The current dynamics of change, including climate change, resource depletion, energy transition, internet of things, etc. will have substantial impacts on the functioning of contemporary business models. Most firms and companies, however, still largely focus on efficiency strategies leading to sub-optimal solutions (reducing bad impact), rather than radically changing their business model to develop new transaction models more appropriate for today’s world (doing better things). However, persistent sustainability issues arising from our changing societal systems, require more radical and structural changes both within and between organizations to change the way we do business. There is limited empirically established research literature on how businesses can be more proactive in this regard and how they can move beyond “management of unsustainability”. In this paper, we present a transformative business model innovation approach inspired by transition theory combining elements of systems thinking, value-inspired envisioning, reflexive design and co-creation that was pioneered for a consortia of reuse centers in the province of Limburg, Belgium. We demonstrate that this approach contributed to the development of new business model concepts, was conducive for mobilizing support and resources to ensure follow up activity and holds potential to promote a more proactive role for businesses as agents of transformative change.

Growing global plastic production combined with poor waste collection has led to increasing amounts of plastic debris being found in oceans, rivers and on shores. The goal of this study is to provide an overview on currently available technological solutions to tackle marine plastic litter and to assess their potential use in developing countries. To compile an inventory of technological solutions, a dedicated online platform was developed. A total of 51 out of initially 75 submitted solutions along the plastics value chain were assessed by independent experts. Collection systems represent more than half of the shortlisted solutions. A quarter include processing and treatment technologies, either as a stand-alone solution (30%) or, more commonly, in combination with a first litter capturing step. Ten percent offer digital solutions. The rest focuses on integrated waste management solutions. For each stage in the source-to-sea spectrum—land, rivers, sea—two illustrative examples are described in detail. This study concludes that the most cost-effective type of solution tackles land-based sources of marine litter and combines technology with people-oriented practices, runs on own energy sources, connects throughout the plastics value chain with a convincing valorization plan for captured debris, and involves all relevant stakeholders.

In a small, open and resource-poor economy, import and export dependency have an ever-growing impact on local policy decisions, which makes local (environmental) policy-makers increasingly depend on global data. This increases the interest in models that link local production and consumption data to global production, trade and environmental data. The recent increase in availability of global environmentally extended multi-regional input-output tables (EE-MRIO tables) provides an opportunity to link them with existing local environmentally extended input-output tables (EE-RIO tables). These combined tables make it possible (1) to analyse the links between local and global production and consumption and (2) to study global value chains, material use and environmental impacts simultaneously. However, estimations using input-output (I–O) analyses contain errors due to imperfect databases. In this article the magnitude of specification, aggregation and time errors are estimated and compared. The results show the need to combine local datasets with multi-regional ones and show that highest detailed (country and sector levels) as well as time series of I–O tables are the way forward for using I–O analyses in local policy-making. The paper provides guidance on trading off investments in model adoption and/or extension and the reliability of estimation results.