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"Reducing Emissions from Deforestation and Forest Degradation (REDD+) has become important for Indonesia because this mechanism will provide financial benefits and it adheres to the Indonesian commitment to participate in climate change mitigation. However, a weak forest governance system and lack of transparency have undermined Indonesia’s goals to reduce deforestation and to manage and distribute the compensation appropriately. The idea of transformational change to reform Indonesian forest governance might be hindered by path dependency that has become entrenched within the Indonesian government. This paper, therefore, attempts to analyze path dependency of Indonesian forest governance and to examine the implication of path dependency in the development of REDD+ in Indonesia. Using the political economy lens, the diagnosis of path dependency is determined if there are positive feedbacks for the Ministry of Forestry, as the leading agency in the administration of Indonesian forests, to maintain the status quo. This paper shows that there are four positive or reinforcing feedbacks for the Ministry of Forestry: (1) vested interests for utilizing the forests as an income source, (2) network effects from managing the forest resources, (3) sunk costs invested to strengthen the institution, and (4) inclusiveness of the institution in managing the forests. This paper also highlights that path dependency within the Ministry of Forestry causes a complexity in the REDD+ debate in Indonesia, particularly regarding which institutional arrangement will best implement REDD+. On the other hand, this paper shows that various policies and activities related to REDD+ could break path dependency."

"Deforestasi di Kalimantan Timur perlu dikendalikan melalui pemodelan perubahan tutupan lahan. Penelitian ini bertujuan untuk menganalisis klasifikasi dan validasi peta penutup lahan multi temporal, menganalisis investigasi model optimal, dan mensintesis prediksi tutupan lahan tahun 2036 serta analisis pola spasial perubahan tutupan lahan 2016-2036. Pemodelan perubahan tutupan lahan menggunakan pendekatan Multilayer Perceptron melalui pemilihan periode dan interval kalibrasi optimal untuk menghasilkan model yang akurat dan objektif. Empat belas skenario interval kalibrasi disusun menggunakan 11 peta penutup lahan yang diklasifikasi dari Citra Landsat Time Series menggunakan metode Random Forest pada lingkungan komputasi awan Google Earth Engine. Simulasi pemodelan menggunakan modul Land Change Modeler dengan mempertimbangkan 14 variabel pendorong. Hasil klasifikasi menunjukkan akurasi yang baik dengan Overall Accuracy 71,43-85,14% dan nilai Kappa 0,667-0,827. Periode dan interval kalibrasi optimal untuk memprediksi tutupan lahan tahun 2036 adalah periode 2016-2021 dengan interval 5 tahun dan dengan lama prediksi tiga kali periode kalibrasi. Perubahan tutupan lahan berupa fenomena deforestasi dan reforestasi ditemukan di kawasan pertambangan, perkebunan, dan hutan produksi. Hasil analisis spasial menemukan adanya penurunan luas tutupan hutan dari tahun 2016 hingga 2021 dengan laju deforestasi 651 km2/tahun. Diperkirakan luas tutupan hutan tahun 2036 masih tersisa 69.203 km2. Sebagian besar perubahan tutupan lahan terjadi pada kemiringan tanah kurang dari 4 derajat pada ketinggian di bawah 100m. Topografi merupakan variabel yang paling berpengaruh dalam mendorong perubahan tutupan lahan di Kalimantan Timur.

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Deforestation in East Kalimantan needs to be controlled through land cover change modeling. This study aims to analyze the classification and validation of multi-temporal land cover maps, analyze optimal model investigations, synthesize land cover predictions in 2036, and analyze spatial patterns of land cover changes in 2016-2036. Land cover change modeling uses the Multilayer Perceptron approach by selecting the suitable calibration period and intervals to produce an accurate and objective model. Fourteen calibration interval scenarios were prepared using 11 land cover maps classified from Landsat Time Series images using the Random Forest method in the Google Earth Engine cloud computing environment. The modeling simulation uses the Land Change Modeler module by considering 14 driving variables. The classification results show good accuracy, with an Overall Accuracy of 71.43-85.14% and a Kappa value of 0.667-0.827. The optimal calibration period and interval to predict land cover in 2036 is the 2016-2021 period with 5-year intervals and three times the calibration period. Changes in land cover in the form of deforestation and reforestation are found in mining areas, plantations, and production forests. The spatial analysis results found a decrease in forest cover area from 2016 to 2021 with a deforestation rate of 651 km2/year. It is estimated that in 2036 there will still be 69,203 km2 of forest cover remaining. Most land cover changes occur at less than 4 degrees land slopes at elevations below 100m. Topography is the most influential variable driving land cover change in East Kalimantan"