• Energy Research

AbstractRapeseed 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.

An amendment to this paper has been published and can be accessed via the original article.

The German Renewable Energy Sources Act (EEG) has been deemed successful in promoting German biogas production. However, the German state-level biogas production development (BPD) under the EEG has not been systematically studied and compared. This research aimed to study the German state-level BPD using the multivariate linear regression model with a dummy variable, and to spatially quantify the environmental and agricultural consequences using the geographic information system (GIS) technique to identify the necessities of regional-based analysis on Germany’s BPD. The empirical results indicated that Saxony-Anhalt was advanced in BPD, while farmers’ response from Bavaria to EEG was the weakest. The reason behind could be the differences in farmers’ personality traits and risk cognitions toward the biogas production investment. The spatial analysis indicated that Saxony-Anhalt had more severe environmental problems caused by the biogas production expansion than Bavaria. Therefore, to promote BPD in states such as Bavaria, an increase in the nationwide unified subsidy might lead to an overreaction of the EEG strong response states, e.g., Saxony-Anhalt, leading to more serious environmental problems. In the end, there is a need for more regional-based research on studying the BPD in Germany in the future to avoid the ambiguity of large-scale studies.

Abstract Background: The German energy transition strategy calls for a reform of the German energy sector. As a result, the Germany Renewable Energy Sources Act (EEG) was passed in 2000 and is widely regarded as successful legislation for promoting bioenergy development. More than 1,000 biogas plants were constructed in Central Germany (CG) between 2000 and 2014. Despite this, few studies have been conducted for this period that systematically investigate how environmental, social and economic factors, as well as various EEG amendments have impacted biogas production and what the environmental consequence of biogas production development in CG have been. Methods: The impacts of environmental, social and economic factors and different EEG amendments on biogas production decisions in CG were quantified using multivariate linear regression model and the event study econometric technique. A GIS-based spatial analysis was also conducted to provide insight into the changes to agricultural land use that resulted from the development of biogas plants during the EEG period. Results: The main finding was that the income diversification effect resulting from biogas production was the most important factor in a farmer’s decision to adopt biogas production. In addition, all of the EEG amendments had a significant influence on the adoption of biogas production, however EEG III and IV, which tried to promote small-scale plants, were unable to reduce the average size of the plants constructed in these two amendment periods. From a landscape perspective, there was a striking increase in the cultivation of silage maize in CG from 2000 to 2014. Silage maize was intensively cultivated in regions with a high installed biogas plant capacity. Since the first EEG amendment, permanent grassland area slightly increased while arable land area declined in CG. Conclusions: The adoption of biogas production in CG was strongly driven by economic incentives for the farmers, more precisely, by the incentive to diversify their income sources. In addition to increase the subsidy, future EEG amendments should find new measures to encourage the adoption of small-scale biogas plants, which had been unsuccessful in EEG amendments III and IV.

Connections to world markets facilitate local markets developments to support more efficient capital allocation and greater investment and growth opportunities. Under the framework of cross-market rebalancing theory, in this study, we aim to systematically examine the market connections among world financial, energy, renewable energy and European carbon markets by measuring the return spillovers from 2008 to 2021. We find that the renewable energy market is more closely connected to the world financial and energy markets in the sense of the return transmission, while the carbon market is less connected to them. However, due to improved market regulations and determinations related to fighting climate change, the connections between the carbon market and other markets have gradually intensified. Plotting the return spillover indexes, we observe that strong return spillovers from the renewable energy market to other markets occurred when large investment plans were announced. Regarding the carbon market, regulation changes introduced by the EU Commission to improve and stabilize market environment induced intensified return transmission from carbon market to other markets. Another interesting finding is that the highly intensified return transmission among markets due to the COVID-19 crisis started to loosen when COVAX published the first interim distribution forecast on 3 February 2021.

The current Ukraine War underlines the importance of grain self-sufficiency. After the adoption of the Paris Agreement, two major challenges developing countries are facing in the coming decades are increasing agricultural production to ensure food security and reducing carbon emissions (CE). The key to such an “environment-development dilemma” is to improve agricultural carbon emission efficiency (CEE). Using China as the study site, we systematically analyze the impacts of agricultural management activities on agricultural CEE from 1997 to 2019. Global and local Moran's I index tests provide evidence of a positive spatial dependence of agricultural CEE. Using the LISA cluster map, we observe that high CEE regions tend to be distributed together, dominated by environmental conditions. However, with the promotion of agricultural management activities, such a clustering pattern vanished. Our spatial Durbin model (SDM) estimation results indicate that there are significant nonlinear relationships between agricultural practices and agricultural CEE. While the consumption of fertilizers and pesticides has economies of scale effects, the deployment of agricultural machinery and irrigation have diseconomies of scale effects on local CEE. Based on the SDM results, the direct and indirect effect estimation results suggest that the significant direct and spillover effects of many practices on agricultural CEE have opposite nonlinear shapes, implying a more complicated situation in promoting these activities, as the positive regional effect of an agricultural activity might have a negative impact on adjacent regions. All the results indicate that local policymakers should carefully tailor agricultural development policies based on local environmental conditions.