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A profound transformation of China’s energy system is required to achieve carbon neutrality. Here, we couple Monte Carlo analysis with a bottom-up energy-environment-economy model to generate 3,000 cases with different carbon peak times, technological evolution pathways and cumulative carbon budgets. The results show that if emissions peak in 2025, the carbon neutrality goal calls for a 45–62% electrification rate, 47–78% renewable energy in primary energy supply, 5.2–7.9 TW of solar and wind power, 1.5–2.7 PWh of energy storage usage and 64–1,649 MtCO 2 of negative emissions, and synergistically reducing approximately 80% of local air pollutants compared to the present level in 2050. The emission peak time and cumulative carbon budget have significant impacts on the decarbonization pathways, technology choices, and transition costs. Early peaking reduces welfare losses and prevents overreliance on carbon removal technologies. Technology breakthroughs, production and consumption pattern changes, and policy enhancement are urgently required to achieve carbon neutrality. China’s transition path toward carbon neutrality remains uncertain. Here the authors combine Monte Carlo analysis with an energy-environment-economy model to present a probabilistic view of China’s energy transition across 3,000 cases.

Cost of capital is an important driver of investment decisions, including the large investments needed to execute the low-carbon energy transition. Most models, however, abstract from country or technology differences in cost of capital and use uniform assumptions. These might lead to biased results regarding the transition of certain countries towards renewables in the power mix and potentially to a sub-optimal use of public resources. In this paper, we differentiate the cost of capital per country and technology for European Union (EU) countries to more accurately reflect real-world market conditions. Using empirical data from the EU, we find significant differences in the cost of capital across countries and energy technologies. Implementing these differentiated costs of capital in an energy model, we show large implications for the technology mix, deployment, carbon emissions and electricity system costs. Cost-reducing effects stemming from financing experience are observed in all EU countries and their impact is larger in the presence of high carbon prices. In sum, we contribute to the development of energy system models with a method to differentiate the cost of capital for incumbent fossil fuel technologies as well as novel renewable technologies. The increasingly accurate projections of such models can help policymakers engineer a more effective and efficient energy transition.

Countries are urged to advance the energy transition in a just, orderly, and equitable manner, yet the appropriate pathway remains unclear. Using a provincial-level, hourly-dispatched power system model of China that incorporates intertemporal decisions on early retirement and carbon capture retrofitting, our study reveals that for coal-rich but gas-poor economies, repositioning coal power from a baseload resource to a flexibility provider can accelerate net-zero transition of the power system in three aspects. First, when achieving the same emissions reduction target, it mitigates stranded assets by decreasing the average lifespan loss of coal power by 7.9-9.6 years and enhancing the long-term competitiveness of retrofitted coal power. Second, it enables the integration of an additional 194-245 gigawatts of variable renewables by 2030 under the same carbon emissions reduction trajectory. Third, it reduces China’s power system transition costs by approximately 176 billion U.S. Dollars, particularly in the face of costly gas power and energy storage technologies. These robust findings underscore the need for appropriate policies to incentivize the flexible dispatch, orderly retirement, and carbon capture retrofitting of coal power, thereby accelerating the decarbonization of China’s power system. A study on China finds that repositioning coal power from a baseload resource to a flexibility provider can accelerate the net-zero transition by mitigating stranded assets, enabling greater integration of renewables, and reducing transition costs.

The same cumulative carbon emission reduction target can correspond to multiple emission reduction pathways. This study explores how different coal power transition pathways with the same cumulative emissions reductions impact the transition costs, by assessing the dynamic transition processes for coal plants adopting multiple mitigation technologies concurrently or sequentially, such as flexibility operation, biomass and coal co-firing, carbon capture and storage, and compulsory retirement. We develop a plant-level dynamic optimization model and apply it to China’s 4200+ coal plants. We find that under deep decarbonization, the majority of Chinese coal plants retrofit with multiple technologies to reduce emissions and retire naturally at lower costs while contributing to grid stability. Optimizing the pathway can potentially save over 700 billion U.S. Dollars for achieving the same target or increase cumulative emissions reduction from 30% to 50% at no additional cost. This analysis can help inform a cost-effective coal phase-out under China’s carbon neutrality. A well-designed national coal phase-out pathway in China that considers diverse technology portfolios and plant-level sequential decision-making processes can save over 700 billion dollars and elevate mitigation potential.

Policy, business, finance and civil society stakeholders are increasingly looking to compare future emissions pathways across both their associated physical climate risks stemming from increasing temperatures and their transition climate risks stemming from the shift to a low-carbon economy. Here, we present an integrated framework to explore near-term (to 2030) transition risks and longer-term (to 2050) physical risks, globally and in specific regions, for a range of plausible greenhouse gas emissions and associated temperature pathways, spanning 1.5–4 °C levels of long-term warming. By 2050, physical risks deriving from major heatwaves, agricultural drought, heat stress and crop duration reductions depend greatly on the temperature pathway. By 2030, transition risks most sensitive to temperature pathways stem from economy-wide mitigation costs, carbon price increases, fossil fuel demand reductions and coal plant capacity reductions. Considering several pathways with a 2 °C target demonstrates that transition risks also depend on technological, policy and socio-economic factors.There is a balance in mitigation pathway design between economic transition cost and physical climate threats. This study provides a comprehensive framework to assess the near- and long-term risks under various warming scenarios globally and in particular regions.

Over the last decades, the Cache Side Channel Attack (CSCA) seriously threatens the security of user information, which repeatedly highlights the security of cache. Even though the secure cache design can mitigate or defend against this attack, the high design period and tap-out cost reduce the development of the specific designs. Therefore, many researchers focus on proposing a secure cache model to analyse the CSCA and simplify the secure design. Nowadays, the cache model can be divided into 1) Indicator analysis model; 2) Cache verify model; 3) Cache abstract model. However, most of these models focus on the establishment phase of CSCA, regardless of the usage phase of CSCA. On the other hand, the granularity of the cache model is either the entire cache or a single cache line, which can’t describe the cache behaviour in CSCA clearly. To overcome these limitations, we proposed a novel cache model based on state transition and gave a formal specification of CSCA by this model in this work. Firstly, we summarize 11 basic states of the cache set and point to the target state in the process of CSCA. Then we describe the CSCA from three aspects: pre-attack cost, state transition cost, and detection accuracy, which gives a more comprehensive analysis of CSCA.

CO 2 capture and storage (CCS) in geological reservoirs is expected to play a large role in low-emissions scenarios from multi-sector human Earth system models. Yet these scenarios have often projected near-term CCS deployments that far exceed what is currently planned, l et al one operational. They have also failed to consider regional differences in capacity to deploy large-scale CO 2 capture, transport, and subsurface injection. Here, we update a leading integrated energy-economy-land model by recalibrating maximum deployments to publicly announced CCS projects through to 2030. We also quantify a range of regionally explicit future scaling and maximum injection rates for the overall CCS value chain and evaluate their implications for emissions trajectories, energy mix, use of rate-limited storage capacity, and mitigation costs. Under limited CCS growth rates, deployment at mid-century and 2100 could be reduced by a factor of 7 relative to a scenario that does not consider injectivity or growth rate limits. However, sustained efforts to rapidly scale CCS could reduce transition costs by nearly $11 trillion (20%) globally, with cost reductions most heavily concentrated in regions such as China and India. Delayed mitigation combined with slower-than-expected CCS deployment could result in large and prolonged temperature overshoot. Conversely, there are lower peak and long-term temperatures with aggressive emissions cuts in anticipation of slow CCS scaling that subsequently far exceeds expectations.

Adjusting green public support programmes to green premiums can reduce public spending, yet this is challenged by uncertainty. Underfunding green technologies can delay the green transition, and overfunding them can increase transition costs. Both risks of under- and overfunding can be reduced using responsive adjustments.

Amidst escalating global concerns over climate change and the pressing need for sustainable development, this study conducts a comparative analysis across 24 nations that have successfully achieved carbon peaking, evaluating their socioeconomic characteristics and carbon reduction strategies. Simultaneously, it examines China’s policy evolution and strategic responses within the context of its economic and urban development. The analysis reveals that countries with successful carbon peak outcomes typically exhibit high GDP per capita and advanced urbanization rates. Critical to their success are comprehensive adjustments in energy consumption structures and industrial transformation, which are supported by robust environmental policies and technological innovation. The study categorizes global carbon reduction policies into three primary categories and seven sub-categories, reflecting the dynamic evolution of policy approaches driven by global climate agendas and varying stages of national development. Strategies including legal frameworks, carbon pricing mechanisms, international cooperation, and technological innovation are critically assessed for their potential to refine China’s carbon policies. Significant challenges in policy implementation are identified, particularly in aligning ambitious environmental strategies with economic objectives and managing transition costs in critical sectors such as energy and transportation. The study emphasizes the necessity of a phased policy implementation approach, which begins with enhancing public and corporate environmental awareness, advances through the promotion of low-carbon technologies, and concludes with the establishment of stringent legal and regulatory frameworks.

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task, a cognitive test in which participants respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework’s effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.