# Sources of China’s Economic Growth: An Empirical Analysis Based on the BML Index with Green Growth Accounting

^{1}

^{2}

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## Abstract

**:**

_{2}emissions with environmental regulation restrain economic growth in some provinces; and (3) overall, physical capital accumulation is the most important driving force for economic take-off, irrespective of whether the government adopts environmental regulations.

## 1. Introduction

_{2}emissions were generated by primary energy doubled from 3.47 to 7.18 billion tonnes from 2000 to 2009, making China a major contributor of CO

_{2}emissions in the world. By the end of 2006, however, China had made some substantial progress in pollution reduction. The economic benefits arising from the implementation of its cleaner production program amounted to 4.4 billion CNY, and the direct economic benefits generated by energy saving amounted to 5.5 billion CNY. However, the costs of environmental pollution abatement are still increasing along with economic development. In 2011, the country’s environmental pollution control investment was 602.62 billion CNY, accounting for 1.27% of GDP. Consequently, the high intensity of resource consumption and environmental pollution shows that Chinese economic growth is still mainly based on the extensive growth feature of high input, high consumption, high emission and growth without development, and is not driven by the green growth approach of improving TFP.

## 2. Literature Review

_{2}emissions with environmental regulation. Energy and the environment are endogenous variables and double rigid constraints on economic growth. The dual reversed transmission mechanism of the influence of energy conservation and environmental regulation on economic growth, can promote energy productivity and the transformation of the pattern of economic development. Energy consumption is a forceful driver for the growth of GDP [17,18,19,20]. Koop [21] noted that input changes of CO

_{2}emissions are a negligible factor in explaining growth. Chang [22] concluded that GDP growth is indissociable from increases in both energy consumption and CO

_{2}emissions. Wang et al. [23] found that reducing CO

_{2}emissions may handicap China’s economic growth to some degree. Kareem et al. [24] found a causal relationship between CO

_{2}emissions and economic growth with the causality running from CO

_{2}emissions to economic growth. However, most of the researchers investigated the existence and direction of Granger causality between economic growth, energy, and CO

_{2}emissions, rather than growth accounting incorporating energy and environmental regulation.

_{2}emissions, a measure of environmental regulation variables, can be chosen as an input [26,27] or an output by using the DDF with the nonparametric method such as data envelopment analysis (DEA) [28,29,30,31]. This nonparametric method does not set the form of production functions and especially can be used for growth accounting with multiple inputs and outputs. Therefore, to contribute to the existing literature, this study first expands the biennial Malmquist productivity index proposed by Pastor et al. [32] and constructs a biennial Malmquist–Luenberger productivity index with the biennial environmental DEA technology, which can avoid infeasibilities and measures technological progress and regress. Second, this study adds energy and environmental factors into the decomposition and proposes a GGAF emphasizing energy saving and environmental protection. The change in economic growth can be decomposed into seven components: technical efficiency change, technological change, and the effects of labor, capital, energy, output structure, and CO

_{2}emissions with environmental regulation. Finally, this study utilizes the Silverman test to test for multimodality and the nonparametric Li-Fan-Ullah test to analyze the distribution dynamics of economic growth between actual and counterfactual distributions.

## 3. Method

#### 3.1. Environmental Production Technology

_{+ }denote an input vector for labor, capital and energy, respectively; Y Є R

_{+}denotes the desirable or good output of gross regional product (GRP); and C Є R

_{+}represents undesirable or bad output of CO

_{2}emissions.

_{i}is the weight assigned to each observation when constructing the production possibilities frontier; and z

_{i}> 0 means that the production technology exhibits constant returns-to-scale (CRS). In addition, the output set at the time period t + 1 can be similarly defined as S(t + 1).

#### 3.2. Biennial Malmquist–Luenberger Index

_{y}, − g

_{c}) is a direction vector, and measures the maximum proportional expansion of both desirable and undesirable outputs (Y

^{t}, C

^{t}), given the input vector (L

^{t}, K

^{t}, E

^{t}) and the biennial technology in the direction g. Following [32], we introduce a TFP index called the biennial Malmquist–Luenberger index (hereafter, BML index). Taking the biennial production technology as a reference, the BML index between period t and t + 1 is given by

#### 3.3. Green Growth Accounting Framework

^{t}, Y

^{t+1}represent the actual desirable output at times t and t + 1 respectively; the functions F

^{t}(L

^{t}, K

^{t}, E

^{t}, Y

^{t}, C

^{t}, g) and F

^{t+1}(L

^{t+1}, K

^{t+1}, E

^{t+1}, Y

^{t+1}, C

^{t+1}, g) represent the maximum potential desirable output at times t and t +1 given input, desirable output and technology, respectively; and similarly, the functions F

^{B}(L

^{t}, K

^{t}, E

^{t}, Y

^{t}, C

^{t}, g) and F

^{B}(L

^{t+1}, K

^{t+1}, E

^{t+1}, Y

^{t+1}, C

^{t+1}, g) represent the maximum potential desirable output given the biennial technology at times t and t + 1, respectively.

^{t+1}/Y

^{t}= EFF × TC × LE × KE × EE × CAE × OSE = TFP × IME × OME

_{2}emissions (CAE) and output structure (OSE) with environmental regulation, respectively. The product of LE, KE and EE is the change in the input mix effect (IME). Similarly, the product of the last two effects is the change in the output mix effect (OME).

_{2}emissions. Because one output, GRP, is considered, the effect of the change in output structure is equal to 1 and we can ignore it in the decomposition. Thus, according to the above-mentioned accounting idea, the decomposition of economic growth in the traditional model without environment regulation is given by:

^{t+1}/Y

^{t}= EFF × TC × LE × KE = TFP × IME

## 4. Data and Empirical Results

#### 4.1. Data

_{2}emissions) in each region. As we aim to study the effects of resources and the environment on economic growth, especially the effect of CO

_{2}emissions on economic growth, we select energy consumption as one of inputs and CO

_{2}emissions as one of outputs [35,36]. CO

_{2}, an environmental factor, is an undesirable by-product accompanied by the production. In addition, CO

_{2}is mainly due to the use of energy (especially fossil energy), so we should take energy into account.

_{1900}is the initial value of the capital stock in 1900, I is the real value of gross fixed capital formation, and δ is the depreciation rate. To estimate the capital stock, we need to determine the initial capital stock and depreciation rate. We assume that the initial capital stock in 1900 is 0. This assumption is based on the fact that the capital stock from 1900 to 1952 was completely depreciated. Using investment data from 1952 to 2012 obtained in all provinces, we perform regressions between the logarithmic of the existing investment data and time series data. In addition, then we simulate the 1900 to 1951 sequence investment data for all provinces. Following a recent study by [40], we adopt different depreciation rates for each province.

_{2}emissions cannot be obtained directly from the official data. CO

_{2}emissions mainly result from fossil energy consumption. The publication Guidelines for National Greenhouse Gas Inventories [41] provides a reference formula to estimate CO

_{2}emissions. Following this method, we can use provincial-level energy consumption to forecast CO

_{2}emissions in each province. The forecasting equation is given by:

_{2}is 23,104.14 (10000 tonnes). In addition, those of labor, capital stock and energy consumption are 2304.33 (10000 persons), 18,642.29 (100 million CNY), 8797.01 (10000 tonnes), respectively. From these values, we can know that China is a big country in terms of energy consumption and CO

_{2}emissions. Clearly, the high growth in China shows obvious features of high investment, high energy consumption and high emissions. Therefore, the study of China’s economic growth can no longer ignore the source of energy and environmental elements.

#### 4.2. Empirical Results

_{2}emissions, and energy consumption, including five other components.

Variables | Mean | S.D. | Max | Min |
---|---|---|---|---|

Gross regional product (100 million CNY) | 6,620.05 | 6,671.37 | 42,860.33 | 223.88 |

Carbon dioxide emissions (10000 tonnes) | 23,104.14 | 18,408.14 | 106,667.02 | 892.85 |

Labor (10000 persons) | 2,304.33 | 1,525.40 | 6,288.00 | 230.40 |

Capital stock (100 million CNY) | 18,642.29 | 17,905.59 | 110,064.98 | 953.54 |

Energy consumption (10000 tonnes) | 8,797.01 | 6,970.46 | 40,630.76 | 384.48 |

Provinces | TFP | EFF | TC | OSE | CAE | LE | KE | EE |
---|---|---|---|---|---|---|---|---|

Beijing | 1.004 | 0.983 | 1.022 | 1.018 | 1.087 | |||

1.031 | 1.002 | 1.029 | 1.002 | 0.998 | 1.079 | 1.037 | 0.964 | |

Tianjin | 1.034 | 1.014 | 1.019 | 1.015 | 1.090 | |||

1.044 | 0.991 | 1.053 | 0.994 | 0.978 | 1.037 | 1.106 | 0.983 | |

Hebei | 0.982 | 0.986 | 0.995 | 1.005 | 1.126 | |||

0.997 | 0.991 | 1.006 | 1.000 | 0.939 | 1.005 | 1.180 | 1.000 | |

Shanxi | 0.972 | 0.977 | 0.995 | 1.004 | 1.143 | |||

0.891 | 0.760 | 1.172 | 0.938 | 0.916 | 1.007 | 1.452 | 0.997 | |

Inner Mongolia | 0.995 | 0.990 | 1.004 | 1.006 | 1.155 | |||

0.979 | 0.751 | 1.303 | 0.899 | 0.879 | 1.022 | 1.485 | 0.986 | |

Liaoning | 1.029 | 0.997 | 1.032 | 1.009 | 1.076 | |||

1.006 | 0.864 | 1.164 | 0.953 | 0.975 | 1.059 | 1.177 | 0.959 | |

Jilin | 0.988 | 0.986 | 1.002 | 1.005 | 1.130 | |||

0.993 | 0.983 | 1.010 | 0.997 | 0.951 | 1.005 | 1.186 | 1.000 | |

Heilongjiang | 1.007 | 1.011 | 0.996 | 1.005 | 1.094 | |||

0.978 | 0.939 | 1.042 | 0.976 | 0.980 | 1.018 | 1.175 | 0.990 | |

Shanghai | 1.018 | 1.000 | 1.018 | 1.032 | 1.056 | |||

1.014 | 1.000 | 1.014 | 0.999 | 1.001 | 1.110 | 1.041 | 0.947 | |

Jiangsu | 1.036 | 1.005 | 1.031 | 1.008 | 1.076 | |||

1.023 | 1.000 | 1.022 | 1.000 | 0.961 | 1.016 | 1.089 | 1.034 | |

Zhejiang | 1.023 | 0.993 | 1.030 | 1.014 | 1.076 | |||

1.010 | 0.994 | 1.016 | 1.000 | 0.921 | 1.012 | 1.130 | 1.049 | |

Anhui | 0.980 | 1.000 | 0.980 | 1.000 | 1.139 | |||

1.006 | 0.999 | 1.007 | 1.000 | 0.938 | 1.000 | 1.182 | 1.000 | |

Fujian | 1.013 | 0.997 | 1.016 | 1.012 | 1.091 | |||

1.005 | 0.987 | 1.018 | 1.000 | 0.975 | 1.020 | 1.049 | 1.067 | |

Jiangxi | 0.969 | 0.989 | 0.980 | 1.000 | 1.152 | |||

1.005 | 0.999 | 1.006 | 1.000 | 0.871 | 1.000 | 1.210 | 1.054 | |

Shandong | 0.999 | 0.995 | 1.004 | 1.005 | 1.119 | |||

0.999 | 0.993 | 1.007 | 1.000 | 0.953 | 1.006 | 1.142 | 1.026 | |

Henan | 0.959 | 0.974 | 0.984 | 1 | 1.164 | |||

1.004 | 0.996 | 1.007 | 1.000 | 0.865 | 1.000 | 1.287 | 0.998 | |

Hubei | 0.980 | 0.996 | 0.984 | 1.004 | 1.135 | |||

1.006 | 1.001 | 1.005 | 1.000 | 0.833 | 1.004 | 1.316 | 1.008 | |

Hunan | 0.973 | 0.993 | 0.980 | 1.000 | 1.145 | |||

1.006 | 1.000 | 1.006 | 1.000 | 0.910 | 1.000 | 1.175 | 1.036 | |

Guangdong | 1.001 | 1.000 | 1.001 | 1.018 | 1.099 | |||

1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.053 | 1.060 | 1.003 | |

Guangxi | 0.946 | 0.965 | 0.981 | 0.999 | 1.181 | |||

0.993 | 0.988 | 1.005 | 1.000 | 0.865 | 1.000 | 1.214 | 1.071 | |

Hainan | 1.032 | 1.003 | 1.029 | 1.015 | 1.059 | |||

0.997 | 0.989 | 1.008 | 1.000 | 0.858 | 1.002 | 1.182 | 1.095 | |

Chongqing | 0.974 | 0.994 | 0.980 | 1.000 | 1.154 | |||

1.009 | 1.004 | 1.005 | 1.000 | 0.837 | 1.000 | 1.254 | 1.061 | |

Sichuan | 0.981 | 1.001 | 0.980 | 1.000 | 1.141 | |||

1.010 | 1.005 | 1.005 | 1.000 | 0.893 | 1.000 | 1.180 | 1.051 | |

Guizhou | 0.981 | 1.002 | 0.980 | 1.000 | 1.133 | |||

1.004 | 1.002 | 1.002 | 1.000 | 0.743 | 1.000 | 1.490 | 1.000 | |

Yunnan | 0.971 | 0.991 | 0.980 | 1.000 | 1.137 | |||

1.000 | 0.995 | 1.004 | 1.000 | 0.741 | 1.000 | 1.433 | 1.040 | |

Shaanxi | 1.004 | 1.008 | 0.996 | 1.002 | 1.119 | |||

1.003 | 0.998 | 1.005 | 1.000 | 0.704 | 1.002 | 1.527 | 1.042 | |

Gansu | 0.974 | 0.994 | 0.980 | 1.000 | 1.140 | |||

1.006 | 1.000 | 1.006 | 1.000 | 0.896 | 1.000 | 1.231 | 1.000 | |

Qinghai | 1.029 | 1.000 | 1.030 | 1.010 | 1.075 | |||

1.008 | 0.998 | 1.009 | 1.000 | 0.649 | 1.006 | 1.618 | 1.051 | |

Ningxia | 1.018 | 0.995 | 1.023 | 1.010 | 1.085 | |||

0.996 | 0.993 | 1.003 | 1.000 | 0.651 | 1.010 | 1.704 | 1.000 | |

Xinjiang | 1.020 | 0.992 | 1.028 | 1.011 | 1.069 | |||

1.004 | 0.991 | 1.014 | 1.000 | 0.801 | 1.011 | 1.355 | 1.000 | |

Weighted Mean | 0.996 | 0.994 | 1.002 | 1.007 | 1.115 | |||

1.001 | 0.974 | 1.032 | 0.992 | 0.883 | 1.016 | 1.256 | 1.017 |

_{2}emissions are less than 1 on average, considering environmental regulation. These results indicate that the exacerbation of output structure and CO

_{2}emissions effects have an adverse impact on economic growth and inhibit the growth of GRP in the long run. The output structure effect in most provinces is not less than 1 and promotes economic growth. Compared with Beijing, Chongqing, Gansu and other provinces, Tianjin, Heilongjiang, Inner Mongolia, Liaoning and Shanxi exhibit the smallest output structure effects, which are 0.994, 0.976, 0.899, 0.953 and 0.938, respectively. In terms of the CO

_{2}emissions effect, 28 provinces have scores far smaller than 1, while rich regions such as Shanghai and Guangdong exhibit values equal to one. In fact, if there is no cycle of production technology and other advanced conditions, the more CO

_{2}emissions, the more inputs such as raw materials are needed in the production process, which causes enterprises to produce less desirable outputs. Thus, the regulation of CO

_{2}emissions could restrain GDP growth to some extent.

_{2}emissions and labor did not significantly contribute to convergence, even though the contribution of CO

_{2}emissions had a wide dispersion. Figure 1g shows that the regression coefficient for change in physical capital accumulation was negative and statistically significant, indicating that the change contributed to convergence. Finally, as shown in Figure 1h, even if the contribution of the energy effect showed a wide dispersion and its coefficient was negative, the coefficient was statistically insignificant, suggesting that the energy effect also contributed little to convergence.

## 5. Analysis of Distributions Dynamics of Economic Growth

Distributions | p-values | |
---|---|---|

H0: One Mode H1: More than One Mode | H0: Two Modes H1: More than Two Modes | |

Y98 | 0.262 (H0 not reject) | 0.323 (H0 not reject) |

Y12 | 0.043 (H0 reject) | 0.523 (H0 not reject) |

^{98}, Y

^{1}

^{2}denote GRP across provinces in 1998 and 2012, respectively.

_{0}: f(x) = g(x) for all x, and the alternative is H

_{1}: f(x) ≠ g(x) for some x. The decomposition of economic growth change with environment regulation in Equation (8) can be re-expressed as:

^{12}= (EFF × TC × LE × KE × EE × CAE × OSE) × Y

^{98}

_{2}emissions and labor successively in Figure 3b–e, these results would not change dramatically. Until it takes physical capital accumulation into account, there is almost no significant difference between the counterfactual and the 2012 distributions (see Figure 3f). The results in Figure 3a–f are consistent with Test 2, 9, 30, 65, 100 and 121 in Appendix Table A1.

_{2}emissions and labor in Figure 4c,d, the counterfactual distribution moves much closer to the actual 1998 distribution. When physical capital accumulation is added to the counterfactual distribution in Figure 4e, its mode shifts to the left and it moves much closer to the actual 2012 distribution. This demonstrates that physical capital accumulation is the core contributor to change in economic growth. Figure 4f describes the counterfactual distribution with the joint effect of six components including the change in energy consumption. It illustrates that the counterfactual distribution moves further toward the actual 2012 distribution, though its shape changes only slightly and its tail extends. In addition, the six panels of Figure 4 can be reinforced by the nonparametric test corresponding to Tests 3, 15, 45, 85, 115 and 127 in Appendix Table A1.

**Figure 3.**Counterfactual and actual distributions of economic growth with the effect of technical efficiency change (EFF).

_{2}emissions, labor, physical capital accumulation and energy consumption in combination with each other. However, regardless of whether the combinations include two, three, four, five or six components of the decomposition, the shape of the counterfactual distribution does not change significantly. Only when physical capital accumulation is added to these combinations does the counterfactual distribution increasingly exhibit a bimodal shape and appear identical to the actual 2012 distribution. In summary, along with the process of economic growth, physical capital accumulation plays the most important role in changing the distribution from unimodal to bimodal during the period of 1998–2012.

## 6. Conclusions

_{2}emissions, labor, physical capital accumulation and energy consumption on economic growth.

_{2}emissions with the emergence of a serious deterioration discouraged economic growth in some provinces between 1998 and 2012. Labor and energy are important sources of economic growth and their increase stimulates rapid economic growth. The energy effect contributed little to convergence. Undoubtedly, a high physical capital accumulation is the most important driving force for economic take-off, irrespective of whether the government implements environmental regulation or not, and it also makes a big contribution to convergence. In other words, economic growth is still mainly dependent on factor inputs, but not efficiency or TFP in China.

## Acknowledgments

## Author Contributions

## Appendix

Null Hypothesis (H_{0}) | t-Test Statistics | Null Hypothesis (H_{0}) | t-Test Statistics |
---|---|---|---|

1. f(Y^{12}) = g(Y^{98}) | 3.0925 | 43. f(Y^{12}) = g(Y^{98} × EFF × LE × EE) | 1.6660 * |

2. f(Y^{12}) = g(Y^{98} × EFF) | 3.1722 | 44. f(Y^{12}) = g(Y^{98} × EFF × KE × EE) | 7.2167 |

3. f(Y^{12}) = g(Y^{98} × TC) | 3.2513 | 45. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE) | 3.9089 |

4. f(Y^{12}) = g(Y^{98} × OSE) | 3.1999 | 46. f(Y^{12}) = g(Y^{98} × TC × OSE × LE) | 0.6358* |

5. f(Y^{12}) = g(Y^{98} × CAE) | 4.5551 | 47. f(Y^{12}) = g(Y^{98} × TC × OSE × KE) | 8.3981 |

6. f(Y^{12}) = g(Y^{98} × LE) | 2.4344 | 48. f(Y^{12}) = g(Y^{98} × TC × OSE × EE) | 1.5624 * |

7. f(Y^{12}) = g(Y^{98} × KE) | −0.2006 * | 49. f(Y^{12}) = g(Y^{98} × TC × CAE × LE) | 0.7044 * |

8. f(Y^{12}) = g(Y^{98} × EE) | 2.6783 | 50. f(Y^{12}) = g(Y^{98} × TC × CAE × KE) | 9.9558 |

9. f(Y^{12}) = g(Y_{98} × EFF × TC) | 2.7458 | 51. f(Y^{12}) = g(Y^{98} × TC × CAE × EE) | 2.7983 |

10. f(Y^{12}) = g(Y^{98} × EFF × OSE) | 3.1313 | 52. f(Y^{12}) = g(Y^{98} × TC × LE × KE) | 0.5224 * |

11. f(Y^{12}) = g(Y^{98} × EFF × CAE) | 5.3561 | 53. f(Y^{12}) = g(Y^{98} × TC × LE × EE) | 0.0716 * |

12. f(Y^{12}) = g(Y^{98} × EFF × LE) | 2.8901 | 54. f(Y^{12}) = g(Y^{98} × TC × KE × EE) | 9.1998 |

13. f(Y^{12}) = g(Y^{98} × EFF × KE) | 2.8901 | 55. f(Y^{12}) = g(Y^{98} × OSE × CAE × LE) | 4.1468 |

14. f(Y^{12}) = g(Y^{98} × EFF × EE) | 2.2788 | 56. f(Y^{12}) = g(Y^{98} × OSE × CAE × KE) | 0.0276* |

15. f(Y^{12}) = g(Y^{98} × TC × OSE) | 2.0906 * | 57. f(Y^{12}) = g(Y^{98} × OSE × CAE × EE) | 5.1708 |

16. f(Y^{12}) = g(Y^{98} × TC × CAE) | 2.9263 | 58. f(Y^{12}) = g(Y^{98} × OSE × LE × KE) | 7.7696 |

17. f(Y^{12}) = g(Y^{98} × TC × LE) | −0.0902 * | 59. f(Y^{12}) = g(Y^{98} × OSE × LE × EE) | 1.8249* |

18. f(Y^{12}) = g(Y^{98} × TC × KE) | 0.2784 * | 60. f(Y^{12}) = g(Y^{98} × OSE × KE × EE) | 7.5675 |

19. f(Y^{12}) = g(Y^{98} × TC × EE) | 0.9731 * | 61. f(Y^{12}) = g(Y^{98} × CAE × LE × KE) | 9.1998 |

20. f(Y^{12}) = g(Y^{98} × OSE × CAE) | 4.8776 | 62. f(Y^{12}) = g(Y^{98} × CAE × LE × EE) | 3.3905 |

21. f(Y^{12}) = g(Y^{98} × OSE × LE) | 2.5605 | 63. f(Y^{12}) = g(Y^{98} × CAE × KE × EE) | 0.0212* |

22. f(Y^{12}) = g(Y^{98} × OSE × KE) | 8.1129 | 64. f(Y^{12}) = g(Y^{98} × LE × KE × EE) | 8.7493 |

23. f(Y^{12}) = g(Y^{98} × OSE × EE) | 2.4269 | 65. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE) | 4.5586 |

24. f(Y^{12}) = g(Y^{98} × CAE × LE) | 3.7703 | 66. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × LE) | 2.1208 * |

25. f(Y^{12}) = g(Y^{98} × CAE × KE) | −0.0399 * | 67. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × KE) | 8.6480 |

26. f(Y^{12}) = g(Y^{98} × CAE × EE) | 4.9706 | 68. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × EE) | 2.3043 * |

27. f(Y^{12}) = g(Y^{98} × LE × KE) | 0.5651 * | 69. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × LE) | 3.2199 |

28. f(Y^{12}) = g(Y^{98} × LE × EE) | 1.9149 * | 70. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × KE) | −0.1126 * |

29. f(Y^{12}) = g(Y^{98} × KE × EE) | 8.7209 | 71. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × EE) | 4.6026 |

30. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE) | 2.9007 | 72. f(Y^{12}) = g(Y^{98} × EFF × TC × LE × KE) | 1.6153 * |

31. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE) | 4.2116 | 73. f(Y^{12}) = g(Y^{98} × EFF × TC × LE × EE) | 1.3714 * |

32. f(Y^{12}) = g(Y^{98} × EFF × TC × LE) | 1.9787 * | 74. f(Y^{12}) = g(Y^{98} × EFF × TC × KE × EE) | 7.6552 |

33. f(Y^{12}) = g(Y^{98} × EFF × TC × KE) | 8.5744 | 75. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × LE) | 4.7471 |

34. f(Y^{12}) = g(Y^{98} × EFF × TC × EE) | 2.4802 | 76. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × KE) | 0.7412 * |

35. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE) | 5.4615 | 77. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × EE) | 5.2877 |

36. f(Y^{12}) = g(Y^{98} × EFF × OSE × LE) | 2.8215 | 78. f(Y^{12}) = g(Y^{98} × EFF × OSE × LE × KE) | 7.9339 |

37. f(Y^{12}) = g(Y^{98} × EFF × OSE × KE) | 8.3418 | 79. f(Y^{12}) = g(Y^{98} × EFF × OSE × LE × EE) | 1.5858 * |

38. f(Y^{12}) = g(Y^{98} × EFF × OSE × EE) | 2.2165 * | 80. f(Y^{12}) = g(Y^{98} × EFF × OSE × KE × EE) | 7.1302 |

39. f(Y^{12}) = g(Y^{98} × EFF × CAE × LE) | 4.7027 | 81. f(Y^{12}) = g(Y^{98} × EFF × CAE × LE × KE) | −0.0305 * |

40. f(Y^{12}) = g(Y^{98} × EFF × CAE × KE) | 0.7840 * | 82. f(Y^{12}) = g(Y^{98} × EFF × CAE × LE × EE) | 3.9011 |

41. f(Y^{12}) = g(Y^{98} × EFF × CAE × EE) | 5.2502 | 83. f(Y^{12}) = g(Y^{98} × EFF × CAE × KE × EE) | 0.0233 * |

42. f(Y^{12}) = g(Y^{98} × EFF × LE × KE) | −0.0651 * | 84. f(Y^{12}) = g(Y^{98} × EFF × LE × KE × EE) | 7.2847 |

85. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × LE) | 2.1985 * | 107. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × LE × EE) | 2.8757 |

86. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × KE) | 6.5094 | 108. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × KE × EE) | −0.0267 * |

87. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × EE) | 3.9165 | 109. f(Y^{12}) = g(Y^{98} × EFF × TC × LE × KE × EE) | 7.7859 |

88. f(Y^{12}) = g(Y^{98} × TC × OSE × LE × KE) | 8.1778 | 110. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × LE × KE) | 0.0060 * |

89. f(Y^{12}) = g(Y^{98} × TC × OSE × LE × EE) | 0.8540 * | 111. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × LE × EE) | 3.9147 |

90. f(Y^{12}) = g(Y^{98} × TC × OSE × KE × EE) | 8.5051 | 112. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × KE × EE) | −0.0191 * |

91. f(Y^{12}) = g(Y^{98} × TC × CAE × LE × KE) | 10.2244 | 113. f(Y^{12}) = g(Y^{98} × EFF × OSE × LE × KE × EE) | 7.1642 |

92. f(Y^{12}) = g(Y^{98} × TC × CAE × LE × EE) | 1.0477 * | 114. f(Y^{12}) = g(Y^{98} × EFF × CAE × LE × KE × EE) | −0.0649* |

93. f(Y^{12}) = g(Y^{98} × TC × CAE × KE × EE) | 9.8342 | 115. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × LE × KE) | 2.1411 |

94. f(Y^{12}) = g(Y^{98} × TC × LE × KE × EE) | 8.9758 | 116. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × LE × EE) | 2.2456 * |

95. f(Y^{12}) = g(Y^{98} × OSE × CAE × LE × KE) | −0.1248 * | 117. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × KE × EE) | 5.1559 |

96. f(Y^{12}) = g(Y^{98} × OSE × CAE × LE × EE) | 3.6693 | 118. f(Y^{12}) = g(Y^{98} × TC × OSE × LE × KE × EE) | 8.8583 |

97. f(Y^{12}) = g(Y^{98} × OSE × CAE × KE × EE) | –0.1008 * | 119. f(Y^{12}) = g(Y^{98} × TC × CAE × LE × KE × EE) | 9.9377 |

98. f(Y^{12}) = g(Y^{98} × OSE × LE × KE × EE) | 7.5982 | 120. f(Y^{12}) = g(Y^{98} × OSE × CAE × LE × KE × EE) | −0.0889 * |

99. f(Y^{12}) = g(Y^{98} × CAE × LE × KE × EE) | 0.5346 * | 121. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE × LE × KE) | −0.1959 * |

100. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE × LE) | 3.5822 | 122. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE × LE × EE) | 3.1966 |

101. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE × KE) | 0.3618 * | 123. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE × KE × EE) | 0.1709 * |

102. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × CAE × EE) | 4.8423 | 124. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × LE × KE × EE) | 7.1949 |

103. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × LE × KE) | 7.6422 | 125. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × LE × KE × EE) | 0.1431 * |

104. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × LE × EE) | 1.4114 * | 126. f(Y^{12}) = g(Y^{98} × EFF × OSE × CAE × LE × KE × EE) | −0.0987 * |

105. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE × KE × EE) | 7.2751 | 127. f(Y^{12}) = g(Y^{98} × TC × OSE × CAE × LE × KE × EE) | 2.1060 * |

106. f(Y^{12}) = g(Y^{98} × EFF × TC × CAE × LE × KE) | 0.1907 * | 128. f(Y^{12}) = g(Y^{98} × EFF × TC × OSE ×CAE × LE × KE × EE) | 0.00 * |

## Conflicts of Interest

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## Share and Cite

**MDPI and ACS Style**

Du, M.; Wang, B.; Wu, Y.
Sources of China’s Economic Growth: An Empirical Analysis Based on the BML Index with Green Growth Accounting. *Sustainability* **2014**, *6*, 5983-6004.
https://doi.org/10.3390/su6095983

**AMA Style**

Du M, Wang B, Wu Y.
Sources of China’s Economic Growth: An Empirical Analysis Based on the BML Index with Green Growth Accounting. *Sustainability*. 2014; 6(9):5983-6004.
https://doi.org/10.3390/su6095983

**Chicago/Turabian Style**

Du, Minzhe, Bing Wang, and Yanrui Wu.
2014. "Sources of China’s Economic Growth: An Empirical Analysis Based on the BML Index with Green Growth Accounting" *Sustainability* 6, no. 9: 5983-6004.
https://doi.org/10.3390/su6095983