Gene expression data from isolated stele cells after roots were treated with 140 mM NaCl for 32 hour Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Gene expression data from isolated stele cells Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Gene expression data from isolated stele cells Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Gene expression data from isolated stele cells Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Across continental Africa, more than 300 new hydropower projects are under consideration to meet the future energy demand that is expected based on the growing population and increasing energy access. Yet large uncertainties associated with hydroclimatic and socioeconomic changes challenge hydropower planning. In this work, we show that only 40 to 68% of the candidate hydropower capacity in Africa is economically attractive. By analyzing the African energy systems' development from 2020 to 2050 for different scenarios of energy demand, land-use change, and climate impacts on water availability, we find that wind and solar outcompete hydropower by 2030. An additional 1.8 to 4% increase in annual continental investment ensures reliability against future hydroclimatic variability. However, cooperation between countries is needed to overcome the divergent spatial distribution of investment costs and potential energy deficits.

Carbon-emitting technologies often cost less than carbon-emission-free alternatives; this difference in cost is known as the Green Premium. Innovations that decrease the Green Premium contribute to achieving climate goals, but a conceptual framework to quantify that contribution has been lacking. Here, we devise a framework to translate reductions in the Green Premium into equivalent reductions in carbon emissions. We introduce a new integrated assessment model designed for teaching and communication, the Climate Optimized INvestment model, to facilitate transparent investigation of cost-saving innovation. We look at consequences of introducing a new technology with potential for learning and improvement for scenarios with three levels of stringency of carbon constraint: an Unlimited budget scenario in which carbon emissions abatement is determined only by balancing marginal costs; a Large budget scenario with a maximum budget for future cumulative emissions equivalent to 50 times the initial-year emissions; and a Small budget scenario with a maximum budget for future cumulative emissions equivalent to 15 times the initial-year emissions. At all of these stringency levels, we find the least-cost solutions involve investing in a learning subsidy to bring the cost of the new technology down the learning curve. Reducing the Green Premium can lead to enhanced carbon abatement, lower abatement costs even after reaching net-zero emissions, less climate damage, and increased net-present-value of consumption. We find both the value of Green Premium reductions and the value of carbon dioxide removal are greater under more stringent mitigation targets. Our study suggests a crucial role for both public and private sectors in promoting and developing innovations that can contribute to achieving zero emissions goals.

Plans for decarbonized electricity systems rely on projections of highly uncertain future technology costs. We use a stylized model to investigate the influence of future cost uncertainty, as represented by different projections in the National Renewable Energy Laboratory 2021 Annual Technology Baseline dataset, on technology mixes comprising least-cost decarbonized electricity systems. Our analysis shows that given the level of future cost uncertainty as represented by these projections, it is not possible to predict with confidence which technologies will play a dominant role in future least-cost carbon emission-free energy systems. Successful efforts to reduce costs of individual technologies may or may not lead to system cost reductions and widespread deployments, depending on the success of cost-reduction efforts for competing and complementary technologies. These results suggest a portfolio approach to reducing technology costs. Reliance on uncertain cost breakthroughs risks costly outcomes. Iterative decision-making with learning can help mitigate these risks.

Efforts to make food systems more sustainable have emphasized reducing adverse environmental impacts of agriculture. In contrast, chemical and biological processes that could produce food without agriculture have received comparatively little attention or resources. Although there is a possibility that someday a wide array of attractive foods could be produced chemosynthetically, here we show that dietary fats could be synthesized with <0.8 g CO2-eq kcal(-1), which is much less than the >1.5 g CO2-eq kcal(-1) now emitted to produce palm oil in Brazil or Indonesia. Although scaling up such synthesis could disrupt agricultural economies and depend on consumer acceptance, the enormous potential reductions in greenhouse gas emissions as well as in land and water use represent a realistic possibility for mitigating the environmental footprint of agriculture over the coming decade.

The 2022 Indus floods in Pakistan underscore the urgency of adapting to more frequent and severe natural disasters in a warming world. Post-disaster reconstruction offers a chance to built-in adaptation measures, but identifying feasible and cost-effective adaptation options is challenging, especially in data-scarce regions. Here we employ a high-resolution rapid assessment of flood stages combined with demographic data to identify adaptation opportunities and costs for the Indus floodplains. Under a plausible set of assumptions, we find that rebuilding houses in a flood-proof, elevated manner ('moving up') or (temporary) relocation ('moving over') could have protected 13%, respectively 16% of people affected during the 2022 floods, while the remaining 70% of people were exposed to shallow water levels that could have been addressed with low-cost adaptation. Implementing these measures during ongoing reconstruction could be an effective adaptation to future floods but will come with substantial costs. Rebuilding in a flood-proofed manner ('moving up') alone would already increase costs by 26%-63% ($1.5bn-$3.6bn) compared to estimated reconstruction costs without adaptation ($5.8bn). Additional costs would be incurred by relocation and adaptation of other infrastructure. The absence of local flood stage and socio-economic data creates uncertainty and points to future research avenues. Yet, our prototype approach demonstrates the value of rapid assessments for guiding post-disaster adaptation of livelihoods to future floods.