The Driving Effect of Spatial Differences of Water Intensity in China

Shi, Zhen; Huang, Huinan; Wu, Fang; Chiu, Yung‐ho; Zhang, Chenjun

doi:10.1007/s11053-019-09602-5

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…High developed metropolises like Jingjintang (JJT), Yangtze River Delta (YRD) and Peral River Delta (PRD) depict higher WUE index, owing to the more developed water allocation strategy and water utilizing technique. The WUE in Guangdong province is higher than the YRD (Shanghai Jiangsu), but lower than the JJT region (Liu et al, 2020;Shi et al, 2020). Although some core PRD cities, such as Guangzhou, showed the best decoupling status between the urban economic output and water usage (Wang et al, 2018b) improvement.…”

Section: Management Implicationsmentioning

confidence: 99%

Evaluation of Water Resources Utilization Efficiency in Guangdong Province Based on the DEA–Malmquist Model

Cheng

Song

Xie

2022

Front. Environ. Sci.

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In this study, we examined the temporal and spatial trends of water utilization efficiency in the highly developed Guangdong Province based on a data envelopment analysis (DEA) model and Malmquist index, trying to evaluate and predict the water utilization status in prefecture-level cities from the input–output perspective. The results showed that the water utilization efficiency in Guangdong Province in 2012–2018 was on a fluctuating upward trend, although the utilization efficiency had not reached the DEA efficiency; spatially, the water utilization efficiency in Guangdong Province was relatively high in the Pearl River Delta and the surrounding cities, but low in the Western Guangdong region; in the perspective of efficiency decomposition factors, technological change was the most pervasive driving force of the water utilization efficiency. Pearl River Delta and Northern Guangdong made progress in productivity due to the advantages of scale expansion benefit. Eastern Guangdong achieved improvement in all-over parameters and made the largest growth in water utilization efficiency, while Western Guangdong basically profited from organizational management during the study period; the prediction analysis revealed a continuous increase in the water utilization efficiency in Guangdong Province during 2019–2025 with an average annual growth rate of about 0.6%, benefiting mainly from the technological innovation and secondly from management renewal, while scale expansion benefit tended to decline. Furthermore, spatial heterogeneity exhibited a decreasing tendency in the future owing to the rapid water utilization efficiency increase in the Western and Eastern Guangdong. It is suggested that the technological innovation and the integrated management capability in water use should be paid more attention to enhance the water utilization efficiency in Guangdong Province. Achieving a balance between economic growth and water resource protection, and promoting the sustainable development of the nature-economy-society compound system is of the utmost priority in Guangdong.

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Section: Management Implicationsmentioning

confidence: 99%

Evaluation of Water Resources Utilization Efficiency in Guangdong Province Based on the DEA–Malmquist Model

Cheng

Song

Xie

2022

Front. Environ. Sci.

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“…Water use efficiency ( WUE and IWUE ): Water use efficiency is a measure of the input and output of water resources over a given time period. We utilize the water use of 10,000 Yuan GDP and the water use of 10,000 Yuan industrial GDP to reflect water use efficiency, which is an indication of the improvement in water‐saving capacity and the performance of water resource input (Shi et al, 2020). Water use per unit of output is apparently in a positive correlation with TWU.…”

Section: Methodology and Datamentioning

confidence: 99%

Forecasting China's water use peak path under coordinating perspective from economic development and technological progress

Zhou

Zhang

et al. 2023

Natural Resources Forum

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China holds the distinction of being the foremost global consumer of water resources and contends with a significant level of water scarcity. In light of this quandary, China has been assiduously endeavoring to foster water conservation practices with the ultimate objective of reaching a peak point in water use. In this paper, we construct a framework for forecasting China's total water use (TWU) peak path. In this framework, we select influencing factors in terms of both economy and technology to forecast the peak path of China's TWU under six different scenarios, and analyze the economic and technological circumstances at the peak. Our findings indicate that China's TWU will persist in its upward trend based on both the high growth and business as usual scenarios. The projected peak period is estimated to transpire in 2037, 2032, 2030, and 2028, respectively, with the corresponding peak levels amounting to 644.03, 633.93, 625.79, and 620.92 billion m3. However, in the low growth scenarios, China's TWU reached its peak in 2013. The extent of economic development plays a pivotal role in determining the timing and manner of water usage peaking. Early peaking of water usage could potentially result in a loss of gross domestic product (GDP). Furthermore, technological advancements hold the potential to facilitate a reduction in water use without requiring a significant trade‐off between resource conservation and economic development. The results of sensitivity analysis show that the average acreage of water used for irrigation on cropland, GDP, water use efficiency, and population contribute the most to TWU, while the increase in the proportion of industrial, service, and water‐saving irrigation area can reduce water use. Our analytical framework provides a commonly applicable solution for the water use forecast in areas seeking to reach the water use peak or to achieve sustainable development.

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“…A similar spatial-temporal IDA method was applied not only for energy intensity and emissions, but also for other issues, especially water intensity [22,35,36]. The studies on water intensity in Chinese regions also showed that significant difference in water intensity is accompanied by the difference in the influencing mechanisms of these differences [22,35].…”

Section: Research Outcomesmentioning

confidence: 99%

Spatial Differences in Carbon Intensity in Polish Households

Sidorczuk-Pietraszko

2020

Energies

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Knowledge about the driving forces behind greenhouse gasses (GHG) emissions is crucial for informed and evidence-based policy towards mitigation of GHG emission and changing production and consumption patterns. Both national and regional-level authorities are capable of addressing their actions more effectively if they have information about the spatial distribution of phenomena related to the policies they conduct. In this context, the main aim of this paper is to explain the regional differences in carbon intensity in Poland. The differences in carbon intensity between regions and the national average were analysed using index decomposition analysis (IDA). Aggregate carbon intensity for regional economies as well as the carbon intensity of households was investigated. For both levels of analysis: total emissions and emission from households economic development is the key factor responsible for the inter-regional differences in carbon emission per capita. In the case of total emissions, the second important factor influencing these differences is the structure of the national power system, i.e., its concentration and the production of energy from fossil fuels. For households, disposable income per capita is a key factor of differences in CO2 emission per capita between regions. Higher households’ incomes contribute to higher emission per capita, mostly due to the shift in consumption towards more energy- and material-intensive goods. The contribution of energy emissivity is quite low and not as varied as in the case of income. This suggests that policy instruments targeted at the consumption of fuels can be rather uniform across regions, while more developed regions should also be subject to measures supporting less energy-intensive consumption. On the other hand, policy in less developed regions should prevent them from following the path of per capita emissions growth.

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The Driving Effect of Spatial Differences of Water Intensity in China

Cited by 6 publications

References 15 publications

Evaluation of Water Resources Utilization Efficiency in Guangdong Province Based on the DEA–Malmquist Model

Evaluation of Water Resources Utilization Efficiency in Guangdong Province Based on the DEA–Malmquist Model

Forecasting China's water use peak path under coordinating perspective from economic development and technological progress

Spatial Differences in Carbon Intensity in Polish Households

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