Decomposition methods for analyzing changes of industrial water use

Shang, Yizi; Lu, Shilei; Shang, Ling; Li, Xiaofei; Wei, Yongping; Lei, Xiaohui; Wang, Chao; Wang, Hao

doi:10.1016/j.jhydrol.2016.10.053

Cited by 45 publications

(16 citation statements)

References 32 publications

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“…Existing research on the decomposition of driving factors of water resources utilization by LMDI model, Zhang et al (2015) [ 42 ] adopted LMDI model to decompose it into economic development level, population size, industrial water use efficiency and water resources development and utilization level, when studying the changes of total water consumption and water use efficiency in Urumqi from 1995 to 2012. Based on the improved Laspeyres model and LMDI model, Shang et al (2016) [ 43 ] decomposed the driving forces of industrial water consumption into output force, technical force and structural force. Regarding the LMDI model combined with the theory of WF, when studying China’s provincial gray water footprint characteristic, Zhang et al (2019) [ 44 ] decomposed the driving force of the gray WF into economic effects, strength effects, technology effect and development effect, based on the Kaya equation and LMDI model.…”

Section: Methods and Modelmentioning

confidence: 99%

Research on the Decoupling of Water Resources Utilization and Agricultural Economic Development in Gansu Province from the Perspective of Water Footprint

Shi

Yuan

Pang

et al. 2020

IJERPH

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Objectively evaluating the decoupling status of water resources utilization and economic development is an important sign of judging the sustainability of regional economic development. From the perspective of water footprint (WF), this paper expands the scope of water resources accounting by assessing agricultural blue WF, green WF and gray WF. The Tapio decoupling index was used to explore the decoupling status of agricultural WF and economic development in Gansu Province from 2006 to 2015, and the logarithmic mean divisor index (LMDI) decomposition model was used to identify the main driving factors of agricultural WF changes and explore the degree of divergence between agricultural economic development and water resources utilization. The results showed that agricultural economic growth was a main factor for the increase of WF; the improvement of agricultural production technology had a restraining effect, and the population effect and structural effect had a lesser effect. During the research period, the relationship between agricultural WF and economic growth in Gansu Province changed from weak decoupling to strong decoupling, and the contributing factors to decoupling were in descending order: economic, technological, structural and population. Finally, this paper puts forward suggestions on optimizing planting structure, improving agricultural technology and economic development mode to promote the sustainable development of local agriculture.

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Section: Methods and Modelmentioning

confidence: 99%

Research on the Decoupling of Water Resources Utilization and Agricultural Economic Development in Gansu Province from the Perspective of Water Footprint

Shi

Yuan

Pang

et al. 2020

IJERPH

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“…While its use has increased rapidly in the last decade in energy and environment fields, to our knowledge there have been only a handful of applications to water (i.e. Cazcarro et al (2013); Kohler (2016); Shang et al (2016Shang et al ( , 2017a) partly due to the lack of available water data at the sectoral level (Duarte and Yang, 2011).…”

Section: Technological Developments and Sectoral Enhancementsmentioning

confidence: 99%

“…Shang et al (2016) refer to these respectively as the "Output", "Structure" and "Technology" effects respectively.…”

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confidence: 99%

Scotland's industrial water use: Understanding recent changes and examining the future

Allan

McGrane

Roy

et al. 2020

Environmental Science & Policy

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Future climate scenarios predict significant changes in the availability of water resources at global and regional scales. Knowledge of the possible economic consequences of this are limited by a shortage of data linking economic activity with physical water use. Matching a unique premise-level dataset to economic indicators at industrial/sector level, this paper undertakes a decomposition of changes in industrial water demand for Scotland between 2012 and 2016. Results highlight the importance of taking a sectoral approach, as changes in sectoral water intensity are significant. Furthermore, changes in the structure of the economy, i.e. a move away from water-intensive industries, highlight further reductions in overall water consumption. By considering future scenarios for Scottish water resources, this paper identifies key multi-disciplinary research challenges to address the major obstacles in developing a climate-ready water policy, which also captures the potential economic opportunities for Scotland from an awareness of the role of water in the economy.

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“…Therefore, probability in the next period can be found in the drought probability transition matrix based on the drought situations of this period. Alcántara-Ayala [24] has researched on each natural disaster's inducing mechanism and process, established a monitoring, evaluation, and early warning system, and studied the disaster-forming conditions and general inducing models.…”

Section: Introductionmentioning

confidence: 99%

Evaluation on Early Drought Warning System in the Jinghui Channel Irrigation Area

Shang

Zhang

2020

IJERPH

Self Cite

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With the economic growth, continuous global environment deterioration, and increasingly serious water resources shortage, droughts have become more and more serious and produced great impacts on both the regional ecology and sustainable economic development. This paper has established the “green, blue, yellow, orange, and red lights” as the early warning grades for agricultural droughts. By using the two influencing factors, namely precipitation and soil moisture, this paper has established the drought assessment index evaluation model using weighted coupling method. It has carried out the analogue simulation of the early drought warning based on the Jinghui Channel’s 2013 water source situations. The soil moisture in January and February is relatively ideal, and the actual early drought warning is expressed by the “green light”. The soil moisture deficit is comparatively serious in March, but the situation concerning water inflow is ideal with the “green light”. Actually, the early warning signal is basically consistent with the soil moisture drought degree between April and August. The actual early warning is expressed by the “green light” as well, but the soil moisture is not so ideal, however, this is the seeding time of the winter wheat so the lack of soil moisture has no impact on the crops output. In November and December, the winter wheat is at the growth and development stage and does not need much moisture. At this stage, the soil moisture is relatively poor. By integrating the time effects into the early drought warning system, this paper provides administrators of irrigation areas with a scientific decision-making based on the drought control measures.

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Decomposition methods for analyzing changes of industrial water use

Cited by 45 publications

References 32 publications

Research on the Decoupling of Water Resources Utilization and Agricultural Economic Development in Gansu Province from the Perspective of Water Footprint

Research on the Decoupling of Water Resources Utilization and Agricultural Economic Development in Gansu Province from the Perspective of Water Footprint

Scotland's industrial water use: Understanding recent changes and examining the future

Evaluation on Early Drought Warning System in the Jinghui Channel Irrigation Area

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