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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

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Front. Eng    2015, Vol. 2 Issue (3) : 211-223    https://doi.org/10.15302/J-FEM-2015043
ENGINEERING MANAGEMENT TREATISES
Driving Factors of Green Mining in Coal Mining Enterprises in China
Lin-xiu Wang1,Mu-xi Yu1,*(),Si-jia Wang2
1. School of Mechanic and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
2. Smeal College of Business, Pennsylvania State University, State College, PA 16801, USA
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Abstract

This study constructs a Green Mining model based on identification and analysis of the four key driving factors of Green Mining in coal mining enterprises in China. Twelve research propositions are raised and empirically tested by using Structural Equation Model. The result shows that four key factors affect the intended implementation of Green Mining by improving a corporations efficiency and reputation. The diffusion of Green Mining technology has direct positive influence on the intended Green Mining implementation; Stakeholders’ green appeal has a positive influence on the decision makers’ attitude and values. The empirical study provides support for the government to establish a reasonable regulation and control mechanism to improve enterprises’ enthusiasm for Green Mining. Four Green Mining incentive policies are raised to regulate and motivate the coal mining enterprises to improve their environmental behaviors. The government can adopt piece(s) of them to change the enterprises’ roles from traditional passive to modern initiative.
Keywords Green Mining      driving factor      Structural Equation Model      empirical study     
Corresponding Author(s): Mu-xi Yu   
Online First Date: 18 February 2016    Issue Date: 21 March 2016
 Cite this article:   
Lin-xiu Wang,Mu-xi Yu,Si-jia Wang. Driving Factors of Green Mining in Coal Mining Enterprises in China[J]. Front. Eng, 2015, 2(3): 211-223.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2015043
https://academic.hep.com.cn/fem/EN/Y2015/V2/I3/211
Fig.1  Driving process of environmental-economic instruments.
Fig.2  Myers’ Corporate Social Performance Theory.
Fig.3  Conceptual model of driving factors of Green Mining.
Fig.4  Benefits of Green Mining.
EFF REP WIS AGE AST NUM
EFF Pearson Correlation 1 0.537** 0.443** 0.226** 0.120* 0.014
Sig. (2-tailed) 0.000 0.000 0.015 0.085 0.815
N 290 290 290 290 290 290
REP Pearson Correlation 0.537** 1 0.445** 0.132** 0.093 0.159**
Sig. (2-tailed) 0.000 0.000 0.024 0.140 0.016
N 290 290 290 290 290 290
WIS Pearson Correlation 0.443** 0.445** 1 0.186** 0.130* 0.069
Sig. (2-tailed) 0.000 0.000 0.022 0.065 0.239
N 290 290 290 290 290 290
AGE Pearson Correlation 0.226** 0.132** 0.186** 1 0.285** 0.459**
Sig. (2-tailed) 0.015 0.024 0.022 0.000 0.000
N 290 290 290 290 290 290
AST Pearson Correlation 0.120* 0.093 0.130* 0.285** 1 0.748**
Sig. (2-tailed) 0.085 0.140 0.065 0.000 0.000
N 290 290 290 290 290 290
NUM Pearson Correlation 0.014 0.159** 0.069 0.459** 0.748** 1
Sig. (2-tailed) 0.815 0.016 0.239 0.000 0.000
N 290 290 290 290 290 290
Tab.1  Correlation Coefficient Matrix of Relative Variables
Variable 1 Variable 2 Correlation Coefficient P-value Correlation Relationship
WIS EFF 0. 443 P<0.01 Positive
WIS REP 0. 445 P<0.01 Positive
WIS AGE 0.186 P<0.01 Positive
WIS AST 0.130 P<0.05 Positive
EFF REP 0.537 P<0.01 Positive
EFF AGE 0.226 P<0.01 Positive
EFF AST 0.120 P<0.05 Positive
REP AGE 0.186 P<0.01 Positive
REP NUM 0.130 P<0.05 Positive
Tab.2  Correlations Analysis of Relative Variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett’s Test of Sphericity
Approx. Chi-Square df Sig.
0.822 5888.501 990 0
Tab.3  KMO and Bartlett’s Test
Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Total % of Variance Cumulative/% Total % of Variance Cumulative/% Total % of Variance Cumulative/%
1 13.36 25.207 25.207 13.36 25.207 25.207 11.79 22.247 22.247
2 6.632 12.513 37.719 6.632 12.513 37.719 5.18 9.770 32.017
3 5.393 10.175 47.894 5.393 10.175 47.894 4.34 8.182 40.199
4 3.319 6.262 54.156 3.319 6.262 54.156 3.96 7.464 47.662
5 2.056 3.879 58.036 2.056 3.879 58.036 3.20 6.034 53.697
6 1.945 3.670 61.705 1.945 3.670 61.705 3.19 6.022 59.718
7 1.737 3.277 64.982 1.737 3.277 64.982 2.79 5.268 64.982
Tab.4  Final Total Variance Contribution Rates (N=290)
Measurement Index Absolute Fitting Test Increment Fitting Test Brief Fitting Test
Chi-Square Text CMIN/DF(c2/df ) RMR GFI RMSEA AGFI NFI NNFI CFI PNFI PGFI
RecommendedStandard P<0.05 P<3 P<0.08 P>0.9 P<0.1 P>0.7 P>0.9 P>0.9 P>0.9 P>0.5 P>0.5
Standard 2350.58 (P= 0.00) 2.48 0.073 0.92 0.071 0.7 0.92 0.87 0.88 0.78 0.67
Tab.5  Fitting Test of Confirmatory Factor Analysis
Variable Cronbach’s a-value
LED 0.851
TEC 0.794
GOV 0.817
STA 0.832
EFF 0.796
REP 0.826
WIS 0.807
Reliability 0.906
Tab.6  Reliability Analysis of Data
Fig.5  Optimal model of SEM.
Path Standardized Estimate t-value
GOV→TEC 0.33 4.18
GOV→LED 0.29 3.87
GOV→EFF 0.29 3.95
GOV→REP 0.23 3.09
STA→LED 0.14 2.05
STA→REP 0.36 5.13
LED→EFFLED→REPTEC→WISEFF→WISREP→WIS 0.400.090.170.450.33 22.121.262.556.004.65
Tab.7  Standardized Path Coefficients
Variables Relationship Direct Effect Indirect Effect Whole effect
GOV→TEC 0.29 - - 0.29
GOV→LED 0.31 - - 0.31
GOV→EFF 0.33 0.10 0.43
GOV→REP 0.28 0.03 0.31
STA→LED 0.11 - - 0.11
STA→REP 0.32 0.01 0.33
LED→EFF 0.42 - - 0.42
LED→REP 0.10 - - 0.10
TEC→WIS 0.18 -0.04 0.14
EFF→WIS 0.37 - - 0.37
REP→WIS 0.26 - - 0.26
Tab.8  Direct Effect, Indirect Effect, and the Whole Effect among Variable
Fig.6  (a) Emission permits transaction of different enterprises. (b) Emission permits transaction between different pollution regions in the enterprise.
Fig.7  Incentive effect analysis of supportive subsidy used in stimulation towards Green Mining process.
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