Has Economic Competition Improved China’s Provincial Energy Ecological Efficiency under Fiscal Decentralization?
Abstract
:1. Introduction
2. Literature Review and Research Hypothesis
2.1. The Definition of Energy Ecological Efficiency (EEE)
2.2. Fiscal Decentralization and EEE
2.3. Economic Competition and EEE
2.4. Fiscal Decentralization, Economic Competition and EEE
3. Energy Eco-Efficiency Measurement and Regional Difference Analysis
3.1. Measurement Method and Index Selection
3.1.1. Input Indicators
- (1)
- Energy. There are many kinds of energy consumption in China, including coal, oil, natural gas, nuclear energy, water energy, wind energy, solar energy and so on. Since every province has different resource endowments and different types of energy consumption account for different proportions of the energy system, it is not appropriate to use certain specific energy consumption to represent all of China’s regional energy inputs. We select total energy consumption as energy input, which is the total energy consumed by various industries and households in a certain area (country, region) in a certain period. The unit is 10,000 tons of standard coal. This part of the data comes from the 2001–2016 China Energy Statistical Yearbook. [60]
- (2)
- Labor. Employees mainly refer to those who have reached the age of 16 and could participate in social work for gaining remuneration or making income. The employment personnel can better reflect the actual use of labor resources in the current year. Therefore, this paper selects the number of employed persons as the measurement index for measuring labor input. Given the difference between annual statistical data and actual annual labor, we specifically take the average of the final number of employees in a year and the final number of employees in a previous year [61]. Data is offered by the China Statistical Yearbook (2001–2016) [62].
- (3)
- Capital. Unavailable in statistical yearbooks, capital stock is indirectly calculated by the perpetual inventory method proposed by Shan [63]. We also assume that the discount rate of total fixed assets varies from province to province [64]. All data are obtained from the China Statistical Yearbook (2001–2016) and are at constant prices for the year 2000 [62].
3.1.2. Output Indicators
- (1)
- Regional GDP (GDP). Strictly speaking, factor input can produce diverse desirable outputs, thus requiring the classification and aggregation of different outputs in practice. Hence, regional GDP, a variable for aggregate outputs in this paper, is taken as the proxy variable for desirable outputs [65]. All data on regional GDP are obtained from the China Statistical Yearbook (2001–2016) [62] and are at constant prices for the year 2000.
- (2)
- Social welfare index. At present, the measurement indicators of social welfare mainly include the human development index (HDI) of the United Nations Development Program and the life expectancy per capita [66]. However, due to lack of statistics, continuous data on life expectancy per capita in 30 provinces in China between 2000 and 2015 cannot be obtained. Hence, according to the 13th five-year plan on employment, education, culture, social security, medical care, housing and other social welfare systems, we respectively select the employment rate, the number of years of education per capita, the total number of books printed per capita, the rate of participation in social security, the rate of ownership of the number of health technical personnel, and the newly-increased construction area per capita, and calculate the six variables in the social welfare index by using the entropy method. All data come from the China Statistical Yearbook (2001–2016) [62].
- (3)
- Undesirable outputs. Since the 10th five-year plan, the Chinese government has viewed sulfur dioxide (SO2) and chemical oxygen demand (COD) as two main environmental pollutants and set a 10% reduction in the total discharge of major pollutants (SO2 and COD) as one of its energy efficiency goals in the 11th five-year plan. In addition, extensive attention has been paid to global climate change in recent years (mainly focusing on greenhouse gases such as CO2). The Chinese government has made a commitment to achieve a 40–45% reduction in carbon dioxide emissions per unit of GDP by 2020, as compared to the level in 2005. Thus, we choose SO2, COD, and CO2 as undesirable environmental outputs. Data on SO2 and COD are obtained from the China Statistical Yearbook on the Environment (2001–2016), and CO2 emissions are estimated using the conversion standard of the Intergovernmental Panel on Climate Change (IPCC) (2006) and based on energy fuel (mainly coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas) consumption. Data on energy fuel consumption are obtained from the China Energy Statistical Yearbook (2001–2016) [60].
3.2. Regional Difference Analysis on Energy Eco-Efficiency
4. Empirical Analysis
4.1. Model Settings
4.2. Sample Selection and Variable Settings
4.3. Result Analysis
4.4. Robustness Analysis
5. Regional Economic Competition and EEE
6. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Variable | Mean | Std. Dev. | Min | Max | Observations |
---|---|---|---|---|---|
EEE | 0.501 | 0.290 | 0.012 | 1 | 480 |
fd | 0.506 | 0.177 | 0.148 | 0.906 | 480 |
com | −0.013 | 0.069 | −0.243 | 0.095 | 480 |
open | 0.345 | 0.800 | 0.006 | 14.722 | 480 |
market | 0.417 | 0.217 | 0.030 | 1.968 | 480 |
ave_gdp | 17,664.32 | 12,304.36 | 2759 | 79,132.93 | 480 |
tec | 0.092 | 0.016 | 0.048 | 0.131 | 480 |
invest | 0.572 | 0.220 | 0.252 | 1.328 | 480 |
ir | 0.060 | 0.034 | −0.007 | 0.223 | 480 |
gs | 0.192 | 0.085 | 0.077 | 0.628 | 480 |
Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
---|---|---|---|---|---|
fd | 0.482 *** (3.21) | 0.475 *** (3.18) | 0.435 ** (2.59) | 0.308 ** (2.28) | |
com | −0.606 *** (−2.69) | −0.607 *** (−2.79) | −0.794 *** (−5.12) | −0.607 *** (−7.78) | |
fd*com | −1.570 * (−1.66) | −1.735 * (−1.89) | −1.458 * (−2.09) | ||
open | 0.028 ** (2.14) | 0.030 ** (2.39) | 0.033 * (1.89) | 0.033 (1.78) | 0.031 * (1.82) |
market | −0.283 *** (−4.26) | −0.310 *** (−4.64) | −0.333 *** (-4.62) | −0.394 *** (−6.50) | −0.380 *** (−4.50) |
tec | 0.073 *** (6.35) | 0.067 *** (5.44) | 0.049 *** (7.16) | 0.032 ** (2.77) | 0.044 *** (5.06) |
ave_gdp | 7.15 × 10−6 *** (3.35) | 7.62 × 10−6 *** (3.39) | 1.41 × 10−5 *** (5.87) | 1.58 × 10−5 *** (10.01) | 1.19 × 10−5 *** (4.92) |
invest | −0.568 *** (−6.96) | −0.543 *** (−6.32) | −0.435 *** (−4.00) | −0.401 ** (−3.45) | −0.388 *** (−3.48) |
ir | 0.036 (0.10) | 0.417 (1.01) | 0.293 (1.03) | 0.719 ** (2.38) | 0.794 ** (2.97) |
gs | 0.887 *** (3.60) | 1.059 *** (4.10) | 1.708 *** (11.25) | 1.178 *** (11.22) | 1.369 *** (9.28) |
R-squared | 0.570 | 0.571 | 0.579 | 0.585 | |
F | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Hausman | 0.0000 | 0.0000 | 0.0000 | ||
Observations | 480 | 480 | 480 | 480 | 480 |
2000–2007 | 2008–2015 | |||||
---|---|---|---|---|---|---|
Variables | Model 6 | Model 7 | Model 8 | Model 9 | Model 10 | Model 11 |
fd | 0.517 * (2.42) | 0.431 ** (2.26) | 0.598 *** (4.74) | 0.521 *** (6.20) | ||
com | −0.675 *** (−3.86) | −0.543 ** (−2.89) | −0. 833 *** (−5.72) | −0.494 *** (−5.62) | ||
fd*com | −1.073 ** (−2.39) | −1.717 *** (−4.15) | ||||
open | 0.398 ** (5.65) | 0.439 *** (11.53) | 0.475 *** (11.73) | 0.014 (1.24) | 0.014 (1.01) | 0.013 (1.24) |
market | −0.224 (−2.23) | −0.065 (−1.03) | −0.147 (−1.80) | −0. 414 *** (−6.50) | −0. 474 *** (−9.88) | −0.453 *** (−7.77) |
tec | 0.108 *** (6.14) | 0.067 ** (5.44) | 0.123 *** (8.51) | 0. 045 *** (7.70) | 0.020 * (2.10) | 0.040 *** (5.97) |
ave_gdp | 1.79 × 10−5 * (2.57) | 1.82 × 10−5 *** (6.12) | 2 × 10−5 *** (6.25) | 6.67 × 10−6 *** (6.38) | 9.83 × 10−6 *** (31.43) | 5.01 × 10−6 *** (7.37) |
invest | −0.451 ** (−3.21) | −0.456 *** (−6.06) | −0.438 *** (−7.94) | −0. 425 ** (−4.22) | −0.369 ** (−3.21) | −0.364 *** (−3.21) |
ir | 0.127 (0.34) | 0.415 (0.92) | 0.367 (0.84) | 1.023 (1.89) | 0.680 (1.21) | 0.594 (1.13) |
gs | 0.449 (1.06) | 0.021 (0.56) | 0.442 (1.51) | 1.660 *** (11.86) | 1.078 *** (30.20) | 1.435 *** (13.95) |
R-squared | 0.461 | 0.454 | 0.517 | 0.521 | 0.522 | 0.537 |
F | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Hausman | 0.0000 | 0.0000 | 0.0003 | 0.0000 | 0.0000 | 0.0001 |
Observations | 240 | 240 | 240 | 240 | 240 | 240 |
Variables | Model 12 | Model 13 | Model 14 | Model 15 |
---|---|---|---|---|
DumE*com | −0.892 ** (−2.66) | −0.869*** (−3.63) | −1.276*** (−9.10) | −1.310 *** (−9.35) |
DumM*com | −0.782 (−1.40) | −0.835 ** (−2.32) | −0.698 *** (−3.88) | −0.697 *** (−3.75) |
DumW*com | 1.437 (1.86) | 1.501996 *** (4.87) | 0.8472537 *** (4.07) | 0.846 *** (4.04) |
fd | 0.572 ** (2.26) | 0.521 *** (3.74) | 0.253 * (2.15) | 0.279 ** (2.35) |
market | −0.207 * (−1.99) | −0.234 *** (−3.96) | −0.360 *** (−5.80) | −0.360 *** (−5.70) |
tec | 0.084 ** (2.53) | 0.076 ** (6.62) | 0.030 ** (2.27) | 0.028 * (2.13) |
ave_gdp | 8.78 × 10−6 ** (2.17) | 9.41 × 10−6 *** (4.53) | 1.06 × 10−5 *** (5.13) | 1.06 × 10−5 *** (5.06) |
invest | −0.565 ** (−2.48) | −0.5652029 *** (−6.92) | −0.399 *** (−3.30) | −0.417 *** (−3.38) |
ir | −0.384 (−0.57) | −0.153 (−0.44) | 0.789 ** (2.82) | 0.742 ** (2.57) |
gs | 1.218 ** (2.57) | 1.384 *** (5.76) | 1.616 *** (9.40) | 1.651 *** (9.56) |
open | 0.035 (1.24) | 0.038 *** (2.85) | 0.029 (1.70) | |
R-squared | 0.5404 | 0.6183 | 0.6118 | |
F | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Hausman | 0.0000 | 0.0000 | ||
Observations | 480 | 480 | 480 | 480 |
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Zhou, M.; Wang, T.; Yan, L.; Xie, X.-B. Has Economic Competition Improved China’s Provincial Energy Ecological Efficiency under Fiscal Decentralization? Sustainability 2018, 10, 2483. https://doi.org/10.3390/su10072483
Zhou M, Wang T, Yan L, Xie X-B. Has Economic Competition Improved China’s Provincial Energy Ecological Efficiency under Fiscal Decentralization? Sustainability. 2018; 10(7):2483. https://doi.org/10.3390/su10072483
Chicago/Turabian StyleZhou, Min, Teng Wang, Liang Yan, and Xiong-Biao Xie. 2018. "Has Economic Competition Improved China’s Provincial Energy Ecological Efficiency under Fiscal Decentralization?" Sustainability 10, no. 7: 2483. https://doi.org/10.3390/su10072483