Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Structural decomposition analysis (SDA) is a well-known approach to studying factors contributing to changes of an aggregate indicator in energy and emissions studies. Such studies normally rely on yearly data since input-output (I-O) tables are needed. With energy and economic transitions and seasonal factors, variations in renewable energy supply and in final demands of goods and services are becoming more prominent within a year in many countries. If monthly data are incorporated, some temporal dynamics within a year can be investigated in SDA application. In this paper, we propose an additive SDA framework and a multiplicative SDA framework that include monthly data to respectively reveal the drivers of temporal dynamics associated with energy/emissions embodiments and aggregate embodied intensity indicators. Based on China's 2018 and 2020 I-O tables, an empirical study is conducted using the proposed frameworks. The results obtained show that the increased granularity helps to reveal temporal dynamics mechanisms which will otherwise be overlooked. We discuss the findings and present areas for future research.