Tasnia, Rifah (2025) FINANCING ENERGY INFRASTRUCTURE FOR THE AI ECONOMY: LNG EXPORTS, GRID EXPANSION, AND DATA CENTER POWER DEMAND. Journal of Economic and Economic Policy, 2 (3). ISSN 3047-4892

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Abstract

Objective: The accelerated adoption of artificial intelligence (AI) technology has resulted in greater energy consumption by data centers, thereby posing new financial problems regarding the construction of reliable and sustainable energy infrastructure. Method: This is a quantitative study employing an online questionnaire survey among 155 individuals based in the United States belonging to the fields of energy, AI, finance, government, and academia. Statistical analysis including descriptive statistics, Pearson’s correlation, and multiple linear regression was performed on the collected data using IBM SPSS Statistics 27. Results: The variable of Energy Infrastructure Financing received the highest mean score (4.31), while Data Center Power Demand scored second (4.24). It was found that Grid Expansion was rated as the top priority for investments by 87.1% of the survey participants. Correlation analysis revealed the highest correlation between Energy Infrastructure Financing and Data Center Power Demand (r = 0.701). The results of regression analysis proved that Data Center Power Demand (β = 0.325), AI Economy Growth (β = 0.281), Grid Expansion (β = 0.268), LNG Export Investment (β = 0.214), and Renewable Energy Integration (β = 0.172) were significant factors influencing the process of financing. The high explanatory power of the regression model was confirmed by R² = 0.716. Novelty: It is necessary to use combined strategies of investment that will include modernization of the grid infrastructure, diversification of energy sources, and using renewable energy in order to cope with the electricity needs due to artificial intelligence.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, Energy infrastructure financing, Data center power demand, Grid expansion, Renewable energy integration
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	25 Aug 2026 02:47
Last Modified:	25 Aug 2026 02:47
URI:	http://eprints.umsida.ac.id/id/eprint/17002

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