Socio-economic Drivers of Energy Consumption: Evidence from Three Urban Agglomerations in the Yangtze River Economic Belt

This study explores the Socio-economic drivers of energy consumption in three Urban Agglomerations and cities in the Yangtze River Economic Belt during the 2011–2020 period. Using the logarithmic mean Divisia index (LMDI) method decompose the change in energy consumption into five factors: energy intensity effect ( $$\varDelta C_{EI}^{{}}$$ Δ C EI ), per urban population GDP effect ( $$\varDelta C_{PG}^{{}}$$ Δ C PG ), urbanization rate effect ( $$\varDelta C_{UR}^{{}}$$ Δ C UR ), investment population support coefficient effect ( $$\varDelta C_{IP}^{{}}$$ Δ C IP ) and investment effect ( $$\varDelta C_{I}^{{}}$$ Δ C I ). The main results showed the following: (1) $$\varDelta C_{I}^{{}}$$ Δ C I ranks the first most important factor in three Urban Agglomerations and in whole cities from 2011–2020 period; (2) The impact of $$\varDelta C_{UR}^{{}} $$ Δ C UR on the reduction of energy consumption is negative in three Urban Agglomerations and in whole cities. (3) $$\varDelta C_{EI}^{{}}$$ Δ C EI has a strong impact on swelling energy consumption in three Urban Agglomerations and in 68 cities during the study period. The most of the cities mainly focusing on Urban Agglomeration in the middle reaches of the Yangtze River as well as Yangtze River Delta Urban Agglomeration. (4) $$\varDelta C_{IP}^{{}}$$ Δ C IP has a most powerful force to reduce energy consumption in three Urban Agglomerations and in whole cities over the whole study period. (5) The role of $$\varDelta C_{PG}^{{}}$$ Δ C PG in the increase of energy consumption cannot be disregarded in three Urban Agglomerations as well as in cities particularly showing in Shanghai, Nanjing. Therefore, decision-makers should balance the social and economic implications of energy usage in addition to developing suitable fixed asset investment strategies.