ComActivate responds to growing levels of energy poverty across the EU, especially in the Central and Eastern European (CEE) region, by addressing the poor energy efficiency of buildings as a major driver of energy poverty, and as a driver of climate change. The project aims to ensure the energy sufficiency of vulnerable people living in multi-family apartment buildings in CEE by developing best practices for clean energy production, consumption and efficiency measures at the neighbourhood level.

The project holds a specific objective to develop, demonstrate and co-create innovative Neighbourhood Energy Sufficiency Roadmaps (NESRs) for three energy-poor multi-family apartment building (MFAB) neighbourhoods in three vulnerable districts in Bulgaria, Hungary and Lithuania.

The aim of this report and the annexed Excel files is to enable relevant authorities to establish efficient and effective policy & finance mechanisms that enable large-scale investments in their residential sectors’ final energy consumption. Each analysis serves as a reliable planning tool, enabling local authorities to get clear insight in entire residential stock on their territory and scope of pertaining energy-saving and emission-reduction needs and opportunities.

Each analysis includes:

• an examination of the situation at the time of conducting the research
• possible measures to improve energy efficiency and generate energy savings and investments
• the situation after implementation of the proposed energy efficiency measures
• environmental, economic and job-creation effects of implementing the proposed measures

Building stock database

The first phase of this research involved defining the scope of the neighbourhood in each target area. Upon successful completion of this phase, site visits to collect data took place. During this process, where applicable, already available documents were consulted, such as:

• walkthrough energy audits
• (district) heating development plans
• renewable energy possibilities
• anything related to the current building stock or energy use

The number of MFABs analysed in target regions is as follows:

• Józsefváros municipality in Budapest, Hungary: 325 buildings,
• The city of Burgas, Bulgaria: 476 buildings,
• Elektrenai and Kaišiadorys municipalities in Lithuania: 308 buildings

The scope of analysis was adapted according to the needs of local communities. The full analyses (Excel files) come as an appendix to the main report.

Some general notes about these Excel files:

• All Excels have a DATA tab, where specific values can be changed in accordance with local regulations, actual or current values, or according to standards/laws.
• Buildings were separated by entrances where it made sense.
• As a general rule, a separation was made by addresses, except where one building has two, three or four addresses.
• Construction characteristics (envelope characteristics) were taken into account based on what was visible from pictures and maps. Significant problems here were the inner courtyards and parts of the buildings not visible from the street. This applies to Budapest and Burgas, whereas the buildings in Lithuania do not have such a complex geometry.
• The same calculation methodology was used for all locations, except where we had data based on sources provided by pilots, which is a very small number of buildings.
• Savings ranges (%) are reasonable and well within of what is typically obtainable on buildings of comparable age, type, size and shape factor. All needs and savings are calculated based on an approximate budget, using estimated geometry.
• Energy sources used in individual entrances are taken based on the appearance of the building, information from pilots or data available on the Internet.
• Electricity consumption for air conditioners in transitional periods is neglected.
• Electricity consumption for lighting has not been considered, nor can it be taken into account based on these input data.

3D models of target neighbourhoods

3D GIS models of energy use and energy saving assessments for MFAB clusters in selected neighbourhoods have been created based on the data obtained through the building stock analyses. The model allows “walking” through each neighbourhood, clicking on any particular building and getting all relevant data about it such as outer dimensions, surface area, heated surface, energy needs, energy sources used for heating, investments needed for implementation of energy efficiency measures, and possible savings.