RQ: How have previous researchers used Input-Output models to study the Food-Energy-Water nexus and how does this differ from efforts to use optimization?
The Water- Food- Energy nexus (WEFN) relation is intricate and interdependent that agriculture accounted for 70% of the global fresh water withdrawal, while food production and its supply chain consumes 30% of the energy according to FAO report. Every intervention in WEF is likely to have corresponding effects on the other sectors and since WEF elements are critical for survival of any region, their inefficient use poses potential challenge as world is reaching its sustainable limit of resource availability. Multi regional Input output (MRIO) tools which are constructed from region’s economic input output or Supply and Use tables are capable of capturing the WEF elements interlinkages across different economic sectors and different supply chain paths. Therefore it provides accounting method for WEF element linkages from consumption and production perspective to help manage them efficiently by tracking the synergies and tradeoffs that one has on the other.
Three challenges in optimizing the WEFN have been identified from the literature and how these challenges can be overcome by the integration of IO models and optimization tools, have been discussed comprehensively. The challenges identified are 1) Multi scale nature of WEFN interactions 2) System boundary definition and 3) Multiple stake holders involvement having different objective functions. In addition to this this papers also discuss about the future research avenues with regard to the use of IO models and optimization tool to study WEFN.
Key words: MRIO, Sustainability, Water-Energy-Food Nexus, Optimization, Interdisciplinary.
Almost every conceivable human activity consumes water, energy, food or the combination of the three. For example to produce electricity water is needed to produce steam that turns the turbine. Also water is required for cooling purposes and in other processes to produce electricity. 15% of the global water withdrawal are used for energy purposes (Birol, 2010). For the production of food, water is needed for irrigation of crops and processing of secondary food items. Water is also required for cooking and production of fertilizers. 70% of the global water usage is linked to the agriculture (Aquastat, 2011). For the distribution and production of food, energy is required and it is estimated that 30% of the global energy is embodied in the food supply chains (FOA, 2011). For the pumping, treating, distributing and desalination of water, energy is required. It is estimated that 8% of the global energy used is associated with water usage (United Nations, 2014). Clearly, all the three sectors are highly interconnected and dependent on each other: a phenomena that is termed as Water, Energy Food Nexus (WEFN). A summary of the WEFN interconnections is demonstrated in Figure1.
United Nations’ Sustainable Development Goals for zero poverty, ending hunger and food insecurity, ensuring water security, access to clean energy, sustainable economic growth, responsible consumption and production, and conservation, protection, and sustaining life under water are closely interlinked and success in achieving them will depend heavily on ensuring the sustainable use and management of water, energy, land (food), and other natural resources (Rasul, 2016). The point being that these goals are not only interdependent, they also possess both reinforcing and imposing constraints properties on each other (Rasul, 2014) and each goal being strongly interlinked in a different way to other. For example accomplishing the goal of food security and zero hunger, is strongly dependent on accomplishing the goal of energy and water sufficiency which is necessary to make sure water and energy is available for irrigation or agricultural output. In a same way the ability to accomplish the goal of water and energy sufficiency will heavily rely on the ways in which food is produced, harvested, processed and distributed (Hussey and Pittock, 2012).
Despite the significance of WEFN for ensuring sustainability, 1.2 billion people lack access to clean drinking water (Watkins, 2006). The global population is expected to touch 9.6 billion by 2050 (UNEP, 2012), which means efficient WEFN systems must designed that are resilient, sustainable, and well-managed to satisfy the earth’s increasing demand for water, food and energy. Rapid global change is increasing the demand for water, energy, food, and other resources. In the recent period the concept of Water Energy Food (WEF) nexus has been gaining momentum in the scholarly research literatures and in the media (World Economic
Forum, 2011; Bazilian et al., 2011; FAO, 2014; Lawford et al., 2013; Rahaman and Varis, 2005;) as Water, Energy and food are the most significant resources in any social and economic system (Biggs, 2015). Several studies has demonstrated the significance of nexus research among these three resources on regional economic and social sustainability (Guijun, 2016). Managing water, energy, and food nexus is one of the most challenging research areas for achieving global sustainability (Taniguchi et al, 2017).
Figure 1 Summary of WEFN
Since WEFN is a very vast topic (Daniel, 2016; Bazilian, 2011), this review article will be restricted to the recent studies on the use of optimization tools and input output (IO) models to address the WEFN challenge. After the literature review some major challenges and knowledge gaps in the current literature with regard to the application of optimization and IO tools in modelling WEFN will be presented. The three challenges discussed will be 1) multi scale challenge, 2) Challenges pertaining to uncertainty and 3) challenges associated with objectives of multiple stake holders. In addition to this, the possible avenues in the research of optimizing WEF nexus will be identified to foster the development of sustainability science. Though there are several papers that have applied optimization on water-energy, food-water and water-food relationship. This, article could have highlighted on the numerous interactions existing within WEFN. Instead, the purpose of this review is to briefly highlight the relevant literature, and spot on the gaps and challenges pertaining to optimizing to WEFN optimization. A summary of the articles reviewed that have applied optimization on WEF using IO model, hybrid LCA and LCA is shown in Table1.
What are IO models and how they can be useful in understanding WEFN sustainability?
The increase of trade globally has made the water energy and food relation more convoluted, the consumptions are embedded across the multi levels of supply chain and are interlinked with each other. Therefore such challenges demand the use of a powerful accounting methods. Fortunately, there exists method to do that and input-output analysis (IOA) is one of them. IOA has been recommended to understand the interconnectivities across multiple sectors (Owen, 2017), together with the part of trade, industries, products and final consumers. The Multi Regional Input-Output (MRIO) databases center on the evaluation of trade flows between regions and industrial sectors, using a flow matrix approach. MRIO analysis is capable of quantifying full product consumption across the levels of supply chain, and also to which region the final demand corresponds too. Over the last decade the advances in the computational power, efficient algorithms and the data availability has caused to build larger databases (Owen, 2017). MRIO databases have been used to capture the Greenhouse gases (GHG) footprints. There lies a great potential in MRIO models to untap energy-water-food nexus relationships by quantifying them (Owen, 2017).
The IO table for an economy records the trade flows between different sectors of the economy. It is comprised of economic transactions among different sectors in a matric form. When IO table is converted into a matrix form that shows the economic purchases required from each sector of the economy to have a unit currency output, for different economic activities, that matrix is called ‘A’matrix. Algebraically, the total economic purchases required from each sector ‘X’ (vector of different sectors) to satisfy the final demand ‘Y’ (vector of different sectors) within an economy can be represented by Equation (1).
X [I-A] = Y (1)
Now if the final demand vector Yis known, the purchases made from each industrial sectors to satisfy the final demand Y, can be calculated by readjusting Equation (1) to Equation (2), where matrix [I-A]-1is known as Leontief inverse.
|Authors||Optimizing Objective||Relationship||Optimization tool||Model||Scale|
|1||Ahmetovic, 2010||Minimize Water network cost & energy||WEF||Non convex Nonlinear Programming||LCA||Ethanol Plant|
|2||Bernadi, 2012||Environmental and economical||WEF||Mixed integer Linear Programming||LCA||Bioethanol Supply Chain|
|3||Cucek, 2014||Economic, environment & social||WEF||Mixed integer Linear Programming||LCA||Regional Biomass energy supply chain|
|4||Garcia, 2015||Water footprint, energy & economics||WEF||Mixed integer non Linear Programming||LCA||Biomass cultivation to bio fuel production|
|5||Kursun, 2015||Land, water, cost, GWP,
%Re, EYR, ELR
|WEF||Linear programming||Hybrid LCA||Optimum energy mix of Rampura, India|
|6||Aviso, 2015||Maximize trade with water constraint||WF||Fuzzy optimization||IO model||2 case studies: Bio fuel & tile manufacturing|
|7||Ermolieva, 2015||Maximize wellbeing of customer & producers||WEF||Stochastic optimization||EPIC*||20 main agricultural crops in EPIC*|
|8||Moradi, 2004||Minimize annual cost||WEF||Genetic Algorithm||Irrigation pumps|
|9||You, 2011||Cost, environment, social||WEF||Mixed Integer Linear programming||LCA and IO model||Ethanol supply chain|
|10||Tan, 2008||Minimize emissions and land use||FE||Fuzzy Linear programming optimization||Matrix- Based LCA||Bio fuels for transportation|
|11||Tan, 2009||Minimize water, carbon and land use||WEF||Fuzzy Linear programming optimization||IO based LCA||Ethanol production from bio refinery|
|12||Al-Ansari, 2015||Resource efficiency||WEF||Process modelling and optimization||LCA||Case study set in Qatar on agricultural sub systems|
|13||Zhang, 2016||Minimize total cost||WEF||WEFO linear mathematical model||—||Thermoelectric powerplant|
|14||Aviso, 2010||Minimize water footprints||WEF||Fuzzy optimization||IO model||Production of bio fuel|
Table 1 Summary of the papers reviewed on application of optimization with process based and IO models for WEFN
X =[I-A]-1Y (2)
If the emissions intensity coefficient diagonal matrix B is multiplied by the total purchases made by economic sectors, vector X, then the environmental burden vector E, representing emission caused by each economic sector can be calculated by Equation 3. When IO models are used in this way to calculate the environmental impact they are called IO-LCA and when used to quantify social impacts, are called social- LCA.
E = BX=B[I-A]-1Y (3)
Since MRIO data bases are new developments, very little literature is available on exploiting the use of it (Inomata, 2014). Galli et al (2012) claims that the literature on the use to explore the socio economic impact by IO tables is often missing. The recent development of large data bases has made them to be of greater use (Owen, 2017). Though there is an extensive literature to document the limitations of IO models (Lenzen, 2000), but it is certain that future IO tables will be more accurate and advance (Wiedmann, 2011). The major problem associated with the IO models is the level of aggregation in the data, instead of quantifying the environmental impact of particular process, it shows the impact caused by a sector. Therefore IO models can be quit unhelpful when trying to optimize a particular process as it cannot identify the major hotspots due to aggregation in the data. While much of the literature available on optimizing the WEF interactions have used process based models (Garcia, 2015) which has a major drawback of truncation error in the results due to tight system boundary. The more big the system boundary is the better the results are for optimization as explained by Garcia and You (2015), in which they found that the optimization results were significantly different when considered system boundary from biofuel cultivation to biofuel production in contrast with the system boundary that included biomass cultivation till the fuel combustion. The IO model can be integrated with Life Cycle Assessment (LCA) models to from Hybrid LCA, that can capture process levels details with greater system boundary. IO model combined with LCA can be very helpful in filling this problem in optimization. The effect of system boundary on optimization will be discussed comprehensively in the research gap section.
IO tables have focused on the economic and environmental impacts associated by WEF resources consumption. It has been applied several times to analyze single resource only (Li, 2016) but have been rarely used for the optimization of WEFN and is evident from the literature review by Garcia (2016). Daniel did the literature review of the optimization approaches that have been applied on WEFN using
the process based models and found that no hybrid-LCA has been performed so far in this area that can help in making more thorough studies.
Some of the researches in which IO tool was used to study the water energy food nexus was by Vanham (2016). Vanham claimed that by tracking the water consumption in the energy and food products, some very useful insights of the water energy food nexus can be obtained which is not possible to study using conventional water management methods. Wang (2016) used this method by quantifying energy-related water consumption and water-related energy consumption by the help of input-output (IO) method on a case study of Beijing. Fang and Chen (2017) also calculated the water and energy consumption using IO analysis on Beijing to identity their significance in water food energy nexus. Holland et al. (2015) used MRIO model to trace the water consumption to identify where the fresh water is being consumed. Duan and Chen (2017) used a network approach to analyze the water and energy dependency on a country for global trade. White et al (2017) used IO analysis to account for the food use and its effect on the water and energy and found Construction and Agricultural products to be the greatest water-energy-food consumers. There are lot of studies pertaining to IO analysis on WEF, but very little literature can be found on using IO analysis on WEFN for optimization as can be seen from Table1.
Why an interdisciplinary approach is needed to optimize the WEFN?
The intervention is any one sector of WEF will have the impact on the other (Bazilian et al., 2011; Hussey and Pittock, 2012). Food choices and agricultural practices heavily influence water and energy need. Likewise, water, energy and land demand is influenced by range of policies, like those pertaining to agriculture, energy, land usage, food, subsidies, taxes, loans and prices (Rasul, 2016). The best way to minimize the trade-offs and resource conflicts is by enhancing the efficient usage of water, energy, and food. However, taking measures by ensuring the efficient usage of the resources will not be sufficient in longer run unless the ecosystem is preserved and does not get changed, therefore sustainable use of the resources is needed. To achieve a healthy ecosystem the condition of sustainable production is required (Rasul, 2014; FAO, 2014). In addition to it, a particular goal is insufficient to achieve healthy people in the society; it relies on several goals varying from ensuring food, water, and energy supply to ecological system preservation, healthy ecosystems, and environmental protection. Like the food, water, and energy nexus, the sustainable goals are closely interlinked. Thus food, water, energy sufficiency, and the sustainability goals is required to be approached in an integrated way (Rasul, 2016). Though the awareness of nexus approach has increased in the past years, however, the literature regarding the
methods for nexus assessments for policy making and management is still in infancy (Taniguchi, 2017). There lies an immense need for Disciplinary and broad interdisciplinary science approach in advancing both the theoretical and applied aspects of the WEF Nexus, mainly because of the inherent trade-offs and synergies that are associated with the Nexus (Taniguchi, 2017). Figure 2 shows the Water, Energy and Food interaction among each other and with the externalities, this demonstrates the dynamic nature of the WEF nexus and the interconnection with different parameters. Water, energy and food nexus apart from being linked with each other they are also influenced by a spectrum of drivers that lies in different domains of economic, social and environmental sciences.
Drivers: Policies Environment Society Urbanization
Drivers: Politics Investment Culture
Sustainability and human well being
Figure 2 The Dynamics of Water, Energy and Food nexus
Despite the inherent interlinkages among food, water, and energy production, organizations often work in a disjointed and isolated way (Rasul, 16). It demands significant change in the decision-making approach towards having a holistic view and there is a strong need to develop institutional mechanisms to foster the coordination in the actions of various agents that may strengthen complementarities and synergies among the water, energy and food sectors. Not accounting for the underlying interconnectedness of these three sectors, policies experiences the unintentional consequence of transferring a crisis from one sector to another (Tomain, 2011), also policies and decisions taken in silos, regardless of accounting their impact on other sectors, can aggravate resource constraints (Hermann et al., 2012; Scott et al., 2011). Yet water, energy and food security are considered separately (Taniguchi, 2017). For example the agricultural policy has caused an increase in the food production, it possess a huge challenge for the sustainability of the water and energy resources that in turn adversely affect the irrigation systems (Pingali, 2007). Subsidies
on water and energy could result in their over usage that may have adverse consequences on the nexus. Intensive agriculture can lead towards waterlogging and salinity in soils and makes crops prone to waterborne and water-linked infections that will exacerbate the food production (Pingali, 2007). The cross-sectoral linkages are the reasons for extra burden on, water, energy, and other scarce resources that will adversely affect the over sustainability of water, food and energy (Shah, 2009). Mostly the agreed principle is that for achieving sustainable development there is a need to have different policies from environmental, social and economic perspective collectively with further integrated decision-making across all sectors in a region (Nilsson et al. 2012).
How the researchers have used the optimization and IO models to study the WEFN?
Among the WEF relationship, the water-energy and Process-Scale Modeling/Optimization is the most explored region of the WEFN by the researchers, However relatively little attention has be paid particularly in the Water-food, food-energy and Water-Energy-Food combined, in fact they present beginnings for novel research endeavors (Garcia, 2015). In the optimization of water-energy-food interaction combined, by most and at large the researchers have applied optimization on the bioethanol plant’s production or supply chains.
The use of IO model to analyze the aspects of the WEFN in an integrated way is limited in literature. However, in the past few years some research has been done on this area using the IOA that indicates the emergence of optimizing the WEFN that would guide the further research. These research on WEF as whole have been mostly related to optimizing the bioethanol supply chains, holistic WEFN framework proposals and optimization pertaining to irrigation network. Biofuel is considered as clean and renewable source of energy and to reduce dependencies on the petroleum oil for energy many governments and industries globally has chosen to produce energy from bioenergy sources that are obtained from crops and food. For example US produces bioenergy from corn while Brazil is producing bioenergy from sugar cane. Both corn and sugarcane are crops that need to be cultivated and demands extensive land, water and energy. Sugarcane and corn are the food crops that typically demands higher amount of water for cultivation than other energy crops like woody biomass (Mekonnen and Hoekstra, 2011). Therefore, attempts have been made by the researchers to optimize the water metrics for biofuel crops cultivation. For example Ahmetovi’s et al. (2010) attempted to optimize the cost associated with water and energy of the bioethanol plant. The main goal was to reduce the fresh water intake and waste water discharge, for
which they used non convex and nonlinear programming for the optimization to minimize the amount of water intake by heating and cooling utilities. They found that there is room in current industrial operations to reduce the level of fresh water intake and discharge water by optimization. Aviso et al. (2011) used a multi-regional fuzzy input output model for optimizing the supply chain from consumption and production aspect for biofuel production while considering the water footprint. They found that the crops like, sugarcane and corn, are more water intensive and have higher water footprints. They used technological coefficient matrix with max-min aggregation to develop a linear programming model. The model simultaneously considered the fuzzy goals set by various stake holders involved. ˇCuˇcek et al. (2012) and Bernardi et al. (2012) also established a multi-objective optimization model using mixed integer linear programming to minimize the water footprints as one of the objective while considering the bio energy supply chain. Garcia (2015) used mixed integer linear programming to optimize water footprint, energy and economics of biofuel conversion process and product network. The also found that the water footprint of the food based process is much lesser than using the energy crops such as woody biomass.
The WEFN studies outside the domains of bio energy and bio fuels, have focused on the water-energy, and water-food interactions primarily (Garcia, 2015). Some notable exception are of Moradi-Jala et al. (2004) who used the genetic algorithm to minimize the annual cost by optimizing the schedule of the irrigation pumps operation, selecting pump type and number of units of the pumps required. Thus he aimed at reducing the cost by minimizing the energy consumption of the irrigation pumps. This study included the work on addressing the energy from pumps, water for irrigation and food from irrigation aspects. While the rest of the research on this area aimed at forming the frameworks for the WEFN analysis rather having specific studies related to the WEFN. Most of such study that focused on forming the framework of WEFN have emerged just recently over the last year (Garcia, 2015). This shows the importance of considering the optimization from transdisciplinary approach is being understood and acted upon. For example Kursun et al. (2015) used applied the hybrid life Cycle and energy concepts to develop an linear programming optimization model to investigate the optimum energy mix for Rampura village in India. They considered several objectives that corresponded to the domains of economic and environmental sciences like land, water, cost, GWP and etc. The found that it is optimum to use the biogas produced in rural areas for cooking purposes and to generate electricity for satisfying the irrigation and lighting requirements. Ermolieva et al. (2015) used stochastic optimization on twenty main agricultural crops that have been categorized to contribute more to carbon emissions by EPIC. His objective was to maximize the wellbeing of the consumers and the producers, and focused on the food, energy and water systems with Global Biosphere Management Model (GLOBIOM). This model is capable of handling
different WEF provision scenarios for different regions such as Japan and Ukraine. Al-Ansari et al. (2015) developed a system based tool by the help of LCA. The system defines the WEF subsystems of the overall process and integrates the Life cycle inventory data into one model and applied to a case study in Qatar.
There lies a whole new area of opportunities for research investigating all three resources in an integrative way at all levels of the Water- Energy- Food Nexus. The challenges and gaps in the literature that can be filled by the help of optimization tools and IO models will be discussed in the following section.
What are the challenges in the literature and how IO Models and optimization integration can fill the gap?
Decision makers currently lack effective tools to understand the tradeoff between different systems (Daher 2015) and need to expand the scope of thoughts to consider all three WEF elements simultaneously (Shinde 2017). Due to the vastness of the WEF areas there lies difficulties in considering them in an integrative way to support decision making at the nexus (Bazilian 2011). The relationship among macro-economic and sectoral policies and cross-sectoral impacts are not incorporated into national policies (Rasul, 2015). One of the major weakness of the current research approach lies in the methodological difficulty in WEF interdependency quantification (Li, 2016). Moreover, the current quantification literature on regional impact studies due to the consumption of WEF resources is scarce (Chang, 2016; Li, 2016). There lies several challenges ahead in addressing the Water-Energy-Food Nexus research (Taniguchi, 2017). In order to mitigate the complexity and challenges associated with WEF nexus, following consideration will prove useful in the analysis and optimization of WEF collectively-
Challenges associated with Multi scale-
There are several layers of scales at both spatial and temporal level in WEFN. Intervention in any scale can have direct or indirect, far reaching consequences or effects on the other scales within WEFN system. Households, communities, regions and globes are affected in different ways depending on their WEF interactions across different scale. The point is that WEF is not only interconnected to each other one single level, but they have interconnection over multiple levels. For example the decision made on the bioethanol production plants at community scale will have far reaching impact on the water, energy and food usage at the regional scale. Another example can be the changes in the consumers dietary pattern at house hold level can have significant impact on the regional or global scales on food processing and
energy requirements. Therefore it is extremely important to consider various levels within the WEFN when modelling the system for water, energy and food optimization. It is entirely possible that WEF optimization one scale could cause severe inefficiencies on the other scales, thus leading towards cumulative disadvantage with regard to the allocation of the resources. If the scope is being narrowed towards optimizing a single level then the interconnected nature of the WEFN and the synergies and tradeoffs among WEF linkages gets ignored.
In the optimization literature available on WEFN, usually very specific scale is addressed. For example when dealing with WEFN at regional level, their supply chain is being considered only and the levels related to house hold consumption and other scales are ignored. When optimizing the WEFN, many levels should be taken into consideration to treat it as a system to achieve more global optimization. The optimization tools can be very helpful to achieve this by taking into account the constraints from every level when modelling optimization problem for a WEFN system. For example when optimizing the water consumption at particular level, as much details must be incorporated into the optimization models as possible. Though this will cause to much efforts but a balance should be reached in which considerable information is used for the model while the rest can be ignored. The Hybrid LCA can be useful in this regard that can integrate the details from smaller (i.e process level) level to covering the supply chain across regions by the integration of LCA with IO models.
The definition of the system boundary in WEFN-
One of the main step of the WEFN study is defining the system boundary. Considerable amount of efforts should be directed to define system boundaries in such a way that takes into account the social, environmental and economic effects that is to be optimized. For example if a bioethanol power plant is aiming to reduce the emission and water within the plant, then most important part that is contributing towards the emissions and water usage can be missed, that is in the transportation of the bioethanol which lies outside the system boundary of the plant. Therefore the purpose of the study cannot be perfectly full filled if the objective is to reduce emissions, and optimization for reducing emission within the system boundary can lead towards substantial emission outside the system boundary .
Garcia (2015) found out that in optimizing the bioconversion product and process network, the role of system boundary allocation is very critical. They identified the results of optimization on the emission were very different, when system boundary of biomass cultivation to ethanol production was defined to
the system boundary where cultivation of ethanol to its consumption was considered. Most application of Life Cycle Optimization (LCO) has utilized the process based LCA which has a major draw back in defining the system boundary and often leads in truncation error. Up till now, no LCO research has utilized hybrid- LCA approaches. The utilization of Hybrid- LCA can significantly help over come this gap in the literature. Therefore integration of Hybrid- LCA and optimization can be very helpful in fulfilling the purpose of the study of WEFN system.
Multiple stake holders with multiple and different objectives-
Having multiple stake holders with different objectives is one of the main challenge when modelling and optimizing WEFN. The objectives of industry and supply chain stake holders are well defined, but the objectives of the house and the communities have been given very little importance (Garcia, 2015). The stakeholders involved have conflicting objectives that lies at different scales of the WEFN. With the increasing number of stake holders involvement and their multiple objectives, increased the complexity of the WEFN system to model for optimization. Up till now the past researches have considered very limited objectives to maximize or minimize the resources, and most of them have focused on reducing the costs. In reality, when multiple objective exists then suitable optimization algorithm have to be chosen to model the WEFN. Game theory optimization coupled with IO model can help optimize the WEFN by taking into account the multiple objectives. It is best used for multi objective optimization and optimization under conflicting interests.
Discussion and Conclusion
Since the WEFN is complex and intricate, understanding it by disciplinary approaches have limitations, water, energy and food nexus should be studied using the transdisciplinary approaches that considers the knowledge from different domains of environmental, economic and social sciences. To understand the nexus and be able to address to different objectives of the stakeholders involved, it is crucial to take use of the interdisciplinary knowledge. Therefore, a transdisciplinary way to nexus research is mandatory to allow the full spectrum of stakeholders to recognize optimal policy (Taniguchi et al., 2013).
The three challenges in the current identified from the current literature on WEFN optimization are multi scale challenge, system boundary definition challenge and addressing to the multiple stakeholders’ multi objective function challenge. The multi scale challenge can be well addressed by incorporating LCA into
IO models to form hybrid-LCA, in this way the WEF interactions on one scale can be linked to the WEF interactions on different scales. The second challenge of the system boundary definition can also be overcome this way by the help of hybrid LCA in with the truncation error associated with the process based models can be minimized or eliminated. The third challenge is modelling WEFN while considering multiple objectives of different stake holders and this challenge can be addressed by using multi objective optimization functions like the game theory. Also uncertainty in the WEFN at different spatial and temporal scale can be modelled by stochastic optimization. Therefore these challenges in WEFN can be best overcome by using optimization tools and hybrid models. Hybrid models will overcome the first two challenges identified and the optimization tool can overcome the third challenge and help the stake holders work in integration.
The past trend in sectoral investments have mainly been targeted to achieve, sector specific objectives and optimization without considering cross-sectoral coordination, thus significantly jeopardizing the WEFN to a great risk of unforeseen side-effects and a cumulative undesirable sectoral trade-offs. Newell et al. (2011) mentions “a system’s performance cannot be optimized by optimizing the performance ofits subsystems taken in isolation from one another …efforts to avoid unwanted policy outcomes and to identify leverage points for effective change must take into account the effect of interactions between sectors”. Therefore optimization as a whole system should be considered rather optimizing individual sectoral linkages. Hence to achieve well human being and global sustainability, optimal governance of the WEFN is mandatory (Taniguchi, 2017).
Due to the nexus complication and limitations of disciplinary solutions, water-energy-food nexus issues related to the climate change and land-use policies should be studied using an interdisciplinary approach that includes the natural, social, and human sciences. In order to understand the nexus, different kinds of knowledge is required from different domains. A transdisciplinary approach to nexus research is mandatory to engage the stake holders for optimal policy making across different scales in WEFN (Taniguchi et al., 2013).
- Ahmetović, Elvis, Mariano Martín, and Ignacio E. Grossmann. “Optimization of energy and water consumption in corn-based ethanol plants.” Industrial & Engineering Chemistry Research49.17 (2010): 7972-7982.
- Al-Ansari, Tareq, et al. “Development of a life cycle assessment tool for the assessment of food production systems within the energy, water and food nexus.” SustainableProductionandConsumption2 (2015): 52- 66.
- Aquastat, F. A. O. “FAO’s information system on water and agriculture.” (2011).
- Aviso, Kathleen B., et al. “Fuzzy input–output model for optimizing eco-industrial supply chains under water footprint constraints.” Journal of Cleaner Production 19.2 (2011): 187-196.
- Bazilian, Morgan, et al. “Considering the energy, water and food nexus: Towards an integrated modelling approach.” Energy Policy 39.12 (2011): 7896-7906.
- Bernardi, Andrea, Sara Giarola, and Fabrizio Bezzo. “Optimizing the economics and the carbon and water footprints of bioethanol supply chains.” Biofuels, Bioproducts and Biorefining 6.6 (2012): 656-672.
- Biggs, Eloise M., et al. “Sustainable development and the water–energy–food nexus: A perspective on livelihoods.” Environmental Science & Policy 54 (2015): 389-397.
- Birol, Ekin, Phoebe Koundouri, and Yiannis Kountouris. “Assessing the economic viability of alternative water resources in water-scarce regions: Combining economic valuation, cost-benefit analysis and discounting.” Ecological Economics 69.4 (2010): 839-847.
- Chang, Yuan, et al. “Quantifying the Water-Energy-Food Nexus: Current Status and Trends.” Energies 9.2 (2016): 65.
- Čuček, Lidija, et al. “Total footprints-based multi-criteria optimisation of regional biomass energy supply chains.” Energy 44.1 (2012): 135-145.
- Duan, Cuncun, and Bin Chen. “Energy–water nexus of international energy trade of China.” Applied Energy 194 (2017): 725-734.
- Ermolieva, T. Yu, et al. “Systems analysis of robust strategic decisions to plan secure food, energy, and water provision based on the stochastic GLOBIOM model.” CyberneticsandSystemsAnalysis51.1 (2015): 125.
- Europe, Forest. “UNECE and FAO. 2011.” State of Europe’s forests (2011): 337.
- Fang, Delin, and Bin Chen. “Linkage analysis for the water–energy nexus of city.” Applied Energy 189 (2017): 770-779.
- FAO. The Water-energy-food Nexus: a New Approach in Support of Food Security and Sustainable Agriculture,FAO, Rome (2014).
- Galli, Alessandro, et al. “Integrating ecological, carbon and water footprint into a “footprint family” of indicators: definition and role in tracking human pressure on the planet.” Ecological indicators 16 (2012): 100-112.
- Garcia, Daniel J., and Fengqi You. “The water-energy-food nexus and process systems engineering: a new focus.” Computers & Chemical Engineering 91 (2016): 49-67.
- Hermann, Sebastian, et al. “Climate, land, energy and water (CLEW) interlinkages in Burkina Faso: An analysis of agricultural intensification and bioenergy production.” Natural Resources Forum. Vol. 36. No. 4. 2012.
- Hussey, Karen, and Jamie Pittock. “The energy–water nexus: Managing the links between energy and water for a sustainable future.” Ecology and Society 17.1 (2012).
- Inomata, Satoshi, and Anne Owen. “Comparative evaluation of MRIO databases.” (2014): 239-244.
- Kursun, Berrin, et al. “Life cycle and emergy based design of energy systems in developing countries: Centralized and localized options.” Ecological Modelling 305 (2015): 40-53.
- Lenzen, Manfred. “Errors in conventional and Input‐Output—based Life—Cycle inventories.” Journal of Industrial Ecology4.4 (2000): 127-148.
- Li, Guijun, Daohan Huang, and Yulong Li. “China’s input-output efficiency of water-energy-food nexus based on the Data Envelopment Analysis (DEA) model.” Sustainability 8.9 (2016): 927.
- Li, Guijun, Daohan Huang, and Yulong Li. “China’s input-output efficiency of water-energy-food nexus based on the Data Envelopment Analysis (DEA) model.” Sustainability 8.9 (2016): 927.
- Mekonnen, M., & Hoekstra, A. Y. (2011). The green, blue and grey water footprint of crops and derived crops products. Hydrology and earth system sciences discussions, 8(47), 763-809.
- Mihelcic, James R., Linda D. Phillips, and David W. Watkins Jr. “Integrating a global perspective into education and research: Engineering international sustainable development.” EnvironmentalEngineering Science 23.3 (2006): 426-438.
- Moradi-Jalal, Mahdi, Sergey I. Rodin, and Miguel A. Mariño. “Use of genetic algorithm in optimization of irrigation pumping stations.” Journal of irrigation and drainage engineering130.5 (2004): 357-365.
- Newell, Barry, Debborah Marsh, and Deepak Sharma. “Enhancing the resilience of the Australian national electricity market: Taking a systems approach in policy development.” EcologyandSociety16.2 (2011).
- Nilsson, Måns, et al. “Understanding policy coherence: analytical framework and examples of sector– environment policy interactions in the EU.” Environmental Policy and Governance 22.6 (2012): 395-423.
- Owen, Anne, Kate Scott, and John Barrett. “Identifying critical supply chains and final products: an input- output approach to exploring the energy-water-food nexus.” AppliedEnergy(2017).
- Pingali, Prabhu. “Agricultural growth and economic development: a view through the globalization lens.” Agricultural Economics 37.s1 (2007): 1-12.
- Rahaman, Muhammad Mizanur, and Olli Varis. “Integrated water resources management: evolution, prospects and future challenges.” (2005).
- Rasul, Golam. “Food, water, and energy security in South Asia: a nexus perspective from the Hindu Kush Himalayan region☆.” Environmental Science & Policy 39 (2014): 35-48.
- Rasul, Golam. “Managing the food, water, and energy nexus for achieving the Sustainable Development Goals in South Asia.” Environmental Development 18 (2016): 14 -25.
- Ringler, Claudia, Anik Bhaduri, and Richard Lawford. “The nexus across water, energy, land and food (WELF): potential for improved resource use efficiency?.” Current Opinion in Environmental Sustainability 5.6 (2013): 617-624.
- Scott, Christopher A., et al. “Policy and institutional dimensions of the water–energy nexus.” Energy Policy 39.10 (2011): 6622-6630.
- Shah, Tushaar. Taming the anarchy: Groundwater governance in South Asia. Routledge, 2010.
- Taniguchi, Makoto, et al. “Water-Energy-Food Nexus in the Asia-Pacific Region.” (2017): 1-8.
- Tomain, Joseph P. “The Politics of Clean Energy: Moving beyond the Beltway.” San Diego J. Climate & Energy L. 3 (2011): 299.
- UNEP (2012) Responsible resource management for a sustainable world: findings from the international resource panel. UNEP, Paris, 36 p
- Vanham, D. “Does the water footprint concept provide relevant information to address the water–food– energy–ecosystem nexus?.” Ecosystem services 17 (2016): 298-307.
- Wang, Saige, Tao Cao, and Bin Chen. “Urban energy–water nexus based on modified input–output analysis.” Applied Energy 196 (2017): 208-217.
- Water, U. N. “The United Nations world water development report 2014: water and energy.” United Nations, Paris(2014).
- White, David J., et al. “The Water-Energy-Food Nexus in East Asia: A tele-connected value chain analysis using inter-regional input-output analysis.” Applied Energy(2017).
- Wiedmann, Thomas, et al. “Quo Vadis MRIO? Methodological, data and institutional requirements for multi-region input–output analysis.” Ecological Economics 70.11 (2011): 1937-1945.
- World Economic Forum. Global Risks 2011 Report, 6th ed.; World Economic Forum: Cologne, Germany, 2011.
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