Roadmapping green economic restructuring: A Ricardian gradient approach

Alexander Vaninsky

Energy Economics, 2023, vol. 125, issue C

Abstract: This study presents a dynamic model for an optimal green restructuring of a national economy. The model aims to achieve an optimal increase in the gross domestic product (GDP), a decrease in energy consumption, and mitigation of greenhouse gas (GHG) emissions. It utilizes the environmentally- and energy-extended input-output model and Ricardian gradients (RGs) to handle the time-varying adjustment of the sectoral structure of the gross output (GO). The components of the RGs reveal the comparative advantage of the sectors of the economy with respect to the GDP to GO, energy to GO, or GHG to GO ratios, respectively. The trajectory of the structural change created by the model forms optimal acute angles with all RGs at each moment in time. This property makes the trajectory locally optimal. The objective of global optimization was to maximize the minimal improvement in all indicators at the final point. To achieve this goal, the model optimizes the trajectory parameters: the weight coefficients of the GDP, energy consumption, GHG emissions, and the speed of restructuring. A combination of local and global optimization enables the economic system to achieve short- and long-term economic and environmental goals together. Application of the model to the Chinese economy from 1995 to 2009 showed an increase in the GDP by 26.23%, along with a decrease in energy consumption by 28.95% and mitigation of GHG emissions by 33.78%.

Keywords: Economy–energy–environment optimization; Multi-criteria dynamic optimal economic restructuring; Locally and globally optimal trajectory; Ricardian gradient; Economy–energy–environment (E3) frame; Degrowth effect (search for similar items in EconPapers)
JEL-codes: C6 C67 O11 Q56 Q58 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:125:y:2023:i:c:s0140988323003869

DOI: 10.1016/j.eneco.2023.106888

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