Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age

Density of Biogas Power Plants as An Indicator of Bioenergy Generated Transformation of Agricultural Landscapes

The increasing use of biogas, produced from energy crops like silage maize, is supposed to noticeably change the structures and patterns of agricultural landscapes in Europe. The main objective of our study is to quantify this assumed impact of intensive biogas production with the example of an agrarian landscape in Northern Germany. Therefore, we used three different datasets; Corine Land Cover (CLC), local agricultural statistics (Agrar-Struktur-Erhebung, ASE), and data on biogas power plants. Via kernel density analysis, we delineated impact zones which represent different levels of bioenergy-generated transformations of agrarian landscapes. We cross-checked the results by the analyses of the land cover and landscape pattern changes from 2000 to 2012 inside the impact zones. We found significant correlations between the installed electrical capacity (IC) and land cover changes. According to our findings, the landscape pattern of cropland—expressed via landscape metrics (mean patch size (MPS), total edge (TE), mean shape index (MSI), mean fractal dimension index (MFRACT)—increased and that of pastures decreased since the beginning of biogas production. Moreover, our study indicates that the increasing number of biogas power plants in certain areas is accompanied with a continuous reduction in crop diversity and a homogenization of land use in the same areas. We found maximum degrees of land use homogenisation in areas with highest IC. Our results show that a Kernel density map of the IC of biogas power plants might offer a suitable first indicator for monitoring and quantifying landscape change induced by biogas production