Determinants of agricultural water demand in China

Li, C.; Jiang, Tianliang; Luan, Xiaobo; Yin, Yupeng; Wu, Pute; Wang, Y.B.; Sun, Shikun

doi:10.1016/j.jclepro.2020.125508

Cited by 29 publications

(14 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This proves that water productivity change and technology change are significantly different for the three regions of China. Average WUE, MLI, and TC are different in the three regions of China and were also proved by researchers in different periods [ 75 , 76 ]. However, our last hypothesis, that the distribution of the Avg.…”

Section: Resultsmentioning

confidence: 82%

Impact of “Three Red Lines” Water Policy (2011) on Water Usage Efficiency, Production Technology Heterogeneity, and Determinant of Water Productivity Change in China

Shah

Hao

et al. 2022

IJERPH

View full text Add to dashboard Cite

This research evaluates the effects of the Three Red Lines policy on water usage efficiency (WUE), production technology heterogeneity, and water productivity change in 31 Chinese provinces between 2006 and 2020. SMB-DEA, Meta-frontier analysis, and Malmquist–Luenberger index (MLI) techniques were employed for estimation. Results revealed that the mean WUE (2006–2020) in all Chinese provinces was 0.52, with an improvement potential of 48%. Shanghai, Beijing, Shaanxi, and Tianjin were the best performers. The WUE scores before (2006–2011) and after (2012–2020) water policy implementation were 0.58 and 0.48, respectively; on average, there was more than a 9% decline in WUE after the implementation of the water policy. The eastern region has the most advanced water utilization technology as its technology gap ratio (TGR) is nearly 1. The average MLI (2006–2020) score was 1.13, suggesting that the MLI has increased by 12.57% over the study period. Further technology change (TC) is the key predictor of MLI growth, whereas efficiency change (EC) diminished from 2006 to 2020. The mean MLI score for 2006–2011 was 1.16, whereas the MLI Score for the period 2012–2020 was 1.10, indicating a modest decline following the implementation of the water policy. All three Chinese regions experienced MLI growth during 2006–2020, with TC the main change factor.

show abstract

Section: Resultsmentioning

confidence: 82%

Impact of “Three Red Lines” Water Policy (2011) on Water Usage Efficiency, Production Technology Heterogeneity, and Determinant of Water Productivity Change in China

Shah

Hao

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…This result is similar to those of the other existing studies (Deng et al, 2020;White et al, 2018), which showed that China's agricultural sector has the largest water footprint mainly due to its high water consumption. Li et al (2021a) also showed that S01 is a water-intensive industry with a high proportion of water consumption but a low proportion of output value. Although S01 has high water lock-in characteristics, the consumption intensity of agriculture, forestry, animal husbandry and fishery on water resources decreased in the long term.…”

Section: Analysis Of Intra-sectorial Water Lock-in Statusmentioning

confidence: 99%

Water lock-in within China’s economic industry based on the input–output method and social network analysis

Wang

Wei

2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

As one of the countries with the most severe water resource problems, China faces enormous challenges in the intensive use of water resources. Rapid economic development has led to serious waste of water resources in the industry, resulting in path dependence on water-consuming technologies, namely the concept of 'water lock-in'. This study aims to estimate the water lock-in effects in various industries in China from 1997 to 2017. To this end, a novel combination of the input-output analysis and social network analysis methods is used to calculate 'integrated, intra-sectorial and inter-sectorial' water lockin, identify the complex water resource dependence relationship and explore the dynamic evolution process of the lock-in mechanism. The research results are as follows. (1) From 1997 to 2017, the integrated, intra-sectorial and inter-sectorial water lock-in coefficients decreased by 82.08%, 77.92% and 83.14%, respectively. (2) Non-metallic minerals and other mining products underwent the largest decline in water lock-in within the sectors, whereas coal, oil and gas extraction products underwent the most significant decline in water lock-in between the sectors. (3) Water lock-in conduction is most durable and obvious from S01 (agriculture, forestry, fishery products and services) to S06 (textiles). Policy recommendations are suggested to realise the water-unlocking path.Keywords Water lock-in • Input-output analysis method • Social network analysis • Unlock • China * Yigang Wei

show abstract

“…In recent years, several effective studies have shown that there is a rapid and high demand for water [2], [3]. We have also noticed that there has been an intense shift from traditional irrigation to smart irrigation based on Internet of Things technology to fully automate and remotely examine water requirements [4], [5].…”

Section: Related Workmentioning

confidence: 99%

Monitoring of submersible pumps using ESP32 microcontroller and photovoltaic panels

Didi

Azami

2023

IJEECS

View full text Add to dashboard Cite

In this article, the Moroccan climate is currently undergoing several changes that have negatively affected agricultural activities, especially in the field of irrigation. That's why we relied on internet of things (IoT) technology to overcome these problems. In this article, we present the design and realization of an intelligent irrigation system via a solar submersible pump. We used the ESP32 microcontroller which reads the temperature and humidity values measured by the soil moisture sensor. The communication between the blocks is ensured by the radiofrequency module. We have implemented a Web server to monitor the measured quantities. The graphical representation of the data will be ensured using the ThingSpeak platform which makes it possible to store and collect the data coming from the sensors via the hypertext transfer protocol (HTTP). Our achievement was executed and tested without any problems detected, which shows that our smart irrigation study was very successful.

show abstract

Determinants of agricultural water demand in China

Cited by 29 publications

References 35 publications

Impact of “Three Red Lines” Water Policy (2011) on Water Usage Efficiency, Production Technology Heterogeneity, and Determinant of Water Productivity Change in China

Impact of “Three Red Lines” Water Policy (2011) on Water Usage Efficiency, Production Technology Heterogeneity, and Determinant of Water Productivity Change in China

Water lock-in within China’s economic industry based on the input–output method and social network analysis

Monitoring of submersible pumps using ESP32 microcontroller and photovoltaic panels

Contact Info

Product

Resources

About