# Debunking competition - Global ecologically unequal exchange explained by exploitation and control relations

Raffaele Guarino, Giulio Corsi, Enrique Muñoz-Ulecia

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Raffaele Guarino, Giulio Corsi, Enrique Muñoz-Ulecia. Debunking competition - Global ecologically unequal exchange explained by exploitation and control relations. Ecological Economics, 2026, 242, pp.108909. 10.1016/j.ecolecon.2025.108909. hal-05449904

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# Debunking competition - Global ecologically unequal exchange explained by exploitation and control relations

Raffaele Guarino<sup>a, *,</sup> Giulio Corsi<sup>a</sup>, Enrique Muñoz-Ulecia<sup>b,c,d</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

International trade

Environmental extended multi-regional input-output analysis

Ecological network analysis

Green growth

## ABSTRACT

According to mainstream economic theory, competition is the fundamental force that regulates production and exchange in the global economy. It is presumed that countries compete in international markets with mutually beneficial outcomes for all parties. However, this assumption is challenged by increasing socioeconomic inequalities across and within countries, as well as global environmental degradation. Among other critical theories, Ecologically Unequal Exchange argues that international trade triggers asymmetries in the distribution of benefits and costs between Core and Periphery regions of the global economic system. We combined Environmentally Extended Multi-Regional Input-Output analysis and Ecological Network analysis to empirically determine which type of ecological relation (competition, control, exploitation or mutualism) characterizes the interaction between countries in international trade. Our research revealed that exploitation and control relations are far more prevalent than competition or mutualism in the global economy. Although the Periphery and Semi-periphery exhibit higher environmental intensities, the responsibility for most environmental degradation can be attributed to a few Core countries that drive resources' extraction and appropriation. Consequently, the Core enhances its economic and environmental performance by exploiting and controlling the Semi-periphery and Periphery. Our findings demonstrate that green growth and dematerialization policies may be unable to achieve socially fair and environmentally sustainable societies at the global level if the structure of the global economy remains unchanged.

## 1. Introduction

Since the beginning of economic thought, competition has been considered the regulating principle of economic behavior, driving trade, economic growth and human welfare (McNulty, 1968). Economists have argued that competition guides the specialization of national production, ensuring maximum benefits to all parties through exchanges (Ricardo, 2004; Smith, 1981). The idea of competition still dominates mainstream economic perspectives. For instance, current green growth and dematerialization narratives argue that the pressure exerted by competition in the market promotes technological innovation and efficient use of resources, leading to decoupling economic growth from environmental degradation (Mol et al., 2020; Roberts and Parks, 2009). Consequently, competition is viewed as a market-regulating mechanism

that leads to win-win solutions for addressing the social-ecological crisis (Hanaček et al., 2020; Hickel, 2019; Kallis et al., 2018).

This narrative is based on the assumptions that higher economic growth leads to greater human wellbeing, and that greater efficiency leads to less environmental impact. However, theoretical and empirical studies have questioned both assumptions. Firstly, although human wellbeing is linked to a certain level of economic development, ever-increasing levels of growth do not necessarily lead to greater wellbeing (Max-Neef, 1995). Secondly, greater efficiency leads to less environmental impact (or less use of resources) per unit of product, but to date this has only resulted in greater cumulative impact and consumption of resources, falling into the Jevons' paradox (Gómez-Baggethun, 2020). Furthermore, while efficiency reduces the unitary amount of resources, their degradation through the economic

<footnote><sup>a</sup> International PhD Programme, UNESCO Chair "Environment, Resources and Sustainable Development", Department of Science and Technology, Parthenope University of Naples, Italy</footnote>

<footnote><sup>b</sup> AGIR, University of Toulouse, INRAE, Castanet-Tolosan, France</footnote>

<footnote><sup>c</sup> Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, Spain</footnote>

<footnote><sup>d</sup> Centro de Investigación y Tecnología Alimentaria de Aragón (CITA), Zaragoza, Spain</footnote>

<footnote>* Corresponding author.
E-mail address: raffaele.guarino003@studenti.uniparthenope.it (R. Guarino).</footnote>
process according to the second law of thermodynamics points out the energetic and material impossibility of absolute decoupling (Costanza, 1980; Georgescu-Roegen, 1971; Parrique et al., 2019; Ward et al., 2016).

The current capitalist economy (even in its green version) advocates economic growth. If decoupling can be only a relative phenomenon (Haberl et al., 2020), then countries' interaction in international trade becomes a struggle to acquire an increasing share of finite environmental space. This ultimately results in an unequal distribution of environmental benefits and costs (Bruckner et al., 2023; Dorninger et al., 2017; Wiedmann and Lenzen, 2018; World Bank, 2020). For some countries to win, others must inevitably lose. Among other critical theories, Ecologically Unequal Exchange (EUE) argues that these inequalities are not expressions of punctual failures in a fair and competitive equilibrium, where world regions compete for resources on global markets, but are instead achieved through systematic appropriation and exploitation dynamics (Corsi et al., 2024). These dynamics are perpetrated by Core regions (developed economic centers, sometimes overlapping with the term Global North) towards regions of the Periphery (underdeveloped regions at the margins of world's economic organization, sometimes overlapping with the term Global South); commonly through regions in the middle that mediate exchanges (Semi-periphery) (Hickel, 2019; Roberts and Parks, 2009). This exploitation is perpetrated through international trade, where asymmetric environmental and labour balances are masked by apparently reciprocal market exchanges (Hickel, 2019; Hickel et al., 2022; Hornborg, 2009).

EUE challenges theoretically the idea that competition is the fundamental principle of economic organization driving shared growth and progress, whether it regards individuals, businesses or nations (Stigler, 1957). Instead, it emphasizes that social and environmental inequalities are not a contingent or temporary outcome resulting from competitive market failures that can be resolved; rather, they are a necessary condition imposed by the Core to sustain constantly increasing levels of resource consumption. That is, some regions (i.e., the Periphery) have to function as exploited nodes in the network so that others (i.e., the Core) can pursue the capitalistic need for perpetual economic growth. From this perspective, the current consumption levels of the Core are achieved through the exploitation and the control of the Periphery in the form of the appropriation and capitalization of ecological resources (Moore, 2015; Althouse et al., 2023).

Empirically proving such an assertion, however, is intricate (Dorninger and Hornborg, 2015). This study contributes to this debate by (i) providing a model to empirically analyse the direct and indirect effects of global trade on the distribution of natural resources and environmental impacts. (ii) Assessing each nation's level of influence and dependence on the environmental pressures tied to international trade. And (iii), identifying which kind of relation (competition, control, exploitation or mutualism) dominates countries' interaction in international trade, ultimately indicating the level of mutualism and synergy of the global economy.

To do so, we analyse the state of global international trade in 2022 using the Environmentally Extended Multi-Regional Input-Output (EEMRIO) database EXIOBASE v.3.8.2 (Stadler et al., 2018). This database contains annual input-output tables for 163 industries in 49 countries and regions, including all 27 EU member states. We considered the year 2022, which is the latest year available in EXIOBASE v.3.8.2. Based on previous research (Bruckner et al., 2023), we select nine indicators of natural resource use and environmental pressures: Land Use Change (LUC); Greenhouse Gasses emissions (GHG); Blue Water Consumption (WCB); Freshwater Aquatic Ecotoxicity (FAE); Marine Aquatic Ecotoxicity (MAE); Terrestrial Ecotoxicity (TEX); Photochemical Oxidation (PHO); Particulate Matter (PMR); and Domestic Extraction Used (DEU). Following the methodology proposed by Costanza (1980), we analysed the distribution and exchange of these environmental pressures in global international trade.

Additionally, we performed an Ecological Network Analysis (Fath

et al., 2007) on these flows to assess the direct and indirect dependence of each world region on resources from the rest of the world, as well as their contribution to the economic system as a whole. This integrated analysis has previously been conducted at the city, regional and national levels (Galychyn et al., 2022; Guarino et al., 2023a; Zhai et al., 2019). This study is novel in that it combines EEMRIO and ENA incorporating nine environmental indicators to the global economic system with final demand internalised (i.e., considering the global economy as a closed system). Although ENA also has a long tradition (Fath et al., 2007), it often treats the economic system as an open system. However, treating the global economy as a closed system is more aligned with biophysical principles, allowing us to capture internal feedback loops of the system and indirect effects hidden when final demand is treated as exogenous.

The remainder of this paper is organized as follows. Section 2 outlines the methodology and datasets employed, emphasizing their distinctive features as well as inherent limitations. Section 3 presents the empirical results and discusses them through the lens of the EUE theoretical framework, with particular attention to their relevance for the ongoing debate on green growth. Section 4 concludes by synthesizing the main findings and reflecting on their broader policy implications.

## 2. Material and methods

### 2.1. EXIOBASE data

Input-output analysis, originating as a method for scrutinizing the interdependencies among productive sectors within economic systems, has evolved over time to encompass broader multi-regional analyses, enabling the examination of trade dynamics between nations (Leontief, 1986; Miller and Blair, 2013). The progressive expansion of this analytical framework has given rise to its environmental extension, which has found application in assessing the environmental implications associated with specific products and entire economic sectors. One prominent manifestation of this application is exemplified by EEMRIO models, which facilitates the quantification of environmental footprints by capturing the intricate web of environmental pressures and costs entailed in the production of goods and services across diverse global economies (Wackernagel and Rees, 1996).

Regarding the indicators addressed in this study, according to Bruckner et al. (2023), the indicator employed for measuring GHG emissions considers multiple GHGs (CO2, CH4, N2O, SF6, HFC, and PFC) in kgCO2eq, aligning with their 100-year global warming potential, as recommended by the IPCC report (IPCC, 2007). Notably, EXIOBASE's GHG dataset encompasses emissions from combustion, non-combustion, agriculture, and waste but excludes those from land use, land use change, and forestry. Calculations for associated material use involved aggregating coefficients for various categories such as domestic crop extraction, grazing, forestry, etc., to derive a total material use indicator (in kt). A similar approach was taken for land use, where 20 different categories were summed to ascertain total land use (in km2). Water consumption was determined by aggregating coefficients for total blue water use across different sectors like agriculture, manufacturing, or electricity. Freshwater aquatic, marine aquatic, and terrestrial ecotoxicity were quantified via an aggregated approach from EXIOBASE, which considers the impacts of each emission of a toxic substance to air, water, or soil, measured in kilograms of 1,4-dichlorbenzene equivalents. To address photochemical oxidation from emissions like non-methane volatile organic chemicals or carbon monoxide, an aggregated and weighted indicator measuring kilograms of formed ozone was utilized, following the maximum incremental reactivities method suggested by Steinmann and colleagues (Steinmann et al., 2018). Particulate matter emissions were determined using emission-weighted coefficients in kilograms PM2.5 equivalents. Value added was computed using coefficients for value added in million dollars from EXIOBASE v.3.8.2. Additional details on the chosen indicators are available in Supplementary Material, Table SM1.
## 2.2. Model construction and data analysis

The EEMRIO methodology consists of accounting for trade that occurs between m economic sectors in n nations. Consequently, the resulting matrix Z attains a size of (nm x nm). Beyond this, the final demand, which by definition consists of “Households”, “Government”, and “Gross Capital Formation”, is also accounted for each nation. This study endogenizes “Households” and “Government” components into the transaction matrix and considers “Gross Capital Formation” as a net-output. This approach builds upon foundational work by Costanza (1979) and aligns with established traditions in input-output analysis (Appelbaum, 1992; Duchin and Steenge, 2007) and social accounting matrix analysis (Stone, 1947; Pyatt and Thorbecke, 1976). Traditional input-output systems treat final demand as exogenous, meaning that shocks, such as changes in government spending, generate only direct, limited effects. In contrast, endogenization embeds these components within the system’s feedback structure, allowing their interactions to be dynamically determined. The implications are profound: when households and government are endogenized, an initial demand stimulus (e.g., increased public investment) triggers higher incomes and profits, which in turn could spur further private investment, productivity gains, and wage growth. Open-system frameworks overlook these feedback mechanisms, inherently underestimating economic cycle (Guarino et al., 2023b).

The goal of endogenization is to transform the open IO system into a closed one. The operations involve defining a balance between the taxes and the wages of household and government consumption. These sectors receive goods and services based on the proportions of personal consumption expenditures and government expenditures, while rendering services to other sectors in accordance with employee compensation, indirect business taxes, and a specific fraction of property-type income. It is possible to define this percentage through our own readjustment of the formulas reported by Costanza (1979). In particular, referring to Government sector, it is possible to define a balance equation as follows:

$$GP = IBT + X_g \times PTI \quad (1)$$

While for the Household sector:

$$PCE = C + X_h \times PTI \quad (2)$$

Where, IBT represents “Indirect Business Taxes”, PTI is “Property Type Income”, GP is “Government Purchases” and PCE is “Personal Consumption Expenditures”. In this way it is possible to calculate $X_g$ and $X_h$, which are the coefficients of the PTI calculated for the government and households, respectively.

$$X_g = \frac{(GP - IBT)}{PTI} \quad (3)$$

$$X_h = \frac{(PCE - EC)}{PTI} \quad (4)$$

The remaining part (called $X_e$) is considered as “net profit”:

$$X_e = 1 - X_g - X_h \quad (5)$$

Following this approach, government and households are considered as dummies sectors. Consequently, the transaction matrix Z is now composed of (m + 2)n x (m + 2)n. On the other hand, gross capital formation is considered as net production and in terms of incorporation into economic/ecological structures such as infrastructure. Firstly, the economic sectors have been merged to create a matrix in which the main diagonal is the national economy, and the other cells represent trades between countries. The environmental extension of the MRIO is applied through the balance equation between ecological inputs and economic outputs.

$$e\hat{X} = E + eZ \quad (6)$$

Where E represents the environmental input inflow matrix, e

represents the embodied intensity factor and X is the economic output of the system. The hat on X indicates that the vector has been diagonalized, that is, it has become the principal diagonal of a matrix with all other values equal to zero in order to perform the mathematical operation with matrices. It is possible to solve for e in the following way:

$$e = E(\hat{X} - Z)^{-1} \quad (7)$$

These coefficients quantify the amount of environmental resources used, or impact produced, directly and indirectly, per unit of monetary value.

ENA is a systems-oriented approach employed for the analysis of interactions within a system, aiming to reveal holistic properties that may not be readily apparent through direct observations (Fath et al., 2007). The combined application of IO and ENA enables to explore the hierarchy of countries and the functional connections across all metabolic pathways of ecological and socioeconomic flows exchanged within an economy and between the economy and its environment (Galychyn et al., 2022). To conduct ecological network analysis, it is imperative for the model to satisfy steady-state conditions. In essence, the model must adhere to the principles of energy and mass conservation, as stipulated by the first law of thermodynamics (Fath and Patten, 1999). The steady-state requirement is expressed by the following equation:

$$T_j = T_i \quad (8)$$

Here, $T_i$ represents the aggregate of all flows (embodied environmental pressures in monetary intercountry transactions) directed towards each country i, and $T_j$ signifies the total of all outflows originating from each country j. Consequently, the system attains a state of equilibrium when the overall volume of inputs and outputs from each country i and the environment, denoted as $E_i$, equals the total volume of outputs and inputs from each country i and the environment, denoted as $Y_i$, that is the vector of embodied energy in the net output, or, in other words, the product of energy coefficient multiplied for economic value of gross capital formation, our storage. The preceding equation can be reformulated in the following manner:

$$f_{ij} + E_i = f_{ji} + Y_i \quad (9)$$

After computing the ecological intensity coefficients, we proceed to multiply them by the corresponding monetary flows in the transaction matrix. This process results in a matrix that delineates the ecological components embedded in transactions among sectors and countries. For instance, the term $f_{ij}$ denotes the ecological elements incorporated in the transaction between country i and country j. To advance, it is necessary to estimate the indirect and integral flows between every pair of system components. This can be accomplished by calculating the non-dimensional input-oriented transfer efficiencies across all pathways within the system. Subsequently, the elements of the non-dimensional output-oriented transfer efficiency matrix (G) can be ascertained using the following equation:

$$g_{ij} = \frac{f_{ij}}{T_i} \quad (10)$$

This matrix illustrates the proportion of the initial ecological input that is actually used in the exchange process between each set of countries. The calculation of a dimensionless integral flow intensity matrix (N) can be done on the basis of the inversion of this matrix, as done above:

$$N = (1 - G)^{-1} \quad (11)$$

By multiplying the integral flow intensity matrix with the diagonal elements of the flow matrix, denoted as diag($T_i$), a matrix Y can be generated, illustrating the relative contribution of each country:

$$Y = \text{diag}(T_i) \times N \quad (12)$$

This matrix allows for the computation of the “integral pulling force
weight” ($W_i$) and the “integral driving force weight” ($W_j$):

$$W_i = \frac{\sum_{(i=1)}^{n} Y_{ij}}{\sum_{(j=1)}^{n} \sum_{(i=1)}^{n} Y_{ij}} \qquad (13)$$

$$W_j = \frac{\sum_{(j=1)}^{n} Y_{ij}}{\sum_{(j=1)}^{n} \sum_{(i=1)}^{n} Y_{ij}} \qquad (14)$$

The term “pull weight” conveys the capacity of nation i to acquire ecological inputs from other nations through forward linkages, specifically demand linkages. This metric delineates the degree to which nation i relies on other nations within the system. Conversely, the “driving force weight” signifies the capacity of nation i to furnish ecological assistance to other nations through backward linkages, denoting supply linkages. This parameter gauges the extent of influence that nation i exerts on other nations within the system. Conceptually, these relationships can be construed as upstream and downstream, respectively. Moreover, it’s possible to determine and compare both indirect and direct pulling and driving force weights:

$$F_i = \frac{\sum_{(j=1)}^{n} f_{ij}}{\sum_{(j=1)}^{n} \sum_{(i=1)}^{n} f_{ij}} \qquad (15)$$

$$F_j = \frac{\sum_{(i=1)}^{n} f_{ij}}{\sum_{(j=1)}^{n} \sum_{(i=1)}^{n} f_{ij}} \qquad (16)$$

Where, $F_i$ and $F_j$ are direct pulling and driving force weights, respectively. The numerator of (15) is the direct contribution of the system to the country i. From this, indirect effects can be derived simply by difference, as follows:

$$I_i = W_i - F_i \qquad (17)$$

$$I_j = W_j - F_j \qquad (18)$$

The distribution of benefits and costs among each pair of combinations of system components can be determined through Utility Analysis (Fath et al., 2007; Fath and Patten, 1999). Consequently, it becomes feasible to investigate the nature of the relationship extant between each pair of countries. This is accomplished through the computation of the direct utility intensity matrix (D), followed by an examination of the integral intensity matrix (U). The matrix D is constituted by the net flows between countries i and j, as explicated in the ensuing equation:

$$d_{ij} = \frac{(f_{ij} - f_{ji})}{T_i} \qquad (19)$$

The D-matrix solely signifies the character of the direct association, whereas the integral utility intensity matrix (U) offers a comprehensive understanding of the overall benefits or drawbacks associated with any linkage between any pair of countries. Put differently, it incorporates indirect effects into consideration as well. Specifically, element $d_{ij}$ represents the net embodied ecological benefits or costs exchanged between countries i and j. The formula for the integral utility intensity matrix (U) is presented in the following equation:

$$U = (1 - D)^{-1} \qquad (20)$$

Matrix U can exhibit positive or negative values, indicating whether the association is advantageous or detrimental. We computed five categories of relationships: exploitation (+, -), where one country gains more than the other; control (-, +), where one component’s outputs are influenced by the other; competition (-, -), causing negative effects on both components; neutralism (0,0), with no impact on either component; and mutualism (+, +), where both components benefit. Discerning the relationship type between two countries offers insights into the overall structure and dynamics of the system. Ultimately, the analysis enables an evaluation of the system’s comprehensive mutualistic and synergistic attributes. These two metrics facilitate an appraisal of the

system’s condition concerning fitness and symbiosis (Liu et al., 2011). The mutualistic index is derived from the ratio of positive to negative utilities within the network system, whereas the synergistic index pertains to the overall significance of mutualistic and competitive relationships. Their calculations are as follows:

$$MI = \frac{\sum U(+) - \sum U(-)}{\sum U(+) + \sum U(-)} \qquad (21)$$

$$SI = \sum U(+) + \sum U(-) \qquad (22)$$

where $\sum U(+) $ and $\sum U(-)$ are sum of flows with positive and negative utilities, respectively. If MI is greater than 1, it means that the total benefits of interactions outweigh costs in the system and hence the system can be considered mutualistic and healthy (Tan et al., 2018). The index of synergism is defined as the algebraic sum of integral flows characterized by positive utilities and those with negative utilities (Tan et al., 2018). A positive value of S indicates the presence of synergism, signifying that systems exhibit a net positive outcome (benefits outweigh costs) resulting from interactions between each pair of sectors (Fath and Borrett, 2006; Zhang et al., 2016). To facilitate the understanding of equations we have added a simplified example based on Costanza (1979) in the Supplementary Material 1, Example of the methodology. All computations were executed using MATLAB (version R2018A), and graphical representation was accomplished using R (R Core Team, 2022).

## 2.3. Limitations of the modelling approach

There are several limitations associated with using EXIOBASE v.3.8.2 MRIO data in our analysis. This data, estimated in 2022 from macroeconomic indicators such as trade and GDP provided by the International Monetary Fund, should be interpreted with caution. Specifically, the environmental accounts in EXIOBASE v.3.8.2 are updated with real data points from different years: 2015 for energy, 2019 for all GHG emissions (with non-fuel, non-CO2 now casted from 2018), 2013 for materials, and 2011 for most other indicators, including land and water. Indeed, EXIOBASE v.3.8.2 is known to have deviations from official national accounts, and its structure for a projected year like 2022 inherently relies on the (partly deviated) structures of previous years. We recognize that alternative databases like EORA/GLORIA or FIGARO-E3, which often show a closer alignment with official SNA figures and offer higher spatial or sectoral granularity, could enable a more precise tracing of bilateral flows and asymmetric relations, particularly for peripheral economies. The choice of database ultimately depends on the research focus, and such refinements are left to future work, as they would extend beyond the scope of this paper’s foundational framework and objective. Despite these mismatches, we believe our analysis remains valid as the methodology captures the structure of the economic system in terms of relationships between nations. While the absolute amounts of energy consumption, land use, or other materials may not be exact, our study focuses on the flows between nations. As long as the estimates do not deviate significantly enough to alter the direction of these flows, our results and conclusions can be considered valid. Therefore, assuming that the macroeconomic projection is sufficient to maintain existing trade relationships and structures between nations, our results can be considered robust.

However, to address these weaknesses further, we ran a sensitivity analysis to assess how sensitive the results are to changes in the environmental inputs. This confirmed the robustness of our results to both fixed and random perturbation scenarios (Supplementary Material 2, Sensitivity analysis). Moreover, the large-scale aggregation of countries within RoW (Rest of the World) regions in Africa (WF), Asia (WA), South America and Greenland (WL), and Eastern Europe (WE), poses a notable constraint when utilizing EXIOBASE v.3.8.2. This limitation is twofold. Firstly, it impedes accurately trace the intricate flows between
individual countries, resulting in a homogenization of the diverse territories under consideration. Secondly, these aggregated regions emerge as substantial nodes within the network, akin to countries with huge GDPs. For instance, WA comprises 62 countries, collectively generating 4 % of global economic output, an amount comparable to that of Japan alone. A similar scenario unfolds with WF, encompassing 58 countries and yielding the 1.68 % of total economic output approaching that of Brazil. It is crucial to underscore that these macro-regions encompass all nations in the global South, where natural resource extraction is prevalent, and environmental impacts are often more pronounced. In the ENA, the size of each node and the overall node count holds pivotal importance, significantly influencing each position within the system. Consequently, the merged results of countries within RoW regions surpass the sum of their individual contributions, amplifying their significance. Within this context, RoW regions emerge as influential players on the global scenario, portraying a cohesive unit despite comprising diverse political, economic, and social entities. While it is essential to acknowledge these complexities, the results do offer valuable insights about the structure of the global economy, the interaction of countries in international trade and the insights to assess ecological macroeconomic policies.

# 3. Results and discussion

## 3.1. Global environmental pressures' intensities and impacts

The first key insight from this research is the variable distribution of environmental pressures' intensities across global regions (Figs. 1 and 2). Environmental pressure's intensity informs about the ecological elements embedded in economic production, representing the amount of natural resources used or environmental impacts produced, directly and indirectly, to generate one unit of economic output for each country. Fig. 1 shows the geographical distribution of environmental pressures' intensity among world regions for each impact indicator (coefficient *e* of eq. 7). Fig. 2 ranks world regions according to their normalized embedded environmental pressures per capita.

Despite differences in the distribution of environmental pressures between world regions, a clear pattern emerges. Depending on the type of impact considered, either Semi-periphery regions (e.g., India, Brazil, Indonesia, RoW America, Eastern Europe) or Periphery regions (e.g., RoW Africa, RoW Asia) produce the highest levels of impact per unit of economic output (Fig. 1 and Supplementary Material 3 Fig. SM2A). These results align with previous evidence of higher environmental pressures in less developed regions than in developed ones (Wood et al., 2018). Greece presents a particular case. Despite being a Global North country, it shows extremely high values in all impact categories, ranking 1st in terms of environmental pressures' intensity (Supplementary Material 3 Fig. SM2A). This is mainly due to the model and database used. EXIOBASE groups many countries of the Global South by considering them as a single nation. Thus, low-income nations in the Global North show similar features to those in the Global South because they play a similar role in the network (e.g., small economic output means being a smaller node).

The results in Fig. 1 and Supplementary Material 3 Fig. SM2A could mislead to think that the world's Periphery has higher responsibilities than Core regions in the current global environmental crisis. By reallocating the responsibility for environmental impacts from the producer to the consumer, it is possible to account for the environmental impacts embedded in products and services and assign them to the importing country after tracing economic exchanges in global trade. From this perspective, the roles of China, the United States and India emerge, being the biggest world economies and actually responsible for the highest proportion of most environmental pressures (Supplementary Material 3 Figs. SM1 and SM3). These results are consistent with the EUE perspective of dematerialization and decoupling processes in Global North countries conceived as merely relative dynamics, whose impacts

are transferred to Global South regions (Wiedmann and Lenzen, 2018) and hidden in international trade (Hornborg, 1998).

Looking at the environmental pressures per capita offers another complementary angle to understand the role of each world region. From this perspective, those regions with the highest life standards (e.g., Luxembourg, Ireland, Denmark) appear at the top, surpassing producing countries and large economies (Fig. 2 and Supplementary Material 3 Fig. SM2B). That is, regions that are highly efficient at the national level and present low environmental pressures' intensities (Fig. 1, Supplementary Material 3 Fig. SM2A), can do so given the displacement of environmentally damaging industries to third countries, not due to a real reduction of resources consumption (absolute dematerialization) in their economy. Therefore, these countries turn their position upside down in a per capita assessment (Fig. 2 and Supplementary Material 3 Fig. SM2B).

On the opposite side, the low position of India, RoW Africa and RoW Asia Pacific signals that their high levels of environmental impacts are primarily caused by demographic pressure and aggregation of highly populated regions, respectively (Fig. 2). The embedded environmental pressures per capita also signals a strong influence of demographic factors on China's performances, although less than in the case of India (Fig. 2); which could be a matter of China's higher economic output per capita. Contrarily, population has almost no influence on the performance of the United States (Fig. 2) whose high values are more related to their large economic output per capita (Supplementary Material 3 Table SM4). The high position occupied by the United States even in the per capita ranking highlights that their high levels of embedded environmental impacts are dependent on output production dynamics, which gives empirical support to theoretical studies signalling Global North appropriation of global environmental space (Hickel et al., 2022).

## 3.2. Countries' dependence and influence on the global system

The second key insight from this study is that the capacity to attract (Pulling Force Weight, PFW) and push (Driving Force Weight, DFW) the environmental pressures of the global economic system is highly concentrated in a handful of regions of the Core and the Semi-periphery (Figs. 3 and 4 displaying results from eqs. 13 to 18). This means that while environmental pressures tend to manifest with higher intensities in the Semi-periphery and the Periphery (Fig. 1), their drivers mostly reside within Core and Semi-periphery regions (Fig. 3). This aligns with previous findings that emphasise how Core countries take advantage of their position in global value chains to externalize ecological burdens while capturing economic value (Althouse et al., 2023). In this way, it reveals the mediating role of the Semi-periphery in exchanges between the Core and the Periphery.

Results show that 12 regions (out of the 49 analysed) have the capacity to attract (Fig. 3A) and push (Fig. 3B) most global ecological impacts (from 74 % to 85 %). Moreover, there is an asymmetric distribution of the share of impacts between the two or three leading regions (positions mainly occupied by China, the United States and India) and the rest (Fig. 3). It deserves mention that if the contribution of all European Union's countries were grouped, it would rank stable in third/fourth position for most indicators, stressing the relevance of this Core region when considered as an aggregate. The high levels of PFW and DFW in Core and Semi-periphery countries (Fig. 3) highlights that these regions have large capacity to acquire and push ecological impacts. These results may be capturing the high capacity of the Core in acquiring environmental pressures embedded in processed products or semi finished products, while the Semi-periphery shows high dependence and
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Global coefficients of environmental pressures' intensity embedded in economic production in 2022. All coefficients assess the amount of natural resources required, or the environmental impact produced, to generate a unit of economic output. Colors represent from low (blueish) to high (reddish) environmental pressures' intensity. LUC = Land Use Change; WCB = Water Consumption Blue; GHG = Greenhouse gasses emissions; FAE = Freshwater Aquatic Ecotoxicity; MAE = Marine Aquatic Ecotoxicity; TEX = Terrestrial Ecotoxicity; PHO = Photochemical Oxidation; PMR = Particulate Matter/Respiratory; DEU = Domestic Extraction Used. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

influence on ecological impacts presumably linked to the acquisition of raw materials from the Periphery (Yu et al., 2014).

Fig. 4 shows the overall dependence (Fig. 4A) and influence (Fig. 4B) of each region in the global economic system organized by continents as classified by EXIOBASE for land use change (LUC).<fnref n="1" /> In this regard, we found a high and positive correlation (from 0.73 to 0.99, p-value<0.001) between the capacity of each world region to acquire and influence ecological elements from the rest of the world and its economic output (Supplementary Material 3 Tables SM2 and SM3). This finding supports criticism towards dematerialization theories, suggesting that a region's economic power is linked to its influence and dependence on global ecological resources. In this regard, China presents the largest ability to attract ecological elements from the global economy (more than 20 % on average) and the capacity to push around 23 % of them (Supplementary Material 3 Fig. SM4A and SM4B).

Upon closer examination, the dominance of Asia and Oceania emerges prominently. On average, they possess the capacity to attract nearly 45 % of ecological elements (Fig. 4A) and direct 49 % of them (Fig. 4B). Conversely, Europe and North America consistently exhibit a greater capacity to attract elements (Fig. 4A) than to influence them (Fig. 4B). These findings offer insights beyond the traditional models of global asymmetries between the Core and the Periphery or the Global North and the Global South, revealing a notable shift in global production dynamics from the West to the East. These results resonate with the recent identification of three categories of countries according to their capacity to capture value and deal with environmental impacts

(Althouse et al., 2023). Moreover, our results indicate that roughly half of the world's resources and environmental impacts must traverse Asia and Oceania before reaching their final destinations for consumption. This underscores the pivotal role played by Asia and Oceania in the global ecological and economic landscape. Additionally, the aggregation of many countries in macro-regions leads some of them (RoW Asia and Pacific and RoW Africa) to present high capacity to acquire and influence ecological pressures in the global system (Supplementary Material 3 Fig. SM4). As already mentioned, it should be kept in mind that macro-regions group together countries with different political, social and economic contexts.

Distinguishing whether a region's dependence or influence is direct or indirect (i.e., second degree interaction with other regions) reveals that indirect relations are beneficial for the United States, Japan, and European Union, while detrimental for China, India, and macro-regions (Supplementary Material 3 Figs. SM5 and SM6). This suggests that the structure of the global economic system benefits Core regions through indirect effects. However, the same does not hold true for other regions that, even with similarly high capacity to acquire and drive ecological impacts, belong to a Semi-periphery context. These results offer empirical evidence to EUE theories arguing that Core regions exploit the Semi-periphery by importing processed goods, while the Semi-periphery exploits the Periphery by importing raw materials, ultimately leading to ecologically unequal exchange dynamics benefitting the Core (Yu et al., 2014).

<footnote n="1">¹ Given constraints to display all indicators in the same plot with enough quality, the rest of indicators are provided in Supplementary Material 3, Extended Fig. 4. When referring to Fig. 4 in the text we speak about the average of the nine environmental indicators.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Global distribution of embedded environmental impacts per capita in 2022. Each dot represents an environmental pressure, the star represents the average pressure for all indicators of each world region. The values presented are homogenized using min-max normalization for visual comparison of indicators. For every indicator, the minimum value of that indicator gets transformed into a 0, the maximum value gets transformed into a 1, and every other value gets transformed into a decimal between 0 and 1. Supplementary Material Fig. SM1 presents the raw values.</figcaption></figure>

### 3.3. Exploitation and control relations drive international trade

The third and foremost result of our analysis is that exploitation and control relations instead of competition dominate countries' interactions in international trade (Fig. 5).<fnref n="2" /> Competition is used here in its ecological connotation, describing interactions among individuals (i.e., countries) vying for the same resources (i.e., environmental resources and pressures embedded in economic products) (Abrams, 2022; Fath, 2007). Traditionally, economic competition has been regarded as a positive mechanism operating on a win-win logic, an invisible hand regulating markets and fostering innovation and technological efficiency (Dasgupta, 1981). Despite this conceptual disparity, mainstream economic theories and policies continue to hinge upon this concept. However, critical perspectives have contended that competition - in its economic sense - indeed engenders inequalities (Martinez-Alier and O'Connor, 1995). More recently, through studies conducted within the framework of EUE, there has been a movement to transcend competition as the regulating force of economic behavior, openly highlighting the predatory nature of relations between the Global North and South (Hornborg, 2009; Hornborg and Martinez-Alier, 2016). This pattern of exploitation and control has been argued to replicate across various

scales, including Core-Periphery, urban-rural, and society-nature dynamics. Here we examined the exchanges between world regions to empirically assess what kind of relations characterize their interaction in global international trade. Relations are classified following the ecological categorization as mutualism (when both interacting countries win), competition (when both countries lose), exploitation (when one country gains more than the other) and control (when one country's outputs are influenced by the other) (Fath, 2007).

Examining these connections reveals an asymmetry in trade-related advantages and disadvantages among most countries (Fig. 5). Without considering the relationship between each country and itself, at most one relationship for each indicator shows a mutual benefit for the partners involved (Fig. 5). In addition, no matter the environmental indicator considered, competition relations between countries range from 0 to a maximum of 4 relationships (Fig. 5). Consequently, the majority of relationships (95 % on average) are based on exploitation and control. Exploitation relations range from 11 to 38 relations, while Control relations range from 10 to 37, with an average of almost 25 and 22 relationships for all indicators considered (Fig. 5). These results indicate that in the pairwise relations taking place in international trade there is almost always a winner and a loser. These results are in line with the hypothesis of zero-sum game (Hornborg, 2009) where any nation's gain necessarily comes at another nation's loss. To clarify how these ecological relationship types manifest in real-world trade, we offer illustrative examples from our results. An exploitation relation is found between France and Rest of World Africa where France benefits

<footnote n="2">² As for Fig. 4, we provide the other eight environmental indicators in Supplementary Material 3, Extended Fig. 5. When referring to Fig. 5 in the text we speak about the average of the nine environmental indicators.</footnote>
A. Pull force weights per environmental pressure

<figure><img src="image_6.png" /></figure>

B. Driving force weights per environmental pressure

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Pull and driving force weights per environmental pressure in 2022 – Distribution by countries. Pull force weights (Fig. 3A) and driving force weights (Fig. 3B) assess, respectively, the levels of dependence and influence of regions for each environmental pressure. The distribution of values for the top 12 world regions for each environmental pressure is displayed; their share in total is represented as a ratio. DEU = Domestic Extraction Used; GHG = Greenhouse gasses emissions; MAE = Marine Aquatic Ecotoxicity; PMR = Particulate Matter/Respiratory; WCB = Water Consumption Blue; FAE = Freshwater Aquatic Ecotoxicity; LUC = Land Use Change; PHO = Photochemical Oxidation; TEX = Terrestrial Ecotoxicity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Average (A) pull and (B) driving force weights for land use change (LUC) in 2022 – All world regions grouped by continents. In Fig. 4A and 4B, the first ring represents the coefficients joined by continents. The second ring shows the proportion of each region within them. Notably, Asia and Oceania are merged, and South America includes Greenland. Full data of the PFW and DFW of each indicator for each world region is available in Supplementary Material Tables SM4 and SM5. The other eight indicators are provided in Supplementary Material, Extended Fig. 4.</figcaption></figure>

ecologically from the relationship while Rest of World Africa incurs in environmental costs. A control dynamic is evident between the United States and Mexico, where the United States consumption patterns strongly shape Mexico production systems, even when not directly importing high-impact goods. Meanwhile, competition was observed between Japan and Germany incurring mutual ecological costs, where both nations compete for global markets. Lastly, a case of mutualism occurs between Cyprus and the Netherlands, where their mutual benefit is derived not from direct resource exchange but from their position in the network, enabling their participation in a trade architecture that structurally reduces their exposure to ecological degradation. Their role in the systemic structure of global trade and the indirect benefits they provide each other (i.e., their reciprocal indirect ecological relief)

reflects a kind of ecological symbiosis whereby each country provides system-level resilience and functional buffering to the other. This interdependence exemplifies network mutualism as defined by Fath et al. (2007), wherein entities (i.e., countries) not directly linked can experience bilateral benefits due to the structure of intervening flow paths – indirect routes mediated by third parties (i.e., countries) that create system-level mutualism even when direct exchanges might suggest otherwise (Fath and Patten, 1999).

Considering not only the pairwise interactions, but also the properties of the global economic system as a whole, the mutualism index (Table 1) shows whether the costs of international trade exceed the benefits. If the index is greater than 1, the system can be said to be mutualistic. We found that costs exceed benefits in international trade
<figure><img src="image_9.png" /><figcaption>Fig. 4. (continued).</figcaption></figure>

for all the environmental pressures considered (Table 1). In addition, the degree of synergy of the system denotes, when it is positive, that there is a net benefit from the interactions taking place in the system. As it can be seen, the synergy index indicates a net benefit from international trade for all indicators (Table 1). However, when domestic national benefits - that is, the interaction of each country with itself - are removed (Synergism1), the index collapses to values near zero (Table 1). Hence, although the ecological relations show that some countries (particularly the Global North) are benefiting from international trade, the mutualism and synergism indexes reveal that these exchanges are detrimental for the well-functioning of the global economic system as a whole.

### 3.4. Implications for green growth policies

While green growth and dematerialization have become prominent in recent sustainability narratives, it can fairly be argued that their relatively short policy lifespan is theoretically grounded in traditional

economics principles of competition, where technological improvement results into a win-win scenario of economic growth and a simultaneous decrease of ecological impacts (Feola, 2020; Jackson and Victor, 2019; Vogel and Hickel, 2023). Our findings outline that the structure of the current global economy is far from being a win-win situation. In fact, the structure of the global economy is dominated by asymmetric (win-lose) relationships where Core regions (the Global North) improve their economic and environmental performance by taking resources from and displacing environmental impacts to the Periphery and Semi-Periphery (the Global South). In this regard, our findings aligns with previous studies challenging that the opening of new markets to international competition and the commodification of environmental resources is sufficient to engender reciprocal benefits through increases in eco-efficiency (Feola, 2020; Hickel and Kallis, 2020; Jackson and Victor, 2019; Vogel and Hickel, 2023). More specifically, recent research in ecological macroeconomics highlights the limitations of green growth strategies in an open economy context, emphasizing how pollution and
Ecological relations of each country for LUC by Core-Periphery classification

<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Ecological relationships between each world region for land use change (LUC) in 2022. The index ranges from 0 to 1 and indicates the proportion of each type of relationship for each environmental pressure. The plots for the other environmental indicators are provided in Supplementary Material, Extended Fig. 5. Data on countries' classification taken from Babones (2005).</figcaption></figure>

<caption>**Table 1**</caption>

Indexes of mutualism and synergy in 2022. Values represent the average for all environmental pressures. 1 Without considering domestic national benefits.

<table><thead><tr><th>Index</th><th>Mean</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Mutualism</td><td>0,97</td><td>0,95</td><td>0,98</td></tr><tr><td>Synergism</td><td>50,01</td><td>49,43</td><td>51,08</td></tr><tr><td>Synergism1</td><td>0,92</td><td>-0,45</td><td>2,9</td></tr></tbody></table>

resource-intensive industries are unevenly distributed globally, often at the expense of the Periphery (Althouse et al., 2020).

Therefore, maintaining current policies can lead to polarization rather than improvement in the distribution of environmental pressures, potentially hampering the compliance of sustainability goals (Jorgenson, 2009; Rice, 2007). The environmental and socio-economic exploitation of the Global South is not an exception but intrinsic to the current economic model (Dorninger et al., 2021; Hickel and Slamersak, 2022; Hornborg, 2014). While our model cannot establish causality between specific sustainability policies and observed exploitation patterns, it does highlight how the existing configuration of international trade may constrain the potential of policy instruments that do not actively address these systemic imbalances. We argue that economic approaches integrating social and biophysical aspects, such as the one used in this study or others (Hickel et al., 2022; Hornborg and Martinez-Alier, 2016; Muradian and Martinez-Alier, 2001), may be better empirically and theoretically grounded to understand the functioning of the global economy and, therefore, design better-informed policies to face the current social-ecological crisis.

## 4. Conclusion

The findings provided in this study empirical support Ecologically

Unequal Exchange's theories. In the current economic system, Core countries present low national environmental intensities but high environmental intensities per capita. Conversely, countries from the Periphery and Semi-periphery present high national environmental intensities but low environmental intensities per capita. Moving from a production to a consumption approach and considering all indirect relations, we found that the capacity to appropriate environmental resources and drive their extraction is highly concentrated in the most advanced economies. Meanwhile, the Semi-periphery and Periphery bear a disproportionate amount of environmental pressures and have little capacity to acquire environmental resources or influence the distribution of ecological impacts.

These interactions are characterized by exploitation and control relations, rather than competition or mutualism. At the global level, some countries must inevitably lose (bear the burden of environmental impacts) in order for others to win (capture environmental resources and improve their national resource-use efficiency). Contrary to the current economic theory assumption, competition is not central in the global economy neither in its ecological meaning (lose-lose interaction) nor the economic one (specialization and exchange to maximize efficiency under win-win interactions).

Our findings suggest that green growth and dematerialization policies may be unable to achieve socially fair and environmentally sustainable societies at the global level if the structure of the global economy remains unchanged.

## CRediT authorship contribution statement

**Raffaele Guarino:** Writing – original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. **Giulio Corsi:** Writing – review & editing, Investigation, Conceptualization. **Enrique Muñoz-Ulecia:** Writing – review & editing, Visualization, Validation, Supervision, Software, Investigation, Conceptualization.
Declaration of generative AI and AI-assisted technologies in the
writing process

During the preparation of this work the authors used ChatGPT, DeepL and Grammarly in order to improve the readability of the text. After using this tool, authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Funding sources

This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.

Declaration of competing interest

We hereby declare that there are no conflicts of interest regarding the publication of this manuscript. All authors have contributed to the work independently, and there are no financial or personal relationships with other individuals or organizations that could inappropriately influence (bias) the work reported in this manuscript.

Acknowledgements

We would like to express our gratitude to Professor Martini for his invaluable support in the development of the model. The feedback received from the ISEE 2023 conference participants was instrumental in refining our approach, and we are deeply appreciative of their insights. We also extend our heartfelt thanks to PhD student Arianna Petrosino for her essential assistance in creating the maps within the GIS environment.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecolecon.2025.108909.

Data availability

Data will be made available on request.

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