Measurement, Regional Differences and Convergence Characteristics of Comprehensive Green Transformation of China’s Economy and Society
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. Construction of the Indicator System
3.2. Description of the Research Methodology and Data
3.2.1. Research Methodology
- (1)
- Entropy Weight Method
- (2)
- KDE Method
- (3)
- Thiel’s Index Method
- (4)
- Convergence Model
3.2.2. Data Description
3.3. Analysis of the Development Index for CGT at Both the National Level and Across Four Regional Divisions
4. Results and Discussion
4.1. Trend Analysis of the Dynamic Evolution of a CGT
4.2. Analysis of Regional Differences in the CGT
4.3. Spatial Convergence Analysis for a CGT
4.3.1. Conducting a σ-Convergence Test on CGT
4.3.2. β-Convergence Test for a Full Green Transition
- (1)
- Conducting an Absolute β-Convergence Test for CGT
- (2)
- Conducting a conditional β-convergence test for CGT
5. Conclusions and Implications
5.1. Research Conclusions
5.2. Policy Implications
5.3. Research Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Nationwide | Eastern Part | Central Part | Western Part | Northwest | ||
---|---|---|---|---|---|---|
p | p | p | p | p | ||
LM | Spatial error: (Robust) Lagrange multiplier | 0.101 | 0.726 | 0.955 | 0.793 | 0.882 |
Spatial lag: (Robust) Lagrange multiplier | 0.001 | 0.074 | 0.429 | 0.239 | 0.943 | |
Hausman | 0.000 | 0.0274 | 0.6370 | 0.0001 | 0.5072 | |
LR | Likelihood-ratio test (individual) | 0.000 | 0.000 | 0.0887 | ||
Likelihood-ratio test (time) | 0.000 | 0.0198 | 0.027 | |||
Model Selection | two-way fixed SAR | two-way fixed SDM | two-way fixed OLS | two-way fixed OLS | random OLS |
Nationwide | Eastern Part | Central Part | Western Part | Northwest | ||
---|---|---|---|---|---|---|
p | p | p | p | p | ||
LM | Spatial error:(Robust) Lagrange multiplier | 0.000 | 0.609 | 0.347 | 0.093 | 0.599 |
Spatial lag: (Robust) Lagrange multiplier | 0.639 | 0.551 | 0.701 | 0.346 | 0.73 | |
Hausman | 0.000 | 0.1303 | 0.054 | 0.000 | 0.8207 | |
LR | Likelihood-ratio test (individual) | 0.000 | 0.0692 | 0.000 | ||
Likelihood-ratio test (time) | 0.000 | 0.0060 | 0.000 | |||
Model Selection | two-way fixed SEM | random OLS | two-way fixed OLS | two-way fixed SEM | random OLS |
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Primary Indicators | Secondary Indicators | Tertiary Indicators | Measurement Method | Nature of the Indicator |
---|---|---|---|---|
comprehensive transformation | optimization of spatial patterns | greening coverage in built-up areas | green coverage area of Built-up Area/built-up area | + |
transportation convenience | public transportation vehicles per 10,000 population (standard units) | + | ||
industrial restructuring | advanced industrial structure | tertiary industry added value/secondary industry added value | + | |
rationalization of industrial structure | + | |||
heightened industrial structure | + | |||
improvements in production methods | energy consumption per unit of GDP | total energy consumption/GDP (tons of standard coal/billion dollars) | − | |
emissions of sulfur dioxide from exhaust gasses | sulfur dioxide emissions from exhaust gasses (tons) | − | ||
Synergistic transformation | industrial synergy | resource recycling rate | utilization rate of general industrial solid waste/generation rate of general industrial solid waste | + |
industrial structure | tertiary value added/GDP | + | ||
carbon intensity | total carbon emissions/GDP | − | ||
regional synergy | gap in people’s living standards | consumption expenditure per capita for the whole population (yuan) | + | |
urban-rural consumption gap | urban-to-rural disposable income ratio | − | ||
innovative transformation | innovation drive | R&D intensity | internal expenditure on R&D funds/GDP | + |
green technology patent status | number of green technology patents granted/number of green technology patents filed | + | ||
number of R&D staff | full-time equivalent of R&D personnel (person-years) | + | ||
innovation output | technology transaction activity | technology market turnover (billions of dollars) | + | |
innovation environment | innovation policy environment | number of innovation policy-related documents (number) | + | |
safe transformation | environmental safety | proportion of pollution control investment | proportion of environmental pollution control investment/GDP | + |
sewage treatment rate | sewage treatment/total sewage discharge | + | ||
energy safety | energy mix (level of electricity consumption) | electricity consumption/total energy consumption | + | |
energy consumption elasticity coefficient | average annual growth rate of energy consumption/average annual growth rate of national economy | − | ||
food safety | grain production per unit area | total annual food production/area of arable land actually occupied by food crops (kg/ha) | + | |
foreign trade dependence on food | total food imports and exports/gross agricultural product | − |
Area | Province | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | Annual Rate of Growth (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Eastern Region | Beijing | 0.526 | 0.547 | 0.568 | 0.581 | 0.596 | 0.613 | 0.628 | 0.618 | 0.623 | 0.607 | 0.599 | 0.627 | 0.016 |
Fujian | 0.364 | 0.372 | 0.383 | 0.384 | 0.407 | 0.423 | 0.439 | 0.445 | 0.447 | 0.461 | 0.461 | 0.474 | 0.024 | |
Guangdong | 0.410 | 0.433 | 0.437 | 0.441 | 0.457 | 0.488 | 0.505 | 0.512 | 0.530 | 0.547 | 0.554 | 0.559 | 0.029 | |
Hainan | 0.334 | 0.351 | 0.369 | 0.373 | 0.380 | 0.402 | 0.411 | 0.421 | 0.460 | 0.455 | 0.454 | 0.476 | 0.033 | |
Hebei | 0.349 | 0.352 | 0.360 | 0.370 | 0.381 | 0.408 | 0.430 | 0.440 | 0.442 | 0.448 | 0.445 | 0.469 | 0.027 | |
Jiangshu | 0.421 | 0.434 | 0.451 | 0.465 | 0.479 | 0.505 | 0.527 | 0.521 | 0.529 | 0.544 | 0.557 | 0.571 | 0.028 | |
Shandong | 0.382 | 0.397 | 0.412 | 0.423 | 0.441 | 0.480 | 0.499 | 0.507 | 0.506 | 0.531 | 0.547 | 0.563 | 0.036 | |
Shanghai | 0.432 | 0.451 | 0.462 | 0.476 | 0.490 | 0.512 | 0.523 | 0.520 | 0.533 | 0.542 | 0.562 | 0.575 | 0.026 | |
Tianjin | 0.459 | 0.472 | 0.493 | 0.500 | 0.505 | 0.525 | 0.534 | 0.525 | 0.539 | 0.541 | 0.554 | 0.561 | 0.018 | |
Zhejiang | 0.409 | 0.425 | 0.435 | 0.445 | 0.454 | 0.486 | 0.493 | 0.505 | 0.518 | 0.519 | 0.535 | 0.519 | 0.022 | |
averages | 0.409 | 0.424 | 0.437 | 0.446 | 0.459 | 0.484 | 0.499 | 0.501 | 0.513 | 0.520 | 0.527 | 0.539 | 0.026 | |
Central Region | Anhui | 0.353 | 0.365 | 0.381 | 0.398 | 0.417 | 0.450 | 0.473 | 0.476 | 0.469 | 0.473 | 0.499 | 0.498 | 0.032 |
Henan | 0.315 | 0.329 | 0.345 | 0.353 | 0.367 | 0.409 | 0.433 | 0.436 | 0.451 | 0.451 | 0.448 | 0.465 | 0.036 | |
Hubei | 0.348 | 0.355 | 0.377 | 0.386 | 0.394 | 0.415 | 0.427 | 0.434 | 0.449 | 0.457 | 0.464 | 0.483 | 0.030 | |
Hunan | 0.342 | 0.354 | 0.366 | 0.382 | 0.399 | 0.411 | 0.424 | 0.437 | 0.442 | 0.453 | 0.446 | 0.474 | 0.030 | |
Jiangxi | 0.341 | 0.348 | 0.342 | 0.347 | 0.361 | 0.374 | 0.400 | 0.406 | 0.417 | 0.418 | 0.429 | 0.443 | 0.024 | |
Shanxi | 0.286 | 0.305 | 0.320 | 0.325 | 0.343 | 0.377 | 0.374 | 0.389 | 0.396 | 0.400 | 0.390 | 0.403 | 0.032 | |
averages | 0.331 | 0.343 | 0.355 | 0.365 | 0.380 | 0.406 | 0.422 | 0.430 | 0.437 | 0.442 | 0.446 | 0.461 | 0.031 | |
Western Region | Gansu | 0.264 | 0.289 | 0.312 | 0.314 | 0.330 | 0.354 | 0.356 | 0.402 | 0.381 | 0.381 | 0.371 | 0.383 | 0.034 |
Guangxi | 0.288 | 0.323 | 0.330 | 0.339 | 0.344 | 0.359 | 0.382 | 0.395 | 0.406 | 0.399 | 0.401 | 0.410 | 0.033 | |
Guizhou | 0.302 | 0.316 | 0.334 | 0.344 | 0.351 | 0.367 | 0.385 | 0.389 | 0.395 | 0.399 | 0.408 | 0.428 | 0.032 | |
Neimeng | 0.308 | 0.321 | 0.345 | 0.364 | 0.381 | 0.396 | 0.411 | 0.396 | 0.411 | 0.431 | 0.423 | 0.425 | 0.030 | |
Ningxia | 0.307 | 0.339 | 0.353 | 0.363 | 0.362 | 0.383 | 0.374 | 0.376 | 0.386 | 0.389 | 0.410 | 0.416 | 0.028 | |
Qinghai | 0.261 | 0.266 | 0.271 | 0.278 | 0.283 | 0.323 | 0.324 | 0.332 | 0.349 | 0.341 | 0.340 | 0.349 | 0.027 | |
Shanxi | 0.308 | 0.323 | 0.333 | 0.344 | 0.354 | 0.373 | 0.379 | 0.378 | 0.389 | 0.396 | 0.405 | 0.420 | 0.029 | |
Sichuan | 0.337 | 0.358 | 0.370 | 0.379 | 0.392 | 0.416 | 0.434 | 0.444 | 0.451 | 0.456 | 0.457 | 0.470 | 0.031 | |
Xinjiang | 0.320 | 0.343 | 0.367 | 0.379 | 0.386 | 0.389 | 0.401 | 0.413 | 0.420 | 0.423 | 0.420 | 0.439 | 0.029 | |
Yunnan | 0.341 | 0.345 | 0.353 | 0.361 | 0.373 | 0.384 | 0.403 | 0.410 | 0.403 | 0.406 | 0.405 | 0.428 | 0.021 | |
Chongqing | 0.359 | 0.358 | 0.376 | 0.375 | 0.385 | 0.405 | 0.409 | 0.411 | 0.420 | 0.431 | 0.443 | 0.451 | 0.021 | |
averages | 0.309 | 0.326 | 0.340 | 0.349 | 0.358 | 0.377 | 0.387 | 0.395 | 0.401 | 0.405 | 0.408 | 0.420 | 0.028 | |
Northeast China | Heilongjiang | 0.281 | 0.289 | 0.313 | 0.320 | 0.338 | 0.364 | 0.361 | 0.370 | 0.376 | 0.385 | 0.394 | 0.404 | 0.034 |
Jilin | 0.353 | 0.361 | 0.369 | 0.387 | 0.394 | 0.410 | 0.418 | 0.415 | 0.425 | 0.428 | 0.433 | 0.444 | 0.021 | |
Liaoning | 0.332 | 0.349 | 0.361 | 0.357 | 0.373 | 0.401 | 0.427 | 0.417 | 0.428 | 0.429 | 0.439 | 0.431 | 0.024 | |
averages | 0.322 | 0.333 | 0.348 | 0.355 | 0.368 | 0.392 | 0.402 | 0.401 | 0.409 | 0.414 | 0.422 | 0.426 | 0.026 | |
National average | 0.348 | 0.362 | 0.376 | 0.385 | 0.397 | 0.420 | 0.433 | 0.438 | 0.446 | 0.451 | 0.456 | 0.469 | 0.028 |
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
Nationwide | Eastern Part | Central Part | Western Part | Northwest | |
Model Selection | two-way fixed SAR | two-way fixed SDM | two-way fixed OLS | two-way fixed OLS | random OLS |
lny1 | −0.3798 *** | −0.1599 *** | −0.0499 | −0.695 ** | −0.1054 ** |
(−9.73) | (−2.70) | (−1.26) | (−2.45) | (−2.06) | |
_cons | −0.0418 | −0.0868 | |||
(−1.46) | (−1.46) | ||||
Wx | |||||
lny1 | 0.1905 | ||||
(0.90) | |||||
Spatial | −0.0164 | ||||
rho | −0.2154 | −0.2458 | (−0.44) | ||
(−1.06) | (−1.23) | ||||
Variance | |||||
sigma2_e | 0.0003 *** | 0.0002 *** | |||
(12.82) | (7.35) | ||||
N | 330 | 110 | 66 | 121 | 33 |
r2 | 0.0960 | 0.0182 | 0.0287 | 0.0520 | |
r2_w | 0.2218 | 0.0068 | 0.0287 | 0.0520 | 0.5515 |
r2_o | 0.1199 | 0.1877 | 0.5645 | ||
ll | 854.8177 | 305.5536 | 167.7983 | 280.7609 |
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
Nationwide | Eastern Part | Central Part | Western Part | Northwest | |
Model Selection | two-way fixed SEM | random OLS | two-way fixed OLS | two-way fixed SEM | random OLS |
lny1 | −0.4066 *** | −0.0578 ** | −0.1155 ** | −0.5706 *** | −0.2087 ** |
(−10.01) | (−1.94) | (−2.11) | (−7.48) | (−2.00) | |
x1 | 0.8858 | −0.0108 | −0.5844 | 0.9734 | 5.4998 |
(0.98) | (−0.03) | (−0.50) | (0.66) | (1.49) | |
x2 | 0.0339 * | 0.0111 | 0.0876 | 0.0449 | 0.1095 |
(1.87) | (1.07) | (0.90) | (0.64) | (1.09) | |
x3 | 0.0086 | −0.0084 | −0.2007 * | 0.0269 | 0.3380 |
(0.19) | (−0.26) | (−1.73) | (0.30) | (0.19) | |
_cons | −0.0227 | −0.3461 * | |||
(−0.62) | (−1.94) | ||||
Spatial | |||||
lambda | −0.1263 | −0.6992 *** | |||
(−0.60) | (−2.78) | ||||
Variance | |||||
sigma2_e | 0.0003 *** | 0.0003 *** | |||
(12.84) | (7.53) | ||||
N | 330 | 110 | 66 | 121 | 33 |
r2 | 0.1132 | 0.0853 | 0.1854 | ||
r2_w | 0.2081 | 0.4201 | 0.0853 | 0.2871 | 0.6049 |
r2_o | 0.4082 | 0.1260 | 0.6195 | ||
ll | 858.0435 | 169.7798 | 310.1562 |
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Wu, Y.; Wang, J.; Jia, M. Measurement, Regional Differences and Convergence Characteristics of Comprehensive Green Transformation of China’s Economy and Society. Sustainability 2025, 17, 3971. https://doi.org/10.3390/su17093971
Wu Y, Wang J, Jia M. Measurement, Regional Differences and Convergence Characteristics of Comprehensive Green Transformation of China’s Economy and Society. Sustainability. 2025; 17(9):3971. https://doi.org/10.3390/su17093971
Chicago/Turabian StyleWu, Yongjie, Jingwen Wang, and Mengxuan Jia. 2025. "Measurement, Regional Differences and Convergence Characteristics of Comprehensive Green Transformation of China’s Economy and Society" Sustainability 17, no. 9: 3971. https://doi.org/10.3390/su17093971
APA StyleWu, Y., Wang, J., & Jia, M. (2025). Measurement, Regional Differences and Convergence Characteristics of Comprehensive Green Transformation of China’s Economy and Society. Sustainability, 17(9), 3971. https://doi.org/10.3390/su17093971