Quantifying the Effects of Renewable Energy Advancement: A Statistical Analysis Method

• Main Authors: Chakim, Mochamad Heru Riza, Aini, Qurotul, Lutfiani, Ninda, Ramadhona, Nova, Ariyanto, Ferry, Hardini, Marviola
• Format: Conference Proceeding
• Language: English
• Subjects: Banking; Correlation; Costs; Economic indicators; Energy Mix; Government; Monetary Policy; Renewable Energy; Renewable energy sources; Reviews
• ISSN: 2770-159X
• DOI: 10.1109/CITSM60085.2023.10455729

This research aims to analyze the diverse impacts of renewable energy progress using a statistical analysis method. The study investigates correlations between variables, including current account balance, interest rates, money flows, electricity, industry application, and international energy trade balance. Employing multiple regression and principal component analysis (PCA), the research is structured in two phases. Initially, multiple regression is utilized to examine the influence of monetary policy on renewable energy growth, employing nine macroeconomic variables and four energy indicators from the World Bank database (2017). Each economic indicator is subjected to regression analysis against energy variables. Subsequently, principal component analysis is performed on the 13 variables, revealing a strong correlation between renewable energy share in government spending and growth (GSG). Moreover, a notable correlation is observed between government spending, foreign exchange reserves, and industrial application, contributing to significant cost savings. This research highlights limitations such as limited access to renewable energy sources and data gaps, which could be addressed through enhanced datasets from the World Bank and the International Renewable Energy Agency.