title: The prosecution of environmental offences in New Zealand

authors: Daya-Winterbottom, Trevor

subjects: law

abstract: This paper will examine the background law regarding environmental offences under the Resource Management Act 1991 (NZ), prosecution trends, sentencing for RMA offences (including principles of sentencing, sentencing discretion, legislative guidance, appellate guidance, guideline judgments, and tariffs), the use of costs in conjunction with sentencing, recent appeal judgments, and finally draw conclusions regarding consistency and sentencing in relation to environmental offences. Where relevant comparisons will be made with the Australian jurisdictions.

text: The prosecution of environmental offences in New Zealand This paper will examine the background law regarding environmental offences under the Resource Management Act 1991 (NZ), prosecution trends, sentencing for RMA offences (including principles of sentencing, sentencing discretion, legislative guidance, appellate guidance, guideline judgments, and tariffs), the use of costs in conjunction with sentencing, recent appeal judgments, and finally draw conclusions regarding consistency and sentencing in relation to environmental offences. Where relevant comparisons will be made with the Australian jurisdictions.

year: 2008

title: Efficient online evaluation of big data stream classifiers

authors: Bifet, Albert

subjects: Data Streams

abstract: The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.

text: Efficient online evaluation of big data stream classifiers The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.

year: 2015

title: The role of coastal marae in natural hazard response and climate change adaptation

authors: Bailey-Winiata, Akuhata

abstract: Marae are much more than infrastructure. They are spiritual places and community hubs. Marae are often positioned in places that are known to be safe based on mātauranga a whānau/hapū/iwi (often more generally referred to as mātauranga Māori - Māori knowledge), and often near waterbodies including rivers/streams, estuaries, and the ocean. Although there are many advantages to living near waterbodies, such features are prone to natural hazards such as flooding and erosion. The impact of these hazards is being exacerbated by climate change and sea level rise.

Hapū and iwi in the past adapted to the impact of natural hazards, and continue to do so, and are now adapting to climate change through a carefully considered process informed by mātauranga Māori developed through generations of observations. Coastal adaptation is generally categorised into three broad types: 1) Protect, 2) Accommodate and 3) Retreat, and many coastal marae are already adapting across all these responses.

Heading into the future, marae are likely to play an increasingly important role for communities in Aotearoa New Zealand. They are examples of mātauranga in action and will continue to protect and shelter people from hazards and following disasters.

text: The role of coastal marae in natural hazard response and climate change adaptation Marae are much more than infrastructure. They are spiritual places and community hubs. Marae are often positioned in places that are known to be safe based on mātauranga a whānau/hapū/iwi (often more generally referred to as mātauranga Māori - Māori knowledge), and often near waterbodies including rivers/streams, estuaries, and the ocean. Although there are many advantages to living near waterbodies, such features are prone to natural hazards such as flooding and erosion. The impact of these hazards is being exacerbated by climate change and sea level rise.

Hapū and iwi in the past adapted to the impact of natural hazards, and continue to do so, and are now adapting to climate change through a carefully considered process informed by mātauranga Māori developed through generations of observations. Coastal adaptation is generally categorised into three broad types: 1) Protect, 2) Accommodate and 3) Retreat, and many coastal marae are already adapting across all these responses.

Heading into the future, marae are likely to play an increasingly important role for communities in Aotearoa New Zealand. They are examples of mātauranga in action and will continue to protect and shelter people from hazards and following disasters.

year: 2022-11