Research into the Path and Mechanism by Which Intelligent Manufacturing Promotes Carbon Emission Reductions
Abstract
:1. Introduction
1.1. Background and Significance of Topic
1.2. Intelligent Manufacturing and Carbon Emissions
1.3. Research Hypothesis
2. Variable Selection
2.1. Variable Selection
2.2. Data Sources
2.3. Descriptive Analysis of Sample
3. Empirical Modeling
3.1. Spatial Correlation Analysis Model
3.2. Spatial Weighting Matrix Setting
3.3. Spatial Panel Modeling Setup
4. Model Testing and Analysis of Results
4.1. Spatial Correlation Test
4.2. Spatial Econometric Modeling Tests
4.3. Analysis of Empirical Results of the Benchmark Model
4.4. Mechanical Testing
4.5. Heterogeneity Analysis
4.6. Robustness Check
5. Results
6. Conclusions
7. Research Weaknesses and Limitations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Level 1 Indicator | Level 2 Indicator | Description of Indicator (Unit) |
---|---|---|
Basic inputs | Intelligent equipment fixed assets | Investment in fixed assets in towns and cities for the manufacture of communication electronic equipment (units/tens of thousands of people) |
Intelligent equipment import inputs | Imports in the electronics industry as a proportion of industrial main business income (CNY million) | |
Staffing inputs | Average number of workers in the electronics and communications manufacturing industry (persons) | |
Intelligent infrastructure | Internet broadband access port count (in units of 10,000) | |
Investment in scientific research | R&D personnel in electronics and communications manufacturing (persons) | |
Software support | Popularization of software applications | Revenue from basic software products as a share of industrial main business income (%) |
Revenue from software operations | Revenue from software operations (million US dollars) | |
Investment in scientific research | Software developers (persons) | |
Software business exports | Export revenue from software business (million US dollars) | |
Market practices | Economic efficiency | Profit of the electronic and communication equipment manufacturing industry (CNY billion) |
Social efficiency | Total assets of communication, computer, and other electronic equipment in the manufacturing industry (CNY billion/person) | |
Innovation capacity | Number of patent applications in the electronics and communications manufacturing industry (pieces) |
Variable | Designation | Sample | Unit | Average | Minimum | Maximum |
---|---|---|---|---|---|---|
CE | Carbon emission | 330 | 10 million tons | 31.153 | 3.495 | 89.278 |
IM | Intelligent manufacturing level | 330 | — | 0.087 | 0.002 | 0.830 |
UL | Urbanization level | 330 | % | 58.981 | 34.975 | 89.616 |
CG | Urban greenery | 330 | % | 33.385 | 4.0201 | 66.802 |
EI | Energy intensity | 330 | Tons/million | 1.224 | 0.376 | 4.453 |
PR | Productivity level | 330 | millions | 2.619 | 0.097 | 7.072 |
GI | Government intervention | 330 | % | 2.355 | 1.074 | 6.603 |
TI | Technological innovation | 330 | % | 1.758 | 0.410 | 6.530 |
Year | CE | IM | ||
---|---|---|---|---|
p-Value | p-Value | |||
2011 | 0.1164 | 0.024 | 0.0368 | 0.081 |
2012 | 0.0982 | 0.030 | 0.0433 | 0.065 |
2013 | 0.0902 | 0.038 | 0.0466 | 0.059 |
2014 | 0.0823 | 0.045 | 0.0446 | 0.060 |
2015 | 0.0723 | 0.053 | 0.0611 | 0.040 |
2016 | 0.0626 | 0.075 | 0.0531 | 0.037 |
2017 | 0.0507 | 0.080 | 0.0403 | 0.047 |
2018 | 0.0551 | 0.089 | 0.0447 | 0.044 |
2019 | 0.0502 | 0.088 | 0.0532 | 0.038 |
2020 | 0.0416 | 0.080 | 0.0539 | 0.042 |
2021 | 0.0236 | 0.086 | 0.0534 | 0.041 |
Test Method | Statistic | p-Value |
---|---|---|
LM—spatial error | 160.965 | 0.000 |
LM—spatial error (robust) | 159.136 | 0.000 |
LM—spatial lag | 6.443 | 0.011 |
LM—spatial lag (robust) | 4.613 | 0.032 |
Hausman test | 11.560 | 0.041 |
Wald—spatial lag | 47.780 | 0.000 |
Wald—spatial error | 47.640 | 0.000 |
LR—spatial lag | 68.680 | 0.000 |
LR—spatial error | 63.700 | 0.000 |
Variable | Hybrid OLS | SDM |
---|---|---|
IM | −40,396.817 *** | −18,244.542 *** |
(10,282.619) | (4078.715) | |
UL | 597.175 *** | 425.622 *** |
(102.180) | (85.328) | |
CG | −181.862 *** | −197.164 * |
(54.690) | (107.820) | |
EI | 8928.837 *** | 15,222.401 *** |
(1515.935) | (1264.532) | |
PR | 11.215 *** | 3.917 *** |
(0.720) | (0.694) | |
W × IM | 59,666.469 *** | |
(7737.912) | ||
W × PR | −4.469 *** | |
(1.704) | ||
Province effect | YES | |
Time effect | YES | |
Rho | −34,784.065 *** | −0.103 |
(9091.494) | (0.063) | |
N | 330 | 330 |
R-squared | 0.5375 | 0.3550 |
Log-likelihood | −3002.9006 |
Variable | Model (1) | Model (2) | Model (3) |
---|---|---|---|
CE | TI | CE | |
IM | −18,244.542 *** | 2.249 *** | −12,750.950 *** |
(4078.715) | (0.309) | (4242.909) | |
UL | 425.622 *** | 0.020 *** | 488.835 *** |
(85.328) | (0.007) | (85.163) | |
CG | −197.164 * | 0.017 ** | −161.168 |
(107.820) | (0.008) | (105.998) | |
EI | 15,222.401 *** | 0.235 ** | 15,940.086 *** |
(1264.532) | (0.100) | (1252.181) | |
PR | 3.917 *** | −0.000 | 3.735 *** |
(0.694) | (0.000) | (0.681) | |
TI | −2651.235 *** | ||
(690.298) | |||
W × IM | 59,666.469 *** | −1.708 *** | 53,953.508 *** |
(7737.912) | (0.623) | (7722.046) | |
W × PR | −4.469 *** | −0.000 | −5.027 *** |
(1.704) | (0.000) | (1.674) | |
Province effect | YES | YES | YES |
Time effect | YES | YES | YES |
Rho | −0.103 | −0.086 | −0.072 |
(0.063) | (0.073) | (0.063) | |
N | 330 | 330 | 330 |
R-squared | 0.3550 | 0.4278 | 0.3144 |
Log-likelihood | −3002.9006 | 116.2295 | −2995.6414 |
Variable | Regression Results |
---|---|
IM | −54,032.181 *** |
(7599.355) | |
GI | −2546.790 *** |
(821.541) | |
Interact | 58,412.464 *** |
(7425.177) | |
UL | 574.310 *** |
(86.744) | |
CG | −204.305 ** |
(103.485) | |
EI | 15,372.953 *** |
(1225.720) | |
PR | 3.393 *** |
(0.671) | |
W × IM | −3.449 ** |
(1.672) | |
W × PR | −40,903.755 *** |
(7461.820) | |
Province effect | YES |
Time effect | YES |
rho | −0.078 |
(0.062) | |
N | 330 |
R-squared | 0.4148 |
Log-likelihood | −2988.3796 |
Variable | 2011–2016 | 2017–2019 | 2020–2021 |
---|---|---|---|
IM | −27,779.705 *** | −28,924.355 ** | 29,684.736 *** |
(5978.821) | (12,372.989) | (9914.710) | |
UL | 440.918 *** | −365.855 | −250.917 |
(112.576) | (386.334) | (546.807) | |
CG | −259.117 ** | 25.553 | −11,260.292 * |
(115.664) | (138.715) | (6506.012) | |
EI | 12,350.680 *** | 24,359.267 *** | 6445.511 ** |
(1339.240) | (2970.673) | (2557.839) | |
PR | 2.044 ** | 4.180 | 36.685 *** |
(0.983) | (4.163) | (10.325) | |
W × IM | 52,240.808 *** | 107,751.445 *** | 40,093.032 ** |
(11,146.701) | (26,455.721) | (18,289.626) | |
W × PR | −5.847 *** | −12.787 | 10.807 |
(2.195) | (8.602) | (20.246) | |
Province effect | YES | YES | YES |
Time effect | YES | YES | YES |
rho | −0.140 | −0.046 | 0.048 |
(0.101) | (0.096) | (0.183) | |
N | 180 | 90 | 60 |
R-squared | 0.3452 | 0.4444 | 0.4552 |
Log-likelihood | −1529.8810 | −755.7205 | −473.6559 |
Variable | Eastern Region | Central Region | Western Region |
---|---|---|---|
IM | 7698.916 | −64,783.024 ** | −68,432.924 *** |
(4784.663) | (25,446.517) | (16,090.736) | |
UL | 720.963 *** | −494.132 * | 12.352 |
(95.031) | (290.326) | (246.343) | |
CG | −163.425 | −1676.370 *** | 36.441 |
(104.387) | (544.801) | (141.455) | |
EI | 13,345.405 *** | 26,038.359 *** | 14,160.557 *** |
(2716.112) | (3186.138) | (1697.012) | |
PR | −3.819 *** | 1.155 | 7.682 *** |
(1.394) | (1.142) | (1.666) | |
W × IM | −30,459.057 | 68,274.565 | 60,007.379 |
(21,107.463) | (69,169.727) | (52,418.906) | |
W × PR | −7.316 | −7.570 *** | −12.018 * |
(6.164) | (2.874) | (7.280) | |
Province effect | YES | YES | YES |
Time effect | YES | YES | YES |
Rho | −0.312 | −0.312 * | −0.485 *** |
(0.215) | (0.163) | (0.187) | |
N | 121 | 99 | 110 |
R-squared | 0.2044 | 0.0211 | 0.1744 |
Log-likelihood | −1032.3224 | −885.6298 | −970.6475 |
Variable | Regression Results |
---|---|
L.CE | 1.009 *** |
(0.033) | |
IM | −25,898.667 *** |
(4797.717) | |
UL | 412.176 *** |
(139.981) | |
CG | 59.948 |
(111.433) | |
EI | 4968.172 ** |
(2124.822) | |
PR | 1.982 *** |
(0.568) | |
W × IM | 33,592.655 *** |
(11,265.414) | |
W × PR | −1.352 |
(1.048) | |
rho | −35,402.912 ** |
(14,078.943) | |
AR(1) | 0.087 |
AR(2) | 0.298 |
Hansen test | 0.713 |
Variable | Spatial Economic Distance Matrix |
---|---|
IM | −9381.465 ** |
(4178.922) | |
UL | 373.380 *** |
(84.033) | |
CG | −275.556 *** |
(102.273) | |
EI | 15,250.201 *** |
(1255.503) | |
PR | 2.897 *** |
(0.734) | |
W × IM | 254,596.632 *** |
(34,503.413) | |
W × PR | −15.974 *** |
(4.421) | |
Province effect | YES |
Time effect | YES |
Rho | −0.801 *** |
(0.229) | |
N | 330 |
R-squared | 0.4100 |
Log-likelihood | −3000.6099 |
Variable | TCE |
---|---|
IM | −14.272 *** |
(1.811) | |
UL | 0.221 *** |
(0.040) | |
CG | −0.130 *** |
(0.049) | |
EI | 7.716 *** |
(0.580) | |
PR | 0.001 *** |
(0.000) | |
W × IM | 0.872 |
(3.504) | |
W × PR | 0.001 |
(0.001) | |
Province effect | YES |
Time effect | YES |
Rho | −0.224 *** |
(0.070) | |
N | 330 |
R-squared | 0.1392 |
Log-likelihood | −467.4802 |
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Tang, J.; Wang, W.; Ding, W. Research into the Path and Mechanism by Which Intelligent Manufacturing Promotes Carbon Emission Reductions. Energies 2024, 17, 3925. https://doi.org/10.3390/en17163925
Tang J, Wang W, Ding W. Research into the Path and Mechanism by Which Intelligent Manufacturing Promotes Carbon Emission Reductions. Energies. 2024; 17(16):3925. https://doi.org/10.3390/en17163925
Chicago/Turabian StyleTang, Jiahui, Wan Wang, and Wangwang Ding. 2024. "Research into the Path and Mechanism by Which Intelligent Manufacturing Promotes Carbon Emission Reductions" Energies 17, no. 16: 3925. https://doi.org/10.3390/en17163925
APA StyleTang, J., Wang, W., & Ding, W. (2024). Research into the Path and Mechanism by Which Intelligent Manufacturing Promotes Carbon Emission Reductions. Energies, 17(16), 3925. https://doi.org/10.3390/en17163925