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November 30, 2025Oxford Review of Economic Policy3 citationsOpen Access

Understanding and modelling structural economic change as a dynamic resource creation process—an application to low-carbon transitions

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DZDimitri ZenghelisHPHector PollittJMJean-François Mercure

Key Points

  • Structural change drives innovation in a low-carbon economy, enabling adoption of new technologies and behaviors.
  • Evidence shows interactions of strategic complementarities and spillovers influence technological change.
  • Observational analysis of economic models demonstrates limitations with static optimization in predicting dynamic changes.
  • This framework highlights the need for robust scenario analysis to inform policy choices and manage risks.

Abstract

Abstract The global economy is constantly undergoing large-scale structural change. Policy-makers face the task of deciding how technological change is directed, where limited public funds should be spent, and how to induce private investment. This paper provides an understanding of structural economic change as a dynamic process of resource creation, applying findings to the transition to a low carbon economy. The aim of this paper is to provide an explicit account of the mechanisms and processes that drive and steer innovation and adoption of new technologies, networks, and behaviours. These include strategic complementarities, expectation formation, and the role of multiple actors. We distinguish between structural change within a sector and knock-on cascades and spillovers across sectors, both of which play crucial roles in driving transformational change. Understanding these processes then allows us to develop the analytical tools to guide appropriate policy choices. With the support of a simple modelled illustration, we show that structural change cannot be analysed using a static optimization approach based on historic data. Economic models remain valuable in providing insights, but have fundamental limitations when making predictions in the context of reinforcing feedbacks and increasing returns. Instead, they are best used to inform risks and steer policy in the direction suited to achieve strategic objectives. We find that a variety of models, complemented by a range of qualitative and non-modelling analytical approaches, with different strengths and weaknesses, can help to identify tipping points and allow scenario analysis to articulate risks and opportunities, thereby guiding policy choice.

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Cite This Study

Zenghelis et al. (2025) studied this question.

synapsesocial.com/papers/692b9da01d383f2b2a37a3c2https://doi.org/10.1093/oxrep/graf036
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