Browsing by Author "Beriro, Darren"

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    Advancing the development and application of decision support systems for sustainable brownfield redevelopment.
    (Cranfield University, 2023-12) Hammond, Ellis Bernard; Coulon, Frederic; Hallett, Stephen; Beriro, Darren
    The redevelopment of brownfield sites is a vital part of ensuring sustainable urban development but has a range of challenges, including contamination and/or geotechnical hazards, leading to risk and cost implications. Brownfield redevelopment involves multiple stakeholders, from land use planners, land developers, and specialist consultants, to local community groups, and neighbouring residents. Understanding complex data and information can be difficult for decision-makers, which is exacerbated when communicating development scenarios and options with others. To support stakeholders, digital tools are often used, including specialised Decision Support Systems (DSSs). This PhD research investigates and contributes to the advancement of brownfield redevelopment DSSs. Existing and emerging challenges are evaluated, identifying improvement opportunities through a critical review of literature and large-scale sector- wide stakeholder consultation. A novel WebGIS-based DSS was developed in collaboration with land use planning stakeholders, applying the DSS to an area of post- industrial land within the Liverpool city region, UK. The DSS was evaluated through user testing, where improvements were identified and implemented, and verified, using a combination of empirical and user-testing methods. Overall, the approach and application of this PhD research demonstrates modern user led DSS development for brownfield applications, overcoming many of the limitations of existing work. The use of the DSS to support early-stage planning and redevelopment of brownfield land is aligned with and informs, multiple current policies for sustainable development and the use of applied digital technologies in planning and land development.
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    A decision support system to assess the feasibility of onshore renewable energy infrastructure
    (Elsevier, 2022-07-16) Beriro, Darren; Nathanail, Judith; Salazar, Juan; Kingdon, Andrew; Marchant, Andrew; Richardson, Steve; Gillet, Andy; Rautenberg, Svea; Hammond, Ellis; Beardmore, John; Moore, Terry; Angus, Phil; Waldron, Julie; Rodrigues, Lucelia; Nathanail, Paul
    This article introduces a new web-based decision support system created for early-stage feasibility assessments of renewable energy technologies in England, UK. The article includes a review of energy policy and regulation in England and a critical evaluation of literature on similar decision support systems. Overall, it shows a novel solution for a repeatable, scalable digital evidence base for the policy compliant deployment of renewable energy technologies. Data4Sustain is a spatial decision support system developed to quickly identify the feasibility of seven renewable energy technologies across large areas including wind, solar, hydro, shallow and geothermal. A multi-actor approach was used to identify the key factors that influence the technical feasibility of these technologies to generate electricity or heat for local consumption or regional distribution. The research demonstrates opportunities to improve the links between policy and regulation with deployment of renewable energy technologies using novel approaches to digital planning. Deployed, resilient, cost-effective and societally accepted renewable energy generation infrastructure has a role to play in ensuring universal access to affordable, reliableand modern energy supply. This is central to supporting a concerted transition to a low-carbon future in order to address climate change. The selection and siting of renewable energy technology is driven by natural resource availability and physical and regulatory constraints. These factors inform early-stage feasibility of renewables, helping to focus investment of time and money. Understanding their relative importance and identifying the most suitable technologies is a highly complex task due to the disparate and often unconnected sources of data and information needed. Data4Sustain help to overcome these challenges.