Scuola di studi superiori Ferdinando Rossi

Tecnologie, economia e geografia per la sostenibilita

Relatore: Prof. Bagliani

The effect of the International TradeNetwork on the Ecological Footprint

Author:

Marco PangalloDate:

21/04/2015

Abstract

The possible tradeoff between growth and sustainability is one of the most pressing issuesof our time. Since the pollution level in the most affluent countries is decreasing the publicopinion in those countries may think that sustainability has been achieved. This is not trueon the global scale, as the Ecological Footprint indicator proves. The production of pollutinggoods has simply been externalized to developing countries, and Jorgenson and Rice (2005)demonstrate it in a quantitative way. The goal of the present work is to check whether thisassertion holds using a rigorous metrics, the PageRank centrality, developed in the contextof the World Wide Web, and used here for the International Trade Network. When thePageRank centrality of a country is high, it means that that country is a net receiver of goodsproduced elsewhere; when it is low, that country exports most of its good. I find a statisticallysignificant positive relation between the PageRank centrality and the Ecological Footprint, insupport of the externalization assertion. It is important to stress that if one considers onlytotal import/export values there is no meaningful relation, but when the network structure ofthe International Trade Network comes into play things change dramatically.

1 Introduction

Is the environmental impact of nations on therise? Are we consuming more resources thanthe Earth can provide? The answer to thesequestions has been debated for long, as manypolicy issues depend on it. Most answers wereideologically based, without a sound scien-tific background. Only recently some scientificinstruments were developed to address thesequestions: one of the most promising is theEcological Footprint (EF) (Rees, 1992; Wack-ernagel and Rees, 1996).

In order to achieve strong sustainability ourmarginal use of natural capital in productionbust be decreasing and eventually go to zeroif endless economic growth is to be preserved(Caviglia-Harris et al., 2009). The EF can tellwhether this is happening or not. The de-bate over the sustainability of economic growthcan be cast in the framework of the Environ-mental Kuznets Curve (EKC). The hypothe-sis in Kuznets (1955) is that income inequal-ity is low in poor countries, becomes high inthe early stage of economic development, be-

comes low again in developed countries, fol-lowing thus an inverted U-shaped relation in aplot where GDP per capita is on the x-axisand inequality is on the y-axis. Some sci-entists were tempted to consider environmen-tal impact rather than income inequality, andcome roughly to the same conclusion. The in-terpretation would be that the richest coun-tries can afford caring more about environmen-tal issues, and act consequently. The empir-ical evidence for the EKC depends cruciallyon which indicators are taken into account.Some pollutants in the atmosphere seem actu-ally to show an inverted-U relation with GDPper-capita, whereas the pollution of the wa-ter decreases monotonically as countries getmore affluent. Some production-based indica-tors do not exhibit any relation with the GDPat all(Caviglia-Harris et al., 2009). What ismore, Bagliani et al. (2008) claim that manypapers in the EKC literature have economet-ric weaknesses. Rothman (1998) suggests thatconsumption-based indicators, such as the EF,shall be used. Bagliani et al. (2008) andCaviglia-Harris et al. (2009), with minor dif-

1

ferences, find that the EF is monotonically in-creasing with the GDP, at least in a reasonablerange, with no support for an EKC. It shouldbe noted that the EF intensity (EF per unitof GDP) seems to be decreasing (York et al.,2004), but since the GDP itself increases fasterthe total EF rises.

If the richest countries have such a highenvironmental impact, why is there not con-cern among their citizens? This is the ori-gin of the Ekins (1997)’s pollution-haven hy-pothesis: the richest countries externalize theirenvironmental impact to the developing coun-tries, rising the pollution levels in such nationsand maintaining a good environmental qualitywithin their borders. Most of the studies onEkins’ hypothesis focus on the trade of pollut-ing materials (Bagliani et al., 2008). Jorgen-son and Rice (2005) consider instead an indi-cator which quantifies the extent to which thepoorest countries export to the richest ones. Informulas, their weighted-export flows indicatorreads:

Di =

n∑j=1

pijaj (1)

Here pij is the export from country i to coun-try j and aj is the GDP per capita of countryj, assigning actually a higher weight to the ex-ports sent to the n richest countries. Jorgensonand Rice (2005) find that a negative coefficienton Di is statistically significant, meaning thatthe EF of export-oriented countries is on aver-age lower.

The goal of this work is to check Jorgensonand Rice (2005)’s result using a more rigorousmetrics, namely PageRank (Page et al., 1999).At the basis of Google’s search engine, this al-gorithm finds the most relevant webpages fora given query by “navigating” in the networkstructure of the World Wide Web. Here I con-sider the International Trade Network (ITN),a network whose nodes are nations and whose

weighted edges represent the direct import/-export flows, and I compute the PageRank ofevery country. In this context, the figure quan-tifies the level of resource appropriation fromother nations. I find that a robust relationholds between the PageRank and the EF, con-firming Jorgenson and Rice (2005)’s result andEkins (1997)’s pollution-haven hypothesis.

The rest of this essay is structured as follows:Section 2 explains the Ecological Footprint, theInternational Trade Network and the PageR-ank algorithm; Section 3 provides details aboutthe data set, the analysis and the results; Sec-tion 4 concludes.

2 Theoretical background

2.1 Ecological footprint

The ecological footprint is an indicator whichmeasures the biologically productive area re-quired, directly and indirectly, to produce theresources consumed by a population and to ab-sorb its waste. The EF distinguishes amongseveral types of land: cropland, grazing land(pastures), forest land and sea area which pro-vide natural resources; built-up land for humansettlements; the “energy land”, which is theamount of forest that would be needed to ab-sorb carbon dioxide emissions, and thus quan-tifies the carbon footprint of human activities.The latter is the major component of the EF indeveloped countries, but it is not the only im-pact on Earth’s ecosystems. In order to com-pute the EF, the consumption of a country isdisaggregated in all its n goods and services:

F =∑n

Fn =∑n

Cn

pn=

∑n

Cnqn (2)

Here F is the total footprint, Fn is the n-thgood footprint, Cn is the n-th good consump-tion, pn is the yield per unit good, whose mea-surement unit is the inverse of hectares (ha), qn

2

is just the inverse of pn. In detail, every goodor service is assigned a surface on one or moretypes of land (for instance, bread needs bothcropland for the wheat and energy land for theproduction process). These surfaces are thenmultiplied by a yield factor, telling how effi-cient is a country in using its land (German’scropland is 2.31 times more efficient than aver-age1), and by an equivalence factor, higher formore productive types of land (cropland is themost productive). These components are thenmeasured in global hectares (gha) and can beconsistently summed to get the unit footprintof good n, qn in Eq. (2). Using the identityConsumption = Production + Import - Exportin Eq. (2) it is possible to get an insightfulperspective: when a country is mostly export-oriented, its EF is likely to be low.

F =∑n

(Pn + In − En)qn (3)

The Ecological Footprint received severalcritics. First, it is an indicator, i.e. it usesa measurable quantity as a proxy for a non-measurable one, and thus has some weaknessesby definition. Second, it only considers wastethat can be assimilated by ecosystems, andso neglects heavy and radioactive materials.Finally, some authors criticize the way con-sumption is converted in used land, others theaggregation procedures (Caviglia-Harris et al.,2009). In any case, every indicator has its ownweakness, and indexes are often arbitrary, so atleast the strenghts and weaknesses of the EFare well known (Caviglia-Harris et al., 2009).Some assets of the EF are:

• It lets disentangle local and global scale,by focusing on consumption and not onproduction. Namely, it considers theconsumption of polluting goods, and notwhere they were produced.

1http://www.footprintnetwork.org/en/index.php/GFN/page/glossary

• It is possible to consider ecological ac-counting. On the one hand, the actual ca-pability of the Earth to provide productiveland is computed, given that a fraction ofits surface should be used to maintain thebiodiversity; on the other hand, the hu-man demand for land is calculated. Atthe present, 1.5 Earths would be neededto satisfy human demand2.

An interesting issue is what drives the Eco-logical Footprint. Jorgenson and Rice (2005)consider urbanization, GDP per capita, GDPgrowth, human capital, income inequality, ecc.As a framework, one can write the identity(IPAT equation):

E =∑n

En =∑n

YYnY

En

Yn(4)

Here E is the total impact, En is the impactof product n, Y is per-capita GDP, Yn is theGDP of product n. The first term in the sum-mation is the scale effect, driven by the sizeof the economy; the second is the compositioneffect, telling which sectors are most impor-tant; the third is the technological effect, i.e.how efficient is a country in producing good n.Bagliani et al. (2008) consider possible expla-nations for the rise or drop of the EF in moreaffluent nations: it may drop if the marginalconsumption decreases (scale effect), it mayrise if the share of sectors whose EF is higherincreases (composition effect), or if policies toreduce the pollution are counter-productive onthe global level (technology effect, see Mayeret al. (2005)).

2.2 International trade network

Complex Networks (Newman, 2010) are an in-terdisciplinary field which studies the network

2http://www.footprintnetwork.org/en/index.php/GFN/page/

world footprint/

3

structure of biological, technological, seman-tic, social, information and economic networks.Many insightful results can be obtained whenit is considered the way in which the elementsof a Complex System are connected, ratherthan simply focusing on the elements them-selves or assuming trivial mean-field interac-tions. In the present study it is not only thetotal import/export of a given country whichis taken into account, but also how much eachcountry imports from and exports to any other.The international trade network is by defini-tion a complete network: in theory, every coun-try is connected with all other countries. Inpractice, the majority of the import/exportflows are negligible, and it is possible to con-sider a threshold below which two countries areconsidered as not connected. A visual repre-sentation of the ITN after the weakest edgeshave been removed is provided in Figure 1 (thedataset used to create the ITN is described inSection 3). It is possible to see a dense corewhich comprises most countries (the largestconnected component) and some nodes whichare disconnected from the network (most ofthem are Pacific Ocean’s islands).

A more insightful perspective can be ob-tained in Fig. 2. The algorithm that drawsthe network divides it into communities: theEuropean countries on the one side, the Amer-ican and Asian countries on the other.

Fig. 2 also highlights a problem with us-ing the total trade values: biggest countriesin terms of population are overly weighted. Tosolve this issue, in the rest of the analysis I con-sider normalized import/export flows. If wij isthe total import from country i with popula-tion Pi to country j with population Pj , wij isdivided by the average population (Pi + Pj) /2.This way the trade between USA and Canadacan be sensibly compared to that occurring be-tween Belgium and Netherlands: while the for-mer was the highest flow before the normaliza-tion, the latter is the strongest connection after

Figure 1: The international trade network.The sizes of the edges and of the arrows isscaled according to the total trade going onbetween countries.

Figure 2: The international trade network.The nodes size is scaled according to the GDP,the other specifications are as in Fig. 1

4

populations have been taken into account.

2.3 PageRank Centrality

The PageRank Centrality measures how muchtime a random walker on a directed networkwould spend in each of its nodes. In the ITNthe random walker can be depicted as a prod-uct being randomly traded among countries.A random walker is simply choosing between astarting node and the connected nodes, with aprobability proportional to the strength of thelinks. Formally, if the random walker startsfrom node i, pij = wij/

∑j wij is the proba-

bility that it goes to node j (transition proba-bility), σi(σj) is the probability it is in nodei (j ) (state probability). Then σi(t + 1) =∑

j∈N(i) pjiσj(t), where N(i) is the set of nodesconnected to node i and t is a time label. Invector form:

σ(t) = PTσ(t− 1) (5)

where σ is a n × 1 vector and P = {Pij} an × n transition matrix. Eq. (5) describes aMarkov Chain relaxing to a stationary state,which is the PageRank:

σ = limt→∞

σ(t) (6)

This is true provided that the Markov Chainis irreducible and aperiodic. Namely, theremust be no isolated nodes or loops where therandom walker would be trapped. This prob-lem is solved by considering a transition matrix

P′ = (1− α)P + α1 (7)

where 1 is a matrix of 1s. α is usually chosensmall, and it represents here a small import/export flow between all countries.

3 Data analysis and results

The import/export, GDP and population dataare taken from the publicly available dataset

described in Gleditsch (2002). GDP and pop-ulation data come mostly from the Penn WorldTables, with some interpolation when datawere not declared. GDP is in constant USdollars (base 1996). Trade data come mostlyfrom the IMF Direction of Trade database(year 2000), and are in current year US dol-lars. Since these figures are based on customsdeclaration, it often happens that the importfrom i to j wimp

ij is different than the exportfrom i to j wexp

ij : I just take the average value

wij = (wimpij + wexp

ij )/2. The cross-countrydata for the Ecological Footprint in 2000 arealso publicly available, and are taken from theWWF Living Planet Report 2004 (WWF andGlobal Footprint Network, 2004).

As a first test, I plot the EF against the GDPper capita. The result can be seen in Fig. 3.The increasing trend is apparent: I consider alinear fit

EFi = a+ b ·GDPi + ui (8)

where ui is the residual, and get an R2 =0.794, with a = (0.977 ± 0.103)gha/cap and

b = (2.11± 0.09)10−4 gha/capUSD/cap . The hypothesis

that b < 0 is rejected at the 1% significancelevel.

It is also interesting to plot the EFagainst the commercial balance per capita, i.e.(EXPi − IMPi)/POPi. It can be expectedthat the most positive the commercial bal-ance, the lowest the EF, but no clear relationemerges from the data, as it can be seen in Fig.4.

However, once the network structure is takeninto account, things radically change. As de-scribed in Section 2.3 I compute the PageRankcentrality of every country. I first remove thenodes with out-degree 0, and then try to com-pute the PageRank with α = 0 in Eq. (7).The result is that Gambia and Senegal share50% each of the PageRank, with all other coun-tries to zero! The reason is that there is a loop

5

0 5000 10000 15000 20000 25000 30000 35000 40000 45000

GDP p.c. (USD/cap.)

0

2

4

6

8

10

12

14

Eco

logic

al fo

otp

rint

p.c

. (g

ha/c

ap.)

Figure 3: The Ecological Footprint as a func-tion of GDP per capita

0.2 0.1 0.0 0.1 0.2 0.3 0.4 0.5

Balance of trade p.c. (USD/cap.)

0

2

4

6

8

10

12

14

Eco

logic

al fo

otp

rint

p.c

. (g

ha/c

ap.)

Figure 4: The Ecological Footprint as a func-tion of the commercial balance

Country PR Country PR

Germany 0.06545 Austria 0.03052

Netherlands 0.06455 Spain 0.02638

Belgium 0.06238 Canada 0.02480

US 0.05557 Denmark 0.02416

France 0.05158 Japan 0.02077

UK 0.04836 Ireland 0.02075

Switzerland 0.04380 Finland 0.02031

Singapore 0.03750 Norway 0.02015

Italy 0.03642 Malaysia 0.01930

Sweden 0.03473 Korea 0.01562

Table 1: Top 20 countries in term of theirPageRank (PR) in the international trade net-work

between them without any out-edge, so prod-ucts are “trapped” there. Setting α = 0.01solves the problem. In Table 1 it is possibleto see some countries and their PageRank. Itshould be stressed that the PageRank consid-ers only import/export fluxes, and not countryattributes such as GDP.

As explained in Section 1, the objective ofthis work is to study the relation between thePageRank and the EF (Figure 5). To do so, Iconsider a linear fit

EFi = a+ b · log(PRi) + ui (9)

(since the PageRank scales on a log scale, itis consistent to use the log for the fit), and getan R2 = 0.438, with a = (7.76± 0.51)gha/capand b = (0.78± 0.08)gha/cap. The hypothesisthat b < 0 is rejected at the 1% significancelevel.

A visual depiction of the result is provided inFig. 6. The nodes with most links are drawnby the algorithm in the center, and they areactually those with the highest EF. The nodesat the periphery, which mostly export towardsricher countries, are light-coloured, i.e. theirEF is low.

6

10 9 8 7 6 5 4 3 2

PageRank Centrality

0

2

4

6

8

10

12

14

Eco

logic

al fo

otp

rint

p.c

. (g

ha/c

ap.)

Figure 5: The Ecological Footprint as a func-tion of the PageRank centrality

Figure 6: The international trade network.Nodes are coloured according to their EF. Allother specifications are as in Fig. 1

0.000 0.005 0.010 0.015 0.020

Closeness Centrality

0

2

4

6

8

10

12

14

Eco

logic

al fo

otp

rint

p.c

. (g

ha/c

ap.)

Figure 7: The Ecological Footprint as a func-tion of the closeness centrality

I finally consider another metrics on net-works, the closeness centrality. It tells whichnodes are central in the network in that theyare not too far from any other node in the net-work itself (Newman, 2010). A standard pro-cedure with weighted networks is to assign adistance inversely proportional to the weight ofthe edge: dij = 1

wij. Plotting the EF against

the closeness centrality is not as insightful asconsidering the PageRank centrality, as it canbe seen in Fig. 7.

4 Conclusion

I wanted to test whether Ekins (1997)’spollution-haven hypothesis held using the in-ternational trade network. I went beyondJorgenson and Rice (2005) because I consid-ered all countries and a metrics less “ad-hoc”than their indicator. The PageRank Central-ity quantifies the extent to which countriesare used to produce the resources that willbe brought to other countries, and presumablyconsumed there. I found a statistically signif-icant positive relation between the PageRankCentrality and the Ecological Footprint. It isinteresting to see that just considering total

7

import/export per capita does not yield to anymeaningful conclusion (Fig. 4), but once thenetwork structure is taken into account a clearrelation emerges.

An interesting perspective would be to con-sider the trade network of single products, andrelate the study to the Economic Complexityindex (Cristelli et al., 2015). In general an in-terdisciplinary collaboration is needed to ad-dress long-term sustainability issues and to un-derstand whether our impact on the Earth willever start to decrease.

8

5 Bibliography

Bagliani, M., Bravo, G. and Dalmazzone, S. (2008). A consumption-based approach to environmentalKuznets curves using the ecological footprint indicator . In �Ecological Economics�, vol. 65(3), pp.650–661.

Caviglia-Harris, J. L., Chambers, D. and Kahn, J. R. (2009). Taking the ”U” out of Kuznets: Acomprehensive analysis of the EKC and environmental degradation. In �Ecological Economics�,vol. 68(4), pp. 1149–1159.

Cristelli, M., Tacchella, A. and Pietronero, L. (2015). The heterogeneous dynamics of economic com-plexity . In �PloS one�, vol. 10(2), p. e0117174.

Ekins, P. (1997). The Kuznets curve for the environment and economic growth: examining the evidence.In �Environment and planning a�, vol. 29(5), pp. 805–830.

Gleditsch, K. S. (2002). Expanded trade and GDP data. In �Journal of Conflict Resolution�, vol. 46(5),pp. 712–724.

Jorgenson, A. K. and Rice, J. (2005). Structural dynamics of international trade and material consump-tion: A cross-national study of the ecological footprints of less-developed countries. In �Journal ofWorld-Systems Research�, vol. 11(1), pp. 57–77.

Kuznets, S. (1955). Economic growth and income inequality . In �The American economic review�, pp.1–28.

Mayer, A. L., Kauppi, P. E., Angelstam, P. K., Zhang, Y., Tikka, P. M. et al. (2005). Importing timber,exporting ecological impact . American Association for the Advancement of Science.

Newman, M. (2010). Networks: an introduction. Oxford University Press.

Page, L., Brin, S., Motwani, R. and Winograd, T. (1999). The PageRank citation ranking: Bringingorder to the web.. In .

Rees, W. E. (1992). Ecological footprints and appropriated carrying capacity: what urban economicsleaves out . In �Environment and urbanization�, vol. 4(2), pp. 121–130.

Rothman, D. S. (1998). Environmental Kuznets curvesareal progress or passing the buck?: a case forconsumption-based approaches. In �Ecological economics�, vol. 25(2), pp. 177–194.

Wackernagel, M. and Rees, W. (1996). Our ecological footprint: reducing human impact on the earth.New Society Publishers.

WWF, W. and Global Footprint Network, G. (2004). Living planet report 2004 . In �World WildlifeFound Editor�.

York, R., Rosa, E. A. and Dietz, T. (2004). The ecological footprint intensity of national economies. In�Journal of Industrial Ecology�, vol. 8(4), pp. 139–154.

9