title: Changing parameters of consultation in local government: loss or gain for democracy?

authors: Mackness, Kate

abstract: A key tenet of the Resource Management Act (RMA) when enacted in 1991 was to provide for significant rights of public participation in decision-making processes in district and regional planning processes. The Local Government Act (LGA) when enacted in 2002 continued to extend the mechanisms for public participation in decision-making processes through partnership arrangements and a greater requirement to consult. These provisions recognise the interests of the general public and those specifically affected, as well as enhancing the quality of local decision making. We argue that the reforms to the LGA and the RMA since the  election of the National-led government in 2008 have created a policy context where these public participation rights could be eroded. Our textual analysis of the amendments to the legislation demonstrates that the current National-led government’s pursuit of greater efficiency is resulting in reduced consultation under the RMA, and potential opportunities for erosion of public participation to occur under the LGA – although conversely, the greater flexibility also provided may generate new democratic opportunities.

text: Changing parameters of consultation in local government: loss or gain for democracy? A key tenet of the Resource Management Act (RMA) when enacted in 1991 was to provide for significant rights of public participation in decision-making processes in district and regional planning processes. The Local Government Act (LGA) when enacted in 2002 continued to extend the mechanisms for public participation in decision-making processes through partnership arrangements and a greater requirement to consult. These provisions recognise the interests of the general public and those specifically affected, as well as enhancing the quality of local decision making. We argue that the reforms to the LGA and the RMA since the  election of the National-led government in 2008 have created a policy context where these public participation rights could be eroded. Our textual analysis of the amendments to the legislation demonstrates that the current National-led government’s pursuit of greater efficiency is resulting in reduced consultation under the RMA, and potential opportunities for erosion of public participation to occur under the LGA – although conversely, the greater flexibility also provided may generate new democratic opportunities.

year: 2016

title: Forecasting seasonal drawdowns in Whangamata town supply wells

authors: Jelley, Neil

subjects: groundwater

abstract: The coastal township of Whangamata's reticulated water supply is provided by a
number of groundwater bores, extracting water from local fractured rhyolite and
andesite aquifers. A need has arisen to create a greater understanding of the aquifers,
because of an increased demand for water abstraction. Water demand in Whangamata
increases dramatically during the summer vacation period. Occupant numbers
increase from 4,000 up to 50,000 during peak times, resulting in increased water
demand. Over the past five years an increase in groundwater abstraction has produced
an evident downward trend in bore water levels. Electrical conductivity is also
increasing in several aquifers, posing a realistic threat of sea water intrusion and
questioning the sustainability of current abstraction volumes.
Multiple linear regression and an artificial neural network model were investigated as
simple empirical forecasting tools for well drawdowns to predict the effect of future
increases in groundwater demand. This approach was adopted as opposed to a
groundwater numerical model because of poor time resolution of available data and
the complex, fractured nature of the aquifer. By using pumping volumes as variables,
seasonal bore water level variations and long term trends were predicted. The models
were evaluated with independent validation data sets. The actual ability of a model to
predict bore water level seasonal variation and long term trends was assessed using a
comparison with a moving average of the validation data set.
Multiple linear regression proved superior to the neural network in almost every bore
modelled. Although neural networks proved capable of modelling seasonal bore
water level variations it was not to the same degree of accuracy as the regression
approach. The regression approach yielded a modified index of agreement of 0.6-0.74
when comparing a moving average of observed data with the validation data sets. The
developed models were used to forecast well water levels with varying abstraction
volumes aiming to prevent further long term decline in bore water levels.

text: Forecasting seasonal drawdowns in Whangamata town supply wells The coastal township of Whangamata's reticulated water supply is provided by a
number of groundwater bores, extracting water from local fractured rhyolite and
andesite aquifers. A need has arisen to create a greater understanding of the aquifers,
because of an increased demand for water abstraction. Water demand in Whangamata
increases dramatically during the summer vacation period. Occupant numbers
increase from 4,000 up to 50,000 during peak times, resulting in increased water
demand. Over the past five years an increase in groundwater abstraction has produced
an evident downward trend in bore water levels. Electrical conductivity is also
increasing in several aquifers, posing a realistic threat of sea water intrusion and
questioning the sustainability of current abstraction volumes.
Multiple linear regression and an artificial neural network model were investigated as
simple empirical forecasting tools for well drawdowns to predict the effect of future
increases in groundwater demand. This approach was adopted as opposed to a
groundwater numerical model because of poor time resolution of available data and
the complex, fractured nature of the aquifer. By using pumping volumes as variables,
seasonal bore water level variations and long term trends were predicted. The models
were evaluated with independent validation data sets. The actual ability of a model to
predict bore water level seasonal variation and long term trends was assessed using a
comparison with a moving average of the validation data set.
Multiple linear regression proved superior to the neural network in almost every bore
modelled. Although neural networks proved capable of modelling seasonal bore
water level variations it was not to the same degree of accuracy as the regression
approach. The regression approach yielded a modified index of agreement of 0.6-0.74
when comparing a moving average of observed data with the validation data sets. The
developed models were used to forecast well water levels with varying abstraction
volumes aiming to prevent further long term decline in bore water levels.

year: 2007

title: E hoki mai nei ki te ūkaipō | Return to your place of nourishment [Te Kōputu a te Whanga a Toi | Whakatāne Art Gallery]

authors: Apiata, Hāwea

subjects: toi Māori

abstract: E Hoki Mai Nei ki te Ūkaipō | Return to Your Place of Nourishment brings together works by four wāhine Māori artists, Aimee Ratana, Aisha Roberts, Hollie Tawhiao and Maraea Timutimu, which explore narratives of pakanga
(conflict) through toi (art). In this show, the four artists weave together their unique practices and stories to present a cohesive yet diverse examination of conflicts, with a particular focus on the artists’ own whānau, hapū, and iwi narratives.
Curated by Hāwea Apiata.

text: E hoki mai nei ki te ūkaipō | Return to your place of nourishment [Te Kōputu a te Whanga a Toi | Whakatāne Art Gallery] E Hoki Mai Nei ki te Ūkaipō | Return to Your Place of Nourishment brings together works by four wāhine Māori artists, Aimee Ratana, Aisha Roberts, Hollie Tawhiao and Maraea Timutimu, which explore narratives of pakanga
(conflict) through toi (art). In this show, the four artists weave together their unique practices and stories to present a cohesive yet diverse examination of conflicts, with a particular focus on the artists’ own whānau, hapū, and iwi narratives.
Curated by Hāwea Apiata.

year: 2025-04-17