The Bird's Head Seascape (BHS), Papua, Indonesia is located within the epicenter of global marine biodiversity and has been the focus of recent conservation efforts to protect marine resources. Here, we provide an overview of Marine Protected Areas (MPAs) progress in the BHS over the past decade, including establishment history, changes in management effectiveness and ecosystem health, as well as examining trends in tourism growth. While generally viewed as a conservation success story, we reflect on both successes and challenges in the BHS, identifying where we need to continue to improve and adapt in response to rapid economic and environmental change. As of 2020, BHS MPAs cover 5.1 million ha across 23 MPAs. As expected, management effectiveness is steadily increasing in BHS MPAs-although newer MPAs face Purwanto and Dominic A. Andradi-Brown should be considered joint first authors.
Panel Data Regression Analysis is a combination of time series data and cross section data. The purpose of this study is to determine the best model for panel data regression analysis on HDI in West Papua Province and to determine the HDI model in West Papua Province. The data used in this study are West Papua data in the 2019 Publication Figures and 2019 Publication Human Development Index data. In the process of determining the best model, estimating model parameters with 3 approaches namely CEM, FEM and REM, then testing model selection, classical assumption test, model equation checking and finally model interpretation. The results of this study indicate that the best regression model is FEM with individual effects and time effects with a good model of 91% which means that HDI in West Papua Province is explained by GRDP, RLS, JPM and UHH. The equation model is as follows: Based on the equations that have been obtained, the variables that have a significant effect on HDI in West Papua Province are RLS and UHH.
In the 2015-2035 national industrial development master plan, it is stated that micro and small industries are one of the contributors to the national economy. This means that the even distribution of industrial and regional development according to the potential of natural resources in each region is the main target. Merauke regency has the second largest number of micro and small industries in Papua Province. Business development in this industry was found to be difficult. Raw materials are one of the supporting factors in developing the micro and small industries business. The availability of sufficient and sustainable raw materials will facilitate production. The number of available business units also affects the absorption of labor. Increased employment can reduce unemployment and poverty rates. Information on the potential of raw materials and business units can be used as the basis for consideration of decisions, policies and efforts to increase the distribution of development and industrial estates according to the potential of natural resources. Biplot analysis was used in this study to map the potential raw materials for each district. This analysis describes summary table data in a two-dimensional graph, which is based on singular value decomposition. The results show that the first is the availability of raw materials for food crops which is high in the district of Jagebob, Kurik, Muting, Semangga and Ulilin. Animha and Naunkenjerai districts have high availability of raw materials for fishery commodities. The highest raw material for agricultural commodities is found in the Merauke district. Second is the number of business units for livestock and plant commodities, which is quite high in the Merauke district. The number of business unit for plantation commodities and services is quite high in the Kurik district. The number of service business unit is mostly found in Malind district. The resulting feasibility measure is 0.804 raw materials and 0.765 business units.
The developments research in the cluster analysis using the fuzzy method. The fuzzy method allocates to each group with membership value located at interval [0, 1], showing the magnitude of the possibility of an object being a member into a particular group. Outlier in data very important known before grouping, because affect the final result. Grouping by using the mean value as the center of the group will be more sensitive than using the median value, so this research applies fuzzy c-means and fuzzy c-medoid method to the grouping of villages in Sorong Regency Year 2016 based on the underdevelopment status and examine the goodness of both methods. There are 23.2% of villages that do not change when done grouping with both methods. Overall average distance of group center object and varians in the resulting group the two methods are the same, the varins between groups of fuzzy c-means is greater than the fuzzy c-medoid method.
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