Capture and utilization of CO2 as alternative carbon feedstock for fuels, chemicals, and materials aims at reducing greenhouse gas emissions and fossil resource use. For capture of CO2, a large variety of CO2 sources exists. Since they emit much more CO2 than the expected demand for CO2 utilization, the environmentally most favorable CO2 sources should be selected. For this purpose, we introduce the environmental-merit-order (EMO) curve to rank CO2 sources according to their environmental impacts over the available CO2 supply. To determine the environmental impacts of CO2 capture, compression and transport, we conducted a comprehensive literature study for the energy demands of CO2 supply, and constructed a database for CO2 sources in Europe. Mapping these CO2 sources reveals that CO2 transport distances are usually small. Thus, neglecting transport in a first step, we find that environmental impacts are minimized by capturing CO2 first from chemical plants and natural gas processing, then from paper mills, power plants, and iron and steel plants. In a second step, we computed regional EMO curves considering transport and country-specific impacts for energy supply. Building upon regional EMO curves, we identify favorable locations for CO2 utilization with lowest environmental impacts of CO2 supply, so-called CO2 oases.

District heating is seen as an important concept to decarbonize heating systems and meet climate mitigation goals. However, the decision related to where central heating is most viable is dependent on many different aspects, like heating densities or current heating structures. An urban energy simulation platform based on 3D building objects can improve the accuracy of energy demand calculation on building level, but lacks a system perspective. Energy system models help to find economically optimal solutions for entire energy systems, including the optimal amount of centrally supplied heat, but do not usually provide information on building level. Coupling both methods through a novel heating grid disaggregation algorithm, we propose a framework that does three things simultaneously: optimize energy systems that can comprise all demand sectors as well as sector coupling, assess the role of centralized heating in such optimized energy systems, and determine the layouts of supplying district heating grids with a spatial resolution on the street level. The algorithm is tested on two case studies; one, an urban city quarter, and the other, a rural town. In the urban city quarter, district heating is economically feasible in all scenarios. Using heat pumps in addition to CHPs increases the optimal amount of centrally supplied heat. In the rural quarter, central heat pumps guarantee the feasibility of district heating, while standalone CHPs are more expensive than decentral heating technologies.

The shift to electric mobility lags pursued goals. In this article we analyze the consumer's perspective and examine, which technology attributes and person‐related factors influence electric vehicle (EV) adoption, and whether differences in person‐related factors affect vehicle attribute importance. A total of 1922 participants took part in a Germany‐wide, representative study comprising a questionnaire measuring person‐related factors and a discrete choice experiment determining the importance of technology‐specific attributes. Results from the choice experiment for all vehicles classes reveal that purchase price was the most important vehicle attribute. Less important for the choice of a small‐sized vehicle were in descending order: range, fuel costs, fuel type, refueling infrastructure, CO2‐emissions and CO2‐tax. Regression analyses further indicate that subjective norms, collective efficacy, technological risk attitude and perceived information were the strongest predictors for purchase intention in the questionnaire. Participants showing high values on these factors also weighted attribute importance in the choice experiment differently, but throughout favoring EVs, than participants with low values on these factors. Factors that are disadvantageous for EV, such as range and price, were de‐emphasized by these respondents. In addition, preference shares for battery electric vehicles were more than twice as high as for conventional vehicles in three out of four groups with high values. Socio‐psychological factors, therefore, seem to relativize the impact of mere techno‐economic factors on electric vehicle adoption. Hence, we recommend that these factors receive greater attention in the discourse on policy measures.

District heating (DH) systems are often seen as a good practical approach to meet the local heat demand of districts. Yet, under today's regulations to renovate buildings on high efficiency standards, the local heat demand is decreasing. Therefore, the operation of DH systems is also affected by the changing heat demand profile, which might lead to less profit for the operators of DH systems. Thus, the operators strive for an optimal operation at which the heat demand is met and the profits are maximized. In this work, a control strategy for optimal operation of a combined heat and power (CHP) based DH is presented. The proposed control strategy couples the operation of CHPs to the European energy exchange (EEX) price by implementing different operation constraints. This configuration is accompanied with another, which is the installation of additional storage volume. Thereby it is held to provide the optimal operation for the plant technically and economically.

District Heating (DH) systems are often seen as a good practical approach to meet the local heat demand of the districts due to its ability to provide affordable and low carbon energy to the consumers. Yet, under today's regulations to renovate the buildings into more energyefficient ones, the local heat demand is decreasing. Therefore, the operation of DH systems is also affected by the changing heat demand profile, which might lead to less profit for the operators of DH systems. Thus, the operators of DH systems strive for an optimal operation at which the heat demand is met and the profits are maximized. Due to the fact that these systems are complex-physical systems, therefore it is difficult to conduct any experimental investigation on them in order to examine the optimal operation. Accordingly, it is crucial to create fundamental models to investigate the optimal operation of such systems. In this paper, a power-based model is built to represent the heating station as part of a DH system. Then, the model is validated using real data from an existing heating station in Freiburg, Germany. The validation results reveal that the goodness-of-fit for the model is held to be good enough to test it for operational optimization cases.