Open AccessPreliminary study of building a low-carbon emission concept for Bali with nocturnal light analysis
Author(s) -
I Ketut Swardika,
Putri Alit Widyastuti Santiary,
I Wayan Suasnawa
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1450/1/012038
Subject(s) - tourism , climate change , global warming , electricity , satellite , histogram , nocturnal , environmental science , environmental economics , energy (signal processing) , metre , greenhouse gas , meteorology , computer science , environmental resource management , natural resource economics , business , geography , engineering , economics , statistics , mathematics , ecology , artificial intelligence , physics , archaeology , aerospace engineering , astronomy , electrical engineering , image (mathematics) , biology
Energy crisis and increase energy consume initiate depletion of natural resources and environmental degradation and that will leads to global warming and climate change. Nowadays, tourism considered being one of the important industries in the world. It also acknowledged as significant largest consumers of energy through many sectors including supporting facilities for tourists that focused on this paper. Bali’s most important tourist destination and become proponent of economic has many resorts surrounded by business trade support. Increasing electricity demand becomes present issues. This paper proposes a method to build community-based initiatives for reducing carbon emissions and saving energy. The method consists of procedural to build light threshold regulation. This research uses light-meter survey, a night-time satellite dataset, and other supporting data. The light threshold uses night-time satellite dataset. Classes of light thresholds are defined from histogram analysis. Results show a relationship of lux light-meter survey mean with night-time satellite dataset mean. From results created maps of class regions that show approximate of level energy used.