Spatiotemporal Characteristics and Influencing Factors of Water Resources’ Green Utilization Efficiency in China: Based on the EBM Model with Undesirable Outputs and SDM Model
Abstract
:1. Introduction
2. Data and Methods
2.1. Research Area
2.2. Index Selection and Data Sources
WSGUE Assessment Indicators
- (1)
- Input indicators. We selected the total water consumption, employee index, and capital stock as input indicators. The total water consumption, including agricultural, industrial, domestic, and ecological water, referred to the data from NBSC [2]. The employee data came from the statistical yearbook of the Chinese provinces (2010–2020) [27].
- (2)
- The capital stock was estimated through the perpetual inventory method in this paper. According to Zhang et al. [28], the depreciation rate registered at 9.6%, and the capital stock in 2009 was equal to the investment in fixed assets divided by 10%. The price indices of the investment in fixed assets were converted to 2009 prices based in accordance with China Fixed Capital Investment Yearbook (2010–2013, 2015–2018) [29], China Investment Statistical Bulletin (2014) [30], China Investment Statistical Yearbook (2019–2020) [31], and NBSC [2].
- (3)
- The desirable output indicator. Gross regional product (GDP) was selected as the desirable output indicator in this paper. The provincial GDP was converted to 2009 prices based on GDP deflator. Relevant data were obtained from NBSC [2].
- (4)
- The undesirable output indicators. COD and nitrogen emissions from wastewater were selected, which have been the key monitored objects by the related department of environmental management in China for a long time, which are selected as two undesirable output indicators.
2.3. Driving Factors of WRGUE
2.3.1. Economic Development Level
2.3.2. Water Resources Utilization Structure
2.3.3. Technical Progress
2.3.4. Opening-Up Policy
2.3.5. Urbanization
2.3.6. Population Density
2.4. Methods
2.4.1. The EBM Model with Undesirable Outputs
2.4.2. Spatial Autocorrelation Analysis
2.4.3. Spatial Durbin Model
3. Results
4. Discussions
4.1. Spatial Autocorrelation Analysis of WRGUE
4.2. Discussion on Influencing Factors for of WRGUE
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Primary Indicators | Specific Indicators | Mean | Min | Max |
---|---|---|---|---|
Input indicators | The total water consumption (108 tons) | 201.1952 | 22.5 | 619.1 |
The capital stock (RMB 108 Yuan) | 130,946.5 | 7982.3 | 530,575 | |
The social employee (104 person) | 2719.8 | 303.26 | 7150.25 | |
Desirable output indicator | GDP (RMB 108 Yuan) | 19,083.8 | 939.7 | 87,731.7 |
Undesirable output indicator | The COD of wastewater (104 tons) | 50.62 | 1.97 | 198.3 |
The nitrogen of wastewater (104 tons) | 5.06 | 0.1 | 23.09 |
Explanatory Variable | Variables’ Definition and Unit | Pre-Judgment |
---|---|---|
Economic development level | Per capita GDP (RMB 104 Yuan) | Positive |
Water resources use structure | Proportion of agricultural water to the total water consumption (%) | Negative |
Technical progress level | Proportion of R& D expenditure to GDP (%) | Positive |
Opening-up level | Proportion of the foreign trade to GDP (%) | Positive |
Urbanization level | Proportion of the urban population to the total resident (%) | Unknown |
Population density | Resident population per square kilometer (person/sq.km) | Unknown |
Fixed Effects | Random Effects | |
---|---|---|
Wald test spatial lag | 46.56 *** | 112.08 *** |
LR test spatial lag | 42.80 *** | 80.20 *** |
Wald test spatial error | 27.99 *** | 53.92 *** |
LR test spatial error | 34.12 *** | 77.79 *** |
InDEL | InWSUS | InTDL | InOPL | InUL | InPD | Mean VIF | |
---|---|---|---|---|---|---|---|
VIF | 5.44 | 1.74 | 3.32 | 2.76 | 5.81 | 3.47 | 3.76 |
1/VIF | 0.184 | 0.575 | 0.301 | 0.362 | 0.172 | 0.288 |
Regions | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Tianjing | 0.581 | 0.595 | 0.629 | 0.584 | 0.656 | 0.666 | 0.610 | 1.004 | 0.555 | 0.627 | 0.595 | 0.646 |
Hebei | 0.387 | 0.381 | 0.384 | 0.352 | 0.382 | 0.368 | 0.355 | 0.368 | 0.332 | 0.336 | 0.331 | 0.362 |
Shanghai | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Jiangsu | 0.558 | 0.567 | 0.575 | 0.539 | 0.605 | 0.620 | 0.607 | 0.681 | 0.561 | 0.580 | 0.548 | 0.586 |
Zhejiang | 0.635 | 0.641 | 0.639 | 0.571 | 0.636 | 0.623 | 0.598 | 0.632 | 0.548 | 0.571 | 0.558 | 0.605 |
Fujian | 0.571 | 0.579 | 0.562 | 0.507 | 0.562 | 0.541 | 0.509 | 0.550 | 0.473 | 0.470 | 0.456 | 0.525 |
Shandong | 0.495 | 0.490 | 0.486 | 0.454 | 0.496 | 0.495 | 0.474 | 0.501 | 0.454 | 0.469 | 0.472 | 0.481 |
Guangdong | 0.819 | 0.823 | 0.792 | 0.692 | 0.794 | 0.775 | 0.722 | 0.802 | 0.661 | 0.648 | 0.610 | 0.740 |
Hainan | 0.447 | 0.459 | 0.445 | 0.386 | 0.422 | 0.400 | 0.367 | 0.385 | 0.329 | 0.319 | 0.305 | 0.388 |
Eastern region | 0.649 | 0.653 | 0.651 | 0.609 | 0.655 | 0.649 | 0.624 | 0.692 | 0.591 | 0.602 | 0.588 | 0.633 |
Shanxi | 0.443 | 0.436 | 0.441 | 0.402 | 0.436 | 0.415 | 0.379 | 0.375 | 0.355 | 0.368 | 0.362 | 0.401 |
Anhui | 0.352 | 0.361 | 0.359 | 0.335 | 0.363 | 0.355 | 0.336 | 0.382 | 0.324 | 0.315 | 0.306 | 0.344 |
Jiangxi | 0.336 | 0.339 | 0.334 | 0.314 | 0.340 | 0.335 | 0.321 | 0.331 | 0.301 | 0.297 | 0.287 | 0.321 |
Henan | 0.417 | 0.422 | 0.433 | 0.392 | 0.440 | 0.438 | 0.415 | 0.443 | 0.383 | 0.390 | 0.376 | 0.414 |
Hubei | 0.467 | 0.475 | 0.474 | 0.428 | 0.471 | 0.457 | 0.427 | 0.467 | 0.400 | 0.390 | 0.378 | 0.439 |
Hunan | 0.444 | 0.453 | 0.452 | 0.404 | 0.451 | 0.439 | 0.408 | 0.435 | 0.382 | 0.371 | 0.355 | 0.418 |
Central region | 0.410 | 0.414 | 0.415 | 0.379 | 0.417 | 0.406 | 0.381 | 0.405 | 0.358 | 0.355 | 0.344 | 0.390 |
Inner Mongolia | 0.393 | 0.397 | 0.398 | 0.374 | 0.419 | 0.410 | 0.402 | 0.476 | 0.403 | 0.441 | 0.432 | 0.413 |
Guangxi | 0.357 | 0.352 | 0.354 | 0.314 | 0.348 | 0.336 | 0.310 | 0.330 | 0.275 | 0.264 | 0.246 | 0.317 |
Chongqing | 0.396 | 0.414 | 0.440 | 0.423 | 0.471 | 0.474 | 0.467 | 0.640 | 0.478 | 0.535 | 0.516 | 0.478 |
Sichuan | 0.370 | 0.383 | 0.404 | 0.370 | 0.415 | 0.404 | 0.380 | 0.407 | 0.366 | 0.364 | 0.355 | 0.383 |
Guizhou | 0.431 | 0.431 | 0.433 | 0.383 | 0.425 | 0.403 | 0.371 | 0.382 | 0.325 | 0.310 | 0.292 | 0.381 |
Yunnan | 0.413 | 0.414 | 0.412 | 0.374 | 0.419 | 0.401 | 0.376 | 0.415 | 0.354 | 0.344 | 0.328 | 0.386 |
Shaanxi | 0.397 | 0.405 | 0.419 | 0.396 | 0.429 | 0.424 | 0.406 | 0.423 | 0.390 | 0.404 | 0.401 | 0.408 |
Gansu | 0.369 | 0.365 | 0.361 | 0.319 | 0.354 | 0.336 | 0.307 | 0.353 | 0.287 | 0.286 | 0.278 | 0.329 |
Qinghai | 0.333 | 0.331 | 0.334 | 0.311 | 0.325 | 0.308 | 0.288 | 0.293 | 0.278 | 0.276 | 0.270 | 0.304 |
Ningxia | 0.326 | 0.321 | 0.322 | 0.293 | 0.317 | 0.309 | 0.295 | 0.315 | 0.278 | 0.276 | 0.264 | 0.301 |
Xinjiang | 0.409 | 0.400 | 0.394 | 0.337 | 0.377 | 0.362 | 0.327 | 0.343 | 0.284 | 0.279 | 0.262 | 0.343 |
Western region | 0.381 | 0.383 | 0.388 | 0.354 | 0.391 | 0.379 | 0.357 | 0.398 | 0.338 | 0.344 | 0.331 | 0.368 |
Liaoning | 0.385 | 0.388 | 0.398 | 0.373 | 0.408 | 0.403 | 0.398 | 0.453 | 0.389 | 0.404 | 0.402 | 0.400 |
Jilin | 0.289 | 0.291 | 0.290 | 0.283 | 0.309 | 0.306 | 0.291 | 0.328 | 0.289 | 0.291 | 0.283 | 0.296 |
Heilongjiang | 0.388 | 0.384 | 0.373 | 0.324 | 0.356 | 0.344 | 0.321 | 0.355 | 0.302 | 0.296 | 0.286 | 0.339 |
Northeast | 0.354 | 0.354 | 0.354 | 0.327 | 0.358 | 0.351 | 0.337 | 0.379 | 0.327 | 0.330 | 0.324 | 0.345 |
China | 0.474 | 0.477 | 0.478 | 0.441 | 0.481 | 0.472 | 0.449 | 0.496 | 0.425 | 0.431 | 0.418 | 0.458 |
Year | Global Moran’s I | Z-Score | p-Value |
---|---|---|---|
2009 | 0.224 ** | 2.249 | 0.025 |
2010 | 0.237 ** | 2.355 | 0.019 |
2011 | 0.247 ** | 2.446 | 0.014 |
2012 | 0.231 ** | 2.375 | 0.018 |
2013 | 0.251 ** | 2.465 | 0.014 |
2014 | 0.263 *** | 2.567 | 0.010 |
2015 | 0.254 ** | 2.522 | 0.012 |
2016 | 0.297 *** | 2.775 | 0.006 |
2017 | 0.208 ** | 2.158 | 0.031 |
2018 | 0.236 ** | 2.362 | 0.018 |
2019 | 0.225 *** | 2.294 | 0.022 |
Spatial Fixed-Effects | Time Fixed-Effects | Spatial and Time Fixed-Effects | |
---|---|---|---|
InDEL | 0.8086419 *** | 0.7220803 *** | 0.6974106 *** |
InWSUS | 0.0004369 | −0.0032236 | −0.0004647 |
InTDL | 0.0937041 *** | 0.0039324 | 0.0824482 ** |
InOPL | 0.0625741 *** | 0.0125481 | 0.0717951 *** |
InUL | −0.9996273 *** | −0.2393367 *** | −0.9468609 *** |
InPD | −0.0295799 | 0.1158211 *** | −0.1191172 |
W*InDEL | −0.8510972 *** | −0.4911258 *** | −1.53070 *** |
W*InWSUS | −0.0068093 | −0.0752403 *** | 0.0045217 |
W*InTDL | −0.0155968 | −0.1458118 ** | 0.014417 |
W*InOPL | −0.0166994 | 0.1628485 *** | 0.0597447 * |
W*InUL | 0.9070408 *** | −0.0868307 | 1.122323 *** |
W*InPD | −0.3018763 | −0.0216836 | −0.9419549 *** |
Variance sigma2_e | 0.0030164 *** | 0.0136224 *** | 0.0024756 *** |
R-squared | 0.3562 | 0.0010 | 0.2760 |
Log-likelihood | 472.5766 | 240.4700 | 521.8122 |
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Zeng, L.; Li, P.; Yu, Z.; Nie, Y.; Li, S.; Gao, G.; Huang, D. Spatiotemporal Characteristics and Influencing Factors of Water Resources’ Green Utilization Efficiency in China: Based on the EBM Model with Undesirable Outputs and SDM Model. Water 2022, 14, 2908. https://doi.org/10.3390/w14182908
Zeng L, Li P, Yu Z, Nie Y, Li S, Gao G, Huang D. Spatiotemporal Characteristics and Influencing Factors of Water Resources’ Green Utilization Efficiency in China: Based on the EBM Model with Undesirable Outputs and SDM Model. Water. 2022; 14(18):2908. https://doi.org/10.3390/w14182908
Chicago/Turabian StyleZeng, Liangen, Peilin Li, Zhao Yu, Yang Nie, Shengzhang Li, Guangye Gao, and Di Huang. 2022. "Spatiotemporal Characteristics and Influencing Factors of Water Resources’ Green Utilization Efficiency in China: Based on the EBM Model with Undesirable Outputs and SDM Model" Water 14, no. 18: 2908. https://doi.org/10.3390/w14182908