The Impact of Digital Technology on Water Resources Management: Evidence from China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Model Formulation
2.1.1. Entropy Weight–TOPSIS
2.1.2. Benchmark Regression Model
2.1.3. Mediation Effect Model
2.1.4. Threshold Effect Model
2.1.5. Coupling Coordination Degree Model
2.2. Variable Indicators and Data Source
2.2.1. Description of Indicators
2.2.2. Data Source
3. Results
3.1. Data Description
3.2. Scatter Plots
3.3. Benchmark Estimation Analysis
3.4. Endogeneity Test and Robustness Test
3.5. Mechanism Effect Test
3.6. Heterogeneity Analysis
3.7. Threshold Effect Test
3.8. Further Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ahmed, A.A.; Sayed, S.; Abdoulhalik, A.; Moutari, S.; Oyedele, L. Applications of machine learning to water resources management: A review of present status and future opportunities. J. Clean. Prod. 2024, 441, 140715. [Google Scholar] [CrossRef]
- Shams, A.K.; Muhammad, N.S. Toward sustainable water resources management: Critical assessment on the implementation of integrated water resources management and water–energy-food nexus in Afghanistan. Water Policy 2022, 24, 1–18. [Google Scholar] [CrossRef]
- Dinar, A. Challenges to water resource management: The role of economic and modeling approaches. Water 2024, 16, 610. [Google Scholar] [CrossRef]
- Lv, B.; Liu, C.; Li, T.; Meng, F.; Fu, Q.; Ji, Y.; Hou, R. Evaluation of the water resource carrying capacity in Heilongjiang, eastern China, based on the improved TOPSIS model. Ecol. Indic. 2023, 150, 110208. [Google Scholar] [CrossRef]
- Grison, C.; Koop, S.; Eisenreich, S.; Hofman, J.; Chang, I.; Wu, J.; Savic, D.; Leeuwen, K. Integrated water resources management in cities in the world: Global challenges. Water Resour. Manag. 2023, 37, 2787–2803. [Google Scholar] [CrossRef]
- Bian, D.; Yang, X.; Lu, Y.; Chen, H.; Sun, B.; Wu, F.; Chen, Y.; Xiang, W. Analysis of the spatiotemporal patterns and decoupling effects of China’s water resource spatial equilibrium. Environ. Res. 2023, 216, 114719. [Google Scholar] [CrossRef]
- Kamyab, H.; Khademi, T.; Chelliapan, S.; SaberiKamarposhti, M.; Rezania, S.; Yusuf, M.; Farajnezhad, M.; Abbas, M.; Jeon, B.; Ahn, Y. The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management. Results Eng. 2023, 20, 101566. [Google Scholar] [CrossRef]
- Abegeja, D. The application of satellite sensors, current state of utilization, and sources of remote sensing dataset in hydrology for water resource management. J. Water Health 2024, 22, 1162–1179. [Google Scholar] [CrossRef]
- Zhang, C.; Oki, T. Water pricing reform for sustainable water resources management in China’s agricultural sector. Agric. Water Manag. 2023, 275, 108045. [Google Scholar] [CrossRef]
- Timotewos, M.T.; Barjenbruch, M. Examining the Prospects of Residential Water Demand Management Policy Regulations in Ethiopia: Implications for Sustainable Water Resource Management. Sustainability 2024, 16, 5625. [Google Scholar] [CrossRef]
- Gupta, A.D.; Pandey, P.; Feijóo, A.; Yaseen, Z.M.; Bokde, N.D. Smart water technology for efficient water resource management: A review. Energies 2020, 13, 6268. [Google Scholar] [CrossRef]
- Ivanov, D.; Dolgui, A.; Sokolov, B. The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. Int. J. Prod. Res. 2019, 57, 829–846. [Google Scholar] [CrossRef]
- Ciarli, T.; Kenney, M.; Massini, S.; Piscitello, L. Digital technologies, innovation, and skills: Emerging trajectories and challenges. Res. Policy 2021, 50, 104289. [Google Scholar] [CrossRef]
- Available online: https://www.digitalchina.gov.cn/ (accessed on 3 September 2024).
- Alsufyani, N.; Gill, A.Q. Digitalisation performance assessment: A systematic review. Technol. Soc. 2022, 68, 101894. [Google Scholar] [CrossRef]
- Ghimire, A.; Ali, S.; Long, X.; Chen, L.; Sun, J. Effect of Digital Silk Road and innovation heterogeneity on digital economy growth across 29 countries: New evidence from PSM-DID. Technol. Forecast. Soc. Chang. 2024, 198, 122987. [Google Scholar] [CrossRef]
- Hasan, M.M.; Yajuan, L.; Khan, S. Promoting China’s inclusive finance through digital financial services. Glob. Bus. Rev. 2020, 23, 984–1006. [Google Scholar] [CrossRef]
- Costa, F.; Frecassetti, S.; Rossini, M.; Portioli-Staudacher, A. Industry 4.0 digital technologies enhancing sustainability: Applications and barriers from the agricultural industry in an emerging economy. J. Clean. Prod. 2023, 408, 137208. [Google Scholar] [CrossRef]
- Arefiev, S.; Zhyhlei, I.; Pereguda, Y.; Kryvokulska, N.; Lushchyk, M. The use of digital technologies to ensure environmental safety in the context of the green economy development. Rev. Univ. Zulia. 2024, 15, 353–369. [Google Scholar] [CrossRef]
- Peng, H.; Zhang, Y.; Liu, J. The energy rebound effect of digital development: Evidence from 285 cities in China. Energy 2023, 270, 126837. [Google Scholar] [CrossRef]
- Chen, Y.; Wang, Y.; Zhao, C. From riches to digitalization: The role of AMC in overcoming challenges of digital transformation in resource-rich regions. Technol. Forecast. Soc. Chang. 2024, 200, 123153. [Google Scholar] [CrossRef]
- Liu, Y.; Tang, T.; Ah, R.; Luo, L. Has digital technology promoted the restructuring of global value chains? Evidence from China. Econ. Anal. Policy 2024, 81, 269–280. [Google Scholar] [CrossRef]
- Popkova, E.G.; De Bernardi, P.; Tyurina, Y.G.; Sergi, B.S. A theory of digital technology advancement to address the grand challenges of sustainable development. Technol. Soc. 2022, 68, 101831. [Google Scholar] [CrossRef]
- Tang, M.; Liu, Y.; Hu, F.; Wu, B. Effect of digital transformation on enterprises’ green innovation: Empirical evidence from listed companies in China. Energy Econ. 2023, 128, 107135. [Google Scholar] [CrossRef]
- Satı, Z.E. Comparison of the criteria affecting the digital innovation performance of the European Union (EU) member and candidate countries with the entropy weight-TOPSIS method and investigation of its importance for SMEs. Technol. Forecast. Soc. Chang. 2024, 200, 123094. [Google Scholar] [CrossRef]
- Wang, H.; Wang, Y.; Zeng, G.; Qian, Z.; Lu, S. The differential impact of the digital economy on urban energy efficiency in China: The mediating mechanism of FDI. Environ. Dev. Sustain. 2024, 1–28. [Google Scholar] [CrossRef]
- Irfan, M.; Razzaq, A.; Sharif, A.; Yang, X. Influence mechanism between green finance and green innovation: Exploring regional policy intervention effects in China. Technol. Forecast. Soc. Chang. 2022, 182, 121882. [Google Scholar] [CrossRef]
- Sun, L.; Fang, J.; Li, J.; Wang, L. Research on the impact of the integration of digital economy and real economy on enterprise green innovation. Technol. Forecast. Soc. Chang. 2024, 200, 123097. [Google Scholar] [CrossRef]
- Cheikh, N.B.; Rault, C. Financial inclusion and threshold effects in carbon emissions. Res. Policy 2024, 192, 114265. [Google Scholar]
- Tomal, M. Evaluation of coupling coordination degree and convergence behaviour of local development: A spatiotemporal analysis of all Polish municipalities over the period 2003–2019. Sustain. Cities Soc. 2021, 71, 102992. [Google Scholar] [CrossRef]
- Godinez-Madrigal, J.; Van Cauwenbergh, N.; van der Zaag, P. Production of competing water knowledge in the face of water crises: Revisiting the IWRM success story of the Lerma-Chapala Basin, Mexico. Geoforum 2019, 103, 3–15. [Google Scholar] [CrossRef]
- Bozorg-Haddad, O.; Baghban, S.; Loáiciga, H.A. Assessment of global hydro-social indicators in water resources management. Sci. Rep. 2021, 11, 17424. [Google Scholar] [CrossRef]
- Maurya, S.P.; Singh, P.K.; Ohri, A.; Singh, R. Identification of indicators for sustainable urban water development planning. Ecol. Indic. 2020, 108, 105691. [Google Scholar] [CrossRef]
- Wang, H.; Guo, J. New way out of efficiency-equity dilemma: Digital technology empowerment for local government environmental governance. Technol. Forecast. Soc. Chang. 2024, 200, 123184. [Google Scholar] [CrossRef]
- Liu, J.; Yu, Q.; Chen, Y.; Liu, J. The impact of digital technology development on carbon emissions: A spatial effect analysis for China. Resour. Conserv. Recycl. 2022, 185, 106445. [Google Scholar] [CrossRef]
- Chenic, A.Ș.; Burlacu, A.; Dobrea, R.C.; Tescan, L.; Creţu, A.I.; Stanef-Puica, M.R.; Godeanu, T.N.; Manole, A.M.; Virjan, D.; Moroianu, N. The impact of digitalization on macroeconomic indicators in the new industrial age. Electronics 2023, 12, 1612. [Google Scholar] [CrossRef]
- Wan, Q.; Tang, S.; Jiang, Z. Does the development of digital technology contribute to the innovation performance of China’s high-tech industry? Technovation 2023, 124, 102738. [Google Scholar] [CrossRef]
- Chen, R.; Ramzan, M.; Hafeez, M.; Ullah, S. Green innovation-green growth nexus in BRICS: Does financial globalization matter? J. Innov. Knowl. 2023, 8, 100286. [Google Scholar] [CrossRef]
- Available online: https://www.stats.gov.cn/ (accessed on 1 September 2024).
- Tiwari, S.; Mentel, G.; Mohammed, K.S.; Mohd Ziaur Rehman, M.Z.; Lewandowska, A. Unveiling the role of natural resources, energy transition and environmental policy stringency for sustainable environmental development: Evidence from BRIC+ 1. Res. Policy 2024, 96, 105204. [Google Scholar]
- Zhao, L.; Chen, H.; Ding, X.; Chen, Y. Does Digital Village Construction Empower the Green Allocation of Agricultural Water Resources? Systems 2024, 12, 214. [Google Scholar] [CrossRef]
- Cassidy, J.; Barbosa, B.; Damião, M.; Ramalho, P.; Ganhão, A.; Santos, A.; Feliciano, J. Taking water efficiency to the next level: Digital tools to reduce non-revenue water. J. Hydroinform. 2021, 23, 453–465. [Google Scholar] [CrossRef]
- Yuan, S.; Pan, X. Inherent mechanism of digital technology application empowered corporate green innovation: Based on resource allocation perspective. J. Environ. Manag. 2023, 345, 118841. [Google Scholar] [CrossRef] [PubMed]
- Hansen, B.E. Threshold effects in non-dynamic panels: Estimation, testing, and inference. J. Econom. 1999, 93, 345–368. [Google Scholar] [CrossRef]
- Zhao, G.; Deng, Z.; Liu, C. Assessment of the Coupling Degree between Agricultural Modernization and the Coordinated Development of Black Soil Protection and Utilization: A Case Study of Heilongjiang Province. Land 2024, 13, 288. [Google Scholar] [CrossRef]
- Wang, Q.; Wang, S. A comparison of decomposition the decoupling carbon emissions from economic growth in transport sector of selected provinces in eastern, central and western China. J. Clean. Prod. 2019, 229, 570–581. [Google Scholar] [CrossRef]
- Alves, R.G.; Maia, R.F.; Lima, F. Development of a Digital Twin for smart farming: Irrigation management system for water saving. J. Clean Prod. 2023, 388, 135920. [Google Scholar] [CrossRef]
- Yin, W.; Hu, Q.; Liu, W.; Liu, J.; He, P.; Zhu, D.; Kornejady, A. Harnessing Game Engines and Digital Twins: Advancing Flood Education, Data Visualization, and Interactive Monitoring for Enhanced Hydrological Understanding. Water 2024, 16, 2528. [Google Scholar] [CrossRef]
Primary Indicators | Secondary Indicators | Unit |
---|---|---|
Water resources development and utilization | Water consumption per CNY 10,000 of GDP | Cubic meters/CNY 10,000 |
Water consumption per CNY 10,000 of industrial added value | Cubic meters/CNY 10,000 | |
Per capita water consumption | Cubic meters per person | |
Water resources development and utilization rate | % | |
Water environment governance | Wastewater and sewage discharge per CNY 10,000 of GDP | Cubic meters/CNY 10,000 |
Urban sewage centralized treatment rate | % | |
Total chemical oxygen demand discharge from wastewater | 10,000 tons | |
Total ammonia nitrogen emissions from wastewater | 10,000 tons | |
Water ecological restoration | Forest coverage rate | % |
Greening coverage in built-up areas | % | |
Groundwater water use rate | % |
Primary Indicators | Secondary Indicators | Unit |
---|---|---|
Fundamentals of digital technology | Internet penetration rate | % |
Mobile phone penetration rate | % | |
Length of long-distance fiberoptic cable per unit area | Kilometers per square kilometer | |
Mobile base station density | Units per square kilometer | |
IPV4 address count | 10,000 | |
Digital factors inputs | The proportion of employees in the software and information technology service industry among urban unit employees | % |
R&D expenditure of industrial enterprises above the designated size | CNY 10,000 | |
Full-time equivalent of R&D personnel in industrial enterprises above the designated size | Person | |
Digital technology applications | Per capita telecommunication business volume | CNY 10,000 per person |
The proportion of information technology service revenue in GDP | % | |
Digital inclusive finance development index | - |
Variables | N | Mean | SD | Min | Max |
---|---|---|---|---|---|
DT | 300 | 0.185 | 0.142 | 0.038 | 0.589 |
WRM | 300 | 0.565 | 0.115 | 0.316 | 0.825 |
GI | 300 | 7.756 | 1.306 | 3.434 | 10.722 |
City | 300 | 0.614 | 0.114 | 0.379 | 0.896 |
Eco | 300 | 10.947 | 0.432 | 10.003 | 12.156 |
Pop | 300 | 0.047 | 0.071 | 0.001 | 0.393 |
Inf | 300 | 2.783 | 0.360 | 1.413 | 3.332 |
Variable | WRM | |||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
DT | 0.287 *** (4.98) | 0.236 ** (3.22) | 0.252 *** (4.22) | 0.221 *** (2.95) |
City | −0.416 *** (−2.70) | −0.506 ** (−2.00) | ||
Eco | 0.044 ** (2.06) | 0.023 (0.51) | ||
Pop | 0.017 (0.09) | 1.881 (0.67) | ||
Inf | −0.018 (−0.08) | 0.024 (1.01) | ||
_cons | 0.398 *** (23.39) | 0.392 *** (26.84) | 1.179 (1.07) | 0.252 (0.56) |
Province | No | Yes | No | Yes |
Year | No | Yes | No | Yes |
N | 300 | 300 | 300 | 300 |
R2 | 0.097 | 0.186 | 0.177 | 0.230 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
WRM | WRM | WRM | WRM | |
DT | 0.636 *** (3.19) | 0.222 *** (2.63) | 0.230 *** (3.18) | 0.215 *** (2.86) |
Control | Yes | Yes | Yes | Yes |
Kleibergen–Paap rk LM statistic | 12.245 *** | |||
Kleibergen–Paap rk Wald F statistic | 75.896 *** | |||
Province | Yes | Yes | Yes | Yes |
Year | Yes | Yes | Yes | Yes |
N | 270 | 260 | 300 | 300 |
R2 | 0.028 | 0.301 | 0.254 | 0.227 |
Variable | (1) GI | (2) WRM |
---|---|---|
DT | 0.181 ** (2.48) | 0.171 *** (2.75) |
GI | 0.244 *** (4.62) | |
City | −0.520 *** (−3.58) | −0.664 *** (−4.64) |
Eco | 0.024 (0.60) | 0.018 (0.47) |
Pop | 1.261 (0.71) | 1.511 (0.89) |
Inf | 0.022 (1.09) | 0.014 (0.72) |
_cons | 0.275 (0.69) | −0.066 (−0.17) |
Control | Yes | Yes |
Province | Yes | Yes |
Year | Yes | Yes |
R2 | 0.864 | 0.874 |
N | 300 | 300 |
Sobel test | 0.044 ** | |
Bootstrap test | [0.010, 0.110] |
Variable | Higher Lever (4) WRM | Lower Lever (5) WRM |
---|---|---|
DT | 0.270 ** (2.03) | 0.125 ** (2.20) |
City | −0.017 (−0.27) | −0.0224 (−1.21) |
Eco | 0.071 * (1.86) | −0.025 (−0.75) |
Pop | 0.010 ** (2.19) | 0.009 (0.77) |
Inf | −0.344 (−0.55) | −0.157 (−0.44) |
_cons | 1.603 (1.54) | 2.244 *** (4.09) |
Province | Yes | Yes |
Year | Yes | Yes |
N | 80 | 210 |
R2 | 0.417 | 0.388 |
Variable | Threshold | P | Crit10 | Crit5 | Crit1 | Threshold Value |
---|---|---|---|---|---|---|
GI | Single | 0.024 | 14.947 | 16.951 | 23.056 | 1.840 |
Double | 0.672 | 12.956 | 15.886 | 21.310 | 1.972 |
Variable | Coefficient | std | P | [95% conf. Interval] | |
---|---|---|---|---|---|
City | −0.606 *** | 0.182 | 0.002 | −0.979 | 0.234 |
Eco | 0.052 * | 0.030 | 0.086 | −0.007 | 0.113 |
Pop | 1.829 | 2.420 | 0.456 | −3.119 | 6.778 |
Inf | 0.016 | 0.022 | 0.470 | −0.028 | 0.060 |
GI < 1.840 | −0.025 | 0.121 | 0.839 | −0.272 | 0.223 |
GI ≥ 1.840 | 0.282 *** | 0.080 | 0.001 | 0.119 | 0.445 |
Control | Yes | ||||
N | 300 | ||||
R2 | 0.242 |
Interval | Type |
---|---|
0–0.1 | Extremely imbalanced |
0.1–0.2 | Hyper imbalanced |
0.2–0.3 | Moderately imbalanced |
0.3–0.4 | Mildly imbalanced |
0.4–0.5 | On the verge of imbalanced |
0.5–0.6 | Barely adequate coordination |
0.6–0.7 | Primary coordination |
0.7–0.8 | Intermediate coordination |
0.8–0.9 | Good coordination |
0.9–1.0 | High-quality coordination |
Region | Province | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|---|---|
Eastern China | Beijing | 0.770 | 0.771 | 0.780 | 0.797 | 0.789 | 0.790 | 0.785 | 0.801 | 0.796 | 0.751 |
Fujian | 0.507 | 0.462 | 0.365 | 0.361 | 0.391 | 0.405 | 0.406 | 0.428 | 0.433 | 0.421 | |
Guangdong | 0.443 | 0.450 | 0.442 | 0.436 | 0.446 | 0.463 | 0.488 | 0.474 | 0.481 | 0.499 | |
Hainan | 0.510 | 0.494 | 0.509 | 0.509 | 0.517 | 0.606 | 0.603 | 0.453 | 0.433 | 0.465 | |
Hebei | 0.582 | 0.542 | 0.535 | 0.553 | 0.511 | 0.496 | 0.495 | 0.466 | 0.452 | 0.478 | |
Jiangsu | 0.783 | 0.778 | 0.776 | 0.751 | 0.751 | 0.739 | 0.739 | 0.697 | 0.689 | 0.707 | |
Liaoning | 0.424 | 0.431 | 0.423 | 0.444 | 0.464 | 0.471 | 0.435 | 0.474 | 0.476 | 0.463 | |
Shandong | 0.578 | 0.568 | 0.574 | 0.549 | 0.543 | 0.550 | 0.537 | 0.542 | 0.525 | 0.512 | |
Shanghai | 0.684 | 0.711 | 0.720 | 0.699 | 0.696 | 0.693 | 0.700 | 0.740 | 0.762 | 0.741 | |
Tianjin | 0.479 | 0.434 | 0.412 | 0.408 | 0.413 | 0.420 | 0.432 | 0.459 | 0.458 | 0.444 | |
Zhejiang | 0.577 | 0.576 | 0.569 | 0.564 | 0.563 | 0.567 | 0.470 | 0.570 | 0.528 | 0.528 | |
Average | 0.576 | 0.565 | 0.555 | 0.552 | 0.553 | 0.564 | 0.554 | 0.555 | 0.548 | 0.546 | |
Central China | Anhui | 0.530 | 0.525 | 0.518 | 0.516 | 0.517 | 0.520 | 0.536 | 0.534 | 0.475 | 0.492 |
Heilongjiang | 0.502 | 0.474 | 0.595 | 0.559 | 0.437 | 0.416 | 0.434 | 0.409 | 0.424 | 0.438 | |
Henan | 0.610 | 0.580 | 0.569 | 0.535 | 0.527 | 0.528 | 0.546 | 0.495 | 0.495 | 0.518 | |
Hubei | 0.599 | 0.575 | 0.537 | 0.527 | 0.531 | 0.517 | 0.531 | 0.495 | 0.495 | 0.525 | |
Hunan | 0.545 | 0.523 | 0.522 | 0.472 | 0.460 | 0.454 | 0.441 | 0.458 | 0.455 | 0.465 | |
Jiangxi | 0.599 | 0.579 | 0.575 | 0.559 | 0.539 | 0.506 | 0.496 | 0.473 | 0.466 | 0.491 | |
Jilin | 0.508 | 0.483 | 0.475 | 0.483 | 0.490 | 0.444 | 0.407 | 0.434 | 0.437 | 0.429 | |
Shanxi | 0.579 | 0.572 | 0.562 | 0.553 | 0.553 | 0.569 | 0.570 | 0.498 | 0.486 | 0.504 | |
Average | 0.559 | 0.539 | 0.544 | 0.526 | 0.507 | 0.494 | 0.495 | 0.475 | 0.467 | 0.483 | |
Western China | Chongqing | 0.660 | 0.644 | 0.641 | 0.625 | 0.626 | 0.620 | 0.612 | 0.553 | 0.570 | 0.577 |
Gansu | 0.753 | 0.731 | 0.723 | 0.732 | 0.747 | 0.753 | 0.743 | 0.686 | 0.675 | 0.706 | |
Guangxi | 0.467 | 0.460 | 0.465 | 0.476 | 0.493 | 0.509 | 0.423 | 0.448 | 0.411 | 0.443 | |
Guizhou | 0.513 | 0.500 | 0.508 | 0.508 | 0.529 | 0.549 | 0.543 | 0.539 | 0.553 | 0.470 | |
Inner Mongolia | 0.522 | 0.490 | 0.475 | 0.488 | 0.501 | 0.517 | 0.463 | 0.456 | 0.468 | 0.477 | |
Ningxia | 0.493 | 0.406 | 0.396 | 0.390 | 0.425 | 0.446 | 0.378 | 0.448 | 0.443 | 0.419 | |
Qinghai | 0.652 | 0.616 | 0.618 | 0.638 | 0.640 | 0.601 | 0.564 | 0.534 | 0.528 | 0.565 | |
Shaanxi | 0.500 | 0.476 | 0.470 | 0.476 | 0.486 | 0.485 | 0.395 | 0.422 | 0.418 | 0.543 | |
Sichuan | 0.580 | 0.573 | 0.575 | 0.565 | 0.570 | 0.599 | 0.609 | 0.678 | 0.583 | 0.540 | |
Xinjiang | 0.499 | 0.506 | 0.495 | 0.505 | 0.527 | 0.455 | 0.425 | 0.459 | 0.435 | 0.435 | |
Yunnan | 0.750 | 0.747 | 0.746 | 0.737 | 0.744 | 0.738 | 0.734 | 0.716 | 0.718 | 0.686 | |
Average | 0.581 | 0.559 | 0.556 | 0.558 | 0.572 | 0.570 | 0.535 | 0.540 | 0.527 | 0.533 | |
National average | 0.573 | 0.556 | 0.552 | 0.547 | 0.548 | 0.548 | 0.531 | 0.528 | 0.519 | 0.524 |
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Zhao, X.; Yang, D.; Zhou, Y. The Impact of Digital Technology on Water Resources Management: Evidence from China. Water 2024, 16, 2867. https://doi.org/10.3390/w16192867
Zhao X, Yang D, Zhou Y. The Impact of Digital Technology on Water Resources Management: Evidence from China. Water. 2024; 16(19):2867. https://doi.org/10.3390/w16192867
Chicago/Turabian StyleZhao, Xiaochun, Danjie Yang, and Ying Zhou. 2024. "The Impact of Digital Technology on Water Resources Management: Evidence from China" Water 16, no. 19: 2867. https://doi.org/10.3390/w16192867