Eco-innovation and openness: Mapping the growth trajectories and the knowledge structure of open eco-innovation

Open innovation runs contrary to the individualistic mentality of traditional corporate R&D implementation while embracing external cooperation in a complex world. Our main motivation for the study is to assess and characterize knowledge structure that represents radical transformation toward accelerating co-development of sustainable innovations. Our review points to the role of the open eco-innovation research landscape as an emerging research domain of potential contributions to sustainable development. Specifically, in this systematic analysis, we apply exploratory, bibliometric, and network visualization techniques to characterize the available knowledge in the field. We trace the growth trajectory of this emerging literature and map the knowledge base of the open eco-innovation (OE) research field. We conceptualised four phases of research domain development and recognised that OE is at the acceleration phase. We emphasized that a synthetic knowledge base is one of the basic ingredients of an open eco-innovation model in addition to analytic and symbolic knowledge bases. Finally, we highlighted what might seem to be budding theoretical perspectives underlining open eco-innovation

Measuring environmental policy stringency: Approaches, validity, and impact on environmental innovation and energy efficiency

Solid tests of the impact of environmental and energy policy on important economic outcomes, such as innovation, productivity, competitiveness and energy and carbon efficiency are impaired by the lack of appropriate empirical proxies for the commitment to, and stringency of, environmental policy. We contribute to the literature by: (1) computing different indicators of environmental policy stringency, (2) testing to what extent they convey similar insights through a statistical comparison exercise, and (3) showing the implications of using one or the other indicator in two illustrative empirical applications focused on environmental innovation and energy efficiency. We conclude by highlighting the implications of our analysis for empirical research focusing on the evaluation of policy impacts, and highlight fruitful future research avenues

Channeling diverse innovation pressures to support European sustainability transitions

Innovation patterns and processes must be aligned, and harnessed and accelerated across multiple domains to address our climate objectives and wider sustainability challenges. In this Perspective, we draw from original case studies on specific technologies and their related innovation systems in agriculture, buildings, electricity, ICT, industry, and transport across Germany, Italy, Poland, and the United Kingdom. Across these innovation systems, the Research Note discusses the technologies, infrastructure, actors, policies and institutions that may lead to, or prevent, successful and unsuccessful technology transitions. We synthesize this diverse evidence to offer five key findings on technology costs and configurations, diversity and multiplicity of actors, diversity of value systems, and countervailing pressures. These insights support the design of effective innovation and decarbonization policies to promote low-carbon transitions

‘The future costs of nuclear power using multiple expert elicitations: effects of RD&D and elicitation design

Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans

The effects of expert selection, elicitation design and R&D assumptions on experts' estimates of the future costs of photovoltaics

Expert elicitations of future energy technology costs can improve energy policy design by explicitly characterizing uncertainty. However, the recent proliferation of expert elicitation studies raises questions about the reliability and comparability of the results. In this paper, we standardize disparate expert elicitation data from five EU and US studies, involving 65 experts, of the future costs of photovoltaics (PV) and evaluate the impact of expert and study characteristics on the elicited metrics. The results for PV suggest that in-person elicitations are associated with more optimistic 2030 PV cost estimates and in some models with a larger range of uncertainty than online elicitations. Unlike in previous results on nuclear power, expert affiliation type and nationality do not affect central estimates. Some specifications suggest that EU experts are more optimistic about breakthroughs, but they are also less confident in that they provide larger ranges of estimates than do US experts. Higher R&D investment is associated with lower future costs. Rather than increasing confidence, high R&D increases uncertainty about future costs, mainly because it improves the base case (low cost) outcomes more than it improves the worst case (high cost) outcomes

Future prospects for energy technologies: insights from expert elicitations

Expert elicitation is a structured approach for obtaining judgments from experts about items of interest to decision makers. This method has been increasingly applied in the energy domain to collect information on the future cost, technical performance, and associated uncertainty of specific energy technologies. This article has two main objectives: (1) to introduce the basics of expert elicitations, including their design and implementation, highlighting their advantages and disadvantages and their potential to inform policymaking and energy system decisions; and (2) to discuss and compare the results of a subset of the most recent expert elicitations on energy technologies, with a focus on future cost trajectories and implied cost reduction rates. We argue that the data on future energy costs provided by expert elicitations allows for more transparent and robust analyses that incorporate technical uncertainty, which can then be used to support the design and assessment of energy and climate change mitigation policies.V. Bosetti would like to acknowledge funding from the ERC (grant agreement 336703 – RISICO). L.D. Anadón, L. Aleluia Reis and E. Verdolini would like to acknowledge funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement 730403 – INNOPATHS)

Transition Towards a Green Economy in Europe: Innovation and Knowledge Integration in the Renewable Energy Sector

This paper investigates the fragmentation of the EU innovation system in the field of renewable energy sources (RES) by estimating the intensity and direction of knowledge spillovers over the years 1985-2010. We modify the original double exponential knowledge diffusion model proposed by Caballero and Jaffe (1993) to provide information on the degree of integration of EU countries’ RES knowledge bases and to assess how citation patterns changed over time. We show that EU RES inventors have increasingly built “on the shoulders of the other EU giants”, intensifying their citations to other member countries and decreasing those to domestic inventors. Furthermore, the EU strengthened its position as source of RES knowledge for the US. Finally, we show that this pattern is peculiar to RES, with other traditional (i.e. fossil-based) energy technologies and other radically new technologies behaving differently. We provide suggestive, but convincing evidence that such decrease in fragmentation around the turn of the century emerged as a result of the EU increased support for RES taking mainly the form of demand-pull policies

A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury

The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

The extreme variability in symptom presentation reveals that individuals diagnosed with a first-episode psychosis (FEP) may encompass different sub-populations with potentially different illness courses and, hence, different treatment needs. Previous studies have shown that sociodemographic and family environment factors are associated with more unfavorable symptom trajectories. The aim of this study was to examine the dimensional structure of symptoms and to identify individuals’ trajectories at early stage of illness and potential risk factors associated with poor outcomes at follow-up in non-affective FEP. One hundred and forty-four non-affective FEP patients were assessed at baseline and at 2-year follow-up. A Principal component analysis has been conducted to identify dimensions, then an unsupervised machine learning technique (fuzzy clustering) was performed to identify clinical subgroups of patients. Six symptom factors were extracted (positive, negative, depressive, anxiety, disorganization and somatic/cognitive). Three distinct clinical clusters were determined at baseline: mild; negative and moderate; and positive and severe symptoms, and five at follow-up: minimal; mild; moderate; negative and depressive; and severe symptoms. Receiving a low-dose antipsychotic, having a more severe depressive symptomatology and a positive family history for psychiatric disorders were risk factors for poor recovery, whilst having a high cognitive reserve and better premorbid adjustment may confer a better prognosis. The current study provided a better understanding of the heterogeneous profile of FEP. Early identification of patients who could likely present poor outcomes may be an initial step for the development of targeted interventions to improve illness trajectories and preserve psychosocial functioning