Consistent characterisation factors at midpoint and endpoint relevant to agricultural water scarcity arising from freshwater consumption
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Abstract
Purpose
The shortage of agricultural water from freshwater sources is a growing concern because of the relatively large amounts needed to sustain food production for an increasing population. In this context, an impact assessment methodology is indispensable for the identification and assessment of the potential consequences of freshwater consumption in relation to agricultural water scarcity. This paper reports on the consistent development of midpoint and endpoint characterisation factors (CFs) for assessing these impacts.
Methods
Midpoint characterisation factors focus specifically on shortages in food production resulting from agricultural water scarcity. These were calculated by incorporating country-specific compensation factors for physical availability of water resources and socio-economic capacity in relation to the irrigation water demand for agriculture. At the endpoint, to reflect the more complex impact pathways from food production losses to malnutrition damage from agricultural water scarcity, international food trade relationships and economic adaptation capacity were integrated in the modelling with measures of nutritional vulnerability for each country.
Results and discussion
The inter-country variances of CFs at the midpoint revealed by this study were larger than those derived using previously developed methods, which did not integrate compensation processes by food stocks. At the endpoint level, both national and trade-induced damage through international trade were quantified and visualised. Distribution of malnutrition damage was also determined by production and trade balances for commodity groups in water-consuming countries, as well as dependency on import ratios for importer countries and economic adaptation capacity in each country. By incorporating the complex relationships between these factors, estimated malnutrition damage due to freshwater consumption at the country scale showed good correlation with total reported nutritional deficiency damage.
Conclusions
The model allows the establishment of consistent CFs at the midpoint and endpoint for agricultural water scarcity resulting from freshwater consumption. The complex relationships between food production supply and nutrition damage can be described by considering the physical and socio-economic parameters used in this study. Developed CFs contribute to a better assessment of the potential impacts associated with freshwater consumption in global supply chains and to life cycle assessment and water footprint assessments.
Keywords
Agricultural water scarcity Economic adaptation Food stock compensation Freshwater consumption International trade Undernourishment Water footprint1 Introduction
Demand for freshwater has been increasing due to population and economic growth, while freshwater resources are limited. The severity of this limitation varies among regions because of differences in the physical availability of water resources and in local socio-economic conditions. In addition, the uneven distribution of freshwater availability and demand is responsible for various types of environmental impacts (World Water Assessment Programme 2009). Global supply chains of products and services also contribute to the potential environmental risks related to water use, but these are hidden in material and service flows. Nevertheless, the importance of assessing the impacts of water use in life cycle assessment (LCA) has been recognized in recent years (Köehler 2008; Bayart et al. 2010; Berger and Finkbeiner 2012; Boulay et al. 2013; Kounina et al. 2013; Motoshita 2013; Tendal et al. 2013).
Many methods for assessing the impacts of water use in LCA have been developed (Kounina et al. 2013). According to the previously described impact pathway of freshwater use (Bayart et al. 2010; Kounina et al. 2013), freshwater deficits will result in impacts on human life and on biotic and abiotic environments. Specifically, damage related to water issues has been estimated to represent up to 9.1 % of total human health damage (Prüss-Üstün et al. 2008). This is the same level as the total health damage caused by cardiovascular diseases (WHO 2008). Thus, health damage in human populations resulting from water use should be a key concern of human society. Appropriate indicators for assessing the impacts on human health related to freshwater consumption need to be developed.
The primary impact factors leading to human health problems can be found in domestic/agricultural/aquacultural water scarcity (Bayart et al. 2010; Kounina et al. 2013). Specifically, agriculture is responsible for almost 70 % of freshwater use in the world (FAO 2010a). Thus, freshwater consumption may result in large impacts on agricultural water users. Several methods for assessing freshwater scarcity (Frischknecht et al. 2006; Pfister et al. 2009; Ridoutt and Pfister 2010; Hoekstra et al. 2011; Boulay et al. 2011) have been developed at the midpoint level. However, indicators clearly focusing on the impact for each water user have not been proposed to date. At the endpoint level, methods for assessing human health damage due to freshwater consumption have been developed (Pfister et al. 2009; Motoshita et al. 2010; Boulay et al. 2011). In the context of agricultural water scarcity, two models developed by Pfister et al. (2009) and Boulay et al. (2011) assess malnutrition damage resulting from freshwater consumption in the context of consideration of physical water availability and social vulnerability (human development index (HDI) and economic adaptation capacity). Models for assessing the impact of agricultural water scarcity can be improved by considering more direct factors relevant to vulnerability to food supply shortages (such as food stock and distribution of nutritional conditions within a population). In addition, the international trade in food may compensate for and/or shift food shortages to other countries.
This study aimed to develop appropriate indicators for assessing agricultural water scarcity due to freshwater consumption at the midpoint and endpoint. At the midpoint, the robustness of an indicator is one of the most critical aspects. Thus, indicators were proposed based on physical water stress and the consideration of region-specific factors addressing the extent of agricultural water demand and vulnerability to food supply shortage. At the endpoint, human health damage from malnutrition was modelled to improve previous models by incorporating economic adaptation capacity and the effects of the international food trade to reflect the total damage from food production losses.
2 Methods
2.1 Outline of agricultural water scarcity modelling
The cause-effect chain in the modelling of water scarcity impact
Freshwater consumption will potentially lower water availability for agriculture and agricultural production, but the severity of this depends on irrigation intensity and societal capacity for compensation. Depending on absolute levels of water scarcity, a shortage of irrigation water might be compensated for by inputting additional water from other available freshwater resources and food stocks. In this context, alternative freshwater resources and food stocks can represent physical and socio-economic vulnerability to water scarcity, respectively, and were therefore included in characterisation factors (CFs) at the midpoint.
On the other hand, food production shortages may not be fully compensated for within a local system. Any local production shortages may also be spread through international food trade. As a result, food supply shortages due to agricultural water scarcity can cause health damage as a major endpoint impact. However, the spread effects of shortages in food production will depend on the commodity balance of water consumers, the vulnerability of economy and food stocks in food-importing countries. In addition, the significance of food supply shortages will also be controlled by overall nutritional conditions and their variability in each region. Suitable characterisation factors at the endpoint were developed by integrating these parameters.
As mentioned above, physical factors such as freshwater resources and social factors relating to food production and consumption were included in the modelling at both the midpoint and endpoint. However, the availability of statistical data for many socio-economic indices is often limited to the national level. Therefore, the geographic resolution of both the midpoint and endpoint was set at the scale of individual countries, even if some factors could be analysed at higher spatial resolution. Details of the modelling methodology are described in the following sections.
2.2 Midpoint modelling
where R Agr,i [dimensionless] is the ratio of agricultural water use to total water withdrawal in country i, IDR i [dimensionless] is the irrigation dependency ratio for crop production in country i, PCF i [dimensionless] expresses the physical compensation capacity of country i, and SCF i [dimensionless] expresses the social compensation capacity of country i.
The meaning of the CFs at the midpoint is the ratio of potential “net” production loss of irrigated crops resulting from freshwater consumption. Based on the hypothesis that all water users are proportionally affected and that production loss is proportional to irrigation loss, the product of R Agr,i and IDR i expresses the potentially lost share of agricultural production. The gross value is converted to the net loss by considering vulnerability of physical (in terms of water resources) and social (in terms of food stocks) aspects.
where WScI i is the scarcity index expressing a function of the ratio of water use to available water resources, AID ij [m3/year] expresses the annual demand of irrigation water for producing commodity j in country i, ATD ij [m3/year] expresses the annual total demand (including irrigation and rain) of water for producing commodity j in country i, AFP ij [kcal/year] means the average annual production of commodity j in country i for 10 years (from 2000 to 2009), and AFS ij [kcal/year] is the average annual stock of commodity j in country i for 10 years (from 2000 to 2009). For calculating AFP ij and AFS ij , the amounts of annual production and stocks in metric tons were converted into dietary energy based on FAOSTAT data (FAO 2013). Details of calculation of AID ij , ATD ij , AFP ij and AFS ij are described in Section 2.3.1.
2.3 Endpoint modelling
Malnutrition damage at the endpoint will occur when agricultural water scarcity causes losses of crop production and food supply cannot be locally compensated for. Consequences of crop production loss may be spread to other countries through international trade in food. Any resulting shortage of food in each country (not only water consumer but also food-importing countries) may cause malnutrition damage. For these reasons, the modelling incorporates three modules (food production loss assessment, food supply shortage assessment and health damage assessment) as described in the following sections.
2.3.1 The food production loss assessment module
where CPL ij [ton/m3] indicates the amount of production loss of commodity j in country i, IDR ij [dimensionless] expresses the demand ratio of irrigation water for commodity j to total irrigation water demand in country i, CY ij [ton/m3] means the crop yield of commodity j in country i per unit irrigation water input, and SCF ij [dimensionless] is the social compensation capacity of commodity j in country i.
where FPL ij [kcal/m3] expresses the food production loss of commodity j in country i (in terms of dietary energy), DEC ij [kcal/ton] means the unit conversion factor of food from metric weight to dietary energy, FPR ij [dimensionless] is the ratio of feed production amount to total crop production amount, and AFPR ij [dimensionless] is the ratio of each type of animal food commodity to total animal food (both in terms of dietary energy).
2.3.2 Food supply shortage assessment module
where FSLWC, j [kcal/m3] expresses the supply shortage of commodity j in the water consumer country, DSRWC, j [dimensionless] indicates the ratio of the domestic supply amount of commodity j to the total supply in the water consumer country, EACWC [dimensionless] describes the economic adaptation capacity in the water consumer country, FPLWC, j [kcal/m3] is the production loss of commodity j in the water consumer country, FSL ij [kcal/m3] is the supply shortage of commodity j in country i, ISR ij [dimensionless] is the import sharing ratio of commodity j in country i, and EAC i [dimensionless] is the economic adaptation capacity in country i.
where NIA ij [kcal] expresses the net import amount of commodity j in country i.
where GNI i [current US$;] is the per-capita gross national income of country i reported by the World Bank (2014b).
2.3.3 The health damage assessment module
Loss of food supplies will result in a prevalence of undernourished populations. While the size of the undernourished population also depends on many aspects of nutrition in each country, we chose average dietary energy supply per capita as the main parameter for describing fundamental nutrient conditions at the national level. Additionally, a gap in nutrient conditions in each country was assumed to be a potential factor in the creation of an undernourished population (Thompson and Meerman 2010). Therefore, the level of the undernourished population in each country was modelled using two parameters of nutrient conditions (average dietary energy supply per capita and the Gini coefficient of dietary energy consumption). Non-linear multiple regression analysis was applied to model the relationship between the undernourished population and the above two parameters in accordance with the procedures described in Motoshita et al. (2010). Details of the analysis procedure and results are found in the Electronic Supplementary Material of this paper. Fundamental data on the above two explanatory variables was collected from the FAO database (FAO 2010b, 2013).
where RUP i [%] expresses the rate of undernourished population in country i, ADES i [kcal/capita/day] is the average daily dietary energy supply per capita in country i, and GC i [dimensionless] is the Gini coefficient of dietary energy consumption per capita in country i.
The level of health damage (disability adjusted life years (DALYs)) from malnutrition on a per case basis (HDM i [DALY/case]) was calculated for each country by dividing total health damage from malnutrition in each country by the level of undernourished population (WHO 2008; FAO 2010b). By multiplying predicted undernourished population increases due to food supply shortages with the level of health damage of malnutrition per individual case, any increases in health damage from undernourishment caused by food supply shortages can be estimated. A point of notice is the limitation that the shortages in food supply are assumed to affect all population equally without considering the depth of lacking dietary energy for a specific part of the population. Thus, health damage calculated from Eq. (13) only describes the impact on all population at an average nutrient condition in each country.
2.3.4 The characterisation factor at the endpoint level
where P i [capita] expresses the population of country i.
The impact on freshwater consumption from agricultural water scarcity can be separately identified for national and trade-induced damage by using these characterization factors at the endpoint (specific characterisation factors are shown in Annex A2 in the Electronic Supplementary Material).
3 Results of the development of characterisation factors
3.1 Comparison with characterization factors of previous models at the midpoint
According to the distribution map of CF ranking (Fig. 2), the sensitivity to freshwater consumption in each country documents the differences in the CFs based on the generic and user-specific approaches. Some countries in North Africa, the Mediterranean, South-East Asia and South America recorded a lower ranking (are less sensitive) from the user-specific approach and a higher one from the generic approach. In addition, the differences in CFs among countries were larger in the user-specific approach (coefficient of variance = 3.38) than in the generic approach (coefficient of variance = 1.14). These differences can be attributed to the complexities in agricultural water demand and food compensation capacity in each country. However, no significant differences could be found in either cases when applying the water scarcity indices of WSI derived from Pfister et al. (2009) and those of Alpha obtained from Boulay et al. (2011) (details are found in the Electronic Supplementary Material).
In addition to the two WScI methods described above, the methods constructed by Frischknecht et al. (2006) and Hoekstra et al. (2012) may be made applicable by modifying the geographical scale and coverage of countries. Basically, a space for the inclusion of WScI in Eq. (2) is left for the users of this midpoint indicator when assessing the impact of freshwater consumption (Annex A3 and A4 in the Electronic Supplementary Material). However, it was pointed out in a previous study (Boulay et al. 2014) that the modelling choices of each indicator might lead to significant differences in analytical results. Actually, differences among CFs based on the two water stress indices (Pfister et al. 2009; Boulay et al. 2011) in the present analysis are found in some countries (Fig. S2 in the Electronic Supplementary Material), but the trends seem to be fairly similar on the whole. Thus, the default choice of WScI is discussed in the following section to achieve consistency in the endpoint assessment.
3.2 Comparison with characterization factors of previous models at the endpoint
As shown in Fig. 3, the CFs derived by Pfister et al. (2009) were relatively lower (average, 0.305 × 10−6 [DALY/m3]) on the whole than for the other two methods (the average of Boulay et al. (2011), 8.71 × 10−6 [DALY/m3]; the average of this study, 5.75 × 10−6 [DALY/m3]). The reason for the lower CFs in Pfister et al. (2009) can be found in the lower impact factor of health damage resulting from the shortage of a unit of agricultural water (Boulay et al. 2014). The regional relative vulnerability of CFs in these two models showed some similarities throughout (generally vulnerable in South American, South-East Asian, Mediterranean and African countries), even though the scale of the CFs in both models was different. On the other hand, the CFs derived in the present study seemed to be similar in scale to those of Boulay et al. (2011), but regional relative vulnerability was different from those derived in the first two methods. Developed countries in North America, Oceania, Asia and Europe also showed relatively high CFs in this study. In Fig. 3, both national and trade-induced damage in other countries caused by water consumer countries result in relatively higher CFs in developed countries than CFs of the other two models.
Shares of national and trade-included damage in the CFs derived for this study for water consumption in four countries
4 Discussion
4.1 Verification of the assessment model
4.1.1 Estimation approach of malnutrition damage related to freshwater consumption
where AFS i [m3] expresses the amount of freshwater shortage in country i, WS i [m3] indicates the amount of total water supply (rain and irrigation) for agriculture in country i, MD i [m3] means the minimum demand of agricultural water, WC i [m3] is the amount of consumed freshwater in country i, and HHMalnutrition,i [DALY] is the health damage of malnutrition due to freshwater consumption in country i.
4.1.2 The results of estimated malnutrition damage
Estimated annual damage from malnutrition due to total global freshwater consumption
| Nutritional deficiencies reported by WHO [DALY/year] | Estimated damage from malnutrition due to freshwater consumption [DALY/year] | ||||
|---|---|---|---|---|---|
| CFs derived by Pfister et al. (2009) | CFs derived by Boulay et al. (2011) | CFs derived in this study | |||
| Total | National damage (as water consumer) | ||||
| Africa | 11,751,606 | 36,767 | 208,981 | 1,647,242 | 83,670 |
| America | 2,294,151 | 1642 | 49 | 242,135 | 2407 |
| East Mediterranean | 4,289,188 | 39,556 | 404,137 | 338,292 | 118,244 |
| Europe | 1,893,581 | 267 | 0 | 24,413 | 4 |
| Southeast Asia | 13,499,624 | 0 | 0 | 252,278 | 0 |
| Western Pacific | 4,912,083 | 0 | 0 | 120,145 | 0 |
| World | 38,640,234 | 78,232 | 613,167 | 2,624,505 | 204,326 |
The estimated damage based on the CFs reported in previous models showed big variations with the damage of nutritional deficiencies reported by WHO. Specifically, no damage was estimated in Southeast Asia and the West Pacific because many countries in these regions showed no shortage of agricultural water (see Eq. (15) and Table S5 in the Electronic Supplementary Material). On the other hand, the estimated damage based on the CFs developed in this study showed relatively higher levels of damage on the whole. In addition, some damage was estimated in Southeast Asia and the West Pacific even though no shortage of agricultural water was found in these locations. In these regions, some countries were affected by the levels of water consumption in other countries through international food trade as a result of their lower economic adaptation capacities, even though their direct demand for freshwater for agriculture may be satisfied under current supply situations.
The relationship between estimated damage from malnutrition due to agricultural water scarcity (based on the CFs derived by this study) and total damage from nutritional deficiencies (WHO 2008) at a country scale
4.2 Choices of water scarcity index
Correlation coefficients of estimated malnutrition endpoint damage with two water stress indices and the total damage from nutritional deficiencies on a regional scale
| Estimation based on CFs with water stress index by Pfister et al. (2009) [ND] | Estimation based on CFs with water stress index by Boulay et al. (2011) [ND] | |
|---|---|---|
| Africa | 0.347 | 0.311 |
| America | 0.212 | 0.164 |
| Eastern Mediterranean | 0.469 | 0.145 |
| Europe | 0.200 | 0.125 |
| Southeast Asia | 0.239 | −0.104 |
| Western Pacific | 0.208 | −0.138 |
5 Conclusions
Agricultural use of freshwater is generally the largest proportion of total freshwater demand for human activities. Food production shortages due to agricultural water scarcity may cause severe effects on human health in terms of a region’s nutritional conditions. On the other hand, the shortage of food production in a water consumer country can be compensated for by accumulating food stocks and/or spread to other countries through the international trade in food. These adaptation and trade-induced effects are now quantified in our model.
At the midpoint level, characterisation factors were developed in a manner consistent with those derived in the modelling of the endpoint by focusing on physical and social compensation factors (water resource availability, agricultural irrigation demand and food stock compensation capacity). At the endpoint level, malnutrition damage was assessed as a more specific result of freshwater consumption, along with consideration of the complex linkages among countries owing to commodity balance and economic vulnerability within the international food trade. As a consequence, characterisation factors could then reflect the actual social situation in describing the impacts of agricultural water scarcity. Specifically, the completeness of the assessment improved by specifying the impacts of national and trade-induced damage in the endpoint model. Achieving consistency between the CFs at midpoint and endpoint is expected to make the results of assessing the potential impacts of water use in worldwide supply chains more reliable.
In terms of water use for agriculture, the regional scale and temporal variations are very significant factors. These aspects have been integrated in midpoint models (Ridoutt and Pfister 2010; Hoekstra et al. 2012; Pfister and Bayer 2013). More specific modelling in terms of differences in regional and temporal resolutions is left as a future task, while the modelling in this study was performed at a country and annual scale due to limitations in the background data required for the analysis. The depth of lacking dietary energy for a specific part of the population may affect the severity of health damage of each malnutrition case, while the aspect was excluded in this analysis. This point is also expected to be discussed in future analyses for improving the endpoint modelling.
Supplementary material
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