Abstract
The management of water resources requires a correct understanding of the simultaneous management of food and energy resources. The framework of water–food–energy correlation with the approach of sustainability of resources and uses analyzes the combined management and exploitation of water, food, and energy resources with the help of scenario planning. In addition to sustainability concepts, environmental costs such as the emission of carbon dioxide from fossil fuels and its impact on the environment are also discussed. In this research, according to the five defined indicators and based on the potential of using solar energy and the possibility of exploiting renewable energy sources such as solar energy, various management scenarios have been developed. After examining the virtual water management model developed in the Hunan basin as a case study, the development of the water–food–energy nexus model and its calibration, and four scenarios including improving water use efficiency, energy saving, increasing food productivity, and nexus sustainability were developed. The results showed that the nexus strategy can provide sustainability goals according to the weight of each component. After the combined scenario, improving the efficiency of water consumption can be the component with the highest priority in the decision-making model in dry areas.
HIGHLIGHTS
The water–food–energy nexus is evaluated as a conceptual approach for achieving sustainable management.
Improving water use efficiency, energy saving, increasing food productivity, and nexus sustainability were considered.
The developed approach provides a significant contribution to achieving regional sustainable development goals.
INTRODUCTION
The twenty-first century is witnessing an explosion in environmental changes, global population, agricultural land disintegration, and geopolitical instabilities (Salem et al. 2022). The development of industry, population growth, and increasing use of water, energy, and food resources has caused a challenge to sustainable management. In addition to this, climate change and the reduction of ecosystem services have caused problems in the use of water resources, food security, and energy supply (Xiang et al. 2016). The use of renewable energy such as solar energy instead of fossil fuels helps to better manage water and food resources. The use of this type of energy in the agricultural energy supply sector should be considered as an alternative policy for energy supply. Considering the increasing severity and extent of droughts in the world and the need to evaluate solutions to reduce the effects of drought on the production of agricultural products, as well as the reduction of surface water resources and the lowering of the underground water level (Cai & Rosegrant 2004; Liu et al. 2019), as well as its consequences on the economic and social life of the people, there is a need to propose solutions for sustainable development in energy, water, and food sectors (Chen et al. 2020).
Water–energy–food nexus
Water, energy, and food are three key factors in managing these critical conditions in the future, and improving decision-making systems is not possible without paying attention to them (Xia & Pahl-Wostl 2012; Li et al. 2020). Some researchers have reported that the frameworks developed so far have not been able to sufficiently ensure the goals of sustainable development (Biggs et al. 2015). To create a new approach for considering the dynamic behavior and evaluation of water–energy–food nexus, a system dynamic model platform was used by El Gafy et al. (2017) in Egypt under different scenarios and alternatives. The main goals were determination of water and energy footprints of crop production, estimating the virtual water and energy import and export and the national water and energy saving balance due to trade of agricultural commodities. The main finding of the study was the necessity of considering the water–food–energy nexus in developing national strategies. Biggs et al. (2015) provided a critical review of water–food–energy nexus approaches and identified potential linkages with sustainable livelihoods theory and practice to deepen the understanding of the interrelated dynamics between human populations and the natural environment. In this regard, the concept of ‘Environmental Livelihood Security,’ which includes the balance between the supply of natural resources and the human demand for the environment, was created to promote sustainability. The result of this structure was an integrated framework with the capacity to measure and monitor the environmental livelihood security of entire systems through accounting for water, energy, and food required for livelihoods at multiple spatial scales and organizational levels. Albrecht et al. (2018) provided a knowledge base of existing approaches and promoted further development of analytical methods that align with nexus thinking. The literature review in this research showed that the concept of linkage can be identified using approaches that focus on four key features including innovation, social and political context, collaboration, and implementation in policy and practice. The use of interdisciplinary and hybrid methods and the combination of transdisciplinary or collaborative approaches in this field have been suggested as promising approaches. Interdisciplinary and hybrid approaches that combine quantitative and qualitative methods from different disciplines can contribute to the physical and social aspects of water, energy, and food systems. It has been recommended that analysis should target policy and societal scales.
Katyaini et al. (2021) showed that the main priorities for managing the water–food nexus in arid and semi-arid regions of India are to reduce the overexploitation of groundwater and to investigate the uncertainty of rainfall, which directly affects agriculture. Cazcarro & Dilekli (2021) evaluated the strategies of future food and energy demands as well as direct and indirect resource uses to generate a substantial number of economic and environmental scenarios. The clear policy implication was that, in all scenarios, processes of energy transition, raw material use reduction, and recycling must be strengthened. Orimoloye (2022) evaluated the gaps and implementations of water, energy, and food nexus in published articles between 2015 and 2021. Findings showed that nexus modeling should be combined with influencing factors including population growth, environmental change impacts (including climate change), climate change adaptation and climate resilience regimes, biodiversity loss, and sustainable nature. Issues such as the protection of water resources and management strategies and tools or mechanisms for the use of water assets and agricultural innovations under the commitments of sustainable use should be considered (Salem et al. 2022). Norouzi (2022) presented a conceptual model of water–food–energy nexus using a dynamic system for Iran and analyze the factors affecting this interconnection. The developed model applied to improve the economic productivity index of water–energy–food for 2005–2018. Results showed that for reducing the risk of energy–water–food planning, these three parts should be examined in regular operation and coordination.
Virtual water
One of the emerging concepts in water-scarce countries is the concept of ‘virtual water’ to determine agricultural and industrial production strategies. As a new approach to addressing water scarcity and security issues, the concept of virtual water can be used to support sustainable development in water-scarce areas. With growing consumption, the virtual water trade has become an important element in the water sustainability of a nation (Goswami & Nishad 2015). Virtual water flow analysis based on water footprint has important insights for sustainable economic management in the agricultural sector and modification of water resources management patterns (Katyaini et al. (2021). The concept of virtual water was first proposed by Allen (1993) to refer to the amount of water consumed in the production of goods and services. Since 2002, this concept has gained widespread attention worldwide. In China, virtual water has been proposed as a potential approach for water resource conservation, especially in water-scarce areas such as the northwest.
Goswami & Nishad (2015) estimated the virtual water trades of two populous nations, India and China, to present certain quantitative measures and time scales. Results showed that the export of virtual water alone can lead to the loss of water sustainability. In general, water sustainability has emerged as a major global concern, with uncertainties and added vulnerability due to climate change. An emerging issue of growing importance and debate in the context of water and food sustainability is the virtual water trade. Virtual trade of water has become an important component of global fresh water demand and supply and has resulted in globalization of water resources. Wang et al. 2020 investigated interprovincial virtual water trade in Gansu Province, China, using an input–output method and spatial flow patterns. Based on the obtained results, it was found that in the current structure, virtual water is mainly exported to develop coastal areas and their adjacent provinces or other water-rich areas. Therefore, for sustainable development, the current business model should be adjusted to reduce virtual water output and at the same time increase its input to achieve balanced economic development and water resource security. Khaneiki et al. (2022) showed that virtual water has historically been an adaptation strategy that enabled some arid regions to develop a prosperous economy without putting pressure on their scarce water resources in arid central Iran. This article concluded that a similar model of virtual water can remedy the ongoing water crisis in central Iran, where groundwater reserves are overexploited, and many rural and urban centers are teetering on the edge of socio-ecological collapse.
Achieving water security to overcome water shortage has been the goal and subject of attention of researchers in recent years. In this process, it is necessary to evaluate the mutual connection and inherent dependencies between water and its related factors and systems. Past researches have provided programs for simulation, optimization, uncertainty analysis, and sensitivity analysis of water, food, and energy nexus, but their flexibility and reliability have not been paid enough attention. Based on the concept of virtual water, this article deals with the connection between water, food, and energy. Planning has been done based on the structure of nexus and virtual water in the form of five criteria of reliability, durability, vulnerability, adaptability, and resiliency.
METHODS
Water–food–energy nexus description
Different interpretations of the water–food–energy nexus have been expressed in recent years, but in general, it is the reaction between the subsystems with respect to the larger system. In fact, this definition shows the interaction between the three parts of water, food, and energy to reach the complex characteristics of a universal integrated system. Nexus is considered as the factor of simultaneous dependence between the energy and water sectors and the simultaneous coupling of production, methods, distribution, and the way of using resources, and when it comes to food, it becomes a complete cycle. Evaluating system performance only by considering its sub-sections and considering each sub-section alone does not lead to improvement and sustainable development in the overall system. Therefore, there is a need to explain indicators to evaluate the performance of the system and the way of supply and demand in the water, energy, and food sectors. The correlation of water–energy–food was proposed as a new approach to issues related to the interconnected management of water-energy-food resources. Many efforts have been made to examine this model from various aspects, including resource consumption flow calculations and technology performance evaluation. Based on the predefined structure, five evaluation indicators have been considered to measure the efficiency of the system.
Resiliency
Durability
Reliability
where R is the reliability of the simulated model (0 ≤ R ≤ 1).
Vulnerability
Adaptability
Best–worst method
The best–worst method is based on pairwise comparisons and inspired by the hierarchical analysis method. In this method, the basis of work is choosing the best and worst criteria or options. If it is assumed that a decision matrix with n criteria is considered, in this matrix, a pairwise comparison between the criteria should be made and the relative importance of each of the indicators should be evaluated. Similar to the method of paired comparisons in hierarchical analysis, the equality of two indicators means equal importance of two criteria in relation to each other and the relative importance of two times, i.e., the first criterion is two times more important compared to the second criterion. Therefore, two concepts, (1) the principle of consistency of paired comparisons and (2) the principle of invertibility of the decision matrix, are the basis of weighting. Two factors of direction and intensity of preference of one criterion over another can be applied. The direction of preference is determined by the decision maker, but the main challenge is the intensity of preference and the superiority of one criterion over another, which causes inconsistency in paired comparisons.




Agricultural virtual water
Virtual water in this study is referred to as the total amount of water that is consumed to produce agricultural yields. Two strategic crops including wheat and barley were simulated in 2021–2022 growing season in Hunan region, China. Field information was measured through a field experiment in the study area for three wheat fields with an area of 165 hectares and three barley fields with an area of 137 hectares. The required parameters regarding water balance and performance in the last 5 years (2018–2022) were recorded and used to calibrate water and energy and yield production by AquaCrop software. In addition, carbon dioxide concentration (CO2) is one of the primary parameters required by the model, which is summarized in Table 1.
Required parameters for simulating water–food–energy nexus
Yield . | Factor . | Unit . | 2018 . | 2019 . | 2020 . | 2021 . | 2022 . |
---|---|---|---|---|---|---|---|
Wheat | Virtual water | m3·ton−1 | 2,825 | 2,940 | 2,812 | 3,120 | 3,080 |
1,000 m3·ha−1 | 12.3 | 12.4 | 12.9 | 14.9 | 14.0 | ||
Transpiration | mm·year−1 | 379 | 396 | 364 | 417 | 409 | |
CO2 emission | ton | 128 | 142 | 156 | 167 | 161 | |
Production | ton·ha−1 | 4.36 | 4.23 | 4.62 | 4.78 | 4.56 | |
Barley | Virtual water | m3·ton−1 | 2,814 | 2,910 | 2,823 | 3,048 | 3,034 |
1,000 m3·ha−1 | 11.2 | 11.8 | 11.5 | 12.6 | 12.2 | ||
Transpiration | mm·year−1 | 352 | 338 | 348 | 379 | 373 | |
CO2 emission | ton | 129 | 140 | 137 | 152 | 146 | |
Production | ton·ha−1 | 3.98 | 4.05 | 4.08 | 4.12 | 4.03 |
Yield . | Factor . | Unit . | 2018 . | 2019 . | 2020 . | 2021 . | 2022 . |
---|---|---|---|---|---|---|---|
Wheat | Virtual water | m3·ton−1 | 2,825 | 2,940 | 2,812 | 3,120 | 3,080 |
1,000 m3·ha−1 | 12.3 | 12.4 | 12.9 | 14.9 | 14.0 | ||
Transpiration | mm·year−1 | 379 | 396 | 364 | 417 | 409 | |
CO2 emission | ton | 128 | 142 | 156 | 167 | 161 | |
Production | ton·ha−1 | 4.36 | 4.23 | 4.62 | 4.78 | 4.56 | |
Barley | Virtual water | m3·ton−1 | 2,814 | 2,910 | 2,823 | 3,048 | 3,034 |
1,000 m3·ha−1 | 11.2 | 11.8 | 11.5 | 12.6 | 12.2 | ||
Transpiration | mm·year−1 | 352 | 338 | 348 | 379 | 373 | |
CO2 emission | ton | 129 | 140 | 137 | 152 | 146 | |
Production | ton·ha−1 | 3.98 | 4.05 | 4.08 | 4.12 | 4.03 |
RESULTS AND DISCUSSION
Evaluation of nexus system
Evaluation of existing strategies for water (W), food (F), energy (E), and nexus.
Evaluation of existing strategies for water (W), food (F), energy (E), and nexus.
Class-based improvement
Three levels of improvement of decision-making components (5, 10, and 25%) were evaluated to compare the progress of the five indicators. The results of the improvement in the decision-making components and the nexus model are summarized in Table 2. As shown in the table, the vulnerability of the system will increase in conditions of improvement of irrigation efficiency. The main reason for increased vulnerability is due to environmental stress for crop production, which reduces the range of soil moisture. Reliability and adaptability for barley have been more than wheat, which shows that the sensitivity of this product to deficit irrigation is less than that of wheat. In general, maximizing food productivity increases the criteria of reliability, resiliency, adaptability, and durability of the model compared to improving water use efficiency and energy saving. The use of the nexus system has improved the reliability and resiliency of the model by more than 80%. In addition, the results showed that using the nexus method can take into account the ability of all components to create optimal conditions and reduce vulnerability to less than 20%.
Class-based estimation of evaluation criteria under optimal condition
. | Wheat . | Barley . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Strategies . | Level . | S . | D . | R . | V . | A . | Level . | S . | D . | R . | V . | A . |
Improving water use efficiency | 5% | 0.67 | 0.56 | 0.78 | 0.29 | 0.63 | 5% | 0.64 | 0.57 | 0.79 | 0.31 | 0.66 |
10% | 0.61 | 0.52 | 0.73 | 0.31 | 0.58 | 10% | 0.59 | 0.53 | 0.74 | 0.34 | 0.62 | |
25% | 0.54 | 0.46 | 0.67 | 0.35 | 0.52 | 25% | 0.52 | 0.47 | 0.68 | 0.37 | 0.58 | |
Energy saving | 5% | 0.63 | 0.47 | 0.65 | 0.26 | 0.66 | 5% | 0.62 | 0.46 | 0.64 | 0.27 | 0.64 |
10% | 0.59 | 0.44 | 0.62 | 0.29 | 0.64 | 10% | 0.59 | 0.43 | 0.61 | 0.29 | 0.60 | |
25% | 0.53 | 0.39 | 0.56 | 0.34 | 0.59 | 25% | 0.54 | 0.38 | 0.54 | 0.33 | 0.56 | |
Increasing food productivity | 5% | 0.74 | 0.61 | 0.79 | 0.18 | 0.64 | 5% | 0.73 | 0.62 | 0.78 | 0.19 | 0.63 |
10% | 0.69 | 0.58 | 0.74 | 0.22 | 0.60 | 10% | 0.68 | 0.59 | 0.73 | 0.23 | 0.61 | |
25% | 0.61 | 0.52 | 0.68 | 0.26 | 0.57 | 25% | 0.62 | 0.53 | 0.67 | 0.26 | 0.58 | |
Nexus sustainability | 5% | 0.81 | 0.63 | 0.82 | 0.12 | 0.71 | 5% | 0.82 | 0.64 | 0.80 | 0.13 | 0.72 |
10% | 0.78 | 0.60 | 0.79 | 0.14 | 0.68 | 10% | 0.79 | 0.60 | 0.77 | 0.15 | 0.69 | |
25% | 0.72 | 0.53 | 0.74 | 0.17 | 0.63 | 25% | 0.74 | 0.54 | 0.72 | 0.18 | 0.64 |
. | Wheat . | Barley . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Strategies . | Level . | S . | D . | R . | V . | A . | Level . | S . | D . | R . | V . | A . |
Improving water use efficiency | 5% | 0.67 | 0.56 | 0.78 | 0.29 | 0.63 | 5% | 0.64 | 0.57 | 0.79 | 0.31 | 0.66 |
10% | 0.61 | 0.52 | 0.73 | 0.31 | 0.58 | 10% | 0.59 | 0.53 | 0.74 | 0.34 | 0.62 | |
25% | 0.54 | 0.46 | 0.67 | 0.35 | 0.52 | 25% | 0.52 | 0.47 | 0.68 | 0.37 | 0.58 | |
Energy saving | 5% | 0.63 | 0.47 | 0.65 | 0.26 | 0.66 | 5% | 0.62 | 0.46 | 0.64 | 0.27 | 0.64 |
10% | 0.59 | 0.44 | 0.62 | 0.29 | 0.64 | 10% | 0.59 | 0.43 | 0.61 | 0.29 | 0.60 | |
25% | 0.53 | 0.39 | 0.56 | 0.34 | 0.59 | 25% | 0.54 | 0.38 | 0.54 | 0.33 | 0.56 | |
Increasing food productivity | 5% | 0.74 | 0.61 | 0.79 | 0.18 | 0.64 | 5% | 0.73 | 0.62 | 0.78 | 0.19 | 0.63 |
10% | 0.69 | 0.58 | 0.74 | 0.22 | 0.60 | 10% | 0.68 | 0.59 | 0.73 | 0.23 | 0.61 | |
25% | 0.61 | 0.52 | 0.68 | 0.26 | 0.57 | 25% | 0.62 | 0.53 | 0.67 | 0.26 | 0.58 | |
Nexus sustainability | 5% | 0.81 | 0.63 | 0.82 | 0.12 | 0.71 | 5% | 0.82 | 0.64 | 0.80 | 0.13 | 0.72 |
10% | 0.78 | 0.60 | 0.79 | 0.14 | 0.68 | 10% | 0.79 | 0.60 | 0.77 | 0.15 | 0.69 | |
25% | 0.72 | 0.53 | 0.74 | 0.17 | 0.63 | 25% | 0.74 | 0.54 | 0.72 | 0.18 | 0.64 |
Virtual water changes
CO2 emission
CONCLUSION
The water–food–energy nexus is being promoted as a conceptual approach for achieving sustainable management. According to the concepts in the relationship between water, food, and energy in this study in three general parts of the analysis of the internal relationship between the components, the analysis of the influence of planning factors and the evaluation of the developed systems have been developed. We anticipate that the developed approach will make a significant contribution to achieving regional sustainable development goals and will be effective in improving the technical knowledge of the water, food, and energy relationship at the national and global levels. In future research, transdisciplinary and collaborative approaches can be addressed from the perspective of decision-makers, beneficiaries, and policy-makers in the fields of water, energy, and food. These components can help align relevant research with policy needs and support applied use.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.