Global Transitions最新文献

Product Development Partnerships (PDPs) are organizations that target economically-deprived markets, aiming to develop a product by integrating contributions of diverse partners. They have gained importance in the global health arena by targeting and developing drugs for neglected tropical diseases. Their projects are difficult to manage given the multiplicity of roles, objectives and institutional logics of the partners that participate in the collaboration. We explore activities and strategies that platform PDPs – PDPs that orchestrate hybrid project networks – employ to stimulate collaboration between heterogeneous actors. Based on the analysis of two platform PDP projects targeting poverty-related diseases, we propose a framework outlining two innovation collaboration models. With this we support the better understanding of PDPs, which are gaining momentum to facilitate socio-technical transitions across the globe to tackle poverty-related diseases.

In this study, we analyse the role of forced lockdowns on electricity consumption behaviour and its effect on momentary transition in electricity use. Electricity consumption data for residential, commercial and industrial consumers within the Lagos metropolis representing 259 electrical feeder locations were collected and analysed under three scenarios: first, we analyse a business-as-usual scenario without a lockdown; secondly, we analyse the case of a partial lockdown; and finally, we analyse the case of a total lockdown. The study revealed that aside government announcement of the lockdown, certain social practices triggered changes in electricity consumption and use leading to momentary energy transition. Within the residential sector, increased cooking, home laundry, showering, and some professional practices that moved to the homes impacted on higher electricity consumption. Reduced manufacturing practices limited to those involved in food, personal care and pharmaceutical products led to a reduction in electricity use within the industrial sector, while reduced electricity use in the commercial sector was triggered mainly by a scaling down of trading services to essentials. The study concludes by highlighting the impact of changes in electricity demand and consumption under these scenarios and its implications for energy transition and electricity planning.

In collective self-consumption (CSC) communities, citizens come together to produce renewable energy and need to find ways to organise the sharing of consumption at the (micro-)local level. The articulation between the exposure of individual practices and the collective objective of lowering consumption outside solar periods leads to dynamics of social control and privacy preservation that vary according to the nature of spaces. Observing two operations of solar energy sharing in multi-dwelling buildings, our ethnographic analysis investigates the practices of occupying different types of space – from the common to the private - as well as the scenes of discussion among individuals. In this sense, our research reveals a strong intertwining between, on the one hand, the governance of energy communities and, on the other, the spaces in which consumption practices, energy accounting and deliberation processes take place.

The importance of and interest to research and investigations of atmospheric composition and its modeling for different applications are substantially increased. Air quality forecast (AQF) and assessment systems help decision makers to improve air quality and public health, mitigate the occurrence of acute air pollution episodes, particularly in urban areas, and reduce the associated impacts on agriculture, ecosystems and climate. Advanced approaches in AQF combine an ensemble of state-of-the-art models, high-resolution emission inventories, satellite observations, and surface measurements of most relevant chemical species to provide hindcasts, analyses, and forecasts from global to regional air pollution and downscaling for selected countries, regions, and urban areas. Based on published reviews and recent analyses, the article discusses main gaps, challenges, applications and advances, main trends and research needs in further advancements of atmospheric composition and air quality modeling and forecasting.

With a large international sample (n = 8317), the present study examined which beliefs and attitudes about COVID-19 predict 1) following government recommendations, 2) taking health precautions (including mask wearing, social distancing, handwashing, and staying at home), and 3) encouraging others to take health precautions. The results demonstrate the importance of believing that taking health precautions will be effective for avoiding COVID-19 and generally prioritizing one’s health. These beliefs continued to be important predictors of health behaviors after controlling for demographic and personality variables. In contrast, we found that perceiving oneself as vulnerable to COVID-19, the perceived severity of catching COVID-19, and trust in government were of relatively little importance. We also found that women were somewhat more likely to engage in these health behaviors than men, but that age was generally unrelated to voluntary compliance behaviors. These findings may suggest avenues and dead ends for behavioral interventions during COVID-19 and beyond.

Spatial planning systems at local and regional levels are often not well-adapted to the growth of small-scale and local social innovations in renewable energy. Participatory decision support tools have been developed to support the implementation of many areas of environmental policy, but are less common in energy contexts. In response to this knowledge gap, we discuss, compare and contrast the participatory development of two different types of digital support tools for the cases of Spain and the Netherlands, leading to insights into the characteristics that local-level stakeholders find particularly desirable. We adopt an integrative approach, hybridizing implementation theory and action research for, respectively, analysis of implementation characteristics of key actors, and knowledge co-construction with participant stakeholders. The tools developed represent two extremes of the spatial decision support tool spectrum, a simple touchscreen application on the one hand (COLLAGE) and a more complicated spatial model on the other (APoLUS). COLLAGE was used and well-liked by stakeholders, whereas APoLUS was not adopted by the participant group, who nevertheless contributed much essential information to its development. We identify eight key differences between the two tools which shed light on the nature of bottom-up energy transition processes: 1: Target users; 2: Target scale of action; 3: Relevance to users’ needs; 4: Interactive quality; 5: Key emphasis; 6: Level of complexity; 7: Ease of communication of tool rationale; 8: Cost. The differences between these tools also relate to a recognized dichotomy in sustainability transition research, with complex spatial support systems like APoLUS tending towards descriptive-analytical modes of sustainability science and simpler tools like COLLAGE being more clearly related to transformational modes. Approaches to supporting local-scale energy transitions that are able to span both modes are likely to become increasingly relevant as the climate crisis evolves. We also identify a research gap between support tools for implementation of established policy and support tools for transformative actions at local scales, and suggest the study of digital “transition support tools” as a promising avenue for future research.

Predictive modeling based on machine learning with medical data has great potential to improve healthcare and reduce costs. However, two hurdles, among others, impede its widespread adoption in healthcare. First, medical data are by nature longitudinal. Pre-processing them, particularly for feature engineering, is labor intensive and often takes 50–80% of the model building effort. Predictive temporal features are the basis of building accurate models, but are difficult to identify. This is problematic. Healthcare systems have limited resources for model building, while inaccurate models produce suboptimal outcomes and are often useless. Second, most machine learning models provide no explanation of their prediction results. However, offering such explanations is essential for a model to be used in usual clinical practice. To address these two hurdles, this paper outlines: 1) a data-driven method for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling; and 2) a method of using these features to automatically explain machine learning prediction results and suggest tailored interventions. This provides a roadmap for future research.

Environmental or ‘ecological’ footprints have been widely used in recent years as indicators of resource consumption and waste absorption transformed on the basis of biologically productive land area [in global hectares (gha)] required per capita with prevailing technology. It has been employed here to estimate the footprints associated with three low carbon, more electric transition pathways for the United Kingdom (UK): described as ‘Market Rules’ (MR), ‘Central Co-ordination’ (CC) and ‘Thousand Flowers’ (TF) respectively. These pathways focus on the power sector, including the potential for increasing use of low-carbon electricity for heating and transport, within the context of critical European Union developments and policies. Their overall environmental footprint has been disaggregated into various components: bioproductive and built land, carbon emissions, embodied energy, materials and waste, transport, and water consumption. This component-based approach provides, for example, a means for evaluating the implications for the so-called ‘energy-land-water nexus’. Electricity demand was projected to decrease significantly under the TF pathway by 2050, but its total environmental footprint (EF) was greater than either that under the MR or CC pathways. This is mainly due to the increase in the use of bioproductive land associated with solid biofuel production and that of the carbon footprint, which are both seen to be larger than under either the MR or CC cases. Water and waste footprint components made almost negligibly small contributions under all three transition pathways. Lessons can clearly be drawn for other industrialised nations attempting to decarbonise their electricity generation systems, although local circumstances will determine the country-specific findings.

Climate change is disruptive because virtually all aspects of our lives are best located in the Goldilocks zone: the place where it is “not too hot and not too cold but just right”. Rising greenhouse gases are heating the globe faster than has ever occurred before, and record-breaking intense, extreme weather is becoming more common. In the last three years unexpectedly severe hurricanes, heatwaves and forest fires have affected millions of people. What makes disruption dangerous? I suggest low predictability, high scale, speed and lack of reversibility are good guides. Risks to health are direct and indirect and include also the “transition risks” associated with responses to climate change. Sometimes disruption is welcome because it provides opportunities for radical action that would not be possible otherwise: in this vein, it has been argued that climate change is “not just a challenge, but the greatest public health opportunity of the 21st century”. The co-benefits agenda (justifying climate interventions on the basis of positive outcomes in other sectors) is beguiling: it promises a relatively smooth way forward, but might an emphasis on win-win interventions distract from the radical changes that are needed? There are other reasons for caution – the intersection of climate and health policies may contain trade-offs as well as synergies, and the prospect of future gains that outweigh immediate losses is seldom, on its own, sufficient to change in-grained behaviours and policies.

Open innovation strategies in large firms have been changing considerably during the last 15 years. Some multinationals are now taking a long-term, strategic approach to Open innovation, thereby actively developing a regionally bounded innovation ecosystem. This approach goes beyond the tradition of open innovation, which emphasized the opening of firms’ boundaries for inbound and outbound knowledge flows. In the new approach, multinationals actively shape their innovation environment to better exploit external talent and expertise, share public infrastructure, raise funds and influence public policies - the key enablers for establishing a vibrant, world-class research and development (R&D) environment. We examine one such regionally embedded innovation ecosystem set up by Janssen Pharmaceuticals at its global R&D centre in Beerse, Belgium.

We develop a conceptual framework by integrating Open innovation, Innovation Ecosystems and Regional Economics literature streams. This combination of the three distinct theoretical approaches is required to explain the benefits and working of Janssen Pharmaceuticals’ regionally embedded innovation ecosystem.