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Vertical fiscal imbalance and energy intensity in China

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Abstract

The effective evaluation of the factors of energy intensity (EI) at the system level has become an engaging topic in the high-quality development of the economy. However, none of the existing studies have explored the relationship between vertical fiscal imbalance (VFI) and EI systematically and deeply. In this work, Chinese provincial panel data for the period of 1998–2017 are used to analyze the influence of VFI on EI by using the spatial Durbin model. Findings demonstrate an evident spatial correlation for the effect of VFI on EI, indicating that EI in local and surrounding regions can increase as VFI improves. Results are tested for robustness using different methods. Findings further show that VFI has a positive effect on EI by improving intergovernmental fiscal transfers and hindering technological innovation. The findings of this work could help the central and provincial governments of China reduce EI problems through fiscal methods.

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Fig. 1
Fig. 2

Source: CESY and CSY

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Notes

  1. VFI refers to the degree of mismatch between local governments’ expenditure responsibilities and their own source revenues (Jia et al. 2021).

  2. The 1994 tax-sharing reform is an important reform that deals with basic revenue problems by providing sufficient resources, especially to the central government, simplifying the tax structure, and unifying the tax burden on taxpayers (Shen et al. 2012).

  3. Since 1994, the value-added tax has been divided between the central and local governments at a ratio of 75:25 as a basis rate. Since 2002, corporate and personal income taxes have become the central–local sharing taxes: the central government has a 50% share of income taxes in 2002 and 60% thereafter.

  4. The benign growth of national economy refers to the virtuous cycle of energy development and economic growth to a certain extent.

  5. IFTs are important policy instruments employed by the central government to achieve legitimate goals (Boadway 2002).

  6. The optimal extent of decentralization is usually smaller on the revenue than on the expenditure side; thus, considering some extent of VFI is theoretically efficient (Eyraud and Lusinyan 2013).

  7. At present, the common spatial weight matrix includes the binary contiguity matrix, shared boundary ratio matrix, k-nearest matrix, distance function matrix, and kernel function matrix. According to the connotation and characteristics of the above spatial weight matrices, although the binary contiguity matrix cannot reflect the feature that spatial correlation decreases with the increase of distance, the binary contiguity matrix has strict symmetry and convenience compared with other spatial weight matrices. Therefore, the binary contiguity matrix is used as the spatial weight matrix for empirical testing, and the distance function matrix is used for robustness testing.

  8. The number of empirical samples is 570 because explanatory variables are treated by one lag period.

  9. First, as the lagging VFI has already occurred, it can be regarded as the “predetermined variable” (its value has been given from the current point of view) and is not related to the disturbance term in the current period, which satisfies the exogeneity of the IVs. Second, since the 1994 tax-sharing reform, China’s fiscal system has demonstrated a strong mismatch between revenue and expenditure responsibilities, that is, VFI (Jia et al. 2014), indicating that VFI has a certain continuity. Thus, VFI in the previous period is highly related to VFI in the current period, which satisfies the correlation of the IVs.

  10. Urbanization (URB) is represented by urban population as share of the total population. Industrialization (IND) is defined as industrial value added as share of regional GDP.

  11. Longitude and latitude coordinates in all regions are taken from the National Fundamental Geographic Information System of China.

  12. The calculation method for the IFT variable is the net IFTs from the central government to a provincial government as a share of the total population of each province, where the net IFTs equal the subsidy from the central government minus the remittance to the central government (Sun and He 2018). The data on IFTs come from the YFC and CSY.

  13. Because a central issue for technological innovation is the transformation of innovation inputs efficiently into commercially successful innovation outputs (Fu 2012), we use technological innovation efficiency to measure technological innovation. The selection of input and output indicators is critical for the evaluation of technological innovation efficiency. Following Sharma and Thomas (2008), we use the intramural expenditure on research and development (R&D) during a given period as the input index. For the other input index, we employ the full-time-equivalent of R&D personnel (Bai 2013). The former reflects funds input, and the latter reflects labor input. For the output, we use the number of patent applications as an index to determine the output of the innovative process. Another index chooses the value of contract deals in technical markets, which reflects the capacity of transforming technology into economic benefits in the market. The current study employs the linear programming technique of data envelopment analysis to estimate technological innovation efficiency. The data on technological innovation efficiency come from the China Statistical Yearbook on Science and Technology.

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Acknowledgements

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

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  1. School of Public Finance and Taxation, Nanjing University of Finance & Economics, Nanjing, China

    Liangliang Liu

  2. School of Management, University of Science and Technology of China, Hefei, China

    Wenqing Zhang

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Liu, L., Zhang, W. Vertical fiscal imbalance and energy intensity in China. Environ Resource Econ 83, 509–526 (2022). https://doi.org/10.1007/s10640-022-00714-w

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