The recent report (the Sixth Assessment Report of Working Group 1) of the Intergovernmental Panel on Climate Change clearly points to the urgency of global climate action. Optimal governance regimes are necessary for climate change mitigation in major greenhouse gas-emitting countries and economic sectors. Given our study's domain, the 'climate change governance' concept suffices as our theoretical framework. Adopting comparative legal and policy analysis and the case study approach, we examine the climate governance regimes in four regional carbon-intensive countries - South Africa, China, Germany, and the United States of America - with the primary objective of identifying areas for improving their regulatory capacity to mitigate climate change. Thus, we examine the relationship between climate policy goals/targets, the prevailing policy environment, and supportive strategies for achieving policy goals in our case study countries. Drawing on the climate governance literature, we adopt 'target setting', 'supportive measures/strategies', 'comprehensiveness', and 'oversight body' as the indices for our analysis. Our results reveal striking similarities and contrasts in the focus countries, signalling opportunities for improvements across different parameters. The study suggests the need for policy symmetry between governance (emissions reduction) goals and regime design elements such as proportional supportive strategies and comprehensive coverage of carbon-intensive economy sectors. We also canvass the necessity of avoiding policy and institutional fragmentations that hamper the emergence of clear and well-coordinated climate policy direction in countries. As climate change governance is now at a critical juncture, this study will significantly improve the regimes of our focus countries and other jurisdictions with similar peculiarities.

Understanding the dynamic behaviour of Sub-Saharan African households as they move along the energy ladder is essential for the energy transition in developing countries. This study applies Fixed and Random effect panel data models to analyse the drivers of rural and urban households' energy transition in Nigeria from 2010 to 2018. The estimation results from the panel models with robust standard errors show that rural households tend to increase their expenses on fuel sources that potentially substitute the energy source whose prices have increased. However, there is no significant relationship between the price and expenditure on different fuels in urban households. Irrespective of spatiality, we find that aside from income - education, household size, and internet access are essential drivers of household fuel choices. More importantly, we find evidence of reverse energy transition. We argue that this reverse energy transition limits the shift to cleaner fuels and increases the economic vulnerabilities of rural households. Our analysis also reveals that Nigerians' preference for fuels is shifting to be price inelastic. We make a strong case for policies and interventions that raise household income, empower women, reduce the cost of living, and improve clean and affordable energy access to encourage energy transition.

Lack of access to clean cooking energy systems negatively affects the health and welfare of millions of people in developing countries. Different factors such as household income, household size, fuel price, and information spread have been identified as barriers to the widespread uptake of clean cooking systems. However, analyses exploring the dynamic influences of these factors towards accelerating clean cooking from the long-term perspective are limited. Here, we employ a system dynamics modelling framework to simulate how various strategies could affect the adoption of clean cooking systems in Nigeria over time. Our results reveal that clean cooking adoption is a fluctuating process, and the trends present a non-linear behaviour. We found that the adoption of clean cooking energy systems would occur faster early in the simulating year among urban households than in rural households. The results indicate that, at low prices of liquefied petroleum gas, many rural households will switch to clean cookstoves with higher adoption rates than consumers in urban households. Additionally, results from baseline scenario analysis revealed that, without significant policy interventions, not all households would switch to clean cooking. Our analysis further indicates that households with fewer members tend to transition quicker to clean cooking options than larger households. The impact of clean cooking due to communication among households would be more significant among rural households than among urban households. While the model results are perceptive, we emphasise that potent policies are needed to accelerate the diffusion and adoption of clean cooking energy systems in Nigeria and other African countries. (C) 2022 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved..

The cement sector in South Africa contributes approximately 1 % to the South African national green-house gas inventory. In line with the country's GDP increasing which will cause population to increase, the demand for housing, public and private structural infrastructures continues to increase. It is assumed that the demand for cement will also increase proportionally as well as its associated CO2 emission. The purpose of this study is to firstly; review previous energy modelling strategies in South Africa and specifically in the cement industry if any in order to examine the alignment of the cement industry's strategies with that of the South African government since becoming part of the Paris agreement signatories. Previous studies of energy efficiency, modelling, and GHG emissions were analyzed and reviewed for the understanding of the readiness of South Africa's cement industry to tackle her energy and global warming issues. South Africa is in a position to tackle the energy efficiency and GHG emissions problem through redesigning and consolidated critical data collection will, driven by the government and responsible stakeholders. Secondly; if any previous strategies are in place but in isolation, to examine the potential for long-term energy efficiency strategies and CO2 mitigation options for the cement sector in South Africa; as these two elements have a distinguishable link. Cement production is an energy-intensive process that consists of combustion-and process-related emissions. This study will employ-two modelling frameworks to examine strategies for reducing cement sector energy demand and associated CO2 emission for the period 2015 to 2050 (35 years) with 5-year intervals parallel to the Paris agreement. After reviewing energy models, the study will employ the low emissions analysis platform (LEAP) to model possible energy saving methods in the combustion process and other forms of energy supply in the context of South African cement sector. The existing reviewed methods and results will be compared and the LEAP will provide a second set of comparable results in order to influence policy and inform decision-making. For process-related emissions in the sector, the study will develop an excel-based model to examine possible strategies for reducing CO2 emission in the sector. The results will be examined in terms of cumulative energy savings, GHG emissions, and marginal abatement cost of carbon. (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Engineering for a Sustainable World.

The most common household fuel utilized in the six geopolitical zones of Nigeria is kerosene, liquified petroleum of gas (LPG), firewood, charcoal, and electricity. These energy commodities are contributing to simplify people's life. They are used in satisfying energy demands such as cooking, heating, and lighting for every single home. The energy prices were collected from 2010 to 2021, and we forecasted from 2022 to 2024. There is data available from 2010-2021 about prices for some of these commodities, but they are scattered, narrow, and in some cases, there is just a general-referred value for the whole nation and only for a single year from the past. These situations have limited the development of economic studies which undertake analyses regarding consumers' behavior. The forecasted fares for kerosene and LPG were calculated under the basis of accessible information but limited by the National Bureau of Statistics of Nigeria. The available electricity tariffs were collected from the Nigerian Electricity Regulatory Commission from the existing eleven private electricity distribution companies (DISCOS). In the case of firewood and charcoal, the costs were estimated departing from the research work of Gujba etal. [1]. In the second part, we specify the way how data was obtained and its treatment for specific time periods. The statistics include the values for each fuel in the different geopolitical regions and for the most popular presentations available to the end customers. The forecasting was developed for past and future years during the under-study period of time. The information presented in the article refers to the research study: Urban and rural household energy transition in Sub-Saharan Africa: Does spatial heterogeneity reveal the direction of the transition?

Dinophytes are widely distributed in marine- and fresh-waters, but have yet to be conclusively documented in terrestrial environments. Here, we evaluated the presence of these protists from an environmental DNA metabarcoding dataset of Neotropical rainforest soils. Using a phylogenetic placement approach with a reference alignment and tree, we showed that the numerous sequencing reads that were phylogenetically placed as dinophytes did not correlate with taxonomic assignment, environmental preference, nutritional mode, or dormancy. All the dinophytes in the soils are rather windblown dispersal units of aquatic species and are not biologically active residents of terrestrial environments.

Numerous studies covering some aspects of SARS-CoV-2 data analyses are being published on a daily basis, including a regularly updated phylogeny on nextstrain.org. Here, we review the difficulties of inferring reliable phylogenies by example of a data snapshot comprising a quality-filtered subset of 8,736 out of all 16,453 virus sequences available on May 5, 2020 from gisaid.org. We find that it is difficult to infer a reliable phylogeny on these data due to the large number of sequences in conjunction with the low number of mutations. We further find that rooting the inferred phylogeny with some degree of confidence either via the bat and pangolin outgroups or by applying novel computational methods on the ingroup phylogeny does not appear to be credible. Finally, an automatic classification of the current sequences into subclasses using the mPTP tool for molecular species delimitation is also, as might be expected, not possible, as the sequences are too closely related. We conclude that, although the application of phylogenetic methods to disentangle the evolution and spread of COVID-19 provides some insight, results of phylogenetic analyses, in particular those conducted under the default settings of current phylogenetic inference tools, as well as downstream analyses on the inferred phylogenies, should be considered and interpreted with extreme caution.

Motivation: Previously we presented swarm, an open-source amplicon clustering programme that produces fine-scale molecular operational taxonomic units (OTUs) that are free of arbitrary global clustering thresholds. Here, we present swarm v3 to address issues of contemporary datasets that are growing towards tera-byte sizes.

Priority effects, where arrival order and initial relative abundance modulate local species interactions, can exert taxonomic, functional, and evolutionary influences on ecological communities by driving them to alternative states. It remains unclear if these wide-ranging consequences of priority effects can be explained systematically by a common underlying factor. Here, we identify such a factor in an empirical system. In a series of field and laboratory studies, we focus on how pH affects nectar-colonizing microbes and their interactions with plants and pollinators. In a field survey, we found that nectar microbial communities in a hummingbird-pollinated shrub, Diplacus (formerly Mimulus) aurantiacus, exhibited abundance patterns indicative of alternative stable states that emerge through domination by either bacteria or yeasts within individual flowers. In addition, nectar pH varied among D. aurantiacus flowers in a manner that is consistent with the existence of these alternative stable states. In laboratory experiments, Acinetobacter nectaris, the bacterium most commonly found in D. aurantiacus nectar, exerted a strongly negative priority effect against Metschnikowia reukaufii, the most common nectar-specialist yeast, by reducing nectar pH. This priority effect likely explains the mutually exclusive pattern of dominance found in the field survey. Furthermore, experimental evolution simulating hummingbird-assisted dispersal between flowers revealed that M. reukaufii could evolve rapidly to improve resistance against the priority effect if constantly exposed to A. nectaris-induced pH reduction. Finally, in a field experiment, we found that low nectar pH could reduce nectar consumption by hummingbirds, suggesting functional consequences of the pH-driven priority effect for plant reproduction. Taken together, these results show that it is possible to identify an overarching factor that governs the eco-evolutionary dynamics of priority effects across multiple levels of biological organization.

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e.g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first 10years. In particular, the goals of this review are 1) to motivate the usage of phylogenetic placement and illustrate some of its use cases, 2) to outline the full workflow, from raw sequences to publishable figures, including best practices, 3) to introduce the most common tools and methods and their capabilities, 4) to point out common placement pitfalls and misconceptions, 5) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.