Listen

As urbanisation continues to accelerate in China, the contradiction between rapid economic development and water scarcity in semi-arid cities is becoming increasingly evident. Consequently, the study of the relationship between water resources use and economic growth is of particular importance. Lanzhou City was selected as the study area, an evaluation index system was established to comprehensively evaluate the status of water resources utilisation, meanwhile, the water footprint method and Tapio decoupling model were adopted to measure the decoupling status between water utilisation and economic development from 2002 to 2021. The result showed that the total water footprint and per capita water footprint followed an increasing and then decreasing trend. The water footprint varied significantly by industrial sector, with agriculture accounting for 72.87% of total water use. The self-sufficiency rate of water resources was above 96.5%. The economic value increased substantially from 22.25 CNY/m3 in 2002 to 183.99 CNY/m3 in 2021. The water scarcity index and the pressure index were high, with annual mean values of 0.96 and 1, respectively. Water consumption and economic growth are generally decoupled weakly and strongly, with the number of strong decoupling occurrences increasing significantly from 2011 to 2021.

Listen

  • In the semi-arid regions of Lanzhou City, agricultural water use was the largest flow of water resources.

  • To relieve stress on water resources and promote stable socio-economic development, this study combined the Tapio decoupling model to analyze the water resources.

  • Paths of realising sustainable water consumption while developing economy are discussed.

decoupling model, Lanzhou City, water footprint, water resource evaluation
Listen

Water is the source of life and the indispensable material for human survival and development (Wang & Li 2018). In recent years, economic growth and population expansion have led to a global shortage of water around the world, a problem that is particularly acute in rapidly developing countries (Süme et al. 2024). Despite China's 7% share of the world's freshwater resources, it must supply water to approximately 20% of the world's population, this makes water scarcity a threat to the sustainable development of some cities (Sun et al. 2021). Consequently, scientific analyses of the sustainable use of urban water resources and studies of the relationship between urban water consumption and economic development can assist in the achievement of sustainable economic development and the resolution of water resource issues.

A substantial body of research has been conducted on the utilisation of water resources. The principal methods employed for the evaluation of water resource utilisation are the system dynamics model (Li et al. 2023), the analytic hierarchy process (Lu et al. 2017) and fuzzy comprehensive evaluation methods (Wang et al. 2021a; Abbaszadeh et al. 2024), etc. For example, Li et al. (2023) constructed an integrated water resources assessment system and a system dynamics model to assess and predict the water resources carrying capacity of Xiongan from 2010 to 2035, they found that scenarios combining the three models of primary development, environmental protection and efficient water conservation not only meets the balance of water resources supply and demand but also has the best-carrying index. Lu et al. (2017) used the hierarchical analysis method (Analytical Hierarchy Process (AHP)) to study the carrying capacity of Huai'an water environment from 2005 to 2014, they found that water environment carrying capacity appeared an upward tendency. The application of these evaluation models and methods can obtain better results; however, these major methods have certain limitations. They are based on statistics of water consumption in various industries and fail to encompass water resources and their dynamics within products and services. Meanwhile, the above methods are difficult to use to explore the relationship between changes in water use and economic development. Therefore, the water footprint theory has been introduced to the evaluation of water utilisation.

The water footprint theory was first proposed by Hoekstra in 2002, this footprint was defined as the virtual water amount of all products (or services) consumed by the population in a certain area during their normal life in a certain period (Hoekstra 2003). Scholars have primarily focused on calculating the water footprint of different industries, the evaluation and analysis of national water resources (Hossain et al. 2021), and the study of the water footprint of specific product consumption (Agnusdei et al. 2022; Xiao et al. 2022). Most of their research concentrated in pivotal regions such as river basins (Fu et al. 2022; Lu et al. 2022a), and provinces (Jiang et al. 2022; Wen et al. 2023). For example, Fu et al. (2022) analyzed the grey water footprint in the Yangtze River Basin, they found the average grey water footprint in the central regions was higher than eastern region and western region. Despite the considerable research on footprint theory, but there are still some shortcomings, previous studies have concentrated on larger regions, with comparatively little research having been conducted on the differences between smaller regions. However, in the context of China's development planning, the city levels are more conducive to the implementation of policies. Moreover, many studies in the literature do not study the decoupling of water use and economic growth in the context of analysing the water scarcity situation in semi-arid regions. This is an obstacle to the achievement of sustainable water resources development in semi-arid regions of Northwest China. Consequently, the relationship between water resources utilisation and economic development at the smaller spatial unit level requires further study, which will facilitate the formulation of specific and critical zoning management strategies.

The water footprint model can illustrate the extent to which human activities in a specific region utilise water resources. By comparing the water resources available in the region and calculating the degree of regional water scarcity, it is possible to assess the security of the water in the region. Meanwhile, Lanzhou City is an important node city on the Silk Road and an important provincial capital centre in Northwest China. The region's natural characteristics and the region's economic development are highly representative in Northwest Arid Zone. Therefore, in this study, Lanzhou is selected as the research object, and the water footprint model is combined to calculate the water footprint from 2002 to 2021. Then the water resources evaluation indicator system is established to comprehensive evaluation of the degree of water use. Finally, the Tapio model is established to analyze the relationship between water resources and economic development (Figure 1). The study will further examine the water resource utilisation in semi-arid cities in Northwest China, and provide a scientific basis for addressing the challenges of water shortage in semi-arid provincial capital cities.
Figure 1
Graphical abstract.
View largeDownload slide

Graphical abstract.

Figure 1
Graphical abstract.
View largeDownload slide

Graphical abstract.

Close modal
Listen

Study area

Listen
Lanzhou is situated in the northwestern region of China. (Figure 2(a)). The total area of the region is 13,100 km². The topography of the region exhibits a pronounced elevation gradient, with the highest elevations situated in the western and southern regions and the lowest elevations in the northeastern region (Figure 2(b)). Lanzhou is the only provincial capital city where the Yellow River runs through the city, flowing through it for more than 150 km. It is an important central city in the western region and an important node city on the Silk Road Economic Belt.
Figure 2
Geographical location map: (a) map of the study area and (b) geographical location map of Lanzhou City.
View largeDownload slide

Geographical location map: (a) map of the study area and (b) geographical location map of Lanzhou City.

Figure 2
Geographical location map: (a) map of the study area and (b) geographical location map of Lanzhou City.
View largeDownload slide

Geographical location map: (a) map of the study area and (b) geographical location map of Lanzhou City.

Close modal

Lanzhou City has a temperate continental climate, with an average annual temperature of 11.2 °C. Precipitation falls predominantly from June to September, with an average annual precipitation of about 327 mm (Hu et al. 2017). There are few self-produced water resources in Lanzhou City, and the per capita water resources stand at only one-quarter of the national per capita level (Zhang et al. 2020). The security of the water supply is primarily contingent upon the Yellow River surface water. Under the restrictions of the Yellow River water allocation scheme, the available water resources are very limited. Problems concerning water resources such as unequal distribution of water resources and imbalance between supply and demand have directly hindered the efficient and sustainable development of Lanzhou City.

In 2021, the total population of Lanzhou City was 4,384,300, the urban population totalled 3,663,500, and the rural population was 720,800. Meanwhile, the urbanisation rate was 83.56%, and the natural population growth rate stood at 1.77‰. The city's gross regional product was CNY 323,129 million, representing a 6.1% increase over the previous year. The value added by the primary, secondary, and tertiary industries was CNY 6,252 million, CNY 111,391 million, and CNY 205,486 million, respectively, and the ratio of the industrial structure was 1.94:34.47:63.59. Based on the permanent population, the per capita GDP was CNY 73,807 (https://tjj.lanzhou.gov.cn/).

Data sources

Listen

The data for this study were sourced from the Gansu Water Resources Bulletin, the Gansu Provincial Statistical Yearbook, the Lanzhou Municipal Statistical Yearbook, and previous research results. The data employed in this study were categorised into two distinct groups: socio-economic data and water resource data. The initial category encompasses data pertaining to the primary agricultural and livestock products, the population of Lanzhou City at the end of the year, its gross domestic product (GDP), and its import and export trade values. The data presented here were obtained from the Statistical Yearbooks of Gansu Province and the Statistical Yearbooks of Lanzhou City, covering the period from 2002 to 2021. (https://tjj.gansu.gov.cn/, https://tjj.lanzhou.gov.cn/). The second category included domestic water consumption, industrial water consumption, and ecological water consumption. These data were from 2002 to 2021 Gansu Water Resources Bulletin (https://slt.gansu.gov.cn/slt/c106726/c106732/c106773/zcfg.shtml). The available water resources in Lanzhou City from 2002 to 2012 were based on the findings of Feng's research (Feng 2012).

The agricultural water footprint included two components: water demand for crop products, and water demand for animal products. The main crop products were wheat, corn, potatoes, oil, vegetable, and fruit. Livestock products mainly include pork, beef, mutton, milk, poultry eggs, and aquatic product. The virtual water content per unit of product was based on the previous research results (Wang et al. 2021b; Lu et al. 2022b; Liu et al. 2022a) (Table 1).

Table 1

Virtual water contents of main agricultural and livestock products

Product categoryProduct nameVirtual water content per unit of product
Agricultural products Wheat 0.69 
Corn 0.80 
Potato 0.28 
Oil 2.20 
Vegetable 0.22 
Fruits 0.82 
Livestock products Pork 2.20 
Beef 12.56 
Mutton 5.20 
Milk 1.03 
Poultry eggs 3.55 
Aquatic products 5.00 
Product categoryProduct nameVirtual water content per unit of product
Agricultural products Wheat 0.69 
Corn 0.80 
Potato 0.28 
Oil 2.20 
Vegetable 0.22 
Fruits 0.82 
Livestock products Pork 2.20 
Beef 12.56 
Mutton 5.20 
Milk 1.03 
Poultry eggs 3.55 
Aquatic products 5.00 

Unit: m3/kg.

View Large

Methods

Listen

Water footprint calculation model

Listen
The water footprint was used to describe the total amount of water consumed by the products and services needed in a particular area at a particular time (Hoekstra 2011), which can be divided into internal and external water footprints according to the source, with the calculation formula as follows:
(1)
where WFP is the total water footprint, IWFP is the internal water footprint (100 million m3), and EWFP is the external water footprint (100 million m3).
There were two main approaches to accounting for the water footprint. The bottom-up approach was based on the principle that the water footprint of all commodities consumed in the region was obtained by multiplying the quantities and adding them together. The top-down method considered the fact that due to the trade between regions, the consumption of products and services was mobile, the consumption of water resources may come from other regions, and the virtual water trade of products cannot be ignored (Lu et al. 2022c). Therefore, this study adopted the top-down method to calculate the water footprint of Lanzhou City. In this method, the internal water footprint was defined as the quantity of water consumption in the production of the goods and services consumed by residents in a region over the course of a year. This was equal to the sum of the region's agricultural, industrial, domestic, and ecological water consumption minus the virtual amount of water exported to other regions through trade, namely:
(2)
where AWU is the agricultural water footprint (100 million m3); IWU, DWU, and EWU are, respectively, the industrial, domestic, and ecological water consumption (100 million m3); and VWU is the virtual water volume exported from the study area (100 million m3).
The external water footprint referred to the water resources that a region takes on from other regions through trade and other means. The external water footprint was the imported virtual water volume minus that which was re-exported, namely:
(3)
where VWI is the imported virtual water volume (100 million m3), and VWE is the virtual amount of water imported and re-exported, which is generally ignored.
Calculating the agricultural water consumption required multiplying the output of agricultural and livestock products by the virtual water content per unit of product, namely:
(4)
where In is the unit of product virtual water quantity (m3/kg), and Qn is the output of agricultural and livestock products (kg).
The import and export virtual water volumes were collectively referred to as the virtual water trade volume. It is difficult to calculate the exact amount of virtual water due to the different types and quantities of exports. The calculation formula is as follows:
(5)
(6)
where ET is the export trade value of Lanzhou City (CNY 100 million), IT is the import trade value of Lanzhou City (CNY 100 million), GDP is the gross regional product (CNY 100 million), and TWC is the total water consumption of the area (100 million m3).

The construction of a water resources evaluation indicator system

Listen

To evaluate water resource utilisation in Lanzhou, this study referred to the evaluation index system constructed by Qi et al. (2011) and Liu et al. (2022b). Six indicators were selected with which to evaluate the water resources of Lanzhou City based on three aspects: the water footprint structure, water footprint benefit, and water footprint security.

The structural indicators of the water footprint included water resource self-sufficiency and water import dependency (WD). WD is the ratio of the external water footprint to the total water footprint of the study area. It reflects the dependence of the region on external water resources. The higher the WD value, the more dependent the region was on external water resources. The water self-sufficiency rate (WSS) is the proportion of the internal water footprint to the total water footprint. This reflects the dependence of the region on local water resources. When WSS decreases, this indicates that the demand for external water resources is gradually increasing, and water resources can be imported from external sources to meet the local demand. The calculation formula is as follows:
(7)
(8)
Water footprint benefit indicators included the economic footprint benefit value and water footprint net trade volume. The economic footprint benefit value of the water footprint (WFEB) represents the economic value created by the consumption unit of the water footprint. A greater economic benefit value is indicative of a higher regional water resources utilisation efficiency. The net trade volume of the water footprint (WFNTV) is the difference in the virtual water volumes of import and export in a certain period. It reflects the net outflow size of the water footprint. If the water footprint of exports is greater than the water footprint of imports, the WFNTV is positive. The region is an exporter of water resources, which is not conducive to the sustainable development of water resources. Meanwhile, if it is negative, it is an importer of water resources and contributes to the sustainability of local water resources. The calculation formula is as follows:
(9)
(10)
The security indicators of water footprint in this study were water scarcity index and water stress index. The water footprint scarcity (WS) Index is a measure of the degree of water stress in a region. The WS Index is calculated by dividing the total water footprint of the region by the amount of available water resources. The larger the index, the more stressful the region is. If the value exceeds 1, it indicates that the region's demand for water exceeds the amount of water available at the same time, showing a state of extreme water scarcity. Water resources stress index reflects the regional water resources stress status. It characterises the loads placed on water resources by human processes and the relative scarcity of water resources in the region. The calculation formula is as follows:
(11)
(12)
where WA is the regional available water amount (100 million m3).

Construction of the Tapio decoupling model

Listen

Decoupling refers to the situation in which energy consumption does not increase with economic growth in the process of economic development but decreases with economic growth (Schandl et al. 2016). The use of decoupling models has been employed to assess the relationship between water resources and economic development in various regions. The decoupling models used by scholars usually encompass the Tapio decoupling model, the IGT decoupling model and the OECD decoupling model. However, the IGT decoupling model and the OECD model are not comprehensive enough in evaluating the specific types of decoupling, including undecoupling, relative decoupling, and absolute decoupling. The Tapio decoupling model is advantageous in terms of accuracy, comprehensiveness, scientific rigour, and thus this paper employed the Tapio decoupling model (Du et al. 2021; Pan et al. 2022).

This study divided eight decoupling states according to the research of Tapio and the dynamic characteristics of the research data. The most desirable one is a strong decoupling that showed an inverse relationship between water consumption and economic growth. Strong decoupling indicates that economic growth is no longer dependent on increasing the amount of water use, in other words, efficiency has increased to a stage of sustainable use. A weak decoupling state indicates that the economy is growing faster than the water footprint, and the efficiency of water use is improving. Expensive coupling and expensive negative decoupling states indicate that the economy is still growing despite higher water use. The four states of weak negative decoupling, recessive coupling, recessive decoupling, and strong negative decoupling tend to occur during economic recessions. These four states mean that water resources are being depleted while the economy is not being developed (Table 2). The formula of the decoupling model is as follows:
(13)
where e is the decoupling index, ΔWFP refers to the annual growth rate of the water footprint (100 million m3), ΔGDP is the annual growth rate of the gross product (CNY 100 million), WFPn refers to the total water footprint at the end of period n (100 million m3),WFPn−1 is the total water footprint at the end of period n−1 (100 million m3), GDPn is the gross product at the end of the year n (100 million m3), and GDPn−1 refers to the gross product at the end of the year n–1 (100 million m3).
Table 2

Decoupling state classification criteria

ClassifyDecoupling typeΔWFPΔGDPE
Decoupling Strong decoupling − + (−∞, 0) 
Weak decoupling + + (0, 0.8) 
Recessive decoupling − − (1.2, +∞) 
Coupling Expensive coupling + + (0.8, 1.2) 
Recessive coupling − − (0.8, 1.2) 
Negative decoupling strong negative decoupling + − (−∞, 0) 
Weak negative decoupling − − (0, 0.8) 
Expensive negative decoupling + + (1.2, +∞) 
ClassifyDecoupling typeΔWFPΔGDPE
Decoupling Strong decoupling − + (−∞, 0) 
Weak decoupling + + (0, 0.8) 
Recessive decoupling − − (1.2, +∞) 
Coupling Expensive coupling + + (0.8, 1.2) 
Recessive coupling − − (0.8, 1.2) 
Negative decoupling strong negative decoupling + − (−∞, 0) 
Weak negative decoupling − − (0, 0.8) 
Expensive negative decoupling + + (1.2, +∞) 
View Large
Listen

Composition of water footprint

Listen
The total water footprint of Lanzhou City exhibited an overall trend of increasing and then decreasing from 2002 to 2021 (Figure 3). The maximum value of the water footprint was 1998 million m³ in 2011, and the minimum value was 1,568 million m³ in 2018 (Table 3). The total water footprint for the multi-year average was 1,783 million m³. From 2003 to 2011, the water footprint exhibited an upward trend, with an average annual growth rate of 2.55%. From 2012 to 2021, the water footprint exhibited considerable fluctuations and demonstrated a downward trend. The overall per capita water footprint was declining. The maximum value was 578.37 m³ in 2009, while the minimum value was 358.79 m³ in 2020. The average annual rate of reduction in the per capita water footprint is 1.53%. The per capita water footprint in Lanzhou City was found to be below the national average, indicating a more efficient utilisation of water resources than in the majority of other regions in China. (Pan & Xu 2019).
Table 3

Water footprint composition of Lanzhou City from 2012 to 2021

YearAWUIWUEWUDWUVWIVWUTotal WFPWFP per Capita
2002 7.59 8.38 0.03 1.86 0.58 1.28 17.38 577.50 
2003 7.94 7.98 0.05 1.69 0.47 1.88 16.25 533.96 
2004 8.34 7.56 0.05 1.76 0.37 1.44 16.64 539.97 
2005 8.56 7.74 0.23 1.82 0.51 1.17 17.69 567.50 
2006 9.20 8.23 0.23 1.91 0.41 1.22 18.76 573.65 
2007 9.57 7.10 0.97 1.91 0.25 0.95 18.85 572.24 
2008 9.15 7.21 0.98 1.98 0.17 0.79 18.71 565.25 
2009 9.77 6.62 0.99 1.98 0.21 0.36 19.21 578.37 
2010 10.28 6.69 1.00 2.12 0.19 0.84 19.44 537.20 
2011 10.81 6.53 0.99 2.11 0.51 0.96 19.98 551.77 
2012 11.47 5.16 0.99 2.16 0.41 1.58 18.61 512.70 
2013 12.27 3.96 0.40 1.90 0.20 1.55 17.18 471.74 
2014 12.90 3.77 0.40 1.92 0.20 1.48 17.71 483.27 
2015 13.33 3.92 0.71 1.98 0.22 1.87 18.30 495.39 
2016 13.75 3.94 0.71 1.99 0.32 1.23 19.47 525.52 
2017 9.52 4.27 0.71 2.04 0.26 0.36 16.44 440.75 
2018 9.47 3.31 0.84 2.14 0.26 0.34 15.68 417.85 
2019 10.02 3.56 1.15 2.34 0.19 0.29 16.97 447.65 
2020 10.50 1.53 1.54 1.99 0.24 0.11 15.69 358.79 
2021 11.14 1.62 2.33 2.25 0.35 0.12 17.56 400.56 
YearAWUIWUEWUDWUVWIVWUTotal WFPWFP per Capita
2002 7.59 8.38 0.03 1.86 0.58 1.28 17.38 577.50 
2003 7.94 7.98 0.05 1.69 0.47 1.88 16.25 533.96 
2004 8.34 7.56 0.05 1.76 0.37 1.44 16.64 539.97 
2005 8.56 7.74 0.23 1.82 0.51 1.17 17.69 567.50 
2006 9.20 8.23 0.23 1.91 0.41 1.22 18.76 573.65 
2007 9.57 7.10 0.97 1.91 0.25 0.95 18.85 572.24 
2008 9.15 7.21 0.98 1.98 0.17 0.79 18.71 565.25 
2009 9.77 6.62 0.99 1.98 0.21 0.36 19.21 578.37 
2010 10.28 6.69 1.00 2.12 0.19 0.84 19.44 537.20 
2011 10.81 6.53 0.99 2.11 0.51 0.96 19.98 551.77 
2012 11.47 5.16 0.99 2.16 0.41 1.58 18.61 512.70 
2013 12.27 3.96 0.40 1.90 0.20 1.55 17.18 471.74 
2014 12.90 3.77 0.40 1.92 0.20 1.48 17.71 483.27 
2015 13.33 3.92 0.71 1.98 0.22 1.87 18.30 495.39 
2016 13.75 3.94 0.71 1.99 0.32 1.23 19.47 525.52 
2017 9.52 4.27 0.71 2.04 0.26 0.36 16.44 440.75 
2018 9.47 3.31 0.84 2.14 0.26 0.34 15.68 417.85 
2019 10.02 3.56 1.15 2.34 0.19 0.29 16.97 447.65 
2020 10.50 1.53 1.54 1.99 0.24 0.11 15.69 358.79 
2021 11.14 1.62 2.33 2.25 0.35 0.12 17.56 400.56 

Note: Unit of AWU, IWU, EWU, DWU, VWI, VWU, and total WFP is 100 million m3. Unit of WFP per capita is m3.

View Large
Figure 3
Water footprint and per capita water footprint of Lanzhou City from 2002 to 2021.
View largeDownload slide

Water footprint and per capita water footprint of Lanzhou City from 2002 to 2021.

Figure 3
Water footprint and per capita water footprint of Lanzhou City from 2002 to 2021.
View largeDownload slide

Water footprint and per capita water footprint of Lanzhou City from 2002 to 2021.

Close modal
The water footprint composition of Lanzhou City was extremely unbalanced, with large differences in the values of each type of water footprint. The proportion of agricultural water consumption was the highest, with a value of up to 72.87% (Table 3). This was followed by industrial water use, which accounted for a high proportion in 2002–2019, with the highest value of 49.10%. Agricultural water consumption showed a trend of rising and then falling, with an overall upward trend. It rose from 759 million m³ in 2002 to 1114 million m³ in 2021 with an average annual growth rate of 2.33%, which indicates that the pressure on agricultural water use in semi-arid areas should not be ignored (Table 3 and Figure 4). Industrial water consumption showed a downward trend in Lanzhou City, decreasing from 838 million m3 in 2002 to 162 million m3 in 2021. This phenomenon can be attributed to the restructuring of the industrial landscape and the introduction of novel development paradigms Industrial development is progressively embracing environmentally conscious, low-carbon, and harmonious approaches. Ecological water use was in a state of fluctuation, increasing from 3 million m3 in 2002 to 233 million m3 in 2021. This may be due to the vigorous promotion of ecological restoration and protection in Northwest China in recent years, resulting in an increase in ecological water consumption. From 2002 to 2019, the volume of exported virtual water was greater than that of imported virtual water, and from 2020 onwards, the amount of imported virtual water was greater than that of exported virtual water, which was conducive to the sustainable development of regional water resources (Table 3 and Figure 4). Overall, the agricultural water use sector continues to be the primary consumer of water resources. Therefore, enhancing agricultural water use efficiency represents a pivotal strategy for regulating the total quantity of water utilised and a fundamental prerequisite for attaining sustainable and efficient utilisation of water resources within the context of Lanzhou City.
Figure 4
Composition of the water footprint of Lanzhou City from 2002 to 2021.
View largeDownload slide

Composition of the water footprint of Lanzhou City from 2002 to 2021.

Figure 4
Composition of the water footprint of Lanzhou City from 2002 to 2021.
View largeDownload slide

Composition of the water footprint of Lanzhou City from 2002 to 2021.

Close modal

Water resources evaluation indicator system

Listen
From 2002 to 2021, the self-sufficiency rate of water resources in Lanzhou City was above 96.5%, indicating that the local water resources could largely meet the development needs. Accordingly, the dependence on external water resources in this region was low, with the dependence on imports remaining between 0.9% and 3.4% (Table 4 and Figure 5(a)). The economic benefit value of the water footprint increased from 22.25 CNY/m3 in 2002 to 183.99 CNY/m3 in 2021 (Figure 5(b)), the data show that the economic efficiency of water resources in Lanzhou City has improved significantly during this period, and the level of water resources utilisation has also increased significantly. The volume of water footprint trade was positive from 2002 to 2019, and Lanzhou City was a net exporter of water resources in virtual water trade (Table 4). From 2020, the import of virtual water was higher than the export of virtual water, which was conducive to the sustainable development of water resources in Lanzhou City (Figure 5(b)).
Table 4

Lanzhou City water footprint evaluation index system

YearWater footprint structural indicators
Water footprint benefit indicators
Water footprint security indicators
WDWSSWFEBWFNTVWSWP
2002 3.36 96.64 22.25 0.69 0.94 0.97 
2003 2.89 97.11 27.08 1.41 0.87 0.95 
2004 2.25 97.75 30.33 1.07 0.89 0.95 
2005 2.86 97.14 32.05 0.66 0.95 0.99 
2006 2.18 97.82 34.04 0.81 1.01 1.05 
2007 1.32 98.68 38.87 0.70 1.01 1.05 
2008 0.93 99.07 45.23 0.62 1.01 1.04 
2009 1.11 98.89 48.20 0.15 1.03 1.04 
2010 0.98 99.02 56.60 0.65 1.05 1.08 
2011 2.53 97.47 68.07 0.45 1.07 1.10 
2012 2.22 97.78 84.05 1.17 1.00 1.06 
2013 1.18 98.82 106.47 1.35 0.92 1.00 
2014 1.15 98.85 112.94 1.28 0.95 1.02 
2015 1.22 98.78 114.56 1.64 0.98 1.07 
2016 1.63 98.37 116.27 0.91 1.05 1.10 
2017 1.59 98.41 152.14 0.10 0.88 0.89 
2018 1.67 98.33 174.24 0.08 0.84 0.85 
2019 1.12 98.88 167.20 0.10 0.91 0.92 
2020 1.54 98.46 184.04 −0.13 0.84 0.84 
2021 1.98 98.02 183.99 −0.23 0.94 0.93 
Average 1.79 98.21 89.93 0.67 0.96 1.00 
YearWater footprint structural indicators
Water footprint benefit indicators
Water footprint security indicators
WDWSSWFEBWFNTVWSWP
2002 3.36 96.64 22.25 0.69 0.94 0.97 
2003 2.89 97.11 27.08 1.41 0.87 0.95 
2004 2.25 97.75 30.33 1.07 0.89 0.95 
2005 2.86 97.14 32.05 0.66 0.95 0.99 
2006 2.18 97.82 34.04 0.81 1.01 1.05 
2007 1.32 98.68 38.87 0.70 1.01 1.05 
2008 0.93 99.07 45.23 0.62 1.01 1.04 
2009 1.11 98.89 48.20 0.15 1.03 1.04 
2010 0.98 99.02 56.60 0.65 1.05 1.08 
2011 2.53 97.47 68.07 0.45 1.07 1.10 
2012 2.22 97.78 84.05 1.17 1.00 1.06 
2013 1.18 98.82 106.47 1.35 0.92 1.00 
2014 1.15 98.85 112.94 1.28 0.95 1.02 
2015 1.22 98.78 114.56 1.64 0.98 1.07 
2016 1.63 98.37 116.27 0.91 1.05 1.10 
2017 1.59 98.41 152.14 0.10 0.88 0.89 
2018 1.67 98.33 174.24 0.08 0.84 0.85 
2019 1.12 98.88 167.20 0.10 0.91 0.92 
2020 1.54 98.46 184.04 −0.13 0.84 0.84 
2021 1.98 98.02 183.99 −0.23 0.94 0.93 
Average 1.79 98.21 89.93 0.67 0.96 1.00 

Note: Unit of WD and WSS is %. Unit of WFEB is CNY/m3. Unit of WFNTV is 100 million m3.

View Large
Figure 5
Indicators for the evaluation of water resource utilisation. (a) Water footprint structural indicators; (b) water footprint benefit indicators; and (c) water footprint security indicators.
View largeDownload slide

Indicators for the evaluation of water resource utilisation. (a) Water footprint structural indicators; (b) water footprint benefit indicators; and (c) water footprint security indicators.

Figure 5
Indicators for the evaluation of water resource utilisation. (a) Water footprint structural indicators; (b) water footprint benefit indicators; and (c) water footprint security indicators.
View largeDownload slide

Indicators for the evaluation of water resource utilisation. (a) Water footprint structural indicators; (b) water footprint benefit indicators; and (c) water footprint security indicators.

Close modal

Over the 20-year period under study, the water scarcity index of Lanzhou City exhibited a pattern of initial increase followed by a decline. The peak value of water resource scarcity was observed in 2011, with a score of 1.07, while the lowest value was recorded in 2020, with a score of 0.84. The annual average value was 0.96 (Figure 5(c)). The change in the water resource stress index was essentially the same as that of the water resource scarcity index. The maximum value of the water resource stress index was 1.10 in 2011, the minimum value was 0.84 in 2020, and the multi-year average was 1.00 (Table 4). From 2006 to 2012, the water stress index and scarcity index of Lanzhou City exceeded 1, indicating that the city is experiencing a shortage of water resources. Its actual consumption of water resources exceeds the available water resources, which greatly limits the sustainable development of the city's economy, society, and ecology. In recent years, the water scarcity index and pressure index of Lanzhou City have decreased. However, the increase in the water pressure index and shortage index still requires control to manage the overall level of water resources utilisation in the city.

Analysis of the decoupling relationship between water resource utilisation and economic growth

Listen

The GDP exhibited a consistent upward trend over the course of the study period, while the total water footprint exhibited significant fluctuations. In terms of growth rates, the rate of increase in GDP was considerably faster than that of the water footprint (Table 5). This indicates that the socio-economic development is favourable despite the water resources consume.

Table 5

Decoupling state of water resource utilisation and economic growth in Lanzhou City in 2002–2021

YearRate of change in water footprintRate of change in GDPDecoupling indexDecoupling state
2002–2003 −6.51 13.78 −0.47 Strong decoupling 
2003–2004 2.37 14.67 0.16 Weak decoupling 
2004–2005 6.34 12.36 0.51 Weak decoupling 
2005–2006 6.03 12.60 0.48 Weak decoupling 
2006–2007 0.49 14.76 0.03 Weak decoupling 
2007–2008 −0.75 15.50 −0.05 Strong decoupling 
2008–2009 2.68 9.42 0.28 Weak decoupling 
2009–2010 1.20 18.84 0.06 Weak decoupling 
2010–2011 2.76 23.60 0.12 Weak decoupling 
2011–2012 −6.83 15.03 −0.45 Strong decoupling 
2012–2013 −7.71 16.91 −0.46 Strong decoupling 
2013–2014 3.10 9.37 0.33 Weak decoupling 
2014–2015 3.30 4.78 0.69 Weak decoupling 
2015–2016 6.44 8.03 0.80 Expensive coupling 
2016–2017 −15.59 10.45 −1.49 Strong decoupling 
2017–2018 −4.58 9.28 −0.49 Strong decoupling 
2018–2019 8.19 3.82 2.14 Expensive negative decoupling 
2019–2020 −7.57 1.74 −4.35 Strong decoupling 
2020–2021 11.96 11.94 1.00 Expensive coupling 
YearRate of change in water footprintRate of change in GDPDecoupling indexDecoupling state
2002–2003 −6.51 13.78 −0.47 Strong decoupling 
2003–2004 2.37 14.67 0.16 Weak decoupling 
2004–2005 6.34 12.36 0.51 Weak decoupling 
2005–2006 6.03 12.60 0.48 Weak decoupling 
2006–2007 0.49 14.76 0.03 Weak decoupling 
2007–2008 −0.75 15.50 −0.05 Strong decoupling 
2008–2009 2.68 9.42 0.28 Weak decoupling 
2009–2010 1.20 18.84 0.06 Weak decoupling 
2010–2011 2.76 23.60 0.12 Weak decoupling 
2011–2012 −6.83 15.03 −0.45 Strong decoupling 
2012–2013 −7.71 16.91 −0.46 Strong decoupling 
2013–2014 3.10 9.37 0.33 Weak decoupling 
2014–2015 3.30 4.78 0.69 Weak decoupling 
2015–2016 6.44 8.03 0.80 Expensive coupling 
2016–2017 −15.59 10.45 −1.49 Strong decoupling 
2017–2018 −4.58 9.28 −0.49 Strong decoupling 
2018–2019 8.19 3.82 2.14 Expensive negative decoupling 
2019–2020 −7.57 1.74 −4.35 Strong decoupling 
2020–2021 11.96 11.94 1.00 Expensive coupling 
View Large

During the study period, the relationship between water resources and economic development in Lanzhou City was observed to assume four states: weak decoupling, strong decoupling, expensive coupling, and expensive negative decoupling. Over the course of the study period, there were 16 years of decoupling, with the level of decoupling increasing over time. This may be attributed to the implementation of a series of water conservation and management measures in Lanzhou in recent years, achieving a notable decoupling of water use from economic development. Lanzhou City achieved a state of coordinated development of water resource utilisation and economic development, in general, and water consumption decreased with economic growth (Table 5). A state of expensive negative decoupling occurred in 2018–2019, during which economic growth did not lead to a reduction in water consumption. Furthermore, in 2015–2016 and 2020–2021, the two variables were in a state of expensive coupling. This was the intermediate state of negative decoupling and decoupling transition. A correlation was observed between the utilisation of water resources and economic growth, although the relationship was not particularly strong.

Listen

The water footprint of Lanzhou City showed a fluctuating trend, but the magnitude was not significant, thereinto, the agricultural water footprint was the main component of the water footprint, and its proportion was approximately accounted for 72.87% between 2002 and 2021. It is consistent with the composition of the overall water footprint in other regions of China (Kong et al. 2019). The relatively large share of agricultural water consumption may be attributed to the relatively underdeveloped socio-economic context and the prevalence of agricultural mechanisation in Northwest China, as well as the relatively rudimentary mode of agricultural development. This has led to the increasingly prominent issues of agricultural water use efficiency and structural scarcity of water resources. The inefficiency of agricultural water use affected the intensity of agricultural water use (Lu et al. 2021). Meanwhile, as urbanisation progresses and the population increases, the demand for agricultural and livestock products also increases, so the water consumption of the agricultural and livestock industries will also increase accordingly. The ecological water footprint of Lanzhou City has increased by 76.67% since 2002, may be attributed to the 12th Five-Year Plan and 13th Five-Year Plan for Ecological Construction and Development of Lanzhou City, which accelerated the construction of an ecological civilisation.

This study analyzed three aspects to determine the sustainable utilisation of water resources in Lanzhou. The growing value of the economic benefits of the water footprint in Lanzhou city indicates an improvement in the efficiency of water resources utilisation, which is consistent with the findings of Liang et al. (2023). The results of the analyses of the water stress index and water scarcity index demonstrate that the degree of water shortage has been reduced in recent years, and the contradiction between the supply and demand of water resources has been alleviated. Feng and Zhu's study indicated that water consumption in Gansu Province has been significantly reduced, alleviating the pressure on water resources to a certain extent (Feng & Zhu 2022). The results of this study are further argued by Feng and Zhu's study. This reduction can be attributed to the implementation of a rigorous water resources management system proposed by the Ministry of Water Resources of China in 2011, which provides an important basis for decoupling economic growth from water consumption.

The relationship between water resource utilisation and economic growth in Lanzhou City exhibited a decoupled state overall, weakly so from 2002 to 2011 and then more strongly from 2012 to 2021. This finding is similar to the findings of (Wang & Wang 2020). Their research indicated a pronounced decoupling between water use and economic growth in over 60% of China's 31 provinces from 2011 onwards. This period coincided with the acceleration of the construction of a water-saving society. In recent years, several organisations have indicated that as global water scarcity expands and intensifies, the decoupling of water use from economic growth is critical to sustaining economic growth and human well-being (United Nations Environment Programme 2011). Long et al. (2019) and Zhang et al. (2021) have both reached the clear conclusion that the intensity is the most important driver of the inhibition of the growth of total water use. Furthermore, Richter found that cities could decouple economic growth from water use by adapting to population growth while reducing water consumption (Richter et al. 2020). This study found that agricultural and industrial water use in Lanzhou City constituted a significant proportion of total water use (Bao & Xu 2023). Therefore, it seems that optimising the industrial structure, improving the efficiency of agricultural water use, and increasing the research and development of core water-saving technologies stand as important goals in promoting the decoupling of water resource use from economic development.

Listen

The resolution of the contradiction between economic growth and water resources in semi-arid regions is a key factor in the achievement of regional sustainable development. For this reason, this study took Lanzhou City as the study area. It combined the water footprint model, the Tapio decoupling model and other methods to evaluate the sustainable use of water resources in Lanzhou City. The objective is to identify a feasible way to achieve the synchronisation between the sustainable development of regional water resources and economic growth. This will provide scientific guidance for the achievement of regional sustainable development.

The water footprint of Lanzhou City has exhibited a gradual increase since 2003, followed by a gradual decrease after 2012. Furthermore, the composition of the water footprint of Lanzhou City was highly imbalanced, exhibiting considerable discrepancies in the water footprint values of various categories. During the study period, local water resources were found to be capable of meeting the city's water supply requirements, with a low dependence on outside sources. The efficiency of water use has demonstrably improved, from CNY 22.25/m³ in 2002 to CNY 183.99/m³ in 2021. However, the city continues to confront the challenges of water scarcity and water stress. The relationship between water uses and economic growth exhibited four distinct states: weak decoupling, strong decoupling, expensive coupling, and expensive negative decoupling. The strength of the decoupling gradually increased, accompanied by a corresponding decrease in the dependence of economic growth on water consumption.

In conclusion, it is of paramount importance to accelerate the decoupling of water consumption from economic growth and to achieve sustainable water resources development, which facilitates the formulation of targeted economic development plans. In the agricultural sector, it is imperative to vigorously promote water-saving irrigation, implement refined management of agricultural water use, scientifically determine reasonable irrigation quotas, and optimise and adjust the crop planting structure. In the industrial sector, it is crucial to promote the integration of information technology and industrialisation, increase the research and development of water-saving core technologies, and improve China's water-saving equipment manufacturing innovation capacity.

Listen

This research has been supported by Gansu Province Water Resources Science Experimental Research and Technology Promotion Programme Project (23GSLK082, 24GSLK073), Gansu Provincial Key R&D Projects (22YF7GA107), Gansu Provincial Science and Technology Major Project (23ZDFA009).

Listen

All relevant data are included in the paper or its Supplementary Information.

Listen

The authors declare there is no conflict.

Abbaszadeh
H.
,
Daneshfaraz
R.
,
Sume
V.
 &
Abraham
J.
2024
Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions
.
AQUA – Water Infrastructure, Ecosystems and Society
jws2024010
.
https://doi.org/10.2166/aqua.2024.010
.
Google Scholar
 
Agnusdei
G. P.
,
Coluccia
B.
,
Pacifico
A. M.
 &
Miglietta
P. P.
2022
Towards circular economy in the agrifood sector: Water footprint assessment of food loss in the Italian fruit and vegetable supply chains
.
Ecological Indicators
137
,
108781
.
https://doi.org/10.1016/j.ecolind.2022.108781
.
Google Scholar
Crossref
Search ADS
 
Bao
C.
 &
Xu
M.
2023
Spatiotemporal characteristics of water use change and its driving factors in China's urban agglomerations
.
Ecological Indicators
151
,
110279
.
https://doi.org/10.1016/j.ecolind.2023.110279
.
Google Scholar
Crossref
Search ADS
 
Du
X.
,
Shen
L.
,
Wong
S.
,
Meng
C.
 &
Yang
Z.
2021
Night-time light data based decoupling relationship analysis between economic growth and carbon emission in 289 Chinese cities
.
Sustainable Cities and Society
73
,
103119
.
https://doi.org/10.1016/j.scs.2021.103119
.
Google Scholar
Crossref
Search ADS
 
Feng
X.
2012
Analysis and research on water resources development and utilization situation and existing problems in Lanzhou City
.
Gansu Water Resources and Hydropower Technology
48
(
3
),
11
–
13
.
Google Scholar
 
Fu
T.
,
Xu
C.
,
Yang
L.
,
Hou
S.
 &
Xia
Q.
2022
Measurement and driving factors of grey water footprint efficiency in Yangtze River Basin
.
Science of the Total Environment
802
,
149587
.
https://doi.org/10.1016/j.scitotenv.2021.149587
.
Google Scholar
Crossref
Search ADS
PubMed
 
Hoekstra
A. Y.
2003
Virtual water: An introduction
.
Virtual Water Trade
Edited by A.Y. Hoekstra. Water Research Report Series No. 13, Delft, The Netherlands
.
Google Scholar
 
Hoekstra
A. Y.
2011
The Water Footprint Assessment Manual: Setting the Global Standard
.
Routledge, London, England
.
Google Scholar
 
Hossain
I.
,
Imteaz
M. A.
 &
Khastagir
A.
2021
Water footprint: Applying the water footprint assessment method to Australian agriculture
.
Journal of the Science of Food and Agriculture
101
(
10
),
4090
–
4098
.
https://doi.org/10.1002/jsfa.11044
.
Google Scholar
Crossref
Search ADS
PubMed
 
Hu
J.
,
Liu
W.
,
Chang
Z.
 &
Zhang
J.
2017
Study on variation characteristics of precipitation in LanZhou from 1951 to 2015
.
Water Conservancy and Hydropower Technology
48
(
4
),
8
–
12
.
https://doi.org/10.13928/j.cnki.wrahe.2017.04.002
.
Google Scholar
 
Kong
Y.
,
He
W.
,
Yuan
L.
,
Shen
J.
,
An
M.
,
Degefu
D. M.
,
Gao
X.
,
Zhang
Z.
,
Sun
F.
 &
Wan
Z.
2019
Decoupling analysis of water footprint and economic growth: A case study of Beijing–Tianjin–Hebei Region from 2004 to 2017
.
International Journal of Environmental Research and Public Health
16
(
23
),
4873
.
https://doi.org/10.3390/ijerph16234873
.
Google Scholar
Crossref
Search ADS
PubMed
 
Li
W.
,
Jiang
S.
,
Zhao
Y.
,
Li
H.
,
Zhu
Y.
,
Ling
M.
,
Qi
T.
 &
Wang
H.
2023
Comprehensive evaluation and scenario simulation of water resources carrying capacity: A case study in Xiong'an New Area, China
.
Ecological Indicators
150
,
110253
.
https://doi.org/10.1016/j.ecolind.2023.110253
.
Google Scholar
Crossref
Search ADS
 
Liang
X.
,
Li
J.
,
Guo
G.
,
Li
S.
 &
Gong
Q.
2023
Urban water resource utilization efficiency based on SBM-undesirable–Gini coefficient–kernel density in Gansu Province, China
.
Environment, Development and Sustainability
25
(
11
),
13015
–
13034
.
https://doi.org/10.1007/s10668-022-02601-2
.
Google Scholar
Crossref
Search ADS
 
Lu
Y.
,
Xu
H.
,
Wang
Y.
 &
Yang
Y.
2017
Evaluation of water environmental carrying capacity of city in Huaihe River Basin based on the AHP method: A case in Huai'an City
.
Water Resources and Industry
18
,
71
–
77
.
https://doi.org/10.1016/j.wri.2017.10.001
.
Google Scholar
Crossref
Search ADS
 
Lu
N.
,
Linur
G.
 &
Feng
P.
2022b
Decoupling relations between water utilization and economic development in the capital cities of five northwestern provinces
.
Journal of Arid Land Resources and Environment
36
(
12
),
107
–
112
.
https://doi.org/10.13448/j.cnki.jalre.2022.308
.
Google Scholar
 
Lu
N.
,
Zhu
J.
,
Tang
Z.
,
Zhang
J.
 &
Chi
H.
2022c
Decreasing water dependency for economic growth in water-scarce regions by focusing on water footprint and physical water: A case study of Xi'an, China
.
Sustainable Cities and Society
85
,
104092
.
https://doi.org/10.1016/j.scs.2022.104092
.
Google Scholar
Crossref
Search ADS
 
Liu
X.
,
Yuan
D.
,
Han
F.
,
Tang
Y.
 &
Lu
Y.
2022a
Decoupling analysis of water resource utilization and economic growth in gansu province based on water footprint theory
.
Pearl River
43
(
7
),
52
–
60
.
Google Scholar
 
Liu
C.
,
Jin
W.
,
Sun
Y.
 &
Chen
K.
2022b
Evaluation of water resources utilization in the taihu basin from the perspective of water footprint
.
China Rural Water and Hydropower
8
,
70
–
77
.
https://doi.org/10.27748/d.cnki.gszkj.2022.000010
.
Google Scholar
 
Long
H.
,
Lin
B.
,
Ou
Y.
 &
Chen
Q.
2019
Spatio-temporal analysis of driving factors of water resources consumption in China
.
Science of the Total Environment
690
,
1321
–
1330
.
https://doi.org/10.1016/j.scitotenv.2019.06.311
.
Google Scholar
Crossref
Search ADS
PubMed
 
Pan
Z. W.
 &
Xu
C. H.
2019
Decoupling analysis of water resources utilization and economic growth in China
.
Journal of South China Agricultural University (Social Science Edition)
18
(
2
),
97
–
108
.
Google Scholar
 
Pan
X.
,
Guo
S.
,
Xu
H.
,
Tian
M.
,
Pan
X.
 &
Chu
J.
2022
China's carbon intensity factor decomposition and carbon emission decoupling analysis
.
Energy
239
,
122175
.
https://doi.org/10.1016/j.energy.2021.122175
.
Google Scholar
Crossref
Search ADS
 
Qi
R.
,
Geng
Y.
 &
Zhu
Q.
2011
Evaluation of regional water resources utilization based on water footprint method
.
Journal of Natural Resources
26
,
486
–
495
.
Google Scholar
 
Richter
B. D.
,
Benoit
K.
,
Dugan
J.
,
Getacho
G.
,
LaRoe
N.
,
Moro
B.
,
Rynne
T.
,
Tahamtani
M.
 &
Townsend
A.
2020
Decoupling urban water use and growth in response to water scarcity
.
Water
12
(
10
),
2868
.
https://doi.org/10.3390/w12102868
.
Google Scholar
Crossref
Search ADS
 
Schandl
H.
,
Hatfield-Dodds
S.
,
Wiedmann
T.
,
Geschke
A.
,
Cai
Y. Y.
,
West
J.
,
Newth
D.
,
Baynes
T.
,
Lenzen
M.
 &
Owen
A.
2016
Decoupling global environmental pressure and economic growth: Scenarios for energy use, materials use and carbon emissions
.
Journal of Cleaner Production
132
,
45
–
56
.
https://doi.org/10.1016/j.jclepro.2015.06.100
.
Google Scholar
Crossref
Search ADS
 
Süme
V.
,
Daneshfaraz
R.
,
Kerim
A.
,
Abbaszadeh
H.
 &
Abraham
J.
2024
Investigation of clean energy production in drinking water networks
.
Water Resources Management
38
(
6
),
2189
–
2208
.
Google Scholar
Crossref
Search ADS
 
Sun
S.
,
Zhou
X.
,
Liu
H.
,
Jiang
Y.
,
Zhou
H.
,
Zhang
C.
 &
Fu
G.
2021
Unraveling the effect of inter-basin water transfer on reducing water scarcity and its inequality in China
.
Water Research
194
,
116931
.
https://doi.org/10.1016/j.watres.2021.116931
.
Google Scholar
Crossref
Search ADS
PubMed
 
United Nations Environment Programme. International Resource Panel, United Nations Environment Programme. Sustainable Consumption, & Production Branch
.
2011
Decoupling Natural Resource use and Environmental Impacts From Economic Growth
.
Nairobi UNEP/Earthprint
, Nairobi.
Wang
N.
,
Li
Z.
,
Tian
F.
 &
Wang
T.
2021b
Water footprint intensity and spatial association of the Yellow River Basin: Based on ESDA model
.
Environmental Science & Technology
44
(
2
),
196
–
202
.
https://doi.org/10.19672/j.cnki.1003-6504.2021.02.024
.
Google Scholar
 
Wen
F.
,
Fang
X.
,
Khanal
R.
 &
An
M.
2023
The effect of sectoral differentiated water tariff adjustment on the water saving from water footprint perspective: A case study of Henan Province in China
.
Journal of Cleaner Production
393
,
136152
.
https://doi.org/10.1016/j.jclepro.2023.136152
.
Google Scholar
Crossref
Search ADS
 
Zhang
Y.
,
Li
E.
,
Luo
D.
,
Chai
X.
 &
Du
Q.
2020
Fuzzy comprehensive evaluation of water environment safety of Lanzhou based on AHP-entropy weight method
.
Journal of Safety and Environment
20
(
2
),
709
–
718
.
https://doi.org/10.13637/j.issn.1009-6094.2019.1034
.
Google Scholar
 
Zhang
C.
,
Zhao
Y.
,
Shi
C.
 &
Chiu
Y. H.
2021
Can China achieve its water use peaking in 2030? A scenario analysis based on LMDI and Monte Carlo method
.
Journal of Cleaner Production
278
,
123214
.
https://doi.org/10.1016/j.jclepro.2020.123214
.
Google Scholar
Crossref
Search ADS
 
© 2024 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).