ALBERT

Description: The results of a search for gluino and squark pair production with the pairs decaying via the lightest charginos into a final state consisting of two W bosons, the lightest neutralinos ($$ilde{chi }^0_1$$ χ ~ 1 0 ), and quarks, are presented: the signal is characterised by the presence of a single charged lepton ($$e^{pm }$$ e ± or $$mu ^{pm }$$ μ ± ) from a W boson decay, jets, and missing transverse momentum. The analysis is performed using 139 fb$$^{-1}$$ - 1 of proton–proton collision data taken at a centre-of-mass energy $$sqrt{s}=13$$ s = 13   delivered by the Large Hadron Collider and recorded by the ATLAS experiment. No statistically significant excess of events above the Standard Model expectation is found. Limits are set on the direct production of squarks and gluinos in simplified models. Masses of gluino (squark) up to 2.2  (1.4 ) are excluded at 95% confidence level for a light $$ilde{chi }^0_1$$ χ ~ 1 0 .

Description: A correction to this paper has been published: 10.1140/epjc/s10052-021-09344-w

Description: The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.

Description: Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3–11% historically to 10–20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Description: Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations.

Description: Chinese cities are core in the national carbon mitigation and largely affect global decarbonisation initiatives, yet disparities between cities challenge country-wide progress. Low-carbon transition should preferably lead to a convergence of both equity and mitigation targets among cities. Inter-city supply chains that link the production and consumption of cities are a factor in shaping inequality and mitigation but less considered aggregately. Here, we modelled supply chains of 309 Chinese cities for 2012 to quantify carbon footprint inequality, as well as explored a leverage opportunity to achieve an inclusive low-carbon transition. We revealed significant carbon inequalities: the 10 richest cities in China have per capita carbon footprints comparable to the US level, while half of the Chinese cities sit below the global average. Inter-city supply chains in China, which are associated with 80% of carbon emissions, imply substantial carbon leakage risks and also contribute to socioeconomic disparities. However, the significant carbon inequality implies a leveraging opportunity that substantial mitigation can be achieved by 32 super-emitting cities. If the super-emitting cities adopt their differentiated mitigation pathway based on affluence, industrial structure, and role of supply chains, up to 1.4 Gt carbon quota can be created, raising 30% of the projected carbon quota to carbon peak. The additional carbon quota allows the average living standard of the other 60% of Chinese people to reach an upper-middle-income level, highlighting collaborative mechanism at the city level has a great potential to lead to a convergence of both equity and mitigation targets.

Description: Molecular clocks are the basis for dating the divergence between lineages over macroevolutionary timescales (~105 to 108 years). However, classical DNA-based clocks tick too slowly to inform us about the recent past. Here, we demonstrate that stochastic DNA methylation changes at a subset of cytosines in plant genomes display a clocklike behavior. This “epimutation clock” is orders of magnitude faster than DNA-based clocks and enables phylogenetic explorations on a scale of years to centuries. We show experimentally that epimutation clocks recapitulate known topologies and branching times of intraspecies phylogenetic trees in the self-fertilizing plant Arabidopsis thaliana and the clonal seagrass Zostera marina, which represent two major modes of plant reproduction. This discovery will open new possibilities for high-resolution temporal studies of plant biodiversity.

Description: Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the “full world” model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China’s provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China’s overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.

Description: As the forerunner and policy test field of the sustainable development, the sustainable development pilot zones are an important strategy for China to explore the mechanism and model of the coordinated development of human and land in different regional units. However, the impact of sustainable development pilot zones, especially on the improvement of environmental efficiency, needs to be assessed. In this paper, 187 prefecture-level cities in China were taken as samples (22 sustainable development pilot zones and 165 nonpilot ones). Firstly, the environmental efficiency of 187 prefecture-level cities between 2006 and 2016 was measured by data envelopment analysis (DEA). Then, the effect of construction of sustainable development pilot zones on environmental efficiency was assessed using the difference-in-difference (DID) model. The assessment results were further verified by propensity score matching with difference-in-difference (PSM-DID). In addition, the impact mechanism of construction of the sustainable development pilot zones on the environmental efficiency was discussed. Results show that the environmental efficiency of sustainable development pilot zones is 27.7∼31.7% greater than that of nonsustainable one, which is mainly attributed to the environmental regulation and industrial structure adjustment.

Description: Boosted by impressive technological innovation and cost reductions, renewable energy in a growing number of countries is now primarily considered for its social and economic benefits. Among the renewable energy promotion actions at the global level, photovoltaic poverty alleviation (PVPA) program in China is very unique since the targeted users are villagers in poverty regions. Under this condition, it would be quite necessary to understand how are the PVPA program carrying out. In this study, three poverty villages are selected in the northwestern part of China to conduct the social impact analysis. An evaluation system including four categories and thirteen indicators was established. Site investigation and questionnaire interview was carried out to collect required information. Our findings reveal that the poor families in the three counties can increase their income by around 3000 RMB per year with the implementation of PVPA program. The final social impact indicator of Yanchi County, Dingbian County and Guazhou County are 2.61, 2.09 and 2.15 respectively. Villagers’ living standards in the three counties are prominently improved because of the solar power supply. The factors that hinder the development of PVPA projects are the lack of investment funds, poor quality of solar panels, low public awareness, high abandon rate of photovoltaic, etc. Recommendations for improving the sustainable development of PVPA program based on the findings are also proposed.