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

Social innovation enablers to unlock a low energy demand future

We initiate the process of developing a comprehensive low energy demand (LED) innovation narrative by applying the framework 'Functions of Innovation Systems' (FIS) and identifying the key conditions under which technology interventions can be improved and scaled up over the next three decades to contribute to climate change mitigation. Several studies have argued that the potential for LED-focused mitigation is much larger than previously portrayed and have shown that adopting a wide variety of energy-reducing activities would achieve emissions reductions compatible with a 1.5 C temperature target. Yet, how realistic achieving such a scenario might be or what processes would need to be in place to create a pathway to a LED outcome in mid-century, remain overlooked. This study contributes to understanding LED's mitigation potential by outlining narratives of LED innovation in three end-use sectors: industry, transport, and buildings. Our analysis relies on the FIS approach to assess three innovations in these sectors. A key insight is that the distinct characteristics of LED technology make enabling social innovations crucial for their widespread adoption. Finally, we identify a set of eight social enablers required for unlocking LED pathways

Assessment of vocal cord nodules: A case study in speech processing by using Hilbert-Huang Transform

Vocal cord nodules represent a pathological condition for which the growth of unnatural masses on vocal folds affects the patients. Among other effects, changes in the vocal cords' overall mass and stiffness alter their vibratory behaviour, thus changing the vocal emission generated by them. This causes dysphonia, i.e. abnormalities in the patients' voice, which can be analysed and inspected via audio signals. However, the evaluation of voice condition through speech processing is not a trivial task, as standard methods based on the Fourier Transform, fail to fit the non-stationary nature of vocal signals. In this study, four audio tracks, provided by a volunteer patient, whose vocal fold nodules have been surgically removed, were analysed using a relatively new technique: the Hilbert-Huang Transform (HHT) via Empirical Mode Decomposition (EMD); specifically, by using the CEEMDAN (Complete Ensemble EMD with Adaptive Noise) algorithm. This method has been applied here to speech signals, which were recorded before removal surgery and during convalescence, to investigate specific trends. Possibilities offered by the HHT are exposed, but also some limitations of decomposing the signals into so-called intrinsic mode functions (IMFs) are highlighted. The results of these preliminary studies are intended to be a basis for the development of new viable alternatives to the softwares currently used for the analysis and evaluation of pathological voice

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)

A research and development investment strategy to achieve the Paris climate agreement

Climate stabilization requires the deployment of several low-carbon options, some of which are still not available at large scale or are too costly. Governments will have to make important decisions on how to incentivize Research and Development (R&D). Yet, current assessments of climate neutrality typically do not include research-driven innovation. Here, we link two integrated assessment models to study R&D investment pathways consistent with climate stabilization and suggest a consistent financing scheme. We focus on five low-carbon technologies and on energy efficiency measures. We find that timely R&D investment in these technologies lowers mitigation costs and induces positive employment effects. Achieving 2 °C (1.5 °C) requires a global 18% (64%) increase in cumulative low-carbon R&D investment relative to the reference scenario by mid-century. We show that carbon revenues are sufficient to both finance the additional R&D investment requirements and generate economic benefits by reducing distortionary taxation, such as payroll taxes, thus enhancing job creation