Abstract
The use of clean energy can promote the coordinated development of the economy and the ecological environment. However, few studies have paid attention to the changes in the health status of residents in the process of economic development and energy use. To fill this gap, this paper uses health energy intensity, which refers to the amount of energy consumed per unit of health status (composed of population mortality, maternal mortality, and perinatal mortality), to explore the impact of clean energy (expressed by the share of clean energy consumption in total energy consumption) on economic development and healthy energy intensity. By using the panel data of 30 provinces and cities in China from 2005 to 2019, this paper constructs a simultaneous equation model for empirical analysis from the perspective of the whole country and areas with different income levels. The results show that from the national perspective, in the early stage of clean energy development, the level of economic development and healthy energy intensity increased; however, with the further development of clean energy, the sample period shows that the level of economic development and the healthy energy intensity decreased. Heterogeneity analysis shows that in both high-income and moderate-income areas, clean energy has a U-shaped effect on economic development; but in low-income areas, clean energy has an inverted U-shaped effect on economic development. In high-income and low-income areas, clean energy has an inverted U-shaped effect on healthy energy intensity; but in moderate-income areas, clean energy has a U-shaped effect on healthy energy intensity. China’s clean energy market is still in its early stage, and the research conclusions of this paper provide a theoretical basis for the realization of China’s clean energy development.
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Data availability
The data and material used or analyzed during the current study are available from the corresponding author on reasonable request.
Notes
Here is a brief introduction to some indicators of these five aspects: product quality, such as the rate of superior products; service quality, such as the density of star-rated hotels; economic benefits, such as the rate of land output; social benefits, such as public transport vehicles per 10,000 people; in terms of ecological benefits, such as park green space per capita; in terms of economic performance, such as the real growth rate of GDP. For the detailed composition of the indicators, see (Nie C and Jian X 2020).
See Appendix 1 for the calculation formula and steps of the entropy weight method.
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This research was funded by “Liaoning Social Science foundation (L18BJL011).”
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H.H. was responsible for the methodology, software, analysis, writing, review, and editing; S.Y. was responsible for the resources, investigation, data curation, and analysis.
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Appendices
Appendix 1. Calculation formula and steps of entropy weight method
The entropy weighting method is an objective weighting method, and its idea is to determine the index weight according to the information provided by the observed values of each index. The specific calculation methods are as follows:
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(1)
Firstly, set the metrics. There are m provinces and cities, and n evaluation indicators together constitute the initial global evaluation matrix: \(X=\{{x}_{ij}{\}}_{\mathrm{mxn}}\), where \({x}_{ij}\) represents the j index value of the i province and city.
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(2)
Secondly, process the data. The entropy weight method requires that the original value of the index should be standardized before calculating the weight of the index. In order to avoid the interference of dimensions and positive and negative orientations between indicators, the method of extreme difference one is used to carry out dimensionless normalization processing on the original data. To eliminate the effects of negative numbers and zeros, data panning is performed at the same time. For negative indicators (smaller is better) use the formula:
\({y}_{ij}=\frac{{x}_{j\mathrm{ max}}-{x}_{ij}}{{x}_{j\mathrm{ max}}-{x}_{j\mathrm{ min}}}\)+0.0001 (The logarithmic operation will be performed later, and 0.0001 is added to ensure that the true number is not 0.) This formula makes the negative index positive and standardizes at the same time (in fractions, the denominator is fixed, but the smaller the numerator \({x}_{ij}\), the larger the value of the whole fraction, which shows that the smaller \({x}_{ij}\), the better).
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(3)
Calculate the characteristic proportion or contribution degree of the i province and city under the j index.
The formula is: \({p}_{ij}=\frac{{y}_{ij}}{\sum_{i=1}^{m}{y}_{ij}}\).
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(4)
Calculate the entropy value. The formula is \({e}_{j}=-\frac{1}{\mathrm{ln}m}\sum_{i=1}^{m}{p}_{ij}\mathrm{ln}{p}_{ij}\). In the formula, \({e}_{j}\) is the entropy value of the j index, 0 ≤ \({e}_{j}\)≤1.
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(5)
Calculate the coefficient of variance. The formula is \({g}_{j}=1-{e}_{j}\). In the formula, \({g}_{j}\) is the difference coefficient, and the larger the value of \({g}_{j}\), the more important the index is.
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(6)
Determine weights. The formula is: \({w}_{j}=\frac{{g}_{j}}{\sum_{i=1}^{m}{g}_{j}}\). In the formula, \({w}_{j}\) is the weight of the j indicator, 0 ≤ \({w}_{j}\)≤1, \(\sum_{j=1}^{n}{w}_{j}=1\).
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(7)
Comprehensive index calculation. The formula is: \({s}_{i}=\sum_{j=1}^{n}{w}_{j}{y}_{ij}\). So far, the comprehensive index value has been calculated.
Appendix 2. Multicollinearity test results
In doing the multicollinearity test, we will explore each of the three single equations separately. In the single equation of economic development, the core variable of the main research is clean energy, so we did the tests for lnurban, lntp, lnl, and lnclean; in the single equation of healthy energy intensity, the core variable of the main study is clean energy, so we did the tests for lntp, lnil, lnphe, and lnclean; in the single equation of clean energy, the core variable of the main research is economic development, so we did the tests for lntp, lnis, lnil, and lned. The test results are shown in the table below (Table 8).
As can be seen from the results of the above tests, all VIF values are less than 10. It can be considered that in the single equation of economic development, lnurban, lntp, lnl, and lnclean do not have serious multicollinearity; in the single equation of health energy intensity, lntp, lnil, lnphe, and lnclean do not have serious multicollinearity; in the single equation of clean energy, lntp, lnis, lnil, and lned do not have serious multicollinearity.
For each single equation, there is no serious multicollinearity between the core variable and the control variable, so the parameter estimates of the control variables will not affect the parameter estimates of the core variables. The paper wants to study these three core variables: clean energy, economic development, and healthy energy intensity. In simultaneous equation model, these three core variables are endogenous. For the endogeneity problem of the simultaneous equation model, many studies have used the 3SLS method to solve it. Therefore, the paper also uses the 3SLS method to solve the endogeneity problem between the core variables, and further empirical analysis is carried out on this basis.
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Hou, H., Yang, S. Clean energy, economic development and healthy energy intensity: an empirical analysis based on China’s inter-provincial panel data. Environ Sci Pollut Res 29, 80366–80382 (2022). https://doi.org/10.1007/s11356-022-21322-7
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DOI: https://doi.org/10.1007/s11356-022-21322-7