Search

Abstract Background A large proportion of individuals experience functional impairment in everyday life due to climate worry. However, the current understanding of this functional impairment is limited by the use of suboptimal measures. Furthermore, it is not known whether functional impairment due to climate worry affects pro-environmental behaviors (PEBs) or whether individuals who experience such impairment perceive a need for support. The aims of the current study were (1) to extend previous research using an established measure of functional impairment (the Work and Social Adjustment Scale, WSAS), (2) to explore the associations between climate worry, functional impairment, and PEBs, and (3) to describe the characteristics and the perceived need for support of individuals with functional impairment due to climate worry. Methods A cross-sectional survey targeting adult individuals who experience climate worry. Participants were recruited nationally in Sweden between September and October 2022. The survey included measures of climate worry severity, climate worry frequency, functional impairment, PEBs, depressive symptoms, sleep problems, and questions related to perceived need for support. Results A total of 1221 adults (75% women, mean age 46.3 years) were included in the analyses. Multivariate structural equation modeling revealed that climate worry severity and frequency were significantly associated with PEBs (β = 0.34 and β = 0.45, respectively). Climate worry frequency was associated with functional impairment (β = 0.41). Functional impairment was only marginally associated with PEBs (β = 0.05). Approximately 40% of the sample (n = 484) reported a high frequency and high severity of climate worry. Among these, one-third (n = 153) scored above the cutoff for significant impairment on the WSAS. Individuals in this group (high severity and frequency of climate worry as well as significant functional impairment) were more likely to experience depressed mood and sleep problems and were more interested in receiving support, specifically concerning strategies for worry management and sustainable behavior change. Conclusions Using an established measure of functional impairment, we found an association of climate worry with functional impairment and PEBs. Importantly, as there is a perceived need for support in individuals with impairment due to climate worry, interventions targeting this specific subgroup should be developed.

Abstract The ‘decade of action’ intended to accomplish the ambitious 17 Sustainable Development Goals (SDGs) faces notable challenges. Our investigation into the impact of the COVID-19 crisis on SDG progress reveals important lessons for shaping effective policy interventions to ensure resilient societies and overall well-being. Through systematic mapping and a rapid review approach, our analysis reveals that nearly 90% of the SDGs, specifically 144 targets, were adversely affected by the COVID-19 pandemic. Yet, there is a glimmer of opportunity: 66 targets stand to gain from the crisis-induced transformations, provided that the right choices are made. Achieving this goal demands a comprehensive approach and decisive leadership to steer an inclusive economic recovery that also safeguards the environment while safeguarding the environment. The intricate interplay between the ongoing planetary and post-COVID-19 crises, environmental challenges, and conflicts underscores the need for a proactive, deliberate and well-informed approach, marked by collaborative decision-making, which is imperative for effectively steering the ‘decade of action’ toward achieving the SDGs. These complex challenges demand collective, decisive action, all with the overarching aim of securing a just and sustainable future for all.

Abstract The energy sector is the largest emitter of greenhouse gas emissions, accounting for 37% of the world’s combined emissions, and plays a key role in achieving the Sustainable Development Goals. However, there is no systematic means for planners and practitioners to integrate climate considerations throughout the lifecycle of energy infrastructure projects. Using a thematic document analysis, we provide a comprehensive list of activities related to climate mitigation and adaptation which can be systematically mainstreamed into the energy sector project lifecycle to support a range of sustainable development outcomes. Two renewable energy projects were used to integrate the results into a practical context and demonstrate the range of potential SDG target synergies. The case studies demonstrate the varied means by which climate action can be integrated through mainstreaming in project lifecycle stages, holistically achieving wider SDG impacts. This work provides a practical means to maximise progress within the framework of climate-compatible development.

The GINGER (gyroscopes in general relativity) project foresees the construction of an array of large frame ring laser gyroscopes, rigidly connected to the Earth. Large frame ring laser gyroscopes are high-sensitivity instruments used to measure angular velocity with respect to the local inertial frame. In particular, they can provide sub-daily variations in the Earth rotation rate, a measurement relevant for geodesy and for fundamental physics at the same time. Sensitivity is the key point in determining the relevance of this instrument for fundamental science. The most recent progress in sensitivity evaluation, obtained on a ring laser prototype, indicates that GINGER should reach the level of 1 part in 1011 of the Earth’s rotation rate. The impact on fundamental physics of this kind of apparatus is reviewed.

Ring Laser Gyroscopes, based on the Sagnac effect, are currently the most sensitive rotation sensors. GINGERINO, a RLG installed underground, shows a proved sensitivity that enters the few frad/s regime in about 2.5 days of integration time. On one hand, this sensitivity is well below the shot–noise–level as predicted applying to GINGERINO the so called independent beam model. On the other hand, it paves the way to the use of RLG in fundamental and quantum physics research. Indeed, high sensitivity rotation measurement opens to test general relativity and alternative theory of gravity. Moreover, it make possible to study the interplay between quantum effects in the optical domain and non-inertial reference frames.

Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the task of uncovering the sentiment of Twitter users concerning climate-related issues. This is done by applying modern natural-language-processing (NLP) methods, i.e. VADER, TextBlob, and BERT, to estimate the sentiment of a gathered dataset based on climate-change keywords. A transfer-learning-based model applied to a pre-trained BERT model for embedding and tokenizing with logistic regression for sentiment classification outperformed the rule-based methods VADER and TextBlob; based on our analysis, the proposed approach led to the highest accuracy: 69%. The collected data contained significant noise, especially from the keyword ‘energy’. Consequently, using more specific keywords would improve the results. The use of other methods, like BERTweet, would also increase the accuracy of the model. The overall sentiment in the analyzed data was positive. The distribution of the positive, neutral, and negative sentiments was very similar in the different SDGs.

Abstract This study aimed at supporting robust decision-making for planning and management of water–energy–food Nexus systems in the country of Jordan. Nexus priority challenges in Jordan were identified as (1) water scarcity, (2) agricultural productivity and water quality, and (3) shift to energy independence. We created a water–energy–food Nexus model that integrates three modelling frameworks: (1) the Water Evaluation and Planning system WEAP model to estimate water demands, supplies and allocation; (2) the MABIA model to estimate crop production, and, (3) a GIS-based energy modelling tool to estimate energy requirements of the water system. Through a set of scenario runs, results show how desalination is needed to address water scarcity, but it has to be coupled with low-carbon electricity generation in order to not exacerbate climate change. Improving water productivity in agriculture improves most of the studied dimensions across the water–energy–food security nexus; however, it does little for water scarcity at the municipal level. Reducing non-revenue water can have positive effects on municipal unmet demand and reduction of energy for pumping, but it does not improve agricultural water productivity and may have negative feedback effects on the Jordan Valleys aquifer levels. Energy efficiency can support energy-intensive projects, like desalination, by substantially reducing the load on the energy system, preventing increased emissions and achieving a more resilient water system. Finally, when all interventions are considered together all of the major drawbacks are reduced and the benefits augmented, producing a more holistic solution to the WEF Nexus challenges in Jordan.

Without targeted climate adaptation, impacts of climate change threaten achievement of all 169 SDG targets. Fuldauer et al. provide an actionable framework to assess these impacts and help systematically align national adaptation plans with the SDGs.

The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use Artificial-Intelligence-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities), and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with apparent differences in some SDGs. Finally, the number of abstracts the code cannot identify decreases with time, possibly showing the scientific community's awareness of SDG.

Access to electricity is a prerequisite for development, included in both the Agenda for Sustainable Development and the African Union’s Agenda 2063. Still, universal access to electricity is elusive to large parts of the global population. In Somalia, approximately one-third of the population has access to electricity. The country is unique among non-island countries as it has no centralized grid network. This paper applies a geospatial electrification model to examine paths towards universal access to electricity in Somalia under different timelines and with regard to different levels of myopia in the modeling process. This extends the previous scientific literature on geospatial electrification modeling by studying the effect of myopia for the first time and simultaneously presenting the first geospatial electrification analysis focused on Somalia. Using the Open Source Spatial Electrification Tool (OnSSET), the least-cost electrification options towards 2030 and 2040, respectively, are compared. We find that under the shorter timeline, a deployment of mini-grids and stand-alone PV technologies alone provides the least-cost option under all but one scenario. However, under the longer timeline, the construction of a national transmission backbone would lower overall costs if there is high demand growth and/or low cost of centralized grid electricity generation. We also compare different levels of myopia in the modeling process. Here, OnSSET is first run directly until 2040, then in five-year time-steps and annual time-steps. We find that running the model directly until 2040 leads to the lowest costs overall. Running the model myopically leads to a sub-optimal, more costly technology mix, with a lock-in effect towards stand-alone systems. On the other hand, the myopic approach does provide additional insights into the development of the system over time. We find that longer-term planning favors the centralized grid network, whereas short-sighted myopic planning can lead to higher costs in the long term and a technology mix with a higher share of stand-alone PV.

Abstract The sensitivity to angular rotation of the top class Sagnac gyroscope GINGERINO is carefully investigated with standard statistical means, using 103 days of continuous operation and the available geodesic measurements of the Earth angular rotation rate. All features of the Earth rotation rate are correctly reproduced. The unprecedented sensitivity of fractions of frad/s is attained for long term runs. This excellent sensitivity and stability put Sagnac gyroscopes at the forefront for fundamental physics, in particular for tests of general relativity and Lorentz violation, where the sensitivity plays the key role to provide reliable data for deeper theoretical investigations.

Measurement(s) electricity access • urban area • rural area Technology Type(s) digital curation Factor Type(s) Administrative boundaries • Population • Night-time lights Sample Characteristic - Environment populated place Sample Characteristic - Location Sub-Saharan Africa Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13516283

Abstract Ring laser gyroscopes are top sensitivity inertial sensors used in the measurement of angular rotation. It is well known that the response of such remarkable instruments can in principle access the very low frequency band, but the occurrence of nonlinear effects in the laser dynamics imposes severe limitations in terms of sensitivity and stability. We report here general relationships aimed at evaluating corrections able to effectively account for nonlinear laser dynamics. The so-derived corrections are applied to analyse thirty days of continuous operation of the large area ring laser gyroscope GINGERINO leading to duly reconstruct the Sagnac frequency $$\omega _s$$ ωs . The analysis shows that the evaluated corrections affect the measurement of the Earth rotation rate $$\varOmega _{\oplus }$$ Ω⊕ at the level of 1 part in $$1.5\times 10^{3}$$ 1.5×103 . The null shift term $$\omega _{ns}$$ ωns plays a non negligible role. It turns out proportional to the optical losses $$\mu $$ μ of the ring cavity, which are changing in time at the level of $$10\%$$ 10% within the considered period of thirty days. The Allan deviation of estimated $$\varOmega _{\oplus }$$ Ω⊕ shows a remarkable long term stability, leading to a sensitivity better than $$10^{-10}$$ 10-10 rad/s with more than 10 s of integration time, and approaching $$(8.5\pm 0.5)\times 10^{-12}$$ (8.5±0.5)×10-12 rad/s with $$4.5\times 10^{5}$$ 4.5×105 s of integration time.

This paper presents the first application of the scenario discovery approach in geospatial electrification modelling. 1944 electrification simulations were constructed for Burkina Faso from a combination seven input levers, including four grid-extension strategies. The scenario discovery analysis identifies a scenario described by a high grid electricity generation cost in combination with an intensification strategy for grid-extension, as most likely to lead to a high cost of electricity in Burkina Faso. Thus, to avoid such a high cost, decisions in the country could be targeted either at lowering grid electricity generation costs or to choose one of the other two grid-extension strategies, or both. For each of the grid-extension strategies, a number of drivers causing a high LCOE were identified. Common drivers for all strategies were the grid electricity generation cost and discount rate. The scenario discovery approach was used to identify the key drivers of high electrification costs and their interactions, providing useful information that might not be gained from a traditional scenario-axes approach. This approach provided a structured way to analyze more parameters than found in previous electrification studies for Burkina Faso. The paper discusses on the pros compared to a traditional scenario-axes approach, such as reduced risk of perceived bias and improved ability to deal with multiple uncertain parameters, but also notes the additional computational requirements.

The introduction of geospatial data into modelling efforts carries many advantages but also introduces numerous challenges. A common challenge is the Modifiable Areal Unit Problem (MAUP), describing how results change as the spatial aggregation of data changes. Here, we have studied MAUP in geospatial least-cost electrification modelling. We do this by assessing the effects of using 26 different population bases each for Benin, Malawi and Namibia. We use the population bases to generate 2080 electrification scenarios per country and conducting a global sensitivity analysis using the Delta Moment-Independent Measure. We identify population aggregation to be highly influential to the model results with regards to method of aggregation (delta values of 0.06–0.24 depending on output studied), administrative division (0.05–0.14), buffer chosen in the clustering process (0.05–0.32) and the minimum number of neighbours within the buffer required for clustering (0.05–0.19). Based on our findings, we conclude that geospatial electrification studies are not robust concerning the choice of population data. We suggest, that modelers put larger emphasis on different population aggregation methods in their sensitivity analyses and that the methods chosen to conduct sensitivity analysis are global in nature (i.e. moving all inputs simultaneously through their possible range of values).

Artificial intelligence (AI) is becoming more and more common in people’s lives. Here, the authors use an expert elicitation method to understand how AI may affect the achievement of the Sustainable Development Goals.

Abstract Inertial sensors are important at application level and also in fundamental physics. Ring laser gyroscopes, which measure angular rotation rates, are among the most sensitive ones. Large area ring laser reach sensitivities at the level of fractions of prad/s, allowing measurements of relevant geophysical signals. Improvements of a factor 10–100 would make these instruments able to measure general relativity effects; this is the goal e.g. of the GINGER project, an Earth based experiment aiming to test the Lense–Thirring effect with an accuracy of $$1\%$$ 1% . However, the laser induces non-linearities, effects larger in small scale instruments. We discuss a novel technique to analyse data, able to reduce non-linear laser effects. We apply this technique to data from two ring laser prototypes, and compare the precision of the measurement of the angular rotation rate obtained with the new and the standard methods. We show that the back-scatter problem of the ring laser gyroscopes is negligible with a proper analysis of the data. These results not only allow to improve the performance of large scale ring laser gyroscopes but also pave the way to the development of small scale instruments with nrad/s sensitivity, which are precious for environmental studies and as inertial platforms.

Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high, with experts believing that AI paves the way for managing and handling various global challenges. However, the significant enabling and inhibiting influence of AI for sustainable development needs to be assessed carefully, given that the technology diffuses rapidly and affects millions of people worldwide on a day-to-day basis. To address this challenge, a panel discussion was organized by the KTH Royal Institute of Technology, the AI Sustainability Center and MIT Massachusetts Institute of Technology, gathering a wide range of AI experts. This paper summarizes the insights from the panel discussion around the following themes: The role of AI in achieving the Sustainable Development Goals (SDGs); AI for a prosperous 21st century; Transparency, automated decision-making processes, and personal profiling; and Measuring the relevance of Digitalization and Artificial Intelligence (D&AI) at the indicator level of SDGs. The research-backed panel discussion was dedicated to recognize and prioritize the agenda for addressing the pressing research gaps for academic research, funding bodies, professionals, as well as industry with an emphasis on the transportation sector. A common conclusion across these themes was the need to go beyond the development of AI in sectorial silos, so as to understand the impacts AI might have across societal, environmental, and economic outcomes. The recordings of the panel discussion can be found at:https://www.kth.se/en/2.18487/evenemang/the-role-of-ai-in-achieving-the-sdgs-enabler-or-inhibitor-1.1001364?date=2020–08–20&length=1&orglength=185&orgdate=2020–06–30Short link: https://bit.ly/2Kap1tE

The occurrence of plasmon resonances on metallic nanometer-scale structures is an intrinsically nanoscale phenomenon, given that the two resonance conditions (i.e., negative dielectric permittivity and large free-space wavelength in comparison with system dimensions) are realized at the same time on the nanoscale. Resonances on surface metallic nanostructures are often experimentally found by probing the structures under investigation with radiation of various frequencies following a trial-and-error method. A general technique for the tuning of these resonances is highly desirable. In this paper we address the issue of the role of local surface patterns in the tuning of these resonances as a function of wavelength and electric field polarization. The effect of nanoscale roughness on the surface plasmon polaritons of randomly patterned gold films is numerically investigated. The field enhancement and relation to specific roughness patterns is analyzed, producing many different realizations of rippled surfaces. We demonstrate that irregular patterns act as metal–dielectric–metal local nanogaps (cavities) for the resonant plasmonic field. In turn, the numerical results are compared to experimental data obtained via aperture scanning near-field optical microscopy.

A Correction to this paper has been published: https://doi.org/10.1140/epjc/s10052-021-09199-1

Measuring in a single location on Earth its angular rotation rate with respect to the celestial frame, with a sensitivity enabling access to the tiny Lense-Thirring effect, is an extremely challenging task. GINGERINO is a large frame ring laser gyroscope, operating as free running and unattended inside the underground laboratory of the Gran Sasso, Italy. The main geodetic signals, i.e., annual and Chandler wobbles, daily polar motion, and length of the day, are recovered from GINGERINO data using standard linear regression methods, demonstrating a sensitivity approaching tens of frad/s, therefore close to the requirements for Earth-based Lense-Thirring and Lorentz violation tests.

The time-dependent population dynamics of hyperfine (HF) sublevels of n 2 p 3 / 2 atomic states upon laser excitation in a cold medium of alkali atoms is examined. We demonstrate some peculiarities of the absorption HF multiplet formation in D2-line resulting from a long interaction time (∼200 μ s) interaction between light and Na ( n = 3 ) and Cs ( n = 6 ) atoms in a cold and slow sub-thermal ( T ∼ 1 K) beam. We analytically describe a number of D2-line-shape effects that are of interest in spectroscopic studies of cold dusty white dwarfs: broadening by optical pumping, intensity redistribution within components of D2-line HF multiplet for partially closed transitions and asymmetry of absorption lines induced by AC Stark shifts for cyclic transitions.

A study of the phototermal instabilities in a Fabry–Perot cavity is reported, where one mirror consists of a silicon-nitride membrane coated by the molecular organic semiconductor tris(8-hydroxyquinoline) aluminum and silver layers. We propose a theoretical model to describe the back-action associated with the delayed response of the cavity field to the radiation pressure force and the photothermal force. For the case under investigation, the photothermal force response occurs on a timescale that is comparable to that of mirror oscillations and dominates over the radiation pressure force. A phase diagram analysis has been performed to map the stability of the static solution as a function of the control parameters. The model equations are integrated numerically and the time history is compared to experimental measurements of the transmitted field and displacement of the membrane. In both experimental and theoretical data we observe large amplitude oscillations when the cavity length is scanned at a low speed compared to the growth rate of the instability. The perturbation is found to evolve through three regimes: sinusoidal oscillations, double peaks and single peaks followed by a lethargic regime. When the cavity length is scanned in opposite directions, dynamical hysteresis is observed, whose extension has a power law dependence on the scanning rate.

Energy for cooking is considered essential in achieving modern energy access. Despite this, almost three billion people worldwide still use solid fuels to meet their cooking needs. To better support practitioners and policy-makers, this paper presents a new model for comparing cooking solutions and its key output metric: the ‘levelized cost of cooking a meal’ (LCCM). The model is applied to compare several cooking solutions in the case study area of Nyeri County in Kenya. The cooking access targets are connected to the International Workshop Agreement and Global Tracking Framework’s tiers of cooking energy access. Results show how an increased energy access with improved firewood and charcoal cookstoves could reduce both household’s LCCMs and the total costs compared to traditional firewood cooking over the modelling period. On the other hand, switching to cleaner cooking solutions, such as LPG- and electricity, would result in higher costs for the end-user highlighting that this transition is not straightforward. The paper also contextualizes the results into the wider socio-economic context. It finds that a tradeoff is present between minimizing costs for households and meeting household priorities, thus maximizing the potential benefits of clean cooking without dismissing the use of biomass altogether.

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards. [ABSTRACT FROM AUTHOR]