The global biodiversity implications of a UK transition to a low carbon economy

Lead Research Organisation: University of Southampton
Department Name: School of Biological Sciences

Abstract

Conserving global biodiversity in the face of climate change is widely recognized to be a major global challenge (e.g. Thomas et al. 2004; Pearson et al. 2014) that is likely to be considerably exacerbated by the potential synergistic and additive effects of other drivers of species declines such as land use change (Jetz et al. 2007; Eigenbrod et al. 2014). Furthermore, analyses of global trade patterns have shown that it is Northern demand for goods and services that is largely driving greenhouse gas emissions and land use change in the developing world (e.g. Weinzettel et al. 2013). While decarbonisation of global energy supplies is likely to reduce the impacts of climate change on biodiversity, the impacts of such shifts in energy production away from fossil fuels may have other negative impacts on biodiversity (Agarwala et al. 2014), with the global-scale impacts of decarbonisation on biodiversity remaining largely unknown.
The aim of this PhD is examine the potential global biodiversity implications of a UK transition to a low carbon economy. This work will consist of spatially modelling the degree to which current and future UK (and global) energy demand will spatially coincide with areas of known high biodiversity (e.g., biodiversity hotspots, ecoregions) and ranges of species classified as endangered on the IUCN's Red List. It will also look at potential synergistic impacts of climate change and energy demand on species distributions. This work will build on existing spatial models (e.g. Eigenbrod et al. 2014) as well as coupled global trade hydrological model (in review) developed in Southampton by the lead supervisor Dr Felix Eigenbrod and co-supervisor Prof Gail Taylor, as well as extensive bioclimatic niche modelling expertise of Dr Richard Pearson at UCL (e.g. Pearson et al 2014), who will be a co-advisor on this studentship. This studentship is part of a major (£1.5 million) research consortium - (ADVENT), which is quantifying linkages between energyand environment, and which includes researchers at UEA, UCL, Southampton, Aberdeen and Plymouth Marine Laboratory. As such, the successful applicant will engage with a large community of researchers from across Britain throughout their PhD. The successful applicant will have a good (at least high 2.1) first degree in biology, physical geography or a related discipline, and proven expertise and interest in GIS modelling and statistical analyses. A MSc or MRes in a relevant field would be an asset, but is not a requirement.
The student will based at the Centre for Biological Sciences in Southampton, and will join the community of 60 PhD students in Biology, as well as wider community of ecologists within Biology, Oceanography, Geography and Environment at the University of Southampton. The student will take part in the weekly Ecosystem Conservation Club discussion group in Biology, as well as the weekly Environmental Biosciences seminar series. They will also be expected to give poster and oral presentations in the annual Biological Sciences Student conference, and be given the opportunity to present their findings at international conferences throughout their PhD.
The student will also undergo both generic and specialist research training as part of their PhD in Biological Sciences, including advanced techniques in statistical analysis in R and in GIS, as well as training in CV writing, job interviews, time-management, diversity, and workshops on presentation, teaching and public engagement. The student will also be trained in supervision, and have the opportunity to demonstrate to undergraduates. In combination, this training will equip the student with key transferable skills as outlined by the NERC-led "Review of Skills Needs in the Environment Sector". These include: Multidisciplinarity, Data Management (working with large data sets on field and experimental, and historical data), Translating Research (science to policy), Statistical Analysis; Modelling (part

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/M019764/1 30/06/2015 30/06/2021
1789894 Studentship NE/M019764/1 01/09/2016 31/08/2020 Sebastian Dunnett
 
Description A new global harmonised database of wind and solar installations with first order estimates of power capacities is presented as a result of this award funding. There is currently a dearth of open access data describing the global distribution of the renewable energy infrastructure estate: these data aim to close that gap. A paper describing the methodology is currently in review at Scientific Data.
Exploitation Route Accurate location information of wind and solar installations opens the door for a whole host of other scientific disciplines to interrogate siting of wind turbines and solar panels. Social scientists can investigate the socioeconomic drivers of installation placement, whilst engineers can calculate the efficiency of installation siting based solely on geophysical variables. The data can also act as an important metric for assessing progress towards clean energy development goals, which previously relied on largely financing mechanisms.
Sectors Energy,Environment

URL https://doi.org/10.6084/m9.figshare.11310269.v1
 
Title Harmonised global datasets of wind and solar farm locations and power 
Description Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet's limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code for enabling users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate this data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? No  
Impact Publication currently in review at Scientific Data. 
URL https://doi.org/10.6084/m9.figshare.11310269.v1