Biased innovation and network evolution: digital driver for green innovation of manufacturing in China

Figures & data

Table 1. GIBM evaluation index system.

Guideline Layer	Indicator Layer	Proxy Indicators
Input factors	Capital input	Investment in fixed assets in manufacturing (billion yuan)
	Labor input	Employment in manufacturing (million people)
	Land input	Industrial land area (km^2)
	Energy input	Comprehensive energy consumption per unit of industrial added value (t standard coal/million yuan)
Expected Output	Economic output	Industrial added value as a share of GDP (%)
	Technology output	Number of green patents of manufacturing companies
Unexpected Output	COD emissions	COD emission per unit of industrial added value (t/million yuan) (%)
	Ammonia emission	Ammonia nitrogen emission per unit of industrial added value (t/100 million yuan) (%)
	Sulfur dioxide emission	SO2 emissions per unit of industrial added value/(t/billion yuan) (%)
	Nitrogen oxide emissions	NOx emission per unit of industrial added value/(t/billion yuan) (%)
	Wastewater emissions	Wastewater emission per unit of industrial added value/(t/billion yuan) (%)
	Solid waste production	Solid waste generation per unit of industrial added value/(t/yuan) (%)

Table 2. Classification basis of the four major sectors.

Intra-sectors relationship	Intre-sectors relationship
	≈0	>0
$\ge (g_q-1)/(\mathit{g}-1)$	Bidirectional overflow	Net overflow
$>(g_q-1)/(\mathit{g}-1)$	Net gain	Irrelevant

Figure 1. Overall evolution of GIBM in China.

Figure 2. GIBM trends in China.

Figure 3. Overall evolution of GIIM in China.

Figure 4. China GIIM Spatial Association Network in 2012.

Figure 5. China GIIM Spatial Association Network in 2016.

Figure 6. China GIIM Spatial Association Network in 2021.

Figure 7. Network density and association from 2012 to 2021.

Table 3. Connectedness，hierarchy and efficiency.

year	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Connectedness	1	1	1	1	1	1	1	1	1	1
Hierarchy	0	0	0	0	0	0	0	0	0	0
Efficiency	0.5394	0.4901	0.4557	0.4286	0.4187	0.4212	0.3768	0.3842	0.3227	0.3128

Figure 8. Evolution of small-world characteristics of spatially linked networks.

Figure 9. Centrality analysis in 2012.

Figure 10. Centrality analysis in 2016.

Figure 11. Centrality analysis in 2021.

Figure 12. Structural hole indicator of China’s GIIM in 2021.

Figure 13. Core-edge structure of GIIM spatial associations, 2012, 2016 and 2021.

Figure 14. Correlation among the four GIIM blocks in 2021.

Table 4. Classification of GIBM space-related network blocks in 2012.

Segment	Segment Matrix				provinces in each segment	overflow relation-ships	accepted relation-ships	Desired internal relationship ratio	Actual internal relationship ratio
	I	II	III	IV
I	2	12	24	20	2	56	55	3.45%	3.45%
II	12	21	69	42	6	123	106	17.24%	4.65%
III	23	68	40	17	12	108	109	37.93%	10.00%
IV	20	26	16	15	10	62	79	31.03%	13.89%

Table 5. Classification of GIBM space-related network blocks in 2016.

Segment	Segment Matrix				provinces in each segment	overflow relation-ships	accepted relation-ships	Desired internal relationship ratio	Actual internal relationship ratio
	I	II	III	IV
I	7	24	39	18	3	81	81	6.90%	3.57%
II	24	33	97	33	8	154	142	24.14%	4.94%
III	39	97	57	5	13	141	139	41.38%	8.44%
IV	18	21	3	12	6	42	56	17.24%	12.50%

Table 6. Classification of GIBM space-related network blocks in 2021.

Segment	Segment Matrix				provinces in each segment	overflow relation-ships	accepted relation-ships	Desired internal relationship ratio	Actual internal relationship ratio
	I	II	III	IV
I	30	29	54	60	6	143	144	17.24%	4.03%
II	30	12	45	43	5	118	112	13.79%	4.07%
III	54	41	21	37	9	132	140	27.59%	6.38%
IV	60	42	41	22	10	143	140	31.03%	6.54%

Table 7. OLS regression results of the whole network structure effect.

Model	(1)	(2)
Network Density	2.420**(2.66)
Network Efficiency		−2.357**(−2.43)
Interception	−2.939***(−5.58)	−0.569(−1.40)
R2	0.4697	9.4256
Adj.R2	0.435	0.3537

Notes: (1)*, **, *** represent at 1%, 5%, and 10% significance levels, respectively; (2)The figures in () indicate the t-values.

Table 8. Regression results of the ego-network structure effect.

Model	(1)	(2)	(3)
Degree centrality	0.109**（2.30）
Betweenness centrality		0.266**（2.19）
Eigenvector centrality			0.130*（1.75）
Interception	0.502***（2.59）	−0.196（-0.38）	0.531**（2.24）
Wald	5.28**	4.82**	3.06*
R2	0.0509	0.0448	0.0427
Hausman statistic	0.36	0.09	1.03
FE/RE	RE	RE	RE

Notes: (1）*, **, *** represent at 1%, 5%, and 10% significance levels, respectively;（2）The figures in () indicate the t-values.

Table 9. Correlation analysis.

Independent	2012	Significance	2016	Significance	2021	Significance
D	−0.359***	0.000	−0.376***	0.000	−0.391***	0.000
E	−0.062***	0.001	−0.086***	0.000	−0.037**	0.017
T	0.749***	0.000	0.679***	0.000	0.678***	0.000

Notes: *, **, *** represent at 1%, 5%, and 10% significance levels, respectively.

Table 10. QAP regression analysis.

Variable	2012			2016			2021
	Coef	Prob 1	Prob 2	Coef	Prob 1	Prob 2	Coef	Prob 1	Prob 2
D	−0.0089***	0.991	0.009	−0.0129**	1.000	0.000	−0.0132***	1.000	0.000
E	−0.0002***	1.000	0.000	−3.5553**	0.987	0.014	−0.00003***	1.000	0.000
T	0.7074***	0.000	1.000	0.6320***	0.000	1.000	0.5506***	0.000	1.000