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The 6th IEEE International Energy Conference (EnergyCon 2020), Gammarth, Tunisia (held online due to coronavirus outbreak), 28 September - 1 October 2020 Sub-Saharan Africa requires affordable, reliable, and sustainable electricity to boost its economic, social, and human development. The main challenge posed to the region’s electricity sector is the large investment gap needed to finance new power projects. The employment of new and innovative financing options is required to bridge this investment gap. Independent power projects have become one of the fastest-growing sources of new finance in the region. However, their development is constrained by the limited availability of debt finance for project implementation. The limited capital and bureaucratic burden of traditional financial institutions coupled with the high risks in the region ensures that the debt finance required by independent power projects is raised only after an arduous voyage and at high interest rates. We address these challenges by proposing a novel decentralized finance instrument, a blockchain special purpose vehicle that streamlines the processes in the financial layer of a traditional special purpose vehicle–finance mobilization, revenue collection, and revenue disbursal. Specifically, the proposed decentralized finance instrument facilitates the mobilization of finance for the special purpose vehicle from a location-independent crowd, revenue collection from the electricity off taker in a risk-mitigated manner, and disbursal of eventual project revenues to investors. Sustainable Energy Authority of Ireland (SEAI)

Trace elements (TE) are known to play a crucial role in microbial metabolism, and to improve biogas output in anaerobic digestion (AD), although the mechanisms are not well characterised.We have characterised the microbial communities associated with anaerobic mono-digestion of grass silage through initiation, stable operation, inhibition at high organic loading rates, and rescue through addition of trace elements (iron, nickel and cobalt). Comparison of these communities with communities from unsupplemented anaerobic co-digestion of a grass silage:slurry mix shows TE supplementation to be significantly associated with increases in genera known to be involved in the metabolism of volatile fatty acids (Gelria, Anaerovorax, Dethiobacter), hydrolysis (Clostridia), and in particular the uncharacterised clostridial order MBA03. However, a decrease was seen in the abundance of methanogenic Archaea, indicating TE supplementation improves mono-digestion of grass silage through augmented fermentation rather than improved methanogenesis. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Module Descriptor for lecturer professional development in Educating for Food Sustainability. In response to SDG 4.7, which requires learners acquire knowledge and skills for sustainability, this module addresses a gap identified by the National Strategy on Education for Sustainable Development in the ‘preparedness of lecturers to facilitate the type of participatory learning’ associated with balancing social and economic well-being with Earth’s ability to replenish its natural resources. For food-sector educators committed to embedding sustainability in their academic practice, this module aims to develop a community of practice, comprising faculty, students, industry and community that can support authentic and transformative living-lab experiences for a sustainable future of our food systems. Deploying inquiry-based, evidence-based and action-learning approaches, this module focuses on addressing future-of-food issues within a holistic framework informed by the 4 Cs of culture, campus, curriculum and community as a means for transforming food-sector higher education.

Correlative species distribution models are based on the observed relationship between species’ occurrence and macroclimate or other environmental variables. In climates predicted less favourable populations are expected to decline, and in favourable climates they are expected to persist. However, little comparative empirical support exists for a relationship between predicted climate suitability and population performance. We found that the performance of 93 populations of 34 plant species worldwide – as measured by in situ population growth rate, its temporal variation and extinction risk – was not correlated with climate suitability. However, correlations of demographic processes underpinning population performance with climate suitability indicated both resistance and vulnerability pathways of population responses to climate: in less suitable climates, plants experienced greater retrogression (resistance pathway) and greater variability in some demographic rates (vulnerability pathway). While a range of demographic strategies occur within species’ climatic niches, demographic strategies are more constrained in climates predicted to be less suitable. Supporting InformationFile (1) COMPADRE Plant Matrix Database version 3.0.0 downloaded on July 21, 2014; File (2) Data spreadsheet with population-level demographic metrics and climate suitability values (calculated using the R codes appended here); File (3) R scripts for demographic metric calculations, Species Distribution Models and statistical analyses; File (4) Projected maps based on the Species Distribution Models for species in this study.Csergo et al ELE.zip

The 6th IEEE International Energy Conference (EnergyCon 2020), Gammarth, Tunisia (held online due to coronavirus outbreak), 28 September - 1 October 2020 This paper proposes and discusses the idea of using nascent blockchain hosted prediction markets as a decentralised crowd sourcing method for renewable energy forecasting. This method is further used as a risk management and hedging tool against volatility in weather variables they depend on. While existing approaches have been centralised by nature, with limited sources of input data and models, prediction markets allow anyone to participate in forecasting by betting on an outcome and earning profits for correct results. Since they have mercenary motivations, these participants are most likely to provide reliable and accurate information. Moreover, renewable energy producers can participate in these prediction markets to hedge against low-income periods due to poor weather conditions. This paper delivers a conceptual framework to exploit prediction markets in a blockchain platform with the aim of forecasting and hedging of renewable energy sources. The potential financial gain from applying this approach has been demonstrated through a case study for a typical small wind power producer. Sustainable Energy Authority of Ireland (SEAI)

Optional Module (Level 8) in Food Sector Sustainabiliyt. In response to SDG 4.7, this module contributes to the National Strategy on Education for Sustainable Development’s objective of “equipping learners with relevant knowledge, skills and values for becoming informed active citizens who take action for a more sustainable future”. For food-sector students committed to embedding sustainability in their professional learning and practice, this module explores how we can transform our food systems to be more resilient, ensuring the safe and nutritious food for everyone. It aims to provide authentic and transformative living-lab experiences of sustainable food systems through a community of practice, comprising faculty, students, industry and community stakeholders. The module will provide a comprehensive overview of sustainable development goals (SDGs), targets and policies relating to food systems, diets and nutrition and the design-thinking processes underpinning the co-creating innovative solutions to complex food sustainability challenges.

This OER is a Food Analysis module descriptor that has been reviewed to embed sustainability learning outcomes, activities, and reading material. This is an output from the IMPACT project Sustainable-Food-Curriculum CoCreate. It includes learning outcomes related to green chemical analysis, and impact of climate change on food analysis.

The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre