Systemic Risk in the Global Water IO Network

Transcription

1 Motivation and RQ Data and definitions SDA Network Conclusions Systemic Risk in the Global Input-Output Network Tiziano Distefano 1 Massimo Riccaboni 1 Giovanni Marin 2 1 IMT Institute for Advanced Studies, Lucca 2 IRCrES-CNR, Milano; SEEDS, Ferrara IAERE Conference Bologna, February 2016

2 Motivation and RQ Data and definitions SDA Network Conclusions Outline Motivation and research questions Water and sustainable development Water in the current policy debate Objective and related literature Data and definitions Data Definitions Balance of Virtual Water Trade Structural Decomposition Analysis Methodology Results of the SDA Network Analysis VW trade as a network Description of the VW trade network Conclusions

3 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Water in the debate on sustainable development Water is a primary input for many critical sectors (agriculture, food and beverage, energy production, pulp and paper, primary metals, etc) Water resources are not (always) correctly priced or property rights not adequately enforced overexploitation Water is not evenly distributed across regions (and in time) Water-scarce regions rely on import of water-intensive (direct and indirect) products to satisfy its domestic demand for Virtual Water The dependence on foreign Virtual Water exposes water-scarce countries to shocks in the water network Issue of access to water and water security at the core of the recent policy debate on sustainable development (SDG 2015) and climate change adaptation (CoP21 in Paris, 2015)

4 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Water in the debate on sustainable development Water is a primary input for many critical sectors (agriculture, food and beverage, energy production, pulp and paper, primary metals, etc) Water resources are not (always) correctly priced or property rights not adequately enforced overexploitation Water is not evenly distributed across regions (and in time) Water-scarce regions rely on import of water-intensive (direct and indirect) products to satisfy its domestic demand for Virtual Water The dependence on foreign Virtual Water exposes water-scarce countries to shocks in the water network Issue of access to water and water security at the core of the recent policy debate on sustainable development (SDG 2015) and climate change adaptation (CoP21 in Paris, 2015)

5 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje

6 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Water in CoP21 in Paris (2015) Water security is clearly a key development priority for most Parties and therefore various types of action related to the protection of water resources have been included in the adaptation components. These generally aim at saving water, ensuring security of supply, enhancing the allocation of water and broadening the resource base. [...] A few Parties are putting in place integrated water management systems. Some Parties seek to develop water-saving irrigation systems, while others referred to their consideration of climate criteria in their water management efforts. Some Parties outlined more specific techniques, such as digging wells, rainwater harvesting or the substitution of water withdrawal from aquifers with surface water. Synthesis report on the aggregate effect of the intended nationally determined contributions

7 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Objective(s) of the paper 1. Evaluate the role played by different drivers of VW Particular emphasis on the trade-related components 2. Description of the VW trade network Focus on the assessment of its topology and vulnerability Community structure (probably there will be no time for that...)

8 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Related literature IO and water use Lenzen et al. (2013) IO models include both direct and indirect water use along the supply chain Arto et al. (2012) production vs consumption based accounting of environmental pressures (including water) Cazcarro et al. (2013), Roson and Sartori (2015) SDA on water consumption Structural change (of production and consumption) has been in the direction of reducing water use Beyond drivers: network analysis Carvalho (2012) network theory has been extensively used to analyse trade flows as it enables to find non-local interdependencies among countries (nodes) and grasp information on the topology of trade patterns Acemoglu et al. (2012) Interconnections between different sectors play a key role in the potential propagation of idiosyncratic shocks throughout the economy Research on Virtual Water Network both interconnected and interdependent Vulnerability to exogenous perturbations (Sartori and Schiavo, 2014)

9 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Related literature IO and water use Lenzen et al. (2013) IO models include both direct and indirect water use along the supply chain Arto et al. (2012) production vs consumption based accounting of environmental pressures (including water) Cazcarro et al. (2013), Roson and Sartori (2015) SDA on water consumption Structural change (of production and consumption) has been in the direction of reducing water use Beyond drivers: network analysis Carvalho (2012) network theory has been extensively used to analyse trade flows as it enables to find non-local interdependencies among countries (nodes) and grasp information on the topology of trade patterns Acemoglu et al. (2012) Interconnections between different sectors play a key role in the potential propagation of idiosyncratic shocks throughout the economy Research on Virtual Water Network both interconnected and interdependent Vulnerability to exogenous perturbations (Sartori and Schiavo, 2014)

10 Motivation and RQ Data and definitions SDA Network Conclusions Water and sustainable development Water in the current policy debate Obje Our contribution We combine results of SDA and network analysis duality of trade We go beyond analysis of water related to food and/or agriculture The nodes of the network are country-sector pairs and not countries shocks may hit specific sectors in a country and these shocks propagate through input-output relationships

11 Motivation and RQ Data and definitions SDA Network Conclusions Data Definitions Balance of Virtual Water Trade Brief description of data Data sources: WIOD: World Input Output Database World IO tables Environmental extensions (blue, gray and green water use) FAOSTAT: water estimation (Mekonnen and Hoekstra, 2011) WORLD BANK: data on population Coverage: Countries: 40 (EU27, Usa, Japan, Brazil, China, India among others) + Rest of the World; Sectors: 35 (primary, secondary and tertiary); Period: from 1995 to 2009.

12 Motivation and RQ Data and definitions SDA Network Conclusions Data Definitions Balance of Virtual Water Trade Definitions Blue Water Blue water refers to the consumptive use of ground or surface water. Its supply is costly because it requires infrastructure. Blue water is mobile, it can be abstracted, pumped, stored, treated, distributed, collected, and recycled Each m3 saved can be directed toward alternative uses by industry and households. Virtual Water Virtual water is the volume of water needed to supply a commodity or a services. This includes both direct water use and indirect (along the supply chain) water use

13 Motivation and RQ Data and definitions SDA Network Conclusions Data Definitions Balance of Virtual Water Trade Definitions Blue Water Blue water refers to the consumptive use of ground or surface water. Its supply is costly because it requires infrastructure. Blue water is mobile, it can be abstracted, pumped, stored, treated, distributed, collected, and recycled Each m3 saved can be directed toward alternative uses by industry and households. Virtual Water Virtual water is the volume of water needed to supply a commodity or a services. This includes both direct water use and indirect (along the supply chain) water use

14 Motivation and RQ Data and definitions SDA Network Conclusions Data Definitions Balance of Virtual Water Trade Virtual Water trade balance vs water availability

15 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

16 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

17 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

18 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

19 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

20 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

21 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA We decompose changes in blue water consumption into several components following Xu and Dietzenbacher (2014) w = Θ(IE, T, H, POP, Q C, Q cap, D ) (1) IE represents the change in the vector of direct water use per unit of produced output H is the contribution to water footprint of changes in the technical coefficients matrix (with a fixed geographical structure of intermediate inputs) T accounts for changes in the geographical composition of the mix of intermediate inputs for a fixed mix of intermediates Q C represents changes in water footprint due to change in the structure of final demand (with a fixed geographical structure) similar to H D is the contribution of changes in geographical composition of final demand similar to T Q cap is the change in the level of aggregate final demand per capita POP represents the change in population

22 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA Link to the simple Leontief model Leontief model: w = e L F (2) where: e = k < x > 1 is the vector of water use (vector k) per unit of output L is the Leontief matrix F is the matrix of final demand Link to components: IE = e L F H + T = ē L F Q C + D + Q cap + POP = ē L F

23 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA Link to the simple Leontief model Leontief model: w = e L F (2) where: e = k < x > 1 is the vector of water use (vector k) per unit of output L is the Leontief matrix F is the matrix of final demand Link to components: IE = e L F H + T = ē L F Q C + D + Q cap + POP = ē L F

24 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA Figure: SDA of water demand for the World 60% 40% 20% 0% -20% -40% -60% IE H T Q_C D* Q_CAP POP Tot

25 Motivation and RQ Data and definitions SDA Network Conclusions Methodology Results of the SDA SDA: summing up Not surprisingly scale-related variables (level of final demand per capita and population growth) contributed to substantial increases of blue water consumption (about +65 percent) Many differences across countries (that directly reflect differences in economic and demographic growth) Composition of final demand in the direction of reducing water (i.e. reduction in relative consumption of water-intensive products) But composition of mix of intermediates goes in the opposite direction Changes in trade play a very marginal role (and positive...) Other factors (not related to water) influence trade patterns

26 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Virtual Water Trade as a network Virtual Water Trade can be seen as a network: Country-sector pairs are the nodes The amount of virtual water traded between nodes is the (weighted) link The network is asymmetric Our approach: We evaluate the directed (i.e. asymmetric) network of water embodied in the exchange in intermediate inputs Only consider links with weight > 1000m 3 We consider both overall trade (including domestic exchanges between sectors) and pure international trade (excluding domestic exchanges)

27 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Some indicators In-degree k in,t Number of country-sector pairs that are exporting (water) to country-sector pair i In-strength S in,t Total amount of water embodied in intermediate input purchased by country-sector pair i Out-degree k out,t Number of country-sector pairs that are importing (water) from country-sector pair i Out-strength S out,t Total amount of water embodied in intermediate input sold by country-sector pair i We also compute second-order degree and strength weighted sum of the degrees/strengths of the country-sector pairs that purchase/sell country-sector pair i s product as inputs/outputs, with weights given by the corresponding input/output shares.

28 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Some indicators In-degree k in,t Number of country-sector pairs that are exporting (water) to country-sector pair i In-strength S in,t Total amount of water embodied in intermediate input purchased by country-sector pair i Out-degree k out,t Number of country-sector pairs that are importing (water) from country-sector pair i Out-strength S out,t Total amount of water embodied in intermediate input sold by country-sector pair i We also compute second-order degree and strength weighted sum of the degrees/strengths of the country-sector pairs that purchase/sell country-sector pair i s product as inputs/outputs, with weights given by the corresponding input/output shares.

29 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Some indicators In-degree k in,t Number of country-sector pairs that are exporting (water) to country-sector pair i In-strength S in,t Total amount of water embodied in intermediate input purchased by country-sector pair i Out-degree k out,t Number of country-sector pairs that are importing (water) from country-sector pair i Out-strength S out,t Total amount of water embodied in intermediate input sold by country-sector pair i We also compute second-order degree and strength weighted sum of the degrees/strengths of the country-sector pairs that purchase/sell country-sector pair i s product as inputs/outputs, with weights given by the corresponding input/output shares.

30 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Some indicators In-degree k in,t Number of country-sector pairs that are exporting (water) to country-sector pair i In-strength S in,t Total amount of water embodied in intermediate input purchased by country-sector pair i Out-degree k out,t Number of country-sector pairs that are importing (water) from country-sector pair i Out-strength S out,t Total amount of water embodied in intermediate input sold by country-sector pair i We also compute second-order degree and strength weighted sum of the degrees/strengths of the country-sector pairs that purchase/sell country-sector pair i s product as inputs/outputs, with weights given by the corresponding input/output shares.

31 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Some indicators In-degree k in,t Number of country-sector pairs that are exporting (water) to country-sector pair i In-strength S in,t Total amount of water embodied in intermediate input purchased by country-sector pair i Out-degree k out,t Number of country-sector pairs that are importing (water) from country-sector pair i Out-strength S out,t Total amount of water embodied in intermediate input sold by country-sector pair i We also compute second-order degree and strength weighted sum of the degrees/strengths of the country-sector pairs that purchase/sell country-sector pair i s product as inputs/outputs, with weights given by the corresponding input/output shares.

32 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network In-strength vs in-degree (2009) Idea elasticity > 1 means that nodes with many connections also have generally heavier connections Figure: In-strength (y-axis) vs in-degree (x-axis) β = 2.41

33 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network In-strength vs in-degree (2009) Idea elasticity > 1 means that nodes with many connections also have generally heavier connections Figure: In-strength (y-axis) vs in-degree (x-axis) β = 2.41

34 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Out-strength vs out-degree (2009) Figure: Out-strength (y-axis) vs out-degree (x-axis) β = 1.98

35 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Distribution of first and second order degrees Idea Fat-tailed distributions indicate a vulnerability of the network to shocks Extreme events are more likely than in the Gaussian distribution Fat-tailed nature of the degree distribution has also important consequences on the network resilience in case of removal of vertices or exogenous shocks to vertices (Carvalho, 2012) Figure: Distribution of first-order degrees Figure: Distribution of second-order degrees

36 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Distribution of first and second order degrees Idea Fat-tailed distributions indicate a vulnerability of the network to shocks Extreme events are more likely than in the Gaussian distribution Fat-tailed nature of the degree distribution has also important consequences on the network resilience in case of removal of vertices or exogenous shocks to vertices (Carvalho, 2012) Figure: Distribution of first-order degrees Figure: Distribution of second-order degrees

37 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network In/out degree and strength correlations Idea Positive correlation implies that country-sector pairs that have a higher number of input demand relations, i.e. a high in-degree, also tend to supply their output to a relatively higher number of other country-sector pairs Degree correlation (in-out) (1995), (2001), (2009) Strength correlation (in-out) (1995), (2001), (2009)

38 Motivation and RQ Data and definitions SDA Network Conclusions VW trade as a network Description of the VW trade network Assortativity measures Assortativity measures the similarity of connections in the graph with respect to the node strength correlation coefficient between the strengths (weighted degrees) of all nodes on two opposite ends of a link Useful to evaluate whether relatively high degree nodes have a higher tendency to be connected to other high degree nodes A positive assortativity coefficient indicates that nodes tend to link to other nodes with the same or similar strength Four possible combinations (in-in, out-out, in-out, out-in) Results indicate a slightly negative correlation in all cases and years (disassortativity) High strength country-sector pairs tend to have trade relationships with small strength country-sector pairs more often than expected in a random network, suggesting a potential benefit of redistribution of VW trade from big (high endowed) countries toward water scarce regions/industries. Presence of disassortativity indicates that countries with large volumes of water are open to trade with many other countries large volumes of water can be reallocated among several country-sector pairs water security tool

39 Motivation and RQ Data and definitions SDA Network Conclusions Combining results from SDA and network analysis Marginal role of pure trade in reducing water footprints Increasing globalization (coupled with correct pricing of water) may deliver large savings Increasing trade in virtual water however results in increasing exposure to imported shocks Increasing trade within the current topology results in increased vulnerability

40 Motivation and RQ Data and definitions SDA Network Conclusions THANK YOU FOR YOUR ATTENTION

41 Motivation and RQ Data and definitions SDA Network Conclusions Figure: IE component (water intensity of output)

42 Motivation and RQ Data and definitions SDA Network Conclusions Figure: T component ( geography of intermediate inputs)

43 Motivation and RQ Data and definitions SDA Network Conclusions Figure: D component ( geography of final demand)

44 Motivation and RQ Data and definitions SDA Network Conclusions H component (structure of intermediate inputs for given geography )

45 Motivation and RQ Data and definitions SDA Network Conclusions Q C component (structure of final demand for given geography )

46 Motivation and RQ Data and definitions SDA Network Conclusions Q cap component (final demand per capita)

47 Motivation and RQ Data and definitions SDA Network Conclusions POP component (population)