Economic spillover from renewable energy industries: an input-output analysis

ABSTRACT

This study aims to identify the changes in the economic relations between industries resulting from the dissemination of renewable energy in Korea. Although previous studies have examined the ripple effect of renewable energy using an input-output table, there are limited studies that have performed an accurate analysis due to the absence of real economic transaction data for renewable energy and other industries. Thus, an input-output analysis was performed using actual data for 2010 and 2015 as well as 2020 estimated by RAS method. Backward and forward linkage effects and inducement effects were estimated. Specifically, the renewable energy value chain industries were classified into Raw material, Machine part, Infra structure, and End product. The main findings are: (1) the effect ratio of renewable energy industry decreased to 0.9572 reaching a similar level to the existing electric power industry; (2) the production inducement effects were 1.1278(2010), 0.7016(2015), and 0.7203(2030), and value-added inducement effects were 0.6043(2010), 0.3860(2015), and 0.4069(2020); and (3) the proportion of inducement effect on the service industry decreased approximately from 40% to 34%. These findings show how the renewable energy industry has changed from the early stages to the present and its economic importance in other industries resulting from renewable energy production and distribution. This study provides basic data for diagnosing the economic spillover and establishing energy policies due to the dissemination of renewable energy.

KEYWORDS:

• Economic spillover
• Input-output analysis
• Linkage effects
• Production inducement effects
• Renewable energy value chain
• Value-added inducement effects

Notes

¹ Submission under the Paris Agreement, The Republic of Korea’s Update of its First Nationally Determined Contribution, Ministry of Foreign Affairs, 2020. 12. 30.

² In Korea, I-O tables are created based on actual data at five-year intervals, and there are annually published I-O tables that are created based on those five-year I-O tables. This study used the actual data (five-year intervals) of 2010 and 2015 in order to increase the accuracy of the estimated values.

³ RAS method is used for estimating an I-O table for the prediction year based on two different years of I-O tables. For detailed explanation of this method, refer to Stone (1961), Miller and Blair (1985, 288), and Hewings (1977).

⁴ Used amount of electricity increased by 33.4% for domestic use, 41.5% for industrial use, 30.5% for commercial use, and 22.7% for housing use over the past 10 years compared to 2009.