Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt
Abstract
:1. Introduction
2. Review of Literature
2.1. GTFP Measurement
2.2. Industrial Structure and GTFP
3. Measurement of GTFP
3.1. Measurement Method
3.2. Selection of Measurement Variables
3.3. Measurement Result
4. Empirical Research Design
4.1. Model Construction
4.2. Variable Selection
4.2.1. Explained Variable: GTFP
4.2.2. Explanatory Variables: Industrial Structure
4.2.3. Regulatory Variable: Environmental Regulation
4.2.4. Control Variables
4.3. Data Sources and Descriptive Statistics
5. Empirical Research Results and Analysis
5.1. Main Regression Results
5.2. Subregional Test
6. Research Conclusions and Suggestions
6.1. Research Conclusions
6.2. Countermeasures and Suggestions
6.2.1. Promote the Industrial Structure Upgrade in Various Regions of the YREB
6.2.2. Promote Environmental Protection
6.2.3. Push Forward the New Urbanization of the YREB
6.3. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Primary Index | Secondary Index | Variable Description |
---|---|---|
Input | Labor input | Number of employed persons in the region (104 persons) |
Capital input | Calculate the capital stock by using the perpetual inventory method (CNY 108) 1 | |
Energy consumption | Electricity consumption (100 million kwh) | |
Desirable output | Regional GDP | Regional GDP (CNY 108) |
The urban green space area | Urban green space area (104 hectares) | |
Undesirable output index | Wastewater emission | Total industrial wastewater discharge (100 million tons) |
Industrial sulfur dioxide emission | Industrial sulfur dioxide emission (100 million tons) |
Year | West | ||||
Yunan | Guizhou | Sichuan | Chongqing | Mean | |
2005 | 0.546 | 0.436 | 0.544 | 0.585 | 0.528 |
2006 | 0.45 | 0.416 | 0.498 | 0.473 | 0.459 |
2007 | 0.411 | 0.411 | 0.456 | 0.454 | 0.433 |
2008 | 0.405 | 0.427 | 0.469 | 0.476 | 0.444 |
2009 | 0.367 | 0.393 | 0.431 | 0.462 | 0.413 |
2010 | 0.347 | 0.371 | 0.431 | 0.464 | 0.403 |
2011 | 0.33 | 0.357 | 0.452 | 0.508 | 0.412 |
2012 | 0.338 | 0.367 | 0.482 | 1.01 | 0.549 |
2013 | 0.35 | 0.406 | 0.506 | 0.563 | 0.456 |
2014 | 0.321 | 0.373 | 0.472 | 0.534 | 0.425 |
2015 | 0.315 | 0.389 | 0.46 | 0.575 | 0.435 |
2016 | 0.295 | 0.395 | 0.451 | 0.665 | 0.452 |
2017 | 0.328 | 0.348 | 0.422 | 1.008 | 0.527 |
2018 | 0.316 | 0.332 | 0.411 | 0.458 | 0.379 |
2019 | 0.329 | 0.344 | 0.541 | 0.483 | 0.424 |
mean | 0.363 | 0.384 | 0.468 | 0.581 | 0.449 |
Year | Midland | ||||
Hubei | Hunan | Anhui | Jiangxi | Mean | |
2005 | 0.599 | 1.027 | 0.61 | 1.015 | 0.813 |
2006 | 0.57 | 1.032 | 0.589 | 1.01 | 0.800 |
2007 | 0.512 | 1.033 | 0.47 | 0.508 | 0.631 |
2008 | 0.523 | 1.03 | 0.453 | 1.003 | 0.752 |
2009 | 0.513 | 0.536 | 0.421 | 0.456 | 0.482 |
2010 | 0.519 | 1.001 | 0.424 | 0.672 | 0.654 |
2011 | 0.517 | 1.009 | 0.431 | 0.451 | 0.602 |
2012 | 0.537 | 1.017 | 0.431 | 0.454 | 0.610 |
2013 | 0.556 | 1.028 | 0.433 | 0.468 | 0.621 |
2014 | 0.536 | 1.025 | 0.406 | 0.436 | 0.601 |
2015 | 0.527 | 1.032 | 0.385 | 0.41 | 0.589 |
2016 | 0.558 | 1.027 | 0.379 | 0.386 | 0.588 |
2017 | 0.484 | 0.508 | 0.365 | 0.35 | 0.427 |
2018 | 0.463 | 0.469 | 0.354 | 0.339 | 0.406 |
2019 | 0.458 | 1.038 | 0.353 | 0.354 | 0.551 |
mean | 0.525 | 0.921 | 0.434 | 0.554 | 0.608 |
Year | East | ||||
Jiangsu | Shanghai | Zhejiang | Mean | ||
2005 | 1.043 | 1.223 | 0.751 | 1.006 | |
2006 | 1.032 | 1.237 | 0.683 | 0.984 | |
2007 | 1.011 | 1.241 | 0.613 | 0.955 | |
2008 | 1.001 | 1.241 | 0.611 | 0.951 | |
2009 | 1.019 | 1.254 | 0.577 | 0.950 | |
2010 | 1.018 | 1.262 | 0.598 | 0.959 | |
2011 | 1.023 | 1.217 | 0.597 | 0.946 | |
2012 | 1.029 | 1.185 | 0.596 | 0.937 | |
2013 | 1.044 | 1.192 | 0.733 | 0.990 | |
2014 | 1.045 | 1.206 | 0.685 | 0.979 | |
2015 | 1.051 | 1.268 | 0.628 | 0.982 | |
2016 | 1.057 | 1.211 | 0.556 | 0.941 | |
2017 | 1.039 | 1.413 | 0.453 | 0.968 | |
2018 | 1.029 | 1.417 | 0.435 | 0.960 | |
2019 | 1.045 | 1.412 | 0.456 | 0.971 | |
mean | 1.032 | 1.265 | 0.598 | 0.965 |
Variable Type | Name | Code | Description |
---|---|---|---|
Explained variable | Green total factor productivity | GTFP | According to 3.3 measurement result |
Explanatory variables | Industry structure upgrade | ISU | Calculated according to formula (5) |
Industry structure rationalization | ISR | Calculated according to formula (7) | |
Regulatory variable | Environmental regulation | ER | Calculated according to formula (8) |
Control variable | Economic development level | EDL | EDL = Per capita GDP (CNY 104) |
Degree of openness | EXP | ESP = Regional export trade volume/regional GDP | |
Local government input | INP | INP = Regional government expenditure/regional GDP | |
Urbanization rate | UR | UR = Regional urban population/total population |
Variable | Obs | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
GTFP | 165 | 0.624 | 0.295 | 0.206 | 1.417 |
ISU | 165 | 0.978 | 0.633 | 0.154 | 3.142 |
ISR | 165 | 0.207 | 0.178 | 0.001 | 0.819 |
ER | 165 | 1.331 | 0.859 | 0.352 | 5.772 |
EDL | 165 | 4.290 | 2.999 | 0.505 | 15.659 |
EXP | 165 | 0.187 | 0.206 | 0.020 | 0.899 |
INP | 165 | 0.207 | 0.071 | 0.090 | 0.402 |
UR | 165 | 0.524 | 0.148 | 0.269 | 0.893 |
Variables | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
Ln ISU | 0.154 *** | 0.203 * | 0.282 *** | 0.328 *** |
(4.773) | (1.935) | (7.661) | (3.039) | |
Ln ISR | −0.047 | −0.002 | −0.030 | −0.005 |
(−1.135) | (−0.059) | (−0.806) | (−0.119) | |
Ln ER | 0.511 *** | 0.319 *** | ||
(6.018) | (3.275) | |||
Ln EDL | 0.687 *** | 0.571 *** | ||
(4.136) | (3.434) | |||
Ln EXP | 0.117 *** | 0.054 | ||
(2.732) | (1.181) | |||
Ln INP | −0.528 *** | −0.405 *** | ||
(−4.390) | (−3.280) | |||
Ln UR | −1.406 *** | −0.998 *** | ||
(−4.188) | (−2.792) | |||
Constant | −0.667 *** | −2.778 *** | −0.725 *** | −2.406 *** |
(−5.165) | (−6.376) | (−6.627) | (−5.376) | |
Observations | 165 | 165 | 165 | 165 |
Number of DMU | 11 | 11 | 11 | 11 |
VARIABLES | West | Midland | East |
---|---|---|---|
Ln ISU | 0.309 *** | 0.690 ** | 2.327 *** |
(3.083) | (1.943) | (3.533) | |
Ln ISR | −0.137 | 0.065 ** | 0.772 *** |
(−1.065) | (2.503) | (4.860) | |
Ln ER | 0.412 *** | 0.039 * | 0.691 *** |
(3.208) | (0.212) | (3.219) | |
Ln EDL | −0.723 *** | 0.072 * | −3.273 *** |
(−2.868) | (1.034) | (−3.885) | |
Ln EXP | 0.068 | −0.268 * | −0.409 |
(1.062) | (−1.878) | (−1.507) | |
Ln INP | −0.126 | −0.134 | −0.528 |
(−0.669) | (−0.446) | (−1.300) | |
Ln UR | 0.478 | −3.975 *** | 2.272 *** |
(1.215) | (−3.362) | (2.915) | |
Constant | 0.076 | −3.907 *** | 5.405 *** |
(0.137) | (−3.393) | (3.631) | |
Observations | 60 | 60 | 45 |
Number of DMU | 4 | 4 | 3 |
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Sun, J.; Tang, D.; Kong, H.; Boamah, V. Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt. Int. J. Environ. Res. Public Health 2022, 19, 3718. https://doi.org/10.3390/ijerph19063718
Sun J, Tang D, Kong H, Boamah V. Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt. International Journal of Environmental Research and Public Health. 2022; 19(6):3718. https://doi.org/10.3390/ijerph19063718
Chicago/Turabian StyleSun, Jinhua, Decai Tang, Haojia Kong, and Valentina Boamah. 2022. "Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt" International Journal of Environmental Research and Public Health 19, no. 6: 3718. https://doi.org/10.3390/ijerph19063718