Do Political Economy Factors Matter in Explaining the Increase in the Production of Bioenergy?
Eric Nazindigouba Kere
The international political and economic context is characterized by a growing awareness of the need to fight against global warming (due largely to the increase in greenhouse gas emissions, according to the Intergovernmental Panel on Climate Change (IPCC 2014) and to find alternatives to fossil fuels. Indeed, the increase in temperature, the multiplication of natural disasters (storms, droughts, floods, etc.), and the volatility of oil prices are signals that should encourage states to act against global warming.
In such a context, the ‘bioenergy with carbon capture and storage’ (BECCS) technology appears as an alternative to fossil fuels. Indeed, these energies have a very favourable carbon footprint because they are produced from agriculture and forestry biomass.1 They are mainly used to produce heat, biofuels, and electricity. For these reasons, many countries have chosen to respond to environmental and energy challenges through increased production of bioenergy. For example, the United States, with the Renewable Fuel Standard Program (RFS2) and the Energy Independence and Security Act of2007, set the goal of incorporating a minimum of 36 billion gallons of biofuels into the fuel market by 2022 (EPA 2010). According to data from the United States Energy Information Administration (EIA 2015a), illustrated in Figure 9.1, world production of bioenergy (ethanol and biodiesel) has increased dramatically over the last decade. Global bioenergy production increased from 300,000 barrels per day (BPD) in 2000 to 1.9 million BPD in 2012. This increase was primarily
If they do not generate indirect land use changes.
Figure 9.1. Evolution of bioenergy production and number of producers.
Source: Author’s calculations based on data from EIA (2015a).
due to strong growth in ethanol production: 1.47 million BPD in 2012 versus 299,000 BPD in 2000. Biodiesel production increased from 15,000 to 431,000 BPD between 2000 and 2012. At the same time, the number of bioenergy- producing countries quadrupled between 2000 and 2011, followed by a slight decline in 2012. Despite this progress, the IPCC (2014: 82) recommends a fourfold increase in investments in BECCS in order to limit global warming to 2°C. Therefore, a better understanding of the determinants of bioenergy production is essential to promote the transition to clean energy.
To the best of our knowledge, only Gan and Smith (2011) have empirically analysed the macroeconomic determinants ofbioenergy. In the OECD (Organization for Economic Cooperation and Development) countries, they showed that gross national product along with bioenergy market deployment policies have significant and positive impacts on the per capita supply of renewable energy and bioenergy. Surprisingly, the impact of political economy factors was ignored in their study, even though several papers have shown that the quality of political and economic governance influences economic activity (including bioenergy production) through its impact on investment and entrepreneurship (Acemoglu, Johnson, and Robinson 2001; Baum and Lake 2003; Christopoulos and Tsionas 2004; La Porta et al. 2008; Nelson and Singh 1998).
We classify political economy factors into two categories: governance quality and macroeconomic policies. Quality of governance reflects the quality of political and economic institutions, and can be captured by the following indicators: environmental policy stringency (EPS) instruments, bureaucracy quality, corruption, investment profile, democratic accountability, government stability, and law and order. Furthermore, according to La Porta, de Silanes, and Shleifer, ‘the historical origin of a country’s laws is highly correlated with a broad range of its legal rules and regulations, as well as with economic outcomes’ (2008: 285), and thus, indirectly, with the economic and political conditions in which bioenergy is produced. High-quality governance helps to create institutional and political dynamics, a transparent and predictable framework that encourages economic actors to invest in growth sectors of the future, including bioenergy. Macroeconomic policies (financial development, interest rates, trade openness, and oil scarcity) may also play an important role in increasing the production of bioenergy. Indeed, financial development facilitates the financing of future projects at low cost. It results in low interest rates and high credit volume. Open trade facilitates access to technology, increases competition, and provides new market opportunities for bioenergy, whereas the increasing scarcity of physical oil (high prices and low reserves) stimulates the transition to clean energy (Grafton etal. 2012; Heun and de Wit 2012). Finally, the amount of renewable energy produced is an important determinant of biofuels production. Energy transition can be achieved through specialization in the production of bioenergy and/or renewable energy based on the comparative advantages of each country.
This study tries to fill the gap in the literature on the macroeconomic drivers of bioenergy by examining the impact of political economy factors, oil production, renewable energies, and macroeconomic factors. First, we present a simple theoretical model in which oil, renewable energy, and bioenergy are produced simultaneously. This formalization allows us to highlight the theoretical connections between oil production, supply of bioenergy, and political economy factors. We show that the supply of bioenergy depends positively on governance quality, financial development, land yields, and market conditions (price, income). On the negative side, oil reserves and renewable energies tend to reduce bioenergy supply. Second, we empirically highlight the determinants of the supply of bioenergy using an unbalanced panel dataset of 112 countries for the period between 2000 and 2012. Motivated by the fact that ‘zeros’ represent a large fraction of the bioenergy data (40 per cent), we use a fixed effects Tobit model (Honore 1992) to address the censoring problem, as well as unobservable heterogeneity specific to each country. Third, we make separate estimates of the supply function of bioenergy for all the countries in our sample of developed and developing countries. This allows us to analyse and compare the effects of political economy factors on bioenergy production, depending on the level of development. Finally, given that most countries do not produce bioenergy, we analyse the determinants leading to the decision of whether or not to produce bioenergy by using a random effects probit model. We show that political economy factors (governance quality and macroeconomic policies) are central in deciding whether or not to produce bioenergy, but their impact on production size is limited. Indeed, when the decision to produce is made, market size and production conditions have a greater influence on the amount of bioenergy. Using a long-term analysis, we find that the countries whose laws are of Germanic, Scandinavian, and French origin produce relatively more bioenergy, whereas those whose laws have a Socialist origin produce less than other countries.
The remainder of the chapter is organized as follows. Section 9.2 outlines the theoretical model, the empirical strategy, and data. Section 9.3 discusses the results and their implications. Section 9.4 concludes by indicating how political economy factors can facilitate the transition to clean energy.
-  Countries (Barbados, Trinidad and Tobago, Kazakhstan, Vietnam, Jamaica, Honduras,Nicaragua, El Salvador, Singapore, Hong Kong, Pakistan, Serbia, Costa Rica, Cambodia,Switzerland, Bosnia and Herzegovina, and Norway) that have stopped production of bioenergybetween 2011 and 2012 account for only 0.4 per cent of production; hence the small effect onglobal production. This phenomenon can be explained by the increase in agricultural pricesin 2012.
-  There are several countries that produce bioenergy throughout the period and others that donot produce anything. Using a discrete choice model with fixed effects removes these countriesfrom the analysis. This is why we choose a random effects model.