Product Service System Innovation in the Smart City

Product service systems (PSS) may usefully form part of the mix of innovations necessary to move society toward more sustainable futures. However, despite such potential, PSS implementation is highly uneven and limited. Drawing on an alternate socio-technical perspective of innovation, this paper provides fresh insights, on among other things the role of context in PSS innovation, to address this issue. Case study research is presented focusing on a use orientated PSS in an urban environment: the Copenhagen city bike scheme. The paper shows that PSS innovation is a situated complex process, shaped by actors and knowledge from other locales. It argues that further research is needed to investigate how actors interests shape PSS innovation. It recommends that institutional spaces should be provided in governance landscapes associated with urban environments to enable legitimate PSS concepts to co-evolve in light of locally articulated sustainability principles and priorities

Critical factors for implementing and diffusing sustainable Product-Service Systems: Insights from innovation studies and companies' experiences

This is the post-print version of the final paper published in Journal for Cleaner Production. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.Eco-efficient Product-Service System (PSS) innovations represent a promising approach to sustainability. However the adoption of such business strategies is still very limited because it often involves significant corporate, cultural and regulatory barriers. An important challenge is not only to conceive eco-efficient PSS concepts, but also to understand the contextual conditions that facilitate their societal embedding, and which strategies and development pathways are the most appropriate. The combination of theoretical insights from innovation studies (in particular Strategic Niche Management and Transition Management) and a case studies research (exploring the innovation journeys made by six companies in introducing their eco-efficient PSS innovations in the market) is used to investigate the factors that influence the implementation and diffusion of this kind of innovations. The article provides a structured overview of these factors, grouping them in four clusters: implementation of socio-technical experiments; establishment of a broad network of actors; building up of a shared project vision; creation of room for broad and reflexive learning processes. Based on these results it is argued that a broader and more strategic system approach should be adopted by companies. Companies should focus not only on the PSS solution and its value chain, but also on the contextual conditions that may favour or hinder the societal embedding of the PSS itself. The article concludes by outlining a key area for future research

Webly Supervised Learning of Convolutional Networks

We present an approach to utilize large amounts of web data for learning CNNs. Specifically inspired by curriculum learning, we present a two-step approach for CNN training. First, we use easy images to train an initial visual representation. We then use this initial CNN and adapt it to harder, more realistic images by leveraging the structure of data and categories. We demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly supervised learning by localizing objects in web images and training a R-CNN style detector. It achieves the best performance on VOC 2007 where no VOC training data is used. Finally, we show our approach is quite robust to noise and performs comparably even when we use image search results from March 2013 (pre-CNN image search era)

Developing cycle hubs in a destination – a how to guide

This ‘how-to’ guide draws on our experience of planning and delivering a project designed to enhance provision for leisure cycling in rural Northumberland. By sharing the lessons learned during 18 months working in Haltwhistle and Wooler between autumn 2009 and spring 2011, we hope that other market town communities in Northumberland and further afield will be able to stimulate and cater for this increasingly important tourism sector

After interaction

Interaction turns on the outmoded idea that there is a natural separation between people and things. Technologies have always been about producing dense, interconnected relationships of humans and non-humans. Seeing design as performing expansive networks of relations opens up possibilities beyond the human-computer interactions. The relations go much deeper than any discrete interactions between user and machine. Extensive investigations have also demonstrated how cognitive capacities can extend beyond the individual's mind and into the interactions between people

Theoretical Framework for the Definition of Locally-Embedded Future Policy Scenarios

This document has been prepared in the framework of the European project SMARTEES – Social Innovation Modelling Approaches to Realizing Transition to Energy Efficiency and Sustainability. Work-package: No. 5. Definition of Future Policy Scenarios. Deliverable 5.1 (Report)[Abstract] This document presents the common conceptual framework for the development of locally embedded policy scenarios in the SMARTEES case studies. The conceptual framework will inform the methodology for the co-creation of policy scenarios in each case, but also aims to be a stand-alone tool that policy-makers can use to conceptualize and implement the policy tools that can lead to citizen engagement with and acceptance of energy policy, the adoption of sustainable energy behaviours and to adequately manage setbacks and conflicts in the process.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 763912https://local-social-innovation.eu/resources/deliverables

The Rise of Innovation Districts: A New Geography of Innovation in America

As the United States slowly emerges from the great recession, a remarkable shify is occurring in the spatial geogrpahy of innovation. For the past 50 years, the landscape of innovation has been dominated by places like Silicon Valley - suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. A new complementary urban model is now emerging, giving rise to what we and others are calling "innovation districts." These districts, by our definition, are geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. They are also physically compact, transit-accessible, and technicall

Data-Driven Optimization for Bicycle Station Location in a Small to Medium-Sized City: The Case Study of Cuenca, Ecuador

Bike sharing systems (BSSs) are an important transportation alternative, and station distribution is a key component of these that is driven by user demand and resource constraints. Designing an effective BSS with appropriate station distribution requires a method that consists of steps structured in a flexible, parameterizable, repeatable, and organized way, based on and aligned with proven or accepted standards---particularly in resource-limited environments. This includes data-driven analysis of information relevant to BSS station design from various sources and in different formats. Models and algorithms are used to organize and examine the data, reduce redundant data, standardize factors, and find patterns that can inform the efficient design and implementation of a BSS. The algorithms and models used in the present study provide a data-driven approach to determining effective BSS station distribution in a city. Factor analysis and principal component analysis (PCA) were used as the various sources of data involved in the design of a BSS (i.e., data on traffic, demographic, and land use) can often overlap and/or have redundant data and these techniques allow minimizing superfluous data without losing relevant information. Econometric models were also used to identify the costs of pollutants, with the aim of locating stations in areas where pollution is a problem, and an emission-free BSS might be of greatest benefit. Patterns of potential users and mobility are derived from unsupervised learning algorithms. Finally, the set covering model (SCP), an optimization model for the distribution of stations, is used to define the number of stations in the city and their locations. This model\u27s objective is to minimize costs while still satisfying user demand. Using this data-driven approach can help guide the strategic design and planning of a BSS. A case study using this method was carried out using data from the city of Cuenca, Ecuador, the third most populous city in this developing country. Cuenca is considered a mid-sized city and is a UNESCO World Heritage Site. When compared to the costly Spanish--Ecuadorian consortium that implemented the currently BSS running in Cuenca, applying the proposed data-driven approach to this real-life practical case study resulted in a 70--90\% match in the locations of stations. The consortium had to study the place of implementation in a great amount of depth and obtained a similar design to that obtained in this case study. This demonstrates the potential of the proposed method as a simple, effective, and low-cost method for the strategic planning of BSSs in small and mid-sized cities. The present study provides an affordable solution to the design of BSS station distribution for cities without many resources. Using this method, cities can take advantage of a standardized platform to define a network of stations through an established step-by-step process. The method of BSS design proposed here demonstrates three significant advantages: 1) in-depth knowledge of the area in which a BSS is to be implemented location is not required, as the design can be driven by existing data and can even be adapted to new data sources; 2) implementation is economical as this reduces the need to hire expensive expert personnel with knowledge and experience in implementing BSSs; 3) the method is versatile since it can accept input data of various kinds, which enables the adaptation of the solution to any small or mid-sized city. This method, therefore, provides small and mid-sized resource-limited cities with a simple and cost-effective method to design a BSS that can be tailored to particular contexts and can be adapted to the specific goals of BSS implementation in a given city