# Changing Characteristics of the Water Consumption Structure in Nanjing City, Southern China

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## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}(30°13′39′′ N–32°36′37′′ N and 118°21′28′′ E–119°15′57′′ E) is located in the southwest of Jiangsu Province and close to the Anhui Province, as shown in Figure 1. Nanjing has a subtropical monsoon climate with ample rainfall, with an average annual precipitation of more than 1000 mm. There are 120 rivers in Nanjing, all of which can be categorized according to four main watersheds: the Nanjing branch of the Yangtze River, Chuhe River, Qinhuai River, and Shuiyangjiang River. The annual average rainfall equates to 3 billion m

^{3}(excluding the passing-through water amount), where the average annual surface water is about 2.4 billion m

^{3}. Nanjing is located in the lower reaches of Yangtze River, Shuiyangjiang River, and Chuhe River, so the average annual amount of passing-through water resources is 898.2 billion m

^{3}, which is nearly 300 times the total amount of local water resources used by Nanjing [39].

^{3}in the last 22 years. In 2011, it reached a maximum of 4.562 billion m

^{3}, but there has been a downward trend in the last three years. Among the different sectors, the most remarkable decrease was AW consumption, whereas there were huge increases in both IW and DW consumption. AW consumption decreased by 335 million m

^{3}between 1993 and 2014. The proportion of AW consumption relative to the total water consumption decreased from 0.51 in 1993 to 0.36 in 2014. In addition, both IW and DW consumption have increased because of industrial development and population growth. The ratios of IW and DW consumption relative to total water consumption increased by 0.36 and 0.25, respectively, from 1993 to 2014, where the water consumption amounts by the IW and DW sectors increased by 379 million m

^{3}and 391 million m

^{3}, respectively. However, IW consumption increased after decreasing initially. In order to address severe environmental and ecological problems that have emerged in recent years, EW consumption measures were initiated in 2005, which mainly comprise water consumption by the renewed inner urban landscape rivers and the outer rivers. EW consumption increased from 20 million m

^{3}in 2005 to 140 million m

^{3}in 2014.

## 3. Data and Methods

#### 3.1. Data and Sources

#### 3.2. Methods

#### 3.2.1. Information Entropy

_{i}, where i represents different sectors. The information entropy H of a water consumption system is as follows.

_{1}= p

_{2}= ...... = p

_{n}= 1/n, the water system is the most highly ordered and ${H}_{max}$ = $\mathit{ln}\left(n\right)$. However, this situation is impossible in the real world, and thus the information entropy of water consumption always satisfies the criterion: ${H}_{min}\le H\le {H}_{max}$.

#### 3.2.2. Grey Incidence Analysis

_{i}and X

_{0}is written as follows [41].

_{0i}can be used to analyze the relationship between the absolute values of the sequences.

_{0i}is calculated as follows.

_{0i}can be used mainly to analyze the changes in the rate from the sequence to the starting point.

_{0i}is obtained by:

## 4. Results and Discussion

#### 4.1. Analysis of Water Consumption Using Entropy

#### 4.1.1. Changes in the Relative Proportions of Water Consumption Sectors

#### 4.1.2. Information Entropy Analysis for Water Consumption

#### 4.2. Analysis of Driving Forces That Affected Water Consumption

#### 4.2.1. Selection and Calculation of Driving Forces

- (1)
- Factors related to AW consumptionRatio of agricultural output (%): ${X}_{11}=Agricultural\text{}output/GDP$Ratio of irrigation area (%): ${X}_{12}=\text{}Irrigation\text{}area/Arable\text{}land$Per capita food production (ton/person): ${X}_{13}=Food\text{}production/Population$Ratio of grain crops to economic crops (%):$${X}_{14}=Graincropplantingarea/Economiccropplantingarea$$Grain crops include wheat, rice, potato, corn, and soybean in Nanjing.
- (2)
- Factors related to IW consumptionRatio of industrial output (%): ${X}_{21}=Industrialoutput/GDP$Ratio of high water consumption industry (%):$${X}_{22}=Highwaterconsumptionindustrialoutput/Industrialoutput$$Reuse ratio for IW (%): ${X}_{23}=Reusewateruse/Industrialwaterconsumption$Per ten thousand Yuan industrial output of water consumption (m
^{3}/ten thousand Yuan):$${X}_{24}=Industrialwaterconsumption/Industrialoutput$$

- (3)
- Factors related to DW consumptionRatio of third industry output (%): ${X}_{31}=Thirdindustryoutput/GDP$Population density (person/km
^{2}): ${X}_{32}=Population/Citylandarea$Daily water consumption per capita (m^{3}/person$\xb7$day):$${X}_{33}=Domesticwaterconsumption/Urbanpopulation*Days$$Natural population growth rate (%): ${X}_{34}=Birthrate-Mortality$ - (4)
- Factors related to EW consumptionPer capita disposable income of urban residents (Yuan):$${X}_{41}=Disposableincomeofurbanhouseholds/Averagefamilynumber$$GDP growth rate (%): ${X}_{42}=ChangeofGDP/GDPoflastyear$Ratio of urban green coverage (%): ${X}_{43}=Urbangreencoverage/Urbanarea$Per capita park green area (m
^{2}/person): ${X}_{44}=Parkgreenarea/Urbanpopulation$

#### 4.2.2. Analysis of Driving Force Factors by Grey Incidence Analysis

- Grey relational ranking of AW consumption: X
_{13}> X_{11}> X_{14}> X_{12} - Grey relational ranking of IW consumption: X
_{22}> X_{23}> X_{21}> X_{24} - Grey relational ranking of DW consumption: X
_{34}> X_{31}> X_{32}> X_{33} - Grey relational ranking of EW consumption: X
_{42}> X_{43}> X_{41}> X_{44}

_{13}) was γ

_{13}= 0.9085, which shows the strong correlation.

_{11}) and the relative ratio of grain crops to economic crops (X

_{14}), respectively. These two factors had major impacts on the relative proportion of AW consumption (γ

_{11}= 0.7370, γ

_{13}= 0.6016). The relative ratio of agriculture decreased from 9.78% to 2.43%, which shows the decrease in economic benefits due to agriculture. In the planting structure, the ratio of grain crops to economic crops decreased from 71:29 to 49:51. These changes to the agricultural structure led to reduced AW consumption, especially agricultural IRW consumption.

^{3}from 6215 million m

^{3}. EPIW accounted for a large relative ratio of the IW consumption and it still comprised the majority of IW consumption in Nanjing.

_{22}) had a major impact on the relative ratio of IW consumption (γ

_{22}= 0.8948). The reuse ratio of IW (X

_{23}) represented the production process level and it tended to increase, with a major impact on the relative ratio of IW consumption (γ

_{23}= 0.6449), which also led to a decline in IW consumption. The decline in the proportion of industrial output (X

_{21}, γ

_{21}= 0.5571) and per ten thousand Yuan industrial output of water consumption (X

_{24}, γ

_{24}= 0.5073) represented industrial structure adjustment and the improvement of industrial efficiency, respectively, which also reflected the changes in IW consumption, although this was not clear in the results.

_{31}), population density (X

_{32}), and natural population growth rate (X

_{34}) had major impacts on the ratio of DW consumption (γ

_{31}= 0.6441, γ

_{32}= 0.6374, γ

_{34}= 0.6490). Due to the economic development of Nanjing, DW consumption increased from 6530 million m

^{3}in 1993 to 10,445 million m

^{3}in 2014, and its relative ratio increased to 24.79% from 17.95%. This can be explained by population growth and development of the third industry. The population of Nanjing reached 8,187,800 people and the proportion of third industry output accounted for 56.49% of the GDP. The development of third industry sectors such as services and tourism were drivers of increased municipal water consumption, which increased the relative ratio of DW consumption.

_{42}) had a major impact on the relative ratio of EW consumption (γ

_{42}= 0.6439), whereas the per capita disposable income of urban residents (X

_{41}), ratio of urban green coverage (X

_{43}), and per capita park green area (X

_{44}) had low impacts on the relative ratio of EW consumption (γ

_{41}= 0.5551, γ

_{43}= 0.5629, γ

_{44}= 0.5226). The deterioration of the ecosystem influences human life and development, so improving living standards and ecological consciousness will increase the demand for EW consumption. EW consumption mainly comprised water consumption for renewing the inner urban landscape rivers and the outer river EW. In theory, increasing the ratio of urban green coverage (X

_{43}) and the per capita park green area (X

_{44}) could increase the demand for environmental water in cities. However, this was not clear from the EW consumption results, which were mainly due to inner river water.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 4.**Changes in relative proportions of water consumption by different sectors in Nanjing from 1993 to 2014. (

**a**) Changes in relative proportions of water consumption by different agricultural sectors; (

**b**) Changes in relative proportions of water consumption by different industrial sectors; (

**c**) Changes in relative proportions of domestic water and ecological water consumption.

Referenced Sequence | Comparison Sequence | N | Mean | SD | Synthetic Incidence Degree $\gamma $ |
---|---|---|---|---|---|

Ratio of AW | X_{11} | 22 | 0.046 | 0.017 | 0.7370 |

X_{12} | 22 | 0.734 | 0.080 | 0.5494 | |

X_{13} | 22 | 0.228 | 0.071 | 0.9085 | |

X_{14} | 22 | 1.197 | 0.650 | 0.6016 | |

Ratio of IW | X_{21} | 22 | 0.471 | 0.031 | 0.5571 |

X_{22} | 22 | 0.328 | 0.054 | 0.8948 | |

X_{23} | 22 | 0.669 | 0.138 | 0.6449 | |

X_{24} | 22 | 238.863 | 178.781 | 0.5073 | |

Ratio of DW | X_{31} | 22 | 0.483 | 0.046 | 0.6441 |

X_{32} | 22 | 883.201 | 70.601 | 0.6374 | |

X_{33} | 22 | 358.086 | 100.066 | 0.5766 | |

X_{34} | 22 | 0.025 | 0.011 | 0.6490 | |

Ratio of EW | X_{41} | 10 | 28,076.2 | 9474.497 | 0.5551 |

X_{42} | 10 | 0.127 | 0.019 | 0.6439 | |

X_{43} | 10 | 44.756 | 0.811 | 0.5629 | |

X_{44} | 10 | 13.620 | 0.853 | 0.5226 |

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**MDPI and ACS Style**

Wu, H.; Wang, X.; Shahid, S.; Ye, M.
Changing Characteristics of the Water Consumption Structure in Nanjing City, Southern China. *Water* **2016**, *8*, 314.
https://doi.org/10.3390/w8080314

**AMA Style**

Wu H, Wang X, Shahid S, Ye M.
Changing Characteristics of the Water Consumption Structure in Nanjing City, Southern China. *Water*. 2016; 8(8):314.
https://doi.org/10.3390/w8080314

**Chicago/Turabian Style**

Wu, Hao, Xiaojun Wang, Shamsuddin Shahid, and Mao Ye.
2016. "Changing Characteristics of the Water Consumption Structure in Nanjing City, Southern China" *Water* 8, no. 8: 314.
https://doi.org/10.3390/w8080314