# Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}, of which 2208.56 km

^{2}is arable land. As the most affluent and populous city in China, Beijing has 21.52 million residents, and the city’s GDP reached 2133.1 billion Yuan, accounting for 3.35% of the GDP of the entire country in 2014 [2]. According to the Beijing Water Authority, the per capita water resources in Beijing were 94 m

^{3}in 2014 [3], which is far below the internationally recognized minimum standard of 1000 m

^{3}per year [4].

^{3}/year in 2002, using an interregional input–output framework [17], and we have also evaluated the gross WF of different sectors in Beijing using an input–output approach [18]. Because this paper aims to account for the water consumed in production processes, we applied the WFN method to quantify the WF.

^{9}m

^{3}, accounting for 63.66% of the total water consumption [20]. With the increasing pressure of agricultural water resources, an urgent response is currently needed, and a better understanding of the influence of anthropogenic factors on the environment should be employed. Structural decomposition analysis (SDA) [21], the Logarithmic-Mean Divisia Index (LMDI) [22,23], and stochastic impact by regression on population, affluence, and technology (STIRPAT) [24] are methods that are commonly used to analyze the driving force of changes in the WF. Dietz and Rosa reformulated the IPAT into STIRPAT, which is used to analyze the impact of non-proportional variables on the environment [25,26]. As a stochastic model, the STIRPAT model allows a hypothesis test. In addition, it permits falsifiable tests of the environmental Kuznets curve and modernization theory [27]. This model has simple, systematic, powerful features. It simply includes key anthropogenic driving forces and clarifies the mathematical relationship between the driving forces and their impacts. In addition, it applies to a wide variety of effects [25]. Compared with other decomposition methods, the STIRPAT model is more flexible and easier to operate, while the SDA model needs an input–output table. Moreover, the STIRPAT model can examine more detailed factors than the LMDI, so it can provide more detailed and reliable information [28].

_{2}emissions using the STIRPAT model [31], and Ignatius used the STIRPAT model to examine the impacts of anthropogenic factors on the environment in Nigeria [32]. Our study differs in several ways from previous studies. In particular, the STIRPAT model was widely used to examine the driving factors of CO

_{2}emissions in previous studies, but few works have undertaken a decomposition analysis of the WF [24]. In addition, we make use of this method at the regional scale, which is more targeted. Most importantly, in previous studies, the main drivers of the agricultural WF were the population, GDP, urbanization level, plantation structure, and diet structure. However, these studies ignored the impact of two major transformations that are occurring in Beijing: the constantly increasing standard of living and improvements in cultivation technology. In 2012, the Engel coefficient of Beijing was 31.3%, compared with 55.3% in 1980. In this process, technology develops rapidly. For example, the area of mechanical cultivation as a proportion of the total area of cultivated land has risen from 61% in 2004 to 82.9% in 2012 [33].

## 2. Methods and Data

#### 2.1. Water Footprint Evaluation

_{total}is the total water footprint of crop production in Beijing (m

^{3}·year

^{−1}), WF

_{green}is the green water footprint (m

^{3}·year

^{−1}), which can be estimated with the CropWat model [35], and WF

_{grey}is the grey water footprint (m

^{3}·year

^{−1}), which refers to the volume of water needed to dilute pollutants to the extent that the quality of ambient water remains above agreed-upon water quality standards.

_{green}and ET

_{blue}refer to the quantity of green water evaporation and the quantity of blue water evaporation (mm), respectively, which can be calculated using the CropWat model. The factor 10 is the conversion coefficient, the depth of the water (mm) can be converted to unit land area of water (m

^{3}/hm

^{2}), and A is the acreage of calculated crops (hm

^{2}).

_{c}is crop evapotranspiration during the growth period (mm), and P

_{eff}is the effective precipitation over that period (mm). The ET

_{c}is calculated using the CropWat model as follows:

_{c}is the crop coefficient and ET

_{0}is the reference crop evapotranspiration (mm), which is calculated according to the FAO Penman–Monteith equation as follows [36]:

_{n}represents net radiation at the crop surface (MJ·m

^{−2}·day

^{−1}); G represents soil heat flux density (MJ·m

^{−2}·day

^{−1}); γ is the psychrometric constant (kPa·°C

^{−1}); T is the average air temperature at a height of 2 m (°C); u

_{2}is the wind speed at a 2 m height (m·s

^{−1}); e

_{s}is the saturation vapor pressure deficit (kPa); e

_{a}is the actual vapor pressure (kPa); Δ refers to the slope of the vapor pressure curve (kPa·°C

^{−1}).

_{eff}can be calculated according to the Soil Conservation Service method, which was developed by U.S. Department of Agriculture (USDA):

^{−2}); α is the leaching ratio, which is in this paper, the percentage of applied nitrogen that leached from the soil was assumed to be 10% for nitrogen fertilizers; C

_{max}is the maximum concentration (kg/m

^{3}); C

_{nat}is the natural background concentrations of pollutants (kg/m

^{3}).

#### 2.2. STIRPAT Model

#### 2.3. Ridge Regression

#### 2.4. Data Sources

## 3. Results

#### 3.1. The Agricultural Water Footprint within Beijing

^{3}·year

^{−1}to 2388.45 million m

^{3}·year

^{−1}(Figure 1). However, the WF experienced varying trends during 1980–2012. During 1980–1990, the WF slowly decreased. Then, it had a significant upward trend and reached its peak during 1991–1996. From 1997, it quickly declined, and the WF in 2003 had the lowest value of 2110.55 million m

^{3}·year

^{−1}. This year also marked a change in the inflection point. Since 2004, the WF tended to remain stable. In the terms of the individual components of the agricultural WF within Beijing, wheat, maize, and vegetables accounted for the largest proportion. Among three types of WF, the blue WF was larger than the other two WF (Figure 2). During the study period, the blue WF was the largest part of the total WF, with a proportion of 50.43%, followed by the grey and green WF, which accounted for 29.55% and 20.02%, respectively. Nevertheless, the blue WF has rapidly declined over the past three decades. This is mainly due to the adjustment of the plantation structure in Beijing. For example, rain-fed maize has largely replaced wheat [17], which has had a significant effect.

#### 3.2. Selection of Influencing Factors

_{1}—GDP per capita, A

_{2}—Engel coefficient, and T

_{3}—total rural power have a high correlation with the WF within Beijing at a significance level below 0.1%. Therefore, we chose these factors for the research.

#### 3.3. Co-Integration Analysis

#### 3.4. Multicollinearity Diagnostics

^{2}= 0.919 and F = 61.562, with sig. = 0.000, and the fit appeared good. However, all the variables cannot pass the t-test in a significant regression. A variance inflation factor (VIF) larger than 10 often indicates that multicollinearity may seriously affect the OLS estimate [42]. Most of the VIF values are larger than 10, ranging from 6.147 to 38.551, which means that there is serious multicollinearity among variables. Thus, it can be judged that the OLS cannot be used to model these dates. Therefore, to eliminate the influence of multiple collinearity between the independent variables, we used ridge regression.

#### 3.5. Ridge Regression Estimation

## 4. Discussion

^{2}to 2830 km

^{2}[33]. This causes a reduction in the agricultural WF within Beijing.

## 5. Conclusions and Policy Implications

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

Variable | Min | Max | Mean | SD | CV |
---|---|---|---|---|---|

Precipitation (0.1 mm) | 0.000 | 3276.600 | 53.070 | 172.864 | 3.257 |

Wind Velocity (0.1 m/s) | 112.320 | 345.600 | 201.833 | 40.180 | 0.199 |

Min Temp (0.1 °C) | −17.000 | 21.200 | 2.211 | 11.034 | 4.992 |

Max Temp (0.1 °C) | 3.300 | 41.900 | 25.061 | 10.109 | 0.403 |

Humidity (%) | 22.000 | 83.000 | 54.061 | 12.914 | 0.239 |

Sunshine Duration (h) | 3.570 | 10.640 | 7.006 | 1.330 | 0.190 |

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**Figure 2.**Changes in the blue, green, and grey water footprint of crop production within Beijing from 1980 to 2012.

Independent Variables | Definition of Measuring Method | Unit of Measurement |
---|---|---|

P—population | million | |

U—urbanization level | the percent of urban population on the total population | % |

A_{1}—economic level | GDP per capita | US dollar |

A_{2}—living standard | Engel coefficient | % |

T_{1}—Cultivated land level | land demand per crop yield | Hectare/10 thousand tons |

T_{2}—technical level | volume of chemical fertilizer use | 1 × 10^{4} ton |

T_{3}—technical level | total rural power consumption | 1 × 10^{4} kwh |

Variable | Min | Max | Mean | SD | CV |
---|---|---|---|---|---|

P (million) | 904.300 | 2069.300 | 1310.745 | 339.001 | 0.259 |

A_{1} (US dollar) | 115.900 | 1217.000 | 503.001 | 61.301 | 0.122 |

A_{2} (%) | 30.900 | 54.100 | 42.201 | 1.402 | 0.033 |

U (%) | 0.576 | 0.862 | 0.734 | 0.100 | 0.137 |

T_{1} (Hectare/10 thousand tons) | 0.152 | 7.5293 | 2.221 | 2.330 | 1.049 |

T_{2} (1 × 10^{4} ton) | 8.200 | 19.800 | 14.273 | 3.091 | 0.217 |

T_{3} (1 × 10^{4} kwh) | 76,753 | 619,806 | 281,006 | 27,601.776 | 0.098 |

P | A_{1} | A_{2} | U | T_{1} | T_{2} | T_{3} | ||
---|---|---|---|---|---|---|---|---|

WF | Pearson correlations | −0.800 ** | −0.833 ** | 0.887 ** | −0.633 ** | −0.249 | 0.141 | −0.754 ** |

Sig. | 0.000 | 0.000 | 0.000 | 0.000 | 0.162 | 0.433 | 0.000 |

Value | I | P | A_{1} | A_{2} | U | T_{3} | D(I) | D(P) | D(A_{1}) | D(A_{2}) | D(U) | D(T_{3}) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

t-statistic | −0.522 | 1.741 | 2.260 | −0.861 | −1.153 | 2.260 | −5.076 | −4.862 | −4.657 | −5.095 | −5.582 | −5.076 | |

Test critical values | 1% level | −3.654 | −3.654 | −3.654 | −3.654 | −3.654 | −3.654 | −3.662 | −3.662 | −3.689 | −3.662 | −3.662 | −3.662 |

5% level | −2.957 | −2.957 | −2.957 | −2.957 | −2.957 | −2.957 | −2.960 | −2.960 | −2.972 | −2.960 | −2.960 | −2.960 | |

10% level | −2.617 | −2.617 | −2.617 | −2.617 | −2.617 | −2.617 | −2.619 | −2.619 | −2.625 | −2.619 | −2.619 | −2.619 |

Variable | Unstandardized Coefficients | Std. Error | t-Statistic | Sig. | VIF |
---|---|---|---|---|---|

C | 10.316 | 2.317 | 4.452 | 0.000 | |

lnP | −0.358 | 0.418 | −0.858 | 0.398 | 38.551 |

lnA_{1} | 0.023 | 0.108 | 0.214 | 0.832 | 37.843 |

lnA_{2} | 2.258 | 0.316 | 7.143 | 0.000 | 14.377 |

lnU | 0.404 | 0.287 | 1.409 | 0.170 | 6.147 |

lnT_{3} | 0.321 | 0.080 | 4.020 | 0.000 | 9.225 |

R-squared | 0.919 | Adjusted R squared | 0.904 | ||

F-statistic | 61.562 | Sig. | 0.000 | ||

Durbin–Watson | 1.896 |

Items | Non-Normalized Coefficient | Normalized Coefficient | VIF | Sig. |
---|---|---|---|---|

P | −0.201 | 0.049 | 0.248 | 0.00 |

A_{1} | −0.078 | 0.013 | 0.179 | 0.00 |

A_{2} | 0.579 | 0.076 | 0.132 | 0.00 |

T_{3} | −0.021 | 0.128 | 0.132 | 0.045 |

U | 0.096 | 0.026 | 0.127 | 0.044 |

Constant | 22.497 | 0.804 | 0.037 | 0 |

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## Share and Cite

**MDPI and ACS Style**

Jin, C.; Huang, K.; Yu, Y.; Zhang, Y.
Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector. *Water* **2016**, *8*, 513.
https://doi.org/10.3390/w8110513

**AMA Style**

Jin C, Huang K, Yu Y, Zhang Y.
Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector. *Water*. 2016; 8(11):513.
https://doi.org/10.3390/w8110513

**Chicago/Turabian Style**

Jin, Chen, Kai Huang, Yajuan Yu, and Yue Zhang.
2016. "Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector" *Water* 8, no. 11: 513.
https://doi.org/10.3390/w8110513