Journal•ISSN: 0040-1625
Technological Forecasting and Social Change
Elsevier BV
About: Technological Forecasting and Social Change is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Biology. It has an ISSN identifier of 0040-1625. Over the lifetime, 7145 publications have been published receiving 291028 citations. The journal is also known as: Technological forecasting & social change.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a Gaussian process classifier was used to estimate the probability of computerisation for 702 detailed occupations, and the expected impacts of future computerisation on US labour market outcomes, with the primary objective of analyzing the number of jobs at risk and the relationship between an occupations probability of computing, wages and educational attainment.
4,853 citations
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TL;DR: It is described that the emergence of a new innovation system and changes in existing innovation systems co-evolve with the process of technological change, and a method for systematically mapping those processes taking place in innovation systems and resulting in technological change is proposed.
2,263 citations
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TL;DR: In this article, the authors present an overview of the greenhouse gas (GHG) emissions scenarios that form the analytical backbone for other contributions to this Special Issue, and analyze the feasibility, costs and uncertainties of meeting a range of different climate stabilization targets in accordance with Article 2 of the United Nations Framework Convention on Climate Change.
1,129 citations
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TL;DR: An overview of the origins of technology roadmapping is provided by means of a brief review of the technology and knowledge management foundations of the technique in the context of the fields of technology strategy and technology transitions.
1,091 citations
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TL;DR: In this article, a substitution model of technological change based upon a simple set of assumptions has been presented, and the mathematical form of the model is shown to fit existing data in a wide variety of substitutions remarkably well.
1,068 citations