Fragmentation in policy making and poor investment in Energy Efficiency: how can data analytics help?
Energy Efficiency (EE) has come to be recognised as the ‘first fuel’ for energy. EE is seen as one of the three pillars of the UN initiative ‘Sustainable Energy for All’ and a major resource to reverse climate change and meet sustainability goals. However, it has been estimated that two-thirds of the economically-viable EE potential through 2035 will remained untapped under the current policy conditions. EE measures are still under-valued in the market by both public and private investors, and significant barriers, such as lack of information, split incentives, energy pricing skewed by subsidies, are hindering further investments.
European buildings, particularly residential ones, are large consumers of energy and the majority of this consumption is associated with buildings built before 1990 and even if demolition rates were to increase, most of these buildings will still be in use by 2050. Renovating existing buildings is far less energy-intensive than demolishing and reconstructing. Thus, it is imperative that EE investments for building renovation are accelerated across all Member countries.
The evidence on multiple benefits of investments in buildings is dispersed and needs to be consolidated, analysed and made available to policy-makers at the regional and municipal level. Public and private investors do not currently pay enough attention to EE opportunities in comparison to supply-side options and there is a need to quantify and highlight the multiple and diverse benefits of EE investments, but this is hindered by a lack of data and of mature/shared methodologies.
The reality shows that this data is either not available or stored in various archives, digitally or on paper, in different departments of the regional/municipal entities. Moreover, the effort required to document the building stock, merge the data, perform the analysis, generate substantial reporting and integrate it in budgeting and planning schemes is overburdening most entities, which often work with limited staff and resources. As a consequence, EE programmes are delayed and are not planned with sufficient detail, often leading to unforeseen obstacles and the dispersion of funding in ‘shallow retrofits’, which achieve limited impact.
Extensive data work along with analytics is needed for achieving EE through existing building stock renovation, as well as the implementation of shared methodologies. This will also involve collecting and consolidating open data sources that will assist with analysis to underpin EE initiatives within municipalities to assist with planning building renovation measures as policy-makers and planners need specific data to represent the local building stock, such as building age, location, dimension, construction type, energy consumption, applicable technologies, etc. Decision support tools, which include data analytics and data visualisation are needed to make this process user-friendly and to allow policymakers to make evidence-based decisions with the aim of prioritising EE in the move towards sustainability and reversing climate change.
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