Trend Analysis of the Impact of Ecological Governance on Industrial Structural Upgrading under the Dual Carbon Target
Abstract
:1. Introduction
- The paper proposes a method that combines gray forecasting models and neural networks to predict the level of industrial structural upgrading and introduces a threshold regression model for yearly prediction of the intensity of ecological governance. It exhibits strong applicability in studying the trend of government decision-making effectiveness.
- Based on the predicted data, fixed effects and threshold regression analyses were conducted on the ecological governance intensity, verifying the mechanisms through which future ecological governance affects the industrial structure.
- By examining the trends of ecological governance intensity and industrial structural transformation, the study reveals the development path of future ecological environmental management.
2. Data Sources and Methods
2.1. Data Source and Processing
2.2. Methods
2.2.1. Prediction Methods
- 1.
- Core Explanatory Variable—EG
- 2.
- Control Variables
- 3.
- Interpreted Variables—TS, CS
2.2.2. Panel Threshold Regression Model
3. Empirical Analysis
3.1. Forecasting Results
3.1.1. Forecasting Results of Core Explanatory Variable
3.1.2. Forecasting Results of Control Variables
3.1.3. Forecasting Results of Interpreted Variables
3.2. Threshold Regression Analysis Based on Predicted Data
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
TS | Industrial Structure Upgrading Index |
CS | Industrial Low-Carbonization Index |
EG | Ecological Governance Efforts |
Threshold Value |
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Indicator Category | Indicator Name |
---|---|
Interpreted variables | Industrial Structure Upgrading Index (TS) |
Industrial Low-Carbonization Index (CS) | |
Core explanatory variable | Ecological Governance Efforts (EG) |
Control variables | Population |
Economy | |
Finance | |
Tec-Edu | |
Business | |
Salary |
Variable | Class I City | Class II City | Class III City | |||
---|---|---|---|---|---|---|
GM(1,1) | GM(2,1) | GM(1,1) | GM(2,1) | GM(1,1) | GM(2,1) | |
Population | 281 | 7 | 286 | 2 | 288 | 0 |
Economy | 288 | 0 | 288 | 0 | 288 | 0 |
Finance | 286 | 2 | 288 | 0 | 288 | 0 |
Tec-Edu | 286 | 2 | 287 | 1 | 287 | 1 |
Business | 275 | 13 | 280 | 8 | 281 | 7 |
Salary | 281 | 7 | 283 | 5 | 287 | 1 |
Name | Parameter Settings |
---|---|
Number of input layer nodes | 7 |
Number of hidden layer nodes | 10 |
Number of output layer nodes | 2 |
Input layer to hidden layer transfer function | Logsig function |
Implicit layer to output layer transfer function | Purelin function |
Activation function | Sigmoid function |
Learning function | Learngdm function |
Training function | Trainlm function |
Training frequency | 10000 |
Target accuracy | 0.0001 |
Learning rate | 0.15 |
Variable | R2 | MSE | MAE |
---|---|---|---|
TS | 0.9474 | 0.0517 | 0.0064 |
CS | 0.9907 | 0.0233 | 0.0142 |
Year | TS | CS | ||||
---|---|---|---|---|---|---|
Number of Thresholds | Threshold | p Value | Number of Thresholds | Threshold | p Value | |
2020 | Single threshold | 1.0016 | 0.0800 | Double threshold | 0.8920 | 0.0500 |
1.3332 | 0.0233 | |||||
2021 | Single threshold | 1.0017 | 0.0900 | Double threshold | 0.9342 | 0.0600 |
1.4119 | 0.0400 | |||||
2022 | Single threshold | 0.9866 | 0.0914 | Double threshold | 0.9651 | 0.0500 |
1.5513 | 0.0220 | |||||
2023 | Absent | — | — | Double threshold | 1.1750 | 0.0533 |
1.5464 | 0.0633 | |||||
2024 | Absent | — | — | Double threshold | 1.1971 | 0.0433 |
1.5446 | 0.0100 | |||||
2025 | Absent | — | — | Double threshold | 1.2982 | 0.0667 |
1.5758 | 0.0000 |
Variable | 2020 | 2021 | 2022 |
---|---|---|---|
ln Population | −0.0517 | 0.0052 | -0.0105 |
ln Economy | −0.2649 *** 1 | −0.7712 *** | −0.8238 *** |
ln Finance | −0.1286 *** | 0.0297 ** | 0.1440 *** |
ln Tec-Edu | −0.1905 * | 0.0352 | 0.0232 |
ln Business | 0.1237 *** | 0.2628 *** | 0.2471 *** |
ln Salary | 0.6457 *** | 0.8059 *** | 0.8322 *** |
Cons | −2.1282 *** | −4.4183 *** | −5.6944 *** |
−0.3967 ** | −0.0202 * | −0.0164 ** | |
0.0769 *** | 0.0580 *** | 0.0418 *** |
Variable | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |
---|---|---|---|---|---|---|
ln Population | 0.2744 *** 1 | 0.2725 *** | 0.2487 *** | 0.1960 *** | 0.2935 *** | 0.2173 *** |
ln Economy | −0.0179 | 0.0014 | 0.0200 | 0.0623 ** | 0.0786 *** | 0.0764 *** |
ln Finance | −0.1423 *** | −0.1535 *** | −0.1348 *** | −0.1231 *** | −0.0951 *** | −0.1106 *** |
ln Tec−Edu | 0.1941 *** | 0.2448 *** | 0.2584 *** | 0.2653 *** | 0.2472 *** | 0.2705 *** |
ln Business | 0.0407 ** | 0.0260 ** | 0.0134 | 0.0131 | 0.0117 | 0.0375 * |
ln Salary | 0.119 *** | 0.1028 *** | 0.0866 *** | 0.0602 *** | 0.0591 ** | 0.0519 ** |
Cons | −2.4933 *** | −2.8389 *** | −3.0405 *** | −3.0029 *** | −3.8523 *** | −3.5374 *** |
0.2976 ** | 0.0171 * | 0.0089 | −0.0165 | −0.0377 * | −0.0539 *** | |
0.0993 *** | 0.0843 *** | 0.0763 *** | 0.0797 *** | 0.0606 *** | 0.0767 *** | |
0.1203 | −0.0317 | −0.0603 * | −0.0984 * | −0.1435 *** | −0.2197 *** |
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You, S.; Zhang, C.; Zhao, H.; Zhou, H.; Li, Z.; Xu, J.; Meng, Y. Trend Analysis of the Impact of Ecological Governance on Industrial Structural Upgrading under the Dual Carbon Target. Sustainability 2023, 15, 11775. https://doi.org/10.3390/su151511775
You S, Zhang C, Zhao H, Zhou H, Li Z, Xu J, Meng Y. Trend Analysis of the Impact of Ecological Governance on Industrial Structural Upgrading under the Dual Carbon Target. Sustainability. 2023; 15(15):11775. https://doi.org/10.3390/su151511775
Chicago/Turabian StyleYou, Siqing, Chaoyu Zhang, Han Zhao, Hongli Zhou, Zican Li, Jiayi Xu, and Yan Meng. 2023. "Trend Analysis of the Impact of Ecological Governance on Industrial Structural Upgrading under the Dual Carbon Target" Sustainability 15, no. 15: 11775. https://doi.org/10.3390/su151511775