Most ocean plastic pollution results from leakage from waste management activities on land, mainly in coastline communities. In this research, the digitalization of waste management will be evaluated to improve the prevention of leakage. The digitalization means introducing mobile apps into the waste bank that can improve waste management efficiency while providing reliable data. The data on waste management were gained from Griya Luhu App which has been used in 13 villages around Gianyar, while the waste generation was calculated from 97 samples. Then, the villages were categorized by their potential risk of waste leakage based on their distances from the shore. First, the growth of digital waste banks based on the number of units, the number of customers and the amount of waste-managed was analyzed. Second, the composition of waste collected was evaluated. Last, inorganic waste generation (IWG) from digital waste banks was reduced. The results showed that digital waste banks and the customers had grown rapidly in 1 year. The number of waste bank units grew from 0 to 80 with an increase to a total of 5500 customers during the same period with a maximum of 20 tons of waste managed per month. In general, digital waste banks have shown promising performance in preventing waste leakage into the ocean with a 54.04% reduction of IWG. Compared to this reduction percentage, Tulikup as a high-risk village has a considerably low reduction (30.30%) and should be prioritized. Furthermore, the ability to manage a village with a high population/number of customers should be improved.

This study was conducted to model fire occurrence within El Nino variability and peatland distribution. These climate and geographical factors have a significant impact on forest fires in tropical areas such as Indonesia. The re-analysis dataset from ECMWF was observed with respect to climate characteristics in Indonesian El Nino events. The INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) was utilized to simulate fires over Borneo Island due to its capability to simulate large-scale fires with simplified parameters. There were some adjustments in this INFERNO model, especially for peat fire as peatland has a significant impact on fires. The first was the contribution of climate to the peat fire which is represented by long-term precipitation. The second was the combustion completeness of peat fire occurrence that is mainly affected by human-induced peat drainage. The result of the model shows that El Nino variability mainly affected peat fires but was unable to well simulate the above-ground fire. It increased the burnt area during strong El Nino but overestimated the fires during low/no El Nino season due to lack of peat fire ignition in the calculation. Moreover, as the model did not provide peat drainage simulation, it underestimated the carbon emission. This model has shown promising results by addressing key features in limited input data, but improving some simulations is necessary for regulating weak/no El Nino conditions and carbon combustion of peat fire.

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