Global patterns of ecologically unequal exchange: Implications for sustainability in the 21st century

Highlights

• We provide empirical evidence that supports the theory of ecologically unequal exchange.

• High-income nations are net importers of embodied materials, energy, land, and labor.

• High-income nations gain a monetary trade surplus via this resource appropriation.

• Lower-income nations provide resources but experience monetary trade deficits.

• The observed inequality is systemic and hampers global sustainability in multiple ways.

Abstract

Ecologically unequal exchange theory posits asymmetric net flows of biophysical resources from poorer to richer countries. To date, empirical evidence to support this theoretical notion as a systemic aspect of the global economy is largely lacking. Through environmentally-extended multi-regional input-output modelling, we provide empirical evidence for ecologically unequal exchange as a persistent feature of the global economy from 1990 to 2015. We identify the regions of origin and final consumption for four resource groups: materials, energy, land, and labor. By comparing the monetary exchange value of resources embodied in trade, we find significant international disparities in how resource provision is compensated. Value added per ton of raw material embodied in exports is 11 times higher in high-income countries than in those with the lowest income, and 28 times higher per unit of embodied labor. With the exception of embodied land for China and India, all other world regions serve as net exporters of all types of embodied resources to high-income countries across the 1990–2015 time period. On aggregate, ecologically unequal exchange allows high-income countries to simultaneously appropriate resources and to generate a monetary surplus through international trade. This has far-reaching implications for global sustainability and for the economic growth prospects of nations.

Introduction

Global use of natural resources has reached unprecedented levels and is expected to further rise in the coming decades (Krausmann et al., 2018; OECD, 2018). International trade volumes have grown rapidly (Kastner et al., 2014; Wood et al., 2018) as domestic requirements for materials, energy, land, and labor have increasingly been met by drawing on non-domestic sources (Wiedmann and Lenzen, 2018; Wiedmann et al., 2015).

The advocacy of international trade is largely premised on the notion that such trade relations are economically beneficial to all parties (Feenstra, 2015). However, this perspective neglects the material aspects of international trade flows. In contrast, the theory of ecologically unequal exchange explicitly considers material aspects of international trade and postulates that there are asymmetric net transfers of resources (including labor) from peripheral to core areas of the global economic system (Hornborg, 2019, Hornborg, 2014, Hornborg, 1998). The exclusive focus on monetary flows implies a disregard for these potentially unequal transfers of biophysical resources, such as materials, energy, land, and labor, embodied in commodities and services traded between regions with differing economic ‘power’.

High-income nations (the ‘core’ of the global economic system) depend on resource-intensive industrial technologies and infrastructures whose efficient functioning is contingent on annual net transfers of resources from distant (peripheral) areas (Frey et al., 2018; Jorgenson and Clark, 2009a). Moreover, high-income nations obtain significantly higher revenues for the resources they export than poorer nations, which is mostly due to the positions occupied in global supply chains and their respective roles in the world economy (Piñero et al., 2019; Prell et al., 2014; UNCTAD, 2013). The asymmetry of international trade, i.e. of the net transfers of resource volumes and monetary values, is a crucial determinant of the capacity of individual nations to accumulate capital and technological infrastructure and to thereby achieve economic growth (e.g., Grossman and Helpman, 1991).

Unequal trade patterns arise from and reproduce global socio-economic inequalities and hamper socio-environmental sustainability through environmental burden-shifting to poorer nations (Wiedmann and Lenzen, 2018). The displacement of extractive frontiers “elsewhere” (Schaffartzik and Pichler, 2017) is linked to socio-environmental conflicts and the rise of environmental justice movements particularly affecting the agricultural, mining, and manufacturing sectors (Temper et al., 2015) as well as commodified sinks for waste produced via economic activities (Hein and Faust, 2014).

To date, empirical evidence to support the theoretical notion of ecologically unequal exchange as a structural feature of the global economy is still very scarce. While there is a range of conceptual work (Hornborg, 2019, Hornborg, 1998) and of case studies that provide empirical evidence for the presence of ecologically unequal exchange between or within single nation states (Dorninger and Eisenmenger, 2016; Infante-Amate and Krausmann, 2019; Yu et al., 2014; Zhang et al., 2018), for specific commodities or indicators (Jorgenson, 2012; Jorgenson and Clark, 2009b), or in historical perspectives (Bogadóttir, 2016; Hornborg, 2006), comprehensive global assessments of ecologically unequal exchange over decadal time periods have not previously been undertaken. The results of the only global assessment – where ecologically unequal exchange was assessed in terms of proportionality of physical and monetary trade, ecological intensity, and net-transfers (Moran et al., 2013) – have been called into question (Dorninger and Hornborg, 2015). Given the increasingly globalized nature of the economic system and the increased focus on understanding teleconnections and sustainability (e.g., Friis et al., 2016; Seto et al., 2012), this represents a significant research gap for ecological economics and sustainability science. To fill this gap, this study assesses the international exchange of key resources at the global scale over a 26-year period (1990–2015). We quantify ecologically unequal exchange in four biophysical resources embodied in traded goods and services:

• raw materials, expressed in ‘raw material equivalents’ (RMEs): materials directly traded plus all materials embodied in traded goods and services (measured in Gigatons [Gt]) (Schaffartzik et al., 2015);

• energy: primary energy used along the whole supply chain to produce a certain good or service (measured in Exajoules [EJ]) (Owen et al., 2017);

• land: land use that is directly and indirectly required for the production of a good or service (measured in hectares [ha]) (Bruckner et al., 2015); and

• labor: all labor expended in the supply chain to produce a certain good or service (measured in person-year equivalents [p-y_eq]) (Simas et al., 2015).

To facilitate a global analysis of large-scale and diverse biophysical and socio-economic flows between countries, we use aggregated indicators that capture all materials, energy, land, or labor used in global supply chains.¹ In contrast to previous studies, we use biophysical resources, labor, and value added in one consistent framework and provide this in a time series analysis. Moreover, we conduct an inferential statistical testing of hypotheses derived from ecologically unequal exchange theory.

We use an environmentally-extended multi-regional input-output analysis (EEMRIO) to generate consumption-based pressure indicators (‘footprints’) in order to capture the displacement effects of international trade (Steinmann et al., 2017; Wiedmann and Lenzen, 2018). A national consumption footprint represents the domestic extraction (materials) or use (energy, land, labor) of biophysical resources within a given nation plus the net trade (imports minus exports, including embodied flows) (Wiedmann et al., 2015). The extractive expansion required for increasing trade volumes is often related to ecological distribution conflicts (Martinez-Alier et al., 2010).

In addition to the environment-related footprint assessments, we also used multi-regional input-output analysis to assess global monetary value chains and the analysis of trade in value added (TiVA). TiVA, which is sometimes referred to as a nation’s ‘value footprint’ (Wiedmann and Lenzen, 2018), accounts for the monetary value added by one country embodied in the final demand of another country, i.e. TiVA represents the monetary value a nation generates through its exports rather than the total value of the goods exported (Stehrer, 2012). The TiVA indicator is the financial counterpart to input-output-based resource footprints and follows the same calculation steps (see Section 3.2). To the best of the authors’ knowledge, the present study is the first to analyze embodied resource flows and TiVA in one consistent framework.

Our analysis is based on the most recent data available from the EEMRIO database Eora (Lenzen et al., 2012b, Lenzen et al., 2013b). In addition to direct international trade flows, EEMRIO models allow calculating embodied resource flows associated with global supply chains, by including the intermediate resources used to produce goods and services for final demand (Wiedmann and Lenzen, 2018; Wiedmann et al., 2015). We analyze the domestic extraction and use of resources and their reallocation through international trade on a global scale and in a temporal perspective. We calculate net international appropriation as well as differences in monetary valuation (TiVA) of materials, energy, land, and labor. Further, we build four structural equation models (SEM), one for each of the examined resources, to statistically assess relationships between predictive socio-economic variables, resource appropriation, and value added generation as suggested by ecologically unequal exchange theory.

Our analysis includes 170 countries, encompassing 99.2% of the world population in 2015, and the bulk of global supply chains and economy-wide resource flows. In order to investigate patterns of trade in relation to income inequality, we group countries into four income classes based on gross national income (GNI) per capita. Inspired by the World Bank’s classification of income and lending groups (World Bank, 2018a), we refer to them as high-income (HI), upper-middle income (UMI), lower-middle income (LMI), and low-income (LI) countries. However, in order to maintain similarly sized groups in terms of total population, our income boundaries deviate slightly from those of the World Bank (for details see Appendix B, Fig. 5 and Table 1).