Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction
Abstract
:1. Introduction
2. Literature Review and Research Hypotheses
2.1. Literature Review
2.2. Research Hypothesis
2.2.1. Impact Mechanism of Industrial Structure on Carbon Emissions
2.2.2. Impact Mechanism of Industrial Structure on Energy Structure
2.2.3. Impact Mechanism of Energy Structure on Carbon Emissions
3. Model and Data
3.1. Model
3.1.1. Quantile Regression Model
3.1.2. Mediation Effect Model
3.2. Variable Selection and Measurement
3.2.1. Interpreted Variables
3.2.2. Core Explanatory Variables
3.2.3. Mediator Variable
3.2.4. Control Variables
3.3. Data Sources and Statistical Characteristics
4. Empirical Test
4.1. Regional Division
4.1.1. Current Status of Carbon Emissions in China
4.1.2. Regional Grouping
4.2. Quantile Regression Analysis
4.2.1. Panel Unit Root Test and Cointegration Test
4.2.2. Model Form
4.2.3. Analysis of Regression Results
4.2.4. Robustness Test
4.3. Mediating Effect Analysis
4.3.1. Stepwise Regression Test
4.3.2. Bootstrap Mediation Effect Test
5. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable Name | Economic Meaning | Metrics | AVG | Std. | Min | Max |
---|---|---|---|---|---|---|
CG | Carbon Intensity: the amount of carbon dioxide emitted per unit of gross national product | Carbon emissions/Gross national product (million tons/billion yuan) | 342.9253 | 270.0545 | 15.7056 | 1564.8340 |
IS | Industrial Structure | The added value of the tertiary industry/the added value of the second industry | 1.1374 | 0.5521 | 0.5271 | 5.2340 |
ES | Energy Consumption Structure | Regional coal consumption/regional total energy consumption (%) | 0.6853 | 0.2701 | 0.0177 | 1.7578 |
OP | Level of Opening-up | Total import and export volume of domestic destinations and sources of goods (100 million yuan) | 7920.0000 | 17,500.0000 | 15.2940 | 128,000.0000 |
TE | Technological Innovation | Number of patent authorizations (pieces) | 24,627.4300 | 53,679.7700 | 56.0000 | 527,390.0000 |
PL | Population | Total population (10,000 people) | 4387.5790 | 2683.5330 | 495.6000 | 12,489.0000 |
UR | Urbanization level | Urban population/total population (%) | 0.2707 | 0.1798 | 0.0197 | 0.8028 |
CM | Carbon Trading Pilot Policy | 0~1 dummy variable | 0 | 1 |
Group | Area |
---|---|
≤0.10 quantile group | Jiangxi, Hunan, Guangxi |
0.10~0.25 quantile group | Beijing, Fujian, Shanghai, Hainan |
0.25~0.50 quantile group | Zhejiang, Jiangsu, Sichuan, Chongqing, Hubei, Yunnan, Tianjin, Anhui |
0.50~0.75 quantile group | Shaanxi, Hebei, Qinghai, Jilin, Liaoning, Heilongjiang, Shandong |
0.75~0.90 quantile group | Gansu, Xinjiang, Guizhou, Guangdong, Henan |
≥0.90 quantile group | Inner Mongolia, Ningxia, Shanxi |
Variable | LLC | IPS | ADF-Fisher Chi-Square | PP-Fisher Chi-Square | Conclusion |
---|---|---|---|---|---|
D_lnCG | −18.4329 *** | −16.4297 *** | 213.9499 *** | 538.7272 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnIS | −13.4503 *** | −11.4533 *** | 198.5181 *** | 246.8631 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnES | −19.0036 *** | −16.7741 *** | 250.4381 *** | 529.6426 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnOP | −16.0971 *** | −14.7765 *** | 263.9364 *** | 452.4454 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnTE | −16.8806 *** | −14.6179 *** | 161.3194 *** | 410.4722 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnPL | −14.8879 *** | −12.1741 *** | 223.7696 *** | 327.3083 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnUR | −17.8013 *** | −18.5908 *** | 320.9167 *** | 631.6294 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
D_lnCM | −19.0570 *** | −15.3160 *** | 147.0966 *** | 407.6537 *** | smooth |
(0.0000) | (0.0000) | (0.0000) | (0.0000) |
Testing Method | INSPECTION FORM | Statistics | p-Value |
---|---|---|---|
Kao | Modified Dickey–Fuller t | −5.4478 | 0.0000 |
Dickey–Fuller t | −4.7316 | 0.0000 | |
Augmented Dickey–Fuller t | −5.2597 | 0.0000 | |
Pedroni | Modified Phillips–Perron t | 8.1915 | 0.0000 |
Phillips–Perron t | −2.9919 | 0.0014 | |
Augmented Dickey–Fuller t | −4.4612 | 0.0000 | |
Westerlund | −2.3487 | 0.0094 |
Variable | lnIS | lnES | lnOP | lnTE | lnPL | lnUR | lnCM | _cons |
---|---|---|---|---|---|---|---|---|
10% | 0.2950 *** | 1.2656 *** | 0.2134 *** | −0.5537 *** | 0.4081 *** | 0.8036 *** | 0.0228 | −3.528 *** |
(0.0077) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.9010) | (0.0040) | |
25% | −0.0114 *** | 1.0875 *** | 0.126 *** | −0.5365 *** | 0.3393 *** | 0.7888 *** | 0.0192 * | −1.3342 * |
(0.0091) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0850) | (0.0530) | |
50% | −0.2105 *** | 1.0122 *** | 0.136 *** | −0.5087 *** | 0.1996 *** | 0.4597 *** | 0.0358 * | 1.3491 ** |
(0.0040) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0656) | (0.0110) | |
75% | −0.1938 *** | 0.8755 *** | 0.0961 *** | −0.4518 *** | 0.1566 *** | 0.3627 *** | 0.0923 ** | 2.6937 *** |
(0.0090) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0258) | (0.0000) | |
90% | −0.1442 *** | 0.8192 *** | 0.0671 *** | −0.4442 *** | 0.1749 *** | 0.4226 *** | 0.0434 | 2.8686 *** |
(0.0015) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.5040) | (0.0000) |
Replace the Explained Variable | Replace Explanatory Variables | |
---|---|---|
(1) | (2) | |
lnPC | lnCG | |
lnIS | −0.2668 *** | |
lnIR | −0.0261 *** | |
lnES | 0.7018 *** | 0.6332 *** |
lnOP | 0.2329 *** | 0.0858 * |
lnTE | −0.1707 *** | −0.2168 *** |
lnPL | 0.5678 *** | 0.8548 *** |
lnUR | 0.4206 *** | 0.2920 *** |
lnCM | −0.0603 | −0.2925 *** |
_cons | 3.0882 *** | −2.0662 |
adjust R2 | 0.8984 | 0.7372 |
Model 1 | Model 2 | Model 3 | |
---|---|---|---|
lnCG | lnES | lnCG | |
IS | −0.3670 *** | −0.1219 *** | −0.2943 *** |
(0.0082) | (0.0000) | (0.0055) | |
ES | 0.5971 *** | ||
(0.0040) | |||
OP | −0.0975 | −0.0096 | −0.1032 |
(0.2920) | (0.9060) | (0.1630) | |
TE | −0.2808 *** | −0.0677 ** | −0.2404 *** |
(0.0020) | (0.0450) | (0.0070) | |
PL | 0.6057 | −0.6529 | 0.9955 * |
(0.3220) | (0.1910) | (0.0700) | |
UR | 0.3087 * | 0.0783 | 0.2620 |
(0.0970) | (0.4020) | (0.1470) | |
CM | −0.4407 *** | −0.2915 ** | −0.2666 *** |
(0.0010) | (0.0190) | (0.0090) | |
_cons | 2.5707 | 4.7036 *** | −3.1802 |
(0.5960) | (0.0100) | (0.5130) | |
R2 | 0.8858 | 0.8286 | 0.9002 |
Mediator Variable | Path | Effect | Effect Coefficient | p-Value |
---|---|---|---|---|
energy structure | lnIS-lnES-lnCG | indirect | −0.0728 | 0.0000 |
lnIS-lnES-lnCG | direct | −0.2943 | 0.0000 | |
lnIS-lnCG | total effect | −0.3670 |
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Fan, G.; Zhu, A.; Xu, H. Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction. Sustainability 2023, 15, 3489. https://doi.org/10.3390/su15043489
Fan G, Zhu A, Xu H. Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction. Sustainability. 2023; 15(4):3489. https://doi.org/10.3390/su15043489
Chicago/Turabian StyleFan, Guoliang, Anni Zhu, and Hongxia Xu. 2023. "Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction" Sustainability 15, no. 4: 3489. https://doi.org/10.3390/su15043489
APA StyleFan, G., Zhu, A., & Xu, H. (2023). Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction. Sustainability, 15(4), 3489. https://doi.org/10.3390/su15043489