• Energy Research
• US close
• GB close
• Tsinghua University close

Net anthropogenic emissions of carbon dioxide (CO2) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts1-5. Yet continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO2 emissions6-13. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO2 (with a range of 226 to 1,479 gigatonnes CO2, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO2. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO2) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO2)5, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO2)5. The remaining carbon budget estimates are varied and nuanced14,15, and depend on the climate target and the availability of large-scale negative emissions16. Nevertheless, our estimates suggest that little or no new CO2-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals17. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable4,18.

The development of methane hydrate extraction technology remains constrained due to the limited physical understanding of hydrate dissociation dynamics. While recent breakthroughs in pore-scale visualization techniques offer intuitive insights into the dissociation process, obtaining a profound grasp of the underlying mechanisms necessitates more than mere experimental observations. In this research, we introduce a two-phase micro-continuum model that facilitates the numerical simulation of methane hydrate dissociation at both single- and multiscale levels. We employed this numerical model to simulate microfluidic experiments and determined the kinetic parameters of methane hydrate dissociation based on experimental data under various dissociation scenarios. The simulations, once calibrated, correspond closely to experimental results. By comprehensively comparing the simulated results with experimental data, the rate constant and the effective diffusion coefficient were reliably determined to be kd = 1.5 × 108 kmol2/(J·s·m2) and Dl = 0.8 × 10−7 m2/s, respectively. Notably, the multiscale model not only matches the precision of the single-scale model but also presents considerable promise for streamlining the simulation of hydrate dissociation across multiscale porous media. Moreover, we contrast hydrate dissociation under isothermal versus adiabatic conditions, wherein the dissociation rate is significantly reduced under adiabatic conditions due to the shifted thermodynamic condition. This comparison highlights the disparities between microfluidic experiments and real-world extraction environments.

AbstractThe inclusive top quark pair ($$t\bar{t}$$ t t ¯ ) production cross-section $$\sigma _{t\bar{t}}$$ σ t t ¯ has been measured in proton–proton collisions at $$\sqrt{s}=13\,\text {TeV}$$ s = 13 TeV , using 36.1 fb$$^{-1}$$ - 1 of data collected in 2015–2016 by the ATLAS experiment at the LHC. Using events with an opposite-charge $$e\mu $$ e μ pair and b-tagged jets, the cross-section is measured to be: $$\begin{aligned} \sigma _{t\bar{t}} = 826.4 \pm 3.6\,\mathrm {(stat)}\ \pm 11.5\,\mathrm {(syst)}\ \pm 15.7\,\mathrm {(lumi)}\ \pm 1.9\,\mathrm {(beam)}\,\mathrm {pb}, \end{aligned}$$ σ t t ¯ = 826.4 ± 3.6 ( stat ) ± 11.5 ( syst ) ± 15.7 ( lumi ) ± 1.9 ( beam ) pb , where the uncertainties reflect the limited size of the data sample, experimental and theoretical systematic effects, the integrated luminosity, and the LHC beam energy, giving a total uncertainty of 2.4%. The result is consistent with theoretical QCD calculations at next-to-next-to-leading order. It is used to determine the top quark pole mass via the dependence of the predicted cross-section on $$m_t^{\mathrm{pole}}$$ m t pole , giving $$m_t^{\mathrm{pole}}=173.1^{+2.0}_{-2.1}\,\text {GeV}$$ m t pole = 173 . 1 - 2.1 + 2.0 GeV . It is also combined with measurements at $$\sqrt{s}=7\,\text {TeV}$$ s = 7 TeV and $$\sqrt{s}=8\,\text {TeV}$$ s = 8 TeV to derive ratios and double ratios of $$t\bar{t}$$ t t ¯ and Z cross-sections at different energies. The same event sample is used to measure absolute and normalised differential cross-sections as functions of single-lepton and dilepton kinematic variables, and the results are compared with predictions from various Monte Carlo event generators.