14^th World Bioenergy Congress and Expo

The contemporary increase in worldwide population drives the generation of energy from conventional and unconventional sources. Energy generated from exhaustible resources endanger the environment and imperils economic development. However, production of energy from naturally replenished resources add-in to economic development and helps address issues of global warming and further grants energy security. This study seeks to investigate the determinants of biomass energy production for International Energy Administration (IEA)-30 countries for the period covering 2000-2015. In our analysis, Gross Domestic Product (GDP) per capita is used as a proxy for economic growth and energy imports is deemed as the main controlling factor.  Our panel fully modified and dynamic ordinary least squares regression shows a significant positive influence of total biomass energy production on economic growth. Thus, a percentage increase in primary biomass energy production increase GDP per capita by 0.04%-0.05%. For our panel vector error correction model based causality nexus, we notice that in both the short and long-run, there exist unidirectional causality running from economic growth and energy imports to total biomass energy production which supports the conservation hypothesis. The findings from the study indicates that economic growth and energy imports significantly influences total biomass energy production. This study guides policymakers in formulating a conclusive biomass energy and trade policies for sustainable economic growth. Synopsis of our Econometric Model Formulation: Our primary focus is to investigate the nexus between biomass energy production, energy imports and economic growth with a panel data fixed-effects regression model specified as follows: 0 1 2 it it it it Z Y C α β β ε = + + + (1) Where  denotes the dependent variable gross domestic product per capita (GDPC) ;  represents total biomass energy produced;  represents each IEA member country-level control variables;  is the intercept or constant and  and are the parameters; is the stochastic error term;  is the subscript of each IEA member states where , and  is the subscript of each IEA member state time dimensions where . More specifically, we explore the relationship between biomass energy production (TBEP), energy imports (EI) and GDPC by employing Granger causality test based on panel vector error correction model (PVECM). For our stationarity analysis, we first employ Im, Pesaran and Shin (IPS) test developed by Im et al. which allows for heterogeneous autoregressive coefficients. We formulate our mathematical model as: 1it i it i it it z vz Y δε − = + + (2) Where itY represents our predictor variables comprising individual time trend; autoregressive coefficients is represented by iv ; and it ε represent the stationary stochastic error terms. As the IPS ensures various orders of serial correlation by averaging the augmented Dickey-Fuller (ADF) unit root test, we formulate our stochastic stationary error term as:   

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iv it ix it x it x ε φ ε µ − = = + ∑

(3) Therefore, by substituting (3) into (2), our mathematical formulation becomes: 1

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iv it i it ix it x i it it x z vz Y φ ε δ µ −− = = + + + ∑ (4) Where the number of lags in ADF regression is represented by. We propose our null hypothesis to be a case where there exists a unit root in each series of our panel data sets whereas alternative hypothesis supposes that at least one individual series in the panel data is stationary. Besides Phillips-Perron (PP), Augmented Dickey-Fuller and Levin, Lin & Chu (LLC) stationarity test are executed.