Customize the use of cookies

This website uses cookies to provide more efficient navigation and analyze visitor traffic. You will find detailed information about them below.

Cookies classified as "Necessary" will be stored in your browser, as they are essential for enabling the basic functionalities of the site. We also use third-party cookies aimed at analytics (Google Analytics), which help us analyze how you use this website. You can choose to enable or disable some of these cookies, but doing so may affect your browsing experience.

Always Active

These cookies are required to provide basic functionality of the website and cannot be disabled. They do not store any private or personally identifiable data.

These cookies allow us to understand how visitors interact with the website and provide information related to the number of visits, traffic sources, and bounce rates.

These cookies are used to provide visitors with personalized ads based on the pages they previously visited and to analyze the effectiveness of advertising campaigns. They are usually related to the integration of social media videos on the website.

ate: 09/03/2023.

Publication type: Research article.

Author(s): Jaime Nieto, Pedro B. Moyano, Diego Moyano, Luis Javier Miguel.

Keywords: Energy & materials, Energy transition, Sustainable development

Short description:

Input–output tables (IOTs) provide a relevant picture of economic structure as they represent the composition and interindustry relationships of an economy. The technical coefficients matrix (A matrix) is considered to capture the technological status of an economy; so, it is of special relevance for the evaluation of long-term, structural transformations, such as sustainability transitions in integrated assessment models (IAMs). The A matrix has typically been considered either static or exogenous. Endogenous structural change has rarely been applied to models. The objective of this paper is to analyze energy intensity, a widely used variable in IAMs, and its role as a driver of structural change. We therefore identify the most relevant technical coefficients in the IOTs time series and estimate an econometric model based on the energy intensity of five different final end-use energy sources. The results of this analysis show that energy intensity has a significant influence on the evolution of the A matrix and should therefore be taken into consideration when analyzing endogenous structural change in models