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Article

The Water Resources Rebound Effect Threatening the Achievement of Sustainable Development Goal 6 (SDG 6)

by
Yingchun Ge
1 and
Jing Wang
2,*
1
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
Gansu Food Inspection and Research Institute, Lanzhou 730030, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(10), 4155; https://doi.org/10.3390/su16104155
Submission received: 18 April 2024 / Revised: 10 May 2024 / Accepted: 13 May 2024 / Published: 15 May 2024
(This article belongs to the Section Sustainable Water Management)
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Abstract

:
Coaction of climate change and human activities exerts a significant impact on the fate of water resources, provoking the rebound effect in water resources and threatening the achievement of SDG (sustainable development goal) 6. However, the mechanisms of interactions between this effect and SDG 6 and how to decrease this effect towards the achievement of SDG 6 are less known. In this paper, a water resources rebound effect (WRRE) model is integrated into a river basin sustainable development decision support system to simulate and project the interactions between the WRRE and SDG 6 under multiple combination scenarios. The results show that multiple drivers, including precipitation, technological advances, and ecological water flow, coaffect the WRRE, not the silo technology factor. The transfer fate of the saved water is a major root cause leading to the WRRE and harming the achievement of SDG 6, and the WRRE is positively correlated to SDG 6 overall, which, nevertheless, can sometimes be reversed by technological advances. Our outcomes indicate that decoupling SDG 6 from the WRRE can promote the achievement of SDG 6 and decrease the rebound effect, relying on holistic integration that couples climatic, socioeconomic, and behavioral interaction between SDG 6 and the WRRE and translation of scientific research into available decision-making information for strict scientific-research-based policy actions.

1. Introduction

Water plays a crucial role in supporting the Earth’s life system and promoting economic development; nevertheless, increasing water demand induced by rapidly growing population and socioeconomic development [1] has exerted a negative effect on global and regional hydrological and water resource systems [2]. Warming climate is threatening water towers around the world [3], posing a huge challenge for sustaining water security [4,5], especially in arid- and semi-arid regions [6]. Water issues have a profound impact on the sustainability of the co-development of humans and nature, from the past to the future [7], in which the rebound effect in water resources is a key factor against the regions’ sustainability [8].
Water resource management is regarded as a significant means to meet water issues on a global and regional scale [9], the sustainability of which is dominated by trade-offs and synergies among water, ecosystem, and socioeconomy [10]. Sustainable Development Goal 6 (SDG 6: ensure availability and sustainable management of water and sanitation for all) presents an important opportunity for addressing the water issues from multiple dimensions [11], focusing on quality, quantity, use efficiency, and disaster of water and potential methods and measures improving water features, for example, applying new technique to increase water use efficiency. Technological advances can save water through the improvement of water use efficiency by means of better methods (e.g., water allocation and hydrological prediction), facilities (e.g., drop irrigation and lined canal), and public education (e.g., improvement of farmer’s water-saving consciousness) particularly in agricultural fields, which, nevertheless, has not prevented the spread of the Aral Sea syndrome [12,13] around the world due to poor management of water resources [14,15]. The world is seemingly away from achieving SDG 6 [16]. A crucial reason leading to this dilemma is the Jevons paradox in water resources (i.e., water resources rebound effect (WRRE)) that improvements in water resources use efficiency often result in the opposite of the original goal [17,18].
The implication of the impact of the rebound effect on water resources represents that improvements in water use efficiency stimulate land transformation from desert, wetland, and forest to farmland because the saved resources can create additional benefits, resulting in an increase in water demand [8], and reduction in natural habitat [19]. The WRRE is caused by the irrigated farmland expansion, increasing the risk of food security from more extreme events induced by climate change [20,21] due to rising water stress and the amount and strength of natural disasters (e.g., drought and flood), particularly in arid and semi-arid regions, which affects the achievement of SDG 6.
Studies on the relationship among impact, population, affluence, and technology (Impact = Population × Affluence × Technology) suggest that the growing population, application of new techniques, and individual desire drive the WRRE [22,23]. “Goden standard” between population and efficiency on environmental impact suggests that the rate of efficiency growth exceeding the rate of population growth is a key principle for sustaining the existing environmental pressure level [24,25]. Most regions around the world can not touch this standard, which reveals that the rebound effect induced by improving water use efficiency causes excessive consumption of water resources, exerting a negative impact on external effects on hydrology and the ecosystem at the macroscale. This mechanism prevents efforts in water-saved projects from reaching their intended goals around the globe [17,22]. Identifying methods and factors slowing down the negative impact of this mechanism on resources and the environment has become a hot topic; for instance, regulating environment efficiency and consumption patterns can weaken the rebound effect [26,27], the interaction of quality of public management and agricultural intensity [28], the resilience of irrigation water demand [29], the improved productivity [30], investment and subsidy [31] in agriculture influence this mechanism. However, little is known about the mechanisms by which the rebound effect affects SDG 6 and how to decrease this effect towards the achievement of SDG 6.
The objective of this study is to identify the potential methods of slowing down this mechanism of negative impacts of water resources rebound effect on achievement of SDG 6 through the development of a framework coupling WRRE and SDG 6 to understand the interaction between both agents. We employed a river basin sustainable development decision support system (RSDSS)[V 1.0] [32,33] to simulate and predict three indicators (i.e., SDG 6.4.1, SDG 6.4.2, and SDG 6.6.1) and WRRE by 2050 under 216 scenario pathways to analyze the impact of key divers on the SDG indicators and WRRE and interaction between them and provide potential suggestions for slowing down WRRE towards the achievement of SDG 6. Then, Section 2 provides a methodology framework. Section 3 presents results on the analysis of interaction among the key drivers, the SDG indicators, and WRRE. The results are discussed in Section 4. Finally, Section 5 presents the conclusion of this study.

2. Materials and Methods

2.1. A Framework

The framework focuses on the integration of WRRE and SDG 6 by means of scenario analysis and RSDSS developed by Ge et al. [32,33] (Figure 1), presents an opportunity to establish a connection among policy assumptions, the model simulation-based trend of change in natural and socioeconomic elements, and the impact of the change on WRRE and SDG 6. By regulating the value of the key drivers, one can map a quantitative scenario pathway into a policy assumption. The scenario pathway can describe the potential policy quantitatively and comprehensively by setting desired values for climate (e.g., temperature and precipitation), socioeconomic (e.g., population, urbanization, and technological advances), and management (e.g., ecological water flow). RSDSS, as a decision tool, can simulate and predict the changes in natural and socioeconomic elements in space and time under a given scenario pathway (i.e., policy assumption) and output state value of these elements that are the foundation to quantify the WRRE and SDG 6 indicators (Figure 1).
By means of the framework, the impact of diverse climate and socioeconomic assumptions on WRRE and SDG 6 can be clearly articulated by repeatedly regulating each scenario parameter, particularly the representation of the cascade effect of how technological advances induce WRRE. The framework can identify the potential policy assumptions that not only reduce WRRE but also promote the achievement of SDG 6.

2.2. Formation of Scenario Pathways

In this study, we choose an endorheic basin—the Heihe River Basin, which is the second largest inland river basin in China—to explore the interaction mechanism between WRRE and SDG 6, and three key drivers affecting WRRE are taken into account as scenario parameters to form scenario pathways (i.e., policy assumptions), including precipitation, technological advances, and ecological water flow. Increasing precipitation can enrich the regional total water resources volume, leading to the expansion of production scales, inducing more water consumption rather than improving the water use regime, in particular the increase in the farmland area. This mechanism is similar to the principle of the rebound effect in that the improvement in the water use efficiency usually results in higher consumption of water because the saved water is transferred to cultivate more land. Ecological water flow, which refers to the flow of water to the ecology, is considered a strategy through which the saved water is diverted to ecology instead of agricultural production. This driver is employed to examine the availability of a policy that reduces the rebound effect on water resources. Three driving factors are the main input parameters of the watershed system model integrated into the RSDSS tool [32], controlling the model output results that are used to quantify the WRRE. We do not deny that other driving factors influence the WRRE; nevertheless, they are not the focus of this study.
All parameters are given resilience ranges by means of scenario analysis models according to the narrative of the shared socioeconomic pathway 1 (SSP 1: sustainability) with rapid economic development, low carbon emission, and friendly environment strategies. Climate-related parameter ranges are identified based on results from the representative concentration pathways 2.6 (RCPs 2.6) from the IPCC fifth assessment report (AR5) [34] and results from regional climate simulation [35] (Table 1). Threshold of the technological advances rate is quantified by the per-capita income in the Heihe River Basin, the reference value of the annual growth rate of total factor productivity for the mid-income country [36], and the average per-capita income in China (Table 1). According to the Heihe water allocation model proposed by the State Council of the People’s Republic of China and the runoff flow under different precipitation conditions in the future, we calculate the range of the ecological water flow [32] (Table 1).
In order to examine the solo and combined effects of the driver factors on WRRE and SDG 6, each scenario parameter is sampled six times with equal interval principles within its range to characterize the variability of the drivers; then, the scenario pathways outlining 216 plausible futures by 2050 can be formed through combining the scenario parameters with different values.

2.3. The RSDSS

The RSDSS was developed as an ensemble artifact to explore the potent sustainability pathways based on a watershed system model, SDGs, SSPs, the surrogate modeling technique, and multiple sources data carrying out the simulation and prediction of interactions between human activities and nature elements and co-impacts of the interactions on SDG goals and targets under different combination scenarios through the watershed system integration based on three connections [33]. Therefore, the watershed system model was developed through the coupling of an upstream ecohydrological model, a mid- and downstream ecohydrological model, and an economic model to simulate joint impacts of land use types changes, climate changes, and technological advances on the relationship between supply and demand of water resources. This model provides 41 output variables, including 27 elements on human and nature processes, 10 SDG indicators, 4 sustainability indexes, and 8 input parameters that are used to set the combination scenario [32]. Accordingly, the RSDSS provides an opportunity to study relationships between the water resources rebound effect and SDG 6.
Based on the RSDSS, the impact of farmland area increase, technological advances, and water resources allocation on the SDG 6 index can be expressed using a conception function as follows [33]:
W U E t , W S t , D I W R M t = g t ( T e t A r g , T e t I n d , T e t S e r , L a n d t − 1 , V g x ∈ X , f t − 1 ( T t , P t , V f x ∈ X , g t − 1 ( . ) ) )
where f t ( ) and g t ( ) are conception models of the integrated ecohydrological model and the economic model, respectively; W U E t ,   W S t ,   a n d   D I W R M t represent the water use efficiency, the water stress, and the degree of integrated water resources management in the tth year, respectively; T e t I n d ,   T e t A r g , T e t S e r , T t , and P t indicate the industrial technological advance rate, the agricultural technological advance rate, the service technological advance rate, temperature, and precipitation in the tth year; L a n d t − 1 expresses land use in (t − 1)th year; and V f x ∈ X and V g x ∈ X represent the set of other input parameters for the integrated ecohydrological model and the economic model, respectively.

2.4. The WRRE

This study employs an interaction mechanism between expected saving water and actual saving water to describe WRRE [37], which is expressed using the following formula:
W R R E = W C b E T A − E b − ( W C b − W C T A ) W C b E T A − E b × 100 %
where WRRE is water resources rebound effect (%), W C b represents water resources consumption at technological advances E b (m3), and W C T A indicates water resources consumption at technological advances E T A (m3). W C b E T A − E b and ( W C b − W C T A ) represent the expected saving of water resources and actual saving of water resources, respectively (m3). The implication of WRRE is to examine the gap between our actions in water resources use and the expected outcome from technological advances, and WRRE decreases with the actual saving of water induced by efficiency improvement.

2.5. Assessment of SDG 6

Aiming at the estimation of co-impacts of diverse divers on SDG 6 and the identification of relationship between WRRE and SDG 6, the WRRE-related three SDG indicators, which include SDG 6.4.1 change in water-use efficiency over time [38], SDG 6.4.2 level of water stress [39] and SDG 6.5.1 degree of integrated water resources management, are chosen to quantify the state of SDG 6 [40]. We assume that the contribution of each indicator on achievement of SDG 6 is same, then they are given the same weight. As a result, an assessment index on SDG 6 is obtained, which is numerically given by the following equation:
S D G   6   i n d e x = W U E − W S + D I W R M 3
where WUE is the water-use efficiency, WS is the water stress, and DIWRM is the degree of integrated water resources management. Three indicators have same weight, and are quantified by output variables from a sustainable development decision support system developed by Ge et al. [32].

3. Results

3.1. Co-Impact of Diverse Drivers on the WRRE

The results revel that the increasing technological advances enhances the water resources rebound effect, which is a common phenomenon (Figure 2). A 3.2% increase in the technological advances rises the average WRRE by 0.6% under different ecological water flow (EWF) scenarios (Figure 2a) and by 1.0% under diverse precipitation scenarios (Figure 2b), respectively.
As shown in Figure 2a, the EWF can slow down WRRE availably, for example, an increase in the EWF of 1.1% would result in a decrease in WRRE of 1.3% (Figure 2a), which stems from the fact that the water saved by the technological advances can be partly transferred to ecosystem through water resources regime (e.g., ecological water flow) [41]. However, the increase in the EWF does not restrain expansion of the farmland (i.e., the irrigated farmland), area of which increases 0.2% after technology improvement, in parallel leading to per capita gross domestic product increase of 0.9%. This outcome reveals that economic interests drive farmers to spontaneously reclaim more land with the saved water, in most cases, these behaviors are not constrained by environment factors including climate change, government management, ecosystem health, and regional sustainability. Figure 2b shows an interesting phenomenon that the ecological water flow strengthens impact of the technological advance on WRRE, for example, an EWF increases from 10.10 to 10.80 causes an increase in the technological advance-induced WRRE growth rate from 0.4% to 0.7%. The phenomenon reveals that increasing ecological water flow further stimulate water resources consumption in economy, in other words, the proportion of the saved-water used in economy increases faster than the proportion of that transferred to ecosystem.
Figure 2 shows that the WRRE decreases with rising precipitation at the same ecological water flow, for example, the precipitation increases from 0% to 10% results in an increase from 0.4% to 1.5% in average growth rate of WRRE induced by the technological advances at the ecological water flow of 1.01 billion m3. This outcome reveals that the precipitation increase enhances the negative impact of the technological advances on the WRRE although it decreases the value of the WRRE. A major reason is that the increasing precipitation is hidden in actual water consumption and not taken account in expected water consumption, rising the “water conservation”. Farmers do not know that the irrigation water is the saved water or the increased precipitation; nevertheless, the precipitation enlarges the impact of the real saved-water on expansion of farm land. In regions where water use efficiency is improving rapidly, the mechanism can be extrapolated to a general principle that an increase in the amount of available water resources in a region can strengthen this negative impact.

3.2. Co-Impact of Diverse Drivers on SDG 6

The solo impact of the technological advances on the SDG 6 index shows a nonlinear trend of first strengthening and then weakening, but the correlation between the two items represents a U change, from strong correlation to moderate correlation, to strong correlation (Figure 3a,b). The SDG 6 index under most pathways correlated nonsignificantly with the technological advances, in other words, increasing technological advances do not necessarily lead to the achievement of SDG 6.
The results derived from correlation analysis (Figure 3a) do not reveal any significant correlation between the EWF and the SDG 6 index under six technological advance scenarios. The relationship between the EWF and the SDG 6 index is highly variable, and the SDG 6 index does not increase with the EWF (Figure 3a), which may be contrary to our common understanding that more water transferred to ecosystem is more conducive to achieving SDG 6. We find that the EWF plays positive and negative roles in achievement of SDG 6 under different technological advances (Figure 3a), indicating that there is an optimal balance point between the EWF and the technological advances under a special precipitation condition, promoting the achievement of SDG 6.
Figure 3b shows that increase in the precipitation has a major negative impact on the achievement of SDG 6, and the negative effect is not reversed by the technological advances that are set on range of 9.67–11.70. For example, an increase in the precipitation of 10% will lead to a reduction in the SDG 6 index of 2.5% (Figure 3b). In addition, the precipitation exerts a nonlinear control on correlation between the technological advances and the SDG 6 index, significance level of which decreases with the increase in the precipitation (Figure 3b).

3.3. Interaction between the WRRE and SDG 6

This results from analysis on the interaction between the WRRE and the SDG 6 index show that there, in general, is a strong positive correlation between the two items (Pearson’s r (Pr) = 0.92, p < 0.001, N = 216) (Figure 4b), controlled by joint impact of the precipitation, the ecological water flow, and the technological advances on them (Figure 4a), which seems to make different statements on the interaction between the WRRE and SDG 6. However, the relationship between the WRRE and the SDG 6 index changes from synergies to trade-offs when the technological advances increase to a high level under all combination scenarios of the precipitation and the ecological water flow (Figure 4a,c–e). The technological advances initially produce a positive effect on the SDG 6 index; nevertheless, the effect is gradually reversed as increasing technological advances, modifying the relationship between the WRRE and the SDG 6 index (Figure 4c–e).
A parallel plot in Figure 4a displays the coaction of three drivers on the WRRE and the SDG 6 index under 216 projected pathways. The precipitation exerts a strong negative control on the WRRE and the SDG 6 index, steered by the ecological water flow and technological advances (Figure 4a). Increasing ecological water flow can not only mitigate the WRRE, but also reduce the SDG 6 index, promoting them to reach the optimal level under the reasonable technological advances (Figure 4a). We find that the optimal level always occurs at the moderate rate of the technological progress. For example, under 0% increase in precipitation, when the technological advances and the ecological water flow are 10.08 and 10.66, respectively, the WRRE can reach a low level (82.94) and the SDG 6 index can reach a high level (1.35) (Figure 4a).

4. Discussion

4.1. Interaction between the WRRE and SDG 6

The technological advances are considered to be very effective approaches to address water scarcity problems and promote achievement of SDG 6 [42,43]. However, we find that the technological advances sometimes exert a negative effect for achieving SDG 6. The precipitation, the technological advances, and the ecological water flow act jointly on water resources and economic system, inducing the WRRE that implies that the vast majority of the technology-saved water is used to enlarge the production scale and rarely flow into ecology, in particular agriculture [44], leading to increase demand of water in economy [45]. For example, development of the water-saving society at Zhangye city in the Heihe River Basin stimulates application of new irrigation techniques including low-pressure pipe irrigation, drip irrigation, and spray irrigation, increasing the efficient water-saving irrigation area by 432.7% (from 7.0 × 103 ha to 37.1 × 103 ha) from 2000 to 2010 at three counties of this city, nevertheless, expanding irrigation area by 12.3%, from 160.3 × 103 ha in 2000 to 180.0 × 103 ha in 2010, which causes that water consumption in agriculture increases 21.2%, from 0.9 × 109 m3 in 2000 to 1.1 × 109 m3 in 2010 [46,47]. In addition, farmers use most of the saved water to develop irrigation area in India, leading to more consumption in groundwater [48]. This saved water is likely to originally plundered from the ecology. Moreover, warming climate plays a significant role in the hydrological processes [6,49], enhancing variability of hydrological processes including precipitation, evapotranspiration, and runoff [50,51] which can significantly affect the WRRE, resulting in increasing risk of achieving SDG 6.
The technological advances modify the relationship between the WRRE and SDG 6 through a complex mechanism that constantly address the conflict between water demand elasticity [29] and the expansion of production scale (e.g., transformation from low-quality land to farmland [8]), driving the transformation of the relationship from synergies to trade-offs (Figure 4c–e). For most endoreic basins, most water saved by the initial technological efficiency gains is used to bridge the gap between water supply and demand [52], which alleviates the water stress and thus facilitating achievement of SDG 6, and the other is transferred to expand farmland and improve productivity [30], inducing the WRRE (Figure 4c–e). After the balance between water supply and demand is come up to, the further increase in the technological level stimulates the further expansion of farmland through farmers’ spontaneous behavior, creating new imbalance in water supply demand enhancing the demand elasticity in irrigation water [29], which, thereby, not only increase the WRRE, but also rise water stress that impedes achievement of SDG 6, leading to the trade-offs between the WRRE and SDG 6. Accordingly, blindly improving technological level not only increase the rebound effect, but also greatly increase the risk of achieving SDG 6.
This mechanism hides a risk transfer process that is driven by the rebound effect. The WRRE essentially increase the water demand and change water distribution in time and space by expanding the agricultural area and adjusting crop structure (e.g., planting crops with high water consumption for more economic benefit) [22,28]. The increase in the water demand from the expansion of farmland not only offsets the water saved by the technological advances, but also transfers the individual risk of farmers to the risk of regional water resources management. The farmers use the saved water to reclaim farmland from the barren land, increasing potentially the risk coping with water scarcity in dry years because these additional water demand gradually intrude into regional water resources management to modify the water allocation scheme, distributing the individual water demand over the whole region. Water stress strengthened by increasing water demand and the risk transfer directly impede the achievement of SDG 6.

4.2. Decoupling between the WRRE and SDG 6

Decoupling SDG 6 from the WRRE is a variable approach that can constrain the negative impact of the WRRE on SDG 6, which requires a rational regulation of allocation of water in economy and ecology according to the water supply (e.g., precipitation) and the technological level (Figure 4a,c–e). “Decoupling” represents the disconnection between the WRRE and SDG 6 [53], which implies that changes in the WRRE will not lead to a concomitant growth in the negative impact for SDG 6.
The rebound effect is not an inevitable consequence of efficiency improvements [54], but improving the technological advance rate as a means of reducing water consumption is futile under everchanging climate and human activities [27], if we do not adopt necessary strategies or policies to reconcile the benefit conflict between economy and ecology of the saved water. This indicates that the occurrence of the WRRE depends on the destination to which the saved-water is transferred and its amount. Around the world, the transfer of most of the saved water to farmlands is the primary inducement inducing the rebound effect in water resources [17,19,44,55], and enhancing the correlation between the WRRE and SDG 6, nevertheless, transferring all of this water to ecology is not unadvisable for a region. High ecological water flow, although not triggering a rebound effect, is likely to increase the water-related risks due to uncertainty in water supply (e.g., precipitation).
It is crucial to quantify the ecological water demand and balance of supply and demand for water for the decoupling between the WRRE and SDG 6, providing scientific evidences for policy making with the purpose of addressing the benefit conflict. However, the precipitation, the technological advances, and the ecological water flow jointly affect the WRRE, SDG 6, and interactions between them (Figure 4a). Identifying objectively optimal flows to ecology and economy according to the water supply condition and the water amount saved by the technological advances plays an important role in restraining the rapid increase in the farmland area, limiting availably water demand and promoting decoupling SDG 6 from the WRRE. Moreover, it is necessary that the policies based on the scientific evidences can be strictly carried out, some of which even if may damage the economic benefits of individuals or organizations.
However, decoupling SDG 6 from the WRRE still face some key challenges that merit further attention in future scientific researches and policy making, including translating scientific findings on thresholds of some key factors such as ecological water flow and reasonable farmland area into policy actions, the failure of implementation of policies related to ecological water transfer policies, and the ineradicable egoistic psychology of water users pursuing more economic benefits. These challenges are intertwined and act as a major barrier to the decoupling processes. To meet these challenges, it is required to develop an approach of addressing nexus challenges as opposed to silo challenge, finding compromise policies that consider trade-offs between economic interests and ecological benefits and change the psychological behavior towards water sustainability, and rigorously transforming these policies into practical actions.

4.3. Next Step

The WRRE is affected by more factors including the water price [52,56], the water right [57,58], the investment [31,59,60] in addition to the precipitation, the ecological water flow, and the technological advances. This suggests that there are more complex mechanisms that dominate the WRRE, affecting SDG 6 in parallel, in which the interaction between the WRRE and SDG 6 is driven by coactions among economy, precipitation, technological advances, policy, and behavior of individuals and organizations. Accordingly, figuring out these mechanisms can further understand joint impacts of the water right, the water price, the investment, the technological advances, and the ecological water flow on the rebound effect under diverse water supply conditions (e.g., precipitation, physical water transfer [61]), and facilitating essentially decoupling SDG 6 from the WRRE.
To elucidate this joint impact mechanism requires integrating more elements in coupled human-nature modeling, such as water rights, subsidies, ecological flows, and telecoupling factors (e.g., virtual water), which control water consumption via multiple mechanisms. The agent modeling and multiple-agent modeling have ability to quantify the interaction between the resources system and the individual behavior [1,62], promoting the decision making [63,64]. Using a system integration methodology coupling agent model, hydrological models, water resources management model, economic models, and climate models can improve our understanding of mitigation mechanisms for the WRRE and the interaction between it and SDG 6 from behavioristic perspective, for example, discovering impacts of the individual (e.g., farmers and stakeholders) behavior on trade-offs among water resources, economy, and ecosystem. The system integration helps to identify potential joint factors that affect the WRRE and SDG 6 [33], and their thresholds, reducing the WRRE and promoting achievement of SDG 6 towards the decoupling between them.
In addition, the decoupling needs scientific decision-making and strict policy actions. Scientific researches can provide quantitative, reliable, objective, and accurate data and information to decision makers and policy makers, most of which, nevertheless, is not used to support decisions due to lack of available methods and measures and the disconnection between scientists and decision makers [65], which poses a huge challenge for bridging the gap between scientific researches and decision making [66]. Accordingly, translation of the scientific researches into the available decision-making information plays a crucial role in making scientific decision makings that promote decoupling SDG 6 from the WRRE. The translation can be realized through diverse methods including research collaborations [67] and the decision rehearsal [33]. In addition, it is a requirement for the decoupling SDG 6 from the WRRE that decision makers (e.g., managers, farmers, and stakeholders) must overcome the self-interest psychology pursuing maximum economic benefit, carrying out the scientific-research-based policy actions strictly. Shaping decision makers’ beliefs using scientific approaches and outcomes [65] can not only mitigate and even stop the rebound effect but also restrain the increase in the water stress. For example, farmers’ awareness of saving water can be improved through education [68] and the participation in individuals in water management [62,69], which are able to stimulate their trust in water authorities [68], using scientific water resources allocation regimes for farming strictly and thereby avoiding the expansion of farmland. Transformation of water users in behaviors is one of the most effective ways that restrain the WRRE. Accordingly, the development of approaches regulating behaviors of water users is likely to promote the decoupling between the WRRE and SDG 6.

5. Conclusions

This study shows that the WRRE is modified by coactions of three drivers including the precipitation, the technological advances, and the ecological water flow, not silo technological factor, and the technological advances significantly positively correlated to the WRRE, nevertheless, the precipitation and the ecological water flow significantly negatively correlated to the WRRE. Three drivers steer SDG 6 through complex nonlinear mechanisms, revealing that increasing these drivers alone will not necessarily promote SDG 6, for example, the precipitation exerts a major negative impact on the achievement of SDG 6, which is contrary to our general understanding. The analysis shows that the WRRE is a crucial factor causing this anomaly.
The results indicate that SDG 6, overall, positively correlates to the WRRE under coactions of three drivers, i.e., increase in the WRRE leading to increase in the SDG 6 index; nevertheless, the technological advances can sometimes reverse the correlation. The correlation is dominated by multiple mechanisms including the impact of the distribution and quantity of high-efficiency-saved water in ecosystem and economy on the WRRE level and the risk achieving SDG 6, the contribution of farmers’ self-interest psychology on expansion of farmland, and the negative effect of the precipitation on SDG 6. Our outcomes suggest that the expansion of production scale (e.g., increase in farmland area) and forced ecological flow diversion are the root causes leading to high rebound effect and low SDG 6 index. Rapid increase in farmland area indicates that most of the saved water is transferred to agriculture, which directly leads to the rebound effect and the increase in water demand in economy that rises the risk of achievement SDG 6. If the saved water is compulsorily moved to ecosystem to reduce the rebound effect, this will cut down economic water support, inducing high water stress.
Decoupling SDG 6 from the WRRE can promote the achievement of SDG 6 and decrease the rebound effect, relying on system integration that allows for clarification of joint impacts of various drivers including climatic, socioeconomic, and behavioral on the interaction between SDG 6 and the WRRE, and strict scientific-research-based policy actions that can identify the optimal allocation of the saved water in economy and ecosystem and restrain the behavior of decision makers including managers, farmers, and stakeholders. This study suggests that the decoupling still need further development of holistic methods and measures in mitigation mechanism of the WRRE and the translation of scientific researches into available decision information.
However, our outcomes are obtained based on analysis of interactions among climate, ecohydrology, and socioeconomy in a typical endorheic basin through a watershed system model integrated in the RSDSS tool, which focuses on the impact of the climate change and the technological advances on the agricultural water use because there is a high water conflict between agriculture and ecology caused by the fact that the water consumption in agriculture amounts to nearly 90 percent of the region’s water resources. These outcomes are likely to apply to most endorheic basins, nevertheless, may not be directly applicable to regions where there is a little water conflict between ecology and agriculture. Accordingly, further efforts are required to integrate more natural and human components, quantify the interaction between WRRE and SDG 6, identify more driving factors affecting decoupling SDG 6 from WRRE, and translate the outcomes into policy actions.

Author Contributions

Conceptualization, Y.G.; Data curation, J.W.; Formal analysis, Y.G.; Funding acquisition, Y.G.; Methodology, Y.G. and J.W.; Resources, J.W.; Software, Y.G.; Validation, Y.G.; Writing—original draft, Y.G.; Writing—review and editing, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Key Research Program of Gansu Province (Grant: 23ZDKA0004) and the National Science Foundation of China (Grant: 41471448).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank Jianxun Fan, from Zhangye Water Affairs Bureau, who provided the model-required data.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. A framework of coupling water resources rebound effect and SDG 6 indicators based on river basin sustainable development decision support system (RSDSS) [32,33].
Figure 1. A framework of coupling water resources rebound effect and SDG 6 indicators based on river basin sustainable development decision support system (RSDSS) [32,33].
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Figure 2. Estimation on impact of technological advances, ecological water flow, and precipitation on the WRRE under different combination scenarios. (a) Correlation of the WRRE and the technological advances and precipitation, thereinto, color lines represent the fitting lines between the technological advances and the WRRE under 6 precipitation scenarios, and P = 0 represents precipitation with growth rate of 0%. (b) Correlation of the WRRE and the technological advances and the ecological water flow, thereinto, color lines represent the fitting lines between the technological advances and the WRRE under 6 ecological water flow scenarios, and EWF = 10.1 indicates that the ecological water flow is 10.1 × 108 m3. Pr is Pearson’ relation coefficient. At the 0.05 level, all fitting functions are significant.
Figure 2. Estimation on impact of technological advances, ecological water flow, and precipitation on the WRRE under different combination scenarios. (a) Correlation of the WRRE and the technological advances and precipitation, thereinto, color lines represent the fitting lines between the technological advances and the WRRE under 6 precipitation scenarios, and P = 0 represents precipitation with growth rate of 0%. (b) Correlation of the WRRE and the technological advances and the ecological water flow, thereinto, color lines represent the fitting lines between the technological advances and the WRRE under 6 ecological water flow scenarios, and EWF = 10.1 indicates that the ecological water flow is 10.1 × 108 m3. Pr is Pearson’ relation coefficient. At the 0.05 level, all fitting functions are significant.
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Figure 3. Estimation on impact of the technological advances, the ecological water flow, and the precipitation on the SDG 6 index under different combination scenarios. (a) Correlation of the SDG 6 index and the technological advances and the ecological water flow, thereinto, color lines represent the fitting lines between the technological advances and the SDG 6 index under 6 ecological water flow scenarios, and EWF = 10.1 indicates that the ecological water flow is 10.1 × 108 m3. At the 0.05 level, the fitting functions for the EWF = 10.10 and EWF = 10.80 scenarios are significant. (b) Correlation of the SDG 6 index and the technological advances and the precipitation, thereinto, color lines represent the fitting lines between the technological advances and the WRRE under 6 precipitation scenarios, and P = 0 represents precipitation with growth rate of 0%. At the 0.05 level, the fitting functions for the P = 0 and P = 2 scenarios are significant.
Figure 3. Estimation on impact of the technological advances, the ecological water flow, and the precipitation on the SDG 6 index under different combination scenarios. (a) Correlation of the SDG 6 index and the technological advances and the ecological water flow, thereinto, color lines represent the fitting lines between the technological advances and the SDG 6 index under 6 ecological water flow scenarios, and EWF = 10.1 indicates that the ecological water flow is 10.1 × 108 m3. At the 0.05 level, the fitting functions for the EWF = 10.10 and EWF = 10.80 scenarios are significant. (b) Correlation of the SDG 6 index and the technological advances and the precipitation, thereinto, color lines represent the fitting lines between the technological advances and the WRRE under 6 precipitation scenarios, and P = 0 represents precipitation with growth rate of 0%. At the 0.05 level, the fitting functions for the P = 0 and P = 2 scenarios are significant.
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Figure 4. Interactions between the WRRE and the SDG 6 index. (a) Parallel plot indicates impacts of three drivers (i.e., precipitation (%/(39 year)), ecological water flow (108 m3), and technological advances) on the interactions under 216 combination scenarios. (b) Correlation of the WRRE and the SDG 6 index, and at the 0.05 level, the fitting function is significant. (c–e) represent impacts of the technological advances on interactions between the WRRE and the SDG 6 index under three levels (i.e., low, moderate, and high) of the precipitation and the ecological water flow.
Figure 4. Interactions between the WRRE and the SDG 6 index. (a) Parallel plot indicates impacts of three drivers (i.e., precipitation (%/(39 year)), ecological water flow (108 m3), and technological advances) on the interactions under 216 combination scenarios. (b) Correlation of the WRRE and the SDG 6 index, and at the 0.05 level, the fitting function is significant. (c–e) represent impacts of the technological advances on interactions between the WRRE and the SDG 6 index under three levels (i.e., low, moderate, and high) of the precipitation and the ecological water flow.
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Table 1. Range of scenario parameters.
Table 1. Range of scenario parameters.
Scenario ParameterRange
Precipitation (%/(39 year))[0.00, 10.00]
Technological advances rate[9.67, 11.70]
Ecological water flow (108 m3)[10.10, 10.80]
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Ge, Y.; Wang, J. The Water Resources Rebound Effect Threatening the Achievement of Sustainable Development Goal 6 (SDG 6). Sustainability 2024, 16, 4155. https://doi.org/10.3390/su16104155

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Ge Y, Wang J. The Water Resources Rebound Effect Threatening the Achievement of Sustainable Development Goal 6 (SDG 6). Sustainability. 2024; 16(10):4155. https://doi.org/10.3390/su16104155

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Ge, Yingchun, and Jing Wang. 2024. "The Water Resources Rebound Effect Threatening the Achievement of Sustainable Development Goal 6 (SDG 6)" Sustainability 16, no. 10: 4155. https://doi.org/10.3390/su16104155

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