Providing equitable food security for a growing population while minimizing environmental impacts and enhancing resilience to climate shocks is an ongoing challenge. Here, we quantify the resource intensity, environmental impacts and nutritional output of a small (0.075 ha) low-input subsistence Mediterranean agroecological farm in a developed nation that is based on intercropping and annual crop rotation. The farm provides one individual, the proprietor, with nutritional self-sufficiency (adequate intake of an array of macro- and micro-nutrients) with limited labor, no synthetic fertilizers or herbicides, and zero waste, effectively closing a full farm-table-farm cycle. We find that the agroecological farm outperforms conventional farming as practiced in the same country in terms of both lower environmental burdens, across all examined environmental metrics (63% lower on average) per kg produce, and higher nutritional score (66% higher on average). Per equal farmland, the environmental lopsidedness was even higher (79% lower than conventional farming on average), with nearly the same nutritional score (3% lower on average). Moreover, when considering total land area, which includes farmland and supporting non-agricultural lands, as well as postgate impacts and food losses, the advantage of the agroecological system over conventional farming is even more pronounced. Situated within a Mediterranean region that is undergoing rapid climate change, this food system is a unique case study of nutrition- and environment-oriented food production system. While its deployment potential is limited by lack of supportive policies, it nonetheless represents one of the most starkly bold alternatives to current food systems.
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We present an economic impacts model based on direct expenditures for European cycle routes, originally designed in 2009 as part of a study commissioned by the European Parliament. At its request, the study was updated in 2012, including a refined version of our model which takes some limitations of the former model into account. Our main findings are that cycle tourists’ daily spending is comparable to that of other tourists, and that cycle tourism can contribute significantly in particular to rural economies that have not previously enjoyed mainstream tourism development. (European) cycle tourism thus proves to be useful as an (additional) tool for regional rural development. We arrived at a total estimated direct expenditures in Europe of almost €44 billion (€35 billion from day trips and €8.94 billion from overnight trips). We applied the model to the routes of EuroVelo, the European cycle route network which is currently being developed, showing their considerable economic potential of over €7 billion in direct expenditures. Furthermore, cycle tourism has a far lower negative impact on the environment (in terms of carbon dioxide emissions) than other forms of tourism. Cycle tourism is therefore a good example of a low carbon tourism product which could be developed as a major slow travel opportunity across (rural) Europe.
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This booklet contains the analyses and designs that were produced by international teams of students, designers and researchers on the revitalization of public space in the district of Kerkrade – West (Limburg, the Netherlands) in December 2017 during the International Design Workshop (re)CYCLE LIMBURG 2. It was partially built on knowledge, experiences and ideas from the preceding workshop in December 2016. The outcomes of the workshop are mainly presented in the form of drawings, maps, schemes, collages, artistic impressions etc. Both workshops were framed in the interdisciplinary project Kerkrade-West of Zuyd UAS and its Research Centre for Smart Urban ReDesign (SURD).
The livability of the cities and attractiveness of our environment can be improved by smarter choices for mobility products and travel modes. A change from current car-dependent lifestyles towards the use of healthier and less polluted transport modes, such as cycling, is needed. With awareness campaigns, cycling facilities and cycle infrastructure, the use of the bicycle will be stimulated. But which campaigns are effective? Can we stimulate cycling by adding cycling facilities along the cycle path? How can we design the best cycle infrastructure for a region? And what impact does good cycle infrastructure have on the increase of cycling?To find answers for these questions and come up with a future approach to stimulate bicycle use, BUas is participating in the InterReg V NWE-project CHIPS; Cycle Highways Innovation for smarter People transport and Spatial planning. Together with the city of Tilburg and other partners from The Netherlands, Belgium, Germany and United Kingdom we explore and demonstrate infrastructural improvements and tackle crucial elements related to engaging users and successful promotion of cycle highways. BUas is responsible for the monitoring and evaluation of the project. To measure the impact and effectiveness of cycle highway innovations we use Cyclespex and Cycleprint.With Cyclespex a virtual living lab is created which we will use to test several readability and wayfinding measures for cycle infrastructure. Cyclespex gives us the opportunity to test different scenario’s in virtual reality that will help us to make decisions about the final solution that will be realized on the cycle highway. Cycleprint will be used to develop a monitoring dashboard where municipalities of cities can easily monitor and evaluate the local bicycle use.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
