The impact of new public management on the roles of elected councillors, management and the community sector in Irish local government: a case study of Cork County Council

The fundamental aim of this thesis is to examine the effect of New Public Management (NPM) on the traditional roles of elected representatives, management and community activists in Irish local government. This will be achieved through a case study analysis of one local authority, Cork County Council. NPM promises greater democracy in decision-making. Therefore, one can hypothesise that the roles of the three key groupings identified will become more influenced by principles of participatory decision-making. Thus, a number of related questions will be addressed by this work, such as, have the local elected representatives been empowered by NPM? Has a managerial revolution taken place? Has local democracy been enhanced by more effective community participation? It will be seen in chapter 2 that these questions have not been adequately addressed to date in NPM literature. The three groups identified can be regarded as stakeholders although the researcher is cautious in using this term because of its value-laden nature. Essentially, in terms of Cork County Council, stakeholders can be defined as decision-makers and people within the organization and its environment who are interested in or could be affected directly or indirectly by organizational performance. This is an all-embracing definition and includes all citizens, residents, community groups and client organizations. It is in this context that the term 'stakeholder' should be understood when it is occasionally used in this thesis. In this case, the perceptions of elected councilors, management and community representatives with regard to their changing roles are as significant as the changes themselves. The chapter begins with a brief account of the background to this research. This is followed by an explanation of the methodology which is used and then concludes with short statements about the remaining chapters in the thesis.

Practical wireless sensor networks power consumption metrics for building energy management applications

The power consumption of wireless sensor networks (WSN) module is an important practical concern in building energy management (BEM) system deployments. A set of metrics are created to assess the power profiles of WSN in real world condition. The aim of this work is to understand and eventually eliminate the uncertainties in WSN power consumption during long term deployments and the compatibility with existing and emerging energy harvesting technologies. This paper investigates the key metrics in data processing, wireless data transmission, data sensing and duty cycle parameter to understand the system power profile from a practical deployment prospective. Based on the proposed analysis, the impacts of individual metric on power consumption in a typical BEM application are presented and the subsequent low power solutions are investigated.

Specification of optimum holistic building environmental and energy performance information to support informed decision making

Political drivers such as the Kyoto protocol, the EU Energy Performance of Buildings Directive and the Energy end use and Services Directive have been implemented in response to an identified need for a reduction in human related CO2 emissions. Buildings account for a significant portion of global CO2 emissions, approximately 25-30%, and it is widely acknowledged by industry and research organisations that they operate inefficiently. In parallel, unsatisfactory indoor environmental conditions have proven to negatively impact occupant productivity. Legislative drivers and client education are seen as the key motivating factors for an improvement in the holistic environmental and energy performance of a building. A symbiotic relationship exists between building indoor environmental conditions and building energy consumption. However traditional Building Management Systems and Energy Management Systems treat these separately. Conventional performance analysis compares building energy consumption with a previously recorded value or with the consumption of a similar building and does not recognise the fact that all buildings are unique. Therefore what is required is a new framework which incorporates performance comparison against a theoretical building specific ideal benchmark. Traditionally Energy Managers, who work at the operational level of organisations with respect to building performance, do not have access to ideal performance benchmark information and as a result cannot optimally operate buildings. This thesis systematically defines Holistic Environmental and Energy Management and specifies the Scenario Modelling Technique which in turn uses an ideal performance benchmark. The holistic technique uses quantified expressions of building performance and by doing so enables the profiled Energy Manager to visualise his actions and the downstream consequences of his actions in the context of overall building operation. The Ideal Building Framework facilitates the use of this technique by acting as a Building Life Cycle (BLC) data repository through which ideal building performance benchmarks are systematically structured and stored in parallel with actual performance data. The Ideal Building Framework utilises transformed data in the form of the Ideal Set of Performance Objectives and Metrics which are capable of defining the performance of any building at any stage of the BLC. It is proposed that the union of Scenario Models for an individual building would result in a building specific Combination of Performance Metrics which would in turn be stored in the BLC data repository. The Ideal Data Set underpins the Ideal Set of Performance Objectives and Metrics and is the set of measurements required to monitor the performance of the Ideal Building. A Model View describes the unique building specific data relevant to a particular project stakeholder. The energy management data and information exchange requirements that underlie a Model View implementation are detailed and incorporate traditional and proposed energy management. This thesis also specifies the Model View Methodology which complements the Ideal Building Framework. The developed Model View and Rule Set methodology process utilises stakeholder specific rule sets to define stakeholder pertinent environmental and energy performance data. This generic process further enables each stakeholder to define the resolution of data desired. For example, basic, intermediate or detailed. The Model View methodology is applicable for all project stakeholders, each requiring its own customised rule set. Two rule sets are defined in detail, the Energy Manager rule set and the LEED Accreditor rule set. This particular measurement generation process accompanied by defined View would filter and expedite data access for all stakeholders involved in building performance. Information presentation is critical for effective use of the data provided by the Ideal Building Framework and the Energy Management View definition. The specifications for a customised Information Delivery Tool account for the established profile of Energy Managers and best practice user interface design. Components of the developed tool could also be used by Facility Managers working at the tactical and strategic levels of organisations. Informed decision making is made possible through specified decision assistance processes which incorporate the Scenario Modelling and Benchmarking techniques, the Ideal Building Framework, the Energy Manager Model View, the Information Delivery Tool and the established profile of Energy Managers. The Model View and Rule Set Methodology is effectively demonstrated on an appropriate mixed use existing ‘green’ building, the Environmental Research Institute at University College Cork, using the Energy Management and LEED rule sets. Informed Decision Making is also demonstrated using a prototype scenario for the demonstration building.

Leveraging lessons learned in organizations through implementing practice-based organizational learning and performance improvement - An opportunity for context-based intelligent assistant support (CIAS)

Organizations that leverage lessons learned from their experience in the practice of complex real-world activities are faced with five difficult problems. First, how to represent the learning situation in a recognizable way. Second, how to represent what was actually done in terms of repeatable actions. Third, how to assess performance taking account of the particular circumstances. Fourth, how to abstract lessons learned that are re-usable on future occasions. Fifth, how to determine whether to pursue practice maturity or strategic relevance of activities. Here, organizational learning and performance improvement are investigated in a field study using the Context-based Intelligent Assistant Support (CIAS) approach. A new conceptual framework for practice-based organizational learning and performance improvement is presented that supports researchers and practitioners address the problems evoked and contributes to a practice-based approach to activity management. The novelty of the research lies in the simultaneous study of the different levels involved in the activity. Route selection in light rail infrastructure projects involves practices at both the strategic and operational levels; it is part managerial/political and part engineering. Aspectual comparison of practices represented in Contextual Graphs constitutes a new approach to the selection of Key Performance Indicators (KPIs). This approach is free from causality assumptions and forms the basis of a new approach to practice-based organizational learning and performance improvement. The evolution of practices in contextual graphs is shown to be an objective and measurable expression of organizational learning. This diachronic representation is interpreted using a practice-based organizational learning novelty typology. This dissertation shows how lessons learned when effectively leveraged by an organization lead to practice maturity. The practice maturity level of an activity in combination with an assessment of an activity’s strategic relevance can be used by management to prioritize improvement effort.

Biodiversity change in the Irish uplands - the effects of grazing management

As a prominent form of land use across much of upland Europe, extensive livestock grazing may hold the key to the sustainable management of these landscapes. Recent agricultural policy reform, however, has resulted in a decline in upland sheep numbers, prompting concern for the biodiversity value of these areas. This study quantifies the effects of varying levels of grazing management on plant, ground beetle and breeding bird diversity and assemblage in the uplands and lowlands of hill sheep farms in County Kerry, Ireland. Farms represent a continuum of light to heavy grazing, measured using a series of field indicators across several habitats, such as the internationally important blanket bog, home to the ground beetle, Carabus clatratus. Linear mixed effects modelling and non-metric multidimensional scaling are employed to disentangle the most influential management and environmental factors. Grazing state may be determined by the presence of Molinia caerulea or Nardus stricta, and variables such as % traditional ewes, % vegetation litter and % scrub prove valuable indicators of diversity. Measures of ecosystem functioning, e.g. plant biomass (nutrient cycling) and % vegetation cover (erosion rates) are influenced by plant diversity, which is influenced by grazing management. Levels of the ecosystem service, soil organic carbon, vary with ground beetle abundance and diversity, potentially influencing carbon sequestration and thereby climate change. The majority of species from all three taxa are found in the lowlands, with the exception of birds such as meadow pipit and skylark. The scale of measurement should be determined by the size and mobility of the species in question. The challenge is to manage these high nature value landscapes using agri-environment schemes which enhance biodiversity by maintaining structural heterogeneity across a range of scales, altitudes and habitats whilst integrating the decisions of people living and working in these marginal areas.

Application of signal detection methods to fisheries management

The abundance of many commercially important fish stocks are declining and this has led to widespread concern on the performance of traditional approach in fisheries management. Quantitative models are used for obtaining estimates of population abundance and the management advice is based on annual harvest levels (TAC), where only a certain amount of catch is allowed from specific fish stocks. However, these models are data intensive and less useful when stocks have limited historical information. This study examined whether empirical stock indicators can be used to manage fisheries. The relationship between indicators and the underlying stock abundance is not direct and hence can be affected by disturbances that may account for both transient and persistent effects. Methods from Statistical Process Control (SPC) theory such as the Cumulative Sum (CUSUM) control charts are useful in classifying these effects and hence they can be used to trigger management response only when a significant impact occurs to the stock biomass. This thesis explores how empirical indicators along with CUSUM can be used for monitoring, assessment and management of fish stocks. I begin my thesis by exploring various age based catch indicators, to identify those which are potentially useful in tracking the state of fish stocks. The sensitivity and response of these indicators towards changes in Spawning Stock Biomass (SSB) showed that indicators based on age groups that are fully selected to the fishing gear or Large Fish Indicators (LFIs) are most useful and robust across the range of scenarios considered. The Decision-Interval (DI-CUSUM) and Self-Starting (SS-CUSUM) forms are the two types of control charts used in this study. In contrast to the DI-CUSUM, the SS-CUSUM can be initiated without specifying a target reference point (‘control mean’) to detect out-of-control (significant impact) situations. The sensitivity and specificity of SS-CUSUM showed that the performances are robust when LFIs are used. Once an out-of-control situation is detected, the next step is to determine how much shift has occurred in the underlying stock biomass. If an estimate of this shift is available, they can be used to update TAC by incorporation into Harvest Control Rules (HCRs). Various methods from Engineering Process Control (EPC) theory were tested to determine which method can measure the shift size in stock biomass with the highest accuracy. Results showed that methods based on Grubb’s harmonic rule gave reliable shift size estimates. The accuracy of these estimates can be improved by monitoring a combined indicator metric of stock-recruitment and LFI because this may account for impacts independent of fishing. The procedure of integrating both SPC and EPC is known as Statistical Process Adjustment (SPA). A HCR based on SPA was designed for DI-CUSUM and the scheme was successful in bringing out-of-control fish stocks back to its in-control state. The HCR was also tested using SS-CUSUM in the context of data poor fish stocks. Results showed that the scheme will be useful for sustaining the initial in-control state of the fish stock until more observations become available for quantitative assessments.