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Abstract: A hybrid method of ecological habitat survey and mapping is presented, bridging paper-based methods and mobile electronic procedures. The hybrid approach uses a hand-held PDA with real-time dGPS to map habitats and record habitat attributes. It retains paper field maps to facilitate ecological decisions and to overcome the need for field surveyors to operate a fully functional mobile GIS system. The transition from paper records to a full GIS implementation is bridged by secondary digitisation in which a GIS specialist, trained in field survey procedures, interacts routinely with the field surveyors, throughout the field season to assure data quality standards. The methodology is practical and affordable, particularly if there are multiple field surveyors. [ABSTRACT FROM AUTHOR]

Introduction: Encouraging safe work practices (SWPs) is challenging in agriculture. Group-based social learning has effectively promoted SWPs and health behaviors in other occupations, and could be applied in agriculture (e.g., through farmer discussion groups (DGs)). In Ireland, dairy DG members are more likely to adopt novel technologies and practices, a relationship that might extend to SWPs. The extent of SWP adoption among Irish dairy farmers is unknown. This paper evaluates a 2018 baseline study of SWP implementation, conducted as part of a dairy DG-based intervention study. Method: A paper-based survey of SWP implementation and safety self-perception was distributed to 1,220 farmers from 84 dairy DGs. For eight SWPs, associated with high-risk farm hazards (livestock, slurry, machinery, or tractors), farmers were asked how frequently they implemented these practices in the previous year, and how frequently they intended to do so next year (0: never, 1: rarely, 2: sometimes, 3: most of the time, 4: all of the time). Results: Surveys were completed by 460 farmers. For the previous year, three SWPs, related to slurry, machinery, and tractor hazards, scored a median frequency of four. Four SWPs, related to livestock, slurry, and machinery hazards, scored a median frequency of three. The lowest median score (two) was for tractor exit behavior. Median intention scores matched or exceeded past frequency for all SWPs, while 73% intended to increase implementation of at least one SWP. Most (96%) considered themselves a "safe farmer." Conclusions: Farmers generally perceived themselves to be safe at work, which is reflected in their SWP implementation. Most farmers intended to increase SWP implementation, suggesting awareness of safety shortcomings and a desire to farm more safely. Practical applications: This study can inform farm safety promotion initiatives. The disconnect between farmers' safety self-perception and SWP implementation merits further research. [ABSTRACT FROM AUTHOR]

• Providing a historical review on inverse optimal control (IOC) and inverse reinforcement learning (IRL). • Providing a comparative view on IOC and IRL. • Categorizing the methods in IOC and IRL. • Explaining the most important methods of IOC and IRL in similar frameworks. • Discussing the challenges existing in IOC and IRL. Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in control systems, robotics, Machine Learning (ML) and optimization taking into account the optimal manners. This paper reviews the history of the IOC and Inverse Reinforcement Learning (IRL) approaches and describes the connections and differences between them to cover the research gap in the existing literature. The general formulation of IOC/IRL is described and the related methods are categorized based on a hierarchical approach. For this purpose, IOC methods are categorized under two classes, namely classic and modern approaches. The classic IOC is typically formulated for control systems, while IRL, as a modern approach to IOC, is considered for machine learning problems. Despite the presence of a handful of IOC/IRL methods, a comprehensive categorization of these methods is lacking. In addition to the IOC/IRL problems, this paper elaborates, where necessary, on other relevant concepts such as Learning from Demonstration (LfD), Imitation Learning (IL), and Behavioral Cloning. Some of the challenges encountered in the IOC/IRL problems are further discussed in this work, including ill-posedness, non-convexity, data availability, non-linearity, the curses of complexity and dimensionality, feature selection, and generalizability. [ABSTRACT FROM AUTHOR]

The Irish government is implementing policies to transition Ireland to a low carbon and environmentally sustainable economy by 2050. Ireland has sectoral targets of 600,000 installed heat pumps by 2030, currently roughly 28,000 are installed. Such a high target of heat pumps will not only have a significant effect on electricity demand but also on the management and operation of the grid. In this paper we explore the demand from homes heated by air source heat pumps using an innovative dataset from a field trial in Ireland. To assess the impact of large-scale adoption of heat pumps, this paper estimates the after diversity maximum demand per heat pump heated home. In particular we explore statistical distributions to best model coincident demand, and estimate after diversity maximum demand per home. We use the software package RStudio to model several different distributions. Based on goodness-of-fit statistics and criteria, a Gamma distribution is the best fit. We apply our methodology to data from a similar heat pump trial in the UK to complement our results. [ABSTRACT FROM AUTHOR]

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of accuracy and computational efficiency. • We proposed new low-voltage aggregation techniques based on portfolio theory to improve probabilistic demand forecasting. • The increased volatility at the low voltage level requires probabilistic forecasting to measure uncertainties. • The proposed methods were found to outperform random aggregation with the real-world data from Korea and Ireland. • A simple forecasting approach minimizing the standard deviation of deseasonalized demands was accurate and efficient. • The new method can contribute to achieving carbon neutrality, enable P2P energy sharing, and benefit prosumers. [ABSTRACT FROM AUTHOR]

• There are numerous implementation structures for sustainability in HEIs. • Policy, Strategies, Initiatives and Outreach approaches are varied. • Numerous drivers, barriers and best practices exist. • Monitoring of sustainability progress is not standardized. • Access to sustainability information can be challenging. Higher Education Institutes are being called upon to integrate sustainable development into their organisation, driven by national and international events, policies and environmental targets. This paper maps sustainability policies and initiatives of a select number of Irish and international Higher Education Institutes, exploring their integration of sustainability on-campus, testing of new ideas and technologies, and the amplification of lessons learned within society and economy. It provides a comparison of Irish Higher Education Institutes with international examples. Sixteen case studies were selected; seven from Ireland and nine international. A literature review was conducted with a focus on papers that dealt explicitly with sustainability education and outreach. Three site visits of Higher Education Institution campuses took place, as did interviews with energy and transport managers and academics of each institute. The results show the integration of sustainable development into Higher Education Institutes follows no standard approach, with the number of initiatives for sustainability outweighing the number of strategies and policies, presenting a general bottom-up process. Overall, Higher Education Institutes lack a systemic approach. The majority of initiatives focus oncampus actions, with outreach neglected. Higher Education Institutes represent significant resources in implementing sustainable development. To maximise their potential a mixture of top-down and bottom-up processes are necessary, with management support essential if sustainable development is to survive in the long-term, and for lessons learned to be successfully amplified to wider society and economy and assist in meeting national and international environmental targets. [ABSTRACT FROM AUTHOR]

Historic rock lighthouses are unusual structures that are situated in hostile marine environments to provide warning to mariners. Even in an era of satellite navigation their role continues to be an important one, but their survivability into the future is not assured. Out of concern for their ongoing service, the multidisciplinary STORMLAMP project is assessing their survivability under wave loading. This paper presents the various stages of investigations into the structural integrity and stability assessment of the Fastnet lighthouse, situated just off the coast of Ireland. The paper describes: Extreme Bayesian analysis to quantify waves of particular return periods resulting in a 1 in 250 year return period wave with H 0.1% of 17.6 m and an associated maximum force of 20,765 kN; logistically challenging field modal tests revealing the key modal parameters, like the modal masses of 1822 t and 1 675 t for 4.8 Hz and 5.0 Hz modes respectively, the cantilevered nature of the overall lighthouse and the directional effects due to the asymmetric contact with the granite rock; and details of a discontinuous finite element model that is used to determine the stability of the tower under the 1 in 250 year return period breaking wave condition, which is well within stability and material strength limits, causing maximum horizontal displacements in the order of 1 mm at the top of the tower. The overall assessment is that the sheer mass of the lighthouse and its interconnected joints are able to withstand the worst of the Atlantic storms. • Bayesian extreme values analysis is applied to 27 years of wave hindcast data. • Prior informative distributions for the shape and scale parameters are defined from the surrounding numerical nodes. • Field modal analysis is performed in order to identify dynamic parameters of the lighthouse. • For the first time, a discontinuous finite element model is applied to the analysis of the coastal structures. • The survivability of the historic Fastnet lighthouse is performed on the basis of the validated numerical structural model. [ABSTRACT FROM AUTHOR]

Consumer research tends to isolate the impact of marketing and social factors. Little has been done to include both. This paper aimed to find out what would happen when these two sets of factors are included. Two models were built in this paper, Model I with the marketing factors only and Model II with both the marketing and social factors. Data was collected in Ireland among more than 1473 transition year students in a personal survey regarding their willingness to learn Chinese. Data were analysed by using structural equation modelling (SEM). Results showed the two social variables, acculturation and intergenerational influence, significantly consolidated the effects of brand awareness on both brand trust and purchase behaviour; and they diminished the impact of brand trust on purchase behaviour. Empirical evidence suggested the worthiness for marketing researchers to examine both marketing and social factors in consumer research. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this paper is to add to the broader literature on socio-technical theory and its value and/or relevance to health information in Ireland. The paper focuses on three factors that can impact on health information; those of policy, infrastructure and people (PIP) and examines how Ireland compares with other countries in relation to these factors.Materials and Methods: Qualitative methods (documentary analysis and semi-structured interviews) were used. Key policy and strategy documents, and original research articles from Australia, Canada, Ireland, the UK and the US were analysed from a comparative perspective. The dimensions of policy, infrastructure and people were then explored through semi-structured interviews with health information experts in Ireland. Their perceptions were compared with and contrasted against the findings from the documentary analysis, and examined thematically.Findings: The views of health information experts support the findings of the review of Ireland's development in this area compared with other countries and that Ireland lags behind others in policy and practice terms.Conclusions: The paper concludes that the three dimensions of policy, infrastructure and people do indeed help to frame the understanding of health information in Ireland and that a socio-technical perspective, combined with a comparative approach, can also help both policy makers and practitioners in identifying the scope for improvement in health information. [ABSTRACT FROM AUTHOR]

• We used disparate data in a decision tree to refine the mapping of Irish peat soils. • Peat Associated Landcover Class concept was applied to locate converted peatlands. • Shallow peat soils ≥ 10 cm were included in the new Irish Peat Soils Map (IPSM). • The IPSM shows a larger peat area than previous peat maps and accuracies ≥ 74 %. • The Adaptative Mapping Framework implemented can be applied in other regions. Accurate mapping for effective management of peat soils is necessary to help reduce GHG emissions and improve environmental quality. However, mapping remains a major challenge as definitions of peat soils vary substantially between jurisdictions and organizations, while field data are sparse and difficult to produce, and remote sensing of limited use for converted peatlands. Using an Adaptive Mapping Framework, this paper compiles disparate data sources to update the Derived Irish Peat Map to better facilitate its application for environmental issues. This includes incorporation of areas considered to be underlain by shallow peat soils (≥ 10 cm and ≥ 8.6 % Organic Matter content), and augmentation of the overall test dataset with an additional ∼ 20,000 points. The workflow for map generation employed 20 Decision Tree Output Maps (DTOMs), aggregated into 33 Map Combinations (MCs). These facilitated the addition of new areas and removal of areas where the presence of peat could not be confidently identified. The MC selected for the update had the highest accuracy metrics (≥ 74 %), consisting of DTOMs with a user accuracy ≥ 66 % and assessed over a minimum number of test points ≥ 50. The resulting map reveals peat to underlie 1.66 M ha of Ireland (∼ 23.3 % of the country), identified with values of 83 % for overall accuracy and F1 score for peat areas. This extent is 13.2 % larger than that delineated in previous versions and at least 23.2 % larger than areas presented in other previous maps. The methodology also allows transparency from which data sources can be distinguished to define different key peat thickness ranges (≥ 10 cm, ≥ 30–40 cm), better facilitating assessment of environmental impacts arising from land use change. This approach has potential relevance for peat mapping globally, notably in areas containing disparate datasets (e.g., land cover, soil map, etc.), or employing different production methods. The accuracy metrics generated also suggest that the approach can be used as a basis for implementing or updating national and international regulations concerning carbon-rich soils in comparable settings to those encountered in Ireland. [ABSTRACT FROM AUTHOR]

This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions. • Reactive strategy for adaptative UAV navigation in tunnels. • UAV 3D close-range surface data acquisition. • Method validation and tunnel defect analysis. [ABSTRACT FROM AUTHOR]

This paper explores the influence of political leaders' populist communication styles on public adherence to government policies regarding COVID-19 containment. We adopt a mixed-methods approach that combines: theory building with a nested multicase study design for Study 1 and an empirical study in a natural setting for Study 2. Based on the results from Studies 1 and 2, we develop two propositions that we further explain theoretically: (P1) countries with political leaders associated with engaging or intimate populist communication styles (i.e., the UK, Canada, Australia, Singapore, and Ireland) exhibit better public adherence to their governments' COVID-19 movement restrictions than do countries with political leaders associated with communication styles that combine the champion of the people and engaging styles (i.e., the US); (P2) the country whose political leader is associated with a combination of engaging and intimate populist communication styles (i.e., Singapore) exhibits better public adherence to the government's COVID-19 movement restrictions than do countries whose political leaders adopted solely engaging or solely intimate styles, namely, the UK, Canada, Australia, and Ireland. This paper contributes to the research on political leadership in crises and populist political communication. [ABSTRACT FROM AUTHOR]

Once released into the environment, herbicides can move through soil or surface water to streams and groundwater. Filters containing adsorbent media placed in fields may be an effective solution to herbicide loss in the environment. However, to date, no study has investigated the use of adsorbent materials in intervention systems at field-scale, nor has any study investigated their optimal configuration. Therefore, the aim of this paper was to examine the efficacy of low-cost, coconut-based activated carbon (CAC) intervention systems, placed in streams and tributaries, for herbicide removal. Two configurations of interventions were investigated in two agricultural catchments and one urban area in Ireland: (1) filter bags and (2) filter bags fitted into polyethylene pipes. Herbicide sampling was conducted using Chemcatcher® passive sampling devices in order to identify trends in herbicide exceedances at the sites, and to quantifiably assess, compare, and contrast the efficiency of the two intervention configurations. While the Chemcatcher® passive sampling devices are capable of analysing eighteen different acid herbicides, only six different acid herbicides (2,4-D, clopyralid, fluroxypyr, MCPA, mecoprop and triclopyr) were ever detected within the three catchment areas, which were also the only acid herbicides used therein. The CAC was capable of complete herbicide removal, when the water flow was slow (0.5–1 m3 s−1), and the interventions spanned the width and depth of the waterway. Overall, the reduction in herbicide concentrations was better for the filter pipes than for the filter bags, with a 48% reduction in detections and a 37% reduction in exceedances across all the sampling sites for the filter pipe interventions compared to a 13% reduction in the number of detections and a 24% reduction in exceedances across all sampling sites for the filter bag interventions (p < 0.05). This study demonstrates, for the first time, that CAC may be an effective in situ remediation strategy to manage herbicide exceedances close to the source, thereby reducing the impact on environmental and public health. [Display omitted] • First reported field-scale assessment of in situ herbicide remediation technology. • Ideal environmental conditions include low flow rates to maximise contact time. • Systems showed capability for herbicide removal to below maximum allowable level. • Optimum configuration for herbicide removal identified as a filter pipe system. • Filter pipe system 35% more effective than the filter bags. [ABSTRACT FROM AUTHOR]

Design thinking is an emerging concept gaining much traction in practice and more recently in academic debate. While prior work has examined the characteristics and processes of design thinking, there is an absence of studies that attempt to measure the extent of its use and effectiveness in industry. This paper reports on the preliminary results of a study targeted at designers operating in dynamic project environments. The aim of this exploratory research is to identify who designers collaborate with, the extent to which users are involved in the design process in practice and the perceived importance of understanding the emotional needs of the user. The study also sought to ascertain whether user interaction is associated with performance. To do this, data was collected from 131 experienced designers in Ireland. Initial findings from this analysis are presented and discussed. This study answers calls for further work in a neglected but essential area and helps to bridge the gap between theory and practice. [ABSTRACT FROM AUTHOR]

A high-quality animal health surveillance service is required to inform policy and decision-making in food-animal disease control, to substantiate claims regarding national animal health status and for the early detection of exotic or emerging diseases. In Ireland, the Department of Agriculture, Food and the Marine provides partially subsidized testing of farm animal samples and postmortem examinations to the Irish agriculture sector (farmers) at 6 regional veterinary laboratories (RVL) throughout the country. Diagnoses and data from these submissions are recorded and reported monthly and annually to enable animal health monitoring and disease surveillance. In a passive surveillance model, both the veterinary practitioner and the farmer play a vital role in sample submission by determining which cases are sent to the laboratory for postmortem or diagnostic testing. This paper identified factors influencing Irish dairy farmers' decisions to submit carcasses to RVL. Behavioral determinants of the submission of samples where veterinary professionals are concerned has been studied previously; however, limited work has studied determinants among farmers. This study conducted qualitative analyses of decisions of Irish dairy farmers relevant to diagnostic sample submission to an RVL and to examine the herd-level characteristics of farmers that submitted cases to an RVL. The biographical narrative interpretive method was used to interview 5 case-study farmers who were classified nonsubmitters, medium, or high submitters to the postmortem service based on the proportion of on-farm mortalities submitted to the laboratory service in 2016. The data obtained from these interviews were supplemented and triangulated through dairy farmer focus groups. The data were thematically analyzed and described qualitatively. In addition, quantitative analysis was undertaken. Data for herds within the catchment area of a central RVL were extracted, and a multivariable logistic regression model was constructed to examine the relationship between herds from which carcasses were submitted to the laboratory and those from which none were submitted. Results from the analysis show that the farmer's veterinary practitioner was the primary influence on submission of carcasses to the laboratory. Similarly, the type of incident, logistical issues with transporting carcasses to the laboratory, influence of peers, presence of alternative private laboratories, and a fear of government involvement were key factors emerging from the case-study interview and focus group data. Herd size was identified in both the qualitative and quantitative analysis as a factor determining submission. In the logistic regression model, herd size and increased levels of expansion were positively correlated with the odds of submission, whereas distance from the laboratory was negatively associated with odds of submission. These results identify the main factors influencing the use of diagnostic services for surveillance of animal health, signaling how services may be made more attractive by policy makers to a potentially wider cohort of users. [ABSTRACT FROM AUTHOR]

Gaseous transport has been successful in European countries such as Germany, Italy and Sweden. However, the majority of European countries have seen little market penetration of gas-powered vehicles. The utilisation of gaseous transport fuels in Ireland, like in many countries, is currently overlooked as a sustainable option to satisfying the European Union renewable transport target of 10% by 2020. A blend of Compressed Natural Gas and its renewable form biomethane (bio-CNG) presents a sustainable alternative which can diversify Ireland's transport energy portfolio. With transport fossil fuel prices at an all-time high; this paper focuses on analysing the bio-CNG policies that can best influence the Irish transport sector through strategic penetration in the captive fleet market. This paper identifies the main barriers to bio-CNG development in Ireland and proposes an aligned strategic solution to deliver a gaseous transport market. A bio-CNG roadmap is devised which provides Ireland with a platform to assist satisfying renewable transport targets through captive fleets. The findings of this paper highlight that bio-CNG has the potential to offer fleet operators a 22% financial savings versus diesel. Finally, the bio-CNG roadmap can meet 1% of Ireland's renewable transport target by fuelling 14,000 commercial gaseous vehicles. [ABSTRACT FROM AUTHOR]

• Value transfer for estimation of water quality benefit values to society • Transferring contingent valuation function using spatial microsimution approach • More accurate as control for differences in site quality and commodity consistency The empirical findings regarding the transfer validity of benefit transfer methods for environmental policy evaluation have been mixed. In this paper the value function from a contingent valuation exercise that estimates the value to the Irish general public of achieving good ecological status, as specified in the Water Framework Directive, are transferred to a spatial microsimulated population within individual water management units (WMUs) across Ireland. The welfare estimates from this novel value function based transfer approach are compared against the results from previous primary valuation studies in one of the WMUs and transfer errors are found to be low. The proposed spatial microsimulation value transfer approach controls for the heterogeneous distribution of populations across the WMUs and for the fact that the willingness to pay will vary according to an individual's geographic proximity to the water body within each catchment and with the current ecological status of the WMU. [ABSTRACT FROM AUTHOR]

Nutrient management on grazed grasslands is of critical importance to maintain productivity levels, as grass is the cheapest feed for ruminants and underpins these meat and milk production systems. Many attempts have been made to model the relationships between controllable (crop and soil fertility management) and noncontrollable influencing factors (weather, soil drainage) and nutrient/productivity levels. However, to the best of our knowledge not much research has been performed on modeling the interconnections between the influencing factors on one hand and nutrient uptake/herbage production on the other hand, by using data-driven modeling techniques. Our paper proposes to use predictive clustering trees (PCT) learned for building models on data from dairy farms in the Republic of Ireland. The PCT models show good accuracy in estimating herbage production and nutrient uptake. They are also interpretable and are found to embody knowledge that is in accordance with existing theoretical understanding of the task at hand. Moreover, if we combine more PCT into an ensemble of PCT (random forest of PCT), we can achieve improved accuracy of the estimates. In practical terms, the number of grazings, which is related proportionally with soil drainage class, is one of the most important factors that moderates the herbage production potential and nutrient uptake. Furthermore, we found the nutrient (N, P, and K) uptake and herbage nutrient concentration to be conservative in fields that had medium yield potential (11 t of dry matter per hectare on average), whereas nutrient uptake was more variable and potentially limiting in fields that had higher and lower herbage production. Our models also show that phosphorus is the most limiting nutrient for herbage production across the fields on these Irish dairy farms, followed by nitrogen and potassium. [ABSTRACT FROM AUTHOR]

• A high spatial resolution model for estimating N 2 O emissions at the farm level. • Findings indicate a 5% reduction in farm-level N 2 O emission estimates. • There is an increase in the variation of N 2 O emissions of 20% at the local level. • Statistical significance of environmental factors in N 2 O emissions estimation. • Substituting 20% of CAN with protected urea reduces 15% of N 2 O emissions. This paper aims to define a high spatial resolution model for estimating nitrous oxide (N 2 O) emissions from agricultural soils in Ireland. In 2020, N 2 O emissions from the management of agricultural soils represented 10% of the total national agricultural Greenhouse Gas (GHG) emissions. The high spatial resolution model employed here takes account of environmental factors that influence N 2 O emissions at a more disaggregated spatial scale than the Intergovernmental Panel on Climate Change (IPCC) national inventory-reporting framework. Activity data from the EU Farm Accountancy Data Network (for Ireland) is used in conjunction with biophysical and climatic data in a geographic information system (GIS) framework to estimate N 2 O emissions at a sub-national level. Results indicate that N 2 O emissions are 5% lower by applying this high spatial resolution modelling approach at the farm level compared to the baseline model (Tier 2). Nevertheless, 25% of the farms in the sample analysed had an overestimation in their N 2 O emissions of 20%, and another 25% of the farm sample had an average underestimation in their N 2 O emissions by 19%, a consequence of the variation of environmental factors among farms. Using a data panel regression, results confirm that the environmental factors examined are statistically significant within the model proposed, and with a simulation, we assessed the switching of 20% of CAN fertiliser for protected urea. According to the high spatial model, this mitigation measure can reduce N 2 O emissions from inorganic fertilisers by up to 15%. However, the reduction is conditioned by local factors and environmental conditions. [ABSTRACT FROM AUTHOR]

• Site suitability assessments are at the heart of renewable energy planning. • A collaborative approach enables developing a fit for purpose decision-support tool. • Stakeholder-driven site suitability criteria supports transparent decisions. • The tool supports informed discussions and fosters consensus on deployment areas. • Data and technological limitations signal action areas for advancing practice. Competing land-use demands associated with planning for renewables is resulting in growing social and political challenges. Spatial decision-support tools are being developed and applied to tackle some of these challenges by facilitating systematic identification of suitable development sites based on technical, environmental and socio-economic criteria. Nevertheless, decision-support tools are often developed with limited input from stakeholders affecting participative and accountable planning and decision-making. This paper aims to address this shortcoming and foster greater transparency in the development of support tools for renewable energy planning, and thus in siting decisions. It present and discusses a stakeholder-driven approach to the co-creation of an online decision-support tool that attends to planning policy, technical considerations, environmental protection and social concerns influencing renewable energy planning decisions in Ireland. This bottom-up approach, easily transferable to other jurisdictions, fosters proactive expert input, awareness raising and greater comprehensibility and acceptance of siting decisions. It also enables developing an interface that responds to users' needs. The paper contributes to advancing renewable energy planning practice through effective and meaningful stakeholder engagement that supports transparent and accountable decisions from the outset. It also highlights ongoing data and technological shortcomings affecting the integration and operationalisation of stakeholders demands and needs, which signal priority areas of action to further advance the development of renewable energy decision-support tools. [ABSTRACT FROM AUTHOR]

Abstract: Current attempts to encourage sustainable consumption are failing to make significant advancements and consumption by households in daily practices continues to grow. Interventions in this area are often based on simplistic behavioural assumptions, which overlook the embeddedness of daily consumption practices and fail to challenge escalating consumption demands and expectations. Against this background there is a need to consider more radical socio-cultural, technological and organisational innovations that may fulfil the goals of daily practices more sustainably in the future. Taking the case of home heating, the most energy intensive practice in Irish households, this paper reports on a participatory backcasting study that developed future scenarios and action plans for sustainable heating. In contrast to conventional behavioural change models and forecasting techniques, this study adopted a social practice orientation. This paper focuses on the operationalisation of the social practice approach with special reference to the iterative processes around scenario development and the resultant backcasting outputs. Participant evaluations of the procedure are presented which indicate that despite significant operational complexity, the process was strongly valued and provided significant opportunity for learning amongst stakeholders involved. Whether such approaches can be integrated into governing systems, however will depend on a willingness amongst stakeholders to embrace a radical reconceptualisation of behaviour. [Copyright &y& Elsevier]

Abstract: The paper critically reviews and explores the impact of renewable energy heat policies to induce technological change in Ireland. It is in response to the challenge of better understanding the broad effects and attributes of renewable energy policy instruments in Ireland and beyond. Using the Irish residential sector as a case study, and analysing data of more than 31,600 technology installations under the Irish Government''s Greener Homes Scheme [GHS], the paper assesses the extent to which policy makers may be informed of the willingness of consumers to adopt these technologies in their homes. In doing so, it attempts to provide valuable guidance for policy evaluation in relation to renewable energy in the residential sector. It represents investment decisions and technological choice by the end user and reflects the growing concern among energy policy markets regarding the representation of user preferences. The overall intent is to investigate the implications of analysing different variables and their contribution to making more balanced renewable energy policies. It provides a critical analysis of the Greener Homes Scheme, the key instrument for implementing Ireland''s renewable heating policy from 2006 to 2010, established to induce technological change within the residential sector. In assessing the impact of the Scheme, attention focuses on decision frameworks for technological choice and the treatment of market and behavioural failures. It considers how consumers have responded to new technological policies aimed at deploying renewable heat technologies, and the specific policies aimed at advancing certain technologies over others. Through a case study investigation, the paper explores the relationship and dependencies that influence consumers'' decision-making regarding the choice of renewable energy heating technologies in the domestic sector in Ireland. Consumer preferences and their subsequent investment decisions, motives and technological choice are reflected. It concludes that while economic factors are important, a much broader variety of determinants is required when analysing the process of adoption of technology. [Copyright &y& Elsevier]

Abstract: This paper presents an assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue in Ireland. The rivers, located in the South of Ireland, are underlain by sandstone, limestones and mudstones, and the catchments are primarily agricultural. A comprehensive database of suspended sediment data is not available for rivers in Ireland. For such situations, it is common to estimate suspended sediment concentrations from the flow rate using the suspended sediment rating curve approach. These rating curves are most commonly constructed by applying linear regression to the logarithms of flow and suspended sediment concentration or by applying a power curve to normal data. Both methods are assessed in this paper for the Rivers Bandon and Owenabue. Turbidity-based suspended sediment loads are presented for each river based on continuous (15min) flow data and the use of turbidity as a surrogate for suspended sediment concentration is investigated. A database of paired flow rate and suspended sediment concentration values, collected between the years 2004 and 2011, is used to generate rating curves for each river. From these, suspended sediment load estimates using the rating curve approach are estimated and compared to the turbidity based loads for each river. Loads are also estimated using stage and seasonally separated rating curves and daily flow data, for comparison purposes. The most accurate load estimate on the River Bandon is found using a stage separated power curve, while the most accurate load estimate on the River Owenabue is found using a general power curve. Maximum full monthly errors of −76% to +63% are found on the River Bandon with errors of −65% to +359% found on the River Owenabue. The average monthly error on the River Bandon is −12% with an average error of +87% on the River Owenabue. The use of daily flow data in the load estimation process does not result in a significant loss of accuracy on either river. Historic load estimates (with a 95% confidence interval) were hindcast from the flow record and average annual loads of 7253±673tonnes on the River Bandon and 1935±325tonnes on the River Owenabue were estimated to be passing the gauging stations. [Copyright &y& Elsevier]

Abstract: Green biorefinery (GBR) is an alternative use of grassland biomass. GBR involves applying technology to chemically and physically fractionate (split) biomass such as grass and grass silage to produce marketable products. Three GBR system models were developed in a companion paper to determine, using scenario analysis, the most technically feasible scenario for the development of a blueprint Irish GBR system. The three GBR system models were a combination of feedstock system and biorefinery technology: Grass/silage – basic technology (GS), Silage – basic technology (S) and Silage – advanced technology (AT). The models were then assessed at different input volumes. The focus of this paper is on the development of the economic modelling component of the three GBR system models in order to identify which of the previously selected GBR scenarios is the most technically and economically viable to develop a blueprint for a first generation GBR. Six scenarios were assessed in this paper. The GS, S and AT GBR systems, at two input volume rates: medium volume and low volume. Additional scenario analyses were also carried out to investigate two possible production scenarios "No Prot" (fibre product only), and "Prot" (includes proteinaceous secondary product). Both the economic and technical scenario analyses of this paper and its companion paper suggest the most appropriate GBR systems for a first generation Irish blueprint are the combination of Silage feedstock – basic biorefinery technology (S), at the medium input volume and both "Prot" and "No Prot" systems. [Copyright &y& Elsevier]

• This paper examines why women are less likely to study STEM in college. • We use administrative data for all secondary school students who apply for college in Ireland. • Find a 22 percentage points raw gender gap that is concentrated in the areas of engineering, technology, and mathematics. • Subject choice in secondary school is the most important predictor of the portion of the gap that can be explained, with a small role for grades achieved in mathematics versus English. • A gender gap of 9% remains even among students who studied the same subjects and achieved the same grades at secondary school. • Find a much smaller gap when we include nursing degrees in STEM. While education levels of women have increased dramatically relative to men, women are still greatly underrepresented in Science, Technology, Engineering, and Mathematics (STEM) college programmes. We use unique data on preference rankings for all secondary school students who apply for college in Ireland and detailed information on school subjects and grades to decompose the sources of the gender gap in STEM. We find that, of the 22 percentage points raw gap, about 13 percentage points is explained by differential subject choices and grades in secondary school. Subject choices are more important than grades – we estimate male comparative advantage in STEM (as measured by subject grades) explains about 3 percentage points of the gender gap. Additionally, differences in overall achievement between girls and boys have a negligible effect. Strikingly, there remains a gender gap of 9 percentage points even for persons who have identical preparation at the end of secondary schooling (in terms of both subjects studied and grades achieved); however, this gap is only 4 percentage points for STEM-ready students. We find that gender gaps are smaller among high-achieving students and for students who go to school in more affluent areas. There is no gender gap in science (the large gaps are in engineering and technology), and we also find a smaller gender gap when we include nursing degrees in STEM, showing that the definition of STEM used is an important determinant of the conclusions reached. [ABSTRACT FROM AUTHOR]

Re-use and preparation for re-use are currently in sharp focus across the European Union's (EU) emerging legislation on waste management and the circular economy. Lifetime extension is of particular importance to electrical and electronic equipment due to the critical nature of many of the raw materials involved and their potential status as hazardous waste. During this research interviews were conducted with stakeholders in the preparation for re-use of waste electrical and electronic equipment (WEEE) in the United Kingdom, Belgium, France, and Austria, where these systems are considered successful, and in Spain, the first country in the EU to have deemed preparation for re-use targets separate to those for recycling necessary. Similar interviews were conducted in Ireland, where the preparation for re-use system has not yet developed to a successful level, and factors reported to be facilitating or inhibiting the success of preparation for re-use were compared. Factors in successful preparation for re-use systems included involving social enterprise, requiring the use of quality standards, providing preparation for re-use organizations access to material at the point of end user surrender, and facilitating positive relationships between preparation for re-use organizations and the producer representative organizations organizing the waste streams. These factors were found to be largely absent in Ireland. This paper recommends these factors be supported in countries which are not yet reporting significant levels of preparation for re-use. Furthermore, where preparation for re-use had not been established practice prior to the WEEE directive, targets separate from those for recycling are recommended as necessary to align the interests of the Producer Representative Organisations and organisations which seek to promote it. • Preparation for re-use is resisted in EU countries which do not have a history of the practice. • Distinct preparation for re-use targets, separate from those for recycling, are recommended. • Successful preparation for re-use organizations access material close to end user. • Use of quality standards contributes to success in preparation for re-use systems. • Involvement of social enterprise benefits preparation for re-use systems. [ABSTRACT FROM AUTHOR]

This paper examines the feasibility of various alternative student loan schemes for Ireland. Using data from a large employer survey, we model the life-cycle earnings distribution for Irish graduates. We then use these estimates to simulate the effects of alternative types of student loans, including mortgage-type loans and income-contingent loans of various designs, incorporating participation and migration patterns into the simulations. The results show that mortgage-type loans entail unsustainably high repayment rates for low income graduates. Through the specification of several alternative income-contingent loan schemes, it is demonstrated that this approach to higher education financing is feasible both in terms of affordability for graduates and with respect to implied government subsidies. There are some important policy design issues to be addressed and we conclude with some recommendations for a future Irish scheme. [ABSTRACT FROM AUTHOR]

The composition and size of the passenger car fleet is often influenced by legislation and policy, including taxation, scrappage, and vehicle registration policies. Numerous measures at national and international level have been investigated to reduce the air pollution and/or climate change impacts of passenger fleets. However, the objectives of climate change and air pollution policy occasionally are conflicting, and this has resulted in a number of well publicised policy shortcomings. This paper outlines the development of a fleet emissions control (FEC) framework which enables the dynamic and adaptive optimisation of fleet emissions through changes in taxation policy in Ireland. The data for analysis was obtained from national sources including datasets from the national emission inventory and COPERT model. Applications of regression-based modelling and statistical analysis were conducted to predict fleet, mileage, and emissions changes. The results of the FEC framework showed that no increase in NO x emissions, above 2007 levels, could be achieved at a cost of a small increase in CO 2 in 2016 (+1.8%). This could be achieved by taxation policy primarily resulting in a shift away from small engine diesel vehicles (<1.4 L) in the fleet which were responsible for the largest contribution to emissions. In the transition of vehicle fleets to full electrification or full decarbonisation, the FEC framework developed here offers the potential to formulate practical and optimised adjustments in static taxation systems. These adjustments will assist in achieving the requirements of national policies on reducing emission. • A Fleet Emissions Control Framework for passenger cars was developed enabling practical optimisation of fleet emissions. • Incentivising one vehicle category over another, is a complex process with potentially positive and negative outcomes. • Incentivising diesel or petrol each have negative consequences. • Policy should incentivise a fleet composition which optimises the total emission. [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]

Abstract: National outlooks of emission levels are important components of international environmental policymaking and associated national policy development. This is the case for both greenhouse gas emissions and transboundary air pollutants. However, there is uncertainty inherent in the production of forecasts. In the climate context, IPCC guidelines have been established to support national teams in quantifying uncertainty within national inventory reporting of historic emissions. These are presented to indicate the potential range of deviation from reported values and to offer added evidence for policy decisions. However, the method and practice of accounting for uncertainty amongst emission forecasts is both less clear and less common. This paper posits that the role of forecasts in setting international targets and planning policy action renders the management of ‘forecast’ uncertainty as important as addressing uncertainty in the context of inventory and compliance work. Failure to explicitly present uncertainty in forecasting delivers an implicit and misplaced confidence in a given future scenario, irrespective of parallel work on other scenarios and sensitivities. However, it is acknowledged that approaches to uncertainty analyses within the literature are often highly technical and the models used are both computationally demanding and time-intensive. This can limit broader adoption where national capacities are limited and scenario development is frequent. This paper describes an approach to presenting uncertainty, where the aim is to balance the technical and temporal demands of uncertainty estimation against a means of delivering regular and practical estimation and presentation of uncertainty for any given scenario. In turn this methodology should help formalise the recognition of the uncertainty dimension in emissions forecasts, for all stakeholders engaged. [Copyright &y& Elsevier]

Abstract: “Green biorefinery” (GBR) could be an alternative option for using grassland biomass. GBR involves applying technology to chemically and physically fractionate (split) biomass such as grass and grass silage into marketable products. From the grass fibre fraction insulation materials can be produced. From the grass juice fraction, proteinaceous products for animal feed and lactic acid (LA) for plastic production (polylactic acid – PLA) can be produced. This paper is the first part in the analysis to develop a blueprint for a first generation Irish GBR system. The focus of this paper is on the technical aspects of developing three GBR system models and subsequent scenario analyses. The three GBR system models were a combination of feedstock system and biorefinery technology; Grass/silage–basic technology (GS), Silage–basic technology (S) and Silage–High Tech (AT). The models, which were integrated mass and energy balances, were then assessed at the three different input volumes, to generate nine scenarios. The scenarios which required further economic analysis in a companion paper to determine their overall feasibility (technical and economic) in the development of blueprint for a first generation Irish GBR system were identified. From the technical analysis outlined in this paper six scenarios were identified which require further economic analysis. These were scenarios generated for the GS, S and AT GBR systems, at two input volume rates. [Copyright &y& Elsevier]

Abstract: Air pollution is becoming a very important issue for the transportation sector, particularly car emissions in urban areas, and there is much interest in evaluating the actual level of emissions. In this paper, a case study of a standard driving cycle in the urban area of Dublin City is presented. On-road, speed-time data was extracted by an on-board diagnostic tool, and saved into a data acquisition package. Firstly, the driving cycle was established for the urban area of the city; one car travelling different routes has been employed to implement this research and some representative results have been achieved. The second part of the project was to estimate the emissions from the same car using the driving cycle obtained and compare the results with those obtained by a gas analyzer attached to the car simultaneously in order to validate the methodology used in this paper. A representative driving cycle reflecting the real-world driving conditions is proposed and estimated vehicle emissions were compared with measured results. The method is easy to follow and the results are in a good fit to the estimated values. [Copyright &y& Elsevier]

Abstract: This paper looks at the economic returns to different fields of study in Ireland in 2004 and also the value placed on various job-related competencies, accumulated on completion of higher education, in the Irish labour market. In examining these issues, the paper also analyses, through quantile regression, how the returns vary across the earnings distribution. The impact that education-job mismatch, both education level and field, has on earnings is also taken into consideration. The results derived indicate that, relative to the base case, there are higher returns to Medicine & Veterinary, Education, Engineering & Architecture, Science and Computers & IT. Small but significant returns were found for some of the competencies analysed, in particular technical skills. The quantile regression analysis reveals that both field and competency returns vary across the wage distribution and that, apart from Medicine & Veterinary and technical skills, field and competency-specific returns diminish as one moves up the earnings distribution. [Copyright &y& Elsevier]

Abstract: This paper aims to contribute to media studies on climate change through a study of the ways in which the adequacy of transnational measures of tackling climate change are represented in media outlets in the UK, Ireland, Sweden and Finland. Media texts on the European Union Energy and Climate Package, introduced in January 2008 and approved in modified form in December 2008, are analyzed in-depth. Through an exploratory cross-societal comparison of media representations, the paper traces some of the dynamics of contemporary environmental debates. We argue that the adequacy of proposed transnational measures for tackling climate change becomes a relative concept in the media: measures are not adequate or inadequate per se, but contingent upon social context and relative with regard to time and to the comparisons made. [ABSTRACT FROM AUTHOR]

Abstract: This paper demonstrates that by understanding how energy is consumed in the manufacturing of reinforced concrete, designers can significantly reduce the overall embodied energy of structures. Embodied energy of products can vary from country to country. Therefore, to accurately estimate the embodied energy of reinforced concrete structures, data specific to the country where they are being constructed must be used. This paper presents the assessment of embodied energy in typical RC building structures in Ireland. The most common methods used to calculate EE are evaluated in this paper and the most suitable method was applied to reinforced concrete. The EE of a typical 30MPa concrete mix in Ireland is calculated to be 1.08MJ/kg. Notably cement is credited with 68% of the total EE. The major contributors of energy consumption are identified, which should aid to minimise energy consumption and optimise efficiency. A case study is presented which compares the EE of a typical reinforced concrete structure in Ireland using two concrete mix designs. The first uses Ordinary Portland Cement, while the second uses GGBS replacing half of the cement content. As expected, the EE of the GGBS mix is significantly lower (30%) than that of its counterpart. [Copyright &y& Elsevier]

Renewable gas has been proposed as a solution to decarbonise industrial processes, specifically heat demand. As part of this effort, the breast-milk substitutes industry is proposing to use renewable gas as a substitute for fossil natural gas. However, decarbonising the industrial processing of breast-milk substitutes can increase social license for these products, potentially undermining breastfeeding. World Health Organisation nutrition targets aim to increase exclusive breastfeeding to at least 50% globally by 2025 to improve maternal, infant, and young child health and nutrition. This target will have implications for the energy transition. A weakness of existing energy models is that demands for end-use products such as breast-milk substitutes are typically not considered explicitly. This paper develops an analytical framework for explicitly representing infant feeding methods in energy systems models. We compare the emissions saved in Ireland from decarbonising the industrial processing of breast-milk substitutes with renewable gas with the emissions saved by an increase in exclusive breastfeeding to 50% in both Ireland and a key export market, China. We demonstrate that the emissions saved from achieving the minimum global breastfeeding target are greater than when renewable gas is used to displace natural gas in the production of breast-milk substitutes in Ireland. We discuss the decarbonisation of breast-milk substitutes in relation to the principle of justice as non-maleficence, a principle based on the commitment to avoid harm, a novel application of a principle of justice. We conclude that breastfeeding support can be considered a demand-side measure for mitigating climate change by reducing the demand for energy services to produce breast-milk substitutes. A key recommendation is to position breastfeeding support as both a public health and a climate justice issue that is relevant for a just transition. The framework developed for this paper could be applied to support the inclusion of a wider range of mitigation options with social justice outcomes in energy system models. • Breastfeeding and breastfeeding support can contribute to mitigating climate change. • Achieving global nutrition targets will save more emissions than fuel-switching. • Breastfeeding support programmes support a just transition. • This work can support the expansion of mitigation options in energy system models. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we outline the development of a spatial microsimulation model that uses a combinational optimisation technique called simulated annealing to match the Irish Census of Agriculture data to a National Farm Survey (NFS). The matched NFS and Census information is then used to produce small area farm population microdata estimates of the probability of participation by Irish farmers in the Rural Environmental Protection Scheme (REPS) for the year 2005. Previous studies that have examined the participation of farmers in agri-environmental schemes across Europe have been unable to quantify the type of habitats being protected under the programmes. This is rectified in this paper by combining the simulated farm population microdata with habitat land cover data within a GIS framework to examine what types of habitats are actually being protected under the agri-environmental programme. The results indicate that wetgrassland, bogland, rocky complexes, forestry and shallow water are habitats that are more likely to be given protection under the REPS programme while heath, dry grassland, built land and cut fen are habitat types that are unlikely to be protected under REPS. [Copyright &y& Elsevier]

Abstract: The services sector has the least amount of energy end use data available, which poses significant challenges to companies within the sector attempting to benchmark their energy performance and inform energy management decisions. This paper explores through a case study analysis the use of simple performance indicators and how additional data and new metrics can greatly enhance the understanding of energy trends and in particular the assessment of building energy performance. The country chosen for the analysis is Ireland, where the services sector has experienced high energy demand growth since 1990 (4.1% annually) compared with the EU-15 (1.5% annually). Despite this growth, the available energy data is poor, in particular for the public service sub-sectors. The case study chosen is an institution within the education sub-sector, University College Cork. The paper presents some simple energy performance indicators that have been used to date to inform energy policy. The paper then introduces new approaches and tools for assessing energy performance in buildings and how these may be utilised to improve the energy policy decision making and energy management. It discusses how these approaches are been implemented for buildings with separate functions, presents some initial results and discusses future planned work. [Copyright &y& Elsevier]

Abstract: A computable general equilibrium model with specific detail in taxation and energy use is developed in this paper to quantify the impact of the implementation of energy taxation to reduce carbon dioxide emissions in Ireland. Benchmark data combining physical energy and emissions data and economic data in the form of a Social Accounting Matrix (SAM) had to be compiled from various data sources, because energy and pollution accounts from the SEEA are not available for Ireland. We find that the reduction target for energy related CO2 emissions in Ireland of 25.8% compared to 1998 levels can be achieved with a carbon energy tax of 10–15 euros per tonne of CO2. Though fuel switching is important in meeting the target, this result is more sensitive to the possibilities for producers to substitute away from energy use. Welfare would fall but only by small percentages. Production and consumption patterns would change more significantly, with a shift in demand from fuels with a high emission factor to energy sources with a lower carbon-intensity and from energy to other commodities. This paper confirms that a carbon energy tax leads to greater emission reductions than an equivalent uniform energy tax. The latter has a stronger negative impact on the less polluting energy sectors whereas the carbon tax greatly stimulates the use of renewable energy and reduces the use of peat and coal. The new SAM, the model and the application to energy taxes contribute to a better informed debate on environmental policy in Ireland. [Copyright &y& Elsevier]

This paper presents aspects of a research investigation into nutrient fluxes into and out of an important Irish estuary, Wexford Harbour, located in the southeast of Ireland. Details of the methodology applied, the data collection and analysis and the models used are outlined. In particular, the paper addresses modelling of chlorophyll_a production in the brackish waterbody and its dependency on light attenuation.There are three distinct stages to the research carried out: (i) collection, collation and analysis of physical, chemical and biological water quality variables; (ii) development, validation and application of a chlorophyll_a production model; (iii) development of a Geographic Information System.The nutrient and chlorophyll_a production model simulates nine interactive water quality parameters; during the first stage these above parameters were measured to provide initial conditions for the model and for calibration purposes. From measurements, relationships were derived between the water quality variables and salinity, which were used to specify initial spatially varied values of the parameters to the model at different seasons. A relationship was also developed between light intensity, turbidity and concentrations of chlorophyll_a, based on field measurements. This relationship was incorporated into the model. The paper presents chlorophyll_a predictions using this relationship and illustrates how the enhanced model can be used as an accurate modelling tool for managing coastal ecosystems. The water quality model was incorporated into the ArcView GIS system providing an easy-to-use graphical user interface to run simulations and interrogate results. [Copyright &y& Elsevier]

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter. • A deep-learning framework for automatic pavement condition rating for roads is presented. • Our proposed framework consists of segmentation, cleaning, resizing and classification. • A large open dataset of real-world images captured from regional and local roads in Ireland. • Images are labelled by expert data analysts using PSCI rating scale used in Ireland. • The unique augmentation enabled the models to be more robust to changes in background. [ABSTRACT FROM AUTHOR]

A 2018 estimate indicates that there were 226,057 radon-attributable lung cancer deaths in 66 countries that had representative radon surveys. This is a shocking figure, and as it comes from only 66 countries it underestimates the worldwide death toll. Any research that enables countries to conduct representative radon surveys and to understand better the risk to citizens from radon is surely welcome. We hope this paper provides a useful methodology for estimating population risk. The estimation of population weighted average indoor radon levels requires statistically valid sampling methodologies that use a representative sample of occupied homes throughout the country. A literature review indicates that in many population weighted surveys, the sampling methodology may not have been designed to do this. This paper describes a simple, resource efficient methodology which produces statistically valid and reliable estimates based on a small scale sample that is representative of the population distribution. The resource efficient design of this study enables it to be repeated at frequent intervals providing for a longitudinal analysis of the population risk from indoor radon. This survey was conducted in Ireland using 653 measurements and a representative sampling strategy to provide a baseline population weighted radon exposure for future comparisons. This study estimates the average population weighted indoor radon concentration in Ireland to be 97.83 Bq m−3 (95% Confidence Interval 90.69 Bq m−3 to 105.53 Bq m−3), and that there are an estimated 350 lung cancer cases and 255 deaths per year due to radon exposure. The mortality rate of 5.3 per 100,000 due to indoor radon, demonstrates that radon remains one of the highest preventable causes of death in Ireland. • Population weighted survey design. • Efficient methodology to produce statistically valid Population Radon Risks. • Study design allows frequent longitudinal studies of Population Radon Risk. • Population weighted indoor radon concentration in Ireland is 97.83 Bq m−3. • 350 lung cancer cases per year in Ireland due to radon exposure. [ABSTRACT FROM AUTHOR]

The relationship between angling effort and catch is well-recognised, in particular that effort influences catch rates. But increased catch, which can be considered an attribute of fishery quality, may influence effort in terms of number of fishing trips. This suggests bi-directional feedback between catch and effort. In many travel cost applications little attention has been given to this endogeneity problem. In this paper we expand the application of structural equation models to address this issue by jointly estimating demand (effort) and catch functions. Using a cross-section dataset of sea bass anglers we propose two separate joint models. First, we include expected catch as an explanatory variable in the demand equation. In the second, we reverse the causality and use the expected number of fishing days as a covariate in the catch function. The two approaches produce similar model estimates, and perform better at predicting anglers' catch and effort than standard models. The findings confirm that sea bass angling is highly valued, with a consumer surplus of about €282–318 per angler per day, though this is likely to be biased upwards. Furthermore higher catches result in more days fished, on average in a 2:1 ratio. Whereas on average, an additional fishing day results in 3–4 additional bass caught. [ABSTRACT FROM AUTHOR]

Bioenergy is likely to play a key role in decarbonising the energy system. The versatility of bioenergy as a transport, heat or electricity fuel is one of its key strengths but can add to the complexity of policy design. Polices aimed at stimulating bioenergy use in one end use sector should consider the impacts of use and uptake in the others. This paper details a methodology for an integrated bioenergy and heat policy decision support tool that is being used to inform policy in Ireland. The previous literature has focused on individual supply chain optimisation, plant sizing and plant locations from an operator’s perspective. The BioHEAT model is a techno-economic model that accounts for the co-dependencies between the end use sectors. It extends the approach to supply chain specification in the literature to incorporate a novel representation of consumer decision making in the heat sector and the flexibility to model various policy types in heat, electricity and transport sectors from a policy making perspective. Three scenarios are examined to demonstrate the functionality of the model, including the interaction between separate policies targeting the Heat and Power sectors. The results demonstrate how the model can be used to examine policy impacts against a range of metrics including the contribution to renewable energy and carbon reduction targets; cost to the exchequer and the marginal cost of carbon abated. The model has helped to inform the development of a renewable heat policy instrument in Ireland. [ABSTRACT FROM AUTHOR]

This paper focuses on high resolution wind power statistical models fitted to meteorological data for the island of Ireland. A discrete Burr model efficiently represents the number of consecutive hours of wind power availability. The models developed in this study may be most useful at time resolutions less than 6 hours to capture zero power and short bursts of wind power potential. They could serve as a useful complement to other wind power modelling approaches such as MERRA reanalysis models. [ABSTRACT FROM AUTHOR]

This paper examined the relationship between radon risk and lung cancer prevalence using a novel dataset combining spatially-coded survey data with a radon risk map. A logit model was employed to test for significant associations between a high risk of indoor radon and lung cancer prevalence using data on 5590 people aged 50+ from The Irish Longitudinal Study on Ageing (TILDA) and radon risk data from Ireland's Environmental Protection Agency (EPA). The use of data at the individual level allowed a wide range of potentially confounding factors (such as smoking) to be included. Results indicate that those who lived in an area in which 10%–20% of households were above the national reference level (200 Bq/m 3 ) were 2.9–3.1 times more likely to report a lung cancer diagnosis relative to those who lived in areas in which less than 1% of households were above the national reference level. [ABSTRACT FROM AUTHOR]

The responsive nature of environmental assessment (EA) subjects it to the political conversations that drive future planning and development, one of the most relevant being the increased attention given to a sustainable transition. This political attention has catalyzed an interest amongst practitioners to urge traditional EA practice towards one with an increased regard for sustainability objectives, understood, in this case, to be guided and driven by the Sustainable Development Goals (SDGs). Using a theoretical framework of a practitioner's 'spaces for practice' and interviews with environmental consultants in Denmark and the Republic of Ireland, this paper unfolds the motivation that consultants have for integrating SDGs in their practice. This includes both outlining the overarching opportunities that enable the adoption of new practices and the perceived capacities that demotivate integration attempts. The results are then compared and correlated across both countries, showing an emerging motivation from consultants for integrating SDGs and shifting EA practice towards aligning with the strategic goals, but also identifying challenges hindering the implementation of this motivation in practice. These range from being intrinsic conditions of the individual practitioner to extrinsic conditions of the relations between practitioners and stakeholder groups. The study suggests directing attention towards theory on how practitioners construct practice to better address the divide between SDG integration in theory and in practice. • An investigation of consultants' motivation for integrating the SDGs in EA. • Interviews with EA consultants reveal common opportunities and capacities to align EA & SDG. • A call for strengthened collaboration between stakeholders on the potential of SDG integration. • The need for established methods for SDG integration to understand influence in practice. • The need for attention to unblocking barriers to harvest opportunities for SDG integration. [ABSTRACT FROM AUTHOR]

[Display omitted] Reinforcement learning (RL) based control is widely considered a promising approach in building automation and control as it has demonstrated the potential to deal with complex objectives in adjacent domains like robotics, autonomous vehicles, gaming applications, and advertisement recommendations. When applied to any environment, model-free RL learns to improve its control performance over time without requiring a control model, by receiving and then analyzing feedback from the building environment after each control action. Operational objectives are becoming increasingly complex through the simultaneous consideration of thermal comfort, carbon emissions, grid services, and indoor air quality. In this context, conventional rule-based control approaches are proving sub-optimal, mostly heuristic, and inadequate. The model-free and self learning nature of RL appears promising and attractive as it may address the scalability issues associated with advanced control approaches. However, it suffers from long training times and unstable control behavior during the early stages of its learning process, which makes it unsuitable to be applied directly to buildings. This paper addresses these issues using an inverse reinforcement learning approach (IRL), a technique utilized to learn the objective of a controller agent which is considered an expert in its respective domain. Here, we consider a rule-based control as the expert demonstrator. IRL is different from a direct imitation (i.e., direct mapping of states to actions) of control actions as it tries to find the underlying intent of an expert's policy, providing the controller with a better-generalized policy for unseen states or environments with slightly different dynamics. This approach propels the RL controller's policy to levels similar to or better than that of a rule-based policy before it starts learning by interacting with the building. This makes RL for building energy management applications more practical as it prevents the erratic and exploratory behavior in the initial training period, simultaneously speeding up the learning process when compared to applying an untrained RL agent directly to a building environment. [ABSTRACT FROM AUTHOR]