Carbon Sequestration Total Factor Productivity Growth and Decomposition: A Case of the Yangtze River Economic Belt of China
Abstract
:1. Introduction
2. Methodology
2.1. Conceptual Framework
2.2. Stochastic Frontier Analysis
2.3. Translog Production Function Setting
2.4. Technical Inefficiency Function Setting
2.5. Decomposition of CSTFP Growth
2.5.1. Rate of Technical Progress
2.5.2. Rate of Technical Efficiency Change
2.5.3. Rate of Scale Efficiency Change
2.5.4. Rate of Factor Allocation Efficiency Change
3. Study Area and Data
3.1. The Yangtze River Economic Belt (YREB) of China
3.2. Variables and Data
3.2.1. Output and Input Variables
3.2.2. Impact Variables of Technical Inefficiency Terms
4. Empirical Estimation
4.1. Model Test
4.2. Parameter Estimation
5. Results
5.1. The CSTFP Growth Is Obviously Improved Owing to Carbon Sequestration’s Synergy Effect
5.2. Three of Four Components Drives Positively the CSTFP Growth
5.3. Technical Efficiency Growth Is the Primary Contributor to the CSTFP Growth
5.4. Scale Efficiency is Stabilized While Factor Allocation Efficiency is Lowered
5.5. Distance on CSTFP Growth in Three Watershed Segments is Significant
6. Discussion
6.1. Technological Progress Is Improved by Synergistic Effect of Energy Inputs and Carbon Sequestration
6.2. Technical Efficiency Growth Is Benefited from Harmonious Symbiosis Influence of Carbon Sequestration
6.3. Inefficient Resource Allocation Is Mainly Due to Conflicts of Interest and Insufficient Coordination among Regions in YREB
6.4. CSTFP Growth Is Inadequate and Uncoordinated among 11 Provinces and Municipalities of YREB
6.5. Total Synergies Are Not Being Unleashed Enough for Promotion of the CSTFP Growth in YREB
7. Conclusions and Policy Implications
7.1. Conclusions
7.2. Policy Implications
7.2.1. Overall Synergies of Carbon Sequestration on Input–Output Mixes Should Be Given Full Play to Driving CSTFP Growth in YREB
7.2.2. Regional Close Cooperation and Harmonious Symbiosis Development Should Be Strengthened among Provinces and Municipalities of YREB
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Marks | Observed Values | Mean | S.D. | Min. | Max. | |
---|---|---|---|---|---|---|---|
Explained variable | Local GDP (RMB 100 million yuan) | Y | 187 | 11,056.22 | 9438.172 | 993.53 | 53,138.6 |
Explanatory variable | Labor (10,000 workers) | L | 187 | 3058.321 | 1201.332 | 752.26 | 4860 |
Capital stock (RMB 100 million yuan) | K | 187 | 22,509.77 | 20,796.25 | 1776.29 | 119,790 | |
Energy consumption (10,000 ton) | E | 187 | 10,154.21 | 5792.988 | 2331.52 | 32,876.9 | |
carbon sequestration (10,000 ton) | CS | 187 | 15,686.54 | 8962.507 | 501.5 | 34,729.8 | |
Exogenous variables | Carbon emission intensity (ton/10,000RMB¥) | CI | 187 | 0.8894 | 0.5141 | 0.3316 | 3.1028 |
Ratio of secondary industry (%) | SI | 187 | 46.04 | 5.3618 | 29.83 | 56.6 | |
Urbanization rate (%) | UE | 187 | 47.51 | 15.77 | 23.36 | 89.6 | |
Research and development level (%) | RD | 187 | 1.2817 | 0.7167 | 0.3478 | 3.726 | |
Opening level (%) | OP | 187 | 30.4 | 38.4908 | 3.3477 | 158.333 |
Indexes | Equivalent Coal Factors | Carbon Emission Factors |
---|---|---|
Raw coal | 0.7143 | 0.7559 |
Coke | 0.9714 | 0.855 |
Crude oil | 1.4286 | 0.5857 |
Petrol | 1.4714 | 0.5538 |
Kerosene | 1.4714 | 0.5714 |
Diesel oil | 1.4571 | 0.5921 |
Fuel oil | 1.4286 | 0.6185 |
Nature gas | 1.33 | 0.4483 |
Coke oven gas | 0.5928 | 0.3548 |
Categories | Factor | Unit |
---|---|---|
Forest [76] | 13.97 | tCO2/hm2 |
Artificial wetland [77,78] | 6.07 | tCO2/hm2 |
Inshore and coastal [77,78] | 3.70 | tCO2/hm2 |
River [77,78] | 2.19 | tCO2/hm2 |
Lake [77,78] | 1.47 | tCO2/hm2 |
Rice [79] | 3.50 | tCO2/t |
Wheat [79] | 2.66 | tCO2/t |
Corn [79] | 2.71 | tCO2/t |
Beans [79] | 2.48 | tCO2/t |
Potatoes [79] | 1.71 | tCO2/t |
Peanut [79] | 3.15 | tCO2/t |
Rapeseed [79] | 1.83 | tCO2/t |
Tobacco [79] | 0.67 | tCO2/t |
Generalized Likelihood Rate | Critical Value | Result | ||
---|---|---|---|---|
227.576 | 141.32 | 17.67 | Refuse | |
286.444 | 17.612 | 5.138 | Refuse | |
279.23 | 21.667 | 7.045 | Refuse | |
158.621 | 279.23 | 10.371 | Refuse |
Variable | Estimation I | Estimation II | Estimation III | |||
---|---|---|---|---|---|---|
Coef. | S.D | Coef. | S.D | Coef. | S.D | |
lnL | 3.278 *** | (0.622) | 1.863 ** | (0.735) | 3.42 *** | (0.756) |
lnK | −0.571 * | (0.347) | −1.368 ** | (0.563) | −1.66 *** | (0.439) |
lnE | 2.366 *** | (0.585) | 0.581 *** | (0.418) | ||
lnCS | −1.491 *** | (0.269) | ||||
t | 0.190 *** | (0.0482) | 0.109 | (0.0719) | 0.277 *** | (0.0564) |
lnL × lnK | −0.559 *** | (0.0575) | −0.196 * | (0.110) | 0.456 *** | (0.087) |
lnL × lnE | 0.199 | (0.127) | −0.487 *** | (0.105) | ||
lnK × lnE | −0.749 *** | (0.108) | −0.583 *** | (0.0855) | ||
lnL × lnCS | 0.524 *** | (0.0744) | ||||
lnK × lnCS | −0.401 *** | (0.0403) | ||||
lnE × lnCS | 0.335 *** | (0.0455) | ||||
lnL × t | 0.0278 *** | (0.00585) | −0.0134 | (0.00877) | −0.0431 *** | (0.0087) |
lnK × t | −0.0468 *** | (0.00974) | −0.0648 *** | (0.0188) | −0.0354 ** | (0.0092) |
lnE × t | 0.0682 *** | (0.0107) | 0.0187 * | (0.0076) | ||
lnCS × t | 0.0269 *** | (0.0038) | ||||
lnL × lnL | 0.140 *** | (0.0307) | 0.148 *** | (0.0471) | −0.472 *** | (0.1021) |
lnK × lnK | 0.318 *** | (0.0394) | 0.551 *** | (0.0788) | 0.4 *** | (0.0444) |
lnE × lnE | 0.170 ** | (0.0834) | 0.327 *** | (0.0669) | ||
lnCS × lnCS | −0.113 *** | (0.015) | ||||
t2 | 0.00169 ** | (0.000737) | 0.000665 | (0.00132) | −0.000186 | (0.000788) |
Constant | −6.730 *** | (2.253) | 7.455 *** | (2.670) | 2.129 | (1.1859) |
Technical inefficiency | ||||||
CI | 0.560 *** | (0.0281) | 0.524 *** | (0.0218) | ||
UE | −3.050 *** | (0.390) | −0.893 *** | (0.214) | −0.975 *** | (0.157) |
SI | −0.923 ** | (0.458) | −0.902 *** | (0.156) | −1.225 *** | (0.1104) |
RD | 0.109 * | (0.0679) | 0.0604 * | (0.0362) | 0.124 *** | (0.017) |
OP | −0.724 ** | (0.343) | −0.134 ** | (0.0588) | −0.138 *** | (0.0367) |
Constant | 1.865 *** | (0.213) | 0.669 *** | (0.125) | 0.769 *** | (0.0922) |
log likelihood | 228.1329 | 319.5497 | 372.2396 | |||
wald chi2(13) | 15,687.35 | 7368.21 | 18878.13 | |||
prob > chi2 | 0 | 0 | 0 | |||
γ | 0.986104207 | 0.93437562 | 0.999995001 |
ΔTFP | ΔTP | CR | ΔTE | CR | ΔSE | CR | ΔAE | CR | |
---|---|---|---|---|---|---|---|---|---|
Estimation 1 (two-factor) | −26.55 | −25.77 | −93.00 | 0.03 | 0.11 | 0.55 | 1.98 | −1.36 | −4.91 |
Estimation 2 (three-factor) | −3.76 | 0.90 | 15.25 | −0.08 | −1.36 | 0.17 | 2.88 | −4.75 | −80.51 |
Estimation 3 (four-factor) | 0.20 | 1.72 | 18.55 | 2.65 | 28.59 | 0.37 | 3.99 | −4.53 | −48.87 |
ΔLTFP | ΔLTP | CE | ΔLTE | CE | ΔSE | CE | ΔAE | CE | |
---|---|---|---|---|---|---|---|---|---|
Upper reaches | 0.62 | 3.86 | 23.84 | 4.06 | 25.07 | 0.49 | 2.99 | −7.80 | −48.09 |
Middle reaches | −0.14 | 2.38 | 20.96 | 3.00 | 26.35 | 0.23 | 2.07 | −5.75 | −50.62 |
Lower reaches | 0.11 | −2.01 | −48.66 | 0.29 | 7.01 | 0.39 | 9.43 | 1.44 | 34.9 |
the YREB | 0.20 | 1.72 | 18.58 | 2.65 | 28.54 | 0.37 | 3.97 | −4.53 | −48.9 |
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Rao, G.; Su, B.; Li, J.; Wang, Y.; Zhou, Y.; Wang, Z. Carbon Sequestration Total Factor Productivity Growth and Decomposition: A Case of the Yangtze River Economic Belt of China. Sustainability 2019, 11, 6809. https://doi.org/10.3390/su11236809
Rao G, Su B, Li J, Wang Y, Zhou Y, Wang Z. Carbon Sequestration Total Factor Productivity Growth and Decomposition: A Case of the Yangtze River Economic Belt of China. Sustainability. 2019; 11(23):6809. https://doi.org/10.3390/su11236809
Chicago/Turabian StyleRao, Guangming, Bin Su, Jinlian Li, Yong Wang, Yanhua Zhou, and Zhaolin Wang. 2019. "Carbon Sequestration Total Factor Productivity Growth and Decomposition: A Case of the Yangtze River Economic Belt of China" Sustainability 11, no. 23: 6809. https://doi.org/10.3390/su11236809