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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.

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.

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).

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