How does bioenergy compare with other land-based renewable energy sources globally?

The potential power generation from land-based bioenergy is predicted globally using a computer model. Simultaneous consideration of land use, cost and carbon restrictions enables practical evaluation of net power output. Comparisons are made with wind and solar power, and a sensitivity analysis is used to explore the effects of different policy assumptions. Biomass is shown to offer only moderate power-generating potential, and would satisfy less than half of current demand even if all suitable existing arable land were used to grow bioenergy crops. However, bioenergy can be cheap to generate given current economics, and is able to remove atmospheric carbon in some cases if coupled with carbon capture and storage. Wind turbines are able to provide more power globally, but photovoltaic solar panels are the only source considered with the potential to satisfy existing demand. Since land-based bioenergy is also restricted by the need to grow food for an expanding population, and technological developments are likely to greatly increase the viability of other renewable sources, the role of land-based bioenergy appears relatively limited and short-term

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Crop growth models are increasingly used as part of research into areas such as climate change and bioenergy, so it is particularly important to understand the effects of environmental inputs on model results. Rather than investigating the effects of separate input parameters, we assess results obtained from a crop growth model using a selection of entire meteorological and soil input datasets, since these define modelled conditions. Yields are found to vary significantly only where the combination of inputs makes the crop vulnerable to drought, rather than being especially sensitive to any single input. Results highlight the significance of soil water parameters, which are likely to become increasingly critical in areas affected by climate change. Differences between datasets demonstrate the need to consider the dataset-dependence of parameterised model terms, both for model validation and predictions based on alternative datasets

Economic and greenhouse gas costs of Miscanthus supply chains in the United Kingdom

Miscanthus has been identified as one of the most promising perennial grasses for renewable energy generation in Europe and the United States [Mitigation and Adaptation Strategies for Global Change 9 (2004) 433]. However, the decision to use Miscanthus depends to a considerable degree on its economic and environmental performance [Soil Use and Management 24 (2008) 235; Renewable and Sustainable Energy Reviews 13 (2009) 1230]. This article assessed the spatial distribution of the economic and greenhouse gas (GHG) costs of producing and supplying Miscanthus in the UK. The average farm-gate production cost of Miscanthus in the UK is estimated to be 40 £ per oven-dried tonne (£ odt 1), and the average GHG emissions from the production of Miscanthus are 1.72 kg carbon equivalent per oven-dried tonnes per year (kg CE odt 1 yr 1). The production cost of Miscanthus varies from 35 to 55 £ odt 1 with the lowest production costs in England, Wales and Northern Ireland, and the highest costs in Scotland. Sensitivity analysis shows that yield of Miscanthus is the most influential factor in its production cost, with precipitation the most crucial input in determining yield. GHG emissions from the production of Miscanthus range from 1.24 to 2.11 kg CE odt 1 yr 1. To maximize the GHG benefit, Miscanthus should be established preferentially on croplands, though other considerations obviously arise concerning suitability and value of the land for food production

Global change synergies and trade-offs between renewable energy and biodiversity

Reliance on fossil fuels is causing unprecedented climate change and is accelerating environmental degradation and global biodiversity loss. Together, climate change and biodiversity loss, if not averted urgently, may inflict severe damage on ecosystem processes, functions and services that support the welfare of modern societies. Increasing renewable energy deployment and expanding the current protected area network represent key solutions to these challenges, but conflicts may arise over the use of limited land for energy production as opposed to biodiversity conservation. Here, we compare recently identified core areas for the expansion of the global protected area network with the renewable energy potential available from land-based solar photovoltaic, wind energy and bioenergy (in the form of Miscanthus 9 giganteus). We show that these energy sources have very different biodiversity impacts and net energy contributions. The extent of risks and opportunities deriving from renewable energy development is highly dependent on the type of renewable source harvested, the restrictions imposed on energy harvest and the region considered, with Central America appearing at particularly high potential risk from renewable energy expansion. Without restrictions on power generation due to factors such as production and transport costs, we show that bioenergy production is a major potential threat to biodiversity, while the potential impact of wind and solar appears smaller than that of bioenergy. However, these differences become reduced when energy potential is restricted by external factors including local energy demand. Overall, we found that areas of opportunity for developing solar and wind energy with little harm to biodiversity could exist in several regions of the world, with the magnitude of potential impact being particularly dependent on restrictions imposed by local energy demand. The evidence provided here helps guide sustainable development of renewable energy and contributes to the targeting of global efforts in climate mitigation and biodiversity conservation

Synergies and trade-offs between renewable energy extraction and biodiversity conservation - a cross-national multi-factor analysis

Increased deployment of renewable energy can contribute towards mitigating climate change and improving air quality, wealth and development. However, renewable energy technologies are not free of environmental impacts; thus, it is important to identify opportunities and potential threats from the expansion of renewable energy deployment. Currently, there is no cross-national comprehensive analysis linking renewable energy potential simultaneously to socio-economic and political factors and biodiversity priority locations. Here, we quantify the relationship between the fraction of land-based renewable energy (including solar photovoltaic, wind and bioenergy) potential available outside the top biodiversity areas (i.e. outside the highest ranked 30% priority areas for biodiversity conservation) within each country, with selected socio-economic and geopolitical factors as well as biodiversity assets. We do so for two scenarios that identify priority areas for biodiversity conservation alternatively in a globally coordinated manner vs. separately for individual countries. We show that very different opportunities and challenges emerge if the priority areas for biodiversity protection are identified globally or designated nationally. In the former scenario, potential for solar, wind and bioenergy outside the top biodiversity areas is highest in developing countries, in sparsely populated countries and in countries of low biodiversity potential but with high air pollution mortality. Conversely, when priority areas for biodiversity protection are designated nationally, renewable energy potential outside the top biodiversity areas is highest in countries with good governance but also in countries with high biodiversity potential and population density. Overall, these results identify both clear opportunities but also risks that should be considered carefully when making decisions about renewable energy policies

PopFor : A new model for estimating poplar yields

Yield and soil data for this project has been supplied by Rainer Schlepphorst, Holger Hartmann and Dieter Murach from HNEE, Germany, from the BIODEM project. Soil water and groundwater data were supplied by Markus Schmidt, HNEE, Germany. We would like to thank Shell and the University of Aberdeen CLSM for funding this work with a studentship. The MiscanFor modelling was supported by UK NERC ADVENT (NE/1806209) and FAB-GGR (NE/P019951/1) project funding.Peer reviewedPostprin

A Mathematical Framework for Agent Based Models of Complex Biological Networks

Agent-based modeling and simulation is a useful method to study biological phenomena in a wide range of fields, from molecular biology to ecology. Since there is currently no agreed-upon standard way to specify such models it is not always easy to use published models. Also, since model descriptions are not usually given in mathematical terms, it is difficult to bring mathematical analysis tools to bear, so that models are typically studied through simulation. In order to address this issue, Grimm et al. proposed a protocol for model specification, the so-called ODD protocol, which provides a standard way to describe models. This paper proposes an addition to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis. The mathematical framework is that of algebraic models, that is, time-discrete dynamical systems with algebraic structure. It is shown by way of several examples how this mathematical specification can help with model analysis.Comment: To appear in Bulletin of Mathematical Biolog