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This paper aims to evaluate the location of formal waste reduction facilities in comparison to the distribution of recyclable plastic waste generation in Denpasar City, Bali Province, Indonesia. The distribution of recyclable plastic waste generation was carried out by conducting primary sampling from 200 houses, following the guidelines of SNI-19-3964-1994. Socioeconomic variables, including house size, population density, Gross Domestic Product (GDP), and area classification, were obtained through interviews and the use of remote sensing data products. The distribution of recyclable plastic waste is modeled using the best of six machine learning models: LGBM (Light Gradient Boosting Machine), Linear Regression, Random Forest, and SVM (Support Vector Machine), XGBoost, and Adaboost. The LGBM model was selected with an R2 of 0.939 in the training dataset, an R2 of 0.954 in the testing dataset, and the lowest RMSE and MAE. The map of recyclable plastic waste generation distribution is created through a spatial analysis that consists of three classes with ranges of <248.5, >248.5 and <732.5, and >732.5 grams/household/week. The effectiveness of the coverage area and capacity through spatial analysis indicates that the waste reduction facilities in Denpasar City are 32% and 46%, respectively.

As a significant contributor of plastic waste to the marine environment, Indonesia is striving to construct a national strategy for reducing plastic debris. Hence, the primary aim of this study is to create a model for plastic waste quantity originating from the mainland, accumulated in estuaries. This was achieved by compiling baseline data of marine plastic disposal from the mainland via comprehensive contextualisation of data generated by remote sensing technology and spatial analysis. The parameters used in this study cover plastic waste generation, land cover, population distribution, and human activity identification. These parameters were then used to generate the plastic waste disposal index; that is, the distribution of waste from the mainland, flowing through the river, and ultimately accumulating in the estuary. The plastic waste distribution is calculated based on the weighting method and overlap analysis between land and coastal areas. The results indicate that 0.6% of Indonesia, including metropolitan cities, account for the highest generation of plastic waste. Indicating of plastic releases to the ocean applied by of developing three different scenarios with the highest estimation 11.94 tonnes on a daily basis in an urban area, intended as the baseline study for setting priority zone for plastic waste management.

Leachate generated from Municipal Solid Waste (MSW) landfill may cause various negative impacts, therefore it should be treated properly in a Leachate Treatment Plant (LTP). The paper aims to select a more sustainable LTP system in Indonesia by applying Analytic Network Process (ANP) as multicriteria analysis approach. 5 alternative LTP systems were analyzed using 5 criteria namely economic, environmental, social, technical, and institutional capacity with a total of 25 sub-criteria. Total 36 respondents from municipality staffs, academics, professionals, and LTP operators were interviewed by adopting pair wise comparison. The results from Driving Dependence Power Analysis (DDPA) showed that sub-criteria for operating costs (E2), maintenance costs (E3), processing effectiveness (T1) and monitoring needs (T3) have the highest dependence power value. Sub-criteria of the operational cost (E2) and the availability of regulations (K2) have the highest driving power value. Main considerations for selecting LTP system at a final disposal site by respondents were economic criteria (weight 0.3219) and water protection sub-criteria (L2, weight 0.17). Top priority of the technology chosen by respondents were Alternative#1 consist of Anaerobic, Facultative and Maturation ponds and Biofilter.

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

A study of waste material flow was carried out in Bali Province to define, evaluate, and analyse the existing condition of waste management as a reference for improving solid waste services. In this paper, waste quantity in every chain of waste management was measured. Waste sampling, observation, and interviews were also conducted to build the material flow model. Around 4.2 million residents and 4.9 million tourists yearly generate waste of up to 822,555 tonnes/year, which is dominated by organic waste for around 65% ww (wet weight) and plastic waste for around 15.67% ww. Around 200,718 tonnes/year (24.40% ww) of waste is transported directly to the landfills and 235,418 tonnes/year (28.62% ww) is collected first at the transfer depos, before being transported to the landfill. As much as 39,566 tonnes/year (4.81% ww) of waste enters the 3R-Transfer Depo for recycling and around 47,030 tonnes/year (5.72% ww) enter the waste bank. Furthermore, due to the lack of integrated waste management, around 283,369 tonnes/year (34.45% ww) waste is illegally dumped into the environment. In the end, up to 444,679 tonnes/year (54.06% ww) of waste is processed in ten landfills which serve Bali Province.

Bali Province is an island of high tourist activity surrounded by the sea. Without a proper mainland waste management, waste problem will impact the aquatic environment. River and beach are waterways that become an important role in the waste flow that will end to the sea. Identification of waste in rivers and beaches is needed to determine the stream waste management policy. Measurements were made by adopting the NOAA (National Oceanic and Atmospheric Administration) method on 4 rivers and 4 beaches. Measurement of waste generation and composition represents the watershed and population density classification groups. The peak hour for the waste streams at 11.00-14.00 WITA (GMT+8). The dominant waste is organic waste (59.10% WW) and hazardous waste (17.12%WW). From the waste measurement on the beach, the waste density is around 0.087 tonnes/m3 and the composition of waste is dominated by plastic waste, paper waste, and textile waste. The average waste density in beach is around 0.007 kg/m2 or 0.53 items/m2. Yeh Gangga Beach is the dirtiest with 0.64 items waste/m2 and Lepang Beach is the cleanest beach with 0.27 items/m2.

Bali Province is an island of high tourist activity surrounded by the sea. Without a proper mainland waste management, waste problem will impact the aquatic environment. River and beach are waterways that become an important role in the waste flow that will end to the sea. Identification of waste in rivers and beaches is needed to determine the stream waste management policy. Measurements were made by adopting the NOAA (National Oceanic and Atmospheric Administration) method on 4 rivers and 4 beaches. Measurement of waste generation and composition represents the watershed and population density classification groups. The peak hour for the waste streams at 11.00-14.00 WITA (GMT+8). The dominant waste is organic waste (59.10% WW) and hazardous waste (17.12%WW). From the waste measurement on the beach, the waste density is around 0.087 tonnes/m3 and the composition of waste is dominated by plastic waste, paper waste, and textile waste. The average waste density in beach is around 0.007 kg/m2 or 0.53 items/m2. Yeh Gangga Beach is the dirtiest with 0.64 items waste/m2 and Lepang Beach is the cleanest beach with 0.27 items/m2.

The aim of this paper is to identify patterns of waste management in the Bali archipelago tourism area. The Nusa Penida District is a new tourism destination located in the Southeast of Bali. In 2018, there were average 391,071 tourists/day coming and 45,520 local residents live in this area. The total amount of waste produced in Nusa Penida District is 15.90 tonnes/day or 173.61 m3/day. High tourist activities have not been handled by a good waste management. Questionnaires were distributed randomly to the public and tourists to find out the pattern of waste management. Observation also conducted to build the material flow analysis as a waste information baseline. Around 48.21% organic waste used as livestock feed and 8.45% dumped carelessly to the environment. Around 32.51% anorganic waste be burnt and 45.68% waste dumped carelessly. Moreover, Nusa Penida District facing offering waste management problem caused by their cultural activities. In total, around 8.82 tonnes/day waste is dumped in landfills and total unmanaged waste around 6.73 tonnes/day.