How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. Assessment of Green Development Level
3.2. Assessment of the Regional Differences of Green Development
3.3. Design of Green Development Index
3.4. Data Sources
4. Research Results
4.1. Assessment of Green Development Level
4.2. Comparison of the Different Stages of Green Development
4.3. Analysis of Regional Differences of China’s Green Development
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Target Index | First Level Index | Second Level Index | Third Level Index (Unit) | Weight |
---|---|---|---|---|
Level of green development | Green economy | Green input | Expenditure on science and technology as a proportion of government expenditure (%) | 0.066 |
Expenditure on energy conservation and protection as a proportion of government expenditure (%) | 0.042 | |||
Green production | Per capital GDP (Yuan) | 0.056 | ||
Proportion of tertiary industry added value in GDP (%) | 0.040 | |||
Per capital disposable income of residents (yuan) | 0.057 | |||
Green ecology | Utilization of resources | Per capital water resources (cubic meters/person) | 0.143 | |
Per capital forest stock (cubic meters/person) | 0.101 | |||
Forest coverage rate (%) | 0.035 | |||
Conservation of ecology | Proportion of newly added area under control of soil erosion in the area under jurisdiction (%) | 0.063 | ||
Green society | Green live | Green coverage rate of built-up areas (%) | 0.042 | |
Total passenger volume of urban public trams (10,000 passengers) | 0.038 | |||
Harmless treatment rate of household garbage (%) | 0.028 | |||
Green consumption | Urban per capital natural gas consumption growth rate (%) | 0.021 | ||
Per capital water consumption decline rate (%) | 0.017 | |||
Green technology | Investment in technology | Proportion of R&D expenditure in GDP of industrial enterprises above designated size (%) | 0.079 | |
Full-time equivalent of R&D personnel of industrial enterprises above designated size (person) | 0.023 | |||
Technical output | Number of patents granted (pieces) | 0.110 |
Region | Province/Year | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|---|---|
Eastern region | Beijing | 0.386 | 0.384 | 0.387 | 0.450 | 0.555 | 0.442 | 0.448 | 0.447 | 0.339 | 0.431 |
Tianjin | 0.262 | 0.291 | 0.284 | 0.203 | 0.310 | 0.320 | 0.330 | 0.333 | 0.260 | 0.330 | |
Hebei | 0.200 | 0.246 | 0.215 | 0.291 | 0.437 | 0.252 | 0.269 | 0.264 | 0.456 | 0.259 | |
Shandong | 0.291 | 0.350 | 0.326 | 0.287 | 0.464 | 0.340 | 0.352 | 0.355 | 0.341 | 0.367 | |
Jiangsu | 0.431 | 0.454 | 0.448 | 0.304 | 0.532 | 0.452 | 0.458 | 0.455 | 0.414 | 0.466 | |
Shanghai | 0.360 | 0.371 | 0.374 | 0.267 | 0.459 | 0.415 | 0.427 | 0.426 | 0.286 | 0.415 | |
Zhejiang | 0.422 | 0.443 | 0.433 | 0.682 | 0.479 | 0.439 | 0.449 | 0.449 | 0.373 | 0.454 | |
Fujian | 0.286 | 0.400 | 0.374 | 0.663 | 0.458 | 0.322 | 0.336 | 0.340 | 0.289 | 0.344 | |
Guangdong | 0.461 | 0.488 | 0.483 | 0.648 | 0.557 | 0.503 | 0.510 | 0.499 | 0.600 | 0.508 | |
Hainan | 0.251 | 0.258 | 0.247 | 0.578 | 0.448 | 0.258 | 0.258 | 0.270 | 0.268 | 0.273 | |
Liaoning | 0.233 | 0.271 | 0.252 | 0.437 | 0.399 | 0.274 | 0.290 | 0.288 | 0.270 | 0.285 | |
Mean | 0.326 | 0.360 | 0.348 | 0.437 | 0.463 | 0.365 | 0.375 | 0.375 | 0.354 | 0.376 | |
Central region | Jiling | 0.262 | 0.285 | 0.264 | 0.474 | 0.310 | 0.300 | 0.315 | 0.299 | 0.459 | 0.283 |
Heilongjiang | 0.319 | 0.349 | 0.329 | 0.526 | 0.359 | 0.352 | 0.363 | 0.351 | 0.473 | 0.338 | |
Shanxi | 0.206 | 0.253 | 0.220 | 0.208 | 0.352 | 0.251 | 0.266 | 0.257 | 0.471 | 0.255 | |
Henan | 0.193 | 0.241 | 0.215 | 0.263 | 0.328 | 0.226 | 0.247 | 0.254 | 0.303 | 0.254 | |
Hubei | 0.217 | 0.271 | 0.240 | 0.394 | 0.365 | 0.269 | 0.282 | 0.276 | 0.441 | 0.275 | |
Hunan | 0.208 | 0.256 | 0.227 | 0.525 | 0.342 | 0.255 | 0.271 | 0.274 | 0.380 | 0.269 | |
Anhui | 0.174 | 0.223 | 0.197 | 0.323 | 0.339 | 0.214 | 0.233 | 0.235 | 0.253 | 0.227 | |
Jiangxi | 0.248 | 0.287 | 0.261 | 0.618 | 0.500 | 0.265 | 0.283 | 0.291 | 0.285 | 0.290 | |
Mean | 0.228 | 0.271 | 0.244 | 0.416 | 0.362 | 0.267 | 0.282 | 0.280 | 0.383 | 0.274 | |
Western region | Guangxi | 0.215 | 0.260 | 0.232 | 0.581 | 0.304 | 0.244 | 0.260 | 0.268 | 0.355 | 0.265 |
Chongqing | 0.227 | 0.262 | 0.237 | 0.426 | 0.416 | 0.269 | 0.283 | 0.284 | 0.463 | 0.275 | |
Sichuan | 0.261 | 0.293 | 0.269 | 0.436 | 0.380 | 0.278 | 0.293 | 0.299 | 0.302 | 0.296 | |
Guizhou | 0.185 | 0.254 | 0.216 | 0.385 | 0.203 | 0.238 | 0.251 | 0.260 | 0.367 | 0.256 | |
Yunnan | 0.311 | 0.340 | 0.320 | 0.565 | 0.397 | 0.335 | 0.347 | 0.348 | 0.455 | 0.337 | |
Shaanxi | 0.277 | 0.293 | 0.272 | 0.459 | 0.384 | 0.284 | 0.301 | 0.298 | 0.431 | 0.296 | |
Gansu | 0.146 | 0.193 | 0.166 | 0.147 | 0.159 | 0.212 | 0.218 | 0.199 | 0.491 | 0.188 | |
Qinghai | 0.413 | 0.379 | 0.370 | 0.231 | 0.336 | 0.380 | 0.379 | 0.394 | 0.579 | 0.395 | |
NeiMonggol | 0.348 | 0.351 | 0.343 | 0.312 | 0.359 | 0.379 | 0.391 | 0.376 | 0.555 | 0.357 | |
Ningxia | 0.216 | 0.255 | 0.227 | 0.178 | 0.376 | 0.277 | 0.288 | 0.277 | 0.569 | 0.265 | |
Xinjiang | 0.200 | 0.242 | 0.212 | 0.140 | 0.326 | 0.227 | 0.247 | 0.252 | 0.325 | 0.247 | |
Mean | 0.254 | 0.284 | 0.260 | 0.351 | 0.331 | 0.284 | 0.296 | 0.296 | 0.445 | 0.289 | |
National mean | 0.274 | 0.308 | 0.288 | 0.400 | 0.388 | 0.309 | 0.321 | 0.321 | 0.395 | 0.317 |
Region | 2010 | 2011 | 2012 | 2013 | 2014 | Growth Rate | 2015 | 2016 | 2017 | 2018 | 2019 | Growth Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|
The First Stage | The Second Stage | |||||||||||
Eastern region | 0.326 | 0.360 | 0.348 | 0.437 | 0.463 | 42% | 0.365 | 0.375 | 0.375 | 0.354 | 0.376 | 2.9% |
Central region | 0.228 | 0.271 | 0.244 | 0.416 | 0.362 | 59% | 0.267 | 0.282 | 0.280 | 0.383 | 0.274 | 2.6% |
Western region | 0.254 | 0.284 | 0.260 | 0.351 | 0.331 | 30% | 0.284 | 0.296 | 0.296 | 0.445 | 0.289 | 1.7% |
Nationwide | 0.274 | 0.308 | 0.288 | 0.400 | 0.388 | 42% | 0.309 | 0.321 | 0.321 | 0.395 | 0.317 | 2.5% |
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Ma, X.-F.; Zhang, R.; Ruan, Y.-F. How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China. Int. J. Environ. Res. Public Health 2023, 20, 1707. https://doi.org/10.3390/ijerph20031707
Ma X-F, Zhang R, Ruan Y-F. How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China. International Journal of Environmental Research and Public Health. 2023; 20(3):1707. https://doi.org/10.3390/ijerph20031707
Chicago/Turabian StyleMa, Xiang-Fei, Ru Zhang, and Yi-Fan Ruan. 2023. "How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China" International Journal of Environmental Research and Public Health 20, no. 3: 1707. https://doi.org/10.3390/ijerph20031707