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Research Commons is the University of Waikato's open access research repository, housing research publications and theses produced by the University's staff and students.
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Item The role of Adaptive Energy Digital Twin technology in decarbonising emissions in the New Zealand process heat sector(Conference Contribution, 2023)New Zealand (NZ) is uniquely placed to meet the goal of net zero emissions in the industry sector by 2050, but only with an 80% expansion of renewable electricity generation (TWh) combined with energy efficiency improvements through advanced process heat integration, improved energy system design and control, future deployment of high temperature heat pumps (HTHP) for process heat up to 200oC and biomass for heat above 200oC, and smart integration of renewable electricity via microgrid connected factories and communities. The development of Adaptive Energy Digital Twin (AEDT) technology applied widely by energy consultants, equipment providers and factory owners, is considered a key development to aid industry decarbonisation. A seven-year research programme is underway in NZ to develop an open source AEDT technology platform and to demonstrate its usefulness for decarbonising a wide variety of industry sectors, such as dairy, meat, food, wood, paper, metals and chemical processing industries of NZ. Electricity supply and demand differences across the regions of NZ for 2020 and predicted for 2050 are also presented.Item Integrating occupancy density into the environmental assessment of residential buildings: Towards embodied impact reduction at both building and urban level(Journal Article, Elsevier BV, 2025)Life Cycle Assessments (LCA) of buildings typically use gross floor area (GFA)-centric functional units, while urban-scale assessments use occupancy-centric ones. This mismatch reflects the dual functions of buildings (providing space and shelter) and can lead to conflicting strategies, where reducing impacts per GFA at the building level may increase impacts per occupant at the urban level. This study compares these assessment approaches and introduces a dual-functional unit approach that evaluates buildings across both functions. Eight detached houses are assessed using LCA across four impact categories and ranked using a multi-criteria decision-making (MCDM) method for each approach. Results show significant variation in rankings among the established approaches, with several buildings having completely opposite outcomes (e.g., ranked 1st in the GFA-centric assessment but 6th in the occupancy-centric assessment), while the proposed approach produces more consistent rankings, representing both functions. Moreover, a scenario-based analysis compares the three assessments to a control (no-assessment) scenario, considering all detached houses built in Aotearoa New Zealand over the past 5 years. The GFA-centric assessment resulted in increased total impacts (mean difference +0.20% across the four impact categories) by promoting larger and less dense buildings (+0.70 m² per occupant), while the occupancy-centric assessment led to mean impact reductions of -1.98%, while significantly reducing the space per occupant by 0.98m². The proposed approach achieved even greater impact reductions (-2.22%) while reducing space by only 0.31m² per occupant. Finally, a correlation with national climate goals was made, showing the approach could achieve 71.65% of the national carbon reduction target.Item Editorial for “Unmasking Racism and Oppression in Psychology” – Part II(Journal Article, Christchurch New Zealand Psychological Society, 2025)Since publishing the first part of the “Unmasking Racism and Oppression in Psychology” special issue in March 2025, we have received immense aroha and gratitude from colleagues and students eager to learn about the perspectives of Indigenous and minoritised groups in psychology and how they can support the development of an anti-racist discipline. Rather than seeking to prove the existence of racism in the field (Crossing et al., 2024), this issue centres on validating our authors’ experiences of navigating oppression, challenging Eurocentric psychology, resisting assimilation, and remaining grounded in Indigenous ontologies and epistemologies while staying accountable to their communities. Racism in psychology has been called out by many colleagues in Aotearoa (to name a few; Levy & Waitoki, 2016; Love, 2008; Pomare et al., 2021), and this issue responds to their concerns by amplifying solutions for change in the teaching, practice, and research of psychology.Publication Performance evaluation of SCATS-controlled intersections in New Zealand with machine-learning delay prediction and signal-timing optimisation(Thesis, The University of Waikato, 2025)Adaptive signal systems such as the Sydney Coordinated Adaptive Traffic System (SCATS) controls traffic signal system by adjusting phase splits and cycle length in real time. The adjustments rely only on the last few seconds of detector data, the system lacks real-time predictive and optimization functionalities, allowing queues and emissions to build. Fine-tuning of signal timing by even a small increment can derive large economic and environmental benefits for the wider network. This short-coming is becoming increasingly significant in New Zealand, where transport already contributes 39 % of national CO₂ emissions and intersection delay costs Auckland more than NZ $1 billion each year. This thesis evaluates present and future performance of two representative SCATS-controlled intersections—Albany, Auckland and Ruakura, Hamilton—and tests whether supervised machine- learning (ML) models can predict delay and recommend cycle lengths that improvises the native SCATS logic. Field data was used to build, calibrate, and validate base SIDRA intersection models; saturation- flow rates were matched within ±5 % of observations and all lane-movements satisfied degree-of- saturation criteria. A Monte-Carlo routine expanded 14 SCATS peak-period volume logs into 98 synthetic volume scenarios, which were re-run in SIDRA to obtain delay, queue, fuel, and emission outputs. Four machine learning models—XGBoost, Random Forest, Support Vector Regression, and k-Nearest Neighbours—were trained on the synthetic dataset; the best two, XGBoost and Random Forest were combined in an ensemble to give a delay-prediction model. Furthermore, baseline analysis (2024) found both sites operating at Level-of-Service D, with several right-turn lanes already oversaturated and 95th-percentile queues exceeding storage. Ten- year growth projections degraded both intersections to LoS F well before the planning horizon. The ML ensemble predicted average control delay with MAE ≈ 4 s veh⁻¹ and, under moderate demand, shortened cycles by 15–46 s, cutting delay by 13 % and CO₂ by 5 % relative to SCATS timings; though the benefits diminished under heavy oversaturation. Data-driven machine learning models can provide cycle-by-cycle delay forecasts and substantial performance gains, but require additional high-degree of saturations and longer cycle lengths training data for robust operation and more generalization during severe congestion. Integration of such predictors and optimizers with SCATS would provide practical step toward meeting New Zealand’s 2035 emission-reduction targets.Item Navigating protection and presence: Trade-offs around data suppression for small Pacific populations(Journal Article, Resource Books Ltd., 2025)Introduction: Datasets, their analytics and their interpretation are key decision support tools for Pacific Island communities, with the potential to shape public policy, healthcare, and social interventions in the Pacific ‘Blue Continent’. However, in the case of numerically small island populations, privacy concerns have motivated widespread use of data suppression. While suppression safeguards privacy, it also risks erasing the visibility of these populations, leading to ‘statistical invisibility’ that obscures the social, health, and economic challenges. This study critically reviews the practice of data suppression, emphasizing its rationale in privacy protection, but also highlighting the impacts on resource allocation, advocacy, and equitable policy-making for Pacific populations. Methods: We explored the rationale behind data suppression, and its legal and regulatory context. Using case studies including the U.S. Census Bureau, Centers for Disease Control and Behavioral Risk Factor Surveillance System, we assess the impact of suppression thresholds and privacy-preserving methods on Pacific Island communities. We present a novel analysis of data suppression impacts on ICD code suppression across different levels of geographical units in the Pacific to illustrate disproportionate impacts. We review alternative privacy-preserving methods, including data smoothing, statistical masking, and synthetic data generation, that could mitigate the effects of suppression without compromising individual privacy. Finding and Conclusions: We recommend inclusive and transparent data practices needed to prevent data suppression compounding systemic marginalization of small Pacific populations. By critically evaluating current practices and proposing alternative strategies grounded in ‘Critical Data Theory’ and Pacific knowledge epistemology, this paper aims to inform policies that balance protection of individual privacy with the accurate representation of small, geographically dispersed populations.
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