Abstract
This study analyzes seven bioeconomy sectors with the aim of establishing the leading contributing sectors to gross domestic product (GDP), and also determines the future relationship between bioeconomy and the national economy in Japan. We use data from World Input–Output Database (WIOD), International Renewable Energy Agency (IRENA), and the World Bank Group for this analysis. First, we use principal component analysis (PCA) techniques to identify the bioeconomy sectors that contribute significantly to the national economy. We find through the PCA that all the bioeconomy sectors that we analyzed contribute almost uniformly and significantly to the national economy. We also find forestry and wood sectors to be the most significant contributing bioeconomy sectors. We use the autoregressive distributed lag (ARDL) bounds test to prove the existence of short-run and long-run relationships between bioeconomy and gross domestic product (GDP). We finally use the vector error correction Granger causality model to establish a bicausality between bioeconomy and GDP in the long-run, but not in the short-run.
Highlights
Since the Industrial Revolution, the world’s economy has heavily depended on fossil feedstocks such as crude oil, natural gas, and coal for industrial use to produce diverse products such as fuel, chemicals, pharmaceuticals, soaps, synthetic fiber, plastics, etc. to meet the growing demand of the population [1]
Several bioeconomy sectors contribute to the gross domestic product (GDP) growth of a country. Which of these sectors are the key contributors? Which of these factors’ contribution is negligible? What are the short-run and the long-run relationships between bioeconomy and GDP growth? What is the causal effect of bioeconomy on the GDP growth and vice versa? The main aim of this study is to answer these important questions by analyzing the principal sectors of bioeconomy that drive its contribution to the GDP growth of Japan
Our study utilized recent econometric techniques such as the autoregressive distributed lag (ARDL) and Vector Error Correction Model (VECM) Granger causality model to reveal the direction of the causality among the variables within the current study period
Summary
Since the Industrial Revolution, the world’s economy has heavily depended on fossil feedstocks such as crude oil, natural gas, and coal for industrial use to produce diverse products such as fuel, chemicals, pharmaceuticals, soaps, synthetic fiber, plastics, etc. to meet the growing demand of the population [1]. Since the Industrial Revolution, the world’s economy has heavily depended on fossil feedstocks such as crude oil, natural gas, and coal for industrial use to produce diverse products such as fuel, chemicals, pharmaceuticals, soaps, synthetic fiber, plastics, etc. Two major concerns have emerged with the continuous use of fossil raw materials. It is fast depleting, which has raised sustainability and cost concerns [2]. It is a significant contributor to the increasing level of CO2 in the atmosphere, which is causing massive environmental problems [3]. The need for a comparatively more environmentally friendly and sustainable energy and raw material source is inevitable. Bioeconomy has subsequently emerged as a concept that can satisfy these needs
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