Nature-based solutions
There are various planetary systems, fluxes and pathways that provide the secure and habitable conditions that facilitates our existence. These systems are in danger of passing various threshold values which will cause massive disruptions to the functioning of nature and as a result, our societies. The great loss of biodiversity at the hands of providing food for an exponentially growing population will have dramatic impacts in the years to come.
Agriculture, deforestation, and land-use change account for almost a quarter of global emissions and are amongst the biggest drivers of biodiversity loss. But natural carbon sinks like the ocean, peatlands and forests can go some way in reducing global emissions, while nature-based solutions, such as protecting and restoring forests, wetland and coastal ecosystems, can also help humanity adapt and build resilience in the face of climate change, lead healthy and productive lives, and stimulate economic development.
Conserving marine environments, School of Life Sciences
Coral reefs are the world’s most biodiverse and productive marine system, providing food and coastal protection to millions. Researchers from the University of Warwick have co-ordinated new approaches to coral reef conservation, restoration and management in the British Indian Ocean Territory, providing the basis for the UK Government’s declaration of a strictly enforced ‘no-take’ Marine Protected Area (MPA). This is the world’s largest strictly protected MPA, and is a major step forward for marine conservation and food security in a region that has undergone massive decline in ecological integrity and its ability to produce protein for many of the world’s poorest countries.
In 2010, under the leadership of Professor Charles Sheppard from the University of Warwick, the research team supplied evidence that underpinned the UK Government’s decision to declare the 650,000 km2 British Indian Ocean Territory as a Marine Protected Area.
Limiting human activity in the area is supporting ecosystem conservation to benefit tropical habitats and is helping to preserve livelihoods and increase food security in some of the poorest countries in that region. The unique biodiversity and lack of human impacts have led to Chagos being established as a global reference site for other research projects, representing the optimal tropical marine ecosystem which can act as a baseline to which standards of other reef ecosystems can be compared.
The studies have shown that where an area is vulnerable to climate change, rapid reef recovery is possible where human activity is restricted. These findings have provided considerable support for the concept of strictly protected Marine Protected Areas.
Using computer simulations to make responsible decisions about landscapes, Department of Statistics
Making responsible decisions about landscapes is facilitated by the use of complex models able to represent multiple competing demands on land use. Decisions about land use require that trade-offs between competing demands be identified, and their consequences through time be characterised. Models consisting of stochastic computer simulations are increasingly used to make realistic predictions about real world processes from socio-ecological systems involving land use. Models attempt to simulate all relevant aspects of a real physical system, they may involve many parameters, some of which will be difficult to set correctly. The final objective of these models is to assess the possible consequences of management decisions, such as the placement of wind turbines, thus it is crucially important that the uncertainty introduced by calibrating parameter values be understood.
In order to make informed decisions, one needs to be able to consider the effects of a number of complex interacting temporal and spatial processes (e.g. hydrological, ecological, agricultural, economic, climate). A project by Dr Rivhard Everitt in the Department for Statistics is developing new techniques in Approximate Bayesian Computation to enable parameter estimation for models for these processes, taking into account the impact of model misspecification. The outcome for beneficiaries of the research will be that they can reliably show the uncertainty in model predictions of the consequences of specific management interventions. This will allow management decisions to be taken while being aware of uncertainty about the consequences of considered interventions.
This project is part of the Strategic Priorities Fund on Landscape Decisions. Entitled "Statistical inference and uncertainty quantification for complex process-based models using multiple data sets" the project is funded by the UK Natural Natural Environmental Research Council and is due to conclude in 2022.
Read more about the project here.
Key UN Sustainable Development Goals in this theme are:
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Research Details |
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Department of Chemistry |
Summary Richard and his team work on the preparation and detailed atomic-scale structure of materials that have properties for practical applications in areas related to sustainability and energy. An understanding of the structure of a material in terms of the arrangement of atoms allows properties to be tuned and by using synthetic chemistry we can target new compositions and structures to tailor-make new materials. They work with the company Johnson Matthey to prepare new materials for clean air applications, such as catalytic converters for vehicle exhausts and new materials for electrocatalysis that find use in fuel cells for clean energy. Richard’s group is working in new materials for battery applications, in collaboration with the Brazilian company CBMM, the world’s largest producer of the metal niobium. The aim is to find new improved materials for rechargeable batteries, and they are collaborating with researchers in the Energy Innovative Centre in WMG to test the materials and put them into use. This includes fast-charging electrodes for portable energy storage. A further research project on sustainable materials for green conversion of biomass is in progress in collaboration with the School of Engineering. This is funded by a Newton Institutional Links project 2020-2022, with universities and industry in Indonesia to guide the work and to put the materials into application. Here, the group is producing new catalysts to convert biomass organic matter into useful chemicals for industry, in an environmentally benign and energy-efficient way.
Publications
Teaching In the Department of Chemistry, Richard teaches on a third/fourth year undergraduate module ‘Energy’, led by Professor Ross Hatton. This introduces recent developments in materials for sustainable energy. This is an optional module but one that is selected by the majority of our students |
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School of Engineering |
Research: 2018-2021: EPSRC/NERC/DST: Pathways and evolution of pollutants: Interactions between physical controlling effects, microbial community composition and pollutant biodegradation. |
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Global Sustainable Development |
Teaching
Interests Climate Emergency project: https://globuswarwick.com/climateemergency/ (which has a new group of students driving for further change, including campaigning for a commitment to universal education about the self declared Climate Emergency for all students and systematic reform of curriculum that fail to engage with these realities sufficiently, yes economics, one of the most conservative departments in the world with no core requirement even to think about the environment as the foundation of the economy. |
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Department of Chemistry |
Summary Professor Bugg's research group is studying the microbial conversion of biopolymer lignin, found in plant biomass, into renewable chemicals. Research interests involve the discovery of novel bacterial enzymes for lignin degradation and metabolic engineering of bacterial lignin degraders to produce target bio products. Research Projects: Publications • “Bacterial enzymes for lignin depolymerisation: new biocatalysts for generation of renewable chemicals from biomass” T.D.H. Bugg, J.J. Williamson, G.M.M. Rashid, Curr. Opin. Chem. Biol., 55, 26-33 (2020).• “Sphingobacterium sp. T2 manganese superoxide dismutase catalyses the oxidative demethylation of polymeric lignin via generation of hydroxyl radical” G.M.M. Rashid, X. Zhang, R.C. Wilkinson, V. Fülöp, B. Cottyn, S. Baumberger, and T.D.H. Bugg, ACS Chem. Biol., 13, 2920-2929 (2018) • “Structural and functional characterisation of a multi-copper oxidase CueO from lignin-degrading bacterium Ochrobactrum sp. reveal its activity towards lignin model compounds and lignosulfonate” R.S. Granja-Travez, R.C. Wilkinson, G.F. Persinoti, F.M. Squina, V. Fülöp, and T.D.H. Bugg, FEBS Journal, 285, 1684-1700 (2018). • “Biocatalytic conversion of lignin to aromatic dicarboxylic acids in Rhodococcus jostii RHA1 by re-routing aromatic degradation pathways” Z. Mycroft, M. Gomis, P. Mines, P. Law & T.D.H. Bugg, Green Chemistry, 17, 4974-4979 (2015) |
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Statistics |
Statistical inference and uncertainty quantification for complex process-based models using multiple data sets" (Jan 2020 - July 2022, funded by NERC) Making responsible decisions about landscapes is facilitated by the use of complex models able to represent multiple competing demands on land use. Decisions about land use require that trade-offs between competing demands be identified, and their consequences through time be characterised. Models consisting of stochastic computer simulations are increasingly used to make realistic predictions about real world processes from socio-ecological systems involving land use. Models attempt to simulate all relevant aspects of a real physical system, they may involve many parameters, some of which will be difficult to set correctly. The final objective of these models is to assess the possible consequences of management decisions, such as the placement of wind turbines, thus it is crucially important that the uncertainty introduced by calibrating parameter values be understood. In order to make informed decisions, one needs to be able to consider the effects of a number of complex interacting temporal and spatial processes (e.g. hydrological, ecological, agricultural, economic, climate). The project will develop new techniques in Approximate Bayesian Computation to enable parameter estimation for models for these processes, taking into account the impact of model misspecification. This project is part of the Strategic Priorities Fund on Landscape Decisions. |