• Energy Research

Abstract The energetic use of residues from agriculture can foster the transition towards a more renewable energy supply. However, sustainability issues have to be considered along the entire provision chain as they affect the resource and energy potential as well as the achievable contribution to climate mitigation. Straw is one of the most important agricultural residues in Germany. It is not yet used for energy purposes extensively and compared to other agricultural feedstock it shows low competition with food, feed or fiber. This paper analyses on the one hand the sustainable potential of cereal straw for energy application in Germany considering the actual agricultural conditions, and on the other hand the global warming potential from different energy provision chains based on straw. Different humus-balance tools that are able to assess the organic matter (OM) demand to presume soil fertility. The analysis of straw potentials was applied at NUTS 3 level for Germany, based on statistical data. The results of this analysis were used as input data for the modeling of concepts for straw provision and use. Greenhouse gas (GHG) emissions were calculated for each concept in order to compare the global warming potential of various energy applications, to investigate the relative contribution of different production steps and to compare them with fossil energy applications. In total, 29.8 Tg of straw (fresh matter) are produced annually in Germany (1999–2007). Approximately 4.8 Tg of the total straw occurrence are annually required by animal husbandry. Between 7.97 and 13.25 Tg straw can be classified as sustainable straw. Highest straw potential (3.99 Mg ha−1) can be found in parts of Schleswig-Holstein, Mecklenburg–West Pomerania, North Rhine-Westphalia and Lower Saxony. But there are also regions that show a net deficit. The cumulated GHG emissions for the resulting concepts are between 8 and 35 g CO2-eq. MJ−1. In comparison to fossil energy applications, the highest reduction potential occurs for concepts for combined heat and power (CHP) provision, i.e. 223 g CO 2 -eq . MJ el - 1 . This study highlights the possible contribution of straw as renewable energy carrier, but also demonstrates that there are regional restrictions for straw use.

The agricultural sector is a primary driver of nitrogen (N) pollution. Several European and German policy measures exist regulating N inputs and fostering mitigation measures in crop management. Life cycle assessment (LCA) is an established tool for assessing environmental impacts which are also broadly applied for crop production systems and evaluation of N management strategies. However, due to the multiple spatial and temporal pathways of N losses from crop production, assessing N-related impacts in LCA is not straightforward. Consequently, this study further developed and applied a novel distance-to-target approach including regional carrying capacity based normalization references for N assessment in LCA. The overall aim was to prove its applicability as regional decision support for the assessment of N management strategies in cropping systems considering environmental interventions with regional N resiliencies. Therefore, environmental interventions were evaluated within a case study for four different N management scenarios for rapeseed cropping systems in five German NUTS-3 regions. Regional carrying capacity based normalization references were derived for two N-related impact categories: terrestrial eutrophication and terrestrial acidification. The regional normalization references also included background interventions of non-crop producing sectors and were provided for all German NUTS-3 regions applicable as distance-to-target values in LCA. Overall results showed that environmental interventions and exceedance of N resilience were lowest in the N-management scenario applying catch crops for both impact categories. The case study demonstrated that considering absolute sustainability references as regional N resilience in LCA is a valuable tool for agricultural decision-makers to evaluate N management strategies for crop production systems.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

Manufacturing processes have a significant contribution to energy consumption and related greenhouse gas (GHG) emissions in a product’s life cycle. Today, information on GHG emissions is increasingly demanded from companies in a life cycle perspective, based on the methodology of Life Cycle Assessment. Manufacturing companies supply producers of final products and are, therefore, requested to provide data on GHG of their manufacturing processes and resulting products. Obtaining such data for real-world manufacturing processes represents a huge effort. This challenge can be overcome with the use of a parameterized model, the Extended Energy Modeling Approach (EEMA), that has been developed for the machining process, which is a widespread industrial manufacturing process. The model calculates the total energy demand from power key values, which report the average power consumption of the constant and variable units of the machinery equipment, the consumer groups, as well as the different operating states of the equipment. Therefore, EEMA enables the reuse of a single measurement campaign for follow-up investigations of the specific machine tool, thereby significantly improving the efficiency of data acquisition for the calculation of the total energy demand and life-cycle-based GHG emissions. To use EEMA for the compilation of life cycle inventory datasets, methodological requirements were analyzed to derive a procedure for LCA-compliant datasets for machine tools. The key findings of applying the EEMA for the case study of a turning machine show that the constant consumer groups have a significant influence on the total energy demand. The share of the variable consumer groups in the total energy demand increases with increasing machine utilization but is always below 5%.

Bulk mineral waste materials are one of the largest waste streams worldwide and their management systems can differ greatly depending on regional conditions. Due to this variation, the decision-making context is of particular importance when studying environmental impacts of mineral waste management systems with life cycle assessment (LCA). We follow the premise that LCA results—if applied in practice—are always used in an improvement (i.e., decision-making) context. But how suitable are existing LCA studies on bulk mineral waste management for decision support? To answer this question, we quantitatively and qualitatively assess 57 peer-reviewed bulk mineral waste management LCA studies against 47 criteria. The results show inadequacies regarding decision support along all LCA phases. Common shortcomings are insufficient attention to the specific decision-making context, lack of a consequential perspective, liberal use of allocation and limited justification thereof, missing justifications for excluded impact categories, inadequately discussed limitations, and incomplete documentation. We identified the following significant issues for bulk mineral waste management systems: transportation, the potential leaching of heavy metals, second-order substitution effects, and the choice to include or exclude avoided landfilling and embodied impacts. When applicable, we provide recommendations for improvement and point to best practice examples.

This study applies a life cycle assessment (LCA) to the shared dockless standing e-scooter system that is established in Brussels. The results are given for four impact categories: global warming potential (GWP), particulate matter formation, mineral resource, and fossil resource scarcity. Regarding GWP, the use of the shared e-scooters in the current system causes 131 g of CO2-eq. per passenger-kilometer while the mode of transportation displaced has an impact of 110 g of CO2-eq. Thus, at present, the use of e-scooters shows a higher impact than the transportation modes they replace. The high results for the shared e-scooter, in terms of GWP, are mainly caused by the short lifespan of the shared e-scooter. Nevertheless, as the market further matures, the lifespan of e-scooters could increase and the impact per kilometer travelled could decrease accordingly. Regarding the use of the personal e-scooter, the LCA results show an impact of around 67 g of CO2-eq. This study quantifies the LC impacts of the current situation based on local, ‘real-life’ data. However, potential changes on soft mobility patterns induced by the use-oriented product-service system (PSS), such as a shared e-scooter system, could not be quantified.