electricity load curve for the year 2020, which was not specifically plotted in the\noriginal manuscript. We present the load profiles for key years and scenarios in\nSupplementary Figure 6.\nChanges/additions to the manuscript:\n(Manuscript, Line 210-213) Comparing the 2060 and 2020 load profiles\n(Supplementary Figure 6a), the load profile undergoes a transformation from a\nrelatively flat distribution (reducing thermal power regulation operations) to one\ncharacterized by daytime peaks (better matching PV generation).\n(Supplementary Information, Supplementary Figure 6) Add Supplementary Fig. 6a.\na\nb\nSupplementary Fig. 6 The load and net load variation of the typical summer day in China\na The real electricity load (except storage charging), b The net load. The net load is the\ndifference between the real electricity load (except storage charging) and the output of variable\nrenewables (wind and PV power generation).\nComment 3.14:\nLine 185-186. You write that “Nowadays, fossil fuels and district heating are the primary\nsources for satisfying the heating demand, while electricity almost exclusively fulfils\ncooling needs.” Is that district heating also fuelled by fossil fuels (e.g. Gas) in China?\nResponse:\nThanks for your question. Nowadays, district heating is mainly supplied by fossil fuels,\nsuch as coal-fired CHP and gas-fired heat boilers. However, low-carbon heating modes\nsuch as industrial waste heat and nuclear energy heat are now being piloted in some\nplaces, and this mode of district heating will be free from fossil fuels.\nChanges/additions to the manuscript:\n(Manuscript, Line 223-224) Nowadays, fossil fuels and district heating (mostly fossil\nfuels) are the primary sources for satisfying the heating demand,\nComment 3.15:\nThe figures cited in the text are inconsistent with the actual figures. Figure 5b is\nelectricity consumption in the transportation sector on a summer workday, while Figure\n5d is missing. Figure 6e is marginal electricity supply costs for different time periods\non a summer workday.\nResponse:\nThank you very much for pointing out the inconsistencies in the figure citations. We\nhave revised the order and combination of figures against the journal's figure\nformatting requirements, and we have double-checked the full-text figure citations in\nthe revised manuscript.\nChanges/additions to the manuscript:\n(Manuscript, Line 243) adjustable load and V2G technology play a pivotal role in\nenhancing system flexibility during periods of severely constrained online capacity by\nfacilitating intraday storage arbitrage (Fig. 6b).\n(Manuscript, Line 246) these interactions significantly diminish the reliance on power-\nbased energy storage, such as pumped hydro (Fig. 4d).\n(Manuscript, Line 256) This, in turn, can effectively lower the marginal cost of electricity\n(Fig. 8e)\nComment 3.16:\nLine 224-230 (line 459-468). Provide more detail on how demand shifting is achieved\nwithin the modelling framework. Price elasticity reducing demand level is not shifting\nthe load. Is demand shifting to that described by Li et al. -\nhttps://www.sciencedirect.com/science/article/pii/S0306261918310237. More\nelaboration is needed.\nResponse:\nThank you very much for mentioning this. Both our paper and the study you mentioned\nfor the UK are based on the TIMES framework and have a similar modeling formulation\nof time-shifting of loads. However, an improvement over this is the introduction of the\namount of value of the load time shift in our model. Based on a three-hour load time-\nshift subsidy rate of 1 CNY/kWh for Yunnan Province, the model conservatively\nassumes that the subsidy per hour shifted (whether advanced or delayed) is about 4\nUS cents/kWh.\nIn response to your question about price elasticity, it is not our intent to indicate that\nprice elasticity enables load hourly shifting. Rather, we are applying price elasticity,\nwhich has traditionally worked only on total annual demand, to the hourly scale,\nthereby enabling price-based load reduction (curtailable load), which is an important\nway in which demand-side response provides system flexibility.\nChanges/additions to the manuscript:\n(Manuscript, Line 558-565) Based on the TIMES open-source framework69,70, the\nChina TIMES 2.0 model models load shifting as a special bi-directional energy storage\ntechnology that is able to represent the maximum allowed deviations from nominal\ndemand loads, maximum advance or delay for meeting the shifted loads, and the value\nper hour shifted by setting the storage capacity, the storage time, and the operating\ncost, respectively. The model conservatively assumes a subsidy of approximately 4\ncents/kWh per hour of shifting, whether advanced or delayed, based on a subsidy of\nRMB 1/kWh for three-hour load shifting in Yunnan Province.\nComment 3.17:\nAs with other sections, clarity on understanding the additional implications of improved\ntemporality on system costs would be useful.\nResponse:\nThank you very much for your valuable suggestions. In terms of total system cost per\ncapita, there will be a clear bifurcation between scenarios that consider sub-annual\nmodeling and those that do not after 2040. In 2060, sub-annual modeling will push up\nsystem costs by about 4% (CN60 vs CN60-noTS), while not considering demand-side\nmanagement measures will further push up system costs by nearly 1% (CN60 vs\nCN60-noLM). Enhanced time resolution places more demand on flexibility resources,\nand the differences between scenarios are mainly in the areas of electricity, hydrogen\nand energy storage. In terms of power supply costs, consideration of sub-annual level\nmodeling would increase costs by 3% until 2030, with a modest cost differential\nbetween 2030-2050 and then again after 2050 to about 3%. In terms of hydrogen costs,\nthe average increase compared to CN60-noTS amounts to 14% from 2035-2060 and\n27% in 2035. This also suggests that sub-annual modeling better identifies hydrogen\nenergy application scenarios and supply patterns. In terms of the cost of energy\nstorage, it is even more significant, and the gap gradually widens as the share of\nrenewable energy increases, with the scenario considering sub-annual modeling rising\n24% over the CN60-noTS scenario by 2060. The degree of demand-side management\nhas a significant impact on energy storage investment, with the CN60-LM scenario\nreducing investment by 10% compared to the CN60 scenario, while the CN60-noLM\nscenario increases investment by more than 12% compared to the CN60 scenario.\nWe have added the CN60-noTS scenario in Supplementary Fig. 13 so that we can\nmore intuitively see the impact by considering sub-annual constraints.\nChanges/additions to the manuscript:\n(Manuscript, Line 270-274) Considering the system operating constraints at the sub-\nannual level, the total system cost would rise by about 4% compared to not considering\nit. Enhanced time resolution places more demand on flexibility resources, and the\ndifferences between scenarios are mainly in the areas of electricity, hydrogen and\nenergy storage. (Supplementary Fig. 13).\n(Manuscript, Line 279-284) By considering the sub-annual operation process, we\nidentified that hydrogen could take advantage of low electricity price periods to reduce\nproduction costs and thus expand application. Comparing CN60 with CN60-noTS,\nthe annual expense of hydrogen increases by 14% from 2035 to 2060, and by 27%\nespecially in 2035. It has been demonstrated that the CN60 increases energy storage\nexpenses by 24% over the CN60-noTS by 2060.\n(Manuscript, Line 287-290) Demand-side response exerts a substantial influence on\nenergy storage expenditures. A 10% reduction in expenses is observed for the CN60-\nLM scenario relative to the CN60 scenario, while the CN60-noLM scenario\ndemonstrates an expense increase of more than 12% compared to the CN60 scenario.\n(Supplementary Information, Supplementary Fig.13) add CN60-noTS scenario.\na\nb c\nd\ne\nSupplementary Fig. 13 Energy transition cost in electricity, hydrogen and storage for\nChina during 2020-2100. a Annual system investment. b Undiscounted annual per capita\nsystem cost. c Undiscounted annual system costs as a share of the year's GDP. d\nUndiscounted annual system costs by sector. e Undiscounted annual system costs by type of\ncost.\nComment 3.18:\nDiscussion: Develop this section more towards the implications of the results for\ndecision makers, and for the modelling community.\nResponse:\nThank you very much for your suggestions. We have implementation suggestions for\npolicymakers and the modeling community in the revised version of the manuscript.\nChanges/additions to the manuscript:\n(Manuscript, Line 321-323) Based on our findings, we offer the following insights into\ndemand management, power planning, storage deployment, demand-side response,\nand market mechanisms for flexibility enhancement and energy system transformation\n(Manuscript, Line 330-333) Therefore, the construction of charging infrastructure\nshould gradually increase the fast-charging proportion and guide the coordination of\ncharging load with renewable energy output through market regulation, policy\nincentives and intelligent instruments.\n(Manuscript, Line 339-341) Enhancing regulation performance and CCS retrofits of\nCFPPs could mitigate asset stranding concerns, thereby facilitating a viable transition\npath for massive CFPPs.\n(Manuscript, Line 353-358) We recommend accelerating pumped hydro construction\nand actively promoting variable speed pumped hydro to effectively manage large-scale,\nrapid power fluctuations. We propose to strengthen the integration of new-type energy\nstorage with the development planning of distribution grids, renewables, EVs, and to\nput efforts into the grid-forming energy storage, to further enhance the ability of energy\nstorage to support the operation of power grids.\n(Manuscript, Line 380-386) Specifically, we advocate for the following measures:\nwidening the peak-valley price difference, expanding the floating range of tariffs and\nthe upper and lower price limits, enriching the trading varieties of auxiliary services\nwith technical characteristics of dispatchable resources such as ramping and system\ninertia, and, with reference to CFPP and pumped hydro, establishing a capacity tariff\nmechanism for new-type energy storage to break up the unfair competition of flexible\nresources.\n(Manuscript, Line 393-400) The challenges of ensuring power system security during\nthe net-zero transition are evidenced by recent global trends, including more frequent\npower interruptions, load control, and electricity price volatility. A pressing need has\nemerged within the broader IAMs community and among energy system modelers to\nprovide sub-annual perspectives that consider the novel changes precipitated by the\nsubstantial integration of renewable energy and the emergence of new electric load\naccess. To enhance the feasibility of long-term mitigation pathways, it is imperative to\ndevise cost-effective system flexibility regulation mechanisms.\nComment 3.19:\nLine 273-274. ‘Most economical’ compared to what other options?\nResponse:\nThank you very much for your question. The other options here we would like to stand\nin for non-demand-side traditional means such as peaking power, energy storage,\nreserve capacity and so on. We have tried to minimize this more absolute formulation\nin the revised version.\nChanges/additions to the manuscript:\n(Manuscript, Line 324-326) Effective demand management is a more economical\nmeans of balancing energy supply and demand than making investments and\ndispatching power plants and storage.\nComment 3.20:\nLine 279-289. While interesting, are these results pertinent to the main argument of\nthe paper?\nResponse:\nThank you very much for your thought-provoking questions. Here we may not have\npassed on background knowledge of China's coal power development, thus creating\na leap of logic. In 2021 the National Development and Reform Commission (NDRC)\nand the National Energy Administration (NEA) proposed to carry out energy-saving\nand carbon-reducing retrofits, heat supply retrofits, and flexibility retrofits (the Three\nRetrofits) for coal power units, in 2024 the NDRC and the NEA proposed to carry out\nthe three major decarbonization retrofits such as biomass blending retrofits, ammonia\nblending retrofits, and carbon capture retrofits for coal power units. In March 2025, the\n(NDRC and the NEA released the Implementation Plan for Coal Power Upgrading\nSpecial Action (2025-2027), which emphasized the need to enhance the flexibility of\ncoal power operation capabilities such as deep peaking, rate of change of loads,\nstarting and stopping peaking, and high efficiency of wide loads. Therefore, we believe\nthat our discussion of retrofitting existing CFPPs and new-built CFPPs is of great\ninterest to policymakers and must be considered to address the transformation of\nChina's coal power. The topic of this paper is about the need for a integrated\nassessment of net-zero paths at the sub-annual level, and the flexibility retrofit of coal\npower happens to be an important source of power system flexibility.\nComment 3.21:\nScenario setting: The scenarios are distinct but need more clarification of common\nassumptions and differentiated assumptions.\nResponse:\nThank you very much for your suggestion, we have significantly improved the\ndescription of common assumptions and differences between scenarios.\nChanges/additions to the manuscript:\n(Manuscript, Line 574-578) In addition, we propose a scenario CN60-noTS without\nsub-annual constraints, which removes the seasonal, weekly, and daily variability\ncarve-outs of renewable energy outputs and loads (assumed to be averaged\ndistributions) and relaxes the power unit flexibility constraints.\n(Manuscript, Line 579-581) For all net-zero scenarios (CN60-noTS, CN60, CN60-\nnoLM and CN60-LM), we set the cumulative carbon emissions for 2019-2100 under\nRCP2.6 assumption based on the carbon budget allocation principle (230 GtCO ).\n2\n(Manuscript, Line 588-592) For NDC and all net-zero scenarios, we set capacity limits\nfor coal, nuclear, wind, and PV power plants based on policy and resource potential.\nFor all end-use sectors, we assumed no further increase in the share of fossil fuel. In\naddition, for all scenarios, we require total carbon emissions to decline monotonically\nafter peaking (no rebound).\n(Manuscript, Line 593-607) The difference in assumptions between scenarios centers\non the difference in the degree of demand-side contribution to system flexibility. CN60-\nnoLM represents a scenario where no demand response measures are implemented,\nand the energy user consumes energy in the same way as they do presently. This\nrepresents the case where energy prices do not affect demand-side behavior at all.\nCN60 is an intermediate scenario that envisions the electricity demand for\ntransportation and hydrogen production adapting to variations in online electricity\ngeneration capacity. In this scenario, energy prices have an impact on demand that\ncan be easily regulated, such as hydrogen production and EV charging, and users\nregulate the time distribution of energy use in response to price signals. Meanwhile,\nCN60-LM is a supply-demand interaction scenario that introduces load time shifting\nacross a wide range of industries alongside V2G technology and home energy storage\nfeed-in. In this scenario, most of the energy service demands are capable of demand-\nside response with price incentives, and in particular, EV batteries, and home energy\nstorage can be dispatched to balance system supply and demand.\nComment 3.22:\nLine 470-423. In your NDC scenario, does it include over 65% reduction in carbon\nintensity and other renewable related target?\nResponse:\nThank you very much for your question. Our NDC scenarios include China's carbon\nintensity reduction target (>65% reduction) and renewable energy development target\n(>1200GW solar and wind capacity) for 2030 in the updated NDC.\nChanges/additions to the manuscript:\n(Manuscript, Line 569-571) Carbon intensity reduction of more than 65% from 2005\nand wind & solar capacity exceeding 1200GW targets are bounded in the NDC\nscenario.\nComment 3.23:\nLine 476-477. The statement “We set the cumulative carbon emissions for 2019-2100\nunder RCP2.6 assumption based on the carbon budget allocation principle (230\nGtCO2)”, needs reference or clarification of the allocation principle, and why is it\napplied for all your scenarios?\nResponse:\nThank you very much for your question. As you mentioned, there is a large uncertainty\nin allocating carbon budgets to countries from RCP2.6 or the two-degree target, and\neach country has its own preferred allocation principles. However, for the sake of multi-\nmodel comparability, we have adopted the proposal suggested by the ENGAGE project.\nThe global carbon budget allocation is calculated according to the “Equal Cumulative\nPer Capita (ECPC)” methodology, with a historical allocation starting point in 1990, and\na global pathway that selects the median pathway of the IPCC Sixth Assessment\nReport Category C4 (limit warming in 2 degrees with >50% probability)\nChanges/additions to the manuscript:\n(Manuscript, Line 581-585) The global carbon budget allocation is calculated\naccording to the “Equal Cumulative Per Capita (ECPC)” methodology, with a historical\nallocation starting point in 1990, and a global pathway that selects the median pathway\nof the IPCC Sixth Assessment Report Category C4 (limit warming in 2 degrees\nwith >50% probability).\nComment 3.24:\nSI Figure 2, why does coal use rebound in 2060 in urban areas within the hot summer–\ncold winter zone under your NDC scenario? However, in the same scenario and\nclimate zone, coal use continues to phase down after 2030.\nResponse:\nThank you very much for your question. The regions that have seen a rebound in coal\nconsumption are all in the south (HSCW, HSWW, M). Buildings in the South do not\ncurrently have mandatory space heating requirements, so space heating penetration\nin 2025 is low, with penetration rates of 29%, 26%, and 9% in the three regions above,\nwhile urban space heating penetration in the North is close to 100%. As the quality of\nlife improves, we expect the heating penetration rate in the South to increase\nsignificantly, and by 2030 and 2060, the heating penetration rate in the above three\nregions will reach 34%, 31%, 12% and 62%, 61%, 30%, respectively, and thus drive-\nup energy consumption. Additionally, the dominant technologies available for space\nheating in the South include coal boilers, natural gas boilers, electric boilers, air\nconditioners, and heat pumps. Since we set the share of fossil fuels not to rise, from a\ncost-optimization perspective, it is relatively inexpensive to choose coal in the NDC\nscenario with no carbon constraints in 2060, so coal boilers rebound in the face of\ndeclining natural gas (which relies heavily on imports and higher fuel costs).\nComment 3.25:\nSI Figure 6 lacks legend\nResponse:\nThank you very much for pointing out this omission, we have added the legend.\nChanges/additions to the manuscript:\n(Supplementary Information, Supplementary Figure 6) Add legend.\na.\nb.\nSupplementary Fig. 6 The load and net load variation of the typical summer day in China\na The real electricity load (except storage charging), b The net load. The net load is the\ndifference between the real electricity load (except storage charging) and the output of variable\nrenewables (wind and PV power generation).\nComment 3.26:\nSI Figure 15 needs more elaboration around data source\nResponse:\nThank you very much for your question. We have included data on the percentage of\nenergy use in the building and transportation sectors that have large differences in\ntemporal distribution in the new Supplementary Table 3.\nThe percentage of energy use by time period in the building sector refers to the national\nstandard GB 55015-2021 “General specifications for building energy efficiency and\nrenewable energy use”. The percentage of energy use by time period in the\ntransportation sector refers to the “New Energy Vehicle Industry Development Plan\n(2021-2035)”. The agriculture sector is assumed to have flat electricity use curve, and\nthe total load residual term excluding building, transportation, and agriculture is\nconsidered as the electricity consumption curve for the industrial sector.\nChanges/additions to the manuscript:\nSupplementary Information, Supplementary 14 caption) The load profile of the building\nsector refers to the national standard GB 55015-2021 “General specifications for\nbuilding energy efficiency and renewable energy use”. The load profile of the\ntransportation sector refers to the “New Energy Vehicle Industry Development Plan\n(2021-2035)”. The agriculture sector is assumed to have flat electricity use curve, and\nthe total load residual term excluding building, transportation, and agriculture is\nconsidered as the electricity consumption curve for the industrial sector.\nReviewer 4\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nResponse:\nThanks very much for your invitation of Early Career Researchers to give us helpful\ncomments.\nResponse to Referees\nNCOMMS-25-08417-A\nAugust 25, 2025\nReviewer 1\nThis version has been properly revised.\nResponse:\nThank you very much for reviewing this paper and for your valuable comments.\nReviewer 2\nComment 2.1:\nSensitivity analysis should be conducted considering different V2G participation rates\nin the assumptions of demand-side response?\nResponse:\nThank you very much for your suggestion. Based on the CN60-LM scenario, we ran\ntwo additional scenarios and made differentiated adjustments to the participation\nlevel of V2G. Specifically, we changed the proportion of the maximum activity level of\nV2G in each timeslice to the electricity consumption of the transportation sector in\ndifferent scenarios. The model is then optimized according to the principle of cost\noptimization.\nScenario 2030 2050 2110\nCN60-LM 20% 30% 50%\nCN60-LM-lowV2G 20% 20% 20%\nCN60-LM-highV2G 40% 60% 70%\nThe results show that an increase in the proportion of V2G applications can promote\nthe consumption of VRE while reducing the operation of energy storage systems.\nV2G is first applied to support peak loads and ramping, and when the application rate\nis higher, it further substitutes for energy storage.\nWe have included the sensitivity analysis and additional figures in the supplementary\ninformation, with references in the main text.\nChanges/additions to the manuscript:\n(Manuscript, Line 224-226) The result of sensitivity analysis of different V2G\napplication rates can be found in Supplementary Notes 3 and Supplementary Figures\n18-19.\n(Supplementary Note 3 and Supplementary Figure 18-19)\nSupplementary Note 3 Sensitivity scenario analysis for V2G application rates\nDemand response, such as V2G, can alleviate peak loads and reduce energy\nstorage requirements. Based on the CN60-LM scenario, we ran two additional\nsensitivity scenarios and made differentiated adjustments to the participation level of\nV2G. Specifically, we changed the proportion of the maximum activity level of V2G in\neach timeslice to the electricity consumption of the transportation sector in different\nscenarios. For the CN60-LM scenario, in 2030, 2050, and 2110, the maximum\nactivity level for charging or discharging in each timeslice are 20%, 30%, and 50% of\nthe original transportation electricity demand for that timeslice, respectively. For the\nLowV2G scenario, these limits are 20%, 20%, and 20%, respectively. For the\nHighV2G scenario, these limits are 40%, 60%, and 70%, respectively.\nFrom the power supply on a typical day, a higher V2G application rate (HighV2G\nscenario) can promote the consumption of wind power and solar power at noon\n(approximately 50GW), while reducing the charging and discharging of energy\nstorage. V2G facilities primarily charge during midday and discharge in the evening.\nConversely, the LowV2G scenario significantly weakens the role of V2G, resulting in\na noticeable flexibility shortage around 9 PM, necessitating increased output from\nvarious power plants such as nuclear, coal-fired, and biomass to provide flexibility.\nFocusing on V2G operations, it can be observed that the primary charging period for\nV2G vehicles is between 8 and 10 am, while the discharge period is between 8 and 9\npm. By increasing the utilization rate of V2G applications, charging during the 9 am to\n2 pm period can be significantly enhanced, while discharge during the 6 pm to 11 pm\nperiod also sees an overall improvement. Through sensitivity analysis, it can be\nsummarized that the most economical role of V2G is to provide power support during\npeak load and ramping periods. Under conditions of higher V2G application rates, it\ncan partially replace the functions of energy storage facilities.\nSupplementary Fig. 18 Differences in electricity supply by technology for the\ntypical day in 2060 between the V2G application rate sensitivity scenario and\nthe core scenario\nSupplementary Fig. 19 V2G operations in 2060 under different scenarios\nComment 2.2:\nThe sensitivity analyses should also consider different assumed costs for the\nflexibility retrofits of coal-fired power plants?\nResponse:\nThank you very much for your suggestion. Your comment is very important. The cost\nand pace of retrofitting will indeed have a certain impact on the development and\noperation of thermal power and nuclear power. Based on the CN60 scenario\n(retrofitting cost of 500 CNY/kW), we further ran two additional scenarios: CN60-\nlowCost and CN60-highCost, corresponding to retrofitting costs of 200 CNY/kW and\nThe results indicate that flexibility retrofits for existing coal-fired power plants are both\nnecessary and cost-effective in the near term. Both high-cost and low-cost retrofit\nscenarios show high retrofit rates. However, in the high retrofit cost scenario, by 2060,\ndaytime renewable energy integration will face greater challenges, with energy storage\nand gas-fired generation taking on more flexible regulation tasks. In the low retrofit cost\nscenario, nuclear power demonstrates greater flexibility, enabling it to increase load\nduring the evening hours when flexibility is scarce and reduce load during midday\nhours.\nWe have included the sensitivity analysis and additional figures in the supplementary\ninformation, with references in the main text.\nChanges/additions to the manuscript:\n(Manuscript, Line 150-153) CFPP flexibility retrofits can significantly improve the\ndispatch performance of existing assets, partially alleviating the lack of flexibility\n(Supplementary Notes 2 and Supplementary Figure 16-17).\n(Supplementary Note 2 and Supplementary Figure 16-17)\nSupplementary Note 2 Sensitivity scenario analysis for flexibility retrofits\nFlexibility retrofits can help fully utilize existing large-scale thermal power assets to\nprovide flexibility to the energy system. The cost and pace of retrofitting will indeed\nhave a certain impact on the development of thermal power and nuclear power. In the\nmodel, retrofit technologies are provided for coal-fired, nuclear, and biomass power\ngeneration units (both with and without CCS). Based on the CN60 scenario (retrofitting\ncost of 500 CNY/kW), we further ran two additional scenarios: CN60-lowCost and\nCN60-highCost, corresponding to retrofitting costs of 200 and 1000 CNY/kW,\nrespectively.\nSensibility scenario simulations indicate that, given the relatively high investment costs\nassociated with energy storage and other flexibility technologies, flexibility retrofits\nremain cost-effective for most power plants. In the CN60-highCost scenario, solar\npower generation is projected to decline moderately by 2060, while wind and nuclear\npower generation are expected to increase correspondingly. Over the 2020–2060\nperiod, total electricity generation from all power sources will experience minor\nfluctuations. In the CN60-lowCost scenario, the impact on total electricity generation is\nminimal, with coal-fired power generation with CCS showing a moderate increase\nbetween 2045 and 2055.\nFrom the hourly operation perspective, it can be seen that under the CN60-highCost\nscenario, daytime solar power generation will decrease, while wind power generation\nwill increase slightly throughout the day. In the evening, gas-fired power plants and\nenergy storage will play a greater role in providing flexibility. Under the CN60-lowCost\nscenario, some nuclear power plants will participate in flexibility renovations and\nengage in peak shaving, while wind power and solar power generation will not be\nsignificant. At the same time, activities related to flexible hydropower and energy\nstorage have also decreased relatively.\nSupplementary Fig. 16 Differences in electricity capacity by technology between\nthe flexibility retrofit sensitivity scenario and the core scenario\nSupplementary Fig. 17 Differences in electricity supply by technology for the\ntypical day in 2060 between the flexibility retrofit sensitivity scenario and the\ncore scenario\nReviewer 3\nGeneral Comment:\nThank you for the authors’ detailed and thoughtful responses to the first-round\ncomments. I appreciate the substantial effort made to address the issues raised,\nincluding the additional analyses, clarifications, and the inclusion of new tables and\ndata where requested. These revisions have strengthened the manuscript\nconsiderably and improved its transparency.\nResponse:\nThank you very much for your review. Your comments have helped us further refine\nthe novelty and key points of our paper. We hope that our revisions to the manuscript\nin this round will adequately address your concerns. Due to changes in the novelty\ndescription and narrative style of the paper, additional revisions not mentioned in the\nfollowing comments are highlighted in color in the manuscript.\nComment 3.1:\nIn response to comments 1.1 and 3.8 regarding the paper’s novelty, the three core\ncomparisons presented are clear. However, I remain unconvinced, as your sub-\nannual modelling approach and key quantitative findings primarily demonstrate that\ntraditional annual-based IAM scenario analyses tend to idealize the pace of thermal\npower phase-out and overestimate the scale of renewable deployment—an issue\nthat is already widely acknowledged. That said, the strength of your model in\nexploring specific pathway combinations can indeed support China in identifying\nmore feasible power system transition pathways. I would therefore recommend\npositioning the paper’s novelty around the feasibility of these transition pathways with\nflexibility management included, rather than framing sub-annual modelling itself as\nthe core innovation. If so, I also suggest refining your key quantitative conclusions in\nthe abstract accordingly.\nResponse:\nWe appreciate the suggestion to position our contribution around feasibility with\nflexibility management. We have revised the Title, Abstract, Introduction, and\nDiscussion to emphasize that our core contribution is to quantify feasibility and least-\nregret portfolios for China’s energy transition when flexibility options are co-optimized\nin a sub-annual framework.\nNow, sub-annual modeling is presented as an enabling methodological\nenhancement, not the novelty itself. We now focus key quantitative conclusions on\nfeasibility and flexibility rather than on differences vs an annual model and highlight\nthe unique integration of demand-side flexibility and V2G in an economy-wide\nframework.\nChanges/additions to the manuscript:\n(Manuscript, Title) Exploring the feasible net-zero transition pathway in China\nconsidering energy system flexibility\n(Manuscript, Abstract)\nNet-zero energy transition is projected to accelerate the replacement of fossil fuels\nwith renewables, leaving system flexibility resources increasingly scarce. Here, we\npresent a sub-annual energy-environment-economy model with endogenous hourly\nenergy demand profiles and power balance dynamics including power dispatch,\nstorage operations and demand-side response that co-optimizes supply- and\ndemand-side flexibility, to map feasible transition pathways for China. The results\nshow that compared with coarser timeslice representative of common modelling\npractice, sub-annual representation tightening flexibility needs with a high variable\nrenewable energy (VRE) and high electrification energy system. Results reveal\nsteeper load ramps and frequent evening price strikes, identify increased activities in\nthermal power, nuclear power, hydrogen production, and energy storage. Accounting\nfor temporal variability in supply and demand, the cost-optimal solution exhibits\nmarginal abatement costs that are over 9% higher, but incorporating demand-side\nflexibility measures can mitigate cost growth and delineates least-regret portfolios for\nreliable, affordable decarbonization. Incentives for demand-side response such as\nload time-shifting and vehicle-to-grid (V2G) can reduce investment in pumped hydro\nby 23% and yield more than a threefold cost-benefit ratio. The study highlights\nenhanced modelling of temporal dynamics within future energy model development\nand incentive-compatible market mechanism design for dispatchable resource\ndevelopment.\n(Manuscript, Line 43-46) Multi-sector energy system model and integrated\nAssessment Model (IAM) are designed to provide a quantitative description of\ncoupled human and natural system processes and their interactions, having been\nwidely applied in long-term planning studies in energy and wider domains.\n(Manuscript, Line 65-72) In this study, we enhanced the modeling of temporal\ndynamics in an energy-environment-economy model, China TIMES 2.0, using 56\nrepresentative timeslices nested at the annual-seasonal-weekly-daily-hourly to\nassess energy transition under operational constraints and flexibility enhancement\nmeasures. This approach establishes an integrated framework that aligns long-term\nstrategic planning with the complexities of short-term operational dynamics. Based\non industry-level energy consumption pattern modeling, the amplitude and shape of\nthe power load curve are optimized endogenously.\n(Manuscript, Line 269-270) With the annual-seasonal-weekly-daily-hourly nesting\nmodeling framework, we observe the dynamics of the energy transition with\noperational constraints.\n(Manuscript, Line 280-285) The booming EV charging and hydrogen production via\nelectrolysis are poised to become valuable resources for managing flexible loads in\nthe coming years. Given that daytime fast charging is a cheap, clean, and flexibility-\nenhancing option, charging infrastructure construction should gradually increase the\nfast-charging proportion and guide the coordination of charging load with VRE output\nthrough market regulation, policy incentives and intelligent instruments.\n(Manuscript, Line 286-287) For safety scheduling and adequate supply\nconsiderations, China has accelerated the approval process for new CFPPs in the\npast 2 years38.\n(Manuscript, Line 291-294) Therefore, enhancing regulation performance of CFPPs\nand promoting low-carbon retrofits are essential options for mitigating the risk of\nstranded assets. In a high-VRE power system, hydro, abated CFPPs and BECCS\nneed to reposition and transform into primary flexibility providers.\n(Manuscript, Line 295) Diversified energy storage portfolio addresses various\nflexibility needs.\n(Manuscript, Line 312-314) Conventional objectives in power dispatch are\nprogressively proving ineffective in aligning with the optimal configuration\nrequirements for system flexibility in high-VRE power systems.\n(Manuscript, Line 316-317) Current time-of-use tariffs encourage narrowing the peak-\nto-valley load gap, thereby reducing the need for thermal power regulation for\neconomic efficiency.\n(Manuscript, Line 320-327) More importantly, energy consumers must be able to\ndiscern and react to price fluctuations, thereby empowering stakeholders to engage\nin incentive-compliant actions that foster system flexibility. Specifically, we propose\nthe implementation of the following measures: a) The expansion of the peak-valley\nprice discrepancy, b) The augmentation of the floating range of tariffs. c) The\ndiversification of trading varieties in auxiliary service markets, including ramping and\nsystem inertia. d) The establishment of a capacity tariff mechanism for new-type energy\nstorage, rectifying inequitable competition among flexible resources.\n(Manuscript, Line 333-336) A pressing need has emerged within the broader IAMs\ncommunity and among energy system modelers to enhanced modelling of sub-\nannual dynamics and flexibility management measures.\nComment 3.2:\nPart of the problem of the current argument of novelty relates to the starting position\nof the paper, and the scenario design. Under comment 3.4, the statement on the lack\nof temporal resolution in models needs to be evidenced. You use one reference (8) in\nlines 48-49, but this paper is not relevant and fails to back this assertion up. Most\nnational TIMES models for example will almost certainly be using a sub-annual\ntimeslice resolution – so this starting position for the paper is not accurate (unless\nyou have references to suggest otherwise). A stronger position would be that while\nthere are some attempts at sub-annual representation in similar models, this paper is\narguing for improving this, based on enhanced modelling of temporal dynamics.\nResponse:\nThank you very much for your suggestion. We acknowledge that our previous\nstatement was too absolute. We have replaced it with a more objective position.\nSpecifically, some national TIMES models represent sub-annual dynamics through\ncoarse timeslices (e.g., seasonal × day/night or day types), while several global IAMs\ndefault to annual energy balances with optional timeslices. However, coarse timeslice\noften does not capture diurnal ramps, electricity scarcity, or the operational synergies\nfrom demand-side flexibility and V2G. We now cite model documentation and peer-\nreviewed studies of representative models, and highlight the research gap of finer\ntemporal dynamics.\nChanges/additions to the manuscript:\n(Manuscript, Line 46-51) Some national energy system models, such as TIMES-\nfamily, employ coarse sub-annual timeslices (timeslices by season and daily period)8,\nand several global IAMs operate at annual with limited operational detail, although\noptional timeslices exist9,10. Coarse timeslice modeling, however, often cannot fully\ncapture diurnal ramps, peak load hours, or the operational synergies from demand-\nside flexibility.\n(References) Add references\nsation of passenger light-duty vehicles using spatially resolved TIMES-Irel\nand Model. Applied Energy 316, 119078 (2022). https://doi.org/10.1016/j.a\npenergy.2022.119078\noss-cutting analysis of energy, climate, the environment, and sustainable\ndevelopment. Environmental Modelling & Software 112, 143–156 (2019).\nhttps://doi.org/10.1016/j.envsoft.2018.11.012\n2). Zenodo (2025). https://doi.org/10.5281/zenodo.15581183\nComment 3.3:\nThis then calls into question the scenario design, where you have used a ‘noTS’\nscenario. The problem of comparison against this scenario is undermined if indeed\nmost national TIMES models do have some level of TS resolution. Comparisons of\nscenarios with and without TS, and the emerging results, are therefore not surprising\n– given that you are using quite an extreme comparator i.e. the noTS case.\nResponse:\nWe're grateful for your comments. We agree that CN60-noTS is a relatively extreme\ncomparator to the TIMES model with TS characterization.\nWe conducted further reviews on sub-annual modeling both domestically and\ninternationally. Power models such as SWITCH-China, RESPO, CISPO, and GTEP\nhave relatively fine time granularity (often hourly, or even full-year 8760 hours),\nbut they provide limited insights into future end-use sector transitions. Multi-sector\nmodels can generally be categorized into energy economic models, which focus on\neconomic analysis, and energy technology models, which emphasize technological\ndynamics. Teams in countries such as Switzerland, the UK, the US, and Ireland have\nconducted sub-annual national TIMES. Most Chinese modeling teams constructed\nenergy economic models, such as CE3METL, CHEER, C-GEM, C-REM, IMED|CGE,\nand C3IAM|CEEPA, which typically study the dynamics of net-zero transition from a\ntop-down perspective and find it difficult to characterize multiple timeslices. There are\nrelatively few active energy technology models in China. Representative models\ninclude China-MAPLE, C3IAM|NET, IPAC-Technology and IMED|TEC. These models\ntypically contain rich technical details and describe technological evolution from the\nbottom-up perspective, but none of them focus on sub-annual modeling. For the\nlocalized version of the international IAM model, MESSAGEix-China is currently\ndeveloping a sub-annual version (4 seasons × 2 weeks × 6 daily timeslices). GCAM-\nChina 7.0 has been open-sourced, calculating credible capacity by setting\ncontribution coefficients for system flexibility. Our team’s model China TIMES 1.0\nconsiders diurnal variations, and China TIMES-30PE considers 4 seasons × 5 daily\ntimeslices. Hence, in order to enhance the comparison between different time\ngranularities, we added the CN60-DN scenario, which considers two timeslices for\nday and night, and the 32-timeslice scenario CN60-noHour, which considers 4\nseasons × 2 weeks × 4 daily timeslices. For modifications to the scenario setting\ndescription, please refer to the reply to the next comment (Comment 3.4). We\nmodified Figure 2 by adding the CN60-noHour and CN60-DN scenarios. The results\nof the section “Assessing the energy transition at the sub-annual level” have also\nbeen included in the comparison of the two scenarios mentioned above.\nChanges/additions to the manuscript:\n(Manuscript, Line 105-110) The finer temporal resolution reveals operational\nconstraints that are not visible at coarse resolution which tighten feasible pathways\nfor high VRE shares and fast electrification unless flexibility is enhanced accordingly.\nIn addition, the distinct trajectories of thermal power, green hydrogen, and energy\nstorage in China TIMES 2.0, which differ from other IAMs, clearly demonstrate the\nunique impact of system flexibility on long-term planning.\n(Manuscript, Line 113-117) When temporal variability is explicitly represented, the\ncost-optimal solution exhibits marginal abatement costs that are over 9% higher and\ntotal final energy about 3% lower by 2060 relative to a coarse-timeslice baseline.\nThese differences arise because the model internalizes flexibility requirements and\noperational constraints that are muted at the annual scenario.\n(Manuscript, Line 121-126) Thermal power capacity in 2050 could increase by 16-\n21% and 30-34% in the coarser and hourly modeling scenario, respectively. From the\nhourly perspective, we identify thermal power achieves a shift from electricity\nprovider to flexibility provider, mitigating the possible shocks of rapid\ndecommissioning. On the other hand, renewables proportion among energy mix\nwould decrease by 2.2% for CN60-nohour and 3.0% for CN60.\n(Manuscript, Line 128-139) The coarse time granularity masks the impact of different\nflexibility measures. Under the CN60-LM scenario, which considers active demand-\nside flexibility, photovoltaics (PV) generation would decrease by 2% compared to the\nannual-scale model, while in the CN60-noLM scenario, which does not include\ndemand-side flexibility measures, it would decrease by 12%. For scenarios\nconsidering 2-timeslice (CN60-DN) and 32-timeslice (CN60-noHour) scenarios,\npower generation would decrease by 3% and 4%, respectively. Demand for\ndispatchable resources would rise sharply with considerable differences between\nscenarios, with pumped hydro growing by 43-135% under hourly scenarios, and\nCN60-noHour and CN60-DN would grow by 90% and 46% respectively. Grid-\nconnected hydrogen production would increase by more than 45% when seasonal\nfactors are taken into account, but only by 27% when only diurnal variations are\nconsidered.\n(Figure 2) add CN60-noHour and CN60-DN scenarios.\nComment 3.4:\nUnder 3.6, you state that for noTS ‘Such a scenario is equivalent to the scenario of a\ntraditional annual-level IAM model without embedded sub-annual level inscriptions.’\nIs this really the case? Again, please evidence this based on equivalent national\nmodels.\nResponse:\nThank you for this point. We have softened and clarified our wording. Our\nCN60-noTS setup captures annual energy balances without sub-annual operating\nconstraints; it is not universally “equivalent” to all annual-level IAMs, some of which\nmay include stylized capacity adequacy or flexibility constraints. We restructured the\nmodel framework and adjusted different time granularities to represent 1) the status\nof most multi-sector energy models in China, 2) the time resolution of China TIMES\nV1.0, and 3) the time granularity commonly used by leading national TIMES for\ncomparison. At the same time, we have also made modifications to the scenario\nsetting section.\nChanges/additions to the manuscript:\n(Manuscript, Line 89-90) In this study, we propose four hourly scenarios with different\nflexibility management measures and climate ambition:\n(Manuscript, Line 95-100) For comparison, we also present three scenarios\nconsidering coarser temporal resolutions: CN60-noTS, CN60-DN, and CN60-noHour,\ncorresponding to no sub-annual modeling (most multi-sector energy system models\nin China), only considering day and night timeslices (China TIMES 1.0 model), and\nconsidering seasonal-weekly-daily modeling but without hourly modeling (common\npractice in national TIMES models).\n(Manuscript, Line 513-516) CN60-DN represents the fluctuations in renewable\nenergy and load considering only daytime and nighttime, which is the practice of the\nChina TIMES 1.0 model46. CN60-noHour scenario depicts four seasons, weekdays\nand weekends, and four time periods in a day, for a total of 32 timeslices, similar to\nthe leading national TIMES model.\nComment 3.5:\nThe novelty of the paper is likely to be in the innovative way you have introduced\nload shifting and V2G integration. However, the approach to identifying any insights\nbased on a comparison with noTS is problematic, and doesn’t allow you to highlight\nimprovements versus standard modelling approaches.\nResponse:\nThanks for your comments. In the text, except for the first subsection of the results,\nwhere we use several course timeslice scenarios for comparison, the remaining\nsubsections focus on comparisons between the three scenarios CN60, CN60-noLM,\nand CN60-LM, which are characterized at the hourly level. We added the explicit\ndescription of the comparator to the results section in the revised text.\nChanges/additions to the manuscript:\n(Manuscript, Line 113-116) When temporal variability is explicitly represented, the\ncost-optimal solution exhibits marginal abatement costs that are over 9% higher and\ntotal final energy about 3% lower by 2060 relative to a coarse-timeslice baseline.\n(Manuscript, Line 121-126) Thermal power capacity in 2050 could increase by 16-\n21% and 30-34% in the coarser and hourly modeling scenario, respectively. From the\nhourly perspective, we identify thermal power achieves a shift from electricity\nprovider to flexibility provider, mitigating the possible shocks of rapid\ndecommissioning. On the other hand, renewables proportion among energy mix\nwould decrease by 2.2% for CN60-nohour and 3.0% for CN60.\n(Manuscript, Line 129-139) Under the CN60-LM scenario, which considers active\ndemand-side flexibility, photovoltaics (PV) generation would decrease by 2% compared to the\nannual-scale model, while in the CN60-noLM scenario, which does not include\ndemand-side flexibility measures, it would decrease by 12%. For scenarios\nconsidering 2-timeslice (CN60-DN) and 32-timeslice (CN60-noHour) scenarios,\npower generation would decrease by 3% and 4%, respectively. Demand\nfor dispatchable resources would rise sharply with considerable differences between\nscenarios, with pumped hydro growing by 43-135% under hourly scenarios, and\nCN60-noHour and CN60-DN would grow by 90% and 46% respectively. Grid-\nconnected hydrogen production would increase by more than 45% when seasonal\nfactors are taken into account, but only by 27% when only diurnal variations are\nconsidered.\n(Manuscript, Line 180-182) By comparing the CN60 and CN60-noLM scenarios, a\ndiscernible shift is evident in the consumption patterns of adaptive loads, including\nEV charging and hydrogen production.\n(Manuscript, Line 184-187) Model results suggest adoption of fast-charging devices\nwould increase dramatically, projected to increase from 15% in 2019 to 80% by 2060\nin CN60 while remaining stable in CN60-noLM.\n(Manuscript, Line 194-197) Compared with the CN60-noLM scenario, strategic\nhydrogen production in the CN60 scenario has the potential to utilize surplus daytime\nelectricity, thereby mitigating rather than exacerbating power imbalances during peak\ndemand periods.\n(Manuscript, Line 235-236) Considering system operation constraints at the hourly\nlevel (CN60), the total system cost would rise by about 4% compared to the CN60-\nnoTS scenario.\n(Manuscript, Line 239-244) Net-zero transition in the CN60 scenario requires an\nincrement annual investment by 50% compared to the NDC scenario (Fig. 8). Under\nthe CN60 scenario, annual investment in VRE, energy storage, BECCS and\nhydrogen needs to be approximately 250, 30, 12 and 44 billion USD by 2060. Energy\nstorage expenses would be 24% larger in the CN60 compared to the CN60-noTS by\n2060.\nComment 3.6:\nA further concern in this revised manuscript relates to the writing and overall\nstructure. The authors have commendably added a great deal of information to clarify\nthe questions raised by reviewers, which is much appreciated. However, the\nmanuscript as a whole would benefit from further proofreading to ensure it is concise\nenough and better aligned with your research focus and the key conclusions\nemerging from the analysis. Especially in the Results section, the presentation of\nquantitative findings does not fully reflect the structure and insights expected from\nyour three comparisons. In other words, the presentation could be more clearly\norganized or restructured to better convey the main findings.\nResponse:\nThank you very much for your suggestions, which will greatly improve the readability\nof our article. Our narrative logic follows “the need for more refined time granularity\nassessment in high VRE systems”—“power supply needs to be repositioned to\naddress flexibility challenges”—“demand-side flexibility is crucial”—“high levels of\nsupply-demand interaction address flexibility crises”, emphasizing the comparison\nbetween hourly scenarios and coarse-timeslice scenarios, considering the impact of\norderly load (CN60 vs. CN60-noLM), and considering the impact of demand response\nand V2G (CN60-LM vs. CN60). Finally, a comprehensive assessment of cost-\neffectiveness is conducted.\nWe have restructured the Results around the three key comparisons. First, we revised\nthe subheadings in the results section to make each subsection clearer and more\nprogressive. Second, we deleted some background descriptive text that were not\ndirectly related to flexibility, thereby making the paper more focused. Third, we\ncentered around a small set of system-level metrics aligned with flexibility and\nfeasibility.\nChanges/additions to the manuscript:\n(Manuscript, Line 140) Flexibility scarcity leading to power plant repositioning\n(Manuscript, Line 162-165) Its operational focus is anticipated to transition from\ncatering to peak loads to bolstering ramping capacity and accommodating nighttime\nloads, with over 35% of electricity demand at night being met by hydro.\n(Manuscript, Line 174-176) This surge is coupled with an increase in peak load,\nprojected to grow from 1.0 TW in 2019 to at least 2.5 TW in 2060. Peak loads could\nbe even further in case power load fluctuates unpredictably.\n(Manuscript, Line 184-187) Model results suggest adoption of fast-charging devices\nwould increase dramatically, projected to increase from 15% in 2019 to 80% by 2060\nin CN60 while remaining stable in CN60-noLM.\n(Manuscript, Line 215-220) Pumped hydro capacity is expected to reach 285 GW by\n2060, a level deemed feasible with a consistent annual growth rate of 3% (Fig. 4d). In\nthe CN60 scenario, it is necessary to invest pumped hydro facilities in nearly all\neligible candidate sites (368 GW). Even worse, the CN60-noLM scenario exhausts\nthe potential of pumped hydro and requires the intensive construction of other long-\nterm storage facilities (417 GW).\n(Manuscript, Line 234) Financial implications of a feasible energy transition to net-\nzero\n(Manuscript, Line 241-244) Under the CN60 scenario, annual investment in VRE,\nenergy storage, BECCS and hydrogen needs to be approximately 250, 30, 12 and 44\nbillion USD by 2060. Energy storage expenses would be 24% larger in the CN60\ncompared to the CN60-noTS by 2060.\nComment 3.7:\nSince the key focus of the paper is the flexibility of the power sector transition, and\nthe sub-annual model does not show significant differences in transition pathways in\nbroad terms, I recommend tightening this section. Keep the key results concise and\nhighlight where the sub-annual modeling truly makes a difference.\nResponse:\nThank you very much for your suggestion, which has been very helpful in highlighting\nour views. We have significantly reduced the content of this section and moved the\nlengthy description of the energy transition to Supplementary Notes 1. In addition, for\naesthetic and recognizability reasons, we have only shown the four hourly granularity\nscenarios in Figure 1, and the complete figure comparing the 7scenarios is shown in\nSupplementary Figure 15.\nChanges/additions to the manuscript:\n(Manuscript, Line 86-89) China aims for net-zero emissions by 2060 amid rising\nenergy demand and sectoral transition (Fig. 1). China is projected to peak its fossil\nfuel and industrial CO emissions at 12 Gt in 2025. Coal use will decline sharply\npost-2030, with renewables dominating power generation after 2035.\n₂\n(Supplementary Notes 1 and Supplementary Figure 15)\nSupplementary Note 1 China’s energy transition towards carbon neutrality\nChina is projected to reach its peak in fossil fuel and industrial processes (FFI)\ncarbon emissions around 2025 at 12 GtCO (Supplementary Fig. 15a). Unlike the\n2\nrapid coal-fired power plant (CFPP) phase-out pathway proposed by most IAMs, our\nstudy shows the ongoing expansion of CFPPs would persist as renewables are\ninadequate in addressing the incremental demand for electricity. After 2030, the\nenergy system is expected to undergo a marked transition, culminating in the\nachievement of CO net-zero emissions by 2060. Achieving the desired climate\n2\ntargets necessitates a precipitous decline in the coal usage in unabated CFPPs and\nenergy-intensive industries, which would lead to formidable transition pressures\n(Supplementary Fig. 15b). Final energy consumption is projected to rise by 16% from\n2020 to 2030, followed by a gradual decline leading up to 2060 (Supplementary Fig.\n15c). The expansion of electricity applications would lead to changes in both the\nquantity and temporal distribution of electricity consumption (Supplementary Figs. 1-\n5). Renewables are anticipated to dominate electricity generation after 2035\n(Supplementary Fig. 15d). This shift necessitates an accelerated transition within the\npower sector.\nSupplementary Fig. 15 China’s energy system decarbonization pathway for all\nscenarios\na Fossil fuel and industrial processes (FFI) CO emissions by sector, b Primary\n2\nenergy mix by fuel, c Final energy mix by fuel, d Electricity generation by technology.\nComment 3.8:\nLines 114–118: \"Fig. 2 explores the implications of considering sub-annual modeling\nacross the dimensions of mitigation strategy, energy demand, energy supply,\ndispatchable resource, and cost & effort. The sub-annual analysis indicates that\nachieving the energy transition with a high VRE share and a high electrification rate\nwould pose greater challenges than that from the annual perspective.\" This sentence\nappears to suggest that sub-annual analysis poses greater challenges to the energy\ntransition itself, which is confusing. Sub-annual modelling is a change in the\ngranularity of time, and while it may reveal operational challenges, it does not in itself\ncreate obstacles to transition pathways.\nResponse:\nThank you very much for pointing out this issue. What we want to express is that\nmore refined modeling reveals challenges that previous models could not detect,\nrather than creating challenges. We have re-proofread the entire text and resolved\nany ambiguities that such issues may have caused.\nChanges/additions to the manuscript:\n(Manuscript, Line 101-110) Fig. 2 compares outcomes under different temporal\ngranularity and flexibility management across mitigation strategy, energy demand,\nenergy supply, dispatchable resources, and cost. Fig. 3 compares the results with\nprominent IAMs under a harmonized scenario framework with the same carbon\nbudget constraints and net-zero targets. The finer temporal resolution reveals\noperational constraints that are not visible at coarse resolution which tighten feasible\npathways for high VRE shares and fast electrification unless flexibility is enhanced\naccordingly. In addition, the distinct trajectories of thermal power, green hydrogen,\nand energy storage in China TIMES 2.0, which differ from other IAMs, clearly\ndemonstrate the unique impact of system flexibility on long-term planning.\nComment 3.9:\nLines 134–136: \"The scenario with sub-annual modelling would increase marginal\nabatement costs of CO2 by more than 9% and decrease total energy consumption by\n3% in 2060 compared to that without sub-annual modelling.\" This has a similar issue:\nit implies the modeling choice causes these differences rather than reveals them\nthrough a more refined temporal lens. I suggest reframing to clarify.\nResponse:\nThank you very much for pointing out this ambiguous statement. We have corrected\nit in the new statement and explicitly pointed out that the difference in optimization\nresults is due to the internalization of flexibility requirements and operational\nconstraints.\nChanges/additions to the manuscript:\n(Manuscript, Line 113-117) When temporal variability is explicitly represented, the\ncost-optimal solution exhibits marginal abatement costs that are over 9% higher and\ntotal final energy about 3% lower by 2060 relative to a coarse-timeslice baseline.\nThese differences arise because the model internalizes flexibility requirements and\noperational constraints that are muted at the annual scenario.\nComment 3.10:\nThe context in Lines 158–162 could be tightened.\nResponse:\nWe appreciate your feedback. We have refined our original statement and explained\nwhy the expansion of VRE leads to flexibility issues.\nChanges/additions to the manuscript:\n(Manuscript, Line 142-144) China has historically concentrated its flexibility resources\non the supply side, primarily through thermal power and hydropower executing\ndispatch instructions.\nComment 3.11:\nFocus more directly on results or metrics that clearly support the subtitle’s theme—\ni.e., highlighting the scarcity of flexibility in a high-VRE system.\nResponse:\nThank you very much for your comment. We first clarified the focus of this section,\nwhich is the shift in flexibility shortages on the power supply side due to the\ndevelopment of VRE. Therefore, we first revised the section title. Second, we\nexplained at what stage flexibility shortages are likely to occur. Subsequently, we\ndescribed the periods of intraday flexibility shortages indicated by the net load curve\nand discussed the future development patterns of thermal power, hydropower, and\nnuclear power. Finally, we discussed flexibility issues at the seasonal level.\nChanges/additions to the manuscript:\n(Manuscript, Line 140) Flexibility scarcity leading to power plant repositioning\n(Manuscript, Line 141-142) During periods of ramping, generation output variations,\nnet load fluctuations, and peak load hours, flexibility scarcity issues are likely to arise.\n(Manuscript, Line 150-153) CFPP flexibility retrofits can significantly improve the\ndispatch performance of existing assets, partially alleviating the lack of flexibility\n(Supplementary Notes 2 and Supplementary Figure 16-17).\n(Manuscript, Line 156-157) With the rapid spread of VRE, it is essential to rapidly\nscale up low-carbon flexibility resources.\n(Manuscript, Line 161-165) Hydropower has gained significant importance in the\nhigh-VRE power system and is poised to become a central pillar of flexibility. Its\noperational focus is anticipated to transition from catering to peak loads to bolstering\nramping capacity and accommodating nighttime loads, with over 35% of electricity\ndemand at night being met by hydro.\n(Manuscript, Line 166-171) Seasonal pattens in PV and wind output reshape system\nbalance configuration (Fig. 5d). In summer, PV will dominate, requiring massive\nelectricity storage during midday hours and low-load operation capabilities for\nthermal power plants. In winter, wind dominance will increase intraday power load\nfluctuations and multi-hour deficits. These dynamics will increase the demand for\nfast-ramping capacity, short-duration storage, and demand flexibility to contain\ncurtailment and maintain adequacy.\nComment 3.12:\nLines 183–187: \"In the context of seasonal variations in electricity supply, summer is\ndistinguished by the longest periods of daylight, resulting in PV generation\ncontributing ~48% of the electricity mix. Conversely, during the winter and spring\nmonths, wind energy assumes a more prominent role...\" This provides seasonal\ncontext on VRE availability, but the link to \"flexibility scarcity\" is unclear. Consider\nreframing this to focus on how these variations create flexibility challenges.\nResponse:\nThank you very much for reminding us to link seasonal variations with flexibility\nchallenges. In our new statement, we have reframed the issue to show how seasonal\nPV/wind patterns translate into specific flexibility challenges (hourly fluctuations, low-\nsevere oversupply, peak load hours, multi-hour deficits).\nChanges/additions to the manuscript:\n(Manuscript, Line 166-171) Seasonal pattens in PV and wind output reshape system\nbalance configuration (Fig. 5d). In summer, PV will dominate, requiring massive\nelectricity storage during midday hours and low-load operation capabilities for\nthermal power plants. In winter, wind dominance will increase intraday power load\nfluctuations and multi-hour deficits. These dynamics will increase the demand for\nfast-ramping capacity, short-duration storage, and demand flexibility to contain\ncurtailment and maintain adequacy.\nComment 3.13:\nLines 189–191: \"The CN60-noLM scenario demonstrates higher levels of storage\nactivity in comparison to the other two net-zero scenarios, indicating that orderly\ndemand could alleviate power balancing challenges (Fig. 6a).\" This content is more\nappropriate for the next section (Orderly demand easing power imbalance) and\nshould be moved there for better flow.\nResponse:\nThank you very much for your suggestion. We have moved it to the next section and\nrefined the content.\nChanges/additions to the manuscript:\n(Manuscript, Line 189-191) In the CN60-noLM scenario without demand-side\nparticipation, the load curve’s shape remains unchanged, thereby requiring higher\nstorage capacity and activity (Fig. 6a).\nComment 3.14:\nFor the sections Orderly demand easing power imbalance and Addressing the\nflexibility crisis through demand-supply interaction, there is a similar issue of\nattempting to cover too many quantitative details, which results in a lack of clear\nfocus in the discussion. I would suggest the authors to further refine the narrative\nstructure in these sections to strengthen the logical flow and better highlight the key\ninsights.\nResponse:\nThank you very much for pointing out the shortcomings in our expression. First, we\nmade significant cuts to the text and quantitative details, removing content that was\nnot closely related to the main theme of the article.\nIn the section titled “Orderly demand easing power imbalance,” we further highlighted\nthe role of flexible loads such as orderly EV charging and orderly hydrogen\nproduction. Finally, we also explained the load fluctuations in different seasons.\nIn the section “Addressing the flexibility crisis through demand-supply interaction,” we\ndiscuss the two common forms of demand-side response (“shiftable load” and\n“curtailable load”) and V2G technology, which exemplify source-load interaction.\nSince such technologies are often used to smooth peak loads, we observe not only\ntheir activities but also their associated impact on energy storage construction.\nChanges/additions to the manuscript:\n(Manuscript, Line 176) Peak loads could be even further in case power load\nfluctuates unpredictably.\n(Manuscript, Line 177-178) Our study delineates the seasonal and intraday variations\nin power loads, a crucial aspect for integrating more challenging-to-dispatch power\nsystems.\n(Manuscript, Line 182-187) Under the CN60 scenario, results show EV would shift\nfrom charging primarily at night to charging during the day, utilizing PV power and\nalleviating excess electricity supply. Model results suggest adoption of fast-charging\ndevices would increase dramatically, projected to increase from 15% in 2019 to 80%\nby 2060 in CN60 while remaining stable in CN60-noLM.\n(Manuscript, Line 189-191) In the CN60-noLM scenario without demand-side\nparticipation, the load curve’s shape remains unchanged, thereby requiring higher\nstorage capacity and activity (Fig. 6a).\n(Manuscript, Line 202-206) Significant increases in winter electricity consumption are\nexpected in the future due to the growing popularity of electric heating technology,\nthereby narrowing seasonal differences in electricity consumption (Fig. 5b).\nHydrogen produced in spring and autumn is stored for use in summer and winter,\nthereby achieving cross-seasonal energy storage (Fig. 7d).\n(Manuscript, Line 215-220) Pumped hydro capacity is expected to reach 285 GW by\n2060, a level deemed feasible with a consistent annual growth rate of 3% (Fig. 4d). In\nthe CN60 scenario, it is necessary to invest pumped hydro facilities in nearly all\neligible candidate sites (368 GW). Even worse, the CN60-noLM scenario exhausts\nthe potential of pumped hydro and requires the intensive construction of other long-\nterm storage facilities (417 GW).\n(Manuscript, Line 227-229) The energy price increase resulting from emission\nreduction could incentivize demand-side load reduction, which is also an important\nmeans of demand-side response.\nReviewer 4\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nResponse:\nThanks very much for giving us helpful comments."}]

Decision letter