The building and construction industry, which is responsible for 39% of global carbon emissions, is far off track in achieving its net-zero emission targets. Product-service system (PSS) business models are one of the instruments used by the industry in the transition toward reaching these targets. A PSS business model is designed around an end-of-life solution that minimizes material usage and maximizes energy efficiency. It is provided to customers as a marketable set of products and services, jointly capable of fulfilling a customer’s needs. There are signals from practice however, that suggest that the implementation of this type of business model is falling behind. This study investigates this and seeks to identify key challenges and opportunities for sustainable PSS business models in the built environment. Using a grounded theory approach, data from 13 semi-structured interviews across five companies is used to identify challenges and opportunities that suppliers are facing in selling their products through PSS business models. Our preliminary data analysis points to nine challenges and opportunities for PSS business models. We discuss these in the context of the current economic transition toward a sustainable and circular built environment and provide suggestions for further research that could help to overcome resistance toward the implementation of PSS business models. The contribution of this research to researchers and practitioners is that it provides insights into the adoption of new business models in fragmented and competitive business environments.
MULTIFILE
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
One of the most complex and urgent challenges in the energy transition is the large‐scale refurbishment of the existing housing stock in the built environment. In order to comply with the goals of the Paris convention, the aim is to live “energy‐neutral,’’ that is, a dwelling should produce as much sustainable energy as it consumes on a yearly basis. This means that millions of existing houses need to undergo a radical energy retrofit. In the next 30 years, all dwellings should be upgraded to nearly zero‐energy buildings, which is a challenge to accomplish for a reasonable price. Across the EU, many projects have developed successful approaches to the improvement of building technologies and processes, as well a better involvement of citizens. It is important to compare and contrast such approaches and disseminate lessons learned.In practice, it is crucial to raise the level of participation of inhabitants in neighborhood renovation activities. Therefore,the central question of this issue is: How can we increase the involvement of tenants and homeowners into this radicalenergy renovation?
LINK
Positive Energy Districts (PEDs) can play an important part in the energy transition by providing a year-round net positive energy balance in urban areas. In creating PEDs, new challenges emerge for decision-makers in government, businesses and for the public. This proposal aims to provide replicable strategies for improving the process of creating PEDs with a particular emphasis on stakeholder engagement, and to create replicable innovative business models for flexible energy production, consumption and storage. The project will involve stakeholders from different backgrounds by collaborating with the province, municipalities, network operators, housing associations, businesses and academia to ensure covering all necessary interests and mobilise support for the PED agenda. Two demo sites are part of the consortium to implement the lessons learnt and to bring new insights from practice to the findings of the project work packages. These are 1), Zwette VI, part of the city of Leeuwarden (NL), where local electricity congestion causes delays in building homes and small industries. And 2) Aalborg East (DK), a mixed-use neighbourhood with well-established partnerships between local stakeholders, seeking to implement green energy solutions with ambitions of moving towards net-zero emissions.
