Two Birds with One Stone: A Combined Environmental and Economic Performance Assessment of Rapeseed-Based Biodiesel Production

by Xueqing Yang,Yang Liu, Alberto Bezama, Daniela Thrän (GCB-Bioenergy) Rapeseed is the dominant feedstock for biodiesel production in Germany; however, significant decline in crop yields observed during the 2018 drought in Europe poses economic and environmental risks for its sustained use as a fuel crop. Many Life Cycle Assessment (LCA) studies were conducted to quantify the potential environmental impacts of biodiesel production; however, only a few studies have considered the spatial and temporal heterogeneities of the studied regions. Furthermore, previous studies have usually only focused on the greenhouse gas (GHG) savings of biodiesel and have ignored the environmental burden and economic profits of biodiesel production. For the first time, we combined the Regional Environmental LCA model with an economic analysis to evaluate both the environmental impact and the economic benefits of biodiesel production in Central Germany (CG). Our results showed that emissions from rapeseed cultivation were the largest contributor to both global and regional environmental impact categories. In our study region, we found that GHG emissions were around 56%–71% lower for rapeseed-based biodiesel than for fossil fuels. Due to the drought in 2018, we also observed that the regional rapeseed supply could not meet the demand of biodiesel production in CG. An economic analysis of biodiesel production found significant economies of scale effect in the biodiesel industry. In addition, none of the studied biodiesel plants were able to operate at their designed installed capacities without causing indirect land-use change. Furthermore, the profitability of biodiesel production was closely related to the feedstock cultivation cost. Based on these findings, we concluded that a regionalized LCA model would be able to more accurately evaluate the environmental influence of biodiesel production by taking site-specific conditions into consideration. We also suggest that potential biodiesel plant operators take the regional biodiesel production density and feedstock cultivation conditions into account when deciding on plant size.

1 INTRODUCTION

The Paris Agreement has been ground breaking in its efforts to obtain global involvement in combating climate change. Its aim is to limit global warming to <2°C above the pre-industrial average (UNFCCC, 2015). The European Green Deal provides an action plan for implementing climate actions for a sustainable, resource-efficient and environmental-friendly economy in Europe (EU Commission, 2019). To achieve these goals, the European Union (EU) commission put forth its proposal in April 2020 to enshrine in legislation the EU’s political commitment to be climate neutral by 2050 (EU Commission, 2020). One measure that has already been put into practice is sustainably certified biogenic raw materials and biofuels. For instance, approximately 9.5 million tonnes of CO₂ eq. were avoided in 2018 through the use of biofuels (BLE, 2019).

Biodiesel is a critical type of biofuel that has many advantages over petroleum diesel, such as lower pollution rates, lower greenhouse gas (GHG) emissions and a higher degradability (Firoz, 2017). The feedstocks for biodiesel production are generally oilseed crops, animal fats and microalgae. Of the different oilseed crops, soybeans are the primary feedstock for biofuel production in the United States. In Asia, China's most common feedstocks are used and imported vegetable oils and jatropha, and in Malaysia, Indonesia and Thailand, palm oil is the primary raw material for biodiesel production (Koçar & Civaş, 2013). In the EU, the primary crops are rapeseed, sunflower and soybeans. Among the EU’s 27 member countries, France, Germany and Poland were the top three rapeseed producers in 2018, producing 4.9, 3.6 and 2.2 million tonnes, respectively (FAOSTAT, 2018). In Germany, biodiesel is the most produced biofuel (fatty acid methyl esters, FAME), comprising approximately 64% of the total output in 2017, followed by bioethanol at 34% (FNR, 2019). Biofuels made up approximately 5% of the total fuel consumption in the transport sector in 2018, 3.6% of this being biodiesel (FNR, 2020).

However, the production of biofuel is under debate as a result of the potential environmental burdens caused by the cultivation of bioenergy crops. These include land competition with food crops (Muscat et al., 2020; Valentine et al., 2011), nutrient pollution in groundwater (Diaz-Chavez et al., 2011; Nyakatawa et al., 2006; Wu et al., 2018), potential biodiversity loss (Di Fulvio et al., 2019; Immerzeel et al., 2014; Meehan et al., 2010) and decline in soil quality (Wu et al., 2018). The biggest argument against the cultivation of bioenergy crops is direct and indirect land-use change (dLUC and iLUC), which pose both an environmental problem and ethical problem (e.g. crop scarcity, malnutrition). In contrast to dLUC, there is little consensus on the approaches used to evaluate iLUC, making it difficult to quantify. Nevertheless, many studies still report that the emissions from iLUC could offset any GHG savings from biofuels (Lapola et al., 2010). In addition, the Renewable Energy Directive (RED) 2009/28/EC (EU Commission, 2009), as the most crucial policy for promoting biofuel production, is also being criticized for using standard values (default values) to measure GHG savings as part of its sustainability criteria. For instance, when comparing two widely used GHG accounting tools, scholars pointed out the need to include deep harmonization in the calculation processes under the current methodological framework of the RED (Hennecke et al., 2013). Additionally, many researchers believe that Germany's rapeseed biodiesel might fail to reach the 35% GHG reduction goal when weather conditions are unfavourable and actual N₂O field emission values are taken into account (Pehnelt & Vietze, 2012, 2013). Therefore, in 2015, the RED was redrafted to take iLUC effects into consideration and the EU Parliament introduced a cap (ILUC Directive) of 7% on the amount of biofuel crops. Three years later, the RED II re-casted the RED to promote renewable energy utilization and establish a framework for the country's renewable energy policy for the period from 2021 to 2030. Moreover, the RED II proposed differentiating between low-risk and high-risk iLUC feedstocks. Unlike low-risk feedstocks, such as maize, sugarcane and rapeseed, high-risk feedstocks like palm oil will be subject to a sub-cap (below the 7% biofuel crop-based cap) keeping it at 2019 consumption levels from 2021 to 2023 and then phasing it out to 0% by 2030 (Dusser, 2019).

Scholars generally agree that Life Cycle Assessments (LCA) are one of the most effective approaches for evaluating the environmental influences of bioenergy production (Campbell et al., 2011; Krohn & Fripp, 2012; Liang et al., 2013; Rocha et al., 2014; Varanda et al., 2011). The scientific literature includes many LCA studies that evaluate the environmental impact of rapeseed-based biodiesel systems (Table 1). These studies mainly follow the generic LCA approach to enable comparison among the various systems and to utilize such Life Cycle Impact Assessment (LCIA) methods as CML 2001 (Guinée et al., 2002), Eco-indicator 99 (Goedkoop & Spriensma, 2001), ReCiPe 2008 (Goedkoop et al., 2008), EDIP 1997 (Wenzel et al., 1997) and IPCC (2014) in their evaluations. However, the downside of using generic LCAs is that this model is generally based on averages for material and process flows without including temporal and spatial heterogeneities. Therefore, many researchers dispute the reliability and accuracy of applying generic LCAs (Finnveden, 2000; Hellweg & Milà i Canals, 2014). To better incorporate temporal and spatial heterogeneities, more regionalized LCIAs are needed, since the characterization factors of the impact categories are calculated based on site-specific characteristics, for example, topographical condition, land use, soil types, etc. (Rodríguez et al., 2014), the selected spatial scales (Manneh et al., 2010; Yang, Liu, Wang, et al., 2021) and temporal scales (Yang, Liu, Thrän, et al., 2021). Due to the complexity of geographically differentiated processes on a regional scale, a tool that integrates Geographical Information Systems (GIS) and LCA (GIS-LCA) could enable both spatial and temporal information to be incorporated into the regionalized LCIA (Gasol et al., 2011; Hellweg & Milà i Canals, 2014). READ MORE