How does vertical fiscal imbalance affect energy consumption in China?

Abstract

Evaluating the influencing factors of energy consumption at the system level has become a focus in high-quality economic development. However, the existing literature has not yet explored whether and how vertical fiscal imbalance (VFI) affects energy consumption. Thus, we aim to address this gap. We empirically examine the effect of VFI on energy consumption and its possible mechanisms by using China’s panel data at the province level from 1998 to 2018. We find that VFI significantly increases energy consumption, but this influence has evident regional heterogeneity. Moreover, VFI indirectly affects energy consumption through industrial structure upgrading, and the positive influence of VFI on energy consumption is strengthened by the improvement in intergovernmental fiscal transfers. These findings give political references to deepen fiscal reform, reduce energy consumption, and implement strategies for energy saving and emission reduction.

Keywords:

• vertical fiscal imbalance
• energy consumption
• industrial structure upgrading
• intergovernmental fiscal transfers
• Chinese provinces

Acknowledgements

We express our genuine appreciation to the Natural Science Foundation of Jiangsu Province of China (BK20190792) and the Innovation and Entrepreneurship Doctoral Program of Jiangsu for supporting this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The 1994 tax-sharing system reform initiates a number of fiscal system reforms that centralize the tax revenues by the central government while devolving more spending responsibilities to local governments (Du, Fang, and Jin 2014).

2 The central system levies central taxes, whereas the local system levies local taxes. The central system first collects central-local shared taxes, but the earnings from these taxes are distributed between the central and local governments.

3 Local officials are not elected by local voters, but, rather, are nominated by central officials (He 2015).

4 The panel data form of the STIRPAT model is $I=\alpha P_{it}^b{A}_{it}^c{T}_{it}^{d}e,$ where I, P, A, T denote environmental impact, population size, per capita wealth, and technological level. After taking logarithms on both sides, we can obtain $\ln I_{it}=\alpha +b\ln P_{it}+c\ln A_{it}+d\ln T_{it}+e_{it}.$ One of the advantages of the STIRPAT model is that it not only allows the coefficients to be estimated as parameters but also allows the appropriate decomposition and improvement of the influence factors (Shao et al. 2011).

5 Considering the increase of population size in the process of urbanization, the study analyzes the effect of structural changes in population size on energy consumption by introducing the level of urbanization.

6 Energy consumption in the current period affects VFI in the current period or later periods. In other words, if we can find that the lag phase of VFI significantly affects the current period of energy consumption, we can confirm the causal relationship between VFI and energy consumption to some extent.

7 The coastal areas consist of 11 regions: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The inland areas consist of 19 regions: Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.