Human behaviour change is necessary to meet targets set by the Paris Agreement to mitigate climate change. Restrictions and regulations put in place globally to mitigate the spread of COVID-19 during 2020 have had a substantial impact on everyday life, including many carbon-intensive behaviours such as transportation. Changes to transportation behaviour may reduce carbon emissions. Behaviour change theory can offer perspective on the drivers and influences of behaviour and shape recommendations for how policy-makers can capitalise on any observed behaviour changes that may mitigate climate change. For this commentary, we aimed to describe changes in data relating to transportation behaviours concerning working from home during the COVID-19 pandemic across the Netherlands, Sweden and the UK. We display these identified changes in a concept map, suggesting links between the changes in behaviour and levels of carbon emissions. We consider these changes in relation to a comprehensive and easy to understand model of behaviour, the Opportunity, Motivation Behaviour (COM-B) model, to understand the capabilities, opportunities and behaviours related to the observed behaviour changes and potential policy to mitigate climate change. There is now an opportunity for policy-makers to increase the likelihood of maintaining pro-environmental behaviour changes by providing opportunities, improving capabilities and maintaining motivation for these behaviours.
To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.
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Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion systemcan give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.
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Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. While extensive attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC.
Horticulture crops and plants use only a limited part of the solar spectrum for their growth, the photosynthetically active radiation (PAR); even within PAR, different spectral regions have different functionality for plant growth, and so different light spectra are used to influence different properties of the plant, such as leaves, fruiting, longer stems and other plant properties. Artificial lighting, typically with LEDs, has been used to provide these specified spectra per plant, defined by their light recipe. This light is called steering light. While the natural sunlight provides a much more sustainable and abundant form of energy, however, the solar spectrum is not tuned towards specific plant needs. In this project, we capitalize on recent breakthroughs in nanoscience to optimally shape the solar spectrum, and produce a spectrally selective steering light, i.e. convert the energy of the entire solar spectrum into a spectrum most useful for agriculture and plant growth to utilize the sustainable solar energy to its fullest, and save on artificial lighting and electricity. We will take advantage of the developed light recipes and create a sustainable alternative to LED steering light, using nanomaterials to optimally shape the natural sunlight spectrum, while maintaining the increased yields. As a proof of concept, we are targeting the compactness of ornamental plants and seek to steer the plants’ growth to reduce leaf extension and thus be more valuable. To realize this project the Peter Schall group at the UvA leads this effort together with the university spinout, SolarFoil, whose expertise lies in the development of spectral conversion layers for horticulture. Renolit - a plastic manufacturer and Chemtrix, expert in flow synthesis, provide expertise and technical support to scale the foil, while Ludvig-Svensson, a pioneer in greenhouse climate screens, provides the desired light specifications and tests the foil in a controlled setting.
