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Sodium-ion batteries have garnered notable attention as a potentially low-cost alternative to lithium-ion batteries, which have experienced supply shortages and price volatility for key minerals. Here we assess their techno-economic competitiveness against incumbent lithium-ion batteries using a modelling framework incorporating componential learning curves constrained by minerals prices and engineering design floors. We compare projected sodium-ion and lithium-ion price trends across over 6,000 scenarios while varying Na-ion technology development roadmaps, supply chain scenarios, market penetration and learning rates. Assuming that substantial progress can be made along technology roadmaps via targeted research and development, we identify several sodium-ion pathways that might reach cost-competitiveness with low-cost lithium-ion variants in the 2030s. In addition, we show that timelines are highly sensitive to movements in critical minerals supply chains—namely that of lithium, graphite and nickel. Our modelled outcomes suggest that being price advantageous against low-cost lithium-ion variants in the near term is challenging and increasing sodium-ion energy densities to decrease materials intensity is among the most impactful ways to improve competitiveness. Sodium-ion batteries are considered a promising substitute for Li-ion, but the timeline and conditions for achieving cost-competitiveness remain uncertain. This study evaluates their techno-economic potential, showing that while challenging, they could compete with low-cost Li-ion batteries by the 2030s under specific conditions.

The recent sharp decline in the cost of renewable energy suggests that the production of hydrogen from renewable power through a power-to-gas process might become more economical. Here we examine this alternative from the perspective of an investor who considers a hybrid energy system that combines renewable power with an efficiently sized power-to-gas facility. The available capacity can be optimized in real time to take advantage of fluctuations in electricity prices and intermittent renewable power generation. We apply our model to the current environment in both Germany and Texas and find that renewable hydrogen is already cost competitive in niche applications (€3.23 kg −1 ), although not yet for industrial-scale supply. This conclusion, however, is projected to change within a decade (€2.50 kg −1 ) provided recent market trends continue in the coming years. Hydrogen fuel, produced from renewable power, could be critical in the decarbonization of the electricity and transportation sectors. Here, a thorough economic analysis shows that hydrogen obtained from wind power is already cost competitive in niche applications and may become widely competitive in the foreseeable future.

Causal inference has recently attracted substantial attention in the machine learning and artificial intelligence community. It is usually positioned as a distinct strand of research that can broaden the scope of machine learning from predictive modelling to intervention and decision-making. In this Perspective, however, we argue that ideas from causality can also be used to improve the stronghold of machine learning, predictive modelling, if predictive stability, explainability and fairness are important. With the aim of bridging the gap between the tradition of precise modelling in causal inference and black-box approaches from machine learning, stable learning is proposed and developed as a source of common ground. This Perspective clarifies a source of risk for machine learning models and discusses the benefits of bringing causality into learning. We identify the fundamental problems addressed by stable learning, as well as the latest progress from both causal inference and learning perspectives, and we discuss relationships with explainability and fairness problems. Machine learning performs well at predictive modelling based on statistical correlations, but for high-stakes applications, more robust, explainable and fair approaches are required. Cui and Athey discuss the benefits of bringing causal inference into machine learning, presenting a stable learning approach.

Research over the past decades has demonstrated the explanatory power of emotions, feelings, motivations, moods, and other affective processes when trying to understand and predict how we think and behave. In this consensus article, we ask: has the increasingly recognized impact of affective phenomena ushered in a new era, the era of affectivism?

The emergence of SARS-CoV-2 and attempts to limit its spread have resulted in a contraction of the global economy. Here we document the socioeconomic impacts of the pandemic among households, adults and children in low-income countries. To do so, we rely on longitudinal household survey data from Ethiopia, Malawi, Nigeria and Uganda, originating from pre-COVID-19 face-to-face household surveys plus phone surveys implemented during the pandemic. We estimate that 256 million individuals—77% of the population—live in households that have lost income during the pandemic. Attempts to cope with this loss are exacerbated by food insecurity and an inability to access medicine and staple foods. Finally, we find that student–teacher contact has dropped from a pre-COVID-19 rate of 96% to just 17% among households with school-aged children. These findings can inform decisions by governments and international organizations on measures to mitigate the effects of the COVID-19 pandemic. Recent phone survey data from Ethiopia, Malawi, Nigeria and Uganda reveals the breadth of the socioeconomic impacts of the COVID-19 pandemic on individuals and households.

Environmental, Social, and Governance (ESG) is highly consistent with the “Dual Carbon” goals proposed by China and has become an important indicator to measure enterprises’ high-quality development. This study explores the impact of intelligent manufacturing on corporate ESG performance and its potential mechanisms. Using the dataset of China’s A-share listed companies from 2009 to 2021, we treat the intelligent manufacturing pilot programs (IMPP) as a quasi-natural experiment and use the staggered difference-in-difference model for empirical analysis. The results show that corporate ESG performance is significantly improved after participating in IMPP, and the placebo and entropy balancing tests further reconfirm the results. The mechanism analysis shows that IMPP works in two main ways: promoting enterprises’ green innovation and reducing the misallocation of financial resources. The heterogeneity analysis shows that the IMPP significantly improves the ESG performance of Non-state-owned, high-tech, and heavy-polluting enterprises. In addition, the IMPP improves the enterprises’ green total factor productivity and brings significant economic benefits. This study has far-reaching policy implications for promoting the quality-driven development of China’s manufacturing industry and the sustainable development of enterprises.

Cobalt, widely used in the layered oxide cathodes needed for long-range electric vehicles (EVs), has been identified as a key EV supply bottleneck. Many reports have proposed that nickel-rich, cobalt-free cathodes can—in addition to supply chain benefits—herald significant increases in energy density and reductions in EV cost if they can be stabilized. Here we present a contrasting viewpoint. We show that cobalt’s thermodynamic stability in layered structures is essential in enabling access to higher energy densities without sacrificing performance or safety, effectively lowering battery costs per kWh despite increasing raw material costs. We additionally show that the supply growth required to support intermediate cobalt content cathodes for 1.3 billion EVs by 2050 is within historical trends for major industrial metals—although supply concentration in challenging jurisdictions is likely to remain a problem. We predict that these techno-economic factors will drive the continued use of cobalt in nickel-based EV batteries. The development of high-energy Li-ion batteries is being geared towards cobalt-free cathodes because of economic and social–environmental concerns. Here the authors analyse the chemistry, thermodynamics and resource potential of these strategic transition metals, and propose that the use of cobalt will likely continue.

The literature provides conflicting guidance about the appropriate time-matching requirement between electricity consumption by electrolysers and contracted variable renewable energy (VRE) for qualifying hydrogen (H 2 ) as ‘low carbon’. Here we show that these findings are highly influenced by different interpretations of additionality. Substantially lower consequential emissions are achievable under annual time matching when presuming that VRE for non-H 2 electricity demand does not compete with VRE contracted for H 2 , as opposed to when assuming that all VRE resources are in direct competition. Further analysis considering four energy system-relevant policies suggests that the latter interpretation of additionality is likely to overestimate the emissions impacts of annual matching and underestimate those of hourly matching. We argue for starting with annual time matching in the near term for the attribution of the H 2 US production tax credits, where conditions resemble the ‘non-compete’ framework, followed by phase-in and subsequent phase-out of hourly time-matching requirements as the grid is deeply decarbonized. There is debate about when electrolytic hydrogen produced from grid-connected renewables should qualify as ‘low carbon’. Here the authors explore how additionality and the degree of time matching between electrolysers’ electricity consumption and contracted renewable energy generation impacts emissions and costs.

Optimal wind farm locations require a strong and reliable wind resource and access to transmission lines. As onshore and offshore wind energy grows, preferred locations become saturated with numerous wind farms. An upwind wind farm generates ‘wake effects’ (decreases in downwind wind speeds) that undermine a downwind wind farm’s power generation and revenues. Here we use a diverse set of analysis tools from the atmospheric science, economic and legal communities to assess costs and consequences of these wake effects, focusing on a West Texas case study. We show that although wake effects vary with atmospheric conditions, they are discernible in monthly power production. In stably stratified atmospheric conditions, wakes can extend 50+ km downwind, resulting in economic losses of several million dollars over six years for our case study. However, our investigation of the legal literature shows no legal guidance for protecting existing wind farms from such significant impacts. Wakes from upwind wind farms can reduce energy generation at downwind farms. Here, using power production data and atmospheric simulations, researchers quantify the economic impacts of wakes, explain the physics of wake variability and highlight that no legal framework exists to protect downwind farms.