A Multidimensional Framework for Quantifying Brazil–China Commodity Trade Dependence Using the Commodity-Specific Sustainability Index

Abstract

We propose the Commodity-Specific Sustainability Index (CSSI), a multidimensional system for quantifying Brazil–China commodity trade dependence that integrates environmental, economic, and social sustainability metrics with conventional trade dynamics. Traditional trade metrics often overlook sustainability risks due to their focus on volume or monetary value. The CSSI combines three dimensions of sustainability risk (environmental impact, economic resilience, and social well-being) into a single assessment framework for major commodities, including soybeans and iron ore. The framework uses a dynamic weighting mechanism that adjusts sub-indices depending on policy priorities and stakeholder inputs, and a Transformer-based time series model captures relationships between CSSI trends with bilateral trade flows along with external shocks, enabling the predictive analysis of sustainability-driven trade adjustments. Furthermore, the CSSI replaces conventional trade volumes with sustainability-adjusted counterparts that are then incorporated into standard trade frameworks such as gravity equations. Our analysis of soybeans and iron ore from 2015 to 2022 shows that conventional dependence metrics overestimate trade dependence by 12–19% (95% CI: 10.8–21.2%, p < 0.001) for commodities with a high environmental footprint. The predictive model, built entirely based on publicly accessible data sources, produces a mean absolute error of 5.5% (±0.8%) in forecasting quarterly trade flows, outperforming ARIMA (6.8% ± 0.5%) and LSTM (6.1% ± 0.6%). The CSSI’s novelty is its holistic approach to sustainability–trade connections, providing policy makers and researchers with a tool to assess long-term commodity resilience, beyond traditional economic metrics.

1. Introduction

The Brazil–China bilateral trade relationship represents one of the most significant commodity-dependent partnerships in the global economy, fundamentally shaped by China’s expanding industrial requirements for Brazilian commodities to support its economic development, including soybeans, iron ore, and crude oil [1]. This relationship exemplifies the broader challenge of integrating sustainability considerations into traditional trade analysis, as conventional metrics often fail to capture the environmental, social, and long-term economic implications of commodity dependence. This trade relationship has been extensively studied using standard approaches, such as the gravity model [2] and input–output analysis [3], which focus on trade volumes and monetary flows. However, many of these approaches overlook the complexity of sustainability issues related to commodity transactions, such as environmental degradation, labor conditions, and long-term stability [4,5].

The magnitude of this relationship cannot be underestimated. In 2022, Brazil exported the majority of its primary commodities to China, with soybeans, iron ore, and petroleum accounting for 78% of the total export value [4]. This reflects China’s need for raw materials to support industrialization and food security, whereas Brazil enjoys stable income and foreign exchange earnings. However, this dependence exposes systemic risks, as fluctuations in international commodity prices can significantly impact trade balances and economic stability. For example, a 10% decline in iron ore prices reduces Brazil’s trade surplus to China by approximately USD 2.1 billion annually [5].

1.1. Limitations of Traditional Trade Dependence Metrics

Traditional analysis of Brazil–China trade relies heavily on established metrics, such as revealed comparative advantage (RCA) [6] and trade concentration indices [7], which consistently emphasize Brazil’s increasing dependence on China as a raw material market. These approaches, while providing valuable economic insights, fundamentally overlook the sustainability dimensions that increasingly influence trade relationships in the 21st century.

Economic dependence indices, such as the Trade Dependence Index [8] and the Herfindahl–Hirschman Index [9], provide useful information but focus only on economic aspects. The revealed comparative advantage (RCA) index (derived by Balassa [6]) measures Brazil’s export performance in specific commodities relative to its export profile and trade patterns, consistently yielding values above 2.0 for primary commodities, indicating strong comparative advantages in resource-based sectors. However, these indices seldom account for price volatility, terms-of-trade shocks, or the environmental and social externalities that can exacerbate economic vulnerability and create systemic risks in trade relationships [5].

Sustainability assessments such as Life Cycle Assessment (LCA) [10] and Ecological Footprint [11] are rarely integrated into trade analyses and leave little room for economic and environmental policy decisions, especially for Brazil, where raw material exports to China represent a major part of GDP, but their impact on the environment raises questions about long-term sustainability [12].

Gravity equations, which are used extensively to predict trade flows between nations, have been modified to include geopolitical factors such as diplomatic alignment [13], cultural proximity [14], and institutional quality [2]. While these modifications represent important advances, commodities are still treated as homogeneous entities without accounting for differences in their ecological or social effects, thereby oversimplifying the risk of “resource curse effects” where low-value-added exports inhibit technological progress and economic diversification [15].

1.2. Emerging Sustainability–Trade Integration Approaches

The bilateral relationship is further complicated by geopolitical complexities and structural differences. China’s infrastructure investment initiatives, such as the Belt and Road Initiative (BRI) [16], often involve long-term commodity supply agreements that create complex interdependencies between trading partners [17]. Recent work has increasingly emphasized the need to integrate sustainability considerations into trade analysis, as traditional economic measures fail to account for environmental and social externalities in international trade [18].

Life Cycle Assessment (LCA) studies reveal significant variation in environmental impacts within commodity groups. Brazilian soybean production exhibits carbon footprints ranging from 0.5 to 3.0 kg CO₂e/kg, depending on production methods and land-use change [19]. These findings highlight the inadequacy of aggregate trade statistics that mask significant differences in environmental impacts, demonstrating the need for commodity-specific sustainability assessments.

1.3. The CSSI Framework: Bridging Trade and Sustainability Analysis

This study presents the Commodity-Specific Sustainability Index (CSSI), which is designed to bridge the gap between trade and sustainability analyses. The CSSI represents a paradigm shift from economic evaluations to a multidimensional approach, where environmental, economic, and social sustainability metrics are combined within a single framework. Unlike other composite indices, such as the Environmental Performance Index [20] or the Sustainable Development Index [21], the CSSI is designed for bilateral commodity trade analysis with three main improvements.

First, we implement a dynamic weighting system that adjusts sub-indices (environmental footprint, economic resilience, and social impact) according to policy objectives and time-varying trends. This ensures that the index adapts to changing policy priorities and international commitments such as the Paris Agreement [22] or United Nations (UN) Sustainable Development Goals [23]. Machine learning algorithms optimize the balance between different sustainability measures based on observed correlations with trade resilience and policy effectiveness.

Second, the CSSI introduces a sustainability-weighted trade volume metric that adjusts dependence calculations by penalizing exports with high environmental or social risk. The adjustment transforms volume-based measures into sustainability-weighted measures, which provide a deeper understanding of trade relations, accounting for the hidden costs of unsustainable practices. The adjustment is calibrated by the environmental, social, and economic effects of different commodity sectors.

Third, the framework implements a machine learning model using Transformer networks that detects relationships between CSSI variations and geopolitical or macroeconomic events, allowing for prediction scenarios and policy simulation, distinguishing the CSSI from static assessment tools, and allowing for forward-looking analysis of commodity trade relationships.

1.4. Contributions and Implications

The CSSI contributes to the literature in several ways. Its approach extends beyond descriptive trade indices and embeds causality analysis using panel data [24] and regression discontinuity [25], which isolates the effect of sustainability performance on trade dependence. This enables researchers and policymakers to distinguish correlation and cause in sustainability–trade relationships, providing a stronger basis for policy interventions.

Conceptually, this framework operationalizes the notion of “sustainability-adjusted dependence”: dependence on a trading partner is measured not only by trade volume but also by the environmental and social costs of maintaining the relationship. This concept is fundamentally changing how trade dependence is understood and measured: it takes a step beyond traditional economic indicators to include sustainability considerations that affect long-term trade viability.

Practically, the CSSI can be applied to other commodity-dependent countries facing similar trade–environment tensions, for example, Argentina, Indonesia, or Chile [26]. The modular design and methodological transparency of the CSSI enable adaptation to different countries and commodity portfolios to improve adoption and comparison.

The implementation of the CSSI on Brazil–China commodity trade yields several important findings that challenge traditional views of trade dependence. Our empirical analysis demonstrates that conventional measures overestimate Brazil’s dependence on China by 12–19% (95% CI: 10.8–21.2%, p < 0.001) for commodities with high environmental footprints, with the overestimation being most pronounced for soybeans (18.7% ± 2.1%) compared with iron ore (13.4% ± 1.8%). The framework’s predictive capabilities enable identification of sustainability-driven trade disruptions up to six months in advance, providing early warning capabilities for policymakers and market participants.

This study addresses three primary research questions: (1) How do sustainability considerations alter traditional measures of bilateral trade dependence? (2) Can machine learning approaches effectively predict sustainability-driven trade adjustments? (3) What are the policy implications of integrating sustainability metrics into trade analysis?

Our identification strategy relies primarily on associational analysis rather than strict causal inference. While we employ panel data methods and examine policy discontinuities, we acknowledge that establishing definitive causal relationships requires stronger identifying assumptions than our current design permits. Therefore, we position our findings as robust associations that inform policy discussions rather than definitive causal claims.

The remainder of this paper is organized as follows: Section 2 provides theoretical foundations and preliminary knowledge to understand the CSSI framework, including background on commodity trade patterns, sustainability metrics, and data integration issues. Section 3 presents detailed methodology for CSSI development, including mathematical formulations, algorithms, and technical implementations. Section 4 reports results of applying the CSSI to Brazil’s soybean and iron ore exports, including performance comparisons with existing methods and sensitivity analyses. Section 5 presents policy implications, limitations, and future directions, while Section 6 summarizes key results and general implications for sustainable trade policy.

2. Theoretical Foundations and Data Framework

This section provides the theoretical foundations necessary for understanding the CSSI framework, focusing on four key areas: (1) Brazil–China commodity trade dynamics and their sustainability implications; (2) methodological approaches to sustainability assessment in supply chains; (3) data integration challenges in transnational sustainability studies; and (4) technical foundations for real-time sustainability monitoring and prediction. Unlike traditional literature reviews, this section establishes the conceptual and empirical groundwork specifically required for the development of the CSSI.

2.1. Brazil–China Commodity Trade Dynamics and Sustainability Implications

The Brazil–China trade relationship represents one of the most significant shifts in global commodity markets over the past two decades. By 2022, Brazil’s exports to China comprised most of its primary goods (soybeans, iron ore, petroleum, etc.), with 78% of the total export value [27]. China has a major market for commodities to support its continued economic growth and food security objectives, while Brazil receives stable revenue and foreign exchange earnings for macroeconomic stability.

The soybean trade illustrates this relationship. Brazil is the world’s largest soybean producer and exporter, with production increasing from 55 million MT in 2005/2006 to 150 million MT in 2022/2023 [28]. China imported 84 million MT of Brazilian soybeans in 2022, which represents 60% of Brazil’s soybean exports [4]. This trade relationship is driven by China’s growing demand for animal feed to support its expanding livestock sector and Brazil’s competitive prices and complementary seasonal production cycles, which enable Brazil to supply soybeans during China’s off-season.

However, this concentration creates systemic vulnerabilities that extend beyond simple price volatility. Fluctuations in international commodity markets can severely impact trade balances and the economic stability of both countries. Economic modeling demonstrates that a 10% decline in iron ore prices would reduce Brazil’s trade surplus with China by USD 2.1 billion annually, equivalent to 0.12% of Brazil’s GDP [29]. These effects are magnified by the high correlation between different commodity prices (ρ = 0.73 for soybean–iron ore price correlation, 2015–2022) and can produce synchronized shocks across multiple sectors.

The iron ore trade has additional problems, namely, market concentration and price power. Brazil’s iron ore exports to China are dominated by two major producers, Vale and CSN, which together account for more than 70% of the exports [30], and China’s steel industry, as a major global producer, represents approximately 60% of global iron ore demand. This creates a concentrated market structure that requires careful coordination to maintain price stability and mutual benefits for both countries.

The bilateral relationship is further complicated by geopolitical asymmetries and power dynamics. China’s state-supported investments (e.g., the Belt and Road Initiative [31]) link infrastructure financing to long-term commodity supply contracts, creating dependencies that can limit Brazil’s sovereign decision-making power. These arrangements often involve complex financing structures linking Brazilian commodity exports to Chinese infrastructure investments, with long-term commitments that may limit Brazil’s ability to adapt to changing market conditions or policy priorities.

The “middle-income trap” [17] adds another layer of complexity: Brazil’s low-value-added exports may inhibit technological upgrading and economic diversification, potentially limiting long-term development. Bresser-Pereira [32] argues that commodity dependence can lead to exchange rate appreciation (Dutch disease effect), which reduces the competitiveness of manufacturing sectors, creating a self-reinforcing cycle of deindustrialization and increased commodity dependence.

Recent changes in global trade policy have further raised uncertainties in the Brazil–China commodity trade relationship. Environmental trade measures, such as the Carbon Border Adjustment Mechanism proposed by the European Union [33], create new incentives for sustainable production, which can affect the competitiveness of Brazilian commodities in global markets. China’s own environmental policies, such as its commitment to carbon neutrality before 2060 [34], may affect future demand patterns for different types of commodities based on their environmental characteristics.

2.2. Sustainability Metrics for Commodity Supply Chains

Sustainability assessment of commodity trade requires integration of three complementary dimensions that address environmental, economic, and social performance. Understanding these approaches is essential for developing the integrated assessment framework that CSSI represents, as each dimension contributes unique insights into the long-term viability of trade relationships.

2.2.1. Environmental Footprinting

Environmental footprinting is the most developed area of sustainability assessment, and Life Cycle Assessment (LCA) is the main tool for measuring resource consumption and pollutant emissions over production cycles. The Brazilian soybean carbon footprint exhibits significant variation depending on production methods and land use patterns, ranging from 0.5 to 2.8 kg CO₂e per kg of soybean [19]. This variation is primarily attributed to land use change, with production on recently deforested land exhibiting considerably higher carbon footprints than production on established agricultural land.

Carbon footprints are calculated using international standards, such as ISO 14,067 [10], which establishes the limits of greenhouse gas emissions for products over time. For agricultural commodities, emissions from land use change, fertilizer production and application, fuel consumption for machinery, transport, and processing are mainly considered. Time limits for land use change emissions remain a subject of ongoing debate, with different approaches (20-year vs. 100-year amortization periods) yielding significantly different results and affecting trade competitiveness assessments.

Water scarcity footprints (in cubic meters) vary similarly due to regional differences in irrigation and water availability. Mekonnen and Hoekstra [35] demonstrated that soybean production in Brazil’s Cerrado region requires an average of 2100 m³ of water per ton of soybeans (compared with 1800 m³ per ton in the Amazon region) due to different precipitation patterns, irrigation requirements, and water use efficiency.

The concept of virtual water trade proposed provides a framework for understanding the water implications of commodity trade [36]. Brazil’s soybean exports to China represent a virtual water transfer of 180 billion cubic meters [37], equivalent to the annual water consumption of Germany, which can have significant effects on water security in both countries, as China can cover part of its food security needs using Brazil’s water.

2.2.2. Social Impact Assessment

Social impacts are a relatively new but increasingly important component of sustainability assessment. The Social Hotspots Database [38] provides standardized assessments of working conditions, human rights, and community well-being across sectors and regions. For mining in Brazil, occupational injury rates reach 15.2 injuries per 1000 workers per year, compared with the national industrial average of 6.6 injuries per 1000 workers [39]. These disparities highlight significant social costs associated with commodity production for export that are not captured in traditional economic analyses.

There are a number of difficulties associated with measuring social impacts, especially in terms of data availability, standardization, and cultural context. Unlike environmental impacts (usually measured using standard physical units), social impacts involve subjective assessments of well-being, rights, and community development, which differ greatly across cultures and within institutions. Standardized social impact assessment methods are still being developed by organizations such as the International Labour Organization [40] and the UN Global Compact [41], aiming at common frameworks and indicators that can be applied across different national contexts.

2.2.3. Economic Resilience Measures

Economic resilience measures provide the third dimension of sustainability assessment, in the context of long-term economic stability. Price elasticity measures the response of demand for a commodity to changes in its price, providing information on market stability and vulnerability to external shocks. Soybean exports exhibit a price elasticity of −0.7 versus Chinese import tariffs [5], suggesting moderate responsiveness to trade barriers and policy changes.

Market diversification scores measure the concentration of trade relations and the risk of over-dependence on markets/partners. The Herfindahl–Hirschman Index (HHI) measures concentration of trade relationships, with values higher than 0.25 representing high concentration and potential vulnerability to market disruption [42]. Brazil’s soybean exports have an HHI of 0.42 for destination markets, indicating high concentration and significant dependence on the Chinese market.

The integration of different sustainability dimensions poses significant methodological challenges. Traditional approaches treat environmental, social, and economic indicators as separate domains, making it difficult to assess trade-offs and synergies between different sustainability goals. Integrated assessment frameworks require sophisticated weighting schemes, normalization procedures, and aggregation methods that can combine disparate indicators while preserving information about individual dimensions.

2.3. Data Integration Challenges in Transnational Sustainability Assessment

Cross-border sustainability analysis faces three fundamental data challenges that complicate the development of comprehensive assessment frameworks: spatiotemporal variability, indicator heterogeneity, and verification gaps. Each of these challenges requires specific methodological and technological solutions that inform the CSSI design.

2.3.1. Spatiotemporal Variability

Spatiotemporal differences arise when different data sources are misaligned in terms of time resolution, coverage, and reporting period. Satellite-derived deforestation data from systems such as Global Forest Watch [43] exhibit temporal misalignment with trade statistics due to different reporting periods and jurisdictional boundaries. Global Forest Watch provides annual forest loss data on a calendar-year basis, while trade statistics may follow different fiscal year reporting periods. This temporal misalignment can lead to apparent inconsistencies between environmental data and trade data that must be addressed through interpolation and synchronization methods.

Spatial misalignment is another challenge. Satellite data are usually organized according to geographic coordinates or administrative boundaries, whereas trade data are organized according to political jurisdictions and commercial relationships. The attribution of environmental effects to specific trade flows requires spatial analysis linking production locations with export destinations through complex supply chain networks.

The time delay between environmental impacts and trade flows further complicates integrated analyses. Deforestation may occur when agriculture expands several years before the resulting agricultural products enter international trade, creating temporal gaps that must be accounted for in sustainability studies. Pendrill et al. [44] demonstrate that the average time between deforestation and agricultural export ranges from 2 to 8 years, depending on the commodity and production system.

2.3.2. Indicator Heterogeneity

Indicator heterogeneity is another major issue because measurement standards, methodologies, and institutions differ from country to country. Chinese environmental standards differ substantially from Brazil’s forest code, reflecting different national contexts, priorities, and institutional capacities.

Harmonization of environmental standards reflects different national contexts, priorities, and capacities. What constitutes sustainable practice in one country may not be feasible in another due to differences in climate, ecology, economic development, or capacity. The development of internationally comparable sustainability indicators requires balancing standardization needs with legitimate differences in national contexts and priorities.

Methodological differences in data collection and processing may also result in apparent differences between national datasets. For example, Brazil’s greenhouse gas inventory follows IPCC guidelines but uses country-specific emission factors reflecting local conditions and practices [45]. China’s greenhouse gas inventory follows similar IPCC methods but uses different emission factors and activity data sources [31]. These differences can result in systematic biases in comparisons that must be addressed through harmonization procedures or uncertainty analyses.

2.3.3. Verification and Transparency Gaps

Verification gap is the third major challenge, stemming from data quality, transparency, and accountability issues in different institutions. Multi-source verification systems using publicly accessible databases can potentially improve data quality and transparency in supply chain assessments; however, they require standardization between Brazilian and Chinese data reporting systems, which employ different methodological frameworks and institutional standards.

Cross-border data verification faces both methodological and institutional challenges. Different countries may use different data collection protocols, quality assurance procedures, and reporting standards, making cross-platform integration complex. Additionally, the reliance on multiple independent data sources for verification requires careful consideration of data consistency and harmonization procedures to ensure reliable sustainability assessments.

2.4. Technical Foundations for Real-Time Sustainability Monitoring

The development of the CSSI requires integration of several emerging technologies that enable real-time monitoring and predictive analysis of sustainability–trade relationships. These technical foundations include satellite-based monitoring systems, machine learning approaches for time series analysis, and federated learning frameworks for cross-border data collaboration.

Satellite-derived monitoring systems provide near-real-time data on environmental changes that can be linked to trade flows. Systems such as Global Forest Watch [43], which integrates multiple satellite data sources, including MODIS and Landsat, provide user-friendly access to forest monitoring data and have been increasingly used by international buyers to assess the sustainability of Brazilian products. Recent advances in cloud-based processing platforms such as Google Earth Engine [46] enable more accessible monitoring and automated detection of environmental changes without requiring specialized technical expertise.

Machine learning approaches, particularly Transformer-based architectures, offer significant advantages for analyzing complex time series relationships between sustainability indicators and trade flows. Unlike traditional econometric approaches, Transformer models can capture non-linear relationships and long-range dependencies in time series data, making them particularly suitable for analyzing the complex interactions between environmental, social, and economic factors in trade relationships.

Open data integration represents another technological innovation with potential applications in trade analysis, especially when data sharing between countries may be facilitated through standardized public databases and APIs. This approach allows multiple parties to access and analyze sustainability data collaboratively using publicly available sources, making it an attractive tool for international trade analysis where data transparency and accessibility are important considerations [47].

The literature reveals several important gaps that the CSSI framework addresses: (1) trade analysis methods do not systematically integrate sustainability metrics and fail to account for the full range of effects in trade relationships; (2) existing sustainability assessment frameworks lack the temporal resolution and predictive capability necessary for trade policy analysis; (3) there are no standardized approaches to combine the environmental, economic, and social aspects of trade assessment; and (4) data integration issues in international sustainability assessment, particularly regarding data privacy and cross-border collaboration, remain largely unaddressed.

3. Methodology

3.1. The CSSI Framework Architecture

The CSSI framework integrates three sustainability dimensions (environmental, economic, and social) through a dynamic weighting mechanism and employs machine learning techniques for predictive analysis. The overall CSSI score for commodity c in period t is calculated as follows:

$$CSS{I}_{c,t}=w_{E,t}\times E_{c,t}{w}_{Ec,t}\times E{c}_{c,t}{w}_{S,t}\times S_{c,t}$$

where $w_{E,t}$, $w_{Ec,t}$, and $w_{S,t}$ represent time-varying weights for environmental, economic, and social dimensions, respectively, subject to the constraint $\Sigma w_{i,t}=1$ and $w_{i,t}\ge 0$. The dynamic nature of these weights allows the index to adapt to changing policy priorities and stakeholder preferences while maintaining analytical consistency.

3.2. Sub-Index Development

3.2.1. Environmental Sub-Index Formulation

The environmental sub-index, $E_{c,t},$ combines carbon footprint, water stress, and land use change indicators:

$$E_{c,t}={\alpha }_1\times C{F}_{c,t}+{\alpha }_2\times W{S}_{c,t}+{\alpha }_3\times LU{C}_{c,t}$$

where ${\alpha }_1$ = 0.45, ${\alpha }_2$ = 0.30, and ${\alpha }_3$ = 0.25 represent the relative weights for carbon footprint, water stress, and land use change, respectively. These weights were determined through expert consultation and validated using sensitivity analysis (Section 3.6.2).

The carbon footprint component is calculated as follows:

$$C{F}_{c,t}={\displaystyle \frac{GH{G}_{c,t}}{\mathrm{m}\mathrm{a}\mathrm{x}(GH{G}_{all,t})}}$$

where $GH{G}_{c,t}$ represents greenhouse gas emissions per unit of commodity c in period t, normalized by the maximum emissions across all commodities and time periods. For soybeans, GHG emissions range from 0.5 to 2.8 kg CO₂e/kg, depending on production methods and land use change [19]. For iron ore, emissions typically range from 0.8 to 1.5 kg CO₂e/kg, depending on mining and processing methods.

Water stress is measured using the Water Stress Index (WSI):

$$W{S}_{c,t}={\displaystyle \frac{W{U}_{c,t}}{W{A}_{c,t}}}\times {\displaystyle \frac{1}{\mathrm{m}\mathrm{a}\mathrm{x}(WS{I}_{all,t})}}$$

where $W{U}_{c,t}$ is water used for the production of commodity c, and $W{A}_{c,t}$ is water availability in the production region. The normalization ensures comparability across commodities and time periods.

Land use change impact is calculated as follows:

$$LU{C}_{c,t}={\displaystyle \frac{\Delta F_{c,t}\times C{F}_{LUC}}{P_{c,t}\times \mathrm{m}\mathrm{a}\mathrm{x}(LU{C}_{all,t})}}$$

where $\Delta F_{c,t}$ represents the change in forest area due to commodity c in period t, $C{F}_{LUC}$ is the carbon factor for land use change (typically 100–200 tCO₂/ha for tropical forests based on IPCC guidelines), and $P_{c,t}$ is production volume. The normalization using $\mathrm{m}\mathrm{a}\mathrm{x}(LU{C}_{all,t})$ ensures comparability across commodities.

3.2.2. Economic Sub-Index Formulation

The economic sub-index, $E{c}_{c,t}$, captures trade competitiveness and market stability through three components:

$$E{c}_{c,t}={\beta }_1\times RC{A}_{c,t}+{\beta }_2\times \left(1-{\sigma }_{c,t}\right)+{\beta }_3\times DI{V}_{c,t}$$

where ${\beta }_1$ = 0.40, ${\beta }_2$ = 0.35, and ${\beta }_3$ = 0.25 represent weights for revealed comparative advantage, price stability, and market diversification, respectively. These weights were calibrated using historical trade data and validated through cross-validation analysis.

The revealed comparative advantage follows Balassa’s formulation:

$$RC{A}_{c,t}={\displaystyle \frac{X_{c,t}/X_{total,t}}{X_{c,world,t}/X_{world,t}}}$$

where $X_{c,t}$ represents exports of commodity $c$ in period $t$, $X_{total,t}$ represents total exports, and world subscripts indicate global aggregates.

Price volatility is estimated using a GARCH (1, 1) model to capture time-varying volatility patterns:

$${\sigma }_t^2=\omega +\alpha \times {\epsilon }_{t-1}^2+\beta \times {\sigma }_{t-1}^2$$

where ${\epsilon }_{t-1}$ represents the previous period’s price shock, and ω, α, and β are estimated parameters. For the Brazil–China commodity trade analysis, typical parameter values are ω = 0.001, α = 0.08, and β = 0.90, estimated using maximum likelihood estimation on monthly price data from 2010 to 2022.

Market diversification is measured using the inverse of the Herfindahl–Hirschman Index:

$$DI{V}_{c,t}=1-\sum _j(s_{j,c,t}{)}^2$$

where $s_{j,c,t}$ is the market share of destination country j for commodity c in period t.

3.2.3. Social Sub-Index Development

The social sub-index, $S_{c,t}$, integrates indicators associated with labor rights, workplace safety, community development, and human rights protection:

$$S_{c,t}={\gamma }_1\times L{R}_{c,t}+{\gamma }_2\times W{S}_{c,t}+{\gamma }_3\times C{D}_{c,t}+{\gamma }_4\times H{R}_{c,t}$$

where ${\gamma }_1$ = 0.30, ${\gamma }_2$ = 0.25, ${\gamma }_3$ = 0.25, and ${\gamma }_4$ = 0.20 represent weights for labor rights, workplace safety, community development, and human rights, respectively. These weights were determined through stakeholder consultation involving representatives from labor organizations, civil society groups, and government agencies.

Labor rights compliance is assessed using ILO conventions and national labor standards:

$$L{R}_{c,t}={\displaystyle \frac{{\sum }_k{w}_k\times C_{k,c,t}}{{\sum }_k{w}_k}}$$

where $C_{k,c,t}$ represents conformance to labor standard k for commodity c in period t, and $w_k$ is the relative weight of standard k. The assessment covers eight core ILO conventions, including freedom of association (C87, C98), forced labor (C29, C105), child labor (C138, C182), and discrimination (C100, C111). Conformance scores are derived from official ILO reports, national labor inspections, and third-party audits.

Workplace safety indicators,$W{S}_{c,t}$, are measured using standardized metrics, including injury rates, fatality rates, and safety training coverage. For Brazilian mining operations, baseline injury rates of 15.2 per 1000 workers are used for normalization, while agricultural operations use 8.4 per 1000 workers as the baseline [39].

Community development impact, ${CD}_{c,t}$, is assessed through indicators such as local employment rates, infrastructure development, and community investment programs. Human rights protection scores, ${HR}_{c,t}$, are derived from international human rights databases, including the CIRI Human Rights Dataset and Freedom House indices, adjusted for sector-specific considerations.

3.3. Dynamic Weighting Mechanism

The dynamic weighting system represents a key innovation of the CSSI, allowing the index to adapt to changing policy priorities and stakeholder preferences while maintaining analytical consistency and transparency.

Policy targets are established through a structured expert consultation process involving three stakeholder groups: (1) academic researchers specializing in sustainability and trade economics (n = 12); (2) policy practitioners from relevant government agencies and international organizations (n = 8); (3) industry representatives from commodity trading and sustainability certification bodies (n = 6).

Expert selection criteria included the following: minimum 5 years of relevant experience; published research or policy work in the sustainability–trade nexus; and geographic representation across Brazil, China, and international organizations. Experts were recruited through professional networks and snowball sampling to ensure diverse perspectives.

The weight parameters (α, β, γ) were determined through a modified Delphi process: (1) initial round: experts provided individual weight preferences based on policy priorities; (2) second round: experts reviewed aggregate results and provided revised weights; (3) final round: consensus weights were established with a coefficient of variation <0.15 across expert responses.

Policy targets for each dimension are set annually based on national sustainability commitments (e.g., NDCs under the Paris Agreement), international trade agreements with sustainability provisions, and stakeholder priorities identified through the expert consultation process.

3.3.1. Adaptive Weight Update Algorithm

The weight update process follows a gradient-based optimization approach with policy feedback integration:

$$w_{i,t+1}=w_{i,t}+\eta \times \nabla L\left(w_{i,t}\right)+\lambda \times (targe{t}_{i,t}-actua{l}_{i,t})$$

where η = 0.05 is the learning rate parameter (determined through grid search optimization), $\nabla L\left(w_{i,t}\right)$ is the gradient of the loss function with respect to weight i, λ = 0.1 is the policy response coefficient, $targe{t}_{i,t}$ represents policy targets for dimension i in period t, and $actua{l}_{i,t}$ represents observed performance for dimension i in period t.

The loss function, L, combines prediction accuracy and policy alignment objectives:

$$L(w)=\sum _t\left[(CSS{I}_{pred,t}-CSS{I}_{actual,t}{)}^2+\mu \times \sum _i(w_{i,t}-w_{target,i,t}{)}^2\right]$$

where μ = 0.2 controls the relative importance of policy alignment versus prediction accuracy. This parameter was calibrated through cross-validation to balance predictive performance with policy responsiveness.

3.3.2. Constraint Optimization Implementation

The weight update must comply with the simplex constraint (weights sum to 1 and are non-negative). This is achieved through Lagrangian optimization:

$$L_{constrained}=L\left(w\right)+\lambda \times \left(\sum _i{w}_i-1\right)+\sum _i{\mu }_i\times \mathrm{m}\mathrm{a}\mathrm{x}(0,-w_i)$$

The optimal weights are obtained by solving the first-order conditions: $\partial L_{constrained}/\partial w_i=0$,$\forall i$, $\partial L_{constrained}/\partial \lambda =0$

3.3.3. Policy Feedback Integration

The policy feedback system monitors alignment between observed outcomes and policy targets, automatically adjusting weights to promote policy-consistent assessments:

$$w_{E,t+1}=w_{E,t}+\kappa \times \mathrm{m}\mathrm{a}\mathrm{x}(0,ga{p}_{E,t})$$

where $ga{p}_{E,t}=(targe{t}_{E,t}-actua{l}_{E,t})/targe{t}_{E,t}$ is the normalized policy gap for the environmental dimension, and κ = 0.05 is the feedback sensitivity parameter. Similar adjustments are applied to economic and social dimensions.

Beyond the reported Monte Carlo ranges, we conduct a comprehensive sensitivity analysis across alternative weight specifications:

Alternative Prior Distributions: We test uniform priors (equal weights), literature-based priors (weights from existing sustainability indices), and data-driven priors (weights optimized for predictive performance). The results show ranking correlations > 0.85 across all specifications, indicating robust index performance.

Fixed vs. Dynamic Weight Comparison: Static weights (time-invariant) yield 15% higher prediction errors compared with dynamic weights, with particularly poor performance during policy transition periods (2018–2019 soy moratorium expansion, 2020–2021 mining regulation changes). Dynamic weights capture policy responsiveness that static approaches miss.

Extreme Scenario Testing: Under extreme weight perturbations (±50% from baseline), 89% of commodity rankings remain stable, with changes concentrated in middle-tier commodities rather than top/bottom performers.

3.4. Transformer-Based Prediction Architecture

The CSSI framework employs a Transformer neural network architecture to capture complex temporal dependencies and predict sustainability–trade relationships. This approach can capture long-range dependencies and non-linear patterns that traditional time series methods might miss.

3.4.1. Multi-Head Attention Mechanism

The Transformer’s core feature is a multi-head attention mechanism that allows the model to focus on different parts of the input sequence simultaneously:

$$\mathrm{MultiHead}(\mathrm{Q},\mathrm{K},\mathrm{V})=\mathrm{Concat}(\mathrm{h}\mathrm{e}\mathrm{a}{\mathrm{d}}_1,\dots ,\mathrm{h}\mathrm{e}\mathrm{a}{\mathrm{d}}_{\mathrm{h}}){\mathrm{W}}^{\mathrm{O}}$$

where each attention head is computed as follows:

$$hea{d}_i=\mathrm{Attention}(Q{W}_i^Q,K{W}_i^K,V{W}_i^V)$$

The attention function implements scaled dot-product attention:

$$\mathit{Attention}(Q,K,V)=\mathit{softmax}\left({\displaystyle \frac{Q{K}^T}{\sqrt{d_k}}}\right)V$$

where $d_k$ = 64 is the dimension of the key vectors, and the softmax function ensures that attention weights sum to 1.

3.4.2. Model Architecture Specifications

The Transformer model employs the following configuration, optimized for sustainability–trade prediction through hyperparameter tuning:

• Encoder layers: Six (determined through an ablation study).
• Attention heads: 12 (optimal for capturing multi-dimensional relationships).
• Hidden dimension: 512 (balanced between model capacity and computational efficiency).
• Feed-forward dimension: 2048 (standard 4× hidden dimension ratio).
• Dropout rate: 0.1 (prevents overfitting while maintaining learning capacity)
• Learning rate: 1 × 10⁻⁴ with Adam optimizer (β₁ = 0.9, β₂ = 0.999).
• Sequence length: 24 months (captures seasonal and cyclical patterns).
• Prediction horizon: 6 months (optimal for policy planning purposes).
• Batch size: 32 (computational efficiency consideration).
• Training epochs: 100 with early stopping (patience = 10).

3.4.3. Training Strategy and Loss Function

The model is trained using teacher forcing with a mean squared error loss function:

$$\mathit{Loss}={\displaystyle \frac{1}{N}}\times \sum _n(y_{pred,n}-y_{true,n}{)}^2$$

Training employs early stopping to prevent overfitting, with training termination if validation loss fails to improve for 10 consecutive epochs. The dataset is split into training (70%), validation (15%), and test (15%) sets using temporal splits to avoid data leakage.

3.4.4. Technical Specifications and Implementation Details

Feature Set: The model uses 24 input features: (1) CSSI components (3 sub-indices + composite score); (2) trade variables (bilateral trade volume, price indices, exchange rates); (3) economic indicators (GDP growth, inflation, commodity price volatility); (4) policy variables (binary indicators for major policy changes, sustainability commitments); (5) external factors (global commodity prices, weather indices, geopolitical events).

Temporal Split Protocol: We implement strict temporal splitting to prevent data leakage: training data (2015–2019), validation data (2020–2021), test data (2022). No future information is used in feature engineering or model training. Cross-validation uses an expanding window approach with a minimum of 24-month training periods.

Computational Requirements: Training time: 45 min on NVIDIA V100 GPU; memory usage: 8 GB GPU memory; inference time: <1 s per prediction. The model is implemented in PyTorch 2.7.0 with standard optimization (Adam optimizer, learning rate 0.001, batch size 32).

The analysis of sequence length reveals a clear trend in model performance. At six months, the Mean Absolute Error (MAE) was 8.2% (±1.1%), which decreased to an optimal value of 7.1% (±1.0%) at the twelve-month baseline. Performance then gradually declined with longer sequences, resulting in an MAE of 7.3% (±1.2%) at eighteen months and 7.8% (±1.3%) at twenty-four months. Consequently, the optimal sequence length is determined to be twelve months, as it best balances the need for sufficient temporal context against the risk of overfitting.

The evaluation of attention heads shows that performance improves as the number of heads increases, up to a point. With four heads, the MAE was 7.8% (±1.2%), which decreased to 7.3% (±1.1%) with eight heads. The optimal performance was achieved with twelve heads, yielding a baseline MAE of 7.1% (±1.0%). Beyond this, performance plateaued, as sixteen heads only achieved a comparable MAE of 7.2% (±1.1%). This indicates that twelve heads are sufficient to meet the model’s capacity requirements, with additional heads providing no significant benefit.

3.5. Sustainability-Adjusted Trade Volume Calculation

The CSSI framework introduces sustainability-adjusted trade volumes, which modify standard trade statistics to account for hidden costs and risks associated with unsustainable practices.

3.5.1. Basic Adjustment Formula

The sustainability-adjusted trade volume is calculated as follows:

$$V_{adjusted,c,t}=V_{original,c,t}\times CSS{I}_{c,t}^{\gamma }$$

where γ = 1.2 is the adjustment intensity parameter, calibrated through cross-validation to optimize predictive performance while maintaining economic interpretability.

3.5.2. Non-Linear Adjustment Function

For more sophisticated modeling of sustainability impacts, a sigmoid adjustment function is employed:

$$\mathit{adjustment\_factor}={\displaystyle \frac{1}{1+\mathrm{e}\mathrm{x}\mathrm{p}(-k\times (CSS{I}_{c,t}-\mathit{threshold}))}}$$

where k = 10 controls the steepness of the adjustment, and threshold = 0.5 represents the sustainability benchmark. These parameters were calibrated using historical data to ensure realistic adjustment magnitudes.

3.5.3. Integration with Gravity Equations

The sustainability-adjusted volumes are integrated into standard gravity equation frameworks:

$$\mathrm{l}\mathrm{n}\left({\mathit{Trade}}_{ij,c,t}\right)={\beta }_0+{\beta }_1\mathrm{l}\mathrm{n}\left(GD{P}_i\right)+{\beta }_2\mathrm{l}\mathrm{n}\left(GD{P}_j\right)+{\beta }_3\mathrm{l}\mathrm{n}\left({\mathrm{Distance}}_{ij}\right)+{\beta }_4\mathrm{l}\mathrm{n}\left(V_{adjusted,c,t}\right)+{\epsilon }_{ij,c,t}$$

where ${\mathit{Trade}}_{ij,c,t}$ represents bilateral trade flows, $GD{P}_i$ and $GD{P}_j$ are economic sizes, ${\mathrm{Distance}}_{ij}$ is geographical distance, and $V_{adjusted,c,t}$ is the sustainability-adjusted volume. This specification allows for direct comparison with traditional gravity models while incorporating sustainability considerations.

3.6. Model Validation and Robustness Testing

The CSSI employs comprehensive validation procedures to ensure reliability and robustness under different conditions and applications.

3.6.1. Time Series Cross-Validation

To avoid data leakage and ensure realistic performance assessment, time series cross-validation is employed with expanding windows:

$$\mathit{Train}: [\mathrm{1,2},\dots ,t]$$

$$\mathit{Test}: [t+1,t+2,\dots ,t+h]$$

where h = 6 represents the prediction horizon length. The validation process uses five expanding windows, each adding 12 months of data, to assess model stability across different time periods.

3.6.2. Monte Carlo Sensitivity Analysis

Weight sensitivity is assessed through 10,000 Monte Carlo simulations:

$$w_i^{(j)}~\mathit{Uniform}(w_i-\delta ,w_i+\delta )$$

where δ = 0.05 represents the perturbation magnitude. Ranking stability is measured as follows:

$$\mathit{Stability}={\displaystyle \frac{1}{J}}\times \sum _{j}I({\mathit{rank}}_j={\mathit{rank}}_{\mathit{baseline}})$$

where $I(·)$ is the indicator function, and J = 10,000 is the number of simulations. The results show ranking stabilities of 0.89 for soybeans and 0.92 for iron ore, indicating robust performance under weight variations.

3.6.3. Statistical Significance Testing

The 12–19% overestimation by conventional metrics is tested using paired t-tests:

$$H_0:{\mu }_{\mathrm{conventional}}={\mu }_{\mathrm{CSSI}}, H_1:{\mu }_{\mathrm{conventional}}\ne {\mu }_{\mathrm{CSSI}}$$

The results show t = 4.73 (p < 0.001) for soybeans and t = 3.89 (p < 0.001) for iron ore, confirming statistically significant differences. Confidence intervals are calculated using bootstrap resampling (n = 1000), yielding 95% CI [10.8%, 21.2%] for the overall overestimation.

3.7. Identification Strategy and Methodological Limitations

Our empirical approach combines descriptive analysis, predictive modeling, and associational inference. We employ panel data methods to examine relationships between sustainability metrics and trade patterns, but acknowledge several limitations in establishing causal identification:

Panel Data Specifications: We use fixed-effects models to control for time-invariant country–commodity characteristics, but cannot rule out time-varying confounders that may bias our estimates. The specification follows $Trad{e}_{ct}=\alpha +\beta \times CSS{I}_{ct}+\gamma \times X_{ct}+{\delta }_c+{\tau }_t+{\epsilon }_{ct}$, where ${\delta }_c$ and ${\tau }_t$ represent commodity and time-fixed effects, respectively.

Policy Discontinuity Analysis: While we examine changes around policy interventions (e.g., the Amazon Soy Moratorium), we lack the randomized variation necessary for regression discontinuity design. Our analysis should be interpreted as examining associations around policy changes rather than causal effects.

Identifying Assumptions: Our analysis assumes that sustainability metrics are not simultaneously determined with trade flows within our observation period. This assumption may be violated if traders anticipate sustainability regulations, limiting causal interpretation.

Given these limitations, we interpret our results as robust associations that provide valuable insights for policy discussions while acknowledging the need for future research with stronger identification strategies.

3.8. Data Sources and Processing

The CSSI framework integrates multiple publicly accessible data sources to ensure comprehensive coverage of sustainability dimensions while maintaining research reproducibility and accessibility.

Environmental metrics were sourced from authoritative global databases. Deforestation data were obtained from Global Forest Watch (annual, 2001–2022) and PRODES/INPE, processed via Google Earth Engine. Carbon footprint data were compiled from the Emissions Database for Global Atmospheric Research (EDGAR) and supplemented by a systematic review of published Life Cycle Assessment (LCA) studies. Water stress indicators were derived from FAO AQUASTAT and the World Resources Institute’s Aqueduct database, while climate data were sourced from the World Bank Climate Data portal and NOAA Climate Data Online.

Economic variables were assembled from diverse international and national sources. Trade statistics were extracted from the UN Comtrade database and national customs statistical offices. Price data were collected from FRED Economic Data (Federal Reserve Bank of St. Louis), the World Bank Commodity Price Data platform, and official commodity exchange records. Market concentration metrics were derived from the SEC EDGAR database for public companies and industry association reports. Exchange rate data were obtained from central bank official databases and the IMF International Financial Statistics.

Social indicators were drawn from globally recognized institutions. Labor statistics were acquired from the ILOSTAT database and national labor ministry reports. Safety indicators were based on ILO occupational safety statistics and the WHO Global Health Observatory. Human rights indices incorporated the V-Dem Democracy Dataset, Freedom House, Polity IV, and the World Justice Project Rule of Law Index. Community development metrics were sourced from World Bank Open Data and UN Human Development Reports.

Rigorous quality control protocols were implemented. Missing data were handled using multiple imputation by chained equations, executed in R/Python. Outliers were identified via the isolation forest algorithm with a 0.1 contamination rate using scikit-learn. Temporal alignment was achieved through linear interpolation for monthly synchronization, supplemented by spline interpolation for complex patterns. Cross-validation ensured reliability, with each indicator verified against at least two independent data sources.

The framework prioritizes open data sources to ensure research reproducibility. All primary data sources are freely accessible through official government databases, international organizations, or academic repositories. Technical processing requirements are minimized through the use of user-friendly platforms (Global Forest Watch, World Bank Open Data) and cloud-based processing tools (Google Earth Engine).

4. Results

This section presents empirical results obtained from applying the CSSI framework to Brazil–China commodity trade for soybeans and iron ore from 2015 to 2022. The analysis integrates satellite-based environmental data, official trade statistics, and multi-source verification systems using publicly accessible databases to demonstrate the framework’s analytical capabilities and predictive performance. The results show that the CSSI framework provides novel insights into sustainability–trade relationships with superior predictive power compared with traditional approaches.

4.1. CSSI Scores and Temporal Trends

The CSSI analysis of Brazilian soybeans and iron ore exports reveals distinct sustainability trajectories that diverge significantly from conventional trade volume trends. Figure 1 illustrates the CSSI framework architecture, showing how environmental, economic, and social sub-indices are dynamically integrated through the adaptive weighting mechanism and Transformer-based prediction engine.

The evolution of CSSI scores (Figure 2) demonstrates that the two commodities exhibit contrasting sustainability patterns. Soybeans show improving sustainability performance, with CSSI scores increasing from 0.52 in 2015 to 0.61 in 2022, representing a 17.3% improvement over the analysis period. This improvement is primarily driven by enhanced environmental performance, reflecting zero-deforestation commitments from major trading companies and improved agricultural practices in key production regions.

Iron ore exhibits a contrasting sustainability trajectory, with CSSI scores declining from 0.68 in 2015 to 0.55 in 2022, representing a 19.1% decrease. This decline is attributed to intensified mining operations, increased social conflicts in mining communities, and volatility in global steel markets. These divergent trends underscore the importance of commodity-specific sustainability assessments, as aggregate trade statistics would not reveal these fundamental differences.

Table 1. CSSI scores and trade volumes for key commodities (2015–2022).

Commodity	Year	Environmental Score	Economic Score	Social Score	CSSI Score	Trade Volume (MT)	Adjusted Volume (MT)
Soybeans	2015	0.45	0.58	0.53	0.52	54.2	50.1
Soybeans	2018	0.53	0.58	0.57	0.56	83.3	78.1
Soybeans	2020	0.59	0.58	0.60	0.59	82.9	78.9
Soybeans	2022	0.65	0.58	0.62	0.61	84.2	82.1
Iron Ore	2015	0.72	0.65	0.67	0.68	344.1	303.6
Iron Ore	2018	0.64	0.63	0.62	0.63	385.8	342.9
Iron Ore	2020	0.57	0.60	0.57	0.58	400.9	350.7
Iron Ore	2022	0.52	0.58	0.54	0.55	405.4	345.8

summarizes CSSI scores and trade volumes for both commodities over the analysis period. While conventional trade volumes increased for both commodities (soybeans by 55.4% and iron ore by 17.8%), sustainability-adjusted volumes show different patterns: soybeans increased by 63.9% while iron ore decreased by 13.9%. This divergence highlights the importance of incorporating sustainability considerations into trade dependence calculations.

4.2. Sub-Index Analysis and Dimensional Performance

The decomposition of CSSI scores into environmental, economic, and social sub-indices reveals the specific drivers of overall sustainability performance and identifies areas where policy intervention may be most effective. For soybeans, the environmental sub-index shows the most significant improvement, increasing from 0.45 in 2015 to 0.65 in 2022. This improvement reflects the impact of the Amazon Soy Moratorium [48], enhanced satellite monitoring systems, and adoption of sustainable farming practices by major producers.

The economic sub-index for soybeans remains relatively stable around 0.58 throughout the analysis period, indicating that soybeans maintain their competitiveness and market position despite increasing environmental requirements. This stability suggests that sustainability improvements have not compromised economic performance, supporting the business case for sustainable production practices.

The social sub-index for soybeans shows steady improvement from 0.53 to 0.62, reflecting enhanced labor conditions, community engagement programs, and improved safety standards in agricultural operations. This improvement is particularly notable given the scale of Brazil’s soybean production and the challenges of implementing social standards across diverse production regions.

Iron ore exhibits a different pattern, with all three sub-indices showing declining trends. The environmental sub-index decreases from 0.72 to 0.52, reflecting increased water consumption, adverse impacts on air quality, and biodiversity loss resulting from intensified mining operations. The economic sub-index declines from 0.65 to 0.58, indicating reduced competitiveness due to increasing production costs and market volatility. The social sub-index shows the most concerning decline, from 0.67 to 0.54, reflecting ongoing conflicts with indigenous communities, occupational safety issues, and limited community development investments.

4.3. Predictive Performance Analysis

The Transformer-based prediction model demonstrates superior performance compared with traditional time series approaches.

Table 2. Predictive performance comparison across models and horizons.

Model	One-Month MAE (%)	Three-Month MAE (%)	Six-Month MAE (%)	RMSE (%)	R2
CSSI–Transformer	5.5 ± 0.8	6.2 ± 0.9	7.1 ± 1.0	8.9 ± 1.2	0.78 ± 0.03
ARIMA(2,1,2)	6.8 ± 0.5	8.1 ± 0.6	9.7 ± 0.7	12.3 ± 0.8	0.65 ± 0.04
LSTM	6.1 ± 0.6	7.4 ± 0.7	8.8 ± 0.8	11.1 ± 0.9	0.69 ± 0.04
SVR	7.2 ± 0.7	8.9 ± 0.8	10.5 ± 0.9	13.1 ± 1.0	0.61 ± 0.05
Random Forest	5.1 ± 0.6	6.3 ± 0.7	7.9 ± 0.8	10.2 ± 0.9	0.71 ± 0.04
Baseline (Naive)	8.7 ± 1.2	12.4 ± 1.5	15.8 ± 1.8	18.9 ± 2.1	0.45 ± 0.05

Note: Performance metrics are reported as mean ± standard deviation across 5-fold time series cross-validation. MAE = mean absolute error, RMSE = root mean square error, R² = coefficient of determination.

presents comprehensive performance metrics across different forecasting horizons and model specifications.

The CSSI–Transformer model achieves a 6-month forecasting MAE of 7.1% (±1.0%), significantly outperforming the ARIMA (9.7% ± 0.7%) and LSTM (8.8% ± 0.8%) approaches. Statistical significance testing using paired t-tests confirms the superior performance: t = 6.42, p < 0.001 vs. ARIMA; t = 3.18, p < 0.01 vs. LSTM. The model’s performance demonstrates the effectiveness of publicly accessible data sources for sustainability–trade analysis.

Figure 3 provides a visual comparison of predictive performance across different models and forecasting horizons, illustrating the consistent superiority of the CSSI–Transformer approach across all evaluation metrics and time periods.

The performance advantage of the CSSI–Transformer model is particularly pronounced for longer forecasting horizons (6 months), where the attention mechanism effectively captures long-range dependencies in sustainability–trade relationships that traditional models fail to identify.

4.4. Trade Dependence Overestimation Analysis

A key finding of this study is that conventional trade dependence metrics systematically overestimate Brazil’s dependence on China for commodities with high environmental footprints.

Table 3. Conventional vs. CSSI-adjusted trade dependence metrics.

Commodity	Metric	Conventional	CSSI-Adjusted	Overestimation (%)	95% CI	p-Value
Soybeans	Trade Intensity	0.847	0.689	18.7 ± 2.1	[16.2, 21.2]	<0.001
Soybeans	Export Share	0.623	0.521	16.4 ± 1.8	[14.1, 18.7]	<0.001
Soybeans	HHI Concentration	0.421	0.348	17.3 ± 1.9	[14.9, 19.7]	<0.001
Iron Ore	Trade Intensity	0.734	0.636	13.4 ± 1.8	[11.2, 15.6]	<0.001
Iron Ore	Export Share	0.589	0.518	12.1 ± 1.6	[10.1, 14.1]	<0.001
Iron Ore	HHI Concentration	0.398	0.341	14.3 ± 1.7	[12.2, 16.4]	<0.001
Overall	Weighted Average	0.685	0.576	15.9 ± 1.2	[13.8, 18.0]	<0.001

Note: Overestimation calculated as (conventional—CSSI-adjusted)/CSSI-adjusted × 100%. Statistical significance was assessed using paired t-tests, with Bonferroni correction for multiple comparisons.

presents a detailed analysis of this overestimation across different metrics and time periods.

The analysis reveals that conventional metrics overestimate trade dependence by 12–19% across different measures, with soybeans showing higher overestimation (16.4–18.7%) compared with iron ore (12.1–14.3%). This overestimation is statistically significant across all metrics (p < 0.001) and remains robust across different time periods and sensitivity analyses.

4.5. Dynamic Weighting Evolution

The dynamic weighting mechanism demonstrates adaptive behavior in response to changing policy priorities and market conditions. The commodity clustering analysis (Figure 4) reveals distinct groupings based on CSSI scores and trade dependence levels, providing insights into the sustainability–trade nexus for different commodity types.

The clustering analysis identifies three distinct commodity groups: (1) high sustainability, low dependence (sustainable diversified commodities); (2) moderate sustainability, high dependence (transition commodities such as soybeans); and (3) low sustainability, high dependence (resource-intensive commodities such as iron ore). This classification provides a framework for targeted policy interventions and risk management strategies.

We acknowledge potential simultaneity between trade flows and sustainability-adjusted volumes, as both may respond to common unobserved factors. To address these concerns, we implement several robustness checks:

Alternative Specifications:

(1) Component-based specification: We enter environmental, economic, and social sub-indices separately as trade–cost shifters rather than using the composite sustainability-adjusted volume:

$$ln\left(Trad{e}_{ijt}\right)=\alpha +{\beta }^1\times Environmenta{l}_{jt}+{\beta }^2\times Economi{c}_{jt}+{\beta }^3\times Socia{l}_{jt}+\gamma \times Controls+{\epsilon }_{ijt}$$

(2) Lagged specification: We use one-year lagged CSSI components to reduce simultaneity concerns:

$$ln\left(Trad{e}_{ijt}\right)=\alpha +\beta \times CSS{I}_{j,t-1}+\gamma \times Controls+{\epsilon }_{ijt}$$

Instrumental Variable Approach:

We implement sustainability-adjusted volume using exogenous sustainability drivers: rainfall patterns (for agricultural commodities), global commodity price volatility (for economic sub-index), and international sustainability policy announcements (for policy responsiveness). First-stage F-statistics >10 indicate strong instruments, though we acknowledge limitations in exclusion restrictions.

Results Comparison:

All specifications yield qualitatively similar results: sustainability factors significantly predict trade patterns with effect sizes within 20% of baseline estimates. The component-based specification shows that environmental factors have the strongest predictive power (β₁ = 0.34, p < 0.01), followed by economic factors (β₂ = 0.28, p < 0.05).

Interpretation: While we cannot definitively rule out simultaneity, the consistency across specifications and the use of lagged variables support our interpretation that sustainability characteristics influence trade patterns, though bidirectional causality remains possible.

4.6. Policy Scenario Analysis

Table 4. Policy scenario analysis and impact assessment.

Policy Scenario	Soybeans CSSI	Iron Ore CSSI	Soybean Dependence Change (%)	Iron Ore Dependence Change (%)	Total Trade Impact (USD Billion)	Environmental Benefit Score	Implementation Cost (USD Million)
Baseline	0.61	0.55	0	0	0	0	0
Carbon Tax (+20%)	0.64	0.59	−5	−12	−2.1	0.15	150
Deforestation Penalty	0.68	0.56	−15	−5	−3.8	0.35	280
Enhanced Labor Standards	0.63	0.58	−3	−8	−1.2	0.08	95
Combined Policies	0.71	0.62	−22	−18	−6.5	0.52	420

presents the results of policy scenario analysis, demonstrating how different interventions could affect CSSI scores and trade dependence patterns. The analysis considers four policy scenarios: carbon taxation, deforestation penalties, enhanced labor standards, and combined policies.

The combined policy scenario has the most significant impact, with potential CSSI improvements of 16.4% for soybeans and 12.7% for iron ore, corresponding to trade dependence reductions of 22% and 18%, respectively. These results suggest that comprehensive sustainability policies can simultaneously address environmental and social concerns while enhancing economic security through reduced commodity market dependence.

Cost estimates were derived from empirical benchmarks and program data. Monitoring costs were calculated based on satellite monitoring service pricing, ranging from USD 0.50 to 2.00 per hectare annually. Certification costs reflected the industry average for sustainability certification, estimated at USD 500–1500 per producer annually. Technology adoption expenses were derived from pilot programs, encompassing equipment and training costs ranging from USD 2000 to 8000 per farm. Administrative costs accounted for government program implementation, estimated at 15–25% of direct costs.

Environmental benefits were quantified using standardized economic metrics. CO₂ reduction was valued at USD 50 per ton CO₂e based on the social cost of carbon. Biodiversity protection was assessed using ecosystem service valuations (USD/hectare), and water conservation was priced via shadow pricing (USD/m³). These monetary values were subsequently normalized to a 0–100 scale to enable comparative analysis across intervention types.

Cost and benefit estimates are presented with 95% confidence intervals. Enhanced Monitoring showed benefits of 23.4 (18.7–28.1) against costs of 12.1 (9.8–14.4). Certification Programs yielded benefits of 31.2 (25.9–36.5) with costs of 18.7 (15.2–22.2). Technology Adoption generated benefits of 28.9 (23.1–34.7) at costs of 25.3 (20.8–29.8). The Combined Scenario demonstrated synergistic effects, with benefits of 67.8 (58.2–77.4) and costs of 48.9 (42.1–55.7).

Intervention synergies and trade-offs were analyzed through cross-impact assessment. The combined scenario demonstrated non-linear benefit accumulation, particularly in monitoring-certification and technology-certification pairings. Resource allocation constraints and institutional capacity factors were incorporated as scaling limitations, with diminishing returns observed beyond regional implementation thresholds.

Estimates assume current technology costs and benefit valuations. Actual outcomes may vary due to technological progress, the quality of policy implementation, and market responses not captured in our model.

Figure 5 illustrates the policy impact analysis, showing the trade-offs between implementation costs, environmental benefits, and trade dependence reduction across different policy scenarios. The analysis reveals that deforestation penalties offer the highest environmental benefit per dollar invested, while combined policies provide the most comprehensive sustainability improvements.

4.7. Robustness and Sensitivity Analysis

Comprehensive robustness tests demonstrate the stability and reliability of the CSSI under various conditions and assumptions.

Table 5. Robustness tests and sensitivity analysis results.

Robustness Test	Test Description	CSSI Rank Stability (%)	Prediction Accuracy Change (%)	Statistical Significance	Confidence Interval (95%)
Weight Sensitivity	Monte Carlo (10,000 iterations)	98.2	−0.8	p < 0.001	[0.59, 0.63]
Data Latency	Satellite delay ≤30 days	99.1	−0.3	p < 0.01	[0.60, 0.62]
Missing Data	10% random missing values	96.7	−2.1	p < 0.05	[0.57, 0.65]
Outlier Removal	Remove top/bottom 5%	97.8	−1.2	p < 0.01	[0.58, 0.64]
Cross-Validation	5-fold cross-validation	95.4	−1.8	p < 0.001	[0.58, 0.64]

presents results from multiple robustness tests, including Monte Carlo simulations, data quality assessments, and cross-validation procedures.

Monte Carlo simulations using 10,000 iterations show that CSSI rankings maintain 98.2% consistency when weights are perturbed by ±10% of their optimal values. Prediction accuracy remains within 2% when up to 10% of input data are missing, demonstrating the framework’s robustness to data quality issues common in international trade analysis.

Sensitivity analysis reveals that the environmental sub-index is the most influential factor in CSSI scores. A 10% change in environmental indicators results in a 6.2% change in CSSI scores for soybeans and a 7.8% change for iron ore. Economic indicators show moderate sensitivity (3.1% for soybeans; 4.2% for iron ore), while social indicators show lower but significant sensitivity (2.3% for soybeans; 2.8% for iron ore).

The evolution of dynamic weights over the analysis period (Figure 6) demonstrates the adaptive nature of the CSSI framework in response to changing policy priorities and market conditions. Environmental weights increased from 0.35 in 2015 to 0.42 in 2022, reflecting growing policy emphasis on climate change and deforestation concerns.

Economic weights remain relatively stable (0.38–0.40), while social weights show a moderate increase from 0.27 to 0.31, reflecting increased attention to labor rights and community development issues. This adaptive weighting mechanism ensures that the CSSI remains relevant and responsive to evolving sustainability priorities.

4.8. Comparative Analysis with Existing Indices

The CSSI framework demonstrates significant advantages over existing trade and sustainability indices in terms of comprehensiveness, adaptability, and predictive power.

Table 6. Comparison of the CSSI with existing sustainability and trade indices.

Index/Framework	Environmental Dimension	Economic Dimension	Social Dimension	Dynamic Weighting	Commodity-Specific	Predictive Capability	Real-Time Updates	Trade Policy Integration
Trade Dependence Index	No	Yes	No	No	Partial	No	No	Limited
Herfindahl–Hirschman Index	No	Yes	No	No	Partial	No	No	Limited
Environmental Performance Index	Yes	No	No	No	No	No	Annual	No
Sustainable Development Index	Yes	Partial	Yes	No	No	No	Annual	No
Ecological Footprint	Yes	No	No	No	No	No	Annual	No
CSSI (This Study)	Yes	Yes	Yes	Yes	Yes	Yes	Monthly	Yes

provides a systematic comparison of the CSSI with major existing frameworks.

The CSSI’s multidimensional assessment, dynamic weighting, commodity-specific analysis, and predictive capabilities represent significant advances over existing approaches. Unlike static indices that provide periodic snapshots, the CSSI enables continuous monitoring and forward-looking analysis that can inform policy and business planning decisions.

4.9. Gravity Equation Integration Results

The integration of sustainability-adjusted volumes into gravity equation frameworks demonstrates the practical applicability of the CSSI for trade policy analysis.

Table 7. Gravity equation estimation results: traditional vs. CSSI-enhanced models.

Variable	Traditional Model	CSSI-Enhanced Model	Difference	t-Statistic
ln(GDP_Brazil)	0.847 *** (0.089)	0.823 *** (0.084)	−0.024	−1.92
ln(GDP_China)	1.234 *** (0.112)	1.198 *** (0.106)	−0.036	−2.14 *
ln(Distance)	−1.456 *** (0.134)	−1.389 *** (0.128)	0.067	3.21 **
ln(Trade_Volume)	0.678 *** (0.067)	-	-	-
ln(CSSI_Adjusted_Volume)	-	0.734 *** (0.071)	0.056	4.89 ***
Constant	−2.134 ** (0.892)	−2.567 ** (0.934)	−0.433	−2.78 **
R2	0.743	0.789	0.046	-
Adjusted R2	0.721	0.768	0.047	-
F-statistic	89.34 ***	112.67 ***	-	-
N	96	96	-	-

Note: Standard errors in parentheses. *, *, and *** indicate significance at 10%, 5%, and 1% levels, respectively. Models estimated using quarterly data from 2015 to 2022.

presents estimation results comparing traditional and CSSI-enhanced gravity models.

The CSSI-enhanced gravity model shows improved explanatory power (R² = 0.789 vs. 0.743) and better model fit (F-statistic = 112.67 vs. 89.34). The coefficient on sustainability-adjusted volume (0.734) is significantly larger than the traditional trade volume coefficient (0.678), indicating that sustainability considerations enhance the explanatory power of gravity models for bilateral trade flows.

5. Discussion

The application of the CSSI to Brazil–China commodity trade yields several important findings that challenge conventional approaches to trade dependence analysis and provide new insights into the complex relationships between sustainability and international trade. This section discusses the theoretical and practical implications of these results, examines policy recommendations, addresses limitations, and outlines directions for future research.

5.1. Theoretical Implications and Contributions

The CSSI framework makes several significant theoretical contributions to the fields of trade economics and sustainability science. First, the framework operationalizes the concept of “sustainability-adjusted dependence,” demonstrating that traditional trade metrics systematically misrepresent the true nature of bilateral trade relationships when sustainability considerations are taken into account. The finding that conventional trade metrics overestimate Brazil’s dependence on China by 12–19% for commodities with high environmental footprints suggests that sustainability characteristics fundamentally alter trade risk profiles and strategic implications.

This result has profound implications for trade theory, particularly in how we measure and interpret economic interdependence. The gravity model framework for predicting trade flows, traditionally based on economic size and geographical distance, may require substantial modifications to account for sustainability factors that increasingly influence trade patterns. The CSSI results suggest that sustainability characteristics act as a form of “trade friction” that either facilitates or impedes commerce depending on the alignment between production practices and importing country preferences.

The dynamic weighting mechanism represents a major methodological innovation that allows composite indices to adapt to changing policy priorities and stakeholder preferences while maintaining analytical consistency. Unlike static indices, where weights remain fixed regardless of context or time period, CSSI demonstrates that optimal weighting schemes evolve in response to changing policy environments, market conditions, and technological developments. This approach has broad applicability to other composite indices in economics and policy analysis.

The successful application of machine learning techniques, particularly Transformer architectures, to trade analysis represents another significant theoretical contribution. The superior predictive performance of the CSSI–Transformer model (MAE 7.1% vs. 9.7% for ARIMA) demonstrates that complex temporal dependencies and non-linear relationships in trade data can be effectively captured using attention mechanisms originally developed for natural language processing. This suggests broad potential applications for Transformer architectures in economic forecasting and policy analysis.

The open data integration approach addresses fundamental challenges in international economic research related to data accessibility and transparency. The successful demonstration that comprehensive sustainability analysis can be achieved using publicly accessible data sources opens new possibilities for international research collaboration in areas such as trade, environmental monitoring, and social development. This approach enables more comprehensive and accurate analyses of global economic phenomena while promoting transparency and reproducibility in academic research.

5.2. Policy Implications and Recommendations

The CSSI framework provides several actionable insights for policymakers seeking to balance economic growth and sustainability objectives in international trade. The contrasting sustainability trajectories observed for soybeans (improving) and iron ore (declining) suggest that commodity-specific policy approaches are necessary to address the unique challenges and opportunities of different sectors.

5.2.1. Soybean Sector Recommendations

The positive sustainability trend in the soybean sector (CSSI improvement from 0.52 to 0.61) demonstrates that environmental improvements can be achieved without compromising economic competitiveness. The stability of the economic sub-index throughout the analysis period indicates that sustainability investments have not undermined Brazil’s comparative advantage in soybean production. This supports the business case for sustainable agriculture and suggests that similar approaches could be applied to other agricultural commodities.

Specific policy recommendations for the soybean sector include (1) expanding zero-deforestation commitments to additional production regions and supply chains beyond the current Amazon Soy Moratorium coverage; (2) enhancing satellite monitoring systems to provide real-time deforestation alerts and enable rapid response to violations; (3) developing green financing mechanisms such as sustainability-linked loans and green bonds to support sustainable production practices; and (4) strengthening traceability systems to enable premium pricing for sustainably produced soybeans in international markets.

5.2.2. Iron Ore Sector Interventions

The iron ore sector faces more complex challenges, with declining performance across all three sustainability dimensions (environmental, economic, and social). The decline of the CSSI score from 0.68 to 0.55 indicates systemic problems that require comprehensive policy interventions addressing environmental impacts, social conflicts, and economic volatility. The concentration of production in the Carajás region presents particular challenges associated with biodiversity conservation, indigenous rights, and community development.

Policy recommendations for the iron ore sector include (1) implementing stricter environmental standards for mining operations, including mandatory environmental impact assessments and biodiversity offset requirements; (2) establishing community development funds financed through mining royalties to support social impact mitigation and local economic diversification; (3) strengthening occupational safety regulations and enforcement to reduce injury rates and improve working conditions; and (4) promoting economic diversification in mining regions to reduce dependence on extractive industries.

5.2.3. Integrated Policy Approaches

The policy scenario analysis demonstrates that combined interventions can significantly improve both sustainability performance and trade resilience. The combined policy scenario shows potential CSSI improvements of 16.4% for soybeans and 12.7% for iron ore, with corresponding trade dependence reductions of 22% and 18%, respectively. These results suggest that comprehensive sustainability policies can simultaneously address environmental and social concerns while enhancing economic security through reduced commodity market dependence.

The framework’s ability to predict sustainability-driven risks and trade vulnerabilities up to six months in advance enables proactive policy interventions. This early warning capability represents a significant improvement over reactive policy approaches that respond only after problems have already caused economic or environmental damage. The 6-month prediction horizon facilitates sufficient time for policy adjustments, market interventions, and stakeholder engagement to mitigate emerging risks.

5.3. Implications for International Trade Governance

The CSSI framework has significant implications for international trade governance, particularly as sustainability considerations become increasingly integrated into trade agreements and dispute resolution mechanisms. The framework’s ability to quantify sustainability–trade relationships provides empirical foundations for developing trade rules that account for environmental and social externalities.

The emergence of environmental trade measures, such as the EU’s Carbon Border Adjustment Mechanism, creates new demands for standardized sustainability assessments that can support fair and effective trade policies. The CSSI framework could serve as a model for developing international sustainability standards that enable consistent assessment across countries and commodities while respecting national sovereignty and development priorities.

The federated learning architecture addresses sovereignty concerns that have historically limited international cooperation in trade monitoring and assessment. By enabling collaboration without raw data sharing, the CSSI could facilitate the development of multilateral sustainability monitoring systems that respect national sovereignty while promoting global cooperation on environmental and social challenges.

5.4. Business and Market Implications

The CSSI framework provides valuable insights for companies operating in international commodity markets on how sustainability considerations can be integrated into strategic planning and risk management. The framework’s ability to predict sustainability-driven trade disruptions up to six months in advance enables companies to adapt supply chains, investment strategies, and market positioning to capitalize on emerging opportunities and mitigate risks.

For commodity producers, CSSI results demonstrate that sustainability investments can enhance, rather than undermine, their competitive position in international markets. The positive correlation between sustainability performance and trade resilience suggests that companies with strong environmental and social practices may be better positioned to navigate market volatility and regulatory changes. This supports the business case for sustainability investments and suggests that ESG considerations should be integrated into core business strategies rather than treated as peripheral concerns.

The framework’s real-time monitoring capabilities enable dynamic supply chain management that can respond rapidly to changing sustainability conditions and market preferences. Companies can use CSSI data to identify emerging sustainability leaders, adjust procurement strategies to favor sustainable suppliers, and develop premium products that command higher prices in sustainability-conscious markets.

5.5. Limitations and Methodological Considerations

While the CSSI framework represents a significant advance in sustainability–trade analysis, several limitations must be acknowledged. The reliance on satellite data for environmental monitoring, while providing unprecedented spatial and temporal coverage, may miss certain types of environmental impacts that are not visible from space. Ground-based monitoring and field verification remain important complements to satellite-based assessments.

The standardization of social impact measures across different cultural and institutional contexts presents ongoing challenges. Unlike environmental impacts that can often be measured using standardized physical units, social impacts involve subjective assessments of well-being, rights, and community development that vary significantly across cultures and institutions. The framework’s social sub-index, while comprehensive, may not fully capture the nuanced social dynamics specific to different production regions and communities.

The complexity of the dynamic weighting mechanism, while providing adaptive capabilities, may reduce transparency and interpretability for some users. The trade-off between methodological sophistication and practical usability requires careful consideration in different application contexts. Future developments should focus on providing user-friendly interfaces and clear explanations of how weights are determined and adjusted.

The current analysis focuses on two commodities (soybeans and iron ore) and one bilateral relationship (Brazil–China). While these represent significant portions of global commodity trade, the generalizability of findings to other commodities and trade relationships requires empirical validation. The framework’s modular design facilitates such extensions; however, systematic testing across diverse contexts is necessary to establish broader applicability.

6. Conclusions

This study introduces the Commodity-Specific Sustainability Index (CSSI), a novel framework for integrating sustainability considerations into trade dependence analysis. Applied to Brazil–China commodity trade, CSSI provides significant theoretical contributions, methodological innovations, and practical applications that address critical gaps in both trade analysis and sustainability assessment.

6.1. Key Empirical Findings

The empirical results demonstrate that conventional trade dependence measures systematically overestimate bilateral trade dependence by 12–19% (95% CI: 10.8–21.2%, p < 0.001) for commodities with high environmental impacts, suggesting that sustainability considerations fundamentally alter the risk profiles and strategic implications of international trade relationships. The contrasting sustainability trajectories for soybeans (17.3% improvement) and iron ore (19.1% decline) illustrate the importance of commodity-specific assessments and the limitations of aggregate trade statistics for understanding complex sustainability–trade interactions.

The dynamic weighting mechanism represents a significant improvement over static composite indices, enabling adaptive responses to changing policy priorities and stakeholder preferences while maintaining analytical integrity and transparency. The Transformer-based machine learning architecture achieves superior predictive performance (7.1% mean absolute error (MAE)) compared with conventional approaches (9.7% for autoregressive integrated moving average (ARIMA) and 8.8% for long short-term memory (LSTM)), demonstrating the value of advanced analytics for capturing temporal dependencies and non-linear relationships in sustainability–trade data.

6.2. Theoretical and Methodological Contributions

The CSSI framework makes several important theoretical contributions. It operationalizes the concept of “sustainability-adjusted dependence,” demonstrating that sustainability characteristics act as trade frictions that either facilitate or impede commerce, depending on the alignment between production practices and importing country preferences. The framework advances composite index construction through dynamic weighting, incorporates machine learning techniques into trade analysis, and addresses international data sharing challenges through the implementation of federated learning.

Methodologically, the framework provides a comprehensive approach to integrating environmental, economic, and social dimensions into trade analysis. The integration of satellite monitoring, blockchain traceability, and machine learning techniques creates a robust foundation for real-time sustainability assessment and predictive analysis. The implementation of federated learning enables international collaboration while respecting data sovereignty and privacy requirements.

6.3. Policy and Practical Implications

For policymakers, the CSSI framework provides an evidence-based tool for balancing economic growth and sustainability objectives in international trade policy. The real-time monitoring capabilities and 6-month prediction horizon enable proactive policy interventions that can prevent emerging risks from escalating into economic or environmental crises. This forward-looking approach represents a significant improvement over reactive policy frameworks and provides a strong foundation for better coordination between economic and environmental policies.

The policy simulation capabilities demonstrate that comprehensive sustainability measures can improve environmental and social performance while enhancing economic security through reduced dependence on volatile commodity markets. Combined policy scenarios could reduce soybean dependence by 22% and iron ore dependence by 18%, with corresponding CSSI improvements of 16.4% and 12.7%, respectively.

For companies operating in international commodity markets, the CSSI framework demonstrates that sustainability investments can enhance, rather than undermine, competitive position, supporting the business case for ESG integration into core business strategies. The framework’s predictive capabilities enable companies to adapt supply chains and investment strategies to capitalize on emerging opportunities and mitigate sustainability-driven trade disruptions.

6.4. Broader Implications and Significance

The broader implications of this work extend beyond Brazil–China trade to fundamental questions about the role of sustainability in international economic relations and the governance frameworks needed to balance economic growth with environmental protection and social equity. The CSSI contributes to ongoing efforts to align economic development with sustainability objectives, providing a foundation for developing more sophisticated tools for sustainable development policy.

As global trade increasingly faces sustainability-related disruptions and policy interventions, there is a growing need for comprehensive analytical frameworks that can capture the complex interactions between economic, environmental, and social factors. The CSSI framework represents a significant step toward meeting this need and can serve as a model for developing similar frameworks in other sectors and contexts.

The successful implementation of the CSSI framework demonstrates both the feasibility and value of integrating sustainability considerations into trade analysis, opening new avenues for research and policy development toward more sustainable and resilient international economic relations. The framework’s combination of theoretical rigor, methodological innovation, and practical applicability positions it as a valuable tool for researchers, policymakers, and practitioners working to achieve sustainability objectives in an increasingly complex global economy.

6.5. Future Research Directions

Future research should explore the application of the CSSI to additional commodities and bilateral trade relationships to establish broader generalizability. The framework’s modular design facilitates such extensions, but systematic testing across diverse contexts is necessary. Additionally, investigation of alternative machine learning architectures and ensemble methods could further improve predictive performance.

Enhanced integration of ground-based monitoring data with satellite observations could improve environmental impact assessments. Development of standardized social impact measurement protocols that account for cultural and institutional differences would strengthen the social dimension of the framework. Integration of real-time economic data streams could enhance the responsiveness of the economic sub-index.

Systematic evaluation of CSSI implementation in different policy contexts would provide valuable insights into optimal application strategies. Comparative studies across different countries and regions could identify best practices for framework adaptation and implementation. Long-term impact assessments of policies informed by CSSI analysis would validate the framework’s practical value.

Advancement of federated learning techniques for international trade analysis could enable broader collaborative research while maintaining data sovereignty. Development of blockchain-based traceability systems integrated with CSSI could enhance supply chain transparency and accountability. Integration of Internet of Things (IoT) sensors for real-time environmental and social monitoring could provide more granular and timely data.

Development of formal economic models that incorporate sustainability-adjusted trade measures could advance our theoretical understanding of sustainability–trade relationships. Investigation of network effects and spillover mechanisms in sustainability–trade systems could reveal important systemic dynamics. Integration of behavioral economics insights could improve our understanding of how sustainability considerations influence trade decisions.

These research directions would contribute to the continued development of sophisticated analytical tools for sustainable development policy and practice, supporting the global transition toward more sustainable and resilient economic systems.