Graham Wall

The decarbonisation of energy systems has been a key driver for the advancement of renewable energy sources, including offshore wind, tidal, wave, and ocean thermal energies. As these resources increasingly become integral to national energy matrices, particularly in marine energy, it is crucial to analyse them using Multi-Criteria Decision-Making (MCDM) methods. This study aims to identify the primary topics addressed in MCDM applications related to Marine Renewable Energies (MREs) through a systematic literature review spanning the years 2000–2024. The research process involved five stages: (1) defining the problem, (2) surveying relevant papers, (3) conducting a preliminary analysis of the papers, (4) performing a detailed review, and (5) selecting the final sample of articles for analysis. The findings reveal a strong focus on offshore wind, wave, tidal, and ocean thermal energy studies, often combining multiple technologies. A classification of the research topics indicates that the majority of publications address site selection and spatial planning problems, employing methods such as AHP, PROMETHEE, TOPSIS, and Fuzzy techniques, frequently integrated with GIS tools. Conversely, research on governance, legal, and licencing aspects of these technologies within the broader marine sector remains limited. While the development of MREs is recognised as a significant opportunity, challenges such as technological maturity, design standardisation, reliability, accessibility, and governance must be addressed to enable their widespread adoption.

This paper on participatory budgeting for public involvement in environmental sustainability at a Thai university is well written and presented. The literature review presents several different ways in which participatory budgeting can happen and int...

Reservoir modeling has become an indispensable tool in the oil and gas industry to measure risk associated with alternative production scenarios. It is no secret that nowadays more and more operators use deterministic approach for history matching but even so, the task remains technically challenging. Given a large number of uncertainties associated with subsurface models, it has become critical to produce a set of history-matched models and use them for risk assessment in predicting the future performance of the reservoir. Quantification of uncertainties is a vital process in complex fields to ensure a proper field development plan (FDP) is in place. In this paper, we present a comprehensive workflow that incorporates all processes from building static and dynamic models to producing ensemble of history matched models. To assess the mismatch with historical production data, a number of geological realizations were created and simulated using a variety of uncertainty parameters. Based on the obtained mismatch value, the posterior probability distribution for uncertain parameters updated. Different optimization methods are used to find candidates with improved match quality, and this loop is repeated until a set of diverse history-matched models are generated. These calibrated reservoir models are then used to estimate prediction uncertainties for further FDP optimization. This study is focused on automating workflows and generating multiple geological realizations that produce a set of history-matched models to probabilistically estimate prediction uncertainties and optimize FDP. We also show how these models are critical in the process of decision making and risk evaluation.

The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster.

Purpose: Offshore renewable energy technologies provide many new opportunities for coastal regions around the world, and although the energy policy literature has documented the success stories of many “first mover” regions, there is little guidance for “second mover” or “follower” regions. This paper aims to investigate the strategic challenges faced by coastal regions in the Channel area that are not first movers. Design/methodology/approach: The authors use a multiple case study approach to analyse the behaviour of regional stakeholders when planning and assessing their participation in the renewable energy sector. Findings: The paper reveals the tendency of regional planners to idealise investments in renewable energy. The negative consequences of idealisation are inadequate strategic visions. Research limitations/implications: The findings are only relevant in the context of the regions that are part of the case study. Practical implications: The paper illustrates how idealisation of technology or strategy is created and how it impacts strategic decision-making. It also discusses how to address idealisation. Social implications: Although much of the energy policy literature discusses the challenge of social acceptance, this paper documents an opposite phenomenon, idealisation. There is a need in the energy sector to find a middle ground between these two extremes. Originality/value: The paper provides evidence and a theoretical analysis of a decision-making bias, idealisation, which is not discussed in the literature.

The offshore wind sector has achieved significant cost reductions in recent years. However, there is still work to be done to maintain and surpass these savings across current and future farms. There is increased competition to reduce costs within the industry itself. Additional challenges are foreseen at future sites located further from shore, in harsher conditions and deeper waters. Larger turbines and projects also mean new equipment, logistics and maintenance requirements. Moreover, farms are approaching the decommissioning phase where there is little experience. Modelling is a safe and cost-effective way to evaluate and optimise operations. However, there is a lack of comprehensive decision-support tools, detailed enough to provide insight into the effects of technological innovations and novel strategies. To address the gap, the EU FP7 LEANWIND project developed a suite of state-of-the-art logistics optimisation and financial simulation models. They can assess a farm scenario in detail at every stage of the project lifecycle and supply-chain, identifying potential cost reductions and more efficient strategies. This paper introduces the models including: an overview of their scope and capabilities; how they can be applied; and the potential end users.