Research ArticleENVIRONMENTAL ECONOMICS

Enhanced economic connectivity to foster heat stress–related losses

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Science Advances  10 Jun 2016:
Vol. 2, no. 6, e1501026
DOI: 10.1126/sciadv.1501026
  • Fig. 1 Production loss propagation.

    (A) Basic setup of the study. Production reductions in the sectors of agriculture and fishing, mining and quarrying, and construction due to heat stress (measured in USD at day level) are computed for tropical countries (shaded in red). These first-order losses can induce production reductions in other regions and sectors (higher-order losses) through linkages in the global supply network. The cascading of production losses is simulated by use of the model Acclimate. (B) Schematic illustration of the model Acclimate. Production losses are passed on from one production site to another (or a consumption site) through demand and supply relations. Storage, transport time, and the possibility of demand redistribution can buffer the propagation. (C) Perfect complementarity as main nonlinearity of loss propagation. Production in regional sector ir is limited by the strongest relative failure of its suppliers (here, js and ku with jk).

  • Fig. 2 Production losses change over time and the effect of higher-order losses is significant.

    (A) Decomposition of total production loss. Total production losses are composed of first- and higher-order losses that both increase over the simulation period (1991–2011). (B) Role of storage for loss propagation. Higher-order production losses vary with storage size (given in days of input volume; here, 3 to 15 days), but larger storage capacities can reduce higher-order production losses only to a certain extent. They saturate when above a storage size of about 10 days’ worth of total input is reached. (C) Ratio of higher- to first-order production loss. Higher-order production losses are about 12 to 20% of first-order losses for storage capacities of 15 days.

  • Fig. 3 Changes in network structure increase relative production loss.

    (A) Relative production losses in each simulation year. The ratio of total production loss to annual production level (in %) increases with time. (B) Relative production losses increase with the static network measure SPC. The static network measure SPC correlates with the losses in each year.

  • Fig. 4 Exemplary illustration of network measure SPC.

    (A and B) Role of different supply regions of the same input. In this simplified example, the fourth Japanese sector obtains the input good i3 from three different tropical countries. The respective SPC values result from the share that each of these production sites has on the total supply of i3 to the Japanese sector. These shares can change over time but always add up to 1. (C and D) Role of different inputs from the same region. The dependence of Japan on supplies from India is determined by the maximal sectoral impact that India has on each sector in Japan. (E and F) Role of enhanced interregional connectedness. The global average of all regional SPC values can change either because of new connections in the network or because the established link structure varies.

  • Fig. 5 Regional SPC for the case of India.

    (A to C) Maps show how dependent a country’s production is on supplies from India according to the regional SPC values in 1991 (A), 2001 (B), and 2011 (C). Dark red color denotes that the dependency is equal to or larger than 24.5%.

  • Fig. 6 Change of economic network’s SPC over time.

    (A) Number of network connections. Global network measure SPC increases over time and is strongly correlated with the number of connections in the network. (B) Log-log frequency distribution of regional SPC values. The histogram of regional SCP values is shifted toward larger SCP values over time as represented by the mean (vertical dashed lines), suggesting that dependencies on supplies from tropical countries have become larger on average. The mean values for all years are given in fig. S5.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/6/e1501026/DC1

    Description of the model Acclimate

    fig. S1. Examples of forcing time series of first-order daily production loss due to heat stress.

    fig. S2. Independence of main result on modeling assumptions.

    fig. S3. Annually averaged global mean temperature over time.

    fig. S4. Contribution of first-order and higher-order losses to total annual production loss.

    fig. S5. Mean value from log-log histogram of regional SPC values.

    fig. S6. Heat stress–induced production losses under the RCP 8.5 warming scenario for different economic structures over time.

    table S1. Alphabetical list of all sectors used in simulations.

    table S2. Alphabetical list of all countries used in simulations.

  • Supplementary Materials

    This PDF file includes:

    • Description of the model acclimate
    • fig. S1. Examples of forcing time series of first-order daily production loss due to heat stress.
    • fig. S2. Independence of main result on modeling assumptions.
    • fig. S3. Annually averaged global mean temperature over time.
    • fig. S4. Contribution of first-order and higher-order losses to total annual production loss.
    • fig. S5. Mean value from log-log histogram of regional SPC values.
    • fig. S6. Heat stress–induced production losses under the RCP 8.5 warming scenario for different economic structures over time.
    • table S1. Alphabetical list of all sectors used in simulations.
    • table S2. Alphabetical list of all countries used in simulations.

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