Low Emissions Energy Development for Global Climate Change Mitigation

This paper study on the potential of low-emission energy that can be developed in Indonesia, which comes from natural resources. This study is considered important as the attention of Indonesia to the increase in carbon emissions from energy and forestry sectors, as well as efforts to mitigate global climate change. Study of low-emission energy potential in this research are to: wind energy, energy mini / micro hydro, solar energy, geothermal energy, and biodiesel energy. Development of low emission energy has not reached the optimal point in the development of energy except the mini / micro hydro, and biodiesel. Development of low-emission energy in the future is expected to reach the target of national energy mix in 2025 through special policy and technology development

Impact of international emission reduction on energy and forestry sector of Indonesia

We extended the MERGE model to develop a set of energy projections for a reference and various mitigation scenarios to the year 2100. We included coal as a tradable good. In Indonesia, oil imports will increase while coal exports will decrease. If the OECD countries reduce their emissions, oil price would fall, Indonesia would import more oil but less gas and its per capita income would fall slightly. With international trade in emission permits, Indonesian energy development is similar to the earlier scenario, but Indonesia would gain some income. If all countries reduce their emissions, Indonesia would export more coal and would substitute coal by gas and carbon free technologies in energy consumption. If Indonesian commits to emissions reduction, per capita income would slightly fall. Population and economic growth are the driving forces of deforestation. In the reference scenario, deforestation increase by 60% in 2020 relative to today, indicating that Indonesia has large potential to mitigate emissions in the forestry sector. International climate policy would slightly increase the deforestation rate, mainly because of more rapid economic growth. Indonesia would gain from the sale of emission permits from reduced deforestation.Emission reduction; deforestation; Indonesia

PROYEKSI AWAL MUSIM DI JAWA BERBASIS HASIL DOWNSCALING CONFORMAL CUBIC ATMOSPHERIC MODEL (CCAM) (SEASON ONSET PROJECTION IN JAVA BASED ON CCAM DOWNSCALING OUTPUT)

Penelitian mengenai awal musim dengan kriteria Badan Meteorologi, Klimatologi dan Geofisika (BMKG) yang menggunakan curah hujan dasarian di Indonesia telah banyak dilakukan, namun data yang digunakan masih memiliki keterbatasan dalam periode analisis, resolusi spasial yang masih rendah dan masih belum dapat menghasilkan proyeksi ke depan. Penggunaan model iklim adalah jawaban untuk mengatasi semua keterbatasan tersebut. Penelitian ini menggunakan Conformal Cubic Atmospheric Model (CCAM) untuk downscaling dari data model iklim global dan reanalisis National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR). Periode waktu yang digunakan adalah periode 1991 2010 (20 tahun) sebagai periode baseline (kondisi saat ini) dan periode 2011 2030 sebagai periode proyeksi ke depan. Penentuan awal musim pada penelitian ini menggunakan kriteria curah hujan dasarian dari Badan Meteorologi, Klimatologi dan Geofisika (BMKG). Hasil dari penelitian ini menunjukkan bahwa proyeksi dengan menggunakan skenario A2 IPCC menyimpulkan bahwa Awal Musim Kemarau (AMK) di sebagian besar daerah Pulau Jawa datang lebih cepat, sedangkan Awal Musim Hujan (AMH) cenderung mundur atau datang lebih lambat dari baseline. Dengan kata lain, Pulau Jawa diproyeksikan mengalami musim kemarau yang lebih panjang dan musim hujan yang lebih pendek dibandingkan kondisi saat ini.Kata kunci: CCAM, Downscaling, AMK, AM

Implementation of Information System on the Planting Time Prediction Based on Climate Modelling

Changes in rainfall patterns due to climate change have resulted in losses of farmers in Indramayu. Farmers has loss of economic due to failing of planting and harvesting every year in this location. Therefore, farmers need information on future rainfall prediction to plant rice accurately and on time. The information system should be able to be easily accessed by farmers and extension. This paper discuss research result on rainfall prediction system and planting rice with high resolution in dasarian (10-days) scale. The information system includes predictive technology using Geographic Information System (GIS) with input from Smart Climate Modelling using stochastic approach. Information delivery system was developed in line with the predictions of rainfall information and feedback planting season comes to evaluate the predicted results. Such information will be dynamically run through overlaying with Google maps on the web server. This system can be accessed by the public and was designed to automatically generate maps of rainfall and planting prediction that can be viewed directly in the Google maps application. This research result will be effort to adapt to climate change that has impacted to agriculture sector in Indonesia

KONSEP FORECAST-BASED-FINANCING UNTUK PERTANIAN PRESISI DI INDONESIA

Sebagai negara berkembang, aktivitivas ekonomi Indonesia hampir sepenuhnya bergantung pada aktivitas agrikultur. Meskipun sebagai produser makanan terbesar di Asia Tenggara, Pemerintah Indonesia didominasi oleh kekhawatiran mengenai ketahanan pangan dalam negeri. Sehingga, dalam upaya menghadapi kekhawatiran tersebut, Pemerintah Indonesia mengupayakan perbaikan dan peningkatan fasilitas di sektor pertanian. Namun, tujuan peningkatan ini menghadapi banyak kendala yang muncul seperti dampak dan risiko ketidakpastian cuaca ekstrim atau peristiwa iklim. Salah satu cara alternatif untuk memitigasi risiko adalah dengan pendekatan yang dikenal sebagai Climate Smart Agriculture (CSA). Konsep CSA juga membantu petani dengan melalui pembiayaan berbasis prediksi, penggabungan asuransi berbasis indeks dan prediksi indeks, dalam konteks spesifik dari El Nino Southern Oscillation (ENSO) terkait peristiwa cuaca. Penelitian ini akan memperkenalkan konsep pembiayaan perkiraan yang menggabungkan asuransi berbasis indeks dan prediksi indeks untuk perlindungan kegiatan pertanian Indonesia. Secara umum, asuransi berbasis indeks menyajikan alternatif unik untuk produk asuransi tradisional, terutama di Indonesia, di mana kapasitas administrasi rendah, kredit terbatas, dan bantuan untuk bencana masa lalu telah tertunda. Asuransi berbasis indeks dapat memainkan peran besar dalam meningkatkan ketahanan masyarakat

Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest)

This paper was aimed to analyse the effect of temporal decorrelation in carbon stocks estimation. Estimation of carbon stocks plays important roles particularly to understand the global carbon cycle in the atmosphere regarding with climate change mitigation effort. PolInSAR technique combines the advantages of Polarimetric Synthetic Aperture Radar (PolSAR) and Interferometry Synthetic Aperture Radar (InSAR) technique, which is evidenced to have significant contribution in radar mapping technology in the last few years. In carbon stocks estimation, PolInSAR provides information about vertical vegetation structure to estimate carbon stocks in the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric with 46 days temporal baseline were used in this research. The study was carried out in Southeast Sulawesi tropical forest. The research method was by comparing three interferometric phase coherence images affected by temporal decorrelation and their impacts on Random Volume over Ground (RvoG) model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease from R2=0.7525 (standard deviation of tree heights is 2.75 meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772 (standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence image to produce a good accuracy of carbon stocks.

Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest)

This paper was aimed to analyse the effect of temporal decorrelation in carbon stocks estimation. Estimation of carbon stocks plays important roles particularly to understand the global carbon cycle in the atmosphere regarding with climate change mitigation effort. PolInSAR technique combines the advantages of Polarimetric Synthetic Aperture Radar (PolSAR) and Interferometry Synthetic Aperture Radar (InSAR) technique, which is evidenced to have significant contribution in radar mapping technology in the last few years. In carbon stocks estimation, PolInSAR provides information about vertical vegetation structure to estimate carbon stocks in the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric with 46 days temporal baseline were used in this research. The study was carried out in Southeast Sulawesi tropical forest. The research method was by comparing three interferometric phase coherence images affected by temporal decorrelation and their impacts on Random Volume over Ground (RvoG) model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease from R2=0.7525 (standard deviation of tree heights is 2.75 meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772 (standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence image to produce a good accuracy of carbon stocks

Kerangka Konseptual Pengembangan Sistem Informasi Cerdas Agribisnis (SICA) di Indonesia Berbasis Prediksi Iklim

Sebagai negara agraris, Indonesia memiliki potensi besar di pasar pertanian. Di sisi lain, pengaruh iklim terhadap pola pertanian di Indonesia sangat signifikan. Dengan dukungan sistem pendukung keputusan dalam kalender tanam berbasis prediksi iklim, petani dapat menghasilkan panen dengan baik karena mempertimbangkan pola iklim dalam strategi tanamnya. Tetapi di sisi lain, dengan petani mengetahui waktu terbaik untuk menanam tanaman, maka permintaan benih, pestisida, air, dan pasokan pupuk menjadi sangat tinggi dan tidak bisa dipenuhi sepenuhnya karena pasar tidak punya waktu dalam mempersiapkan semua kebutuhan petani tersebut. Penelitian ini bertujuan untuk mengembangkan kerangka kerja konseptual untuk memenuhi kebutuhan pasar untuk mengetahui permintaan petani pada waktu tertentu untuk mempersiapkan pasokan di wilayah tertentu dengan sudah dikembangkannya Sistem Informasi Cerdas Agribisnis (SICA) dalam platform website dan android. Sistem dirancang untuk mengintegrasikan kalender penanaman tanaman berbasis prediksi iklim dengan penawaran dan permintaan kebutuhan petani dalam aktivitas tanam. Selanjutnya, dengan menggunakan sistem ini, petani dapat mengetahui harga dan permintaan terbaik dari pasar untuk produksi tanaman mereka