Sustainable economic growth within environmental limits hypothetical case study: 'limitville'

A new approach to delivering ‘sustainable economic growth within environmental limits’ has been developed for the East Midlands. The approach is described in detail in a guidance document prepared by Land Use Consultants and GHK Consulting for emda and partners. The ‘Limitville’ case study was developed to test and illustrate the new approach

Sustainable economic growth within environmental limits. volume 1: guidance for the east midlands

LUC and GHK have developed a tool for setting environmental limits that local communities and other stakeholders can use. The three-step approach is based on the participation of the local community and other interested stakeholders, such as statutory agencies and businesses. It enables them to debate the use of important national, regional and local environmental assets, and the potential economic and social implications of setting environmental limits. The approach also provides a tool that can usefully support and inform other assessment requirements, such as Sustainability Appraisal/Strategic Environmental Assessment, Habitats Regulations Assessment and Equality Impact Assessment/Health Impact Assessment

Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition

Driven by real-world applications such as fitness, wellbeing and healthcare, accelerometry-based activity recognition has been widely studied to provide context-awareness to future pervasive technologies. Accurate recognition and energy efficiency are key issues in enabling long-term and unobtrusive monitoring. While the majority of accelerometry-based activity recognition systems stream data to a central point for processing, some solutions process data locally on the sensor node to save energy. In this paper, we investigate the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes. An empirical energy model is presented and used to evaluate the energy efficiency of both systems, and a practical case study (monitoring the physical activities of office workers) is developed to evaluate the effect on classification accuracy. The results show a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer’s activity

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

The Cooperative Participatory Evaluation of Renewable Technologies on Ecosystem Services (CORPORATES)

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