سلولهای خورشیدی حساس به رنگ
Abstract
1. Introduction
2. Related literature
3. Framework and data
4. Results and findings
5. Conclusions and discussions
Acknowledgments
References
Abstract
The increasingly uncertain dynamics of technological change pose special challenges to traditional technology forecasting tools, which facilitates future-oriented technology analysis (FTA) tools to support the policy processes in the fields of science, technology & innovation (ST&I) and the management of technology (MOT), rather than merely forecasting incremental advances via analyses of continuous trends. Dye-sensitized solar cells are a promising third-generation photovoltaic technology that can add functionality and lower costs to enhance the value proposition of solar power generation in the early years of the 21st century. Through a series of technological forecasting studies analyzing the R&D patterns and trends in Dye-sensitized solar cells technology over the past several years, we have come to realize that validating previous forecasts is useful for improving ST&I policy processes. Yet, rarely do we revisit forecasts or projections to ascertain how well they fared. Moreover, few studies pay much attention to assessing FTA techniques. In this paper, we compare recent technology activities with previous forecasts to reveal the influencing factors that led to differences between past predictions and actual performance. Beyond our main aim of checking accuracy, in this paper we also wish to gain some sense of how valid those studies were and whether they proved useful to others in some ways.
Introduction
Newly emerging science and technologies (NESTs) are expected to bring both considerable wealth and numerous opportunities and challenges. As NESTs can be radically novel, relatively fast-growing, and characterized by a certain degree of coherence, these forms of technologies tend to be more dependent on intermittent advances (Rotolo et al., 2017). The anticipated (disruptive) impacts on markets and on society are more difficult to foresee than a steady and incremental innovation process, and the highly uncertain dynamics of NESTs pose special challenges to traditional technology forecasting tools. In an environment facing the complexity of a growing number of NESTs, decision makers need to capture current and strategic intelligence on a range of technologies and make forward-looking assessments. Over the years, future-oriented technology analysis (FTA) tools have expanded beyond forecasting incremental advances. Two papers made a case for methodological enrichment to address expanding challenges, contributing to the FTA blend of “technology forecasting” and “foresight” approaches (Coates et al., 2001; Technology Futures Analysis Methods Working Group, 2004). Others compile alternative FTA-related methods, distinguishing types and study purposes (Porter, 2010; Rader and Porter, 2008). Most FTA endeavors now purport to inform policy processes for those addressing Science, Technology & Innovation (ST&I) and the management of technology (MOT). Hence, the ability to explore multiple potential innovation pathways (Robinson and Propp, 2008) becomes essential.