## 4 Discussion  
In order to speed up the process of decarbonization, some countries in Europe, like e.g., Denmark and Germany, introduced regulations regarding the coverage of the heat demand on a wider scale. Germany, as one of the leaders in this topic, obligates all its communities to have heating plans for coverage of the heat demand by 2050, as described in the Introduction. The strategy and methods to achieve this target are not solved until now, and there is a urgent need for doing this in a rapid and efficient manner. Although the previous works, especially through our German neighbors, show that the generation of potential heating networks using the GIS methodology is possible and provides reliable results, there is still great potential for the optimization of these techniques and methods. The investigations carried out within this study indicate that automatization of this process with reliable results is possible in a very easy and fast manner, as the proposed method is based on very few input data.  
The LCPA method tested and presented in this paper shows very good results in the case of a small test area, generating the end heating network appropriately. This means that the network was generated along the street network without closing spaces and connects all heat consumers. Based only on this small example, it could be supposed that this method is good enough to achieve the target of this work, which is the generation of potential heating networks in a very automatic manner at different spatial levels. Showing in the results from the selected study area, one important week point occurs due to the generation of some unnecessary heat pipes.  
Figure 15: DH network in the study area as part of Wroclaw county.  
Summarizing, the LCPA-based heating pipe routing method proposed in this paper uses a simple and nevertheless efficient and easily automated way to generate heating networks on the basis of very few input data, as they are the building footprints and the street network, which are available open-source for all communities in the European Union under the Inspire Directive [27]. In addition, volunteered geographic information (VGI) data sources such as the Open Street Map in case public data are not openly available.  
Furthermore, this method is very easy to use and takes only few minutes of computation time, and it is therefore suitable for use for the first general estimation of potential DH networks at different even higher (like the whole vovodeship) levels.  
In order to avoid generating unnecessary heating pipes, a better method using a Closest Facility tool was developed and implemented in a Model Builder of the ArcGIS Pro environment. This method requires more computational time: for the selected study area, it took 5 h for 1127 connections, which involves 220 connections/hour but generates a more efficient (as many pipes as needed) DH network.  
The limitations of the current study are as follows:  
The proposed method deals only with the first rough generation of DH networks without considering the energy issue.  
The computation time is rather high (220 connections/hour) in the case of the Closest Facility method.  
Further directions of this study would be the development of a tool for dimensioning the heating pipes on the basis of the available heat demand data of individual heat consumers in order to consider the energy issue and perform the economic analysis.