Economic metrics to estimate current and future resource use, with a focus on water withdrawals

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Sustainable Production and Consumption journal homepage: www.elsevier.com/locate/spc

Economic metrics to estimate current and future resource use, with a focus on water withdrawals Janez Sušnik UNESCO-IHE Institute for Water Education, Integrated Water Systems and Governance Department, PO Box 3015, 2601DA Delft, The Netherlands

A B S T R A C T

Demand for, and use of, water, food and energy resources is already straining mostly non-renewable supplies (e.g. freshwater resources). Population growth, climate change and shifting socio-economic standards mean that these resources will be stretched to critical levels. This paper uses historical data of GDP and GDP-per-capita to identify correlations with 19 water, food and energy metrics (e.g. water withdrawals, total food production and electricity generation and consumption), and subsequently attempts to estimate plausible resource use and demand for a suite of seven GDP growth scenarios, focusing on water resources. It is shown that GDP-per-capita is weakly correlated with all metrics, however total GDP shows stronger correlation. Best-fit regressions and the statistical distributions of historical data were used to replicate the historical data and validate derived correlations. Following this, the GDP scenarios were used to estimate plausible global total water withdrawals, food production and electricity generation and consumption to 2100. If recent GDP growth is maintained, then the ‘safe planetary boundary’ for total freshwater withdrawal of 4000 km3 yr−1 is surpassed in c. 29% of simulations (out of 8700 simulations). However, in scenarios of global GDP shrinkage, the ‘safe’ limit was exceeded in only 14% of simulations. Estimates reported here for total water withdrawals agree closely with similar estimates from the literature, which tend to use more complex methods and models. Similarly, the food production and electricity generation totals can be related to available viable agricultural land or potential CO2 emissions respectively (e.g. through assumptions of future energy generation methods). It is hoped that this work will contribute to the growing global policy and development debate on resource use and sustainable development, particularly through the provision of probabilistic estimates for resource use and by using open-data sources and a relatively simple methodology. Keywords: GDP–resource relationships; Sustainable development; Water–food–energy system c 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. ⃝

1.

Introduction

Over one billion people currently lack access to safe drinking water and over 2.5 billion lack access to reasonable levels of sanitation (Moe and Rheingans, 2006). Likewise, about 840 million people are chronically malnourished (World Hunger, 2013) and approximately one billion lack access to a reliable electricity supply (World Bank, 2013). A growing population, projected to reach over nine billion people by 2050 (although this relies heavily on fertility assumptions; UNDP, 2013) and

improving lifestyles mean that the demand for water, food and energy is likely to increase in the future (Population Institute, 2003; RAEng, 2010), further stressing finite global resources. These resources include: heavily and over-exploited water resources around the world (e.g. vastly over-exploited groundwater sources, Gleeson et al., 2012); freshwater supply, leading to so-called ‘closed basins’ (Falkenmark and Molden, 2008); land for viable agriculture, which competes with land for growing urbanisation and is undergoing desertification and wastage and; traditional (i.e. fossil fuel based) energy

E-mail address: [email protected]. Received 25 February 2015; Received in revised form 19 May 2015; Accepted 20 May 2015; Published online 28 May 2015. http://dx.doi.org/10.1016/j.spc.2015.05.003 c 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. 2352-5509/⃝

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supply, sources, security and prices. On the agricultural side, recent work has shown that the yield increases for many staple crops have ‘peaked’ over the last 20 years (Grassini et al., 2015), having critical implications when the burgeoning global population is taken into account. The potential for a global water shortage is also critically important as water is deemed one of, if not the most important societal risk (WEF, 2015), with shortages and scarcity having knockon implications for a wide range of sectors including food, energy, health and economic development (e.g. Chenoworth, 2008; King et al., 2008; Allouche, 2011; Cai et al., 2014). Without sufficient water, our modern society is unable to function effectively. The recent ‘megadrought’ in North America is just one example demonstrating the crippling impact of water shortages to regional and continental food production and economic development, with such events predicted to get more frequent and intense through the 21st century (Cook et al., 2015). Water shortages are leading to increased incidents of water-related conflicts and security (e.g. Nordhas and Gleditsch, 2007; http://worldwater.org/water-conflict/). Being able to estimate and predict water resource use across a range of sectors would lead to a better understanding of how critical the water situation may get under plausible development scenarios, may help to plan for potential resource shortages, and may lead to efforts towards mitigation and reduction in consumption of different resources. There have been many efforts to predict future water supply due to climate change and socioeconomic development impacts (e.g. Vandecasteel et al., 2013; Zhang and Balay, 2014; Wada and Bierkens, 2014). Some studies have attempted to predict sectoral water requirements (e.g. domestic or agricultural sectors) as the climate changes and as land use is altered (e.g. Yoshikawa et al., 2014). Many of these methodologies rely on spatially explicit (often interpolated) data such as land use data, population density data and climatological data, to assess water supply and withdrawals. In addition, many also use reduced complexity pseudo-physically based representations for various hydrological processes, and make further assumptions regarding water demand. While sophisticated, the computational and data requirements can be prohibitive, and there are considerable sources of uncertainty. This work uses standard datasets and does not rely on the same assumptions of other studies, nor on complex computational processes. However, predicting future water use is notoriously difficult as it relates to many factors including local climatic conditions and supply/demand constraints, socio-economic development levels, local/regional/national water allocation legislation, capacity to enforce legislation, the relative importance of different economic sectors for a nation, and so on. One potentially promising avenue for the prediction of water withdrawals (here defined as the total quantity of water withdrawn from supply, regardless of how much is actually consumed, i.e. ‘lost’ from supply due to evaporation) is through the use of economic metrics such as the GDP or GDP-per-capita. Recent studies (Duarte et al., 2013) have suggested that water use per capita can be related through percapita income, with Duarte et al. (2013) suggesting that an Environmental Kuznets Curve (EKC) could be identified in relationships between these variables. The EKC is a curve with an inverted U-shape profile. The rising limb (of resource use for example) is usually associated with initial rapid economic growth and development, while the later falling limb may represent technological efficiency gains and changing societal values. However Katz (2015) showed that while some evidence

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of an EKC-relationship between economic variables and water use can be supported, this evidence is weak and strongly depends on the statistical methods used and the dataset(s). Results are also shown to be highly sector specific, with Katz (2015) concluding that EKC has limited value for water policy planning. Likewise, Gleick (2003) suggests that there is no discernible relationship between GDP and per-capita water use, although using a very limited dataset. While these studies are useful, they have their shortcomings. The studies of Duarte et al. (2013) and Katz (2015) focus closely on the theory of EKCs approached from an economic perspective. They also use relatively limited datasets in terms of geographic and temporal coverage, which is also a criticism of the study by Gleick (2003). With regard to food production, which is intimately linked to the finite viable land resource available and to water and energy availability, it is estimated that total production needs to double to 2050 in order to feed nine billion people with improving lifestyles, although it is also recognised that much of this increase could be met by significant reduction in wastage at all locations along the food production chain (IMechE, 2013). Predictions of food production are complicated by trade relations, market prices, climate change and shifting diets. For food, the focus could be placed on further land intensification, improvement of crop yields, or smarter cropping regimes that optimise land, climate and water conditions. Food production is intimately related to water withdrawal (IMechE, 2013; also note the importance of ‘virtual’ or ‘embedded’ water; Hoekstra and Hung, 2005), however while critically important this coupling is not the focus for this paper. For energy, the focus is on reducing our current reliance on fossil fuel derived energy sources, on realising massive (technological) efficiency gains and on substitution to clean and/or renewable sources for energy generation. Predicting energy demand is also difficult due to market price volatility, changes in societal preferences, climate change impacts and the development of new, more efficient technologies that could decouple economies from fossil fuel reliance. For fossilfuel resources, such predictions of consumption would put our reliance on finite resources into perspective, and could help the current drive towards replacement of fossil fuels. As with attempting to forecast water use, similar work has been carried out in the food and energy fields (e.g. LotzeCampen et al., 2008; Unler, 2008), and being able to model potential future energy generation and consumption and food production globally using a single coherent metric would offer useful insights for policy makers, and would contribute to the current dialogue on so-called ‘nexus thinking’. In the energy field for example, work relating energy intensity (energy use per-capita) to GDP per capita (when weighted to purchasing power parity; IMF, 2011) which shows a nonlinear relationship between these variables for different nations, suggesting that using these two variables alone may not lead to strong predictive capability. The work in this paper builds on previous work by attempting to define general relationships between resource use and economic indicators. While the central theme is focused on the withdrawal of freshwater resources, water is not the sole focus, with food and energy metrics also being analysed and discussed. It uses extensive global datasets both spatially and temporally to overcome limitations from previous work. This study presents the relationships between GDP and GDP-per-capita with a number of water, food and energy related statistics including annual water withdrawals

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per capita and total water withdrawals. In addition, this paper also seeks to identify relationships between GDP and GDPper-capita and various food and energy metrics such as total food production and electricity generation and consumption. The underlying idea is that economic measures can be used to broadly estimate water, food and energy production and consumption values at a global scale whilst recognising the inherent uncertainties. While food production and electricity generation may not be considered resources per-se, they do rely on finite resources to be available for conversion and use (e.g. food requires a finite, viable land resource, and electricity generation requires finite fossil fuel resources for the majority of methods). The methods used in this work present some advantages over previous methods to estimate (water) resource use. Firstly, large, spatially-explicit datasets, which often need extrapolating/interpolating and/or some form of scaling, are not required, reducing uncertainty in an already uncertain field of study. In addition, many previous studies consider water, food and energy in isolation, whereas with the methods and data used here, all three are studied together, implying they are part of a wider system, although it is made clear that explicit links and feedbacks between the metrics are not considered here. Finally, many economic analyses require considerable specialist knowledge and access to economic modelling packages. This study requires neither of these while still offering useful, robust analyses and conclusions and comparability with alternative methodologies. In terms of usefulness of this work, it is intended that the numerical analyses are accessible to anyone with basic analytical and computational experience, particularly as specialist software is not required. The results of this work are intended to inform academia for further research into the currently burgeoning ‘water–food–energy’ discussions as are therefore useful to researchers in many fields. The results may also be useful for policy/decisions at (inter-)national scale. By offering a range of development scenarios (using GDP as a proxy) and by accounting for uncertainty in results, this work hopes to bring clarity and transparency to those dealing with national level development issues.

2.

Data and methods

This study uses data from a number of sources in order to form relationships between the economic metrics (GDP and GDP-per-capita) and the resource-related statistics (water, food and energy related). All primary data used in this study is at national spatial and annual temporal resolution. Table 1 outlines the data used, along with statistics regarding these data and their sources. For comparison, the study of Gleick (2003) uses a single year of data for selected countries, whereas this work includes all matching countries (see below and Fig. 1) for 1960–2008, and includes many more metrics than the Gleick study. The study by Duarte et al. (2013), while using a long dataset as is the case here, focus only on per-capita water withdrawals and per-capita GDP, whereas this work uses many more water, food and energy metrics, and also includes total national GDP. Finally, while the study of Katz (2015) also uses the FAO AQAUSTAT database, only the period 2003–2008 is used unlike here where all data are included. Katz (2015) does use an OECD dataset which is not used in this work. The economic metrics (GDP and GDP-per-capita) were used as independent variables, with the resource parameters

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Fig. 1 – Outlining the main stages carried out in this study. (Table 1) being the dependent variables. The analysis in this paper consists of two main parts: (i) global-scale nationallevel analysis of historical economic and resource data in order to define potential correlations between the economy and resource use accounting for local and regional variability and; (ii) global-level analysis based on the results from (i) to estimate global water, food and energy use to 2100 while accounting for the variability analysed in (i). Prior to analysis, the original, raw datasets were split approximately 50:50 into ‘training’ and validation datasets. This was done by selecting pseudo-randomly from the data c. 50% of the data for the ‘training’ set (used to generate the best-fit relationships). The remaining c. 50% were used for validation, and are plotted with the simulated data. These two sets are mutually exclusive. The general analysis process in part (i) used in this study was (Fig. 1): (1) Find a match in country names between independent and dependent variable datasets. Variations on names were accounted for (e.g. United States and United States of America). Exclude non-matches. Exclusion represents a small number of countries (usually ∼10). (2) For each dataset (dependent and independent), data were rationalised in order to make them directly comparable, country-by-country, year-by-year. Null/empty data were treated as a blank or zero (blank data were filtered out in Step 3). (3) The rationalised data were processed to include only instances where there was a data entry for a given country and a given year in both the economic and resource metrics. If both metrics had no data, or if only one metric had data for a given country and year, these were excluded. All filtered ‘complete entries’ were selected and transformed using log10 . This ‘complete’ dataset was split approximately 50:50 as described above. Only the ‘training’ set of the data was used in Step 4.

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Table 1 – Details of the GDP and 19 resource metrics, the number of countries in each dataset, the coverage of the data and the sources of the data. Metric

Total number of countries in dataseta

Temporal coverage [min range; maximum range; completeness]

Data source

GDP

203

World Bank (August 2014; http://data.worldbank.org)

GDP per capita

218

Water withdrawal per capita

183

Agricultural water withdrawal

183

Industrial water withdrawal

183

Municipal water withdrawal

183

Total water withdrawal

183

Water withdrawal as percent of total renewable supply Cereals productionb

183

Root crops productionb

176

Pulses productionb

176

Treenut crop productionb

176

Oil crop productionb

176

1960–2013 [1 year; 54 years; variable completeness from totally complete 1960–2013 to single entry and patchy] 1970–2012 [5 years; 43 years; continuous where data are supplied] 1962–2012. Data are reported in approximately five-year intervals, although this varies between countries. [1 entry; 9 entries; variable from complete records to only one value entered] 1962–2012. Data are reported in approximately five-year intervals, although this varies between countries. [1 entry; 9 entries; variable from complete records to only one value entered] 1962–2012. Data are reported in approximately five-year intervals, although this varies between countries. [1 entry; 9 entries; variable from complete records to only one value entered] 1962–2012. Data are reported in approximately five-year intervals, although this varies between countries. [1 entry; 9 entries; variable from complete records to only one value entered] 1962–2012. Data are reported in approximately five-year intervals, although this varies between countries. [1 entry; 9 entries; variable from complete records to only one value entered] 1962–2012. Data are reported in approximately five-year intervals, although this varies between countries. [1 entry; 9 entries; variable from complete records to only one value entered] 1961–2013. Data are reported at annual intervals for each country. [14 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [8 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] UN FAOSTAT database (August 2014) 1961–2013. Data are reported at annual intervals for each country. [6 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [5 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [4 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.]

176

UN Data (January 2014; https://data.un.org) UN FAO AQUASTAT database (August 2014; http://www.fao.org/nr/water/aquastat/main/index.stm)

UN FAO AQUASTAT database (August 2014)

UN FAO AQUASTAT database (August 2014)

UN FAO AQUASTAT database (August 2014)

UN FAO AQUASTAT database (August 2014)

UN FAO AQUASTAT database (August 2014)

UN FAOSTAT database (http://faostat3.fao.org/; August 2014)

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

(continued on next page)

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Table 1 (continued) Metric

Total number of countries in dataseta

Temporal coverage [min range; maximum range; completeness]

Data source

Vegetable crop productionb

176

UN FAOSTAT database (August 2014)

Fibre crops productionb

176

Fruits productionb

176

Citrus crop productionb

176

Coarse grain crop productionb

176

Total crop productionb

176

Total net electricity generation

184

Total net electricity consumption

184

1961–2013. Data are reported at annual intervals for each country. [7 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [13 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [7 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [9 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [7 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1961–2013. Data are reported at annual intervals for each country. [8 years; complete coverage; where data is available, coverage is good. Countries with no data/no production are not included here.] 1980–2011. Data are reported at annual intervals for each country. [6 years; complete coverage; where data are recorded, coverage is good] 1980–2011. Data are reported at annual intervals for each country. [6 years; complete coverage; where data are recorded, coverage is good]

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

UN FAOSTAT database (August 2014)

US Energy Information Administration (www.eia.gov; August 2014)

US Energy Information Administration (www.eia.gov; August 2014)

a This is the total number of countries with at least one data entry in the timeseries. These countries are not all necessarily used in the

relationship analyses, which depends on there being corresponding data available in both of the variables. Only when data for a given value and year are available in both parameters is that country used. See Section 2. b Production of each crop type (all in kg) was calculated from statistics on individual crop yields per each country in the data set (data from the UN FAOSTAT database) and on the area planted by each crop type in each country (data from UN FAOSTAT database). The total crop production is the sum of the different crop types.

(4) log10 transformed ‘training’ data were plotted (x–y scatter) to identify possible regression relationships between metrics. Plotting was done (a) for all data combined (i.e. all countries and years together) and (b) for all countries but individual years in order to assess potential long-term structures in relationships. As an extra step in order to test the robustness of any derived relationships between metrics, the best-fit regression equation between metrics (Step 4 above and Section 3) and the best-fit statistical distribution (see below) of the entire matched dataset (result after Step 3 above) was used in Monte-Carlo simulations in order to replicate historic resource metric values using only the economic metrics whilst maintaining the structure of the original dataset. Replicated values were assessed against the validation part of the dataset. These simulations were performed in MATLAB, and consist of 5000 runs with psuedorandomly chosen economic values within the range of observed values from the

original data. The economic values were assumed to have a uniform distribution. For each psuedorandom choice of economic value (5000 values), the best-fit regression relations (Table 2) were used to calculate the mean of the dependent resource-related variable. In order to avoid assumptions regarding the distribution of the dependent data, for each dataset, the best-fit distribution of the training data was assessed. This distribution and its associated parameters were then sampled to represent the spread of the observed data. The best-fit distribution was chosen from a set of 17 distributions (beta, Birnbaum–Saunders, exponential, extremevalue, gamma, generalised extreme-value, generalised Pareto, inverse Gaussian, logistic, log–logistic, lognormal, Nakagami, normal, Rayleigh, Rician, t location-scale and Weibull). The simulation results for the resource metric were compared to the ‘validation’ datasets. The point is to assess whether the statistical relationships can be used to recreate the broad structure of the historical data with a view for reasonable fu-

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Table 2 – Statistics of the best-fit regressions and distributions for each GDP–resource metric pairing. Relationship

Total number of data points

Best-fit regression equationa

R2 value

Best-fit distribution

GDP–per-capita water withdrawal GDP–agricultural water withdrawal GDP–industrial water withdrawal GDP–municipal water withdrawal GDP–total water withdrawal

232

0.21

Weibull (scale: 2.6509, shape: 6.2996)

238

y = −0.0553x2 + 1.3795x − 5.8193 y = 0.6581x − 6.9587

0.25

Normal (mean: −0.1040, SD: 1.2592)

209

y = 0.976x − 10.723

0.62

251

y = 0.7927x − 8.5802

0.74

Generalised extreme value (shape: −0.2096, scale: 1.0872, location: 1.1513) Normal (mean: −0.4632, SD: 0.8929)

243

0.57

Normal (mean: 0.4466, SD: 1.0885)

0.11

Not assessed

Extreme value (location: 9.4270, scale: 1.1427) Extreme value (location: 9.0054, scale: 1.0540) Weibull (scale: 7.9410, shape: 7.8484) Generalised extreme value (shape: −0.3040, scale: 0.9706, location: 6.6189) Weibull (scale: 8.0459, shape: 7.3926)

GDP–water withdrawal as percentage of renewable resource GDP–cereal production GDP–root crops production GDP–pulses production GDP–treenuts production

212

y = −0.0446x2 + 1.7509x − 12.932 y = 0.2888x − 2.1701

3451 3562 3249 1691

y = 0.8448x + 0.3569 y = 0.6229x + 2.2359 y = 0.5952x + 1.558 y = 0.528x + 1.5414

0.39 0.3 0.33 0.32

GDP–oil crop production GDP–vegetables production GDP–fibre crop production GDP–fruits production GDP–citrus production

3382 3632 2259 3499 4382

y = 0.5408x + 2.1361 y = 0.7763x + 0.6643 y = 0.5155x + 1.8801 y = 0.629x + 2.2634 y = 0.7399x + 0.322

0.24 0.61 0.18 0.39 0.37

GDP–coarse grains production GDP–total food production

3360 3706

0.4 0.51

GDP–net electricity generation

2360

y = 0.8313x + 0.1753 y = −0.0316x2 + 1.3624x − 0.9717 y = 10.57ln(x) − 23.703

0.87

GDP–net electricity consumption

2608

y = 10.573ln(x) − 23.771

0.9

Weibull (scale: 7.5923, shape: 6.5582) Extreme value (location: 9.0134, scale: 0.9112) Generalised extreme value (shape: −0.4083, scale: 1.3234, location: 7.3410) Extreme value (location: 9.1083, scale: 1.1115) Weibull (scale: 9.8531, shape: 9.9631) Generalised extreme value (shape: −0.3304, scale: 1.2219, location: 0.2589) Generalised extreme value (shape: −0.3155, scale: 1.1848 location: 0.2111)

a x is the independent variable (GDP), y is the dependent variable (the resource metric of interest). Regression and best-fit distributions were

created from the ‘training’ set of data (see Section 2 for details). SD = Standard Deviation.

ture projections to be made. Regional differences in economic growth, population development, and resource use, and the relationships between these variables, would probably lead to different national or regional relationships than the globalaverage assessed here. This study accounts for this variability by assessing the statistical distribution of each relationship, and using this in probabilistic forecasting (see below). Because the future direction of global development is extremely uncertain, it is becoming conventional to model future predictions under a range of scenarios of plausible pathways (e.g. the Shared Socioeconomic Pathways; Kriegler et al., 2012). For part (ii) of the study, in-keeping with the idea of pathways, in order to forecast global resource use to 2100, a suite of seven global GDP development scenarios are used to estimate potential resource use. Using global GDP growth forecasts from the International Monetary Fund (IMF), the average global GDP growth between 2014 and 2019 is predicted to be 3.8% year−1 (IMF, 2014). From 2014 to 2019, this IMF growth rate was used in all scenarios (expect the last, see below), after which the differences in scenarios take hold. For each scenario, global GDP was approximated every year from 2020 to 2100. The seven global GDP scenarios are: 1. The IMF 2014–2019 average growth value of 3.8% yr−1 was assumed to remain constant from 2020 to 2100. 2. For the second scenario, country-level GDP data for 214 countries from 1960 to 2013 from the World Bank was

3. 4. 5. 6. 7.

used (data.worldbank.org; last accessed August 2014). The GDP for each nation was aggregated for each year, giving an annual global GDP estimate between 1960 and 2013 (i.e. country-specific information is now lost). The percent change in globally-estimated GDP between two years (e.g. 1960 to 1961, 1961 to 1962, etc.) was calculated through the time series. Linear regression through the %-change time-series yielded the following: GDP%change = −0.1319(YEAR) +12.023 (R2 = 0.13). This equation allows an estimate of the %-change in GDP from one year to the next to be made for any year, and was used to estimate the %-change in GDP from 2020 to 2100. The results from this equation were used to estimate GDP annually from 2020 to 2100 by using the % change from one year to the next to estimate GDP. For this scenario, a constant GDP growth rate from 2020 to 2100 of 2% yr−1 was assumed. A constant GDP growth rate of 5% yr−1 was assumed. A constant GDP growth rate of −2% yr−1 was assumed. A constant GDP growth rate of −4% yr−1 was assumed. For the final scenario, the IMF (2014) dataset was again exploited, but this time, country-level estimates of GDP growth from 2014 to 2019 were used for 189 countries. For each country, the average predicted GDP growth from 2014 to 2019 was calculated. This average was then assumed constant for that country from 2020 to 2100. The GDPs for the 189 countries were estimated based on the specific growth rates, then aggregated for each year, giving global

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emissions), global totals are more important, while for other such as water resources, regional or local totals are more important, although the focus here is on global-scale analysis. For the forecast simulations, the estimated global GDP value at each year for each scenario is used. The resource metric for each year was then estimated using the global GDP, the relevant regression relationship and best-fit statistical distribution (Section 3, Table 2). For each of the 87 years from 2014 to 2100, a GDP value was estimated and 100 Monte-Carlo simulations were run (total therefore = 8700 datapoints) to approximate the distribution of the resource metric. Fig. 2 – Historical global GDP growth and the seven GDP growth scenarios to 2100 used in this study. Numbers represent the GDP scenarios described in Section 2.

3.

Results

3.1. Relationships with GDP-per-capita as the independent variable

GDP estimates from 2020 to 2100 based on per-country GDP growth estimates.

When GDP-per-capita is plotted against the water withdrawal metrics (Table 1), no discernible relationship is evident for any metric (Fig. 3). The lack of relationship also holds over time where there are sufficient data to make observations (Fig. 4, i.e. time-variant relations are not ‘lost’ in the entire dataset). All plots, whether the entire dataset or broken down per-year (Figs. 3 and 4 respectively) show an unstructured point cloud of data. That is, there is no finer-timescale structure hidden within the entire dataset. With respect to the food production metrics (Table 1), the situation is very similar, with no obviously discernible relationship between GDP-per-capita and any of the food metrics (Fig. 5). As with the water metrics, this lack of relationship holds over the time span of the dataset. Regarding the electricity generation and consumption metrics (Table 1), while there is some hint of a relationship (Fig. 6), these relationships are weak, and are defined mainly

Fig. 2 shows the trajectories of global GDP under the seven scenarios described above. While some of these scenarios may appear extreme, they are designed to cover most probable GDP futures. In addition, the ranges are within recent historical GDP growth ranges (UN, 2015), and are also within the range of most national forecasts to 2019 (IMF, 2014). It is important to note that while the historical analysis and replication described above are based on country-level data, the suite of forecasts are for global GDP totals between 2014 and 2100. Although country-level detail is lost, information regarding global totals is gained. By using the national-level data for the historical analysis, local level variability and uncertainty could be accounted for in probabilistic globallevel forecasting, something that would not be possible if only global-level per-year values were used in the earlier analysis. For some resources (e.g. ores, those with considerable CO2

a

b

c

d

Fig. 3 – GDP-per-capita vs. (a) per-capita water withdrawals; (b) agricultural water withdrawals; (c) municipal water withdrawals and; (d) total water withdrawals.

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Fig. 4 – GDP-per-capita vs. (a) per-capita water withdrawals; (b) agricultural water withdrawals and; (c) total water withdrawals over time. Each box is one year. Top left box is 1970, and boxes are read left-to-right. Absence of a box indicates no data for that year. All x-axes are log 10 GDP-per-capita. All y-axes are log 10 [metric], with [metric] being water withdrawal per-capita in (a), agricultural water withdrawal in (b) and total water withdrawal in (c).

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Fig. 5 – GDP-per-capita vs. (a) cereal crop production; (b) vegetable production; (c) coarse grain production and; (d) total crop production. Light grey symbols express national variability: circles—USA; triangles—China; squares—Kenya; diamonds—Germany and; crosses—Solomon Islands. Note that for plot (c), data for Solomon Islands were not available.

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Fig. 6 – GDP-per-capita vs. (a) net electricity generation and; (b) net electricity consumption. Light grey symbols express national variability: circles—USA; triangles—China; squares—Kenya; diamonds—Germany and; crosses—Solomon Islands. by the extensive scatter in the plots. The trends are certainly not as strong as those compared against GDP (Section 3.2). Over time, the trends are stable. While these figures generally show no global relationship, some trends within the set are country specific. These are shown in Figs. 4–6 as highlighted points, and reflect the heterogeneity in resource use that reflects regional/local availability, infrastructure, social norms and development.

3.2.

Relationships with GDP as the independent variable

When national GDP is plotted against the same water withdrawal data, relationships of variable robustness are discernible for all metrics (Fig. 7). Many of these appear to be structurally stable over time when there are sufficient data to make these relationships out for a given year. Interestingly, the relationship between GDP and per-capita water withdrawals (Fig. 7) appears to show the beginning of an EKC-style inverted-U shaped relation, although the relationship is weak. Moving on to the food production metrics, these too show relationships of variable strength when plotted against GDP.

All relationships are considerably stronger than for GDP-percapita, as with the water data. All relationships for the food production metrics are linear, except for the relationship with total food production, for which a second-order polynomial regression was found to be strongest. For the energy metrics, the relationship with GDP is particularly strong for both generation and consumption (Fig. 8), and is structurally very consistent over time (Fig. 9). Table 2 lists each best-fit regression relationship, the total number of data points, the regression coefficients obtained and the characteristics of the best-fit statistical distribution of the dataset. These relationships and their temporal robustness suggest that GDP can be tentatively used to estimate resource-related metrics, although there is some considerable scatter to account for. This is explored in the next section.

3.3. Using identified relationships to reconstruct observed metrics From the results in Section 3.2, regression relationships of variable robustness were observed between the economic and ‘validation’ set of resource metrics, although with variable

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Fig. 7 – GDP vs. (a) per-capita water withdrawals; (b) agricultural water withdrawals; (c) municipal water withdrawals and; (d) total water withdrawals. Data shown are from the ‘training’ set of observed data (see Section 2 for details).

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Fig. 8 – GDP vs. (a) net electricity generation and; (b) net electricity consumption. Data shown are from the ‘training’ set of observed data (see Section 2 for details). amounts of scatter (expressed in the statistics of the bestfit distribution of the dataset, Table 2). In all cases, the relationships are stronger for GDP than for GDP-per-capita. Therefore, total GDP is chosen as the economic metric to use in order to reconstruct historical values. A MATLAB routine was used to generate 5000 synthetic data points for each economic–resource metric comparison. The regression equations (Table 2) were used to estimate the mean of the resource metric for each GDP value sampled (from a uniform distribution between the minimum and maximum observed GDP in each dataset), while the best-fit distribution statistics replicated the scatter during the simulations. Fig. 10 shows the results of the simulations reconstructing some of the water metrics, and the comparison with the relevant ‘validation’ historical dataset. The simulated data reproduce the ‘validation’ observed datasets well, and replicate the observed range of scatter throughout the datasets. The spread of the historical data is captured reasonably well by the simulations, suggesting that the bestfit distributions and regression equations can be used to reasonably replicate the structure of the observed data series’. Fig. 11 shows the same results for some of the ‘validation’ food production metrics. As with the water metrics, the simulated data replicate the historic data reasonably well,

even capturing the more extreme outlying points, suggesting that the derived best-fit distributions are representative of the data. The ‘validation’ historic energy data are captured very well by the simulations (Fig. 12), and are replicated accurately by the simulations. The relationships found here can be confidently used to recreate energy metrics from GDP values, even more so than for the food or water sectors. For all these simulations, observed results show greater spread at the extremes of the GDP values than in the centre when compared to the historic data. This is because these extremes are located at the earliest or most recent data entries in the records, and are generally less complete than those in the middle of the data series. With more data, it would be expected that these extremes would also show a greater spread in the historic data series.

3.4. Forecasts of future resource use under the global GDP scenarios The future forecasts of resource use are based on global GDP totals, as opposed to the national-level totals used in previous sections. Therefore, although national-level detail is lost, information regarding global resource use is gained

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Fig. 9 – GDP vs. (a) net electricity generation and; (b) net electricity consumption over time. Each box is one year. Top left box is 1980, and boxes are read left-to-right. Absence of a box indicates no data for that year. Data shown are from the ‘training’ set of observed data (see Section 2 for details). while the variability in resource use due to local and regional level differences is still captured. Results for total global water withdrawals are shown in Fig. 13. The trends in withdrawals clearly reflect the underlying GDP scenarios (Fig. 2), though with considerable spread in results. Figs. 14–16 show the predictions for food production and electricity generation and consumption respectively. These results clearly reflect the GDP scenarios, with the scatter being dictated by the statistics of the historical data. These results are discussed further in the next section.

4.

Discussion and implications

The results of this research show that while economic metrics may be used as predictors of water resources withdrawals, the strength of observed relationships strongly depends on the economic metric used and on the metric under

consideration, agreeing with the conclusion of Katz (2015) for water withdrawals. This study shows that GDP per-capita apparently has no strong relationship with any water-related metric (Figs. 3, 5 and 6). This is somewhat surprising because it is commonly thought, for example, that as per-capita GDP increases, per-capita water consumption also increases for example (e.g. Duarte et al., 2013). Clear relationships between GDP per-capita and the other metrics were also not found. It is possible that countryspecific factors such as technology, culture, policy decisions, and local climate, are masking broader trends. However, despite this local variability, global trends still emerge when total national GDP is used. GDP does show relationships with the resource-related metrics, although the strength of these relationships is variable (Figs. 7, 8 and Table 2). Despite this, the relationships are always stronger than for GDP-per-capita. When the statistical properties of the relationships (Table 2) were used

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Fig. 10 – Replicated historical data for GDP vs. (a) per-capita water withdrawals; (b) agricultural water withdrawals; (c) municipal water withdrawals and; (d) total water withdrawals. Light grey dots are simulated data. Black dots are historical data. Note that for the historical data, only the ‘validation’ set from the entire record is shown (see Section 2).

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Fig. 11 – Replicated historical data for GDP vs. (a) cereal crop production; (b) vegetable production; (c) coarse grain production and; (d) total crop production. Light grey dots are simulated data. Black dots are historical data. Note that for the historical data, only the ‘validation’ set from the entire record is shown (see Section 2). in Monte-Carlo simulation, very good agreement between the simulated and observed data was found, suggesting that in lieu of complete datasets, GDP could be used to reconstruct or potentially predict a reasonable range of global resource-related values such as municipal or industrial water withdrawals, or electricity consumption.

The relationships found as part of this work have been used tentatively to predict a range of plausible future resource use based on various scenarios of global GDP development. Many current estimates for water supply and demand rely on predictions to future population (e.g. Alcamo et al., 2007; Yoshikawa et al., 2014), climate change predictions and the

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Fig. 12 – Replicated historical data for GDP vs. (a) net electricity generation and; (b) net electricity consumption. Light grey dots are simulated data. Black dots are historical data. Note that for the historical data, only the ‘validation’ set from the entire record is shown (see Section 2). impacts on crop and domestic demand (e.g. Lissner et al., 2014; Wada et al., 2014), or similar extrapolated trends (e.g. Amarasinghe and Smakhtin, 2014). This work is different, relying instead on GDP scenarios to estimate future water resource use, and therefore adding to the growing literature on estimating future resource use by providing an additional metric with which to estimate future resource use. The richness of the historical data in terms of temporal and spatial coverage means that robust statistical relationships were formed that capture most of the uncertainty and spread inherent in trying to predict such metrics between different countries with very different levels of development, technology, policy and culture. Still, considerable uncertainty comes about due to differences in local/national social structures and preferences with respect to water resource withdrawal and demand, economic development levels and climate variables for example. To recognise some of this uncertainty, seven different GDP scenarios were simulated (Fig. 2, Section 2). By capturing some of the range of uncertainty means that forecasts of water withdrawal will likely be able to represent the range of plausible future withdrawals for a given sector or in total. Figs. 12–16 therefore illustrate (a) a range of uncertainty in GDP forecasts and (b) the inherent global heterogeneity of resource use (including food and energy), deriving from local and regional scale circumstances. The global-level future estimates for resource use allow for the broad-scale systems picture to be assessed, without ‘getting lost’ in regional or local scale details, which are nevertheless important. Results show that estimates of future water resource use closely follow the GDP scenarios (Figs. 12–16). The scatter, which in some cases can partially mask the GDP signature, derive from the statistics of the underlying historical data (Table 2). Of particular interest here is that of global total water withdrawals (Fig. 14) due to the global importance of sufficient freshwater resources. Steffan et al. (2015) place the ‘safe planetary boundary’ for global water withdrawals at 4000 km3 yr−1 , with a current global estimate at 2600 km3 yr−1 . These simulations offer estimates of if the safe boundary will be exceeded, and if so, with what probability for each GDP scenario. Fig. 14 indicates the 4000 km3 boundary, while Table 3 shows how often this limit is exceeded throughout the simulation period for each scenario. For example, for GDP scenario 1 (Section 2), the safe boundary is exceeded in c. 28.9% of simulations, while in scenario 6 it is exceeded in only 14.2% of simulations. For

Table 3 – Percentage of simulations where estimated total global water withdrawals exceeded the safe planetary boundary of 4000 km3 yr−1 (Steffan et al., 2015). For GDP scenario definitions, see Section 2. GDP growth scenario

Percent of simulations where total water withdrawals were >4000 km3 yr−1

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7

28.9 22.9 25.5 30.7 17.9 14.2 30.3

the GDP scenario based on per-country growth rates (scenario 7), the proposed safe boundary was exceeded in 30.3% of simulations. These results clearly show how future economic growth could dramatically affect water withdrawals, and how in turn, this could severely impact the sustainability of this critical resource, assuming current management practices and societal values. With regard to estimates of current total water withdrawals, Steffan et al. (2015) give a value of 2600 km3 yr−1 , while Wada and Bierkens (2014) give a value of 4000 km3 yr−1 , and Hanasaki et al. (2013a,b), give 3214 km3 yr−1 . By 2100, Wada and Bierkens project total withdrawals of 6000 km3 yr−1 , and Hanasaki et al. (2013a,b) projected at least 5802 km3 yr−1 (industrial withdrawals were not accounted for). Previous studies project a range of values due to differences in underlying datasets and differences in the models used including spatial and temporal resolution, and different assumptions regarding water withdrawals. The range of values estimated here, both current and future, is consistent with other recent estimates using very different methods, and expands on previous estimates by offering a range of uncertainty for these estimates, and the probability that the proposed global safe limit is exceeded. Similarly, the global totals for food production could be related to total viable agricultural land availability and use through a land-use intensity metric to determine if the land required to grow the predicted food totals is available, assuming current yields remain constant. Zooming in to country-level data would allow assessment of local land availability constraints. This is an aspect for future research. On the energy aspect, the future electricity total generation estimates given here could be related to various predictions regarding future energy mixes, and thus in turn be used to estimate finite fossil fuel probable lifetimes at

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Fig. 13 – Future estimates of total global water withdrawals under GDP scenario (a) 1; (b) 2; (c) 3; (d) 4; (e) 5; (f) 6 and; (g) 7. See Section 2 for scenario definitions. Horizontal line indicates the 4000 km3 yr−1 safe planetary boundary proposed by Steffan et al. (2015).

present reserve estimates and extraction rates and likely

the proposed safe boundary of 350ppm can be returned to

values for CO2 emissions, and therefore whether or not

(note: current CO2 levels are 400ppm and rising). However,

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Fig. 14 – Future estimates of total global food production under GDP scenario (a) 1; (b) 2; (c) 3; (d) 4; (e) 5; (f) 6 and; (g) 7. See Section 2 for scenario definitions. forecasting CO2 emissions into the distant future will

integrated assessment or energy–economic models might be

naturally require considerable assumptions about local and

more suitable in this situation.

regional generation methods and energy mix scenarios,

This work did not account for what happens if one, or

which while they may be reasonably well known for the

more, of the planetary boundaries are exceeded. Largely,

next 5–10 years, are unknown much beyond that. Additional

this is because these impacts are unknown. It is likely that

uncertainty comes from unknowns regarding technological

economic activity will be impacted, possibly severely, as

advances, public perceptions and policy decisions regarding

these safe limits are neared and surpassed and as resource

energy generation. Other modelling approaches such as

limits are neared. However, the nature and intensity of these

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Fig. 15 – Future estimates of total global electricity generation under GDP scenario (a) 1; (b) 2; (c) 3; (d) 4; (e) 5; (f) 6 and; (g) 7. See Section 2 for scenario definitions. impacts will probably vary globally and will vary differently

is unknown. This work did not consider the fact that water,

according to which boundary is surpassed, and by how much.

food and energy resources form a complex system at global

It is very possible that resource use, whether this is for

scale, and there is no coupling between these sectors in

water, food or energy, will be forcibly reduced, although this

this study. Of course, in reality, changes to one sector (e.g. a

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Fig. 16 – Future estimates of total global electricity consumption under GDP scenario (a) 1; (b) 2; (c) 3; (d) 4; (e) 5; (f) 6 and; (g) 7. See Section 2 for scenario definitions. growing shortage of water) will have critical implications for

2008) with local and regional variability. This means that the

the food and energy sectors, and the economy. Linked to

impacts to water availability, and therefore to food production

this is the suggestion that CO2 concentration increases could

and energy generation, will also be affected, but in ways that

lead to intensification of the hydrological cycle (Kundzewizc,

are not yet clear and with high variability. In addition, there

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are natural limits imposed on many resources. For example, the volume of freshwater resources is limited over useful time periods, as is the viable cultivable land area and the finite fossil fuel reserves. These limits could be physical, such as the total land area that is suitable for agricultural development, the total amount of fossil fuel reserves that are viable for extraction, or the total available volume of renewable freshwater that can be sustainably abstracted (suggested as 4000 km3 yr−1 ; Steffan et al., 2015), or ‘guidance’ limits, such as those proposed by Steffan et al. (2015) for humanity to remain with its ‘safe operating space’. These limits are also not considered in this work, but will likely have a critical effect on society as we approach them unless we can achieve significant and sustained decoupling of the economy from resource consumption (one example here is the use of desalination to decouple freshwater withdrawals from the economy, although this comes with considerable energy implications). As more resources are used, changes will be forced upon other sectors. For example, as water shortages intensify, water productivity in agriculture may be forced by improve for example, or there may be impacts on thermal electricity generation. Robust future estimates of plausible resource withdrawal and production could aid planners and policy makers in shaping (inter-)national agendas for resource development and extraction, and could aid in the transition to a globally green economy (Hoekstra and Wiedmann, 2014; Schaffartzik et al., 2014; Damerau et al., 2015). These results, and especially the probabilistic nature of the results, could play a role in framing this discussion, but must be used in conjunction with other research and with complementary analysis. For example, with regard to electricity generation and consumption, this research accounts only for the net values, and is not broken down by generation method and therefore by primary fuel use rates or CO2 emission equivalents. By combining this research with data on energy mixes (and future projections thereof), CO2 -equivalent emissions measures could be estimated leading to assessments regarding total CO2 generation and the effectiveness of different energy-mix profiles in meeting emissions targets and of the impact of different mixes on water use. In addition, any projections of electricity generation (as a proxy of resource use) made from the work in this paper do not take into account limits to production or consumption. This work could therefore contribute to a better understanding of where we stand relative to some of those safe limits, and how our potential consumption patterns may drive us towards or away from these limits that threaten the stability of the global ecosystem.

5.

Conclusions

Water, food and energy availability and use are all currently under intense scrutiny as the global population increases and as climate and development changes take effect. The work in this paper has taken a step to attempt to estimate future water, food and energy demands by using economic metrics as a predictor for these sectors. Using comprehensive historical datasets on GDP and water, food and energy demands, it has been shown that GDP-per-capita shows no discernible relationship with any of the metrics evaluated. However, national GDP does show relationships of variable robustness, with many of these trends being apparently stable over time. Using best-fit regression relationships and the

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best-fit statistical distributions of the datasets, the nationallevel historical values for the various water, food and energy metrics were well replicated in Monte-Carlo simulations. Subsequently, seven global-level GDP scenarios were used in conjunction with the derived historical relationships to estimate a range of plausible probabilistic water, food and energy demands to 2100. These estimates can be framed in the broader context of safe planetary boundaries. For example, for total water withdrawal, it is shown that if historical average GDP growth continues, then the safe boundary limit for water abstractions (4000 km3 yr−1 ) was exceeded in 28.9% of simulations. Water withdrawal estimates agree well with similar estimates from the literature. For food, the production values could be tied to land availability, and for energy the generation estimates could be related to CO2 emissions by combination with various assumptions about future energy generation methods. While this work is a first step, and there are various assumptions and uncertainties to be addressed in future work, it is hoped that this work will contribute to the growing global debate on resource use and use efficiency, policy and development directions and how best to achieve a sustainable global future.

Acknowledgements This work made extensive use of open-access data sources made available by many different organisations, as listed in the references. I express my gratitude for these data. I thank all colleagues with whom I have had valuable discussions about this research. I also thank Gerald Corzo Perez and Dimitri Solomatine (both UNESCO-IHE) for assistance with the MATLAB aspect of the work. I gratefully acknowledge two anonymous reviewers who contributed to making this paper considerably stronger.

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