| Summary: | Various studies of regional carrying capacity are necessary for leading development to be
suitable with its limit and potency, so it is able to support sustainable development. The
development of prediction model of developmental sustainability based on regional carrying
capacity is important to be done to direct the development more rational and optimal. This
research aims to formulate and determine the calculation of regional carrying capacity
comprehensively and to study a variation of value, a spatial distribution pattern, and factors that
determine the amount of regional carrying capacity in DIY. The next objective is formulating the
model of developmental sustainability based on regional carrying capacity by the use of Artificial
Neural Networks � Backpropagation and investigating the relation pattern of regional carrying
capacity balance and developmental sustainability.
Data processing of carrying capacity is done by quantitative approach, which is by using
mathematical formulation obtained from concept and theoretical development in regional carrying
capacity. Classification, variation of value, spatial distribution pattern, and determining factor of
regional carrying capacity are studied by using Artificial Neural Networks � Self Organizing
Maps, Hot Spot Analysis (Getis-Ord Gi*), and Discriminant Analysis. Model making is done by
Artificial Neural Networks (ANN) with Backpropagation topology. Assessment unit of regional
carrying capacity used is a village unit throughout Yogyakarta Special Region, amounting to 438
villages.
The calculation result of DIY regional carrying capacity shows the value variation among
components and among regional parts (regency/city). Bioecological carrying capacity (land
resources) of DIY is considered low and is having bioecological deficit (land resources deficit) of
0.085 global hectares per capita. It is similar to food carrying capacity having carrying capacity of
only 0.773, and it still needs a large additional supply of rice. A different condition is found for
components of water, air, residential, goods and waste, as well as service that their values of
carrying capacity are quite high. Spatially, cluster distribution of regional carrying capacity is
affected more by factor of urbanity level, population, physiographic aspect, and land use. The
determining factor of the amount of regional carrying capacity is quite varied, multi-role, and
multi-trait. From the training result by Artificial Neural Networks � Self Organizing Maps, five
clusters of regional carrying capacity are produced, and it forms leveling. Furthermore, from the
training that has been done by the use of Artificial Neural Networks (ANN) � Backpropagation, a
prediction model of developmental sustainability level based on regional carrying capacity is
produced. The achievement of optimal value from the level of developmental sustainability is not
determined by individual value of each regional carrying capacity component, but it is more
determined by the value composition of carrying capacity according to the aspect/component. The
composition variation of regional carrying capacity according to the component really determines
the level of developmental sustainability and the application of Carrying Capacity
Multicomponent Zero-Sum Game Theory. The region that has carrying capacity value that is
relatively balanced among aspects/components tends to have a higher developmental sustainability
level. From the result of prediction model use, it describes the existence of level variation spatially
in the level of developmental sustainability in DIY. The arrangement of land use composition by
regional spatial structure plan is the key instrument to obtain the balance of regional carrying
capacity, so it is expected to be able to improve the developmental sustainability. Additionally, the
need to be implemented the principles of eco-efficiency in resources utilization.
|
|---|