Improved granularity in input-output analysis of embodied energy and emissions: The use of monthly data

Abstract

Input-output (I-O) analysis has been widely used in national energy and energy-related emission studies. These studies are generally conducted using annual data. In a growing number of countries, significant variations in renewable energy supply and in final demands of goods and services are observed over time within a year. These temporal variations cannot be captured in I-O analysis using annual data. To investigate such temporal dynamics, we propose an I-O analysis framework that uses monthly data. Further to that, the drivers in embodiments and aggregate embodied intensity (AEI) indicators are studied via Structural Decomposition Analysis (SDA). Additive SDA and multiplicative SDA are applied to reveal the temporal dynamics associated with energy and emission embodiments and AEI indicators, respectively. An application study using China's 2018 datasets show the importance of temporal dynamics in studying its embodiments and AEI indicators, with drivers of their changes show significant variations over months. It is shown that increased data granularity reveals useful information which would otherwise undetected if annual data are employed. Implications of the findings on future research are discussed.