Short-Run Dynamics of Energy Consumption and Socioeconomic Drivers in Trinidad and Tobago: A Novel Approach using VAR Analysis

Sharona Mohammed (), Dillon Ramsook (), Donnie Boodlal () and Rean Maharaj ()
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Sharona Mohammed: Department of Process Engineering, University of Trinidad and Tobago, Point Lisas, Couva, Trinidad and Tobago
Dillon Ramsook: Department of Process Engineering, University of Trinidad and Tobago, Point Lisas, Couva, Trinidad and Tobago
Donnie Boodlal: Department of Process Engineering, University of Trinidad and Tobago, Point Lisas, Couva, Trinidad and Tobago
Rean Maharaj: Department of Process Engineering, University of Trinidad and Tobago, Point Lisas, Couva, Trinidad and Tobago

International Journal of Energy Economics and Policy, 2025, vol. 15, issue 5, 310-326

Abstract: Trinidad and Tobago (T&T), while a minor contributor to global greenhouse gas (GHG) emissions, ranks among the highest per capita emissions due to its fossil-fuel dependent economy and energy-intensive sectors. To support effective climate action under its Nationally Determined Contributions (NDCs), it is critical to understand how socioeconomic factors drive energy consumption and emissions. This study examined the dynamic relationship between key socioeconomic drivers (GDP and population) and energy consumption (electricity, LPG, diesel and gasoline) in T&T's residential and road transportation sectors using annual data from 2000 to 2023. Correlation analysis (Pearson and Spearman) assessed the strength and direction of the relationships, followed by time series techniques including unit root and Johansen cointegration tests, Granger causality and impulse response functions. Results showed strong correlations between electricity consumption for GDP and population, while diesel and gasoline were more closely linked to GDP. LPG showed weak, positive correlations with both drivers. The absence of long-run cointegration justified the use of a Vector Autoregression (VAR). Granger causality tests indicated weak causal relationships, except for electricity and population, where population shocks had a sustained impact on demand. This study presents a novel approach to GHG forecasting and supports the development of data-driven, sector-specific NDCs.

Keywords: Energy Demand Forecasting, VAR Modelling, Correlation Analysis; Socioeconomic Drivers, NDCs, SIDS (search for similar items in EconPapers)
Date: 2025
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DOI: 10.32479/ijeep.20106

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