Randomised controlled trials are strongly advocated to evaluate the effects of intervention programmes on household energy saving behaviours. While randomised controlled trials are the ideal, in many cases, they are not feasible. Notably, many intervention studies rely on voluntary participation of households in the intervention programme, in which case random selection and random assignment are seriously challenged. Moreover, studies employing randomised controlled trials typically do not study the underlying processes causing behaviour change. Yet, the latter is highly important to improve theory and practice. We propose a systematic approach to causal inference based on graphical causal models to study effects of intervention programmes on household energy saving behaviours when randomised controlled trials are not feasible. Using a simple example, we explain why such an approach not only provides a formal tool to accurately establish effects of intervention programmes, but also enables a better understanding of the processes underlying behaviour change.
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Energy efficiency has gained a lot of prominence in recent debates on urban sustainability and housing policy due to its potential consequences for climate change. At the local, national and also international level, there are numerous initiatives to promote energy savings and the use of renewable energy to reduce the environmental burden. There is a lot of literature on energy saving and other forms of energy efficiency in housing. However, how to bring this forward in the management of individual housing organisations is not often internationally explored. An international research project has been carried out to find the answers on management questions of housing organisations regarding energy efficiency. Eleven countries have been included in this study: Germany, the United Kingdom (more specifically: England), France, Sweden, Denmark, the Netherlands, Switzerland, Slovenia, the Czech Republic, Austria and Canada. The state of the art of energy efficiency in the housing management of non-profit housing organisations and the embedding of energy efficiency to improve the quality and performance of housing in management practices have been investigated, with a focus on how policy ambitions about energy efficiency are brought forward in investment decisions at the estate level. This paper presents the conclusions of the research
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Reducing energy consumption in urban households is essential for reaching the necessary climate research and policy targets for CO2 reduction and sustainability. The dominant approach has been to invest in technological innovations that increase household energy efficiency. This article moves beyond this approach, first by emphasising the need to prioritise reducing energy demand over increasing energy efficiency and, second, by addressing the challenge of energy consumption at the level of the community, not the individual household. It argues that energy consumption is shaped in and by social communities, which construct consciousness of the energy implications of lifestyle choices. By analysing a specific type of community, a digital community, it looks at the role that communication on online discussion boards plays in the social process of questioning energy needs and shaping a “decent lifestyle”. The article explores three social processes of community interaction around energy practices – coercive, mimetic, and normative – questioning the ways in which they contribute to the activation of energy discursive consciousness. In conclusion, the article reflects on the potential implications of these social processes for future research and interventions aimed at reducing energy demand. To illustrate how the three selected social processes influence one another, the article builds on the results of a research project conducted in Amsterdam, analysing the potential contribution of online discussion boards in shaping energy norms in the Sustainable Community of Amsterdam Facebook group.
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Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Client: Blue Plan regional activity centre (UNEP/MAP), subcontracted through TEC Conseille, Marseille As part of a regional workshop organized by the Blue Plan in July 2008, one of the conclusions of the Group "Tourism and Climate Change” was the need for saving energy in tourism transportation and particularly of air transport, as air transport is responsible for the largest share of greenhouse gas emissions caused by tourism. In the period 1998-2005, the share of international arrivals by air in the Mediterranean area rose from 23% to 40%, respectively, or in numbers, from 47 to 122 million tourists. Some countries, particularly islands, almost entirely depend on air transport for their international tourism. For example in 2005 air transport is used by 87%, 78%, 73%, 64% and 51% of international tourists arriving in, respectively, Israel, Egypt, Spain, Tunisia and Morocco. According to Plan Bleu forecasts on international arrivals, assuming that the share of air transport remains the same, the number of tourists travelling by plane will reach over 158 million by 2025. Given the role of aviation in the emissions of greenhouse gases (GHG), such a development is clearly not sustainable in the light of the necessary reduction of emissions to avoid dangerous climate change. The overall aim of the study is to inform policy makers and entrepreneurs in both destination and in origin countries, on possible options to reduce emissions of greenhouse gases from air travel, while at the same time not impairing the economic development of tourism. To do this, CSTT has developed a tourism scenario model for all countries with Mediterranean coasts describing inbound and outbound international tourism and domestic tourism by all available transport modes and giving both contributions to GDP and total GHG emissions. This model responses to global mitigation policies (increasing the cost of carbon emissions) as well as national policies (taxes, subsidies and changes in transport quality per transport mode). Using the model both global and national policies can be assessed as well as the risks of global mitigation policies for specific countries.
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
